Biology subjects

Ivanova, N.

Publications and source records attributed to Ivanova, N..

10 recordsLinked to original sources

bioRxiv2025

Planetary-scale metagenomic search reveals new patterns of CRISPR targeting

Interactions between microbes and their mobile genetic elements (MGEs), including viruses and plasmids, are critical drivers of microbiome structures and processes. CRISPR-Cas systems are important regulators of these host-MGE interactions, but a global understanding of CRISPR loci diversity, activity, and roles across Earths biomes is still lacking. Here, we use an optimized computational approach to search short-read data and collect [~]800 million CRISPR spacers across [~]450,000 public metagenomes. From this extensive CRISPR spacer dataset, we identified 1.18 billion hits between 41 million spacers and 2.5 million viruses and plasmids. Focusing on the role of CRISPR as anti-phage defense, we observed an over-representation of prevalent and conserved spacers consistent with a positive selection pressure associated with phage targeting. Identification of sustained CRISPR targeting by microbes not expected to be viable hosts suggested that some phage genomes may frequently enter non-host microbial cells. Taken together, these results outline the extensive diversity of CRISPR loci across microbiomes, and highlight key parameters driving CRISPR targeting patterns likely to influence strain-level diversification and selection processes within microbial populations.

microbiology↗

bioRxiv2025

Tetranucleotide frequencies differentiate genomic boundaries and metabolic strategies across environmental microbiomes

Microbiomes are constrained by physicochemical conditions, nutrient regimes, and community interactions across diverse environments, yet genomic signatures of this adaptation remain unclear. Metagenome sequencing is a powerful technique to analyze genomic content in the context of natural environments, establishing concepts of microbial ecological trends. Here, we developed a data discovery tool - a tetranucleotide-informed metagenome stability diagram - that is publicly available in the Integrated Microbial Genomes and Microbiomes (IMG/M) platform for metagenome-ecosystem analyses. We analyzed the tetranucleotide frequencies from quality-filtered and unassembled sequence data of over 12,000 metagenomes to assess ecosystem-specific microbial community composition and function. We found that tetranucleotide frequencies can differentiate communities across various natural environments, and that specific functional and metabolic trends can be observed in this structuring. Our tool places metagenomes sampled from diverse environments into clusters and along gradients of tetranucleotide frequency similarity, suggesting microbiome community compositions specific to gradient conditions. Within the resulting metagenome clusters, we identify protein-coding gene identifiers that are most differentiated between ecosystem classifications. We plan for annual updates to the metagenome stability diagram in IMG/M with new data, allowing for refinement of the ecosystem classifications delineated here. This framework has the potential to inform future studies on microbiome engineering, bioremediation, and the prediction of microbial community responses to environmental change. ImportanceMicrobes adapt to diverse environments influenced by factors like temperature, acidity, and nutrient availability. We developed a new tool to analyze and visualize the genetic makeup of over 12,000 microbial communities, revealing patterns linked to specific functions and metabolic processes. This tool groups similar microbial communities and identifies characteristic genes within environments. By continually updating this tool, we aim to advance our understanding of microbial ecology, enabling applications like microbial engineering, bioremediation, and predicting responses to environmental change.

ecology↗

bioRxiv2024

Stick of Sticks: Structural Features of the Amyloidogenic Peptide-DNA Complex

The search for peptides that can specifically bind to regulatory regions in DNA is a necessary step for creating drugs that can regulate gene expression. The study is dedicated to the peculiarities of binding of a model peptide, which carries an ionic self-complementary motif and can form amyloid-like fibrils [1], with model double-stranded DNAs. The stoichiometric ratios of the components of the complex were found using the retardation method in agarose gel. Using microscale thermophoresis, it was shown that the peptide in the amyloid-like state is capable of binding to model 45-bp double-stranded DNA, with a micromolar equilibrium dissociation constant. Using cryo-electron, transmission electron, and atomic force microscopy, the morphology of peptide-DNA complexes was studied. Using dynamic light scattering and nanoparticle tracking analysis, as well as small-angle neutron scattering, the spatial parameters of the resulting DNA-peptide complexes were characterized. Molecular dynamics simulations showed that the arginine side chains of the peptide are prone to interact with guanine nitrogenous bases. It was shown that the formation of peptide-dsDNA complexes interferes with the operation of restriction endonucleases that have guanine-cytosine pairs in the recognition center, which is consistent with the results of prediction of interaction sites obtained using computer modeling. The results of the work can be used in the development of peptides capable of interacting with functional regions of DNA, as well as in the development of new carriers for transfection of DNA constructs.

biophysics↗

bioRxiv2023

Inferring Metabolic States from Single Cell Transcriptomic Data via Geometric Deep Learning

