Biodiversity Analysis of the Human Gut Microbiome in Healthy Individuals Using Bioinformatics Tools

Biodiversity Analysis of the Human Gut Microbiome in Healthy Individuals Using Bioinformatics Tools | 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 Biodiversity Analysis of the Human Gut Microbiome in Healthy Individuals Using Bioinformatics Tools Tayma Sbei, Abdul Qader Abbady This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7776578/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 Motivation : The human gut microbiome plays a crucial role in maintaining health and preventing disease. This study aims to provide a comprehensive analysis of the gut microbiome composition in healthy individuals using integrated bioinformatics tools. We analyzed gut microbiota samples from ten healthy individuals using UGENE software, employing taxonomy tools such as Kraken, DIAMOND, and MetaPhlan. Alpha diversity indices, including the Shannon and Simpson diversity indices, were calculated. Correlation analyses were performed to explore relationships between microbial diversity, age, and geographic living conditions. Results : The gut microbiome showed significant diversity across samples. Alpha diversity indices indicated high microbial diversity, which tended to decrease with age. Individuals living in mountainous regions exhibited lower diversity than those in villages. This study highlights the complex diversity of the human gut microbiome and its variation with age and geographic location. The presence of both indigenous microbiota and pathobionts genera can lead to possible dysbiosis within the gut ecosystem. High microbial diversity is associated with better health outcomes, emphasizing its importance in maintaining gut health. Future research should aim to further elucidate the functional roles of these microbial communities. Biological sciences/Computational biology and bioinformatics Biological sciences/Ecology Earth and environmental sciences/Ecology Biological sciences/Microbiology Human Gut Microbiome Microbiome Biodiversity UGENE software Metagenomic analysis Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Figure 8 Figure 9 Figure 10 Figure 11 1. Introduction The human gut microbiome (HGM) is a rich and variable community of microorganisms that inhabit the gastrointestinal tract (GIT). The term includes the microbiota and the collection of microbial genomes, including their structural elements, metabolites, and molecules produced by coexisting hosts.( 1 ) HGM plays a crucial role in numerous aspects of human biology, from metabolic health to immune functions. Gut microbiota is more than 100 trillion microorganisms mainly bacteria, and encodes over 3 million genes compared with ~ 23,000 in the human genome ( 2 ). This complex community undergoes dynamic changes throughout the first two years of life ( 3 ), it changes from sterile to an adult-like stable microbiome by the time the infant reaches 2 years of age ( 4 ). Gut microbiota includes bacteria, archaea, fungi, viruses and eukaryotes, with microbial abundance is greatest in the colon (~ 10 11 and 10 12 cells/g stool). Nine distinct phyla are known in the human gut, with Firmicutes and Bacteroides dominating 90% of the community. Examples of taxonomic gut microbiota composition are illustrated in (Fig. 1 ) ( 2 , 5 ). The composition of the gut microbiota is influenced by mode of delivery of a neonate, host genetic features, environmental factors, host immune response, antibiotic usage, and dietary effects. Diet can have a marked impact on the gut environment, including gut transit time and pH; and changing the intakes of the three main macronutrients (carbohydrates, proteins and fats) can significantly affect the composition of the microbiota. ( 6 ). Environmental factors are also important; studies showed the individuals living in rural areas have a significantly distinct microbiome than those in urban areas. The differences have also been observed between nationalities ( 7 ), due to differences in genetics, diet, hygiene, environment, and geography ( 8 ). It’s very important to keep our gut microbiome healthy and balanced because it supports digestion, structural function, metabolic function, immune-system development, protective function, and the gut–brain axis Digestion : Carbohydrates which humans cannot digest are fermented in the large intestine into short-chain fatty acids (SCFAs) such as acetic, propionic and butyric acid. Propionate can switch off the hunger signal and facilitates production of adenosine triphosphate (ATP) in the liver. Butyrate induces apoptosis in malignant epithelial cells lowering bowel cancer risk and providing energy to gut cells. Acetic acid is used by muscles, and gasses like hydrogen, carbon dioxide, and methane are also produced ( 9 ). Structural Function : The microbiome maintains gut epithelium integrity, preventing cytokines in the gut lumen from passing through. This function may be altered by pathogens such as Escherichia coli (E. coli) and Clostridium Difficile (C. difficile), where dysbiosis facilitates cytokines back diffusion ( 10 ). Metabolic Function : Gut microbiome synthesizes all essential and nonessential amino acids, performs bile biotransformation, produces proteases ( 11 ), and synthesizes water-soluble vitamins and fat-soluble vitamins, such as Vitamin K. It also aids in the absorption of magnesium, iron, and calcium ( 12 ). Immune-system development : The local gut microbiome drives the maturation of gut-associated lymphoid tissue (GALT) ( 13 ), maintains barrier function by mucus and antimicrobial peptide production, and influences inflammatory vs non-inflammatory cell phenotypes. Indigenous microbiota or pathobionts may differentiate T helper (Th) cells into Th1, Th2, Th17, and regulatory T (Treg) cells via microbiota metabolites. Dysbiosis may lead to excess proinflammatory cytokines and loss of immune tolerance, contributing to allergic or autoimmune diseases ( 14 ). Protective Function : The microbiome strengthens the gut barrier through SCFAs production and other mechanisms ( 15 ). The gut–brain axis : The microbiota-gut brain axis (MGBA) is a bidirectional network linking the central nervous system (CNS), autonomic nervous system (ANS) (including the enteric nervous system and the vagus nerve), neuroendocrine and neuroimmune system including the hypothalamic-pituitary-adrenal axis (HPA axis), and the gut microbiota (GM) ( 16 , 17 ). The GM can stimulate peptide and hormone release from enteroendocrine cells, affecting the CNS. Chronic stress can dysregulate the HPA axis, altering GM composition, increasing gut permeability, and contributing to inflammation, brain dysfunction and various CNS disorders ( 18 ). Dysbiosis has been linked to autism, anxiety-depressive behavior, Alzheimer’s disease, Parkinson’s disease, multiple sclerosis, and schizophrenia ( 19 – 21 ). A diverse,stable gut microbiota in adults is associated with improved metabolic and immune health, while dysbiosis can lead to life-threatening diseases ( 22 , 23 ). Balance between major phyla and genera is crucial, disruption can contribute to cardiovascular diseases (CVDs), cancer, respiratory diseases, diabetes, inflammatory bowel disease (IBD), brain disorders, chronic kidney and liver diseases (Table 1 ) ( 22 ) 1. Taxonomic Diversity : Variety of microbial taxa identified using 16S rRNA for bacteria and archaea or internal transcribed spacer (ITS) for fungi ( 25 ). Tools have been developed to classify metagenomic data ( 26 , 27 ): Sequence similarity like DIAMOND (DNA to protein classification (BLASTx-like)). K-mers like Kraken and CLARK (DNA to DNA classification (BLASTn-like)). Marker Genes like MetaPhlAn2 ( 28 ) Phylogenies like MEGAN 2. Alpha Diversity : Mean species diversity within a sample, combining richness (number of species/ OTUs) and evenness ( 29 ). Common metrics include : The Index of Community Diversity : such as Shannon diversity index, and Simpson diversity index, access a combination between richness and evenness in a sample. The Index of Community Richness : such as Chao index, and Abundance-based Coverage Estimator (ACE) index. Beta Diversity : Variation in composition between samples using Bray-Curtis dissimilarity, Jaccard similarity index, or UniFrac. Functional Diversity : Assesses functional redundancy or complementarity of microbial communities and helps understand their roles in ecosystem processes, nutrient cycling, and host-microbe interactions. Ecological Interactions : Network and co-occurrence analysis to reveal community dynamics. Temporal and Spatial Dynamics : Track stability, resilience, and adaptability to environmental changes. The software used for this research is UGENE, a free bioinformatics cross-platform integrating multiple tools and algorithms, with graphical and command-line interfaces. It supports sequence alignment, motif discovery, primer design, phylogenetic analysis, and next generation sequencing (NGS) technologies (Variant Calling, RNA-sequencing, and ChIP-sequencing). The UGENE Workflow Designer allows building custom pipelines with quality filtering, mapping, assembly, and taxonomic classification using Kraken, DIAMOND, and MetaPhlAn. Pre-designed workflows can run locally or on servers ( 30 ). 2. Materials and Methods The microbiome data used in this research were obtained from the European Nucleotide Archive (ENA) under the project accession number ( PRJNA1029329 ). The dataset includes sequencing data targeting the 16S rRNA (V3-V4) gene and ITS ( 1 – 2 ) region from fecal samples collected from the JuHoansi San/Bushmen community in the Nyae Nyae region of north-eastern Namibia. For the purpose of this research, 10 samples were selected based on their geographical location within the region. All individuals were reported as healthy at the time of sampling. The primary software used was UGENE software suite ( version 33.0 ) Linux (64-bit) full package. This specific version was chosen for its support of metagenomic workflow feature, which was removed in later releases. The installation followed the official UGENE documentation. Additionally, several external tools were installed to perform metagenomics classification, including NCBI taxonomy classification, Kraken DB, MetaPhlAn2. Also, Python 3.10 with the scikit-bio and matplotlib libraries was used via Jupyter Notebook for statistical analysis and visualization. An integrated UGENE workflow was set up using the workflow designer and ‘Parallel classification for PE reads’ sample was chosen from the NGS sample section (Supplementary Fig. 1). The workflow steps include: Initial quality control assessment using FastQC to evaluate sequence quality, GC content, and adapter content. The sequencing data obtained was of high quality, as indicated by FastQC analysis. Consequently, no additional filtering or trimming steps were necessary prior to taxonomic classification Taxonomic classification of reads with Kraken, DIAMOND, MetaPhlAn2 Weighted Voting Taxonomic Identification (WEVOTE) was used for assembly and annotation. WEVOTE combines the high sensitivity of the naive similarity methods (DIAMOND) and the high precision of the k-mer-based methods (Kraken) in order to identify novel members of a marker family from novel genomes (48) . Classification Report generation that summarizes the results. This report provided a consolidated view of the quality and composition of the metagenomic samples. The entire workflow took approximately 25 hours to complete due to the complexity and volume of data processed. DIAMOND tool took the most time to process. After taxonomy classification was assigned using UGENE, Python and Jupyter Notebook were used to visualize the classification output. A table was created that included the count of bacterial genus within each phylum for each sample, and a grouped bar plot was generated. Alpha Diversity was calculated using two different indices: the Shannon and Simpson Diversity Indices via the scikit-bio library. Ten csv files were loaded simultaneously, and alpha diversity was calculated for each sample individually. Histograms and box plots were generated. A scatter plot was also created to compare both indices. Exploratory Data Analysis (EDA) was applied in order to uncover relationships within the data. The following plots were generated: Correlation Matrix Heatmap Pair Plot (Scatter Plot Matrix) with KDE (Kernel Density Estimation) plots Scatter Plots with Regression Lines These plots helped visualize relationships between alpha diversity indices and metadata variables such as age and location. To study the taxonomic classification and differentiate pathobionts from indigenous microbiota, a bacterial priority pathogens list was obtained from the World Health Organization (WHO). Pathogens from ( Table 3 ) were also added. The final pathogen list was matched against classification reports using Python. The potential diseases related to each patient were then predicted based on the pathogens found. 