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Zakharevich NV, Morozov MD, Kanaeva VA, Filippov MS, Zyubko TI, Ivanov AB, Ulyantsev VI, Klimina KM, Olekhnovich EI. Systemic metabolic depletion of gut microbiome undermines responsiveness to melanoma immunotherapy. Life Sci Alliance 2024; 7:e202302480. [PMID: 38448159 PMCID: PMC10917649 DOI: 10.26508/lsa.202302480] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/14/2023] [Revised: 02/09/2024] [Accepted: 02/12/2024] [Indexed: 03/08/2024] Open
Abstract
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.
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Affiliation(s)
- Natalia V Zakharevich
- Lopukhin Federal Research and Clinical Center of Physical-Chemical Medicine of Federal Medical Biological Agency, Moscow, Russian
| | - Maxim D Morozov
- Lopukhin Federal Research and Clinical Center of Physical-Chemical Medicine of Federal Medical Biological Agency, Moscow, Russian
| | - Vera A Kanaeva
- Lopukhin Federal Research and Clinical Center of Physical-Chemical Medicine of Federal Medical Biological Agency, Moscow, Russian
- Moscow Institute of Physics and Technology, Moscow, Russian
| | - Mikhail S Filippov
- https://ror.org/04btxg914 Bioinformatics Institute, Saint Petersburg, Russian
| | - Tatyana I Zyubko
- https://ror.org/04btxg914 Bioinformatics Institute, Saint Petersburg, Russian
| | - Artem B Ivanov
- Lopukhin Federal Research and Clinical Center of Physical-Chemical Medicine of Federal Medical Biological Agency, Moscow, Russian
- ITMO University, Saint Petersburg, Russian
| | | | - Ksenia M Klimina
- Lopukhin Federal Research and Clinical Center of Physical-Chemical Medicine of Federal Medical Biological Agency, Moscow, Russian
| | - Evgenii I Olekhnovich
- Lopukhin Federal Research and Clinical Center of Physical-Chemical Medicine of Federal Medical Biological Agency, Moscow, Russian
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Noskova E, Abramov N, Iliutkin S, Sidorin A, Dobrynin P, Ulyantsev VI. GADMA2: more efficient and flexible demographic inference from genetic data. Gigascience 2022; 12:giad059. [PMID: 37609916 PMCID: PMC10445054 DOI: 10.1093/gigascience/giad059] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/12/2022] [Revised: 01/31/2023] [Accepted: 07/05/2023] [Indexed: 08/24/2023] Open
Abstract
BACKGROUND Inference of complex demographic histories is a source of information about events that happened in the past of studied populations. Existing methods for demographic inference typically require input from the researcher in the form of a parameterized model. With an increased variety of methods and tools, each with its own interface, the model specification becomes tedious and error-prone. Moreover, optimization algorithms used to find model parameters sometimes turn out to be inefficient, for instance, by being not properly tuned or highly dependent on a user-provided initialization. The open-source software GADMA addresses these problems, providing automatic demographic inference. It proposes a common interface for several likelihood engines and provides global parameters optimization based on a genetic algorithm. RESULTS Here, we introduce the new GADMA2 software and provide a detailed description of the added and expanded features. It has a renovated core code base, new likelihood engines, an updated optimization algorithm, and a flexible setup for automatic model construction. We provide a full overview of GADMA2 enhancements, compare the performance of supported likelihood engines on simulated data, and demonstrate an example of GADMA2 usage on 2 empirical datasets. CONCLUSIONS We demonstrate the better performance of a genetic algorithm in GADMA2 by comparing it to the initial version and other existing optimization approaches. Our experiments on simulated data indicate that GADMA2's likelihood engines are able to provide accurate estimations of demographic parameters even for misspecified models. We improve model parameters for 2 empirical datasets of inbred species.