The ability to measure gene expression at single-cell resolution has elevated our understanding of how biological features emerge from complex and interdependent networks at molecular, cellular, and tissue scales. As technologies have evolved that complement scRNAseq measurements with things like single-cell proteomic, epigenomic, and genomic information, it becomes increasingly apparent how much biology exists as a product of multimodal regulation. Biological processes such as transcription, translation, and post-translational or epigenetic modification impose both energetic and specific molecular demands on a cell and are therefore implicitly constrained by the metabolic state of the cell. While metabolomics is crucial for defining a holistic model of any biological process, the chemical heterogeneity of the metabolome makes it particularly difficult to measure, and technologies capable of doing this at single-cell resolution are far behind other multiomics modalities. To address these challenges, we present GEFMAP (Gene Expression-based Flux Mapping and Metabolic Pathway Prediction), a method based on geometric deep learning for predicting flux through reactions in a global metabolic network using transcriptomics data, which we ultimately apply to scRNAseq. GEFMAP leverages the natural graph structure of metabolic networks to learn both a biological objective for each cell and estimate a mass-balanced relative flux rate for each reaction in each cell using novel deep learning models.

bioinformatics↗

bioRxiv2022

A centimeter-long bacterium with DNA compartmentalized in membrane-bound organelles

Cells of most bacterial species are around 2 {micro}m in length, with some of the largest specimens reaching 750 {micro}m. Using fluorescence, x-ray, and electron microscopy in conjunction with genome sequencing, we characterized Ca. Thiomargarita magnifica, a bacterium with an average cell length greater than 9,000 {micro}m that is visible to the naked eye. We found that these cells grow orders of magnitude over theoretical limits for bacterial cell size through unique biology, display unprecedented polyploidy of more than half a million copies of a very large genome, and undergo a dimorphic life cycle with asymmetric segregation of chromosomes in daughter cells. These features, along with compartmentalization of genomic material and protein synthesis in membrane-bound organelles, indicate gain of complexity in the Thiomargarita lineage, and challenge traditional concepts of bacterial cells. One Sentence SummaryCa. T. magnifica are compartmentalized centimeter-long bacteria

cell biology↗

bioRxiv2022

A five-fold expansion of the global RNA virome reveals multiple new clades of RNA bacteriophages

High-throughput RNA sequencing offers unprecedented opportunities to explore the Earth RNA virome. Mining 5,150 diverse metatranscriptomes uncovered >2.5 million RNA viral contigs. Via analysis of the 330k novel RNA-dependent RNA polymerases (RdRP), this expansion corresponds to a five-fold increase of RNA virus diversity. Extended RdRP phylogeny supports monophyly of the five established phyla, reveals two putative new bacteriophage phyla and numerous putative novel classes and orders. The dramatically expanded Lenarviricota phylum, consisting of bacterial and related eukaryotic viruses, now accounts for a third of the RNA virome diversity. Identification of CRISPR spacer matches and bacteriolytic proteins suggests that subsets of picobirnaviruses and partitiviruses, previously associated with eukaryotes, infect prokaryotic hosts. Gene content analysis revealed multiple domains previously not found in RNA viruses and implicated in virus-host interactions. This vast collection of new RNA virus genomes provides insights into RNA virus evolution and should become a major resource for RNA virology.

genomics↗

bioRxiv2021

Deeplasmid: Deep learning accurately separates plasmids from bacterial chromosomes

Plasmids are mobile genetic elements that play a key role in microbial ecology and evolution by mediating horizontal transfer of important genes, such as antimicrobial resistance genes. Many microbial genomes have been sequenced by short read sequencers and have resulted in a mix of contigs that derive from plasmids or chromosomes. New tools that accurately identify plasmids are needed to elucidate new plasmid-borne genes of high biological importance. We have developed Deeplasmid, a deep learning tool for distinguishing plasmids from bacterial chromosomes based on the DNA sequence and its encoded biological data. It requires as input only assembled sequences generated by any sequencing platform and assembly algorithm and its runtime scales linearly with the number of assembled sequences. Deeplasmid achieves an AUC-ROC of over 93%, and it was much more precise than the state-of-the-art methods. Finally, as a proof of concept, we used Deeplasmid to predict new plasmids in the fish pathogen Yersinia ruckeri ATCC 29473 that has no annotated plasmids. Deeplasmid predicted with high reliability that a long assembled contig is part of a plasmid. Using long read sequencing we indeed validated the existence of a 102 Kbp long plasmid, demonstrating Deeplasmids ability to detect novel plasmids. AvailabilityThe software is available with a BSD license: deeplasmid.sourceforge.io. A Docker container is available on DockerHub under: billandreo/deeplasmid. Contactwandreopoulos@lbl.gov alevy@mail.huji.ac.il

