Cryo-adapted bacterial copiotrophs from a Trans-Himalayan lake-desert ecosystem as biogeothermometers of warming and mitigators of microbiome perturbation

Cryo-adapted bacterial copiotrophs from a Trans-Himalayan lake-desert ecosystem as biogeothermometers of warming and mitigators of microbiome perturbation | 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 Cryo-adapted bacterial copiotrophs from a Trans-Himalayan lake-desert ecosystem as biogeothermometers of warming and mitigators of microbiome perturbation Sumit Chatterjee, Subhajit Dutta, Jit Ghosh, Swapneel Saha, Mahamadul Mondal, and 3 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8996027/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 26 Apr, 2026 Read the published version in Archives of Microbiology → Version 1 posted 11 You are reading this latest preprint version Abstract A Trans-Himalayan lake-desert ecosystem was explored for the low-to-high temperature adaptations of copiotrophic psychrophiles having potentials for substantive carbon remineralization under natural and/or anthropogenically-influenced conditions of high organic matter delivery to the cryospheric environment. Overall 27 bacterial species were isolated from the brackish-water and sediment-surface of Tso Moriri (a massive lake on the Changthang plateau that remains frozen for approximately one third of the year), and the fine talus covering a lake-side rocky mountain. In Luria broth (LB), all isolates grew at 4°C and 15°C; at -10°C, 13 could grow while others remained only metabolically-active. Catabolizing different complex-organic-compounds, all isolates achieved considerable growth at 4°C; 20 accomplished low growth at -10°C. LB-based growth dwindled with rising temperature: 23, 11, and none of the isolates grew at 28°C, 37°C, and 42°C respectively. The isolates’ genomes, and the habitats’ metagenomes, encompassed diverse genes for extreme-temperature adaptation and carbohydrate catabolism. Within high-altitude cryospheres, cessation of organotrophy, in general, would cut-back simple fatty acids, CO 2 and N 2 O production (short-supply of CO 2 and acetate would in turn cutback methanogenesis, if the concerned archaea are present in situ ). Such negative feedback controls of greenhouse gas production at the micro-habitat level can add-up in the biome-scale to mitigate broader environmental warming. However, homeostasis via abolition of growth for indigenous psychrophiles is fraught with the danger of ecosystem takeover by thermally-better-adapted foreign microbes. At 28°C, majority of the actinobacterial isolates inhibited bacteria from discrete warmer habitats; they can, therefore, be viewed as potential defenders of the cold/frigid ecosystem. Trans-Himalayan deserts and lakes psychrophilic and cryo-adapted bacteria climate warming antibiosis microbiome protection organic matter degradation Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Figure 8 Introduction Microbial life at low temperatures is constrained by a number of biophysical and biochemical adversities (Cavicchioli et al. 2000; Öquist et al. 2009). However, across the seasonally or perpetually frozen alpine/polar territories of the Earth’s biosphere, barren soils, outwardly life-less rocky terrains, and most conspicuously, numerous limnic and fluvioglacial features, harbor considerably diversified microbiomes (Cary et al. 2010; Choe et al. 2021) that in turn sustain highly climate-sensitive ecosystems via biogeochemical cycling of carbon and other elements (Finlay et al. 2010; van der Valk 2012; Clow et al. 2015; Elser et al. 2020). Throughout the high-latitude and high-altitude areas of the globe, copious endolithic and chasmolithic microbial communities colonize the rocky terrains and contribute actively or passively to the weathering of rocks into dust and scree, thereby promoting sediment and soil formation (Archer et al. 2017; Samuels et al. 2020). Supraglacial and subglacial microbiomes drive considerable biogeochemical activities (Hodson et al. 2008; Boetius et al. 2015; Yarzábal et al. 2021). The former types accelerate the melting of glaciers (Cook et al. 2020; Williamson et al. 2020), while post deglaciation the latter types release profuse greenhouse gases, such as carbon dioxide (CO 2 ), methane (CH 4 ) and nitrous oxide (N 2 O), into the atmosphere (Aschenbach et al. 2013; Ma et al. 2018; Lamarche-Gagnon et al. 2019; Zhang et al. 2021). Microbiomes thriving in young as well as matured soils, including those associated with permafrosts, and biocrusts covering deglaciated moraines and rock surfaces, render temporally slow but spatially extensive, fixation and remineralization of carbon (Drotz et al. 2010; Nikrad et al. 2016). Furthermore, approximately, 25 million lacustrine bodies (Verpoorter et al. 2014), together with the vast expanses of thermokarst landforms, sequester huge amount of organic carbon by remaining frozen for a substantial part of the year, and emit enormous volumes of CO 2 and CH 4 upon thawing (Kessler et al. 2012; MacIntyre et al. 2018; Serikova et al. 2019; Guo et al. 2020; Johnston et al. 2020). In all temporarily or permanently frozen ecosystems, microbial activities and proliferation are said to remain subdued as long as the temperature remains below or around 0°C (Mohn and Stewart 2000; Nikrad et al. 2016). However, organotrophic growth, and thereby remineralization of carbon and emission of greenhouse gases, starts once the temperature rises on a seasonal or climatic scale, and cryoturbation takes place (Zimov et al. 2006; Graham et al. 2012; Ernakovich et al. 2015; Nikrad et al. 2016; Walz et al. 2017). Greenhouse effect, at any spatiotemporal level, is thought to enhance microbial activities and growth, which in turn induces further thawing of the environment (Walter et al. 2006; Nikrad et al. 2016). Such positive feedback cycles (Walter et al. 2006; Graham et al. 2012; Schneider von Deimling et al. 2012) can, in the long run, alter the structures and functions of microbiomes, and thereby entire ecosystems, within the cold/frigid realm (Nikrad et al. 2016). To understand the functioning of cold/frigid ecosystems, and in order to manage them in a sustainable way, we require comprehensive information on the scopes of carbon cycling and sequestration not only around the freezing point of water but also at temperatures above the levels that are critical (Kosaka et al. 2019) for the growth and survival of indigenous psychrophiles. For that purpose, chemoorganoheterotrophic capabilities of autochthonous microorganisms need to be delineated via extensive pure-culture-based investigations at near-zero and sub-zero degree Celsius, in tandem with which we require comprehensive data on how native psychrophilic microorganisms respond to different levels of warming in terms of population-level growth or survival. Furthermore, in the context of global warming, it is imperative to appreciate the homeostatic vulnerabilities of alpine/polar microbiomes alongside their potential intrinsic resilience against perturbations. In other words, we need to know whether cold-adapted microbial communities have any indigenous safeguard against infiltration and ecological niche-takeover by organisms from warmer territories, i.e. whether they can thwart microbiome (and habitat) alterations triggered by climate warming (Hubert et al. 2009; Pearce et al. 2009). The present study of pure-culture microbiology explored three adjacent environments (habitats) within a Trans-Himalayan lake-desert ecosystem, centered on the massive fresh-to-brackish water body called Tso Moriri (vernacular meaning: lake amid the mountains), which is situated on the cold arid Changthang plateau of eastern Ladakh, India (Figs. 1 a-c). Phylogenetically diverse chemoorganoheterotrophic bacterial psychrophiles were isolated and characterized from Tso Moriri’s sediment, and water that remains frozen for approximately one third of the year. Psychrophilic heterotrophs were also isolated from the weathered rock dust (fine talus and scree particles) that covers the hill overlooking the western bank of the lake (Figs. 1 d-g). It was first investigated whether the copiotrophic psychrophiles isolated in nutrient-rich medium could grow by catabolizing different simple or complex organic substrates at near-zero and sub-zero degree Celsius temperatures. Experiments were then conducted to know how the growth and activity of the isolates were affected by different levels of warming. The data obtained in vitro were evaluated theoretically to explore whether natural populations of the isolated bacteria could act as “biogeothermometers” chronicling in situ temperatures over time and space. From the contemporary perspective of heightened thawing of the cold/frigid realm, we examined whether the bacteria retrieved from the Tso Moriri area (TMA) had any aptitude for resisting incursion of foreign microorganisms from warmer climatic territories. For this purpose, we tested the antibiosis potentials of the current isolates against higher-temperature-adapted bacteria retrieved previously from discrete warmer ecosystems. The ecophysiologically important phenotypes of the new isolates were appraised in the light of the organisms’ genomes and their habitats’ metagenomes. The whole repertoire of data available was eventually interpreted in the context of climate change to envisage if the indigenous psychrophiles had any role in the abatement of environmental warming. Materials and methods Study site The Himalayas and Trans-Himalayas encompass the largest reservoir of snow and ice on Earth outside the Arctic Circle and Antarctica (Meena and Kukreja 2025). Within the cold arid vastness of the Himalayan rain shadow, the territory of Ladakh features an extensive high-altitude plateau, where multifaceted microbial life thrives to support homeostatically fragile ecosystems harboring unique multi-stress-adapted plants and animals (Dvorský et al. 2011; Aschenbach et al. 2013; Dvorský et al. 2013; Janatková et al. 2013; Angel et al. 2016). Covering a large swathe of eastern Ladakh (Fig. 1 a) sprawls the Changthang desert dotted by several small to large lacustrine bodies among which Tso Moriri is one of the most prominent (Fig. 1 b). This gigantic water body (Figs. 1 b-c), located at an altitude of 4522 m, is flanked on all sides by barren hills (Figs. 1 d-e) that rise either from a distance or very close to the bank of the lake (Fig. 1 d). Small patches of lush green meadows, marshes, and wetlands hem only the northern and southwestern shores of Tso Moriri where two big glacial streams pour their water into the lake. Rest of the cold-desert habitat around the lake is characterized by a bleak topography and highly inhospitable physicochemical conditions that are paralleled only by the extreme multi-stressor environments of a few high-altitude areas of the Chilean and Argentinean Andes (Albarracín et al. 2015, 2016). Sampling On 28 October 2021, water (Fig. 1 f) and sediment (Fig. 1 g) samples were collected from near the western shore of Tso Moriri (at GPS coordinates 32°94′ N and 78°27′ E), together with samples of weathered rock dust from the barren slope of a hill overlooking the western bank of the lake (Fig. 1 e). The entire hill slope, rising from ~ 100 m off the Tso Moriri shore, was devoid of any vegetative cover; visibly, not a single blade of grass grew on it. In order to arrest the aquatic microbiota, as described previously (Mondal et al. 2024), five different batches of 500 mL water were sampled from five discrete sites located at intervals of 1 m, and within 1 m from the shore of the lake. Each batch of 500 mL water was aspirated from within 1 cm of the lake surface using a sterile syringe (Tarsons Products Limited, India), and passed through an autoclaved, Swinnex holder-mounted (Merck KGaA, Germany), sterile, mixed cellulose ester membrane filter (Merck Life Science Private Limited, India) having a mesh size of 0.22 µm and diameter of 47 mm. Subsequent to filtration, each membrane corresponding to the cell residue of 500 mL lake-water was dislodged from the Swinnex holder, folded using autoclaved forceps, and inserted into a 7.5 mL autoclaved cryovial that contained 5 mL of 15% (v/v) glycerol supplemented with 0.9% (w/v) NaCl (Roy et al. 2016). From the same five locations where Tso Moriri’s water was sampled, sediments were collected in approximately equal quantities, and pooled inside a single 250 mL polypropylene bottle. Inside the polypropylene bottle the sediment fractions were mixed thoroughly to give rise to a bulk sample which was used for all downstream investigations. At each sampling point, soft deposit was scraped carefully from the sediment-water interface using a flat and wide sterile spatula without disturbing layers deeper than 1 cm from the sediment-surface. Dry and loose, weathered rock dust samples (fine talus and scree particles) were collected in approximately equal quantities from five distinct points situated within one meter from each other, on the lake-facing slope of the aforesaid hill. At each sampling site, fine powdery materials were scraped cautiously from the surface without disturbing layers deeper than the top 1 cm. The five sample fractions were pooled inside a 250 mL polypropylene bottle, and mixed thoroughly with a sterile spatula to yield a bulk sample that was used for all subsequent investigations. After the insertion of a cell-precipitated membrane, or sediment / rock-dust sample, each cryovial or polypropylene bottle was capped tightly, sealed with parafilm (Tarsons Products Limited, India), and put inside a polyethylene bag, which eventually was packed in a heat-insulated ice box and shipped by air to the laboratory. All samples were investigated immediately upon their arrival at the laboratory. In order to extract metagenomic DNA from Tso Moriri’s water and sediment, and also from the rock-dust of the lake-side mountain, sampling was carried out as described above. Only for the water sample a few minor adjustments were made. Total 5 L of the lake-water was passed through five separate (pre-autoclaved) 0.22 µm filters (1 L per filter), which in turn were inserted into five different sterilized cryovials, each containing 5 mL of sterile 50 mM Tris:EDTA (pH 7.8). Enrichment and isolation of cryo-tolerant / cryo-adapted, psychrophilic copiotrophs Prior to the isolation of pure cultures, the water, sediment and talus samples were subjected to iterative cycles of freezing and thawing. This ensured that the bacterial strains retrieved were cryo-adapted (capable of rendering growth, whether little or substantial, at -10°C), or at least cryo-tolerant (adept in population-level survival, i.e. retention of > 1% cells in metabolically-active state, at -10°C), in nature. A separate time-series investigation of biogeochemistry, carried out by our laboratory at the same sample sites explored in this study, have shown that after the summer months (just before the winter), concentration of total carbon in the surficial lake-water reached 80 mg L - 1 , while total carbon content of the surficial samples of lake-sediment and weathered rock dust reached approximately 2% (w/w) and 1% (w/w) respectively (Chatterjee et al., manuscript under preparation). In view of these findings the present enrichment and isolation strategy specifically targeted cryo-adapted/tolerant copiotrophs that possess the potentials for rendering substantive carbon remineralization under cold and frigid conditions. The overall objective was to ensure that the organotrophic growth potentials exhibited by the new isolates in vitro held direct implications for the scope of in situ organic matter degradation across alpine/polar ecosystems experiencing high carbon delivery to the environment, naturally and/or under anthropogenic influence. In order to enrich copiotrophic, cryo-tolerant / cryo-adapted, psychrophiles from Tso Moriri’s water, each 0.22 µm cellulose acetate filter, which contained microbial cell residue from 500 mL lake-water, was shredded with sterile scissors inside the same vial in which it was inserted on-field. The vial was whirled for 15 minutes; after that the filter-shreds were allowed to settle at the bottom; finally, the supernatant was collected in a fresh sterilized vial without disturbing the debris. This process was repeated for all the five filter-containing cryovials involved in lake-water sampling, and the supernatants recovered from each of them were pooled to get an approximately 22 mL cell-suspension in NaCl-glycerol, which corresponded to 2.5 L of bulked lake-water. This ~ 22 mL cell-suspension was added to 80 mL LB prepared in such a way that the actual nutrient concentrations were achieved after the mixing. The inoculum-medium mixture was subjected to three consecutive cycles of “7-day freezing at -10°C, followed by 7-day thawing at 4°C”, after which pure cultures were isolated at an incubation temperature of 4°C by means of dilution plating, picking of visibly-distinct single colonies, and repeated dilution streaking on Luria agar (LA) plates. To enrich copiotrophic, cryo-tolerant / cryo-adapted, psychrophiles from the lake-sediment or rock-dust sample, 1 g of the corresponding material was added to 100 mL LB, and the suspension was subjected to three cycles of “7-day freezing at -10°C, followed by 7-day thawing at 4°C”. After the third round of thawing, pure culture strains were isolated at 4°C in the same way as described above. All the new isolates were maintained in LA slants with a standard transfer interval of 15 days. For routine growth in LB, or fortnightly transfer in LA, all isolates were grown at 4°C. The strains were classified, as described previously (Saha et al. 2019), up to the lowest taxonomic rank that was ascribable based on 16S rRNA gene sequence similarities with validly-published species curated in the List of Prokaryotic names with Standing in Nomenclature (Parte et al. 2020). Determining the temperature window for growth The temperature range over which the new isolates could, or could not, grow was delineated by recording the extent to which CFU density increased, or decreased, in the individual LB cultures of the strains, after aerobic incubation at -10°C, 4°C, 15°C, 28°C, 37°C, and 42°C. For a given experiment, a seed culture of the strain tested was prepared by transferring a loopful of cell mass from a 7-day old LA slant culture to fresh 20 mL LB medium kept in a 50 mL Erlenmeyer flask, which was then incubated aerobically at 15°C until when the culture attained its mid-log stage. From this 20 mL seed culture, 1% inoculum was transferred to fresh 20 mL LB medium to set up the test culture. Experiments checking aerobic growth at incubation temperatures ranging between 4°C and 42°C were carried out in 50 mL Erlenmeyer flasks, whereas those checking aerobic growth at -10°C were carried out in 50 mL polypropylene tubes. To record CFU density at any time point, 1 mL of the experimental culture concerned was serially diluted using 0.9% (w/v) NaCl, and plated on LA in triplicates (in case of -10°C incubations, the frozen cultures were first liquefied via thawing at 4°C, and then subjected to dilution plating). Subsequently, the LA plates were incubated at 15°C, and colonies appearing on them were counted after 2–3 days depending on the growth rate of the culture in question. Finally, the CFU density was calculated by first multiplying the colony-counts of the individual dilution-plates by their corresponding dilution factors, and subsequently adding and averaging the values across the dilution grades and replica plates available. The above experiments were repeated for the comparator organism Escherichia coli by recording the increases or decreases in CFU density that the LB cultures of strain K-12 underwent after incubation at -10°C, 4°C, 15°C, 28°C, 37°C, and 42°C. E . coli seed cultures were prepared in the same as those prepared for the TMA isolates except for the fact that incubations were carried out at 28°C. Experimental cultures too were subjected to the same procedure as above, while CFU densities for K-12 were recorded by incubating the colony-counting LA plates at 28°C for two days. When the final CFU density of an experimental culture recorded after the stipulated period of incubation was higher than its initial (0 hour) CFU density, the growth rate of the concerned isolate, at the temperature in question, was calculated as the percentage change that was recorded in the CFU density over time (percentage of the initial CFU mL - 1 culture that increased day - 1 incubation). $$\text{G}\text{r}\text{o}\text{w}\text{t}\text{h}\text{r}\text{a}\text{t}\text{e}=\frac{\left(\text{F}\text{i}\text{n}\text{a}\text{l}\text{C}\text{F}\text{U}\text{d}\text{e}\text{n}\text{s}\text{i}\text{t}\text{y}-\text{I}\text{n}\text{i}\text{t}\text{i}\text{a}\text{l}\text{C}\text{F}\text{U}\text{d}\text{e}\text{n}\text{s}\text{i}\text{t}\text{y}\right)\times100}{\text{I}\text{n}\text{i}\text{t}\text{i}\text{a}\text{l}\text{C}\text{F}\text{U}\text{d}\text{e}\text{n}\text{s}\text{i}\text{t}\text{y}}\times\frac{1}{\text{T}\text{o}\text{t}\text{a}\text{l}\text{d}\text{a}\text{y}\text{s}\text{o}\text{f}\text{i}\text{n}\text{c}\text{u}\text{b}\text{a}\text{t}\text{i}\text{o}\text{n}}$$ Determining the temperature window for population-level survival When the final CFU density of an experimental culture recorded after the stipulated period of incubation was lower than its initial (0 hour) CFU density, the survival frequency of the concerned isolate’s cell populations at the temperature in question was delineated in terms of what percentage of cells remained metabolically active in the culture. Proportion of metabolically-active cells in a given culture was determined by testing the cells’ ability to imbibe the nonfluorescent and nontoxic substance called fluorescein diacetate (FDA), and subsequently hydrolyze the same to the tracer compound fluorescein by means of esterase activity (Battin 1997). Fluorescein-stained cells, eventually, were detected with the help of flow cytometry, as described previously (Samaddar et al. 2016; Mondal et al. 2022). Cell-pellet was harvested via cold (4°C) centrifugation for 20 minutes at 6000 g , and then resuspended in 2 mL of a 0.9% NaCl solution. From a 0.5% (w/v) FDA (Sigma, USA) solution in dimethyl sulfoxide, 4 µl was added to the cell suspension and incubated at 37°C for 15 minutes. The cells were then washed and resuspended again in 500 µl of 0.9% NaCl. Fluorescence-activated cell sorting (FACS) was carried out with the help of a BD FACSVerse flow cytometer (Becton, Dickinson and Company, USA), where 10 4 cells were analyzed randomly for their ability to fluoresce through 475–495 nm excitation and 520–530 nm emission. The BD FACSuite software package (Becton, Dickinson and Company) was used to present the data in the form of a dot plot depicting the level of fluorescence recorded for each cell as a function of its photodiode-array-detected ability to forward scatter light having a wave length of 488 nm. Positions of the experiment-specific quadrant gates separating the metabolically-active cells from their inactive counterparts were determined by analyzing an unstained version of the sample. Testing chemoorganoheterotrophic growth on simple/complex carbon compounds The TMA isolates were tested for 4°C and − 10°C aerobic growth on various simple to complex organic compounds as sole sources of energy, electron, and carbon. For this purpose, each strain was cultured aerobically on modified basal and mineral salts (MS) solution (Ghosh and Roy 2006) supplemented with any one of the following organic compounds (HiMedia Laboratories, India) at one particular instance (L − 1 double distilled Milli-Q water): acetate (10 mM), agar (2 g), albumin (2 g), benzoate (5 mM), cellulose (5 g), chitin (10 g), n-hexadecane (0.5 mL), pectin (10 g), water-soluble starch (4g) and xylan (10 g). For each experiment, a seed culture of the test strain was prepared as described above. From the 20 mL seed culture, cells were harvested, then washed twice with 0.9% NaCl, and eventually resuspended in 1 mL MS solution. This cell suspension in MS was added to the test medium (MS solution supplemented with any one of the 10 organic compounds mentioned above) in such a way that the specified concentration of the medium was reached only after the addition of the inoculum (final volume of the test culture was 20 mL). Experiments checking growth at 4°C were carried out in 50 mL Erlenmeyer flasks, whereas those checking growth at -10°C were carried out in 50 mL polypropylene tubes. CFU density of a culture at a given time point of incubation was determined as described for the LB-dependent growth experiments. Furthermore, each of the 27 TMA isolates was tested for its ability to grow in MS solution devoid of any organic carbon. These experiments were carried out in the same way as described above for testing growth in MS supplemented with a single organic compound. Testing antibiosis potential Every TMA isolate was tested by agar overlay assay (Berberov et al. 2025) for its antibiosis potentials against higher-temperature-adapted, mesophilic, Gram negative and Gram positive bacteria that had been isolated previously from warmer habitats within the Western-Himalayan and Trans-Himalayan territories. The Gram negative targets included the well-known model microorganism Escherichia coli K-12 (Gammaproteobacteria), plus the temperate soil isolate Advenella kashmirensis WT001 that belonged to Betaproteobacteria (Ghosh 2005, 2013), and the Puga Valley hydrothermal vent isolate Paracoccus sp. SMMA_5 that belonged to Alphaproteobacteria (Roy et al. 2016; Mondal et al. 2022). The Gram positive targets included the Chumathang hydrothermal vent isolates Bacillus subtilis SC_1, Bacillus licheniformis PAMA2_SD1, and Lysinibacillus fusiformis LAPE1_SD1, all of which belonged to the phylum Bacillota (Dutta et al. manuscript under preparation). Each TMA isolate was also tested for its antibiosis capabilities against other isolates obtained from the TMA. The potential antagonists under assessment (these were taken in batches of four isolates at a time) were first grown in LB, at 15°C for 48 h, to generate their seed cultures. 