3. Results 3.1 FastQC Report The FastQC report was generated as an HTML file for each sample in order to assess the sequence quality (Supplementary File 1). All reads were of high quality, with no sequences flagged as poor quality. The GC content ranged from 40% to 60%, with an average of 53%. Sequence lengths varied from 35 to 301 bp. The per-base quality scores remained consistently high across all positions, and no adapter contamination or ambiguous 'N' bases were detected. Sequence duplication levels showed common peaks likely due to overrepresented 16S/ITS amplicons, which is expected in targeted metagenomic datasets. Overrepresented sequences were present but not removed, as they likely reflect highly abundant microbial taxa or amplification bias rather than contamination. These findings justified proceeding with taxonomic classification without additional filtering. 3.2 Kraken Report The kraken report that was generated contain 5 columns (Supplementary File 2) identifying each read’s classification status (U/C), sequence ID, taxonomic ID, sequence length, and the Lowest Common Ancestor (LCA) Mappings which is a space-delimited list indicating the LCA mapping of each k-mer in the sequence and taxonomic origin to distinguish classified from ambiguous nucleotides. 3.3 DIAMOND Report The DIAMOND report that was generated contains 3 columns, listing the sequence reads and classification attempts (Supplementary File 3). While two columns remained empty, the tool's contribution to cross-validation was preserved. 3.4 MetaPhlan2 Report MetaPhlan2 generated 2 reports: a bowtie output file (Supplementary File 4) and a relative abundance output file (Supplementary File 5) . One sample showed 100% relative abundance of a retrovirus: Murine osteosarcoma virus (family: Retroviridae), potentially representing contamination or alignment artifact. The bowtie file has two entries the sample ID and the gene ID. 3.5 Ensemble Report The Ensemble report assembled the sequences read with its corresponding tax ID assigned by Kraken and DIAMOND in a csv file for each sample (Supplementary File 6) . 3.6 WEVOTE Report The WEVOTE report that was generated contains sequencing read name, superkingdom tax ID along with taxonomy identifier according to the NCBI taxonomy classification and WEVOTE conclusion about the reads (Supplementary File 7). 3.7 Classification Report This classification report provides a 24 columns hierarchical breakdown of taxonomic classifications based on sequencing reads, summarizing taxonomy at all levels, showing both direct counts and proportions as well as clade-wide counts and proportions for various taxonomic levels (Supplementary File 8) . This can help in understanding the distribution and abundance of different taxa in the sample. 3.8 Visualization Bar Plot The taxa-counts table ( Fig. 2 , Fig. 3 ) consists of three columns: phylum, genus, and counts per sample. Also, a bar plot was generated for every sample to visualize which is the most abundant genus / phylum in the sample ( Fig. 4 ) . 3.9 Alpha Diversity The diversity results were saved as a csv file (Table 2) which includes the Shannon Index and Simpson Index results for each sample. Table 2 Shannon and Simpson Indices Across 10 Samples A box plot was generated for each index in the Shannon index ( Fig. 6 A ) : The orange line represents the median which is 2.94 The box represents the interquartile range (IQR), which is the range between the first quartile (Q1) 2.77 and the third quartile (Q3) 2.96. The whiskers typically extend to 1.5 times the IQR from the quartiles at the points 2.485 and 3.245. In the Simpson Index box plot ( Fig. 6 B ) : The median is 0.899 Q1 is 0.894 and Q3 is 0.915. The whiskers extend at the points 0.88 and 0.92. The point below the whisker is considered an outlier with a Simpson index of 0.86. To compare between the indices based on a scatter plot, the correlation coefficient (r) was calculated and found to be 0.8 ( Fig. 7 ) indicating a positive correlation. The correlation coefficient ranges from − 1 to 1, meaning that as the Shannon diversity index increases, the Simpson diversity index also tends to increase. To visually demonstrate this, a scatter plot was created ( Fig. 8 ) , with the x axis representing Shannon diversity, and the y axis representing Simpson diversity. Each point represents an observation from the dataset, with its position determined by the values of the two indices. The points tend to go upwards from left to right, this indicates a positive correlation between the two indices. 3.10 Statistical Analysis The correlation matrix displayed in the heatmap shows the relationships between various metadata and alpha diversity indices. Values close to 1 (dark red) indicate a strong positive correlation, values close to -1 (dark blue) indicate a strong negative correlation and as the color gets lighter the correlations decrease. The most significant correlations found in the matrix are ( Fig. 9 ) : Age and Shannon Index : (r = -0.27) indicating a slightly negative correlation. Age and Simpson Index : (r = -0.53) indicating a moderate negative correlation. As age increases, the Simpson Index tends to decrease more noticeably. Age and Experiencing Intestinal Infections : (r = 0.74) indicating a strong positive correlation. Suggesting younger individuals might have experienced more intestinal infections. Alpha Diversity Indices and Uncertain About Taking Antibiotics : a strong negative correlation with Shannon (-0.70) and Simpson (-0.67). Shannon and Simpson Indices : There is a strong positive correlation (0.82), which is expected as both measure microbial diversity. And many more correlations which are less significant. A pair plot (scatterplot matrix) with KDE plot shows the relationship between Age, Shannon index, and Simpson index, separated by villages (Duinpos and Mountain Pos) ( Fig. 10 ) . KDE Plots : The age distributions for Duinpos and Mountain Pos are shown in the top left corner. It appears that both villages have a similar age distribution with a slight difference. The Shannon diversity index distributions for both villages are shown in the middle. Duinpos (blue) has a broader range of Shannon index values compared to Mountain Pos (orange). The Simpson diversity index distributions for both villages are shown at the bottom. The distribution for Duinpos is broader than that for Mountain Pos. Scatter Plots : The scatter plots show the relationship between Age and different diversity indices for both villages. There seems to be a positive correlation between Shannon and Simpson indices for both villages, which is expected as both indices measure biodiversity. The orange points representing Mountain Pos are more clustered in certain areas compared to the blue points representing Duinpos, indicating less variability in the Shannon and Simpson indices. Duinpos (blue) has a wider spread in Shannon and Simpson indices compared to Mountain Pos (orange). The scatter plot on the left shows the relationship between Age and the Shannon diversity index, and the right plot shows between Age and the Simpson diversity index ( Fig. 11 ) : Scatter Points : Each blue dot represents a data point, with Age on the x-axis and the Shannon /Simpson index on the y-axis. The scatter points show a slight negative trend. Trend Line : The red line represents the best fit line for the data points, indicating the overall trend. The downward slope of the red line suggests a negative correlation between Age and the Shannon / Simpson index. Confidence Interval : The shaded pink area around the trend line represents the confidence interval, showing the range within which the true regression line is likely to fall. 3.11 Functional Analysis The pathogen species table shows each patient with its pathogenic genera found in its microbiome with the total pathogenic occurrence. The occurrence varies between 5 pathogens and 11 ( Table 3 ) . The results of the potential diseases that might affect each patient are listed in ( Table 4 ) . 4. Discussion This study conducted a comprehensive biodiversity analysis of the human gut microbiome in healthy individuals using integrated bioinformatics tools for metagenomic analysis. The results provide valuable insights into the composition and diversity of gut microbiota. We found that the gut microbiome of healthy individuals is characterized by significant diversity, with various bacterial genera contributing to the overall microbial ecosystem. The use of UGENE software allowed for precise taxonomic classification using multiple taxonomy tools, such as Kraken, DIAMOND, MetaPhlan. The bar plots provided a clear visualization of the distribution of genera within different phyla across each sample. The most abundant phylum across all samples was Firmicutes, indicating its dominant presence with a high number of genera varying among samples. Genera such as Clostridium and Lactobacillus were prominent with different relative abundance. Firmicutes are known for fermenting dietary fibers to produce SCFAs, crucial for gut health. However, species like Clostridium difficile were also found, which can cause severe diarrhea and colitis, particularly after antibiotic use. Some samples contained Streptococcus, some species, such as Streptococcus pneumoniae, can cause infections ranging from pharyngitis to pneumonia. Proteobacteria was the second most abundant phylum, containing genera like Escherichia and Helicobacter. Proteobacteria can have both indigenous microbiota and pathobionts which some are associated with inflammatory conditions, such as certain E. coli and Helicobacter. Bacteroidetes and Actinobacteria were also abundant. Bacteroidetes including Bacteroides and Prevotella, are essential for the breakdown of complex carbohydrates contributing to energy harvest from diet. Actinobacteria represented by Bifidobacterium and Corynebacterium, are important for maintaining a healthy gut and commonly used as probiotics. Alpha diversity indices showed Shannon values between 2.6–3.1 (normal 1.5–3.5). and Simpson values between 0.87–0.92 (normal 0–1), indicating high diversity. The correlation coefficient was 0.852, this strong positive shows consistency; when one index indicated high diversity, so did the other. While both indices have different mathematical basis and interpretation, they provide complementary insights. The correlation matrix provided a comprehensive overview of how different variables relate to each other. It revealed a strong positive correlation between the alpha diversity indices as mentioned earlier. And also, a negative correlation between age and alpha diversity, as age increases, the microbial diversity tends to decrease. And this was clear in the scatter plot with a regression line. The pair plot along with the KDE plot showed that individuals living in the mountain have less diversity than those living in the village. Comparing genera identified in samples with known pathogenic genera associated with various diseases. This comparison helps in understanding the potential health implications of the gut microbiome composition. The investigation into potential disease relationships identified specific pathogenic bacteria present across the samples, which could indicate a predisposition to certain diseases even in healthy individuals. For instance, Helicobacter pylori was found in patients ( 2 – 3 – 4 – 5 ), associated with peptic ulcers, gastric cancer, and possibly IBD along with the E. coli and C. difficile. Patient ( 6 ) had the most pathobionts (11 genera), such as Streptococcus pneumoniae, Haemophilus Influenzae, Staphylococcus aureus which could be a marker of chronic respiratory diseases and CVD potential according to the pathogenic microbiota table. Other patients varied in composition, which means they have a potential for various diseases. This underscores the potential of early microbial diagnostics in predicting disease risk. 5. Conclusion This study highlights the diversity and complexity of the human gut microbiome in healthy individuals. Alpha diversity indices indicated a high level of microbial diversity, with a strong positive correlation between Shannon and Simpson indices. Diversity tends to decrease with age, and mountain residents exhibit lower diversity compared to village residents. Firmicutes was the most abundant phylum, with Clostridium and Lactobacillus playing key roles in gut health. The presence of potential pathogens like Clostridium difficile suggests a delicate balance between beneficial and harmful bacteria. The second most abundant phylum, Proteobacteria, included genera associated with inflammation, while Bacteroidetes was less represented than expected, suggesting further investigation. High microbial diversity is associated with resilience against pathogens and better overall health. Future research should focus on longitudinal and functional analyses to clarify microbiome roles in human health. 