Metagenomic Information Research Articles

AI for rapid identification of major butyrate-producing bacteria in rhesus macaques (Macaca mulatta)

BackgroundThe gut microbiome plays a crucial role in health and disease, influencing digestion, metabolism, and immune function. Traditional microbiome analysis methods are often expensive, time-consuming, and require specialized expertise, limiting their practical application in clinical settings. Evolving artificial intelligence (AI) technologies present opportunities for developing alternative methods. However, the lack of transparency in these technologies limits the ability of clinicians to incorporate AI-driven diagnostic tools into their healthcare systems. The aim of this study was to investigate an AI approach that rapidly predicts different bacterial genera and bacterial groups, specifically butyrate producers, from digital images of fecal smears of rhesus macaques (Macaca mulatta). In addition, to improve transparency, we employed explainability analysis to uncover the image features influencing the model’s predictions.ResultsBy integrating fecal image data with corresponding metagenomic sequencing information, the deep learning (DL) and machine learning (ML) algorithms successfully predicted 16 individual bacterial genera (area under the curve (AUC) > 0.7) among the 50 most abundant genera in rhesus macaques (Macaca mulatta). The model was successful in predicting functional groups, major butyrate producers (AUC 0.75) and a mixed group including fermenters and short-chain fatty acid (SCFA) producers (AUC 0.81). For both models of butyrate producers and mixed fermenters, the explainability experiments revealed no decline in the AUC when random noise was added to the images. Increased blurring led to a gradual decline in the AUC. The model’s performance was robust against the impact of fecal shape from smearing, with a stable AUC maintained until patch 4 for all groups, as assessed through scrambling. No significant correlation was detected between the prediction probabilities and the total fecal weight used in the smear; r = 0.30 ± 0.3 (p > 0.1) and r = 0.04 ± 0.36 (p > 0.8) for the butyrate producers and mixed fermenters, respectively.ConclusionOur approach demonstrated the ability to predict a wide range of clinically relevant microbial genera and microbial groups in the gut microbiome based on digital images from a fecal smear. The models proved to be robust to the smearing method, random noise and the amount of fecal matter. This study introduces a rapid, non-invasive, and cost-effective method for microbiome profiling, with potential applications in veterinary diagnostics.

Metagenomic and metatranscriptomic analyses reveal uncharted microbial constituents responsible for polyhydroxybutyrate biodegradation in coastal waters.

Metagenomic and metatranscriptomic analyses reveal uncharted microbial constituents responsible for polyhydroxybutyrate biodegradation in coastal waters.

Multi-omic stock of surface ocean microbiome built by monthly, weekly and daily sampling in Dapeng Bay, China

The coastal ocean is the dynamic interface where terrestrial, atmospheric, and marine systems converge, acting as a hotspot for microbial activity, which underpins the intricate web of carbon and nitrogen cycling. Dapeng Bay, a typical semi-enclosed bay along the southern coastline of China, is strongly influenced by monsoon climates and human activities. Despite its ecological importance, long-term observations and investigations into the microbial community structure in this region are notably lacking. To address this gap, we conducted a two-year continuous sampling from May 2021 to June 2023 to explore shifts in nearshore surface microbial communities and assess the long-term effects of environmental stressors. This study presents comprehensive amplicon, metagenomic, and metatranscriptomic information. We identified 3,600 amplicon sequence variants and recovered 1,216 high-quality metagenome-assembled MAGs, representing 17 bacterial and 3 archaeal phyla. Additionally, 587 MAGs were correlated with transcriptional activity, comprising 539 bacterial and 48 archaeal populations. This dataset is anticipated to provide a multi-dimensional perspective, enhancing our understanding of the complexity, dynamics, and adaptability of microbial communities in coastal environments.

Gut microbiota dysfunction in Crohn's disease.

