Microbial Pan-genomes Research Articles

RIBAP: a comprehensive bacterial core genome annotation pipeline for pangenome calculation beyond the species level

Microbial pangenome analysis identifies present or absent genes in prokaryotic genomes. However, current tools are limited when analyzing species with higher sequence diversity or higher taxonomic orders such as genera or families. The Roary ILP Bacterial core Annotation Pipeline (RIBAP) uses an integer linear programming approach to refine gene clusters predicted by Roary for identifying core genes. RIBAP successfully handles the complexity and diversity of Chlamydia, Klebsiella, Brucella, and Enterococcus genomes, outperforming other established and recent pangenome tools for identifying all-encompassing core genes at the genus level. RIBAP is a freely available Nextflow pipeline at github.com/hoelzer-lab/ribap and zenodo.org/doi/10.5281/zenodo.10890871.

Scoary2: rapid association of phenotypic multi-omics data with microbial pan-genomes

Unraveling bacterial gene function drives progress in various areas, such as food production, pharmacology, and ecology. While omics technologies capture high-dimensional phenotypic data, linking them to genomic data is challenging, leaving 40–60% of bacterial genes undescribed. To address this bottleneck, we introduce Scoary2, an ultra-fast microbial genome-wide association studies (mGWAS) software. With its data exploration app and improved performance, Scoary2 is the first tool to enable the study of large phenotypic datasets using mGWAS. As proof of concept, we explore the metabolome of yogurts, each produced with a different Propionibacterium reichii strain and discover two genes affecting carnitine metabolism.

Pangenomes at the limits of evolution

Evolutionary pathways can be random or deterministic. In a recent article, Beavan et al. investigate this balance by applying machine learning models to microbial pangenomes. The presence of almost one-third of genes can be reliably inferred, indicating a surprising amount of predictable evolution.

Trade-off in genome turnover events leading to adaptive evolution of Microcystis aeruginosa species complex

BackgroundNumerous studies in the past have expanded our understanding of the genetic differences of global distributed cyanobacteria that originated around billions of years ago, however, unraveling how gene gain and loss drive the genetic evolution of cyanobacterial species, and the trade-off of these evolutionary forces are still the central but poorly understood issues.ResultsTo delineate the contribution of gene flow in mediating the hereditary differentiation and shaping the microbial evolution, a global genome-wide study of bloom-forming cyanobacterium, Microcystis aeruginosa species complex, provided robust evidence for genetic diversity, reflected by enormous variation in gene repertoire among various strains. Mathematical extrapolation showed an ‘open’ microbial pan-genome of M. aeruginosa species, since novel genes were predicted to be introduced after new genomes were sequenced. Identification of numerous horizontal gene transfer’s signatures in genome regions of interest suggested that genome expansion via transformation and phage-mediated transduction across bacterial lineage as an evolutionary route may contribute to the differentiation of Microcystis functions (e.g., carbohydrate metabolism, amino acid metabolism, and energy metabolism). Meanwhile, the selective loss of some dispensable genes at the cost of metabolic versatility is as a mean of adaptive evolution that has the potential to increase the biological fitness.ConclusionsNow that the recruitment of novel genes was accompanied by a parallel loss of some other ones, a trade-off in gene content may drive the divergent differentiation of M. aeruginosa genomes. Our study provides a genetic framework for the evolution of M. aeruginosa species and illustrates their possible evolutionary patterns.

PanGraph: scalable bacterial pan-genome graph construction.

The genomic diversity of microbes is commonly parameterized as SNPs relative to a reference genome of a well-characterized, but arbitrary, isolate. However, any reference genome contains only a fraction of the microbial pangenome, the total set of genes observed in a given species. Reference-based approaches are thus blind to the dynamics of the accessory genome, as well as variation within gene order and copy number. With the widespread usage of long-read sequencing, the number of high-quality, complete genome assemblies has increased dramatically. In addition to pangenomic approaches that focus on the variation in the sets of genes present in different genomes, complete assemblies allow investigations of the evolution of genome structure and gene order. This latter problem, however, is computationally demanding with few tools available that shed light on these dynamics. Here, we present PanGraph, a Julia-based library and command line interface for aligning whole genomes into a graph. Each genome is represented as a path along vertices, which in turn encapsulate homologous multiple sequence alignments. The resultant data structure succinctly summarizes population-level nucleotide and structural polymorphisms and can be exported into several common formats for either downstream analysis or immediate visualization.

