Bioethanol production from lignocellulosic waste without pre-treatment employing vermicompost and earthworm gut-isolated bacteria: Insights on waste to wealth conversion efficiency towards cleaner lifestyle

Bioethanol production from lignocellulosic waste without pre-treatment employing vermicompost and earthworm gut-isolated bacteria: Insights on waste to wealth conversion efficiency towards cleaner lifestyle | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Bioethanol production from lignocellulosic waste without pre-treatment employing vermicompost and earthworm gut-isolated bacteria: Insights on waste to wealth conversion efficiency towards cleaner lifestyle Ratan Chowdhury, Nazneen Hussain, Sandip Mukherjee, Soma Barman, and 3 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-3876047/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 4 You are reading this latest preprint version Abstract Lignocellulosic wastes (LCW) have enormous potential to be recycled for bioethanol production. Although yeasts ( Saccharomyces sp.) are commonly used bio-agents for fermentation, their efficiency is inhibited in cellulosic feedstocks. This study isolated novel ethanologenic bacteria from vermicomposting systems for bioenergy generation from fruit waste without pre-treatment. Initially, six strains out of 22, showing remarkable ethanol production ability, were characterized via 16S rRNA sequencing. Specifically, two strains ( Bacillus alcalophilus C5 and Rhizobium spp. S10) produced more ethanol (5.5 and 15.7 g L − 1 ) than the yeast (5 g L − 1 ) from banana epicarps. These strains' dramatically high sedimentation rate and ethanol tolerance strongly justified their industrial applicability. Significant upregulation of alcohol dehydrogenase and acetyl CoA synthase endowed greater ethanol-producing capacity in C5 and S10 than in S. cerevisiae . The flow cytometry and confocal microscopy evidenced that ethanologenic bacteria uniquely defend the reactor-induced sugar and ethanol stresses through reverse/delayed apoptosis and robust membrane integrity. The waste-to-wealth conversion efficiency and cost-benefit analyses estimated that bacteria-mediated LCW-to-bioethanol conversion was a more profitable venture than vermicomposting or composting. Overall, this research demonstrated that the C5 and S10 isolates were more effective than widely used commercial yeast strains for bioethanol generation from LCW. ethanologenic bacteria lignocellulosic waste vermicompost earthworm gut defense mechanism Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Statement of Novelty This study marks a pioneering breakthrough in bioethanol production by isolating ethanologenic bacteria from vermicompost for the first time. Derived from earthworm guts, these bacterial strains outperform yeast, yielding 2-3 times more ethanol. Importantly, they exhibit an unprecedented ability to generate ethanol directly from raw biomass without pretreatment. Flow cytometry uncovers a distinctive defense mechanism against sugar stress, highlighting the bacteria's robust membrane integrity and exceptional stress tolerance. This research not only advances sustainable bioethanol production but also unveils novel insights into adaptive strategies of ethanologenic bacteria. 1. Introduction One of the biggest challenges of the modern world is to meet the ever-increasing energy demands. As non-renewable fossil fuels are mainly used for energy generation, the quest for a sustainable energy supply has gained considerable attention in the present [ 1 ]. Consequently, renewable and sustainable energy generation has become integral to the UN’s SDGs [ 2 ]. In this context, the pathways for bioethanol production from wasted lignocellulosic biomass through the utilization of a microorganism-mediated fermentative process, is a sustainable option for fossil fuel substitution [ 3 ]. Conventionally, bioethanol production from lignocellulosic biomass involves steps like pretreatment for feedstock fortification and enzymatic hydrolysis with the help of externally added enzymes for converting cellulosic materials to sugars [ 4 ]. However, the process's success depends on the microbial activity and their compatibility with the feedstock and exposure conditions [ 3 ]. Thus, experimentation for optimizing the bioethanol production process by step reduction and using novel and efficient microbial species should be continued considering the environmental superiority of the technology. In particular, applying food waste as feedstock reduces the ecological risk of dumping such waste on land [ 5 ]. Lignocellulosic wastes (e.g., crop residues and fruit peels) have been widely utilized as raw materials for bioethanol production [ 6 ]. Fruit peels and discarded pulps are mainly disposed of in landfills owing to their low commercial value[ 6 ]. Although these materials are compostable, the economic viability of the process could be better due to time, space, and labor intensiveness. Generally, yeasts ( Saccharomyces sp. ) are the most dominant microorganism utilized for sugar fermentation for decades [ 7 ]. The metabolic efficiency of S accharomyces is greatly affected in peptone (xylose, arabinose, etc.) dominated feedstocks [ 8 ]. Moreover, the most popular yeast, Saccharomyces cerevisiae , cannot utilize cellulose and hemicelluloses [ 9 ]. Hence, the quest for alternative microorganisms for efficient ethanol production from bio-wastes has gained considerable attention in the recent past. Several bacterial communities (thermophilic, lactic acid producing, volatile fatty acid producing, etc.) bacteria have shown high promise for economically viable bioethanol production [ 10 , 11 , 12 ]. Efforts have also been made to induce non-ethanologenic microbes for ethanol production from cellulosic feedstock [ 12 ]. Recently, Maleki et al. have successfully utilized a non-ethanologenic strain of Bacillus subtillis for ethanol production [ 13 ]. As such, earthworm guts and earthworm-mediated lignocellulosic feedstock-based vermicomposting systems may also serve as potential sources of fermentative microorganisms because the gut environment stimulates their growth [ 14 ]. A recent finding strongly suggests that transient microbes in the earthworm gut play a definite role in the fermentation process [ 15 ]. However, vermicomposting beds and earthworm gut have never been utilized to identify ethanol-producing microorganisms. In particular, the vermicomposting bed can be an easy source of beneficial fermentative vis-à-vis ethanologenic bacteria. One of the vital qualities of ethanologenic microorganisms is their osmo-adaptation. The growth rate of several yeasts is significantly inhibited in high-concentration sugar solutions due to the saturation of the sugar uptake capacity of microbial cell [ 16 , 17 ]. However, no study is available about osmo-tolerant characteristics in ethanol-producing bacteria. The industrial suitability of microorganism-mediated processes is vital for applying such technologies. As such, the sedimentation rate, ethanol, and sugar tolerance properties are crucial attributes for the evaluation of the industrial applicability of the ethanologenic microorganisms [ 17 ]. In this context, assessing waste conversion and economic efficiencies is essential to appreciate the sustainability and eco-compatibility of the technology. A few enzymes (Acetyl-CoA synthase, alcohol dehydrogenase, pyruvate decarboxylase, pyruvate kinase, and pyruvate decarboxylase complex) are also essential regulators of industrially scalable bioethanol production. However, these enzymatic pathways have mainly been studied in the yeast-mediated bio-ethanol production system. Hence, analyzing the activation dynamics of these enzymes in bacteria-mediated schemes will be intriguingly novel. Adopting a novel approach, the present study explores earthworm-mediated vermibeds to isolate and utilize ethanologenic bacteria to valorize lignocellulosic wastes. Based on the available literature, the identified research gaps of the current investigation were: (a) The guts of the earthworms grown in lignocellulosic biomass have not been utilized as a source of ethanologenic bacteria; (b) the ethanol-producing potential of such bacterial isolates has not been evaluated from mechanistic viewpoints (i.e., osmo-tolerance, sedimentation rate, ethanol tolerance, etc.) in comparison with a commercially used organism such as yeast ( Saccharomyces cerevisiae ); and (c) it is unknown whether the bacterial isolates could produce bioethanol from lignocellulosic feedstocks without pre-treatment or not; and (d) the waste-to-energy conversion efficiency and the overall economic benefit have also needed to be ascertained for successful application of the technology. Previously, our published work identified and reported the prolific plant growth-promoting roles of a few earthworm gut-derived bacterial species [ 18 ]. Hence, the ethanologenic property of those bacteria has been explored in the present investigation. In addition, a lignocellulosic feedstock-based vermicompost reactor was searched to isolate new ethanol-producing bacterial strains. Initially, several bacterial strains were qualitatively screened based on their cellulose and carbohydrate degrading capacity, and a few selected isolates were for assessing their bio-ethanol-producing potentials. Eventually, the ethanol-producing ability of the characterized bacteria was evaluated in sugar solution and lignocellulosic fruit waste (banana peel) compared with yeast. Moreover, the activities of different key enzymes were assessed to appreciate the underlying mechanism of microbe-mediated bioethanol production. Finally, the membrane permeability kinetics of the selected bacteria was compared with yeast through flow cytometry and fluorescence microscopy to understand the osmo-adaptation mechanism. 2. Materials and Methods 2.1. Vermicomposting and composting with lignocellulosic biomass Vermicomposting was conducted with lignocellulosic waste (vegetable peels, sugarcane bagasse, and rice straw) materials to stimulate cellulose-degrading and ethanologenic microorganisms. The lignocellulosic waste materials were homogenized with urine-free cow dung at a ratio of 3:1. The vermibeds and composting beds were prepared in truncated cone-shaped earthen reactors of 3L capacity [0.45m (height)×0.15m (base radius)×0.30m (top radius)].The feedstocks were pre-composted for five days prior introduction of earthworms in the vermireactors for the thermo-stabilization [ 19 ]. Eventually, non-clitellated juvenile E. fetida specimens (10 earthworm kg − 1 ) were employed in the vermireactors. The incubation was carried out for 60 days by maintaining 40–50% moisture within a temperature range of 27–30°C, and aeration was maintained by intermittent mixing of the feedstocks throughout the incubation period. The whole set of vermicomposting and composting reactors was replicated thrice. Feedstock samples were periodically drawn at 0, 30, and 60 days to assess the physicochemical and microbial changes during biomass degradation. 2.2. Analysis of physicochemical and microbial properties in feedstocks The periodically obtained vermicompost and compost samples were analyzed for pH, total N, available P, and exchangeable K by following standard protocols [ 20 ]. The microbial activity in the compost and vermicompost feedstocks was assessed based on microbial respiration and microbial biomass C (MBC). The microbial respiration was measured by estimating the CO 2 emitted from the samples on incubation with glucose in a closed system at 25°C for 24 hours. The MBC was estimated by the fumigation extraction technique as detailed by Jenkinson (1988) [ 21 ]. In short, compost and vermicompost samples were fumigated in the presence of chloroform. After that, fumigated and un-fumigated samples were extracted in 2M KCl, and the ninhydrin-N level in the filtrates was spectrophotometrically assessed at 570 nm. All analyses were performed in clean glassware rinsed with deionized water. Analytical grade reagents and deionized water were used for preparing all the solutions [ 22 ]. In addition, the total bacterial and fungal counts in the compost and vermicompost samples were analyzed by pour plate technique using nutrient agar (NA) and potato dextrose agar (PDA), respectively [ 18 ]. The colony forming units (CFU) mL − 1 were calculated with the help of the formula given below: \(No. of organism {m L}^{-1}= \frac{No. of colonies \times dilution }{Volume plated}\) ………………………………….. (I) 2.3. Screening of cellulose-degrading and carbohydrate-utilizing bacterial strains Based on the results of the microbial assessment, the vermicompost samples were searched for isolating potential cellulose-degrading and carbohydrate-utilizing bacterial strains. Initially, 14 profusely growing bacterial strains in NA media were isolated from vermicompost and earthworm gut. Eight well-characterized and previously reported bacterial species isolated from earthworm gut were also assessed for their carbohydrate and cellulose utilization properties [ 18 ]. These 22 dominant bacterial strains were evaluated for their carbohydrate utilization efficiency using the Hi-Media Carbo Kit. Based on the outcome of the qualitative carbohydrate solubilizing assessment, a few strains were selected for assessing their cellulose degradation potential. These isolates were allowed to grow for 24 hours at 37°C in Carboxymethyl cellulose and 1% agar media plates, followed by Congo-red staining according to the technique standardized by Smibert and Krieg (1994) [ 23 ]. 2.4. Gas chromatographic estimation of ethanol production from sugar solution and banana peel Sugar solutions (5% w/v) were initially fermented at 37°C for 48 hours in an anaerobic chamber. The sugar solution was then sterilized at 121°C and 210 kPa for 20 minutes in an autoclave. Later, 1% inoculum of the selected bacterial strains was inoculated into the sugar solutions at room temperature. Glucose was the sole energy source for the bacterial inoculums used in this study. Subsequently, after 48 hours, the turbid microbiological media was transferred to sterilized falcon tubes. A strain of yeast ( Saccharomyces cerevisiae MTCC 170), procured from MTCC, was used as a positive control to compare the ethanol production potency of the screened bacterial isolates. Moreover, the ethanol production potential of the selected bacterial isolates was evaluated using non-edible banana ( Musa spp.) epicarps as substrates. The banana epicarps weighing 20g were homogenized with water at 1:2 ratio, and the homogenate (15 ml) was subjected to centrifugation at 8000 rpm for 15 minutes. The supernatant of the centrifuged contents and the filtrate of the sonicated samples were collected in fresh falcon tubes (5 ml in each tube). After that, 1% of the bacterial cultures were inoculated in each tube and allowed to ferment at 28°C. After 48 hours of incubation, the tubes were sealed and used to measure ethanol content. Gas-chromatography The extracted ethanol was analyzed using a Gas chromatograph (GC) (Model: Agilent 7890A) following the procedure described by Ebersole (2016) [ 24 ]. The method of the GC was set as follows: Inlet: 160 0 C, mode: split, Split flow: 80 ml min -1 Oven: Initial-50 0 C, hold time of 1 min and the Ramp (10 0 C min -1 ) till temperature goes to 200 0 C and hold time of 1 min Detector: 200 0 C, Flame Ionisation Detector (FID) Carrier gas: N 2 , 7 mL min -1 Injection volume: 100 to 200 µL QA-QC HPLC-grade ethanol was used as the standard for the calibration of the instrument. The lowest concentration of the working standard taken daily was considered as the limit of detection (LOD) (defined as the lowest concentration of the working standard with a signal-to-noise ratio equal to or exceeding 10:1) and the limit of quantification (LOQ). For this method, the LOD was 1.0% (v/v), and the LOQ was determined to be 0.789 mg L − 1 . 2.5. Sedimentation rate, ethanol tolerance, and sugar tolerance of the microbial cultures The sedimentation rate of the yeast and the selected bacterial isolates was estimated with minor modifications of the method standardized by previous workers [ 25 , 17 ]. The yeast-extract potato dextrose (YPD) medium was used to grow the yeast cells. Bacterial cells were cultured in Luria Bertani (LB) agar medium for 24 hours, centrifuged (14000 × g) for 10 minutes, and pellets were obtained. This method modification was necessary because bacterial cells cannot grow in the YPD medium. Subsequently, the pellets were suspended in NaCl (0.89%) solution for two hours, the change in absorbance at 600 nm was recorded in a UV-Vis spectrophotometer (Cary 60), and the sedimentation rate (SR) was derived using the formula I Moneke et al., (2008) [ 25 ] \(Sedimentation rate \left(SR\right)\%=\left(1- \frac{Drop in absorbance after 2 hours}{Absorbnce at 0 hour}\right)\times 100\) ………….(II) The ethanol (10% and 15%) and sugar tolerance (5%, 8%, and 12%) of the selected bacterial strains was assessed in comparison with yeast ( Saccharomyces cerevisiae MTCC 170). The ethanol tolerance was measured based on the extent of cell viability after exposure to different concentrations of ethanol following the method detailed by Moneke et al. (2008) [ 25 ]. The survival percentage (SP) in different ethanol solutions was calculated as formula II: \(Survival percentage \left(SP\right)= \frac{Number of alive cells after 2 hour exposure}{Number of cells in control \left(sterilized water\right)} \times 100\) ……(III) To assess the sugar tolerance limit, sugar solutions of 5, 8, and 15% concentration (w/v) were sterilized using an autoclave. Once the solutions reached room temperature, 1% yeast culture and selected bacterial strains were inoculated. The inoculums were allowed to grow at 37°C for 72 hours in an anaerobic chamber. After every 24 hours, the optical density of the cultures was noted at 280 nm using the UV-Vis spectrophotometer (Cary 60). 2.6. Identification of microorganisms through Gram staining and 16S rRNA sequencing The Gram staining assay was performed following the standard technique [ 26 ]. Then, the bacterial isolates' micro–Genomic DNA extraction was performed using the QIAamp DNA Mini kit (QIAGEN, Germany). The quality of the extracted genomic DNA was evaluated using 0.8% (w/v) agarose gel to perform electrophoresis before using the DNA extracts of respective microbes as the template for polymerase chain reaction (PCR). The universal bacterial primers 27FA (5/-AGAGTTTGA TCATGGCTAG-3/) or 27FC (5/-AGAGTTTGATCCTGGCTAG-3/) and U1492R (5/-GTTACCTTGTTACGACTT-3/) was chosen to run the PCR program for amplifying full-length 16S rRNA gene fragment [ 18 ]. The PCR product was purified by gel elution technique using QIA quick Gel Extraction Kit (QIAGEN, Germany). The quantity and quality of the DNA extracts were confirmed using nano-drop and electrophoresis on 1% agarose gel. The partial nucleotide sequence was performed at 1st 158 BASE (Malaysia) using Sanger’s method using 27FA or 27FC, or U1492R primers. Here, it is essential to mention that the selected isolates' three ( K. ascorbata S8, Rhizobium sp . S10, and Bacillus sp . S12) were previously reported as N-fixing strains [ 18 ]. However, one among the three newly isolated strains (i.e., C5) was identified by fatty acid methyl ester (FAME) analysis because the broth culture of the organism was unsuccessful after several efforts. The FAME analysis was conducted by outsourcing from Royal Life Sciences Pvt. Ltd. (affiliated with MIDI Sherlock, USA). In short, the colonies of the C5 strains were grown on Trypticase soy broth Agar at 28 o C for 24 hours. Then about 40 mg of bacterial cells were harvested in sterilized Petri plates by streaking. Then FAMEs were extracted in a step-wise manner following the standard protocol (saponification, methylation, extraction, and aqueous wash) [ 27 ]. The extracted FAMEs were analyzed by gas chromatography. The gas chromatography-derived FAME profiles were used as standard profiles. Subsequently, the organism's genus and species were identified with the assistance of the Sherlock software. The strain with a single match of at least 0.600 similarity indexes or 0.600 with < 0.100 distance from the nearest choice was considered a dependable species match. 2.7. Enzyme assays Activities of five enzymes [Acetyl-CoA synthetase (AS), pyruvate dehydrogenase (Pdh), pyruvate kinase (Pyk), pyruvate decarboxylase (PyD), and alcohol dehydrogenase (AD)] that are associated with the central metabolism of ethanologenic microorganisms were studied in the early exponential phase and at the onset of the stationary phase of growth. AS, Pdh, Pyk, and AD activities were measured regarding the NADH generation at 340 nm (εNADH = 6.220 M − 1 cm − 1 ) following standard methods [ 28 – 30 ]. The activity of PyD was assessed following the method of Hoppner and Doelle (1983), and the enzyme unit of pyruvate decarboxylase activity was expressed as 1.0 µM of acetaldehyde min − 1 at 340 nm [ 30 ]. The details of all these methods have been provided in the supplementary information. 