Microbial Diversity, Functional Genomics and Antibiotic Resistance in Integrated Chicken and Fish Farming Systems in Bangladesh

doi:10.21203/rs.3.rs-4892926/v1

The Integrated Fish Farming (IFF) system, practiced in Bangladesh for its economic benefits and resource efficiency, requires an understanding of microbial diversity, functional genomics, and antimicrobial resistance to optimize efficiency and sustainability. This study delves into the microbial compositions, diversity, and antibiotic resistance within diverse environmental samples using 16S rRNA sequencing and KEGG pathway analysis. The taxonomic analysis revealed a microbial community comprising 2838 OTUs, with Bacteria (99.81%) dominating over Archaea (0.19%). Sediment samples exhibited the highest archaeal diversity, primarily consisting of Euryarchaeota, Parvarchaeota, and Crenarchaeota. Bacterial diversity encompassed 70 phyla, with Firmicutes being predominant, particularly in chicken gut samples. Notable bacterial genera included Lactobacillus and Weissella. Alpha diversity analysis highlighted significant microbial richness in sediment and fish intestine samples, while beta diversity analysis using Bray-Curtis PCoA indicated distinct microbial community compositions across sample types. Functional genomic analysis revealed metabolic genes as the most predominant across all samples, focusing on amino acid, carbohydrate, and energy metabolism. Noteworthy pathways included ribosome biogenesis and ABC transporters, particularly abundant in sediment and feed samples. Antibiotic susceptibility testing of 55 isolates demonstrated high resistance rates, notably against Tetracyclines and Fluoroquinolones, with Escherichia coli and Proteus mirabilis exhibiting the highest resistance. Antibiotic resistance genes identified through KEGG pathways, such as bcrC and vanX, were abundant in sediment and chicken gut samples, indicating significant resistance profiles. This comprehensive profiling underscores the diverse and complex microbial ecosystems in various samples, the metabolic dominance in these environments, and the concerning levels of antibiotic resistance among common bacterial pathogens. These findings emphasize the need for ongoing surveillance and targeted interventions to mitigate the spread of antibiotic resistance in microbial communities. This is the first study in the country to reveal microbial diversity, antimicrobial resistance and functional genomics in Integrated Chicken and Fish Farming settings.

Biological sciences/Microbiology

Earth and environmental sciences/Environmental sciences

Biological sciences/Computational biology and bioinformatics

Microbial Diversity

Functional Genomics

Antibiotic Resistance

Integrated Fish Farming

16S rRNA Sequencing

The Integrated Fish Farming (IFF) system is a sustainable agricultural practice that combines fish farming with other agricultural activities such as poultry and livestock rearing [1]. This system is widely practiced in Bangladesh due to its’ economic benefits and resource efficiency [2, 3]. However, the intricate interactions between different farming components can create complex microbial ecosystems [4], which are critical to the overall health and productivity of the system [5]. Understanding the microbial diversity, functional genomics, and antimicrobial resistance within these systems is crucial for optimizing their efficiency and sustainability [6–8].

Microbial communities play a pivotal role in nutrient cycling [9], disease suppression [10], and overall environmental health within integrated farming systems [11]. For instance, Firmicutes and other beneficial bacteria such as Lactobacillus, Bifidobacterium, Bacillus, Clostridium, and Streptococcus are essential for fermentation and nutrient absorption in poultry and livestock guts, contributing to improved growth and health of the animals [12, 13]. In aquatic environments, diverse microbial populations including Proteobacteria, Actinobacteria, Bacteroidetes, Cyanobacteria, and Verrucomicrobia significantly influence fish health and water quality [14, 15]. Despite the acknowledged importance of microbial communities in IFF systems, there is a paucity of comprehensive studies examining their diversity and functional roles in these environments.

Functional genomics, which involves studying gene functions and interactions, is crucial for understanding roles of microbes in a system [15]. By analyzing gene expression and metabolic pathways, functional genomics reveals how microbes aid in processes like nutrient cycling, waste degradation, and antimicrobial resistance [16, 17]. Furthermore, the widespread use of antibiotics in agriculture has led to the emergence of antimicrobial resistance (AMR) [18], posing a significant threat to both animal and human health [19]. Common antibiotic resistance genes (ARGs) such as blaTEM, blaCTX-M, tetM, tetA, sul1, sul2, ermB, and qnrS have been identified in various agricultural settings, contributing to the complexity of managing AMR in integrated farming systems [20, 21]. Antibiotic-resistant bacteria can spread through various environmental pathways, including water, soil, and animal products, potentially leading to zoonotic diseases [22, 23]. Therefore, assessing the prevalence and distribution of antimicrobial resistance genes (ARGs) in IFF systems is essential for developing strategies to mitigate the risk of AMR transmission.

This research aims to provide a comprehensive analysis of microbial diversity, functional genomics, and antimicrobial resistance in an Integrated Fish and Chicken Farming system in Bangladesh. By utilizing high-throughput sequencing and advanced bioinformatics tools, we seek to uncover the microbial composition and functional pathways utilized by these communities. Additionally, through antibiotic susceptibility testing and functional genomic analysis, we aim to identify the prevalence and distribution of ARGs within the system.

Understanding these aspects will provide valuable insights into the ecological dynamics of IFF systems and contribute to the development of sustainable management practices. This is the first study in the country to reveal microbial diversity, antimicrobial resistance and functional genomics in Integrated Chicken and Fish Farming settings.

Ethical Approval and ARRIVE Guidelines

The experiment was approved by the Ethical Review Committee of the Faculty of Biological Sciences at Jashore University of Science and Technology, Jashore, Bangladesh (certification number: ERC/FBST/JUST/2024–200). All possible measures were taken to minimize pain and harm to the experimental animals. The whole study is reported in accordance with ARRIVE guidelines (https://arriveguidelines.org). All methods were performed in accordance with the relevant guidelines and regulations.

2.1 Sample Collection and Preparation of Isolates

Three distinct integrated fish farms, incorporating chicken farms built over ponds, were selected from different locations within the Jashore district, Bangladesh (Fig. 1). Five types of samples were collected from each farm: feed (1), chicken gut (2), chicken droppings (2), fish intestine (2), and pond sediment (2), resulting in a total of 27 samples across the three farms. Feed samples were collected directly from the feeders using sterile containers to avoid any contamination. The chickens were euthanized through cervical dislocation according to the ethical guidelines, dissected, and sections of the gut were carefully removed using sterile instruments. Chicken droppings were collected from the coop floor immediately after defecation using sterile spatulas. Fish intestine samples were collected by euthanizing the fishes through rapid cooling according to the ethical guidelines, followed by dissection and removal of the intestines using sterile instruments. Pond sediment samples were collected from different locations within the pond using a sterile scoop. All samples were collected aseptically, stored at 4°C immediately after collection, and transported to the laboratory within 1-2 hours. Subsequently, the samples were stored at -20°C for further analysis. Detailed information regarding the sampling sites and sample specifications is provided in Supplementary Table-1.

2.2 Antibiotic Susceptibility Testing (AST)

Five types of selective agar media (Blood Agar (BA) - HiMedia Laboratories, MacConkey Agar (MAC) - Oxoid, Xylose Lysine Deoxycholate (XLD) Agar - Merck, Mannitol Salt Agar (MSA) - Becton Dickinson, Cetrimide Agar (CA) - Liofilchem, and Salmonella Shigella (SS) Agar - HiMedia Laboratories) were utilized to culture 27 samples. As a screening procedure, the samples were diluted with tetracycline at a concentration of 16 µg/mL, in accordance with Clinical and Laboratory Standards Institute (CLSI) guidelines [24], and then plated onto the five media. This process resulted in the isolation of 55 distinct organisms (Feed = 4, Chicken gut = 13, Droppings = 19, Fish intestine = 9, Sediments = 10). Following isolation, a series of biochemical tests were performed to identify the presumptive organisms. Antibiotic susceptibility testing was conducted using the conventional Kirby-Bauer disc diffusion method [25], following CLSI guidelines. Nine different classes of antibiotics were tested, including Penicillins [Amoxicillin (AMX, 20 μg)], Cephalosporins [Cefexime (CFM, 30 μg)], Carbapenems [Imipenem (IPM, 10 μg)], Tetracyclines [Tetracycline (TET, 30 μg)], Macrolides [Azithromycin (AZM, 15 μg)], Aminoglycosides [Amikacin (AMK, 30 μg)], Chloramphenicols [Chloramphenicol (CPC, 30 μg)], Polymyxins [Colistin sulfate (COL, 10 μg)], and Fluoroquinolones [Levofloxacin (LEV, 5 μg)]. The results were interpreted according to the clinical breakpoints recommended by CLSI. Further details on the cultural media, biochemical tests, and isolates can be found in Supplementary Data - 1.