Pangenome Evolution in Environmentally Transmitted Symbionts of Deep-Sea Mussels Is Governed by Vertical Inheritance.

Microbial pangenomes vary across species; their size and structure are determined by genetic diversity within the population and by gene loss and horizontal gene transfer (HGT). Many bacteria are associated with eukaryotic hosts where the host colonization dynamics may impact bacterial genome evolution. Host-associated lifestyle has been recognized as a barrier to HGT in parentally transmitted bacteria. However, pangenome evolution of environmentally acquired symbionts remains understudied, often due to limitations in symbiont cultivation. Using high-resolution metagenomics, here we study pangenome evolution of two co-occurring endosymbionts inhabiting Bathymodiolus brooksi mussels from a single cold seep. The symbionts, sulfur-oxidizing (SOX) and methane-oxidizing (MOX) gamma-proteobacteria, are environmentally acquired at an early developmental stage and individual mussels may harbor multiple strains of each symbiont species. We found differences in the accessory gene content of both symbionts across individual mussels, which are reflected by differences in symbiont strain composition. Compared with core genes, accessory genes are enriched in genome plasticity functions. We found no evidence for recent HGT between both symbionts. A comparison between the symbiont pangenomes revealed that the MOX population is less diverged and contains fewer accessory genes, supporting that the MOX association with B. brooksi is more recent in comparison to that of SOX. Our results show that the pangenomes of both symbionts evolved mainly by vertical inheritance. We conclude that genome evolution of environmentally transmitted symbionts that associate with individual hosts over their lifetime is affected by a narrow symbiosis where the frequency of HGT is constrained.

Computational graph pangenomics: a tutorial on data structures and their applications

Computational pangenomics is an emerging research field that is changing the way computer scientists are facing challenges in biological sequence analysis. In past decades, contributions from combinatorics, stringology, graph theory and data structures were essential in the development of a plethora of software tools for the analysis of the human genome. These tools allowed computational biologists to approach ambitious projects at population scale, such as the 1000 Genomes Project. A major contribution of the 1000 Genomes Project is the characterization of a broad spectrum of genetic variations in the human genome, including the discovery of novel variations in the South Asian, African and European populations—thus enhancing the catalogue of variability within the reference genome. Currently, the need to take into account the high variability in population genomes as well as the specificity of an individual genome in a personalized approach to medicine is rapidly pushing the abandonment of the traditional paradigm of using a single reference genome. A graph-based representation of multiple genomes, or a graph pangenome, is replacing the linear reference genome. This means completely rethinking well-established procedures to analyze, store, and access information from genome representations. Properly addressing these challenges is crucial to face the computational tasks of ambitious healthcare projects aiming to characterize human diversity by sequencing 1M individuals (Stark et al. 2019). This tutorial aims to introduce readers to the most recent advances in the theory of data structures for the representation of graph pangenomes. We discuss efficient representations of haplotypes and the variability of genotypes in graph pangenomes, and highlight applications in solving computational problems in human and microbial (viral) pangenomes.