6. Limitations Reliance on publicly available datasets Reliance on publicly available datasets due to lack of local data. Using UGENE software may limit specific features and analyses compared with more specialized bioinformatics tools. Available datasets may have limited sample sizes or demographic diversity, potentially affecting generalizability. Expanding in future studies would improve robustness, Reliance on taxonomic classification without functional analysis means that we can infer associations but not specific biological functions. due to lack of local data. Using UGENE software may limit specific features and analyses compared with more specialized bioinformatics tools. Declarations Competing Interests The authors declare no competing interests. Funding This research received no specific grant from any funding agency in the public, commercial, or not-for-profit sectors. Author Contribution T.S. conceived and designed the study, performed the bioinformatics analysis, interpreted the data, and wrote the manuscript.A.A. provided supervision, methodological guidance, and manuscript review. Acknowledgement I would like to express my sincere gratitude to Dr. Abdulqader Abbady for their invaluable guidance, support, and expertise throughout the research process.Special thanks to my family and friends for their unwavering support and understanding during this academic journey.I also appreciate Dr. Ghassan Shannan for efforts in trying to obtain local data from various resources and abroad. Although data could not be acquired, your willingness to assist was greatly appreciated.Thanks to Nebras Ayoub, IT specialist, for assistance in setting up and downloading the virtual machine and Linux. Your technical support was crucial for smooth research operation.Finally, I thank the UGENE team, especially Dmitrii Sukhomlinov, for prompt responses and valuable advice on software versions and technical setups. Data Availability UGENE software and its integrated metagenomic tools are freely available online (version 33.0) and support Linux and macOS platforms. Statistical analysis and visualization were conducted using Python 3.10 with the libraries scikit-bio and matplotlib, executed via Jupyter Notebook distributed through the Anaconda platform. Anaconda is freely available for download. All tools used in this study are open-source and freely available for non-commercial use.The datasets generated and analyzed during the current study are available from the corresponding author upon reasonable request. References Berg, G. et al. Microbiome definition re-visited: old concepts and new challenges. Microbiome 8 (1), 103 (2020). Rinninella, E. et al. What is the Healthy Gut Microbiota Composition? A Changing Ecosystem across Age, Environment, Diet, and Diseases. Microorganisms ; 7 (1). (2019). Mancabelli, L. et al. Taxonomic and metabolic development of the human gut microbiome across life stages: a worldwide metagenomic investigation. mSystems 9 (4), e0129423 (2024). Fouhy, F., Ross, R. P., Fitzgerald, G. F., Stanton, C. & Cotter, P. D. Composition of the early intestinal microbiota: knowledge, knowledge gaps and the use of high-throughput sequencing to address these gaps. Gut Microbes . 3 (3), 203–220 (2012). Dave, M., Higgins, P. D., Middha, S. & Rioux, K. P. The human gut microbiome: current knowledge, challenges, and future directions. Transl Res. 160 (4), 246–257 (2012). Scott, K. P., Gratz, S. W., Sheridan, P. O., Flint, H. J. & Duncan, S. H. The influence of diet on the gut microbiota. Pharmacol. Res. 69 (1), 52–60 (2013). Hospodsky, D. et al. Hand bacterial communities vary across two different human populations. Microbiol. (Reading Engl) . 160 (Pt 6), 1144–1152 (2014). Clemente, J. C. et al. The microbiome of uncontacted Amerindians. Sci. Adv. ; 1 (3). (2015). Madhogaria, B., Bhowmik, P. & Kundu, A. Correlation between human gut microbiome and diseases. Infect. Med. 1 (3), 180–191 (2022). Saitoh, Y. et al. Tight junctions. Structural insight into tight junction disassembly by Clostridium perfringens enterotoxin. Science 347 (6223), 775–778 (2015). Rawlings, N. D. et al. The MEROPS database of proteolytic enzymes, their substrates and inhibitors in 2017 and a comparison with peptidases in the PANTHER database. Nucleic Acids Res. 46 (D1), D624–D632 (2018). Afzaal, M. et al. Human gut microbiota in health and disease: Unveiling the relationship. Front. Microbiol. 13 , 999001 (2022). Mörbe, U. M. et al. Human gut-associated lymphoid tissues (GALT); diversity, structure, and function. Mucosal Immunol. 14 (4), 793–802 (2021). Adak, A. & Khan, M. R. An insight into gut microbiota and its functionalities. Cell. Mol. Life Sci. 76 (3), 473–493 (2019). Bron, P. A. et al. Can probiotics modulate human disease by impacting intestinal barrier function? Br. J. Nutr. 117 (1), 93–107 (2017). Mayer, E. A. Gut feelings: the emerging biology of gut-brain communication. Nat. Rev. Neurosci. 12 (8), 453–466 (2011). Chakrabarti, A. et al. The microbiota-gut-brain axis: pathways to better brain health. Perspectives on what we know, what we need to investigate and how to put knowledge into practice. Cell. Mol. Life Sci. 79 (2), 80 (2022). Rusch, J. A., Layden, B. T. & Dugas, L. R. Signalling cognition: the gut microbiota and hypothalamic-pituitary-adrenal axis. Front. Endocrinol. (Lausanne) . 14 , 1130689 (2023). Dissanayaka, D. M. S., Jayasena, V., Rainey-Smith, S. R., Martins, R. N. & Fernando, W. M. A. D. B. The role of diet and gut microbiota in alzheimer’s disease. Nutrients ; 16 (3). (2024). Zhang, X., Tang, B. & Guo, J. Parkinson’s disease and gut microbiota: from clinical to mechanistic and therapeutic studies. Transl Neurodegener . 12 (1), 59 (2023). Ju, S. et al. The Gut-Brain Axis in Schizophrenia: The Implications of the Gut Microbiome and SCFA Production. Nutrients ; 15 (20). (2023). Hou, K. et al. Microbiota in health and diseases. Signal. Transduct. Target. Ther. 7 (1), 135 (2022). Hajjo, R., Sabbah, D. A. & Al Bawab, A. Q. Unlocking the potential of the human microbiome for identifying disease diagnostic biomarkers. Diagnostics (Basel) ; 12 (7). (2022). Understand the key terms. in microbiome projects | Biomcare [Internet]. [cited 2024 Jul 1]. Available from: https://biomcare.com/info/key-terms-in-microbiome-projects/ Ye, S. H., Siddle, K. J., Park, D. J. & Sabeti, P. C. Benchmarking metagenomics tools for taxonomic classification. Cell 178 (4), 779–794 (2019). Lindgreen, S., Adair, K. L. & Gardner, P. P. An evaluation of the accuracy and speed of metagenome analysis tools. Sci. Rep. 6 , 19233 (2016). Meyer, F. et al. Assessing taxonomic metagenome profilers with OPAL. Genome Biol. 20 (1), 51 (2019). Truong, D. T. et al. MetaPhlAn2 for enhanced metagenomic taxonomic profiling. Nat. Methods . 12 (10), 902–903 (2015). The Use and Types of Alpha-Diversity Metrics in Microbial NGS. - CD Genomics [Internet]. [cited 2024 Jul 2]. Available from: https://www.cd-genomics.com/microbioseq/the-use-and-types-of-alpha-diversity-metrics-in-microbial-ngs.html Rose, R., Golosova, O., Sukhomlinov, D., Tiunov, A. & Prosperi, M. Flexible design of multiple metagenomics classification pipelines with UGENE. Bioinformatics 35 (11), 1963–1965 (2019). Tables Tables 1 to 4 are available in the Supplementary Files section. Additional Declarations No competing interests reported. Supplementary Files SupplementaryDocuments.zip Supplementary Figure 1. Parallel classification for PE reads Workflow Main Structure Supplementary File 1. FastQC Report for SRR26415149_1.fastq Supplementary File 2. Kraken Classification Report for SRR26415149_1.fastq Supplementary File 3. Diamond Classification Report for SRR26415149_1.fastq Supplementary File 4. Bowtie2 Output File for SRR26415149_1.fastq Supplementary File 5. Relative Abundance Output File for SRR26415149_1.fastq Supplementary File 6. Ensemble Report for SRR26415149_1.fastq Supplementary File 7. WEVOTE Report for SRR26415149_1.fastq Supplementary File 8. Classification Report for SRR26415149_1.fastq Tables.docx 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. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-7776578","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":539243143,"identity":"b1bdb9a1-3322-4460-9dda-5de98e37c3fd","order_by":0,"name":"Tayma Sbei","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA6UlEQVRIiWNgGAWjYBCDBAZm5gNAWkKGoFIehBa2BJAWHjyK0bUw8BggC+AG9uxnTDf8YKjL42fn+fzqRo0FDwP74aMb8NrCk2N2s4fhcLFkM+8265xjQIfxpKXdwO+wHLMbPAwHEjcc5t1mnMMG1CLBY4ZfC/8bs5t/GOoS9x/meWac848YLRI5Zrd5GJgTNzDzMD/ObSNGy41nZbdlDA4XSxxmM2PO7ZPgYSPkF/b+5G0331QAQ6z/8OPPOd/q5PjZDx/Dq4WBgQMYHeAYYWCTAJP4lYPteQBjMX8grHoUjIJRMApGIgAA0mZCX8EcUiMAAAAASUVORK5CYII=","orcid":"","institution":"Syrian Virtual University","correspondingAuthor":true,"prefix":"","firstName":"Tayma","middleName":"","lastName":"Sbei","suffix":""},{"id":539243144,"identity":"3d69dda0-088b-48af-b301-97f1b0ebff46","order_by":1,"name":"Abdul Qader Abbady","email":"","orcid":"","institution":"Atomic Energy Commission of Syria","correspondingAuthor":false,"prefix":"","firstName":"Abdul","middleName":"Qader","lastName":"Abbady","suffix":""}],"badges":[],"createdAt":"2025-10-03 23:38:07","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-7776578/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-7776578/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":95225555,"identity":"0d352b0a-25f6-42a5-bd47-e2fc24d119fa","added_by":"auto","created_at":"2025-11-05 16:25:13","extension":"docx","order_by":0,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":5657836,"visible":true,"origin":"","legend":"","description":"","filename":"ManuscriptTaymaSbei.docx","url":"https://assets-eu.researchsquare.com/files/rs-7776578/v1/6940103ad803871f3a391f1a.docx"},{"id":95141620,"identity":"d591b8d4-9847-481e-b1ca-0504d20b98b9","added_by":"auto","created_at":"2025-11-04 17:27:18","extension":"json","order_by":1,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":5452,"visible":true,"origin":"","legend":"","description":"","filename":"274aea9f10654f048e3ceb4e96a4862c.json","url":"https://assets-eu.researchsquare.com/files/rs-7776578/v1/6e4b7690f23f13114ba64f52.json"},{"id":95141662,"identity":"c2fbdfa8-3acc-4561-a4dd-38b6ad98c9e1","added_by":"auto","created_at":"2025-11-04 17:27:19","extension":"zip","order_by":2,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":32608416,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryDocuments.zip","url":"https://assets-eu.researchsquare.com/files/rs-7776578/v1/b1ad5257f8da3010ce264d09.zip"},{"id":95226726,"identity":"6e0f722f-6e45-4817-a3b0-22106118f123","added_by":"auto","created_at":"2025-11-05 16:31:40","extension":"xml","order_by":3,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":87472,"visible":true,"origin":"","legend":"","description":"","filename":"274aea9f10654f048e3ceb4e96a4862c1enriched.xml","url":"https://assets-eu.researchsquare.com/files/rs-7776578/v1/93571809141334abee165045.xml"},{"id":95226213,"identity":"1c785da5-2360-4f3c-9832-b291adf9851f","added_by":"auto","created_at":"2025-11-05 16:30:41","extension":"jpeg","order_by":4,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":973581,"visible":true,"origin":"","legend":"","description":"","filename":"floatimage1.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-7776578/v1/dfd2531b836646b4baaf24b6.jpeg"},{"id":95141634,"identity":"dfda002a-b6b1-4d3f-a68c-c5e4c4cd01cc","added_by":"auto","created_at":"2025-11-04 17:27:18","extension":"png","order_by":5,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":208882,"visible":true,"origin":"","legend":"","description":"","filename":"floatimage10.png","url":"https://assets-eu.researchsquare.com/files/rs-7776578/v1/3a592c2f3964bab47449b568.png"},{"id":95226634,"identity":"d0f9f29a-c55a-4ee9-a3c6-c98b5160f539","added_by":"auto","created_at":"2025-11-05 16:31:29","extension":"png","order_by":6,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":65564,"visible":true,"origin":"","legend":"","description":"","filename":"floatimage11.png","url":"https://assets-eu.researchsquare.com/files/rs-7776578/v1/9685c17aa184eedf969a94a4.png"},{"id":95141640,"identity":"9bb84fcc-b877-4739-8673-3c45177a628f","added_by":"auto","created_at":"2025-11-04 17:27:18","extension":"png","order_by":7,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":440266,"visible":true,"origin":"","legend":"","description":"","filename":"floatimage12.png","url":"https://assets-eu.researchsquare.com/files/rs-7776578/v1/174020fc6c0ab1a7ebe555ff.png"},{"id":95225376,"identity":"c7621d30-1c0c-4cf5-870e-5141ca73479d","added_by":"auto","created_at":"2025-11-05 16:24:57","extension":"jpeg","order_by":8,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":228234,"visible":true,"origin":"","legend":"","description":"","filename":"floatimage13.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-7776578/v1/c5354235fb4ab8cb080164ba.jpeg"},{"id":95225253,"identity":"4d4ea06e-b8d0-4d4f-acd3-27fe622b4346","added_by":"auto","created_at":"2025-11-05 16:24:47","extension":"jpeg","order_by":9,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":196024,"visible":true,"origin":"","legend":"","description":"","filename":"floatimage14.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-7776578/v1/ccf37e357a537e769f5ec944.jpeg"},{"id":95141647,"identity":"fe8389eb-67a9-480f-8f1b-975529bf9543","added_by":"auto","created_at":"2025-11-04 17:27:18","extension":"png","order_by":10,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":158121,"visible":true,"origin":"","legend":"","description":"","filename":"floatimage2.png","url":"https://assets-eu.researchsquare.com/files/rs-7776578/v1/45e23e19d6733476d20ab9d9.png"},{"id":95141639,"identity":"90b3604b-815b-40af-9a76-30a11a814ca1","added_by":"auto","created_at":"2025-11-04 17:27:18","extension":"jpeg","order_by":11,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":125042,"visible":true,"origin":"","legend":"","description":"","filename":"floatimage3.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-7776578/v1/8515cd5b1629ca2d56966c6c.jpeg"},{"id":95226063,"identity":"4c5026c7-3b57-4cb7-8902-2d2d0aa86fc7","added_by":"auto","created_at":"2025-11-05 16:26:08","extension":"jpeg","order_by":12,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":133232,"visible":true,"origin":"","legend":"","description":"","filename":"floatimage4.