Biology subjects

Gurevich, A.

Publications and source records attributed to Gurevich, A..

17 recordsLinked to original sources

bioRxiv2026

BGC-QUAST: a quality assessment tool for genome mining software

Summary: Biosynthetic gene clusters (BGCs) encode microbial natural products, many of which have important ecological and biomedical roles. Genome mining tools enable large-scale BGC prediction, but their outputs differ substantially, complicating comparison and interpretation. We present BGC-QUAST, a framework for evaluating and comparing BGC predictions across three analysis modes: comparison across samples, assessment of BGC recovery in draft assemblies relative to reference genomes, and comparison of predictions from different tools using overlap analysis. BGC-QUAST provides standardized metrics, interactive visualizations, and integrated outputs for joint inspection of predictions, enabling the comprehensive comparison of genome mining results and facilitating sample prioritisation based on biosynthetic potential. Availability and implementation: BGC-QUAST is publicly available at https://github.com/gurevichlab/bgc-quast

bioinformatics↗

bioRxiv2026

Unveiling the taxonomic diversity and unprecedented biosynthetic treasure of the phylum Myxococcota

Natural products remain vital sources of therapeutics, particularly anti-infectives, and members of the phylum Myxococcota constitute an especially rich reservoir for their discovery. Based on decades of microbiological efforts, we present 154 new Myxococcota genomes and propose a revised taxonomy expanding the number of described families from 11 to 28 and genera from 32 to 90. Comparison with an equivalent set from the prime source Actinomycetota shows that Myxococcota possess a comparable biosynthetic diversity, underscoring their promise for large-scale isolation and sequencing efforts. The vast untapped potential reflected in 2,387 uncharacterized gene cluster families is highlighted by genome mining efforts, yielding four validated compounds exhibiting novel chemistry, including myxolutamids and myxopentacins. We show that many Myxococcota-derived natural products, such as myxolutamid A and two new sorangicin derivatives, are conserved within taxonomic lineages. New described families thus bear high biosynthetic potential underpinning the importance of precise taxonomic classification guiding targeted drug discovery.

microbiology↗

bioRxiv2025

Drug-Eluting, Radiopaque, Tumor-Casting Hydrogels for Endovascular Locoregional Therapy of Hepatocellular Carcinoma

Hepatocellular carcinoma (HCC) is one of the most common cancers worldwide. For patients with unresectable disease, locoregional therapies, such as transarterial chemoembolization (TACE), are the standard of care. However, even with this treatment, the average survival rate is only 27% at 5 years. Recent studies have demonstrated the potential of novel approaches to improve responses through more complete embolization and targeting ischemia-induced molecular dependencies. To build on these findings, we developed an injectable chitosan-based hydrogel system (AuNP-Lys05-gel) that we hypothesized might be a more effective embolic material by (i) enabling a more complete filling of tumor feeding blood vessels in a cast-like manner, (ii) encapsulating Lys05, a potent autophagy inhibitor, and (iii) providing X-ray contrast for direct visualization of the embolization. Chitosan hydrogels of varying composition were formed and characterized. We found that Lys05 could be loaded in this type of hydrogel and that it was released gradually over 7 days. We found that AuNP was better retained by the hydrogel than iopamidol, an FDA-approved contrast agent, indicating that AuNP would provide longer-lasting CT monitoring. In a rat model of HCC, AuNP-Lys05-gel reduced tumor growth and resulted in a 67% objective response rate vs 26% for a clinically used particle embolic. Histology indicated effective vascular occlusion by the gel. These findings indicate that AuNP-Lys05-gel merits further investigation as a treatment for HCC.

cancer biology↗

bioRxiv2024

Nerpa 2: linking biosynthetic gene clusters tononribosomal peptide structures

MotivationNonribosomal peptides (NRPs) are bioactive microbial metabolites with high pharmaceutical potential. Although genome mining enables large-scale detection of biosynthetic gene clusters (BGCs) predicted to encode NRPs, reliably linking these clusters to their chemical products remains challenging due to the flexible and heterogeneous organization of NRP assembly pathways. ResultsWe present Nerpa 2, a probabilistic framework for accurate and scalable linking of NRP BGCs to candidate chemical structures. The method represents assembly lines as hidden Markov models (HMMs) that capture uncertainty and alternative biosynthetic routes. On curated datasets of experimentally validated BGC-product pairs, our tool outperforms existing methods in linking accuracy and pathway reconstruction. When applied to large genome mining datasets, Nerpa 2 efficiently identifies BGCs likely associated with known compounds and highlights potential producers of novel chemistry. Availability and implementationNerpa 2 is freely available at https://github.com/gurevichlab/nerpa.

