The use of gene clusters to infer functional coupling

Accurate Identification of Periplasmic Urea-binding Proteins by Structure- and Genome Context-assisted Functional Analysis

ABC transporters are ancient and ubiquitous nutrient transport systems in bacteria and play a central role in defining lifestyles. Periplasmic solute-binding proteins (SBPs) are components that deliver ligands to their translocation machinery. SBPs have diversified to bind a wide range of ligands with high specificity and affinity. However, accurate assignment of cognate ligands remains a challenging problem in SBPs. Urea metabolism plays an important role in the nitrogen cycle; anthropogenic sources account for more than half of global nitrogen fertilizer. We report identification of urea-binding proteins within a large SBP sequence family that encodes diverse functions. By combining genetic linkage between SBPs, ABC transporter components, enzymes or transcription factors, we accurately identified cognate ligands, as we verified experimentally by biophysical characterization of ligand binding and crystallographic determination of the urea complex of a thermostable urea-binding homolog. Using three-dimensional structure information, these functional assignments were extrapolated to other members in the sequence family lacking genetic linkage information, which revealed that only a fraction bind urea. Using the same combined approaches, we also inferred that other family members bind various short-chain amides, aliphatic amino acids (leucine, isoleucine, valine), γ-aminobutyrate, and as yet unknown ligands. Comparative structural analysis revealed structural adaptations that encode diversification in these SBPs. Systematic assignment of ligands to SBP sequence families is key to understanding bacterial lifestyles, and also provides a rich source of biosensors for clinical and environmental analysis, such as the thermostable urea-binding protein identified here.

Horizontal gene transfer from chloroplast to mitochondria of seagrasses in the yellow-Bohai seas

Seagrasses are ideal for studying plant adaptation to marine environments. In this study, the mitochondrial (mt) and chloroplast (cp) genomes of Ruppia sinensis were sequenced. The results showed an extensive gene loss in seagrasses, including a complete loss of cp-rpl19 genes in Zosteraceae, most cp-ndh genes in Hydrocharitaceae, and mt-rpl and mt-rps genes in all seagrasses, except for the mt-rpl16 gene in Phyllospadix iwatensis. Notably, most ribosomal protein genes were lost in the mt and cp genomes. The deleted cp genes were not transferred to the mt genomes through horizontal gene transfer. Additionally, a significant DNA transfer between seagrass organelles was found, with the mt genomes of Zostera containing numerous sequences from the cp genome. Rearrangement analyses revealed an unreported inversion of the cp genome in R. sinensis. Moreover, four positively selected genes (atp8, nad5, atp4, and ccmFn) and five variable regions (matR, atp4, atp8, rps7, and ccmFn) were identified.

A roadmap to the molecular human linking multiomics with population traits and diabetes subtypes

In-depth multiomic phenotyping provides molecular insights into complex physiological processes and their pathologies. Here, we report on integrating 18 diverse deep molecular phenotyping (omics-) technologies applied to urine, blood, and saliva samples from 391 participants of the multiethnic diabetes Qatar Metabolomics Study of Diabetes (QMDiab). Using 6,304 quantitative molecular traits with 1,221,345 genetic variants, methylation at 470,837 DNA CpG sites, and gene expression of 57,000 transcripts, we determine (1) within-platform partial correlations, (2) between-platform mutual best correlations, and (3) genome-, epigenome-, transcriptome-, and phenome-wide associations. Combined into a molecular network of > 34,000 statistically significant trait-trait links in biofluids, our study portrays "The Molecular Human". We describe the variances explained by each omics in the phenotypes (age, sex, BMI, and diabetes state), platform complementarity, and the inherent correlation structures of multiomics data. Further, we construct multi-molecular network of diabetes subtypes. Finally, we generated an open-access web interface to "The Molecular Human" ( http://comics.metabolomix.com ), providing interactive data exploration and hypotheses generation possibilities.

Comprehensive blueprint of Salmonella genomic plasticity identifies hotspots for pathogenicity genes

Understanding the dynamic evolution of Salmonella is vital for effective bacterial infection management. This study explores the role of the flexible genome, organised in regions of genomic plasticity (RGP), in shaping the pathogenicity of Salmonella lineages. Through comprehensive genomic analysis of 12,244 Salmonella spp. genomes covering 2 species, 6 subspecies, and 46 serovars, we uncover distinct integration patterns of pathogenicity-related gene clusters into RGP, challenging traditional views of gene distribution. These RGP exhibit distinct preferences for specific genomic spots, and the presence or absence of such spots across Salmonella lineages profoundly shapes strain pathogenicity. RGP preferences are guided by conserved flanking genes surrounding integration spots, implicating their involvement in regulatory networks and functional synergies with integrated gene clusters. Additionally, we emphasise the multifaceted contributions of plasmids and prophages to the pathogenicity of diverse Salmonella lineages. Overall, this study provides a comprehensive blueprint of the pathogenicity potential of Salmonella. This unique insight identifies genomic spots in nonpathogenic lineages that hold the potential for harbouring pathogenicity genes, providing a foundation for predicting future adaptations and developing targeted strategies against emerging human pathogenic strains.

Homoeologs in Allopolyploids: Navigating Redundancy as Both an Evolutionary Opportunity and a Technical Challenge-A Transcriptomics Perspective

Allopolyploidy in plants involves the merging of two or more distinct parental genomes into a single nucleus, a significant evolutionary process in the plant kingdom. Transcriptomic analysis provides invaluable insights into allopolyploid plants by elucidating the fate of duplicated genes, revealing evolutionary novelties and uncovering their environmental adaptations. By examining gene expression profiles, scientists can discern how duplicated genes have evolved to acquire new functions or regulatory roles. This process often leads to the development of novel traits and adaptive strategies that allopolyploid plants leverage to thrive in diverse ecological niches. Understanding these molecular mechanisms not only enhances our appreciation of the genetic complexity underlying allopolyploidy but also underscores their importance in agriculture and ecosystem resilience. However, transcriptome profiling is challenging due to genomic redundancy, which is further complicated by the presence of multiple chromosomes sets and the variations among homoeologs and allelic genes. Prior to transcriptome analysis, sub-genome phasing and homoeology inference are essential for obtaining a comprehensive view of gene expression. This review aims to clarify the terminology in this field, identify the most challenging aspects of transcriptome analysis, explain their inherent difficulties, and suggest reliable analytic strategies. Furthermore, bulk RNA-seq is highlighted as a primary method for studying allopolyploid gene expression, focusing on critical steps like read mapping and normalization in differential gene expression analysis. This approach effectively captures gene expression from both parental genomes, facilitating a comprehensive analysis of their combined profiles. Its sensitivity in detecting low-abundance transcripts allows for subtle differences between parental genomes to be identified, crucial for understanding regulatory dynamics and gene expression balance in allopolyploids.

Comparative Transcriptomics to Identify RNA Writers and Erasers in Microalgae

Epitranscriptomics is considered as a new regulatory step in eukaryotes for developmental processes and stress responses. The aim of this study was, for the first time, to identify RNA methyltransferase (writers) and demethylase (erasers) in four investigated species, i.e., the dinoflagellates Alexandrium tamutum and Amphidinium carterae, the diatom Cylindrotheca closterium, and the green alga Tetraselmis suecica. As query sequences for the enzymatic classes of interest, we selected those ones that were previously detected in marine plants, evaluating their expression upon nutrient starvation stress exposure. The hypothesis was that upon stress exposure, the activation/deactivation of specific writers and erasers may occur. In microalgae, we found almost all plant writers and erasers (ALKBH9B, ALKBH10B, MTB, and FIP37), except for three writers (MTA, VIRILIZER, and HAKAI). A sequence similarity search by scanning the corresponding genomes confirmed their presence. Thus, we concluded that the three writer sequences were lacking from the studied transcriptomes probably because they were not expressed in those experimental conditions, rather than a real lack of these genes from their genomes. This study showed that some of them were expressed only in specific culturing conditions. We also investigated their expression in other culturing conditions (i.e., nitrogen depletion, phosphate depletion, and Zinc addition at two different concentrations) in A. carterae, giving new insights into their possible roles in regulating gene expression upon stress.

