PANNZER-A practical tool for protein function prediction

Network analyses predict major regulators of resistance to early blight disease complex in tomato

BACKGROUND

Early blight and brown leaf spot are often cited as the most problematic pathogens of tomato in many agricultural regions. Their causal agents are Alternaria spp., a genus of Ascomycota containing numerous necrotrophic pathogens. Breeding programs have yielded quantitatively resistant commercial cultivars, but fungicide application remains necessary to mitigate the yield losses. A major hindrance to resistance breeding is the complexity of the genetic determinants of resistance and susceptibility. In the absence of sufficiently resistant germplasm, we sequenced the transcriptomes of Heinz 1706 tomatoes treated with strongly virulent and weakly virulent isolates of Alternaria spp. 3 h post infection. We expanded existing functional gene annotations in tomato and using network statistics, we analyzed the transcriptional modules associated with defense and susceptibility.

RESULTS

The induced responses are very distinct. The weakly virulent isolate induced a defense response of calcium-signaling, hormone responses, and transcription factors. These defense-associated processes were found in a single transcriptional module alongside secondary metabolite biosynthesis genes, and other defense responses. Co-expression and gene regulatory networks independently predicted several D clade ethylene response factors to be early regulators of the defense transcriptional module, as well as other transcription factors both known and novel in pathogen defense, including several JA-associated genes. In contrast, the strongly virulent isolate elicited a much weaker response, and a separate transcriptional module bereft of hormone signaling.

CONCLUSIONS

Our findings have predicted major defense regulators and several targets for downstream functional analyses. Combined with our improved gene functional annotation, they suggest that defense is achieved through induction of Alternaria-specific immune pathways, and susceptibility is mediated by modulating hormone responses. The implication of multiple specific clade D ethylene response factors and upregulation of JA-associated genes suggests that host defense in this pathosystem involves ethylene response factors to modulate jasmonic acid signaling.

Evolution of Orthonectida body plan

Orthonectida is an enigmatic group of animals with still uncertain phylogenetic position. Orthonectids parasitize various marine invertebrates. Their life cycle comprises a parasitic plasmodium and free-living males and females. Sexual individuals develop inside the plasmodium; after egress from the host they copulate in the external environment, and the larva, which has developed inside the female infects a new host. In a series of studied orthonectid species simplification of free-living sexual individuals can be clearly traced. The number of longitudinal and transverse muscle fibers is gradually reduced. In the nervous system, simplification is even more pronounced. The number of neurons constituting the ganglion is dramatically reduced from 200 in Rhopalura ophiocomae to 4-6 in Intoshia variabili. The peripheral nervous system undergoes gradual simplification as well. The morphological simplification is accompanied with genome reduction. However, not only genes are lost from the genome, it also undergoes compactization ensured by extreme reduction of intergenic distances, short intron sizes, and elimination of repetitive elements. The main trend in orthonectid evolution is simplification and miniaturization of free-living sexual individuals coupled with reduction and compactization of the genome.

GWAS for the identification of introgressed candidate genes of Sinapis alba with increased branching numbers in backcross lines of the allohexaploid Brassica

Plant architecture is a crucial determinant of crop yield. The number of primary (PB) and secondary branches (SB) is particularly significant in shaping the architecture of Indian mustard. In this study, we analyzed a panel of 86 backcross introgression lines (BCILs) derived from the first stable allohexaploid Brassicas with 170 Sinapis alba genome-specific SSR markers to identify associated markers with higher PB and SB through association mapping. The structure analysis revealed three subpopulations, i.e., P1, P2, and P3, in the association panel containing a total of 11, 33, and 42 BCILs, respectively. We identified five novel SSR markers linked to higher PB and SB. Subsequently, we explored the 20 kb up- and downstream regions of these SSR markers to predict candidate genes for improved branching and annotated them through BLASTN. As a result, we predicted 47 complete genes within the 40 kb regions of all trait-linked markers, among which 35 were identified as candidate genes for higher PB and SB numbers in BCILs. These candidate genes were orthologous to ANT, RAMOSUS, RAX, MAX, MP, SEU, REV, etc., branching genes. The remaining 12 genes were annotated for additional roles using BLASTP with protein databases. This study identified five novel S. alba genome-specific SSR markers associated with increased PB and SB, as well as 35 candidate genes contributing to plant architecture through improved branching numbers. To the best of our knowledge, this is the first report of introgressive genes for higher branching numbers in B. juncea from S. alba.

