Background: Recent advancements in next-generation sequencing (NGS) technology have ushered in significant improvements in sequencing speed and data throughput, thereby enabling the simultaneous analysis of a greater number of samples within a single sequencing run. This technology has proven particularly valuable in the context of microbial community profiling, offering a powerful tool for characterizing the microbial composition at the species level within a given sample. This profiling process typically involves the sequencing of 16S ribosomal RNA (rRNA) gene fragments. By scaling up the analysis to accommodate a substantial number of samples, sometimes as many as 2,000, it becomes possible to achieve cost-efficiency and minimize the introduction of potential batch effects. Our study was designed with the primary objective of devising an approach capable of facilitating the comprehensive analysis of 1,711 samples sourced from diverse origins, including oropharyngeal swabs, mouth cavity swabs, dental swabs, and human fecal samples. This analysis was based on data obtained from 16S rRNA metagenomic sequencing conducted on the Illumina MiSeq and HiSeq sequencing platforms. Results: We have designed a custom set of 10-base pair indices specifically tailored for the preparation of libraries from amplicons derived from the V3-V4 region of the 16S rRNA gene. These indices are instrumental in the analysis of the microbial composition in clinical samples through sequencing on the Illumina MiSeq and HiSeq platforms. The utilization of our custom index set enables the consolidation of a significant number of libraries, enabling the efficient sequencing of these libraries in a single run. Conclusions: The unique array of 10-base pair indices that we have developed, in conjunction with our sequencing methodology, will prove highly valuable to laboratories engaged in sequencing on Illumina platforms or utilizing Illumina-compatible kits.

The COVID-19 pandemic, caused by the SARS-CoV-2 virus, has significantly impacted global healthcare, underscoring the importance of exploring the virus’s effects on infected individuals beyond treatments and vaccines. Notably, recent findings suggest that SARS-CoV-2 can infect the gut, thereby altering the gut microbiota. This study aimed to analyze the gut microbiota composition differences between COVID-19 patients experiencing mild and severe symptoms. We conducted 16S rRNA metagenomic sequencing on fecal samples from 49 mild and 43 severe COVID-19 cases upon hospital admission. Our analysis identified a differential abundance of specific bacterial species associated with the severity of the disease. Severely affected patients showed an association with Enterococcus faecium, Akkermansia muciniphila, and others, while milder cases were linked to Faecalibacterium prausnitzii, Alistipes putredinis, Blautia faecis, and additional species. Furthermore, a network analysis using SPIEC-EASI indicated keystone taxa and highlighted structural differences in bacterial connectivity, with a notable disruption in the severe group. Our study highlights the diverse impacts of SARS-CoV-2 on the gut microbiome among both mild and severe COVID-19 patients, showcasing a spectrum of microbial responses to the virus. Importantly, these findings align, to some extent, with observations from other studies on COVID-19 gut microbiomes, despite variations in methodologies. The findings from this study, based on retrospective data, establish a foundation for future prospective research to confirm the role of the gut microbiome as a predictive biomarker for the severity of COVID-19.

Immunotherapy has proven to be a boon for patients battling metastatic melanoma, significantly improving their clinical condition and overall quality of life. A compelling link between the composition of the gut microbiome and the efficacy of immunotherapy has been established in both animal models and human patients. However, the precise biological mechanisms by which gut microbes influence treatment outcomes remain poorly understood. Using a robust dataset of 680 fecal metagenomes from melanoma patients, a detailed catalog of metagenome-assembled genomes (MAGs) was constructed to explore the compositional and functional properties of the gut microbiome. Our study uncovered significant findings that deepen the understanding of the intricate relationship between gut microbes and the efficacy of melanoma immunotherapy. In particular, we discovered the specific metagenomic profile of patients with favorable treatment outcomes, characterized by a prevalence of MAGs with increased overall metabolic potential and proficiency in polysaccharide utilization, along with those responsible for cobalamin and amino acid production. Furthermore, our investigation of the biosynthetic pathways of short-chain fatty acids, known for their immunomodulatory role, revealed a differential abundance of these pathways among the specific MAGs. Among others, the cobalamin-dependent Wood–Ljungdahl pathway of acetate synthesis was directly associated with responsiveness to melanoma immunotherapy.