Crohn's disease (CD) results from alterations in the gut microbiota and the immune system. However, the exact metabolic dysfunctions of the gut microbiota during CD are still unclear. Here, we investigated metagenomic functions using PICRUSt2 during the course of CD to better understand microbiota-related disease mechanisms and provide new insights for novel therapeutic strategies. We performed 16S rRNA-based microbial profiling of 567 faecal samples collected from a cohort of 383 CD patients, including 291 remissions (CR), 177 mild-moderate (CM) and 99 severe (CS) disease states. Gene and pathway composition was assessed using PICRUSt2 analyses of 16S data. As expected, changes in alpha and beta diversity, in interaction networks and increases in Proteobacteria abundance were associated with disease severity. However, microbial function was more consistently disrupted than composition from CR, to CM and then to CS. Major shifts in oxidative stress pathways and reduced carbohydrate and amino acid metabolism in favour of nutrient transport were identified in CS compared to CR. Virulence factors involved in host invasion, host evasion and inflammation were also increased in CS. This functional metagenomic information provides new insights into community-wide microbial processes and pathways associated with CD pathogenesis. This study paves the way for new advanced strategies to rebalance gut microbiota and/or eliminate oxidative stress, and biofilm to downregulate gut inflammation.

Metagenome Analysis Identified Novel Microbial Diversity of Sandy Soils Surrounded by Natural Lakes and Artificial Water Points in King Salman Bin Abdulaziz Royal Natural Reserve, Saudi Arabia.

Soil microbes play a vital role in the ecosystem as they are able to carry out a number of vital tasks. Additionally, metagenomic studies offer valuable insights into the composition and functional potential of soil microbial communities. Furthermore, analyzing the obtained data can improve agricultural restoration practices and aid in developing more effective environmental management strategies. In November 2023, sandy soil samples were collected from ten sites of different geographical areas surrounding natural lakes and artificial water points in the Tubaiq conservation area of King Salman Bin Abdulaziz Royal Natural Reserve (KSRNR), Saudi Arabia. In addition, genomic DNA was extracted from the collected soil samples, and 16S rRNA sequencing was conducted using high-throughput Illumina technology. Several computational analysis tools were used for gene prediction and taxonomic classification of the microbial groups. In this study, sandy soil samples from the surroundings of natural and artificial water resources of two distinct natures were used. Based on 16S rRNA sequencing, a total of 24,563 OTUs were detected. The metagenomic information was then categorized into 446 orders, 1036 families, 4102 genera, 213 classes, and 181 phyla. Moreover, the phylum Pseudomonadota was the most dominant microbial community across all samples, representing an average relative abundance of 34%. In addition, Actinomycetes was the most abundant class (26%). The analysis of clustered proteins assigned to COG categories provides a detailed understanding of the functional capabilities and adaptation of microbial communities in soil samples. Amino acid metabolism and transport were the most abundant categories in the soil environment. Metagenome analysis of sandy soils surrounding natural lakes and artificial water points in the Tubaiq conservation area of KSRNR (Saudi Arabia) has unveils rich microbial activity, highlighting the complex interactions and ecological roles of microbial communities in these environments.

Analysis of the potential of the electroactive biofilm growth in a microbial fuel cell type H

Microbial fuel cells (MFC) constitute an attractive alternative as an environmental remediation technology since they can generate electrical current using organic waste as a substrate. Since the performance of MFCs depends on the characteristics of the biofilm on the anode surface, it is important to assess the genetic information of the microorganisms that grow on the electrode. For this purpose, a sewage sludge sample was obtained from a wastewater treatment plant and used to inoculate a type H MFC. Electrochemical characterization, on one hand, indicates that while the biofilm has a typical electrochemical performance reflected by the generated voltage (near 0.4 V) and by the electroactivity observed in cyclic voltammetry experiments, and on the other hand, the metagenomic analysis shows that the most abundant genera are Pseudomonacea, Nitrosomonas, Hyphomonas, and Opitutus. The study also indicates that the biofilm’s electroactive microorganisms can metabolize amino acids, lipids, and carbohydrates and possess genetic tools for ionic transport and energy production. Regarding the electron acceptor/donator capabilities, several oxidases, reductases, and complexes were identified, mainly terminal cytochrome C oxidase and respiratory complex I, which could be associated with the exoelectrogenic capacity of the microorganisms. Finally, the metagenomic information indicates that the biofilm can synthesize rhamnose, sialic acid, and alginate molecules, which could possibly be associated with the formation and consolidation of the microbial biofilm.