bioinformatics↗

bioRxiv2020

The DOE JGI Metagenome Workflow

The DOE JGI Metagenome Workflow performs metagenome data processing, including assembly, structural, functional, and taxonomic annotation, and binning of metagenomic datasets that are subsequently included into the Integrated Microbial Genomes and Microbiomes (IMG/M) comparative analysis system (I. Chen, K. Chu, K. Palaniappan, M. Pillay, A. Ratner, J. Huang, M. Huntemann, N. Varghese, J. White, R. Seshadri, et al, Nucleic Acids Rsearch, 2019) and provided for download via the Joint Genome Institute (JGI) Data Portal (https://genome.jgi.doe.gov/portal/). This workflow scales to run on thousands of metagenome samples per year, which can vary by the complexity of microbial communities and sequencing depth. Here we describe the different tools, databases, and parameters used at different steps of the workflow, to help with interpretation of metagenome data available in IMG and to enable researchers to apply this workflow to their own data. We use 20 publicly available sediment metagenomes to illustrate the computing requirements for the different steps and highlight the typical results of data processing. The workflow modules for read filtering and metagenome assembly are available as a Workflow Description Language (WDL) file (https://code.jgi.doe.gov/BFoster/jgi_meta_wdl.git). The workflow modules for annotation and binning are provided as a service to the user community at https://img.jgi.doe.gov/submit and require filling out the project and associated metadata descriptions in Genomes OnLine Database (GOLD) (S. Mukherjee, D. Stamatis, J. Bertsch, G. Ovchinnikova, H. Katta, A. Mojica, I Chen, and N. Kyrpides, and T. Reddy, Nucleic Acids Research, 2018). IMPORTANCEThe DOE JGI Metagenome Workflow is designed for processing metagenomic datasets starting from Illumina fastq files. It performs data pre-processing, error correction, assembly, structural and functional annotation, and binning. The results of processing are provided in several standard formats, such as fasta and gff and can be used for subsequent integration into the Integrated Microbial Genome (IMG) system where they can be compared to a comprehensive set of publicly available metagenomes. As of 7/30/2020 7,155 JGI metagenomes have been processed by the JGI Metagenome Workflow.

bioinformatics↗

bioRxiv2019

Impact of soil salinity on the cowpea nodule-microbiome and the isolation of halotolerant PGPR strains to promote plant growth under salinity stress

Four soil samples (SS-1--SS-4) isolated from semi-arid soils in Punjab, Pakistan were used as inocula for cowpea (Vigna unguiculata L.) grown under salinity stress to analyze the composition of bacteria in the rhizosphere and within nodules through cultivation-dependent and cultivation-independent methods. Two cowpea varieties, 603 and the salt-tolerant CB 46, were each inoculated with four different native soil samples, and data showed that plants inoculated with soil samples SS-2 and SS-4 grew better than plants inoculated with soil samples SS-1 and SS-3. Bacteria were isolated from both soils and nodules, and 34 of the 51 original isolates tested positive for PGPR traits in plate assays with many exhibiting multiple plant growth-promoting properties. A number of isolates were positive for all PGPR traits tested. For the microbiome studies, environmental DNA (eDNA) was isolated from SS-1 and SS-4, which represented the extremes of the Pakistan soils to which the plants responded, and by 16S rRNA gene sequencing analysis were found to consist mainly of Actinobacteria, Firmicutes, and Proteobacteria. However, sequencing analysis of eDNA isolated from cowpea nodules established by the trap plants grown in the four Pakistan soils indicated that the nodule microbiome consisted almost exclusively of Proteobacterial sequences, particularly Bradyrhizobium. Yet, many other bacteria including Rhizobium, Mesorhizobium, Pseudomonas, as well as Paenibacillus, Bacillus as well as non-proteobacterial genera were isolated from the nodules of soil-inoculated cowpea plants. This discrepancy between the bacteria isolated from cowpea nodules (Proteobacteria and non-Proteobacteria) versus those detected in the nodule microbiome (Proteobacteria) needs further study.

plant biology↗

bioRxiv2018

A genome-wide algal mutant library reveals a global view of genes required for eukaryotic photosynthesis

Photosynthetic organisms provide food and energy for nearly all life on Earth, yet half of their protein-coding genes remain uncharacterized1,2. Characterization of these genes could be greatly accelerated by new genetic resources for unicellular organisms that complement the use of multicellular plants by enabling higher-throughput studies. Here, we generated a genome-wide, indexed library of mapped insertion mutants for the flagship unicellular alga Chlamydomonas reinhardtii (Chlamydomonas hereafter). The 62,389 mutants in the library, covering 83% of nuclear, protein-coding genes, are available to the community. Each mutant contains unique DNA barcodes, allowing the collection to be screened as a pool. We leveraged this feature to perform a genome-wide survey of genes required for photosynthesis, which identified 303 candidate genes. Characterization of one of these genes, the conserved predicted phosphatase CPL3, showed it is important for accumulation of multiple photosynthetic protein complexes. Strikingly, 21 of the 43 highest-confidence genes are novel, opening new opportunities for advances in our understanding of this biogeochemically fundamental process. This library is the first genome-wide mapped mutant resource in any unicellular photosynthetic organism, and will accelerate the characterization of thousands of genes in algae, plants and animals.

genomics↗