2.3 Total DNA Extraction and Metagenomic (16S rRNA) Sequencing

For metagenomic sequencing of the 27 raw samples, two different types of kits were employed for total DNA extraction: the DNeasy Powersoil Pro Kit (QIAGEN) and the QIAamp Fast DNA Stool Mini Kit (QIAGEN). The DNA extraction was performed following the manufacturer’s protocols. The extracted total DNA was subsequently processed for metagenomic sequencing. To amplify the V3-V4 region of the bacterial 16S rRNA gene, sequencing libraries were constructed using primer pairs 341F (5′-CCTACGGGNGGCWGCAG-3′) and 806R (5′-GACTACHVGGGTATCTAATCC-3′), which included the Illumina overhang adapter sequence (Illumina, Inc., San Diego, CA, USA). Each PCR reaction consisted of 12.5 µL of 2X KAPA HiFi HotStart Ready Mix (Roche Diagnostic Corporation, Indianapolis, IN, USA), 5 µL of DNA extract, 2.5 µL of each primer, and 2.5 µL of HyClone water (Cytiva Life Sciences, Marlborough, MA, USA). PCR amplifications were conducted using a T100TM heat cycler (Bio-Rad Laboratories, Inc., Hercules, CA, USA). The PCR program included an initial denaturation at 95 °C for three minutes, followed by 25 cycles of 30 seconds at 95 °C, 30 seconds at 62 °C, 30 seconds at 72 °C, and a final extension at 72 °C for four minutes. The PCR products were purified using the epMotion 5075 Liquid Handler (Eppendorf AG, Hamburg, Germany) and AMPure XP beads (Beckman Coulter, Inc., Brea, CA, USA). The indexed PCR products, following three minutes of initial denaturation at 95 °C, 25 cycles of 30 seconds at 95 °C, 30 seconds at 56 °C, 30 seconds at 72 °C, and a final extension at 72 °C for four minutes, were purified using AMPure XP beads. The indexed and purified libraries were then normalized, pooled using an epMotion liquid handler, and loaded onto a v3 600 cycle-kit (P1 flowcell) reagent cartridge for 2 × 300 bp paired-end sequencing using Illumina NextSeq2000. The Georgia Genomics and Bioinformatics Core (GGBC), University of Georgia, Athens, GA, USA (RRID: SCR_010994) managed the library preparation and sequencing.

2.4 Taxonomic Profiling of the Amplicon Sequences

The sequencing of 27 samples was conducted using a read length of 2 × 300 base pairs, ensuring comprehensive coverage of the 16S V3-V4 region for subsequent bioinformatics analysis. The quality of the resulting FASTQ files was assessed with FastQC v0.12.1 [26]. The reads were processed with Trimmomatic v0.39 [27] to remove low-quality ends and adapter sequences, using a sliding window size of 30, a minimum read length of 100bp, and a minimum average quality score of 20. Post-quality control, the 16S samples yielded an average of 767,128 read pairs, with a range from 468,135 to 1,071,120 pairs. For downstream analysis, QIIME 2 v2023.5 [28] was employed as the primary pipeline for Operational Taxonomic Units (OTUs) clustering and taxonomic assignment. The VSEARCH algorithm within QIIME 2 facilitated read joining, sequence dereplication, de novo OTU clustering at 99% identity, and chimera checking to exclude chimeric sequences. Taxonomic assignment was performed using the Greengenes2 v2022.10, which incorporates 99% OTU clustering and associated taxonomy information [29]. The reference database was trained using the 16S sequencing primer pairs and a naive Bayes classifier [30, 31]. The classify-sklearn algorithm was then applied to classify the assigned OTUs within the samples [28].

2.5 Functional Genomic Analysis

For functional genomic analysis Kyoto Encyclopedia of Genes and Genomes (KEGG Orthology), the PICRUSt2 [32] tool was used with the following parameters: SEPP (Skeletal Engine for Phylogenetic Placement) was employed for phylogenetic placement, with the Hidden State Path (HSP) method set to maximum posterior (mp) for accurate alignment. A minimum alignment score of 0.8 was applied, and the maximum Nearest Sequenced Taxon Index (NSTI) value was set to 2 to ensure high-quality functional predictions. The analysis was run with verbosity enabled for detailed output, and an edge exponent of 0 was used to standardize the impact of sequence placement on functional predictions along with their abundance throughout the samples. The identified KO (KEGG Orthology) numbers were assigned to particular pathways by using the R package ggpicrust2 [33]. Then the pathways were further analyzed according to their class and sub-classes.

2.6 Statistical Analysis and Data Visualization

Downstream analysis, including alpha and beta diversity assessments, microbial composition, and statistical comparisons, was performed using the “PHyloseq” package [34] in R software (version 4.4.1) [35] . OTU counts were normalized through rarefaction. Alpha diversity metrics such as observed richness, Chao1, Shannon, Simpson, InvSimpson, and Fisher indices were estimated using the "Vegan" [36], "ggpubr" [37], and "ggplot2" [38] R packages and subsequently visualized. Differences in microbial abundance and diversity between locations were identified using the Wilcoxon rank sum test via the "microbiomeutilities" [39] R package. Beta diversity was evaluated through principal coordinate analysis (PCoA) based on Bray-Curtis dissimilarity, with differences between samples assessed using Permutational Multivariate Analysis of Variance (PERMANOVA) and permutation tests. Taxonomic comparisons, heatmaps, and histograms were generated with the “Vegan,” “PHyloseq,” “microbiomeutilities,” “Tidyr” [40], and “ggplot2” packages. Statistical analyses were conducted in triplicate, and results were presented as the average of these three measurements.

3.1 Microbial Compositions and Diversity

Taxonomic analysis of 16S rRNA sequencing data revealed a diverse microbial community comprising both Archaea and Bacteria. Among the 2838 identified OTUs (Operational Taxonomic Units), Archaea represented 76 OTUs (0.19%) while Bacteria comprised 2762 OTUs (99.81%). In archaeal diversity, sediment samples dominated with 91.65% of the total archaeal abundance, followed by fish intestine samples at 5.49%. The archaeal community was primarily composed of three phyla: Euryarchaeota (79.91%), Parvarchaeota (14.59%), and Crenarchaeota (5.50%). The most prevalent archaeal genera were Methanosaeta (59.08%) and Methanobacterium (13.83%) (Fig. 2A). Notably, feed samples contained unique archaeal genera, including Halolamina, Halobacterium, Halorubrum, and Halococcus.

Regarding bacterial diversity, 70 phyla were identified, with Firmicutes being the most dominant (31.94%). Firmicutes were most abundant in chicken gut samples (43.35%) and least abundant in feed samples (2.90%). Proteobacteria (17.61%) and Cyanobacteria (11.79%) were also significant, with Cyanobacteria being most prevalent in feed samples (38.09%) and least in chicken gut samples (1.03%). Other notable phyla included Planctomycetes (9.05%), Bacteroidetes (8.90%), Verrucomicrobia (5.51%), and Actinobacteria (4.25%). A total of 933 bacterial genera were identified (Fig. 2B), with Lactobacillus being the most abundant (20.32%), particularly in chicken gut samples (69.04%). Weissella was the second most abundant genus (13.47%), predominantly found in droppings (57.37%). The genus Helicobacter (4.07%) was almost exclusively present in chicken gut samples (99.93%) with a minor presence in droppings (0.07%). Other significant genera included Candidatus (4.83%), Bacteroides (5.44%), and Clostridium (4.04%). Sediment samples contained 198 unique bacterial genera, with Dorea and Aquamonas being common to both chicken gut and sediment.