10 µL inocula from the individual seed cultures were spotted on LA plates at minimum mutual distances of 3 cm, following which the plates were incubated at 15°C for 48 h. After fully grown colonies had appeared for all the potential antagonists investigated, 100 µL of a mid-log-phase culture of the target organism against which antibiosis was to be tested, was mixed with 900 µL molten agar (0.4% w/v) having 37°C temperature, and poured uniformly on the LA plates that were already dotted by incumbent colonies of the potential antagonists. Seed culture of the incoming bacterium (target of potential antibiosis) was grown at 28°C or 15°C, according as the organism was a mesophile from a foreign habitat or an isolate from the TMA. Eventually, these test plates were incubated for 48 h (again, at 28°C or 15°C, according as the incoming bacterium was a mesophile from a foreign habitat or a TMA isolate) and checked for the development of lawns of growth, or clear zones of inhibition, for the incoming organism, around the pre-established colonies of the incumbent bacteria (potential antagonists). Genomics Whole genomic DNA was extracted from the LB-grown stationary phase culture of a given TMA isolate using HiPurA Bacterial Genomic DNA Purification Kit (Himedia, India), and sequenced using a Novaseq 6000 (Illumina Inc., USA) as well as a MinION (Oxford Nanopore Technologies, UK) platform. For every genome, 2×150 bp paired-end Illumina reads having Phred score above 20 (Q20) were assembled alongside MinION reads having quality values > 10, with the help of the software Unicycler v0.5.0 run in hybrid assembly mode (Wick et al. 2017). Prodigal v2.6.3 (Hyatt et al. 2010) was used to predict open reading frames (ORFs), or putative genes, within the assembled genome. Subsequently, a catalog of protein-coding gene sequences (CDSs) was delineated by searching the repertoire of ORFs available, against the eggNOG database v5.0 (Huerta-Cepas et al. 2019), with the aid of eggNOG-mapper v2.1.9 (Cantalapiedra et al. 2021), which in turn used the algorithm HMMER. Metagenomics From the lake-water, lake-sediment, and rock-dust samples, metagenomic DNA was extracted, and sequenced on a Novaseq 6000 using 2 × 250 bp paired-end read chemistry, as described previously (Bhattacharya et al. 2021; Mondal et al. 2024). After clipping the adapters and quality-filtering for an average Phred score ≥ 20, 25 million read-pairs were extracted randomly from each metagenomic sequence dataset and assembled de novo using Megahit v1.2.9 with default parameters (Li et al. 2015). Within the > 1000 bp contigs assembled from a metagenome, ORFs were annotated, and the CDS catalog was delineated, as stated for the pure-culture genomes. The dataset of 50 million quality-filtered reads derived for a given metagenome was searched against the rrnDB database v5.10 (Stoddard et al. 2015) using Bowtie2 v2.4.5 (Langmead and Salzberg 2012) in default mode to extricate sequences corresponding to 16S rRNA genes and classify them using the RDP Classifier with a confidence cut-off value of 0.8 (Mondal et al. 2024). To get an idea about the habitat-wise prevalence (relative abundance) of the different TMA species for which whole genomes were sequenced, the proportion of sequence correspondence that existed between a given genome and the metagenome of the habitat under consideration was determined as follows. First, a subject (target) database was created by curating all the 15 whole genome sequences in hand; subsequently, all Q20 metagenomic reads available for a given habitat were mapped onto the subject database using Bowtie2 v2.4.5 in default mode. The alignment output file obtained was processed with SAMtools v1.13 (Li et al. 2009) to report the number of metagenomic reads that matched each genome of the target database individually. Identification of ecophysiologically important genes within the genomes and metagenomes To identify genes associated with low temperature adaptation, and low as well as high temperature adaptation, the eggNOG-derived CDS catalogs were searched on the basis of the information available in the literature for extreme temperature adaptation, alongside the gene orthology information curated in the Kyoto Encyclopedia of Genes and Genomes (KEGG; https://www.genome.jp/kegg/ ). To identify genes encoding carbohydrate-active enzymes, the Prodigal-derived ORF catalog of an isolate or metagenome was annotated directly by searching against the CAZy and dbCAN3 databases using Diamond (Buchfink et al. 2015) and HMMER (Zheng et al. 2023) algorithms (with default parameters for both) respectively. Subsequently, the collective findings of the two search exercises were reported as the catalog of genes encoding carbohydrate-active enzymes within the genome / metagenome in question. To identify genes that are known to be central to the biosynthesis of different classes of antibiotics, each eggNOG-derived CDS catalog was searched based on the information available in the literature for antibiotic biosynthesis, plus the gene orthology information curated in the KEGG database. Furthermore, to detect genes or gene clusters concerned with the biosynthesis of secondary metabolites, each Prodigal-derived ORF catalog was annotated using the bacterial version of the antibiotics and secondary metabolite analysis shell (antiSMASH v7.0) pipeline in strict detection mode (Medema et al. 2011; Blin et al. 2023). Antibiotic resistance genes were identified by annotating the assembled whole genome sequences against the Comprehensive Antibiotic Resistance Database (CARD, version 3.2.8) using the Resistance Gene Identifier (RGI, v6.0.3) tool with its default analysis parameters (Alcock et al. 2023). Results Cold-adapted copiotrophs from Tso Moriri lake-desert ecosystem A sum total of 61 aerobic, copiotrophic, and freeze-thaw resilient bacterial pure-cultures, apparently dissimilar with regard to colony morphology, were enriched and isolated in Luria broth (LB), from the lake-water (Fig. 1 f), lake-sediment (Fig. 1 g), and rock-dust (Fig. 1 e) samples. 22 of these were obtained from the weathered rock dust of the lake-side hill, while five and 34 were from the lake’s water and sediment respectively (Table 1 ). 16S rRNA gene sequence similarities clustered the 61 isolates into 27 species-level entities that in turn were ascribable to 15 genera distributed over the phyla Actinomycetota, Bacillota, Bacteroidota and Pseudomonadota. One representative strain from each species-level cluster was selected for further characterization (Table 1 ). Table 2. Genomic potentials of the TMA isolates for the synthesis of, and resistance against, different classes of antibiotic and secondary metabolite. Isolation source Name of the isolate Antibiotics potentially synthesized Secondary metabolites synthesized Antibiotics potentially resisted Weathered rock dust Arthrobacter sp. TRD_SC_6 - NAPAAs 1 , NI-siderophores 2 - Mycetocola sp. TRD_SC_2 - NAPAAs, Type-III polyketides - Paenarthrobacter sp. TRD_SC_7 Penicillins and cephalosporins NAPAAs, NI-siderophores, NRPs 3 Rifamycins Pseudarthrobacter sp. TRD_SC_9 Staurosporines NAPAAs, NI-siderophores, Type-III polyketides - Streptomyces sp. TRD_SC_5 Staurosporines, Vancomycins Class-II Lanthipeptides, Class-III Lanthipeptides, NI-siderophores, NRPs, Terpenes, Type-I polyketides Rifamycins Lake -water Acinetobacter sp. TW_SC_4 - - - Ancylobacter sp. TW_SC_1 - NRPs, Type-I polyketides - Microbacterium sp. TW_SC_2 - Type-III polyketides - Lake-sediment Aeromonas sp. TS_SC_11 - NRPs, NRP-metallophores 4 , Terpenes Carbapenems, Cephalosporins, Elfamycins, Penicillin and other beta-lactams Cryobacterium sp. TS_SC_7 Prodigiosin NAPAAs, Terpenes, Type-III polyketides - Flavobacterium sp. TS_SC_5 - Terpenes - Pseudomonas sp. TS_SC_3 - - Diaminopyrimidines, Fluoroquinolones, Phenicols Psychrobacter sp. TS_SC_6 - - - Sanguibacter sp. TS_SC_8 Tetracyclines NAPAAs Rifamycins Trichococcus sp. TS_SC_9 - Type-III polyketides - 1 NAPAAs: non-alpha poly-amino acids 2 NI-siderophores: siderophores independent of non-ribosomal peptide synthase 3 NRPs: non-ribosomal peptides 4 NRP-metallophores: non-ribosomal peptide (NRP) metallophores Cold adaptations of the TMA isolates In LB medium, all the 27 species retrieved from the Tso Moriri area could grow at 4°C (Figs. 2 c-d) and 15°C (Figs. 2 e-f), whereas only 13 could grow at -10°C (Figs. 2 a-b). In terms of what percentage of the starting CFU density (colony-forming units mL − 1 ) remained in the culture after the stipulated period of incubation (Tables S1-S3), the extents of growth recorded at -10°C were far lower than those recorded at 4°C and 15°C. After 14 days at 4°C, the cultures of Flavobacterium TS_SC_2 and Arthrobacter TRD_SC_3 showed the lowest and highest increases in CFU density respectively. After two days at 15°C, lowest and highest increases in CFU density were exhibited by the cultures of Flavobacterium TS_SC_2 and Arthrobacter TRD_SC_8 respectively. After 28 days at -10°C, Pseudarthrobacter TRD_SC_9 and Arthrobacter TRD_SC_6 had the lowest and highest increases in CFU density respectively. Notably, the 14 species, which could not grow in LB at -10°C, managed to maintain considerable proportions of their cell populations in divisible (Figs. 2 a-b) and/or metabolically-active (Figs. 2 g and S1) states, as evidenced by CFU density data, and fluorescein diacetate (FDA) staining followed by flow cytometry, respectively. After 28 days at this temperature, Acinetobacter TW_SC_4 and Aeromonas TS_SC_11 retained the lowest and highest proportions (2% and 75%) of the initial CFU density respectively, while Pseudarthrobacter TS_SC_4 had the lowest (19%), and Cryobacterium TS_SC_7 and Sanguibacter TS_SC_8 had the highest (99%), proportions of cell stained with FDA. Furthermore, for every bacterium lacking growth at -10°C, the percentage of metabolically-active cells exceeded the percentage of cells retaining their divisibility after 28 days of incubation, even though no significant correlation existed between the proportions of divisible and metabolically-active cells (Fig. S2 a). Growth on different simple/complex carbon compounds at zero and sub-zero degree Celsius After 14 days of incubation at 4°C, all the bacterial species isolated from the TMA rendered at least a little growth by utilizing no less than three out of the 10, simple or complex, organic compounds tested as single chemoorganoheterotrophic substrates (Tables S4-S13). The extent of growth differed across the isolates in terms of the percentage increase in CFU density that was attributable to the utilization of the carbon compound in question (Fig. 3 a). 10 TMA isolates were found to accomplish low, moderate, or high growth on all the 10 compounds tested; these were Aeromonas TS_SC_11, Arthrobacter TRD_SC_3, Cryobacterium TS_SC_7, Flavobacterium TS_SC_2, Flavobacterium TS_SC_5, Microbacterium TRD_SC_10, Sanguibacter TS_SC_8, Pseudomonas TS_SC_12, Pseudomonas TS_SC_13 and Psychrobacter TS_SC_6. Although unable to use acetate, Streptomyces TRD_SC_5 could accomplish high growth on maximum number of complex carbon compounds, namely cellulose, chitin, pectin, starch and xylan; this organism also rendered moderate growth on agar, albumin and hexadecane, while its growth on benzoate was low. In contrast, Pseudomonas TS_SC_3, which could only render moderate growth on cellulose, and low growth on albumin and starch, was the least efficient TMA isolate in terms of chemoorganoheterotrophic utilization of the carbon substrates tested. After 28 days of incubation at -10°C, a sum total of 20 TMA isolates were found to render low but definite growth on at least one of the 10 carbon compounds tested (Tables S14-S23), even though in LB medium, only 13 species had exhibited low to high growth at -10°C (Figs. 2 a-b; Table S1 ). Of the 14 TMA isolates that had failed to render − 10°C-growth in LB, seven – namely, Aeromonas TS_SC_11 (benzoate and cellulose), Ancylobacter TW_SC_1 (agar and chitin), Cryobacterium TS_SC_7 (acetate, albumin, cellulose and pectin), Flavobacterium TS_SC_5 (agar), Microbacterium TRD_SC_10 (agar, albumin, benzoate, chitin and starch), Pseudarthrobacter TS_SC_4 (agar and starch), and Pseudomonas TS_SC_10 (agar) – could render a low level of growth attributable to the use of at least one carbon compound (Fig. 3 b). Overall, Arthrobacter TRD_SC_3 accomplished low levels of growth on all the 10 compounds tested, while Streptomyces TRD_SC_5 rendered low levels of growth on all the substrates except acetate, and Mycetocola TRD_SC_2 could do the same on all the carbon compounds except acetate, albumin and benzoate. Of the remaining 24 isolates, 17 could achieve low levels of growth on only a few, or just one, of the 10 organic substrates tested; seven isolates could not grow on any of the compounds tested (Fig. 3 b). Extreme oligotrophy at zero and sub-zero degree Celsius A scrutiny of the increases in the CFU densities of the different isolates recorded after incubation in MS solution at 4°C and − 10°C revealed their extreme oligotrophic potentials. At 4°C, after 14 days of incubation in MS solution, CFU densities of Microbacterium TW_SC_2, and Pseudomonas strains TS_SC_1 and TS_SC_10, increased by > 200% of the initial levels, whereas those of Acinetobacter TW_SC_4, Arthrobacter TRD_SC_1 and TRD_SC_6, Microbacterium TW_SC_3, Mycetocola TRD_SC_2, Paenarthrobacter TRD_SC_7 and Trichococcus TS_SC_9 increased by 6–72% (Table S24). Under these conditions, CFU densities of Arthrobacter TRD_SC_3, Microbacterium TRD_SC_10, and Streptomyces TRD_SC_5 remained unchanged, while those of the remaining 14 strains decreased by 3–90% of the initial levels. At -10°C, after 28 days in MS, CFU densities of Arthrobacter strains TRD_SC_1 and TRD_SC_6, Microbacterium strains TW_SC_2 and TW_SC_3, Mycetocola TRD_SC_2, and Paenarthrobacter TRD_SC_7 increased by 10–50%, whereas those of the remaining species decreased by 2–98%, of the corresponding initial levels (Table S25). Progressive thermal susceptibility of the TMA isolates In LB medium, 23 out of the 27 TMA isolates studied could grow at 28°C (Figs. 4 a-b; Table S26), whereas only 11 could grow at 37°C (Figs. 4 c-d; Table S27), and none at 42°C (Figs. 4 e-f; Table S28). In terms of what percentage of the starting CFU density remained in the culture after the stipulated period of incubation, extents of growth recorded at 28°C were much higher than those recorded at 37°C. Of the four species incapable of growing in LB at 28°C, three did not retain any CFU, while only Arthrobacter TRD_SC_4 retained 28% of the initial CFU density, after four days of incubation at this temperature. At the same time, three of these four isolates had 18–58% cells in metabolically-active conditions, while Cryobacterium TS_SC_7 had 0.2% cells in active condition (Figs. 4 g and S3a). At 37°C, 16 TMA isolates failed to grow in LB. Nine of them had zero or near-zero CFU density, while the other seven had 1–48% of the initial CFU densities, remaining in the cultures after two days of incubation. At the same time, 15 out of these 16 isolates had 4–76% cells in metabolically-active conditions, while Cryobacterium TS_SC_7 had 0.1% cells in active condition (Figs. 4 h and S3b). None of the 27 species retrieved from the Tso Moriri area could grow in LB at 42°C. 22 of them had zero or near-zero CFU density, while the remaining five had 1–33% of the initial CFU densities, present in the cultures after one day of incubation. At the same time, 19 out of the 27 isolates had 1-87.3% cells in metabolically-active conditions, while eight had < 1% cell in active condition (Figs. 4 i and S4). Overall, the trends of LB-based population-level survival of the isolates lacking growth at ≥ 28°C showed that the percentage of cells remaining metabolically active at a given high temperature exceeded the percentage of cells which retained their divisibility at that temperature (Figs. 4 g-i). Furthermore, for all strains susceptible to high temperatures, proportions of divisible, and metabolically active, cells remaining in the cultures after stipulated periods of incubation decreased with increase in temperature. That said, no significant correlation existed between the proportions of divisible and metabolically-active cells, across the species incapable of growth at high temperatures (Figs. S2b-d). Differential temperature windows for growth and survival of cell populations At every incubation temperature tested for LB-based growth, whether in the psychrophilic (Tables S1-S3) or in the mesophilic range (Tables S26-S28), different TMA isolates exhibited different rates of increase or decrease in CFU density. On the flip side, temperature windows for growth (increase in CFU density) and population-level survival (retention of > 1% cells in metabolically-active state), over the tested range, varied across the isolates (Fig. 5 ). Arthrobacter TRD_SC_3 and TRD_SC_8, Microbacterium TW_SC_2 and TW_SC_3, Paenarthrobacter TRD_SC_7, and Streptomyces TRD_SC_5, shared the widest temperature range over which growth was recorded (-10°C to 37°C), whereas Flavobacterium TS_SC_5 and Cryobacterium TS_SC_7 had the narrowest growth window (4°C to 15°C). For all those 4°C-growing isolates whose growth in LB ceased at -10°C, more than 1% cells remained metabolically active at this freezing temperature (Fig. 2 g); the − 10°C-growing isolates, on the other hand, were likely to remain metabolically active at even lower temperatures. At ≥ 28°C, all the TMA isolates, except Arthrobacter TRD_SC_6 and Cryobacterium TS_SC_7, exhibited population-level survival at temperatures above the points where their growth was last recorded (Figs. 5 and 6 a). For TRD_SC_6 and TS_SC_7, growth in LB was last recorded at 37°C and 15°C respectively, but their proportions of metabolically-active cells in the culture dropped below the 1% threshold at the immediately higher temperature points tested, i.e. at 42°C and 28°C respectively (Fig. 5 ). So far as the most suitable temperature for LB-dependent growth was concerned, 22 TMA isolates had their highest growth rates at 15°C (Fig. 5 ). For 11 out of the 22 isolates having growth maxima at 15°C, i.e. for three Arthrobacter species, the two Flavobacterium species, and one species each of Cryobacterium , Microbacterium , Pseudarthrobacter , Pseudomonas , Sanguibacter and Trichococcus , 4°C growth rates were higher than their 28°C growth rates (notably, some of the 28°C growth rates in question were negative). In contrast, for the other half of these 22 isolates, i.e. for two Arthrobacter species, four Pseudomonas species, and one species each of Aeromonas , Microbacterium , Paenarthrobacter , Pseudarthrobacter and Psychrobacter , 28°C growth rates were higher than their 4°C growth rates. For Acinetobacter TW_SC_4, Ancylobacter TW_SC_1, Microbacterium TW_SC_2 and Streptomyces TRD_SC_5, maximum growth rates were recorded at 28°C. While the 15°C growth rates of all these species were higher than their 4°C growth rates, for Microbacterium TW_SC_2 and Streptomyces TRD_SC_5 their 4°C growth rates were higher than the 37°C growth rates, but for Acinetobacter TW_SC_4 and Ancylobacter TW_SC_1 their 37°C growth rates were higher than the 4°C growth rates. For Mycetocola TRD_SC_2 alone, growth rate was highest at 4°C, and then decreased through 15°C and 28°C to become negative at 37°C (Fig. 5 ). Antibiosis by TMA actinobacteria Of the 15 actinobacterial species isolated from the Tso Moriri area, 11 had the ability to inhibit the growth of at least one of the six higher-temperature-adapted foreign bacteria against which antibiosis was tested, namely the Gram negative organisms Escherichia coli K-12, Advenella kashmirensis WT001, Paracoccus sp. SMMA_5, and the Gram positive Bacillus subtilis SC_1, Bacillus licheniformis PAMA2_SD1, and Lysinibacillus fusiformis LAPE1_SD1 (Fig. 7 ). Only the four Arthrobacter species represented by the strains TRD_SC_1, TRD_SC_3, TRD_SC_4 and TRD_SC_8 had no antagonistic activity against any of the target organisms. Pseudarthrobacter TRD_SC_9 and Streptomyces TRD_SC_5 disallowed the growth of all the six foreign bacteria targeted; Paenarthrobacter TRD_SC_7 inhibited all but Advenella kashmirensis , while Arthrobacter TRD_SC_6 deterred only Lysinibacillus fusiformis . On the flip side of the above data, most number of TMA actinobacteria were active against Lysinibacillus fusiformis , Bacillus licheniformis , and Escherichia coli , which in turn were inhibited by 11, nine and seven actinobacteria respectively. Five, four, and three TMA actinobacteria were found to inhibit the growth of Bacillus subtilis , Advenella kashmirensis , and Paracoccus sp. respectively. Notably, none of the 12 non-actinobacterial species isolated from TMA had the ability to inhibit the growth of any of the foreign bacteria against which antibiosis was tested (Fig. 7 ). The same 11 actinobacterial isolates, which had inhibited the growth of foreign mesophilic bacteria, also inhibited at least one or more non-actinobacterial TMA isolate(s) (Fig. 7 ). Microbacterium TW_SC_2 could inhibit the most number of TMA bacteria (nine) outside Actinomycetota. In contrast, Arthrobacter TRD_SC_6 could inhibit only one TMA bacterium outside Actinomycetota. From the reverse perspective, every non-actinobacterial TMA isolate was inhibited by at least three TMA actinobacteria. Aeromonas TS_SC_11 was the most vulnerable TMA bacteria outside Actinomycetota as its growth was inhibited by 11 TMA actinobacteria. In contrast, Ancylobacter TW_SC_1, Flavobacterium TS_SC_2, Pseudomonas TS_SC_12 and Pseudomonas TS_SC_13 were least prone to antibiosis as each of them was susceptible to only three actinobacterial isolates. Out of the 15 TMA species belonging to Actinomycetota, eight could inhibit the growth of fellow actinobacterial isolates (Fig. 7 ). Overall, Microbacterium TRD_SC_10 and Pseudarthrobacter TRD_SC_9 inhibited the most number of actinobacteria (six each). In contrast, Arthrobacter TRD_SC_6, and Microbacterium TW_SC_2, inhibited only one TMA actinobacteria each. From the opposite point of view, 11 TMA actinobacteria were prone to inhibition by isolates belonging to the same phylum (Fig. 7 ). Arthrobacter TRD_SC_8 (inhibited by six TMA actinobacteria) was the most vulnerable TMA isolate belonging to Actinomycetota. In contrast, Microbacterium TW_SC_3, Pseudarthrobacter TRD_SC_9, Paenarthrobacter TRD_SC_7 and Microbacterium TRD_SC_10, were not prone to inhibition by any of the actinobacterial isolates. In the context of antibiosis, it was further noteworthy that none of the non-actinobacterial species isolated from the TMA could inhibit the growth of any other TMA isolate, whatever may be its phylum affiliation (Fig. 7 ). Key attributes of the whole genomes sequenced for selected TMA isolates Complete whole genome sequence was determined for selected isolates from across the three environments explored within the Tso Moriri lake-desert ecosystem (Table 1 ). While one genome was sequenced from each of the 15 genera across which the 27 species-level isolates were classified, it was also ensured that at least one strain was analyzed from each cluster that had formed on the basis of growth or population-level survival at different incubation temperatures (Fig. 6 a). De novo hybrid assembly (statistics given in Table S29) of the short and long DNA sequence reads generated using two different technologies yielded complete or near-complete genome sequences for all the