{"paper_id":"45a73d3e-8b9e-4b0c-9ff4-69d3104e8082","body_text":"Microbial community structure and niche differentiation in healthy and infected samples of Kappaphycus alvarezii in the commercial farming sites of Tamil Nadu coastal, India | 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 Article Microbial community structure and niche differentiation in healthy and infected samples of Kappaphycus alvarezii in the commercial farming sites of Tamil Nadu coastal, India Nagarajan Dhanya, Balakrishnan Prakriti, Arup Ghosh, Lakkakula Satish This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7621295/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Seaweed cultivation, particularly of Kappaphycus alvarezii , plays a significant role in the coastal economy of Tamil Nadu, India. K. alvarezii is frequently affected by ice-ice disease caused by microbial imbalances. This disease leads to tissue bleaching, reduced biomass, and lower carrageenan yield, making the study of seaweed-associated bacterial communities crucial for sustainable cultivation. In this study, healthy and diseased samples of K. alvarezii were collected from five commercial farming sites. High-throughput Illumina sequencing of the V3–V4 region of the 16S rRNA gene was performed to analyze microbial diversity. Bioinformatics tools such as OTU clustering, alpha diversity indices, heat tree visualization, and functional prediction were applied to characterize differences between healthy and diseased samples across locations. The results revealed Proteobacteria as the dominant phylum, with Gammaproteobacteria being prevalent in all samples. Diseased seaweed samples exhibited greater microbial diversity, with genera including Pseudoalteromonas and Vibrio frequently associated with disease, while Cobetia was more abundant in healthy samples. Variations in microbial community structure were closely linked to environmental factors, including temperature, salinity, dissolved oxygen, and nutrient levels. Functional prediction showed that diseased samples had elevated pathways related to fermentation, nitrate reduction, and nitrogen respiration. Whereas healthy samples were enriched in aerobic chemoheterotrophy and hydrocarbon degradation. The study demonstrates that site-specific environmental conditions significantly influence microbial dynamics and disease progression in seaweed farms. Identifying potential pathogens and beneficial bacteria provides a foundation for developing targeted probiotics and disease management strategies that can support sustainable cultivation and improve seaweed health. Biological sciences/Biotechnology Biological sciences/Microbiology Metagenomics disease K. alvarezii microbial diversity K. alvarezii healthy K. alvarezii Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Figure 8 Introduction Seaweeds, aka macroalgae, the cultivation of multiple seaweed species constitutes more than half of the cumulative global marine aquaculture production. This indicates the enormous potential of seaweed aquaculture as a sustainable and lucrative industry. The utilization of seaweed cultivation offers many benefits, ranging from mitigating environmental issues to promoting socio-economic development. Hence, it is imperative to recognize and harness the potential of seaweed aquaculture to ensure a sustainable future (FAO 2020; Wang et al., 2022). The demand for wet seaweeds has skyrocketed in recent years, with over 35 million tons produced worldwide in 2022 (FAO 2022). This versatile seaweed has proven to be a valuable resource, providing various benefits for industries such as food, medicine, and fuel. As a result, the global revenue generated by wet macroalgae production has surged to nearly $ 1.9 billion (FAO 2022). Investing in cultivating and harvesting this valuable resource is wise for businesses looking to capitalize on a growing market (Montúfar et al., 2023). Cultivating seaweed for ecosystem services provides a sustainable solution to ocean acidification and contributes towards carbon fixation, making it a constructive approach to protecting and preserving our oceans (Campbell et al., 2019; Montúfar et al., 2023). K. alvarezii (Doty) is a tropical seaweed that produces a commercially valuable and significant product, carrageenan (Mamat et al., 2024; Munisamy et al., 2024; Mantri et al., 2025). Ice-ice disease is a major concern in the world of K. alvarezii farming. This disease leads to a substantial decline in K. alvarezii biomass yield and reduces the amount of carrageenan relative to the significant percentage of healthy seaweed (Rantetondok et al., 2017; Tahiluddin et al., 2022). The disease is characterized by “bleaching or whitening” indications on the seaweed thallus tissue and can be caused by various opportunistic pathogenic bacteria (Ward et al., 2022; Achmad et al., 2017; Riyaz et al., 2021). It is non-infectious and can arise from adverse environmental factors, including extreme temperature, pH, and salinity, as well as opportunistic bacterial pathogens such as Vibrio sp. (Arasamuthu & Patterson Edward., 2018). The microbial communities associated with seaweeds are highly diverse, yet we have not fully understood their complexities or how they differ from those found in the open sea (Xu et al., 2022). It is vital to examine the epiphytic bacterial microbiota that inhabits seaweed since they are instrumental in influencing algal morphological development, metabolism, growth, and protection against fouling organisms (Nahor et al., 2024). By further exploring this field of study, we can uncover valuable knowledge about the captivating realm of seaweed and its associated microbial communities. The 16S rRNA gene sequencing is the latest approach used for studies of bacterial phylogeny and taxonomy. The major reason for the use of the 16S rRNA gene sequencing is that steady function has not changed over time because it occurs in almost every bacterium, indicating that random sequence change is more accurate and the 16S rRNA gene (1500 bp) is large enough for information purposes (Janda & Abbott., 2007; Clarridge., 2004; Kembel et al., 2012). Experiments show that Ulva species, a type of marine green macroalga, have complex microbiomes essential for their growth. These species associate with specific bacterial communities, and studies using 16S rRNA gene sequencing have explored bacteria-driven growth patterns and the potential of using microbes in algal aquaculture (Wichard., 2023). Red seaweeds have been extensively studied with specific emphasis on Gracilariopsis and Gracilaria species. An integrative analysis of the red macroalga Gracilariopsis lemaneiformis through high-throughput sequencing showed that environmental factors, notably nitrogen and phosphorus levels, profoundly influence the epiphytic bacterial community structure of varying geographic locations. The study illustrated location-specific differences in bacterial community structures and their association with nutrient levels (Pei et al., 2021). Sophisticated multi-omics tools have been used in brown seaweeds. Fucus vesiculosus studies have integrated amplicon sequencing with metabolomics and imaging methods, and it was identified that Alphaproteobacteria are the dominant epibiotic bacteria, whereas fungi of the class Eurotiomycetes were imaged for the first time on an algal surface. This study showed the combination of 16S rRNA sequencing with metabolomics to clarify the surface microbiome-metabolome interactions (Parrot et al., 2019). Phylogenetic and functional characterization of microbiomes of Sargassum seaweed waste determined leading bacterial genera, such as Muricauda, Aminobacterium, Mesorhizobium, Marinobacter, Reichenbachiella, Hyphomonas, Simiduia, Aquamicrobium, Oceanicola, and Alcanivorax , which reflect bacterial diversity within brown seaweed detritus (Mohapatra., 2023). Studies employing 16S rRNA gene sequencing have described bacterial communities related to several species of seaweeds in various marine ecosystems. These researchers have identified that bacteria associated with seaweed are dominated by phyla including Proteobacteria, Bacteroidetes, Firmicutes, Cyanobacteria, Planctomycetes, Actinobacteria, and Verrucomicrobia (Selvarajan et al., 2019; Nahor et al., 2024). In this study, metagenomics was used to analyze the microbial diversity on the surface of diseased and healthy K. alvarezii among five seaweed cultivation sites. This study effectively extends our understanding of the diversity of seaweed thallus-bound microorganisms on both diseased and healthy K. alvarezii . This work unlocks new opportunities for the exploitation and use of seaweed resources in the future, as well as the exploration of valuable microbiomes from active seaweed cultivation sites. Materials and methods Sample collection The source of the biological material was from commercial-scale seaweed farming sites in Tamil Nadu, India. In the present study, K. alvarezii (red seaweed) well-matured, healthy, and infected samples were collected from 5 different commercial farming sites i.e., Mangadu (9.327695\"N 78°59'35.8\"E), Soliyakkudi (9°42'29.6\"N 78°59'35.8\"E), Jegathapattinam (9°57'55.2\"N 79°11'19.5\"E), Nambuthalai (9°43'34.8\"N 79°00'15.1\"E) and Odavimadam (9°59'21.6\"N 79°12'31.3\"E) in the Palk Bay region, Tamil Nadu, India (Fig. 1) Table 1. The samples were immediately placed in a cool pack and transported from the farming sites to the laboratory, and were used to extract the total genomic DNA. All the sites are situated between the villages and in close proximity to shrimp culture ponds. As a result, they are frequently disturbed by heavy anthropogenic pressure from the seaweed farmers and fisher communities that enter the water. The Mangadu site, on the other hand, is located far from the fishers' village and has limited public access, making it an ideal site for seaweed farming with low anthropogenic pressure. Seawater samples were collected from all sites to investigate the relationship between seawater physicochemical parameters and both diseased and healthy seaweed. Table. 1. Collection sites name and their code word Site name Sample Code Mangadu Diseased KaMD Healthy KaMH Soliyakudi Diseased KaSD Healthy KaSH Nambuthalai Diseased KaND Healthy KaNH Jegadhapattinam Diseased KaJD Healthy KaJH Odavimadam Diseased KaOD Healthy KaOH Collection sites, sample types, and their corresponding code names. “Ka” represents Kappaphycus alvarezii , followed by the initials of the site name and sample condition (D – Diseased, H – Healthy). Genomic DNA extraction Genomic DNA was extracted from disease-infected and healthy samples of K. alvarezii . DNA was extracted using the modified method by ONEOMICS PRIVATE LIMITED, Tiruchirappalli, Tamil Nadu (In-house Protocol). Briefly, for around 50 mg of seaweed samples (samples were chopped into pieces in a tube), 200 μL of high salt lysis buffer was added, and then crushed using a sterile micro pestle. To this, 20 μL of proteinase K (20 mg/mL) was added, and the tube was vortexed vigorously and then incubated at 55°C for 10 min. To this, an equal volume of phenol chloroform, iso amyl alcohol was added, followed by centrifugation at 14,000 RPM for 10 min. The supernatant was pipetted out and then two volumes of ice-cold Isopropanol were added and subjected to DNA precipitation at -20°C. Finally, the samples were centrifuged at 14,000 RPM for 20 min, and then the pellet was washed with 70% ethanol. Following isolation, the DNA concentrations were verified using a Qubit 4 Fluorometer (Thermo Fisher Scientific, USA), and DNA quality was determined by Nanodrop (Thermo Scientific, San Diego, CA, USA) and Agarose gel electrophoresis. Next-Generation Amplicon Sequencing During the first PCR, fragments of the V3–V4 regions of the 16S rRNA gene were amplified using the primers 341 F – CCTAYGGGRBGCASCAG and 806 R –GGACTACNNGGGTATCTAAT. The PCR reactions were carried out with Phusion High-Fidelity PCR Master Mix (New England Biolabs), 2 µM of forward (341 F) and reverse (806 R) primers, and about 10 ng template DNA. Thermal cycling consisted of initial denaturation at 98℃ for 1 min, followed by 30 cycles of denaturation at 98℃ for 10 s, annealing at 50℃ for 30 s, and elongation at 72℃ for 30 s, and final extension at 72℃ for 5 min. The reaction was performed on a PTC-0200 G thermocycler (Bio-Rad Laboratories, Inc., USA). Besides the PCR clean-up done to purify the PCR product in this study, it is important to state that a negative control was included in the PCR amplification to ensure there was no contamination of reagents. For PCR product quantification and qualification, the PCR product was mixed with an equal volume of 1X loading buffer (containing SYBR green), and electrophoresis was performed on a 2% agarose gel for detection. Agilent 5400 Fragment Analyzer (Agilent 2100, USA) was used to verify the size of the PCR product. Samples with sharp peaks between 450-480 bp were chosen for further experiments. We purified the amplicon product to remove free primers and primer dimers using AMPure XP beads. Next, we attached dual indices and Illumina sequencing adapters using the Nextera XT DNA Library Preparation Kit (Illumina, USA). The amplicon was then purified again with AMPure XP beads. The libraries generated were quantified using a Qubit 4 Fluorometer (Thermo Fisher Scientific, USA). The amplicons from each reaction mixture were combined in equimolar concentrations and sequencing was carried out using the Illumina NovaSeq 6000 system (Illumina, USA) in a 2 × 250 bp paired-end run following the manufacturer’s guidelines. Bioinformatics analyses The bioinformatics analyses were carried out by ONEOMICS PRIVATE LIMITED (Tiruchirappalli, India). Initially, the raw reads underwent demultiplexing and quality filtering using the deblur plugin in QIIME2 software (https://qiime2.org/). To provide an overview, reads with a length of around 250 base pairs were shortened at positions with an average quality score below 20 over a 50-base-pair sliding window. Reads shorter than 50 base pairs and those containing ambiguous characters were omitted from the subsequent analysis. The operational taxonomic units (OTUs) were generated by clustering using the same deblur plugin. Non-repetitive sequences were extracted to streamline the analysis, and single non-repetitive sequences were removed. The non-repetitive sequences, excluding single sequences, were then clustered based on the OTUs using a similarity threshold of 97%, and chimeras were removed during clustering to obtain the representative OTU sequences. The OTUs obtained were matched against the SILVA database (version 138) using a confidence threshold of 70% using the feature-classifier plugin in QIIME2. Diversity Analyses Variation analysis of alpha diversity indices between groups The statistical analysis of the data from QIIME2 was performed using various R packages. The QIIME2 artifacts were imported and analyzed using the R package qiime2R v0.99.6. Alpha diversity is designed to measure the diversity within a single community, i.e., the bacterial community of K. alvarezii samples. Boxplots were formed to analyze the difference in Alpha diversity indices between groups using the R package Vegan v2.6.6. The alpha diversity index and the Shannon index were used to determine the diversity in each sample. The abundance plots, used to determine the variation in the presence of different taxonomies between the groups, were generated using the R package Phyloseq v1.48.0. The rarefaction curve to determine the adequacy of sequencing depth was also generated using the Vegan R package. The PCA analysis was done using the QIIME2 software and visualized with the qiime2R package. The R package Metacoder R v0.3.8 was used to parse the taxonomy data and visualize the heat tree. Heat Tree Visualization The Heat Tree Analysis utilizes the hierarchical structure of taxonomic classifications to quantitatively (using the median abundance) and statistically depict taxonomic differences between microbial communities. Heat tree visualizations were built from the metacoder package in R v0.3.8 to