2.8. Flow cytometry and confocal microscopy: cellular function for sugar tolerance The fluorescence-activated cell sorting (FACS) and confocal microscopy techniques were utilized to study cellular responses of the yeast ( Saccharomyces cerevisiae MTCC 170) and selected bacterial isolates following methods described by Malakar et al. (2008) and Mukherjee et al. (2014) [ 31 , 32 ]. The yeast and bacterial cells were incubated with 0 (i.e., control), 5, and 15% sugar solutions for 24 hours. Then, the treated and untreated (i.e., control) cells were stained using an Annexin V-FITC apoptosis detection kit (BD Biosciences, San Jose, CA, USA) according to the manufacturer’s protocol and subjected to flow cytometry analysis (BD Accuri, BD Biosciences). Apoptosis phases were detected by distinct double staining patterns: viable (Annexin V- and PI-, lower left square), early apoptotic (Annexin V + and propidium iodide (PI)-, lower right square), late apoptotic (Annexin V + and PI+, upper right square) and necrotic cells (Annexin V- and PI+, upper left square). To evade the coincidence of cells, the flow rate was adjusted to the lowest setting mode (data rate, 200–300 events per second). At least 10,000 events were recorded for each sample with three replicates. Cells were collected on the forward scatter with logarithmic amplifiers for 5,000 events to determine the cell size. As the membrane potential of different organisms varies based on their cell size, all data were expressed as the ratio of membrane potential according to their size. The acquired data were accumulated as list mode files and examined offline using the System II V.3 software (Beckman-Coulter). For determining the membrane permeability, the 0 (i.e., control), 5, and 15% sugar-exposed cells were stained by two nucleic acid staining reagents, propidium iodide (PI) and FITC, using a Live/Dead BacLight kit (Molecular Probes, Invitrogen, Cergy-Pontoise, France). The membrane integrity of the yeast and bacterial cells was assessed by staining the cells at first with green fluorescing FITC, which can enter all cells when used alone, and then with the red-fluorescing PI that specifically invades cells with injured cytoplasmic membranes. The appropriate mixture of the FITC and PI stains enables differentiation between live organisms with intact cytoplasmic membranes and dead organisms with permeable cellular membranes. The method has been adequately standardized for observing apoptotic response in bacteria and yeast in previous studies [ 33 , 34 ]. However, the staining exercise was repeated several times for the yeast cells because a few cells were not responding uniformly to the PI. The morphology of the stained organisms was studied with the help of a Zeiss LSM 510 confocal fluorescence microscope with a 63X objective. 2.9. Waste to wealth conversion efficiency and economic evaluation Waste-to-wealth conversion efficiency (WCE) of vermicomposting and bacteria-driven bioethanol production systems was computed by modifying the formula given by Lalander et al. (2015) to ascertain their environmental compatibility, as shown below [ 33 ] \(Waste conversion efficiency \left(WCE\right)=\frac{End product fresh weight \left(kg\right)}{Initial feedstock fresh weight \left(kg\right)}\times 100\) ……....(IV) Moreover, the economic feasibility of both systems was evaluated on a large-scale basis (100 kg capacity) followed by computing the benefit-cost ratio (BCR) according to the formula V given by Dilon and Hardaker [ 34 ] \(Benefit cost ratio \left(BCR\right)= \frac{Gross return \left(Rupees\right)}{Total operational cost \left(Rupees\right)}\) ………………………………..(V) Where, \(Gross return=Present market price of the end product \left(i.e., vermicompost or ethanol\right)\times total quantity produced \left(kg\right)\) The details of the variables considered for the above calculations are provided in supplementary material 4. 2.10. Statistical Analysis The temporal data on the bio-composting experiment was analyzed for two-way ANOVA with three observations per cell followed by the Least Significant Difference (LSD) test to differentiate the efficiency of various treatments at the probability level of p < 0.05 using SPSS. One-way ANOVA followed by an LSD posthoc test was also performed for all other experiments. 3. Results and discussion 3.1. Chemical and microbial changes of composting and vermicomposting beds – determining the source viability for potential ethanol producers The composting and vermicomposting were primarily conducted to create a microbe-enriched substrate, which could be used as a ready source for ethanol-producing microorganisms. The changes in the chemical and microbial properties of the vermibeds and composting beds are presented in Table 1 . The pH sharply reduced under composting and vermicomposting, strongly indicating the microbe-induced organic matter decomposition process [ 19 ]. However, the increment in NPK bioavailability was significantly greater under vermicompost than under composting. This suggests that the presence of earthworms augmented the nutrient levels by accelerating the microbial activity [ 35 ]. Correspondingly, the microbial biomass carbon and microbial respiration were remarkably enhanced by about 3.35 folds and 2.31 folds in the vermibeds compared to the composting beds (P for treatment < 0.01; LSD = 15.94). These results Table 1 Temporal changes in pH, available P (Av P), total Nitrogen (TN), exchangeable K (Av K), Compost respiration ( Comp. Res),Microbial biomass carbon ( MBC), bacterial and fungal count during the bioconversion experiment (mean ± standard deviation) Parameters Compost Vermicompost P values 0D 30D 60D 0D 30D 60D P treatment P time P treatment×time LSD treatment pH 8.49 ± 0.18 8.38 ± 0.29 6.44 ± 0.34 8.15 ± 0.12 8.33 ± 0.18 6.36 ± 0.25 NS < 0.01 NS - Av P (mg kg − 1 ) 26.41 ± 0.28 40.22 ± 0.18 63.34 ± 0.42 27.56 ± 0.4 51.42 ± 0.36 78.37 ± 0.26 < 0.01 < 0.01 < 0.01 10.65 Total N(%) 0.34 ± 0.12 0.61 ± 0.1 0.64 ± 0.12 0.55 ± 0.12 0.70 ± 0.14 0.85 ± 0.10 NS < 0.01 NS 0.06 Av K (mg kg − 1 ) 34.33 ± 0.20 54.48 ± 2.87 62.45 ± 0.38 32.47 ± 0.40 60.53 ± 0.27 76.38 ± 0.2 < 0.01 < 0.01 < 0.01 8.69 Comp. Res (mg kg − 1 ) 5.29 ± 0.35 7.53 ± 0.35 10.44 ± 0.43 7.42 ± 0.2 10.4 ± 0.36 17.32 ± 0.29 < 0.01 < 0.01 < 0.01 7.84 MBC (mg kg − 1 ) 85.60 ± 0.44 185.30 ± 3.3 215.94 ± 6.81 85.8 ± 14.04 248 ± 34.3 259.08 ± 37.25 < 0.01 < 0.01 < 0.01 15.91 Bacterial count [log(CFU)] 7.06 ± 0.03 - 7.64 ± 0.005 8.16 ± 0.02 - 8.21 ± 0.02 < 0.01 < 0.01 < 0.01 - Fungal count[log(CFU)] 6.03 ± 0.08 - 6.2 ± 0.08 6.5 ± 0.02 - 6.94 ± 0.01 < 0.01 < 0.01 < 0.01 - indicate that microbial proliferation and activity were considerably more remarkable in the vermibeds than in composting beds. The results of the total bacterial and fungal count (Table 1 ) also strongly substantiated that microbial growth was significantly promoted under vermicomposting. Earthworms enrich the microbial diversity in vermibeds by contributing through their intestinal microflora [ 18 ]. Hence, we postulated that the vermibeds would be better substrates for searching for potential ethanol-producing organisms than the composting beds. 3.2. Screening of potential strains-Cellulose degrading (Congo red assay) and carbohydrate solubilizing efficiency Based on the results of the composting and vermicomposting experiments, 14 isolates were initially screened out from the vermibeds considering their high relative dominance (RD) in Nutrient agar plates (SI 1). The RD is an authentic and dependable parameter for assessing the aggressivity of microbial strains in congregations [ 18 ]. Eight previously reported strains were also considered for the present study because the data about their molecular identity and general characteristics were readily available. Cellulolytic capability in microorganisms signifies the effectiveness of the organisms for rapid transformation of obstinate cellulose-rich biomass [ 36 ]. On the other hand, the extent of carbohydrate solubilization efficiency in microorganisms indicates their ability to derive energy from recalcitrant substrates [ 37 ]. Hence, 22 strains were selected to study their cellulose degradation and carbohydrate solubilization potentials (Fig. 1 & SI 2). Overall, six strains were able to solubilize ~ 28–30 different types of sugars (glycerol, mannitol, adonitol, etc.), and the C1, C5, and S10 could solubilize 30–31 out of 35 tested carbohydrates (SI 2). The C5 and S10 exhibited significantly higher cellulose degrading efficiency (i.e., halo zone Index) compared to other strains (Fig. 2 ; p<; LSD = 0.039). On the other hand, a few different strains (T3, T20, OS7, and B5) exhibited high carbohydrate solubilization efficiency (SI 1). Interestingly, among eight previously reported strains, three (S8, S10, and S12) showed high cellulolytic and carbohydrate solubilization potential (SI2). As mentioned in the previous section, the N-fixing and P-solubilizing traits of K. ascorbata S8 (previously reported as IN2), Rhizobium sp. S10 (previously reported as IN4), and Bacillus sp. Hussain et al. have reported S12 (previously reported as IN5) (Table 2 ) [ 18 ]. Table 2 Strain information of all the dominant bacterial taxa Strain Code (For experimental Purposes) Organism Name Seq. Length Accession Number Differential Staining Percentage identity References S8 Kluyvera ascorbata 956 KU321346 Gram Negative 100% Hussain et al. (2016) S10 Rhizobium sp. 989 KU321348 Gram Negative 100% Hussain et al.(2016) S12 Bacillus sp. 1024 KU321350 Gram Positive 100% Hussain et al.(2016) C1 Kosakonia sacchari 464 MH174457 Gram Negative 100% C3 Enterobacter cloacae 456 MH174458 Gram Negative 100% C5 Bacillus alcalophilus NA FAME analysis Gram Positive 100% However, their carbohydrate solubilization and cellulose degradation properties have yet to be evaluated. Although plant-originated sugar and polymers are dependable feedstocks for biofuel production, their recalcitrant character is the major obstacle to their solubilization [ 3 ]. Therefore, the bacterial isolates' sugar and cellulolytic degrading potential were promising indicators of potential bioethanol-producing organisms. 3.3 Ethanol production potential of bacterial strains in different substrates (5% sugar solution and banana peel) and their molecular characterization Based on the cellulolytic and sugar degradation efficacy of the 22 bacterial strains, their ethanol production potential from different substrates was assessed. However, only six out of 22 could produce ethanol from sugar solution; among which three ( Kosakonia sacchari C1, Enterobacter cloacae C3, and Bacillus alcalophlius C5) were from the 14 newly isolated strains (Fig. 2 a). Ethanol production potential of the bacterial isolates was compared with yeast ( S. cerevisiae MTCC 170). The ethanol produced by the bacterial isolates and the yeast from the 5% sugar solution was in the order: C5 > S10 = yeast > C1 > S12 > S8 = C3 (P for organism < 0.01; LSD for organism = 5.575; Fig. 3 a). It was interesting to note that Bacillus alcalophlius C5 strain was most efficient in deriving ethanol from banana peel followed by C1 and S10 (Fig. 2 a). The gas chromatographic analysis also confirmed high purity (~ 99%) of the ethanol produced by the bacterial isolates. As such, ethanol produced from banana peel by all the studied strains was significantly greater than that of 5% sugar solution, and the organism-feedstock interaction effect was also significant (P for feedstock & feedstock × organism < 0.01). This implied that the ethanol generation performance of bacteria and yeast would undoubtedly fluctuate depending on the feedstock characteristics. These results agreed with the previous finding [ 38 ], implying that wasted foods, particularly roughage, could be highly effective for biofuel generation, thereby reducing environmental pollution [ 39 ]. At this stage, it was confirmed that six out of 22 isolates were prolific bioethanol producers; and three among the six strains were yet to be characterized. Therefore, the complete 16s rRNA genes of these three strains were amplified and partially sequenced. The sequencing output and identity of the strains and their accession numbers, as obtained from the NCBI database, are presented in Table 2 . The DNA sequences of the isolated strains were utilized to create a phylogenetic tree to explore their similarities (Fig. 2 b). The phylogenetic tree analysis revealed that the Rhizobium sp. IN4 and Bacillus cereus IP4 were closely related, while Kluyvera ascorbata IN2 was distantly linked to Bacillus and Rhizobium . Interestingly, a high similarity was detected between K. Sacchari C1 and E. Cloacace C3. Such high resemblance was because both species originate from the enterobacteria complex. Moreover, according to the phylogenetic tree, all the strains were distantly related to each other. The B.alcalophilus C5 could not be included in the phylogenetic analysis because the organism was identified by FAME analysis; thus, a complete sequence was unavailable. As such, the strength of identification through FAME analysis is generally equal to that of the classical DNA sequencing [ 40 ]. The previously reported strains of Bacillus , Kluyvera , and Rhizobium were primarily utilized for their plant growth-promoting traits [ 18 ]. However, the bio-fuel production potential of Kluyvera has never been reported earlier. However, there has yet to be a report on cellulolytic and ethanol production traits of Rhizobium strains, a few strains of Bacillus ( B. subtilis WB600, and B. subtilis WBN were able to generate ethanol under in vitro condition [ 13 ]. On the other hand, the cellulolytic potential of Rhizobium strains was reported in rhizosphere soil in addition to their N-fixing potential in previous studies [ 38 ]. The K. sacchari , a well-known human pathogen, is known for its multidimensional plant growth promotion activities [ 41 ]. On the other hand, the Enterobacter strains have been studied for alcohol generation from lignocellulose-derived sugars and glycerol [ 42 ]; yet, their ethanol production potential from biomass under in vivo conditions has been estimated for the first time in this investigation. 3.4. Sedimentation rate, ethanol tolerance, sugar tolerance, and enzyme activation- mechanistic understanding of bacteria-mediated bioethanol generation The data on sedimentation rate (SR), ethanol tolerance, and sugar tolerance is presented in Fig. 3 a-e. a. Variation in microbial sedimentation rate. Values represent mean ± standard deviation (n = 3); b. Variation in sugar tolerance potentials of bacteria and yeast at 24 hours. Values represent mean ± standard deviation (n = 3); c. Variation in sugar tolerance potentials of bacteria and yeast at 48 hours. Values represent mean ± standard deviation (n = 3); d. Variation in sugar tolerance potentials of bacteria and yeast at 72 hours. Values represent mean ± standard deviation (n = 3); e. Variation in ethanol tolerance potentials of bacteria and yeast. Values represent mean ± standard deviation (n = 3). Although the SR of C1, C3, and S8 was poor, the rate of sedimentation in LB agar of Bacillus alcalophlius C5 was significantly higher than the yeast ( S. cerevisiae MTCC 170) grown in YPD agar (Fig. 3 a). The SR of Bacillus spp. S12 and Rhizobium spp. S10 was either the same or marginally lower than the yeast. The sedimentation vis-à-vis flocculation features of microorganisms indicate the ease of their separation from the medium after completion of the fermentation, which is immensely important for the recovery and reuse of the organisms, which signifies the industrial suitability of microbe-mediated ethanol production process [ 17 ]. The tolerance to sugar exposure of the bacterial isolates was generally lower than that of the yeast until 72 hours of incubation (Fig. 3 b-d). The cell growth of all six bacterial isolates constantly increased over time, while the yeast growth was significantly retarded at 72 hours in a 5% sugar solution (Fig. 3 b). Similar pattern of yeast cell growth was also evidenced in 8% and 15% sugar solutions (Fig. 3 c & d). However, the temporal growth patterns of the bacteria marginally varied among the strains in 15% sugar solutions — for example, Rhizobium sp. S10 showed a steady increase over time, and activity of the key extracellular enzymes that regulate the microbe-mediated ethanol production process was evaluated in the treated (5% sugar solution) organisms (Table 3 ). Activities of acetyl CoA synthase and alcohol dehydrogenase were significantly greater in Rhizobium sp. S10 and Bacillus alcalophilus C5 inoculated solutions (P < 0.01; LSD: acetyl CoA synthase = 0.447; alcohol dehydrogenase = 0.957). Acetyl CoA synthase catalyzes acetyl CoA synthesis by utilizing acetic acid, thereby arresting the acetate-induced retardation during the fermentation[ 43 ]. At the same time, the alcohol dehydrogenase upregulates the conversion of acetyl CoA to ethanol [ 44 ]. Table 3 Activities of different enzymes by different isolated strains during the experiment (mean ± standard deviation) Enzyme activities Organisms Pyruvate decarboxylase (mM NADH min − 1 ) Pyruvate kinase (mM NADH min − 1 ) Acetyl CoA synthetase (mM NADH min − 1 ) Alcohol dehydrogenase (mM NADH min − 1 ) Pyruvate dehydrogenase (mM NADH min − 1 ) K. ascorbata S8 9.48 ± 0.2 10.43 ± 0.4 4.73 ± 0.2 9.1 ± 0.2 6.43 ± 0.3 Rhizobium spp. S10 23.73 ± 1.1 23.7 ± 0.7 9.2 ± 0.5 24.7 ± 0.9 9.44 ± 0.3 B. cereus S12 18.27 ± 0.6 16.45 ± 0.2 7.44 ± 2.9 14.7 ± 0.9 7.17 ± 0.4 K. sacchari C1 18.84 ± 0.6 21.07 ± 0.7 5.6 ± 0.8 23.2 ± 1.6 8.47 ± 1.3 E. cloacae C3 8.9 ± 0.7 10.3 ± 0.6 7.57 ± 0.9 8.6 ± 1.1 7.57 ± 0.8 B. alcalophilus C5 28.94 ± 0.5 27.7 ± 0.6 8.44 ± 0.9 26.4 ± 1.6 18.27 ± 0.6 P value < 0.01 < 0.01 < 0.01 < 0.01 < 0.01 LSD 0.561 0.479 0.447 0.957 0.604 Interestingly, the ethanol production efficiency of S10 and C5 was remarkably greater than the other strains described in the previous section. Correspondingly, pyruvate decarboxylase, kinase, and dehydrogenase activities were also significantly higher in Bacillus alcalophilus C5 inoculated sugar solution, followed by Rhizobium spp. S10, K. sacchari C1, and Bacillus cereus S12 (Table 3 ). Pyruvate, a component of carbohydrate metabolism, is decarboxylated by pyruvate decarboxylase to produce acetaldehyde, which is eventually reduced to ethanol by alcohol dehydrogenase [ 45 ]. The pyruvate kinase is known to sustain cellular homeostasis via regulation of energy metabolism, which in turn induces thermo-tolerance to S accharomyces cerevisiae , and the pyruvate dehydrogenase complex dramatically enhances the free fatty acids levels in S. Cerevisiae [ 46 ]. Overall, the results strongly postulate that the high ethanol-producing ability of the bacterial isolates (mainly C5 and S10) from sugar solution and lignocellulosic biomass was due to the efficient release of essential enzymes during fermentation. 3.5 Membrane integrity and cellular response of yeast and bacterial to sugar exposure: Understanding the differential defense mechanism using flow cytometry and confocal microscopy Figure 4 represents the results of the FACS (i.e., flow cytometry) analysis of the stained bacteria and yeast cells exposed to sugar solutions of different concentrations. The FACS and confocal microscopic analyses were performed to comprehend the variations in apoptosis-mediated defense mechanisms between yeast and ethanologenic bacterial cells in response to sugar-induced shock. The two most prolific ethanologenic bacterial strains ( Rhizobium sp. S10 and Bacillus alcalophilus C5 ) were selected for this study. The FACS study demonstrated that incubation of yeast cells with 5 or 15% sugar causes a dose-dependent increase in apoptosis compared to the untreated cells. In sugar-treated yeast, a higher percentage (77.2 and 89.3%) of dual positive cells (both Annexin + PI) indicates a robust increase in apoptosis (about two folds more than the control). The shift of cells from early to late (i.e., mature) apoptosis was also evidenced in yeast (Fig. 4 ). This observation indicates that apoptosis is probably the primary defense mechanism in yeast ( S. cerevisiae ) in response to sugar-induced stress. However, bacterial cells responded differently from yeast to sugar exposure. In Rhizobium sp. S10, 5, and 15% sugar exposure mildly induced apoptosis in bacterial cells with a slight exhibition of dose- dependent apoptotic induction. In contrast, in Bacillus alcalophilus C5, sugar incubation showed no apoptotic induction compared to the control. These results imply that the apoptotic response of ethanologenic bacteria in a sugar-enriched environment may vary among species, and reverse apoptosis or delayed apoptosis could be the probable defensive adjustment of sugar-exposed bacterial cells. Although evidence of reverse-apoptosis with increased survivability in human cells has been reported [ 47 ], such instances have not been found in bacteria. However, delaying apoptosis by the CpG motifs in DNA has been detected in some bacterial species [ 48 ]. Hence, studies with more prolonged exposure to varying sugar concentrations may be able to vindicate the present hypotheses. The results of the confocal microscopy were quite intriguing regarding the differential response between ethanologenic bacteria and yeast to sugar exposure (Fig. 5 ). At a glance, it appears that almost 90% of yeast cells were invaded by the PI at 5 and 15% sugar concentrations, which implies that increasing sugar exposure considerably caused fetal impacts on the fungal ( S.cerevisiae ) cells (Fig. 5 ). However, careful observation of the images in SI 3 would clarify quite a few yeast cells have remained unstained while repeating the staining process. This may be due to the yeast cells' poor compatibility with the stains owing to their cell wall-induced inhibition. However, this technique has been previously used for observing apoptosis of yeast cells [ 30 ]; while bacterial cells have often been studied in a similar manner [ 31 ]. Nevertheless, the result clearly shows that yeast's cellular wall and membrane integrity might be severely disrupted due to sugar exposure. Interestingly, 5% sugar exposure caused mild damage to the membrane integrity of B. alcalophilus C5 and Rhizobium sp. S10, but the responses of the two bacterial species to 15% sugar exposure were conspicuously different. The extent of PI invasion was considerably greater in 15% sugar-exposed S10 than the C5 (Fig. 5 ), signifying that the membrane integrity of C5 was more robust than S10. Overall, bacterial membrane integrity was more potent than the affected yeast cells. This observation substantiates the hypothesis that some bacteria, like B. alcalophilus C5, have more robust defense mechanisms than the ethanol-producing yeast and other bacteria. The current results also indicate that more robust membrane integrity could be one of the effective defensive strategies in addition to apoptosis in bacterial species in response to sugar-induced stress. 