Spatial proximity and gene function: a new dimension in prokaryotic gene association network analysis with 3D-GeneNet

Understanding the biological functions and processes of genes, particularly those not yet characterized, is crucial for advancing molecular biology and identifying therapeutic targets. The hypothesis guiding this study is that the 3D proximity of genes correlates with their functional interactions and relevance in prokaryotes. We introduced 3D-GeneNet, an innovative software tool that utilizes high-throughput sequencing data from chromosome conformation capture techniques and integrates topological metrics to construct gene association networks. Through a series of comparative analyses focused on spatial versus linear distances, we explored various dimensions such as topological structure, functional enrichment levels, distribution patterns of linear distances among gene pairs, and the area under the receiver operating characteristic curve by utilizing model organism Escherichia coli K-12. Furthermore, 3D-GeneNet was shown to maintain good accuracy compared to multiple algorithms (neighbourhood, co-occurrence, coexpression, and fusion) across multiple bacteria, including E. coli, Brucella abortus, and Vibrio cholerae. In addition, the accuracy of 3D-GeneNet's prediction of long-distance gene interactions was identified by bacterial two-hybrid assays on E. coli K-12 MG1655, where 3D-GeneNet not only increased the accuracy of linear genomic distance tripled but also achieved 60% accuracy by running alone. Finally, it can be concluded that the applicability of 3D-GeneNet will extend to various bacterial forms, including Gram-negative, Gram-positive, single-, and multi-chromosomal bacteria through Hi-C sequencing and analysis. Such findings highlight the broad applicability and significant promise of this method in the realm of gene association network. 3D-GeneNet is freely accessible at https://github.com/gaoyuanccc/3D-GeneNet.

OrthoRefine: automated enhancement of prior ortholog identification via synteny

BACKGROUND

Identifying orthologs continues to be an early and imperative step in genome analysis but remains a challenging problem. While synteny (conservation of gene order) has previously been used independently and in combination with other methods to identify orthologs, applying synteny in ortholog identification has yet to be automated in a user-friendly manner. This desire for automation and ease-of-use led us to develop OrthoRefine, a standalone program that uses synteny to refine ortholog identification.

RESULTS

We developed OrthoRefine to improve the detection of orthologous genes by implementing a look-around window approach to detect synteny. We tested OrthoRefine in tandem with OrthoFinder, one of the most used software for identification of orthologs in recent years. We evaluated improvements provided by OrthoRefine in several bacterial and a eukaryotic dataset. OrthoRefine efficiently eliminates paralogs from orthologous groups detected by OrthoFinder. Using synteny increased specificity and functional ortholog identification; additionally, analysis of BLAST e-value, phylogenetics, and operon occurrence further supported using synteny for ortholog identification. A comparison of several window sizes suggested that smaller window sizes (eight genes) were generally the most suitable for identifying orthologs via synteny. However, larger windows (30 genes) performed better in datasets containing less closely related genomes. A typical run of OrthoRefine with ~ 10 bacterial genomes can be completed in a few minutes on a regular desktop PC.

CONCLUSION

OrthoRefine is a simple-to-use, standalone tool that automates the application of synteny to improve ortholog detection. OrthoRefine is particularly efficient in eliminating paralogs from orthologous groups delineated by standard methods.

AnnoView enables large-scale analysis, comparison, and visualization of microbial gene neighborhoods

The analysis and comparison of gene neighborhoods is a powerful approach for exploring microbial genome structure, function, and evolution. Although numerous tools exist for genome visualization and comparison, genome exploration across large genomic databases or user-generated datasets remains a challenge. Here, we introduce AnnoView, a web server designed for interactive exploration of gene neighborhoods across the bacterial and archaeal tree of life. Our server offers users the ability to identify, compare, and visualize gene neighborhoods of interest from 30 238 bacterial genomes and 1672 archaeal genomes, through integration with the comprehensive Genome Taxonomy Database and AnnoTree databases. Identified gene neighborhoods can be visualized using pre-computed functional annotations from different sources such as KEGG, Pfam and TIGRFAM, or clustered based on similarity. Alternatively, users can upload and explore their own custom genomic datasets in GBK, GFF or CSV format, or use AnnoView as a genome browser for relatively small genomes (e.g. viruses and plasmids). Ultimately, we anticipate that AnnoView will catalyze biological discovery by enabling user-friendly search, comparison, and visualization of genomic data. AnnoView is available at http://annoview.uwaterloo.ca.

Possible Role of CHAD Proteins in Copper Resistance

Conserved Histidine Alpha-helical Domain (CHAD) proteins attached to the surface of polyphosphate (PolyP) have been studied in some bacteria and one archaeon. However, the activity of CHAD proteins is unknown beyond their interaction with PolyP granules. By using bioinformatic analysis, we report that several species of the biomining acidophilic bacteria contain orthologs of CHAD proteins with high sequence identity. Furthermore, the gene coding for the CHAD protein is in the same genetic context of the enzyme polyphosphate kinase (PPK), which is in charge of PolyP synthesis. Particularly, the group of ppk and CHAD genes is highly conserved. Metallosphaera sedula and other acidophilic archaea used in biomining also contain CHAD proteins. These archaea show high levels of identity in genes coding for a cluster having the same organization. Amongst these genes are chad and ppx. In general, both biomining bacteria and archaea contain high PolyP levels and are highly resistant to heavy metals. Therefore, the presence of this conserved genetic organization suggests a high relevance for their metabolism. It has been formerly reported that a crystallized CHAD protein contains a copper-binding site. Based on this previous knowledge, in the present report, it was determined that all analyzed CHAD proteins are very conserved at their structural level. In addition, it was found that the lack of YgiF, an Escherichia coli CHAD-containing protein, decreases copper resistance in this bacterium. This phenotype was not only complemented by transforming E. coli with YgiF but also by expressing CHAD from Acidithiobacillus ferrooxidans in it. Interestingly, the strains in which the possible copper-binding sites were mutated were also more metal sensitive. Based on these results, we propose that CHAD proteins are involved in copper resistance in microorganisms. These findings are very interesting and may eventually improve biomining operations in the future.

Peptidyl-tRNA hydrolase is the nascent chain release factor in bacterial ribosome-associated quality control

Rescuing stalled ribosomes often involves their splitting into subunits. In many bacteria, the resultant large subunits bearing peptidyl-tRNAs are processed by the ribosome-associated quality control (RQC) apparatus that extends the C termini of the incomplete nascent polypeptides with polyalanine tails to facilitate their degradation. Although the tailing mechanism is well established, it is unclear how the nascent polypeptides are cleaved off the tRNAs. We show that peptidyl-tRNA hydrolase (Pth), the known role of which has been to hydrolyze ribosome-free peptidyl-tRNA, acts in concert with RQC factors to release nascent polypeptides from large ribosomal subunits. Dislodging from the ribosomal catalytic center is required for peptidyl-tRNA hydrolysis by Pth. Nascent protein folding may prevent peptidyl-tRNA retraction and interfere with the peptide release. However, oligoalanine tailing makes the peptidyl-tRNA ester bond accessible for Pth-catalyzed hydrolysis. Therefore, the oligoalanine tail serves not only as a degron but also as a facilitator of Pth-catalyzed peptidyl-tRNA hydrolysis.