Mutual annotation-based prediction of protein domain functions with Domain2GO

Identifying unknown functional properties of proteins is essential for understanding their roles in both health and disease states. The domain composition of a protein can reveal critical information in this context, as domains are structural and functional units that dictate how the protein should act at the molecular level. The expensive and time-consuming nature of wet-lab experimental approaches prompted researchers to develop computational strategies for predicting the functions of proteins. In this study, we proposed a new method called Domain2GO that infers associations between protein domains and function-defining gene ontology (GO) terms, thus redefining the problem as domain function prediction. Domain2GO uses documented protein-level GO annotations together with proteins' domain annotations. Co-annotation patterns of domains and GO terms in the same proteins are examined using statistical resampling to obtain reliable associations. As a use-case study, we evaluated the biological relevance of examples selected from the Domain2GO-generated domain-GO term mappings via literature review. Then, we applied Domain2GO to predict unknown protein functions by propagating domain-associated GO terms to proteins annotated with these domains. For function prediction performance evaluation and comparison against other methods, we employed Critical Assessment of Function Annotation 3 (CAFA3) challenge datasets. The results demonstrated the high potential of Domain2GO, particularly for predicting molecular function and biological process terms, along with advantages such as producing interpretable results and having an exceptionally low computational cost. The approach presented here can be extended to other ontologies and biological entities to investigate unknown relationships in complex and large-scale biological data. The source code, datasets, results, and user instructions for Domain2GO are available at https://github.com/HUBioDataLab/Domain2GO. Additionally, we offer a user-friendly online tool at https://huggingface.co/spaces/HUBioDataLab/Domain2GO, which simplifies the prediction of functions of previously unannotated proteins solely using amino acid sequences.

Exploring the gene expression network involved in the heat stress response of a thermotolerant tomato genotype

BACKGROUND

The increase in temperatures due to the current climate change dramatically affects crop cultivation, resulting in yield losses and altered fruit quality. Tomato is one of the most extensively grown and consumed horticultural products, and although it can withstand a wide range of climatic conditions, heat stress can affect plant growth and development specially on the reproductive stage, severely influencing the final yield. In the present work, the heat stress response mechanisms of one thermotolerant genotype (E42) were investigated by exploring its regulatory gene network. This was achieved through a promoter analysis based on the identification of the heat stress elements (HSEs) mapping in the promoters, combined with a gene co-expression network analysis aimed at identifying interactions among heat-related genes.

RESULTS

Results highlighted 82 genes presenting HSEs in the promoter and belonging to one of the 52 gene networks obtained by the GCN analysis; 61 of these also interact with heat shock factors (Hsfs). Finally, a list of 13 candidate genes including two Hsfs, nine heat shock proteins (Hsps) and two GDSL esterase/lipase (GELPs) were retrieved by focusing on those E42 genes exhibiting HSEs in the promoters, interacting with Hsfs and showing variants, compared to Heinz reference genome, with HIGH and/or MODERATE impact on the translated protein. Among these, the Gene Ontology annotation analysis evidenced that only LeHsp100 (Solyc02g088610) belongs to a network specifically involved in the response to heat stress.

CONCLUSIONS

As a whole, the combination of bioinformatic analyses carried out on genomic and trascriptomic data available for tomato, together with polymorphisms detected in HS-related genes of the thermotolerant E42 allowed to determine a subset of candidate genes involved in the HS response in tomato. This study provides a novel approach in the investigation of abiotic stress response mechanisms and further studies will be conducted to validate the role of the highlighted genes.

Diploid genome assembly of the Malbec grapevine cultivar enables haplotype-aware analysis of transcriptomic differences underlying clonal phenotypic variation

To preserve their varietal attributes, established grapevine cultivars (Vitis vinifera L. ssp. vinifera) must be clonally propagated, due to their highly heterozygous genomes. Malbec is a France-originated cultivar appreciated for producing high-quality wines and is the offspring of cultivars Prunelard and Magdeleine Noire des Charentes. Here, we have built a diploid genome assembly of Malbec, after trio binning of PacBio long reads into the two haploid complements inherited from either parent. After haplotype-aware deduplication and corrections, complete assemblies for the two haplophases were obtained with a very low haplotype switch-error rate (<0.025). The haplophase alignment identified > 25% of polymorphic regions. Gene annotation including RNA-seq transcriptome assembly and ab initio prediction evidence resulted in similar gene model numbers for both haplophases. The annotated diploid assembly was exploited in the transcriptomic comparison of four clonal accessions of Malbec that exhibited variation in berry composition traits. Analysis of the ripening pericarp transcriptome using either haplophases as a reference yielded similar results, although some differences were observed. Particularly, among the differentially expressed genes identified only with the Magdeleine-inherited haplotype as reference, we observed an over-representation of hypothetically hemizygous genes. The higher berry anthocyanin content of clonal accession 595 was associated with increased abscisic acid responses, possibly leading to the observed overexpression of phenylpropanoid metabolism genes and deregulation of genes associated with abiotic stress response. Overall, the results highlight the importance of producing diploid assemblies to fully represent the genomic diversity of highly heterozygous woody crop cultivars and unveil the molecular bases of clonal phenotypic variation.