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Affiliation(s)
- Ekaterina Noskova
- Computer Technologies Laboratory, ITMO University, St. Petersburg 197101, Russia
| | | | - Stanislav Iliutkin
- Computer Technologies Laboratory, ITMO University, St. Petersburg 197101, Russia
| | - Anton Sidorin
- Laboratory of Biochemical Genetics, St. Petersburg State University, St. Petersburg 199034, Russia
| | - Pavel Dobrynin
- Computer Technologies Laboratory, ITMO University, St. Petersburg 197101, Russia
- Human Genetics Laboratory, Vavilov Institute of General Genetics RAS, Moscow 119991, Russia
| | - Vladimir I Ulyantsev
- Computer Technologies Laboratory, ITMO University, St. Petersburg 197101, Russia
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Olekhnovich EI, Vasilyev AT, Ulyantsev VI, Kostryukova ES, Tyakht AV. MetaCherchant: analyzing genomic context of antibiotic resistance genes in gut microbiota. Bioinformatics 2018; 34:434-444. [PMID: 29092015 DOI: 10.1093/bioinformatics/btx681] [Citation(s) in RCA: 22] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/17/2017] [Accepted: 10/27/2017] [Indexed: 11/14/2022] Open
Abstract
Motivation Antibiotic resistance is an important global public health problem. Human gut microbiota is an accumulator of resistance genes potentially providing them to pathogens. It is important to develop tools for identifying the mechanisms of how resistance is transmitted between gut microbial species and pathogens. Results We developed MetaCherchant-an algorithm for extracting the genomic environment of antibiotic resistance genes from metagenomic data in the form of a graph. The algorithm was validated on a number of simulated and published datasets, as well as applied to new 'shotgun' metagenomes of gut microbiota from patients with Helicobacter pylori who underwent antibiotic therapy. Genomic context was reconstructed for several major resistance genes. Taxonomic annotation of the context suggests that within a single metagenome, the resistance genes can be contained in genomes of multiple species. MetaCherchant allows reconstruction of mobile elements with resistance genes within the genomes of bacteria using metagenomic data. Application of MetaCherchant in differential mode produced specific graph structures suggesting the evidence of possible resistance gene transmission within a mobile element that occurred as a result of the antibiotic therapy. MetaCherchant is a promising tool giving researchers an opportunity to get an insight into dynamics of resistance transmission in vivo basing on metagenomic data. Availability and implementation Source code and binaries are freely available for download at https://github.com/ctlab/metacherchant. The code is written in Java and is platform-independent. Cotanct ulyantsev@rain.ifmo.ru. Supplementary information Supplementary data are available at Bioinformatics online.
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Affiliation(s)
- Evgenii I Olekhnovich
- Federal Research and Clinical Centre of Physical and Chemical Medicine, Federal Medical and Biological Agency of Russia, Moscow, Russian Federation
| | - Artem T Vasilyev
- ITMO University, Saint Petersburg, Russian Federation.,JetBrains Research, Russian Federation
| | | | - Elena S Kostryukova
- Federal Research and Clinical Centre of Physical and Chemical Medicine, Federal Medical and Biological Agency of Russia, Moscow, Russian Federation
| | - Alexander V Tyakht
- Federal Research and Clinical Centre of Physical and Chemical Medicine, Federal Medical and Biological Agency of Russia, Moscow, Russian Federation.,ITMO University, Saint Petersburg, Russian Federation
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Ulyantsev VI, Kazakov SV, Dubinkina VB, Tyakht AV, Alexeev DG. MetaFast: fast reference-free graph-based comparison of shotgun metagenomic data. Bioinformatics 2016; 32:2760-7. [PMID: 27259541 DOI: 10.1093/bioinformatics/btw312] [Citation(s) in RCA: 23] [Impact Index Per Article: 2.9] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/05/2015] [Accepted: 05/16/2016] [Indexed: 02/02/2023] Open
Abstract
MOTIVATION High-throughput metagenomic sequencing has revolutionized our view on the structure and metabolic potential of microbial communities. However, analysis of metagenomic composition is often complicated by the high complexity of the community and the lack of related reference genomic sequences. As a start point for comparative metagenomic analysis, the researchers require efficient means for assessing pairwise similarity of the metagenomes (beta-diversity). A number of approaches were used to address this task, however, most of them have inherent disadvantages that limit their scope of applicability. For instance, the reference-based methods poorly perform on metagenomes from previously unstudied niches, while composition-based methods appear to be too abstract for straightforward interpretation and do not allow to identify the differentially abundant features. RESULTS We developed MetaFast, an approach that allows to represent a shotgun metagenome from an arbitrary environment as a modified de Bruijn graph consisting of simplified components. For multiple metagenomes, the resulting representation is used to obtain a pairwise similarity matrix. The dimensional structure of the metagenomic components preserved in our algorithm reflects the inherent subspecies-level diversity of microbiota. The method is computationally efficient and especially promising for an analysis of metagenomes from novel environmental niches. AVAILABILITY AND IMPLEMENTATION Source code and binaries are freely available for download at https://github.com/ctlab/metafast The code is written in Java and is platform independent (tested on Linux and Windows x86_64). CONTACT ulyantsev@rain.ifmo.ru SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online.