GeNLP: a web tool for NLP-based exploration and prediction of microbial gene function

SUMMARY

GeNLP is a web application that enables exploring microbial gene "semantics" and predictions of uncharacterized gene families based on their genomic context. It utilizes a pre-trained language model to uncover gene relationships and allows users to access and utilize the data as well as make their own predictions through an interactive interface.

AVAILABILITY AND IMPLEMENTATION

The web application is accessible from all browsers at: http://gnlp.bursteinlab.org/. All source codes are freely available from GitHub under the MIT license here: https://github.com/burstein-lab/genomic-nlp-server.

Beyond blast: enabling microbiologists to better extract literature, taxonomic distributions and gene neighbourhood information for protein families

Capturing the published corpus of information on all members of a given protein family should be an essential step in any study focusing on specific members of that family. Using a previously gathered dataset of more than 280 references mentioning a member of the DUF34 (NIF3/Ngg1-interacting Factor 3) family, we evaluated the efficiency of different databases and search tools, and devised a workflow that experimentalists can use to capture the most information published on members of a protein family in the least amount of time. To complement this workflow, web-based platforms allowing for the exploration of protein family members across sequenced genomes or for the analysis of gene neighbourhood information were reviewed for their versatility and ease of use. Recommendations that can be used for experimentalist users, as well as educators, are provided and integrated within a customized, publicly accessible Wiki.

OMA orthology in 2024: improved prokaryote coverage, ancestral and extant GO enrichment, a revamped synteny viewer and more in the OMA Ecosystem

In this update paper, we present the latest developments in the OMA browser knowledgebase, which aims to provide high-quality orthology inferences and facilitate the study of gene families, genomes and their evolution. First, we discuss the addition of new species in the database, particularly an expanded representation of prokaryotic species. The OMA browser now offers Ancestral Genome pages and an Ancestral Gene Order viewer, allowing users to explore the evolutionary history and gene content of ancestral genomes. We also introduce a revamped Local Synteny Viewer to compare genomic neighborhoods across both extant and ancestral genomes. Hierarchical Orthologous Groups (HOGs) are now annotated with Gene Ontology annotations, and users can easily perform extant or ancestral GO enrichments. Finally, we recap new tools in the OMA Ecosystem, including OMAmer for proteome mapping, OMArk for proteome quality assessment, OMAMO for model organism selection and Read2Tree for phylogenetic species tree construction from reads. These new features provide exciting opportunities for orthology analysis and comparative genomics. OMA is accessible at https://omabrowser.org.

Beyond Blast: Enabling Microbiologists to Better Extract Literature, Taxonomic Distributions and Gene Neighborhood Information for Protein Families

Capturing the published corpus of information on all members of a given protein family should be an essential step in any study focusing on specific members of that said family. Using a previously gathered dataset of more than 280 references mentioning a member of the DUF34 (NIF3/Ngg1-interacting Factor 3), we evaluated the efficiency of different databases and search tools, and devised a workflow that experimentalists can use to capture the most published information on members of a protein family in the least amount of time. To complement this workflow, web-based platforms allowing for the exploration of protein family members across sequenced genomes or for the analysis of gene neighborhood information were reviewed for their versatility and ease of use. Recommendations that can be used for experimentalist users, as well as educators, are provided and integrated within a customized, publicly accessible Wiki.

Hubs and Bottlenecks in Protein-Protein Interaction Networks

Protein-protein interaction networks (PPINs) represent the physical interactions among proteins in a cell. These interactions are critical in all cellular processes, including signal transduction, metabolic regulation, and gene expression. In PPINs, centrality measures are widely used to identify the most critical nodes. The two most commonly used centrality measures in networks are degree and betweenness centralities. Degree centrality is the number of connections a node has in the network, and betweenness centrality is the measure of the extent to which a node lies on the shortest paths between pairs of other nodes in the network. In PPINs, proteins with high degree and betweenness centrality are referred to as hubs and bottlenecks respectively. Hubs and bottlenecks are topologically and functionally essential proteins that play crucial roles in maintaining the network's structure and function. This article comprehensively reviews essential literature on hubs and bottlenecks, including their properties and functions.

Inferring Interaction Networks from Transcriptomic Data: Methods and Applications

Transcriptomic data is a treasure trove in modern molecular biology, as it offers a comprehensive viewpoint into the intricate nuances of gene expression dynamics underlying biological systems. This genetic information must be utilized to infer biomolecular interaction networks that can provide insights into the complex regulatory mechanisms underpinning the dynamic cellular processes. Gene regulatory networks and protein-protein interaction networks are two major classes of such networks. This chapter thoroughly investigates the wide range of methodologies used for distilling insightful revelations from transcriptomic data that include association-based methods (based on correlation among expression vectors), probabilistic models (using Bayesian and Gaussian models), and interologous methods. We reviewed different approaches for evaluating the significance of interactions based on the network topology and biological functions of the interacting molecules and discuss various strategies for the identification of functional modules. The chapter concludes with highlighting network-based techniques of prioritizing key genes, outlining the centrality-based, diffusion- based, and subgraph-based methods. The chapter provides a meticulous framework for investigating transcriptomic data to uncover assembly of complex molecular networks for their adaptable analyses across a broad spectrum of biological domains.

A link between energy metabolism and plant host adaptation states in the two-spotted spider mite, Tetranychus urticae (Koch)

Energy metabolism is a highly conserved process that balances generation of cellular energy and maintenance of redox homeostasis. It consists of five interconnected pathways: glycolysis, tricarboxylic acid cycle, pentose phosphate, trans-sulfuration, and NAD+ biosynthesis pathways. Environmental stress rewires cellular energy metabolism. Type-2 diabetes is a well-studied energy metabolism rewiring state in human pancreatic β-cells where glucose metabolism is uncoupled from insulin secretion. The two-spotted spider mite, Tetranychus urticae (Koch), exhibits a remarkable ability to adapt to environmental stress. Upon transfer to unfavourable plant hosts, mites experience extreme xenobiotic stress that dramatically affects their survivorship and fecundity. However, within 25 generations, mites adapt to the xenobiotic stress and restore their fitness. Mites' ability to withstand long-term xenobiotic stress raises a question of their energy metabolism states during host adaptation. Here, we compared the transcriptional responses of five energy metabolism pathways between host-adapted and non-adapted mites while using responses in human pancreatic islet donors to model these pathways under stress. We found that non-adapted mites and human pancreatic β-cells responded in a similar manner to host plant transfer and diabetogenic stress respectively, where redox homeostasis maintenance was favoured over energy generation. Remarkably, we found that upon host-adaptation, mite energy metabolic states were restored to normal. These findings suggest that genes involved in energy metabolism can serve as molecular markers for mite host-adaptation.

Unraveling the functional dark matter through global metagenomics

Metagenomes encode an enormous diversity of proteins, reflecting a multiplicity of functions and activities1,2. Exploration of this vast sequence space has been limited to a comparative analysis against reference microbial genomes and protein families derived from those genomes. Here, to examine the scale of yet untapped functional diversity beyond what is currently possible through the lens of reference genomes, we develop a computational approach to generate reference-free protein families from the sequence space in metagenomes. We analyse 26,931 metagenomes and identify 1.17 billion protein sequences longer than 35 amino acids with no similarity to any sequences from 102,491 reference genomes or the Pfam database3. Using massively parallel graph-based clustering, we group these proteins into 106,198 novel sequence clusters with more than 100 members, doubling the number of protein families obtained from the reference genomes clustered using the same approach. We annotate these families on the basis of their taxonomic, habitat, geographical and gene neighbourhood distributions and, where sufficient sequence diversity is available, predict protein three-dimensional models, revealing novel structures. Overall, our results uncover an enormously diverse functional space, highlighting the importance of further exploring the microbial functional dark matter.