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-7776578/v1/55df9f47cd41caf171ebaab0.jpeg"},{"id":95141651,"identity":"6be113c3-f724-462b-8a76-7a51803d5333","added_by":"auto","created_at":"2025-11-04 17:27:19","extension":"jpeg","order_by":13,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":297325,"visible":true,"origin":"","legend":"","description":"","filename":"floatimage5.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-7776578/v1/0be31bf1d7c6f377e64ed9c5.jpeg"},{"id":95141649,"identity":"d9d298aa-fed8-43eb-b1ad-26fc456b463f","added_by":"auto","created_at":"2025-11-04 17:27:18","extension":"jpeg","order_by":14,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":733731,"visible":true,"origin":"","legend":"","description":"","filename":"floatimage6.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-7776578/v1/05ef23b5c544dcefc0377474.jpeg"},{"id":95225258,"identity":"142c4dce-82c7-4593-8d8d-107276b7727c","added_by":"auto","created_at":"2025-11-05 16:24:47","extension":"jpeg","order_by":15,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":51040,"visible":true,"origin":"","legend":"","description":"","filename":"floatimage7.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-7776578/v1/1edb545b7bab423034c2d6c6.jpeg"},{"id":95225749,"identity":"77996c01-4183-4089-9ac7-a6fba9fd4ea1","added_by":"auto","created_at":"2025-11-05 16:25:28","extension":"jpeg","order_by":16,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":55967,"visible":true,"origin":"","legend":"","description":"","filename":"floatimage8.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-7776578/v1/3ba06d960c87b192df644da2.jpeg"},{"id":95141637,"identity":"ba660a63-04c0-429b-b766-c7a160777924","added_by":"auto","created_at":"2025-11-04 17:27:18","extension":"png","order_by":17,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":37185,"visible":true,"origin":"","legend":"","description":"","filename":"floatimage9.png","url":"https://assets-eu.researchsquare.com/files/rs-7776578/v1/5d5a5c5bb678bdcb8e6b798c.png"},{"id":95141653,"identity":"a5f80756-cdc6-4128-a644-aebf396bf396","added_by":"auto","created_at":"2025-11-04 17:27:19","extension":"png","order_by":18,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":205789,"visible":true,"origin":"","legend":"","description":"","filename":"Onlinefloatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-7776578/v1/7029f39cd4f0816c80e1d37a.png"},{"id":95141645,"identity":"a5953082-ae9d-4c0b-8e50-9b6b87d2e353","added_by":"auto","created_at":"2025-11-04 17:27:18","extension":"png","order_by":19,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":54620,"visible":true,"origin":"","legend":"","description":"","filename":"Onlinefloatimage10.png","url":"https://assets-eu.researchsquare.com/files/rs-7776578/v1/7f699a945f7be3845d77c174.png"},{"id":95141644,"identity":"07bdaf59-8e3a-487d-837b-98a5af1c16af","added_by":"auto","created_at":"2025-11-04 17:27:18","extension":"png","order_by":20,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":23298,"visible":true,"origin":"","legend":"","description":"","filename":"Onlinefloatimage11.png","url":"https://assets-eu.researchsquare.com/files/rs-7776578/v1/ea5ffc7de77f38258d2dc4ee.png"},{"id":95141657,"identity":"653530d7-e4bc-465b-b8fe-69e7496e3a06","added_by":"auto","created_at":"2025-11-04 17:27:19","extension":"png","order_by":21,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":41757,"visible":true,"origin":"","legend":"","description":"","filename":"Onlinefloatimage12.png","url":"https://assets-eu.researchsquare.com/files/rs-7776578/v1/bf49935bea9b30c0805b04c3.png"},{"id":95141652,"identity":"b54a20bb-1483-4ec1-95da-ec9c72c5223f","added_by":"auto","created_at":"2025-11-04 17:27:19","extension":"png","order_by":22,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":191526,"visible":true,"origin":"","legend":"","description":"","filename":"Onlinefloatimage13.png","url":"https://assets-eu.researchsquare.com/files/rs-7776578/v1/10fff3848a0bfaf86b487e51.png"},{"id":95141648,"identity":"9041ce4c-b6b5-4940-8fc3-0603cf5dc82b","added_by":"auto","created_at":"2025-11-04 17:27:18","extension":"png","order_by":23,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":132524,"visible":true,"origin":"","legend":"","description":"","filename":"Onlinefloatimage14.png","url":"https://assets-eu.researchsquare.com/files/rs-7776578/v1/89abcb316140546cb6dfe817.png"},{"id":95226181,"identity":"3c93e2dc-1fa2-446b-bf1a-013ff606b869","added_by":"auto","created_at":"2025-11-05 16:30:34","extension":"png","order_by":24,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":55213,"visible":true,"origin":"","legend":"","description":"","filename":"Onlinefloatimage2.png","url":"https://assets-eu.researchsquare.com/files/rs-7776578/v1/6573dbd3cd90d1515f761556.png"},{"id":95225772,"identity":"aad9aff3-201c-4c20-b0e5-dc6232780be6","added_by":"auto","created_at":"2025-11-05 16:25:29","extension":"png","order_by":25,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":33215,"visible":true,"origin":"","legend":"","description":"","filename":"Onlinefloatimage3.png","url":"https://assets-eu.researchsquare.com/files/rs-7776578/v1/4c2ac3ca6ef9b20109e82618.png"},{"id":95226781,"identity":"d147d39b-59f0-489c-838d-6a8916c46a56","added_by":"auto","created_at":"2025-11-05 16:31:43","extension":"png","order_by":26,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":32948,"visible":true,"origin":"","legend":"","description":"","filename":"Onlinefloatimage4.png","url":"https://assets-eu.researchsquare.com/files/rs-7776578/v1/835144d60c34a520177e7703.png"},{"id":95226825,"identity":"9fef5660-5f76-445c-9c41-5b665d26d825","added_by":"auto","created_at":"2025-11-05 16:31:45","extension":"png","order_by":27,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":75685,"visible":true,"origin":"","legend":"","description":"","filename":"Onlinefloatimage5.png","url":"https://assets-eu.researchsquare.com/files/rs-7776578/v1/c11a001c60c4c0b3d3afada8.png"},{"id":95225562,"identity":"59267426-5bfb-49e9-b398-898470842357","added_by":"auto","created_at":"2025-11-05 16:25:13","extension":"png","order_by":28,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":98412,"visible":true,"origin":"","legend":"","description":"","filename":"Onlinefloatimage6.png","url":"https://assets-eu.researchsquare.com/files/rs-7776578/v1/4a374adc9bef0eda5194781a.png"},{"id":95225760,"identity":"6858b7c7-2476-429a-85b7-439f130d5641","added_by":"auto","created_at":"2025-11-05 16:25:29","extension":"png","order_by":29,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":14670,"visible":true,"origin":"","legend":"","description":"","filename":"Onlinefloatimage7.png","url":"https://assets-eu.researchsquare.com/files/rs-7776578/v1/8a57f2fb9c7a199b1b698784.png"},{"id":95226142,"identity":"1dc0a01c-6309-4b98-82af-caa371eed9b6","added_by":"auto","created_at":"2025-11-05 16:26:32","extension":"png","order_by":30,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":16483,"visible":true,"origin":"","legend":"","description":"","filename":"Onlinefloatimage8.png","url":"https://assets-eu.researchsquare.com/files/rs-7776578/v1/4849cf393dea1fb3a04c207d.png"},{"id":95141654,"identity":"d25c8534-1d49-4ae4-ad71-68fc0dc44f45","added_by":"auto","created_at":"2025-11-04 17:27:19","extension":"png","order_by":31,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":11068,"visible":true,"origin":"","legend":"","description":"","filename":"Onlinefloatimage9.png","url":"https://assets-eu.researchsquare.com/files/rs-7776578/v1/9ac4d33e7c3b03190d5d7044.png"},{"id":95225399,"identity":"1b30c8df-3638-481f-a77e-86a59d7dd630","added_by":"auto","created_at":"2025-11-05 16:24:59","extension":"xml","order_by":32,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":87645,"visible":true,"origin":"","legend":"","description":"","filename":"274aea9f10654f048e3ceb4e96a4862c1structuring.xml","url":"https://assets-eu.researchsquare.com/files/rs-7776578/v1/7efcd09fd5e495f33eadea6d.xml"},{"id":95225304,"identity":"2764a52a-25fb-48a7-9a13-c81f77a83332","added_by":"auto","created_at":"2025-11-05 16:24:51","extension":"html","order_by":33,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":100490,"visible":true,"origin":"","legend":"","description":"","filename":"earlyproof.html","url":"https://assets-eu.researchsquare.com/files/rs-7776578/v1/e76124c3b33fded34afef4d7.html"},{"id":95141621,"identity":"4e832baa-3d9c-4c6f-ba41-c999303fb6bf","added_by":"auto","created_at":"2025-11-04 17:27:18","extension":"jpeg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":161867,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cem\u003eExamples of taxonomic gut microbiota composition. In the box are cited examples of bacteria belonging to Phyla Firmicutes and Bacteroidetes, representing 90% of gut microbiota.\u003c/em\u003e\u003c/p\u003e","description":"","filename":"image1.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-7776578/v1/637f4faf9429fae054e2681e.jpeg"},{"id":95141623,"identity":"c8418c57-06bf-4f76-adf3-62a6d36045fc","added_by":"auto","created_at":"2025-11-04 17:27:18","extension":"jpeg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":125042,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cem\u003eTaxonomy Counts Table Output\u003c/em\u003e\u003c/p\u003e","description":"","filename":"image2.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-7776578/v1/7790bfb97c293f56fa915200.jpeg"},{"id":95225784,"identity":"d6dc311b-e65e-4186-b1ee-2e1676a2ed83","added_by":"auto","created_at":"2025-11-05 16:25:29","extension":"jpeg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":133232,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cem\u003eTaxonomy Counts Table, Excel File\u003c/em\u003e\u003c/p\u003e","description":"","filename":"image3.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-7776578/v1/671f847664b4b64f73917058.jpeg"},{"id":95224881,"identity":"82d85180-b12f-4508-8391-7e1e66aec371","added_by":"auto","created_at":"2025-11-05 16:24:25","extension":"jpeg","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":297325,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cem\u003eCounts of Genus within Each Phylum for All Samples Bar Plot\u003c/em\u003e\u003c/p\u003e","description":"","filename":"image4.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-7776578/v1/27c121476746c20a563567b0.jpeg"},{"id":95141628,"identity":"ac70e415-b4a1-42d9-a49d-999641743392","added_by":"auto","created_at":"2025-11-04 17:27:18","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":162192,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cem\u003eHistogram of Shannon and Simpson Diversity Index\u003c/em\u003e\u003c/p\u003e","description":"","filename":"image5.png","url":"https://assets-eu.researchsquare.com/files/rs-7776578/v1/0a90fbd6afcce70cbd8deb5a.png"},{"id":95141636,"identity":"bc2f2b0e-9ed7-4a2d-a6a4-d90b0dcc0ad5","added_by":"auto","created_at":"2025-11-04 17:27:18","extension":"jpeg","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":51040,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cem\u003eShannon and Simpson Diversity Index Box Plot\u003c/em\u003e\u003c/p\u003e","description":"","filename":"image6.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-7776578/v1/1be197db43e14a0a5340a1c4.jpeg"},{"id":95225529,"identity":"68fcfddb-2793-4173-a849-2bd9650e1d9a","added_by":"auto","created_at":"2025-11-05 16:25:11","extension":"jpeg","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":55967,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cem\u003eCorrelation Coefficient Between Shannon \u0026amp; Simpson Diversity Indices\u003c/em\u003e\u003c/p\u003e","description":"","filename":"image7.