Trophic Position of the White Worm (Enchytraeus albidus) in the Context of Digestive Enzyme Genes Revealed by Transcriptomics Analysis

To assess the impact of Enchytraeidae (potworms) on the functioning of the decomposer system, knowledge of the feeding preferences of enchytraeid species is required. Different food preferences can be explained by variations in enzymatic activities among different enchytraeid species, as there are no significant differences in the morphology or anatomy of their alimentary tracts. However, it is crucial to distinguish between the contribution of microbial enzymes and the animal's digestive capacity. Here, we computationally analyzed the endogenous digestive enzyme genes in Enchytraeus albidus. The analysis was based on RNA-Seq of COI-monohaplotype culture (PL-A strain) specimens, utilizing transcriptome profiling to determine the trophic position of the species. We also corroborated the results obtained using transcriptomics data from genetically heterogeneous freeze-tolerant strains. Our results revealed that E. albidus expresses a wide range of glycosidases, including GH9 cellulases and a specific digestive SH3b-domain-containing i-type lysozyme, previously described in the earthworm Eisenia andrei. Therefore, E. albidus combines traits of both primary decomposers (primary saprophytophages) and secondary decomposers (sapro-microphytophages/microbivores) and can be defined as an intermediate decomposer. Based on assemblies of publicly available RNA-Seq reads, we found close homologs for these cellulases and i-type lysozymes in various clitellate taxa, including Crassiclitellata and Enchytraeidae.

Intestinal cell diversity and treatment responses in a parasitic nematode at single cell resolution

BACKGROUND

Parasitic nematodes, significant pathogens for humans, animals, and plants, depend on diverse organ systems for intra-host survival. Understanding the cellular diversity and molecular variations underlying these functions holds promise for developing novel therapeutics, with specific emphasis on the neuromuscular system's functional diversity. The nematode intestine, crucial for anthelmintic therapies, exhibits diverse cellular phenotypes, and unraveling this diversity at the single-cell level is essential for advancing knowledge in anthelmintic research across various organ systems.

RESULTS

Here, using novel single-cell transcriptomics datasets, we delineate cellular diversity within the intestine of adult female Ascaris suum, a parasitic nematode species that infects animals and people. Gene transcripts expressed in individual nuclei of untreated intestinal cells resolved three phenotypic clusters, while lower stringency resolved additional subclusters and more potential diversity. Clusters 1 and 3 phenotypes displayed variable congruence with scRNA phenotypes of C. elegans intestinal cells, whereas the A. suum cluster 2 phenotype was markedly unique. Distinct functional pathway enrichment characterized each A. suum intestinal cell cluster. Cluster 2 was distinctly enriched for Clade III-associated genes, suggesting it evolved within clade III nematodes. Clusters also demonstrated differential transcriptional responsiveness to nematode intestinal toxic treatments, with Cluster 2 displaying the least responses to short-term intra-pseudocoelomic nematode intestinal toxin treatments.

CONCLUSIONS

This investigation presents advances in knowledge related to biological differences among major cell populations of adult A. suum intestinal cells. For the first time, diverse nematode intestinal cell populations were characterized, and associated biological markers of these cells were identified to support tracking of constituent cells under experimental conditions. These advances will promote better understanding of this and other parasitic nematodes of global importance, and will help to guide future anthelmintic treatments.

The updated genome of the Hungarian population of Aedes koreicus

Vector-borne diseases pose a potential risk to human and animal welfare, and understanding their spread requires genomic resources. The mosquito Aedes koreicus is an emerging vector that has been introduced into Europe more than 15 years ago but only a low quality, fragmented genome was available. In this study, we carried out additional sequencing and assembled and characterized the genome of the species to provide a background for understanding its evolution and biology. The updated genome was 1.1 Gbp long and consisted of 6099 contigs with an N50 value of 329,610 bp and a BUSCO score of 84%. We identified 22,580 genes that could be functionally annotated and paid particular attention to the identification of potential insecticide resistance genes. The assessment of the orthology of the genes indicates a high turnover at the terminal branches of the species tree of mosquitoes with complete genomes, which could contribute to the adaptation and evolutionary success of the species. These results could form the basis for numerous downstream analyzes to develop targets for the control of mosquito populations.

The draft genome of Spiraea crenata L. (Rosaceae) - the first complete genome in tribe Spiraeeae

Spiraea crenata L. is a deciduous shrub distributed across the Eurasian steppe zone. The species is of cultural and horticultural importance and occurs in scattered populations throughout its westernmost range. Currently, there is no genomic information on the tribe of Spiraeeae. Therefore we sequenced and assembled the whole genome of S. crenata using second- and third-generation sequencing and a hybrid assembly approach to expand genomic resources for conservation and support research on this horticulturally important lineage. In addition to the organellar genomes (the plastome and the mitochondrion), we present the first draft genome of the species with an estimated size of 220 Mbp, an N50 value of 7.7 Mbp, and a BUSCO score of 96.0%. Being the first complete genome in tribe Spiraeeae, this may not only be the first step in the genomic study of a rare plant but also a contribution to genomic resources supporting the study of biodiversity and evolutionary history of Rosaceae.

In silico characterization of five novel disease-resistance proteins in Oryza sativa sp. japonica against bacterial leaf blight and rice blast diseases

In the current study, gene network analysis revealed five novel disease-resistance proteins against bacterial leaf blight (BB) and rice blast (RB) diseases caused by Xanthomonas oryzae pv. oryzae (Xoo) and Magnaporthe oryzae (M. oryzae), respectively. In silico modeling, refinement, and model quality assessment were performed to predict the best structures of these five proteins and submitted to ModelArchive for future use. An in-silico annotation indicated that the five proteins functioned in signal transduction pathways as kinases, phospholipases, transcription factors, and DNA-modifying enzymes. The proteins were localized in the nucleus and plasma membrane. Phylogenetic analysis showed the evolutionary relation of the five proteins with disease-resistance proteins (XA21, OsTRX1, PLD, and HKD-motif-containing proteins). This indicates similar disease-resistant properties between five unknown proteins and their evolutionary-related proteins. Furthermore, gene expression profiling of these proteins using public microarray data showed their differential expression under Xoo and M. oryzae infection. This study provides an insight into developing disease-resistant rice varieties by predicting novel candidate resistance proteins, which will assist rice breeders in improving crop yield to address future food security through molecular breeding and biotechnology.