bioinformatics↗

bioRxiv2024

GRIP: physics-informed neural network for gradient retention time prediction in liquid chromatography

Gradient liquid chromatography has numerous applications in life sciences. Retention time prediction remains challenging due to complex underlying physical processes and highly variable chromatographic conditions. Here, we present GRIP, a physics-informed neural network for gradient retention time prediction that explicitly uses experimental setup parameters. GRIP demonstrates zero-shot generalization to unseen chromatographic systems while being on par or out-performing the transfer learning-based baseline. This approach can computationally guide the experimental setup configuration tailored to specific compounds of interest.

bioinformatics↗

bioRxiv2024

BGC Atlas: A Web Resource for Exploring the Global Chemical Diversity Encoded in Bacterial Genomes

Secondary metabolites are compounds not essential for an organisms development, but provide significant ecological and physiological benefits. These compounds have applications in medicine, biotechnology, and agriculture. Their production is encoded in biosynthetic gene clusters (BGCs), groups of genes collectively directing their biosynthesis. The advent of metagenomics has allowed researchers to study BGCs directly from environmental samples, identifying numerous previously unknown BGCs encoding unprecedented chemistry. Here, we present the BGC Atlas (https://bgc-atlas.cs.uni-tuebingen.de), a web resource that facilitates the exploration and analysis of BGC diversity in metagenomes. The BGC Atlas identifies and clusters BGCs from publicly available datasets, offering a centralized database and a web interface for metadata-aware exploration of BGCs and gene cluster families (GCFs). We analyzed over 35,000 datasets from MGnify, identifying nearly 1.8 million BGCs, which were clustered into GCFs. The analysis showed that ribosomally synthesized and post-translationally modified peptides (RiPPs) are the most abundant compound class, with most GCFs exhibiting high environmental specificity. We believe that our tool will enable researchers to easily explore and analyze the BGC diversity in environmental samples, significantly enhancing our understanding of bacterial secondary metabolites, and promote the identification of ecological and evolutionary factors shaping the biosynthetic potential of microbial communities.

bioinformatics↗

bioRxiv2024

Transarterial Embolization Using a Liquid Embolic Enhances Tumor Necrosis to Enable Improved Efficacy Compared with a Particle Embolic in a Translational Rat Model of Hepatocellular Carcinoma

PurposeTo compare the effectiveness of transarterial embolization (TAE) using a liquid embolic (LE) to TAE using particle embolics (PE) with respect to tumor ischemia and treatment response in a translational rat model. Materials and MethodsHCC was induced in male Wistar rats using diethylnitrosamine. Rats were randomized into three treatment groups: sham transarterial embolization (TAE), TAE with LE, and TAE with PE. Tumor response to TAE was monitored via serial T2-weighted MRI scans and assessed using RECIST. Tumor necrosis and hypoxia were assessed through hematoxylin and eosin staining and pimonidazole immunohistochemistry, respectively. Statistical analyses were performed using chi-square tests, Kaplan-Meier estimates, and one-way ANOVAs, with significance set at p<0.05. ResultsTwenty-nine rats were randomized to TAE with LE (n=13), PE (n=13), or sham (n=3). TAE with LE demonstrated a significantly higher objective response rate (83%) compared to TAE with PE (28%; {chi}2 = 11.25, P=0.0008). Complete responses were observed in 50% of the LE-treated tumors versus 10% in the PE-treated group. LE treatment prolonged local progression-free survival (hazard ratio = 0.23; p=0.0085). Histological analysis, with an additional 14 rats randomized to TAE with LE (n=5), PE (n=6), or sham (n=3), showed greater necrosis and hypoxia in LE-treated tumors compared to PE. LE induced a significant reduction in viable tumor tissue (p<0.05) and increased necrotic and hypoxic tissue areas compared to PE (p<0.01). ConclusionLE significantly enhanced the therapeutic efficacy of TAE in a rat model of HCC compared to conventional PE. The results highlight LEs potential to improve ischemia and necrosis, thereby offering a promising option improving the efficacy of embolization for HCC treatment.