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-7776578/v1/da2d508ab4626fea47309e8b.jpeg"},{"id":95141626,"identity":"8afe1f57-261b-4d23-a362-04ce82a94d70","added_by":"auto","created_at":"2025-11-04 17:27:18","extension":"png","order_by":8,"title":"Figure 8","display":"","copyAsset":false,"role":"figure","size":37185,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cem\u003eScatter Plot of Shannon vs. Simpson Diversity Indices\u003c/em\u003e\u003c/p\u003e","description":"","filename":"image8.png","url":"https://assets-eu.researchsquare.com/files/rs-7776578/v1/d6eba4247800fbb41eb93c8d.png"},{"id":95226285,"identity":"a0ff8a3b-9bb0-468a-9bac-995b078f20f2","added_by":"auto","created_at":"2025-11-05 16:30:51","extension":"png","order_by":9,"title":"Figure 9","display":"","copyAsset":false,"role":"figure","size":208882,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cem\u003eCorrelation Matrix of Metadata\u003c/em\u003e\u003c/p\u003e","description":"","filename":"image9.png","url":"https://assets-eu.researchsquare.com/files/rs-7776578/v1/de74a8bcd63ab5c0cb2b552e.png"},{"id":95141630,"identity":"10413800-2724-4fa7-b26a-20a6beae58f9","added_by":"auto","created_at":"2025-11-04 17:27:18","extension":"png","order_by":10,"title":"Figure 10","display":"","copyAsset":false,"role":"figure","size":65564,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cem\u003ePair Plot with KDE Plot Showing the Relationship Between Different Variables\u003c/em\u003e\u003c/p\u003e","description":"","filename":"image10.png","url":"https://assets-eu.researchsquare.com/files/rs-7776578/v1/1c7997a4849b3a88870dc3e1.png"},{"id":95141646,"identity":"ebcb85d0-42fa-4807-a0d0-c86d9a97fe3f","added_by":"auto","created_at":"2025-11-04 17:27:18","extension":"png","order_by":11,"title":"Figure 11","display":"","copyAsset":false,"role":"figure","size":440266,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cem\u003eScatter plot with regression line for Age vs. Alpha Diversity Indices\u003c/em\u003e\u003c/p\u003e","description":"","filename":"image11.png","url":"https://assets-eu.researchsquare.com/files/rs-7776578/v1/240140fba26d896b42b4a15a.png"},{"id":97142220,"identity":"0142fb50-181e-43a3-b624-023eb291b7b0","added_by":"auto","created_at":"2025-12-01 10:07:26","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2418281,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7776578/v1/a469bbf6-2315-4b17-9b34-35f12b3d152c.pdf"},{"id":95141666,"identity":"5818fb00-f965-4fee-a03d-d707771f3a76","added_by":"auto","created_at":"2025-11-04 17:27:19","extension":"zip","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":32608416,"visible":true,"origin":"","legend":"\u003cp\u003eSupplementary Figure 1. Parallel classification for PE reads Workflow Main Structure\u003c/p\u003e\n\u003cp\u003eSupplementary File 1. FastQC Report for SRR26415149_1.fastq\u003c/p\u003e\n\u003cp\u003eSupplementary File 2. Kraken Classification Report for SRR26415149_1.fastq\u003c/p\u003e\n\u003cp\u003eSupplementary File 3. Diamond Classification Report for SRR26415149_1.fastq\u003c/p\u003e\n\u003cp\u003eSupplementary File 4. Bowtie2 Output File for SRR26415149_1.fastq\u003c/p\u003e\n\u003cp\u003eSupplementary File 5. Relative Abundance Output File for SRR26415149_1.fastq\u003c/p\u003e\n\u003cp\u003eSupplementary File 6. Ensemble Report for SRR26415149_1.fastq\u003c/p\u003e\n\u003cp\u003eSupplementary File 7. WEVOTE Report for SRR26415149_1.fastq\u003c/p\u003e\n\u003cp\u003eSupplementary File 8. Classification Report for SRR26415149_1.fastq\u003c/p\u003e","description":"","filename":"SupplementaryDocuments.zip","url":"https://assets-eu.researchsquare.com/files/rs-7776578/v1/802485328eff99183623945c.zip"},{"id":95225082,"identity":"fc5b6c83-680b-460a-bd6f-58e2a4884dd1","added_by":"auto","created_at":"2025-11-05 16:24:33","extension":"docx","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":831080,"visible":true,"origin":"","legend":"","description":"","filename":"Tables.docx","url":"https://assets-eu.researchsquare.com/files/rs-7776578/v1/2ce1d9dc202084f669f836a1.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Biodiversity Analysis of the Human Gut Microbiome in Healthy Individuals Using Bioinformatics Tools","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eThe human gut microbiome (HGM) is a rich and variable community of microorganisms that inhabit the gastrointestinal tract (GIT). The term includes the microbiota and the collection of microbial genomes, including their structural elements, metabolites, and molecules produced by coexisting hosts.(\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e)\u003c/p\u003e\u003cp\u003eHGM plays a crucial role in numerous aspects of human biology, from metabolic health to immune functions. Gut microbiota is more than 100 trillion microorganisms mainly bacteria, and encodes over 3\u0026nbsp;million genes compared with ~\u0026thinsp;23,000 in the human genome (\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e). This complex community undergoes dynamic changes throughout the first two years of life (\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e), it changes from sterile to an adult-like stable microbiome by the time the infant reaches 2 years of age (\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eGut microbiota includes bacteria, archaea, fungi, viruses and eukaryotes, with microbial abundance is greatest in the colon (~\u0026thinsp;10\u003csup\u003e11\u003c/sup\u003e and 10\u003csup\u003e12\u003c/sup\u003e cells/g stool). Nine distinct phyla are known in the human gut, with Firmicutes and Bacteroides dominating 90% of the community. Examples of taxonomic gut microbiota composition are illustrated in (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e) (\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e, \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eThe composition of the gut microbiota is influenced by mode of delivery of a neonate, host genetic features, environmental factors, host immune response, antibiotic usage, and dietary effects. Diet can have a marked impact on the gut environment, including gut transit time and pH; and changing the intakes of the three main macronutrients (carbohydrates, proteins and fats) can significantly affect the composition of the microbiota. (\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e). Environmental factors are also important; studies showed the individuals living in rural areas have a significantly distinct microbiome than those in urban areas. The differences have also been observed between nationalities (\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e), due to differences in genetics, diet, hygiene, environment, and geography (\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eIt\u0026rsquo;s very important to keep our gut microbiome healthy and balanced because it supports digestion, structural function, metabolic function, immune-system development, protective function, and the gut\u0026ndash;brain axis\u003c/p\u003e\u003cp\u003e\u003cul\u003e\u003cli\u003e\u003cp\u003e\u003cb\u003eDigestion\u003c/b\u003e:\u003c/p\u003e\u003c/li\u003e\u003cli\u003e\u003cp\u003eCarbohydrates which humans cannot digest are fermented in the large intestine into short-chain fatty acids (SCFAs) such as acetic, propionic and butyric acid. Propionate can switch off the hunger signal and facilitates production of adenosine triphosphate (ATP) in the liver. Butyrate induces apoptosis in malignant epithelial cells lowering bowel cancer risk and providing energy to gut cells. Acetic acid is used by muscles, and gasses like hydrogen, carbon dioxide, and methane are also produced (\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e).\u003c/p\u003e\u003c/li\u003e\u003cli\u003e\u003cp\u003e\u003cb\u003eStructural Function\u003c/b\u003e:\u003c/p\u003e\u003c/li\u003e\u003cli\u003e\u003cp\u003eThe microbiome maintains gut epithelium integrity, preventing cytokines in the gut lumen from passing through. This function may be altered by pathogens such as Escherichia coli (E. coli) and Clostridium Difficile (C. difficile), where dysbiosis facilitates cytokines back diffusion (\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e).\u003c/p\u003e\u003c/li\u003e\u003cli\u003e\u003cp\u003e\u003cb\u003eMetabolic Function\u003c/b\u003e:\u003c/p\u003e\u003c/li\u003e\u003cli\u003e\u003cp\u003eGut microbiome synthesizes all essential and nonessential amino acids, performs bile biotransformation, produces proteases (\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e), and synthesizes water-soluble vitamins and fat-soluble vitamins, such as Vitamin K. It also aids in the absorption of magnesium, iron, and calcium (\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e).\u003c/p\u003e\u003c/li\u003e\u003cli\u003e\u003cp\u003e\u003cb\u003eImmune-system development\u003c/b\u003e:\u003c/p\u003e\u003c/li\u003e\u003cli\u003e\u003cp\u003eThe local gut microbiome drives the maturation of gut-associated lymphoid tissue (GALT) (\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e), maintains barrier function by mucus and antimicrobial peptide production, and influences inflammatory vs non-inflammatory cell phenotypes. Indigenous microbiota or pathobionts may differentiate T helper (Th) cells into Th1, Th2, Th17, and regulatory T (Treg) cells via microbiota metabolites. Dysbiosis may lead to excess proinflammatory cytokines and loss of immune tolerance, contributing to allergic or autoimmune diseases (\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e).\u003c/p\u003e\u003c/li\u003e\u003cli\u003e\u003cp\u003e\u003cb\u003eProtective Function\u003c/b\u003e:\u003c/p\u003e\u003c/li\u003e\u003cli\u003e\u003cp\u003eThe microbiome strengthens the gut barrier through SCFAs production and other mechanisms (\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e).\u003c/p\u003e\u003c/li\u003e\u003cli\u003e\u003cp\u003e\u003cb\u003eThe gut\u0026ndash;brain axis\u003c/b\u003e:\u003c/p\u003e\u003c/li\u003e\u003cli\u003e\u003cp\u003eThe microbiota-gut brain axis (MGBA) is a bidirectional network linking the central nervous system (CNS), autonomic nervous system (ANS) (including the enteric nervous system and the vagus nerve), neuroendocrine and neuroimmune system including the hypothalamic-pituitary-adrenal axis (HPA axis), and the gut microbiota (GM) (\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e, \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e). The GM can stimulate peptide and hormone release from enteroendocrine cells, affecting the CNS. Chronic stress can dysregulate the HPA axis, altering GM composition, increasing gut permeability, and contributing to inflammation, brain dysfunction and various CNS disorders (\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e).\u003c/p\u003e\u003c/li\u003e\u003c/ul\u003e\u003cdiv class=\"BlockQuote\"\u003e\u003cp\u003eDysbiosis has been linked to autism, anxiety-depressive behavior, Alzheimer\u0026rsquo;s disease, Parkinson\u0026rsquo;s disease, multiple sclerosis, and schizophrenia (\u003cspan additionalcitationids=\"CR20\" citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eA diverse,stable gut microbiota in adults is associated with improved metabolic and immune health, while dysbiosis can lead to life-threatening diseases (\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e, \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eBalance between major phyla and genera is crucial, disruption can contribute to cardiovascular diseases (CVDs), cancer, respiratory diseases, diabetes, inflammatory bowel disease (IBD), brain disorders, chronic kidney and liver diseases (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e) (\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e)\u003c/p\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003e\u003cspan type=\"BoldUnderline\" class=\"BoldUnderline\" name=\"Emphasis\"\u003e1. Taxonomic Diversity\u003c/span\u003e: \u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003eVariety of microbial taxa identified using 16S rRNA for bacteria and archaea or internal transcribed spacer (ITS) for fungi\u003c/span\u003e (\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e). \u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003eTools have been developed to classify metagenomic data\u003c/span\u003e (\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e, \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e):\u003c/p\u003e\u003cp\u003e\u003cul\u003e\u003cli\u003e\u003cp\u003eSequence similarity like DIAMOND (DNA to protein classification (BLASTx-like)).\u003c/p\u003e\u003c/li\u003e\u003cli\u003e\u003cp\u003eK-mers like Kraken and CLARK (DNA to DNA classification (BLASTn-like)).\u003c/p\u003e\u003c/li\u003e\u003cli\u003e\u003cp\u003eMarker Genes like MetaPhlAn2 (\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e)\u003c/p\u003e\u003c/li\u003e\u003cli\u003e\u003cp\u003ePhylogenies like MEGAN\u003c/p\u003e\u003c/li\u003e\u003c/ul\u003e\u003c/p\u003e\u003cp\u003e\u003cspan type=\"BoldUnderline\" class=\"BoldUnderline\" name=\"Emphasis\"\u003e2. Alpha Diversity\u003c/span\u003e: \u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003eMean species diversity within a sample, combining richness (number of species/ OTUs) and evenness\u003c/span\u003e (\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e). \u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003eCommon metrics include\u003c/span\u003e:\u003c/p\u003e\u003cp\u003e\u003cul\u003e\u003cli\u003e\u003cp\u003e\u003cb\u003eThe Index of Community Diversity\u003c/b\u003e: such as Shannon diversity index, and Simpson diversity index, access a combination between richness and evenness in a sample.\u003c/p\u003e\u003c/li\u003e\u003cli\u003e\u003cp\u003e\u003cb\u003eThe Index of Community Richness\u003c/b\u003e: such as Chao index, and Abundance-based Coverage Estimator (ACE) index.\u003c/p\u003e\u003c/li\u003e\u003c/ul\u003e\u003c/p\u003e\u003cp\u003e\u003col\u003e\u003cspan\u003e\u003cli\u003e\u003cp\u003e\u003cspan type=\"BoldUnderline\" class=\"BoldUnderline\" name=\"Emphasis\"\u003eBeta Diversity\u003c/span\u003e: \u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003eVariation in composition between samples using Bray-Curtis dissimilarity, Jaccard similarity index, or UniFrac.\u003c/span\u003e\u003c/p\u003e\u003c/li\u003e\u003c/span\u003e\u003cspan\u003e\u003cli\u003e\u003cp\u003e\u003cspan type=\"BoldUnderline\" class=\"BoldUnderline\" name=\"Emphasis\"\u003eFunctional Diversity\u003c/span\u003e: \u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003eAssesses functional redundancy or complementarity of microbial communities and helps understand their roles in ecosystem processes, nutrient cycling, and host-microbe interactions.\u003c/span\u003e\u003c/p\u003e\u003c/li\u003e\u003c/span\u003e\u003cspan\u003e\u003cli\u003e\u003cp\u003e\u003cspan type=\"BoldUnderline\" class=\"BoldUnderline\" name=\"Emphasis\"\u003eEcological Interactions\u003c/span\u003e: \u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003eNetwork and co-occurrence analysis to reveal community dynamics.