represent hierarchical taxonomic hierarchies in K. alvarezii samples from five sampling sites (Mangadu, Soliyakudi, Nambuthalai, Jagadhapattinam, and Odavimadam). Functional prediction The FAPROTAX (Functional Annotation of Prokaryotic Taxa) tool was used to predict potential metabolic functions of important pathways within bacterial communities identified from healthy and diseased samples collected from five seaweed cultivation sites (Table 1). Data availability The metagenomic sequencing data from diseased Kappaphycus alvarezii have been submitted to the NCBI (https://www.ncbi.nlm.nih.gov/sra/PRJNA1248676) under the BioProject ID PRJNA1248676. The associated Sequence Read Archive (SRA) accession numbers are SRR33066784, SRR33066785, SRR33066786, SRR33066787, SRR33066788, SRR33066789, SRR33066790, SRR33066791, SRR33066792, and SRR33066793. RESULTS Physicochemical characterization of seawater of diseased sample collection sites The average of all physicochemical analyses at each site is presented in (Table 2 ). Based on the physicochemical parameter results provided for the five cultivation sites, notable differences occurred across the five locations. Air temperatures ranged from 28.6°C to 30.6°C, with Mangadu having the highest temperature (30.6°C). Salinity was relatively consistent across most sites (33.6–36.6 PSU), except for Mangadu (36.6 PSU). The pH ranged from 8.16 to 8.41 in all sites. Nutrient levels varied, with Jegathapattinam and Odavimadam generally showing higher concentrations of nitrites, nitrates, and phosphates. Dissolved oxygen levels varied significantly, from a low of 4.8 mg/L in Odavimadam to a high of 8.4 mg/L in Mangadu. Table 2 Mean values and standard deviation of physicochemical properties of seawater samples collected from infected seaweed sample collection sites and their dominant microbial phylum level. Cultivation site Soliyakudi Jegathapattinam Nambuthalai Odavimadam Mangadu Physical parameter Air tem. (ᵒC) 28.6 ± 0.5 28.3 ± 0.5 29 ± 0.0 28.3 ± 0.5 30.6 ± 0.5 Water tem. (ᵒC) 27 ± 0.0 26 ± 0.0 26 ± 0.0 25 ± 0.0 28 ± 0.0 Salinity (psu) 33.6 ± 0.5 34 ± 0.0 34 ± 0.8 33.6 ± 1.03 36.6 ± 0.5 pH 8.29 ± 0.03 8.35 ± 0.02 8.41 ± 0.01 8.3 ± 0.01 8.16 ± 0.06 Water current (M/min) 4 ± 0.0 6.5 ± 0.0 3.4 ± 0.0 6 ± 0.0 5 ± 0.0 Chemical parameter No 2 − N (mg/l) 0.003 ± 0.0005 0.009 ± 0.0018 0.004 ± 0.001 0.014 ± 0.002 0.004 ± 0.004 No 3 − N (mg/l) 0.027 ± 0.0005 0.087 ± 0.0019 0.09 ± 0.02 0.044 ± 0.005 0.035 ± 0.003 Po 4 − N (mg/l) 0.038 ± 0.002 0.091 ± 0.001 0.052 ± 0.02 0.044 ± 0.004 0.048 ± 0.047 NH 3 (mg/l) 0.004 ± 0.001 0.007 ± 0.0008 0.005 ± 0.02 0.008 ± 0.0008 0.004 ± 0.001 SiO 2 (mg/l) 0.045 ± 0.0004 0.055 ± 0.005 0.053 ± 0.005 0.105 ± 0.001 0.074 ± 0.001 D.O (mg/l) 5.6 ± 0.1 5.8 ± 0.02 6.3 ± 0.4 4.8 ± 0.7 8.4 ± 0.3 Microbial Diversity (Phylum level) Diseased dominant bacteria Gammaproteobacteria and Alphaproteobacteria Gammaproteobacteria and Clostridia Gammaproteobacteriaand Clostridia Gammaproteobacteria, Clostridia and Alphaproteobacteria Gammaproteobacteria and Alphaproteobacteria Healthy dominant bacteria Gammaproteobacteria Gammaproteobacteria and Clostridia Gammaproteobacteriaand Clostridia Gammaproteobacteriaand Clostridia Gammaproteobacteria Physicochemical properties of seawater from infected seaweed cultivation sites. Values are presented as mean ± standard deviation. The table also shows the dominant microbial phylum present in diseased and healthy samples at each site. Microbial Diversity and Community Composition The bacterial community structure associated with K. alvarezii shows significant variation between diseased and healthy samples from different sites (Fig. 2 ). This study examines bacteria's phylum and genus-level diversity, revealing distinct patterns of abundance and composition that offer insights into the complex relationship between K. alvarezii and its associated bacterial communities. At all the site, Proteobacteria phylum was found to be dominant, among them, Gammaproteobacteria was consistently prevalent. At the phylum level, the diseased samples from Mangadu and Soliyakudi showed a high abundance of both Gammaproteobacteria and Alphaproteobacteria. In contrast, their healthy counterparts showed a low abundance primarily of Gammaproteobacteria. The Nambuthalai and Jegadhapattinam sites displayed a high abundance of Gammaproteobacteria and Clostridia in diseased samples, with low to moderate abundance in healthy samples. Odavimadam uniquely showed an equal abundance in both diseased and healthy samples, though with slightly different compositions. At the genus level (Fig. 3 ), in the Mangadu diseased sample, high abundance of Others, Cobatia, and Halomonas was observed, while its healthy sample contained low abundance of only Cobatia and Halomonas . In Soliyakudidiseased sample was dominated by Pseudoalteromonas and Cobatia , while Nambuthalai's diseased sample showed high abundance of unclassified genera and Pseudoalteromonas . In Jegadhapattinam, the diseased sample was characterized by Pseudoalteromonas and Psychrobacter, and in Odavimadam, the diseased sample contained high levels of unclassified genera and Oceanisphaera . Diversity analysis While conducting ecological analysis and studying ecological communities, it is essential to measure the community diversity. In this study, a total of 2,071,476 high-quality reads were obtained for the bacterial species, among which 80,977 were unique reads with an average length of 250 bp. It was clear that the diseased samples had more unique raw reads as compared to the healthy samples. Overall, among the five sample sites, the raw reads were comparatively fewer (1,145,158) in diseased K. alvarzii samples than in the healthy ones (1,219,912). The bacterial communities from the five closely located sites were compared between the healthy and diseased samples. From Jegathapattinam, Mangadu, Nambuthalai and Odavimadam sites, the healthy (243,634; 268,598; 260,420; 226,420) samples had more raw reads as compared to diseased samples (234,020; 200,594; 260,142; 203,752). Although in the Soliyakkudi site, the healthy sample (220,546) had fewer raw reads as compared to the diseased ones (246,650). A total of 2,020 bacterial OTUs were identified across all 5 seaweed samples, i.e., 331 from Jegathapattinam, 345 from Mangadu, 504 from Nambuthalai, 581 from Odavimadam, and 259 from Soliyakkudi (Supplementary Table 1). This clearly shows that the bacterial diversity is higher in samples from Odavimadam and lower in samples from Soliyakkudi. This difference was statistically studied with the help of rarefaction curves, Shannon index, relative abundance plots, etc. The rarefaction curve is generally used to deduce that the volume of the sequenced reads is reasonable by assessing the abundance of the bacterial OTUs, which are close to saturation. The rarefaction curves revealed that the alpha diversity of the bacterial lineages for the seaweed samples from Nambuthalai was higher among the diseased samples (OTU > 450; Raw Reads > 45,000) and Odavimadam was higher among the healthy samples (OTU > 400; Raw Reads > 50,000) compared to the other 4 sites' samples. (Fig. 4 ). The Shannon index depicts the statistical results of the community diversity in each sample. The higher Shannon index value suggests that the diversity of organisms is rich. The 10 different samples (diseased and healthy each) from 5 different sites illustrate that, between the diseased and healthy samples, the former has more microbial diversity compared to the latter. Overall, the Odavimadam site has a higher Shannon index (diseased > 3.7 and healthy > 3), which portrays that there are more diversified microbes in this site compared to the other 4 sites (Fig. 5 ). Relative Abundance across the sampling sites A total of 22 bacterial phyla were detected in the K. alvarezii healthy seaweed samples, and about 22 bacterial phyla were detected in the K. alvarezii diseased seaweed samples. Proteobacteria (41.16%) and Actinobacteria (38.33%) were the most abundant phyla in each of the seaweed niches. The major phyla in healthy and diseased K. alvarezii seaweed samples were similar, with Proteobacteria (83.4%), Firmicutes (10.2%), and Actinobacteria (4.5%) being the most abundant phyla. The frequency of bacterial phyla in the healthy and diseased K. alvarezii seaweed samples varied greatly. The abundance of Proteobacteria was higher in healthy K. alvarezii seaweed samples (87.4%) compared to diseased K. alvarezii seaweed samples (75.8%), which was the most abundant phylum in all the samples, followed by Firmicutes and Actinobacteria. Of the 167 bacterial classes, Gammaproteobateria, Alphaproteobacteria, Clostridiales, Veillionellaceae had a higher abundance, in diseased K. alvarezii seaweed samples whereas, Gammaproteobateria, Alphaproteobacteria and Clostridiales had a higher abundance, in healthy K. alvarezii seaweed samples (Fig. 6 ). Among the multiple genus identified, Pseudohalomonas , Halomonas , Cobetia , Vibrio and Agathobacter were the most abundant in the diseased K. alvarezii samples however, Pseudohalomonas , Halomonas , Cobetia , and Agathobacter were the most abundant in the healthy K. alvarezii samples (Fig. 6 ). In the Mangadu site’s diseased sample, the major bacterial phyla were Proteobacteria (76%), Firmicutes (13%), Actinobacteria (4%) and Acidobacteria (3%). The major bacterial class were, Gammaproteobacteria (57%), Alphaproteobacteria (18%) and Clostridiales (10%) and the major bacterial genus were Cobetia (37%), Halomonas (11%), Azospirillum (2%), Sphingomonas (4%), Agathobacter (4%), Vibrio (2%), and Pseudohalomonas (2%). In the healthy ones the major bacterial phyla were Proteobacteria (95%), Firmicutes (3%), the major bacterial class were Gammaproteobacteria (95%), and Clostridiales (3%), the major bacterial genera were Cobetia (85%), Halomonas (9%) and Agatobacter (1%). In the Soliyakkudi site, the major bacterial phyla discovered in the diseased sample were Proteobacteria (81%), and Firmicutes (11%), the major bacterial class were Gammaproteobacteria (77%), Clostridiales (8%), Alphaproteobacteria (3%), Veillonellaceae (3%) Gammaproteobacteria (2%), and finally, the major bacterial genus were Paeudohalomonas (38%), Cobetia (23%), Vibrio (10%), Agathobacter (4) and Halomonas (3%), whereas in the healthy ones the major bacterial phyla were Proteobacteria (95%), Firmicutes (4%), the major bacterial class were Gammaproteobacteria (95%), Clostridiales (3%), the major bacterial genus were Cobetia (66%), Paeudohalomonas (23%), Halomonas (6%), Agathobacter (2%). In the Nambuthalai site, the major bacterial phyla identified in the diseased sample were Proteobacteria (68%), Firmicutes (16%), Actinobacteria (9%). The major bacterial class were Gammaproteobacteria (64%), Clostridiales (12%), Actibnobacteria (3%). Majorly bacterial genus such as Cobetia (29%), Pseudohalomonas (22%), Vibrio (6%), Agathobacter (4%), Halomonas (4%), Alphaproteobateria (3%) were observed. In the healthy ones the major bacterial phyla were Proteobacteria (95%), Firmicutes (7%). Gammaproteobacteria (92%), Clostridiales (4%) and Bacilli (2%) were observed as the major bacterial class. The major bacterial genus were Cobetia (66%), Pseudohalomonas (11%), Halomonas (14%), Agathobacter (2%), Lactobacillus (1%). In the Jegathapattinam site, the major bacterial phyla spotted in the diseased sample were Proteobacteria (75%), Firmicutes (19%), Actinobacteria (3%) the major bacterial class were Gammaproteobacteria (74%), Clostridiales (15%), Veillonellaceae (3), Actinobacteria (3%) and Alphaproteobacteria (1%) and the major bacterial genus were Pseudoalteriomonas (28%), Psychobacter (17%), Cobetia (10%), Vibrio (7%), Agathobacter (6%), Shewanella (4%) and Dialister (2%) whereas in the healthy ones the major bacterial phyla were Proteobacteria (78%), Firmicutes (21%), the major bacterial class were Gammaproteobacteria (78%), Clostridiales (17%), and the major bacterial genus were Pseudoalteriomonas (43%), Cobetia (20%), Halomonas (11%), Agathobacer (4%), Clostridiun sensu strico 1 (3%), Pseudostreptococcaceae (3%), Shewanella (2%), and Vibrio (1%). In the Odavimadam site, the major bacterial phyla discovered in the diseased sample were Proteobacteria (79%), Firmicutes (13%), and Actinobacteria (4%), the major bacterial class were Gammaproteobacteria (67%), Alphaproteobacteria (9%), Clostridiales (9%), Deltaproteobacteria (5%), Veillonellaceae (3%), Actinobacteria (2%) and the major bacterial genus were Oceanisephaera (21%), Pseudoateriomonas (12%), Cobetia (11%), Vibrio (10%), Psycrobacter (7%), Agathobacter (4%), Shewanella (2%), Dialister (2%) and Halomonas (1%) whereas in the healthy ones the major bacterial phyla were Proteobacteria (77%), Firmicutes (14%) and Actinobacteria (5%) the major bacterial class were Gammaproteobacteria (75%), Clostridiales (9%), Alphaproteobacteria (9%), Veillonellaceae (3%), Actinobacteria (2%) and the major bacterial genus were Cobetia (50%), Halomonas (13%), Pseudoalteriomonas (10%), Agathobacter (4%), Alphaproteobacteria (2%) (Supplementary Fig. 1). Heat Tree Analysis of Bacterial Communities Heat tree analysis emphasized the taxonomic structure and abundance structures of bacterial communities linked to K. alvarezii across five sampling sites, each with peculiar compositions. Mangadu site shows a complex structure and balanced abundance among diverse bacterial lineages. Moderate complexity with clustered taxa and specific abundance patterns was observed at Soliyakudi site. Whereas, a simplified structure with lower diversity and dominance of certain groups was noted at the Nambuthalai site. The Jagadhapattinam site was observed with intermediate complexity and was characterized by unique patterns. The Odavimadam site revealed the highest complexity and diversity, with even abundance distribution across taxonomic levels. The heat trees effectively illustrated the quantitative differences in bacterial abundance, emphasizing the taxonomic hierarchy within each site microbiome (Fig. 7 a-e). Functional prediction of bacteria associated with diseased and healthy The functional prediction analysis revealed that the functional groups of the bacterial microbiota associated with seaweed were similar (Fig. 8 ). Although the microbial composition associated with both healthy and diseased varied across collection sites. In Mangadu, the diseased state (KaMD) is characterized by high expression of fermentation, nitrate reduction, nitrate respiration, ureolysis, methylotrophy, nitrogen fixation, and methanol oxidation pathways. In contrast, the healthy state (KaMDH) shows upregulation of chemoheterotrophy, aerobic chemoheterotrophy, and hydrocarbon degradation. Soliyakudi shows a diseased condition (KaSD) with enrichment in fermentation, chloroplast activity, sulfate respiration, and nitrogen respiration. The diseased state in Soliyakudi (KaSH) exhibits a high expression of aerobic chemoheterotrophy, chemoheterotrophy, and hydrocarbon degradation. In Namuthalai, the diseased state (KaND) demonstrates elevated activity in fermentation, nitrification, aerobic ammonia oxidation, nitrate reduction, nitrogen respiration, and nitrate respiration. This contrasts with the healthy state (KaNH), which shows strong activity in aerobic chemotrophy, chemoheterotrophy, and hydrocarbon degradation. Jagathapattinam exhibits a diseased condition (KaJD) with upregulation of aerobic chemotrophy, chemoheterotrophy, and hydrocarbon degradation pathways. The healthy state (KaJH) shows strong expression of nitrification, nitrogen respiration, fermentation, nitrate reduction, and sulfate respiration pathways. Finally, Odavimadam reveals a diseased state (KaOD) with enrichment in nitrate reduction, nitrogen respiration, sulfate respiration, respiration of sulfate compounds, and fermentation. The healthy state (KaOH) exhibits high activity in nitrification, hydrocarbon degradation, aerobic chemoheterotrophy, and chemoheterotrophy (Fig. 8 ). Discussion The present study evaluated the microbial community structure of healthy and diseased K. alvarezii collected from five different seaweed