Decolorization and detoxification of direct blue 5B by a Marinobacter-dominated halo-thermoalkalophilic consortium

Decolorization and detoxification of direct blue 5B by a Marinobacter-dominated halo-thermoalkalophilic consortium

An integrated metagenomic, metabolomic and transcriptomic survey of Populus across genotypes and environments

Bridging molecular information to ecosystem-level processes would provide the capacity to understand system vulnerability and, potentially, a means for assessing ecosystem health. Here, we present an integrated dataset containing environmental and metagenomic information from plant-associated microbial communities, plant transcriptomics, plant and soil metabolomics, and soil chemistry and activity characterization measurements derived from the model tree species Populus trichocarpa. Soil, rhizosphere, root endosphere, and leaf samples were collected from 27 different P. trichocarpa genotypes grown in two different environments leading to an integrated dataset of 318 metagenomes, 98 plant transcriptomes, and 314 metabolomic profiles that are supported by diverse soil measurements. This expansive dataset will provide insights into causal linkages that relate genomic features and molecular level events to system-level properties and their environmental influences.

A microbial knowledge graph-based deep learning model for predicting candidate microbes for target hosts.

Predicting interactions between microbes and hosts plays critical roles in microbiome population genetics and microbial ecology and evolution. How to systematically characterize the sophisticated mechanisms and signal interplay between microbes and hosts is a significant challenge for global health risks. Identifying microbe-host interactions (MHIs) can not only provide helpful insights into their fundamental regulatory mechanisms, but also facilitate the development of targeted therapies for microbial infections. In recent years, computational methods have become an appealing alternative due to the high risk and cost of wet-lab experiments. Therefore, in this study, we utilized rich microbial metagenomic information to construct a novel heterogeneous microbial network (HMN)-based model named KGVHI to predict candidate microbes for target hosts. Specifically, KGVHI first built a HMN by integrating human proteins, viruses and pathogenic bacteria with their biological attributes. Then KGVHI adopted a knowledge graph embedding strategy to capture the global topological structure information of the whole network. A natural language processing algorithm is used to extract the local biological attribute information from the nodes in HMN. Finally, we combined the local and global information and fed it into a blended deep neural network (DNN) for training and prediction. Compared to state-of-the-art methods, the comprehensive experimental results show that our model can obtain excellent results on the corresponding three MHI datasets. Furthermore, we also conducted two pathogenic bacteria case studies to further indicate that KGVHI has excellent predictive capabilities for potential MHI pairs.

NURECON: A Novel Online System for Determining Nutrition Requirements Based on Microbial Composition.

Dietary habits have been proven to have an impact on the microbial composition and health of the human gut. Over the past decade, researchers have discovered that gut microbiota can use nutrients to produce metabolites that have major implications for human physiology. However, there is no comprehensive system that specifically focuses on identifying nutrient deficiencies based on gut microbiota, making it difficult to interpret and compare gut microbiome data in the literature. This study proposes an analytical platform, NURECON, that can predict nutrient deficiency information in individuals by comparing their metagenomic information to a reference baseline. NURECON integrates a next-generation bacterial 16S rRNA analytical pipeline (QIIME2), metabolic pathway prediction tools (PICRUSt2 and KEGG), and a food compound database (FooDB) to enable the identification of missing nutrients and provide personalized dietary suggestions. Metagenomic information from total number of 287 healthy subjects was used to establish baseline microbial composition and metabolic profiles. The uploaded data is analyzed and compared to the baseline for nutrient deficiency assessment. Visualization results include gut microbial composition, related enzymes, pathways, and nutrient abundance. NURECON is a user-friendly online platform that provides nutritional advice to support dietitians' research or menu design.