\u003c/span\u003e\u003c/p\u003e\u003c/li\u003e\u003c/span\u003e\u003cspan\u003e\u003cli\u003e\u003cp\u003e\u003cspan type=\"BoldUnderline\" class=\"BoldUnderline\" name=\"Emphasis\"\u003eTemporal and Spatial Dynamics\u003c/span\u003e: \u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003eTrack stability, resilience, and adaptability to environmental changes.\u003c/span\u003e\u003c/p\u003e\u003c/li\u003e\u003c/span\u003e\u003c/ol\u003e\u003c/p\u003e\u003cp\u003eThe software used for this research is UGENE, a free bioinformatics cross-platform integrating multiple tools and algorithms, with graphical and command-line interfaces. It supports sequence alignment, motif discovery, primer design, phylogenetic analysis, and next generation sequencing (NGS) technologies (Variant Calling, RNA-sequencing, and ChIP-sequencing). The UGENE Workflow Designer allows building custom pipelines with quality filtering, mapping, assembly, and taxonomic classification using Kraken, DIAMOND, and MetaPhlAn. Pre-designed workflows can run locally or on servers (\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e).\u003c/p\u003e"},{"header":"2. Materials and Methods","content":"\u003cp\u003eThe microbiome data used in this research were obtained from the European Nucleotide Archive (ENA) under the project accession number (\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003ePRJNA1029329\u003c/span\u003e). The dataset includes sequencing data targeting the 16S rRNA (V3-V4) gene and ITS (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e) region from fecal samples collected from the JuHoansi San/Bushmen community in the Nyae Nyae region of north-eastern Namibia. For the purpose of this research, \u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003e10 samples\u003c/span\u003e were selected based on their geographical location within the region. All individuals were reported as healthy at the time of sampling.\u003c/p\u003e\u003cp\u003eThe primary software used was UGENE software suite (\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003eversion 33.0\u003c/span\u003e) Linux (64-bit) full package. This specific version was chosen for its support of metagenomic workflow feature, which was removed in later releases. The installation followed the official UGENE documentation. Additionally, several external tools were installed to perform metagenomics classification, including NCBI taxonomy classification, Kraken DB, MetaPhlAn2. Also, Python 3.10 with the scikit-bio and matplotlib libraries was used via Jupyter Notebook for statistical analysis and visualization.\u003c/p\u003e\u003cp\u003eAn integrated UGENE workflow was set up using the workflow designer and \u0026lsquo;Parallel classification for PE reads\u0026rsquo; sample was chosen from the NGS sample section (Supplementary Fig.\u0026nbsp;1). The workflow steps include:\u003c/p\u003e\u003cp\u003e\u003col\u003e\u003cspan\u003e\u003cli\u003e\u003cp\u003eInitial quality control assessment using FastQC to evaluate sequence quality, GC content, and adapter content. The sequencing data obtained was of high quality, as indicated by FastQC analysis. Consequently, no additional filtering or trimming steps were necessary prior to taxonomic classification\u003c/p\u003e\u003c/li\u003e\u003c/span\u003e\u003cspan\u003e\u003cli\u003e\u003cp\u003eTaxonomic classification of reads with Kraken, DIAMOND, MetaPhlAn2\u003c/p\u003e\u003c/li\u003e\u003c/span\u003e\u003cspan\u003e\u003cli\u003e\u003cp\u003eWeighted Voting Taxonomic Identification (WEVOTE) was used for assembly and annotation. WEVOTE combines the high sensitivity of the naive similarity methods (DIAMOND) and the high precision of the k-mer-based methods (Kraken) in order to identify novel members of a marker family from novel genomes (48) .\u003c/p\u003e\u003c/li\u003e\u003c/span\u003e\u003cspan\u003e\u003cli\u003e\u003cp\u003eClassification Report generation that summarizes the results. This report provided a consolidated view of the quality and composition of the metagenomic samples.\u003c/p\u003e\u003c/li\u003e\u003c/span\u003e\u003c/ol\u003e\u003c/p\u003e\u003cp\u003eThe entire workflow took approximately 25 hours to complete due to the complexity and volume of data processed. DIAMOND tool took the most time to process. After taxonomy classification was assigned using UGENE, Python and Jupyter Notebook were used to visualize the classification output. A table was created that included the count of bacterial genus within each phylum for each sample, and a grouped bar plot was generated.\u003c/p\u003e\u003cp\u003eAlpha Diversity was calculated using two different indices: the Shannon and Simpson Diversity Indices via the scikit-bio library. Ten csv files were loaded simultaneously, and alpha diversity was calculated for each sample individually. Histograms and box plots were generated. A scatter plot was also created to compare both indices.\u003c/p\u003e\u003cp\u003eExploratory Data Analysis (EDA) was applied in order to uncover relationships within the data. The following plots were generated:\u003c/p\u003e\u003cp\u003e\u003col\u003e\u003cspan\u003e\u003cli\u003e\u003cp\u003eCorrelation Matrix Heatmap\u003c/p\u003e\u003c/li\u003e\u003c/span\u003e\u003cspan\u003e\u003cli\u003e\u003cp\u003ePair Plot (Scatter Plot Matrix) with KDE (Kernel Density Estimation) plots\u003c/p\u003e\u003c/li\u003e\u003c/span\u003e\u003cspan\u003e\u003cli\u003e\u003cp\u003eScatter Plots with Regression Lines\u003c/p\u003e\u003c/li\u003e\u003c/span\u003e\u003c/ol\u003e\u003c/p\u003e\u003cp\u003eThese plots helped visualize relationships between alpha diversity indices and metadata variables such as age and location.\u003c/p\u003e\u003cp\u003eTo study the taxonomic classification and differentiate pathobionts from indigenous microbiota, a bacterial priority pathogens list was obtained from the World Health Organization (WHO). Pathogens from \u003cem\u003e(\u003c/em\u003eTable\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e3\u003c/span\u003e\u003cem\u003e)\u003c/em\u003e were also added. The final pathogen list was matched against classification reports using Python. The potential diseases related to each patient were then predicted based on the pathogens found.\u003c/p\u003e"},{"header":"3. Results","content":"\u003cdiv id=\"Sec4\" class=\"Section2\"\u003e\n \u003ch2\u003e3.1 FastQC Report\u003c/h2\u003e\n \u003cp\u003eThe FastQC report was generated as an HTML file for each sample in order to assess the sequence quality \u003cem\u003e(Supplementary File 1).\u003c/em\u003e All reads were of high quality, with no sequences flagged as poor quality. The GC content ranged from 40% to 60%, with an average of 53%. Sequence lengths varied from 35 to 301 bp. The per-base quality scores remained consistently high across all positions, and no adapter contamination or ambiguous \u0026apos;N\u0026apos; bases were detected.\u003c/p\u003e\n \u003cp\u003eSequence duplication levels showed common peaks likely due to overrepresented 16S/ITS amplicons, which is expected in targeted metagenomic datasets. Overrepresented sequences were present but not removed, as they likely reflect highly abundant microbial taxa or amplification bias rather than contamination. These findings justified proceeding with taxonomic classification without additional filtering.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec5\" class=\"Section2\"\u003e\n \u003ch2\u003e3.2 Kraken Report\u003c/h2\u003e\n \u003cp\u003eThe kraken report that was generated contain 5 columns \u003cem\u003e(Supplementary File 2)\u003c/em\u003e identifying each read\u0026rsquo;s classification status (U/C), sequence ID, taxonomic ID, sequence length, and the Lowest Common Ancestor (LCA) Mappings which is a space-delimited list indicating the LCA mapping of each k-mer in the sequence and taxonomic origin to distinguish classified from ambiguous nucleotides.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec6\" class=\"Section2\"\u003e\n \u003ch2\u003e3.3 DIAMOND Report\u003c/h2\u003e\n \u003cp\u003eThe DIAMOND report that was generated contains 3 columns, listing the sequence reads and classification attempts \u003cem\u003e(Supplementary File 3).\u003c/em\u003e While two columns remained empty, the tool\u0026apos;s contribution to cross-validation was preserved.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec7\" class=\"Section2\"\u003e\n \u003ch2\u003e3.4 MetaPhlan2 Report\u003c/h2\u003e\n \u003cp\u003eMetaPhlan2 generated 2 reports: a bowtie output file \u003cem\u003e(Supplementary File 4)\u003c/em\u003e and a relative abundance output file \u003cem\u003e(Supplementary File 5)\u003c/em\u003e. One sample showed 100% relative abundance of a retrovirus: Murine osteosarcoma virus (family: Retroviridae), potentially representing contamination or alignment artifact. The bowtie file has two entries the sample ID and the gene ID.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e\n \u003ch2\u003e3.5 Ensemble Report\u003c/h2\u003e\n \u003cp\u003eThe Ensemble report assembled the sequences read with its corresponding tax ID assigned by Kraken and DIAMOND in a csv file for each sample \u003cem\u003e(Supplementary File 6)\u003c/em\u003e.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec9\" class=\"Section2\"\u003e\n \u003ch2\u003e3.6 WEVOTE Report\u003c/h2\u003e\n \u003cp\u003eThe WEVOTE report that was generated contains sequencing read name, superkingdom tax ID along with taxonomy identifier according to the NCBI taxonomy classification and WEVOTE conclusion about the reads \u003cem\u003e(Supplementary File 7).\u003c/em\u003e\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec10\" class=\"Section2\"\u003e\n \u003ch2\u003e3.7 Classification Report\u003c/h2\u003e\n \u003cp\u003eThis classification report provides a 24 columns hierarchical breakdown of taxonomic classifications based on sequencing reads, summarizing taxonomy at all levels, showing both direct counts and proportions as well as clade-wide counts and proportions for various taxonomic levels \u003cem\u003e(Supplementary File 8)\u003c/em\u003e. This can help in understanding the distribution and abundance of different taxa in the sample.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e\n \u003ch2\u003e3.8 Visualization Bar Plot\u003c/h2\u003e\n \u003cp\u003eThe taxa-counts table \u003cem\u003e(\u003c/em\u003eFig. \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e, Fig. \u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e\u003cem\u003e)\u003c/em\u003e consists of three columns: phylum, genus, and counts per sample.\u003c/p\u003e\n \u003cp\u003eAlso, a bar plot was generated for every sample to visualize which is the most abundant genus / phylum in the sample \u003cem\u003e(\u003c/em\u003eFig. \u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003e\u003cem\u003e)\u003c/em\u003e.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec12\" class=\"Section2\"\u003e\n \u003ch2\u003e3.9 Alpha Diversity\u003c/h2\u003e\n \u003cp\u003eThe diversity results were saved as a csv file \u003cem\u003e(Table\u0026nbsp;2)\u003c/em\u003e which includes the Shannon Index and Simpson Index results for each sample.\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003eTable\u0026nbsp;2\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cem\u003eShannon and Simpson Indices Across 10 Samples\u003c/em\u003e\u003c/p\u003e\n \u003cp\u003eA box plot was generated for each index in the Shannon index \u003cem\u003e(\u003c/em\u003eFig. \u003cspan class=\"InternalRef\"\u003e6\u003c/span\u003eA\u003cem\u003e)\u003c/em\u003e:\u003c/p\u003e\n \u003cul\u003e\n \u003cli\u003e\n \u003cp\u003eThe orange line represents the median which is 2.94\u003c/p\u003e\n \u003c/li\u003e\n \u003cli\u003e\n \u003cp\u003eThe box represents the interquartile range (IQR), which is the range between the first quartile (Q1) 2.77 and the third quartile (Q3) 2.96.\u003c/p\u003e\n \u003c/li\u003e\n \u003cli\u003e\n \u003cp\u003eThe whiskers typically extend to 1.5 times the IQR from the quartiles at the points 2.485 and 3.245.\u003c/p\u003e\n \u003c/li\u003e\n \u003c/ul\u003e\n \u003cp\u003eIn the Simpson Index box plot \u003cem\u003e(\u003c/em\u003eFig. \u003cspan class=\"InternalRef\"\u003e6\u003c/span\u003eB\u003cem\u003e)\u003c/em\u003e:\u003c/p\u003e\n \u003cul\u003e\n \u003cli\u003e\n \u003cp\u003eThe median is 0.899\u003c/p\u003e\n \u003c/li\u003e\n \u003cli\u003e\n \u003cp\u003eQ1 is 0.894 and Q3 is 0.915.\u003c/p\u003e\n \u003c/li\u003e\n \u003cli\u003e\n \u003cp\u003eThe whiskers extend at the points 0.88 and 0.92.\u003c/p\u003e\n \u003c/li\u003e\n \u003cli\u003e\n \u003cp\u003eThe point below the whisker is considered an outlier with a Simpson index of 0.86.\u003c/p\u003e\n \u003c/li\u003e\n \u003c/ul\u003e\n \u003cp\u003eTo compare between the indices based on a scatter plot, the correlation coefficient (r) was calculated and found to be 0.8 \u003cem\u003e(\u003c/em\u003eFig. \u003cspan class=\"InternalRef\"\u003e7\u003c/span\u003e\u003cem\u003e)\u003c/em\u003e indicating a positive correlation. The correlation coefficient ranges from \u0026minus;\u0026thinsp;1 to 1, meaning that as the Shannon diversity index increases, the Simpson diversity index also tends to increase.