TMA isolates analyzed (Table 1 ). For 10 out of the 15 TMA isolates selected, their complete genomes were encompassed in single circular chromosomes: Arthrobacter TRD_SC_6, Cryobacterium TS_SC_7, Flavobacterium TS_SC_5, Microbacterium TW_SC_2, Mycetocola TRD_SC_2, Paenarthrobacter TRD_SC_7, Pseudarthrobacter TRD_SC_9, Pseudomonas TS_SC_3, Sanguibacter TS_SC_8 and Trichococcus TS_SC_9. For two TMA isolates their complete genomes were incorporated in single circular chromosomes plus multiple circular plasmids: Acinetobacter TW_SC_4 had two such plasmids of 1.8 mb and 81.9 kb length, while Psychrobacter TS_SC_6 had three of them, 6.4 kb, 7.1 kb and 23.1 kb in length. For Aeromonas TS_SC_11, Ancylobacter TW_SC_1 and Streptomyces TRD_SC_5, their near-complete genomes encompassed one or two uncircularized chromosomes plus one or two circular plasmids varying between 43.2 kb and 89 kb in length. Composition of the microbiomes to which the TMA isolates belonged When the 50 million, randomly-selected, Q20 reads available for each of the three metagenomic datasets were assembled (statistics given in Table S30), 196552, 95112, and 269987 contigs (all > 1000 nucleotide long) were obtained for the rock-dust, lake-water and lake-sediment samples respectively. Within the contigs obtained from the rock-dust metagenome, 289601 CDSs were annotated, of which maximum proportions, i.e. 48%, 21% and 4%, were ascribed to Actinomycetota, Pseudomonadota and Bacillota, respectively (Table S31). Within the contigs obtained from the lake-water metagenome, 180125 CDSs were annotated; most of these, i.e. 29%, 22% and 15%, were ascribed to Pseudomonadota, Actinomycetota and Bacteroidota, respectively (Table S31). Within the contigs obtained from the lake-sediment metagenome, 451033 CDSs were annotated, of which maximum proportions, i.e. 46%, 8% and 8%, were ascribed to Pseudomonadota, Bacteroidota and Bacillota, respectively (Table S31). The 50 million, randomly-selected, Q20 reads available for each of the three metagenomic datasets were sorted and classified taxonomically by searching against the rrnDB database. Consequently, 31009, 36123, and 34328 reads - from the rock-dust, lake-water and lake-sediment datasets respectively - were found to be representative of 16S rRNA genes from different bacterial and archaeal sources. Species belonging to the phylum Actinomycetota and Pseudomonadota accounted for the maximum proportions, i.e. 54% and 18%, of all 16S rRNA-related reads present in the rock-dust dataset, respectively (Table S32). Cyanobacteriota and Actinomycetota accounted for the maximum proportions, i.e. 26% and 24%, of all the 16S rRNA-encoding reads that were there in the lake-water dataset, respectively (Table S33). Pseudomonadota and Campylobacterota encompassed the maximum proportions, i.e. 43% and 12%, of all 16S rRNA-related reads present in the lake-sediment dataset, respectively (Table S34). Furthermore, in the rock-dust metagenome, 8529 16S rRNA-related reads were classifiable at the genus level. Of the 213 genera identified in this way, the Actinomycetota-members Rubrobacter and Blastococcus , followed by the archaeon Nitrososphaera , accounted for the maximum of number of reads (1028, 803 and 625 respectively). In the lake-water metagenome, 10610 16S rRNA-related reads were classifiable at the genus level. Of the 271 genera identified in this way, the cyanobacteria Cyanobium and Synechococcus , and the Bacteroidota-member Algoriphagus , encompassed the maximum of number of reads (3295, 690 and 928 respectively). In the lake-sediment metagenome, 14880 16S rRNA-related reads were classifiable at the genus level. Of the total 398 genera identified, the Campylobacterota member Sulfuricurvum , and the Betaproteobacteria Methylotenera and Hydrogenophaga , encompassed the maximum of number of reads (3060, 1785 and 1042 respectively). Small but definite proportions of metagenomic reads obtained from Tso Moriri’s water and sediment samples, as well as the sample of weathered rock dust from the lake-side hill, mapped onto each of the 15 whole genomes that were analyzed for selected TMA isolates. Collectively, the 15 genomes accounted for 0.1%, 0.3% and 0.2% of metagenomic reads from the lake-water, lake-sediment and rock-dust samples respectively (Table S35). Furthermore, considerable proportion of reads matching 16S rRNA gene homologs from diverse species of Acinetobacter , Aeromonas , Arthrobacter , Cryobacterium , Flavobacterium , Pseudarthrobacter , Pseudomonas , Psychrobacter and Streptomyces were detected in the metagenomes analyzed from across the three TMA habitats (Tables S32-S34). Genes concerned with adaptation to extremes of temperature Inspection of the eggNOG-annotated CDS catalogs of the 15 TMA isolates (Tables S36-S50) revealed diverse genes concerned with low and high temperature adaptations (Figs. S5; Table S51). Besides a number of cold-adaptation-related genes concerned with cold-shock response and RNA remodeling, membrane fluidity regulation, and genome maintenance at low temperatures, several such genes were also detected which conferred tandem adaptation to low as well as high temperatures: these encoded chaperones and other protein quality control components, governed DNA repair and oxidative stress responses, coded for global transcriptional regulators, or governed biosynthesis and transport of compatible solutes and other osmoprotectants. Streptomyces TRD_SC_5 had the highest number of cold-adaptation-related genes, whereas Acinetobacter TW_SC_4 and Psychrobacter TS_SC_6 had the lowest. So far as the genes involved in dual adaptation to low as well as high temperatures were concerned, Streptomyces TRD_SC_5 and Psychrobacter TS_SC_6 had the highest and lowest numbers of them, respectively. The assembled metagenomes of the rock-dust, lake-water and lake-sediment samples also encoded diverse genes concerned with low temperature adaptation, and low as well as high temperature adaptation (Tables S52). For most of the gene categories mentioned above in relation to extreme temperature adaptation, maximum numbers of homologs were detected in the lake-sediment, followed by the rock-dust, metagenome. The trend of distribution of these genes essentially mirrored the trend exhibited by the number of CDSs annotated within the contigs obtained from the three assembled metagenomes. Genes encoding carbohydrate-active enzymes (CAZymes) When the CDS catalogs of the selected TMA isolates (Tables S36-S50) were searched against the dbCAN3 and CAZy databases, a wide array of carbohydrate-active enzymes concerned with polysaccharide binding, modification, and degradation were revealed (Table S53). The highest number of putative CAZymes was encoded by the genome of Streptomyces TRD_SC_5, whereas the least number of them was encoded by Psychrobacter TS_SC_6. Overall, the detected CAZymes belonged to six broad functional categories - glycoside hydrolase (GH), glycosyl transferase (GT), carbohydrate esterase, polysaccharide lyase, enzymes with auxiliary activities, and carbohydrate-binding module (Fig. S6). Of the different classes, again, GH and GT accounted for the most number of genes in all the genomes analyzed (collectively, 67–86% of all CAZyme genes possessed by the different strains were GHs and GTs). That said, most of the TMA isolates that were rich in GHs were relatively poorer in GT diversity, and vice versa. Only Streptomyces TRD_SC_5 had equal number of GH and GT genes, while Microbacterium TW_SC_2, Arthrobacter TRD_SC_6 and Mycetocola TRD_SC_2 also had comparable numbers of GH and GT genes. Besides the genomes of the psychrophilic copiotrophs isolated, the assembled metagenomes of the lake-sediment, rock-dust, and lake-water samples also encoded a wide array of carbohydrate-active enzymes (Table S54). For all the CAZyme gene categories mentioned above, maximum numbers of homologs were detected in the lake-sediment, followed by the rock-dust, metagenome, thereby mirroring the trend of CDS annotation from the assembled metagenomes. Genes encoding antibiotics and other secondary metabolites Based on gene content analysis, five classes of antibiotics [(i) penicillins and cephalosporins, (ii) prodigiosins, (iii) staurosporines, (iv) tetracyclines and (v) vancomycins], and nine classes of secondary metabolites [(i) NI-siderophores, i.e. siderophores independent of non-ribosomal peptide synthase (NRPS), (ii) NAPAAs, i.e. non-alpha poly-amino acids, (iii) Class-II lanthipeptides, (iv) Class-III lanthipeptides, (v) NRPs, or non-ribosomal peptides, (vi) NRP-metallophores, (vii) terpenes, (vii) Type-I polyketides, and (ix) Type-III polyketides, all of which can potentially act as antimicrobial agents] were putatively synthesized by the 15 TMA isolates for which whole genomes were sequenced (Table 2). Every actinobacterium that was capable of inhibiting the growth of one or more target organism(s), and for which the complete whole genome sequence was analyzed, possessed genes for synthesizing one or more classes of antibiotics (Table S55) and/or secondary metabolites (Table S56). Among all the 15 isolates for which genomes were analyzed, highest diversity of antibiotics and secondarily metabolites was putatively synthesized by the actinobacterium Streptomyces TRD_SC_5 (this organism possessed key genes for the synthesis of Class-II and Class-III lanthipeptides, non-ribosomal peptides, siderophores independent of non-ribosomal peptide synthase, staurosporines, terpenes, Type-I polyketides and vancomycins). Cryobacterium TS_SC_7 (prodigiosins, NAPAAs, terpenes, and Type-III polyketides), Paenarthrobacter TRD_SC_7 (NAPAAs, NI-siderophores, NRPs, and penicillins and cephalosporins), and Pseudarthrobacter TRD_SC_9 (NAPAAs, NI-siderophores, staurosporines, and Type-III polyketides) possessed key genes for the synthesis of the next highest diversities of antibiotics and secondarily metabolites. Incidentally, these four actinobacteria had inhibited the growth of 14, 9, 15 and 20 out of the total 33 allochthonous and autochthonous microorganisms against which antibiosis was tested (Fig. 7 ). Microbacterium TW_SC_2 did not have genes required for the synthesis of any known antibiotic or secondary metabolite other than Type-III polyketides, but it had inhibited the growth of 14 target organisms (Fig. 7 ), presumably by hitherto unknown antimicrobial agents. On the flip side of the above data, no known antibiotic or secondarily metabolite was apparently synthesized by Acinetobacter sp. TW_SC_4, Pseudomonas sp. TS_SC_3 and Psychrobacter sp. TS_SC_6, which also had shown no antibiosis against any of the 33 targets tested. Aeromonas TS_SC_11, Ancylobacter TW_SC_1, Flavobacterium TS_SC_5, and Trichococcus TS_SC_9 too had not inhibited any target organism; corroboratively, they did not also synthesize any known antibiotic (the four species, however, putatively synthesized a few classes of secondary metabolites; Table 2). Genes conferring potential resistance against antibiotics Of the 15 isolates for which genomes were analyzed, only five were found to possess genes central to resistance against carbapenems, cephalosporins, diaminopyrimidines, elfamycins, fluoroquinolones, penicillins and other beta-lactams, phenicols, rifamycins, teicoplanins, and/or vancomycins (Tables 2 and S57). The remaining 10 genomes encompassed no such gene which is central to resistance against any known antibiotic. While potential resistance against rifamycins was encoded by the highest number of genomes (three), putative resistance against highest number of antibiotic groups (four) was possessed by Aeromonas TS_SC_11. Nevertheless, growth of this strain was inhibited by several actinobacterial isolates (Fig. 7 ), apparently by antimicrobial agents other than penicillins or other beta-lactams, carbapenems, cephalosporins and elfamycins, against which TS_SC_11 possessed putative resistance according to the genome content data (Table 2). Overall, genome-based predictions of the TMA isolates’ potentials for synthesizing and resisting different antibiotics and secondary metabolites were not sufficient to specify the molecular mechanisms underlying their antibiosis phenotypes. However, genomic potentials for secondary metabolites biosynthesis and resistance neither contradicted each other nor were inconsistent with the antibiosis phenotypes recorded (Table 2). For every instance where an isolate had inhibited the growth of another TMA species, the antagonist was found to have the genomic potential for synthesizing at least one such class of antibiotic or secondary metabolite against which the inhibited species had no resistance gene. Discussion Habitat as the key driver of adaptation to extremes of temperature All the genera under which the TMA isolates were classified, excepting Ancylobacter and Paenarthrobacter , have multiple members retrieved previously from other natural or artificial cold/frigid environments (Table S58). Remarkably, each of them also has at least one member retrieved previously from a mesic/hot habitat (Table S59). For all the genera isolated, except Paenarthrobacter , member strains from other parts of the world, especially mesic/hot environments, are known to grow at temperatures higher than the maxima recorded for the TMA counterparts (Fig. S7; Table S60). It, therefore, appears that native environment rather than phylogenetic background (taxonomy) primarily determines a microbial strain’s adaptation to the extremes of temperature. Consistent with the above facts, a number of TMA isolates, despite their affiliation to the same genus, exhibited differential growth/survival at a given incubation temperature (Fig. 6 a). Conversely, similar growth/survival phenotypes across temperatures unified the majority of strains isolated from a given TMA habitat (environment) irrespective of their generic affiliation (Fig. 6 b). All the bacteria isolated from the weathered rock dust of the lake-adjoining hill, with the exception of only Arthrobacter TRD_SC_4 and TRD_SC_6, grew or survived at the population-level through the entire temperature range tested (i.e. -10°C to 42°C). The bacteria from Tso Moriri’s water also accomplished population-level growth or survival through the entire range of incubation temperatures tested. Most strains isolated from Tso Moriri’s sediment had population-level growth/survival across narrower ranges of temperature, compared to the isolates from rock-dust and lake-water. Tso Moriri’s surficial water freezes in the winter and thaws in the summer. Likewise, the rocky slopes of the adjoining hills get covered by snow in the winter and the same melts through the summer; furthermore, the rocks are exposed to extreme heating and cooling on a diurnal basis. Thus, wider temperature windows for population-level growth/survival in bacteria from these two environments could be reflective of a general linkage between the variability of the thermal condition prevailing in the habitat and the inhabitants’ adaptability to extremes of temperature. By the flip side of the same reasoning, it was also quite natural for the bacteria from Tso Moriri’s subsurface to have narrower temperature windows for population-level growth/survival, compared to their surficial neighbors. In relation to environmental control of extreme-temperature adaptation, it was further noteworthy that all the member strains that were known, prior to this study, to grow at the lowest temperature limits for Acinetobacter , Aeromonas , Microbacterium , Paenarthrobcter , Pseudarthrobacter and Trichococcus (Fig. S7; Table S60) had been isolated from mesic/hot environments, even though isolates from cold/frigid environments were also there for all these genera except Paenarthrobacter (Table S58). On the flip side, all the member strains that were known, prior to this study, to grow at the highest temperature limits for Aeromonas and Mycetocola had been isolated from natural or artificial cold/frigid environments (Fig. S7; Table S60) even though strains isolated from mesic/hot environments were also there for these two genera (Table S59). These environment:phenotype mismatches could be the outcomes of drastic microbial transportation across distinct ecosystems. At the same time these anomalies could be indicative of the existence of some hitherto unappreciated metabolic convergence between the high and low temperature adaptations of bacteria. The latter counterintuitive idea was reinforced by the facts that most of the − 10°C-growing TMA isolates could also grow at 28°C and 37°C (Fig. 6 a), while most of the TMA isolates having > 0.1% CFU left in the culture after one day at 42°C (Figs. 4 f and 4 i) could also grow at -10°C (Fig. 2 b). Gene-contents of neither the genomes of the psychrophiles isolated, nor the metagenomes of the habitats explored, had any direct bearing on the phenotype-based trends recorded for habitat:adaptation correspondence. Future studies of transcriptomics, proteomics, and metabolomics - carried out for pure cultures as well as environmental samples, resolved along gradients and fluxes of temperature over time and space - are required to conclude how environmental experiences of microbial populations dictate their adaptation to extremes of temperature (Mondal et al. 2022). Autochthonous populations of heat-susceptible psychrophiles as biogeothermometers of in situ warming TMA isolates, irrespective of their phylogenetic affinities, exhibited different levels of thermal susceptibility. For instance, almost all cells of Cryobacterium TS_SC_7 lost their divisibility, as well as metabolic activity, after a four-day exposure to 28°C (Figs. 4 b and 4 g), so their retrieval from the lake’s sediment could be indicative of the fact that the temperature of this under-water substratum either does not reach 28°C, or even if it does so, it does not remain at that level for several days. On the flip side, elimination of such bacteria from environments where they had been prevalent previously would indicate protracted in situ warming up to and above 28°C. Population dynamics of TS_SC_7-like bacteria can, therefore, be construed and contrived as biogeothermometers of cold/frigid environments that are difficult to monitor physically over time. Almost all the cells of Flavobacterium TS_SC_5 and TS_SC_2 lost their metabolic activity after a full day at 42°C (Figs. 4 f and 4 i), while their divisibility was already abolished after a single day’s exposure to 37°C (Fig. 4 c), or even after a two-day exposure to 28°C (Fig. 4 a). Likewise, almost all the cells of Arthrobacter TRD_SC_4 lost their activity after a full day at 42°C (Figs. 4 f and 4 i), while their divisibility was already abolished after a two-day exposure to 37°C (Figs. 4 d and 4 h), and only a quarter of the cell population retained its divisibility after four days at 28°C (Figs. 4 b and 4 g). Thus, a progressive decline in the prevalence of a natural population of Flavobacterium species similar to TS_SC_5 and TS_SC_2, over time and/or space, can be correlated with a temporal and/or spatial escalation of temperature to 28°C and above. The same for TRD_SC_4-like populations can, in turn, corroborate spatiotemporal increases of in situ temperature to 37°C and above. In the same vein, population dynamics of bacteria like Arthrobacter TRD_SC_6 and Trichococcus TS_SC_9, within cold/frigid ecosystems, can be used as real-time bellwethers signaling shifts of in situ temperature towards 42°C. Thermal susceptibility of autochthonous organotrophs can usher negative feedback control/reversal of warming in cold/frigid ecosystems Within cold/frigid ecosystems microbial activity is thought to remain minimal as long as the temperature remains below or around 0°C (Forbes et al. 2001; Aronson et al. 2011; Geoffroy et al. 2023). However, organic matter degradation, and consequent emission of greenhouse gases, starts once in situ temperature rises and cryoturbation takes place due to season and/or climate change (Knoblauch et al. 2018; Schuur et al. 2015, 2022). Increased greenhouse effect brought about by enhanced microbial activities stimulates the biotic processes all the more, and that in turn ushers further thawing via more greenhouse gas emission. In this way, a positive feedback loop of cyclical warming (Fig. 8 ) is said to be operationalized in the ecosystem (Walter et al. 2006; Graham et al. 2012; Schneider von Deimling et al. 2012). If the trends of growth proficiency and regression recorded for the TMA isolates between 4–15°C and 28–42°C respectively (Figs. 2 and 4 ) hold good for all natural communities of psychrophiles, then the following phenomena can be hypothesized as global attributes of all cold/frigid ecosystems (Fig. 8 ). Microbes-mediated positive feedback cycles can abet environmental warming via greenhouse gas generation, as stated in the existing theory, only within and around the 4–28°C temperature range. Above 28°C, gradual cessation of microbial growth and activity cuts back the delivery of catabolism end-products such as simple fatty acids, CO 2 , N 2 O, etc. to the environment (Bhattacharya et al. 2021; Sui et al. 2024). In the context of the Tso Moriri area, organotrophy and allied catabolic processes such as ammonia oxidation, which can produce CO 2 and N 2 O respectively, assume significance since metagenome analyses showed chemoorganoheterotrophs such as Algoriphagus , Blastococcus , Hydrogenophaga and Rubrobacter , and N 2 O-producers such as Nitrosomonas , Nitrososphaera , Nitrosospira , Pseudomonas and Streptomyces , to be considerably prevalent across the three TMA habitats explored (Tables S32-S34). Short-supply of simple fatty acids and CO 2 , in turn, limits methanogenesis (if the concerned archaea are there in situ ), the terminal process of carbon cycling that utilizes simple fatty acids or CO 2 as its substrates (Hedderich and Whitman 2013). Notably, trace footprints of methanogens such as Methanothrix , Methanosarcina and Methanoregula were identified in the metagenomes analyzed for Tso Moriri’s water and sediment, but not in the metagenome of the rock-dust analyzed from the nearby hill (Tables S32-S34); such archaea were also reported as active from a number of neighboring glacial territories (Aschenbach et al. 2013). Curbs on the biogenic emission of greenhouse gases (CO 2 , N 2 O, and CH 4 , as applicable for individual ecosystems) trigger negative feedback control of warming, and usher over time, course reversal in the vicious cycle of “warming - microbial growth - and further warming”. Negative feedback controls of greenhouse gas production at micro-environment levels add up in the biome scale to mitigate overall environmental warming. Seasonal (winter) cooling eventually lowers the in situ temperature back to the zero and sub-zero degree Celsius levels (Fig. 8 ). On the part of a cold/frigid ecosystem, however, termination of autochthonous microbial growth and reduction of metabolic activity towards the restoration of homeostatic balance is fraught with the danger of indigenous psychrophiles being removed from the habitat, and their ecological niches taken over by more-thermotolerant intruders from discrete geographical territories (Fig. 8 ). Such incursions can transform microbiome architectures if not abated at temperatures below the critical level (Kosaka et al. 2019) where most cells of most of the native species lose their growth as well as activity. Potential microbiome transformations can, in the long run, alter ecosystem structures and functions drastically, and in doing so tilt the equilibrium irreversibly in favour of the positive feedback mechanism that promotes warming (Walter et al. 2006; Graham et al. 2012; Schneider von Deimling et al. 2012). Heat-enduring actinobacterial psychrophiles as defenders of cold/frigid microbiomes In the scenario of an imminent warming-mediated microbiome alteration, antibiosis potentials of native heat-enduring actinobacterial psychrophiles [such as those characterized here from the TMA (Fig. 7 )] can deter foreign microbes from colonizing the habitat and apprehending the ecological niches of the indigenous psychrophiles. In the long run, this kind of niche safeguard enhances the chances of population rejuvenation for all autochthonous cold-adapted species upon reversal of thawing (Fig. 8 ). Actinomycetota members are known for their extraordinary abilities to produce secondary metabolites including antibiotics, which confer them adaptive fitness against diverse environmental challenges, resulting in the high ecological amplitude of the phylum (Claverías et al. 2015; Barka et al. 2016; Lewin et al. 2016). Moreover, they are often abundant in cryospheric microbiomes (Voytsekhovskaya et al. 2018; Shen et al. 2021), and are known to protect indigenous microbial communities from external invaders in other critical ecosystems (Salazar-Hamm et al. 2025). So far as the 15 TMA