Supplementary Information

The online version contains supplementary material available at 10.1007/s13205-023-03893-5.

CryptoCEN: A Co-Expression Network for Cryptococcus neoformans reveals novel proteins involved in DNA damage repair

Elucidating gene function is a major goal in biology, especially among non-model organisms. However, doing so is complicated by the fact that molecular conservation does not always mirror functional conservation, and that complex relationships among genes are responsible for encoding pathways and higher-order biological processes. Co-expression, a promising approach for predicting gene function, relies on the general principal that genes with similar expression patterns across multiple conditions will likely be involved in the same biological process. For Cryptococcus neoformans, a prevalent human fungal pathogen greatly diverged from model yeasts, approximately 60% of the predicted genes in the genome lack functional annotations. Here, we leveraged a large amount of publicly available transcriptomic data to generate a C. neoformans Co-Expression Network (CryptoCEN), successfully recapitulating known protein networks, predicting gene function, and enabling insights into the principles influencing co-expression. With 100% predictive accuracy, we used CryptoCEN to identify 13 new DNA damage response genes, underscoring the utility of guilt-by-association for determining gene function. Overall, co-expression is a powerful tool for uncovering gene function, and decreases the experimental tests needed to identify functions for currently under-annotated genes.

Prioritization of Trypanosoma brucei editosome protein interactions interfaces at residue resolution through proteome-scale network analysis

BACKGROUND

Trypanosoma brucei is the causative agent for trypanosomiasis in humans and livestock, which presents a growing challenge due to drug resistance. While identifying novel drug targets is vital, the process is delayed due to a lack of functional information on many of the pathogen's proteins. Accordingly, this paper presents a computational framework for prioritizing drug targets within the editosome, a vital molecular machinery responsible for mitochondrial RNA processing in T. brucei. Importantly, this framework may eliminate the need for prior gene or protein characterization, potentially accelerating drug discovery efforts.

RESULTS

By integrating protein-protein interaction (PPI) network analysis, PPI structural modeling, and residue interaction network (RIN) analysis, we quantitatively ranked and identified top hub editosome proteins, their key interaction interfaces, and hotspot residues. Our findings were cross-validated and further prioritized by incorporating them into gene set analysis and differential expression analysis of existing quantitative proteomics data across various life stages of T. brucei. In doing so, we highlighted PPIs such as KREL2-KREPA1, RESC2-RESC1, RESC12A-RESC13, and RESC10-RESC6 as top candidates for further investigation. This includes examining their interfaces and hotspot residues, which could guide drug candidate selection and functional studies.

CONCLUSION

RNA editing offers promise for target-based drug discovery, particularly with proteins and interfaces that play central roles in the pathogen's life cycle. This study introduces an integrative drug target identification workflow combining information from the PPI network, PPI 3D structure, and reside-level information of their interface which can be applicable to diverse pathogens. In the case of T. brucei, via this pipeline, the present study suggested potential drug targets with residue-resolution from RNA editing machinery. However, experimental validation is needed to fully realize its potential in advancing urgently needed antiparasitic drug development.

Genome-wide identification, characterization, and expression analysis of MIPS family genes in legume species

BACKGROUND

Evolutionarily conserved in plants, the enzyme D-myo-inositol-3-phosphate synthase (MIPS; EC 5.5.1.4) regulates the initial, rate-limiting reaction in the phytic acid biosynthetic pathway. They are reported to be transcriptional regulators involved in various physiological functions in the plants, growth, and biotic/abiotic stress responses. Even though the genomes of most legumes are fully sequenced and available, an all-inclusive study of the MIPS family members in legumes is still ongoing.

RESULTS

We found 24 MIPS genes in ten legumes: Arachis hypogea, Cicer arietinum, Cajanus cajan, Glycine max, Lablab purpureus, Medicago truncatula, Pisum sativum, Phaseolus vulgaris, Trifolium pratense and Vigna unguiculata. The total number of MIPS genes found in each species ranged from two to three. The MIPS genes were classified into five clades based on their evolutionary relationships with Arabidopsis genes. The structural patterns of intron/exon and the protein motifs that were conserved in each gene were highly group-specific. In legumes, MIPS genes were inconsistently distributed across their genomes. A comparison of genomes and gene sequences showed that this family was subjected to purifying selection and the gene expansion in MIPS family in legumes was mainly caused by segmental duplication. Through quantitative PCR, expression patterns of MIPS in response to various abiotic stresses, in the vegetative tissues of various legumes were studied. Expression pattern shows that MIPS genes control the development and differentiation of various organs, and have significant responses to salinity and drought stress.