3.6. Waste conversion efficacy and economic evaluations The industrial feasibility and environmental sustainability of vermicomposting and bacteria-mediated bioethanol production technologies could only be appreciated by evaluating the studied systems' waste-to-wealth conversion efficiency and economic potential. Our previous studies demonstrated that toxic metals and odorous volatiles in waste materials are significantly neutralized through vermicomposting [ 49 , 50 ]. Accordingly, the possibilities of emissions of obnoxious gases and migration of toxic metals from the end product (i.e., vermicompost) were negligible. On the other hand, bacteria-mediated bioethanol production from biosolids is well recognized for its ecological compatibility [ 51 ]. Therefore, we have computed both processes' waste conversion (Table 4 ). WCE for vermicomposting was significantly higher than composting; however, the WCE for the bacteria-mediated bioethanol production process was highly organism dependent. We recorded extraordinarily high WCE for B. alcalophilus C5, followed by Rhizobium sp. S10 and yeast ( S. cerevisiae MTCC 170) (P < 0.05; LSD = 2.6). These results indicate that vermicomposting is a better waste conversion route than bacteria-mediated ethanol production. Still, the overall efficiency of these waste conversion treatments can only be assessed through the economic evaluation [ 52 ]. The enumerated benefit-cost ratio (BCR) for different processes is presented in Table 4 . Table 4 Waste-to-wealth conversion efficiency (WCE) and benefit-cost ratio (BCR) for bio-composting systems and microbe-mediated bioethanol generation Treatments WCE % Benefit cost ratio (BCR) Compost 61.8 ± 1.33 0.651 ± 0.003 Vermicompost 70.93 ± 3.91 1.898 ± 0.008 P-Value < 0.01 <0.01 Bioethanol generation C1 68.83 ± 0.50 5.18 ± 0.16 C3 64.48 ± 0.26 1.83 ± 0.07 C5 73.05 ± 0.51 2.09 ± 0.08 S8 59.7 ± 0.23 1.21 ± 0.10 S10 72.42 ± 0.95 1.12 ± 0.08 S12 54.72 ± 0.37 1.48 ± 0.07 R1 69.58 ± 0.33 0.82 ± 0.05 P-Value <0.01 <0.01 LSD 1.26 0.15 The ethanol and vermicompost production rate was assumed for 100 kg initial feedstock. However, the productivity and the cost of production were computed based on the real-time data acquired during the experiments (supplementary material 4). Interestingly, the BCR was highest for the B. alcalophilus C5-based ethanol production process (P < 0.05; LSD = 2.6). However, the BCR for Rhizobium sp. S10 and S. cerevisiae -based processes were significantly higher than vermicomposting (Table 4 ). This was mainly due to the increasing price of ethanol in the Indian market, which is a result of the recent shift in Governmental policy to reduce crude oil import and promote biofuel use [ 53 ]. As such, using ethanol as a substitute for fossil fuels also decreases emissions of air pollutants like particulate matter, CO, and volatile hydrocarbons [ 54 ]. 4. Conclusion Novel and industrially suitable ethanologenic bacteria were isolated from lignocellulosic waste-based vermicomposting systems for the first time. Six of 22 efficient sugars and cellulose solubilizing bacterial isolates exhibited high ethanol production ability. In particular, two bacterial strains Bacillus alcalophilus C5 and Rhizobium spp. S10 strains produced significantly more ethanol (~ 5–15 g L − 1 ) than the yeast without pre-treatment or externally supplemented enzymes. Additionally, these strains were strongly tolerant to inhibitory factors like ethanol and sugar shocks that assure their industrial applicability. We postulated through enzyme assay that appropriate activation of enzymes like alcohol dehydrogenase was one of the critical attributes that imparted high ethanol-producing capability in C5 and S10. The study with flow cytometry and confocal microscopy revealed that reverse/delayed apoptosis and strong membrane integrity could be the defense strategies in bacteria that facilitate their growth and maintain ethanol production capacity in sugar-enriched conditions. High waste-to-wealth conversion efficiency with a significant benefit-cost ratio strongly substantiates the practical applicability of the identified organisms for bioethanol generation from recalcitrant biowastes. However, technological improvement for the up-scaling of bacteria-mediated ethanol production systems warrants in-depth studies in the near future. Declarations Statements and Declarations Funding The authors declare that no funds, grants, or other support were received during the preparation of this manuscript. Competing Interests The authors have no relevant financial or non-financial interests to disclose. Author Contributions All authors contributed to the study conception and design. Material preparation, data collection and analysis were performed by Ratan Chowdhury. The first draft of the manuscript was written by Ratan Chowdhury and all authors commented on previous versions of the manuscript. All authors read and approved the final manuscript. Data Availability The datasets generated during the current study are not publicly available due to the reason that the work is considered to be novel. But the data will be available from the corresponding author on reasonable request. References Balaji Panchal, S.R., Zhu, Z., Qin, S., Chang, T., Zhao, Q., Sun, Y., Zhao, C., Wang, J., Bian, K.: The current state and applications of ethyl carbonate with ionic liquid in sustainable biodiesel production: A review. Renew. Energ. 181 , 341–354 (2022). https://doi.org/10.1016/j.renene.2021.09.062 Bakry, X.V.V., Walid, Mallik, G., Nghiem, X.-H., Sinha, A.: Is green finance really green? Examining the long-run relationship between green finance, renewable energy, and environmental performance in developing countries. Renew. Energ. 208 , 341–355 (2023). https://doi.org/10.1016/j.renene.2023.03.020 Lynd, L.R., Beckham, G.T., Guss, A.M., Jayakody, L.N., Karp, E.M., Maranas, C., McCormick, R.L., Amador-Noguez, D., Bomble, Y.J., Davison, B.H., Foster, C., Himmel, M.E., Holwerda, E.K., Laser, M.S., Ng, C.Y., Olson, D.G., Román-Leshkov, Y., Trinh, C.T., Tuskan, G.A., Upadhayay, V., Vardon, D.R., Wang, L., Wyman, C.E.: Toward low-cost biological and hybrid biological/catalytic conversion of cellulosic biomass to fuels. Energy Environ. Sci. 15 , 938–990 (2022). https://doi.org/10.1039/d1ee02540f Asgher, M., Shahid, M., Kamal, S., Iqbal, H.M.N.: Recent trends and valorization of immobilization strategies and ligninolytic enzymes by industrial biotechnology. J. Mol. Catal. 101 , 56–66 (2014). https://doi.org/10.1016/j.molcatb.2013.12.016 Brethauer, S., Studer, M.H.: Consolidated bioprocessing of lignocellulose by a microbial consortium. Energy Environ. Sci. 7 , 1446–1453 (2014). https://doi.org/10.1039/c3ee41753k Sánchez Orozco, R., Balderas Hernández, P., Roa Morales, G., Ureña Núñez, F., Villafuerte, O., Lugo Lugo, J., Flores, V., Ramírez, N., Barrera Díaz, C.E., Cajero Vázquez, P.: Characterization of Lignocellulosic Fruit Waste as an Alternative Feedstock for Bioethanol Production. Bioresour. 9 (2014). https://doi.org/10.15376/biores.9.2.1873-1885 Mohd Azhar, S.H., Abdulla, R., Jambo, S.A., Marbawi, H., Gansau, J.A., Mohd Faik, A.A., Rodrigues, K.F.: Yeasts in sustainable bioethanol production: A review. Biochem. Biophys. Rep. 10 , 52–61 (2017). https://doi.org/10.1016/j.bbrep.2017.03.003 Bušić, A., Mardetko, N., Kundas, S., Morzak, G., Belskaya, H., Šantek, M.I., Komes, D., Novak, S., Šantek, B.: Bioethanol production from renewable raw materials and its separation and purification: A review. Food Technol. Biotechnol. 56 , 289–311 (2018). https://doi.org/10.17113/ftb.56.03.18.5546 Chu, B.C.H., Lee, H.: Genetic improvement of Saccharomyces cerevisiae for xylose fermentation. Biotechnol. Adv. 25 , 425–441 (2007). https://doi.org/10.1016/j.biotechadv.2007.04.001 Scully, S.M., Orlygsson, J.: Recent advances in second-generation ethanol production by thermophilic bacteria. Energies. 8 , 1–30 (2015). https://doi.org/10.3390/en8010001 Moscoviz, R., Kleerebezem, R., Rombouts, J.L.: Directing carbohydrates toward ethanol using mesophilic microbial communities. Curr. Opin. Biotechnol. 67 , 175–183 (2021). https://doi.org/10.1016/j.copbio.2021.01.016 Talarico, L.A., Gil, M.A., Yomano, L.P., Ingram, L.O., Maupin-Furlow, J.A.: Construction and expression of an ethanol production operon in Gram-positive bacteria. Microbiol. 151 , 4023–4031 (2005). https://doi.org/10.1099/mic.0.28375-0 Maleki, F., Changizian, M., Zolfaghari, N., Rajaei, S., Noghabi, K.A., Zahiri, H.S.: Consolidated bioprocessing for bioethanol production by metabolically engineered Bacillus subtilis strains. Sci. Rep. 11 , 1–13 (2021). https://doi.org/10.1038/s41598-021-92627-9 Drake, H.L., Schramm, A., Horn, M.A.: Earthworm Gut Microbial Biomes: Their Importance to Soil Microorganisms, Denitrification, and the Terrestrial Production of the Greenhouse Gas N2O. In Intestinal Microorganisms of Termites and Other Invertebrates. Springer. 65–87 (2006). https://doi.org/10.1007/0-387-31131-X_4 Zeibich, L., Schmidt, O., Drake, H.L.: Fermenters in the earthworm gut: Do transients matter? FEMS Microbiol. Ecol. 95 (1), 1–12 (2019). https://doi.org/10.1093/femsec/fiy221 Manyuchi, M.M., Mbohwa, C., Muzenda, E.: Anaerobic treatment of opaque beer wastewater with enhanced biogas recovery through Acti-zyme bioaugmentation. S Afr. J. Chem. Eng. 26 , 74–79 (2018). https://doi.org/10.1016/j.sajce.2018.10.002 Tesfaw, A., Oner, E.T., Assefa, F.: Optimization of ethanol production using newly isolated ethanologenic yeasts. Biochem. Biophys. Rep. 25 , 100886 (2021). https://doi.org/10.1016/j.bbrep.2020.100886 Hussain, N., Singh, A., Saha, S., Venkata Satish Kumar, M., Bhattacharyya, P., Bhattacharya, S.S.: Excellent N-fixing and P-solubilizing traits in earthworm gut-isolated bacteria: A vermicompost based assessment with vegetable market waste and rice straw feed mixtures. Bioresour Technol. 222 , 165–174 (2016). https://doi.org/10.1016/j.biortech.2016.09.115 Hussain, N., Das, S., Goswami, L., Das, P., Sahariah, B., Bhattacharya, S.S.: Intensification of vermitechnology for kitchen vegetable waste and paddy straw employing earthworm consortium: Assessment of maturity time, microbial community structure, and economic benefit. J. Clean. Prod. 182 , 414–426 (2018). https://doi.org/10.1016/j.jclepro.2018.01.241 Page, D.R., Miller, A.L., Keeney, R.H.: Methods of Soil Analysis. In Chemical and Microbiological Properties (2nd ed.) Madison, Wisconsin, USA. (1982) Jenkinson, D.S.: The Determination of Microbial Biomass Carbon and Nitrogen in Soil. In: Wilson, J.R. (ed.) Advances in Nitrogen Cycling in Agricultural Ecosystems, pp. 368–386. CaB international, Wallingford (1988) U.S. Environmental Protection Agency (USEPA): Soil Sampling Quality Assurance User's Guide. Environmental Monitoring System Laboratory, Las Vegas (1989) Smibert, R.M., Krieg, N.R.: Phenotypic Characterization. In: Gerhardt, N., Murray, P., Wood, R.G.E., W.A. and, Krieg (eds.) Methods in General and Molecular Bacteriology, pp. 607–654. American Society for Microbiology, Washington DC (1994) Ebersole, B.: Single Laboratory Validation of a GC-FID Method for Ethanol in Kombucha. In: NaturPro Scientific LLC, Rich Schmidt, Covance Laboratories and Matt Eckert, Covance Laboratories. (2016) Moneke, A.N., Okolo, B.N., Nweke, A.I., Ezeogu, L.I., Ire, F.S.: Selection and characterisation of high ethanol-tolerant Saccharomyces yeasts from orchard soil. Afr. J. Biotechnol. 7 , 4567–4575 (2008). https://doi.org/10.5897/AJB08.933 Brown, M.D., Hopps, H.C.: Staining of Bacteria in Tissue Sections: A Reliable Gram Stain Method. Am. J. Clin. Pathol. 60 , 234–240 (1973). https://doi.org/10.1093/ajcp/60.2.234 Sasser, M.: Identification of Bacteria by Gas Chromatography of Cellular Fatty Acids. Tech. Note. 101 , 1–6 (2001) Lin, S.J., Kaeberlein, M., Andalis, A.A., Sturtz, L.A., Defossez, P.A., Culotta, V.C., Fink, G.R., Guarente, L.: Calorie restriction extends Saccharomyces cerevisiae lifespan by increasing respiration. Nat. 418 , 344–348 (2002). https://doi.org/10.1038/nature00829 Tamaki, N., Nakamura, M., Kimura, K., Hama, T.: Purification and properties of aldehyde dehydrogenase from Saccharomyces cerevisiae . J. Biochem. 82 , 73–79 (1977). https://doi.org/10.1093/oxfordjournals.jbchem.a131694 Hoppner, T.C., Doelle, H.W.: Purification and kinetic characteristics of pyruvate decarboxylase and ethanol dehydrogenase from Zymomonas mobilis in relation to ethanol production. Eur. J. Appl. Microbiol. 17 , 152–157 (1983). https://doi.org/10.1007/BF00505880 Malakar, A.K.G., Dipankar, Dey, A., Basu, A.: Antiapoptotic role of S-adenosyl-l-methionine against hydrochloric acid induced cell death in Saccharomyces cerevisiae . Biochim. Biophys. Acta Gen. Subj. 1780 , 937–947 (2008). https://doi.org/10.1016/j.bbagen.2008.03.014 Mukherjee, S., Barman, S., Sarkar, S., Mandal, N.C., Bhattacharya, S.: Anti-bacterial activity of Achatina CRP and its mechanism of action. Indian J. Exp. Biol. 52 , 692–704 (2014) Lalander, C.H., Fidjeland, J., Diener, S., Eriksson, S., Vinnerås, B.: High waste-to-biomass conversion and efficient Salmonella spp. reduction using black soldier fly for waste recycling. Agroecol Sustain. Food Syst. 39 (3), 261–271 (2015). https://doi.org/10.1007/s13593-014-0235-4 Dillon, J.L., Hardaker, J.B.: Farm Management Research for Small Farmer Development, p. 6. Food and Agriculture Organization (FAO) of the United Nations (1993) Mondal, A., Goswami, L., Hussain, N., Barman, S., Kalita, E., Bhattacharyya, P., Bhattacharya, S.S.: Detoxification and eco-friendly recycling of brick kiln coal ash using Eisenia fetida: A clean approach through vermitechnology. Chemosphere. 244 , 125470 (2020). https://doi.org/10.1016/j.chemosphere.2019.125470 Gupta, P., Samant, K., Sahu, A.: Isolation of cellulose-degrading bacteria and determination of their cellulolytic potential. Int. J. Microbiol. (2012). https://doi.org/10.1155/2012/578925 Balch, M.L., Chamberlain, M.B., Worthen, R.S., Holwerda, E.K., Lynd, L.R.: Fermentation with continuous ball milling: Effectiveness at enhancing solubilization for several cellulosic feedstocks and comparative tolerance of several microorganisms. Biomass Bioenergy. 134 , 105468 (2020). https://doi.org/10.1016/j.biombioe.2020.105468 Robledo, M., Jiménez-Zurdo, J.I., Velázquez, E., Trujillo, M.E., Zurdo-Piñeiro, J.L., Ramírez-Bahena, M.H., Ramos, B., Díaz-Mínguez, J.M., Dazzo, F., Martínez-Molina, E., Mateos, P.F.: Rhizobium cellulase CelC2 is essential for primary symbiotic infection of legume host roots. Proc. Natl. Acad. Sci. United States Am. (PNAS). 105 (18), 7064–7069 (2008). https://doi.org/10.1073/pnas.0802547105 Naveenkumar Rajendran, J.H.: Techno-economic analysis of food waste valorization for integrated production of polyhydroxyalkanoates and biofuels. Bioresour Technol. 348 (2022). https://doi.org/10.1016/j.biortech.2022.126796 Slabbinck, B., De Baets, B., Dawyndt, P., De Vos, P.: Towards large-scale FAME-based bacterial species identification using machine learning techniques. Syst. Appl. Microbiol. 32 , 163–176 (2009). https://doi.org/10.1016/j.syapm.2009.01.003 Jan-Roblero, J., Cruz-Maya, J.A., Guerrero Barajas, C.C.: Kosakonia. In Beneficial Microbes in Agro-Ecology: Bacteria and Fungi. 213–231 (2020). https://doi.org/10.1016/B978-0-12-823414-3.00012-5 Nwachukwu, R.E.S., Shahbazi, A., Wang, L., Ibrahim, S., Worku, M., Schimmel, K.: Bioconversion of glycerol to ethanol by a mutant Enterobacter aerogenes . AMB express. 2 , 1–6 (2012). https://doi.org/10.1186/2191-0855-2-20 Ding, J., Holzwarth, G., Penner, M.H., Patton-Vogt, J., Bakalinsky, A.T.: Overexpression of acetyl-CoA synthetase in Saccharomyces cerevisiae increases acetic acid tolerance. FEMS Microbiol. Lett. 362 , 1–20 (2015). https://doi.org/10.1093/femsle/fnu042 Cheng, S., Fan, C., Sinha, S., Bobik, T.A.: The PduQ Enzyme Is an Alcohol Dehydrogenase Used to Recycle NAD + Internally within the Pdu Microcompartment of Salmonella enterica . PLOS ONE. 7 , 1–11 (2012). https://doi.org/10.1371/journal.pone.0047144 Eram, M.S., Ma, K.: Decarboxylation of pyruvate to acetaldehyde for ethanol production by hyperthermophiles. Biomol. 3 , 578–596 (2013). https://doi.org/10.3390/biom3030578 Zhang, Y., Su, M., Qin, N., Nielsen, J., Liu, Z.: Expressing a cytosolic pyruvate dehydrogenase complex to increase free fatty acid production in Saccharomyces cerevisiae . Microb. Cell. Factories. 19 , 1–9 (2020). https://doi.org/10.1186/s12934-020-01493-z Ding, A.X., Sun, G., Argaw, Y.G., Wong, J.O., Easwaran, S., Montell, D.J.: CasExpress reveals widespread and diverse patterns of cell survival of caspase-3 activation during development in vivo. eLife. 5 , 1–20 (2016). https://doi.org/10.7554/eLife.10936 József, L., Khreiss, T., Filep, J.G.: CpG motifs in bacterial DNA delay apoptosis of neutrophil granulocytes. FASEB J. 18 , 1776–1778 (2004). https://doi.org/10.1096/fj.04-2048fje Sahariah, B., Goswami, L., Kim, K.H., Bhattacharyya, P., Bhattacharya, S.S.: Metal remediation and biodegradation potential of earthworm species on municipal solid waste: A parallel analysis between Metaphire posthuma and Eisenia fetida . Bioresour Technol. 180 , 230–236 (2015). https://doi.org/10.1016/j.biortech.2014.12.062 Paul, S., Choudhury, M., Deb, U., Pegu, R., Das, S., Bhattacharya, S.S.: Assessing the ecological impacts of ageing on hazard potential of solid waste landfills: A green approach through vermitechnology. J. Clean. Prod. 236 , 117643 (2019). https://doi.org/10.1016/j.jclepro.2019.117643 Adegboye, M.F., Ojuederie, O.B., Talia, P.M., Babalola, O.O.: Bioprospecting of microbial strains for biofuel production: Metabolic engineering, applications, and challenges. Biotechnol. Biofuels. 14 , 1–21 (2021). https://doi.org/10.1186/s13068-020-01853-2 Pata, S.U.K., Kartal, M.T., Adebayo, T.S.: Enhancing environmental quality in the United States by linking biomass energy consumption and load capacity factor. Geosci. Front. 14 (3) (2023). https://doi.org/10.1016/j.gsf.2022.101531 Sakthivel, P., Subramanian, K.A., Mathai, R.: Indian scenario of ethanol fuel and its utilization in the automotive transportation sector. Resour. Conserv. Recycl. 132 , 102–120 (2018). https://doi.org/10.1016/j.resconrec.2018.01.012 Zhang, B., Ji, C., Wang, S.: Performance of a hydrogen-enriched ethanol engine at unthrottled and lean conditions. Energy Convers. Manag. 114 , 68–74 (2016). https://doi.org/10.1016/j.enconman.2016.01.073 Supplementary Files GAforetohpaper.tif ResearchHighlights.docx SUPPLEMENTARY.doc Cite Share Download PDF Status: Under Review Version 1 posted Reviewers agreed at journal 29 Jan, 2024 Reviewers invited by journal 29 Jan, 2024 Editor invited by journal 28 Jan, 2024 First submitted to journal 17 Jan, 2024 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-3876047","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":270180127,"identity":"9e09d9c9-0bbd-4201-942b-289ae0da3d79","order_by":0,"name":"Ratan Chowdhury","email":"","orcid":"","institution":"Tezpur University","correspondingAuthor":false,"prefix":"","firstName":"Ratan","middleName":"","lastName":"Chowdhury","suffix":""},{"id":270180128,"identity":"b240f7a4-2646-4144-a846-b99edd8c2c8a","order_by":1,"name":"Nazneen Hussain","email":"","orcid":"","institution":"Assam Don Bosco University","correspondingAuthor":false,"prefix":"","firstName":"Nazneen","middleName":"","lastName":"Hussain","suffix":""},{"id":270180129,"identity":"bbf3db2a-90f9-4b65-bca2-497cc1bb5769","order_by":2,"name":"Sandip Mukherjee","email":"","orcid":"","institution":"Washington University In St Louis: Washington University in St Louis","correspondingAuthor":false,"prefix":"","firstName":"Sandip","middleName":"","lastName":"Mukherjee","suffix":""},{"id":270180130,"identity":"968b1e31-579c-429e-a585-208bb2942380","order_by":3,"name":"Soma Barman","email":"","orcid":"","institution":"Tezpur University","correspondingAuthor":false,"prefix":"","firstName":"Soma","middleName":"","lastName":"Barman","suffix":""},{"id":270180131,"identity":"cabd2bed-da1a-4023-b79f-62c32e52c9f3","order_by":4,"name":"Himadri Mandal","email":"","orcid":"","institution":"Tezpur University","correspondingAuthor":false,"prefix":"","firstName":"Himadri","middleName":"","lastName":"Mandal","suffix":""},{"id":270180132,"identity":"5e990190-c4fa-4d2e-bbb1-478e3856a856","order_by":5,"name":"Prasanta Kumar Raul","email":"","orcid":"","institution":"DRL: Defence Research Laboratory","correspondingAuthor":false,"prefix":"","firstName":"Prasanta","middleName":"Kumar","lastName":"Raul","suffix":""},{"id":270180133,"identity":"b3948eb8-f69d-4160-9cc3-cc8044744d25","order_by":6,"name":"Satya Sundar Bhattacharya","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA80lEQVRIiWNgGAWjYLACxn82cigCB/CrZwZitjRjHrgAG3FaDif2IGvBC+Tbzx+T+MHDnL6fvf2ZBEPFvTx++QbGwwV4tBicSWaT7JFgy+3hOZAmwXCmuFiyjYHh8Ax8WhiS2SR4DHhyeyQSjkkwtiUkbjgG1MKDR4t8/2M2yT8JEuk88g/bJBj/JSTuJ6SF4UYymzTPAYMEHglmNgnGBqAtbAS0GNx4bGwt25Bg2HMmjdki4VhC4oxjiQ0EHJb48Obbhv/y7O3HH974UJOQ2N98+PBnvA5jYGCRgDMTwCRjA34NwLj8QEjFKBgFo2AUjHAAAChaR4OmID+FAAAAAElFTkSuQmCC","orcid":"https://orcid.org/0000-0002-8618-0000","institution":"Tezpur University","correspondingAuthor":true,"prefix":"","firstName":"Satya","middleName":"Sundar","lastName":"Bhattacharya","suffix":""}],"badges":[],"createdAt":"2024-01-18 14:23:03","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-3876047/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-3876047/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":50521956,"identity":"2bce40ac-2b83-4bfb-a733-6ed46679832e","added_by":"auto","created_at":"2024-02-01 19:17:20","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":351628,"visible":true,"origin":"","legend":"\u003cp\u003eCellulose degradation efficacy of different microbial strains isolated from vermicompost and earthworm gut (Congo red assay)\u003c/p\u003e","description":"","filename":"F1.png","url":"https://assets-eu.researchsquare.com/files/rs-3876047/v1/d587a28bb49ea8117ed121ca.png"},{"id":50521187,"identity":"8f3136fc-ac0c-4fc4-96c4-c3eb3c505f91","added_by":"auto","created_at":"2024-02-01 19:09:19","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":73404,"visible":true,"origin":"","legend":"\u003cp\u003eEthanol production from sugar solution and banana peels by ethanologenic bacterial strains and their characterization.