Metagenomic Sequence Data Research Articles

Characterization and simulation of metagenomic nanopore sequencing data with Meta-NanoSim.

Nanopore sequencing is crucial to metagenomic studies as its kilobase-long reads can contribute to resolving genomic structural differences among microbes. However, sequencing platform-specific challenges, including high base-call error rate, nonuniform read lengths, and the presence of chimeric artifacts, necessitate specifically designed analytical algorithms. The use of simulated datasets with characteristics that are true to the sequencing platform under evaluation is a cost-effective way to assess the performance of bioinformatics tools with the ground truth in a controlled environment. Here, we present Meta-NanoSim, a fast and versatile utility that characterizes and simulates the unique properties of nanopore metagenomic reads. It improves upon state-of-the-art methods on microbial abundance estimation through a base-level quantification algorithm. Meta-NanoSim can simulate complex microbial communities composed of both linear and circular genomes and can stream reference genomes from online servers directly. Simulated datasets showed high congruence with experimental data in terms of read length, error profiles, and abundance levels. We demonstrate that Meta-NanoSim simulated data can facilitate the development of metagenomic algorithms and guide experimental design through a metagenome assembly benchmarking task. The Meta-NanoSim characterization module investigates read features, including chimeric information and abundance levels, while the simulation module simulates large and complex multisample microbial communities with different abundance profiles. All trained models and the software are freely accessible at GitHub: https://github.com/bcgsc/NanoSim.

Benchmarking genome assembly methods on metagenomic sequencing data.

Metagenome assembly is an efficient approach to reconstruct microbial genomes from metagenomic sequencing data. Although short-read sequencing has been widely used for metagenome assembly, linked- and long-read sequencing have shown their advancements in assembly by providing long-range DNA connectedness. Many metagenome assembly tools were developed to simplify the assembly graphs and resolve the repeats in microbial genomes. However, there remains no comprehensive evaluation of metagenomic sequencing technologies, and there is a lack of practical guidance on selecting the appropriate metagenome assembly tools. This paper presents a comprehensive benchmark of 19 commonly used assembly tools applied to metagenomic sequencing datasets obtained from simulation, mock communities or human gut microbiomes. These datasets were generated using mainstream sequencing platforms, such as Illumina and BGISEQ short-read sequencing, 10x Genomics linked-read sequencing, and PacBio and Oxford Nanopore long-read sequencing. The assembly tools were extensively evaluated against many criteria, which revealed that long-read assemblers generated high contig contiguity but failed to reveal some medium- and high-quality metagenome-assembled genomes (MAGs). Linked-read assemblers obtained the highest number of overall near-complete MAGs from the human gut microbiomes. Hybrid assemblers using both short- and long-read sequencing were promising methods to improve both total assembly length and the number of near-complete MAGs. This paper also discussed the running time and peak memory consumption of these assembly tools and provided practical guidance on selecting them.

Palidis: fast discovery of novel insertion sequences.

The diversity of microbial insertion sequences, crucial mobile genetic elements in generating diversity in microbial genomes, needs to be better represented in current microbial databases. Identification of these sequences in microbiome communities presents some significant problems that have led to their underrepresentation. Here, we present a bioinformatics pipeline called Palidis that recognizes insertion sequences in metagenomic sequence data rapidly by identifying inverted terminal repeat regions from mixed microbial community genomes. Applying Palidis to 264 human metagenomes identifies 879 unique insertion sequences, with 519 being novel and not previously characterized. Querying this catalogue against a large database of isolate genomes reveals evidence of horizontal gene transfer events across bacterial classes. We will continue to apply this tool more widely, building the Insertion Sequence Catalogue, a valuable resource for researchers wishing to query their microbial genomes for insertion sequences.

An active microbiome in Old Faithful geyser.