Oral Lactobacillus zeae exacerbates the pathological manifestation of periodontitis in a mouse model.

The worldwide prevalence of periodontitis is considerably high, and its pathogenic mechanisms must be investigated and understood in order to improve clinical treatment outcomes and reduce the disease prevalence and burden. The exacerbation of the host immune system induced by oral microbial dysbiosis and the subsequent tissue destruction are the hallmarks of the periodontitis. However, the oral bacteria involved in periodontitis are not fully understood. We used the Oxford Nanopore Technologies (ONT) sequencing system to analyze metagenomic information in subgingival dental plaque from periodontitis and non-periodontitis patients. The number of Lactobacillus zeae (L. zeae) in the periodontitis patients was 17.55-fold higher than in the non-periodontitis patients, suggesting that L. zeae is a novel periodontitis-associated pathogen. Although several Lactobacillus species are used in vivo as probiotics to treat periodontitis and compete with Porphyromonas gingivalis (P. gingivalis), the roles of L. zeae in periodontitis progression, and the relationship between L. zeae and P. gingivalis needs to be investigated. Both L. zeae and P. gingivalis were inoculated in the ligature-implant site of periodontitis mice. We collected mouse gingival crevicular fluid to analyze inflammatory cytokine secretion using a multiplex assay. Intact or sliced mouse maxilla tissue was used for micro-computed tomography analysis or hematoxylin and eosin staining, immunohistochemistry, and tartrate-resistant acid phosphatase staining to evaluate alveolar bone loss, neutrophil infiltration, and osteoclast activation, respectively. We observed that L. zeae competed with P. gingivalis, and it increased inflammatory cytokine secretion at the ligature-implant site. Similar to P. gingivalis, L. zeae promoted ligature-induced neutrophile infiltration, osteoclast activation, and alveolar bone loss. We, therefore, concluded that L. zeae accelerated the progression of periodontitis in the ligature-induced periodontitis mouse model.

From metagenomes to metabolism: Systematically assessing the metabolic flux feasibilities for “Candidatus Accumulibacter” species during anaerobic substrate uptake

With the rapid growing availability of metagenome assembled genomes (MAGs) and associated metabolic models, the identification of metabolic potential in individual community members has become possible. However, the field still lacks an unbiassed systematic evaluation of the generated metagenomic information to uncover not only metabolic potential, but also feasibilities of these models under specific environmental conditions. In this study, we present a systematic analysis of the metabolic potential in species of "Candidatus Accumulibacter", a group of polyphosphate-accumulating organisms (PAOs). We constructed a metabolic model of the central carbon metabolism and compared the metabolic potential among available MAGs for “Ca. Accumulibacter” species. By combining Elementary Flux Modes Analysis (EFMA) with max-min driving force (MDF) optimization, we obtained all possible flux distributions of the metabolic network and calculated their individual thermodynamic feasibility. Our findings reveal significant variations in the metabolic potential among “Ca. Accumulibacter” MAGs, particularly in the presence of anaplerotic reactions. EFMA revealed 700 unique flux distributions in the complete metabolic model that enable the anaerobic uptake of acetate and its conversion into polyhydroxyalkanoates (PHAs), a well-known phenotype of “Ca. Accumulibacter”. However, thermodynamic constraints narrowed down this solution space to 146 models that were stoichiometrically and thermodynamically feasible (MDF > 0 kJ/mol), of which only 8 were strongly feasible (MDF > 7 kJ/mol). Notably, several novel flux distributions for the metabolic model were identified, suggesting putative, yet unreported, functions within the PAO communities. Overall, this work provides valuable insights into the metabolic variability among "Ca. Accumulibacter" species and redefines the anaerobic metabolic potential in the context of phosphate removal. More generally, the integrated workflow presented in this paper can be applied to any metabolic model obtained from a MAG generated from microbial communities to objectively narrow the expected phenotypes from community members.