cultivation sites in southern coast of Tamil Nadu, India (Fig. 1 ). In the year of 2013, severe impact was observed on cultivation of K. alvarezii according to the reported findings of Mantri et al. (2022), that have an influential role on seaweed cultivators to abandon their practices because of the seaweed diseases which remained unidentified (Mantri et al., 2024). In the beginning, the change of climate was considered to be the cause, however, later it was discovered that an opportunistic pathogen was found to be responsible for disease (Largo et al., 2017). This metagenomic analysis of healthy and diseased K. alvarezii were were collected from five different active cultivation sites helped to decipher the root cause behind seaweed disease in India. Physicochemical parameters at five cultivation sites significantly influence microbial communities associated with seaweed in both healthy and infected samples (Table 2 ). Higher temperatures and salinity at Mangadu (30.6°C, 36.6 PSU) favor specific microbial taxa, altering diversity and metabolic functions. Dissolved oxygen levels (4.8 mg/L in Odavimadam to 8.4 mg/L in Mangadu) influence the abundance of aerobic and facultative anaerobic microorganisms, affecting community composition. Elevated nitrite, nitrate, and phosphate levels in Jegathapattinam and Odavimadam promote unique nutrient-cycling groups that may alter seaweed-microbe interactions. Slight variations in pH (8.16–8.41) can affect enzyme activity and overall metabolism within the seaweed microbiome, influencing responses to environmental factors (Menaa et al., 2020; Minich et al., 2018). The analysis of microbial diversity revealed that the microbiome associated with both healthy and diseased seaweed varies at each collection site (Figs. 2 and 3 ) and may be influenced by environmental factors (Ndawala et al., 2022; Faisan et al., 2024). Abiotic stressors impact the structure of the bacterial community, even during a single intertidal period, due to unstable of levels of desiccation, temperature, light, and humidity (Vigil et al., 2024; Centurion et al., 2021). Consequently, the microbiomes at different sites varied significantly from one another (Vohsen et al., 2024). Furthermore, there is a phenomenon known as the host effect, which refers to the ability of seaweed to produce metabolic substances that selectively influence microbial adhesion and settlement in their environment (Mesquita et al., 2019; Singh et al., 2014). This process contributes to the development of specific microbial communities. It also activates genes in the bacterial community that are essential for metabolizing substrates or producing metabolites, many of which are beneficial to the host (Lu et al., 2025). The identified microbiome exhibited differences from those found in both healthy and diseased individuals at various sites (Fig. 2 ). In the present study, Proteobacteria emerged as the predominant phylum across all sampling sites, with Gammaproteobacteria consistently detected as a major class. The dominance of Proteobacteria in seawater samples from all five locations suggests their ecological versatility and potential role in shaping microbial community structure, as also highlighted in previous reports (Gu et al., 2023). The bacterial phylum distribution across the five sampling sites reveals important patterns in the comparison of diseased and healthy K. alvarezii samples. The dominance of Gammaproteobacteria and Alphaproteobacteria in diseased samples from Mangadu and Soliyakudi suggests these groups may be crucial to disease processes. The proliferation of opportunistic microorganisms in response to seaweed indicates ecological succession (Singh et al., 2018; Egan et al., 2013). The higher abundance of Gammaproteobacteria and Clostridia in diseased samples from Nambuthalai and Jegadhapattinam, compared to healthy ones, suggests a link between these bacteria and disease. Clostridia, being anaerobic, are well-suited to proliferate in environments where tissue degradation leads to diminished oxygen levels and the accumulation of organic matter, thereby creating conditions that support their growth and metabolic activity (Facimoto et al., 2024). In Odavimadam, similar bacterial abundance in both diseased and healthy samples hints at site-specific factors affecting host-microbe interactions, possibly indicating varied disease etiology or stable environmental conditions. These variations at the phylum level highlight the complexity and site-specific nature of K. alvarezii disease, showing that local conditions significantly influence microbial dynamics in seaweed cultivation. The comparison at the genus level of bacterial communities from five sampling locations indicates different patterns associated with K. alvarezii disease and healthy samples (Fig. 3 ). Diseased samples in Mangadu showed high proportions of unclassified genera, Cobetia , and Halomonas , indicating greater taxonomic diversity with disease development. Existing literature demonstrates that bacterial densities on and inside ice-iced shoots of K. alvarezii are 10–100 fold larger than healthy tissue, with microbiome changes playing crucial role a role in the causing ice-ice disease (Riyaz et al., 2020; Largo et al., 1995). In Soliyakudi site, diseased samples showed enrichment of Pseudoalteromonas , a genus reported to secrete bioactive metabolites with the potential to degrade or damage algal tissues, thereby contributing to disease progression. Pseudoalteromonas organisms are linked with larger organisms and possessing extracellular biologically active agents, and these algal cell wall degrading bacteria are capable of damaging algal tissues and facilitate an entry for opportunistic bacteria (Hollants et al., 2013). The adoptive evolution of algal polysaccharide degradation in P. carrageenovora shows them to be specialized and adapted to seaweed habitats, with an estimated half of seaweed biomass made up of polysaccharides that these bacteria are capable of breaking down (Gobet et al., 2018). Nambuthalai site samples also contained high levels of Pseudoalteromonas and unclassified bacteria, suggesting polymicrobial disease communities of high complexity. At Jegadhapattinam, the presence of Pseudoalteromonas and Psychrobacter suggests distinct disease mechanisms, while high occurrences of Oceanisphaera in Odavimadam diseased samples indicate potential new pathogenic associations. The Formation of ice-ice disease in K. alvarezii is related to microbiome changes influencing carrageenan production and seaweed structural integrity (Riyaz et al., 2021) This study generally demonstrated higher bacterial diversity in diseased K. alvarezii samples compared to healthy samples, consistent with the findings of Zozaya et al. (2015), as evidenced by Shannon diversity indices (Figs. 4 and 5 ). The Odavimadam site exhibited the highest overall microbial diversity. This aligns with previous studies suggesting that increased diversity may result from opportunistic colonization when host defense mechanisms are disrupted (Caballero et al., 2023). There are significant differences in bacterial population across sites, with Odavimadam presenting 581 OTUs and Soliyakkudi only 259 (Fig. 4 ). These discrepancies are likely due to the distinct environmental conditions at each site (Gianoulis et al., 2009). At the phylum level, Proteobacteria were the most abundant in both healthy (87.4%) and diseased samples (75.8%). Interestingly, Vibrio sp. was mainly detected in diseased samples, highlighting its possible role as a pathogen (Ward et al., 2022). Overall, it is clearly noticeable that microbial population was strong similar among the Mangadu, Soliyakudi, Nanbuthalai, sites. Alike the Jegadhapattinam and Odavimadam sites showed extreme similarity in microbial community. The heat tree visualization effectively emphasized taxonomic hierarchies, revealing notable site specific variations in the diversity and abundance of K. alvarezii microbiomes across five sampling sites (Fig. 7 ). The complex relationships within microbial communities, showing that different locations host distinct bacterial assemblages with varying taxonomic complexity (Yan et al., 2024; Juhmani et al., 2020; Xie et al., 2024). The architecture of heat trees, from simple structures to complex communities, reflects local environmental and ecological influences on bacterial community assembly (Xu et al., 2022; Egan et al., 2013). Despite the differences between groups, some genera, such as Cobetia , Pseudohalomonas , and Halomonas , were consistently present. Cobetia was more abundant in healthy samples compared to the diseased ones, suggesting its beneficial role. The association of Vibrio with disease highlights its potential as an early microbial indicator (Largo et al., 1998), while beneficial bacteria may provide avenues for developing probiotics to enhance seaweed health (Mahajan et al., 2025). The functional prediction analysis indicated metabolic changes associated with both healthy and diseased samples (Fig. 8 ). In contrast, diseased samples displayed enrichment in fermentation, nitrate reduction, nitrogen respiration, and sulfate metabolism pathways at various sites. Meanwhile, healthy samples consistently showed enhanced expression of aerobic chemoheterotrophy and hydrocarbon degradation functions. These results reveal, discrepancies in physicochemical characteristics influenced the microbial community structures, with Odavimadam showing the lowest DO at 4.8 mg/L and Mangadu showing the highest at 8.4 mg/L. These findings clearly show that environmental determinants and disease status significantly impact the composition and functional potential of the bacterial communities associated with K. alvarezii . Conclusion This study reveals variance in microbial community structures between healthy and diseased K. alvarezii across five cultivation sites in Tamil Nadu, India. Proteobacteria were observed to be the dominant class across all sites, in which Gammaproteobacteria were consistently present. Diseased samples show higher bacterial diversity compared to healthy samples, indicating that opportunistic colonization occurs following disruptions in host defenses. Pseudoalteromonas and Vibrio were primarily identified in diseased samples, indicating their potential role in disease pathology. Cobetia was more abundant in healthy samples, indicating the beneficial association with the host. Divergences in microbial communities were site-specific, with differences in physicochemical parameters such as temperature, salinity, dissolved oxygen, and nutrient levels. Functional prediction analysis revealed that diseased samples had elevated pathways relevant to fermentation, nitrate reduction, and sulfate metabolism. In contrast, healthy samples showed enhanced functions related to aerobic chemoheterotrophy and hydrocarbon degradation. These determinations provide valuable insights into the microbial factors contributing to K. alvarezii diseases in Indian cultivation sites since 2013. Identifying potential pathogens and beneficial bacteria paves the way for developing targeted disease management strategies and probiotics to improve the health of seaweed, thereby supporting the sustainable cultivation of this economically vital resource. Declarations Ethical approval not applicable. Acknowledgements Authors are highly grateful to Dr. Vaibhav A. Mantri Co-DCs of Applied Phycology & Biotechnology Division, CSIR-CSMCRI, for their valuable suggestions throughout the study. Authors also thank PIs, HCP0024 & MLP0073 at CSIR-CSMCRI. Author contribution N. Dhanya is conducting experiments, writing, and analyses; B. Prakriti, writing, and analyses; Arup Ghosh, monitoring the project. Lakkakula Satish, conceptualization; resources; writing, original draft; writing, review, and editing. Funding The authors would like to express their sincere gratitude to the Department of Science and Technology – Science and Engineering Research Board (DST-SERB), Government of India, for their financial support through the Start-up Research Grant (SRG) scheme (Sanction number: SRG/2023/002162). This assistance was instrumental in the successful completion of this research work. Data availability Research data are not shared References Achmad M, Alimuddin A, Widyastuti U, Sukenda S, Suryanti E, Harris E (2016) Molecular identification of new bacterial causative agent of ice-ice disease on seaweed Kappaphycus alvarezii . PeerJ Preprints e2016v1. Arasamuthu A, Edward JP (2018) Occurrence of ice-ice disease in seaweed Kappaphycus alvarezii at Gulf of Mannar and Palk Bay, Southeastern India. Indian Journal of Geo Marine Sciences 47(6):1208–1216. Caballero-Flores G, Pickard JM, Núñez G (2023) Microbiota-mediated colonization resistance: mechanisms and regulation. 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Metagenomic insights reveal the microbial diversity and associated algal-polysaccharide-degrading enzymes on the surface of red algae among remote regions. International Journal of Molecular Sciences. 24(13):11019. Menaa F, Wijesinghe PA, Thiripuranathar G, Uzair B, Iqbal H, Khan BA, Menaa B. (2020). Ecological and industrial implications of dynamic seaweed-associated microbiota interactions. Marine drugs. 18(12):641. Additional Declarations No competing interests reported. Supplementary Files SeaweedMetagenomeSupplementary.docx SeaweedMetagenomeSupplementaryExcel.xlsx Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {\"props\":{\"pageProps\":{\"initialData\":{\"identity\":\"rs-7621295\",\"acceptedTermsAndConditions\":true,\"allowDirectSubmit\":true,\"archivedVersions\":[],\"articleType\":\"Article\",\"associatedPublications\":[],\"authors\":[{\"id\":521825237,\"identity\":\"377cbd87-aa9a-45a8-8188-60b113f9abf3\",\"order_by\":0,\"name\":\"Nagarajan Dhanya\",\"email\":\"\",\"orcid\":\"\",\"institution\":\"Marine Algal Research Station, CSIR - Central Salt and Marine Chemicals Research 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India.\\u003c/p\\u003e\",\"description\":\"\",\"filename\":\"floatimage1.jpeg\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-7621295/v1/8ba4a5fcedd4d598941247e3.jpeg\"},{\"id\":92506940,\"identity\":\"e9187d01-6278-46fc-b37d-c95535d5af10\",\"added_by\":\"auto\",\"created_at\":\"2025-09-30 12:49:46\",\"extension\":\"png\",\"order_by\":2,\"title\":\"Figure 2\",\"display\":\"\",\"copyAsset\":false,\"role\":\"figure\",\"size\":626061,\"visible\":true,\"origin\":\"\",\"legend\":\"\\u003cp\\u003eTaxonomic composition of the microbiota associated with healthy and diseased \\u003cem\\u003eK. alvarezii\\u003c/em\\u003e: Relative abundance at the phylum level at different collection sites: a. Mangadu, b. Soliyakudi, c. Nambuthalai, d. Jegathapattinam, and e. Odavimadam.\\u003c/p\\u003e\",\"description\":\"\",\"filename\":\"floatimage2.png\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-7621295/v1/fea7c395a54fd924b4235fd1.png\"},{\"id\":92506943,\"identity\":\"c2823a8b-5eb3-4ffc-9520-8d292d2dc190\",\"added_by\":\"auto\",\"created_at\":\"2025-09-30 12:49:46\",\"extension\":\"png\",\"order_by\":3,\"title\":\"Figure 3\",\"display\":\"\",\"copyAsset\":false,\"role\":\"figure\",\"size\":419250,\"visible\":true,\"origin\":\"\",\"legend\":\"\\u003cp\\u003eTaxonomic composition of the microbiota associated with healthy and diseased \\u003cem\\u003eK. alvarezii\\u003c/em\\u003e: Relative abundance at genus levels at in the healthy and diseased \\u003cem\\u003eK. alvarezii\\u003c/em\\u003e from 5 different cultivation sites. a. Mangadu, b. Soliyakudi, c. Nambuthalai, d. Jegathapattinam and e. 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More number of OTUs shows that there are more diverse organisms in the sample.