actinobacteria were concerned, a majority of them were substantially thermotolerant (Figs. 4 and 6 a) and capable of inhibiting Gram positive and/or Gram negative bacteria from discrete higher-temperature ecosystems at 28°C (Fig. 7 ), which was lower than the critical high-temperature apparent for most of the TMA isolates (Fig. 4 ). It is, therefore, not unlikely that in the face of plausible microbial incursions from warmer territories, native actinobacterial psychrophiles would thwart foreign occupation and protect indigenous microorganisms from being vanquished from the habitat. Furthermore, in this context it was noteworthy that among the 11 TMA actinobacteria exhibiting antibiosis potentials against foreign bacteria, six had been isolated from the rock-dust sample, while three and two were from the lake’s sediment and water respectively (Fig. 7 ). This showed that microbiome protection by antibiosis-enabled heat-enduring actinobacterial psychrophiles could be widespread across physicochemically distinct cold/frigid environments. Intricate niche partitioning as central to microbiome functioning within cold/frigid ecosystems Several TMA actinobacteria were found to inhibit the growth of not only foreign microorganisms, but also that of a number of isolates from their own habitats as well as adjacent TMA environments (inhibited targets included fellow actinobacterial isolates also; see Fig. 7 ). These findings brought to the fore extensive niche partitioning (Dussud et al. 2018; Shaiber et al. 2020) as a plausible stratagem of microbiome functioning within the cold/frigid ecosystem. However, neither the phenotypic data, nor the genome-based predictions, available at present in relation to antibiosis (and resistance against the same) could elucidate how ecological niche separation pans out in the micro-environment scale, within the Tso Moriri habitats. In other words, it was not possible from the present data to decipher how niche separation within the TMA habitats effectively precluded in situ encounters between the actinobacterial antagonists and the species they inhibited in vitro . This limitation was attributed to the fact that bulk sampling had already smudged all imprints of niche differentiation, and deciphering of the same would require a new generation of very-finely-resolved spatial investigations of community ecology. Summing up In this study, phylogenetically diverse, cold-adapted copiotrophic bacteria were retrieved from a Trans-Himalayan lake-desert ecosystem, and tested for their chemoorganoheterotrophic growth/survival at temperatures between − 10°C and 42°C. Catabolizing copious complex organic substances, all the isolates could grow substantively at 4°C, while two thirds of them could achieve low growth at -10°C. At virtually zero organic carbon concentration (upon incubation in minimal salts solution having no organic matter added to it), one third of the TMA isolates achieved very low but definite levels of growth (increase in CFU density) at 4°C; a few of them also exhibited such growth on minimal salts at -10°C. How these in vitro phenotypes translate into community-level actions in situ is central to our knowledge on the baseline of organic matter degradation (carbon remineralization) within natural and/or anthropogenically-influenced cryospheric ecosystems, whether experiencing high or low organic matter delivery to the environment. Although the specific strains isolated were found to have relatively sparse populations in situ , their ubiquitous presence across the TMA habitats was detected metagenomically, alongside the prevalence of other members of the higher-level taxa to which the isolated species belonged. At the same time, considerable diversity and relative abundance of other chemoorganoheterotrophic bacteria were detected in conjunction with wide arrays of CAZyme-encoding genes, upon analysing the metagenomes of the lake-water, lake-sediment, and rock-dust samples. These facts collectively pointed towards a significant role of organic carbon degradation in the overall biogeochemistry of the Tso Moriri ecosystem. Based on the dwindling of autochthonous organotrophic growth and activity with rising temperature, a general model was conceived for the potential negative feedback control of warming within cryospheric ecosystems. Presence of N 2 O-producing ammonia-oxidizers and CH 4 -producing archaea, besides the preponderance of chemoorganoheterotrophs, across the TMA habitats highlighted that the homeostatic model envisaged could be operational in the Tso Moriri ecosystem itself. Prevalence of Actinomycetota across the ecosystem corroborated that antibiosis could indeed be an effective defence against microbiome takeover by foreign mesophilic organisms in the face of environmental warming. Declarations Competing interest The authors declare no competing interest. Funding The study was financed by Bose Institute through Intramural Research Grants. S.C. and M.M. received fellowships from Department of Biotechnology (DBT), Government of India (GoI). S.D. and J.S. obtained their fellowships from Council of Scientific and Industrial Research, GoI. J. G. and S. S. got fellowships from University Grants Commission, GoI. NM received fellowship from Bose Institute. Bioinformatic analyses were carried out using computational resources available under an EMR project funded by DBT, GoI (BT/PR40174/BTIS/137/45/2022). Author Contribution W.G. conceived the study, designed the experiments, interpreted the results, and wrote the paper. S.C. anchored the program, planned and performed the experiments, analyzed and curated the data, and composed the paper. S.D. performed the experiments, analyzed the data, and composed the paper. J.G., S.S., M.M., J.S., and N.M. performed the experiments. All authors read and approved the manuscript. Acknowledgement Extensive on-field assistance provided by Sri Asgar Ali of Choglamsar, Ladakh, India is gratefully acknowledged. We thank Dr. Soumya Chatterjee, Biodegradation Technology Division, Defence Research Laboratory, India, for valuable discussions on psychrophilic biodegradation. Data Availability GenBank accession numbers for the 16S rRNA genes of the new isolates are as follows:PV789631 (Arthrobacter sp. TRD_SC_1), PV789699 (Arthrobacter sp. TRD_SC_3), PV789726 (Arthrobacter sp. TRD_SC_4); PV789747 (Arthrobacter sp. TRD_SC_6); PV789751 (Arthrobacter sp. TRD_SC_8); PV793422 (Cryobacterium sp. TS_SC_7); PV790046 (Microbacterium sp. TRD_SC_10); PV793448 (Microbacterium sp. TW_SC_2); PV793500 (Microbacterium sp. TW_SC_3); PV789771 (Mycetocola sp. TRD_SC_2); PV682789 (Paenarthrobacter sp. TRD_SC_7); PV790021 (Pseudarthrobacter sp. TRD_SC_9); PV793432 (Pseudarthrobacter sp. TS_SC_4); PV793425 (Sanguibacter sp. TS_SC_8); PV789776 (Streptomyces sp. TRD_SC_5); PV793427 (Trichococcus sp. TS_SC_9); PV793436 (Flavobacterium sp. TS_SC_2); PV793440 (Flavobacterium sp. TS_SC_5); PV790452 (Ancylobacter sp. TW_SC_1); PV793444 (Acinetobacter sp. TW_SC_4); PV793419 (Aeromonas sp. TS_SC_11); PV793374 (Pseudomonas sp. TS_SC_1); PV793375 (Pseudomonas sp. TS_SC_3); PV793377 (Pseudomonas sp. TS_SC_10); PV793378 (Pseudomonas sp. TS_SC_12); PV793393 (Pseudomonas sp. TS_SC_13); PV793413 (Psychrobacter sp. TS_SC_6).All genome and metagenome sequence data have been deposited to the National Center for Biotechnology Information (NCBI), USA under the BioProject accession number PRJNA1335599. Sequence read datasets for the genomes have been deposited to the NCBI Sequence Read Archive (SRA), while the assembled whole genome sequences have been deposited to the GenBank, under the BioSamples accession numbers SAMN52016225 (Acinetobacter sp. TW_SC_4), SAMN52392035 (Aeromonas sp. TS_SC_11), SAMN52016223 (Ancylobacter sp. TW_SC_1), SAMN52016228 (Arthrobacter sp. TRD_SC_6), SAMN52016222 (Cryobacterium sp. TS_SC_7), SAMN52392036 (Flavobacterium sp. TS_SC_5), SAMN52016224 (Microbacterium sp. TW_SC_2), SAMN52016227 (Mycetocola sp. TRD_SC_2), SAMN52392037 (Paen¬arthrobacter sp. TRD_SC_7), SAMN52016226 (Pseudarthrobacter sp. TRD_SC_9), SAMN52016221 (Pseudomonas sp. TS_SC_3), SAMN52392038 (Psychrobacter sp. TS_SC_6), SAMN52016220 (Sanguibacter sp. TS_SC_8), SAMN52392039 (Streptomyces sp. TRD_SC_5), and SAMN52392040 (Trichococcus sp. 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8","display":"","copyAsset":false,"role":"figure","size":4767396,"visible":true,"origin":"","legend":"","description":"","filename":"Fig8.jpg","url":"https://assets-eu.researchsquare.com/files/rs-8996027/v1/dd47a0a66cd4dab617951d03.jpg"},{"id":108289841,"identity":"9192e47b-19d3-4e04-a1bb-cf439f2c10d8","added_by":"auto","created_at":"2026-05-01 17:55:14","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":28381959,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8996027/v1/cc66dc33-5a3a-4ff5-8df3-35c99021e9c7.pdf"},{"id":104031334,"identity":"3a6505c1-938c-4ffc-9186-f9ef228178fb","added_by":"auto","created_at":"2026-03-06 00:24:14","extension":"xlsx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":13054538,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryDataset.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-8996027/v1/9f19c1555fc850551408ff34.xlsx"},{"id":104031335,"identity":"81f77497-6f29-4008-b29f-730e0c0ff785","added_by":"auto","created_at":"2026-03-06 00:24:15","extension":"docx","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":2886096,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryInformation.docx","url":"https://assets-eu.researchsquare.com/files/rs-8996027/v1/d266894f9b9f43bc63618942.docx"},{"id":104031332,"identity":"cc440b15-094e-45da-898c-989ab2415ad0","added_by":"auto","created_at":"2026-03-06 00:24:14","extension":"docx","order_by":3,"title":"","display":"","copyAsset":false,"role":"supplement","size":17220,"visible":true,"origin":"","legend":"","description":"","filename":"Table1.docx","url":"https://assets-eu.researchsquare.com/files/rs-8996027/v1/9a7ca0cbfde8d035ec06e3cb.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Cryo-adapted bacterial copiotrophs from a Trans-Himalayan lake-desert ecosystem as biogeothermometers of warming and mitigators of microbiome perturbation","fulltext":[{"header":"Introduction","content":"\u003cp\u003eMicrobial life at low temperatures is constrained by a number of biophysical and biochemical adversities (Cavicchioli et al. 2000; \u0026Ouml;quist et al. 2009). However, across the seasonally or perpetually frozen alpine/polar territories of the Earth\u0026rsquo;s biosphere, barren soils, outwardly life-less rocky terrains, and most conspicuously, numerous limnic and fluvioglacial features, harbor considerably diversified microbiomes (Cary et al. 2010; Choe et al. 2021) that in turn sustain highly climate-sensitive ecosystems via biogeochemical cycling of carbon and other elements (Finlay et al. 2010; van der Valk 2012; Clow et al. 2015; Elser et al. 2020).\u003c/p\u003e \u003cp\u003eThroughout the high-latitude and high-altitude areas of the globe, copious endolithic and chasmolithic microbial communities colonize the rocky terrains and contribute actively or passively to the weathering of rocks into dust and scree, thereby promoting sediment and soil formation (Archer et al. 2017; Samuels et al. 2020). Supraglacial and subglacial microbiomes drive considerable biogeochemical activities (Hodson et al. 2008; Boetius et al. 2015; Yarz\u0026aacute;bal et al. 2021). The former types accelerate the melting of glaciers (Cook et al. 2020; Williamson et al. 2020), while post deglaciation the latter types release profuse greenhouse gases, such as carbon dioxide (CO\u003csub\u003e2\u003c/sub\u003e), methane (CH\u003csub\u003e4\u003c/sub\u003e) and nitrous oxide (N\u003csub\u003e2\u003c/sub\u003eO), into the atmosphere (Aschenbach et al. 2013; Ma et al. 2018; Lamarche-Gagnon et al. 2019; Zhang et al. 2021). Microbiomes thriving in young as well as matured soils, including those associated with permafrosts, and biocrusts covering deglaciated moraines and rock surfaces, render temporally slow but spatially extensive, fixation and remineralization of carbon (Drotz et al. 2010; Nikrad et al. 2016). Furthermore, approximately, 25\u0026nbsp;million lacustrine bodies (Verpoorter et al. 2014), together with the vast expanses of thermokarst landforms, sequester huge amount of organic carbon by remaining frozen for a substantial part of the year, and emit enormous volumes of CO\u003csub\u003e2\u003c/sub\u003e and CH\u003csub\u003e4\u003c/sub\u003e upon thawing (Kessler et al. 2012; MacIntyre et al. 2018; Serikova et al. 2019; Guo et al. 2020; Johnston et al. 2020).\u003c/p\u003e \u003cp\u003eIn all temporarily or permanently frozen ecosystems, microbial activities and proliferation are said to remain subdued as long as the temperature remains below or around 0\u0026deg;C (Mohn and Stewart 2000; Nikrad et al. 2016). However, organotrophic growth, and thereby remineralization of carbon and emission of greenhouse gases, starts once the temperature rises on a seasonal or climatic scale, and cryoturbation takes place (Zimov et al. 2006; Graham et al. 2012; Ernakovich et al. 2015; Nikrad et al. 2016; Walz et al. 2017). Greenhouse effect, at any spatiotemporal level, is thought to enhance microbial activities and growth, which in turn induces further thawing of the environment (Walter et al. 2006; Nikrad et al. 2016). Such positive feedback cycles (Walter et al. 2006; Graham et al. 2012; Schneider von Deimling et al. 2012) can, in the long run, alter the structures and functions of microbiomes, and thereby entire ecosystems, within the cold/frigid realm (Nikrad et al. 2016).\u003c/p\u003e \u003cp\u003eTo understand the functioning of cold/frigid ecosystems, and in order to manage them in a sustainable way, we require comprehensive information on the scopes of carbon cycling and sequestration not only around the freezing point of water but also at temperatures above the levels that are critical (Kosaka et al. 2019) for the growth and survival of indigenous psychrophiles. For that purpose, chemoorganoheterotrophic capabilities of autochthonous microorganisms need to be delineated via extensive pure-culture-based investigations at near-zero and sub-zero degree Celsius, in tandem with which we require comprehensive data on how native psychrophilic microorganisms respond to different levels of warming in terms of population-level growth or survival. Furthermore, in the context of global warming, it is imperative to appreciate the homeostatic vulnerabilities of alpine/polar microbiomes alongside their potential intrinsic resilience against perturbations. In other words, we need to know whether cold-adapted microbial communities have any indigenous safeguard against infiltration and ecological niche-takeover by organisms from warmer territories, i.e. whether they can thwart microbiome (and habitat) alterations triggered by climate warming (Hubert et al. 2009; Pearce et al. 2009).\u003c/p\u003e \u003cp\u003eThe present study of pure-culture microbiology explored three adjacent environments (habitats) within a Trans-Himalayan lake-desert ecosystem, centered on the massive fresh-to-brackish water body called Tso Moriri (vernacular meaning: lake amid the mountains), which is situated on the cold arid Changthang plateau of eastern Ladakh, India (Figs.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003ea-c). Phylogenetically diverse chemoorganoheterotrophic bacterial psychrophiles were isolated and characterized from Tso Moriri\u0026rsquo;s sediment, and water that remains frozen for approximately one third of the year. Psychrophilic heterotrophs were also isolated from the weathered rock dust (fine talus and scree particles) that covers the hill overlooking the western bank of the lake (Figs.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003ed-g).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eIt was first investigated whether the copiotrophic psychrophiles isolated in nutrient-rich medium could grow by catabolizing different simple or complex organic substrates at near-zero and sub-zero degree Celsius temperatures. Experiments were then conducted to know how the growth and activity of the isolates were affected by different levels of warming. The data obtained \u003cem\u003ein vitro\u003c/em\u003e were evaluated theoretically to explore whether natural populations of the isolated bacteria could act as \u0026ldquo;biogeothermometers\u0026rdquo; chronicling \u003cem\u003ein situ\u003c/em\u003e temperatures over time and space. From the contemporary perspective of heightened thawing of the cold/frigid realm, we examined whether the bacteria retrieved from the Tso Moriri area (TMA) had any aptitude for resisting incursion of foreign microorganisms from warmer climatic territories. For this purpose, we tested the antibiosis potentials of the current isolates against higher-temperature-adapted bacteria retrieved previously from discrete warmer ecosystems. The ecophysiologically important phenotypes of the new isolates were appraised in the light of the organisms\u0026rsquo; genomes and their habitats\u0026rsquo; metagenomes. The whole repertoire of data available was eventually interpreted in the context of climate change to envisage if the indigenous psychrophiles had any role in the abatement of environmental warming.\u003c/p\u003e"},{"header":"Materials and methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eStudy site\u003c/h2\u003e \u003cp\u003eThe Himalayas and Trans-Himalayas encompass the largest reservoir of snow and ice on Earth outside the Arctic Circle and Antarctica (Meena and Kukreja 2025). Within the cold arid vastness of the Himalayan rain shadow, the territory of Ladakh features an extensive high-altitude plateau, where multifaceted microbial life thrives to support homeostatically fragile ecosystems harboring unique multi-stress-adapted plants and animals (Dvorsk\u0026yacute; et al. 2011; Aschenbach et al. 2013; Dvorsk\u0026yacute; et al. 2013; Janatkov\u0026aacute; et al. 2013; Angel et al. 2016). Covering a large swathe of eastern Ladakh (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003ea) sprawls the Changthang desert dotted by several small to large lacustrine bodies among which Tso Moriri is one of the most prominent (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eb). This gigantic water body (Figs.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eb-c), located at an altitude of 4522 m, is flanked on all sides by barren hills (Figs.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003ed-e) that rise either from a distance or very close to the bank of the lake (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003ed). Small patches of lush green meadows, marshes, and wetlands hem only the northern and southwestern shores of Tso Moriri where two big glacial streams pour their water into the lake. Rest of the cold-desert habitat around the lake is characterized by a bleak topography and highly inhospitable physicochemical conditions that are paralleled only by the extreme multi-stressor environments of a few high-altitude areas of the Chilean and Argentinean Andes (Albarrac\u0026iacute;n et al. 2015, 2016).\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eSampling\u003c/h3\u003e\n\u003cp\u003eOn 28 October 2021, water (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003ef) and sediment (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eg) samples were collected from near the western shore of Tso Moriri (at GPS coordinates 32\u0026deg;94\u0026prime; N and 78\u0026deg;27\u0026prime; E), together with samples of weathered rock dust from the barren slope of a hill overlooking the western bank of the lake (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003ee). The entire hill slope, rising from ~\u0026thinsp;100 m off the Tso Moriri shore, was devoid of any vegetative cover; visibly, not a single blade of grass grew on it.\u003c/p\u003e \u003cp\u003eIn order to arrest the aquatic microbiota, as described previously (Mondal et al. 2024), five different batches of 500 mL water were sampled from five discrete sites located at intervals of 1 m, and within 1 m from the shore of the lake. Each batch of 500 mL water was aspirated from within 1 cm of the lake surface using a sterile syringe (Tarsons Products Limited, India), and passed through an autoclaved, Swinnex holder-mounted (Merck KGaA, Germany), sterile, mixed cellulose ester membrane filter (Merck Life Science Private Limited, India) having a mesh size of 0.22 \u0026micro;m and diameter of 47 mm. Subsequent to filtration, each membrane corresponding to the cell residue of 500 mL lake-water was dislodged from the Swinnex holder, folded using autoclaved forceps, and inserted into a 7.5 mL autoclaved cryovial that contained 5 mL of 15% (v/v) glycerol supplemented with 0.9% (w/v) NaCl (Roy et al. 2016).\u003c/p\u003e \u003cp\u003eFrom the same five locations where Tso Moriri\u0026rsquo;s water was sampled, sediments were collected in approximately equal quantities, and pooled inside a single 250 mL polypropylene bottle. Inside the polypropylene bottle the sediment fractions were mixed thoroughly to give rise to a bulk sample which was used for all downstream investigations. At each sampling point, soft deposit was scraped carefully from the sediment-water interface using a flat and wide sterile spatula without disturbing layers deeper than 1 cm from the sediment-surface.\u003c/p\u003e \u003cp\u003eDry and loose, weathered rock dust samples (fine talus and scree particles) were collected in approximately equal quantities from five distinct points situated within one meter from each other, on the lake-facing slope of the aforesaid hill. At each sampling site, fine powdery materials were scraped cautiously from the surface without disturbing layers deeper than the top 1 cm. The five sample fractions were pooled inside a 250 mL polypropylene bottle, and mixed thoroughly with a sterile spatula to yield a bulk sample that was used for all subsequent investigations.\u003c/p\u003e \u003cp\u003eAfter the insertion of a cell-precipitated membrane, or sediment / rock-dust sample, each cryovial or polypropylene bottle was capped tightly, sealed with parafilm (Tarsons Products Limited, India), and put inside a polyethylene bag, which eventually was packed in a heat-insulated ice box and shipped by air to the laboratory. All samples were investigated immediately upon their arrival at the laboratory.\u003c/p\u003e \u003cp\u003eIn order to extract metagenomic DNA from Tso Moriri\u0026rsquo;s water and sediment, and also from the rock-dust of the lake-side mountain, sampling was carried out as described above. Only for the water sample a few minor adjustments were made. Total 5 L of the lake-water was passed through five separate (pre-autoclaved) 0.22 \u0026micro;m filters (1 L per filter), which in turn were inserted into five different sterilized cryovials, each containing 5 mL of sterile 50 mM Tris:EDTA (pH 7.8).\u003c/p\u003e\n\u003ch3\u003eEnrichment and isolation of cryo-tolerant / cryo-adapted, psychrophilic copiotrophs\u003c/h3\u003e\n\u003cp\u003e \u003cdiv class=\"BlockQuote\"\u003e \u003cp\u003ePrior to the isolation of pure cultures, the water, sediment and talus samples were subjected to iterative cycles of freezing and thawing. This ensured that the bacterial strains retrieved were cryo-adapted (capable of rendering growth, whether little or substantial, at -10\u0026deg;C), or at least cryo-tolerant (adept in population-level survival, i.e. retention of \u0026gt;\u0026thinsp;1% cells in metabolically-active state, at -10\u0026deg;C), in nature. A separate time-series investigation of biogeochemistry, carried out by our laboratory at the same sample sites explored in this study, have shown that after the summer months (just before the winter), concentration of total carbon in the surficial lake-water reached 80 mg L\u003csup\u003e-\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u003c/sup\u003e, while total carbon content of the surficial samples of lake-sediment and weathered rock dust reached approximately 2% (w/w) and 1% (w/w) respectively (Chatterjee et al., manuscript under preparation). In view of these findings the present enrichment and isolation strategy specifically targeted cryo-adapted/tolerant copiotrophs that possess the potentials for rendering substantive carbon remineralization under cold and frigid conditions. The overall objective was to ensure that the organotrophic growth potentials exhibited by the new isolates \u003cem\u003ein vitro\u003c/em\u003e held direct implications for the scope of \u003cem\u003ein situ\u003c/em\u003e organic matter degradation across alpine/polar ecosystems experiencing high carbon delivery to the environment, naturally and/or under anthropogenic influence.