In situ metagenomics: A platform for rapid sequencing and analysis of metagenomes in less than one day.

We present here a complete system for metagenomic analysis that allows performing the sequencing and analysis of a medium-size metagenome in less than one day. This unprecedented development was possible due to the conjunction of state-of-the-art experimental and computational advances: a portable laboratory suitable for DNA extraction and sequencing with nanopore technology; the powerful metagenomic analysis pipeline SqueezeMeta, capable to provide a complete analysis in a few hours and using scarce computational resources; and tools for the automatic inspection of the results via a graphical user interface, that can be coupled to a web server to allow remote visualization of data (SQMtools and SQMxplore). We have tested the feasibility of our approach in the sequencing of the microbiota associated to volcanic rocks in La Palma, Canary Islands. Also, we did a two-day sampling campaign of marine waters in which the results obtained on the first day guided the experimental design of the second day. We demonstrate that it is possible to generate metagenomic information in less than one day, making it feasible to obtain taxonomic and functional profiles fast and efficiently, even in field conditions. This capacity can be used in the further to perform real-time functional and taxonomic monitoring of microbial communities in remote areas.

Sutterella and its metabolic pathways positively correlate with vaccine-elicited antibody responses in infant rhesus macaques.

It is becoming clearer that the microbiota helps drive responses to vaccines; however, little is known about the underlying mechanism. In this study, we aimed to identify microbial features that are associated with vaccine immunogenicity in infant rhesus macaques. We analyzed 16S rRNA gene sequencing data of 215 fecal samples collected at multiple timepoints from 64 nursery-reared infant macaques that received various HIV vaccine regimens. PERMANOVA tests were performed to determine factors affecting composition of the gut microbiota throughout the first eight months of life in these monkeys. We used DESeq2 to identify differentially abundant bacterial taxa, PICRUSt2 to impute metagenomic information, and mass spectrophotometry to determine levels of fecal short-chain fatty acids and bile acids. Composition of the early-life gut microbial communities in nursery-reared rhesus macaques from the same animal care facility was driven by age, birth year, and vaccination status. We identified a Sutterella and a Rodentibacter species that positively correlated with vaccine-elicited antibody responses, with the Sutterella species exhibiting more robust findings. Analysis of Sutterella-related metagenomic data revealed five metabolic pathways that significantly correlated with improved antibody responses following HIV vaccination. Given these pathways have been associated with short-chain fatty acids and bile acids, we quantified the fecal concentration of these metabolites and found several that correlated with higher levels of HIV immunogen-elicited plasma IgG. Our findings highlight an intricate bidirectional relationship between the microbiota and vaccines, where multiple aspects of the vaccination regimen modulate the microbiota and specific microbial features facilitate vaccine responses. An improved understanding of this microbiota-vaccine interplay will help develop more effective vaccines, particularly those that are tailored for early life.

A small step to discover candidate biological control agents from preexisting bioresources by using novel nonribosomal peptide synthetases hidden in activated sludge metagenomes.

