Biology subjects

Bundalovic-Torma, C.

Publications and source records attributed to Bundalovic-Torma, C..

5 recordsLinked to original sources

bioRxiv2021

RecPD: A Recombination-Aware Measure of Phylogenetic Diversity

A critical step in studying biological features (e.g., genetic variants, gene families, metabolic capabilities, or taxa) underlying traits or outcomes of interest is assessing their diversity and distribution. Accurate assessments of these patterns are essential for linking features to traits or outcomes and understanding their functional impact. Consequently, it is of crucial importance that the metrics employed for quantifying feature diversity can perform robustly under any evolutionary scenario. However, the standard metrics used for quantifying and comparing the distribution of features, such as prevalence, phylogenetic diversity, and related approaches, either do not take into consideration evolutionary history, or assume strictly vertical patterns of inheritance. Consequently, these approaches cannot accurately assess diversity for features that have undergone recombination or horizontal transfer. To address this issue, we have devised RecPD, a novel recombination-aware phylogenetic-diversity metric for measuring the distribution and diversity of features under all evolutionary scenarios. RecPD utilizes ancestral-state reconstruction to map the presence / absence of features onto ancestral nodes in a species tree, and then identifies potential recombination events in the evolutionary history of the feature. We also derive a number of related metrics from RecPD that can be used to assess and quantify evolutionary dynamics and correlation of feature evolutionary histories. We used simulation studies to show that RecPD reliably identifies evolutionary histories under diverse recombination and loss scenarios. We then apply RecPD in a real-world scenario in a preliminary study type III effector protein families secreted by the plant pathogenic bacterium Pseudomonas syringae and demonstrate that prevalence is an inadequate metric that obscures the potential impact of recombination. We believe RecPD will have broad utility for revealing and quantifying complex evolutionary processes for features at any biological level. AUTHOR SUMMARYPhylogenetic diversity is an important concept utilized in evolutionary ecology which has extensive applications in population genetics to help us understand how evolutionary processes have distributed genetic variation among individuals of a species, and how this impacts phenotypic diversification over time. However, existing approaches for studying phylogenetic diversity largely assume that the genetic features follow vertical inheritance, which is frequently violated in the case of microbial genomes due to horizontal transfer. To address this shortcoming, we present RecPD, a recombination-aware phylogenetic diversity metric, which incorporates ancestral state reconstruction to quantify the phylogenetic diversity of genetic features mapped onto a species phylogeny. Through simulation experiments we show that RecPD robustly reconstructs the evolutionary histories of features evolving under various scenarios of recombination and loss. When applied to a real-world example of type III secreted effector protein families from the plant pathogenic bacterium Pseudomonas syringae, RecPD reveals that horizontal transfer has played an important role in shaping the phylogenetic distributions of aa substantial proportion of families across the P. syringae species complex. Furthermore, we demonstrate that the traditional measures of feature prevalence are unsuitable as a metric for comparing feature diversity.

genomics↗

bioRxiv2021

Predictive Modeling of Pseudomonas syringae Virulence on Bean using Gradient Boosted Decision Trees

