Phylogenetic Clustering of Genes Reveals Shared Evolutionary Trajectories and Putative Gene Functions

PeHVA22 gene family in passion fruit (Passiflora edulis): initial characterization and expression profiling diversity

Passion fruit, an economically valuable fruit crop, is highly vulnerable to adverse climate conditions. The HVA22 genes, recognized as abscisic acid (ABA) and stress-inducible, play vital roles in stress response and growth regulation in diverse eukaryotic organisms. Here, six HVA22 genes were firstly identified in passion fruit genome and all predicted to be localized within the endoplasmic reticulum. Phylogenetic analyses showed that all PeHVA22s were divided into four subgroups. The gene structural features of PeHVA22 genes clustered in the same subgroup were relatively conserved, while the gene structure characteristics of PeHVA22s from different subgroups varied significantly. PeHVA22A and PeHVA22C closely clustered with barley HVA22 in Group II, were also induced by ABA and drought stress treatment, suggesting conserved roles similar to barley HVA22. Meanwhile, most PeHVA22s exhibited induced expression post-drought treatment but were suppressed under salt, low and high-temperature conditions, indicating a unique role in drought response. Additionally, PeHVA22s displayed tissue-specific expression patterns across diverse tissues, except for PeHVA22B which maybe a pseudogene. Notably, PeHVA22C, PeHVA22E, and PeHVA22F predominantly expressed in fruit, indicating their involvement in fruit development. Almost all PeHVA22s showed variable expression at different developmental stages of stamens or ovules, implying their roles in passion fruit's sexual reproduction. The intricate roles of PeHVA22s may result from diverse regulatory factors including transcription factors and CREs related to plant growth and development, hormone and stress responsiveness. These observations highlighted that PeHVA22s might play conserved roles in ABA response and drought stress tolerance, and also be participated in the regulation of passion fruit growth and floral development.

Using multi-scale genomics to associate poorly annotated genes with rare diseases

BACKGROUND

Next-generation sequencing (NGS) has significantly transformed the landscape of identifying disease-causing genes associated with genetic disorders. However, a substantial portion of sequenced patients remains undiagnosed. This may be attributed not only to the challenges posed by harder-to-detect variants, such as non-coding and structural variations but also to the existence of variants in genes not previously associated with the patient's clinical phenotype. This study introduces EvORanker, an algorithm that integrates unbiased data from 1,028 eukaryotic genomes to link mutated genes to clinical phenotypes.

METHODS

EvORanker utilizes clinical data, multi-scale phylogenetic profiling, and other omics data to prioritize disease-associated genes. It was evaluated on solved exomes and simulated genomes, compared with existing methods, and applied to 6260 knockout genes with mouse phenotypes lacking human associations. Additionally, EvORanker was made accessible as a user-friendly web tool.

RESULTS

In the analyzed exomic cohort, EvORanker accurately identified the "true" disease gene as the top candidate in 69% of cases and within the top 5 candidates in 95% of cases, consistent with results from the simulated dataset. Notably, EvORanker outperformed existing methods, particularly for poorly annotated genes. In the case of the 6260 knockout genes with mouse phenotypes, EvORanker linked 41% of these genes to observed human disease phenotypes. Furthermore, in two unsolved cases, EvORanker successfully identified DLGAP2 and LPCAT3 as disease candidates for previously uncharacterized genetic syndromes.

CONCLUSIONS

We highlight clade-based phylogenetic profiling as a powerful systematic approach for prioritizing potential disease genes. Our study showcases the efficacy of EvORanker in associating poorly annotated genes to disease phenotypes observed in patients. The EvORanker server is freely available at https://ccanavati.shinyapps.io/EvORanker/ .