CONCLUSION

The MIPS genes in the genomes of legumes have been identified, characterized and their expression was analysed. The findings pave way for understanding their molecular functions and evolution, and lead to identify the putative MIPS genes associated with different cell and tissue development.

Genome sequencing and molecular networking analysis of the wild fungus Anthostomella pinea reveal its ability to produce a diverse range of secondary metabolites

BACKGROUND

Filamentous fungi are prolific producers of bioactive molecules and enzymes with important applications in industry. Yet, the vast majority of fungal species remain undiscovered or uncharacterized. Here we focus our attention to a wild fungal isolate that we identified as Anthostomella pinea. The fungus belongs to a complex polyphyletic genus in the family of Xylariaceae, which is known to comprise endophytic and pathogenic fungi that produce a plethora of interesting secondary metabolites. Despite that, Anthostomella is largely understudied and only two species have been fully sequenced and characterized at a genomic level.

RESULTS

In this work, we used long-read sequencing to obtain the complete 53.7 Mb genome sequence including the full mitochondrial DNA. We performed extensive structural and functional annotation of coding sequences, including genes encoding enzymes with potential applications in biotechnology. Among others, we found that the genome of A. pinea encodes 91 biosynthetic gene clusters, more than 600 CAZymes, and 164 P450s. Furthermore, untargeted metabolomics and molecular networking analysis of the cultivation extracts revealed a rich secondary metabolism, and in particular an abundance of sesquiterpenoids and sesquiterpene lactones. We also identified the polyketide antibiotic xanthoepocin, to which we attribute the anti-Gram-positive effect of the extracts that we observed in antibacterial plate assays.

CONCLUSIONS

Taken together, our results provide a first glimpse into the potential of Anthstomella pinea to provide new bioactive molecules and biocatalysts and will facilitate future research into these valuable metabolites.

The genome of the toxic invasive species Heracleum sosnowskyi carries an increased number of genes despite absence of recent whole-genome duplications

Heracleum sosnowskyi, belonging to a group of giant hogweeds, is a plant with large effects on ecosystems and human health. It is an invasive species that contributes to the deterioration of grassland ecosystems. The ability of H. sosnowskyi to produce linear furanocoumarins (FCs), photosensitizing compounds, makes it very dangerous. At the same time, linear FCs are compounds with high pharmaceutical value used in skin disease therapies. Despite this high importance, it has not been the focus of genetic and genomic studies. Here, we report a chromosome-scale assembly of Sosnowsky's hogweed genome. Genomic analysis revealed an unusually high number of genes (55106) in the hogweed genome, in contrast to the 25-35 thousand found in most plants. However, we did not find any traces of recent whole-genome duplications not shared with its confamiliar, Daucus carota (carrot), which has approximately thirty thousand genes. The analysis of the genomic proximity of duplicated genes indicates on tandem duplications as a main reason for this increase. We performed a genome-wide search of the genes of the FC biosynthesis pathway and surveyed their expression in aboveground plant parts. Using a combination of expression data and phylogenetic analysis, we found candidate genes for psoralen synthase and experimentally showed the activity of one of them using a heterologous yeast expression system. These findings expand our knowledge on the evolution of gene space in plants and lay a foundation for further analysis of hogweed as an invasive plant and as a source of FCs.

The soil-borne white root rot pathogen Rosellinia necatrix expresses antimicrobial proteins during host colonization

Rosellinia necatrix is a prevalent soil-borne plant-pathogenic fungus that is the causal agent of white root rot disease in a broad range of host plants. The limited availability of genomic resources for R. necatrix has complicated a thorough understanding of its infection biology. Here, we sequenced nine R. necatrix strains with Oxford Nanopore sequencing technology, and with DNA proximity ligation we generated a gapless assembly of one of the genomes into ten chromosomes. Whereas many filamentous pathogens display a so-called two-speed genome with more dynamic and more conserved compartments, the R. necatrix genome does not display such genome compartmentalization. It has recently been proposed that fungal plant pathogens may employ effectors with antimicrobial activity to manipulate the host microbiota to promote infection. In the predicted secretome of R. necatrix, 26 putative antimicrobial effector proteins were identified, nine of which are expressed during plant colonization. Two of the candidates were tested, both of which were found to possess selective antimicrobial activity. Intriguingly, some of the inhibited bacteria are antagonists of R. necatrix growth in vitro and can alleviate R. necatrix infection on cotton plants. Collectively, our data show that R. necatrix encodes antimicrobials that are expressed during host colonization and that may contribute to modulation of host-associated microbiota to stimulate disease development.