Natural thermal geysers are hot springs that periodically erupt liquid water, steam, and gas. They are found in only a few locations worldwide, with nearly half located in Yellowstone National Park (YNP). Old Faithful geyser (OFG) is the most iconic in YNP and attracts millions of visitors annually. Despite extensive geophysical and hydrological study of geysers, including OFG, far less is known of the microbiology of geysed waters. Here, we report geochemical and microbiological data from geysed vent water and vent water that collects in a splash pool adjacent to OFG during eruptions. Both waters contained microbial cells, and radiotracer studies showed that they fixed carbon dioxide (CO2) when incubated at 70°C and 90°C. Shorter lag times in CO2 fixation activity were observed in vent and splash pool waters incubated at 90°C than 70°C, suggesting cells are better adapted or acclimated to temperatures like those in the OFG vent (∼92-93°C). 16S rDNA and metagenomic sequence data indicated that both communities are dominated by the autotroph Thermocrinis, which likely fuels productivity through the aerobic oxidation of sulfide/thiosulfate in erupted waters or steam. Dominant OFG populations, including Thermocrinis and subdominant Thermus and Pyrobaculum strains, exhibited high-strain level genomic diversity (putative ecotypes) relative to populations from nongeysing YNP hot springs that is attributed to the temporal chemical and temperature dynamics caused by eruptions. These findings show that OFG is habitable and that its eruption dynamics promote genomic diversity, while highlighting the need to further research the extent of life in geyser systems such as OFG.

The Lung Immune Prognostic Index (LIPI) stratifies prognostic groups and correlates with gut microbiota (GM) in patients with advanced renal cell carcinoma (RCC).

719 Background: The LIPI score has been reported as an independent prognostic factor in RCC patients treated with immune checkpoint inhibitors (ICI) or tyrosine kinase inhibitors (TKI). Here, we aimed to correlate LIPI score and GM composition in patients with RCC. Methods: We prospectively collected fecal samples of all comers RCC patients who started a 1st or beyond line therapy (standard or clinical trial) in the NCT0457446 at Gustave Roussy. Neutrophil to lymphocyte ratio (dNLR) and lactate dehydrogenase (LDH) were obtained from routine blood tests. Shot gun metagenomic sequencing (MGS) data were analyzed by multivariate and pair-wise/fold ratio . Clinical benefit ratio (CBR, complete response + partial response + stable disease, RECIST1.1) and overall survival (OS, from treatment start) were evaluated. Multivariate analysis (MVA) for OS included age, gender, therapy line, IMDC, histology, hypoalbuminemia. Results: From February 2016 to July 2021, 160 patients were screened and 102 were included. Median age was 61 years (22-89), patients were mostly males (75%) and clear cell histology (90%). Patients were treated in 1st (15%), 2nd (53%) and beyond (32%) lines. Treated with ICI monotherapy (65%), followed by TKI or mTOR (20%) and ICI combination (19%). IMDC was 32% good (G), 54% intermediate (I) and 14% poor (P) and LIPI was 69% G, 25% I and 6% P. Median OS was 42.9, 17.7 and 8.3 months in patients with LIPI G, I and P, respectively (p<0.0001) . Among IMDC risk groups, IMDC G + LIPI G had better OS compared to other subgroups (p=0.017), and those with IMDC P + LIPI P had the worse OS. Overall, LIPI G had higher rates of CBR than I+P LIPI (74% vs 50%; p=0.0158). At MVA, LIPI was independently associated with OS (HR 6.25, 2.02-24.34; p=0.0187). Overall, Parabacteroides merdae and Veillonella parvula were enriched in LIPI I+P, while LIPI G harbored Faecalibacterium prausnitzii. In patients treated with ICI monotherapy, LIPI I+P were enriched with Bacteroides spp ( P. merdae, Phocaeicola vulgatus), and LIPI G had an overrepresentation of Ruminococcaceae unclass bacterium. Conclusions: We report the first MGS study correlating LIPI score and GM composition in RCC patients. LIPI score correlates with clinical outcomes (OS and CBR) and helped to better-stratified IMDC risk groups. Patients LIPI G harbor health-related commensals, while I and P groups are associated with harmful ones. LIPI score could represent a clinically relevant score to stratify mRCC patients. Clinical trial information: NCT04567446 .

Effects of levodopa on gut bacterial antibiotic resistance in Parkinson's disease rat.