PanRV: Pangenome-reverse vaccinology approach for identifications of potential vaccine candidates in microbial pangenome

BackgroundA revolutionary diversion from classical vaccinology to reverse vaccinology approach has been observed in the last decade. The ever-increasing genomic and proteomic data has greatly facilitated the vaccine designing and development process. Reverse vaccinology is considered as a cost-effective and proficient approach to screen the entire pathogen genome. To look for broad-spectrum immunogenic targets and analysis of closely-related bacterial species, the assimilation of pangenome concept into reverse vaccinology approach is essential. The categories of species pangenome such as core, accessory, and unique genes sets can be analyzed for the identification of vaccine candidates through reverse vaccinology.ResultsWe have designed an integrative computational pipeline term as “PanRV” that employs both the pangenome and reverse vaccinology approaches. PanRV comprises of four functional modules including i) Pangenome Estimation Module (PGM) ii) Reverse Vaccinology Module (RVM) iii) Functional Annotation Module (FAM) and iv) Antibiotic Resistance Association Module (ARM). The pipeline is tested by using genomic data from 301 genomes of Staphylococcus aureus and the results are verified by experimentally known antigenic data.ConclusionThe proposed pipeline has proved to be the first comprehensive automated pipeline that can precisely identify putative vaccine candidates exploiting the microbial pangenome. PanRV is a Linux based package developed in JAVA language. An executable installer is provided for ease of installation along with a user manual at https://sourceforge.net/projects/panrv2/.

Genomic variation and strain-specific functional adaptation in the human gut microbiome during early life.

SummaryThe human gut microbiome matures toward the adult composition during the first years of life and is implicated in early immune development. Here, we investigate the effects of microbial genomic diversity on gut microbiome development using integrated early childhood datasets collected in the DIABIMMUNE study in Finland, Estonia and Russian Karelia. We show that gut microbial diversity is associated with household location and linear growth of children. Single nucleotide polymorphism (SNP)- and metagenomic assembly-based strain tracking revealed large and highly dynamic microbial pangenomes, especially in the genus Bacteroides, in which we identified evidence of variability deriving from Bacteroides-targeting bacteriophages. Our analyses revealed functional consequences of strain diversity; only 10% of Finnish infants harbored Bifidobacterium longum subsp. infantis, a subspecies specialized in human milk metabolism, whereas Russian infants commonly maintained a probiotic Bifidobacterium bifidum strain in infancy. Groups of bacteria contributing to diverse, characterized metabolic pathways converged to highly subject-specific configurations over the first two years of life. This longitudinal study extends the current view of early gut microbial community assembly based on strain-level genomic variation.

MSPminer: abundance-based reconstitution of microbial pan-genomes from shotgun metagenomic data.

MotivationAnalysis toolkits for shotgun metagenomic data achieve strain-level characterization of complex microbial communities by capturing intra-species gene content variation. Yet, these tools are hampered by the extent of reference genomes that are far from covering all microbial variability, as many species are still not sequenced or have only few strains available. Binning co-abundant genes obtained from de novo assembly is a powerful reference-free technique to discover and reconstitute gene repertoire of microbial species. While current methods accurately identify species core parts, they miss many accessory genes or split them into small gene groups that remain unassociated to core clusters.ResultsWe introduce MSPminer, a computationally efficient software tool that reconstitutes Metagenomic Species Pan-genomes (MSPs) by binning co-abundant genes across metagenomic samples. MSPminer relies on a new robust measure of proportionality coupled with an empirical classifier to group and distinguish not only species core genes but accessory genes also. Applied to a large scale metagenomic dataset, MSPminer successfully delineates in a few hours the gene repertoires of 1661 microbial species with similar specificity and higher sensitivity than existing tools. The taxonomic annotation of MSPs reveals microorganisms hitherto unknown and brings coherence in the nomenclature of the species of the human gut microbiota. The provided MSPs can be readily used for taxonomic profiling and biomarkers discovery in human gut metagenomic samples. In addition, MSPminer can be applied on gene count tables from other ecosystems to perform similar analyses.Availability and implementationThe binary is freely available for non-commercial users at www.enterome.com/downloads.Supplementary information Supplementary data are available at Bioinformatics online.

Systematic discovery of antiphage defense systems in the microbial pangenome.