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Affiliation(s)
| | | | - Veronika B Dubinkina
- Federal Research and Clinical Centre of Physical-Chemical Medicine, Moscow, Russian Federation Moscow Institute of Physics and Technology (State University), Dolgoprudny, Russian Federation
| | - Alexander V Tyakht
- Federal Research and Clinical Centre of Physical-Chemical Medicine, Moscow, Russian Federation Moscow Institute of Physics and Technology (State University), Dolgoprudny, Russian Federation
| | - Dmitry G Alexeev
- Moscow Institute of Physics and Technology (State University), Dolgoprudny, Russian Federation
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Dubinkina VB, Ischenko DS, Ulyantsev VI, Tyakht AV, Alexeev DG. Assessment of k-mer spectrum applicability for metagenomic dissimilarity analysis. BMC Bioinformatics 2016; 17:38. [PMID: 26774270 PMCID: PMC4715287 DOI: 10.1186/s12859-015-0875-7] [Citation(s) in RCA: 37] [Impact Index Per Article: 4.6] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/07/2015] [Accepted: 12/14/2015] [Indexed: 12/11/2022] Open
Abstract
BACKGROUND A rapidly increasing flow of genomic data requires the development of efficient methods for obtaining its compact representation. Feature extraction facilitates classification, clustering and model analysis for testing and refining biological hypotheses. "Shotgun" metagenome is an analytically challenging type of genomic data - containing sequences of all genes from the totality of a complex microbial community. Recently, researchers started to analyze metagenomes using reference-free methods based on the analysis of oligonucleotides (k-mers) frequency spectrum previously applied to isolated genomes. However, little is known about their correlation with the existing approaches for metagenomic feature extraction, as well as the limits of applicability. Here we evaluated a metagenomic pairwise dissimilarity measure based on short k-mer spectrum using the example of human gut microbiota, a biomedically significant object of study. RESULTS We developed a method for calculating pairwise dissimilarity (beta-diversity) of "shotgun" metagenomes based on short k-mer spectra (5 ≤ k ≤ 11). The method was validated on simulated metagenomes and further applied to a large collection of human gut metagenomes from the populations of the world (n=281). The k-mer spectrum-based measure was found to behave similarly to one based on mapping to a reference gene catalog, but different from one using a genome catalog. This difference turned out to be associated with a significant presence of viral reads in a number of metagenomes. Simulations showed limited impact of bacterial genetic variability as well as sequencing errors on k-mer spectra. Specific differences between the datasets from individual populations were identified. CONCLUSIONS Our approach allows rapid estimation of pairwise dissimilarity between metagenomes. Though we applied this technique to gut microbiota, it should be useful for arbitrary metagenomes, even metagenomes with novel microbiota. Dissimilarity measure based on k-mer spectrum provides a wider perspective in comparison with the ones based on the alignment against reference sequence sets. It helps not to miss possible outstanding features of metagenomic composition, particularly related to the presence of an unknown bacteria, virus or eukaryote, as well as to technical artifacts (sample contamination, reads of non-biological origin, etc.) at the early stages of bioinformatic analysis. Our method is complementary to reference-based approaches and can be easily integrated into metagenomic analysis pipelines.
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Affiliation(s)
- Veronika B Dubinkina
- Research Institute of Physico-Chemical Medicine, Malaya Pirogovskaya, Moscow, 119435, Russia. .,Moscow Institute of Physics and Technology (State University), Institutskiy per., Dolgoprudny, 141700, Russia.
| | - Dmitry S Ischenko
- Research Institute of Physico-Chemical Medicine, Malaya Pirogovskaya, Moscow, 119435, Russia. .,Moscow Institute of Physics and Technology (State University), Institutskiy per., Dolgoprudny, 141700, Russia.
| | | | - Alexander V Tyakht
- Research Institute of Physico-Chemical Medicine, Malaya Pirogovskaya, Moscow, 119435, Russia. .,Moscow Institute of Physics and Technology (State University), Institutskiy per., Dolgoprudny, 141700, Russia.
| | - Dmitry G Alexeev
- Research Institute of Physico-Chemical Medicine, Malaya Pirogovskaya, Moscow, 119435, Russia. .,Moscow Institute of Physics and Technology (State University), Institutskiy per., Dolgoprudny, 141700, Russia.
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