The second most prevalent neurodegenerative ailment, Parkinson's disease (PD), is characterized by both motor and non-motor symptoms. Levodopa is the backbone of treatment for PD at the moment. However, levodopa-induced side effects, such as dyskinesia, are commonly seen in PD patients. Recently, several antibiotics were found to present neuroprotective properties against neurodegenerative and neuro-inflammatory processes, which might be developed to effective therapies against PD. In this study, we aimed to identify if levodopa treatment could influence the gut bacterial antibiotic resistance in PD rat. Fecal samples were collected from healthy rats and 6-OHDA induced PD rats treated with different doses of levodopa, metagenomic sequencing data showed that levodopa resulted in gut bacteria composition change, the biomarkers of gut bacteria analyzed by LEfSe changed as well. More interestingly, compared with levodopa (5 mg/kg)-treated or no levodopa-treated PD rats, levodopa (10 mg/kg) caused a significant decrease in the abundance of tetW and vanTG genes in intestinal bacteria, which were related to tetracycline and vancomycin resistance, while the abundance of AAC6-lb-Suzhou gene increased apparently, which was related to aminoglycosides resistance, even though the total quantity of Antibiotic Resistance Gene (ARG) and Antibiotic Resistance Ontology (ARO) among all groups did not significantly differ. Consequently, our results imply that the combination of levodopa and antibiotics, such as tetracycline and vancomycin, in the treatment of PD may decrease the amount of corresponding antibiotic resistance genes in gut bacteria, which would give a theoretical basis for treating PD with levodopa combined with tetracycline and vancomycin in the future.

Phylogeny analysis of whole protein-coding genes in metagenomic data detected an environmental gradient for the microbiota.

Environmental factors affect the growth of microorganisms and therefore alter the composition of microbiota. Correlative analysis of the relationship between metagenomic composition and the environmental gradient can help elucidate key environmental factors and establishment principles for microbial communities. However, a reasonable method to quantitatively compare whole metagenomic data and identify the primary environmental factors for the establishment of microbiota has not been reported so far. In this study, we developed a method to compare whole proteomes deduced from metagenomic shotgun sequencing data, and quantitatively display their phylogenetic relationships as metagenomic trees. We called this method Metagenomic Phylogeny by Average Sequence Similarity (MPASS). We also compared one of the metagenomic trees with dendrograms of environmental factors using a comparison tool for phylogenetic trees. The MPASS method correctly constructed metagenomic trees of simulated metagenomes and soil and water samples. The topology of the metagenomic tree of samples from the Kirishima hot springs area in Japan was highly similarity to that of the dendrograms based on previously reported environmental factors for this area. The topology of the metagenomic tree also reflected the dynamics of microbiota at the taxonomic and functional levels. Our results strongly suggest that MPASS can successfully classify metagenomic shotgun sequencing data based on the similarity of whole protein-coding sequences, and will be useful for the identification of principal environmental factors for the establishment of microbial communities. Custom Perl script for the MPASS pipeline is available at https://github.com/s0sat/MPASS.

Site Specialization of Human Oral Veillonella Species.

Veillonella species are abundant members of the human oral microbiome with multiple interspecies commensal relationships. Examining the distribution patterns of Veillonella species across the oral cavity is fundamental to understanding their oral ecology. In this study, we used a combination of pangenomic analysis and oral metagenomic information to clarify Veillonella taxonomy and to test the site specialist hypothesis for the Veillonella genus, which contends that most oral bacterial species are adapted to live at specific oral sites. Using isolate genome sequences combined with shotgun metagenomic sequence data, we showed that Veillonella species have clear, differential site specificity: Veillonella parvula showed strong preference for supra- and subgingival plaque, while closely related V. dispar, as well as more distantly related V. atypica, preferred the tongue dorsum, tonsils, throat, and hard palate. In addition, the provisionally named Veillonella sp. Human Microbial Taxon 780 showed strong site specificity for keratinized gingiva. Using comparative genomic analysis, we identified genes associated with thiamine biosynthesis and the reductive pentose phosphate cycle that may enable Veillonella species to occupy their respective habitats. IMPORTANCE Understanding the microbial ecology of the mouth is fundamental for understanding human physiology. In this study, metapangenomics demonstrated that different Veillonella species have clear ecological preferences in the oral cavity of healthy humans, validating the site specialist hypothesis. Furthermore, the gene pool of different Veillonella species was found to be reflective of their ecology, illuminating the potential role of vitamins and carbohydrates in determining Veillonella distribution patterns and interspecies interactions.