Alpha diversity analysis revealed distinct microbial richness and diversity across sample types. Sediment samples exhibited the highest diversity (Wilcoxon Rank Test, p < 0.01), with significantly greater richness according to the Observed and Chao1 indices. Fish intestine samples were richer than feed samples (p < 0.01). Chicken gut and droppings showed lower richness, with no significant difference between them. The Shannon, Simpson, and Inverse Simpson indices indicated moderate diversity in fish intestine samples, significantly higher than in droppings and feed samples (p < 0.01). Chicken gut and feed samples had the lowest diversity, with chicken gut being the least diverse. The Fisher index confirmed higher diversity in fish intestine samples compared to feed samples (p < 0.01) (Fig. 3A).

Beta diversity analysis using Bray-Curtis Principal Coordinate Analysis (PCoA) assessed microbial community composition. The PCoA plot showed distinct clustering of sample types, with Axis 1 explaining 27.6% of variation and Axis 2 explaining 18.7%. Sediment samples formed a unique cluster, while fish intestine samples overlapped with droppings and chicken gut samples. Feed samples were more dispersed, indicating heterogeneity. The separation of microbial communities was significant, Permutational Multivariate Analysis of Variance (PERMANOVA) (R² = 0.473, p = 0.001), highlighting distinct microbial compositions among different sample types (Fig. 3B).

3.2 Relative Abundance and Top Phyla and Genera across Samples

Feed samples were dominated by Proteobacteria (51.45%) and Actinobacteria (37.40%), with minor contributions from Firmicutes (5.71%) and others. Chicken gut samples had Firmicutes as the most abundant (71.86%), followed by Proteobacteria (12.21%) and Actinobacteria (11.81%). Droppings showed a more balanced distribution with Firmicutes (30.71%), Actinobacteria (26.73%), and Proteobacteria (22.33%). Fish intestine samples were primarily composed of Actinobacteria (36.29%) and Firmicutes (31.49%). Sediment samples had Firmicutes (36.84%) as the most prevalent, followed by Actinobacteria (23.47%) and Proteobacteria (16.69%) and multiple other phyla. Across all sample types, Proteobacteria and Firmicutes were the dominant phyla, with significant variations reflecting the unique microbial compositions influenced by specific environmental conditions and interactions within the system (Fig. 4A).

In terms of genera level relative abundance, Feed samples were dominated by Weissella (19.07%) and Bacillus (16.55%), with Saccharopolyspora at 5.73%, and Streptomyces and Staphylococcus at 5.56% and 4.16%. Chicken gut samples were characterized by Lactobacillus (45.04%), followed by Helicobacter (13.06%) and Bacteroides (9.77%), along with Oscillospira (3.73%) and Ruminococcus (2.94%). In droppings, Weissella was most prevalent (27.52%), with Lactobacillus at 17.79%, Acinetobacter (4.70%), and Bacteroides and Corynebacterium at 3.82% and 3.39%. Fish intestine samples featured Weissella (19.50%), Candidatus (17.04%), and Clostridium (12.43%), with Bacteroides and Synechococcus at 4.75% and 3.88%. In sediment samples, Synechococcus was the most abundant (8.46%), followed by Planctomyces (6.16%), Luteolibacter (5.26%), Anaerolinea (4.04%), and Dechloromonas (3.86%) (Fig. 4B).

The box plot analysis of the top 25 phyla revealed significant differences in microbial abundances. Verrucomicrobia, WS3, Planctomycetes, and Proteobacteria showed significant differences across different sample types. Notable phyla with significant differences included Spirochaetes, Synergistetes, TM7, Fusobacteria, Cyanobacteria, Euryarchaeota, Fibrobacteres, Firmicutes, Acidobacteria, Actinobacteria, Armatimonadetes, Bacteroidetes, BRC1, and Chlamydiae (Fig. 5A). The analysis of the top 25 genera revealed that Sphingobacterium, Staphylococcus, Synechococcus, and Weissella showed significant differences across different sample types. Other notable genera with significant differences included Parabacteroides, Phascolarctobacterium, Planctomyces, Prevotella and Ruminococcus (Fig. 5B). Relative abundance of these top phyla and genera are provided in Table. 1.

3.3 Antibiotic Susceptibility Testing (AST)

In the antibiogram analysis of 55 isolates which comprised various presumptive bacteria, with Escherichia coli and Proteus mirabilis being the most abundant, each accounting for 14 isolates. Other notable isolates included Staphylococcus aureus (8 isolates), Vibrio parahaemolyticus (5 isolates), and Pseudomonas aeruginosa (4 isolates).

The resistance profile showed that Tetracyclines had the highest overall resistance rate at 20.08%, followed by Fluoroquinolones at 15.91% and Penicillins at 15.15%. Polymyxins and Macrolides exhibited resistance rates of 12.88% and 11.36%, respectively. Chloramphenicols and Cephalosporins showed moderate resistance (9.85% and 2.65% respectively), while Aminoglycosides and Carbapenems had the lowest rates at 2.65% and 1.89%, respectively. E. coli and Proteus mirabilis were the most resistant organisms, each contributed to 24.62% of overall resistance. Both exhibited significant resistance to Tetracyclines (21.54% and 18.46%, respectively) and Fluoroquinolones (16.92% for both). Staphylococcus aureus (14.39%) showed notable resistance to Tetracyclines (21.05%) and Penicillins (18.42%). Vibrio parahaemolyticus (8.33%) showed resistance mainly against Tetracyclines (22.73%) and Macrolides (18.18%). Pseudomonas aeruginosa (7.58%) demonstrated resistance to Polymyxins and Chloramphenicols, although it accounted for only 7.58% of overall resistance. Less prevalent isolates such as Vibrio cholerae (6.82%), Salmonella spp. (5.30%), Bacillus cereus, Citrobacter spp., and Shigella flexneri each contributed 2.27% to the overall resistance. Resistance varied across sample types, with Droppings exhibiting the highest resistance at 34.85%, followed by Chicken gut samples at 22.73%, Sediment at 18.18%, Fish intestine at 17.42%, and Feed at 6.82%. These data indicate a concerning level of antibiotic resistance among common bacterial pathogens, particularly E. coli and Proteus mirabilis, which are major contributors to the overall resistance burden (Fig. 6).

3.4 Functional Genomic Analysis (KEGG Orthology)

In all samples, pathways regarding metabolism were predominant, followed by genetic information processing and environmental information processing. Within the metabolism category, pathways accounted for the highest percentages across all samples, with 79.38% in feed, 73.48% in chicken gut, 75.96% in droppings, 75.74% in fish intestine, and 74.47% in sediment. Genetic information processing pathways were the second most abundant, with percentages ranging from 11.08–16.09%, highest in the chicken gut sample. Environmental information processing were also notable, making up 4.40–5.49% of the pathways, with the highest proportion in the sediment sample. In the human diseases category, pathway abundance ranged from 0.95–1.05%, indicating a relatively low but consistent presence across all samples. Additionally, cellular processes and organismal systems were represented at lower levels, with cellular processes ranging from 2.35–3.05% and organismal systems from 1.67–1.76%. This comprehensive profiling underscores the metabolic dominance in these samples while highlighting the presence of other significant pathway categories, including those related to human diseases (Fig. 7A).