Genome-wide identification and expression analysis of the KCS gene family in soybean (Glycine max) reveal their potential roles in response to abiotic stress

Very long chain fatty acids (VLCFAs) are fatty acids with chain lengths of 20 or more carbon atoms, which are the building blocks of various lipids that regulate developmental processes and plant stress responses. 3-ketoacyl-CoA synthase encoded by the KCS gene is the key rate-limiting enzyme in VLCFA biosynthesis, but the KCS gene family in soybean (Glycine max) has not been adequately studied thus far. In this study, 31 KCS genes (namely GmKCS1 - GmKCS31) were identified in the soybean genome, which are unevenly distributed on 14 chromosomes. These GmKCS genes could be phylogenetically classified into seven groups. A total of 27 paralogous GmKCS gene pairs were identified with their Ka/Ks ratios indicating that they had undergone purifying selection during soybean genome expansion. Cis-acting element analysis revealed that GmKCS promoters contained multiple hormone- and stress-responsive elements, indicating that GmKCS gene expression levels may be regulated by various developmental and environmental stimuli. Expression profiles derived from RNA-seq data and qRT-PCR experiments indicated that GmKCS genes were diversely expressed in different organs/tissues, and many GmKCS genes were found to be differentially expressed in the leaves under cold, heat, salt, and drought stresses, suggesting their critical role in soybean resistance to abiotic stress. These results provide fundamental information about the soybean KCS genes and will aid in their further functional elucidation and exploitation.

Detecting patterns of accessory genome coevolution in Staphylococcus aureus using data from thousands of genomes

Bacterial genomes exhibit widespread horizontal gene transfer, resulting in highly variable genome content that complicates the inference of genetic interactions. In this study, we develop a method for detecting coevolving genes from large datasets of bacterial genomes based on pairwise comparisons of closely related individuals, analogous to a pedigree study in eukaryotic populations. We apply our method to pairs of genes from the Staphylococcus aureus accessory genome of over 75,000 annotated gene families using a database of over 40,000 whole genomes. We find many pairs of genes that appear to be gained or lost in a coordinated manner, as well as pairs where the gain of one gene is associated with the loss of the other. These pairs form networks of rapidly coevolving genes, primarily consisting of genes involved in virulence, mechanisms of horizontal gene transfer, and antibiotic resistance, particularly the SCCmec complex. While we focus on gene gain and loss, our method can also detect genes that tend to acquire substitutions in tandem, or genotype-phenotype or phenotype-phenotype coevolution. Finally, we present the R package DeCoTUR that allows for the computation of our method.

Exploring the mobilome and resistome of Enterococcus faecium in a One Health context across two continents

Enterococcus faecium is a ubiquitous opportunistic pathogen that is exhibiting increasing levels of antimicrobial resistance (AMR). Many of the genes that confer resistance and pathogenic functions are localized on mobile genetic elements (MGEs), which facilitate their transfer between lineages. Here, features including resistance determinants, virulence factors and MGEs were profiled in a set of 1273 E. faecium genomes from two disparate geographic locations (in the UK and Canada) from a range of agricultural, clinical and associated habitats. Neither lineages of E. faecium, type A and B, nor MGEs are constrained by geographic proximity, but our results show evidence of a strong association of many profiled genes and MGEs with habitat. Many features were associated with a group of clinical and municipal wastewater genomes that are likely forming a new human-associated ecotype within type A. The evolutionary dynamics of E. faecium make it a highly versatile emerging pathogen, and its ability to acquire, transmit and lose features presents a high risk for the emergence of new pathogenic variants and novel resistance combinations. This study provides a workflow for MGE-centric surveillance of AMR in Enterococcus that can be adapted to other pathogens.

The Community Coevolution Model with Application to the Study of Evolutionary Relationships between Genes based on Phylogenetic Profiles