Gut bacterial species in late trimester of pregnant sows influence the occurrence of stillborn piglet through pro-inflammation response.

Maternal gut microbiota is an important regulator for the metabolism and immunity of the fetus during pregnancy. Recent studies have indicated that maternal intestinal microbiota is closely linked to the development of fetus and infant health. Some bacterial metabolites are considered to be directly involved in immunoregulation of fetus during pregnancy. However, the detailed mechanisms are largely unknown. In this study, we exploited the potential correlation between the gut microbiota of pregnant sows and the occurrence of stillborn piglets by combining the 16S rRNA gene and metagenomic sequencing data, and fecal metabolome in different cohorts. The results showed that several bacterial species from Bacteroides, potential pathogens, and LPS-producing bacteria exhibited significantly higher abundances in the gut of sows giving birth to stillborn piglets. Especially, Bacteroides fragilis stood out as the key driver in both tested cohorts and showed the most significant association with the occurrence of stillborn piglets in the DN1 cohort. However, several species producing short-chain fatty acids (SCFAs), such as Prevotella copri, Clostridium butyricum and Faecalibacterium prausnitzii were enriched in the gut of normal sows. Functional capacity analysis of gut microbiome revealed that the pathways associated with infectious diseases and immune diseases were enriched in sows giving birth to stillborn piglets. However, energy metabolism had higher abundance in normal sows. Fecal metabolome profiling analysis found that Lysophosphatidylethanolamine and phosphatidylethanolamine which are the main components of cell membrane of Gram-negative bacteria showed significantly higher concentration in stillbirth sows, while SCFAs had higher concentration in normal sows. These metabolites were significantly associated with the stillborn-associated bacterial species including Bacteroides fragilis. Lipopolysaccharide (LPS), IL-1β, IL-6, FABP2, and zonulin had higher concentration in the serum of stillbirth sows, indicating increased intestinal permeability and pro-inflammatory response. The results from this study suggested that certain sow gut bacterial species in late trimester of pregnancy, e.g., an excess abundance of Bacteroides fragilis, produced high concentration of LPS which induced sow pro-inflammatory response and might cause the death of the relatively weak piglets in a farrow. This study provided novel evidences about the effect of maternal gut microbiota on the fetus development and health.

Expanded catalogue of metagenome-assembled genomes reveals resistome characteristics and athletic performance-associated microbes in horse

BackgroundAs a domesticated species vital to humans, horses are raised worldwide as a source of mechanical energy for sports, leisure, food production, and transportation. The gut microbiota plays an important role in the health, diseases, athletic performance, and behaviour of horses.ResultsHere, using approximately 2.2 Tb of metagenomic sequencing data from gut samples from 242 horses, including 110 samples from the caecum and 132 samples from the rectum (faeces), we assembled 4142 microbial metagenome-assembled genomes (MAG), 4015 (96.93%) of which appear to correspond to new species. From long-read data, we successfully assembled 13 circular whole-chromosome bacterial genomes representing novel species. The MAG contained over 313,568 predicted carbohydrate-active enzymes (CAZy), over 59.77% of which had low similarity match in CAZy public databases. High abundance and diversity of antibiotic resistance genes (ARG) were identified in the MAG, likely showing the wide use of antibiotics in the management of horse. The abundances of at least 36 MAG (e.g. MAG belonging to Lachnospiraceae, Oscillospiraceae, and Ruminococcus) were higher in racehorses than in nonracehorses. These MAG enriched in racehorses contained every gene in a major pathway for producing acetate and butyrate by fibre fermentation, presenting potential for greater amount of short-chain fatty acids available to fuel athletic performance.ConclusionOverall, we assembled 4142 MAG from short- and long-read sequence data in the horse gut. Our dataset represents an exhaustive microbial genome catalogue for the horse gut microbiome and provides a valuable resource for discovery of performance-enhancing microbes and studies of horse gut microbiome.5Q4GzktoVGndspuVBLwUREVideo

Evaluation of genome skimming to detect and characterise human and livestock helminths