\\u003c/p\\u003e\",\"description\":\"\",\"filename\":\"floatimage4.png\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-7621295/v1/cb2063572c9c080451568f31.png\"},{\"id\":92506950,\"identity\":\"d19c6f82-9bb1-42c7-8636-b9f9ad27bc6f\",\"added_by\":\"auto\",\"created_at\":\"2025-09-30 12:49:46\",\"extension\":\"png\",\"order_by\":5,\"title\":\"Figure 5\",\"display\":\"\",\"copyAsset\":false,\"role\":\"figure\",\"size\":294459,\"visible\":true,\"origin\":\"\",\"legend\":\"\\u003cp\\u003eShannon Index. Stastical report of the diverse communities in the healthy and diseased \\u003cem\\u003eK. alvarezii\\u003c/em\\u003e. a. Mangadu, b. Soliyakudi, c. Nambuthalai, d. Jegathapattinam and e. Odavimadam\\u003c/p\\u003e\\n\\u003cp\\u003eDiseased samples have more Shannon index as compared to the healthy ones, depicting that they have more diverse microbial communities. The left graph on each figure depicts the raw data and the right side is the statistically analyzed data showing the Shannon index\\u003c/p\\u003e\",\"description\":\"\",\"filename\":\"floatimage5.png\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-7621295/v1/bf274b0dd9402e52030fd6bf.png\"},{\"id\":92506939,\"identity\":\"191441a4-f445-4a24-a8bd-51e34618b239\",\"added_by\":\"auto\",\"created_at\":\"2025-09-30 12:49:46\",\"extension\":\"png\",\"order_by\":6,\"title\":\"Figure 6\",\"display\":\"\",\"copyAsset\":false,\"role\":\"figure\",\"size\":768644,\"visible\":true,\"origin\":\"\",\"legend\":\"\\u003cp\\u003eComplex Heat Map. Detailed analysis of the major phylum and genus present in the diseased and healthy \\u003cem\\u003eK. alvarezii\\u003c/em\\u003e samples from the 5 different sites a. Mangadu, b. Soliyakudi, c. Nambuthalai, d. Jegathapattinam and e. Odavimadam. The first column shows phylum abundance, the second and third column shows the genus abundance of diseased and healthy samples. In the genus abundance, the 1 shows highest abundance and -1 depicts the lowest abundance.\\u003c/p\\u003e\",\"description\":\"\",\"filename\":\"floatimage6.png\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-7621295/v1/deb7f9dd0b218d5f7cf47dc8.png\"},{\"id\":92508115,\"identity\":\"ca749e14-3381-4c39-9da6-9dfe1dfa2650\",\"added_by\":\"auto\",\"created_at\":\"2025-09-30 12:57:46\",\"extension\":\"jpeg\",\"order_by\":7,\"title\":\"Figure 7\",\"display\":\"\",\"copyAsset\":false,\"role\":\"figure\",\"size\":844281,\"visible\":true,\"origin\":\"\",\"legend\":\"\\u003cp\\u003eHeat tree of microbiome community structure found in the diseased and healthy \\u003cem\\u003eK. alvarezii\\u003c/em\\u003e samples from the 5 different sites a. Mangadu, b. Soliyakudi, c. Nambuthalai, d. Jegathapattinam and e. Odavimadam. Size and color of nodes and edges show their abundance in the microbial community.\\u003c/p\\u003e\",\"description\":\"\",\"filename\":\"floatimage7.jpeg\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-7621295/v1/fb88f656412c0ab1050ca243.jpeg\"},{\"id\":92506949,\"identity\":\"3db6f98c-f469-4bb9-8cf0-09938751208e\",\"added_by\":\"auto\",\"created_at\":\"2025-09-30 12:49:46\",\"extension\":\"png\",\"order_by\":8,\"title\":\"Figure 8\",\"display\":\"\",\"copyAsset\":false,\"role\":\"figure\",\"size\":194195,\"visible\":true,\"origin\":\"\",\"legend\":\"\\u003cp\\u003ePredicted pathways of the microbiota associated with diseased and healthy \\u003cem\\u003eK. alvarezii\\u003c/em\\u003e for biological functions. a. Mangadu, b. Soliyakudi, c. Nambuthalai, d. Jegathapattinam and e. Odavimadam.\\u003c/p\\u003e\",\"description\":\"\",\"filename\":\"floatimage8.png\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-7621295/v1/42e64ae6543a60031b59af8a.png\"},{\"id\":93070554,\"identity\":\"5ebe9e8a-1c1a-46a6-8d97-20b52d1c877b\",\"added_by\":\"auto\",\"created_at\":\"2025-10-08 17:55:07\",\"extension\":\"pdf\",\"order_by\":0,\"title\":\"\",\"display\":\"\",\"copyAsset\":false,\"role\":\"manuscript-pdf\",\"size\":4832466,\"visible\":true,\"origin\":\"\",\"legend\":\"\",\"description\":\"\",\"filename\":\"manuscript.pdf\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-7621295/v1/54a54449-2ed7-40c3-b59d-50bc7c717a49.pdf\"},{\"id\":92508107,\"identity\":\"289841d8-75e3-4fb5-9c57-5dc87af37a99\",\"added_by\":\"auto\",\"created_at\":\"2025-09-30 12:57:46\",\"extension\":\"docx\",\"order_by\":1,\"title\":\"\",\"display\":\"\",\"copyAsset\":false,\"role\":\"supplement\",\"size\":631968,\"visible\":true,\"origin\":\"\",\"legend\":\"\",\"description\":\"\",\"filename\":\"SeaweedMetagenomeSupplementary.docx\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-7621295/v1/a203a3c99b9062b4f74b59af.docx\"},{\"id\":92506944,\"identity\":\"6e33f4c4-0d05-4cfb-ab18-cf0e139b75b2\",\"added_by\":\"auto\",\"created_at\":\"2025-09-30 12:49:46\",\"extension\":\"xlsx\",\"order_by\":2,\"title\":\"\",\"display\":\"\",\"copyAsset\":false,\"role\":\"supplement\",\"size\":33286,\"visible\":true,\"origin\":\"\",\"legend\":\"\",\"description\":\"\",\"filename\":\"SeaweedMetagenomeSupplementaryExcel.xlsx\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-7621295/v1/50bd8b2aed2efa98db94aab4.xlsx\"}],\"financialInterests\":\"No competing interests reported.\",\"formattedTitle\":\"Microbial community structure and niche differentiation in healthy and infected samples of Kappaphycus alvarezii in the commercial farming sites of Tamil Nadu coastal, India\",\"fulltext\":[{\"header\":\"Introduction\",\"content\":\"\\u003cp\\u003eSeaweeds, aka macroalgae, the cultivation of multiple seaweed species constitutes more than half of the cumulative global marine aquaculture production. This indicates the enormous potential of seaweed aquaculture as a sustainable and lucrative industry. The utilization of seaweed cultivation offers many benefits, ranging from mitigating environmental issues to promoting socio-economic development. Hence, it is imperative to recognize and harness the potential of seaweed aquaculture to ensure a sustainable future (FAO 2020; Wang et al., 2022). The demand for wet seaweeds has skyrocketed in recent years, with over 35\\u0026nbsp;million tons produced worldwide in 2022 (FAO 2022). This versatile seaweed has proven to be a valuable resource, providing various benefits for industries such as food, medicine, and fuel. As a result, the global revenue generated by wet macroalgae production has surged to nearly \\u003cspan\\u003e$\\u003c/span\\u003e1.9\\u0026nbsp;billion (FAO 2022). Investing in cultivating and harvesting this valuable resource is wise for businesses looking to capitalize on a growing market (Mont\\u0026uacute;far et al., 2023). Cultivating seaweed for ecosystem services provides a sustainable solution to ocean acidification and contributes towards carbon fixation, making it a constructive approach to protecting and preserving our oceans (Campbell et al., 2019; Mont\\u0026uacute;far et al., 2023).\\u003c/p\\u003e\\u003cp\\u003e\\u003cem\\u003eK. alvarezii\\u003c/em\\u003e (Doty) is a tropical seaweed that produces a commercially valuable and significant product, carrageenan (Mamat et al., 2024; Munisamy et al., 2024; Mantri et al., 2025). Ice-ice disease is a major concern in the world of \\u003cem\\u003eK. alvarezii\\u003c/em\\u003e farming. This disease leads to a substantial decline in \\u003cem\\u003eK. alvarezii\\u003c/em\\u003e biomass yield and reduces the amount of carrageenan relative to the significant percentage of healthy seaweed (Rantetondok et al., 2017; Tahiluddin et al., 2022). The disease is characterized by \\u0026ldquo;bleaching or whitening\\u0026rdquo; indications on the seaweed thallus tissue and can be caused by various opportunistic pathogenic bacteria (Ward et al., 2022; Achmad et al., 2017; Riyaz et al., 2021). It is non-infectious and can arise from adverse environmental factors, including extreme temperature, pH, and salinity, as well as opportunistic bacterial pathogens such as \\u003cem\\u003eVibrio\\u003c/em\\u003e sp. (Arasamuthu \\u0026amp; Patterson Edward., 2018).\\u003c/p\\u003e\\u003cp\\u003eThe microbial communities associated with seaweeds are highly diverse, yet we have not fully understood their complexities or how they differ from those found in the open sea (Xu et al., 2022). It is vital to examine the epiphytic bacterial microbiota that inhabits seaweed since they are instrumental in influencing algal morphological development, metabolism, growth, and protection against fouling organisms (Nahor et al., 2024). By further exploring this field of study, we can uncover valuable knowledge about the captivating realm of seaweed and its associated microbial communities.\\u003c/p\\u003e\\u003cp\\u003eThe 16S rRNA gene sequencing is the latest approach used for studies of bacterial phylogeny and taxonomy. The major reason for the use of the 16S rRNA gene sequencing is that steady function has not changed over time because it occurs in almost every bacterium, indicating that random sequence change is more accurate and the 16S rRNA gene (1500 bp) is large enough for information purposes (Janda \\u0026amp; Abbott., 2007; Clarridge., 2004; Kembel et al., 2012). Experiments show that \\u003cem\\u003eUlva\\u003c/em\\u003e species, a type of marine green macroalga, have complex microbiomes essential for their growth. These species associate with specific bacterial communities, and studies using 16S rRNA gene sequencing have explored bacteria-driven growth patterns and the potential of using microbes in algal aquaculture (Wichard., 2023). Red seaweeds have been extensively studied with specific emphasis on \\u003cem\\u003eGracilariopsis\\u003c/em\\u003e and \\u003cem\\u003eGracilaria\\u003c/em\\u003e species. An integrative analysis of the red macroalga \\u003cem\\u003eGracilariopsis lemaneiformis\\u003c/em\\u003e through high-throughput sequencing showed that environmental factors, notably nitrogen and phosphorus levels, profoundly influence the epiphytic bacterial community structure of varying geographic locations. The study illustrated location-specific differences in bacterial community structures and their association with nutrient levels (Pei et al., 2021). Sophisticated multi-omics tools have been used in brown seaweeds. \\u003cem\\u003eFucus vesiculosus\\u003c/em\\u003e studies have integrated amplicon sequencing with metabolomics and imaging methods, and it was identified that \\u003cem\\u003eAlphaproteobacteria\\u003c/em\\u003e are the dominant epibiotic bacteria, whereas fungi of the class Eurotiomycetes were imaged for the first time on an algal surface. This study showed the combination of 16S rRNA sequencing with metabolomics to clarify the surface microbiome-metabolome interactions (Parrot et al., 2019). Phylogenetic and functional characterization of microbiomes of \\u003cem\\u003eSargassum\\u003c/em\\u003e seaweed waste determined leading bacterial genera, such as \\u003cem\\u003eMuricauda, Aminobacterium, Mesorhizobium, Marinobacter, Reichenbachiella, Hyphomonas, Simiduia, Aquamicrobium, Oceanicola, and Alcanivorax\\u003c/em\\u003e, which reflect bacterial diversity within brown seaweed detritus (Mohapatra., 2023). Studies employing 16S rRNA gene sequencing have described bacterial communities related to several species of seaweeds in various marine ecosystems. These researchers have identified that bacteria associated with seaweed are dominated by phyla including Proteobacteria, Bacteroidetes, Firmicutes, Cyanobacteria, Planctomycetes, Actinobacteria, and Verrucomicrobia (Selvarajan et al., 2019; Nahor et al., 2024).\\u003c/p\\u003e\\u003cp\\u003eIn this study, metagenomics was used to analyze the microbial diversity on the surface of diseased and healthy \\u003cem\\u003eK. alvarezii\\u003c/em\\u003e among five seaweed cultivation sites. This study effectively extends our understanding of the diversity of seaweed thallus-bound microorganisms on both diseased and healthy \\u003cem\\u003eK. alvarezii\\u003c/em\\u003e. This work unlocks new opportunities for the exploitation and use of seaweed resources in the future, as well as the exploration of valuable microbiomes from active seaweed cultivation sites.\\u003c/p\\u003e\"},{\"header\":\"Materials and methods\",\"content\":\"\\u003cp\\u003e\\u003cstrong\\u003eSample collection\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003eThe source of the biological material was from commercial-scale seaweed farming sites in Tamil Nadu, India. In the present study, \\u003cem\\u003eK. alvarezii\\u003c/em\\u003e (red seaweed) well-matured, healthy, and infected samples were collected from 5 different commercial farming sites i.e., Mangadu (9.327695\\u0026quot;N 78\\u0026deg;59\\u0026apos;35.8\\u0026quot;E), Soliyakkudi (9\\u0026deg;42\\u0026apos;29.6\\u0026quot;N 78\\u0026deg;59\\u0026apos;35.8\\u0026quot;E), Jegathapattinam (9\\u0026deg;57\\u0026apos;55.2\\u0026quot;N 79\\u0026deg;11\\u0026apos;19.5\\u0026quot;E), Nambuthalai (9\\u0026deg;43\\u0026apos;34.8\\u0026quot;N 79\\u0026deg;00\\u0026apos;15.1\\u0026quot;E) and Odavimadam (9\\u0026deg;59\\u0026apos;21.6\\u0026quot;N 79\\u0026deg;12\\u0026apos;31.3\\u0026quot;E) in the Palk Bay region, Tamil Nadu, India (Fig. 1) Table 1. The samples were immediately placed in a cool pack and transported from the farming sites to the laboratory, and were used to extract the total genomic DNA. All the sites are situated between the villages and in close proximity to shrimp culture ponds. As a result, they are frequently disturbed by heavy anthropogenic pressure from the seaweed farmers and fisher communities that enter the water. The Mangadu site, on the other hand, is located far from the fishers\\u0026apos; village and has limited public access, making it an ideal site for seaweed farming with low anthropogenic pressure. Seawater samples were collected from all sites to investigate the relationship between seawater physicochemical parameters and both diseased and healthy seaweed.\\u003c/p\\u003e\\n\\u003cp\\u003eTable. 1. Collection sites name and their code word\\u003c/p\\u003e\\n\\u003cdiv align=\\\"\\\"\\u003e\\n \\u003ctable border=\\\"0\\\" cellspacing=\\\"0\\\" cellpadding=\\\"0\\\"\\u003e\\n \\u003ctbody\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 125px;\\\"\\u003e\\n \\u003cp\\u003eSite name\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 125px;\\\"\\u003e\\n \\u003cp\\u003eSample\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 125px;\\\"\\u003e\\n \\u003cp\\u003eCode\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd rowspan=\\\"2\\\" valign=\\\"top\\\" style=\\\"width: 125px;\\\"\\u003e\\n \\u003cp\\u003eMangadu\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 125px;\\\"\\u003e\\n \\u003cp\\u003eDiseased\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 125px;\\\"\\u003e\\n \\u003cp\\u003eKaMD\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 125px;\\\"\\u003e\\n \\u003cp\\u003eHealthy\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 125px;\\\"\\u003e\\n \\u003cp\\u003eKaMH\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd rowspan=\\\"2\\\" valign=\\\"top\\\" style=\\\"width: 125px;\\\"\\u003e\\n \\u003cp\\u003eSoliyakudi\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 125px;\\\"\\u003e\\n \\u003cp\\u003eDiseased\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 125px;\\\"\\u003e\\n \\u003cp\\u003eKaSD\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 125px;\\\"\\u003e\\n \\u003cp\\u003eHealthy\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 125px;\\\"\\u003e\\n \\u003cp\\u003eKaSH\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd rowspan=\\\"2\\\" valign=\\\"top\\\" style=\\\"width: 125px;\\\"\\u003e\\n \\u003cp\\u003eNambuthalai\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 125px;\\\"\\u003e\\n \\u003cp\\u003eDiseased\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 125px;\\\"\\u003e\\n \\u003cp\\u003eKaND\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 125px;\\\"\\u003e\\n \\u003cp\\u003eHealthy\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 125px;\\\"\\u003e\\n \\u003cp\\u003eKaNH\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd rowspan=\\\"2\\\" valign=\\\"top\\\" style=\\\"width: 125px;\\\"\\u003e\\n \\u003cp\\u003eJegadhapattinam\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 125px;\\\"\\u003e\\n \\u003cp\\u003eDiseased\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 125px;\\\"\\u003e\\n \\u003cp\\u003eKaJD\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 125px;\\\"\\u003e\\n \\u003cp\\u003eHealthy\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 125px;\\\"\\u003e\\n \\u003cp\\u003eKaJH\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd rowspan=\\\"2\\\" valign=\\\"top\\\" style=\\\"width: 125px;\\\"\\u003e\\n \\u003cp\\u003eOdavimadam\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 125px;\\\"\\u003e\\n \\u003cp\\u003eDiseased\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 125px;\\\"\\u003e\\n \\u003cp\\u003eKaOD\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 125px;\\\"\\u003e\\n \\u003cp\\u003eHealthy\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 125px;\\\"\\u003e\\n \\u003cp\\u003eKaOH\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003c/tbody\\u003e\\n \\u003c/table\\u003e\\n\\u003c/div\\u003e\\n\\u003cp\\u003eCollection sites, sample types, and their corresponding code names. \\u0026ldquo;Ka\\u0026rdquo; represents \\u003cem\\u003eKappaphycus alvarezii\\u003c/em\\u003e, followed by the initials of the site name and sample condition (D \\u0026ndash; Diseased, H \\u0026ndash; Healthy).