Elucidating the CodY regulon in Staphylococcus aureus USA300 substrains TCH1516 and LAC

CodY is a conserved broad-acting transcription factor that regulates the expression of genes related to amino acid metabolism and virulence in Gram-positive bacteria. Here, we performed the first in vivo determination of CodY target genes using a novel CodY monoclonal antibody in methicillin-resistant Staphylococcus aureus (MRSA) USA300. Our results showed (i) the same 135 CodY promoter binding sites regulating the 165 target genes identified in two closely related virulent S. aureus USA300 TCH1516 and LAC strains; (ii) the differential binding intensity for the same target genes under the same conditions was due to sequence differences in the same CodY-binding site in the two strains; (iii) a CodY regulon comprising 72 target genes that are differentially regulated relative to a CodY deletion strain, representing genes that are mainly involved in amino acid transport and metabolism, inorganic ion transport and metabolism, transcription and translation, and virulence, all based on transcriptomic data; and (iv) CodY systematically regulated central metabolic flux to generate branched-chain amino acids (BCAAs) by mapping the CodY regulon onto a genome-scale metabolic model of S. aureus. Our study performed the first system-level analysis of CodY in two closely related USA300 TCH1516 and LAC strains, revealing new insights into the similarities and differences of CodY regulatory roles between the closely related strains. IMPORTANCE With the increasing availability of whole-genome sequences for many strains within the same pathogenic species, a comparative analysis of key regulators is needed to understand how the different strains uniquely coordinate metabolism and expression of virulence. To successfully infect the human host, Staphylococcus aureus USA300 relies on the transcription factor CodY to reorganize metabolism and express virulence factors. While CodY is a known key transcription factor, its target genes are not characterized on a genome-wide basis. We performed a comparative analysis to describe the transcriptional regulation of CodY between two dominant USA300 strains. This study motivates the characterization of common pathogenic strains and an evaluation of the possibility of developing specialized treatments for major strains circulating in the population.

Emergence of the fungal immune system

Investigation of fungal biology has been frequently motivated by the fact that many fungal species are important plant and animal pathogens. Such efforts have contributed significantly toward our understanding of fungal pathogenic lifestyles (virulence factors and strategies) and the interplay with host immune systems. In parallel, work on fungal allorecognition systems leading to the characterization of fungal regulated cell death determinants and pathways, has been instrumental for the emergent concept of fungal immunity. The uncovered evolutionary trans-kingdom parallels between fungal regulated cell death pathways and innate immune systems incite us to reflect further on the concept of a fungal immune system. Here, I briefly review key findings that have shaped the fungal immunity paradigm, providing a perspective on what I consider its most glaring knowledge gaps. Undertaking to fill such gaps would establish firmly the fungal immune system inside the broader field of comparative immunology.

Differential global distribution of marine picocyanobacteria gene clusters reveals distinct niche-related adaptive strategies

The ever-increasing number of available microbial genomes and metagenomes provides new opportunities to investigate the links between niche partitioning and genome evolution in the ocean, especially for the abundant and ubiquitous marine picocyanobacteria Prochlorococcus and Synechococcus. Here, by combining metagenome analyses of the Tara Oceans dataset with comparative genomics, including phyletic patterns and genomic context of individual genes from 256 reference genomes, we show that picocyanobacterial communities thriving in different niches possess distinct gene repertoires. We also identify clusters of adjacent genes that display specific distribution patterns in the field (eCAGs) and are thus potentially involved in the same metabolic pathway and may have a key role in niche adaptation. Several eCAGs are likely involved in the uptake or incorporation of complex organic forms of nutrients, such as guanidine, cyanate, cyanide, pyrimidine, or phosphonates, which might be either directly used by cells, for example for the biosynthesis of proteins or DNA, or degraded to inorganic nitrogen and/or phosphorus forms. We also highlight the enrichment of eCAGs involved in polysaccharide capsule biosynthesis in Synechococcus populations thriving in both nitrogen- and phosphorus-depleted areas vs. low-iron (Fe) regions, suggesting that the complexes they encode may be too energy-consuming for picocyanobacteria thriving in the latter areas. In contrast, Prochlorococcus populations thriving in Fe-depleted areas specifically possess an alternative respiratory terminal oxidase, potentially involved in the reduction of Fe(III) to Fe(II). Altogether, this study provides insights into how phytoplankton communities populate oceanic ecosystems, which is relevant to understanding their capacity to respond to ongoing climate change.

Genetic and Structural Diversity of Prokaryotic Ice-Binding Proteins from the Central Arctic Ocean

Ice-binding proteins (IBPs) are a group of ecologically and biotechnologically relevant enzymes produced by psychrophilic organisms. Although putative IBPs containing the domain of unknown function (DUF) 3494 have been identified in many taxa of polar microbes, our knowledge of their genetic and structural diversity in natural microbial communities is limited. Here, we used samples from sea ice and sea water collected in the central Arctic Ocean as part of the MOSAiC expedition for metagenome sequencing and the subsequent analyses of metagenome-assembled genomes (MAGs). By linking structurally diverse IBPs to particular environments and potential functions, we reveal that IBP sequences are enriched in interior ice, have diverse genomic contexts and cluster taxonomically. Their diverse protein structures may be a consequence of domain shuffling, leading to variable combinations of protein domains in IBPs and probably reflecting the functional versatility required to thrive in the extreme and variable environment of the central Arctic Ocean.

Chlorine redox chemistry is widespread in microbiology

Chlorine is abundant in cells and biomolecules, yet the biology of chlorine oxidation and reduction is poorly understood. Some bacteria encode the enzyme chlorite dismutase (Cld), which detoxifies chlorite (ClO2-) by converting it to chloride (Cl-) and molecular oxygen (O2). Cld is highly specific for chlorite and aside from low hydrogen peroxide activity has no known alternative substrate. Here, we reasoned that because chlorite is an intermediate oxidation state of chlorine, Cld can be used as a biomarker for oxidized chlorine species. Cld was abundant in metagenomes from various terrestrial habitats. About 5% of bacterial and archaeal genera contain a microorganism encoding Cld in its genome, and within some genera Cld is highly conserved. Cld has been subjected to extensive horizontal gene transfer. Genes found to have a genetic association with Cld include known genes for responding to reactive chlorine species and uncharacterized genes for transporters, regulatory elements, and putative oxidoreductases that present targets for future research. Cld was repeatedly co-located in genomes with genes for enzymes that can inadvertently reduce perchlorate (ClO4-) or chlorate (ClO3-), indicating that in situ (per)chlorate reduction does not only occur through specialized anaerobic respiratory metabolisms. The presence of Cld in genomes of obligate aerobes without such enzymes suggested that chlorite, like hypochlorous acid (HOCl), might be formed by oxidative processes within natural habitats. In summary, the comparative genomics of Cld has provided an atlas for a deeper understanding of chlorine oxidation and reduction reactions that are an underrecognized feature of biology.