The identification of gastrointestinal helminth infections of humans and livestock almost exclusively relies on the detection of eggs or larvae in faeces, followed by manual counting and morphological characterisation to differentiate species using microscopy-based techniques. However, molecular approaches based on the detection and quantification of parasite DNA are becoming more prevalent, increasing the sensitivity, specificity and throughput of diagnostic assays. High-throughput sequencing, from single PCR targets through to the analysis of whole genomes, offers significant promise towards providing information-rich data that may add value beyond traditional and conventional molecular approaches; however, thus far, its utility has not been fully explored to detect helminths in faecal samples. In this study, low-depth whole genome sequencing, i.e. genome skimming, has been applied to detect and characterise helminth diversity in a set of helminth-infected human and livestock faecal material. The strengths and limitations of this approach are evaluated using three methods to characterise and differentiate metagenomic sequencing data based on (i) mapping to whole mitochondrial genomes, (ii) whole genome assemblies, and (iii) a comprehensive internal transcribed spacer 2 (ITS2) database, together with validation using quantitative PCR (qPCR). Our analyses suggest that genome skimming can successfully identify most single and multi-species infections reported by qPCR and can provide sufficient coverage within some samples to resolve consensus mitochondrial genomes, thus facilitating phylogenetic analyses of selected genera, e.g. Ascaris spp. Key to this approach is both the availability and integrity of helminth reference genomes, some of which are currently contaminated with bacterial and host sequences. The success of genome skimming of faecal DNA is dependent on the availability of vouchered sequences of helminths spanning both taxonomic and geographic diversity, together with methods to detect or amplify minute quantities of parasite nucleic acids in mixed samples.

Integrated de novo gene prediction and peptide assembly of metagenomic sequencing data.

Metagenomics is the study of all genomic content contained in given microbial communities. Metagenomic functional analysis aims to quantify protein families and reconstruct metabolic pathways from the metagenome. It plays a central role in understanding the interaction between the microbial community and its host or environment. De novo functional analysis, which allows the discovery of novel protein families, remains challenging for high-complexity communities. There are currently three main approaches for recovering novel genes or proteins: de novo nucleotide assembly, gene callingand peptide assembly. Unfortunately, their information dependency has been overlooked, and each has been formulated as an independent problem. In this work, we develop a sophisticated workflow called integrated Metagenomic Protein Predictor (iMPP), which leverages the information dependencies for better de novo functional analysis. iMPP contains three novel modules: a hybrid assembly graph generation module, a graph-based gene calling module, and a peptide assembly-based refinement module. iMPP significantly improved the existing gene calling sensitivity on unassembled metagenomic reads, achieving a 92-97% recall rate at a high precision level (>85%). iMPP further allowed for more sensitive and accurate peptide assembly, recovering more reference proteins and delivering more hypothetical protein sequences. The high performance of iMPP can provide a more comprehensive and unbiased view of the microbial communities under investigation. iMPP is freely available from https://github.com/Sirisha-t/iMPP.

MStrain: strain-level identification of Yersinia pestis using metagenomic data.

High-resolution target pathogen detection using metagenomic sequencing data represents a major challenge due to the low concentration of target pathogens in samples. We introduced mStrain, a novel Yesinia pestis strain/lineage-level identification tool that utilizes metagenomic data. mStrain successfully identified Y. pestis at the strain/lineage level by extracting sufficient information regarding single-nucleotide polymorphisms (SNPs), which can therefore be an effective tool for identification and source tracking of Y. pestis based on metagenomic data during a plague outbreak. . Assigning the reads in the metagenomic sequencing data to an exactly known or most closely representative Y. pestis strain. Assigning the reads in the metagenomic sequencing data to a specific lineage on the phylogenetic tree. The unique and typical SNPs present in all representative strains. An SNP is defined as the ancestor state when consistent with the allele of Yersinia pseudotuberculosis strain IP32953; otherwise, the SNP is defined as the derived state. The code for running mStrain, the test dataset, and instructions for running the code can be found at the following GitHub repository: https://github.com/xwqian1123/mStrain.