Organismal traits can evolve in a coordinated way, with correlated patterns of gains and losses reflecting important evolutionary associations. Discovering these associations can reveal important information about the functional and ecological linkages among traits. Phylogenetic profiles treat individual genes as traits distributed across sets of genomes and can provide a fine-grained view of the genetic underpinnings of evolutionary processes in a set of genomes. Phylogenetic profiling has been used to identify genes that are functionally linked, and to identify common patterns of lateral gene transfer in microorganisms. However, comparative analysis of phylogenetic profiles and other trait distributions should take into account the phylogenetic relationships among the organisms under consideration. Here we propose the Community Coevolution Model (CCM), a new coevolutionary model to analyze the evolutionary associations among traits, with a focus on phylogenetic profiles. In the CCM, traits are considered to evolve as a community with interactions, and the transition rate for each trait depends on the current states of other traits. Surpassing other comparative methods for pairwise trait analysis, CCM has the additional advantage of being able to examine multiple traits as a community to reveal more dependency relationships. We also develop a simulation procedure to generate phylogenetic profiles with correlated evolutionary patterns that can be used as benchmark data for evaluation purposes. A simulation study demonstrates that CCM is more accurate than other methods including the Jaccard Index and three tree-aware methods. The parameterization of CCM makes the interpretation of the relations between genes more direct, which leads to Darwin's scenario being identified easily based on the estimated parameters. We show that CCM is more efficient and fits real data better than other methods resulting in higher likelihood scores with fewer parameters. An examination of 3786 phylogenetic profiles across a set of 659 bacterial genomes highlights linkages between genes with common functions, including many patterns that would not have been identified under a non-phylogenetic model of common distribution. We also applied the CCM to 44 proteins in the well-studied Mitochondrial Respiratory Complex I and recovered associations that mapped well onto the structural associations that exist in the complex.

Comprehensive Genome-Wide Identification and Expression Profiling of Eceriferum (CER) Gene Family in Passion Fruit (Passiflora edulis) Under Fusarium kyushuense and Drought Stress Conditions

Plant surfaces are covered with cuticle wax and are the first barrier between a plant and environmental stresses. Eceriferum (CER) is an important gene family involved in wax biosynthesis and stress resistance. In this study, for the first time, 34 CER genes were identified in the passion fruit (Passiflora edulis) genome, and PeCER proteins varied in physicochemical properties. A phylogenetic tree was constructed and divided into seven clades to identify the evolutionary relationship with other plant species. Gene structure analyses revealed that conserved motifs ranged from 1 to 24, and that exons ranged from 1 to 29. The cis-element analysis provides insight into possible roles of PeCER genes in plant growth, development and stress responses. The syntenic analysis revealed that segmental (six gene pairs) and tandem (six gene pairs) gene duplication played an important role in the expansion of PeCER genes and underwent a strong purifying selection. In addition, 12 putative ped-miRNAs were identified to be targeting 16 PeCER genes, and PeCER6 was the most targeted by four miRNAs including ped-miR157a-5p, ped-miR164b-5p, ped-miR319b, and ped-miR319l. Potential transcription factors (TFs) such as ERF, AP2, MYB, and bZIP were predicted and visualized in a TF regulatory network interacting with PeCER genes. GO and KEGG annotation analysis revealed that PeCER genes were highly related to fatty acid, cutin, and wax biosynthesis, plant-pathogen interactions, and stress response pathways. The hypothesis that most PeCER proteins were predicted to localize to the plasma membrane was validated by transient expression assays of PeCER32 protein in onion epidermal cells. qRT-PCR expression results showed that most of the PeCER genes including PeCER1, PeCER11, PeCER15, PeCER17, and PeCER32 were upregulated under drought and Fusarium kyushuense stress conditions compared to controls. These findings provide a foundation for further studies on functions of PeCER genes to further facilitate the genetic modification of passion fruit wax biosynthesis and stress resistance.

A comparative whole-genome approach identifies bacterial traits for marine microbial interactions

Microbial interactions shape the structure and function of microbial communities with profound consequences for biogeochemical cycles and ecosystem health. Yet, most interaction mechanisms are studied only in model systems and their prevalence is unknown. To systematically explore the functional and interaction potential of sequenced marine bacteria, we developed a trait-based approach, and applied it to 473 complete genomes (248 genera), representing a substantial fraction of marine microbial communities. We identified genome functional clusters (GFCs) which group bacterial taxa with common ecology and life history. Most GFCs revealed unique combinations of interaction traits, including the production of siderophores (10% of genomes), phytohormones (3-8%) and different B vitamins (57-70%). Specific GFCs, comprising Alpha- and Gammaproteobacteria, displayed more interaction traits than expected by chance, and are thus predicted to preferentially interact synergistically and/or antagonistically with bacteria and phytoplankton. Linked trait clusters (LTCs) identify traits that may have evolved to act together (e.g., secretion systems, nitrogen metabolism regulation and B vitamin transporters), providing testable hypotheses for complex mechanisms of microbial interactions. Our approach translates multidimensional genomic information into an atlas of marine bacteria and their putative functions, relevant for understanding the fundamental rules that govern community assembly and dynamics.