\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eGenomic DNA extraction\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003eGenomic DNA was extracted from disease-infected and healthy samples of \\u003cem\\u003eK. alvarezii\\u003c/em\\u003e. DNA was extracted using the modified method by ONEOMICS PRIVATE LIMITED, Tiruchirappalli, Tamil Nadu (In-house Protocol). Briefly, for around 50 mg of seaweed samples (samples were chopped into pieces in a tube), 200 \\u0026mu;L of high salt lysis buffer was added, and then crushed using a sterile micro pestle. To this, 20 \\u0026mu;L of proteinase K (20 mg/mL) was added, and the tube was vortexed vigorously and then incubated at 55\\u0026deg;C for 10 min. To this, an equal volume of phenol chloroform, iso amyl alcohol was added, followed by centrifugation at 14,000 RPM for 10 min. The supernatant was pipetted out and then two volumes of ice-cold Isopropanol were added and subjected to DNA precipitation at -20\\u0026deg;C. Finally, the samples were centrifuged at 14,000 RPM for 20 min, and then the pellet was washed with 70% ethanol. Following isolation, the DNA concentrations were verified using a Qubit 4 Fluorometer (Thermo Fisher Scientific, USA), and DNA quality was determined by Nanodrop (Thermo Scientific, San Diego, CA, USA) and Agarose gel electrophoresis.\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eNext-Generation Amplicon Sequencing\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003eDuring the first PCR, fragments of the V3\\u0026ndash;V4 regions of the 16S rRNA gene were amplified using the primers 341 F \\u0026ndash; CCTAYGGGRBGCASCAG and 806 R \\u0026ndash;GGACTACNNGGGTATCTAAT. The PCR reactions were carried out with Phusion High-Fidelity PCR Master Mix (New England Biolabs), 2 \\u0026micro;M of forward (341 F) and reverse (806 R) primers, and about 10 ng template DNA. Thermal cycling consisted of initial denaturation at 98℃ for 1 min, followed by 30 cycles of denaturation at 98℃ for 10 s, annealing at 50℃ for 30 s, and elongation at 72℃ for 30 s, and final extension at 72℃ for 5 min. \\u0026nbsp; The reaction was performed on a PTC-0200\\u0026thinsp;G thermocycler (Bio-Rad Laboratories, Inc., USA). Besides the PCR clean-up done to purify the PCR product in this study, it is important to state that a negative control was included in the PCR amplification to ensure there was no contamination of reagents. For PCR product quantification and qualification, the PCR product was mixed with an equal volume of 1X loading buffer (containing SYBR green), and electrophoresis was performed on a 2% agarose gel for detection. Agilent 5400 Fragment Analyzer (Agilent 2100, USA) was used to verify the size of the PCR product. Samples with sharp peaks between 450-480 bp were chosen for further experiments. \\u0026nbsp;We purified the amplicon product to remove free primers and primer dimers using AMPure XP beads. Next, we attached dual indices and Illumina sequencing adapters using the Nextera XT DNA Library Preparation Kit (Illumina, USA). The amplicon was then purified again with AMPure XP beads. The libraries generated were quantified using a Qubit 4 Fluorometer (Thermo Fisher Scientific, USA). The amplicons from each reaction mixture were combined in equimolar concentrations and sequencing was carried out using the Illumina NovaSeq 6000 system (Illumina, USA) in a 2 \\u0026times; 250 bp paired-end run following the manufacturer\\u0026rsquo;s guidelines.\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eBioinformatics analyses\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003eThe bioinformatics analyses were carried out by ONEOMICS PRIVATE LIMITED (Tiruchirappalli, India). Initially, the raw reads underwent demultiplexing and quality filtering using the deblur plugin in QIIME2 software (https://qiime2.org/). To provide an overview, reads with a length of around 250 base pairs were shortened at positions with an average quality score below 20 over a 50-base-pair sliding window. Reads shorter than 50 base pairs and those containing ambiguous characters were omitted from the subsequent analysis.\\u003c/p\\u003e\\n\\u003cp\\u003eThe operational taxonomic units (OTUs) were generated by clustering using the same deblur plugin. Non-repetitive sequences were extracted to streamline the analysis, and single non-repetitive sequences were removed. The non-repetitive sequences, excluding single sequences, were then clustered based on the OTUs using a similarity threshold of 97%, and chimeras were removed during clustering to obtain the representative OTU sequences. The OTUs obtained were matched against the SILVA database (version 138) using a confidence threshold of 70% using the feature-classifier plugin in QIIME2.\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eDiversity Analyses\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003e\\u003cem\\u003eVariation analysis of alpha diversity indices between groups\\u003c/em\\u003e\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003eThe statistical analysis of the data from QIIME2 was performed using various R packages. The QIIME2 artifacts were imported and analyzed using the R package qiime2R v0.99.6. Alpha diversity is designed to measure the diversity within a single community, i.e., the bacterial community of \\u003cem\\u003eK. alvarezii\\u003c/em\\u003e samples. Boxplots were formed to analyze the difference in Alpha diversity indices between groups using the R package Vegan v2.6.6. The alpha diversity index and the Shannon index were used to determine the diversity in each sample. The abundance plots, used to determine the variation in the presence of different taxonomies between the groups, were generated using the R package Phyloseq v1.48.0. The rarefaction curve to determine the adequacy of sequencing depth was also generated using the Vegan R package. \\u0026nbsp;The PCA analysis was done using the QIIME2 software and visualized with the qiime2R package. The R package Metacoder R v0.3.8 was used to parse the taxonomy data and visualize the heat tree.\\u0026nbsp;\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eHeat Tree Visualization\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003eThe Heat Tree Analysis utilizes the hierarchical structure of taxonomic classifications to quantitatively (using the median abundance) and statistically depict taxonomic differences between microbial communities. Heat tree visualizations were built from the metacoder package in R v0.3.8 to represent hierarchical taxonomic hierarchies in \\u003cem\\u003eK. alvarezii\\u003c/em\\u003e samples from five sampling sites (Mangadu, Soliyakudi, Nambuthalai, Jagadhapattinam, and Odavimadam).\\u0026nbsp;\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eFunctional prediction\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003eThe FAPROTAX (Functional Annotation of Prokaryotic Taxa) tool was used to predict potential metabolic functions of important pathways within bacterial communities identified from healthy and diseased samples collected from five seaweed cultivation sites (Table 1).\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eData availability\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003eThe metagenomic sequencing data from diseased \\u003cem\\u003eKappaphycus alvarezii\\u003c/em\\u003e have been submitted to the NCBI (https://www.ncbi.nlm.nih.gov/sra/PRJNA1248676) under the BioProject ID PRJNA1248676. The associated Sequence Read Archive (SRA) accession numbers are SRR33066784, SRR33066785, SRR33066786, SRR33066787, SRR33066788, SRR33066789, SRR33066790, SRR33066791, SRR33066792, and SRR33066793.\\u003c/p\\u003e\"},{\"header\":\"RESULTS\",\"content\":\"\\u003cdiv id=\\\"Sec13\\\" class=\\\"Section2\\\"\\u003e\\u003ch2\\u003ePhysicochemical characterization of seawater of diseased sample collection sites\\u003c/h2\\u003e\\u003cp\\u003eThe average of all physicochemical analyses at each site is presented in (Table\\u0026nbsp;\\u003cspan refid=\\\"Tab2\\\" class=\\\"InternalRef\\\"\\u003e2\\u003c/span\\u003e). Based on the physicochemical parameter results provided for the five cultivation sites, notable differences occurred across the five locations. Air temperatures ranged from 28.6\\u0026deg;C to 30.6\\u0026deg;C, with Mangadu having the highest temperature (30.6\\u0026deg;C). Salinity was relatively consistent across most sites (33.6\\u0026ndash;36.6 PSU), except for Mangadu (36.6 PSU). The pH ranged from 8.16 to 8.41 in all sites. Nutrient levels varied, with Jegathapattinam and Odavimadam generally showing higher concentrations of nitrites, nitrates, and phosphates. Dissolved oxygen levels varied significantly, from a low of 4.8 mg/L in Odavimadam to a high of 8.4 mg/L in Mangadu.\\u003c/p\\u003e\\u003cp\\u003e\\u003cdiv class=\\\"gridtable\\\"\\u003e\\u003ctable float=\\\"Yes\\\" id=\\\"Tab2\\\" border=\\\"1\\\"\\u003e\\u003ccaption language=\\\"En\\\"\\u003e\\u003cdiv class=\\\"CaptionNumber\\\"\\u003eTable 2\\u003c/div\\u003e\\u003cdiv class=\\\"CaptionContent\\\"\\u003e\\u003cp\\u003eMean values and standard deviation of physicochemical properties of seawater samples collected from infected seaweed sample collection sites and their dominant microbial phylum level.\\u003c/p\\u003e\\u003c/div\\u003e\\u003c/caption\\u003e\\u003ccolgroup cols=\\\"6\\\"\\u003e\\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c1\\\" colnum=\\\"1\\\"\\u003e\\u003c/div\\u003e\\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c2\\\" colnum=\\\"2\\\"\\u003e\\u003c/div\\u003e\\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c3\\\" colnum=\\\"3\\\"\\u003e\\u003c/div\\u003e\\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c4\\\" colnum=\\\"4\\\"\\u003e\\u003c/div\\u003e\\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c5\\\" colnum=\\\"5\\\"\\u003e\\u003c/div\\u003e\\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c6\\\" colnum=\\\"6\\\"\\u003e\\u003c/div\\u003e\\u003cthead\\u003e\\u003ctr\\u003e\\u003cth align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003eCultivation site\\u003c/p\\u003e\\u003c/th\\u003e\\u003cth align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003eSoliyakudi\\u003c/p\\u003e\\u003c/th\\u003e\\u003cth align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003eJegathapattinam\\u003c/p\\u003e\\u003c/th\\u003e\\u003cth align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u003cp\\u003eNambuthalai\\u003c/p\\u003e\\u003c/th\\u003e\\u003cth align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u003cp\\u003eOdavimadam\\u003c/p\\u003e\\u003c/th\\u003e\\u003cth align=\\\"left\\\" colname=\\\"c6\\\"\\u003e\\u003cp\\u003eMangadu\\u003c/p\\u003e\\u003c/th\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003cth align=\\\"left\\\" colspan=\\\"6\\\" nameend=\\\"c6\\\" namest=\\\"c1\\\"\\u003e\\u003cp\\u003ePhysical parameter\\u003c/p\\u003e\\u003c/th\\u003e\\u003c/tr\\u003e\\u003c/thead\\u003e\\u003ctbody\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003eAir tem. (ᵒC)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003e28.6\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;0.5\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003e28.3\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;0.5\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u003cp\\u003e29\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;0.0\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u003cp\\u003e28.3\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;0.5\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e\\u003cp\\u003e30.6\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;0.5\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003eWater tem. (ᵒC)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003e27\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;0.0\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003e26\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;0.0\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u003cp\\u003e26\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;0.0\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u003cp\\u003e25\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;0.0\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e\\u003cp\\u003e28\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;0.0\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003eSalinity (psu)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003e33.6\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;0.5\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003e34\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;0.0\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u003cp\\u003e34\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;0.8\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u003cp\\u003e33.6\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;1.03\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e\\u003cp\\u003e36.6\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;0.5\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003epH\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003e8.29\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;0.03\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003e8.35\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;0.02\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u003cp\\u003e8.41\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;0.01\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u003cp\\u003e8.3\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;0.01\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e\\u003cp\\u003e8.16\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;0.06\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003eWater current (M/min)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003e4\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;0.0\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003e6.5\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;0.0\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u003cp\\u003e3.4\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;0.0\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u003cp\\u003e6\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;0.0\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e\\u003cp\\u003e5\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;0.0\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colspan=\\\"6\\\" nameend=\\\"c6\\\" namest=\\\"c1\\\"\\u003e\\u003cp\\u003e\\u003cb\\u003eChemical parameter\\u003c/b\\u003e\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003eNo\\u003csub\\u003e2\\u003c/sub\\u003e\\u003csup\\u003e\\u0026minus;\\u003c/sup\\u003eN (mg/l)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003e0.003\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;0.0005\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003e0.009\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;0.0018\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u003cp\\u003e0.004\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;0.001\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u003cp\\u003e0.014\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;0.002\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e\\u003cp\\u003e0.004\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;0.004\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003eNo\\u003csub\\u003e3\\u003c/sub\\u003e\\u003csup\\u003e\\u0026minus;\\u003c/sup\\u003eN (mg/l)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003e0.027\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;0.0005\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003e0.087\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;0.0019\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u003cp\\u003e0.09\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;0.02\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u003cp\\u003e0.044\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;0.005\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e\\u003cp\\u003e0.035\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;0.003\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003ePo\\u003csub\\u003e4\\u003c/sub\\u003e\\u003csup\\u003e\\u0026minus;\\u003c/sup\\u003e N (mg/l)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003e0.038\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;0.002\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003e0.091\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;0.001\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u003cp\\u003e0.052\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;0.02\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u003cp\\u003e0.044\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;0.004\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e\\u003cp\\u003e0.048\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;0.047\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003eNH\\u003csub\\u003e3\\u003c/sub\\u003e (mg/l)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003e0.004\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;0.001\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003e0.007\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;0.0008\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u003cp\\u003e0.005\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;0.02\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u003cp\\u003e0.008\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;0.0008\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e\\u003cp\\u003e0.004\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;0.001\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003eSiO\\u003csub\\u003e2\\u003c/sub\\u003e (mg/l)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003e0.045\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;0.0004\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003e0.055\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;0.005\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u003cp\\u003e0.053\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;0.005\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u003cp\\u003e0.105\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;0.001\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e\\u003cp\\u003e0.074\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;0.001\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003eD.O (mg/l)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003e5.6\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;0.1\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003e5.8\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;0.02\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u003cp\\u003e6.3\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;0.4\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u003cp\\u003e4.8\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;0.7\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e\\u003cp\\u003e8.4\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;0.3\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colspan=\\\"6\\\" nameend=\\\"c6\\\" namest=\\\"c1\\\"\\u003e\\u003cp\\u003e\\u003cb\\u003eMicrobial Diversity (Phylum level)\\u003c/b\\u003e\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003eDiseased dominant bacteria\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003eGammaproteobacteria and Alphaproteobacteria\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003eGammaproteobacteria and Clostridia\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u003cp\\u003eGammaproteobacteriaand Clostridia\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u003cp\\u003eGammaproteobacteria, Clostridia and Alphaproteobacteria\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e\\u003cp\\u003eGammaproteobacteria and Alphaproteobacteria\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003eHealthy dominant bacteria\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003eGammaproteobacteria\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003eGammaproteobacteria and Clostridia\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u003cp\\u003eGammaproteobacteriaand Clostridia\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u003cp\\u003eGammaproteobacteriaand Clostridia\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e\\u003cp\\u003eGammaproteobacteria\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003c/tbody\\u003e\\u003c/colgroup\\u003e\\u003ctfoot\\u003e\\u003ctr\\u003e\\u003ctd colspan=\\\"6\\\"\\u003ePhysicochemical properties of seawater from infected seaweed cultivation sites. Values are presented as mean\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;standard deviation. The table also shows the dominant microbial phylum present in diseased and healthy samples at each site.\\u003c/td\\u003e\\u003c/tr\\u003e\\u003c/tfoot\\u003e\\u003c/table\\u003e\\u003c/div\\u003e\\u003c/p\\u003e\\u003c/div\\u003e\\u003cdiv id=\\\"Sec14\\\" class=\\\"Section2\\\"\\u003e\\u003ch2\\u003eMicrobial Diversity and Community Composition\\u003c/h2\\u003e\\u003cp\\u003eThe bacterial community structure associated with \\u003cem\\u003eK. alvarezii\\u003c/em\\u003e shows significant variation between diseased and healthy samples from different sites (Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig2\\\" class=\\\"InternalRef\\\"\\u003e2\\u003c/span\\u003e). This study examines bacteria's phylum and genus-level diversity, revealing distinct patterns of abundance and composition that offer insights into the complex relationship between \\u003cem\\u003eK. alvarezii\\u003c/em\\u003e and its associated bacterial communities.\\u003c/p\\u003e\\u003cp\\u003e\\u003c/p\\u003e\\u003cp\\u003eAt all the site, Proteobacteria phylum was found to be dominant, among them, Gammaproteobacteria was consistently prevalent. At the phylum level, the diseased samples from Mangadu and Soliyakudi showed a high abundance of both Gammaproteobacteria and Alphaproteobacteria. In contrast, their healthy counterparts showed a low abundance primarily of Gammaproteobacteria. The Nambuthalai and Jegadhapattinam sites displayed a high abundance of Gammaproteobacteria and \\u003cem\\u003eClostridia\\u003c/em\\u003e in diseased samples, with low to moderate abundance in healthy samples. Odavimadam uniquely showed an equal abundance in both diseased and healthy samples, though with slightly different compositions.\\u003c/p\\u003e\\u003cp\\u003eAt the genus level (Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig3\\\" class=\\\"InternalRef\\\"\\u003e3\\u003c/span\\u003e), in the Mangadu diseased sample, high abundance of Others, Cobatia, and Halomonas was observed, while its healthy sample contained low abundance of only \\u003cem\\u003eCobatia\\u003c/em\\u003e and \\u003cem\\u003eHalomonas\\u003c/em\\u003e. In Soliyakudidiseased sample was dominated by \\u003cem\\u003ePseudoalteromonas\\u003c/em\\u003e and \\u003cem\\u003eCobatia\\u003c/em\\u003e, while Nambuthalai's diseased sample showed high abundance of unclassified genera and \\u003cem\\u003ePseudoalteromonas\\u003c/em\\u003e. In Jegadhapattinam, the diseased sample was characterized by Pseudoalteromonas and Psychrobacter, and in Odavimadam, the diseased sample contained high levels of unclassified genera and \\u003cem\\u003eOceanisphaera\\u003c/em\\u003e.\\u003c/p\\u003e\\u003cp\\u003e\\u003c/p\\u003e\\u003c/div\\u003e\\u003cdiv id=\\\"Sec15\\\" class=\\\"Section2\\\"\\u003e\\u003ch2\\u003eDiversity analysis\\u003c/h2\\u003e\\u003cp\\u003eWhile conducting ecological analysis and studying ecological communities, it is essential to measure the community diversity. In this study, a total of 2,071,476 high-quality reads were obtained for the bacterial species, among which 80,977 were unique reads with an average length of 250 bp. It was clear that the diseased samples had more unique raw reads as compared to the healthy samples. Overall, among the five sample sites, the raw reads were comparatively fewer (1,145,158) in diseased \\u003cem\\u003eK. alvarzii\\u003c/em\\u003e samples than in the healthy ones (1,219,912). The bacterial communities from the five closely located sites were compared between the healthy and diseased samples. From Jegathapattinam, Mangadu, Nambuthalai and Odavimadam sites, the healthy (243,634; 268,598; 260,420; 226,420) samples had more raw reads as compared to diseased samples (234,020; 200,594; 260,142; 203,752). Although in the Soliyakkudi site, the healthy sample (220,546) had fewer raw reads as compared to the diseased ones (246,650).\\u003c/p\\u003e\\u003cp\\u003eA total of 2,020 bacterial OTUs were identified across all 5 seaweed samples, i.e., 331 from Jegathapattinam, 345 from Mangadu, 504 from Nambuthalai, 581 from Odavimadam, and 259 from Soliyakkudi (Supplementary Table\\u0026nbsp;1). This clearly shows that the bacterial diversity is higher in samples from Odavimadam and lower in samples from Soliyakkudi. This difference was statistically studied with the help of rarefaction curves, Shannon index, relative abundance plots, etc.\\u003c/p\\u003e\\u003cp\\u003eThe rarefaction curve is generally used to deduce that the volume of the sequenced reads is reasonable by assessing the abundance of the bacterial OTUs, which are close to saturation. The rarefaction curves revealed that the alpha diversity of the bacterial lineages for the seaweed samples from Nambuthalai was higher among the diseased samples (OTU\\u0026thinsp;\\u0026gt;\\u0026thinsp;450; Raw Reads\\u0026thinsp;\\u0026gt;\\u0026thinsp;45,000) and Odavimadam was higher among the healthy samples (OTU\\u0026thinsp;\\u0026gt;\\u0026thinsp;400; Raw Reads\\u0026thinsp;\\u0026gt;\\u0026thinsp;50,000) compared to the other 4 sites' samples. (Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig4\\\" class=\\\"InternalRef\\\"\\u003e4\\u003c/span\\u003e).\\u003c/p\\u003e\\u003cp\\u003e\\u003c/p\\u003e\\u003cp\\u003eThe Shannon index depicts the statistical results of the community diversity in each sample. The higher Shannon index value suggests that the diversity of organisms is rich. The 10 different samples (diseased and healthy each) from 5 different sites illustrate that, between the diseased and healthy samples, the former has more microbial diversity compared to the latter. Overall, the Odavimadam site has a higher Shannon index (diseased\\u0026thinsp;\\u0026gt;\\u0026thinsp;3.7 and healthy\\u0026thinsp;\\u0026gt;\\u0026thinsp;3), which portrays that there are more diversified microbes in this site compared to the other 4 sites (Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig5\\\" class=\\\"InternalRef\\\"\\u003e5\\u003c/span\\u003e).\\u003c/p\\u003e\\u003cp\\u003e\\u003c/p\\u003e\\u003c/div\\u003e\\u003cdiv id=\\\"Sec16\\\" class=\\\"Section2\\\"\\u003e\\u003ch2\\u003eRelative Abundance across the sampling sites\\u003c/h2\\u003e\\u003cp\\u003eA total of 22 bacterial phyla were detected in the \\u003cem\\u003eK. alvarezii\\u003c/em\\u003e healthy seaweed samples, and about 22 bacterial phyla were detected in the \\u003cem\\u003eK. alvarezii\\u003c/em\\u003e diseased seaweed samples. Proteobacteria (41.16%) and Actinobacteria (38.33%) were the most abundant phyla in each of the seaweed niches. The major phyla in healthy and diseased \\u003cem\\u003eK. alvarezii\\u003c/em\\u003e seaweed samples were similar, with Proteobacteria (83.4%), Firmicutes (10.2%), and Actinobacteria (4.5%) being the most abundant phyla. The frequency of bacterial phyla in the healthy and diseased \\u003cem\\u003eK. alvarezii\\u003c/em\\u003e seaweed samples varied greatly. The abundance of Proteobacteria was higher in healthy \\u003cem\\u003eK. alvarezii\\u003c/em\\u003e seaweed samples (87.4%) compared to diseased \\u003cem\\u003eK. alvarezii\\u003c/em\\u003e seaweed samples (75.8%), which was the most abundant phylum in all the samples, followed by Firmicutes and Actinobacteria. Of the 167 bacterial classes, Gammaproteobateria, Alphaproteobacteria, Clostridiales, Veillionellaceae had a higher abundance, in diseased \\u003cem\\u003eK. alvarezii\\u003c/em\\u003e seaweed samples whereas, Gammaproteobateria, Alphaproteobacteria and Clostridiales had a higher abundance, in healthy \\u003cem\\u003eK. alvarezii\\u003c/em\\u003e seaweed samples (Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig6\\\" class=\\\"InternalRef\\\"\\u003e6\\u003c/span\\u003e). Among the multiple genus identified, \\u003cem\\u003ePseudohalomonas\\u003c/em\\u003e, \\u003cem\\u003eHalomonas\\u003c/em\\u003e, \\u003cem\\u003eCobetia\\u003c/em\\u003e, \\u003cem\\u003eVibrio\\u003c/em\\u003e and \\u003cem\\u003eAgathobacter\\u003c/em\\u003e were the most abundant in the diseased \\u003cem\\u003eK. alvarezii\\u003c/em\\u003e samples however, \\u003cem\\u003ePseudohalomonas\\u003c/em\\u003e, \\u003cem\\u003eHalomonas\\u003c/em\\u003e, \\u003cem\\u003eCobetia\\u003c/em\\u003e, and \\u003cem\\u003eAgathobacter\\u003c/em\\u003e were the most abundant in the healthy \\u003cem\\u003eK. alvarezii\\u003c/em\\u003e samples (Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig6\\\" class=\\\"InternalRef\\\"\\u003e6\\u003c/span\\u003e).\\u003c/p\\u003e\\u003cp\\u003e\\u003c/p\\u003e\\u003cp\\u003eIn the Mangadu site\\u0026rsquo;s diseased sample, the major bacterial phyla were Proteobacteria (76%), Firmicutes (13%), Actinobacteria (4%) and Acidobacteria (3%). The major bacterial class were, Gammaproteobacteria (57%), Alphaproteobacteria (18%) and Clostridiales (10%) and the major bacterial genus were \\u003cem\\u003eCobetia\\u003c/em\\u003e (37%), \\u003cem\\u003eHalomonas\\u003c/em\\u003e (11%), \\u003cem\\u003eAzospirillum\\u003c/em\\u003e (2%), \\u003cem\\u003eSphingomonas\\u003c/em\\u003e (4%), \\u003cem\\u003eAgathobacter\\u003c/em\\u003e (4%), \\u003cem\\u003eVibrio\\u003c/em\\u003e (2%), and \\u003cem\\u003ePseudohalomonas\\u003c/em\\u003e (2%). In the healthy ones the major bacterial phyla were Proteobacteria (95%), Firmicutes (3%), the major bacterial class were Gammaproteobacteria (95%), and Clostridiales (3%), the major bacterial genera were \\u003cem\\u003eCobetia\\u003c/em\\u003e (85%), \\u003cem\\u003eHalomonas\\u003c/em\\u003e (9%) and \\u003cem\\u003eAgatobacter\\u003c/em\\u003e (1%).\\u003c/p\\u003e\\u003cp\\u003eIn the Soliyakkudi site, the major bacterial phyla discovered in the diseased sample were Proteobacteria (81%), and Firmicutes (11%), the major bacterial class were Gammaproteobacteria (77%), Clostridiales (8%), Alphaproteobacteria (3%), Veillonellaceae (3%) Gammaproteobacteria (2%), and finally, the major bacterial genus were \\u003cem\\u003ePaeudohalomonas\\u003c/em\\u003e (38%), \\u003cem\\u003eCobetia\\u003c/em\\u003e (23%), Vibrio (10%), \\u003cem\\u003eAgathobacter\\u003c/em\\u003e (4) and \\u003cem\\u003eHalomonas\\u003c/em\\u003e (3%), whereas in the healthy ones the major bacterial phyla were Proteobacteria (95%), Firmicutes (4%), the major bacterial class were Gammaproteobacteria (95%), Clostridiales (3%), the major bacterial genus were \\u003cem\\u003eCobetia\\u003c/em\\u003e (66%), \\u003cem\\u003ePaeudohalomonas\\u003c/em\\u003e (23%), \\u003cem\\u003eHalomonas\\u003c/em\\u003e (6%), \\u003cem\\u003eAgathobacter\\u003c/em\\u003e (2%).