Pseudomonas syringae is a genetically diverse bacterial species complex responsible for numerous agronomically important crop diseases. Individual P. syringae isolates are typically given pathovar designations based on their host of isolation and the associated disease symptoms, and these pathovar designations are often assumed to reflect host specificity although this assumption has rarely been rigorously tested. Here we developed a rapid seed infection assay to measure the virulence of 121 diverse P. syringae isolates on common bean (Phaseolus vulgaris). This collection includes P. syringae phylogroup 2 (PG2) bean isolates (pathovar syringae) that cause bacterial spot disease and P. syringae phylogroup 3 (PG3) bean isolates (pathovar phaseolicola) that cause the more serious halo blight disease. We found that bean isolates in general were significantly more virulent on bean than non-bean isolates and observed no significant virulence difference between the PG2 and PG3 bean isolates. However, when we compared virulence within PGs we found that PG3 bean isolates were significantly more virulent than PG3 non-bean isolates, while there was no significant difference in virulence between PG2 bean and non-bean isolates. These results indicate that PG3 strains have a higher level of host specificity than PG2 strains. We then employed machine learning to investigate if we could use genomic data to predict virulence on bean. We used gradient boosted decision trees to model the virulence using whole genome kmers, type III secreted effector kmers, and the presence/absence of type III effectors and phytotoxins. Our model performed best using whole genome data and was able to predict virulence with high accuracy (mean absolute error = 0.05). Finally, we functionally validated the model by predicting virulence for 16 strains and found that 15 (94%) had virulence levels within the bounds of estimated predictions. This study demonstrates the power of machine learning for predicting host specific adaptation and strengthens the hypothesis that P. syringae PG2 strains have evolved a different lifestyle than other P. syringae strains. AUTHOR SUMMARYPseudomonas syringae is a genetically diverse Gammaproteobacterial species complex responsible for numerous agronomically important crop diseases. Strains in the P. syringae species complex are frequently categorized into pathovars depending on pathogenic characteristics such as host of isolation and disease symptoms. Common bean pathogens from P. syringae are known to cause two major diseases: the halo blight disease, which is characterized by large necrotic lesions surrounded by a chlorotic zone or halo of yellow tissue; and the bacterial spot disease, which is characterized by brown leaf spots. While halo blight can cause serious crop losses, bacterial spot disease is generally of minor agronomic concern. The application of statistical genetic and machine learning approaches to genomic data has greatly increased our power to identify genes underlying traits of interest, such as host specificity. Machine learning models can be used to predict outcomes from new samples or to identify the genetic feature(s) that carry the most importance when predicting a particular phenotype. Here, we implemented a rapid method for screening a proxy of virulence for P. syringae isolates on common bean, and used this screen to assess virulence of P. syringae strains on bean. We found that halo blight pathogens display a stronger degree of host specificity compared to brown spot pathogens, and that genomic kmers and virulence factors can be used to predict the virulence of P. syringae isolates on bean using machine learning models.

genomics↗

bioRxiv2020

Evolutionary and structural analyses of SARS-CoV-2 D614G spike protein mutation now documented worldwide

The COVID-19 pandemic, caused by the Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2), was declared on March 11, 2020 by the World Health Organization. As of the 31st of May, 2020, there have been more than 6 million COVID-19 cases diagnosed worldwide and over 370,000 deaths, according to Johns Hopkins. Thousands of SARS-CoV-2 strains have been sequenced to date, providing a valuable opportunity to investigate the evolution of the virus on a global scale. We performed a phylogenetic analysis of over 1,225 SARS-CoV-2 genomes spanning from late December 2019 to mid-March 2020. We identified a missense mutation, D614G, in the spike protein of SARS-CoV-2, which has emerged as a predominant clade in Europe (954 of 1,449 (66%) sequences) and is spreading worldwide (1,237 of 2,795 (44%) sequences). Molecular dating analysis estimated the emergence of this clade around mid-to-late January (10 - 25 January) 2020. We also applied structural bioinformatics to assess D614G potential impact on the virulence and epidemiology of SARS-CoV-2. In silico analyses on the spike protein structure suggests that the mutation is most likely neutral to protein function as it relates to its interaction with the human ACE2 receptor. The lack of clinical metadata available prevented our investigation of association between viral clade and disease severity phenotype. Future work that can leverage clinical outcome data with both viral and human genomic diversity is needed to monitor the pandemic.

bioinformatics↗

bioRxiv2019

Discovery and characterization of a Gram-positive Pel polysaccharide biosynthetic gene cluster