The draft genome of Andean Rhodopseudomonas sp. strain AZUL predicts genome plasticity and adaptation to chemical homeostasis

The genus Rhodopseudomonas comprises purple non-sulfur bacteria with extremely versatile metabolisms. Characterization of several strains revealed that each is a distinct ecotype highly adapted to its specific micro-habitat. Here we present the sequencing, genomic comparison and functional annotation of AZUL, a Rhodopseudomonas strain isolated from a high altitude Andean lagoon dominated by extreme conditions and fluctuating levels of chemicals. Average nucleotide identity (ANI) analysis of 39 strains of this genus showed that the genome of AZUL is 96.2% identical to that of strain AAP120, which suggests that they belong to the same species. ANI values also show clear separation at the species level with the rest of the strains, being more closely related to R. palustris. Pangenomic analyses revealed that the genus Rhodopseudomonas has an open pangenome and that its core genome represents roughly 5 to 12% of the total gene repertoire of the genus. Functional annotation showed that AZUL has genes that participate in conferring genome plasticity and that, in addition to sharing the basal metabolic complexity of the genus, it is also specialized in metal and multidrug resistance and in responding to nutrient limitation. Our results also indicate that AZUL might have evolved to use some of the mechanisms involved in resistance as redox reactions for bioenergetic purposes. Most of those features are shared with strain AAP120, and mainly involve the presence of additional orthologs responsible for the mentioned processes. Altogether, our results suggest that AZUL, one of the few bacteria from its habitat with a sequenced genome, is highly adapted to the extreme and changing conditions that constitute its niche.

Ribosomal RNA operons define a central functional compartment in the Streptomyces chromosome

Streptomyces are prolific producers of specialized metabolites with applications in medicine and agriculture. These bacteria possess a large linear chromosome genetically compartmentalized: core genes are grouped in the central part, while terminal regions are populated by poorly conserved genes. In exponentially growing cells, chromosome conformation capture unveiled sharp boundaries formed by ribosomal RNA (rrn) operons that segment the chromosome into multiple domains. Here we further explore the link between the genetic distribution of rrn operons and Streptomyces genetic compartmentalization. A large panel of genomes of species representative of the genus diversity revealed that rrn operons and core genes form a central skeleton, the former being identifiable from their core gene environment. We implemented a new nomenclature for Streptomyces genomes and trace their rrn-based evolutionary history. Remarkably, rrn operons are close to pericentric inversions. Moreover, the central compartment delimited by rrn operons has a very dense, nearly invariant core gene content. Finally, this compartment harbors genes with the highest expression levels, regardless of gene persistence and distance to the origin of replication. Our results highlight that rrn operons are structural boundaries of a central functional compartment prone to transcription in Streptomyces.

Deciphering the mitochondrial genome of Hemerocallis citrina (Asphodelaceae) using a combined assembly and comparative genomic strategy

Hemerocallis citrina is a perennial herbaceous plant that is dedicated to mothers in Chinese culture and is widely distributed across the country. As a popular species with a long history of cultivation and utilization, it is renowned for its remarkable edible and medicinal value. In this study, we integrated Illumina short-read and Oxford Nanopore long-read sequencing to generate a complete mitochondrial genome (mitogenome) assembly of H. citrina. The H. citrina mitogenome has a multiple chromosomal structure consisting of three circular molecules that are 45,607 bp, 239,991 bp, and 182,864 bp long. We correspondingly annotated 66 genes, comprising 45 protein-coding genes (PCGs), 17 tRNA genes, and 4 rRNA genes. Comparative analysis of gene organization indicated that six syntenic gene clusters were conserved in the mitogenomes of the compared plants. The investigation of repeat content revealed repeat-rich nature of the H. citrina mitogenome, for which plentiful dispersed repeats were characterized to correlate with the size of the mitogenome. The codon usage behavior disclosed that Leucine (Leu) and Serine (Ser) were the most preferred amino acids in H. citrina, and nearly all of the codons with relative synonymous codon usage (RSCU) values greater than 1 showed the preference of A or T ending. Moreover, we inferred a total of 679 RNA editing sites in all mitochondrial PCGs, which presented perfect C-to-U types and tended to lead to the alteration of internal codons. Subsequent selective pressure analysis showed that the majority of the PCGs had undergone evolutionary negative selections, with atp9 in particular undergoing strong stabilizing selection, reflecting its indispensable function in mitogenomes. According to the phylogenetic analysis, H. citrina is close to the species Allium cepa (Amaryllidaceae) and Asparagus officinalis (Asparagaceae) in evolutionary terms. Overall, this project presents the first complete mitogenome of H. citrina, which could provide a reference genome for the comprehensive exploration of the Asphodelaceae family and can facilitate further genomic breeding and evolutionary research on this medicine-food homologous plant.

An updated gene regulatory network reconstruction of multidrug-resistant Pseudomonas aeruginosa CCBH4851

BACKGROUND

Healthcare-associated infections due to multidrug-resistant (MDR) bacteria such as Pseudomonas aeruginosa are significant public health issues worldwide. A system biology approach can help understand bacterial behaviour and provide novel ways to identify potential therapeutic targets and develop new drugs. Gene regulatory networks (GRN) are examples of in silico representation of interaction between regulatory genes and their targets.

OBJECTIVES

In this work, we update the MDR P. aeruginosa CCBH4851 GRN reconstruction and analyse and discuss its structural properties.

METHODS

We based this study on the gene orthology inference methodology using the reciprocal best hit method. The P. aeruginosa CCBH4851 genome and GRN, published in 2019, and the P. aeruginosa PAO1 GRN, published in 2020, were used for this update reconstruction process.

FINDINGS

Our result is a GRN with a greater number of regulatory genes, target genes, and interactions compared to the previous networks, and its structural properties are consistent with the complexity of biological networks and the biological features of P. aeruginosa.

MAIN CONCLUSIONS

Here, we present the largest and most complete version of P. aeruginosa GRN published to this date, to the best of our knowledge.

Deciphering microbial gene function using natural language processing

Revealing the function of uncharacterized genes is a fundamental challenge in an era of ever-increasing volumes of sequencing data. Here, we present a concept for tackling this challenge using deep learning methodologies adopted from natural language processing (NLP). We repurpose NLP algorithms to model "gene semantics" based on a biological corpus of more than 360 million microbial genes within their genomic context. We use the language models to predict functional categories for 56,617 genes and find that out of 1369 genes associated with recently discovered defense systems, 98% are inferred correctly. We then systematically evaluate the "discovery potential" of different functional categories, pinpointing those with the most genes yet to be characterized. Finally, we demonstrate our method's ability to discover systems associated with microbial interaction and defense. Our results highlight that combining microbial genomics and language models is a promising avenue for revealing gene functions in microbes.

Purifying selection enduringly acts on the sequence evolution of highly expressed proteins in Escherichia coli

The evolutionary speed of a protein sequence is constrained by its expression level, with highly expressed proteins evolving relatively slowly. This negative correlation between expression levels and evolutionary rates (known as the E–R anticorrelation) has already been widely observed in past macroevolution between species from bacteria to animals. However, it remains unclear whether this seemingly general law also governs recent evolution, including past and de novo, within a species. However, the advent of genomic sequencing and high-throughput phenotyping, particularly for bacteria, has revealed fundamental gaps between the 2 evolutionary processes and has provided empirical data opposing the possible underlying mechanisms which are widely believed. These conflicts raise questions about the generalization of the E–R anticorrelation and the relevance of plausible mechanisms. To explore the ubiquitous impact of expression levels on molecular evolution and test the relevance of the possible underlying mechanisms, we analyzed the genome sequences of 99 strains of Escherichia coli for evolution within species in nature. We also analyzed genomic mutations accumulated under laboratory conditions as a model of de novo evolution within species. Here, we show that E–R anticorrelation is significant in both past and de novo evolution within species in E. coli. Our data also confirmed ongoing purifying selection on highly expressed genes. Ongoing selection included codon-level purifying selection, supporting the relevance of the underlying mechanisms. However, the impact of codon-level purifying selection on the constraints in evolution within species might be smaller than previously expected from evolution between species.