SOCfinder: a genomic tool for identifying social genes in bacteria

Bacteria cooperate by working collaboratively to defend their colonies, share nutrients, and resist antibiotics. Nevertheless, our understanding of these remarkable behaviours primarily comes from studying a few well-characterized species. Consequently, there is a significant gap in our understanding of microbial social traits, particularly in natural environments. To address this gap, we can use bioinformatic tools to identify genes that control cooperative or otherwise social traits. Existing tools address this challenge through two approaches. One approach is to identify genes that encode extracellular proteins, which can provide benefits to neighbouring cells. An alternative approach is to predict gene function using annotation tools. However, these tools have several limitations. Not all extracellular proteins are cooperative, and not all cooperative behaviours are controlled by extracellular proteins. Furthermore, existing functional annotation methods frequently miss known cooperative genes. We introduce SOCfinder as a new tool to find bacterial genes that control cooperative or otherwise social traits. SOCfinder combines information from several methods, considering if a gene is likely to [1] code for an extracellular protein [2], have a cooperative functional annotation, or [3] be part of the biosynthesis of a cooperative secondary metabolite. We use data on two extensively-studied species (P. aeruginosa and B. subtilis) to show that SOCfinder is better at finding known cooperative genes than existing tools. We also use theory from population genetics to identify a signature of kin selection in SOCfinder cooperative genes, which is lacking in genes identified by existing tools. SOCfinder opens up a number of exciting directions for future research, and is available to download from https://github.com/lauriebelch/SOCfinder.

In silico protein function prediction: the rise of machine learning-based approaches

Proteins function as integral actors in essential life processes, rendering the realm of protein research a fundamental domain that possesses the potential to propel advancements in pharmaceuticals and disease investigation. Within the context of protein research, an imperious demand arises to uncover protein functionalities and untangle intricate mechanistic underpinnings. Due to the exorbitant costs and limited throughput inherent in experimental investigations, computational models offer a promising alternative to accelerate protein function annotation. In recent years, protein pre-training models have exhibited noteworthy advancement across multiple prediction tasks. This advancement highlights a notable prospect for effectively tackling the intricate downstream task associated with protein function prediction. In this review, we elucidate the historical evolution and research paradigms of computational methods for predicting protein function. Subsequently, we summarize the progress in protein and molecule representation as well as feature extraction techniques. Furthermore, we assess the performance of machine learning-based algorithms across various objectives in protein function prediction, thereby offering a comprehensive perspective on the progress within this field.

Genome-Wide Investigation and Expression Analysis of the Catalase Gene Family in Oat Plants (Avena sativa L.)

Through the degradation of reactive oxygen species (ROS), different antioxidant enzymes, such as catalase (CAT), defend organisms against oxidative stress. These enzymes are crucial to numerous biological functions, like plant development and defense against several biotic and abiotic stresses. However, despite the major economic importance of Avena sativa around the globe, little is known about the CAT gene's structure and organization in this crop. Thus, a genome-wide investigation of the CAT gene family in oat plants has been carried out to characterize the potential roles of those genes under different stressors. Bioinformatic approaches were used in this study to predict the AvCAT gene's structure, secondary and tertiary protein structures, physicochemical properties, phylogenetic tree, and expression profiling under diverse developmental and biological conditions. A local Saudi oat variety (AlShinen) was used in this work. Here, ten AvCAT genes that belong to three groups (Groups I-III) were identified. All identified CATs harbor the two conserved domains (pfam00199 and pfam06628), a heme-binding domain, and a catalase activity motif. Moreover, identified AvCAT proteins were located in different compartments in the cell, such as the peroxisome, mitochondrion, and cytoplasm. By analyzing their promoters, different cis-elements were identified as being related to plant development, maturation, and response to different environmental stresses. Gene expression analysis revealed that three different AvCAT genes belonging to three different subgroups showed noticeable modifications in response to various stresses, such as mannitol, salt, and ABA. As far as we know, this is the first report describing the genome-wide analysis of the oat catalase gene family, and these data will help further study the roles of catalase genes during stress responses, leading to crop improvement.

A repertoire of candidate effector proteins of the fungus Ceratocystis cacaofunesta

The genus Ceratocystis includes many phytopathogenic fungi that affect different plant species. One of these is Ceratocystis cacaofunesta, which is pathogenic to the cocoa tree and causes Ceratocystis wilt, a lethal disease for the crop. However, little is known about how this pathogen interacts with its host. The knowledge and identification of possible genes encoding effector proteins are essential to understanding this pathosystem. The present work aimed to predict genes that code effector proteins of C. cacaofunesta from a comparative analysis of the genomes of five Ceratocystis species available in databases. We performed a new genome annotation through an in-silico analysis. We analyzed the secretome and effectorome of C. cacaofunesta using the characteristics of the peptides, such as the presence of signal peptide for secretion, absence of transmembrane domain, and richness of cysteine residues. We identified 160 candidate effector proteins in the C. cacaofunesta proteome that could be classified as cytoplasmic (102) or apoplastic (58). Of the total number of candidate effector proteins, 146 were expressed, presenting an average of 206.56 transcripts per million. Our database was created using a robust bioinformatics strategy, followed by manual curation, generating information on pathogenicity-related genes involved in plant interactions, including CAZymes, hydrolases, lyases, and oxidoreductases. Comparing proteins already characterized as effectors in Sordariomycetes species revealed five groups of protein sequences homologous to C. cacaofunesta. These data provide a valuable resource for studying the infection mechanisms of these pathogens in their hosts.