\u003c/p\u003e \u003cp\u003eIn order to enrich copiotrophic, cryo-tolerant / cryo-adapted, psychrophiles from Tso Moriri\u0026rsquo;s water, each 0.22 \u0026micro;m cellulose acetate filter, which contained microbial cell residue from 500 mL lake-water, was shredded with sterile scissors inside the same vial in which it was inserted on-field. The vial was whirled for 15 minutes; after that the filter-shreds were allowed to settle at the bottom; finally, the supernatant was collected in a fresh sterilized vial without disturbing the debris. This process was repeated for all the five filter-containing cryovials involved in lake-water sampling, and the supernatants recovered from each of them were pooled to get an approximately 22 mL cell-suspension in NaCl-glycerol, which corresponded to 2.5 L of bulked lake-water. This\u0026thinsp;~\u0026thinsp;22 mL cell-suspension was added to 80 mL LB prepared in such a way that the actual nutrient concentrations were achieved after the mixing. The inoculum-medium mixture was subjected to three consecutive cycles of \u0026ldquo;7-day freezing at -10\u0026deg;C, followed by 7-day thawing at 4\u0026deg;C\u0026rdquo;, after which pure cultures were isolated at an incubation temperature of 4\u0026deg;C by means of dilution plating, picking of visibly-distinct single colonies, and repeated dilution streaking on Luria agar (LA) plates.\u003c/p\u003e \u003cp\u003eTo enrich copiotrophic, cryo-tolerant / cryo-adapted, psychrophiles from the lake-sediment or rock-dust sample, 1 g of the corresponding material was added to 100 mL LB, and the suspension was subjected to three cycles of \u0026ldquo;7-day freezing at -10\u0026deg;C, followed by 7-day thawing at 4\u0026deg;C\u0026rdquo;. After the third round of thawing, pure culture strains were isolated at 4\u0026deg;C in the same way as described above.\u003c/p\u003e \u003cp\u003eAll the new isolates were maintained in LA slants with a standard transfer interval of 15 days. For routine growth in LB, or fortnightly transfer in LA, all isolates were grown at 4\u0026deg;C. The strains were classified, as described previously (Saha et al. 2019), up to the lowest taxonomic rank that was ascribable based on 16S rRNA gene sequence similarities with validly-published species curated in the List of Prokaryotic names with Standing in Nomenclature (Parte et al. 2020).\u003c/p\u003e \u003c/div\u003e \u003c/p\u003e\n\u003ch3\u003eDetermining the temperature window for growth\u003c/h3\u003e\n\u003cp\u003e \u003cdiv class=\"BlockQuote\"\u003e \u003cp\u003eThe temperature range over which the new isolates could, or could not, grow was delineated by recording the extent to which CFU density increased, or decreased, in the individual LB cultures of the strains, after aerobic incubation at -10\u0026deg;C, 4\u0026deg;C, 15\u0026deg;C, 28\u0026deg;C, 37\u0026deg;C, and 42\u0026deg;C. For a given experiment, a seed culture of the strain tested was prepared by transferring a loopful of cell mass from a 7-day old LA slant culture to fresh 20 mL LB medium kept in a 50 mL Erlenmeyer flask, which was then incubated aerobically at 15\u0026deg;C until when the culture attained its mid-log stage. From this 20 mL seed culture, 1% inoculum was transferred to fresh 20 mL LB medium to set up the test culture. Experiments checking aerobic growth at incubation temperatures ranging between 4\u0026deg;C and 42\u0026deg;C were carried out in 50 mL Erlenmeyer flasks, whereas those checking aerobic growth at -10\u0026deg;C were carried out in 50 mL polypropylene tubes.\u003c/p\u003e \u003cp\u003eTo record CFU density at any time point, 1 mL of the experimental culture concerned was serially diluted using 0.9% (w/v) NaCl, and plated on LA in triplicates (in case of -10\u0026deg;C incubations, the frozen cultures were first liquefied via thawing at 4\u0026deg;C, and then subjected to dilution plating). Subsequently, the LA plates were incubated at 15\u0026deg;C, and colonies appearing on them were counted after 2\u0026ndash;3 days depending on the growth rate of the culture in question. Finally, the CFU density was calculated by first multiplying the colony-counts of the individual dilution-plates by their corresponding dilution factors, and subsequently adding and averaging the values across the dilution grades and replica plates available.\u003c/p\u003e \u003cp\u003eThe above experiments were repeated for the comparator organism \u003cem\u003eEscherichia coli\u003c/em\u003e by recording the increases or decreases in CFU density that the LB cultures of strain K-12 underwent after incubation at -10\u0026deg;C, 4\u0026deg;C, 15\u0026deg;C, 28\u0026deg;C, 37\u0026deg;C, and 42\u0026deg;C. \u003cem\u003eE\u003c/em\u003e. \u003cem\u003ecoli\u003c/em\u003e seed cultures were prepared in the same as those prepared for the TMA isolates except for the fact that incubations were carried out at 28\u0026deg;C. Experimental cultures too were subjected to the same procedure as above, while CFU densities for K-12 were recorded by incubating the colony-counting LA plates at 28\u0026deg;C for two days.\u003c/p\u003e \u003cp\u003eWhen the final CFU density of an experimental culture recorded after the stipulated period of incubation was higher than its initial (0 hour) CFU density, the growth rate of the concerned isolate, at the temperature in question, was calculated as the percentage change that was recorded in the CFU density over time (percentage of the initial CFU mL\u003csup\u003e-\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u003c/sup\u003e culture that increased day\u003csup\u003e-\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u003c/sup\u003e incubation).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Equa\" class=\"Equation\"\u003e \u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equa\" name=\"EquationSource\"\u003e\n$$\\text{G}\\text{r}\\text{o}\\text{w}\\text{t}\\text{h}\\text{r}\\text{a}\\text{t}\\text{e}=\\frac{\\left(\\text{F}\\text{i}\\text{n}\\text{a}\\text{l}\\text{C}\\text{F}\\text{U}\\text{d}\\text{e}\\text{n}\\text{s}\\text{i}\\text{t}\\text{y}-\\text{I}\\text{n}\\text{i}\\text{t}\\text{i}\\text{a}\\text{l}\\text{C}\\text{F}\\text{U}\\text{d}\\text{e}\\text{n}\\text{s}\\text{i}\\text{t}\\text{y}\\right)\\times100}{\\text{I}\\text{n}\\text{i}\\text{t}\\text{i}\\text{a}\\text{l}\\text{C}\\text{F}\\text{U}\\text{d}\\text{e}\\text{n}\\text{s}\\text{i}\\text{t}\\text{y}}\\times\\frac{1}{\\text{T}\\text{o}\\text{t}\\text{a}\\text{l}\\text{d}\\text{a}\\text{y}\\text{s}\\text{o}\\text{f}\\text{i}\\text{n}\\text{c}\\text{u}\\text{b}\\text{a}\\text{t}\\text{i}\\text{o}\\text{n}}$$\u003c/div\u003e \u003c/div\u003e \u003c/p\u003e\n\u003ch3\u003eDetermining the temperature window for population-level survival\u003c/h3\u003e\n\u003cp\u003e \u003cdiv class=\"BlockQuote\"\u003e \u003cp\u003eWhen the final CFU density of an experimental culture recorded after the stipulated period of incubation was lower than its initial (0 hour) CFU density, the survival frequency of the concerned isolate\u0026rsquo;s cell populations at the temperature in question was delineated in terms of what percentage of cells remained metabolically active in the culture. Proportion of metabolically-active cells in a given culture was determined by testing the cells\u0026rsquo; ability to imbibe the nonfluorescent and nontoxic substance called fluorescein diacetate (FDA), and subsequently hydrolyze the same to the tracer compound fluorescein by means of esterase activity (Battin 1997). Fluorescein-stained cells, eventually, were detected with the help of flow cytometry, as described previously (Samaddar et al. 2016; Mondal et al. 2022).\u003c/p\u003e \u003cp\u003eCell-pellet was harvested via cold (4\u0026deg;C) centrifugation for 20 minutes at 6000 \u003cem\u003eg\u003c/em\u003e, and then resuspended in 2 mL of a 0.9% NaCl solution. From a 0.5% (w/v) FDA (Sigma, USA) solution in dimethyl sulfoxide, 4 \u0026micro;l was added to the cell suspension and incubated at 37\u0026deg;C for 15 minutes. The cells were then washed and resuspended again in 500 \u0026micro;l of 0.9% NaCl. Fluorescence-activated cell sorting (FACS) was carried out with the help of a BD FACSVerse flow cytometer (Becton, Dickinson and Company, USA), where 10\u003csup\u003e4\u003c/sup\u003e cells were analyzed randomly for their ability to fluoresce through 475\u0026ndash;495 nm excitation and 520\u0026ndash;530 nm emission. The BD FACSuite software package (Becton, Dickinson and Company) was used to present the data in the form of a dot plot depicting the level of fluorescence recorded for each cell as a function of its photodiode-array-detected ability to forward scatter light having a wave length of 488 nm. Positions of the experiment-specific quadrant gates separating the metabolically-active cells from their inactive counterparts were determined by analyzing an unstained version of the sample.\u003c/p\u003e \u003c/div\u003e \u003c/p\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eTesting chemoorganoheterotrophic growth on simple/complex carbon compounds\u003c/h2\u003e \u003cp\u003e \u003cdiv class=\"BlockQuote\"\u003e \u003cp\u003eThe TMA isolates were tested for 4\u0026deg;C and \u0026minus;\u0026thinsp;10\u0026deg;C aerobic growth on various simple to complex organic compounds as sole sources of energy, electron, and carbon. For this purpose, each strain was cultured aerobically on modified basal and mineral salts (MS) solution (Ghosh and Roy 2006) supplemented with any one of the following organic compounds (HiMedia Laboratories, India) at one particular instance (L\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e double distilled Milli-Q water): acetate (10 mM), agar (2 g), albumin (2 g), benzoate (5 mM), cellulose (5 g), chitin (10 g), n-hexadecane (0.5 mL), pectin (10 g), water-soluble starch (4g) and xylan (10 g).\u003c/p\u003e \u003cp\u003eFor each experiment, a seed culture of the test strain was prepared as described above. From the 20 mL seed culture, cells were harvested, then washed twice with 0.9% NaCl, and eventually resuspended in 1 mL MS solution. This cell suspension in MS was added to the test medium (MS solution supplemented with any one of the 10 organic compounds mentioned above) in such a way that the specified concentration of the medium was reached only after the addition of the inoculum (final volume of the test culture was 20 mL). Experiments checking growth at 4\u0026deg;C were carried out in 50 mL Erlenmeyer flasks, whereas those checking growth at -10\u0026deg;C were carried out in 50 mL polypropylene tubes. CFU density of a culture at a given time point of incubation was determined as described for the LB-dependent growth experiments. Furthermore, each of the 27 TMA isolates was tested for its ability to grow in MS solution devoid of any organic carbon. These experiments were carried out in the same way as described above for testing growth in MS supplemented with a single organic compound.\u003c/p\u003e \u003c/div\u003e \u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eTesting antibiosis potential\u003c/h3\u003e\n\u003cp\u003eEvery TMA isolate was tested by agar overlay assay (Berberov et al. 2025) for its antibiosis potentials against higher-temperature-adapted, mesophilic, Gram negative and Gram positive bacteria that had been isolated previously from warmer habitats within the Western-Himalayan and Trans-Himalayan territories. The Gram negative targets included the well-known model microorganism \u003cem\u003eEscherichia coli\u003c/em\u003e K-12 (Gammaproteobacteria), plus the temperate soil isolate \u003cem\u003eAdvenella kashmirensis\u003c/em\u003e WT001 that belonged to Betaproteobacteria (Ghosh 2005, 2013), and the Puga Valley hydrothermal vent isolate \u003cem\u003eParacoccus\u003c/em\u003e sp. SMMA_5 that belonged to Alphaproteobacteria (Roy et al. 2016; Mondal et al. 2022). The Gram positive targets included the Chumathang hydrothermal vent isolates \u003cem\u003eBacillus subtilis\u003c/em\u003e SC_1, \u003cem\u003eBacillus licheniformis\u003c/em\u003e PAMA2_SD1, and \u003cem\u003eLysinibacillus fusiformis\u003c/em\u003e LAPE1_SD1, all of which belonged to the phylum Bacillota (Dutta et al. manuscript under preparation). Each TMA isolate was also tested for its antibiosis capabilities against other isolates obtained from the TMA.\u003cdiv class=\"BlockQuote\"\u003e\u003cp\u003eThe potential antagonists under assessment (these were taken in batches of four isolates at a time) were first grown in LB, at 15\u0026deg;C for 48 h, to generate their seed cultures. 10 \u0026micro;L inocula from the individual seed cultures were spotted on LA plates at minimum mutual distances of 3 cm, following which the plates were incubated at 15\u0026deg;C for 48 h. After fully grown colonies had appeared for all the potential antagonists investigated, 100 \u0026micro;L of a mid-log-phase culture of the target organism against which antibiosis was to be tested, was mixed with 900 \u0026micro;L molten agar (0.4% w/v) having 37\u0026deg;C temperature, and poured uniformly on the LA plates that were already dotted by incumbent colonies of the potential antagonists. Seed culture of the incoming bacterium (target of potential antibiosis) was grown at 28\u0026deg;C or 15\u0026deg;C, according as the organism was a mesophile from a foreign habitat or an isolate from the TMA. Eventually, these test plates were incubated for 48 h (again, at 28\u0026deg;C or 15\u0026deg;C, according as the incoming bacterium was a mesophile from a foreign habitat or a TMA isolate) and checked for the development of lawns of growth, or clear zones of inhibition, for the incoming organism, around the pre-established colonies of the incumbent bacteria (potential antagonists).\u003c/p\u003e\u003c/div\u003e\u003c/p\u003e\n\u003ch3\u003eGenomics\u003c/h3\u003e\n\u003cp\u003eWhole genomic DNA was extracted from the LB-grown stationary phase culture of a given TMA isolate using HiPurA Bacterial Genomic DNA Purification Kit (Himedia, India), and sequenced using a Novaseq 6000 (Illumina Inc., USA) as well as a MinION (Oxford Nanopore Technologies, UK) platform. For every genome, 2\u0026times;150 bp paired-end Illumina reads having Phred score above 20 (Q20) were assembled alongside MinION reads having quality values\u0026thinsp;\u0026gt;\u0026thinsp;10, with the help of the software Unicycler v0.5.0 run in hybrid assembly mode (Wick et al. 2017). Prodigal v2.6.3 (Hyatt et al. 2010) was used to predict open reading frames (ORFs), or putative genes, within the assembled genome. Subsequently, a catalog of protein-coding gene sequences (CDSs) was delineated by searching the repertoire of ORFs available, against the eggNOG database v5.0 (Huerta-Cepas et al. 2019), with the aid of eggNOG-mapper v2.1.9 (Cantalapiedra et al. 2021), which in turn used the algorithm HMMER.\u003c/p\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003eMetagenomics\u003c/h2\u003e \u003cp\u003eFrom the lake-water, lake-sediment, and rock-dust samples, metagenomic DNA was extracted, and sequenced on a Novaseq 6000 using 2 \u0026times; 250 bp paired-end read chemistry, as described previously (Bhattacharya et al. 2021; Mondal et al. 2024). After clipping the adapters and quality-filtering for an average Phred score\u0026thinsp;\u0026ge;\u0026thinsp;20, 25\u0026nbsp;million read-pairs were extracted randomly from each metagenomic sequence dataset and assembled \u003cem\u003ede novo\u003c/em\u003e using Megahit v1.2.9 with default parameters (Li et al. 2015). Within the \u0026gt;\u0026thinsp;1000 bp contigs assembled from a metagenome, ORFs were annotated, and the CDS catalog was delineated, as stated for the pure-culture genomes.\u003c/p\u003e \u003cp\u003eThe dataset of 50\u0026nbsp;million quality-filtered reads derived for a given metagenome was searched against the rrnDB database v5.10 (Stoddard et al. 2015) using Bowtie2 v2.4.5 (Langmead and Salzberg 2012) in default mode to extricate sequences corresponding to 16S rRNA genes and classify them using the RDP Classifier with a confidence cut-off value of 0.8 (Mondal et al. 2024).\u003c/p\u003e \u003cp\u003eTo get an idea about the habitat-wise prevalence (relative abundance) of the different TMA species for which whole genomes were sequenced, the proportion of sequence correspondence that existed between a given genome and the metagenome of the habitat under consideration was determined as follows. First, a subject (target) database was created by curating all the 15 whole genome sequences in hand; subsequently, all Q20 metagenomic reads available for a given habitat were mapped onto the subject database using Bowtie2 v2.4.5 in default mode. The alignment output file obtained was processed with SAMtools v1.13 (Li et al. 2009) to report the number of metagenomic reads that matched each genome of the target database individually.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003eIdentification of ecophysiologically important genes within the genomes and metagenomes\u003c/h2\u003e \u003cp\u003eTo identify genes associated with low temperature adaptation, and low as well as high temperature adaptation, the eggNOG-derived CDS catalogs were searched on the basis of the information available in the literature for extreme temperature adaptation, alongside the gene orthology information curated in the Kyoto Encyclopedia of Genes and Genomes (KEGG; \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.genome.jp/kegg/\u003c/span\u003e\u003cspan address=\"https://www.genome.jp/kegg/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eTo identify genes encoding carbohydrate-active enzymes, the Prodigal-derived ORF catalog of an isolate or metagenome was annotated directly by searching against the CAZy and dbCAN3 databases using Diamond (Buchfink et al. 2015) and HMMER (Zheng et al. 2023) algorithms (with default parameters for both) respectively. Subsequently, the collective findings of the two search exercises were reported as the catalog of genes encoding carbohydrate-active enzymes within the genome / metagenome in question.\u003c/p\u003e \u003cp\u003eTo identify genes that are known to be central to the biosynthesis of different classes of antibiotics, each eggNOG-derived CDS catalog was searched based on the information available in the literature for antibiotic biosynthesis, plus the gene orthology information curated in the KEGG database. Furthermore, to detect genes or gene clusters concerned with the biosynthesis of secondary metabolites, each Prodigal-derived ORF catalog was annotated using the bacterial version of the antibiotics and secondary metabolite analysis shell (antiSMASH v7.0) pipeline in strict detection mode (Medema et al. 2011; Blin et al. 2023). Antibiotic resistance genes were identified by annotating the assembled whole genome sequences against the Comprehensive Antibiotic Resistance Database (CARD, version 3.2.8) using the Resistance Gene Identifier (RGI, v6.0.3) tool with its default analysis parameters (Alcock et al. 2023).\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec14\" class=\"Section2\"\u003e\n \u003ch2\u003eCold-adapted copiotrophs from Tso Moriri lake-desert ecosystem\u003c/h2\u003e\n \u003cp\u003eA sum total of 61 aerobic, copiotrophic, and freeze-thaw resilient bacterial pure-cultures, apparently dissimilar with regard to colony morphology, were enriched and isolated in Luria broth (LB), from the lake-water (Fig. \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003ef), lake-sediment (Fig. \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003eg), and rock-dust (Fig. \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003ee) samples. 22 of these were obtained from the weathered rock dust of the lake-side hill, while five and 34 were from the lake\u0026rsquo;s water and sediment respectively (Table \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e). 16S rRNA gene sequence similarities clustered the 61 isolates into 27 species-level entities that in turn were ascribable to 15 genera distributed over the phyla Actinomycetota, Bacillota, Bacteroidota and Pseudomonadota. One representative strain from each species-level cluster was selected for further characterization (Table \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e\n \u003cp\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003eTable 2.\u003c/strong\u003e Genomic potentials of the TMA isolates for the synthesis of, and resistance against, different classes of antibiotic and secondary metabolite.\u003c/p\u003e\n \u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 83px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eIsolation source\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 223px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eName of the isolate\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 108px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eAntibiotics potentially synthesized\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 151px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eSecondary metabolites synthesized\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 139px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eAntibiotics potentially resisted\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"5\" style=\"width: 83px;\"\u003e\n \u003cp\u003eWeathered rock dust\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 223px;\"\u003e\n \u003cp\u003e\u003cem\u003eArthrobacter\u003c/em\u003e sp. TRD_SC_6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 108px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 151px;\"\u003e\n \u003cp\u003eNAPAAs\u003csup\u003e1\u003c/sup\u003e,\u003c/p\u003e\n \u003cp\u003eNI-siderophores\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 139px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 223px;\"\u003e\n \u003cp\u003e\u003cem\u003eMycetocola\u003c/em\u003e sp. TRD_SC_2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 108px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 151px;\"\u003e\n \u003cp\u003eNAPAAs,\u003c/p\u003e\n \u003cp\u003eType-III polyketides\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 139px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 223px;\"\u003e\n \u003cp\u003e\u003cem\u003ePaenarthrobacter\u003c/em\u003e sp. TRD_SC_7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 108px;\"\u003e\n \u003cp\u003ePenicillins and cephalosporins\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 151px;\"\u003e\n \u003cp\u003eNAPAAs,\u003c/p\u003e\n \u003cp\u003eNI-siderophores,\u003c/p\u003e\n \u003cp\u003eNRPs\u003csup\u003e3\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 139px;\"\u003e\n \u003cp\u003eRifamycins\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 223px;\"\u003e\n \u003cp\u003e\u003cem\u003ePseudarthrobacter\u003c/em\u003e sp. TRD_SC_9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 108px;\"\u003e\n \u003cp\u003eStaurosporines\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 151px;\"\u003e\n \u003cp\u003eNAPAAs,\u003c/p\u003e\n \u003cp\u003eNI-siderophores,\u003c/p\u003e\n \u003cp\u003eType-III polyketides\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 139px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 223px;\"\u003e\n \u003cp\u003e\u003cem\u003eStreptomyces\u003c/em\u003e sp. TRD_SC_5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 108px;\"\u003e\n \u003cp\u003eStaurosporines, Vancomycins\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 151px;\"\u003e\n \u003cp\u003eClass-II Lanthipeptides,\u003c/p\u003e\n \u003cp\u003eClass-III Lanthipeptides,\u003c/p\u003e\n \u003cp\u003eNI-siderophores,\u003c/p\u003e\n \u003cp\u003eNRPs,\u003c/p\u003e\n \u003cp\u003eTerpenes,\u003c/p\u003e\n \u003cp\u003eType-I polyketides\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 139px;\"\u003e\n \u003cp\u003eRifamycins\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"3\" style=\"width: 83px;\"\u003e\n \u003cp\u003eLake -water\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 223px;\"\u003e\n \u003cp\u003e\u003cem\u003eAcinetobacter\u003c/em\u003e sp. TW_SC_4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 108px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 151px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 139px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 223px;\"\u003e\n \u003cp\u003e\u003cem\u003eAncylobacter\u003c/em\u003e sp. TW_SC_1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 108px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 151px;\"\u003e\n \u003cp\u003eNRPs,\u003c/p\u003e\n \u003cp\u003eType-I polyketides\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 139px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 223px;\"\u003e\n \u003cp\u003e\u003cem\u003eMicrobacterium\u003c/em\u003e sp. TW_SC_2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 108px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 151px;\"\u003e\n \u003cp\u003eType-III polyketides\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 139px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"7\" style=\"width: 83px;\"\u003e\n \u003cp\u003eLake-sediment\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 223px;\"\u003e\n \u003cp\u003e\u003cem\u003eAeromonas\u003c/em\u003e sp. TS_SC_11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 108px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 151px;\"\u003e\n \u003cp\u003eNRPs,\u003c/p\u003e\n \u003cp\u003eNRP-metallophores\u003csup\u003e4\u003c/sup\u003e,\u0026nbsp;\u003c/p\u003e\n \u003cp\u003eTerpenes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 139px;\"\u003e\n \u003cp\u003eCarbapenems,\u003c/p\u003e\n \u003cp\u003eCephalosporins,\u003c/p\u003e\n \u003cp\u003eElfamycins,\u003c/p\u003e\n \u003cp\u003ePenicillin and other beta-lactams\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 223px;\"\u003e\n \u003cp\u003e\u003cem\u003eCryobacterium\u003c/em\u003e sp. TS_SC_7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 108px;\"\u003e\n \u003cp\u003eProdigiosin\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 151px;\"\u003e\n \u003cp\u003eNAPAAs,\u003c/p\u003e\n \u003cp\u003eTerpenes,\u003c/p\u003e\n \u003cp\u003eType-III polyketides\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 139px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 223px;\"\u003e\n \u003cp\u003e\u003cem\u003eFlavobacterium\u003c/em\u003e sp. TS_SC_5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 108px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 151px;\"\u003e\n \u003cp\u003eTerpenes\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 139px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 223px;\"\u003e\n \u003cp\u003e\u003cem\u003ePseudomonas\u003c/em\u003e sp. TS_SC_3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 108px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 151px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 139px;\"\u003e\n \u003cp\u003eDiaminopyrimidines,\u003c/p\u003e\n \u003cp\u003eFluoroquinolones,\u003c/p\u003e\n \u003cp\u003ePhenicols\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 223px;\"\u003e\n \u003cp\u003e\u003cem\u003ePsychrobacter\u003c/em\u003e sp. TS_SC_6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 108px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 151px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 139px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 223px;\"\u003e\n \u003cp\u003e\u003cem\u003eSanguibacter\u003c/em\u003e sp. TS_SC_8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 108px;\"\u003e\n \u003cp\u003eTetracyclines\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 151px;\"\u003e\n \u003cp\u003eNAPAAs\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 139px;\"\u003e\n \u003cp\u003eRifamycins\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 223px;\"\u003e\n \u003cp\u003e\u003cem\u003eTrichococcus\u003c/em\u003e sp. TS_SC_9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 108px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 151px;\"\u003e\n \u003cp\u003eType-III polyketides\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 139px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003cp\u003e\u003csup\u003e1\u0026nbsp;\u003c/sup\u003eNAPAAs: non-alpha poly-amino acids\u003c/p\u003e\n \u003cp\u003e\u003csup\u003e2\u0026nbsp;\u003c/sup\u003eNI-siderophores: siderophores independent of non-ribosomal peptide synthase\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u003csup\u003e3\u0026nbsp;\u003c/sup\u003eNRPs: \u0026nbsp; \u0026nbsp;non-ribosomal peptides\u003c/p\u003e\n \u003cp\u003e\u003csup\u003e4\u0026nbsp;\u003c/sup\u003eNRP-metallophores: non-ribosomal peptide (NRP) metallophores\u003c/p\u003e\n \u003cdiv align=\"left\" class=\"colspec\"\u003e\u003cbr\u003e\u003c/div\u003e\n \u003cp\u003e\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec15\" class=\"Section2\"\u003e\n \u003ch2\u003eCold adaptations of the TMA isolates\u003c/h2\u003e\n \u003cp\u003eIn LB medium, all the 27 species retrieved from the Tso Moriri area could grow at 4\u0026deg;C (Figs. \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003ec-d) and 15\u0026deg;C (Figs. \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003ee-f), whereas only 13 could grow at -10\u0026deg;C (Figs. \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003ea-b). In terms of what percentage of the starting CFU density (colony-forming units mL\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e) remained in the culture after the stipulated period of incubation (Tables S1-S3), the extents of growth recorded at -10\u0026deg;C were far lower than those recorded at 4\u0026deg;C and 15\u0026deg;C. After 14 days at 4\u0026deg;C, the cultures of \u003cem\u003eFlavobacterium\u003c/em\u003e TS_SC_2 and \u003cem\u003eArthrobacter\u003c/em\u003e TRD_SC_3 showed the lowest and highest increases in CFU density respectively. After two days at 15\u0026deg;C, lowest and highest increases in CFU density were exhibited by the cultures of \u003cem\u003eFlavobacterium\u003c/em\u003e TS_SC_2 and \u003cem\u003eArthrobacter\u003c/em\u003e TRD_SC_8 respectively. After 28 days at -10\u0026deg;C, \u003cem\u003ePseudarthrobacter\u003c/em\u003e TRD_SC_9 and \u003cem\u003eArthrobacter\u003c/em\u003e TRD_SC_6 had the lowest and highest increases in CFU density respectively.\u003c/p\u003e\n \u003cp\u003e\u003cbr\u003e\u003c/p\u003e\n \u003cp\u003eNotably, the 14 species, which could not grow in LB at -10\u0026deg;C, managed to maintain considerable proportions of their cell populations in divisible (Figs. \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003ea-b) and/or metabolically-active (Figs. \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003eg and S1) states, as evidenced by CFU density data, and fluorescein diacetate (FDA) staining followed by flow cytometry, respectively. After 28 days at this temperature, \u003cem\u003eAcinetobacter\u003c/em\u003e TW_SC_4 and \u003cem\u003eAeromonas\u003c/em\u003e TS_SC_11 retained the lowest and highest proportions (2% and 75%) of the initial CFU density respectively, while \u003cem\u003ePseudarthrobacter\u003c/em\u003e TS_SC_4 had the lowest (19%), and \u003cem\u003eCryobacterium\u003c/em\u003e TS_SC_7 and \u003cem\u003eSanguibacter\u003c/em\u003e TS_SC_8 had the highest (99%), proportions of cell stained with FDA. Furthermore, for every bacterium lacking growth at -10\u0026deg;C, the percentage of metabolically-active cells exceeded the percentage of cells retaining their divisibility after 28 days of incubation, even though no significant correlation existed between the proportions of divisible and metabolically-active cells (Fig. \u003cspan class=\"InternalRef\"\u003eS2\u003c/span\u003ea).\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec16\" class=\"Section2\"\u003e\n \u003ch2\u003eGrowth on different simple/complex carbon compounds at zero and sub-zero degree Celsius\u003c/h2\u003e\n \u003cp\u003eAfter 14 days of incubation at 4\u0026deg;C, all the bacterial species isolated from the TMA rendered at least a little growth by utilizing no less than three out of the 10, simple or complex, organic compounds tested as single chemoorganoheterotrophic substrates (Tables S4-S13). The extent of growth differed across the isolates in terms of the percentage increase in CFU density that was attributable to the utilization of the carbon compound in question (Fig. \u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003ea). 10 TMA isolates were found to accomplish low, moderate, or high growth on all the 10 compounds tested; these were \u003cem\u003eAeromonas\u003c/em\u003e TS_SC_11, \u003cem\u003eArthrobacter\u003c/em\u003e TRD_SC_3, \u003cem\u003eCryobacterium\u003c/em\u003e TS_SC_7, \u003cem\u003eFlavobacterium\u003c/em\u003e TS_SC_2, \u003cem\u003eFlavobacterium\u003c/em\u003e TS_SC_5, \u003cem\u003eMicrobacterium\u003c/em\u003e TRD_SC_10, \u003cem\u003eSanguibacter\u003c/em\u003e TS_SC_8, \u003cem\u003ePseudomonas\u003c/em\u003e TS_SC_12, \u003cem\u003ePseudomonas\u003c/em\u003e TS_SC_13 and \u003cem\u003ePsychrobacter\u003c/em\u003e TS_SC_6. Although unable to use acetate, \u003cem\u003eStreptomyces\u003c/em\u003e TRD_SC_5 could accomplish high growth on maximum number of complex carbon compounds, namely cellulose, chitin, pectin, starch and xylan; this organism also rendered moderate growth on agar, albumin and hexadecane, while its growth on benzoate was low. In contrast, \u003cem\u003ePseudomonas\u003c/em\u003e TS_SC_3, which could only render moderate growth on cellulose, and low growth on albumin and starch, was the least efficient TMA isolate in terms of chemoorganoheterotrophic utilization of the carbon substrates tested.\u003c/p\u003e\n \u003cp\u003e\u003cbr\u003e\u003c/p\u003e\n \u003cp\u003eAfter 28 days of incubation at -10\u0026deg;C, a sum total of 20 TMA isolates were found to render low but definite growth on at least one of the 10 carbon compounds tested (Tables S14-S23), even though in LB medium, only 13 species had exhibited low to high growth at -10\u0026deg;C (Figs. \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003ea-b; Table \u003cspan class=\"InternalRef\"\u003eS1\u003c/span\u003e). Of the 14 TMA isolates that had failed to render\u0026thinsp;\u0026minus;\u0026thinsp;10\u0026deg;C-growth in LB, seven \u0026ndash; namely, \u003cem\u003eAeromonas\u003c/em\u003e TS_SC_11 (benzoate and cellulose), \u003cem\u003eAncylobacter\u003c/em\u003e TW_SC_1 (agar and chitin), \u003cem\u003eCryobacterium\u003c/em\u003e TS_SC_7 (acetate, albumin, cellulose and pectin), \u003cem\u003eFlavobacterium\u003c/em\u003e TS_SC_5 (agar), \u003cem\u003eMicrobacterium\u003c/em\u003e TRD_SC_10 (agar, albumin, benzoate, chitin and starch), \u003cem\u003ePseudarthrobacter\u003c/em\u003e TS_SC_4 (agar and starch), and \u003cem\u003ePseudomonas\u003c/em\u003e TS_SC_10 (agar) \u0026ndash; could render a low level of growth attributable to the use of at least one carbon compound (Fig. \u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003eb). Overall, \u003cem\u003eArthrobacter\u003c/em\u003e TRD_SC_3 accomplished low levels of growth on all the 10 compounds tested, while \u003cem\u003eStreptomyces\u003c/em\u003e TRD_SC_5 rendered low levels of growth on all the substrates except acetate, and \u003cem\u003eMycetocola\u003c/em\u003e TRD_SC_2 could do the same on all the carbon compounds except acetate, albumin and benzoate. Of the remaining 24 isolates, 17 could achieve low levels of growth on only a few, or just one, of the 10 organic substrates tested; seven isolates could not grow on any of the compounds tested (Fig. \u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003eb).\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec17\" class=\"Section2\"\u003e\n \u003ch2\u003eExtreme oligotrophy at zero and sub-zero degree Celsius\u003c/h2\u003e\n \u003cp\u003eA scrutiny of the increases in the CFU densities of the different isolates recorded after incubation in MS solution at 4\u0026deg;C and \u0026minus;\u0026thinsp;10\u0026deg;C revealed their extreme oligotrophic potentials.\u003c/p\u003e\n \u003cp\u003eAt 4\u0026deg;C, after 14 days of incubation in MS solution, CFU densities of \u003cem\u003eMicrobacterium\u003c/em\u003e TW_SC_2, and \u003cem\u003ePseudomonas\u003c/em\u003e strains TS_SC_1 and TS_SC_10, increased by \u0026gt;\u0026thinsp;200% of the initial levels, whereas those of \u003cem\u003eAcinetobacter\u003c/em\u003e TW_SC_4, \u003cem\u003eArthrobacter\u003c/em\u003e TRD_SC_1 and TRD_SC_6, \u003cem\u003eMicrobacterium\u003c/em\u003e TW_SC_3, \u003cem\u003eMycetocola\u003c/em\u003e TRD_SC_2, \u003cem\u003ePaenarthrobacter\u003c/em\u003e TRD_SC_7 and \u003cem\u003eTrichococcus\u003c/em\u003e TS_SC_9 increased by 6\u0026ndash;72% (Table S24). Under these conditions, CFU densities of \u003cem\u003eArthrobacter\u003c/em\u003e TRD_SC_3, \u003cem\u003eMicrobacterium\u003c/em\u003e TRD_SC_10, and \u003cem\u003eStreptomyces\u003c/em\u003e TRD_SC_5 remained unchanged, while those of the remaining 14 strains decreased by 3\u0026ndash;90% of the initial levels.\u003c/p\u003e\n \u003cp\u003eAt -10\u0026deg;C, after 28 days in MS, CFU densities of \u003cem\u003eArthrobacter\u003c/em\u003e strains TRD_SC_1 and TRD_SC_6, \u003cem\u003eMicrobacterium\u003c/em\u003e strains TW_SC_2 and TW_SC_3, \u003cem\u003eMycetocola\u003c/em\u003e TRD_SC_2, and \u003cem\u003ePaenarthrobacter\u003c/em\u003e TRD_SC_7 increased by 10\u0026ndash;50%, whereas those of the remaining species decreased by 2\u0026ndash;98%, of the corresponding initial levels (Table S25).\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec18\" class=\"Section2\"\u003e\n \u003ch2\u003eProgressive thermal susceptibility of the TMA isolates\u003c/h2\u003e\n \u003cp\u003eIn LB medium, 23 out of the 27 TMA isolates studied could grow at 28\u0026deg;C (Figs. \u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003ea-b; Table S26), whereas only 11 could grow at 37\u0026deg;C (Figs. \u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003ec-d; Table S27), and none at 42\u0026deg;C (Figs. \u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003ee-f; Table S28). In terms of what percentage of the starting CFU density remained in the culture after the stipulated period of incubation, extents of growth recorded at 28\u0026deg;C were much higher than those recorded at 37\u0026deg;C.\u003c/p\u003e\n \u003cp\u003e\u003cbr\u003e\u003c/p\u003e\n \u003cp\u003eOf the four species incapable of growing in LB at 28\u0026deg;C, three did not retain any CFU, while only \u003cem\u003eArthrobacter\u003c/em\u003e TRD_SC_4 retained 28% of the initial CFU density, after four days of incubation at this temperature. At the same time, three of these four isolates had 18\u0026ndash;58% cells in metabolically-active conditions, while \u003cem\u003eCryobacterium\u003c/em\u003e TS_SC_7 had 0.2% cells in active condition (Figs. \u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003eg and S3a).\u003c/p\u003e\n \u003cp\u003eAt 37\u0026deg;C, 16 TMA isolates failed to grow in LB. Nine of them had zero or near-zero CFU density, while the other seven had 1\u0026ndash;48% of the initial CFU densities, remaining in the cultures after two days of incubation. At the same time, 15 out of these 16 isolates had 4\u0026ndash;76% cells in metabolically-active conditions, while \u003cem\u003eCryobacterium\u003c/em\u003e TS_SC_7 had 0.1% cells in active condition (Figs. \u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003eh and S3b).\u003c/p\u003e\n \u003cp\u003eNone of the 27 species retrieved from the Tso Moriri area could grow in LB at 42\u0026deg;C. 22 of them had zero or near-zero CFU density, while the remaining five had 1\u0026ndash;33% of the initial CFU densities, present in the cultures after one day of incubation. At the same time, 19 out of the 27 isolates had 1-87.3% cells in metabolically-active conditions, while eight had\u0026thinsp;\u0026lt;\u0026thinsp;1% cell in active condition (Figs. \u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003ei and S4).\u003c/p\u003e\n \u003cp\u003eOverall, the trends of LB-based population-level survival of the isolates lacking growth at \u0026ge;\u0026thinsp;28\u0026deg;C showed that the percentage of cells remaining metabolically active at a given high temperature exceeded the percentage of cells which retained their divisibility at that temperature (Figs. \u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003eg-i). Furthermore, for all strains susceptible to high temperatures, proportions of divisible, and metabolically active, cells remaining in the cultures after stipulated periods of incubation decreased with increase in temperature. That said, no significant correlation existed between the proportions of divisible and metabolically-active cells, across the species incapable of growth at high temperatures (Figs. S2b-d).\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec19\" class=\"Section2\"\u003e\n \u003ch2\u003eDifferential temperature windows for growth and survival of cell populations\u003c/h2\u003e\n \u003cp\u003eAt every incubation temperature tested for LB-based growth, whether in the psychrophilic (Tables S1-S3) or in the mesophilic range (Tables S26-S28), different TMA isolates exhibited different rates of increase or decrease in CFU density. On the flip side, temperature windows for growth (increase in CFU density) and population-level survival (retention of \u0026gt;\u0026thinsp;1% cells in metabolically-active state), over the tested range, varied across the isolates (Fig. \u003cspan class=\"InternalRef\"\u003e5\u003c/span\u003e). \u003cem\u003eArthrobacter\u003c/em\u003e TRD_SC_3 and TRD_SC_8, \u003cem\u003eMicrobacterium\u003c/em\u003e TW_SC_2 and TW_SC_3, \u003cem\u003ePaenarthrobacter\u003c/em\u003e TRD_SC_7, and Streptomyces TRD_SC_5, shared the widest temperature range over which growth was recorded (-10\u0026deg;C to 37\u0026deg;C), whereas \u003cem\u003eFlavobacterium\u003c/em\u003e TS_SC_5 and \u003cem\u003eCryobacterium\u003c/em\u003e TS_SC_7 had the narrowest growth window (4\u0026deg;C to 15\u0026deg;C).\u003c/p\u003e\n \u003cp\u003e\u003cbr\u003e\u003c/p\u003e\n \u003cp\u003eFor all those 4\u0026deg;C-growing isolates whose growth in LB ceased at -10\u0026deg;C, more than 1% cells remained metabolically active at this freezing temperature (Fig. \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003eg); the \u0026minus;\u0026thinsp;10\u0026deg;C-growing isolates, on the other hand, were likely to remain metabolically active at even lower temperatures. At \u0026ge;\u0026thinsp;28\u0026deg;C, all the TMA isolates, except \u003cem\u003eArthrobacter\u003c/em\u003e TRD_SC_6 and \u003cem\u003eCryobacterium\u003c/em\u003e TS_SC_7, exhibited population-level survival at temperatures above the points where their growth was last recorded (Figs. \u003cspan class=\"InternalRef\"\u003e5\u003c/span\u003e and \u003cspan class=\"InternalRef\"\u003e6\u003c/span\u003ea). For TRD_SC_6 and TS_SC_7, growth in LB was last recorded at 37\u0026deg;C and 15\u0026deg;C respectively, but their proportions of metabolically-active cells in the culture dropped below the 1% threshold at the immediately higher temperature points tested, i.e. at 42\u0026deg;C and 28\u0026deg;C respectively (Fig. \u003cspan class=\"InternalRef\"\u003e5\u003c/span\u003e).\u003c/p\u003e\n \u003cp\u003e\u003cbr\u003e\u003c/p\u003e\n \u003cp\u003eSo far as the most suitable temperature for LB-dependent growth was concerned, 22 TMA isolates had their highest growth rates at 15\u0026deg;C (Fig. \u003cspan class=\"InternalRef\"\u003e5\u003c/span\u003e). For 11 out of the 22 isolates having growth maxima at 15\u0026deg;C, i.e. for three \u003cem\u003eArthrobacter\u003c/em\u003e species, the two \u003cem\u003eFlavobacterium\u003c/em\u003e species, and one species each of \u003cem\u003eCryobacterium\u003c/em\u003e, \u003cem\u003eMicrobacterium\u003c/em\u003e, \u003cem\u003ePseudarthrobacter\u003c/em\u003e, \u003cem\u003ePseudomonas\u003c/em\u003e, \u003cem\u003eSanguibacter\u003c/em\u003e and \u003cem\u003eTrichococcus\u003c/em\u003e, 4\u0026deg;C growth rates were higher than their 28\u0026deg;C growth rates (notably, some of the 28\u0026deg;C growth rates in question were negative). In contrast, for the other half of these 22 isolates, i.e. for two \u003cem\u003eArthrobacter\u003c/em\u003e species, four \u003cem\u003ePseudomonas\u003c/em\u003e species, and one species each of \u003cem\u003eAeromonas\u003c/em\u003e, \u003cem\u003eMicrobacterium\u003c/em\u003e, \u003cem\u003ePaenarthrobacter\u003c/em\u003e, \u003cem\u003ePseudarthrobacter\u003c/em\u003e and \u003cem\u003ePsychrobacter\u003c/em\u003e, 28\u0026deg;C growth rates were higher than their 4\u0026deg;C growth rates.\u003c/p\u003e\n \u003cp\u003eFor \u003cem\u003eAcinetobacter\u003c/em\u003e TW_SC_4, \u003cem\u003eAncylobacter\u003c/em\u003e TW_SC_1, \u003cem\u003eMicrobacterium\u003c/em\u003e TW_SC_2 and \u003cem\u003eStreptomyces\u003c/em\u003e TRD_SC_5, maximum growth rates were recorded at 28\u0026deg;C. While the 15\u0026deg;C growth rates of all these species were higher than their 4\u0026deg;C growth rates, for \u003cem\u003eMicrobacterium\u003c/em\u003e TW_SC_2 and \u003cem\u003eStreptomyces\u003c/em\u003e TRD_SC_5 their 4\u0026deg;C growth rates were higher than the 37\u0026deg;C growth rates, but for \u003cem\u003eAcinetobacter\u003c/em\u003e TW_SC_4 and \u003cem\u003eAncylobacter\u003c/em\u003e TW_SC_1 their 37\u0026deg;C growth rates were higher than the 4\u0026deg;C growth rates.\u003c/p\u003e\n \u003cp\u003eFor \u003cem\u003eMycetocola\u003c/em\u003e TRD_SC_2 alone, growth rate was highest at 4\u0026deg;C, and then decreased through 15\u0026deg;C and 28\u0026deg;C to become negative at 37\u0026deg;C (Fig. \u003cspan class=\"InternalRef\"\u003e5\u003c/span\u003e).\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec20\" class=\"Section2\"\u003e\n \u003ch2\u003eAntibiosis by TMA actinobacteria\u003c/h2\u003e\n \u003cp\u003eOf the 15 actinobacterial species isolated from the Tso Moriri area, 11 had the ability to inhibit the growth of at least one of the six higher-temperature-adapted foreign bacteria against which antibiosis was tested, namely the Gram negative organisms \u003cem\u003eEscherichia coli\u003c/em\u003e K-12, \u003cem\u003eAdvenella kashmirensis\u003c/em\u003e WT001, \u003cem\u003eParacoccus\u003c/em\u003e sp. SMMA_5, and the Gram positive \u003cem\u003eBacillus subtilis\u003c/em\u003e SC_1, \u003cem\u003eBacillus licheniformis\u003c/em\u003e PAMA2_SD1, and \u003cem\u003eLysinibacillus fusiformis\u003c/em\u003e LAPE1_SD1 (Fig. \u003cspan class=\"InternalRef\"\u003e7\u003c/span\u003e). Only the four \u003cem\u003eArthrobacter\u003c/em\u003e species represented by the strains TRD_SC_1, TRD_SC_3, TRD_SC_4 and TRD_SC_8 had no antagonistic activity against any of the target organisms. \u003cem\u003ePseudarthrobacter\u003c/em\u003e TRD_SC_9 and \u003cem\u003eStreptomyces\u003c/em\u003e TRD_SC_5 disallowed the growth of all the six foreign bacteria targeted; \u003cem\u003ePaenarthrobacter\u003c/em\u003e TRD_SC_7 inhibited all but \u003cem\u003eAdvenella kashmirensis\u003c/em\u003e, while \u003cem\u003eArthrobacter\u003c/em\u003e TRD_SC_6 deterred only \u003cem\u003eLysinibacillus fusiformis\u003c/em\u003e. On the flip side of the above data, most number of TMA actinobacteria were active against \u003cem\u003eLysinibacillus fusiformis\u003c/em\u003e, \u003cem\u003eBacillus licheniformis\u003c/em\u003e, and \u003cem\u003eEscherichia coli\u003c/em\u003e, which in turn were inhibited by 11, nine and seven actinobacteria respectively. Five, four, and three TMA actinobacteria were found to inhibit the growth of \u003cem\u003eBacillus subtilis\u003c/em\u003e, \u003cem\u003eAdvenella kashmirensis\u003c/em\u003e, and \u003cem\u003eParacoccus\u003c/em\u003e sp. respectively. Notably, none of the 12 non-actinobacterial species isolated from TMA had the ability to inhibit the growth of any of the foreign bacteria against which antibiosis was tested (Fig. \u003cspan class=\"InternalRef\"\u003e7\u003c/span\u003e).\u003c/p\u003e\n \u003cp\u003e\u003cbr\u003e\u003c/p\u003e\n \u003cp\u003eThe same 11 actinobacterial isolates, which had inhibited the growth of foreign mesophilic bacteria, also inhibited at least one or more non-actinobacterial TMA isolate(s) (Fig. \u003cspan class=\"InternalRef\"\u003e7\u003c/span\u003e). \u003cem\u003eMicrobacterium\u003c/em\u003e TW_SC_2 could inhibit the most number of TMA bacteria (nine) outside Actinomycetota. In contrast, \u003cem\u003eArthrobacter\u003c/em\u003e TRD_SC_6 could inhibit only one TMA bacterium outside Actinomycetota. From the reverse perspective, every non-actinobacterial TMA isolate was inhibited by at least three TMA actinobacteria. \u003cem\u003eAeromonas\u003c/em\u003e TS_SC_11 was the most vulnerable TMA bacteria outside Actinomycetota as its growth was inhibited by 11 TMA actinobacteria. In contrast, \u003cem\u003eAncylobacter\u003c/em\u003e TW_SC_1, \u003cem\u003eFlavobacterium\u003c/em\u003e TS_SC_2, \u003cem\u003ePseudomonas\u003c/em\u003e TS_SC_12 and \u003cem\u003ePseudomonas\u003c/em\u003e TS_SC_13 were least prone to antibiosis as each of them was susceptible to only three actinobacterial isolates.