Complete mitochondrial genome sequence and comparative analysis of the cultivated yellow nutsedge

As a monocotyledonous plant in family Cyperaceae, yellow nutsedge (Cyperus esculentus L.) is unique in accumulating a substantial amount of oil in underground tubers and provides a model system for studying oil accumulation in nonseed tissues. However, no data on the mitochondrial and nuclear genome sequences of this species are available, which greatly limits our understanding of its evolutionary characteristics and some essential biological mechanisms. In the present study, we report the first complete mitochondrial genome sequence of the cultivated yellow nutsedge. The analysis of the genome showed that the yellow nutsedge mitochondrial genome is 1,002,696 bp in size and encodes 62 genes consisting of 36 protein-coding genes (PCGs), 20 transfer RNA (tRNA) genes, and six ribosomal RNA (rRNA) genes. Compared with other angiosperms, yellow nutsedge mitochondrial genome contains much higher percentage of noncoding sequences (95.36%). Sixteen plastid-derived fragments were identified to be strongly associated with mitochondrial genes including one intact plastid-related gene (ndhH). Comparative analysis with seven other sequenced plant mitochondrial genomes revealed that two syntenic gene clusters, rps3-rpl16 and rps12-nad3, are highly conserved in all plant mitochondrial genomes, and the mitochondrial genome of yellow nutsedge is more similar to those of monocotyledons in the gene order. Phylogenetic analysis based on 13 shared protein-encoding genes in eight plant species showed that yellow nutsedge is evolutionarily more closely related to monocotyledonary species. Overall, the species-specific features of the cultivated yellow nutsedge mitochondrial genome provide additional information for the evolutionary and comparative genomic studies in the yellow nutsedge and other Cyperus species of the Cyperaceae family.

Defining hierarchical protein interaction networks from spectral analysis of bacterial proteomes

Cellular behaviors emerge from layers of molecular interactions: proteins interact to form complexes, pathways, and phenotypes. We show that hierarchical networks of protein interactions can be defined from the statistical pattern of proteome variation measured across thousands of diverse bacteria and that these networks reflect the emergence of complex bacterial phenotypes. Our results are validated through gene-set enrichment analysis and comparison to existing experimentally derived databases. We demonstrate the biological utility of our approach by creating a model of motility in Pseudomonas aeruginosa and using it to identify a protein that affects pilus-mediated motility. Our method, SCALES (Spectral Correlation Analysis of Layered Evolutionary Signals), may be useful for interrogating genotype-phenotype relationships in bacteria.

Gene function prediction in five model eukaryotes exclusively based on gene relative location through machine learning

The function of most genes is unknown. The best results in automated function prediction are obtained with machine learning-based methods that combine multiple data sources, typically sequence derived features, protein structure and interaction data. Even though there is ample evidence showing that a gene's function is not independent of its location, the few available examples of gene function prediction based on gene location rely on sequence identity between genes of different organisms and are thus subjected to the limitations of the relationship between sequence and function. Here we predict thousands of gene functions in five model eukaryotes (Saccharomyces cerevisiae, Caenorhabditis elegans, Drosophila melanogaster, Mus musculus and Homo sapiens) using machine learning models exclusively trained with features derived from the location of genes in the genomes to which they belong. Our aim was not to obtain the best performing method to automated function prediction but to explore the extent to which a gene's location can predict its function in eukaryotes. We found that our models outperform BLAST when predicting terms from Biological Process and Cellular Component Ontologies, showing that, at least in some cases, gene location alone can be more useful than sequence to infer gene function.

Genome-wide identification of cold-tolerance genes and functional analysis of IbbHLH116 gene in sweet potato

Sweet potato (Ipomoea batatas L.) originated from South America; therefore, it is vulnerable to low temperature. Here, the evolutionary analysis of 22 cold-responsive genes in 35 plant species revealed that the identified MYC-type basic helix-loop-helix (bHLH) transcription factors exhibit diverse structures. We found that the number of bHLH gene family members was significantly lower than that of cold-tolerant species. We further systematically evaluated the gene structure, promoter analysis, synteny analysis, and expression pattern of 28 bHLH gene family members in sweet potato. The basic helix-loop-helix protein 116 (IbbHLH116) has the closest phylogeny to the AtICE1 protein of A. thaliana. However, the IbbHLH116 protein from cold-tolerant variety FS18 showed a 37.90% of sequence homology with AtICE1 protein. Subcellular localization analysis showed that IbbHLH116 is localized in the nucleus. The transcripts of IbbHLH116 were highly accumulated in cold-tolerant genotype FS18, particularly in new leaves and stems, compared to the cold-sensitive genotype NC1 under cold stress. Overexpression of IbbHLH116 in the wild type (Col-0) A. thaliana significantly enhanced cold tolerance in transgenic plants by regulating activities of oxidative protective enzymes, such as peroxidase (POD), superoxide dismutase (SOD), and the contents of malondialdehyde (MDA), proline and soluble proteins. Moreover, overexpression of IbbHLH116 in ice1 mutant A. thaliana fully rescued the cold-sensitive phenotype by promoting the expression of C-repeat binding factors 3 (CBF3). Overexpression of IbbHLH116 in the sweet potato callus also induced the expression of CBF3 under low temperature. These results imply that IbbHLH116 can perform the function of the ICE1 gene in conferring cold tolerance in sweet potato.

Cross-Species Analysis Reveals Co-Expressed Genes Regulating Antler Development in Cervidae

Antlers constitute an interesting model for basic research in regenerative biology. Despite decades of being studied, much is still unknown about the genes related to antler development. Here, we utilized both the genome and antlerogenic periosteum (AP) transcriptome data of four deer species to reveal antler-related genes through cross-species comparative analysis. The results showed that the global gene expression pattern matches the status of antler phenotypes, supporting the fact that the genes expressed in the AP may be related to antler phenotypes. The upregulated genes of the AP in three-antlered deer showed evidence of co-expression, and their protein sequences were highly conserved. These genes were growth related and likely participated in antler development. In contrast, the upregulated genes in antler-less deer (Chinese water deer) were involved mainly in organismal death and growth failure, possibly related to the loss of antlers during evolution. Overall, this study demonstrates that the co-expressed genes in antlered deer may regulate antler development.

Turtle ants harbor metabolically versatile microbiomes with conserved functions across development and phylogeny

Gut bacterial symbionts can support animal nutrition by facilitating digestion and providing valuable metabolites. However, changes in symbiotic roles between immature and adult stages are not well documented, especially in ants. Here, we explored the metabolic capabilities of microbiomes sampled from herbivorous turtle ant (Cephalotes sp.) larvae and adult workers through (meta)genomic screening and in vitro metabolic assays. We reveal that larval guts harbor bacterial symbionts with impressive metabolic capabilities, including catabolism of plant and fungal recalcitrant dietary fibers and energy-generating fermentation. Additionally, several members of the specialized adult gut microbiome, sampled downstream of an anatomical barrier that dams large food particles, show a conserved potential to depolymerize many dietary fibers. Symbionts from both life stages have the genomic capacity to recycle nitrogen and synthesize amino acids and B-vitamins. With help of their gut symbionts, including several bacteria likely acquired from the environment, turtle ant larvae may aid colony digestion and contribute to colony-wide nitrogen, B-vitamin and energy budgets. In addition, the conserved nature of the digestive capacities among adult-associated symbionts suggests that nutritional ecology of turtle ant colonies has long been shaped by specialized, behaviorally-transferred gut bacteria with over 45 million years of residency.