cancer biology↗

bioRxiv2023

ABC-HuMi: the Atlas of Biosynthetic Gene Clusters in the Human Microbiome

The human microbiome has emerged as a rich source of diverse and bioactive natural products, harboring immense potential for therapeutic applications. To facilitate systematic exploration and analysis of its biosynthetic landscape, we present ABC-HuMi: the Atlas of Biosynthetic Gene Clusters (BGCs) in the Human Microbiome. ABC-HuMi integrates data from major human microbiome sequence databases and provides an expansive repository of BGCs compared to the limited coverage offered by existing resources. Employing state-of-the-art BGC prediction and analysis tools, our database ensures accurate annotation and enhanced prediction capabilities. ABC-HuMi empowers researchers with advanced browsing, filtering, and search functionality, enabling efficient exploration of the resource. At present, ABC-HuMi boasts a catalog of 19,218 representative BGCs derived from the human gut, oral, skin, respiratory and urogenital systems. By capturing the intricate biosynthetic potential across diverse human body sites, our database fosters profound insights into the molecular repertoire encoded within the human microbiome and offers a comprehensive resource for the discovery and characterization of novel bioactive compounds. The database is freely accessible at https://www.ccb.uni-saarland.de/abc_humi/. GRAPHICAL ABSTRACT O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=149 SRC="FIGDIR/small/558305v2_ufig1.gif" ALT="Figure 1"> View larger version (52K): org.highwire.dtl.DTLVardef@15af4f6org.highwire.dtl.DTLVardef@886a27org.highwire.dtl.DTLVardef@1f12de5org.highwire.dtl.DTLVardef@fc24be_HPS_FORMAT_FIGEXP M_FIG C_FIG

bioinformatics↗

bioRxiv2021

Critical Assessment of Metagenome Interpretation - the second round of challenges

Evaluating metagenomic software is key for optimizing metagenome interpretation and focus of the community-driven initiative for the Critical Assessment of Metagenome Interpretation (CAMI). In its second challenge, CAMI engaged the community to assess their methods on realistic and complex metagenomic datasets with long and short reads, created from [~]1,700 novel and known microbial genomes, as well as [~]600 novel plasmids and viruses. Altogether 5,002 results by 76 program versions were analyzed, representing a 22x increase in results. Substantial improvements were seen in metagenome assembly, some due to using long-read data. The presence of related strains still was challenging for assembly and genome binning, as was assembly quality for the latter. Taxon profilers demonstrated a marked maturation, with taxon profilers and binners excelling at higher bacterial taxonomic ranks, but underperforming for viruses and archaea. Assessment of clinical pathogen detection techniques revealed a need to improve reproducibility. Analysis of program runtimes and memory usage identified highly efficient programs, including some top performers with other metrics. The CAMI II results identify current challenges, but also guide researchers in selecting methods for specific analyses.

bioinformatics↗

bioRxiv2020

MolDiscovery: Learning Mass Spectrometry Fragmentation of Small Molecules

Identification of small molecules is a critical task in various areas of life science. Recent advances in mass spectrometry have enabled the collection of tandem mass spectra of small molecules from hundreds of thousands of environments. To identify which molecules are present in a sample, one can search mass spectra collected from the sample against millions of molecular structures in small molecule databases. This is a challenging task as currently it is not clear how small molecules are fragmented in mass spectrometry. The existing approaches use the domain knowledge from chemistry to predict fragmentation of molecules. However, these rule-based methods fail to explain many of the peaks in mass spectra of small molecules. Recently, spectral libraries with tens of thousands of labelled mass spectra of small molecules have emerged, paving the path for learning more accurate fragmentation models for mass spectral database search. We present molDiscovery, a mass spectral database search method that improves both efficiency and accuracy of small molecule identification by (i) utilizing an efficient algorithm to generate mass spectrometry fragmentations, and (ii) learning a probabilistic model to match small molecules with their mass spectra. We show our database search is an order of magnitude more efficient than the state-of-the-art methods, which enables searching against databases with millions of molecules. A search of over 8 million spectra from the Global Natural Product Social molecular networking infrastructure shows that our probabilistic model can correctly identify nearly six times more unique small molecules than previous methods. Moreover, by applying molDiscovery on microbial datasets with both mass spectral and genomics data we successfully discovered the novel biosynthetic gene clusters of three families of small molecules. AvailabilityThe command-line version of molDiscovery and its online web service through the GNPS infrastructure are available at https://github.com/mohimanilab/molDiscovery.

bioinformatics↗

bioRxiv2020

Transarterial embolization modulates the immune response within target and non-target hepatocellular carcinomas.