FunTaxIS-lite: a simple and light solution to investigate protein functions in all living organisms

MOTIVATION

Defining the full domain of protein functions belonging to an organism is a complex challenge that is due to the huge heterogeneity of the taxonomy, where single or small groups of species can bear unique functional characteristics. FunTaxIS-lite provides a solution to this challenge by determining taxon-based constraints on Gene Ontology (GO) terms, which specify the functions that an organism can or cannot perform. The tool employs a set of rules to generate and spread the constraints across both the taxon hierarchy and the GO graph.

RESULTS

The taxon-based constraints produced by FunTaxIS-lite extend those provided by the Gene Ontology Consortium by an average of 300%. The implementation of these rules significantly reduces errors in function predictions made by automatic algorithms and can assist in correcting inconsistent protein annotations in databases.

AVAILABILITY AND IMPLEMENTATION

FunTaxIS-lite is available on https://www.medcomp.medicina.unipd.it/funtaxis-lite and from https://github.com/MedCompUnipd/FunTaxIS-lite.

Functional annotation and comparative genomics analysis of Balamuthia mandrillaris reveals potential virulence-related genes

Balamuthia mandrillaris is a pathogenic protozoan that causes a rare but almost always fatal infection of the central nervous system and, in some cases, cutaneous lesions. Currently, the genomic data for this free-living amoeba include the description of several complete mitochondrial genomes. In contrast, two complete genomes with draft quality are available in GenBank, but none of these have a functional annotation. In the present study, the complete genome of B. mandrillaris isolated from a freshwater artificial lagoon was sequenced and assembled, obtaining an assembled genome with better assembly quality parameter values than the currently available genomes. Afterward, the genome mentioned earlier, along with strains V039 and 2046, were subjected to functional annotation. Finally, comparative genomics analysis was performed, and it was found that homologous genes in the core genome potentially involved in the virulence of Acanthamoeba spp. and Trypanosoma cruzi. Moreover, eleven of fifteen genes were identified in the three strains described as potential target genes to develop new treatment approaches for B. mandrillaris infections. These results describe proteins in this protozoan's complete genome and help prioritize which target genes could be used to develop new treatments.

The proteome of extracellular vesicles of the lung fluke Paragonimus kellicotti produced in vitro and in the lung cyst

Paragonimiasis is a zoonotic, food-borne trematode infection that affects 21 million people globally. Trematodes interact with their hosts via extracellular vesicles (EV) that carry protein and RNA cargo. We analyzed EV in excretory-secretory products (ESP) released by Paragonimus kellicotti adult worms cultured in vitro (EV ESP) and EV isolated from lung cyst fluid (EV CFP) recovered from infected gerbils. The majority of EV were approximately 30-50 nm in diameter. We identified 548 P. kellicotti-derived proteins in EV ESP by mass spectrometry and 8 proteins in EV CFP of which 7 were also present in EV ESP. No parasite-derived proteins were reliably detected in EV isolated from plasma samples. A cysteine protease (MK050848, CP-6) was the most abundant protein found in EV CFP in all technical and biological replicates. Immunolocalization of CP-6 showed strong labeling in the tegument of P. kellicotti and in the adjacent cyst and lung tissue that contained worm eggs. It is likely that CP-6 present in EV is involved in parasite-host interactions. These results provide new insights into interactions between Paragonimus and their mammalian hosts, and they provide potential clues for development of novel diagnostic tools and treatments.

CryptoCEN: A Co-Expression Network for Cryptococcus neoformans reveals novel proteins involved in DNA damage repair

Elucidating gene function is a major goal in biology, especially among non-model organisms. However, doing so is complicated by the fact that molecular conservation does not always mirror functional conservation, and that complex relationships among genes are responsible for encoding pathways and higher-order biological processes. Co-expression, a promising approach for predicting gene function, relies on the general principal that genes with similar expression patterns across multiple conditions will likely be involved in the same biological process. For Cryptococcus neoformans, a prevalent human fungal pathogen greatly diverged from model yeasts, approximately 60% of the predicted genes in the genome lack functional annotations. Here, we leveraged a large amount of publicly available transcriptomic data to generate a C. neoformans Co-Expression Network (CryptoCEN), successfully recapitulating known protein networks, predicting gene function, and enabling insights into the principles influencing co-expression. With 100% predictive accuracy, we used CryptoCEN to identify 13 new DNA damage response genes, underscoring the utility of guilt-by-association for determining gene function. Overall, co-expression is a powerful tool for uncovering gene function, and decreases the experimental tests needed to identify functions for currently under-annotated genes.