Phyletic Distribution and Diversification of the Phage Shock Protein Stress Response System in Bacteria and Archaea

The PspA protein domain is found in all domains of life, highlighting its central role in Psp networks. To date, all insights into the core functions of Psp responses derive mainly from protein network blueprints representing only three bacterial phyla.

The Quest for Orthologs orthology benchmark service in 2022

The Orthology Benchmark Service (https://orthology.benchmarkservice.org) is the gold standard for orthology inference evaluation, supported and maintained by the Quest for Orthologs consortium. It is an essential resource to compare existing and new methods of orthology inference (the bedrock for many comparative genomics and phylogenetic analysis) over a standard dataset and through common procedures. The Quest for Orthologs Consortium is dedicated to maintaining the resource up to date, through regular updates of the Reference Proteomes and increasingly accessible data through the OpenEBench platform. For this update, we have added a new benchmark based on curated orthology assertion from the Vertebrate Gene Nomenclature Committee, and provided an example meta-analysis of the public predictions present on the platform.

Computational Network Inference for Bacterial Interactomics

Since the large-scale experimental characterization of protein–protein interactions (PPIs) is not possible for all species, several computational PPI prediction methods have been developed that harness existing data from other species. While PPI network prediction has been extensively used in eukaryotes, microbial network inference has lagged behind. However, bacterial interactomes can be built using the same principles and techniques; in fact, several methods are better suited to bacterial genomes. These predicted networks allow systems-level analyses in species that lack experimental interaction data. This review describes the current network inference and analysis techniques and summarizes the use of computationally-predicted microbial interactomes to date.

MeWRKY IIas, Subfamily Genes of WRKY Transcription Factors From Cassava, Play an Important Role in Disease Resistance

Cassava (Manihot esculenta Crantz) is an important tropical crop for food, fodder, and energy. Cassava bacterial blight (CBB) caused by Xanthomonas axonopodis pv. manihotis (Xam) occurs in all cassava growing regions and threatens global cassava production. WRKY transcription factor family plays the essential roles during plant growth, development, and abiotic or biotic stress. Particularly, previous studies have revealed the important role of the group IIa WRKY genes in plant disease resistance. However, a comprehensive analysis of group IIa subfamily in cassava is still missing. Here, we identified 102 WRKY members, which were classified into three groups, I, II, and III. Transient expression showed that six MeWRKY IIas were localized in the nucleus. MeWRKY IIas transcripts accumulated significantly in response to SA, JA, and Xam. Overexpression of MeWRKY27 and MeWRKY33 in Arabidopsis enhanced its resistance to Pst DC3000. In contrast, silencing of MeWRKY27 and MeWRKY33 in cassava enhanced its susceptibility to Xam. Co-expression network analysis showed that different downstream genes are regulated by different MeWRKY IIa members. The functional analysis of downstream genes will provide clues for clarifying molecular mechanism of cassava disease resistance. Collectively, our results suggest that MeWRKY IIas are regulated by SA, JA signaling, and coordinate response to Xam infection.

Considerations for Constructing a Protein Sequence Database for Metaproteomics

Mass spectrometry-based metaproteomics has emerged as a prominent technique for interrogating the functions of specific organisms in microbial communities, in addition to total community function. Identifying proteins by mass spectrometry requires matching mass spectra of fragmented peptide ions to a database of protein sequences corresponding to the proteins in the sample. This sequence database determines which protein sequences can be identified from the measurement, and as such the taxonomic and functional information that can be inferred from a metaproteomics measurement. Thus, the construction of the protein sequence database directly impacts the outcome of any metaproteomics study. Several factors, such as source of sequence information and database curation, need to be considered during database construction to maximize accurate protein identifications traceable to the species of origin. In this review, we provide an overview of existing strategies for database construction and the relevant studies that have sought to test and validate these strategies. Based on this review of the literature and our experience we provide a decision tree and best practices for choosing and implementing database construction strategies.

Response to substrate limitation by a marine sulfate-reducing bacterium

Sulfate-reducing microorganisms (SRM) in subsurface sediments live under constant substrate and energy limitation, yet little is known about how they adapt to this mode of life. We combined controlled chemostat cultivation and transcriptomics to examine how the marine sulfate reducer, Desulfobacterium autotrophicum, copes with substrate (sulfate or lactate) limitation. The half-saturation uptake constant (K_m) for lactate was 1.2 µM, which is the first value reported for a marine SRM, while the K_m for sulfate was 3 µM. The measured residual lactate concentration in our experiments matched values observed in situ in marine sediments, supporting a key role of SRM in the control of lactate concentrations. Lactate limitation resulted in complete lactate oxidation via the Wood-Ljungdahl pathway and differential overexpression of genes involved in uptake and metabolism of amino acids as an alternative carbon source. D. autotrophicum switched to incomplete lactate oxidation, rerouting carbon metabolism in response to sulfate limitation. The estimated free energy was significantly lower during sulfate limitation (-28 to -33 kJ mol^-1 sulfate), suggesting that the observed metabolic switch is under thermodynamic control. Furthermore, we detected the upregulation of putative sulfate transporters involved in either high or low affinity uptake in response to low or high sulfate concentration.

Undervalued Pseudo-nifH Sequences in Public Databases Distort Metagenomic Insights into Biological Nitrogen Fixers

Nitrogen fixation, a distinct process incorporating the inactive atmospheric nitrogen into the active biological processes, has been a major topic in biological and geochemical studies. Currently, insights into diversity and distribution of nitrogen-fixing microbes are dependent upon homology-based analyses of nitrogenase genes, especially the nifH gene, which are broadly conserved in nitrogen-fixing microbes. Here, we report the pitfall of using nifH as a marker of microbial nitrogen fixation. We exhaustively analyzed genomes in RefSeq (231,908 genomes) and KEGG (6,509 genomes) and cooccurrence and gene order patterns of nitrogenase genes (including nifH) therein. Up to 20% of nifH-harboring genomes lacked nifD and nifK, which encode essential subunits of nitrogenase, within 10 coding sequences upstream or downstream of nifH or on the same genome. According to a phenotypic database of prokaryotes, no species and strains harboring only nifH possess nitrogen-fixing activities, which shows that these nifH genes are "pseudo"-nifH genes. Pseudo-nifH sequences mainly belong to anaerobic microbes, including members of the class Clostridia and methanogens. We also detected many pseudo-nifH reads from metagenomic sequences of anaerobic environments such as animal guts, wastewater, paddy soils, and sediments. In some samples, pseudo-nifH overwhelmed the number of "true" nifH reads by 50% or 10 times. Because of the high sequence similarity between pseudo- and true-nifH, pronounced amounts of nifH-like reads were not confidently classified. Overall, our results encourage reconsideration of the conventional use of nifH for detecting nitrogen-fixing microbes, while suggesting that nifD or nifK would be a more reliable marker. IMPORTANCE Nitrogen-fixing microbes affect biogeochemical cycling, agricultural productivity, and microbial ecosystems, and their distributions have been investigated intensively using genomic and metagenomic sequencing. Currently, insights into nitrogen fixers in the environment have been acquired by homology searches against nitrogenase genes, particularly the nifH gene, in public databases. Here, we report that public databases include a significant amount of incorrectly annotated nifH sequences (pseudo-nifH). We exhaustively investigated the genomic structures of nifH-harboring genomes and found hundreds of pseudo-nifH sequences in RefSeq and KEGG. Over half of these pseudo-nifH sequences belonged to members of the class Clostridia, which is supposed to be a prominent nitrogen-fixing clade. We also found that the abundance of nitrogen fixers in metagenomes could be overestimated by 1.5 to >10 times due to pseudo-nifH recorded in public databases. Our results encourage reconsideration of the prevalent use of nifH as a marker of nitrogen-fixing microbes.