Biological control agents (BCAs), beneficial organisms that reduce the incidence or severity of plant disease, have been expected to be alternatives to replace chemical pesticides worldwide. To date, BCAs have been screened by culture-dependent methods from various environments. However, previously unknown BCA candidates may be buried and overlooked because this approach preferentially selects only easy-to-culture microbial lineages. To overcome this limitation, as a small-scale test case, we attempted to explore novel BCA candidates by employing the shotgun metagenomic information of the activated sludge (AS) microbiome, which is thought to contain unutilized biological resources. We first performed genome-resolved metagenomics for AS taken from a municipal sewage treatment plant and obtained 97 nonribosomal peptide synthetase (NRPS)/polyketide synthase (PKS)-related gene sequences from 43 metagenomic assembled bins, most of which were assigned to the phyla Proteobacteria and Myxococcota. Furthermore, these NRPS/PKS-related genes are predicted to be novel because they were genetically dissimilar to known NRPS/PKS gene clusters. Of these, the condensation domain of the syringomycin-related NRPS gene cluster was detected in Rhodoferax- and Rhodocyclaceae-related bins, and its homolog was found in previously reported AS metagenomes as well as the genomes of three strains available from the microbial culture collections, implying their potential BCA ability. Then, we tested the antimicrobial activity of these strains against phytopathogenic fungi to investigate the potential ability of BCA by in vitro cultivation and successfully confirmed the actual antifungal activity of three strains harboring a possibly novel NRPS gene cluster. Our findings provide a possible strategy for discovering novel BCAs buried in the environment using genome-resolved metagenomics.

Classifying the bacterial taxonomy with its metagenomic data using the deep neural network model

Because of the two sequenced methods stated above, SG and AMP, are being used in different ways, present a deep learning methodology for taxonomic categorization of the metagenomic information which could be utilized for either. To place the suggested pipeline to a trial, 1000 16 S full-length genomes were used to generate either SG or AMP short-reads. Then, to map sequencing as matrices into such a number space, used a k-mer model. Our analysis of the existing approaches revealed several drawbacks, including limited ability to handle complex hierarchical representations of data and suboptimal feature extraction from grid-like structures. To overcome these limitations, we introduce DBNs for feature learning and dimensionality reduction, and CNNs for efficient processing of grid-like metagenomic data. Finally, a training set for every taxon was obtained by training two distinct deep learning constructions, specifically deep belief network (DBN) and convolutional neural network (CNN). This examined the proposed methodology to determine the best factor that determines and compared findings to the classification abilities offered by the RDP classifier, a standard classifier for bacterium identification. These designs outperform using RDP classifiers at every taxonomic level. So, at the genetic level, for example, both CNN and DBN achieved 91.4% accuracy using AMP short-reads, but the RDP classifier achieved 83.9% with the same information. This paper, suggested a classification method for 16 S short-read sequences created on k-mer representations and a deep learning structure, that every taxon creates a classification method. The experimental findings validate the suggested pipelines as a realistic strategy for classifying bacterium samples; as a result, the technique might be included in the most commonly used tools for the metagenomic research. According to the outcomes, it could be utilized to effectively classify either SG or AMP information.

Vitamin interdependencies predicted by metagenomics-informed network analyses and validated in microbial community microcosms

Metagenomic or metabarcoding data are often used to predict microbial interactions in complex communities, but these predictions are rarely explored experimentally. Here, we use an organism abundance correlation network to investigate factors that control community organization in mine tailings-derived laboratory microbial consortia grown under dozens of conditions. The network is overlaid with metagenomic information about functional capacities to generate testable hypotheses. We develop a metric to predict the importance of each node within its local network environments relative to correlated vitamin auxotrophs, and predict that a Variovorax species is a hub as an important source of thiamine. Quantification of thiamine during the growth of Variovorax in minimal media show high levels of thiamine production, up to 100 mg/L. A few of the correlated thiamine auxotrophs are predicted to produce pantothenate, which we show is required for growth of Variovorax, supporting that a subset of vitamin-dependent interactions are mutualistic. A Cryptococcus yeast produces the B-vitamin pantothenate, and co-culturing with Variovorax leads to a 90-130-fold fitness increase for both organisms. Our study demonstrates the predictive power of metagenome-informed, microbial consortia-based network analyses for identifying microbial interactions that underpin the structure and functioning of microbial communities.