Melanoma is an aggressive malignancy with a significant risk of mortality. In recent years, treatment strategies have undergone a paradigm shift with the advent of immunotherapy, particularly immune checkpoint inhibitors (ICIs). Despite notable clinical success, a substantial proportion of patients fail to respond or eventually develop resistance to ICIs. Emerging evidence highlights the gut microbiota as a critical modulator of host immune responses and is one of the potential determinants of immunotherapy efficacy. We performed a cross-cohort analysis of gut microbiome profiles from melanoma patients treated with ICIs. The study integrated the first Russian cohort (62 patients) with six previously published international datasets, comprising a total of 490 patients across seven cohorts. In all cases, metagenomic sequencing was performed using various Illumina platforms, and raw sequencing data were processed using a unified bioinformatic pipeline. Analysis revealed 527 metagenome-assembled genomes (MAGs) significantly associated with treatment outcome: 239 with response and 288 with non-response. Notably, the species Faecalibacterium sp900539945, Phocaeicola vulgatus, Bifidobacterium adolescentis, Faecalibacterium taiwanense, and Gemmiger qucibialis were consistently associated with response, while Enterobacter ludwigii was linked to non-response. Analysis of the Russian cohort revealed both conserved and population-specific microbial signatures, highlighting the coexistence of globally shared and region-dependent microbiome features. Our results also show that species-level annotations may obscure opposing response associations within the same taxa, highlighting the need for MAGs or strain profiling. Together, this study demonstrates that cross-cohort analysis enables the identification of robust and reproducible bacterial markers of immunotherapy response, providing a foundation for microbiome-based prediction and modulation strategies in melanoma.

Immunotherapy has proven to be a boon for patients grappling with metastatic melanoma, significantly enhancing their clinical condition and overall quality of life. A compelling connection was discovered between the composition of the intestinal microbiome and the effectiveness of immunotherapy substantiated in both animal models and human patients. Nonetheless, the precise biological mechanisms through which gut microbes influence melanoma treatment outcomes remain poorly understood. This study conducted a high-resolution metagenomic meta-analysis, employing cutting-edge bioinformatics techniques including genome-resolved metagenomics, strain profiling, comparative genomics, and metabolic reconstruction. According to the obtained results, the systemic metabolic depletion of the gut microbiome causes a lack of response to immunotherapy. Specifically, the presence of bacteria adept at utilizing polysaccharides, as well as those responsible for cobalamin, amino acids, and fatty acids production, decreased in patients who experienced unfavorable treatment outcomes. In contrast, patients who had successful outcomes after immunotherapy exhibited a prevalence of amino acids and cobalamin prototrophs, while autotrophy in these substances characterized the microbiomes of patients with unsuccessful outcomes. The metabolic reconstruction of short-chain fatty acid biosynthesis pathways did not differentiate bacteria linked to treatment outcomes based on their ability to produce acetate, butyrate, or propionate. However, the cobalamin-dependent Wood-Ljungdahl pathway of acetate synthesis was directly associated with immunotherapy effectiveness.

Motivation: Microbial communities consist of thousands of microorganisms and viruses and have a tight connection with an environment, such as gut microbiota modulation of host body metabolism. However, the direct relationship between the presence of certain microorganism and host state often remains unknown. Toolkits using reference-based approaches are limited to microbes present in databases. Reference-free methods often require enormous resources for metagenomic assembly or results in many poorly interpretable features based on k-mers. Results: Here we present MetaFX – an open-source library for feature extraction from whole-genome metagenomic sequencing data and classification of groups of samples. Using a large volume of metagenomic samples deposited in databases, MetaFX compares samples grouped by metadata criteria (e.g. disease, treatment, etc) and constructs genomic features distinct for certain types of communities. Features constructed based on statistical k-mer analysis and de Bruijn graphs partition. Those features are used in machine learning models for classification of novel samples. Extracted features can be visualised on de Bruijn graphs and annotated for providing biological insights. We demonstrate the utility of MetaFX by building classification models for 590 human gut samples with inflammatory bowel disease. Our results outperform the previous research disease prediction accuracy up to 17%, and improves classification results compared to taxonomic analysis by 9 ± 10% on average. Availability: MetaFX is a feature extraction toolkit applicable for metagenomic datasets analysis and samples classification. The source code, test data, and relevant information for MetaFX are freely accessible at https://github.com/ctlab/metafx under the MIT License. Contact: abivanov@itmo.ru