\u003cstrong\u003ea. \u003c/strong\u003eVariation in ethanol production by the different bacterial strains. Values represent mean±standard deviation (n=3); \u003cstrong\u003eb.\u003c/strong\u003ePhylogenetic relationships of taxa of the bacterial strains isolated from vermicompost and earthworm gut.\u003c/p\u003e","description":"","filename":"F2.png","url":"https://assets-eu.researchsquare.com/files/rs-3876047/v1/5445a70d647c348f5b33c02f.png"},{"id":50521188,"identity":"48a59678-68fa-4ec0-a634-ab946465687c","added_by":"auto","created_at":"2024-02-01 19:09:20","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":80371,"visible":true,"origin":"","legend":"\u003cp\u003eAssessment of tolerance limits to different shock treatments (sugar exposure - at 5%, 8%, and 15 %; ethanol exposure – 10% and 15%) in the bacteria and yeast.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ea.\u003c/strong\u003eVariation in microbial sedimentation rate. Values represent mean±standard deviation (n=3); \u003cstrong\u003eb. \u003c/strong\u003eVariation in sugar tolerance potentials of bacteria and yeast at 24 hours. Values represent mean±standard deviation (n=3); \u003cstrong\u003ec.\u003c/strong\u003eVariation in sugar tolerance potentials of bacteria and yeast at 48 hours. Values represent mean±standard deviation (n=3); \u003cstrong\u003ed. \u003c/strong\u003eVariation in sugar tolerance potentials of bacteria and yeast at 72 hours. Values represent mean±standard deviation (n=3); \u003cstrong\u003ee.\u003c/strong\u003e Variation in ethanol tolerance potentials of bacteria and yeast. Values represent mean±standard deviation (n=3).\u003c/p\u003e","description":"","filename":"F3.png","url":"https://assets-eu.researchsquare.com/files/rs-3876047/v1/8d5d73cb87992f0b36fd37fe.png"},{"id":50521192,"identity":"dfd6c84b-4519-4a67-b5d0-35a91c8d2e28","added_by":"auto","created_at":"2024-02-01 19:09:20","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":400046,"visible":true,"origin":"","legend":"\u003cp\u003eRepresentation of the fluorescence-activated cell sorting (FACS) analysis of the stained microbial and yeast cells exposed to different concentrations (0% (i.e., control), 5% and 15%) of sugar solutions.\u003c/p\u003e","description":"","filename":"F4.png","url":"https://assets-eu.researchsquare.com/files/rs-3876047/v1/a910ae2c5515c8b948175b97.png"},{"id":50521191,"identity":"59eb8524-d2bd-4621-8001-52bf4d71cae4","added_by":"auto","created_at":"2024-02-01 19:09:20","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":321385,"visible":true,"origin":"","legend":"\u003cp\u003eConfocal microscopy-based assessment of cellular integrity of representative bacterial strains and yeast exposed to sugar solutions of different concentrations using specialized stains (green fluorescein-5-isothiocyanate (FITC) and red propidium iodide (PI)). The FITC stains all cells and the PI stains only ruptured cells.\u003c/p\u003e","description":"","filename":"F5.png","url":"https://assets-eu.researchsquare.com/files/rs-3876047/v1/6e51b5293b0ee742bd54d60f.png"},{"id":50522122,"identity":"d141f509-aa40-4132-8a77-1bf2be2aa62a","added_by":"auto","created_at":"2024-02-01 19:25:22","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2022821,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-3876047/v1/60348a84-1690-47c1-bba2-ebdb174d939f.pdf"},{"id":50521194,"identity":"e3e54dba-20dd-4e51-8b36-4bbb4d4ec6eb","added_by":"auto","created_at":"2024-02-01 19:09:20","extension":"tif","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":821996,"visible":true,"origin":"","legend":"","description":"","filename":"GAforetohpaper.tif","url":"https://assets-eu.researchsquare.com/files/rs-3876047/v1/74a710abb0883393db2de4c7.tif"},{"id":50521190,"identity":"2d3abe74-0c70-448e-afca-2207e2d11c15","added_by":"auto","created_at":"2024-02-01 19:09:20","extension":"docx","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":15115,"visible":true,"origin":"","legend":"","description":"","filename":"ResearchHighlights.docx","url":"https://assets-eu.researchsquare.com/files/rs-3876047/v1/b5a1b10a50fdd2b1bb73a6a3.docx"},{"id":50521957,"identity":"2323af56-cdd7-4a24-bfab-9d0d030fc251","added_by":"auto","created_at":"2024-02-01 19:17:20","extension":"doc","order_by":3,"title":"","display":"","copyAsset":false,"role":"supplement","size":600576,"visible":true,"origin":"","legend":"","description":"","filename":"SUPPLEMENTARY.doc","url":"https://assets-eu.researchsquare.com/files/rs-3876047/v1/0ca64151b50e359706df3b37.doc"}],"financialInterests":"","formattedTitle":"Bioethanol production from lignocellulosic waste without pre-treatment employing vermicompost and earthworm gut-isolated bacteria: Insights on waste to wealth conversion efficiency towards cleaner lifestyle","fulltext":[{"header":"Statement of Novelty","content":"\u003cp\u003eThis study marks a pioneering breakthrough in bioethanol production by isolating ethanologenic bacteria from vermicompost for the first time. Derived from earthworm guts, these bacterial strains outperform yeast, yielding 2-3 times more ethanol. Importantly, they exhibit an unprecedented ability to generate ethanol directly from raw biomass without pretreatment. Flow cytometry uncovers a distinctive defense mechanism against sugar stress, highlighting the bacteria\u0026apos;s robust membrane integrity and exceptional stress tolerance. This research not only advances sustainable bioethanol production but also unveils novel insights into adaptive strategies of ethanologenic bacteria.\u003c/p\u003e"},{"header":"1. Introduction","content":"\u003cp\u003eOne of the biggest challenges of the modern world is to meet the ever-increasing energy demands. As non-renewable fossil fuels are mainly used for energy generation, the quest for a sustainable energy supply has gained considerable attention in the present [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. Consequently, renewable and sustainable energy generation has become integral to the UN\u0026rsquo;s SDGs [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. In this context, the pathways for bioethanol production from wasted lignocellulosic biomass through the utilization of a microorganism-mediated fermentative process, is a sustainable option for fossil fuel substitution [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. Conventionally, bioethanol production from lignocellulosic biomass involves steps like pretreatment for feedstock fortification and enzymatic hydrolysis with the help of externally added enzymes for converting cellulosic materials to sugars [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. However, the process's success depends on the microbial activity and their compatibility with the feedstock and exposure conditions [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. Thus, experimentation for optimizing the bioethanol production process by step reduction and using novel and efficient microbial species should be continued considering the environmental superiority of the technology. In particular, applying food waste as feedstock reduces the ecological risk of dumping such waste on land [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eLignocellulosic wastes (e.g., crop residues and fruit peels) have been widely utilized as raw materials for bioethanol production [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. Fruit peels and discarded pulps are mainly disposed of in landfills owing to their low commercial value[\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. Although these materials are compostable, the economic viability of the process could be better due to time, space, and labor intensiveness. Generally, yeasts (\u003cem\u003eSaccharomyces sp.\u003c/em\u003e) are the most dominant microorganism utilized for sugar fermentation for decades [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. The metabolic efficiency of S\u003cem\u003eaccharomyces\u003c/em\u003e is greatly affected in peptone (xylose, arabinose, etc.) dominated feedstocks [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. Moreover, the most popular yeast, \u003cem\u003eSaccharomyces cerevisiae\u003c/em\u003e, cannot utilize cellulose and hemicelluloses [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. Hence, the quest for alternative microorganisms for efficient ethanol production from bio-wastes has gained considerable attention in the recent past. Several bacterial communities (thermophilic, lactic acid producing, volatile fatty acid producing, etc.) bacteria have shown high promise for economically viable bioethanol production [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e, \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e, \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. Efforts have also been made to induce non-ethanologenic microbes for ethanol production from cellulosic feedstock [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. Recently, Maleki et al. have successfully utilized a non-ethanologenic strain of \u003cem\u003eBacillus subtillis\u003c/em\u003e for ethanol production [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. As such, earthworm guts and earthworm-mediated lignocellulosic feedstock-based vermicomposting systems may also serve as potential sources of fermentative microorganisms because the gut environment stimulates their growth [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]. A recent finding strongly suggests that transient microbes in the earthworm gut play a definite role in the fermentation process [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]. However, vermicomposting beds and earthworm gut have never been utilized to identify ethanol-producing microorganisms. In particular, the vermicomposting bed can be an easy source of beneficial fermentative vis-\u0026agrave;-vis ethanologenic bacteria.\u003c/p\u003e \u003cp\u003eOne of the vital qualities of ethanologenic microorganisms is their osmo-adaptation. The growth rate of several yeasts is significantly inhibited in high-concentration sugar solutions due to the saturation of the sugar uptake capacity of microbial cell [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e, \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]. However, no study is available about osmo-tolerant characteristics in ethanol-producing bacteria. The industrial suitability of microorganism-mediated processes is vital for applying such technologies. As such, the sedimentation rate, ethanol, and sugar tolerance properties are crucial attributes for the evaluation of the industrial applicability of the ethanologenic microorganisms [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]. In this context, assessing waste conversion and economic efficiencies is essential to appreciate the sustainability and eco-compatibility of the technology. A few enzymes (Acetyl-CoA synthase, alcohol dehydrogenase, pyruvate decarboxylase, pyruvate kinase, and pyruvate decarboxylase complex) are also essential regulators of industrially scalable bioethanol production. However, these enzymatic pathways have mainly been studied in the yeast-mediated bio-ethanol production system. Hence, analyzing the activation dynamics of these enzymes in bacteria-mediated schemes will be intriguingly novel.\u003c/p\u003e \u003cp\u003eAdopting a novel approach, the present study explores earthworm-mediated vermibeds to isolate and utilize ethanologenic bacteria to valorize lignocellulosic wastes. Based on the available literature, the identified research gaps of the current investigation were: (a) The guts of the earthworms grown in lignocellulosic biomass have not been utilized as a source of ethanologenic bacteria; (b) the ethanol-producing potential of such bacterial isolates has not been evaluated from mechanistic viewpoints (i.e., osmo-tolerance, sedimentation rate, ethanol tolerance, etc.) in comparison with a commercially used organism such as yeast (\u003cem\u003eSaccharomyces cerevisiae\u003c/em\u003e); and (c) it is unknown whether the bacterial isolates could produce bioethanol from lignocellulosic feedstocks without pre-treatment or not; and (d) the waste-to-energy conversion efficiency and the overall economic benefit have also needed to be ascertained for successful application of the technology. Previously, our published work identified and reported the prolific plant growth-promoting roles of a few earthworm gut-derived bacterial species [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]. Hence, the ethanologenic property of those bacteria has been explored in the present investigation. In addition, a lignocellulosic feedstock-based vermicompost reactor was searched to isolate new ethanol-producing bacterial strains. Initially, several bacterial strains were qualitatively screened based on their cellulose and carbohydrate degrading capacity, and a few selected isolates were for assessing their bio-ethanol-producing potentials. Eventually, the ethanol-producing ability of the characterized bacteria was evaluated in sugar solution and lignocellulosic fruit waste (banana peel) compared with yeast. Moreover, the activities of different key enzymes were assessed to appreciate the underlying mechanism of microbe-mediated bioethanol production. Finally, the membrane permeability kinetics of the selected bacteria was compared with yeast through flow cytometry and fluorescence microscopy to understand the osmo-adaptation mechanism.\u003c/p\u003e"},{"header":"2. Materials and Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e2.1. Vermicomposting and composting with lignocellulosic biomass\u003c/h2\u003e \u003cp\u003eVermicomposting was conducted with lignocellulosic waste (vegetable peels, sugarcane bagasse, and rice straw) materials to stimulate cellulose-degrading and ethanologenic microorganisms. The lignocellulosic waste materials were homogenized with urine-free cow dung at a ratio of 3:1. The vermibeds and composting beds were prepared in truncated cone-shaped earthen reactors of 3L capacity [0.45m (height)\u0026times;0.15m (base radius)\u0026times;0.30m (top radius)].The feedstocks were pre-composted for five days prior introduction of earthworms in the vermireactors for the thermo-stabilization [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]. Eventually, non-clitellated juvenile \u003cem\u003eE. fetida\u003c/em\u003e specimens (10 earthworm kg\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e) were employed in the vermireactors. The incubation was carried out for 60 days by maintaining 40\u0026ndash;50% moisture within a temperature range of 27\u0026ndash;30\u0026deg;C, and aeration was maintained by intermittent mixing of the feedstocks throughout the incubation period. The whole set of vermicomposting and composting reactors was replicated thrice. Feedstock samples were periodically drawn at 0, 30, and 60 days to assess the physicochemical and microbial changes during biomass degradation.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e2.2. Analysis of physicochemical and microbial properties in feedstocks\u003c/h2\u003e \u003cp\u003eThe periodically obtained vermicompost and compost samples were analyzed for pH, total N, available P, and exchangeable K by following standard protocols [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]. The microbial activity in the compost and vermicompost feedstocks was assessed based on microbial respiration and microbial biomass C (MBC). The microbial respiration was measured by estimating the CO\u003csub\u003e2\u003c/sub\u003e emitted from the samples on incubation with glucose in a closed system at 25\u0026deg;C for 24 hours. The MBC was estimated by the fumigation extraction technique as detailed by Jenkinson (1988) [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]. In short, compost and vermicompost samples were fumigated in the presence of chloroform. After that, fumigated and un-fumigated samples were extracted in 2M KCl, and the ninhydrin-N level in the filtrates was spectrophotometrically assessed at 570 nm. All analyses were performed in clean glassware rinsed with deionized water. Analytical grade reagents and deionized water were used for preparing all the solutions [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]. In addition, the total bacterial and fungal counts in the compost and vermicompost samples were analyzed by pour plate technique using nutrient agar (NA) and potato dextrose agar (PDA), respectively [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]. The colony forming units (CFU) mL\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003ewere calculated with the help of the formula given below:\u003c/p\u003e \u003cp\u003e \u003cspan class=\"InlineEquation\"\u003e \u003cspan class=\"mathinline\"\u003e\\(No. of organism {m L}^{-1}= \\frac{No. of colonies \\times dilution }{Volume plated}\\)\u003c/span\u003e \u003c/span\u003e \u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;.. (I)\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003e2.3. Screening of cellulose-degrading and carbohydrate-utilizing bacterial strains\u003c/h2\u003e \u003cp\u003eBased on the results of the microbial assessment, the vermicompost samples were searched for isolating potential cellulose-degrading and carbohydrate-utilizing bacterial strains. Initially, 14 profusely growing bacterial strains in NA media were isolated from vermicompost and earthworm gut. Eight well-characterized and previously reported bacterial species isolated from earthworm gut were also assessed for their carbohydrate and cellulose utilization properties [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]. These 22 dominant bacterial strains were evaluated for their carbohydrate utilization efficiency using the Hi-Media Carbo Kit. Based on the outcome of the qualitative carbohydrate solubilizing assessment, a few strains were selected for assessing their cellulose degradation potential. These isolates were allowed to grow for 24 hours at 37\u0026deg;C in Carboxymethyl cellulose and 1% agar media plates, followed by Congo-red staining according to the technique standardized by Smibert and Krieg (1994) [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e].\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003e2.4. Gas chromatographic estimation of ethanol production from sugar solution and banana peel\u003c/h2\u003e \u003cp\u003eSugar solutions (5% w/v) were initially fermented at 37\u0026deg;C for 48 hours in an anaerobic chamber. The sugar solution was then sterilized at 121\u0026deg;C and 210 kPa for 20 minutes in an autoclave. Later, 1% inoculum of the selected bacterial strains was inoculated into the sugar solutions at room temperature. Glucose was the sole energy source for the bacterial inoculums used in this study. Subsequently, after 48 hours, the turbid microbiological media was transferred to sterilized falcon tubes. A strain of yeast (\u003cem\u003eSaccharomyces cerevisiae\u003c/em\u003e MTCC 170), procured from MTCC, was used as a positive control to compare the ethanol production potency of the screened bacterial isolates.\u003c/p\u003e \u003cp\u003eMoreover, the ethanol production potential of the selected bacterial isolates was evaluated using non-edible banana (\u003cem\u003eMusa\u003c/em\u003e spp.) epicarps as substrates. The banana epicarps weighing 20g were homogenized with water at 1:2 ratio, and the homogenate (15 ml) was subjected to centrifugation at 8000 rpm for 15 minutes. The supernatant of the centrifuged contents and the filtrate of the sonicated samples were collected in fresh falcon tubes (5 ml in each tube). After that, 1% of the bacterial cultures were inoculated in each tube and allowed to ferment at 28\u0026deg;C. After 48 hours of incubation, the tubes were sealed and used to measure ethanol content.