The arms race between bacteria and phages led to the development of sophisticated antiphage defense systems, including CRISPR-Cas and restriction-modification systems. Evidence suggests that known and unknown defense systems are located in "defense islands" in microbial genomes. Here, we comprehensively characterized the bacterial defensive arsenal by examining gene families that are clustered next to known defense genes in prokaryotic genomes. Candidate defense systems were systematically engineered and validated in model bacteria for their antiphage activities. We report nine previously unknown antiphage systems and one antiplasmid system that are widespread in microbes and strongly protect against foreign invaders. These include systems that adopted components of the bacterial flagella and condensin complexes. Our data also suggest a common, ancient ancestry of innate immunity components shared between animals, plants, and bacteria.

Pan-Genome Storage and Analysis Techniques.

Computational pan-genome analysis has emerged from the rapid increase of available genome sequencing data. Starting from a microbial pan-genome, the concept has spread to a variety of species, such as plants or viruses. Characterizing a pan-genome provides insights into intra-species evolution, functions, and diversity. However, researchers face challenges such as processing and maintaining large datasets while providing accurate and efficient analysis approaches. Comparative genomics methods are required for detecting conserved and unique regions between a set of genomes. This chapter gives an overview of tools available for indexing pan-genomes, identifying the sub-regions of a pan-genome and offering a variety of downstream analysis methods. These tools are categorized into two groups, gene-based and sequence-based, according to the pan-genome identification method. We highlight the differences, advantages, and disadvantages between the tools, and provide information about the general workflow, methodology of pan-genome identification, covered functionalities, usability and availability of the tools.

Novel Antibiotic Resistance Determinants from Agricultural Soil Exposed to Antibiotics Widely Used in Human Medicine and Animal Farming.

Antibiotic resistance has emerged globally as one of the biggest threats to human and animal health. Although the excessive use of antibiotics is recognized as accelerating the selection for resistance, there is a growing body of evidence suggesting that natural environments are "hot spots" for the development of both ancient and contemporary resistance mechanisms. Given that pharmaceuticals can be entrained onto agricultural land through anthropogenic activities, this could be a potential driver for the emergence and dissemination of resistance in soil bacteria. Using functional metagenomics, we interrogated the "resistome" of bacterial communities found in a collection of Canadian agricultural soil, some of which had been receiving antibiotics widely used in human medicine (macrolides) or food animal production (sulfamethazine, chlortetracycline, and tylosin) for up to 16 years. Of the 34 new antibiotic resistance genes (ARGs) recovered, the majority were predicted to encode (multi)drug efflux systems, while a few share little to no homology with established resistance determinants. We characterized several novel gene products, including putative enzymes that can confer high-level resistance against aminoglycosides, sulfonamides, and broad range of beta-lactams, with respect to their resistance mechanisms and clinical significance. By coupling high-resolution proteomics analysis with functional metagenomics, we discovered an unusual peptide, PPPAZI 4, encoded within an alternative open reading frame not predicted by bioinformatics tools. Expression of the proline-rich PPPAZI 4 can promote resistance against different macrolides but not other ribosome-targeting antibiotics, implicating a new macrolide-specific resistance mechanism that could be fundamentally linked to the evolutionary design of this peptide.IMPORTANCE Antibiotic resistance is a clinical phenomenon with an evolutionary link to the microbial pangenome. Genes and protogenes encoding specialized and potential resistance mechanisms are abundant in natural environments, but understanding of their identity and genomic context remains limited. Our discovery of several previously unknown antibiotic resistance genes from uncultured soil microorganisms indicates that soil is a significant reservoir of resistance determinants, which, once acquired and "repurposed" by pathogenic bacteria, can have serious impacts on therapeutic outcomes. This study provides valuable insights into the diversity and identity of resistance within the soil microbiome. The finding of a novel peptide-mediated resistance mechanism involving an unpredicted gene product also highlights the usefulness of integrating proteomics analysis into metagenomics-driven gene discovery.