To further analyze these dominant pathways, Level 2 pathways (Pathway_Sub-class) were identified where under the metabolic pathways, amino acid, carbohydrate, and energy metabolism, and the metabolism of cofactors and vitamins were most dominant. Under cellular processes, cell motility pathways were most abundant ranging from 1.20% in feed to 2.11% in chicken gut, followed by genes for transport and catabolism. Membrane transport pathways under environmental information processing pathways were prominent, especially in sediment samples (2.86–3.41%). Genetic information processing pathways was highlighted by translation-related genes (4.79–6.89%) with consistent relative abundances. Human disease pathways included antimicrobial resistance, infectious diseases, endocrine and metabolic disorders, and cancer, were present at lower levels (0.95–1.05%). This analysis underscores the predominance of metabolic pathways and highlights the roles of genetic and environmental information processing and human disease-related pathways. More information regarding pathway abundance are available in Supplementary Data 2. Interactions and clustering of these pathways among various samples are visualized through heatmap (Fig. 7B).

Histogram of the top 35 KEGG level 2 pathways across the five samples were generated using z-standardized values of normalized relative abundance (Fig. 8A). The analysis revealed significant variability in pathway relative abundances across different samples where the ABC transporters pathway was relatively abundant across all samples, ranging from 4.01% in the chicken gut to 4.89% in sediment. The Ribosome biogenesis in eukaryotes pathway showed the highest relative abundance, peaking at 7.78% in the chicken gut and maintaining a consistent presence across other samples, with 7.09% in sediment. Conversely, the Mismatch repair pathway exhibited lower abundance, with a minimum of 1.99% in feed and a maximum of 3.02% in the chicken gut. Similar trends were observed for the Nucleotide excision repair and Protein export pathways, with relative abundances varying from 1.42% in feed to 2.60% in the chicken gut and 2.31% in sediment, respectively.

3.5 Antibiotic Resistance Genes Identified through KEGG Pathways

The analysis of antibiotic resistance pathways identified through KEGG Orthology reveals significant insights into genes associated with antibiotic resistance and their prevalence in various samples. Beta-lactam resistance was notably prevalent. The gene bcrC, encoding undecaprenyl-diphosphatase, was highly abundant in sediment samples (58.99%) and chicken gut samples (31.03%), and was also present in droppings (34.99%) and fish intestines (29.23%). Additionally, acrA, mexA, adeI, smeD, mtrC, and cmeA genes were notably abundant in fish intestine samples (25.74%) and sediment samples (6.58%). Vancomycin resistance was observed, primarily mediated by the vanX gene encoding zinc D-Ala-D-Ala dipeptidase, with significant abundances in sediment (10.92%) and droppings (12.07%). Least abundant genes oprM, emhC, ttgC, cusC, adeK, smeF, mtrE, and cmeC, were found in chicken gut samples (7.18%) and sediment samples (1.40%). Abundance of these genes are shown through heatmap (Fig. 8B).

4.1 Microbial Compositions and Diversity

The taxonomic analysis of microbial communities revealed a significant predominance of bacteria over archaea, a pattern consistent with many environmental microbiomes where bacterial diversity often surpasses that of archaea [41, 42]. Firmicutes, as the most dominant phylum, are well-documented for their crucial roles in fermentation and gut health, particularly within avian species [43]. Their high abundance in chicken gut samples highlights their essential function in digestion and nutrient absorption, which aligns with previous findings that underscore the importance of Firmicutes in the gut microbiome [44].

Cyanobacteria are more abundant in feed samples due to the composition of feeds, which includes materials that contain a substantial portion of cyanobacteria [45]. The varying distribution of Proteobacteria across environments underscores their ecological versatility and potential role in nutrient cycling [8]. The detection of unique archaeal genera in specific environments, such as Haloarchaea in feed samples, points to the presence of specialized niches where these microorganisms can thrive [46]. Conversely, the absence or minimal presence of archaea in chicken gut samples supports the notion that certain environmental conditions may be less hospitable to archaea, a trend observed in various studies [47, 48].

The prevalence of Lactobacillus, particularly in chicken gut samples, underscores its crucial role in avian gut health. Lactobacillus species are known for their beneficial effects on host digestion and immunity, often dominating poultry gut microbiota [49]. Weissella's significant presence in droppings highlights its association with lactic acid fermentation and potential probiotic properties [50]. Helicobacter's specific presence in chicken gut samples aligns with its known pathogenic role in avian gastrointestinal health [51]. The presence of genera such as Candidatus, Bacteroides, and Clostridium points to their roles in nutrient cycling and host health, reflecting the complexity of microbial interactions in different environments [52, 53]. The detection of unique bacterial genera in sediment samples and the commonality of certain genera across different sample types suggest potential microbial transfer between environmental sources and host organisms. This supports the concept of environmental reservoirs influencing gut microbiota composition [54].

Alpha diversity metrics revealed distinct patterns of microbial richness and diversity across different sample types, highlighting the influence of environmental heterogeneity. Sediment samples exhibited the highest diversity, consistent with their complex nature [55]. The higher richness in fish intestine samples compared to feed samples indicates a more diverse microbial environment within the fish gut, influenced by varied dietary inputs and environmental interactions as in an integrated fish farming system along with chickens, food consumption of fish harbors the highest level of versatility [56]. The lower richness and diversity in chicken gut and droppings reflect the selective pressures within the gut environment that favor certain microbial taxa as found earlier that the gut microbiota of chickens are mostly composed of Lactobacillus and Weisella [57].

For beta diversity analysis, the distinct clustering of sediment samples suggests a unique microbial composition, which aligns with previous findings that sediments harbor diverse and specialized microbial communities [58]. The overlap of fish intestine samples with droppings and chicken gut samples suggests some shared microbial communities among these environments, possibly due to similar dietary influences and host interactions. This overlap is consistent with studies indicating that gut microbiota can be influenced by diet and environmental exposure [54, 59]. The significant separation of microbial communities, as indicated by PERMANOVA, highlights the distinct microbial compositions among different sample types. This separation suggests that specific environmental and host-related factors play critical roles in shaping these communities [60]. Such findings are in line with research emphasizing the impact of environmental conditions, host physiology, and dietary inputs on microbial community structure [61, 62].

4.2 Relative Abundance and Top Phyla and Genera across Samples

Feed samples were dominated by Proteobacteria and Actinobacteria besides, Weissella and Bacillus genera, reflecting their adaptability to nutrient-rich environments [63–66]. Chicken gut samples had a high prevalence of Firmicutes, along with Lactobacillus essential for digestion and pathogen inhibition [44]. Droppings showed a balanced distribution of Firmicutes, Actinobacteria, and Proteobacteria with Weissella and Lactobacillus, along with environmental microbes like Acinetobacter and Corynebacterium indicating complex microbial interactions influenced by diet and habitat [67–69]. Fish intestine samples featured Actinobacteria and Firmicutes including Weissella, Candidatus, and Clostridium highlighting their roles in nutrient absorption in aquatic environments [65, 70, 71]. Sediment samples displayed diverse microbial communities which were prevalent in all other samples types indicating that sediments act like a reservoir of organisms in the whole aquatic system [54, 72]. Besides Synechococcus and Planctomyces, were uniquely found in sediments indicative of photosynthesis and biogeochemical cycling roles [54, 72].

Significant variation in phyla such as Verrucomicrobia, WS3, Planctomycetes, and Proteobacteria reflects their adaptability to different environmental conditions. Verrucomicrobia, involved in polysaccharide degradation [73], and Planctomycetes, important in nitrogen cycling, vary with nutrient availability [74]. Proteobacteria's metabolic versatility is evident in their significant differences across environments [8]. Significant differences in Chlamydiae and BRC1 suggest niche-specific adaptations [75]. Genera like Sphingobacterium, Staphylococcus, Synechococcus, and Weissella showed significant differences, reflecting their roles in organic degradation [76], host interactions [77], and fermentation [50]. Genera like Enterococcus, Acinetobacter, and Corynebacterium adapt to diverse environments, from gut to soil [78, 79].