\\u003c/p\\u003e\\u003cp\\u003eIn the Nambuthalai site, the major bacterial phyla identified in the diseased sample were Proteobacteria (68%), Firmicutes (16%), Actinobacteria (9%). The major bacterial class were Gammaproteobacteria (64%), Clostridiales (12%), Actibnobacteria (3%). Majorly bacterial genus such as \\u003cem\\u003eCobetia\\u003c/em\\u003e (29%), \\u003cem\\u003ePseudohalomonas\\u003c/em\\u003e (22%), Vibrio (6%), \\u003cem\\u003eAgathobacter\\u003c/em\\u003e (4%), \\u003cem\\u003eHalomonas\\u003c/em\\u003e (4%), \\u003cem\\u003eAlphaproteobateria\\u003c/em\\u003e (3%) were observed. In the healthy ones the major bacterial phyla were Proteobacteria (95%), Firmicutes (7%). Gammaproteobacteria (92%), Clostridiales (4%) and Bacilli (2%) were observed as the major bacterial class. The major bacterial genus were \\u003cem\\u003eCobetia\\u003c/em\\u003e (66%), \\u003cem\\u003ePseudohalomonas\\u003c/em\\u003e (11%), \\u003cem\\u003eHalomonas\\u003c/em\\u003e (14%), \\u003cem\\u003eAgathobacter\\u003c/em\\u003e (2%), \\u003cem\\u003eLactobacillus\\u003c/em\\u003e (1%).\\u003c/p\\u003e\\u003cp\\u003eIn the Jegathapattinam site, the major bacterial phyla spotted in the diseased sample were Proteobacteria (75%), Firmicutes (19%), Actinobacteria (3%) the major bacterial class were Gammaproteobacteria (74%), Clostridiales (15%), Veillonellaceae (3), Actinobacteria (3%) and Alphaproteobacteria (1%) and the major bacterial genus were \\u003cem\\u003ePseudoalteriomonas\\u003c/em\\u003e (28%), \\u003cem\\u003ePsychobacter\\u003c/em\\u003e (17%), \\u003cem\\u003eCobetia\\u003c/em\\u003e (10%), \\u003cem\\u003eVibrio\\u003c/em\\u003e (7%), \\u003cem\\u003eAgathobacter\\u003c/em\\u003e (6%), \\u003cem\\u003eShewanella\\u003c/em\\u003e (4%) and \\u003cem\\u003eDialister\\u003c/em\\u003e (2%) whereas in the healthy ones the major bacterial phyla were Proteobacteria (78%), Firmicutes (21%), the major bacterial class were Gammaproteobacteria (78%), Clostridiales (17%), and the major bacterial genus were \\u003cem\\u003ePseudoalteriomonas\\u003c/em\\u003e (43%), \\u003cem\\u003eCobetia\\u003c/em\\u003e (20%), \\u003cem\\u003eHalomonas\\u003c/em\\u003e (11%), \\u003cem\\u003eAgathobacer\\u003c/em\\u003e (4%), \\u003cem\\u003eClostridiun sensu strico 1\\u003c/em\\u003e (3%), \\u003cem\\u003ePseudostreptococcaceae\\u003c/em\\u003e (3%), \\u003cem\\u003eShewanella\\u003c/em\\u003e (2%), and \\u003cem\\u003eVibrio\\u003c/em\\u003e (1%).\\u003c/p\\u003e\\u003cp\\u003eIn the Odavimadam site, the major bacterial phyla discovered in the diseased sample were Proteobacteria (79%), Firmicutes (13%), and Actinobacteria (4%), the major bacterial class were Gammaproteobacteria (67%), Alphaproteobacteria (9%), Clostridiales (9%), Deltaproteobacteria (5%), Veillonellaceae (3%), Actinobacteria (2%) and the major bacterial genus were \\u003cem\\u003eOceanisephaera\\u003c/em\\u003e (21%), \\u003cem\\u003ePseudoateriomonas\\u003c/em\\u003e (12%), \\u003cem\\u003eCobetia\\u003c/em\\u003e (11%), \\u003cem\\u003eVibrio\\u003c/em\\u003e (10%), \\u003cem\\u003ePsycrobacter\\u003c/em\\u003e (7%), \\u003cem\\u003eAgathobacter\\u003c/em\\u003e (4%), \\u003cem\\u003eShewanella\\u003c/em\\u003e (2%), \\u003cem\\u003eDialister\\u003c/em\\u003e (2%) and \\u003cem\\u003eHalomonas\\u003c/em\\u003e (1%) whereas in the healthy ones the major bacterial phyla were Proteobacteria (77%), Firmicutes (14%) and Actinobacteria (5%) the major bacterial class were Gammaproteobacteria (75%), Clostridiales (9%), Alphaproteobacteria (9%), Veillonellaceae (3%), Actinobacteria (2%) and the major bacterial genus were \\u003cem\\u003eCobetia\\u003c/em\\u003e (50%), \\u003cem\\u003eHalomonas\\u003c/em\\u003e (13%), \\u003cem\\u003ePseudoalteriomonas\\u003c/em\\u003e (10%), \\u003cem\\u003eAgathobacter\\u003c/em\\u003e (4%), \\u003cem\\u003eAlphaproteobacteria\\u003c/em\\u003e (2%) (Supplementary Fig.\\u0026nbsp;1).\\u003c/p\\u003e\\u003c/div\\u003e\\u003cdiv id=\\\"Sec17\\\" class=\\\"Section2\\\"\\u003e\\u003ch2\\u003eHeat Tree Analysis of Bacterial Communities\\u003c/h2\\u003e\\u003cp\\u003eHeat tree analysis emphasized the taxonomic structure and abundance structures of bacterial communities linked to \\u003cem\\u003eK. alvarezii\\u003c/em\\u003e across five sampling sites, each with peculiar compositions. Mangadu site shows a complex structure and balanced abundance among diverse bacterial lineages. Moderate complexity with clustered taxa and specific abundance patterns was observed at Soliyakudi site. Whereas, a simplified structure with lower diversity and dominance of certain groups was noted at the Nambuthalai site. The Jagadhapattinam site was observed with intermediate complexity and was characterized by unique patterns. The Odavimadam site revealed the highest complexity and diversity, with even abundance distribution across taxonomic levels. The heat trees effectively illustrated the quantitative differences in bacterial abundance, emphasizing the taxonomic hierarchy within each site microbiome (Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig7\\\" class=\\\"InternalRef\\\"\\u003e7\\u003c/span\\u003ea-e).\\u003c/p\\u003e\\u003cp\\u003e\\u003c/p\\u003e\\u003c/div\\u003e\\u003cdiv id=\\\"Sec18\\\" class=\\\"Section2\\\"\\u003e\\u003ch2\\u003eFunctional prediction of bacteria associated with diseased and healthy\\u003c/h2\\u003e\\u003cp\\u003eThe functional prediction analysis revealed that the functional groups of the bacterial microbiota associated with seaweed were similar (Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig8\\\" class=\\\"InternalRef\\\"\\u003e8\\u003c/span\\u003e). Although the microbial composition associated with both healthy and diseased varied across collection sites. In Mangadu, the diseased state (KaMD) is characterized by high expression of fermentation, nitrate reduction, nitrate respiration, ureolysis, methylotrophy, nitrogen fixation, and methanol oxidation pathways. In contrast, the healthy state (KaMDH) shows upregulation of chemoheterotrophy, aerobic chemoheterotrophy, and hydrocarbon degradation. Soliyakudi shows a diseased condition (KaSD) with enrichment in fermentation, chloroplast activity, sulfate respiration, and nitrogen respiration. The diseased state in Soliyakudi (KaSH) exhibits a high expression of aerobic chemoheterotrophy, chemoheterotrophy, and hydrocarbon degradation. In Namuthalai, the diseased state (KaND) demonstrates elevated activity in fermentation, nitrification, aerobic ammonia oxidation, nitrate reduction, nitrogen respiration, and nitrate respiration. This contrasts with the healthy state (KaNH), which shows strong activity in aerobic chemotrophy, chemoheterotrophy, and hydrocarbon degradation. Jagathapattinam exhibits a diseased condition (KaJD) with upregulation of aerobic chemotrophy, chemoheterotrophy, and hydrocarbon degradation pathways. The healthy state (KaJH) shows strong expression of nitrification, nitrogen respiration, fermentation, nitrate reduction, and sulfate respiration pathways. Finally, Odavimadam reveals a diseased state (KaOD) with enrichment in nitrate reduction, nitrogen respiration, sulfate respiration, respiration of sulfate compounds, and fermentation. The healthy state (KaOH) exhibits high activity in nitrification, hydrocarbon degradation, aerobic chemoheterotrophy, and chemoheterotrophy (Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig8\\\" class=\\\"InternalRef\\\"\\u003e8\\u003c/span\\u003e).\\u003c/p\\u003e\\u003cp\\u003e\\u003c/p\\u003e\\u003c/div\\u003e\"},{\"header\":\"Discussion\",\"content\":\"\\u003cp\\u003eThe present study evaluated the microbial community structure of healthy and diseased \\u003cem\\u003eK. alvarezii\\u003c/em\\u003e collected from five different seaweed cultivation sites in southern coast of Tamil Nadu, India (Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig1\\\" class=\\\"InternalRef\\\"\\u003e1\\u003c/span\\u003e). In the year of 2013, severe impact was observed on cultivation of \\u003cem\\u003eK. alvarezii\\u003c/em\\u003e according to the reported findings of Mantri et al. (2022), that have an influential role on seaweed cultivators to abandon their practices because of the seaweed diseases which remained unidentified (Mantri et al., 2024). In the beginning, the change of climate was considered to be the cause, however, later it was discovered that an opportunistic pathogen was found to be responsible for disease (Largo et al., 2017). This metagenomic analysis of healthy and diseased \\u003cem\\u003eK. alvarezii\\u003c/em\\u003e were were collected from five different active cultivation sites helped to decipher the root cause behind seaweed disease in India.\\u003c/p\\u003e\\u003cp\\u003ePhysicochemical parameters at five cultivation sites significantly influence microbial communities associated with seaweed in both healthy and infected samples (Table\\u0026nbsp;\\u003cspan refid=\\\"Tab2\\\" class=\\\"InternalRef\\\"\\u003e2\\u003c/span\\u003e). Higher temperatures and salinity at Mangadu (30.6\\u0026deg;C, 36.6 PSU) favor specific microbial taxa, altering diversity and metabolic functions. Dissolved oxygen levels (4.8 mg/L in Odavimadam to 8.4 mg/L in Mangadu) influence the abundance of aerobic and facultative anaerobic microorganisms, affecting community composition. Elevated nitrite, nitrate, and phosphate levels in Jegathapattinam and Odavimadam promote unique nutrient-cycling groups that may alter seaweed-microbe interactions. Slight variations in pH (8.16\\u0026ndash;8.41) can affect enzyme activity and overall metabolism within the seaweed microbiome, influencing responses to environmental factors (Menaa et al., 2020; Minich et al., 2018).\\u003c/p\\u003e\\u003cp\\u003eThe analysis of microbial diversity revealed that the microbiome associated with both healthy and diseased seaweed varies at each collection site (Figs.\\u0026nbsp;\\u003cspan refid=\\\"Fig2\\\" class=\\\"InternalRef\\\"\\u003e2\\u003c/span\\u003e and \\u003cspan refid=\\\"Fig3\\\" class=\\\"InternalRef\\\"\\u003e3\\u003c/span\\u003e) and may be influenced by environmental factors (Ndawala et al., 2022; Faisan et al., 2024). Abiotic stressors impact the structure of the bacterial community, even during a single intertidal period, due to unstable of levels of desiccation, temperature, light, and humidity (Vigil et al., 2024; Centurion et al., 2021). Consequently, the microbiomes at different sites varied significantly from one another (Vohsen et al., 2024). Furthermore, there is a phenomenon known as the host effect, which refers to the ability of seaweed to produce metabolic substances that selectively influence microbial adhesion and settlement in their environment (Mesquita et al., 2019; Singh et al., 2014). This process contributes to the development of specific microbial communities. It also activates genes in the bacterial community that are essential for metabolizing substrates or producing metabolites, many of which are beneficial to the host (Lu et al., 2025).\\u003c/p\\u003e\\u003cp\\u003eThe identified microbiome exhibited differences from those found in both healthy and diseased individuals at various sites (Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig2\\\" class=\\\"InternalRef\\\"\\u003e2\\u003c/span\\u003e). In the present study, Proteobacteria emerged as the predominant phylum across all sampling sites, with Gammaproteobacteria consistently detected as a major class. The dominance of Proteobacteria in seawater samples from all five locations suggests their ecological versatility and potential role in shaping microbial community structure, as also highlighted in previous reports (Gu et al., 2023). The bacterial phylum distribution across the five sampling sites reveals important patterns in the comparison of diseased and healthy \\u003cem\\u003eK. alvarezii\\u003c/em\\u003e samples. The dominance of Gammaproteobacteria and Alphaproteobacteria in diseased samples from Mangadu and Soliyakudi suggests these groups may be crucial to disease processes. The proliferation of opportunistic microorganisms in response to seaweed indicates ecological succession (Singh et al., 2018; Egan et al., 2013). The higher abundance of Gammaproteobacteria and Clostridia in diseased samples from Nambuthalai and Jegadhapattinam, compared to healthy ones, suggests a link between these bacteria and disease. Clostridia, being anaerobic, are well-suited to proliferate in environments where tissue degradation leads to diminished oxygen levels and the accumulation of organic matter, thereby creating conditions that support their growth and metabolic activity (Facimoto et al., 2024). In Odavimadam, similar bacterial abundance in both diseased and healthy samples hints at site-specific factors affecting host-microbe interactions, possibly indicating varied disease etiology or stable environmental conditions. These variations at the phylum level highlight the complexity and site-specific nature of \\u003cem\\u003eK. alvarezii\\u003c/em\\u003e disease, showing that local conditions significantly influence microbial dynamics in seaweed cultivation.\\u003c/p\\u003e\\u003cp\\u003eThe comparison at the genus level of bacterial communities from five sampling locations indicates different patterns associated with \\u003cem\\u003eK. alvarezii\\u003c/em\\u003e disease and healthy samples (Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig3\\\" class=\\\"InternalRef\\\"\\u003e3\\u003c/span\\u003e). Diseased samples in Mangadu showed high proportions of unclassified genera, \\u003cem\\u003eCobetia\\u003c/em\\u003e, and \\u003cem\\u003eHalomonas\\u003c/em\\u003e, indicating greater taxonomic diversity with disease development. Existing literature demonstrates that bacterial densities on and inside ice-iced shoots of \\u003cem\\u003eK. alvarezii\\u003c/em\\u003e are 10\\u0026ndash;100 fold larger than healthy tissue, with microbiome changes playing crucial role a role in the causing ice-ice disease (Riyaz et al., 2020; Largo et al., 1995).\\u003c/p\\u003e\\u003cp\\u003eIn Soliyakudi site, diseased samples showed enrichment of \\u003cem\\u003ePseudoalteromonas\\u003c/em\\u003e, a genus reported to secrete bioactive metabolites with the potential to degrade or damage algal tissues, thereby contributing to disease progression. \\u003cem\\u003ePseudoalteromonas\\u003c/em\\u003e organisms are linked with larger organisms and possessing extracellular biologically active agents, and these algal cell wall degrading bacteria are capable of damaging algal tissues and facilitate an entry for opportunistic bacteria (Hollants et al., 2013). The adoptive evolution of algal polysaccharide degradation in \\u003cem\\u003eP. carrageenovora\\u003c/em\\u003e shows them to be specialized and adapted to seaweed habitats, with an estimated half of seaweed biomass made up of polysaccharides that these bacteria are capable of breaking down (Gobet et al., 2018).\\u003c/p\\u003e\\u003cp\\u003eNambuthalai site samples also contained high levels of \\u003cem\\u003ePseudoalteromonas\\u003c/em\\u003e and unclassified bacteria, suggesting polymicrobial disease communities of high complexity. At Jegadhapattinam, the presence of \\u003cem\\u003ePseudoalteromonas\\u003c/em\\u003e and \\u003cem\\u003ePsychrobacter\\u003c/em\\u003e suggests distinct disease mechanisms, while high occurrences of Oceanisphaera in Odavimadam diseased samples indicate potential new pathogenic associations. The Formation of ice-ice disease in \\u003cem\\u003eK. alvarezii\\u003c/em\\u003e is related to microbiome changes influencing carrageenan production and seaweed structural integrity (Riyaz et al., 2021)\\u003c/p\\u003e\\u003cp\\u003eThis study generally demonstrated higher bacterial diversity in diseased \\u003cem\\u003eK. alvarezii\\u003c/em\\u003e samples compared to healthy samples, consistent with the findings of Zozaya et al. (2015), as evidenced by Shannon diversity indices (Figs.