\u003c/p\u003e \u003cp\u003e \u003cem\u003eGas-chromatography\u003c/em\u003e \u003c/p\u003e \u003cp\u003eThe extracted ethanol was analyzed using a Gas chromatograph (GC) (Model: Agilent 7890A) following the procedure described by Ebersole (2016) [\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]. The method of the GC was set as follows:\u003c/p\u003e \u003cp\u003e \u003cul\u003e \u003cli\u003e \u003cp\u003eInlet: 160\u003csup\u003e0\u003c/sup\u003eC, mode: split, Split flow: 80 ml min\u003csup\u003e-1\u003c/sup\u003e\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eOven: Initial-50\u003csup\u003e0\u003c/sup\u003eC, hold time of 1 min and the Ramp (10\u003csup\u003e0\u003c/sup\u003eC min\u003csup\u003e-1\u003c/sup\u003e) till temperature goes to 200\u003csup\u003e0\u003c/sup\u003eC and hold time of 1 min\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eDetector: 200\u003csup\u003e0\u003c/sup\u003eC, Flame Ionisation Detector (FID)\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eCarrier gas: N\u003csub\u003e2\u003c/sub\u003e, 7 mL min\u003csup\u003e-1\u003c/sup\u003e\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eInjection volume: 100 to 200 \u0026micro;L\u003c/p\u003e \u003c/li\u003e \u003c/ul\u003e \u003c/p\u003e \u003cp\u003e \u003cem\u003eQA-QC\u003c/em\u003e \u003c/p\u003e \u003cp\u003eHPLC-grade ethanol was used as the standard for the calibration of the instrument. The lowest concentration of the working standard taken daily was considered as the limit of detection (LOD) (defined as the lowest concentration of the working standard with a signal-to-noise ratio equal to or exceeding 10:1) and the limit of quantification (LOQ). For this method, the LOD was 1.0% (v/v), and the LOQ was determined to be 0.789 mg L\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003e2.5. Sedimentation rate, ethanol tolerance, and sugar tolerance of the microbial cultures\u003c/h2\u003e \u003cp\u003eThe sedimentation rate of the yeast and the selected bacterial isolates was estimated with minor modifications of the method standardized by previous workers [\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e, \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]. The yeast-extract potato dextrose (YPD) medium was used to grow the yeast cells. Bacterial cells were cultured in Luria Bertani (LB) agar medium for 24 hours, centrifuged (14000 \u0026times; g) for 10 minutes, and pellets were obtained. This method modification was necessary because bacterial cells cannot grow in the YPD medium. Subsequently, the pellets were suspended in NaCl (0.89%) solution for two hours, the change in absorbance at 600 nm was recorded in a UV-Vis spectrophotometer (Cary 60), and the sedimentation rate (SR) was derived using the formula I Moneke et al., (2008) [\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]\u003c/p\u003e \u003cp\u003e \u003cspan class=\"InlineEquation\"\u003e \u003cspan class=\"mathinline\"\u003e\\(Sedimentation rate \\left(SR\\right)\\%=\\left(1- \\frac{Drop in absorbance after 2 hours}{Absorbnce at 0 hour}\\right)\\times 100\\)\u003c/span\u003e \u003c/span\u003e\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;.(II)\u003c/p\u003e \u003cp\u003eThe ethanol (10% and 15%) and sugar tolerance (5%, 8%, and 12%) of the selected bacterial strains was assessed in comparison with yeast (\u003cem\u003eSaccharomyces cerevisiae\u003c/em\u003e MTCC 170). The ethanol tolerance was measured based on the extent of cell viability after exposure to different concentrations of ethanol following the method detailed by Moneke et al. (2008) [\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]. The survival percentage (SP) in different ethanol solutions was calculated as formula II:\u003c/p\u003e \u003cp\u003e \u003cspan class=\"InlineEquation\"\u003e \u003cspan class=\"mathinline\"\u003e\\(Survival percentage \\left(SP\\right)= \\frac{Number of alive cells after 2 hour exposure}{Number of cells in control \\left(sterilized water\\right)} \\times 100\\)\u003c/span\u003e \u003c/span\u003e\u0026hellip;\u0026hellip;(III)\u003c/p\u003e \u003cp\u003eTo assess the sugar tolerance limit, sugar solutions of 5, 8, and 15% concentration (w/v) were sterilized using an autoclave. Once the solutions reached room temperature, 1% yeast culture and selected bacterial strains were inoculated. The inoculums were allowed to grow at 37\u0026deg;C for 72 hours in an anaerobic chamber. After every 24 hours, the optical density of the cultures was noted at 280 nm using the UV-Vis spectrophotometer (Cary 60).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003e2.6. Identification of microorganisms through Gram staining and 16S rRNA sequencing\u003c/h2\u003e \u003cp\u003eThe Gram staining assay was performed following the standard technique [\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e]. Then, the bacterial isolates' micro\u0026ndash;Genomic DNA extraction was performed using the QIAamp DNA Mini kit (QIAGEN, Germany). The quality of the extracted genomic DNA was evaluated using 0.8% (w/v) agarose gel to perform electrophoresis before using the DNA extracts of respective microbes as the template for polymerase chain reaction (PCR). The universal bacterial primers 27FA (5/-AGAGTTTGA TCATGGCTAG-3/) or 27FC (5/-AGAGTTTGATCCTGGCTAG-3/) and U1492R (5/-GTTACCTTGTTACGACTT-3/) was chosen to run the PCR program for amplifying full-length 16S rRNA gene fragment [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]. The PCR product was purified by gel elution technique using QIA quick Gel Extraction Kit (QIAGEN, Germany). The quantity and quality of the DNA extracts were confirmed using nano-drop and electrophoresis on 1% agarose gel. The partial nucleotide sequence was performed at 1st 158 BASE (Malaysia) using Sanger\u0026rsquo;s method using 27FA or 27FC, or U1492R primers. Here, it is essential to mention that the selected isolates' three (\u003cem\u003eK. ascorbata\u003c/em\u003e S8, \u003cem\u003eRhizobium sp\u003c/em\u003e. S10, and \u003cem\u003eBacillus sp\u003c/em\u003e. S12) were previously reported as N-fixing strains [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]. However, one among the three newly isolated strains (i.e., C5) was identified by fatty acid methyl ester (FAME) analysis because the broth culture of the organism was unsuccessful after several efforts.\u003c/p\u003e \u003cp\u003eThe FAME analysis was conducted by outsourcing from Royal Life Sciences Pvt. Ltd. (affiliated with MIDI Sherlock, USA). In short, the colonies of the C5 strains were grown on Trypticase soy broth Agar at 28\u003csup\u003eo\u003c/sup\u003eC for 24 hours. Then about 40 mg of bacterial cells were harvested in sterilized Petri plates by streaking. Then FAMEs were extracted in a step-wise manner following the standard protocol (saponification, methylation, extraction, and aqueous wash) [\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e]. The extracted FAMEs were analyzed by gas chromatography. The gas chromatography-derived FAME profiles were used as standard profiles. Subsequently, the organism's genus and species were identified with the assistance of the Sherlock software. The strain with a single match of at least 0.600 similarity indexes or 0.600 with \u0026lt;\u0026thinsp;0.100 distance from the nearest choice was considered a dependable species match.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003e2.7. Enzyme assays\u003c/h2\u003e \u003cp\u003eActivities of five enzymes [Acetyl-CoA synthetase (AS), pyruvate dehydrogenase (Pdh), pyruvate kinase (Pyk), pyruvate decarboxylase (PyD), and alcohol dehydrogenase (AD)] that are associated with the central metabolism of ethanologenic microorganisms were studied in the early exponential phase and at the onset of the stationary phase of growth. AS, Pdh, Pyk, and AD activities were measured regarding the NADH generation at 340 nm (εNADH\u0026thinsp;=\u0026thinsp;6.220 M\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003ecm\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e) following standard methods [\u003cspan additionalcitationids=\"CR29\" citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e]. The activity of PyD was assessed following the method of Hoppner and Doelle (1983), and the enzyme unit of pyruvate decarboxylase activity was expressed as 1.0 \u0026micro;M of acetaldehyde min\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e at 340 nm [\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e]. The details of all these methods have been provided in the supplementary information.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003e2.8. Flow cytometry and confocal microscopy: cellular function for sugar tolerance\u003c/h2\u003e \u003cp\u003eThe fluorescence-activated cell sorting (FACS) and confocal microscopy techniques were utilized to study cellular responses of the yeast (\u003cem\u003eSaccharomyces cerevisiae\u003c/em\u003e MTCC 170) and selected bacterial isolates following methods described by Malakar et al. (2008) and Mukherjee et al. (2014) [\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e, \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e]. The yeast and bacterial cells were incubated with 0 (i.e., control), 5, and 15% sugar solutions for 24 hours. Then, the treated and untreated (i.e., control) cells were stained using an Annexin V-FITC apoptosis detection kit (BD Biosciences, San Jose, CA, USA) according to the manufacturer\u0026rsquo;s protocol and subjected to flow cytometry analysis (BD Accuri, BD Biosciences). Apoptosis phases were detected by distinct double staining patterns: viable (Annexin V- and PI-, lower left square), early apoptotic (Annexin V\u0026thinsp;+\u0026thinsp;and propidium iodide (PI)-, lower right square), late apoptotic (Annexin V\u0026thinsp;+\u0026thinsp;and PI+, upper right square) and necrotic cells (Annexin V- and PI+, upper left square). To evade the coincidence of cells, the flow rate was adjusted to the lowest setting mode (data rate, 200\u0026ndash;300 events per second). At least 10,000 events were recorded for each sample with three replicates. Cells were collected on the forward scatter with logarithmic amplifiers for 5,000 events to determine the cell size. As the membrane potential of different organisms varies based on their cell size, all data were expressed as the ratio of membrane potential according to their size. The acquired data were accumulated as list mode files and examined offline using the System II V.3 software (Beckman-Coulter).\u003c/p\u003e \u003cp\u003eFor determining the membrane permeability, the 0 (i.e., control), 5, and 15% sugar-exposed cells were stained by two nucleic acid staining reagents, propidium iodide (PI) and FITC, using a Live/Dead BacLight kit (Molecular Probes, Invitrogen, Cergy-Pontoise, France). The membrane integrity of the yeast and bacterial cells was assessed by staining the cells at first with green fluorescing FITC, which can enter all cells when used alone, and then with the red-fluorescing PI that specifically invades cells with injured cytoplasmic membranes. The appropriate mixture of the FITC and PI stains enables differentiation between live organisms with intact cytoplasmic membranes and dead organisms with permeable cellular membranes. The method has been adequately standardized for observing apoptotic response in bacteria and yeast in previous studies [\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e, \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e]. However, the staining exercise was repeated several times for the yeast cells because a few cells were not responding uniformly to the PI. The morphology of the stained organisms was studied with the help of a Zeiss LSM 510 confocal fluorescence microscope with a 63X objective.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003e2.9. Waste to wealth conversion efficiency and economic evaluation\u003c/h2\u003e \u003cp\u003eWaste-to-wealth conversion efficiency (WCE) of vermicomposting and bacteria-driven bioethanol production systems was computed by modifying the formula given by Lalander et al. (2015) to ascertain their environmental compatibility, as shown below [\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e]\u003c/p\u003e \u003cp\u003e \u003cspan class=\"InlineEquation\"\u003e \u003cspan class=\"mathinline\"\u003e\\(Waste conversion efficiency \\left(WCE\\right)=\\frac{End product fresh weight \\left(kg\\right)}{Initial feedstock fresh weight \\left(kg\\right)}\\times 100\\)\u003c/span\u003e \u003c/span\u003e\u0026hellip;\u0026hellip;....(IV)\u003c/p\u003e \u003cp\u003eMoreover, the economic feasibility of both systems was evaluated on a large-scale basis (100 kg capacity) followed by computing the benefit-cost ratio (BCR) according to the formula V given by Dilon and Hardaker [\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e]\u003c/p\u003e \u003cp\u003e \u003cspan class=\"InlineEquation\"\u003e \u003cspan class=\"mathinline\"\u003e\\(Benefit cost ratio \\left(BCR\\right)= \\frac{Gross return \\left(Rupees\\right)}{Total operational cost \\left(Rupees\\right)}\\)\u003c/span\u003e \u003c/span\u003e\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;..(V)\u003c/p\u003e \u003cp\u003eWhere,\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(Gross return=Present market price of the end product \\left(i.e., vermicompost or ethanol\\right)\\times total quantity produced \\left(kg\\right)\\)\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e \u003cp\u003eThe details of the variables considered for the above calculations are provided in supplementary material 4.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003e2.10. Statistical Analysis\u003c/h2\u003e \u003cp\u003eThe temporal data on the bio-composting experiment was analyzed for two-way ANOVA with three observations per cell followed by the Least Significant Difference (LSD) test to differentiate the efficiency of various treatments at the probability level of p\u0026thinsp;\u0026lt;\u0026thinsp;0.05 using SPSS. One-way ANOVA followed by an LSD posthoc test was also performed for all other experiments.\u003c/p\u003e \u003c/div\u003e"},{"header":"3. Results and discussion","content":"\u003cp\u003e \u003cem\u003e3.1. Chemical and microbial changes of composting and vermicomposting beds \u0026ndash; determining the source viability for potential ethanol producers\u003c/em\u003e \u003cdiv class=\"BlockQuote\"\u003e \u003cp\u003eThe composting and vermicomposting were primarily conducted to create a microbe-enriched substrate, which could be used as a ready source for ethanol-producing microorganisms. The changes in the chemical and microbial properties of the vermibeds and composting beds are presented in Table \u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e. The pH sharply reduced under composting and vermicomposting, strongly indicating the microbe-induced organic matter decomposition process [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]. However, the increment in NPK bioavailability was significantly greater under vermicompost than under composting. This suggests that the presence of earthworms augmented the nutrient levels by accelerating the microbial activity [\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e]. Correspondingly, the microbial biomass carbon and microbial respiration were remarkably enhanced by about 3.35 folds and 2.31 folds in the vermibeds compared to the composting beds (P for treatment\u0026thinsp;\u0026lt;\u0026thinsp;0.01; LSD\u0026thinsp;=\u0026thinsp;15.94). These results\u003c/p\u003e \u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003e\u003cb\u003eTemporal changes in pH, available P (Av P), total Nitrogen (TN), exchangeable K (Av K), Compost respiration\u003c/b\u003e (\u003cb\u003eComp. Res),Microbial biomass carbon\u003c/b\u003e (\u003cb\u003eMBC), bacterial and fungal count during the bioconversion experiment (mean\u0026thinsp;\u0026plusmn;\u0026thinsp;standard deviation)\u003c/b\u003e\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"11\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c10\" colnum=\"10\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c11\" colnum=\"11\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eParameters\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c4\" namest=\"c2\"\u003e \u003cp\u003eCompost\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c7\" namest=\"c5\"\u003e \u003cp\u003eVermicompost\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c9\"\u003e \u003cp\u003eP values\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0D\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e30D\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e60D\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0D\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e30D\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e60D\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u003cb\u003eP\u003c/b\u003e\u003csub\u003e\u003cb\u003etreatment\u003c/b\u003e\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e\u003cb\u003eP\u003c/b\u003e\u003csub\u003e\u003cb\u003etime\u003c/b\u003e\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e\u003cb\u003eP\u003c/b\u003e\u003csub\u003e\u003cb\u003etreatment\u0026times;time\u003c/b\u003e\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e\u003cb\u003eLSD\u003c/b\u003e\u003csub\u003e\u003cb\u003etreatment\u003c/b\u003e\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003epH\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e8.49\u0026thinsp;\u0026plusmn;\u0026thinsp;0.18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e8.38\u0026thinsp;\u0026plusmn;\u0026thinsp;0.29\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e6.44\u0026thinsp;\u0026plusmn;\u0026thinsp;0.34\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e8.15\u0026thinsp;\u0026plusmn;\u0026thinsp;0.12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e8.33\u0026thinsp;\u0026plusmn;\u0026thinsp;0.18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e6.36\u0026thinsp;\u0026plusmn;\u0026thinsp;0.25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eNS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003eNS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAv P (mg kg\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e26.41\u0026thinsp;\u0026plusmn;\u0026thinsp;0.28\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e40.22\u0026thinsp;\u0026plusmn;\u0026thinsp;0.18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e63.34\u0026thinsp;\u0026plusmn;\u0026thinsp;0.42\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e27.56\u0026thinsp;\u0026plusmn;\u0026thinsp;0.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e51.42\u0026thinsp;\u0026plusmn;\u0026thinsp;0.36\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e78.37\u0026thinsp;\u0026plusmn;\u0026thinsp;0.26\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e10.65\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTotal N(%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.34\u0026thinsp;\u0026plusmn;\u0026thinsp;0.12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.61\u0026thinsp;\u0026plusmn;\u0026thinsp;0.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.64\u0026thinsp;\u0026plusmn;\u0026thinsp;0.12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.55\u0026thinsp;\u0026plusmn;\u0026thinsp;0.12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.70\u0026thinsp;\u0026plusmn;\u0026thinsp;0.14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.85\u0026thinsp;\u0026plusmn;\u0026thinsp;0.10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eNS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003eNS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e0.06\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAv K (mg kg\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e34.33\u0026thinsp;\u0026plusmn;\u0026thinsp;0.20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e54.48\u0026thinsp;\u0026plusmn;\u0026thinsp;2.87\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e62.45\u0026thinsp;\u0026plusmn;\u0026thinsp;0.38\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e32.47\u0026thinsp;\u0026plusmn;\u0026thinsp;0.40\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e60.53\u0026thinsp;\u0026plusmn;\u0026thinsp;0.27\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e76.38\u0026thinsp;\u0026plusmn;\u0026thinsp;0.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e8.69\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eComp. Res (mg kg\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e5.29\u0026thinsp;\u0026plusmn;\u0026thinsp;0.35\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e7.53\u0026thinsp;\u0026plusmn;\u0026thinsp;0.35\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e10.44\u0026thinsp;\u0026plusmn;\u0026thinsp;0.43\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e7.42\u0026thinsp;\u0026plusmn;\u0026thinsp;0.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e10.4\u0026thinsp;\u0026plusmn;\u0026thinsp;0.36\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e17.32\u0026thinsp;\u0026plusmn;\u0026thinsp;0.29\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e7.84\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMBC (mg kg\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e85.60\u0026thinsp;\u0026plusmn;\u0026thinsp;0.44\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e185.30\u0026thinsp;\u0026plusmn;\u0026thinsp;3.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e215.94\u0026thinsp;\u0026plusmn;\u0026thinsp;6.81\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e85.8\u0026thinsp;\u0026plusmn;\u0026thinsp;14.04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e248\u0026thinsp;\u0026plusmn;\u0026thinsp;34.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e259.08\u0026thinsp;\u0026plusmn;\u0026thinsp;37.25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e15.91\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBacterial count [log(CFU)]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e7.06\u0026thinsp;\u0026plusmn;\u0026thinsp;0.03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e7.64\u0026thinsp;\u0026plusmn;\u0026thinsp;0.005\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e8.16\u0026thinsp;\u0026plusmn;\u0026thinsp;0.02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e8.21\u0026thinsp;\u0026plusmn;\u0026thinsp;0.02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFungal count[log(CFU)]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e6.03\u0026thinsp;\u0026plusmn;\u0026thinsp;0.08\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e6.2\u0026thinsp;\u0026plusmn;\u0026thinsp;0.08\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e6.5\u0026thinsp;\u0026plusmn;\u0026thinsp;0.02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e6.94\u0026thinsp;\u0026plusmn;\u0026thinsp;0.