4.3 Antibiotic Susceptibility Testing (AST)

The results of this study underscore the growing concern of antibiotic resistance among common bacterial pathogens, particularly in samples associated with integrated fish farming systems. The high resistance rates observed in Escherichia coli and Proteus mirabilis are alarming, as these organisms are known to harbor multiple resistance genes and can act as reservoirs for the transfer of these genes to other bacteria [80, 81]. The predominance of resistance to Tetracyclines, Fluoroquinolones, and Penicillins points to the widespread use of these antibiotics, both in human medicine and agricultural practices. This is consistent with findings from other studies that have reported high resistance rates to these antibiotics due to their extensive and often indiscriminate use [82]. The relatively lower resistance rates observed for Aminoglycosides and Carbapenems suggest that these antibiotics are still effective, likely due to their more restricted use [83]. The presence of resistance in less prevalent isolates, such as Vibrio cholerae and Salmonella spp., also warrants attention. These pathogens are significant from a public health perspective, as they are associated with serious infectious diseases [84]. Their resistance to multiple antibiotics could complicate treatment options and lead to increased morbidity and mortality [85]. The variation in resistance across different sample types indicates that environmental factors, such as the type of sample and its exposure to antibiotic use, play a crucial role in shaping resistance profiles [86]. For instance, higher resistance in droppings and chicken gut samples suggests direct exposure to antibiotics used in poultry farming, whereas resistance in sediment and fish intestine samples reflects the impact of environmental contamination.

4.4 Functional Genomic Analysis (KEGG Orthology)

Functional genomic analysis based on KEGG Orthology revealed that metabolic pathways dominate across all sample types. Pathways, including amino acid, carbohydrate, and energy metabolism, are essential for microbial growth, nutrient acquisition, and environmental adaptation [87]. The high proportion of metabolic genes in feed samples reflects active microbial processing of nutrients [88]. Genetic information processing genes, particularly abundant in chicken gut samples, emphasize the importance of transcription and translation for microbial adaptation and survival [89, 90]. Environmental information processing genes, more prevalent in sediments, underline the role of microbes in responding to environmental changes [91, 92]. Despite their lower abundance, The presence of genes associated with human diseases, including antimicrobial resistance and infectious diseases, suggests potential zoonotic risks [93]. These genes reflect the complex interactions between microbial communities and their potential impact on human health. Genes related to cell motility are significant within cellular processes, suggesting their role in microbial adaptation and interaction with the environment [94]. Cell motility aids in navigating towards nutrients and away from harmful conditions, enhancing survival and competitiveness [95].

The high prevalence of transporters and ribosome biogenesis pathways in the chicken gut and sediment suggests an active metabolic environment are driven by nutrient-rich conditions that favor growth and protein synthesis [96]. The prominence of repair mechanisms, such as mismatch repair and nucleotide excision repair are indicative of the need for maintaining genomic integrity in response to environmental stressors, including oxidative damage or exposure to antimicrobial agents commonly found in agricultural settings [97]. This pattern aligns with previous studies that highlight the influence of selective pressures in shaping microbial functional profiles in diverse environments [98, 99]. Additionally, the consistent abundance of protein export pathways underscore the importance of protein secretion in microbial interactions and host colonization, particularly in the gut ecosystem [100]. These findings suggest that microbial communities are finely tuned to their specific environments, balancing growth, stress response, and interaction capabilities to thrive under varying conditions.

4.5 Antibiotic Resistance Genes Identified through KEGG Pathways

Beta-lactam resistance, particularly mediated by genes such as bcrC, indicates the extensive use and consequent resistance to beta-lactam antibiotics. This resistance is consistent with global trends and emphasizes the need for more prudent use of these antibiotics in both clinical and agricultural settings [101]. The detection of multi-drug resistance genes such as acrA, mexA, adeI, smeD, mtrC, and cmeA suggests a broad spectrum of resistance to antibiotics including tetracyclines, chloramphenicol, macrolides, and fluoroquinolones [102]. These genes are associated with efflux pump mechanisms, which are a major cause of multi-drug resistance in Gram-negative bacteria [103]. It is evident that all our antibiotics in AST were of Beta-lactam group and similar pattern of resistance is observed both in phenotypic and genotypic category which strongly supports our study. This highlights the complexity of tackling multi-drug resistant pathogens, necessitating the development of new antibiotics and alternative therapeutic strategies. Vancomycin resistance, primarily mediated by the vanX gene, poses a serious challenge as it limits the efficacy of one of the last-resort antibiotics used to treat Gram-positive bacterial infections [104]. The environmental presence of vancomycin resistance genes underscores the risk of these genes being transferred to pathogenic bacteria, further complicating treatment options [105]. The identification of genes such as oprM, emhC, ttgC, cusC, adeK, smeF, mtrE, and cmeC, which confer resistance to beta-lactams, aminoglycosides, and fluoroquinolones, further emphasizes the pervasive nature of antibiotic resistance [106]. These findings suggest that even genes present at lower frequencies can significantly contribute to the overall resistance burden [86].

In conclusion, our study represents the first investigation into microbial diversity in integrated fish and chicken farming systems within the country. Our findings offer valuable insights into microbial dynamics and antibiotic resistance, supporting the development of targeted management strategies to improve nutrient cycling and address resistance issues. Despite its pioneering nature, the study has limitations, including a narrow range of sample types and environments, and a lack of detailed mechanistic insights into antibiotic resistance. Future research should expand the scope of sampling and incorporate longitudinal studies to explore temporal dynamics. Additionally, employing advanced techniques like metatranscriptomics could enhance our understanding of microbial functionality. Overall, these outcomes are pivotal for advancing integrated farming practices and ensuring environmental and public health.

Conflict of Interest:

The authors declare no conflict of interest.

Data availability:

The data that support the findings of this study are openly available in NCBI BioProject at https://www.ncbi.nlm.nih.gov/bioproject/ reference number PRJNA1145595.

Acknowledgements:

This study has received a generous instrument support from the Department of Microbiology, Jashore University of Science and Technology. Reagents and consumable were provided by partial grants from Ministry of Science and Technology, Govt. of Bangladesh; Faculty of Biological Sciences, Jashore University of Science and Technology.

Funding:

The study was funded (JUST/Research Cell/Research Project/2022-23/22-FoBST 05) by Jashore University of Science and Technology, Jashore-7408, Bangladesh. We are also grateful to Ministry of Science and Technology (MOST, Fiscal Year 2022-23, Project Id: SRG-221252) for supporting the research project.

Contributions:

SMK conducted the microbial diversity and KEGG analysis, generated figures, curated data, and drafted the original manuscript. JFS and SA were responsible for sample collection, DNA extraction, culturing of isolates, and validation of wet lab data. JJ and KTI executed Antibiotic Susceptibility Testing, validated isolate data, and analyzed KEGG resistance pathways. MSR, OKI, and TC provided critical manuscript review and contributed to drafting. MSH and NS architected the study. MTI conceived the research, secured reagent support, rigorously reviewed the manuscript, and directed the entire project.