Recent successes in the field of immuno-oncology have generated considerable interest in the investigation of approaches that combine standard of care treatments with immunotherapies. Transarterial embolization (TAE) represents an attractive candidate for this approach given the potential for immune system stimulation, however little is known about the influence of TAE on the tumor immunoenvironment. The purpose of this study was to perform a cellular analysis of tumor infiltrating lymphocytes (TILs) and PD-L1 expression following TAE in a translational rat model of hepatocellular carcinoma (HCC) and to identify factors that influence this response. We show that TAE causes dynamic changes in immune cell populations, with variable increases in CD3, CD4, and CD8 cells within embolized tumors over time. We also show that TAE alters the immunobiology of distant, non-target tumors as demonstrated by an increased number of CD4, CD8, and Foxp3+ cells within the intratumoral compartment of non-target tumors. We demonstrate that, in response to TAE, tumor cells up-regulate expression of PD-L1. Finally, we demonstrate marked differences in terms of the foreign body reactions induced by two commonly used embolic particles, and show changes in lymphocyte and macrophage recruitment that depend on the type of embolic particles and their propensity to extravasate beyond the vasculature and into the tumor parenchyma. These findings hold important implications for the on-going development of novel therapeutic strategies combining locoregional therapy with immunomodulators, as well as for the development of techniques and materials that can further leverage the unique modulation of the tumor immune microenvironment.

immunology↗

bioRxiv2020

Tutorial: Assessing metagenomics software with the CAMI benchmarking toolkit

Computational methods are key in microbiome research, and obtaining a quantitative and unbiased performance estimate is important for method developers and applied researchers. For meaningful comparisons between methods, to identify best practices, common use cases, and to reduce overhead in benchmarking, it is necessary to have standardized data sets, procedures, and metrics for evaluation. In this tutorial, we describe emerging standards in computational metaomics benchmarking derived and agreed upon by a larger community of researchers. Specifically, we outline recent efforts by the Critical Assessment of Metagenome Interpretation (CAMI) initiative, which supplies method developers and applied researchers with exhaustive quantitative data about software performance in realistic scenarios and organizes community-driven benchmarking challenges. We explain the most relevant evaluation metrics to assess metagenome assembly, binning, and profiling results, and provide step-by-step instructions on how to generate them. The instructions use simulated mouse gut metagenome data released in preparation for the second round of CAMI challenges and showcase the use of a repository of tool results for CAMI data sets. This tutorial will serve as a reference to the community and facilitate informative and reproducible benchmarking in microbiome research.

bioinformatics↗

bioRxiv2019

TandemMapper and TandemQUAST: mapping long reads and assessing/improving assembly quality in extra-long tandem repeats

Extra-long tandem repeats (ETRs) are widespread in eukaryotic genomes and play an important role in fundamental cellular processes, such as chromosome segregation. Although emerging long-read technologies have enabled ETR assemblies, the accuracy of such assemblies is difficult to evaluate since there is no standard tool for their quality assessment. Moreover, since the mapping of long error-prone reads to ETR remains an open problem, it is not clear how to polish draft ETR assemblies. To address these problems, we developed the tandemMapper tool for mapping reads to ETRs and the tandemQUAST tool for polishing ETR assemblies and their quality assessment. We demonstrate that tandemQUAST not only reveals errors in and evaluates ETR assemblies, but also improves them. To illustrate how tandemMapper and tandemQUAST work, we apply them to recently generated assemblies of human centromeres.