Genome-Wide Identification and Expression Profiling of KCS Gene Family in Passion Fruit (Passiflora edulis) Under Fusarium kyushuense and Drought Stress Conditions

Plant and fruit surfaces are covered with cuticle wax and provide a protective barrier against biotic and abiotic stresses. Cuticle wax consists of very-long-chain fatty acids (VLCFAs) and their derivatives. β-Ketoacyl-CoA synthase (KCS) is a key enzyme in the synthesis of VLCFAs and provides a precursor for the synthesis of cuticle wax, but the KCS gene family was yet to be reported in the passion fruit (Passiflora edulis). In this study, thirty-two KCS genes were identified in the passion fruit genome and phylogenetically grouped as KCS1-like, FAE1-like, FDH-like, and CER6-like. Furthermore, thirty-one PeKCS genes were positioned on seven chromosomes, while one PeKCS was localized to the unassembled genomic scaffold. The cis-element analysis provides insight into the possible role of PeKCS genes in phytohormones and stress responses. Syntenic analysis revealed that gene duplication played a crucial role in the expansion of the PeKCS gene family and underwent a strong purifying selection. All PeKCS proteins shared similar 3D structures, and a protein-protein interaction network was predicted with known Arabidopsis proteins. There were twenty putative ped-miRNAs which were also predicted that belong to nine families targeting thirteen PeKCS genes. Gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) annotation results were highly associated with fatty acid synthase and elongase activity, lipid metabolism, stress responses, and plant-pathogen interaction. The highly enriched transcription factors (TFs) including ERF, MYB, Dof, C2H2, TCP, LBD, NAC, and bHLH were predicted in PeKCS genes. qRT-PCR expression analysis revealed that most PeKCS genes were highly upregulated in leaves including PeKCS2, PeKCS4, PeKCS8, PeKCS13, and PeKCS9 but not in stem and roots tissues under drought stress conditions compared with controls. Notably, most PeKCS genes were upregulated at 9th dpi under Fusarium kyushuense biotic stress condition compared to controls. This study provides a basis for further understanding the functions of KCS genes, improving wax and VLCFA biosynthesis, and improvement of passion fruit resistance.

TAGOPSIN: collating taxa-specific gene and protein functional and structural information

Background

The wealth of biological information available nowadays in public databases has triggered an unprecedented rise in multi-database search and data retrieval for obtaining detailed information about key functional and structural entities. This concerns investigations ranging from gene or genome analysis to protein structural analysis. However, the retrieval of interconnected data from a number of different databases is very often done repeatedly in an unsystematic way.

Results

Here, we present TAxonomy, Gene, Ontology, Protein, Structure INtegrated (TAGOPSIN), a command line program written in Java for rapid and systematic retrieval of select data from seven of the most popular public biological databases relevant to comparative genomics and protein structure studies. The program allows a user to retrieve organism-centred data and assemble them in a single data warehouse which constitutes a useful resource for several biological applications. TAGOPSIN was tested with a number of organisms encompassing eukaryotes, prokaryotes and viruses. For example, it successfully integrated data for about 17,000 UniProt entries of Homo sapiens and 21 UniProt entries of human coronavirus.

Conclusion

TAGOPSIN demonstrates efficient data integration whereby manipulation of interconnected data is more convenient than doing multi-database queries. The program facilitates for instance interspecific comparative analyses of protein-coding genes in a molecular evolutionary study, or identification of taxa-specific protein domains and three-dimensional structures. TAGOPSIN is available as a JAR file at https://github.com/ebundhoo/TAGOPSIN and is released under the GNU General Public License.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12859-021-04429-5.