Research progress and applications of genome‐wide association study in farm animals

AbstractExploring the genetic loci underlying economic traits is foundational for innovation in modern animal breeding technology. Genome‐wide association studies (GWAS) have been valid and commonly used tools to dissect the genomic variants associated with phenotypes for the past ∼20 years and have contributed to our understanding of genetic and molecular bases of various traits. Here, we comprehensively review the recent research progress on GWAS methods. We highlight the methodological advancements enabled by the combination of new proteome, transcriptome, epigenome, and metagenome information and multi‐omics analysis algorithms. The advances in GWAS investigations of chickens, pigs, cattle, and other animals during the last 5 years are also described. Finally, we discuss the current applications of GWAS in cutting‐edge breeding technologies and overall future perspectives of the post‐GWAS era.

Microbiome analysis of Brazilian women cervix reveals specific bacterial abundance correlation to RIG-like receptor gene expression.

The relationship among microbiome, immunity and cervical cancer has been targeted by several studies, yet many questions remain unanswered. We characterized herein the virome and bacteriome from cervical samples and correlated these findings with innate immunity gene expression in a Brazilian convenience sample of HPV-infected (HPV+) and uninfected (HPV-) women. For this purpose, innate immune gene expression data were correlated to metagenomic information. Correlation analysis showed that interferon (IFN) is able to differentially modulate pattern recognition receptors (PRRs) expression based on HPV status. Virome analysis indicated that HPV infection correlates to the presence of Anellovirus (AV) and seven complete HPV genomes were assembled. Bacteriome results unveiled that vaginal community state types (CST) distribution was independent of HPV or AV status, although bacterial phyla distribution differed between groups. Furthermore, TLR3 and IFNαR2 levels were higher in the Lactobacillus no iners-dominated mucosa and we detected correlations among RIG-like receptors (RLR) associated genes and abundance of specific anaerobic bacteria. Collectively, our data show an intriguing connection between HPV and AV infections that could foster cervical cancer development. Besides that, TLR3 and IFNαR2 seem to create a protective milieu in healthy cervical mucosa (L. no iners-dominated), and RLRs, known to recognize viral RNA, were correlated to anaerobic bacteria suggesting that they might be related to dysbiosis.

Strains Colonizing Different Intestinal Sites within an Individual Are Derived from a Single Founder Population.

Metagenomics has improved our understanding of commensal bacteria that colonize human intestines yet relies almost exclusively on fecal samples. Thus, spatial information about the niche range of these gut microbes and the level of specialized adaptation that they undergo has been inaccessible to fecal metagenomic studies. Here, we leveraged metagenomic data obtained through colonoscopy aspirates from three intestinal sites of healthy adults, and reconstructed metagenome-assembled genomes of several common gut bacteria to address intestinal site-specific evolution. We show that the genomes of bacterial strains at specific intestinal sites are clearly distinct yet are interrelated and are derived from a single founder strain colonizing multiple sites. We also reveal that within those intestinal sites, purifying selection is the dominant evolutionary force acting on Escherichia coli genomes within human hosts. Importantly, no site-specific adaptations at the level of accessory genes were detected, implying that these commensals are well-adapted to several host microniches and can therefore colonize multiple intestinal sites with high efficiency. Nevertheless, bacterial in situ growth rates differ markedly across different sections of the intestine. Metagenomics of aspirate samples can reveal unique strain- and intestinal tissue-specific genomic information. Such information may be critical for understanding bacterial contribution to gastrointestinal diseases, which involve only a part of the intestine, as is often the case in inflammatory bowel disease. IMPORTANCE By reconstructing bacterial genomes from samples taken from specific sites within the human intestines, via aspiration, we show that strains at specific intestinal sites are genetically distinct yet interrelated and are derived from a single founder population. Organ-specific metagenomic information represents a powerful tool to generate insights into gastrointestinal diseases, which involve only a part of the intestine, such as inflammatory bowel disease.