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"BlockQuote\"\u003e \u003cp\u003eindicate that microbial proliferation and activity were considerably more remarkable in the vermibeds than in composting beds. The results of the total bacterial and fungal count (Table \u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e) also strongly substantiated that microbial growth was significantly promoted under vermicomposting. Earthworms enrich the microbial diversity in vermibeds by contributing through their intestinal microflora [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]. Hence, we postulated that the vermibeds would be better substrates for searching for potential ethanol-producing organisms than the composting beds.\u003c/p\u003e \u003c/div\u003e \u003c/p\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003e3.2. Screening of potential strains-Cellulose degrading (Congo red assay) and carbohydrate solubilizing efficiency\u003c/h2\u003e \u003cp\u003e \u003cdiv class=\"BlockQuote\"\u003e \u003cp\u003eBased on the results of the composting and vermicomposting experiments, 14 isolates were initially screened out from the vermibeds considering their high relative dominance (RD) in Nutrient agar plates (SI 1). The RD is an authentic and dependable parameter for assessing the aggressivity of microbial strains in congregations [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]. Eight previously reported strains were also considered for the present study because the data about their molecular identity and general characteristics were readily available. Cellulolytic capability in microorganisms signifies the effectiveness of the organisms for rapid\u003c/p\u003e \u003cp\u003etransformation of obstinate cellulose-rich biomass [\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e]. On the other hand, the extent of carbohydrate solubilization efficiency in microorganisms indicates their ability to derive energy from recalcitrant substrates [\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e]. Hence, 22 strains were selected to study their cellulose degradation and carbohydrate solubilization potentials (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e \u0026amp; SI 2).\u003c/p\u003e \u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"BlockQuote\"\u003e \u003cp\u003eOverall, six strains were able to solubilize\u0026thinsp;~\u0026thinsp;28\u0026ndash;30 different types of sugars (glycerol, mannitol, adonitol, etc.), and the C1, C5, and S10 could solubilize 30\u0026ndash;31 out of 35 tested carbohydrates (SI 2). The C5 and S10 exhibited significantly higher cellulose degrading efficiency (i.e., halo zone Index) compared to other strains (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e; p\u0026lt;; LSD\u0026thinsp;=\u0026thinsp;0.039). On the other hand, a few different strains (T3, T20, OS7, and B5) exhibited high carbohydrate solubilization efficiency (SI 1). Interestingly, among eight previously reported strains, three (S8, S10, and S12) showed high cellulolytic and carbohydrate solubilization potential (SI2). As mentioned in the previous section, the N-fixing and P-solubilizing traits of \u003cem\u003eK. ascorbata\u003c/em\u003e S8 (previously reported as IN2), \u003cem\u003eRhizobium sp.\u003c/em\u003e S10 (previously reported as IN4), and \u003cem\u003eBacillus sp.\u003c/em\u003e Hussain et al. have reported S12 (previously reported as IN5) (Table \u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e) [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e].\u003c/p\u003e \u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eStrain information of all the dominant bacterial taxa\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"7\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eStrain Code\u003c/p\u003e \u003cp\u003e(For experimental Purposes)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eOrganism Name\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSeq.\u0026nbsp;Length\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eAccession Number\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eDifferential Staining\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003ePercentage identity\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eReferences\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eS8\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eKluyvera ascorbata\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e956\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003eKU321346\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003eGram Negative\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e100%\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eHussain et al. (2016)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eS10\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eRhizobium sp.\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e989\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003eKU321348\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003eGram Negative\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e100%\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eHussain et al.(2016)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eS12\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eBacillus sp.\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e1024\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003eKU321350\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003eGram Positive\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e100%\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eHussain et al.(2016)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eC1\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eKosakonia sacchari\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e464\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003eMH174457\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003eGram Negative\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e100%\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eC3\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eEnterobacter cloacae\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e456\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003eMH174458\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003eGram Negative\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e100%\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eC5\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eBacillus alcalophilus\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003eNA\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003eFAME analysis\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003eGram Positive\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e100%\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"BlockQuote\"\u003e \u003cp\u003eHowever, their carbohydrate solubilization and cellulose degradation properties have yet to be evaluated. Although plant-originated sugar and polymers are dependable feedstocks for biofuel production, their recalcitrant character is the major obstacle to their solubilization [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. Therefore, the bacterial isolates' sugar and cellulolytic degrading potential were promising indicators of potential bioethanol-producing organisms.\u003c/p\u003e \u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cem\u003e3.3 Ethanol production potential of bacterial strains in different substrates (5% sugar solution and banana peel) and their molecular characterization\u003c/em\u003e \u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003c/ol\u003e \u003c/p\u003e \u003cp\u003eBased on the cellulolytic and sugar degradation efficacy of the 22 bacterial strains, their ethanol production potential from different substrates was assessed. However, only six out of 22 could produce ethanol from sugar solution; among which three (\u003cem\u003eKosakonia sacchari\u003c/em\u003e C1, \u003cem\u003eEnterobacter cloacae\u003c/em\u003e C3, and \u003cem\u003eBacillus alcalophlius\u003c/em\u003e C5) were from the 14 newly isolated strains (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003ea). Ethanol production potential of the bacterial isolates was compared with yeast (\u003cem\u003eS. cerevisiae\u003c/em\u003e MTCC 170). The ethanol produced by the bacterial isolates and the yeast from the 5% sugar solution was in the order: C5\u0026thinsp;\u0026gt;\u0026thinsp;S10\u0026thinsp;=\u0026thinsp;yeast\u0026thinsp;\u0026gt;\u0026thinsp;C1\u0026thinsp;\u0026gt;\u0026thinsp;S12\u0026thinsp;\u0026gt;\u0026thinsp;S8\u0026thinsp;=\u0026thinsp;C3 (P for organism\u0026thinsp;\u0026lt;\u0026thinsp;0.01; LSD for organism\u0026thinsp;=\u0026thinsp;5.575; Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e3\u003c/span\u003ea).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eIt was interesting to note that \u003cem\u003eBacillus alcalophlius\u003c/em\u003e C5 strain was most efficient in deriving ethanol from banana peel followed by C1 and S10 (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003ea). The gas chromatographic analysis also confirmed high purity (~\u0026thinsp;99%) of the ethanol produced by the bacterial isolates. As such, ethanol produced from banana peel by all the studied strains was significantly greater than that of 5% sugar solution, and the organism-feedstock interaction effect was also significant (P for feedstock \u0026amp; feedstock \u0026times; organism\u0026thinsp;\u0026lt;\u0026thinsp;0.01). This implied that the ethanol generation performance of bacteria and yeast would undoubtedly fluctuate depending on the feedstock characteristics. These results agreed with the previous finding [\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e], implying that wasted foods, particularly roughage, could be highly effective for biofuel generation, thereby reducing environmental pollution [\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e]. At this stage, it was confirmed that six out of 22 isolates were prolific bioethanol producers; and three among the six strains were yet to be characterized. Therefore, the complete 16s rRNA genes of these three strains were amplified and partially sequenced. The sequencing output and identity of the strains and their accession numbers, as obtained from the NCBI database, are presented in Table \u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e. The DNA sequences of the isolated strains were utilized to create a phylogenetic tree to explore their similarities (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eb). The phylogenetic tree analysis revealed that the \u003cem\u003eRhizobium sp.\u003c/em\u003e IN4 and \u003cem\u003eBacillus cereus\u003c/em\u003e IP4 were closely related, while \u003cem\u003eKluyvera ascorbata\u003c/em\u003e IN2 was distantly linked to \u003cem\u003eBacillus\u003c/em\u003e and \u003cem\u003eRhizobium\u003c/em\u003e.\u003c/p\u003e \u003cp\u003eInterestingly, a high similarity was detected between \u003cem\u003eK. Sacchari\u003c/em\u003e C1 and \u003cem\u003eE. Cloacace\u003c/em\u003e C3. Such high resemblance was because both species originate from the enterobacteria complex. Moreover, according to the phylogenetic tree, all the strains were distantly related to each other. The \u003cem\u003eB.alcalophilus\u003c/em\u003e C5 could not be included in the phylogenetic analysis because the organism was identified by FAME analysis; thus, a complete sequence was unavailable. As such, the strength of identification through FAME analysis is generally equal to that of the classical DNA sequencing [\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThe previously reported strains of \u003cem\u003eBacillus\u003c/em\u003e, \u003cem\u003eKluyvera\u003c/em\u003e, and \u003cem\u003eRhizobium\u003c/em\u003e were primarily utilized for their plant growth-promoting traits [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]. However, the bio-fuel production potential of \u003cem\u003eKluyvera\u003c/em\u003e has never been reported earlier. However, there has yet to be a report on cellulolytic and ethanol production traits of \u003cem\u003eRhizobium\u003c/em\u003e strains, a few strains of \u003cem\u003eBacillus\u003c/em\u003e (\u003cem\u003eB. subtilis\u003c/em\u003e WB600, and \u003cem\u003eB. subtilis\u003c/em\u003e WBN were able to generate ethanol under \u003cem\u003ein vitro\u003c/em\u003e condition [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. On the other hand, the cellulolytic potential of \u003cem\u003eRhizobium\u003c/em\u003e strains was reported in rhizosphere soil in addition to their N-fixing potential in previous studies [\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e]. The \u003cem\u003eK. sacchari\u003c/em\u003e, a well-known human\u003c/p\u003e \u003cp\u003epathogen, is known for its multidimensional plant growth promotion activities [\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e]. On the other hand, the \u003cem\u003eEnterobacter\u003c/em\u003e strains have been studied for alcohol generation from lignocellulose-derived sugars and glycerol [\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e]; yet, their ethanol production potential from biomass under \u003cem\u003ein vivo\u003c/em\u003e conditions has been estimated for the first time in this investigation.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003e3.4. Sedimentation rate, ethanol tolerance, sugar tolerance, and enzyme activation- mechanistic understanding of bacteria-mediated bioethanol generation\u003c/h2\u003e \u003cp\u003eThe data on sedimentation rate (SR), ethanol tolerance, and sugar tolerance is presented in Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e3\u003c/span\u003ea-e.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003cb\u003ea.\u003c/b\u003e Variation in microbial sedimentation rate. Values represent mean\u0026thinsp;\u0026plusmn;\u0026thinsp;standard deviation (n\u0026thinsp;=\u0026thinsp;3); \u003cb\u003eb.\u003c/b\u003e Variation in sugar tolerance potentials of bacteria and yeast at 24 hours. Values represent mean\u0026thinsp;\u0026plusmn;\u0026thinsp;standard deviation (n\u0026thinsp;=\u0026thinsp;3); \u003cb\u003ec.\u003c/b\u003e Variation in sugar tolerance potentials of bacteria and yeast at 48 hours. Values represent mean\u0026thinsp;\u0026plusmn;\u0026thinsp;standard deviation (n\u0026thinsp;=\u0026thinsp;3); \u003cb\u003ed.\u003c/b\u003e Variation in sugar tolerance potentials of bacteria and yeast at 72 hours. Values represent mean\u0026thinsp;\u0026plusmn;\u0026thinsp;standard deviation (n\u0026thinsp;=\u0026thinsp;3); \u003cb\u003ee.\u003c/b\u003e Variation in ethanol tolerance potentials of bacteria and yeast. Values represent mean\u0026thinsp;\u0026plusmn;\u0026thinsp;standard deviation (n\u0026thinsp;=\u0026thinsp;3).\u003c/p\u003e \u003cp\u003eAlthough the SR of C1, C3, and S8 was poor, the rate of sedimentation in LB agar of \u003cem\u003eBacillus alcalophlius\u003c/em\u003e C5 was significantly higher than the yeast (\u003cem\u003eS. cerevisiae\u003c/em\u003e MTCC 170) grown in YPD agar (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e3\u003c/span\u003ea). The SR of \u003cem\u003eBacillus spp.\u003c/em\u003e S12 and \u003cem\u003eRhizobium spp.\u003c/em\u003e S10 was either the same or marginally lower than the yeast. The sedimentation vis-\u0026agrave;-vis flocculation features of microorganisms indicate the ease of their separation from the medium after completion of the fermentation, which is immensely important for the recovery and reuse of the organisms, which signifies the industrial suitability of microbe-mediated ethanol production process [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThe tolerance to sugar exposure of the bacterial isolates was generally lower than that of the yeast until 72 hours of incubation (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e3\u003c/span\u003eb-d). The cell growth of all six bacterial isolates constantly increased over time, while the yeast growth was significantly retarded at 72 hours in a 5% sugar solution (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e3\u003c/span\u003eb). Similar pattern of yeast cell growth was also evidenced in 8% and 15% sugar solutions (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e3\u003c/span\u003ec \u0026amp; d). However, the temporal growth patterns of the bacteria marginally varied among the strains in 15% sugar solutions \u0026mdash; for example, \u003cem\u003eRhizobium sp.\u003c/em\u003e S10 showed a steady increase over time, and activity of the key extracellular enzymes that regulate the microbe-mediated ethanol production process was evaluated in the treated (5% sugar solution) organisms (Table \u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). Activities of acetyl CoA synthase and alcohol dehydrogenase were significantly greater in \u003cem\u003eRhizobium sp.\u003c/em\u003e S10 and \u003cem\u003eBacillus alcalophilus\u003c/em\u003e C5 inoculated solutions (P\u0026thinsp;\u0026lt;\u0026thinsp;0.01; LSD: acetyl CoA synthase\u0026thinsp;=\u0026thinsp;0.447; alcohol dehydrogenase\u0026thinsp;=\u0026thinsp;0.957). Acetyl CoA synthase catalyzes acetyl CoA synthesis by utilizing acetic acid, thereby arresting the acetate-induced retardation during the fermentation[\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e]. At the same time, the alcohol dehydrogenase upregulates the conversion of acetyl CoA to ethanol [\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e].\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eActivities of different enzymes by different isolated strains during the experiment (mean\u0026thinsp;\u0026plusmn;\u0026thinsp;standard deviation)\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"7\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colspan=\"6\" nameend=\"c7\" namest=\"c2\"\u003e \u003cp\u003eEnzyme activities\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOrganisms\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePyruvate decarboxylase (mM NADH min\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePyruvate kinase (mM NADH min\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eAcetyl CoA synthetase\u003c/p\u003e \u003cp\u003e(mM NADH min\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eAlcohol dehydrogenase\u003c/p\u003e \u003cp\u003e(mM NADH min\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003ePyruvate dehydrogenase (mM NADH min\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"1\" nameend=\"c7\" namest=\"c7\"\u003e\u0026nbsp;\u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eK. ascorbata\u003c/em\u003e S8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e9.48\u0026thinsp;\u0026plusmn;\u0026thinsp;0.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e10.43\u0026thinsp;\u0026plusmn;\u0026thinsp;0.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4.73\u0026thinsp;\u0026plusmn;\u0026thinsp;0.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e9.1\u0026thinsp;\u0026plusmn;\u0026thinsp;0.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e6.43\u0026thinsp;\u0026plusmn;\u0026thinsp;0.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c7\" namest=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eRhizobium spp.\u003c/em\u003e S10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e23.73\u0026thinsp;\u0026plusmn;\u0026thinsp;1.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e23.7\u0026thinsp;\u0026plusmn;\u0026thinsp;0.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e9.2\u0026thinsp;\u0026plusmn;\u0026thinsp;0.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e24.7\u0026thinsp;\u0026plusmn;\u0026thinsp;0.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e9.44\u0026thinsp;\u0026plusmn;\u0026thinsp;0.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c7\" namest=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eB. cereus\u003c/em\u003e S12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e18.27\u0026thinsp;\u0026plusmn;\u0026thinsp;0.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e16.45\u0026thinsp;\u0026plusmn;\u0026thinsp;0.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e7.44\u0026thinsp;\u0026plusmn;\u0026thinsp;2.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e14.7\u0026thinsp;\u0026plusmn;\u0026thinsp;0.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e7.17\u0026thinsp;\u0026plusmn;\u0026thinsp;0.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c7\" namest=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eK. sacchari\u003c/em\u003e C1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e18.84\u0026thinsp;\u0026plusmn;\u0026thinsp;0.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e21.07\u0026thinsp;\u0026plusmn;\u0026thinsp;0.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e5.6\u0026thinsp;\u0026plusmn;\u0026thinsp;0.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e23.2\u0026thinsp;\u0026plusmn;\u0026thinsp;1.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e8.47\u0026thinsp;\u0026plusmn;\u0026thinsp;1.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c7\" namest=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eE. cloacae\u003c/em\u003e C3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e8.9\u0026thinsp;\u0026plusmn;\u0026thinsp;0.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e10.3\u0026thinsp;\u0026plusmn;\u0026thinsp;0.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e7.57\u0026thinsp;\u0026plusmn;\u0026thinsp;0.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e8.6\u0026thinsp;\u0026plusmn;\u0026thinsp;1.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e7.57\u0026thinsp;\u0026plusmn;\u0026thinsp;0.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c7\" namest=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eB. alcalophilus\u003c/em\u003e C5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e28.94\u0026thinsp;\u0026plusmn;\u0026thinsp;0.