An efficient, non-phylogenetic method for detecting genes sharing evolutionary signals in phylogenomic datasets

Assessing the compatibility between gene family phylogenies is a crucial and often computationally demanding step in many phylogenomic analyses. Here, we describe the Evolutionary Similarity Index (IES), a means to assess shared evolution between gene families using a weighted orthogonal distance regression model applied to sequence distances. The utilization of pairwise distance matrices circumvents comparisons between gene tree topologies, which are inherently uncertain and sensitive to evolutionary model choice, phylogenetic reconstruction artifacts, and other sources of error. Furthermore, IES enables the many-to-many pairing of multiple copies between similarly evolving gene families. This is done by selecting non-overlapping pairs of copies, one from each assessed family, and yielding the least sum of squared residuals. Analyses of simulated gene family data sets show that IES’s accuracy is on par with popular tree-based methods while also less susceptible to noise introduced by sequence alignment and evolutionary model fitting. Applying IES to an empirical data set of 1,322 genes from 42 archaeal genomes identified eight major clusters of gene families with compatible evolutionary trends. The most cohesive cluster consisted of 62 genes with compatible evolutionary signal, which occur as both single-copy and multiple homologs per genome; phylogenetic analysis of concatenated alignments from this cluster produced a tree closely matching previously published species trees for Archaea. Four other clusters are mainly composed of accessory genes with limited distribution among Archaea and enriched toward specific metabolic functions. Pairwise evolutionary distances obtained from these accessory gene clusters suggest patterns of interphyla horizontal gene transfer. An IES implementation is available at https://github.com/lthiberiol/evolSimIndex.

CladeOScope: functional interactions through the prism of clade-wise co-evolution

Mapping co-evolved genes via phylogenetic profiling (PP) is a powerful approach to uncover functional interactions between genes and to associate them with pathways. Despite many successful endeavors, the understanding of co-evolutionary signals in eukaryotes remains partial. Our hypothesis is that 'Clades', branches of the tree of life (e.g. primates and mammals), encompass signals that cannot be detected by PP using all eukaryotes. As such, integrating information from different clades should reveal local co-evolution signals and improve function prediction. Accordingly, we analyzed 1028 genomes in 66 clades and demonstrated that the co-evolutionary signal was scattered across clades. We showed that functionally related genes are frequently co-evolved in only parts of the eukaryotic tree and that clades are complementary in detecting functional interactions within pathways. We examined the non-homologous end joining pathway and the UFM1 ubiquitin-like protein pathway and showed that both demonstrated distinguished co-evolution patterns in specific clades. Our research offers a different way to look at co-evolution across eukaryotes and points to the importance of modular co-evolution analysis. We developed the 'CladeOScope' PP method to integrate information from 16 clades across over 1000 eukaryotic genomes and is accessible via an easy to use web server at http://cladeoscope.cs.huji.ac.il.

PhyloCorrelate: inferring bacterial gene-gene functional associations through large-scale phylogenetic profiling

MOTIVATION

Statistical detection of co-occurring genes across genomes, known as "phylogenetic profiling", is a powerful bioinformatic technique for inferring gene-gene functional associations. However, this can be a challenging task given the size and complexity of phylogenomic databases, difficulty in accounting for phylogenetic structure, inconsistencies in genome annotation, and substantial computational requirements.

RESULTS

We introduce PhyloCorrelate-a computational framework for gene co-occurrence analysis across large phylogenomic datasets. PhyloCorrelate implements a variety of co-occurrence metrics including standard correlation metrics and model-based metrics that account for phylogenetic history. By combining multiple metrics, we developed an optimized score that exhibits a superior ability to link genes with overlapping GO terms and KEGG pathways, enabling gene function prediction. Using genomic and functional annotation data from the Genome Taxonomy Database and AnnoTree, we performed all-by-all comparisons of gene occurrence profiles across the bacterial tree of life, totaling 154,217,052 comparisons for 28,315 genes across 27,372 bacterial genomes. All predictions are available in an online database, which instantaneously returns the top correlated genes for any PFAM, TIGRFAM, or KEGG query. In total, PhyloCorrelate detected 29,762 high confidence associations between bacterial gene/protein pairs, and generated functional predictions for 834 DUFs and proteins of unknown function.

AVAILABILITY

PhyloCorrelate is available as a web-server at phylocorrelate.uwaterloo.ca as well as an R package for analysis of custom datasets. We anticipate that PhyloCorrelate will be broadly useful as a tool for predicting function and interactions for gene families.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.