Laghari, M.Y., P.K. Lashari, and Z.A. Palh, Integrated Farming Approach, in Climate Change Impacts on Agriculture: Concepts, Issues and Policies for Developing Countries. 2023, Springer. p. 223-237.
Bisht, D., Integrated fish farming for food, nutritional security and economic efficiency in mid hills of Indian Central Himalaya. Research Journal of Fisheries and Hydrobiology, 2011. 6(1): p. 1-6.
Haobijam, J. and S. Ghosh, Strengths, weaknesses, opportunities and threats (SWOT) of integrated fish farming system (IFFS) as perceived by the farmers in Manipur. 2020.
Walters, J.P., et al., Exploring agricultural production systems and their fundamental components with system dynamics modelling. Ecological modelling, 2016. 333: p. 51-65.
Surve, U., J. Shinde, and E. Patil, Performance of integrated farming system models for economic viability, water productivity, employment generation, energy balance and soil health improvement under irrigated conditions. 2015.
Armalytė, J., et al., Microbial Diversity and Antimicrobial Resistance Profile in Microbiota From Soils of Conventional and Organic Farming Systems. Frontiers in Microbiology, 2019. 10.
Buza, T. and F.M. McCarthy, Functional genomics: applications to production agriculture. CABI Reviews, 2014(2013): p. 1-21.
Herlambang, A., M. Murwantoko, and I. Istiqomah, Dynamic change in bacterial communities in the integrated rice–fish farming system in Sleman, Yogyakarta, Indonesia. Aquaculture research, 2021. 52(11): p. 5566-5578.
Koshila Ravi, R., et al., Microbial interactions in soil formation and nutrient cycling. Mycorrhizosphere and pedogenesis, 2019: p. 363-382.
Mazzola, M., Assessment and management of soil microbial community structure for disease suppression. Annu. Rev. Phytopathol., 2004. 42(1): p. 35-59.
Suman, J., et al., Microbiome as a key player in sustainable agriculture and human health. Frontiers in Soil Science, 2022. 2: p. 821589.
Cisek, A. and M. Binek, Chicken intestinal microbiota function with a special emphasis on the role of probiotic bacteria. Polish journal of veterinary sciences, 2014. 17(2).
Mahesh, M., R.K. Mohanta, and A.K. Patra, Probiotics in livestock and poultry nutrition and health. Advances in Probiotics for Sustainable Food and Medicine, 2021: p. 149-179.
Liu, Z., et al., Comparative analysis of microbial community structure in the ponds with different aquaculture model and fish by high-throughput sequencing. Microbial pathogenesis, 2020. 142: p. 104101.
Nie, Z., et al., Effects of submerged macrophytes (Elodea nuttallii) on water quality and microbial communities of largemouth bass (Micropterus salmoides) ponds. Frontiers in Microbiology, 2023. 13: p. 1050699.
Singh, J.S., Microbes play major roles in the ecosystem services. Climate Change and Environmental Sustainability, 2015. 3(2): p. 163-167.
Fletcher, S., Understanding the contribution of environmental factors in the spread of antimicrobial resistance. Environmental health and preventive medicine, 2015. 20: p. 243-252.
Hazards, E.P.o.B., et al., Role played by the environment in the emergence and spread of antimicrobial resistance (AMR) through the food chain. Efsa Journal, 2021. 19(6): p. e06651.
Ahmad, I., H.A. Malak, and H.H. Abulreesh, Environmental antimicrobial resistance and its drivers: a potential threat to public health. Journal of Global Antimicrobial Resistance, 2021. 27: p. 101-111.
Liao, C.-Y., et al., Antimicrobial resistance of Escherichia coli from aquaculture farms and their environment in Zhanjiang, China. Frontiers in Veterinary Science, 2021. 8: p. 806653.
Zhu, T., et al., Antibiotic resistance genes in layer farms and their correlation with environmental samples. Poultry Science, 2021. 100(12): p. 101485.
Vaz‐Moreira, I., et al., Sources of Antibiotic resistance: zoonotic, human, environment. Antibiotic drug resistance, 2019: p. 211-238.
Dafale, N.A., S. Srivastava, and H.J. Purohit, Zoonosis: an emerging link to antibiotic resistance under “one health approach”. Indian journal of microbiology, 2020. 60: p. 139-152.
Barry, A.L., An overview of the Clinical and Laboratory Standards Institute (CLSI) and its impact on antimicrobial susceptibility tests. Antimicrobial susceptibility testing protocols, 2007. 1.
Hudzicki, J., Kirby-Bauer disk diffusion susceptibility test protocol. American society for microbiology, 2009. 15(1): p. 1-23.
FastQC. 2015.
Bolger, A.M., M. Lohse, and B. Usadel, Trimmomatic: a flexible trimmer for Illumina sequence data. Bioinformatics, 2014. 30(15): p. 2114-2120.
Bolyen, E., et al., Reproducible, interactive, scalable and extensible microbiome data science using QIIME 2. Nature Biotechnology, 2019. 37(8): p. 852-857.
McDonald, D., et al., Greengenes2 unifies microbial data in a single reference tree. Nature biotechnology, 2024. 42(5): p. 715-718.
Abellan-Schneyder, I., et al., Primer, pipelines, parameters: issues in 16S rRNA gene sequencing. Msphere, 2021. 6(1): p. 10.1128/msphere. 01202-20.
Bayes, T., Naive bayes classifier. Article Sources and Contributors, 1968: p. 1-9.
Douglas, G.M., et al., PICRUSt2 for prediction of metagenome functions. Nature biotechnology, 2020. 38(6): p. 685-688.
Yang, C., et al., ggpicrust2: an R package for PICRUSt2 predicted functional profile analysis and visualization. Bioinformatics, 2023. 39(8): p. btad470.
McMurdie, P.J. and S. Holmes, phyloseq: an R package for reproducible interactive analysis and graphics of microbiome census data. PloS one, 2013. 8(4): p. e61217.
R Core Team, R., R Foundation for Statistical Computing; Vienna, Austria: 2020. R: A language and environment for statistical computing. 2017.
Oksanen, J., et al., Package ‘vegan’. Community ecology package, version, 2013. 2(9): p. 1-295.
Kassambara, A., ggpubr: 'ggplot2' Based Publication Ready Plots. 2023.
Wickham, H., ggplot2. Wiley interdisciplinary reviews: computational statistics, 2011. 3(2): p. 180-185.
Shetty, S. and L. Lahti, microbiomeutilities: microbiomeutilities: An R package with utility functions for the microbiome R package. 2024.
Wickham, H., D. Vaughan, and M. Girlich, tidyr: Tidy Messy Data. 2024.
Hu, X., et al., Distinct patterns of distribution, community assembly and cross-domain co-occurrence of planktonic archaea in four major estuaries of China. Environmental Microbiome, 2023. 18(1): p. 75.
Cheng, X., et al., Archaea and their interactions with bacteria in a karst ecosystem. Frontiers in Microbiology, 2023. 14: p. 1068595.
Kogut, M.H., Role of diet-microbiota interactions in precision nutrition of the chicken: facts, gaps, and new concepts. Poultry Science, 2022. 101(3): p. 101673.
Segura-Wang, M., et al., Genome-resolved metagenomics of the chicken gut microbiome. Frontiers in Microbiology, 2021. 12: p. 726923.
Uguz, S. and A. Sozcu, Nutritional Value of Microalgae and Cyanobacteria Produced with Batch and Continuous Cultivation: Potential Use as Feed Material in Poultry Nutrition. Animals, 2023. 13(21): p. 3431.
Nagrale, D.T. and S.P. Gawande, Archaea: ecology, application, and conservation. Microbial Resource Conservation: Conventional to Modern Approaches, 2018: p. 431-451.
Bailey, R., Intestinal microbiota and the pathogenesis of dysbacteriosis in broiler chickens. 2010, University of East Anglia.
Craft, J., et al., Increased microbial diversity and decreased prevalence of common pathogens in the gut microbiomes of wild turkeys compared to domestic turkeys. Applied and Environmental Microbiology, 2022. 88(5): p. e01423-21.
Yadav, S. and R. Jha, Strategies to modulate the intestinal microbiota and their effects on nutrient utilization, performance, and health of poultry. Journal of animal science and biotechnology, 2019. 10: p. 1-11.
Eveno, M., et al., Biodiversity and phylogenetic relationships of novel bacteriocinogenic strains isolated from animal’s droppings at the zoological garden of Lille, France. Probiotics and antimicrobial proteins, 2021. 13: p. 218-228.
Solnick, J.V. and D.B. Schauer, Emergence of diverse Helicobacter species in the pathogenesis of gastric and enterohepatic diseases. Clinical microbiology reviews, 2001. 14(1): p. 59-97.
Aruwa, C.E., et al., Poultry gut health–microbiome functions, environmental impacts, microbiome engineering and advancements in characterization technologies. Journal of Animal Science and Biotechnology, 2021. 12: p. 1-15.
Richards-Rios, P., Understanding the chicken intestinal microbiome: towards a rational approach to feed-based interventions. 2020, The University of Liverpool (United Kingdom).
Kers, J.G., et al., Host and environmental factors affecting the intestinal microbiota in chickens. Frontiers in microbiology, 2018. 9: p. 235.
Wang, Y., et al., Comparison of the levels of bacterial diversity in freshwater, intertidal wetland, and marine sediments by using millions of illumina tags. Applied and environmental microbiology, 2012. 78(23): p. 8264-8271.