\u003c/p\u003e\n \u003cp\u003eTo visually demonstrate this, a scatter plot was created \u003cem\u003e(\u003c/em\u003eFig. \u003cspan class=\"InternalRef\"\u003e8\u003c/span\u003e\u003cem\u003e)\u003c/em\u003e, with the x axis representing Shannon diversity, and the y axis representing Simpson diversity. Each point represents an observation from the dataset, with its position determined by the values of the two indices. The points tend to go upwards from left to right, this indicates a positive correlation between the two indices.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec13\" class=\"Section2\"\u003e\n \u003ch2\u003e3.10 Statistical Analysis\u003c/h2\u003e\n \u003cp\u003eThe correlation matrix displayed in the heatmap shows the relationships between various metadata and alpha diversity indices. Values close to 1 (dark red) indicate a strong positive correlation, values close to -1 (dark blue) indicate a strong negative correlation and as the color gets lighter the correlations decrease.\u003c/p\u003e\n \u003cp\u003eThe most significant correlations found in the matrix are \u003cem\u003e(\u003c/em\u003eFig. \u003cspan class=\"InternalRef\"\u003e9\u003c/span\u003e\u003cem\u003e)\u003c/em\u003e:\u003c/p\u003e\n \u003cul\u003e\n \u003cli\u003e\n \u003cp\u003e\u003cstrong\u003eAge and Shannon Index\u003c/strong\u003e: (r = -0.27) indicating a slightly negative correlation.\u003c/p\u003e\n \u003c/li\u003e\n \u003cli\u003e\n \u003cp\u003e\u003cstrong\u003eAge and Simpson Index\u003c/strong\u003e: (r = -0.53) indicating a moderate negative correlation. As age increases, the Simpson Index tends to decrease more noticeably.\u003c/p\u003e\n \u003c/li\u003e\n \u003cli\u003e\n \u003cp\u003e\u003cstrong\u003eAge and Experiencing Intestinal Infections\u003c/strong\u003e: (r\u0026thinsp;=\u0026thinsp;0.74) indicating a strong positive correlation. Suggesting younger individuals might have experienced more intestinal infections.\u003c/p\u003e\n \u003c/li\u003e\n \u003cli\u003e\n \u003cp\u003e\u003cstrong\u003eAlpha Diversity Indices and Uncertain About Taking Antibiotics\u003c/strong\u003e: a strong negative correlation with Shannon (-0.70) and Simpson (-0.67).\u003c/p\u003e\n \u003c/li\u003e\n \u003cli\u003e\n \u003cp\u003e\u003cstrong\u003eShannon and Simpson Indices\u003c/strong\u003e: There is a strong positive correlation (0.82), which is expected as both measure microbial diversity.\u003c/p\u003e\n \u003c/li\u003e\n \u003c/ul\u003e\n \u003cp\u003eAnd many more correlations which are less significant.\u003c/p\u003e\n \u003cp\u003eA pair plot (scatterplot matrix) with KDE plot shows the relationship between Age, Shannon index, and Simpson index, separated by villages (Duinpos and Mountain Pos) \u003cem\u003e(\u003c/em\u003eFig. \u003cspan class=\"InternalRef\"\u003e10\u003c/span\u003e\u003cem\u003e)\u003c/em\u003e.\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003eKDE Plots\u003c/strong\u003e:\u003c/p\u003e\n \u003cul\u003e\n \u003cli\u003e\n \u003cp\u003eThe age distributions for Duinpos and Mountain Pos are shown in the top left corner. It appears that both villages have a similar age distribution with a slight difference.\u003c/p\u003e\n \u003c/li\u003e\n \u003cli\u003e\n \u003cp\u003eThe Shannon diversity index distributions for both villages are shown in the middle. Duinpos (blue) has a broader range of Shannon index values compared to Mountain Pos (orange).\u003c/p\u003e\n \u003c/li\u003e\n \u003cli\u003e\n \u003cp\u003eThe Simpson diversity index distributions for both villages are shown at the bottom. The distribution for Duinpos is broader than that for Mountain Pos.\u003c/p\u003e\n \u003c/li\u003e\n \u003c/ul\u003e\n \u003cp\u003e\u003cstrong\u003eScatter Plots\u003c/strong\u003e:\u003c/p\u003e\n \u003cul\u003e\n \u003cli\u003e\n \u003cp\u003eThe scatter plots show the relationship between Age and different diversity indices for both villages.\u003c/p\u003e\n \u003c/li\u003e\n \u003cli\u003e\n \u003cp\u003eThere seems to be a positive correlation between Shannon and Simpson indices for both villages, which is expected as both indices measure biodiversity.\u003c/p\u003e\n \u003c/li\u003e\n \u003cli\u003e\n \u003cp\u003eThe orange points representing Mountain Pos are more clustered in certain areas compared to the blue points representing Duinpos, indicating less variability in the Shannon and Simpson indices.\u003c/p\u003e\n \u003c/li\u003e\n \u003cli\u003e\n \u003cp\u003eDuinpos (blue) has a wider spread in Shannon and Simpson indices compared to Mountain Pos (orange).\u003c/p\u003e\n \u003c/li\u003e\n \u003c/ul\u003e\n \u003cp\u003eThe scatter plot on the left shows the relationship between Age and the Shannon diversity index, and the right plot shows between Age and the Simpson diversity index \u003cem\u003e(\u003c/em\u003eFig. \u003cspan class=\"InternalRef\"\u003e11\u003c/span\u003e\u003cem\u003e)\u003c/em\u003e:\u003c/p\u003e\n \u003cul\u003e\n \u003cli\u003e\n \u003cp\u003e\u003cstrong\u003eScatter Points\u003c/strong\u003e: Each blue dot represents a data point, with Age on the x-axis and the Shannon /Simpson index on the y-axis. The scatter points show a slight negative trend.\u003c/p\u003e\n \u003c/li\u003e\n \u003cli\u003e\n \u003cp\u003e\u003cstrong\u003eTrend Line\u003c/strong\u003e: The red line represents the best fit line for the data points, indicating the overall trend. The downward slope of the red line suggests a negative correlation between Age and the Shannon / Simpson index.\u003c/p\u003e\n \u003c/li\u003e\n \u003cli\u003e\n \u003cp\u003e\u003cstrong\u003eConfidence Interval\u003c/strong\u003e: The shaded pink area around the trend line represents the confidence interval, showing the range within which the true regression line is likely to fall.\u003c/p\u003e\n \u003c/li\u003e\n \u003c/ul\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec14\" class=\"Section2\"\u003e\n \u003ch2\u003e3.11 Functional Analysis\u003c/h2\u003e\n \u003cp\u003eThe pathogen species table shows each patient with its pathogenic genera found in its microbiome with the total pathogenic occurrence. The occurrence varies between 5 pathogens and 11 \u003cem\u003e(\u003c/em\u003eTable \u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e\u003cem\u003e)\u003c/em\u003e.\u003c/p\u003e\n \u003cp\u003eThe results of the potential diseases that might affect each patient are listed in \u003cem\u003e(\u003c/em\u003eTable \u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003e\u003cem\u003e)\u003c/em\u003e.\u003c/p\u003e\n\u003c/div\u003e"},{"header":"4. Discussion","content":"\u003cp\u003eThis study conducted a comprehensive biodiversity analysis of the human gut microbiome in healthy individuals using integrated bioinformatics tools for metagenomic analysis. The results provide valuable insights into the composition and diversity of gut microbiota. We found that the gut microbiome of healthy individuals is characterized by significant diversity, with various bacterial genera contributing to the overall microbial ecosystem. The use of UGENE software allowed for precise taxonomic classification using multiple taxonomy tools, such as Kraken, DIAMOND, MetaPhlan. The bar plots provided a clear visualization of the distribution of genera within different phyla across each sample.\u003c/p\u003e\u003cp\u003eThe most abundant phylum across all samples was Firmicutes, indicating its dominant presence with a high number of genera varying among samples. Genera such as Clostridium and Lactobacillus were prominent with different relative abundance. Firmicutes are known for fermenting dietary fibers to produce SCFAs, crucial for gut health. However, species like Clostridium difficile were also found, which can cause severe diarrhea and colitis, particularly after antibiotic use. Some samples contained Streptococcus, some species, such as Streptococcus pneumoniae, can cause infections ranging from pharyngitis to pneumonia.\u003c/p\u003e\u003cp\u003eProteobacteria was the second most abundant phylum, containing genera like Escherichia and Helicobacter. Proteobacteria can have both indigenous microbiota and pathobionts which some are associated with inflammatory conditions, such as certain E. coli and Helicobacter. Bacteroidetes and Actinobacteria were also abundant. Bacteroidetes including Bacteroides and Prevotella, are essential for the breakdown of complex carbohydrates contributing to energy harvest from diet. Actinobacteria represented by Bifidobacterium and Corynebacterium, are important for maintaining a healthy gut and commonly used as probiotics.\u003c/p\u003e\u003cp\u003eAlpha diversity indices showed Shannon values between 2.6\u0026ndash;3.1 (normal 1.5\u0026ndash;3.5). and Simpson values between 0.87\u0026ndash;0.92 (normal 0\u0026ndash;1), indicating high diversity. The correlation coefficient was 0.852, this strong positive shows consistency; when one index indicated high diversity, so did the other. While both indices have different mathematical basis and interpretation, they provide complementary insights.\u003c/p\u003e\u003cp\u003eThe correlation matrix provided a comprehensive overview of how different variables relate to each other. It revealed a strong positive correlation between the alpha diversity indices as mentioned earlier. And also, a negative correlation between age and alpha diversity, as age increases, the microbial diversity tends to decrease. And this was clear in the scatter plot with a regression line. The pair plot along with the KDE plot showed that individuals living in the mountain have less diversity than those living in the village.\u003c/p\u003e\u003cp\u003eComparing genera identified in samples with known pathogenic genera associated with various diseases. This comparison helps in understanding the potential health implications of the gut microbiome composition. The investigation into potential disease relationships identified specific pathogenic bacteria present across the samples, which could indicate a predisposition to certain diseases even in healthy individuals. For instance, Helicobacter pylori was found in patients (\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e), associated with peptic ulcers, gastric cancer, and possibly IBD along with the E. coli and C. difficile. Patient (\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e) had the most pathobionts (11 genera), such as Streptococcus pneumoniae, Haemophilus Influenzae, Staphylococcus aureus which could be a marker of chronic respiratory diseases and CVD potential according to the pathogenic microbiota table. Other patients varied in composition, which means they have a potential for various diseases. This underscores the potential of early microbial diagnostics in predicting disease risk.\u003c/p\u003e"},{"header":"5. Conclusion","content":"\u003cp\u003eThis study highlights the diversity and complexity of the human gut microbiome in healthy individuals. Alpha diversity indices indicated a high level of microbial diversity, with a strong positive correlation between Shannon and Simpson indices. Diversity tends to decrease with age, and mountain residents exhibit lower diversity compared to village residents. Firmicutes was the most abundant phylum, with Clostridium and Lactobacillus playing key roles in gut health. The presence of potential pathogens like Clostridium difficile suggests a delicate balance between beneficial and harmful bacteria. The second most abundant phylum, Proteobacteria, included genera associated with inflammation, while Bacteroidetes was less represented than expected, suggesting further investigation. High microbial diversity is associated with resilience against pathogens and better overall health. Future research should focus on longitudinal and functional analyses to clarify microbiome roles in human health.\u003c/p\u003e"},{"header":"6. Limitations","content":"\u003cp\u003e\u003cul\u003e\u003cli\u003e\u003cp\u003eReliance on publicly available datasets \u003cul\u003e\n \u003cli\u003eReliance on publicly available datasets due to lack of local data.\u003c/li\u003e\n \u003cli\u003eUsing UGENE software may limit specific features and analyses compared with more specialized bioinformatics tools.\u003c/li\u003e\n \u003cli\u003eAvailable datasets may have limited sample sizes or demographic diversity, potentially affecting generalizability. Expanding in future studies would improve robustness,\u003c/li\u003e\n \u003cli\u003eReliance on taxonomic classification without functional analysis means that we can infer associations but not specific biological functions.\u003c/li\u003e\n\u003c/ul\u003edue to lack of local data.\u003c/p\u003e\u003c/li\u003e\u003cli\u003e\u003cp\u003eUsing UGENE software may limit specific features and analyses compared with more specialized bioinformatics tools.\u003c/p\u003e\u003c/li\u003e\u003c/ul\u003e\u003c/p\u003e"},{"header":"Declarations","content":"\u003ch2\u003eCompeting Interests\u003c/h2\u003e\u003cp\u003eThe authors declare no competing interests.\u003c/p\u003e\u003ch2\u003eFunding\u003c/h2\u003e\u003cp\u003eThis research received no specific grant from any funding agency in the public, commercial, or not-for-profit sectors.\u003c/p\u003e\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eT.S. conceived and designed the study, performed the bioinformatics analysis, interpreted the data, and wrote the manuscript.A.A. provided supervision, methodological guidance, and manuscript review.