Abundance-based reconstitution of microbial pan-genomes from shotgun metagenomic data

Abundance-based reconstitution of microbial pan-genomes from shotgun metagenomic data

Sequence data analysis and preprocessing for oligo probe design in microbial genomes

A good oligo probe design in DNA microarray experiments is crucial to obtain the better results of gene expression analysis. However, sequence data from a very large microbial genome or pan-genome will produce a reduced number of oligos and affect the design quality if processed by a probe designer. Gene redundancies and discrepancies across resources of the same species or strain and their sequence similarity and homology are responsible for the poor quantity of oligos designed. We addressed these issues and problems with sequences and introduced the concept of open reading frame (ORF) sequence segmentation from which quality oligos can be selected. Analysis and pre-processing of sequence data were performed using our Perl-based pipeline ORF-Purger 2.0. ORFs were purged of redundancy, discrepancy, invalidity, overlapping, similarity and, optionally, homology, such that the quantity and quality of oligos to be designed were drastically improved. Probe integrity was proposed as the first probe selection criterion since the fully physical availability of all possible probes corresponding to their targets in a nucleic acid sample is necessary for a best probe design.

Pan-Tetris: an interactive visualisation for Pan-genomes

BackgroundLarge-scale genome projects have paved the way to microbial pan-genome analyses. Pan-genomes describe the union of all genes shared by all members of the species or taxon under investigation. They offer a framework to assess the genomic diversity of a given collection of individual genomes and moreover they help to consolidate gene predictions and annotations. The computation of pan-genomes is often a challenge, and many techniques that use a global alignment-independent approach run the risk of not separating paralogs from orthologs. Also alignment-based approaches which take the gene neighbourhood into account often need additional manual curation of the results. This is quite time consuming and so far there is no visualisation tool available that offers an interactive GUI for the pan-genome to support curating pan-genomic computations or annotations of orthologous genes.ResultsWe introduce Pan-Tetris, a Java based interactive software tool that provides a clearly structured and suitable way for the visual inspection of gene occurrences in a pan-genome table. The main features of Pan-Tetris are a standard coordinate based presentation of multiple genomes complemented by easy to use tools compensating for algorithmic weaknesses in the pan-genome generation workflow. We demonstrate an application of Pan-Tetris to the pan-genome of Staphylococcus aureus.ConclusionsPan-Tetris is currently the only interactive pan-genome visualisation tool. Pan-Tetris is available from http://bit.ly/1vVxYZT

ITEP: An integrated toolkit for exploration of microbial pan-genomes

BackgroundComparative genomics is a powerful approach for studying variation in physiological traits as well as the evolution and ecology of microorganisms. Recent technological advances have enabled sequencing large numbers of related genomes in a single project, requiring computational tools for their integrated analysis. In particular, accurate annotations and identification of gene presence and absence are critical for understanding and modeling the cellular physiology of newly sequenced genomes. Although many tools are available to compare the gene contents of related genomes, new tools are necessary to enable close examination and curation of protein families from large numbers of closely related organisms, to integrate curation with the analysis of gain and loss, and to generate metabolic networks linking the annotations to observed phenotypes.ResultsWe have developed ITEP, an Integrated Toolkit for Exploration of microbial Pan-genomes, to curate protein families, compute similarities to externally-defined domains, analyze gene gain and loss, and generate draft metabolic networks from one or more curated reference network reconstructions in groups of related microbial species among which the combination of core and variable genes constitute the their "pan-genomes". The ITEP toolkit consists of: (1) a series of modular command-line scripts for identification, comparison, curation, and analysis of protein families and their distribution across many genomes; (2) a set of Python libraries for programmatic access to the same data; and (3) pre-packaged scripts to perform common analysis workflows on a collection of genomes. ITEP’s capabilities include de novo protein family prediction, ortholog detection, analysis of functional domains, identification of core and variable genes and gene regions, sequence alignments and tree generation, annotation curation, and the integration of cross-genome analysis and metabolic networks for study of metabolic network evolution.ConclusionsITEP is a powerful, flexible toolkit for generation and curation of protein families. ITEP's modular design allows for straightforward extension as analysis methods and tools evolve. By integrating comparative genomics with the development of draft metabolic networks, ITEP harnesses the power of comparative genomics to build confidence in links between genotype and phenotype and helps disambiguate gene annotations when they are evaluated in both evolutionary and metabolic network contexts.