Metagenomic Strain-Typing Combined with Isolate Sequencing Provides Increased Resolution of the Genetic Diversity of Campylobacter jejuni Carriage in Wild Birds

As the world's leading cause of human gastro-enteritis, the food- and waterborne pathogen Campylobacter needs to be intensively monitored through a One Health approach. Particularly, wild birds have been hypothesized to contribute to the spread of human clinical recurring C. jejuni genotypes across several countries. A major concern in studying epidemiological dynamics is resolving the large genomic diversity of strains circulating in the environment and various reservoirs, challenging to achieve with isolation techniques. Here, we applied a passive-filtration method to obtain isolates and in parallel recovered genotypes from metagenomic sequencing data from associated filter sweeps. For genotyping mixed strains, a reference-based computational workflow to predict allelic profiles of nine extended-MLST loci was utilized. We validated the pipeline by sequencing artificial mixtures of C. jejuni strains and observed the highest prediction accuracy when including obtained isolates as references. By analyzing metagenomic samples, we were able to detect over 20% additional genetic diversity and observed an over 50% increase in the potential to connect genotypes across wild-bird samples. With an optimized filtration method and a computational approach for genotyping strain mixtures, we provide the foundation for future studies assessing C. jejuni diversity in environmental and clinical settings at improved throughput and resolution.

Differential response to prolonged amoxicillin treatment: long-term resilience of the microbiome versus long-lasting perturbations in the gut resistome

ABSTRACT The collateral impact of antibiotics on the microbiome has attained increasing attention. However, the ecological consequences of long-term antibiotic exposure on the gut microbiome, including antibiotic resistance, are still limited. Here, we investigated long-term exposure effects to amoxicillin on the human gut microbiome and resistome. Fecal samples were collected from 20 patients receiving 3-months of amoxicillin or placebo treatment as part of a Norwegian multicenter clinical trial on chronic low back pain (AIM study). Samples were collected at baseline, last day of treatment, and 9 months after antibiotic cessation. The abundance and diversity of microbial and resistome composition were characterized using whole shotgun and functional metagenomic sequencing data. While the microbiome profiles of placebo subjects were stable over time, discernible changes in diversity and overall microbiome composition were observed after amoxicillin treatment. In particular, health-associated short-chain fatty acid producing species significantly decreased in proportion. However, these changes were short-lived as the microbiome showed overall recovery 9 months post-treatment. On the other hand, exposure to long-term amoxicillin was associated with an increase in total antimicrobial resistance gene load and diversity of antimicrobial resistance genes, with persistent changes even at 9 months post-treatment. Additionally, beta-lactam resistance was the most affected antibiotic class, suggesting a targeted response to amoxicillin, although changes at the gene level varied across individuals. Overall, our results suggest that the impact of prolonged amoxicillin exposure was more explicit and long-lasting in the fecal resistome than in microbiome composition. Such information is relevant for designing rational administration guidelines for antibiotic therapies.

Phage-Host Prediction Using a Computational Tool Coupled with 16S rRNA Gene Amplicon Sequencing.

Metagenomics studies have revealed tremendous viral diversity in aquatic environments. Yet, while the genomic data they have provided is extensive, it is unannotated. For example, most phage sequences lack accurate information about their bacterial host, which prevents reliable phage identification and the investigation of phage-host interactions. This study aimed to take this knowledge further, using a viral metagenomic framework to decipher the composition and diversity of phage communities and to predict their bacterial hosts. To this end, we used water and sediment samples collected from seven sites with varying contamination levels in the Ebrié Lagoon in Abidjan, Ivory Coast. The bacterial communities were characterized using the 16S rRNA metabarcoding approach, and a framework was developed to investigate the virome datasets that: (1) identified phage contigs with VirSorter and VIBRANT; (2) classified these contigs with MetaPhinder using the phage database (taxonomic annotation); and (3) predicted the phages' bacterial hosts with a machine learning-based tool: the Prokaryotic Virus-Host Predictor. The findings showed that the taxonomic profiles of phages and bacteria were specific to sediment or water samples. Phage sequences assigned to the Microviridae family were widespread in sediment samples, whereas phage sequences assigned to the Siphoviridae, Myoviridae and Podoviridae families were predominant in water samples. In terms of bacterial communities, the phyla Latescibacteria, Zixibacteria, Bacteroidetes, Acidobacteria, Calditrichaeota, Gemmatimonadetes, Cyanobacteria and Patescibacteria were most widespread in sediment samples, while the phyla Epsilonbacteraeota, Tenericutes, Margulisbacteria, Proteobacteria, Actinobacteria, Planctomycetes and Marinimicrobia were most prevalent in water samples. Significantly, the relative abundance of bacterial communities (at major phylum level) estimated by 16S rRNA metabarcoding and phage-host prediction were significantly similar. These results demonstrate the reliability of this novel approach for predicting the bacterial hosts of phages from shotgun metagenomic sequencing data.