\u003c/p\u003e\u003ch2\u003eAcknowledgement\u003c/h2\u003e\u003cp\u003eI would like to express my sincere gratitude to Dr. Abdulqader Abbady for their invaluable guidance, support, and expertise throughout the research process.Special thanks to my family and friends for their unwavering support and understanding during this academic journey.I also appreciate Dr. Ghassan Shannan for efforts in trying to obtain local data from various resources and abroad. Although data could not be acquired, your willingness to assist was greatly appreciated.Thanks to Nebras Ayoub, IT specialist, for assistance in setting up and downloading the virtual machine and Linux. Your technical support was crucial for smooth research operation.Finally, I thank the UGENE team, especially Dmitrii Sukhomlinov, for prompt responses and valuable advice on software versions and technical setups.\u003c/p\u003e\u003ch2\u003eData Availability\u003c/h2\u003e\u003cp\u003eUGENE software and its integrated metagenomic tools are freely available online (version 33.0) and support Linux and macOS platforms. Statistical analysis and visualization were conducted using Python 3.10 with the libraries scikit-bio and matplotlib, executed via Jupyter Notebook distributed through the Anaconda platform. Anaconda is freely available for download. All tools used in this study are open-source and freely available for non-commercial use.The datasets generated and analyzed during the current study are available from the corresponding author upon reasonable request.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n \u003cli\u003e\u003cspan\u003eBerg, G. et al. Microbiome definition re-visited: old concepts and new challenges. \u003cem\u003eMicrobiome\u003c/em\u003e \u003cstrong\u003e8\u003c/strong\u003e (1), 103 (2020).\u003c/span\u003e\u003c/li\u003e\n \u003cli\u003e\u003cspan\u003eRinninella, E. et al. What is the Healthy Gut Microbiota Composition? A Changing Ecosystem across Age, Environment, Diet, and Diseases. \u003cem\u003eMicroorganisms\u003c/em\u003e ;\u003cstrong\u003e7\u003c/strong\u003e(1). (2019).\u003c/span\u003e\u003c/li\u003e\n \u003cli\u003e\u003cspan\u003eMancabelli, L. et al. Taxonomic and metabolic development of the human gut microbiome across life stages: a worldwide metagenomic investigation. \u003cem\u003emSystems\u003c/em\u003e \u003cstrong\u003e9\u003c/strong\u003e (4), e0129423 (2024).\u003c/span\u003e\u003c/li\u003e\n \u003cli\u003e\u003cspan\u003eFouhy, F., Ross, R. P., Fitzgerald, G. F., Stanton, C. \u0026amp; Cotter, P. D. Composition of the early intestinal microbiota: knowledge, knowledge gaps and the use of high-throughput sequencing to address these gaps. \u003cem\u003eGut Microbes\u003c/em\u003e. \u003cstrong\u003e3\u003c/strong\u003e (3), 203\u0026ndash;220 (2012).\u003c/span\u003e\u003c/li\u003e\n \u003cli\u003e\u003cspan\u003eDave, M., Higgins, P. D., Middha, S. \u0026amp; Rioux, K. P. The human gut microbiome: current knowledge, challenges, and future directions. \u003cem\u003eTransl Res.\u003c/em\u003e \u003cstrong\u003e160\u003c/strong\u003e (4), 246\u0026ndash;257 (2012).\u003c/span\u003e\u003c/li\u003e\n \u003cli\u003e\u003cspan\u003eScott, K. P., Gratz, S. W., Sheridan, P. O., Flint, H. J. \u0026amp; Duncan, S. H. The influence of diet on the gut microbiota. \u003cem\u003ePharmacol. Res.\u003c/em\u003e \u003cstrong\u003e69\u003c/strong\u003e (1), 52\u0026ndash;60 (2013).\u003c/span\u003e\u003c/li\u003e\n \u003cli\u003e\u003cspan\u003eHospodsky, D. et al. Hand bacterial communities vary across two different human populations. \u003cem\u003eMicrobiol. (Reading Engl)\u003c/em\u003e. \u003cstrong\u003e160\u003c/strong\u003e (Pt 6), 1144\u0026ndash;1152 (2014).\u003c/span\u003e\u003c/li\u003e\n \u003cli\u003e\u003cspan\u003eClemente, J. C. et al. The microbiome of uncontacted Amerindians. \u003cem\u003eSci. Adv.\u003c/em\u003e ;\u003cstrong\u003e1\u003c/strong\u003e(3). (2015).\u003c/span\u003e\u003c/li\u003e\n \u003cli\u003e\u003cspan\u003eMadhogaria, B., Bhowmik, P. \u0026amp; Kundu, A. Correlation between human gut microbiome and diseases. \u003cem\u003eInfect. Med.\u003c/em\u003e \u003cstrong\u003e1\u003c/strong\u003e (3), 180\u0026ndash;191 (2022).\u003c/span\u003e\u003c/li\u003e\n \u003cli\u003e\u003cspan\u003eSaitoh, Y. et al. Tight junctions. Structural insight into tight junction disassembly by Clostridium perfringens enterotoxin. \u003cem\u003eScience\u003c/em\u003e \u003cstrong\u003e347\u003c/strong\u003e (6223), 775\u0026ndash;778 (2015).\u003c/span\u003e\u003c/li\u003e\n \u003cli\u003e\u003cspan\u003eRawlings, N. D. et al. The MEROPS database of proteolytic enzymes, their substrates and inhibitors in 2017 and a comparison with peptidases in the PANTHER database. \u003cem\u003eNucleic Acids Res.\u003c/em\u003e \u003cstrong\u003e46\u003c/strong\u003e (D1), D624\u0026ndash;D632 (2018).\u003c/span\u003e\u003c/li\u003e\n \u003cli\u003e\u003cspan\u003eAfzaal, M. et al. Human gut microbiota in health and disease: Unveiling the relationship. \u003cem\u003eFront. Microbiol.\u003c/em\u003e \u003cstrong\u003e13\u003c/strong\u003e, 999001 (2022).\u003c/span\u003e\u003c/li\u003e\n \u003cli\u003e\u003cspan\u003eM\u0026ouml;rbe, U. M. et al. Human gut-associated lymphoid tissues (GALT); diversity, structure, and function. \u003cem\u003eMucosal Immunol.\u003c/em\u003e \u003cstrong\u003e14\u003c/strong\u003e (4), 793\u0026ndash;802 (2021).\u003c/span\u003e\u003c/li\u003e\n \u003cli\u003e\u003cspan\u003eAdak, A. \u0026amp; Khan, M. R. An insight into gut microbiota and its functionalities. \u003cem\u003eCell. Mol. Life Sci.\u003c/em\u003e \u003cstrong\u003e76\u003c/strong\u003e (3), 473\u0026ndash;493 (2019).\u003c/span\u003e\u003c/li\u003e\n \u003cli\u003e\u003cspan\u003eBron, P. A. et al. Can probiotics modulate human disease by impacting intestinal barrier function? \u003cem\u003eBr. J. Nutr.\u003c/em\u003e \u003cstrong\u003e117\u003c/strong\u003e (1), 93\u0026ndash;107 (2017).\u003c/span\u003e\u003c/li\u003e\n \u003cli\u003e\u003cspan\u003eMayer, E. A. Gut feelings: the emerging biology of gut-brain communication. \u003cem\u003eNat. Rev. Neurosci.\u003c/em\u003e \u003cstrong\u003e12\u003c/strong\u003e (8), 453\u0026ndash;466 (2011).\u003c/span\u003e\u003c/li\u003e\n \u003cli\u003e\u003cspan\u003eChakrabarti, A. et al. The microbiota-gut-brain axis: pathways to better brain health. Perspectives on what we know, what we need to investigate and how to put knowledge into practice. \u003cem\u003eCell. Mol. Life Sci.\u003c/em\u003e \u003cstrong\u003e79\u003c/strong\u003e (2), 80 (2022).\u003c/span\u003e\u003c/li\u003e\n \u003cli\u003e\u003cspan\u003eRusch, J. A., Layden, B. T. \u0026amp; Dugas, L. R. Signalling cognition: the gut microbiota and hypothalamic-pituitary-adrenal axis. \u003cem\u003eFront. Endocrinol. (Lausanne)\u003c/em\u003e. \u003cstrong\u003e14\u003c/strong\u003e, 1130689 (2023).\u003c/span\u003e\u003c/li\u003e\n \u003cli\u003e\u003cspan\u003eDissanayaka, D. M. S., Jayasena, V., Rainey-Smith, S. R., Martins, R. N. \u0026amp; Fernando, W. M. A. D. B. The role of diet and gut microbiota in alzheimer\u0026rsquo;s disease. \u003cem\u003eNutrients\u003c/em\u003e ;\u003cstrong\u003e16\u003c/strong\u003e(3). (2024).\u003c/span\u003e\u003c/li\u003e\n \u003cli\u003e\u003cspan\u003eZhang, X., Tang, B. \u0026amp; Guo, J. Parkinson\u0026rsquo;s disease and gut microbiota: from clinical to mechanistic and therapeutic studies. \u003cem\u003eTransl Neurodegener\u003c/em\u003e. \u003cstrong\u003e12\u003c/strong\u003e (1), 59 (2023).\u003c/span\u003e\u003c/li\u003e\n \u003cli\u003e\u003cspan\u003eJu, S. et al. The Gut-Brain Axis in Schizophrenia: The Implications of the Gut Microbiome and SCFA Production. \u003cem\u003eNutrients\u003c/em\u003e ;\u003cstrong\u003e15\u003c/strong\u003e(20). (2023).\u003c/span\u003e\u003c/li\u003e\n \u003cli\u003e\u003cspan\u003eHou, K. et al. Microbiota in health and diseases. \u003cem\u003eSignal. Transduct. Target. Ther.\u003c/em\u003e \u003cstrong\u003e7\u003c/strong\u003e (1), 135 (2022).\u003c/span\u003e\u003c/li\u003e\n \u003cli\u003e\u003cspan\u003eHajjo, R., Sabbah, D. A. \u0026amp; Al Bawab, A. Q. Unlocking the potential of the human microbiome for identifying disease diagnostic biomarkers. \u003cem\u003eDiagnostics (Basel)\u003c/em\u003e ;\u003cstrong\u003e12\u003c/strong\u003e(7). (2022).\u003c/span\u003e\u003c/li\u003e\n \u003cli\u003e\u003cspan\u003eUnderstand the key terms. in microbiome projects | Biomcare [Internet]. [cited 2024 Jul 1]. Available from: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://biomcare.com/info/key-terms-in-microbiome-projects/\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\n \u003cli\u003e\u003cspan\u003eYe, S. H., Siddle, K. J., Park, D. J. \u0026amp; Sabeti, P. C. Benchmarking metagenomics tools for taxonomic classification. \u003cem\u003eCell\u003c/em\u003e \u003cstrong\u003e178\u003c/strong\u003e (4), 779\u0026ndash;794 (2019).\u003c/span\u003e\u003c/li\u003e\n \u003cli\u003e\u003cspan\u003eLindgreen, S., Adair, K. L. \u0026amp; Gardner, P. P. An evaluation of the accuracy and speed of metagenome analysis tools. \u003cem\u003eSci. Rep.\u003c/em\u003e \u003cstrong\u003e6\u003c/strong\u003e, 19233 (2016).\u003c/span\u003e\u003c/li\u003e\n \u003cli\u003e\u003cspan\u003eMeyer, F. et al. Assessing taxonomic metagenome profilers with OPAL. \u003cem\u003eGenome Biol.\u003c/em\u003e \u003cstrong\u003e20\u003c/strong\u003e (1), 51 (2019).\u003c/span\u003e\u003c/li\u003e\n \u003cli\u003e\u003cspan\u003eTruong, D. T. et al. MetaPhlAn2 for enhanced metagenomic taxonomic profiling. \u003cem\u003eNat. Methods\u003c/em\u003e. \u003cstrong\u003e12\u003c/strong\u003e (10), 902\u0026ndash;903 (2015).\u003c/span\u003e\u003c/li\u003e\n \u003cli\u003e\u003cspan\u003eThe Use and Types of Alpha-Diversity Metrics in Microbial NGS. - CD Genomics [Internet]. [cited 2024 Jul 2]. Available from: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.cd-genomics.com/microbioseq/the-use-and-types-of-alpha-diversity-metrics-in-microbial-ngs.html\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\n \u003cli\u003e\u003cspan\u003eRose, R., Golosova, O., Sukhomlinov, D., Tiunov, A. \u0026amp; Prosperi, M. Flexible design of multiple metagenomics classification pipelines with UGENE. \u003cem\u003eBioinformatics\u003c/em\u003e \u003cstrong\u003e35\u003c/strong\u003e (11), 1963\u0026ndash;1965 (2019).\u003c/span\u003e\u003c/li\u003e\n\u003c/ol\u003e"},{"header":"Tables","content":"\u003cp\u003eTables 1 to 4 are available in the Supplementary Files section.\u003c/p\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":"[email protected]","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":"Human Gut Microbiome, Microbiome, Biodiversity, UGENE software, Metagenomic analysis","lastPublishedDoi":"10.21203/rs.3.rs-7776578/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7776578/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cb\u003eMotivation\u003c/b\u003e: The human gut microbiome plays a crucial role in maintaining health and preventing disease. This study aims to provide a comprehensive analysis of the gut microbiome composition in healthy individuals using integrated bioinformatics tools.\u003c/p\u003e\u003cp\u003eWe analyzed gut microbiota samples from ten healthy individuals using UGENE software, employing taxonomy tools such as Kraken, DIAMOND, and MetaPhlan. Alpha diversity indices, including the Shannon and Simpson diversity indices, were calculated. Correlation analyses were performed to explore relationships between microbial diversity, age, and geographic living conditions.\u003c/p\u003e\u003cp\u003e\u003cb\u003eResults\u003c/b\u003e: The gut microbiome showed significant diversity across samples. Alpha diversity indices indicated high microbial diversity, which tended to decrease with age. Individuals living in mountainous regions exhibited lower diversity than those in villages.\u003c/p\u003e\u003cp\u003eThis study highlights the complex diversity of the human gut microbiome and its variation with age and geographic location. The presence of both indigenous microbiota and pathobionts genera can lead to possible dysbiosis within the gut ecosystem. High microbial diversity is associated with better health outcomes, emphasizing its importance in maintaining gut health. Future research should aim to further elucidate the functional roles of these microbial communities.\u003c/p\u003e","manuscriptTitle":"Biodiversity Analysis of the Human Gut Microbiome in Healthy Individuals Using Bioinformatics Tools","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-11-04 17:27:13","doi":"10.21203/rs.3.rs-7776578/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","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":"e1a9ab90-f8d1-4636-8cef-f0a52bba1629","owner":[],"postedDate":"November 4th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":57348538,"name":"Biological sciences/Computational biology and bioinformatics"},{"id":57348539,"name":"Biological sciences/Ecology"},{"id":57348540,"name":"Earth and environmental sciences/Ecology"},{"id":57348541,"name":"Biological sciences/Microbiology"}],"tags":[],"updatedAt":"2025-12-01T07:24:17+00:00","versionOfRecord":[],"versionCreatedAt":"2025-11-04 17:27:13","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7776578","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7776578","identity":"rs-7776578","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}