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e27.7\u0026thinsp;\u0026plusmn;\u0026thinsp;0.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e8.44\u0026thinsp;\u0026plusmn;\u0026thinsp;0.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e26.4\u0026thinsp;\u0026plusmn;\u0026thinsp;1.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e18.27\u0026thinsp;\u0026plusmn;\u0026thinsp;0.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c7\" namest=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eP\u003c/b\u003e\u003csub\u003e\u003cb\u003evalue\u003c/b\u003e\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c7\" namest=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eLSD\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.561\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.479\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.447\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.957\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.604\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c7\" namest=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eInterestingly, the ethanol production efficiency of S10 and C5 was remarkably greater than the other strains described in the previous section. Correspondingly, pyruvate decarboxylase, kinase, and dehydrogenase activities were also significantly higher in \u003cem\u003eBacillus alcalophilus\u003c/em\u003e C5 inoculated sugar solution, followed by \u003cem\u003eRhizobium spp.\u003c/em\u003e S10, \u003cem\u003eK. sacchari\u003c/em\u003e C1, and \u003cem\u003eBacillus cereus\u003c/em\u003e S12 (Table \u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). Pyruvate, a component of carbohydrate metabolism, is decarboxylated by pyruvate decarboxylase to produce acetaldehyde, which is eventually reduced to ethanol by alcohol dehydrogenase [\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e]. The pyruvate kinase is known to sustain cellular homeostasis via regulation of energy metabolism, which in turn induces thermo-tolerance to S\u003cem\u003eaccharomyces cerevisiae\u003c/em\u003e, and the pyruvate dehydrogenase complex dramatically enhances the free fatty acids levels in \u003cem\u003eS. Cerevisiae\u003c/em\u003e [\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e]. Overall, the results strongly postulate that the high ethanol-producing ability of the bacterial isolates (mainly C5 and S10) from sugar solution and lignocellulosic biomass was due to the efficient release of essential enzymes during fermentation.\u003c/p\u003e \u003cp\u003e \u003cem\u003e3.5 Membrane integrity and cellular response of yeast and bacterial to sugar exposure: Understanding the differential defense mechanism using flow cytometry and confocal microscopy\u003c/em\u003e \u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003c/ol\u003e \u003c/p\u003e \u003cp\u003eFigure\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e4\u003c/span\u003e represents the results of the FACS (i.e., flow cytometry) analysis of the stained bacteria and yeast cells exposed to sugar solutions of different concentrations. The FACS and confocal microscopic analyses were performed to comprehend the variations in apoptosis-mediated defense mechanisms between yeast and ethanologenic bacterial cells in response to sugar-induced shock. The two most prolific ethanologenic bacterial strains (\u003cem\u003eRhizobium sp.\u003c/em\u003e S10 and \u003cem\u003eBacillus alcalophilus C5\u003c/em\u003e) were selected for this study. The FACS study demonstrated that incubation of yeast cells with 5 or 15% sugar causes a dose-dependent increase in apoptosis compared to the untreated cells. In sugar-treated yeast, a higher percentage (77.2 and 89.3%) of dual positive cells (both Annexin\u0026thinsp;+\u0026thinsp;PI) indicates a robust increase in apoptosis (about two folds more than the control). The shift of cells from early to late (i.e., mature) apoptosis was also evidenced in yeast (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e4\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eThis observation indicates that apoptosis is probably the primary defense mechanism in yeast (\u003cem\u003eS. cerevisiae\u003c/em\u003e) in response to sugar-induced stress. However, bacterial cells responded differently from yeast to sugar exposure. In \u003cem\u003eRhizobium sp.\u003c/em\u003e S10, 5, and 15% sugar exposure mildly induced apoptosis in bacterial cells with a slight exhibition of dose- dependent apoptotic induction. In contrast, in \u003cem\u003eBacillus alcalophilus\u003c/em\u003e C5, sugar incubation showed no apoptotic induction compared to the control. These results imply that the apoptotic response\u003c/p\u003e \u003cp\u003eof ethanologenic bacteria in a sugar-enriched environment may vary among species, and reverse apoptosis or delayed apoptosis could be the probable defensive adjustment of sugar-exposed bacterial cells. Although evidence of reverse-apoptosis with increased survivability in human cells has been reported [\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e], such instances have not been found in bacteria. However, delaying apoptosis by the CpG motifs in DNA has been detected in some bacterial species [\u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e]. Hence, studies with more prolonged exposure to varying sugar concentrations may be able to vindicate the present hypotheses.\u003c/p\u003e \u003cp\u003eThe results of the confocal microscopy were quite intriguing regarding the differential response between ethanologenic bacteria and yeast to sugar exposure (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e5\u003c/span\u003e). At a glance, it appears that almost 90% of yeast cells were invaded by the PI at 5 and 15% sugar concentrations, which implies that increasing sugar exposure considerably caused fetal impacts on the fungal (\u003cem\u003eS.cerevisiae\u003c/em\u003e) cells (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e5\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eHowever, careful observation of the images in SI 3 would clarify quite a few yeast cells have remained unstained while repeating the staining process. This may be due to the yeast cells' poor compatibility with the stains owing to their cell wall-induced inhibition. However, this technique has been previously used for observing apoptosis of yeast cells [\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e]; while bacterial cells have often been studied in a similar manner [\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e]. Nevertheless, the result clearly shows that yeast's cellular wall and membrane integrity might be severely disrupted due to sugar exposure. Interestingly, 5% sugar exposure caused mild damage to the membrane integrity of \u003cem\u003eB. alcalophilus\u003c/em\u003e C5 and \u003cem\u003eRhizobium sp.\u003c/em\u003e S10, but the responses of the two bacterial species to 15% sugar exposure were conspicuously different. The extent of PI invasion was considerably greater in 15% sugar-exposed S10 than the C5 (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e5\u003c/span\u003e), signifying that the membrane integrity of C5 was more robust than S10. Overall, bacterial membrane integrity was more potent than the affected yeast cells.\u003c/p\u003e \u003cp\u003eThis observation substantiates the hypothesis that some bacteria, like \u003cem\u003eB. alcalophilus\u003c/em\u003e C5, have more robust defense mechanisms than the ethanol-producing yeast and other bacteria. The current results also indicate that more robust membrane integrity could be one of the effective defensive strategies in addition to apoptosis in bacterial species in response to sugar-induced stress.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec16\" class=\"Section2\"\u003e \u003ch2\u003e3.6. Waste conversion efficacy and economic evaluations\u003c/h2\u003e \u003cp\u003eThe industrial feasibility and environmental sustainability of vermicomposting and bacteria-mediated bioethanol production technologies could only be appreciated by evaluating the studied systems' waste-to-wealth conversion efficiency and economic potential. Our previous studies demonstrated that toxic metals and odorous volatiles in waste materials are significantly neutralized through vermicomposting [\u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e, \u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e50\u003c/span\u003e]. Accordingly, the possibilities of emissions of obnoxious gases and migration of toxic metals from the end product (i.e., vermicompost) were negligible. On the other hand, bacteria-mediated bioethanol production from biosolids is well recognized for its ecological compatibility [\u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e51\u003c/span\u003e]. Therefore, we have computed both processes' waste conversion (Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e). WCE for vermicomposting was significantly higher than composting; however, the WCE for the bacteria-mediated bioethanol production process was highly organism dependent. We recorded extraordinarily high WCE for \u003cem\u003eB. alcalophilus\u003c/em\u003e C5, followed by \u003cem\u003eRhizobium sp.\u003c/em\u003e S10 and yeast (\u003cem\u003eS. cerevisiae\u003c/em\u003e MTCC 170) (P\u0026thinsp;\u0026lt;\u0026thinsp;0.05; LSD\u0026thinsp;=\u0026thinsp;2.6). These results indicate that vermicomposting is a better waste conversion route than bacteria-mediated ethanol production. Still, the overall efficiency of these waste conversion treatments can only be assessed through the economic evaluation [\u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e52\u003c/span\u003e]. The enumerated benefit-cost ratio (BCR) for different processes is presented in Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eWaste-to-wealth conversion efficiency (WCE) and benefit-cost ratio (BCR) for bio-composting systems and microbe-mediated bioethanol generation\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"3\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTreatments\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eWCE %\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eBenefit cost ratio (BCR)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eCompost\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e61.8\u0026thinsp;\u0026plusmn;\u0026thinsp;1.33\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.651\u0026thinsp;\u0026plusmn;\u0026thinsp;0.003\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eVermicompost\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e70.93\u0026thinsp;\u0026plusmn;\u0026thinsp;3.91\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.898\u0026thinsp;\u0026plusmn;\u0026thinsp;0.008\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eP-Value\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.01 \u0026lt;0.01\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eBioethanol generation\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eC1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e68.83\u0026thinsp;\u0026plusmn;\u0026thinsp;0.50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5.18\u0026thinsp;\u0026plusmn;\u0026thinsp;0.16\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eC3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e64.48\u0026thinsp;\u0026plusmn;\u0026thinsp;0.26\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.83\u0026thinsp;\u0026plusmn;\u0026thinsp;0.07\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eC5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e73.05\u0026thinsp;\u0026plusmn;\u0026thinsp;0.51\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.09\u0026thinsp;\u0026plusmn;\u0026thinsp;0.08\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eS8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e59.7\u0026thinsp;\u0026plusmn;\u0026thinsp;0.23\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.21\u0026thinsp;\u0026plusmn;\u0026thinsp;0.10\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eS10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e72.42\u0026thinsp;\u0026plusmn;\u0026thinsp;0.95\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.12\u0026thinsp;\u0026plusmn;\u0026thinsp;0.08\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eS12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e54.72\u0026thinsp;\u0026plusmn;\u0026thinsp;0.37\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.48\u0026thinsp;\u0026plusmn;\u0026thinsp;0.07\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eR1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e69.58\u0026thinsp;\u0026plusmn;\u0026thinsp;0.33\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.82\u0026thinsp;\u0026plusmn;\u0026thinsp;0.05\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eP-Value\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e\u0026lt;0.01 \u0026lt;0.01\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eLSD\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.26\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.15\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eThe ethanol and vermicompost production rate was assumed for 100 kg initial feedstock. However, the productivity and the cost of production were computed based on the real-time data acquired during the experiments (supplementary material 4). Interestingly, the BCR was highest for the \u003cem\u003eB. alcalophilus\u003c/em\u003e C5-based ethanol production process (P\u0026thinsp;\u0026lt;\u0026thinsp;0.05; LSD\u0026thinsp;=\u0026thinsp;2.6). However, the BCR for \u003cem\u003eRhizobium sp.\u003c/em\u003e S10 and \u003cem\u003eS. cerevisiae\u003c/em\u003e-based processes were significantly higher than vermicomposting (Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e). This was mainly due to the increasing price of ethanol in the Indian market, which is a result of the recent shift in Governmental policy to reduce crude oil import and promote biofuel use [\u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e53\u003c/span\u003e]. As such, using ethanol as a substitute for fossil fuels also decreases emissions of air pollutants like particulate matter, CO, and volatile hydrocarbons [\u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e54\u003c/span\u003e].\u003c/p\u003e \u003c/div\u003e"},{"header":"4. Conclusion","content":"\u003cp\u003eNovel and industrially suitable ethanologenic bacteria were isolated from lignocellulosic waste-based vermicomposting systems for the first time. Six of 22 efficient sugars and cellulose solubilizing bacterial isolates exhibited high ethanol production ability. In particular, two bacterial strains \u003cem\u003eBacillus alcalophilus\u003c/em\u003e C5 and \u003cem\u003eRhizobium spp.\u003c/em\u003e S10 strains produced significantly more ethanol (~\u0026thinsp;5\u0026ndash;15 g L\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e) than the yeast without pre-treatment or externally supplemented enzymes. Additionally, these strains were strongly tolerant to inhibitory factors like ethanol and sugar shocks that assure their industrial applicability. We postulated through enzyme assay that appropriate activation of enzymes like alcohol dehydrogenase was one of the critical attributes that imparted high ethanol-producing capability in C5 and S10. The study with flow cytometry and confocal microscopy revealed that reverse/delayed apoptosis and strong membrane integrity could be the defense strategies in bacteria that facilitate their growth and maintain ethanol production capacity in sugar-enriched conditions. High waste-to-wealth conversion efficiency with a significant benefit-cost ratio strongly substantiates the practical applicability of the identified organisms for bioethanol generation from recalcitrant biowastes. However, technological improvement for the up-scaling of bacteria-mediated ethanol production systems warrants in-depth studies in the near future.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eStatements and Declarations\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that no funds, grants, or other support were received during the preparation of this manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting Interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors have no relevant financial or non-financial interests to disclose.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor Contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll authors contributed to the study conception and design. Material preparation, data collection and analysis were performed by Ratan Chowdhury. The first draft of the manuscript was written by Ratan Chowdhury and all authors commented on previous versions of the manuscript. All authors read and approved the final manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData Availability\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe datasets generated during the current study are not publicly available due to the reason that the work is considered to be novel. But the data will be available from the corresponding author on reasonable request.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eBalaji Panchal, S.R., Zhu, Z., Qin, S., Chang, T., Zhao, Q., Sun, Y., Zhao, C., Wang, J., Bian, K.: The current state and applications of ethyl carbonate with ionic liquid in sustainable biodiesel production: A review. Renew. Energ. \u003cb\u003e181\u003c/b\u003e, 341\u0026ndash;354 (2022). \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.renene.2021.09.062\u003c/span\u003e\u003cspan address=\"10.1016/j.renene.2021.09.062\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBakry, X.V.V., Walid, Mallik, G., Nghiem, X.-H., Sinha, A.: Is green finance really green? Examining the long-run relationship between green finance, renewable energy, and environmental performance in developing countries. Renew. Energ. \u003cb\u003e208\u003c/b\u003e, 341\u0026ndash;355 (2023). \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.renene.2023.03.020\u003c/span\u003e\u003cspan address=\"10.1016/j.renene.2023.03.020\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLynd, L.R., Beckham, G.T., Guss, A.M., Jayakody, L.N., Karp, E.M., Maranas, C., McCormick, R.L., Amador-Noguez, D., Bomble, Y.J., Davison, B.H., Foster, C., Himmel, M.E., Holwerda, E.K., Laser, M.S., Ng, C.Y., Olson, D.G., Rom\u0026aacute;n-Leshkov, Y., Trinh, C.T., Tuskan, G.A., Upadhayay, V., Vardon, D.R., Wang, L., Wyman, C.E.: Toward low-cost biological and hybrid biological/catalytic conversion of cellulosic biomass to fuels. Energy Environ. Sci. \u003cb\u003e15\u003c/b\u003e, 938\u0026ndash;990 (2022). \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1039/d1ee02540f\u003c/span\u003e\u003cspan address=\"10.1039/d1ee02540f\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAsgher, M., Shahid, M., Kamal, S., Iqbal, H.M.N.: Recent trends and valorization of immobilization strategies and ligninolytic enzymes by industrial biotechnology. J. Mol. Catal. \u003cb\u003e101\u003c/b\u003e, 56\u0026ndash;66 (2014). \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.molcatb.2013.12.016\u003c/span\u003e\u003cspan address=\"10.1016/j.molcatb.2013.12.016\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBrethauer, S., Studer, M.H.: Consolidated bioprocessing of lignocellulose by a microbial consortium. Energy Environ. Sci. \u003cb\u003e7\u003c/b\u003e, 1446\u0026ndash;1453 (2014). \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1039/c3ee41753k\u003c/span\u003e\u003cspan address=\"10.1039/c3ee41753k\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eS\u0026aacute;nchez Orozco, R., Balderas Hern\u0026aacute;ndez, P., Roa Morales, G., Ure\u0026ntilde;a N\u0026uacute;\u0026ntilde;ez, F., Villafuerte, O., Lugo Lugo, J., Flores, V., Ram\u0026iacute;rez, N., Barrera D\u0026iacute;az, C.E., Cajero V\u0026aacute;zquez, P.: Characterization of Lignocellulosic Fruit Waste as an Alternative Feedstock for Bioethanol Production. Bioresour. 9 (2014). \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.15376/biores.9.2.1873-1885\u003c/span\u003e\u003cspan address=\"10.15376/biores.9.2.1873-1885\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMohd Azhar, S.H., Abdulla, R., Jambo, S.A., Marbawi, H., Gansau, J.A., Mohd Faik, A.A., Rodrigues, K.F.: Yeasts in sustainable bioethanol production: A review. Biochem. Biophys. Rep. \u003cb\u003e10\u003c/b\u003e, 52\u0026ndash;61 (2017). \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.bbrep.2017.03.003\u003c/span\u003e\u003cspan address=\"10.1016/j.bbrep.2017.03.003\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBušić, A., Mardetko, N., Kundas, S., Morzak, G., Belskaya, H., Šantek, M.I., Komes, D., Novak, S., Šantek, B.: Bioethanol production from renewable raw materials and its separation and purification: A review. Food Technol. Biotechnol. \u003cb\u003e56\u003c/b\u003e, 289\u0026ndash;311 (2018). \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.17113/ftb.56.03.18.5546\u003c/span\u003e\u003cspan address=\"10.17113/ftb.56.03.18.5546\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eChu, B.C.H., Lee, H.: Genetic improvement of Saccharomyces cerevisiae for xylose fermentation. Biotechnol. Adv. \u003cb\u003e25\u003c/b\u003e, 425\u0026ndash;441 (2007). \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.biotechadv.2007.04.001\u003c/span\u003e\u003cspan address=\"10.1016/j.biotechadv.2007.04.001\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eScully, S.M., Orlygsson, J.: Recent advances in second-generation ethanol production by thermophilic bacteria. Energies. \u003cb\u003e8\u003c/b\u003e, 1\u0026ndash;30 (2015). \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.3390/en8010001\u003c/span\u003e\u003cspan address=\"10.3390/en8010001\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMoscoviz, R., Kleerebezem, R., Rombouts, J.L.: Directing carbohydrates toward ethanol using mesophilic microbial communities. Curr. Opin. Biotechnol. \u003cb\u003e67\u003c/b\u003e, 175\u0026ndash;183 (2021). \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.copbio.2021.01.016\u003c/span\u003e\u003cspan address=\"10.1016/j.copbio.2021.01.016\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eTalarico, L.A., Gil, M.A., Yomano, L.P., Ingram, L.O., Maupin-Furlow, J.A.: Construction and expression of an ethanol production operon in Gram-positive bacteria. Microbiol. \u003cb\u003e151\u003c/b\u003e, 4023\u0026ndash;4031 (2005). \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1099/mic.0.28375-0\u003c/span\u003e\u003cspan address=\"10.1099/mic.0.28375-0\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMaleki, F., Changizian, M., Zolfaghari, N., Rajaei, S., Noghabi, K.A., Zahiri, H.S.: Consolidated bioprocessing for bioethanol production by metabolically engineered \u003cem\u003eBacillus subtilis\u003c/em\u003e strains. Sci. Rep. \u003cb\u003e11\u003c/b\u003e, 1\u0026ndash;13 (2021). \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1038/s41598-021-92627-9\u003c/span\u003e\u003cspan address=\"10.1038/s41598-021-92627-9\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDrake, H.L., Schramm, A., Horn, M.A.: Earthworm Gut Microbial Biomes: Their Importance to Soil Microorganisms, Denitrification, and the Terrestrial Production of the Greenhouse Gas N2O. In Intestinal Microorganisms of Termites and Other Invertebrates. Springer. 