Computational modelling of chromosomally clustering protein domains in bacteria

BACKGROUND

In bacteria, genes with related functions-such as those involved in the metabolism of the same compound or in infection processes-are often physically close on the genome and form groups called clusters. The enrichment of such clusters over various distantly related bacteria can be used to predict the roles of genes of unknown function that cluster with characterised genes. There is no obvious rule to define a cluster, given their variability in size and intergenic distances, and the definition of what comprises a "gene", since genes can gain and lose domains over time. Protein domains can cluster within a gene, or in adjacent genes of related function, and in both cases these are chromosomally clustered. Here, we model the distances between pairs of protein domain coding regions across a wide range of bacteria and archaea via a probabilistic two component mixture model, without imposing arbitrary thresholds in terms of gene numbers or distances.

RESULTS

We trained our model using matched gene ontology terms to label functionally related pairs and assess the stability of the parameters of the model across 14,178 archaeal and bacterial strains. We found that the parameters of our mixture model are remarkably stable across bacteria and archaea, except for endosymbionts and obligate intracellular pathogens. Obligate pathogens have smaller genomes, and although they vary, on average do not show noticeably different clustering distances; the main difference in the parameter estimates is that a far greater proportion of the genes sharing ontology terms are clustered. This may reflect that these genomes are enriched for complexes encoded by clustered core housekeeping genes, as a proportion of the total genes. Given the overall stability of the parameter estimates, we then used the mean parameter estimates across the entire dataset to investigate which gene ontology terms are most frequently associated with clustered genes.

CONCLUSIONS

Given the stability of the mixture model across species, it may be used to predict bacterial gene clusters that are shared across multiple species, in addition to giving insights into the evolutionary pressures on the chromosomal locations of genes in different species.

Genome-wide identification and expression profiling of the COBRA-like genes reveal likely roles in stem strength in rapeseed (Brassica napus L.)

The COBRA-like (COBL) genes play key roles in cell anisotropic expansion and the orientation of microfibrils. Mutations in these genes cause the brittle stem and induce pathogen responsive phenotypes in Arabidopsis and several crop plants. In this study, an in silico genome-wide analysis was performed to identify the COBL family members in Brassica. We identified 44, 20 and 23 COBL genes in B. napus and its diploid progenitor species B. rapa and B. oleracea, respectively. All the predicted COBL genes were phylogenetically clustered into two groups: the AtCOB group and the AtCOBL7 group. The conserved chromosome locations of COBLs in Arabidopsis and Brassica, together with clustering, indicated that the expansion of the COBL gene family in B. napus was primarily attributable to whole-genome triplication. Among the BnaCOBLs, 22 contained all the conserved motifs and derived from 9 of 12 subgroups. RNA-seq analysis was used to determine the tissue preferential expression patterns of various subgroups. BnaCOBL9, BnaCOBL35 and BnaCOBL41 were highly expressed in stem with high-breaking resistance, which implies these AtCOB subgroup members may be involved in stem development and stem breaking resistance of rapeseed. Our results of this study may help to elucidate the molecular properties of the COBRA gene family and provide informative clues for high stem-breaking resistance studies.

In silico characterization, docking, and simulations to understand host-pathogen interactions in an effort to enhance crop production in date palms

Food safety remains a significant challenge despite the growth and development in agricultural research and the advent of modern biotechnological and agricultural tools. Though the agriculturist struggles to aid the growing population's needs, many pathogen-based plant diseases by their direct impact on cell division and tissue development have led to the loss of tons of food crops every year. Though there are many conventional and traditional methods to overcome this issue, the amount and time spend are huge. Scientists have developed systems biology tools to study the root cause of the problem and rectify it. Host-pathogen protein interactions (HPIs) have a promising role in identifying the pathogens' strategy to conquer the host organism. In this paper, the interactions between the host Rhynchophorus ferrugineus (an invasive wood-boring pest that destroys palm) and the pathogens Proteus mirabilis, Serratia marcescens, and Klebsiella pneumoniae are comprehensively studied using protein-protein interactions, molecular docking, and followed by 200 ns molecular dynamic simulations. This study elucidates the structural and functional basis of these proteins leading towards better plant health, production, and reliability.

Trends in biological data integration for the selection of enzymes and transcription factors related to cellulose and hemicellulose degradation in fungi

Fungi are key players in biotechnological applications. Although several studies focusing on fungal diversity and genetics have been performed, many details of fungal biology remain unknown, including how cellulolytic enzymes are modulated within these organisms to allow changes in main plant cell wall compounds, cellulose and hemicellulose, and subsequent biomass conversion. With the advent and consolidation of DNA/RNA sequencing technology, different types of information can be generated at the genomic, structural and functional levels, including the gene expression profiles and regulatory mechanisms of these organisms, during degradation-induced conditions. This increase in data generation made rapid computational development necessary to deal with the large amounts of data generated. In this context, the origination of bioinformatics, a hybrid science integrating biological data with various techniques for information storage, distribution and analysis, was a fundamental step toward the current state-of-the-art in the postgenomic era. The possibility of integrating biological big data has facilitated exciting discoveries, including identifying novel mechanisms and more efficient enzymes, increasing yields, reducing costs and expanding opportunities in the bioprocess field. In this review, we summarize the current status and trends of the integration of different types of biological data through bioinformatics approaches for biological data analysis and enzyme selection.

KinOrtho: a method for mapping human kinase orthologs across the tree of life and illuminating understudied kinases

BACKGROUND

Protein kinases are among the largest druggable family of signaling proteins, involved in various human diseases, including cancers and neurodegenerative disorders. Despite their clinical relevance, nearly 30% of the 545 human protein kinases remain highly understudied. Comparative genomics is a powerful approach for predicting and investigating the functions of understudied kinases. However, an incomplete knowledge of kinase orthologs across fully sequenced kinomes severely limits the application of comparative genomics approaches for illuminating understudied kinases. Here, we introduce KinOrtho, a query- and graph-based orthology inference method that combines full-length and domain-based approaches to map one-to-one kinase orthologs across 17 thousand species.

RESULTS

Using multiple metrics, we show that KinOrtho performed better than existing methods in identifying kinase orthologs across evolutionarily divergent species and eliminated potential false positives by flagging sequences without a proper kinase domain for further evaluation. We demonstrate the advantage of using domain-based approaches for identifying domain fusion events, highlighting a case between an understudied serine/threonine kinase TAOK1 and a metabolic kinase PIK3C2A with high co-expression in human cells. We also identify evolutionary fission events involving the understudied OBSCN kinase domains, further highlighting the value of domain-based orthology inference approaches. Using KinOrtho-defined orthologs, Gene Ontology annotations, and machine learning, we propose putative biological functions of several understudied kinases, including the role of TP53RK in cell cycle checkpoint(s), the involvement of TSSK3 and TSSK6 in acrosomal vesicle localization, and potential functions for the ULK4 pseudokinase in neuronal development.

CONCLUSIONS

In sum, KinOrtho presents a novel query-based tool to identify one-to-one orthologous relationships across thousands of proteomes that can be applied to any protein family of interest. We exploit KinOrtho here to identify kinase orthologs and show that its well-curated kinome ortholog set can serve as a valuable resource for illuminating understudied kinases, and the KinOrtho framework can be extended to any protein-family of interest.