Huntington, T. and M.R. Hasan, Fish as feed inputs for aquaculture–practices, sustainability and implications: a global synthesis. FAO Fisheries and Aquaculture Technical Paper, 2009. 518: p. 1-61.
Wang, L., et al., Effects of lactic acid bacteria isolated from Tibetan chickens on the growth performance and gut microbiota of broiler. Frontiers in Microbiology, 2023. 14: p. 1171074.
Petro, C., et al., Microbial community assembly in marine sediments. Aquatic Microbial Ecology, 2017. 79(3): p. 177-195.
Ringø, E., et al., Effect of dietary components on the gut microbiota of aquatic animals. A never‐ending story? Aquaculture nutrition, 2016. 22(2): p. 219-282.
Ruuskanen, M.O., et al., Modelling spatial patterns in host‐associated microbial communities. Environmental Microbiology, 2021. 23(5): p. 2374-2388.
Robinson, C.J., B.J. Bohannan, and V.B. Young, From structure to function: the ecology of host-associated microbial communities. Microbiology and Molecular Biology Reviews, 2010. 74(3): p. 453-476.
Read, M.N. and A.J. Holmes, Towards an integrative understanding of diet–host–gut microbiome interactions. Frontiers in immunology, 2017. 8: p. 538.
Haberecht, S., et al., Poultry feeds carry diverse microbial communities that influence chicken intestinal microbiota colonisation and maturation. AMB Express, 2020. 10: p. 1-10.
Olson, E., et al., Microbiome analyses of poultry feeds: Part II. Comparison of different poultry feeds. Journal of Environmental Science and Health, Part B, 2024: p. 1-9.
Li, Z., Comparative analysis of the role of Bacillus species as food fermenting and food spoilage organisms. 2022.
Sturino, J.M., Literature-based safety assessment of an agriculture-and animal-associated microorganism: Weissella confusa. Regulatory Toxicology and Pharmacology, 2018. 95: p. 142-152.
Rychlik, I., Composition and function of chicken gut microbiota. Animals, 2020. 10(1): p. 103.
Rychlik, I., D. Karasova, and M. Crhanova, Microbiota of chickens and their environment in commercial production. Avian Diseases, 2023. 67(1): p. 1-9.
Boukerb, A.M., et al., Comparative analysis of fecal microbiomes from wild waterbirds to poultry, cattle, pigs, and wastewater treatment plants for a microbial source tracking approach. Frontiers in Microbiology, 2021. 12: p. 697553.
Sun, F., et al., Insights into the intestinal microbiota of several aquatic organisms and association with the surrounding environment. Aquaculture, 2019. 507: p. 196-202.
Burgos Valverde, F.A., Effect of Aquaculture Practices on Fish Microbial Communities. 2018.
Liu, T., et al., Integrated biogeography of planktonic and sedimentary bacterial communities in the Yangtze River. Microbiome, 2018. 6: p. 1-14.
Sichert, A., et al., Verrucomicrobia use hundreds of enzymes to digest the algal polysaccharide fucoidan. Nature microbiology, 2020. 5(8): p. 1026-1039.
Delmont, T.O., et al., Nitrogen-fixing populations of Planctomycetes and Proteobacteria are abundant in surface ocean metagenomes. Nature microbiology, 2018. 3(7): p. 804-813.
Raiyani, N.M. and S.P. Singh, Taxonomic and functional profiling of the microbial communities of Arabian Sea: a metagenomics approach. Genomics, 2020. 112(6): p. 4361-4369.
Ye, Y.-f., H. Min, and Y.-f. Du, Characterization of a strain of Sphingobacterium sp. and its degradation to herbicide mefenacet. Journal of Environmental Sciences, 2004. 16(2): p. 343-347.
Howden, B.P., et al., Staphylococcus aureus host interactions and adaptation. Nature Reviews Microbiology, 2023. 21(6): p. 380-395.
Yan, Y., et al., Metagenomic and Culturomics Analysis of Microbial Communities within Surface Sediments and the Prevalence of Antibiotic Resistance Genes in a Pristine River: The Zaqu River in the Lancang River Source Region, China. Microorganisms, 2024. 12(5): p. 911.
Selvarajan, R., et al., Taxonomic and functional distribution of bacterial communities in domestic and hospital wastewater system: implications for public and environmental health. Antibiotics, 2021. 10(9): p. 1059.
Katale, B.Z., et al., Genetic diversity and risk factors for the transmission of antimicrobial resistance across human, animals and environmental compartments in East Africa: a review. Antimicrobial Resistance & Infection Control, 2020. 9: p. 1-20.
Lupo, A., S. Coyne, and T.U. Berendonk, Origin and evolution of antibiotic resistance: the common mechanisms of emergence and spread in water bodies. Frontiers in microbiology, 2012. 3: p. 18.
Manyi-Loh, C., et al., Antibiotic use in agriculture and its consequential resistance in environmental sources: potential public health implications. Molecules, 2018. 23(4): p. 795.
Elshamy, A.A. and K.M. Aboshanab, A review on bacterial resistance to carbapenems: epidemiology, detection and treatment options. Future science OA, 2020. 6(3): p. FSO438.
Ballal, M., Trends in antimicrobial resistance among enteric pathogens: a global concern. Antibiotic Resistance, 2016: p. 63.
Friedman, N.D., E. Temkin, and Y. Carmeli, The negative impact of antibiotic resistance. Clinical microbiology and infection, 2016. 22(5): p. 416-422.
Hughes, D. and D.I. Andersson, Environmental and genetic modulation of the phenotypic expression of antibiotic resistance. FEMS microbiology reviews, 2017. 41(3): p. 374-391.
Philippot, L., et al., The interplay between microbial communities and soil properties. Nature Reviews Microbiology, 2024. 22(4): p. 226-239.
Yan, W., et al., Gut metagenomic analysis reveals prominent roles of Lactobacillus and cecal microbiota in chicken feed efficiency. Scientific reports, 2017. 7(1): p. 45308.
Cogburn, L., et al., Functional genomics of the chicken—a model organism. Poultry Science, 2007. 86(10): p. 2059-2094.
Rios Galicia, B., Novel bacterial species from the chicken gastrointestinal tract and their functional diversity. 2024.
Stoeva, M.K., et al., Microbial community structure in lake and wetland sediments from a high Arctic polar desert revealed by targeted transcriptomics. PLoS One, 2014. 9(3): p. e89531.
Sun, J., et al., Bacterial abundant taxa exhibit stronger environmental adaption than rare taxa in the Arctic Ocean sediments. Marine Environmental Research, 2024: p. 106624.
Jones, B.A., et al., Zoonosis emergence linked to agricultural intensification and environmental change. Proceedings of the national academy of sciences, 2013. 110(21): p. 8399-8404.
Yang, D.C., K.M. Blair, and N.R. Salama, Staying in shape: the impact of cell shape on bacterial survival in diverse environments. Microbiology and Molecular Biology Reviews, 2016. 80(1): p. 187-203.
Young, K.D., The selective value of bacterial shape. Microbiology and molecular biology reviews, 2006. 70(3): p. 660-703.
Wellawa, R.M.D.H., CHARACTERIZING THE ROLE OF PUTATIVE VIRULENCE GENES ASSOCIATED WITH INFECTION, COLONIZATION AND PERSISTENCE OF SALMONELLA ENTERITIDIS IN CHICKEN USING A BIOLUMINESCENT REPORTER. 2022, University of Saskatchewan.
Meng, Y.-n., et al., Transcriptional regulation of secondary metabolism and autophagy genes in response to DNA replication stress in Setosphaeria turcica. Journal of Integrative Agriculture, 2023. 22(4): p. 1068-1081.
Nguyen, J., J. Lara-Gutiérrez, and R. Stocker, Environmental fluctuations and their effects on microbial communities, populations and individuals. FEMS microbiology reviews, 2021. 45(4): p. fuaa068.
Vieira-Silva, S., et al., Species–function relationships shape ecological properties of the human gut microbiome. Nature microbiology, 2016. 1(8): p. 1-8.
Pickard, J.M., et al., Gut microbiota: Role in pathogen colonization, immune responses, and inflammatory disease. Immunological reviews, 2017. 279(1): p. 70-89.
Ogawara, H., Antibiotic resistance in pathogenic and producing bacteria, with special reference to beta-lactam antibiotics. Microbiological reviews, 1981. 45(4): p. 591-619.
Dalhoff, A., Global fluoroquinolone resistance epidemiology and implictions for clinical use. Interdisciplinary perspectives on infectious diseases, 2012. 2012(1): p. 976273.
Nikaido, H., Antibiotic resistance caused by gram-negative multidrug efflux pumps. Clinical Infectious Diseases, 1998. 27(Supplement_1): p. S32-S41.
Li, G., M.J. Walker, and D.M. De Oliveira, Vancomycin resistance in Enterococcus and Staphylococcus aureus. Microorganisms, 2022. 11(1): p. 24.
Lerminiaux, N.A. and A.D. Cameron, Horizontal transfer of antibiotic resistance genes in clinical environments. Canadian journal of microbiology, 2019. 65(1): p. 34-44.
Sepúlveda-Correa, A., et al., Genes associated with antibiotic tolerance and synthesis of antimicrobial compounds in a mangrove with contrasting salinities. Marine Pollution Bulletin, 2021. 171: p. 112740.