Different Characteristics in Gut Microbiome between Advanced Adenoma Patients and Colorectal Cancer Patients by Metagenomic Analysis.

The occurrence and development of colorectal cancer (CRC) and advanced adenoma (AA) are closely related to the gut microbiome, and AA has a high cancerization progression rate to CRC. Current studies have revealed that bacteriological analysis cannot identify CRC from AA. The objective was to explore microbial targets that could identify CRC and AA from a microecological perspective and to figure out the best way to identify CRC based on fecal microbes. The metagenomic sequencing data were used to describe the gut microbiome profile and analyze the differences between microbial abundance and microbial single nucleotide polymorphism (SNP) characteristics in AA and CRC patients. It was found that there were no significant differences in the diversity between the two groups. The abundance of bacteria (e.g., Firmicutes, Clostridia, and Blautia), fungi (Hypocreales), archaea (Methanosarcina, Methanoculleus, and Methanolacinia), and viruses (Alphacoronavirus, Sinsheimervirus, and Gammaretrovirus) differed between AA and CRC patients. Multiple machine-learning algorithms were used to establish prediction models, aiming to identify CRC and AA. The accuracy of the random forest (RF) model based on the gut microbiome was 86.54%. Nevertheless, the accuracy of SNP was 92.31% in identifying CRC from AA. In conclusion, using microbial SNP was the best method to identify CRC, it was superior to using the gut microbiome, and it could provide new targets for CRC screening. IMPORTANCE There are differences in characteristic microorganisms between AA and CRC. However, current studies have indicated that bacteriological analysis cannot identify CC from AA, and thus, we wondered if there were some other targets that could be used to identify CRC from AA in the gut microbiome. The differences of SNPs in the gut microbiota of intraindividuals were significantly smaller than those of interindividuals. In addition, compared with intestinal microbes, SNP was less affected by time with certain stability. It was discovered that microbial SNP was better than the gut microbiome for identifying CRC from AA. Therefore, screening characteristic microbial SNP could provide a new research direction for identifying CRC from AA.

Diverse viromes in polar regions: A retrospective study of metagenomic data from Antarctic animal feces and Arctic frozen soil in 2012-2014.

Antarctica and the Arctic are the coldest places, containing a high diversity of microorganisms, including viruses, which are important components of polar ecosystems. However, owing to the difficulties in obtaining access to animal and environmental samples, the current knowledge of viromes in polar regions is still limited. To better understand polar viromes, this study performed a retrospective analysis using metagenomic sequencing data of animal feces from Antarctica and frozen soil from the Arctic collected during 2012-2014. The results reveal diverse communities of DNA and RNA viruses from at least 23 families from Antarctic animal feces and 16 families from Arctic soils. Although the viral communities from Antarctica and the Arctic show a large diversity, they have genetic similarities with known viruses from different ecosystems and organisms with similar viral proteins. Phylogenetic analysis of Microviridae, Parvoviridae, and Larvidaviridae was further performed, and complete genomic sequences of two novel circular replication-associated protein (rep)-encoding single-stranded (CRESS) DNA viruses closely related to Circoviridae were identified. These results reveal the high diversity, complexity, and novelty of viral communities from polar regions, and suggested the genetic similarity and functional correlations of viromes between the Antarctica and Arctic. Variations in viral families in Arctic soils, Arctic freshwater, and Antarctic soils are discussed. These findings improve our understanding of polar viromes and suggest the importance of performing follow-up in-depth investigations of animal and environmental samples from Antarctica and the Arctic, which would reveal the substantial role of these viruses in the global viral community.