\\u0026nbsp;\\u003cspan refid=\\\"Fig4\\\" class=\\\"InternalRef\\\"\\u003e4\\u003c/span\\u003e and \\u003cspan refid=\\\"Fig5\\\" class=\\\"InternalRef\\\"\\u003e5\\u003c/span\\u003e). The Odavimadam site exhibited the highest overall microbial diversity. This aligns with previous studies suggesting that increased diversity may result from opportunistic colonization when host defense mechanisms are disrupted (Caballero et al., 2023). There are significant differences in bacterial population across sites, with Odavimadam presenting 581 OTUs and Soliyakkudi only 259 (Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig4\\\" class=\\\"InternalRef\\\"\\u003e4\\u003c/span\\u003e). These discrepancies are likely due to the distinct environmental conditions at each site (Gianoulis et al., 2009). At the phylum level, Proteobacteria were the most abundant in both healthy (87.4%) and diseased samples (75.8%). Interestingly, \\u003cem\\u003eVibrio\\u003c/em\\u003e sp. was mainly detected in diseased samples, highlighting its possible role as a pathogen (Ward et al., 2022). Overall, it is clearly noticeable that microbial population was strong similar among the Mangadu, Soliyakudi, Nanbuthalai, sites. Alike the Jegadhapattinam and Odavimadam sites showed extreme similarity in microbial community.\\u003c/p\\u003e\\u003cp\\u003eThe heat tree visualization effectively emphasized taxonomic hierarchies, revealing notable site specific variations in the diversity and abundance of \\u003cem\\u003eK. alvarezii\\u003c/em\\u003e microbiomes across five sampling sites (Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig7\\\" class=\\\"InternalRef\\\"\\u003e7\\u003c/span\\u003e). The complex relationships within microbial communities, showing that different locations host distinct bacterial assemblages with varying taxonomic complexity (Yan et al., 2024; Juhmani et al., 2020; Xie et al., 2024). The architecture of heat trees, from simple structures to complex communities, reflects local environmental and ecological influences on bacterial community assembly (Xu et al., 2022; Egan et al., 2013).\\u003c/p\\u003e\\u003cp\\u003eDespite the differences between groups, some genera, such as \\u003cem\\u003eCobetia\\u003c/em\\u003e, \\u003cem\\u003ePseudohalomonas\\u003c/em\\u003e, and \\u003cem\\u003eHalomonas\\u003c/em\\u003e, were consistently present. \\u003cem\\u003eCobetia\\u003c/em\\u003e was more abundant in healthy samples compared to the diseased ones, suggesting its beneficial role. The association of \\u003cem\\u003eVibrio\\u003c/em\\u003e with disease highlights its potential as an early microbial indicator (Largo et al., 1998), while beneficial bacteria may provide avenues for developing probiotics to enhance seaweed health (Mahajan et al., 2025).\\u003c/p\\u003e\\u003cp\\u003eThe functional prediction analysis indicated metabolic changes associated with both healthy and diseased samples (Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig8\\\" class=\\\"InternalRef\\\"\\u003e8\\u003c/span\\u003e). In contrast, diseased samples displayed enrichment in fermentation, nitrate reduction, nitrogen respiration, and sulfate metabolism pathways at various sites. Meanwhile, healthy samples consistently showed enhanced expression of aerobic chemoheterotrophy and hydrocarbon degradation functions. These results reveal, discrepancies in physicochemical characteristics influenced the microbial community structures, with Odavimadam showing the lowest DO at 4.8 mg/L and Mangadu showing the highest at 8.4 mg/L. These findings clearly show that environmental determinants and disease status significantly impact the composition and functional potential of the bacterial communities associated with \\u003cem\\u003eK. alvarezii\\u003c/em\\u003e.\\u003c/p\\u003e\"},{\"header\":\"Conclusion\",\"content\":\"\\u003cp\\u003eThis study reveals variance in microbial community structures between healthy and diseased \\u003cem\\u003eK. alvarezii\\u003c/em\\u003e across five cultivation sites in Tamil Nadu, India. Proteobacteria were observed to be the dominant class across all sites, in which Gammaproteobacteria were consistently present. Diseased samples show higher bacterial diversity compared to healthy samples, indicating that opportunistic colonization occurs following disruptions in host defenses. \\u003cem\\u003ePseudoalteromonas\\u003c/em\\u003e and \\u003cem\\u003eVibrio\\u003c/em\\u003e were primarily identified in diseased samples, indicating their potential role in disease pathology. \\u003cem\\u003eCobetia\\u003c/em\\u003e was more abundant in healthy samples, indicating the beneficial association with the host. Divergences in microbial communities were site-specific, with differences in physicochemical parameters such as temperature, salinity, dissolved oxygen, and nutrient levels.\\u003c/p\\u003e\\u003cp\\u003eFunctional prediction analysis revealed that diseased samples had elevated pathways relevant to fermentation, nitrate reduction, and sulfate metabolism. In contrast, healthy samples showed enhanced functions related to aerobic chemoheterotrophy and hydrocarbon degradation. These determinations provide valuable insights into the microbial factors contributing to \\u003cem\\u003eK. alvarezii\\u003c/em\\u003e diseases in Indian cultivation sites since 2013. Identifying potential pathogens and beneficial bacteria paves the way for developing targeted disease management strategies and probiotics to improve the health of seaweed, thereby supporting the sustainable cultivation of this economically vital resource.\\u003c/p\\u003e\"},{\"header\":\"Declarations\",\"content\":\"\\u003cp\\u003eEthical approval not applicable.\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eAcknowledgements\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003eAuthors are highly grateful to Dr. Vaibhav A. Mantri Co-DCs of Applied Phycology \\u0026amp; Biotechnology Division, CSIR-CSMCRI, for their valuable suggestions throughout the study. Authors also thank PIs, HCP0024 \\u0026amp; MLP0073 at CSIR-CSMCRI.\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eAuthor contribution\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003eN. Dhanya is conducting experiments, writing, and analyses; B. Prakriti, writing, and analyses; Arup Ghosh, monitoring the project. Lakkakula Satish, conceptualization; resources; writing, original draft; writing, review, and editing.\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eFunding\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003eThe authors would like to express their sincere gratitude to the Department of Science and Technology \\u0026ndash; Science and Engineering Research Board (DST-SERB), Government of India, for their financial support through the Start-up Research Grant (SRG) scheme (Sanction number: SRG/2023/002162). This assistance was instrumental in the successful completion of this research work.\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eData availability\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003eResearch data are not shared\\u003c/p\\u003e\"},{\"header\":\"References\",\"content\":\"\\u003col\\u003e\\n\\u003cli\\u003eAchmad M, Alimuddin A, Widyastuti U, Sukenda S, Suryanti E, Harris E (2016) Molecular identification of new bacterial causative agent of ice-ice disease on seaweed \\u003cem\\u003eKappaphycus alvarezii\\u003c/em\\u003e. \\u003cem\\u003ePeerJ Preprints\\u003c/em\\u003e e2016v1.\\u003c/li\\u003e\\n\\u003cli\\u003e\\u003cstrong\\u003eArasamuthu A, Edward JP (2018) Occurrence of ice-ice disease in seaweed \\u003c/strong\\u003e\\u003cem\\u003eKappaphycus alvarezii\\u003c/em\\u003e\\u003cstrong\\u003e at Gulf of Mannar and Palk Bay, Southeastern India. \\u003c/strong\\u003e\\u003cem\\u003eIndian Journal of Geo Marine Sciences\\u003c/em\\u003e\\u003cstrong\\u003e 47(6):1208\\u0026ndash;1216.\\u003c/strong\\u003e\\u003c/li\\u003e\\n\\u003cli\\u003e\\u003cstrong\\u003eCaballero-Flores G, Pickard JM, N\\u0026uacute;\\u0026ntilde;ez G (2023) Microbiota-mediated colonization resistance: mechanisms and regulation. \\u003c/strong\\u003e\\u003cem\\u003eNature Reviews Microbiology\\u003c/em\\u003e\\u003cstrong\\u003e 21(6):347\\u0026ndash;360.\\u003c/strong\\u003e\\u003c/li\\u003e\\n\\u003cli\\u003e\\u003cstrong\\u003eCampbell I, Macleod A, Sahlmann C, Neves L, Funderud J, \\u0026Oslash;verland M, Hughes AD, Stanley M (2019) The environmental risks associated with the development of seaweed farming in Europe\\u0026mdash;prioritizing key knowledge gaps. \\u003c/strong\\u003e\\u003cem\\u003eFrontiers in Marine Science\\u003c/em\\u003e\\u003cstrong\\u003e 6:379461.\\u003c/strong\\u003e\\u003c/li\\u003e\\n\\u003cli\\u003eCenturion VB, Lacerda-J\\u0026uacute;nior GV, Duarte AWF, Silva TR, Silva LJ, Rosa LH, Oliveira VM (2021) Dynamics of microbial stress responses driven by abiotic changes along a temporal gradient in Deception Island, Maritime Antarctica. \\u003cem\\u003eScience of the Total Environment\\u003c/em\\u003e 758:143671.\\u003c/li\\u003e\\n\\u003cli\\u003e\\u003cstrong\\u003eClarridge JE III (2004) Impact of 16S rRNA gene sequence analysis for identification of bacteria on clinical microbiology and infectious diseases. \\u003c/strong\\u003e\\u003cem\\u003eClinical Microbiology Reviews\\u003c/em\\u003e\\u003cstrong\\u003e 17(4):840\\u0026ndash;862.\\u003c/strong\\u003e\\u003c/li\\u003e\\n\\u003cli\\u003eDe Mesquita MMF, Crapez MAC, Teixeira VL, Cavalcanti DN (2019) Potential interactions bacteria\\u0026ndash;brown algae. \\u003cem\\u003eJournal of Applied Phycology\\u003c/em\\u003e 31(2):867\\u0026ndash;883.\\u003c/li\\u003e\\n\\u003cli\\u003eEgan S, Harder T, Burke C, Steinberg P, Kjelleberg S, Thomas T (2013) The seaweed holobiont: understanding seaweed\\u0026ndash;bacteria interactions. \\u003cem\\u003eFEMS Microbiology Reviews\\u003c/em\\u003e 37(3):462\\u0026ndash;476.\\u003c/li\\u003e\\n\\u003cli\\u003eFacimoto CT, Clements KD, White WL, Handley KM (2024) Bacteroidia and Clostridia are equipped to degrade a cascade of polysaccharides along the hindgut of the herbivorous fish \\u003cem\\u003eKyphosus sydneyanus\\u003c/em\\u003e. \\u003cem\\u003eISME Communications\\u003c/em\\u003e 4(1): ycae102.\\u003c/li\\u003e\\n\\u003cli\\u003eFaisan JP Jr, Samson EJD, Sollesta-Pitogo HT, Dayrit R, Balinas VT, de la Pe\\u0026ntilde;a LD (2024) Seasonal growth, carrageenan properties, and resistance to disease and epiphytic pests between \\u003cem\\u003eKappaphycus alvarezii\\u003c/em\\u003e (Rhodophyta) var. tambalang (brown) tissue-cultured and farm-sourced seaweeds. \\u003cem\\u003eJournal of Applied Phycology\\u003c/em\\u003e 36(3):1377\\u0026ndash;1389.\\u003c/li\\u003e\\n\\u003cli\\u003eFAO (2020) The state of world fisheries and aquaculture: sustainability in action. 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Marine drugs. 18(12):641.\\u003c/li\\u003e\\n\\u003c/ol\\u003e\"}],\"fulltextSource\":\"\",\"fullText\":\"\",\"funders\":[],\"hasAdminPriorityOnWorkflow\":false,\"hasManuscriptDocX\":true,\"hasOptedInToPreprint\":true,\"hasPassedJournalQc\":\"\",\"hasAnyPriority\":false,\"hideJournal\":true,\"highlight\":\"\",\"institution\":\"\",\"isAcceptedByJournal\":false,\"isAuthorSuppliedPdf\":false,\"isDeskRejected\":\"\",\"isHiddenFromSearch\":false,\"isInQc\":false,\"isInWorkflow\":false,\"isPdf\":false,\"isPdfUpToDate\":true,\"isWithdrawnOrRetracted\":false,\"journal\":{\"display\":true,\"email\":\"info@researchsquare.com\",\"identity\":\"researchsquare\",\"isNatureJournal\":false,\"hasQc\":true,\"allowDirectSubmit\":true,\"externalIdentity\":\"\",\"sideBox\":\"\",\"snPcode\":\"\",\"submissionUrl\":\"/submission\",\"title\":\"Research Square\",\"twitterHandle\":\"researchsquare\",\"acdcEnabled\":true,\"dfaEnabled\":false,\"editorialSystem\":\"\",\"reportingPortfolio\":\"\",\"inReviewEnabled\":false,\"inReviewRevisionsEnabled\":true},\"keywords\":\"Metagenomics, disease K. alvarezii, microbial diversity, K. alvarezii, healthy K. alvarezii\",\"lastPublishedDoi\":\"10.21203/rs.3.rs-7621295/v1\",\"lastPublishedDoiUrl\":\"https://doi.org/10.21203/rs.3.rs-7621295/v1\",\"license\":{\"name\":\"CC BY 4.0\",\"url\":\"https://creativecommons.org/licenses/by/4.0/\"},\"manuscriptAbstract\":\"\\u003cp\\u003eSeaweed cultivation, particularly of \\u003cem\\u003eKappaphycus alvarezii\\u003c/em\\u003e, plays a significant role in the coastal economy of Tamil Nadu, India. \\u003cem\\u003eK. alvarezii\\u003c/em\\u003e is frequently affected by ice-ice disease caused by microbial imbalances. This disease leads to tissue bleaching, reduced biomass, and lower carrageenan yield, making the study of seaweed-associated bacterial communities crucial for sustainable cultivation. In this study, healthy and diseased samples of \\u003cem\\u003eK. alvarezii\\u003c/em\\u003e were collected from five commercial farming sites. High-throughput Illumina sequencing of the V3\\u0026ndash;V4 region of the 16S rRNA gene was performed to analyze microbial diversity. Bioinformatics tools such as OTU clustering, alpha diversity indices, heat tree visualization, and functional prediction were applied to characterize differences between healthy and diseased samples across locations. The results revealed Proteobacteria as the dominant phylum, with Gammaproteobacteria being prevalent in all samples. Diseased seaweed samples exhibited greater microbial diversity, with genera including \\u003cem\\u003ePseudoalteromonas\\u003c/em\\u003e and \\u003cem\\u003eVibrio\\u003c/em\\u003e frequently associated with disease, while \\u003cem\\u003eCobetia\\u003c/em\\u003e was more abundant in healthy samples. Variations in microbial community structure were closely linked to environmental factors, including temperature, salinity, dissolved oxygen, and nutrient levels. Functional prediction showed that diseased samples had elevated pathways related to fermentation, nitrate reduction, and nitrogen respiration. Whereas healthy samples were enriched in aerobic chemoheterotrophy and hydrocarbon degradation. The study demonstrates that site-specific environmental conditions significantly influence microbial dynamics and disease progression in seaweed farms. Identifying potential pathogens and beneficial bacteria provides a foundation for developing targeted probiotics and disease management strategies that can support sustainable cultivation and improve seaweed health.\\u003c/p\\u003e\",\"manuscriptTitle\":\"Microbial community structure and niche differentiation in healthy and infected samples of Kappaphycus alvarezii in the commercial farming sites of Tamil Nadu coastal, India\",\"msid\":\"\",\"msnumber\":\"\",\"nonDraftVersions\":[{\"code\":1,\"date\":\"2025-09-30 12:49:41\",\"doi\":\"10.21203/rs.3.rs-7621295/v1\",\"editorialEvents\":[{\"type\":\"communityComments\",\"content\":0}],\"status\":\"published\",\"journal\":{\"display\":true,\"email\":\"info@researchsquare.com\",\"identity\":\"researchsquare\",\"isNatureJournal\":false,\"hasQc\":true,\"allowDirectSubmit\":true,\"externalIdentity\":\"\",\"sideBox\":\"\",\"snPcode\":\"\",\"submissionUrl\":\"/submission\",\"title\":\"Research Square\",\"twitterHandle\":\"researchsquare\",\"acdcEnabled\":true,\"dfaEnabled\":false,\"editorialSystem\":\"\",\"reportingPortfolio\":\"\",\"inReviewEnabled\":false,\"inReviewRevisionsEnabled\":true}}],\"origin\":\"\",\"ownerIdentity\":\"4c7c532a-7cb9-4e19-bb32-1d41d29a4890\",\"owner\":[],\"postedDate\":\"September 30th, 2025\",\"published\":true,\"recentEditorialEvents\":[],\"rejectedJournal\":[],\"revision\":\"\",\"amendment\":\"\",\"status\":\"posted\",\"subjectAreas\":[{\"id\":55461133,\"name\":\"Biological sciences/Biotechnology\"},{\"id\":55461134,\"name\":\"Biological sciences/Microbiology\"}],\"tags\":[],\"updatedAt\":\"2025-10-08T17:23:17+00:00\",\"versionOfRecord\":[],\"versionCreatedAt\":\"2025-09-30 12:49:41\",\"video\":\"\",\"vorDoi\":\"\",\"vorDoiUrl\":\"\",\"workflowStages\":[]},\"version\":\"v1\",\"identity\":\"rs-7621295\",\"journalConfig\":\"researchsquare\"},\"__N_SSP\":true},\"page\":\"/article/[identity]/[[...version]]\",\"query\":{\"redirect\":\"/article/rs-7621295\",\"identity\":\"rs-7621295\",\"version\":[\"v1\"]},\"buildId\":\"8U1c8b4HqxoKbykW_rLl7\",\"isFallback\":false,\"isExperimentalCompile\":false,\"dynamicIds\":[84888],\"gssp\":true,\"scriptLoader\":[]}","source_license":"CC-BY-4.0","license_restricted":false}