Table 1: Top 25 Phyla and Genera Relative Abundance

Phylum	Feed	Chicken Gut	Droppings	Fish Intestine	Sediment	Genus	Feed	Chicken Gut	Droppings	Fish Intestine	Sediment
Firmicutes	9.98%	69.21%	41.57%	29.99%	5.72%	Lactobacillus	7.91%	53.30%	21.63%	4.18%	7.92%
Proteobacteria	33.52%	13.67%	16.28%	15.79%	18.36%	Weissella	52.77%	0.00%	33.44%	23.65%	2.52%
Cyanobacteria	48.30%	0.61%	14.96%	11.08%	5.72%	Bacteroides	2.26%	11.56%	4.64%	5.77%	1.35%
Planctomycetes	0.12%	0.24%	2.60%	14.14%	20.42%	Candidatus	0.04%	0.13%	0.30%	20.67%	6.59%
Bacteroidetes	0.62%	12.48%	14.30%	6.46%	7.08%	Helicobacter	0.00%	15.45%	0.01%	0.00%	0.00%
Verrucomicrobia	0.08%	1.00%	1.05%	11.93%	9.31%	Clostridium	1.11%	0.89%	0.35%	15.07%	11.78%
Actinobacteria	7.07%	1.19%	5.65%	6.14%	2.95%	Acinetobacter	2.59%	0.01%	5.72%	4.40%	2.29%
Chloroflexi	0.19%	0.00%	0.58%	0.95%	9.55%	Synechococcus	0.00%	0.00%	0.42%	4.70%	25.92%
Acidobacteria	0.03%	0.00%	0.58%	0.73%	5.81%	Leuconostoc	0.31%	0.00%	3.36%	3.08%	0.01%
OD1	0.01%	0.00%	0.19%	0.36%	3.96%	Planctomyces	0.73%	0.00%	0.27%	2.70%	18.88%
OP3	0.00%	0.00%	0.07%	0.06%	2.47%	Phascolarctobacterium	0.96%	1.48%	4.09%	0.15%	0.16%
Spirochaetes	0.00%	0.00%	0.09%	0.13%	1.88%	Brachybacterium	0.91%	0.12%	4.10%	1.44%	0.41%
Fusobacteria	0.02%	0.12%	0.79%	0.56%	0.50%	Lactococcus	1.23%	0.08%	3.65%	1.97%	0.05%
Synergistetes	0.01%	1.48%	0.49%	0.01%	0.03%	Oscillospira	0.51%	4.41%	0.55%	0.03%	0.08%
Nitrospirae	0.04%	0.00%	0.22%	0.27%	0.97%	Corynebacterium	7.10%	0.34%	4.12%	0.35%	1.70%
TM7	0.00%	0.00%	0.09%	0.58%	0.45%	Enterobacter	2.00%	0.07%	2.59%	2.44%	0.14%
Chlorobi	0.00%	0.00%	0.05%	0.06%	0.79%	Ruminococcus	2.00%	3.48%	0.73%	0.11%	0.37%
WS3	0.01%	0.00%	0.13%	0.14%	0.64%	Sphingobacterium	1.56%	0.00%	3.56%	0.98%	0.53%
BRC1	0.00%	0.00%	0.04%	0.12%	0.57%	Enterococcus	2.79%	2.75%	0.83%	0.41%	0.90%
Chlamydiae	0.01%	0.00%	0.04%	0.26%	0.36%	Staphylococcus	11.52%	2.66%	0.53%	0.37%	0.13%
Euryarchaeota	0.00%	0.00%	0.02%	0.04%	0.56%	Prevotella	1.28%	0.01%	3.68%	0.16%	0.30%
OP8	0.00%	0.00%	0.04%	0.03%	0.55%	Akkermansia	0.10%	1.26%	0.10%	2.48%	0.39%
NC10	0.00%	0.00%	0.12%	0.11%	0.40%	Methylocaldum	0.00%	0.00%	0.02%	3.81%	1.26%
Fibrobacteres	0.00%	0.00%	0.00%	0.00%	0.52%	Luteolibacter	0.01%	0.17%	0.26%	0.56%	16.13%
Armatimonadetes	0.00%	0.00%	0.04%	0.05%	0.45%	Parabacteroides	0.30%	1.83%	1.05%	0.54%	0.17%

No competing interests reported.

SupplementaryTable1.docx
Supplementary Table – 1 Details on sampling sites and sample IDs.
SupplementaryData1.xlsx
Supplementary Data - 1 Provides detailed information on AST, isolates, biochemical tests and media used for culturing.
SupplementaryData2.xlsx
Supplementary Data – 2 Taxonomic profiling, metadata, sequence quality reports.
SupplementaryData3.xlsx
Supplementary Data - 3 Details of combined abundance of archaeal and bacterial diversity and percentages.
SupplementaryData4.xlsx
Supplementary Data – 4 KEGG Pathway abundance, percentages, resistance genes, and Ko numbers from the Picrust2 analysis.
SupplementaryFigures.docx
Supplementary Figures Biochemical tests and culturing images.

Microbial Diversity, Functional Genomics and Antibiotic Resistance in Integrated Chicken and Fish Farming Systems in Bangladesh

Status:

Version 1

Abstract

Figures

1.0 Introduction

2.0 Methodology

3.0 Results

3.1 Microbial Compositions and Diversity

3.2 Relative Abundance and Top Phyla and Genera across Samples

3.3 Antibiotic Susceptibility Testing (AST)

3.4 Functional Genomic Analysis (KEGG Orthology)

3.5 Antibiotic Resistance Genes Identified through KEGG Pathways

4.0 Discussion

4.1 Microbial Compositions and Diversity

4.2 Relative Abundance and Top Phyla and Genera across Samples

4.3 Antibiotic Susceptibility Testing (AST)

4.4 Functional Genomic Analysis (KEGG Orthology)

4.5 Antibiotic Resistance Genes Identified through KEGG Pathways

5.0 Conclusion

Declarations

References

Table

Additional Declarations

Supplementary Files

Status:

Version 1