65\u0026ndash;87 (2006). \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1007/0-387-31131-X_4\u003c/span\u003e\u003cspan address=\"10.1007/0-387-31131-X_4\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eZeibich, L., Schmidt, O., Drake, H.L.: Fermenters in the earthworm gut: Do transients matter? FEMS Microbiol. Ecol. \u003cb\u003e95\u003c/b\u003e(1), 1\u0026ndash;12 (2019). \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1093/femsec/fiy221\u003c/span\u003e\u003cspan address=\"10.1093/femsec/fiy221\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eManyuchi, M.M., Mbohwa, C., Muzenda, E.: Anaerobic treatment of opaque beer wastewater with enhanced biogas recovery through Acti-zyme bioaugmentation. S Afr. J. Chem. Eng. \u003cb\u003e26\u003c/b\u003e, 74\u0026ndash;79 (2018). \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.sajce.2018.10.002\u003c/span\u003e\u003cspan address=\"10.1016/j.sajce.2018.10.002\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eTesfaw, A., Oner, E.T., Assefa, F.: Optimization of ethanol production using newly isolated ethanologenic yeasts. Biochem. Biophys. Rep. \u003cb\u003e25\u003c/b\u003e, 100886 (2021). \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.bbrep.2020.100886\u003c/span\u003e\u003cspan address=\"10.1016/j.bbrep.2020.100886\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHussain, N., Singh, A., Saha, S., Venkata Satish Kumar, M., Bhattacharyya, P., Bhattacharya, S.S.: Excellent N-fixing and P-solubilizing traits in earthworm gut-isolated bacteria: A vermicompost based assessment with vegetable market waste and rice straw feed mixtures. Bioresour Technol. \u003cb\u003e222\u003c/b\u003e, 165\u0026ndash;174 (2016). \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.biortech.2016.09.115\u003c/span\u003e\u003cspan address=\"10.1016/j.biortech.2016.09.115\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHussain, N., Das, S., Goswami, L., Das, P., Sahariah, B., Bhattacharya, S.S.: Intensification of vermitechnology for kitchen vegetable waste and paddy straw employing earthworm consortium: Assessment of maturity time, microbial community structure, and economic benefit. J. Clean. Prod. \u003cb\u003e182\u003c/b\u003e, 414\u0026ndash;426 (2018). \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.jclepro.2018.01.241\u003c/span\u003e\u003cspan address=\"10.1016/j.jclepro.2018.01.241\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePage, D.R., Miller, A.L., Keeney, R.H.: Methods of Soil Analysis. In Chemical and Microbiological Properties (2nd ed.) Madison, Wisconsin, USA. (1982)\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eJenkinson, D.S.: The Determination of Microbial Biomass Carbon and Nitrogen in Soil. In: Wilson, J.R. (ed.) Advances in Nitrogen Cycling in Agricultural Ecosystems, pp. 368\u0026ndash;386. CaB international, Wallingford (1988)\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eU.S. Environmental Protection Agency (USEPA): Soil Sampling Quality Assurance User's Guide. Environmental Monitoring System Laboratory, Las Vegas (1989)\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSmibert, R.M., Krieg, N.R.: Phenotypic Characterization. In: Gerhardt, N., Murray, P., Wood, R.G.E., W.A. and, Krieg (eds.) Methods in General and Molecular Bacteriology, pp. 607\u0026ndash;654. American Society for Microbiology, Washington DC (1994)\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eEbersole, B.: Single Laboratory Validation of a GC-FID Method for Ethanol in Kombucha. In: NaturPro Scientific LLC, Rich Schmidt, Covance Laboratories and Matt Eckert, Covance Laboratories. (2016)\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMoneke, A.N., Okolo, B.N., Nweke, A.I., Ezeogu, L.I., Ire, F.S.: Selection and characterisation of high ethanol-tolerant Saccharomyces yeasts from orchard soil. Afr. J. Biotechnol. \u003cb\u003e7\u003c/b\u003e, 4567\u0026ndash;4575 (2008). \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.5897/AJB08.933\u003c/span\u003e\u003cspan address=\"10.5897/AJB08.933\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBrown, M.D., Hopps, H.C.: Staining of Bacteria in Tissue Sections: A Reliable Gram Stain Method. Am. J. Clin. Pathol. \u003cb\u003e60\u003c/b\u003e, 234\u0026ndash;240 (1973). \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1093/ajcp/60.2.234\u003c/span\u003e\u003cspan address=\"10.1093/ajcp/60.2.234\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSasser, M.: Identification of Bacteria by Gas Chromatography of Cellular Fatty Acids. Tech. Note. \u003cb\u003e101\u003c/b\u003e, 1\u0026ndash;6 (2001)\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLin, S.J., Kaeberlein, M., Andalis, A.A., Sturtz, L.A., Defossez, P.A., Culotta, V.C., Fink, G.R., Guarente, L.: Calorie restriction extends Saccharomyces cerevisiae lifespan by increasing respiration. Nat. \u003cb\u003e418\u003c/b\u003e, 344\u0026ndash;348 (2002). \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1038/nature00829\u003c/span\u003e\u003cspan address=\"10.1038/nature00829\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eTamaki, N., Nakamura, M., Kimura, K., Hama, T.: Purification and properties of aldehyde dehydrogenase from \u003cem\u003eSaccharomyces cerevisiae\u003c/em\u003e. J. Biochem. \u003cb\u003e82\u003c/b\u003e, 73\u0026ndash;79 (1977). \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1093/oxfordjournals.jbchem.a131694\u003c/span\u003e\u003cspan address=\"10.1093/oxfordjournals.jbchem.a131694\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHoppner, T.C., Doelle, H.W.: Purification and kinetic characteristics of pyruvate decarboxylase and ethanol dehydrogenase from \u003cem\u003eZymomonas mobilis\u003c/em\u003e in relation to ethanol production. Eur. J. Appl. Microbiol. \u003cb\u003e17\u003c/b\u003e, 152\u0026ndash;157 (1983). \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1007/BF00505880\u003c/span\u003e\u003cspan address=\"10.1007/BF00505880\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMalakar, A.K.G., Dipankar, Dey, A., Basu, A.: Antiapoptotic role of S-adenosyl-l-methionine against hydrochloric acid induced cell death in \u003cem\u003eSaccharomyces cerevisiae\u003c/em\u003e. Biochim. Biophys. Acta Gen. Subj. \u003cb\u003e1780\u003c/b\u003e, 937\u0026ndash;947 (2008). \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.bbagen.2008.03.014\u003c/span\u003e\u003cspan address=\"10.1016/j.bbagen.2008.03.014\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMukherjee, S., Barman, S., Sarkar, S., Mandal, N.C., Bhattacharya, S.: Anti-bacterial activity of Achatina CRP and its mechanism of action. Indian J. Exp. Biol. \u003cb\u003e52\u003c/b\u003e, 692\u0026ndash;704 (2014)\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLalander, C.H., Fidjeland, J., Diener, S., Eriksson, S., Vinner\u0026aring;s, B.: High waste-to-biomass conversion and efficient Salmonella spp. reduction using black soldier fly for waste recycling. Agroecol Sustain. Food Syst. \u003cb\u003e39\u003c/b\u003e(3), 261\u0026ndash;271 (2015). \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1007/s13593-014-0235-4\u003c/span\u003e\u003cspan address=\"10.1007/s13593-014-0235-4\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDillon, J.L., Hardaker, J.B.: Farm Management Research for Small Farmer Development, p. 6. Food and Agriculture Organization (FAO) of the United Nations (1993)\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMondal, A., Goswami, L., Hussain, N., Barman, S., Kalita, E., Bhattacharyya, P., Bhattacharya, S.S.: Detoxification and eco-friendly recycling of brick kiln coal ash using Eisenia fetida: A clean approach through vermitechnology. Chemosphere. \u003cb\u003e244\u003c/b\u003e, 125470 (2020). \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.chemosphere.2019.125470\u003c/span\u003e\u003cspan address=\"10.1016/j.chemosphere.2019.125470\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGupta, P., Samant, K., Sahu, A.: Isolation of cellulose-degrading bacteria and determination of their cellulolytic potential. Int. J. Microbiol. (2012). \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1155/2012/578925\u003c/span\u003e\u003cspan address=\"10.1155/2012/578925\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBalch, M.L., Chamberlain, M.B., Worthen, R.S., Holwerda, E.K., Lynd, L.R.: Fermentation with continuous ball milling: Effectiveness at enhancing solubilization for several cellulosic feedstocks and comparative tolerance of several microorganisms. Biomass Bioenergy. \u003cb\u003e134\u003c/b\u003e, 105468 (2020). \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.biombioe.2020.105468\u003c/span\u003e\u003cspan address=\"10.1016/j.biombioe.2020.105468\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRobledo, M., Jim\u0026eacute;nez-Zurdo, J.I., Vel\u0026aacute;zquez, E., Trujillo, M.E., Zurdo-Pi\u0026ntilde;eiro, J.L., Ram\u0026iacute;rez-Bahena, M.H., Ramos, B., D\u0026iacute;az-M\u0026iacute;nguez, J.M., Dazzo, F., Mart\u0026iacute;nez-Molina, E., Mateos, P.F.: Rhizobium cellulase CelC2 is essential for primary symbiotic infection of legume host roots. Proc. Natl. Acad. Sci. United States Am. (PNAS). \u003cb\u003e105\u003c/b\u003e(18), 7064\u0026ndash;7069 (2008). \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1073/pnas.0802547105\u003c/span\u003e\u003cspan address=\"10.1073/pnas.0802547105\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eNaveenkumar Rajendran, J.H.: Techno-economic analysis of food waste valorization for integrated production of polyhydroxyalkanoates and biofuels. Bioresour Technol. \u003cb\u003e348\u003c/b\u003e (2022). \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.biortech.2022.126796\u003c/span\u003e\u003cspan address=\"10.1016/j.biortech.2022.126796\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSlabbinck, B., De Baets, B., Dawyndt, P., De Vos, P.: Towards large-scale FAME-based bacterial species identification using machine learning techniques. Syst. Appl. Microbiol. \u003cb\u003e32\u003c/b\u003e, 163\u0026ndash;176 (2009). \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.syapm.2009.01.003\u003c/span\u003e\u003cspan address=\"10.1016/j.syapm.2009.01.003\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eJan-Roblero, J., Cruz-Maya, J.A., Guerrero Barajas, C.C.: Kosakonia. In Beneficial Microbes in Agro-Ecology: Bacteria and Fungi. 213\u0026ndash;231 (2020). \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/B978-0-12-823414-3.00012-5\u003c/span\u003e\u003cspan address=\"10.1016/B978-0-12-823414-3.00012-5\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eNwachukwu, R.E.S., Shahbazi, A., Wang, L., Ibrahim, S., Worku, M., Schimmel, K.: Bioconversion of glycerol to ethanol by a mutant \u003cem\u003eEnterobacter aerogenes\u003c/em\u003e. AMB express. \u003cb\u003e2\u003c/b\u003e, 1\u0026ndash;6 (2012). \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1186/2191-0855-2-20\u003c/span\u003e\u003cspan address=\"10.1186/2191-0855-2-20\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDing, J., Holzwarth, G., Penner, M.H., Patton-Vogt, J., Bakalinsky, A.T.: Overexpression of acetyl-CoA synthetase in \u003cem\u003eSaccharomyces cerevisiae\u003c/em\u003e increases acetic acid tolerance. FEMS Microbiol. Lett. \u003cb\u003e362\u003c/b\u003e, 1\u0026ndash;20 (2015). \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1093/femsle/fnu042\u003c/span\u003e\u003cspan address=\"10.1093/femsle/fnu042\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCheng, S., Fan, C., Sinha, S., Bobik, T.A.: The PduQ Enzyme Is an Alcohol Dehydrogenase Used to Recycle NAD\u0026thinsp;+\u0026thinsp;Internally within the Pdu Microcompartment of \u003cem\u003eSalmonella enterica\u003c/em\u003e. PLOS ONE. \u003cb\u003e7\u003c/b\u003e, 1\u0026ndash;11 (2012). \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1371/journal.pone.0047144\u003c/span\u003e\u003cspan address=\"10.1371/journal.pone.0047144\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eEram, M.S., Ma, K.: Decarboxylation of pyruvate to acetaldehyde for ethanol production by hyperthermophiles. Biomol. \u003cb\u003e3\u003c/b\u003e, 578\u0026ndash;596 (2013). \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.3390/biom3030578\u003c/span\u003e\u003cspan address=\"10.3390/biom3030578\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eZhang, Y., Su, M., Qin, N., Nielsen, J., Liu, Z.: Expressing a cytosolic pyruvate dehydrogenase complex to increase free fatty acid production in \u003cem\u003eSaccharomyces cerevisiae\u003c/em\u003e. Microb. Cell. Factories. \u003cb\u003e19\u003c/b\u003e, 1\u0026ndash;9 (2020). \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1186/s12934-020-01493-z\u003c/span\u003e\u003cspan address=\"10.1186/s12934-020-01493-z\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDing, A.X., Sun, G., Argaw, Y.G., Wong, J.O., Easwaran, S., Montell, D.J.: CasExpress reveals widespread and diverse patterns of cell survival of caspase-3 activation during development in vivo. eLife. \u003cb\u003e5\u003c/b\u003e, 1\u0026ndash;20 (2016). \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.7554/eLife.10936\u003c/span\u003e\u003cspan address=\"10.7554/eLife.10936\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eJ\u0026oacute;zsef, L., Khreiss, T., Filep, J.G.: CpG motifs in bacterial DNA delay apoptosis of neutrophil granulocytes. FASEB J. \u003cb\u003e18\u003c/b\u003e, 1776\u0026ndash;1778 (2004). \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1096/fj.04-2048fje\u003c/span\u003e\u003cspan address=\"10.1096/fj.04-2048fje\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSahariah, B., Goswami, L., Kim, K.H., Bhattacharyya, P., Bhattacharya, S.S.: Metal remediation and biodegradation potential of earthworm species on municipal solid waste: A parallel analysis between \u003cem\u003eMetaphire posthuma\u003c/em\u003e and \u003cem\u003eEisenia fetida\u003c/em\u003e. Bioresour Technol. \u003cb\u003e180\u003c/b\u003e, 230\u0026ndash;236 (2015). \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.biortech.2014.12.062\u003c/span\u003e\u003cspan address=\"10.1016/j.biortech.2014.12.062\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePaul, S., Choudhury, M., Deb, U., Pegu, R., Das, S., Bhattacharya, S.S.: Assessing the ecological impacts of ageing on hazard potential of solid waste landfills: A green approach through vermitechnology. J. Clean. Prod. \u003cb\u003e236\u003c/b\u003e, 117643 (2019). \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.jclepro.2019.117643\u003c/span\u003e\u003cspan address=\"10.1016/j.jclepro.2019.117643\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAdegboye, M.F., Ojuederie, O.B., Talia, P.M., Babalola, O.O.: Bioprospecting of microbial strains for biofuel production: Metabolic engineering, applications, and challenges. Biotechnol. Biofuels. \u003cb\u003e14\u003c/b\u003e, 1\u0026ndash;21 (2021). \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1186/s13068-020-01853-2\u003c/span\u003e\u003cspan address=\"10.1186/s13068-020-01853-2\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePata, S.U.K., Kartal, M.T., Adebayo, T.S.: Enhancing environmental quality in the United States by linking biomass energy consumption and load capacity factor. Geosci. Front. \u003cb\u003e14\u003c/b\u003e(3) (2023). \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.gsf.2022.101531\u003c/span\u003e\u003cspan address=\"10.1016/j.gsf.2022.101531\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSakthivel, P., Subramanian, K.A., Mathai, R.: Indian scenario of ethanol fuel and its utilization in the automotive transportation sector. Resour. Conserv. Recycl. \u003cb\u003e132\u003c/b\u003e, 102\u0026ndash;120 (2018). \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.resconrec.2018.01.012\u003c/span\u003e\u003cspan address=\"10.1016/j.resconrec.2018.01.012\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eZhang, B., Ji, C., Wang, S.: Performance of a hydrogen-enriched ethanol engine at unthrottled and lean conditions. Energy Convers. Manag. \u003cb\u003e114\u003c/b\u003e, 68\u0026ndash;74 (2016). \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.enconman.2016.01.073\u003c/span\u003e\u003cspan address=\"10.1016/j.enconman.2016.01.073\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"waste-and-biomass-valorization","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"wave","sideBox":"Learn more about [Waste and Biomass Valorization](http://link.springer.com/journal/12649)","snPcode":"12649","submissionUrl":"https://submission.nature.com/new-submission/12649/3","title":"Waste and Biomass Valorization","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"ethanologenic bacteria, lignocellulosic waste, vermicompost, earthworm gut, defense mechanism","lastPublishedDoi":"10.21203/rs.3.rs-3876047/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-3876047/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eLignocellulosic wastes (LCW) have enormous potential to be recycled for bioethanol production. Although yeasts (\u003cem\u003eSaccharomyces\u003c/em\u003e sp.) are commonly used bio-agents for fermentation, their efficiency is inhibited in cellulosic feedstocks. This study isolated novel ethanologenic bacteria from vermicomposting systems for bioenergy generation from fruit waste without pre-treatment. Initially, six strains out of 22, showing remarkable ethanol production ability, were characterized via 16S rRNA sequencing. Specifically, two strains (\u003cem\u003eBacillus alcalophilus\u003c/em\u003e C5 and \u003cem\u003eRhizobium spp.\u003c/em\u003e S10) produced more ethanol (5.5 and 15.7 g L\u003csup\u003e− 1\u003c/sup\u003e) than the yeast (5 g L\u003csup\u003e− 1\u003c/sup\u003e) from banana epicarps. These strains' dramatically high sedimentation rate and ethanol tolerance strongly justified their industrial applicability. Significant upregulation of alcohol dehydrogenase and acetyl CoA synthase endowed greater ethanol-producing capacity in C5 and S10 than in \u003cem\u003eS. cerevisiae\u003c/em\u003e. The flow cytometry and confocal microscopy evidenced that ethanologenic bacteria uniquely defend the reactor-induced sugar and ethanol stresses through reverse/delayed apoptosis and robust membrane integrity. The waste-to-wealth conversion efficiency and cost-benefit analyses estimated that bacteria-mediated LCW-to-bioethanol conversion was a more profitable venture than vermicomposting or composting. Overall, this research demonstrated that the C5 and S10 isolates were more effective than widely used commercial yeast strains for bioethanol generation from LCW.\u003c/p\u003e","manuscriptTitle":"Bioethanol production from lignocellulosic waste without pre-treatment employing vermicompost and earthworm gut-isolated bacteria: Insights on waste to wealth conversion efficiency towards cleaner lifestyle","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-02-01 19:09:15","doi":"10.21203/rs.3.rs-3876047/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"reviewerAgreed","content":"","date":"2024-01-30T04:50:37+00:00","index":0,"fulltext":""},{"type":"reviewersInvited","content":"","date":"2024-01-30T04:36:31+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"Waste and Biomass Valorization","date":"2024-01-28T06:32:05+00:00","index":"","fulltext":""},{"type":"submitted","content":"Waste and Biomass Valorization","date":"2024-01-17T09:11:58+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"waste-and-biomass-valorization","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"wave","sideBox":"Learn more about [Waste and Biomass Valorization](http://link.springer.com/journal/12649)","snPcode":"12649","submissionUrl":"https://submission.nature.com/new-submission/12649/3","title":"Waste and Biomass Valorization","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"28cf1491-3ad0-4137-a237-dddbda76adfb","owner":[],"postedDate":"February 1st, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2024-04-18T01:29:55+00:00","versionOfRecord":[],"versionCreatedAt":"2024-02-01 19:09:15","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-3876047","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-3876047","identity":"rs-3876047","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}