Our understanding of the biofilm matrix components utilized by Gram-positive bacteria, and the signalling pathways that regulate their production are largely unknown. In a companion study, we developed a computational pipeline for the unbiased identification of homologous bacterial operons and applied this algorithm to the analysis of synthase-dependent exopolysaccharide biosynthetic systems (https://doi.org/10.1101/769745). Here, we explore the finding that many species of Gram-positive bacteria have operons with similarity to the Pseudomonas aeruginosa pel locus. Our characterization of the pelDEADAFG operon from Bacillus cereus ATCC 10987, presented herein, demonstrates that this locus is required for biofilm formation and produces a polysaccharide structurally similar to Pel. We show that the degenerate GGDEF domain of the B. cereus PelD ortholog binds cyclic-3,5-dimeric guanosine monophosphate (c-di-GMP), and that this binding is required for biofilm formation. Finally, we identify a diguanylate cyclase, CdgF, and a c-di-GMP phosphodiesterase, CdgE, that reciprocally regulate the production of Pel. The discovery of this novel c-di-GMP regulatory circuit significantly contributes to our limited understanding of c-di-GMP signalling in Gram-positive organisms. Furthermore, conservation of the core pelDEADAFG locus amongst many species of Bacilli, Clostridia, Streptococci, and Actinobacteria suggests that Pel may be a common biofilm matrix component in many Gram-positive bacteria.\n\nAuthor summaryThe Pel polysaccharide is required for biofilm formation in P. aeruginosa and we have previously found that the genes necessary for biosynthesis of this polymer are broadly distributed across Gram-negative bacteria. Herein, we show that many species of Gram-positive bacteria also possess Pel biosynthetic genes and demonstrate that these genes are used Bacillus cereus for biofilm formation. We show that Pel production in B. cereus is regulated by c-di-GMP and have identified two enzymes, a diguanylate cyclase, CdgF, and a phosphodiesterase, CdgE, that control the levels of this bacterial signalling molecule. While Pel production in B. cereus also requires the binding of c-di-GMP to the receptor PelD, the divergence of this protein in Streptococci suggests a c-di-GMP independent mechanism of regulation is used in this species. The discovery of a Pel biosynthetic gene cluster in Gram-positive bacteria and our characterization of the pel operon in B. cereus suggests that Pel is a widespread biofilm component across all bacteria.

microbiology↗

bioRxiv2019

A systematic pipeline for classifying bacterial operons reveals the evolutionary landscape of biofilm machineries

In bacterial functionally related genes comprising metabolic pathways and protein complexes are frequently encoded in operons and are widely conserved across phylogenetically diverse species. The evolution of these operon-encoded processes is affected by diverse mechanisms such gene duplication, loss, rearrangement, and horizontal transfer. These mechanisms can result in functional diversification of gene-families, increasing the potential evolution of novel biological pathways, and serves to adapt pre-existing pathways to the requirements of particular environments. Despite the fundamental importance that these mechanisms play in bacterial environmental adaptation, a systematic approach for studying the evolution of operon organization is lacking. Herein, we present a novel method to study the evolution of operons based on phylogenetic clustering of operon-encoded protein families and genomic-proximity network visualizations of operon architectures. We applied this approach to study the evolution of the synthase dependent exopolysaccharide (EPS) biosynthetic systems: cellulose, acetylated-cellulose, poly-{beta}-1,6-N-acetyl-D-glucosamine (PNAG), Pel, and alginate. These polymers have important roles in biofilm formation, antibiotic tolerance, and as virulence factors in opportunistic pathogens. Our approach reveals the complex evolutionary landscape of EPS machineries, and enabled operons to be classified into evolutionarily distinct lineages. Cellulose operons show phyla-specific operon lineages resulting from gene loss, rearrangement, and the acquisition of accessory loci, and the occurrence of whole-operon duplications arising through horizonal gene transfer. Our evolutionary-based classification also distinguishes between the evolution of PNAG production between Gram-negative and Gram-positive bacteria on the basis of structural and functional evolution of the acetylation modification domains shared by PgaB and IcaB loci, respectively. We also predict several pel-like operon lineages in Gram-positive bacteria, and demonstrate in our companion paper (BIORXIV/2019/768473) that Bacillus cereus produces a Pel-dependent biofilm that is regulated by cyclic-3,5-dimeric guanosine monophosphate (c-di-GMP).\n\nAUTHOR SUMMARYIn bacterial genomes biological processes are frequently encoded as operons of co-transcribed neighbouring genes belonging to diverse protein families. Therefore, studying the evolution of bacterial operons provides valuable insight into understanding the biological roles of genes involved in environmental adaptation. However, no systematic approach has yet been devised to examine both the evolutionary relationships of operon encoded genes and the evolution of operon organization as a whole. To address this challenge, we present a novel method to study operon evolution by integrating phylogenetic tree based clustering and genomic-context networks. We apply this approach to perform the first systematic survey of all known synthase dependent bacterial biofilm machineries, demonstrating the generalizability of our approach for operons of diverse size, protein family composition, and species distribution. Our approach is able to identify distinct biofilm operon clades across phylogenetically diverse bacteria, resulting from gene rearrangement, duplication, loss, fusion, and horizontal gene transfer. We also elucidate different evolutionary trajectories of Gram-negative and Gram-positive biofilm production machineries, and in a companion paper (BIORXIV/2019/768473) present the experimental validation of a novel mode of biofilm production in a subset of Gram-positive bacteria.

bioinformatics↗