\u003c/p\u003e\n \u003cp\u003eOut of the 15 TMA species belonging to Actinomycetota, eight could inhibit the growth of fellow actinobacterial isolates (Fig. \u003cspan class=\"InternalRef\"\u003e7\u003c/span\u003e). Overall, \u003cem\u003eMicrobacterium\u003c/em\u003e TRD_SC_10 and \u003cem\u003ePseudarthrobacter\u003c/em\u003e TRD_SC_9 inhibited the most number of actinobacteria (six each). In contrast, \u003cem\u003eArthrobacter\u003c/em\u003e TRD_SC_6, and \u003cem\u003eMicrobacterium\u003c/em\u003e TW_SC_2, inhibited only one TMA actinobacteria each. From the opposite point of view, 11 TMA actinobacteria were prone to inhibition by isolates belonging to the same phylum (Fig. \u003cspan class=\"InternalRef\"\u003e7\u003c/span\u003e). \u003cem\u003eArthrobacter\u003c/em\u003e TRD_SC_8 (inhibited by six TMA actinobacteria) was the most vulnerable TMA isolate belonging to Actinomycetota. In contrast, \u003cem\u003eMicrobacterium\u003c/em\u003e TW_SC_3, \u003cem\u003ePseudarthrobacter\u003c/em\u003e TRD_SC_9, \u003cem\u003ePaenarthrobacter\u003c/em\u003e TRD_SC_7 and \u003cem\u003eMicrobacterium\u003c/em\u003e TRD_SC_10, were not prone to inhibition by any of the actinobacterial isolates.\u003c/p\u003e\n \u003cp\u003eIn the context of antibiosis, it was further noteworthy that none of the non-actinobacterial species isolated from the TMA could inhibit the growth of any other TMA isolate, whatever may be its phylum affiliation (Fig. \u003cspan class=\"InternalRef\"\u003e7\u003c/span\u003e).\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec21\" class=\"Section2\"\u003e\n \u003ch2\u003eKey attributes of the whole genomes sequenced for selected TMA isolates\u003c/h2\u003e\n \u003cp\u003eComplete whole genome sequence was determined for selected isolates from across the three environments explored within the Tso Moriri lake-desert ecosystem (Table \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e). While one genome was sequenced from each of the 15 genera across which the 27 species-level isolates were classified, it was also ensured that at least one strain was analyzed from each cluster that had formed on the basis of growth or population-level survival at different incubation temperatures (Fig. \u003cspan class=\"InternalRef\"\u003e6\u003c/span\u003ea).\u003c/p\u003e\n \u003cp\u003e\u003cem\u003eDe novo\u003c/em\u003e hybrid assembly (statistics given in Table S29) of the short and long DNA sequence reads generated using two different technologies yielded complete or near-complete genome sequences for all the TMA isolates analyzed (Table \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e). For 10 out of the 15 TMA isolates selected, their complete genomes were encompassed in single circular chromosomes: \u003cem\u003eArthrobacter\u003c/em\u003e TRD_SC_6, \u003cem\u003eCryobacterium\u003c/em\u003e TS_SC_7, \u003cem\u003eFlavobacterium\u003c/em\u003e TS_SC_5, \u003cem\u003eMicrobacterium\u003c/em\u003e TW_SC_2, \u003cem\u003eMycetocola\u003c/em\u003e TRD_SC_2, \u003cem\u003ePaenarthrobacter\u003c/em\u003e TRD_SC_7, \u003cem\u003ePseudarthrobacter\u003c/em\u003e TRD_SC_9, \u003cem\u003ePseudomonas\u003c/em\u003e TS_SC_3, \u003cem\u003eSanguibacter\u003c/em\u003e TS_SC_8 and \u003cem\u003eTrichococcus\u003c/em\u003e TS_SC_9. For two TMA isolates their complete genomes were incorporated in single circular chromosomes plus multiple circular plasmids: \u003cem\u003eAcinetobacter\u003c/em\u003e TW_SC_4 had two such plasmids of 1.8 mb and 81.9 kb length, while \u003cem\u003ePsychrobacter\u003c/em\u003e TS_SC_6 had three of them, 6.4 kb, 7.1 kb and 23.1 kb in length. For \u003cem\u003eAeromonas\u003c/em\u003e TS_SC_11, \u003cem\u003eAncylobacter\u003c/em\u003e TW_SC_1 and \u003cem\u003eStreptomyces\u003c/em\u003e TRD_SC_5, their near-complete genomes encompassed one or two uncircularized chromosomes plus one or two circular plasmids varying between 43.2 kb and 89 kb in length.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec22\" class=\"Section2\"\u003e\n \u003ch2\u003eComposition of the microbiomes to which the TMA isolates belonged\u003c/h2\u003e\n \u003cp\u003eWhen the 50\u0026nbsp;million, randomly-selected, Q20 reads available for each of the three metagenomic datasets were assembled (statistics given in Table S30), 196552, 95112, and 269987 contigs (all \u0026gt;\u0026thinsp;1000 nucleotide long) were obtained for the rock-dust, lake-water and lake-sediment samples respectively. Within the contigs obtained from the rock-dust metagenome, 289601 CDSs were annotated, of which maximum proportions, i.e. 48%, 21% and 4%, were ascribed to Actinomycetota, Pseudomonadota and Bacillota, respectively (Table S31). Within the contigs obtained from the lake-water metagenome, 180125 CDSs were annotated; most of these, i.e. 29%, 22% and 15%, were ascribed to Pseudomonadota, Actinomycetota and Bacteroidota, respectively (Table S31). Within the contigs obtained from the lake-sediment metagenome, 451033 CDSs were annotated, of which maximum proportions, i.e. 46%, 8% and 8%, were ascribed to Pseudomonadota, Bacteroidota and Bacillota, respectively (Table S31).\u003c/p\u003e\n \u003cp\u003eThe 50\u0026nbsp;million, randomly-selected, Q20 reads available for each of the three metagenomic datasets were sorted and classified taxonomically by searching against the rrnDB database. Consequently, 31009, 36123, and 34328 reads - from the rock-dust, lake-water and lake-sediment datasets respectively - were found to be representative of 16S rRNA genes from different bacterial and archaeal sources.\u003c/p\u003e\n \u003cp\u003eSpecies belonging to the phylum Actinomycetota and Pseudomonadota accounted for the maximum proportions, i.e. 54% and 18%, of all 16S rRNA-related reads present in the rock-dust dataset, respectively (Table S32). Cyanobacteriota and Actinomycetota accounted for the maximum proportions, i.e. 26% and 24%, of all the 16S rRNA-encoding reads that were there in the lake-water dataset, respectively (Table S33). Pseudomonadota and Campylobacterota encompassed the maximum proportions, i.e. 43% and 12%, of all 16S rRNA-related reads present in the lake-sediment dataset, respectively (Table S34).\u003c/p\u003e\n \u003cp\u003eFurthermore, in the rock-dust metagenome, 8529 16S rRNA-related reads were classifiable at the genus level. Of the 213 genera identified in this way, the Actinomycetota-members \u003cem\u003eRubrobacter\u003c/em\u003e and \u003cem\u003eBlastococcus\u003c/em\u003e, followed by the archaeon \u003cem\u003eNitrososphaera\u003c/em\u003e, accounted for the maximum of number of reads (1028, 803 and 625 respectively). In the lake-water metagenome, 10610 16S rRNA-related reads were classifiable at the genus level. Of the 271 genera identified in this way, the cyanobacteria \u003cem\u003eCyanobium\u003c/em\u003e and \u003cem\u003eSynechococcus\u003c/em\u003e, and the Bacteroidota-member \u003cem\u003eAlgoriphagus\u003c/em\u003e, encompassed the maximum of number of reads (3295, 690 and 928 respectively). In the lake-sediment metagenome, 14880 16S rRNA-related reads were classifiable at the genus level. Of the total 398 genera identified, the Campylobacterota member \u003cem\u003eSulfuricurvum\u003c/em\u003e, and the Betaproteobacteria \u003cem\u003eMethylotenera\u003c/em\u003e and \u003cem\u003eHydrogenophaga\u003c/em\u003e, encompassed the maximum of number of reads (3060, 1785 and 1042 respectively).\u003c/p\u003e\n \u003cp\u003eSmall but definite proportions of metagenomic reads obtained from Tso Moriri\u0026rsquo;s water and sediment samples, as well as the sample of weathered rock dust from the lake-side hill, mapped onto each of the 15 whole genomes that were analyzed for selected TMA isolates. Collectively, the 15 genomes accounted for 0.1%, 0.3% and 0.2% of metagenomic reads from the lake-water, lake-sediment and rock-dust samples respectively (Table S35). Furthermore, considerable proportion of reads matching 16S rRNA gene homologs from diverse species of \u003cem\u003eAcinetobacter\u003c/em\u003e, \u003cem\u003eAeromonas\u003c/em\u003e, \u003cem\u003eArthrobacter\u003c/em\u003e, \u003cem\u003eCryobacterium\u003c/em\u003e, \u003cem\u003eFlavobacterium\u003c/em\u003e, \u003cem\u003ePseudarthrobacter\u003c/em\u003e, \u003cem\u003ePseudomonas\u003c/em\u003e, \u003cem\u003ePsychrobacter\u003c/em\u003e and \u003cem\u003eStreptomyces\u003c/em\u003e were detected in the metagenomes analyzed from across the three TMA habitats (Tables S32-S34).\u003c/p\u003e\n \u003cdiv id=\"Sec23\" class=\"Section3\"\u003e\n \u003ch2\u003eGenes concerned with adaptation to extremes of temperature\u003c/h2\u003e\n \u003cp\u003eInspection of the eggNOG-annotated CDS catalogs of the 15 TMA isolates (Tables S36-S50) revealed diverse genes concerned with low and high temperature adaptations (Figs. S5; Table S51). Besides a number of cold-adaptation-related genes concerned with cold-shock response and RNA remodeling, membrane fluidity regulation, and genome maintenance at low temperatures, several such genes were also detected which conferred tandem adaptation to low as well as high temperatures: these encoded chaperones and other protein quality control components, governed DNA repair and oxidative stress responses, coded for global transcriptional regulators, or governed biosynthesis and transport of compatible solutes and other osmoprotectants. \u003cem\u003eStreptomyces\u003c/em\u003e TRD_SC_5 had the highest number of cold-adaptation-related genes, whereas \u003cem\u003eAcinetobacter\u003c/em\u003e TW_SC_4 and \u003cem\u003ePsychrobacter\u003c/em\u003e TS_SC_6 had the lowest. So far as the genes involved in dual adaptation to low as well as high temperatures were concerned, \u003cem\u003eStreptomyces\u003c/em\u003e TRD_SC_5 and \u003cem\u003ePsychrobacter\u003c/em\u003e TS_SC_6 had the highest and lowest numbers of them, respectively.\u003c/p\u003e\n \u003cp\u003eThe assembled metagenomes of the rock-dust, lake-water and lake-sediment samples also encoded diverse genes concerned with low temperature adaptation, and low as well as high temperature adaptation (Tables S52). For most of the gene categories mentioned above in relation to extreme temperature adaptation, maximum numbers of homologs were detected in the lake-sediment, followed by the rock-dust, metagenome. The trend of distribution of these genes essentially mirrored the trend exhibited by the number of CDSs annotated within the contigs obtained from the three assembled metagenomes.\u003c/p\u003e\n \u003c/div\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec24\" class=\"Section2\"\u003e\n \u003ch2\u003eGenes encoding carbohydrate-active enzymes (CAZymes)\u003c/h2\u003e\n \u003cp\u003eWhen the CDS catalogs of the selected TMA isolates (Tables S36-S50) were searched against the dbCAN3 and CAZy databases, a wide array of carbohydrate-active enzymes concerned with polysaccharide binding, modification, and degradation were revealed (Table S53). The highest number of putative CAZymes was encoded by the genome of \u003cem\u003eStreptomyces\u003c/em\u003e TRD_SC_5, whereas the least number of them was encoded by \u003cem\u003ePsychrobacter\u003c/em\u003e TS_SC_6. Overall, the detected CAZymes belonged to six broad functional categories - glycoside hydrolase (GH), glycosyl transferase (GT), carbohydrate esterase, polysaccharide lyase, enzymes with auxiliary activities, and carbohydrate-binding module (Fig. S6). Of the different classes, again, GH and GT accounted for the most number of genes in all the genomes analyzed (collectively, 67\u0026ndash;86% of all CAZyme genes possessed by the different strains were GHs and GTs). That said, most of the TMA isolates that were rich in GHs were relatively poorer in GT diversity, and vice versa. Only \u003cem\u003eStreptomyces\u003c/em\u003e TRD_SC_5 had equal number of GH and GT genes, while \u003cem\u003eMicrobacterium\u003c/em\u003e TW_SC_2, \u003cem\u003eArthrobacter\u003c/em\u003e TRD_SC_6 and \u003cem\u003eMycetocola\u003c/em\u003e TRD_SC_2 also had comparable numbers of GH and GT genes.\u003c/p\u003e\n \u003cp\u003eBesides the genomes of the psychrophilic copiotrophs isolated, the assembled metagenomes of the lake-sediment, rock-dust, and lake-water samples also encoded a wide array of carbohydrate-active enzymes (Table S54). For all the CAZyme gene categories mentioned above, maximum numbers of homologs were detected in the lake-sediment, followed by the rock-dust, metagenome, thereby mirroring the trend of CDS annotation from the assembled metagenomes.\u003c/p\u003e\n \u003cdiv id=\"Sec25\" class=\"Section3\"\u003e\n \u003ch2\u003eGenes encoding antibiotics and other secondary metabolites\u003c/h2\u003e\n \u003cp\u003eBased on gene content analysis, five classes of antibiotics [(i) penicillins and cephalosporins, (ii) prodigiosins, (iii) staurosporines, (iv) tetracyclines and (v) vancomycins], and nine classes of secondary metabolites [(i) NI-siderophores, i.e. siderophores independent of non-ribosomal peptide synthase (NRPS), (ii) NAPAAs, i.e. non-alpha poly-amino acids, (iii) Class-II lanthipeptides, (iv) Class-III lanthipeptides, (v) NRPs, or non-ribosomal peptides, (vi) NRP-metallophores, (vii) terpenes, (vii) Type-I polyketides, and (ix) Type-III polyketides, all of which can potentially act as antimicrobial agents] were putatively synthesized by the 15 TMA isolates for which whole genomes were sequenced (Table\u0026nbsp;2).\u003c/p\u003e\n \u003cp\u003eEvery actinobacterium that was capable of inhibiting the growth of one or more target organism(s), and for which the complete whole genome sequence was analyzed, possessed genes for synthesizing one or more classes of antibiotics (Table S55) and/or secondary metabolites (Table S56). Among all the 15 isolates for which genomes were analyzed, highest diversity of antibiotics and secondarily metabolites was putatively synthesized by the actinobacterium \u003cem\u003eStreptomyces\u003c/em\u003e TRD_SC_5 (this organism possessed key genes for the synthesis of Class-II and Class-III lanthipeptides, non-ribosomal peptides, siderophores independent of non-ribosomal peptide synthase, staurosporines, terpenes, Type-I polyketides and vancomycins). \u003cem\u003eCryobacterium\u003c/em\u003e TS_SC_7 (prodigiosins, NAPAAs, terpenes, and Type-III polyketides), \u003cem\u003ePaenarthrobacter\u003c/em\u003e TRD_SC_7 (NAPAAs, NI-siderophores, NRPs, and penicillins and cephalosporins), and \u003cem\u003ePseudarthrobacter\u003c/em\u003e TRD_SC_9 (NAPAAs, NI-siderophores, staurosporines, and Type-III polyketides) possessed key genes for the synthesis of the next highest diversities of antibiotics and secondarily metabolites. Incidentally, these four actinobacteria had inhibited the growth of 14, 9, 15 and 20 out of the total 33 allochthonous and autochthonous microorganisms against which antibiosis was tested (Fig. \u003cspan class=\"InternalRef\"\u003e7\u003c/span\u003e). \u003cem\u003eMicrobacterium\u003c/em\u003e TW_SC_2 did not have genes required for the synthesis of any known antibiotic or secondary metabolite other than Type-III polyketides, but it had inhibited the growth of 14 target organisms (Fig. \u003cspan class=\"InternalRef\"\u003e7\u003c/span\u003e), presumably by hitherto unknown antimicrobial agents. On the flip side of the above data, no known antibiotic or secondarily metabolite was apparently synthesized by \u003cem\u003eAcinetobacter\u003c/em\u003e sp. TW_SC_4, \u003cem\u003ePseudomonas\u003c/em\u003e sp. TS_SC_3 and \u003cem\u003ePsychrobacter\u003c/em\u003e sp. TS_SC_6, which also had shown no antibiosis against any of the 33 targets tested. \u003cem\u003eAeromonas\u003c/em\u003e TS_SC_11, \u003cem\u003eAncylobacter\u003c/em\u003e TW_SC_1, \u003cem\u003eFlavobacterium\u003c/em\u003e TS_SC_5, and \u003cem\u003eTrichococcus\u003c/em\u003e TS_SC_9 too had not inhibited any target organism; corroboratively, they did not also synthesize any known antibiotic (the four species, however, putatively synthesized a few classes of secondary metabolites; Table 2).\u003c/p\u003e\n \u003c/div\u003e\n \u003cdiv id=\"Sec26\" class=\"Section3\"\u003e\n \u003ch2\u003eGenes conferring potential resistance against antibiotics\u003c/h2\u003e\n \u003cp\u003eOf the 15 isolates for which genomes were analyzed, only five were found to possess genes central to resistance against carbapenems, cephalosporins, diaminopyrimidines, elfamycins, fluoroquinolones, penicillins and other beta-lactams, phenicols, rifamycins, teicoplanins, and/or vancomycins (Tables 2 and S57). The remaining 10 genomes encompassed no such gene which is central to resistance against any known antibiotic. While potential resistance against rifamycins was encoded by the highest number of genomes (three), putative resistance against highest number of antibiotic groups (four) was possessed by \u003cem\u003eAeromonas\u003c/em\u003e TS_SC_11. Nevertheless, growth of this strain was inhibited by several actinobacterial isolates (Fig. \u003cspan class=\"InternalRef\"\u003e7\u003c/span\u003e), apparently by antimicrobial agents other than penicillins or other beta-lactams, carbapenems, cephalosporins and elfamycins, against which TS_SC_11 possessed putative resistance according to the genome content data (Table\u0026nbsp;2).\u003c/p\u003e\n \u003cp\u003eOverall, genome-based predictions of the TMA isolates\u0026rsquo; potentials for synthesizing and resisting different antibiotics and secondary metabolites were not sufficient to specify the molecular mechanisms underlying their antibiosis phenotypes. However, genomic potentials for secondary metabolites biosynthesis and resistance neither contradicted each other nor were inconsistent with the antibiosis phenotypes recorded (Table\u0026nbsp;2). For every instance where an isolate had inhibited the growth of another TMA species, the antagonist was found to have the genomic potential for synthesizing at least one such class of antibiotic or secondary metabolite against which the inhibited species had no resistance gene.\u003c/p\u003e\n \u003c/div\u003e\n\u003c/div\u003e"},{"header":"Discussion","content":"\u003cdiv id=\"Sec28\" class=\"Section2\"\u003e \u003ch2\u003eHabitat as the key driver of adaptation to extremes of temperature\u003c/h2\u003e \u003cp\u003eAll the genera under which the TMA isolates were classified, excepting \u003cem\u003eAncylobacter\u003c/em\u003e and \u003cem\u003ePaenarthrobacter\u003c/em\u003e, have multiple members retrieved previously from other natural or artificial cold/frigid environments (Table S58). Remarkably, each of them also has at least one member retrieved previously from a mesic/hot habitat (Table S59). For all the genera isolated, except \u003cem\u003ePaenarthrobacter\u003c/em\u003e, member strains from other parts of the world, especially mesic/hot environments, are known to grow at temperatures higher than the maxima recorded for the TMA counterparts (Fig. S7; Table S60). It, therefore, appears that native environment rather than phylogenetic background (taxonomy) primarily determines a microbial strain\u0026rsquo;s adaptation to the extremes of temperature.\u003c/p\u003e \u003cp\u003eConsistent with the above facts, a number of TMA isolates, despite their affiliation to the same genus, exhibited differential growth/survival at a given incubation temperature (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003ea). Conversely, similar growth/survival phenotypes across temperatures unified the majority of strains isolated from a given TMA habitat (environment) irrespective of their generic affiliation (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eb).\u003c/p\u003e \u003cp\u003e \u003col\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003eAll the bacteria isolated from the weathered rock dust of the lake-adjoining hill, with the exception of only \u003cem\u003eArthrobacter\u003c/em\u003e TRD_SC_4 and TRD_SC_6, grew or survived at the population-level through the entire temperature range tested (i.e. -10\u0026deg;C to 42\u0026deg;C).\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003eThe bacteria from Tso Moriri\u0026rsquo;s water also accomplished population-level growth or survival through the entire range of incubation temperatures tested.\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003eMost strains isolated from Tso Moriri\u0026rsquo;s sediment had population-level growth/survival across narrower ranges of temperature, compared to the isolates from rock-dust and lake-water.\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003c/ol\u003e \u003c/p\u003e \u003cp\u003eTso Moriri\u0026rsquo;s surficial water freezes in the winter and thaws in the summer. Likewise, the rocky slopes of the adjoining hills get covered by snow in the winter and the same melts through the summer; furthermore, the rocks are exposed to extreme heating and cooling on a diurnal basis. Thus, wider temperature windows for population-level growth/survival in bacteria from these two environments could be reflective of a general linkage between the variability of the thermal condition prevailing in the habitat and the inhabitants\u0026rsquo; adaptability to extremes of temperature. By the flip side of the same reasoning, it was also quite natural for the bacteria from Tso Moriri\u0026rsquo;s subsurface to have narrower temperature windows for population-level growth/survival, compared to their surficial neighbors.\u003c/p\u003e \u003cp\u003eIn relation to environmental control of extreme-temperature adaptation, it was further noteworthy that all the member strains that were known, prior to this study, to grow at the lowest temperature limits for \u003cem\u003eAcinetobacter\u003c/em\u003e, \u003cem\u003eAeromonas\u003c/em\u003e, \u003cem\u003eMicrobacterium\u003c/em\u003e, \u003cem\u003ePaenarthrobcter\u003c/em\u003e, \u003cem\u003ePseudarthrobacter\u003c/em\u003e and \u003cem\u003eTrichococcus\u003c/em\u003e (Fig. S7; Table S60) had been isolated from mesic/hot environments, even though isolates from cold/frigid environments were also there for all these genera except \u003cem\u003ePaenarthrobacter\u003c/em\u003e (Table S58). On the flip side, all the member strains that were known, prior to this study, to grow at the highest temperature limits for \u003cem\u003eAeromonas\u003c/em\u003e and \u003cem\u003eMycetocola\u003c/em\u003e had been isolated from natural or artificial cold/frigid environments (Fig. S7; Table S60) even though strains isolated from mesic/hot environments were also there for these two genera (Table S59). These environment:phenotype mismatches could be the outcomes of drastic microbial transportation across distinct ecosystems. At the same time these anomalies could be indicative of the existence of some hitherto unappreciated metabolic convergence between the high and low temperature adaptations of bacteria. The latter counterintuitive idea was reinforced by the facts that most of the \u0026minus;\u0026thinsp;10\u0026deg;C-growing TMA isolates could also grow at 28\u0026deg;C and 37\u0026deg;C (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003ea), while most of the TMA isolates having\u0026thinsp;\u0026gt;\u0026thinsp;0.1% CFU left in the culture after one day at 42\u0026deg;C (Figs.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003ef and \u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003ei) could also grow at -10\u0026deg;C (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eb).\u003c/p\u003e \u003cp\u003eGene-contents of neither the genomes of the psychrophiles isolated, nor the metagenomes of the habitats explored, had any direct bearing on the phenotype-based trends recorded for habitat:adaptation correspondence. Future studies of transcriptomics, proteomics, and metabolomics - carried out for pure cultures as well as environmental samples, resolved along gradients and fluxes of temperature over time and space - are required to conclude how environmental experiences of microbial populations dictate their adaptation to extremes of temperature (Mondal et al. 2022).\u003c/p\u003e \u003cp\u003e \u003cb\u003eAutochthonous populations of heat-susceptible psychrophiles as biogeothermometers of\u003c/b\u003e \u003cb\u003ein situ\u003c/b\u003e \u003cb\u003ewarming\u003c/b\u003e\u003c/p\u003e \u003cp\u003eTMA isolates, irrespective of their phylogenetic affinities, exhibited different levels of thermal susceptibility. For instance, almost all cells of \u003cem\u003eCryobacterium\u003c/em\u003e TS_SC_7 lost their divisibility, as well as metabolic activity, after a four-day exposure to 28\u0026deg;C (Figs.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eb and \u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eg), so their retrieval from the lake\u0026rsquo;s sediment could be indicative of the fact that the temperature of this under-water substratum either does not reach 28\u0026deg;C, or even if it does so, it does not remain at that level for several days. On the flip side, elimination of such bacteria from environments where they had been prevalent previously would indicate protracted \u003cem\u003ein situ\u003c/em\u003e warming up to and above 28\u0026deg;C. Population dynamics of TS_SC_7-like bacteria can, therefore, be construed and contrived as biogeothermometers of cold/frigid environments that are difficult to monitor physically over time.