A domain sequence approach to pangenomics: applications to Escherichia coli

The study of microbial pangenomes relies on the computation of gene families, i.e. the clustering of coding sequences into groups of essentially similar genes. There is no standard approach to obtain such gene families. Ideally, the gene family computations should be robust against errors in the annotation of genes in various genomes. In an attempt to achieve this robustness, we propose to cluster sequences by their domain sequence, i.e. the ordered sequence of domains in their protein sequence. In a study of 347 genomes from Escherichia coli we find on average around 4500 proteins having hits in Pfam-A in every genome, clustering into around 2500 distinct domain sequence families in each genome. Across all genomes we find a total of 5724 such families. A binomial mixture model approach indicates this is around 95% of all domain sequences we would expect to see in E. coli in the future. A Heaps law analysis indicates the population of domain sequences is larger, but this analysis is also very sensitive to smaller changes in the computation procedure. The resolution between strains is good despite the coarse grouping obtained by domain sequence families. Clustering sequences by their ordered domain content give us domain sequence families, who are robust to errors in the gene prediction step. The computational load of the procedure scales linearly with the number of genomes, which is needed for the future explosion in the number of re-sequenced strains. The use of domain sequence families for a functional classification of strains clearly has some potential to be explored.

Evolutionary consequences of antibiotic use for the resistome, mobilome and microbial pangenome

The widespread use and abuse of antibiotic therapy has evolutionary and ecological consequences, some of which are only just beginning to be examined. One well known consequence is the fixation of mutations and lateral gene transfer (LGT) events that confer antibiotic resistance. Sequential selection events, driven by different classes of antibiotics, have resulted in the assembly of diverse resistance determinants and mobile DNAs into novel genetic elements of ever-growing complexity and flexibility. These novel plasmids, integrons, and genomic islands have now become fixed at high frequency in diverse cell lineages by human antibiotic use. Consequently they can be regarded as xenogenetic pollutants, analogous to xenobiotic compounds, but with the critical distinction that they replicate rather than degrade when released to pollute natural environments. Antibiotics themselves must also be regarded as pollutants, since human production overwhelms natural synthesis, and a major proportion of ingested antibiotic is excreted unchanged into waste streams. Such antibiotic pollutants have non-target effects, raising the general rates of mutation, recombination, and LGT in all the microbiome, and simultaneously providing the selective force to fix such changes. This has the consequence of recruiting more genes into the resistome and mobilome, and of increasing the overlap between these two components of microbial genomes. Thus the human use and environmental release of antibiotics is having second order effects on the microbial world, because these small molecules act as drivers of bacterial evolution. Continued pollution with both xenogenetic elements and the selective agents that fix such elements in populations has potentially adverse consequences for human welfare.

A domain sequence approach to pangenomics: applications to Escherichia coli

The study of microbial pangenomes relies on the computation of gene families, i.e. the clustering of coding sequences into groups of essentially similar genes. There is no standard approach to obtain such gene families. Ideally, the gene family computations should be robust against errors in the annotation of genes in various genomes. In an attempt to achieve this robustness, we propose to cluster sequences by their domain sequence, i.e. the ordered sequence of domains in their protein sequence. In a study of 347 genomes from Escherichia coli we find on average around 4500 proteins having hits in Pfam-A in every genome, clustering into around 2500 distinct domain sequence families in each genome. Across all genomes we find a total of 5724 such families. A binomial mixture model approach indicates this is around 95% of all domain sequences we would expect to see in E. coli in the future. A Heaps law analysis indicates the population of domain sequences is larger, but this analysis is also very sensitive to smaller changes in the computation procedure. The resolution between strains is good despite the coarse grouping obtained by domain sequence families. Clustering sequences by their ordered domain content give us domain sequence families, who are robust to errors in the gene prediction step. The computational load of the procedure scales linearly with the number of genomes, which is needed for the future explosion in the number of re-sequenced strains. The use of domain sequence families for a functional classification of strains clearly has some potential to be explored.