Abstract B003: Cross-reacting antigens between the intestinal microbiome and tumor neoantigen are associated with immune checkpoint inhibitor response in a gastrointestinal tumor cohort

Abstract Background: Immune checkpoint inhibitor (ICI) is a revolutionary therapy that promotes the activation of effector T cells against tumors. Despite the potential for long-term benefit, a large proportion of patients does not respond to ICI therapy. One reason for this heterogenic response is due to the high variability in the intestinal microbiota among different patients. The abundance of certain bacteria has been associated with different responses and fecal meta transplant from responding donors was shown to rescue a beneficial phenotype in refractory patients. The presence of cross-reactive antigens (CRAgs) between the gut microbiome and tumor neoantigens has been proposed as a potential mechanism that could link the intestinal microbiota with ICI response. However, empirical evidence of CRAg’s role in ICI therapy is limited to a few studies. Methods: We investigated ICI response in a cohort of 22 stage IV gastrointestinal (GI) tumor patients in which whole-exome and gut metagenomic sequencing data were available. This cohort included patients with gastric cancer (n=11), esophageal carcinoma (n=6), and colon cancer (n=5). For each patient, we characterized the tumor neoantigen load based on epitopes that show high affinity and presentability in HLA molecules. Neoantigens were matched to the intestinal microbiome to identify corresponding CRAgs. CRAg quality, which ranks CRAgs according to their immunogenic potential, was defined based on matches in immunogenic protein or epitope databases. Results: We found the total number of CRAgs varied per tumor type, with a higher abundance in colon cancer (median µ=619) followed by esophageal (µ =287.5) and gastric (µ = 243). Interestingly, colon cancer patients showed a better progression-free survival (PFS) curve followed by esophageal and gastric cancer patients, respectively. We also observed that CRAg load was better associated with longer PFS (p=.03) than neoantigen load (p=0.07). Moreover, CRAg’s quality showed to be an important predictor of PFS. Patients with a high proportion of CRAgs matching at least one of the quality databases showed better PFS (p=0.02). Further, a taxa-specific analysis indicated that commensal CRAgs load was a better predictor of PFS (p<0.008) than relative abundance. Altogether, our work supports a mechanistic link between the microbiome, neoantigens, and immuno-therapy. Citation Format: Ravin Poudel, Die Dai, Fang Li, Xiaochen Yin, Yan Kou, Antonio L. C. Gomes. Cross-reacting antigens between the intestinal microbiome and tumor neoantigen are associated with immune checkpoint inhibitor response in a gastrointestinal tumor cohort [abstract]. In: Proceedings of the AACR Special Conference on Colorectal Cancer; 2022 Oct 1-4; Portland, OR. Philadelphia (PA): AACR; Cancer Res 2022;82(23 Suppl_1):Abstract nr B003.

Standardized multi-omics of Earth’s microbiomes reveals microbial and metabolite diversity

Despite advances in sequencing, lack of standardization makes comparisons across studies challenging and hampers insights into the structure and function of microbial communities across multiple habitats on a planetary scale. Here we present a multi-omics analysis of a diverse set of 880 microbial community samples collected for the Earth Microbiome Project. We include amplicon (16S, 18S, ITS) and shotgun metagenomic sequence data, and untargeted metabolomics data (liquid chromatography-tandem mass spectrometry and gas chromatography mass spectrometry). We used standardized protocols and analytical methods to characterize microbial communities, focusing on relationships and co-occurrences of microbially related metabolites and microbial taxa across environments, thus allowing us to explore diversity at extraordinary scale. In addition to a reference database for metagenomic and metabolomic data, we provide a framework for incorporating additional studies, enabling the expansion of existing knowledge in the form of an evolving community resource. We demonstrate the utility of this database by testing the hypothesis that every microbe and metabolite is everywhere but the environment selects. Our results show that metabolite diversity exhibits turnover and nestedness related to both microbial communities and the environment, whereas the relative abundances of microbially related metabolites vary and co-occur with specific microbial consortia in a habitat-specific manner. We additionally show the power of certain chemistry, in particular terpenoids, in distinguishing Earth’s environments (for example, terrestrial plant surfaces and soils, freshwater and marine animal stool), as well as that of certain microbes including Conexibacter woesei (terrestrial soils), Haloquadratum walsbyi (marine deposits) and Pantoea dispersa (terrestrial plant detritus). This Resource provides insight into the taxa and metabolites within microbial communities from diverse habitats across Earth, informing both microbial and chemical ecology, and provides a foundation and methods for multi-omics microbiome studies of hosts and the environment.