\u003c/p\u003e \u003cp\u003eAlmost all the cells of \u003cem\u003eFlavobacterium\u003c/em\u003e TS_SC_5 and TS_SC_2 lost their metabolic activity after a full day at 42\u0026deg;C (Figs.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003ef and \u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003ei), while their divisibility was already abolished after a single day\u0026rsquo;s exposure to 37\u0026deg;C (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003ec), or even after a two-day exposure to 28\u0026deg;C (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003ea). Likewise, almost all the cells of \u003cem\u003eArthrobacter\u003c/em\u003e TRD_SC_4 lost their activity after a full day at 42\u0026deg;C (Figs.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003ef and \u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003ei), while their divisibility was already abolished after a two-day exposure to 37\u0026deg;C (Figs.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003ed and \u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eh), and only a quarter of the cell population retained its divisibility after four days at 28\u0026deg;C (Figs.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eb and \u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eg). Thus, a progressive decline in the prevalence of a natural population of \u003cem\u003eFlavobacterium\u003c/em\u003e species similar to TS_SC_5 and TS_SC_2, over time and/or space, can be correlated with a temporal and/or spatial escalation of temperature to 28\u0026deg;C and above. The same for TRD_SC_4-like populations can, in turn, corroborate spatiotemporal increases of \u003cem\u003ein situ\u003c/em\u003e temperature to 37\u0026deg;C and above. In the same vein, population dynamics of bacteria like \u003cem\u003eArthrobacter\u003c/em\u003e TRD_SC_6 and \u003cem\u003eTrichococcus\u003c/em\u003e TS_SC_9, within cold/frigid ecosystems, can be used as real-time bellwethers signaling shifts of \u003cem\u003ein situ\u003c/em\u003e temperature towards 42\u0026deg;C.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec29\" class=\"Section2\"\u003e \u003ch2\u003eThermal susceptibility of autochthonous organotrophs can usher negative feedback control/reversal of warming in cold/frigid ecosystems\u003c/h2\u003e \u003cp\u003eWithin cold/frigid ecosystems microbial activity is thought to remain minimal as long as the temperature remains below or around 0\u0026deg;C (Forbes et al. 2001; Aronson et al. 2011; Geoffroy et al. 2023). However, organic matter degradation, and consequent emission of greenhouse gases, starts once \u003cem\u003ein situ\u003c/em\u003e temperature rises and cryoturbation takes place due to season and/or climate change (Knoblauch et al. 2018; Schuur et al. 2015, 2022). Increased greenhouse effect brought about by enhanced microbial activities stimulates the biotic processes all the more, and that in turn ushers further thawing via more greenhouse gas emission. In this way, a positive feedback loop of cyclical warming (Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003e) is said to be operationalized in the ecosystem (Walter et al. 2006; Graham et al. 2012; Schneider von Deimling et al. 2012).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eIf the trends of growth proficiency and regression recorded for the TMA isolates between 4\u0026ndash;15\u0026deg;C and 28\u0026ndash;42\u0026deg;C respectively (Figs.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e and \u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e) hold good for all natural communities of psychrophiles, then the following phenomena can be hypothesized as global attributes of all cold/frigid ecosystems (Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003col\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003eMicrobes-mediated positive feedback cycles can abet environmental warming via greenhouse gas generation, as stated in the existing theory, only within and around the 4\u0026ndash;28\u0026deg;C temperature range.\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003eAbove 28\u0026deg;C, gradual cessation of microbial growth and activity cuts back the delivery of catabolism end-products such as simple fatty acids, CO\u003csub\u003e2\u003c/sub\u003e, N\u003csub\u003e2\u003c/sub\u003eO, etc. to the environment (Bhattacharya et al. 2021; Sui et al. 2024). In the context of the Tso Moriri area, organotrophy and allied catabolic processes such as ammonia oxidation, which can produce CO\u003csub\u003e2\u003c/sub\u003e and N\u003csub\u003e2\u003c/sub\u003eO respectively, assume significance since metagenome analyses showed chemoorganoheterotrophs such as \u003cem\u003eAlgoriphagus\u003c/em\u003e, \u003cem\u003eBlastococcus\u003c/em\u003e, \u003cem\u003eHydrogenophaga\u003c/em\u003e and \u003cem\u003eRubrobacter\u003c/em\u003e, and N\u003csub\u003e2\u003c/sub\u003eO-producers such as \u003cem\u003eNitrosomonas\u003c/em\u003e, \u003cem\u003eNitrososphaera\u003c/em\u003e, \u003cem\u003eNitrosospira\u003c/em\u003e, \u003cem\u003ePseudomonas\u003c/em\u003e and \u003cem\u003eStreptomyces\u003c/em\u003e, to be considerably prevalent across the three TMA habitats explored (Tables S32-S34).\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003eShort-supply of simple fatty acids and CO\u003csub\u003e2\u003c/sub\u003e, in turn, limits methanogenesis (if the concerned archaea are there \u003cem\u003ein situ\u003c/em\u003e), the terminal process of carbon cycling that utilizes simple fatty acids or CO\u003csub\u003e2\u003c/sub\u003e as its substrates (Hedderich and Whitman 2013). Notably, trace footprints of methanogens such as \u003cem\u003eMethanothrix\u003c/em\u003e, \u003cem\u003eMethanosarcina\u003c/em\u003e and \u003cem\u003eMethanoregula\u003c/em\u003e were identified in the metagenomes analyzed for Tso Moriri\u0026rsquo;s water and sediment, but not in the metagenome of the rock-dust analyzed from the nearby hill (Tables S32-S34); such archaea were also reported as active from a number of neighboring glacial territories (Aschenbach et al. 2013).\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003eCurbs on the biogenic emission of greenhouse gases (CO\u003csub\u003e2\u003c/sub\u003e, N\u003csub\u003e2\u003c/sub\u003eO, and CH\u003csub\u003e4\u003c/sub\u003e, as applicable for individual ecosystems) trigger negative feedback control of warming, and usher over time, course reversal in the vicious cycle of \u0026ldquo;warming - microbial growth - and further warming\u0026rdquo;.\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003eNegative feedback controls of greenhouse gas production at micro-environment levels add up in the biome scale to mitigate overall environmental warming.\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003eSeasonal (winter) cooling eventually lowers the \u003cem\u003ein situ\u003c/em\u003e temperature back to the zero and sub-zero degree Celsius levels (Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003e).\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003c/ol\u003e \u003c/p\u003e \u003cp\u003eOn the part of a cold/frigid ecosystem, however, termination of autochthonous microbial growth and reduction of metabolic activity towards the restoration of homeostatic balance is fraught with the danger of indigenous psychrophiles being removed from the habitat, and their ecological niches taken over by more-thermotolerant intruders from discrete geographical territories (Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003e). Such incursions can transform microbiome architectures if not abated at temperatures below the critical level (Kosaka et al. 2019) where most cells of most of the native species lose their growth as well as activity. Potential microbiome transformations can, in the long run, alter ecosystem structures and functions drastically, and in doing so tilt the equilibrium irreversibly in favour of the positive feedback mechanism that promotes warming (Walter et al. 2006; Graham et al. 2012; Schneider von Deimling et al. 2012).\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003e\u003c/h3\u003e\n\u003cdiv class=\"Heading\"\u003e\u003cb\u003eHeat-enduring actinobacterial psychrophiles as defenders of cold/frigid microbiomes\u003c/b\u003e\u003c/div\u003e \u003cp\u003eIn the scenario of an imminent warming-mediated microbiome alteration, antibiosis potentials of native heat-enduring actinobacterial psychrophiles [such as those characterized here from the TMA (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003e)] can deter foreign microbes from colonizing the habitat and apprehending the ecological niches of the indigenous psychrophiles. In the long run, this kind of niche safeguard enhances the chances of population rejuvenation for all autochthonous cold-adapted species upon reversal of thawing (Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eActinomycetota members are known for their extraordinary abilities to produce secondary metabolites including antibiotics, which confer them adaptive fitness against diverse environmental challenges, resulting in the high ecological amplitude of the phylum (Claver\u0026iacute;as et al. 2015; Barka et al. 2016; Lewin et al. 2016). Moreover, they are often abundant in cryospheric microbiomes (Voytsekhovskaya et al. 2018; Shen et al. 2021), and are known to protect indigenous microbial communities from external invaders in other critical ecosystems (Salazar-Hamm et al. 2025). So far as the 15 TMA actinobacteria were concerned, a majority of them were substantially thermotolerant (Figs.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e and \u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003ea) and capable of inhibiting Gram positive and/or Gram negative bacteria from discrete higher-temperature ecosystems at 28\u0026deg;C (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003e), which was lower than the critical high-temperature apparent for most of the TMA isolates (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e). It is, therefore, not unlikely that in the face of plausible microbial incursions from warmer territories, native actinobacterial psychrophiles would thwart foreign occupation and protect indigenous microorganisms from being vanquished from the habitat. Furthermore, in this context it was noteworthy that among the 11 TMA actinobacteria exhibiting antibiosis potentials against foreign bacteria, six had been isolated from the rock-dust sample, while three and two were from the lake\u0026rsquo;s sediment and water respectively (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003e). This showed that microbiome protection by antibiosis-enabled heat-enduring actinobacterial psychrophiles could be widespread across physicochemically distinct cold/frigid environments.\u003c/p\u003e \u003cdiv id=\"Sec31\" class=\"Section2\"\u003e \u003ch2\u003eIntricate niche partitioning as central to microbiome functioning within cold/frigid ecosystems\u003c/h2\u003e \u003cp\u003eSeveral TMA actinobacteria were found to inhibit the growth of not only foreign microorganisms, but also that of a number of isolates from their own habitats as well as adjacent TMA environments (inhibited targets included fellow actinobacterial isolates also; see Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003e). These findings brought to the fore extensive niche partitioning (Dussud et al. 2018; Shaiber et al. 2020) as a plausible stratagem of microbiome functioning within the cold/frigid ecosystem. However, neither the phenotypic data, nor the genome-based predictions, available at present in relation to antibiosis (and resistance against the same) could elucidate how ecological niche separation pans out in the micro-environment scale, within the Tso Moriri habitats. In other words, it was not possible from the present data to decipher how niche separation within the TMA habitats effectively precluded \u003cem\u003ein situ\u003c/em\u003e encounters between the actinobacterial antagonists and the species they inhibited \u003cem\u003ein vitro\u003c/em\u003e. This limitation was attributed to the fact that bulk sampling had already smudged all imprints of niche differentiation, and deciphering of the same would require a new generation of very-finely-resolved spatial investigations of community ecology.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec32\" class=\"Section2\"\u003e \u003ch2\u003eSumming up\u003c/h2\u003e \u003cp\u003eIn this study, phylogenetically diverse, cold-adapted copiotrophic bacteria were retrieved from a Trans-Himalayan lake-desert ecosystem, and tested for their chemoorganoheterotrophic growth/survival at temperatures between \u0026minus;\u0026thinsp;10\u0026deg;C and 42\u0026deg;C. Catabolizing copious complex organic substances, all the isolates could grow substantively at 4\u0026deg;C, while two thirds of them could achieve low growth at -10\u0026deg;C. At virtually zero organic carbon concentration (upon incubation in minimal salts solution having no organic matter added to it), one third of the TMA isolates achieved very low but definite levels of growth (increase in CFU density) at 4\u0026deg;C; a few of them also exhibited such growth on minimal salts at -10\u0026deg;C. How these \u003cem\u003ein vitro\u003c/em\u003e phenotypes translate into community-level actions \u003cem\u003ein situ\u003c/em\u003e is central to our knowledge on the baseline of organic matter degradation (carbon remineralization) within natural and/or anthropogenically-influenced cryospheric ecosystems, whether experiencing high or low organic matter delivery to the environment.\u003c/p\u003e \u003cp\u003eAlthough the specific strains isolated were found to have relatively sparse populations \u003cem\u003ein situ\u003c/em\u003e, their ubiquitous presence across the TMA habitats was detected metagenomically, alongside the prevalence of other members of the higher-level taxa to which the isolated species belonged. At the same time, considerable diversity and relative abundance of other chemoorganoheterotrophic bacteria were detected in conjunction with wide arrays of CAZyme-encoding genes, upon analysing the metagenomes of the lake-water, lake-sediment, and rock-dust samples. These facts collectively pointed towards a significant role of organic carbon degradation in the overall biogeochemistry of the Tso Moriri ecosystem.\u003c/p\u003e \u003cp\u003eBased on the dwindling of autochthonous organotrophic growth and activity with rising temperature, a general model was conceived for the potential negative feedback control of warming within cryospheric ecosystems. Presence of N\u003csub\u003e2\u003c/sub\u003eO-producing ammonia-oxidizers and CH\u003csub\u003e4\u003c/sub\u003e-producing archaea, besides the preponderance of chemoorganoheterotrophs, across the TMA habitats highlighted that the homeostatic model envisaged could be operational in the Tso Moriri ecosystem itself. Prevalence of Actinomycetota across the ecosystem corroborated that antibiosis could indeed be an effective defence against microbiome takeover by foreign mesophilic organisms in the face of environmental warming.\u003c/p\u003e \u003c/div\u003e"},{"header":"Declarations","content":"\u003ch2\u003eCompeting interest\u003c/h2\u003e\n\u003cp\u003eThe authors declare no competing interest.\u003c/p\u003e\n\u003ch2\u003eFunding\u003c/h2\u003e\n\u003cp\u003eThe study was financed by Bose Institute through Intramural Research Grants. S.C. and M.M. received fellowships from Department of Biotechnology (DBT), Government of India (GoI). S.D. and J.S. obtained their fellowships from Council of Scientific and Industrial Research, GoI. J. G. and S. S. got fellowships from University Grants Commission, GoI. NM received fellowship from Bose Institute. Bioinformatic analyses were carried out using computational resources available under an EMR project funded by DBT, GoI (BT/PR40174/BTIS/137/45/2022).\u003c/p\u003e\n\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\n\u003cp\u003eW.G. conceived the study, designed the experiments, interpreted the results, and wrote the paper. S.C. anchored the program, planned and performed the experiments, analyzed and curated the data, and composed the paper. S.D. performed the experiments, analyzed the data, and composed the paper. J.G., S.S., M.M., J.S., and N.M. performed the experiments. All authors read and approved the manuscript.\u003c/p\u003e\n\u003ch2\u003eAcknowledgement\u003c/h2\u003e\n\u003cp\u003eExtensive on-field assistance provided by Sri Asgar Ali of Choglamsar, Ladakh, India is gratefully acknowledged. We thank Dr. Soumya Chatterjee, Biodegradation Technology Division, Defence Research Laboratory, India, for valuable discussions on psychrophilic biodegradation.\u003c/p\u003e\n\u003ch2\u003eData Availability\u003c/h2\u003e\n\u003cp\u003eGenBank accession numbers for the 16S rRNA genes of the new isolates are as follows:PV789631 (Arthrobacter sp. TRD_SC_1), PV789699 (Arthrobacter sp. TRD_SC_3), PV789726 (Arthrobacter sp. TRD_SC_4); PV789747 (Arthrobacter sp. TRD_SC_6); PV789751 (Arthrobacter sp. TRD_SC_8); PV793422 (Cryobacterium sp. TS_SC_7); PV790046 (Microbacterium sp. TRD_SC_10); PV793448 (Microbacterium sp. TW_SC_2); PV793500 (Microbacterium sp. TW_SC_3); PV789771 (Mycetocola sp. TRD_SC_2); PV682789 (Paenarthrobacter sp. TRD_SC_7); PV790021 (Pseudarthrobacter sp. TRD_SC_9); PV793432 (Pseudarthrobacter sp. TS_SC_4); PV793425 (Sanguibacter sp. TS_SC_8); PV789776 (Streptomyces sp. TRD_SC_5); PV793427 (Trichococcus sp. TS_SC_9); PV793436 (Flavobacterium sp. TS_SC_2); PV793440 (Flavobacterium sp. TS_SC_5); PV790452 (Ancylobacter sp. TW_SC_1); PV793444 (Acinetobacter sp. TW_SC_4); PV793419 (Aeromonas sp. TS_SC_11); PV793374 (Pseudomonas sp. TS_SC_1); PV793375 (Pseudomonas sp. TS_SC_3); PV793377 (Pseudomonas sp. TS_SC_10); PV793378 (Pseudomonas sp. TS_SC_12); PV793393 (Pseudomonas sp. TS_SC_13); PV793413 (Psychrobacter sp. TS_SC_6).All genome and metagenome sequence data have been deposited to the National Center for Biotechnology Information (NCBI), USA under the BioProject accession number PRJNA1335599. Sequence read datasets for the genomes have been deposited to the NCBI Sequence Read Archive (SRA), while the assembled whole genome sequences have been deposited to the GenBank, under the BioSamples accession numbers SAMN52016225 (Acinetobacter sp. TW_SC_4), SAMN52392035 (Aeromonas sp. TS_SC_11), SAMN52016223 (Ancylobacter sp. TW_SC_1), SAMN52016228 (Arthrobacter sp. TRD_SC_6), SAMN52016222 (Cryobacterium sp. TS_SC_7), SAMN52392036 (Flavobacterium sp. TS_SC_5), SAMN52016224 (Microbacterium sp. TW_SC_2), SAMN52016227 (Mycetocola sp. TRD_SC_2), SAMN52392037 (Paen\u0026not;arthrobacter sp. TRD_SC_7), SAMN52016226 (Pseudarthrobacter sp. TRD_SC_9), SAMN52016221 (Pseudomonas sp. TS_SC_3), SAMN52392038 (Psychrobacter sp. TS_SC_6), SAMN52016220 (Sanguibacter sp. TS_SC_8), SAMN52392039 (Streptomyces sp. TRD_SC_5), and SAMN52392040 (Trichococcus sp. TS_SC_9).Sequence read datasets for the metagenomes have been deposited to the NCBI SRA with the following BioSample accession numbers: SAMN53833703 (weathered rock dust), SAMN53833704 (lake-water), and SAMN53833705 (lake-sediment). 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Science 312:1612\u0026ndash;1613. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1126/science.1128908\u003c/span\u003e\u003cspan address=\"10.1126/science.1128908\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"},{"header":"Table 1","content":"\u003cp\u003eTable 1 is available in the Supplementary Files section.\u003c/p\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":"archives-of-microbiology","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"aomi","sideBox":"Learn more about [Archives of Microbiology](https://www.springer.com/journal/203)","snPcode":"203","submissionUrl":"https://submission.nature.com/new-submission/203/3","title":"Archives of Microbiology","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"Trans-Himalayan deserts and lakes, psychrophilic and cryo-adapted bacteria, climate warming, antibiosis, microbiome protection, organic matter degradation","lastPublishedDoi":"10.21203/rs.3.rs-8996027/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8996027/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eA Trans-Himalayan lake-desert ecosystem was explored for the low-to-high temperature adaptations of copiotrophic psychrophiles having potentials for substantive carbon remineralization under natural and/or anthropogenically-influenced conditions of high organic matter delivery to the cryospheric environment. Overall 27 bacterial species were isolated from the brackish-water and sediment-surface of Tso Moriri (a massive lake on the Changthang plateau that remains frozen for approximately one third of the year), and the fine talus covering a lake-side rocky mountain. In Luria broth (LB), all isolates grew at 4\u0026deg;C and 15\u0026deg;C; at -10\u0026deg;C, 13 could grow while others remained only metabolically-active. Catabolizing different complex-organic-compounds, all isolates achieved considerable growth at 4\u0026deg;C; 20 accomplished low growth at -10\u0026deg;C. LB-based growth dwindled with rising temperature: 23, 11, and none of the isolates grew at 28\u0026deg;C, 37\u0026deg;C, and 42\u0026deg;C respectively. The isolates\u0026rsquo; genomes, and the habitats\u0026rsquo; metagenomes, encompassed diverse genes for extreme-temperature adaptation and carbohydrate catabolism. Within high-altitude cryospheres, cessation of organotrophy, in general, would cut-back simple fatty acids, CO\u003csub\u003e2\u003c/sub\u003e and N\u003csub\u003e2\u003c/sub\u003eO production (short-supply of CO\u003csub\u003e2\u003c/sub\u003e and acetate would in turn cutback methanogenesis, if the concerned archaea are present \u003cem\u003ein situ\u003c/em\u003e). Such negative feedback controls of greenhouse gas production at the micro-habitat level can add-up in the biome-scale to mitigate broader environmental warming. However, homeostasis via abolition of growth for indigenous psychrophiles is fraught with the danger of ecosystem takeover by thermally-better-adapted foreign microbes. At 28\u0026deg;C, majority of the actinobacterial isolates inhibited bacteria from discrete warmer habitats; they can, therefore, be viewed as potential defenders of the cold/frigid ecosystem.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e","manuscriptTitle":"Cryo-adapted bacterial copiotrophs from a Trans-Himalayan lake-desert ecosystem as biogeothermometers of warming and mitigators of microbiome perturbation","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-03-06 00:24:04","doi":"10.21203/rs.3.rs-8996027/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2026-03-18T01:43:05+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-03-10T20:46:20+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"48163464588444251235980670981758209874","date":"2026-03-07T23:02:17+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"30354731065784521912786075577454269957","date":"2026-03-05T05:08:55+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-03-04T10:14:23+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"314001125631128334497331385010631976804","date":"2026-03-03T09:04:53+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"39847239453364009523140151710633145484","date":"2026-03-03T07:44:33+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2026-03-02T16:19:09+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2026-03-02T16:13:35+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2026-03-02T15:02:42+00:00","index":"","fulltext":""},{"type":"submitted","content":"Archives of Microbiology","date":"2026-02-28T14:18:33+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"archives-of-microbiology","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"aomi","sideBox":"Learn more about [Archives of Microbiology](https://www.springer.com/journal/203)","snPcode":"203","submissionUrl":"https://submission.nature.com/new-submission/203/3","title":"Archives of Microbiology","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"0b8de7fb-99b0-4be1-8307-0b6a590d65a2","owner":[],"postedDate":"March 6th, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2026-05-01T17:54:54+00:00","versionOfRecord":{"articleIdentity":"rs-8996027","link":"https://doi.org/10.1007/s00203-026-04904-8","journal":{"identity":"archives-of-microbiology","isVorOnly":false,"title":"Archives of Microbiology"},"publishedOn":"2026-04-27 00:00:00","publishedOnDateReadable":"April 27th, 2026"},"versionCreatedAt":"2026-03-06 00:24:04","video":"","vorDoi":"10.1007/s00203-026-04904-8","vorDoiUrl":"https://doi.org/10.1007/s00203-026-04904-8","workflowStages":[]},"version":"v1","identity":"rs-8996027","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8996027","identity":"rs-8996027","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}