bioinformatics↗

bioRxiv2019

Feature-based Molecular Networking in the GNPS Analysis Environment

Molecular networking has become a key method used to visualize and annotate the chemical space in non-targeted mass spectrometry-based experiments. However, distinguishing isomeric compounds and quantitative interpretation are currently limited. Therefore, we created Feature-based Molecular Networking (FBMN) as a new analysis method in the Global Natural Products Social Molecular Networking (GNPS) infrastructure. FBMN leverages feature detection and alignment tools to enhance quantitative analyses and isomer distinction, including from ion-mobility spectrometry experiments, in molecular networks.

bioinformatics↗

bioRxiv2019

De Novo Peptide Sequencing Reveals a Vast Cyclopeptidome in Human Gut and Other Environments

Cyclic and branch cyclic peptides (cyclopeptides) represent an important class of bioactive natural products that include many antibiotics and anti-tumor compounds. However, little is known about cyclopeptides in the human gut, despite the fact that humans are constantly exposed to them. To address this bottleneck, we developed the CycloNovo algorithm for de novo cyclopeptide sequencing that employs de Bruijn graphs, the workhorse of DNA sequencing algorithms. CycloNovo reconstructed many new cyclopeptides that we validated with transcriptome, metagenome, and genome mining analyses. Our benchmarking revealed a vast hidden cyclopeptidome in the human gut and other environments and suggested that CycloNovo offers a much-needed step-change for cyclopeptide discovery. Furthermore, CycloNovo revealed a wealth of anti-microbial cyclopeptides from food that survive the complete human gastrointestinal tract, raising the question of how these cyclopeptides might affect the human microbiome.\n\nSIGNIFICANCEThe golden age of antibiotics was followed by a decline in the pace of antibiotics discovery in the 1990s. The key prerequisite for the resurgence of antibiotics research is the development of a computational discovery pipeline for antibiotics sequencing. We describe such pipeline for cyclic and branch cyclic peptides (cyclopeptides) that represent an important class of bioactive natural products such as antibiotics and anti-tumor compounds. Our CycloNovo algorithm for cyclopeptide sequencing reconstructed many new cyclopeptides that we validated with transcriptome, metagenome, and genome mining analyses. CycloNovo revealed a wealth of anti-microbial cyclopeptides from food that survive the complete human gastrointestinal tract, raising the question of how these cyclopeptides might affect the human microbiome.

bioinformatics↗

bioRxiv2017

MetaRiPPquest: A Peptidogenomics Approach for the Discovery of Ribosomally Synthesized and Post-translationally Modified Peptides

Ribosomally synthesized and post-translationally modified peptides (RiPPs) are an important class of natural products that include many antibiotics and a variety of other bioactive compounds. While recent breakthroughs in RiPP discovery raised the challenge of developing new algorithms for their analysis, peptidogenomic-based identification of RiPPs by combining genome/metagenome mining with analysis of tandem mass spectra remains an open problem. We present here MetaRiPPquest, a software tool for addressing this challenge that is compatible with large-scale screening platforms for natural product discovery. After searching millions of spectra in the Global Natural Products Social (GNPS) molecular networking infrastructure against just six genomic and metagenomic datasets, MetaRiPPquest identified 27 known and discovered 5 novel RiPP natural products.

bioinformatics↗

bioRxiv2017

Critical Assessment of Metagenome Interpretation - a benchmark of computational metagenomics software

In metagenome analysis, computational methods for assembly, taxonomic profiling and binning are key components facilitating downstream biological data interpretation. However, a lack of consensus about benchmarking datasets and evaluation metrics complicates proper performance assessment. The Critical Assessment of Metagenome Interpretation (CAMI) challenge has engaged the global developer community to benchmark their programs on datasets of unprecedented complexity and realism. Benchmark metagenomes were generated from ~700 newly sequenced microorganisms and ~600 novel viruses and plasmids, including genomes with varying degrees of relatedness to each other and to publicly available ones and representing common experimental setups. Across all datasets, assembly and genome binning programs performed well for species represented by individual genomes, while performance was substantially affected by the presence of related strains. Taxonomic profiling and binning programs were proficient at high taxonomic ranks, with a notable performance decrease below the family level. Parameter settings substantially impacted performances, underscoring the importance of program reproducibility. While highlighting current challenges in computational metagenomics, the CAMI results provide a roadmap for software selection to answer specific research questions.

bioinformatics↗