Genome-wide and constrained ordination-based analyses of EC code data support reclassification of the species of Massilia La Scola et al. 2000 into Telluria Bowman et al. 1993, Mokoshia gen. nov. and Zemynaea gen. nov

Based on genome-wide data, Massilia species belonging to the clade including Telluria mixta LMG 11547T should be entirely transferred to the genus Telluria owing to the nomenclatural priority of the type species Telluria mixta. This results in the transfer of 35 Massilia species to the genus Telluria. The presented data also supports the creation of two new genera since peripherally branching Massilia species are distinct from Telluria and other related genera. It is proposed that 13 Massilia species are transferred to Mokoshia gen. nov. with the type species designated Mokoshia eurypsychrophila comb. nov. The species Massilia arenosa is proposed to belong to the genus Zemynaea gen. nov. as the type species Zemynaea arenosa comb. nov. The genome-wide analysis was well supported by canonical ordination analysis of Enzyme Commission (EC) codes annotated from genomes via pannzer2. This new approach was performed to assess the conclusions of the genome-based data and reduce possible ambiguity in the taxonomic decision making. Cross-validation of EC code data compared within canonical plots validated the reclassifications and correctly visualized the expected genus-level taxonomic relationships. The approach is complementary to genome-wide methodology and could be used for testing sequence alignment based data across genetically related genera. In addition to the proposed broader reclassifications, invalidly described species 'Massilia antibiotica', 'Massilia aromaticivorans', 'Massilia cellulosiltytica' and 'Massilia humi' are described as Telluria antibiotica sp. nov., Telluria aromaticivorans sp. nov., Telluria cellulosilytica sp. nov. and Pseudoduganella humi sp. nov., respectively. In addition, Telluria chitinolytica is reclassified as Pseudoduganella chitinolytica comb. nov. The use of combined genome-wide and annotation descriptors compared using canonical ordination clarifies the taxonomy of Telluria and its sibling genera and provides another way to evaluate complex taxonomic data.

Elucidating the Obligate Nature and Biological Capacity of an Invasive Fungal Corn Pathogen

Tar spot is a devasting corn disease caused by the obligate fungal pathogen Phyllachora maydis. Since its initial identification in the United States in 2015, P. maydis has become an increasing threat to corn production. Despite this, P. maydis has remained largely understudied at the molecular level, due to difficulties surrounding its obligate lifestyle. Here, we generated a significantly improved P. maydis nuclear and mitochondrial genome, using a combination of long- and short-read technologies, and also provide the first transcriptomic analysis of primary tar spot lesions. Our results show that P. maydis is deficient in inorganic nitrogen utilization, is likely heterothallic, and encodes for significantly more protein-coding genes, including secreted enzymes and effectors, than previous determined. Furthermore, our expression analysis suggests that, following primary tar spot lesion formation, P. maydis might reroute carbon flux away from DNA replication and cell division pathways and towards pathways previously implicated in having significant roles in pathogenicity, such as autophagy and secretion. Together, our results identified several highly expressed unique secreted factors that likely contribute to host recognition and subsequent infection, greatly increasing our knowledge of the biological capacity of P. maydis, which have much broader implications for mitigating tar spot of corn. [Formula: see text] Copyright © 2023 The Author(s). This is an open access article distributed under the CC BY-NC-ND 4.0 International license.

Insight into pathogenomics and phylogeography of hypervirulent and highly-lethal Mycobacterium tuberculosis strain cluster

BACKGROUND

. The Mycobacterium tuberculosis Beijing genotype is globally spread lineage with important medical properties that however vary among its subtypes. M. tuberculosis Beijing 14717-15-cluster was recently discovered as both multidrug-resistant, hypervirulent, and highly-lethal strain circulating in the Far Eastern region of Russia. Here, we aimed to analyze its pathogenomic features and phylogeographic pattern.

RESULTS

. The study collection included M. tuberculosis DNA collected between 1996 and 2020 in different world regions. The bacterial DNA was subjected to genotyping and whole genome sequencing followed by bioinformatics and phylogenetic analysis. The PCR-based assay to detect specific SNPs of the Beijing 14717-15-cluster was developed and used for its screening in the global collections. Phylogenomic and phylogeographic analysis confirmed endemic prevalence of the Beijing 14717-15-cluster in the Asian part of Russia, and distant common ancestor with isolates from Korea (> 115 SNPs). The Beijing 14717-15-cluster isolates had two common resistance mutations RpsL Lys88Arg and KatG Ser315Thr and belonged to spoligotype SIT269. The Russian isolates of this cluster were from the Asian Russia while 4 isolates were from the Netherlands and Spain. The cluster-specific SNPs that significantly affect the protein function were identified in silico in genes within different categories (lipid metabolism, regulatory proteins, intermediary metabolism and respiration, PE/PPE, cell wall and cell processes).

CONCLUSIONS

. We developed a simple method based on real-time PCR to detect clinically significant MDR and hypervirulent Beijing 14717-15-cluster. Most of the identified cluster-specific mutations were previously unreported and could potentially be associated with increased pathogenic properties of this hypervirulent M. tuberculosis strain. Further experimental study to assess the pathobiological role of these mutations is warranted.

Characterization of metagenome-assembled genomes from the International Space Station

BACKGROUND

Several investigations on the microbial diversity and functional properties of the International Space Station (ISS) environment were carried out to understand the influence of spaceflight conditions on the microbial population. However, metagenome-assembled genomes (MAGs) of ISS samples are yet to be generated and subjected to various genomic analyses, including phylogenetic affiliation, predicted functional pathways, antimicrobial resistance, and virulence characteristics.

RESULTS

In total, 46 MAGs were assembled from 21 ISS environmental metagenomes, in which metaSPAdes yielded 20 MAGs and metaWRAP generated 26 MAGs. Among 46 MAGs retrieved, 18 bacterial species were identified, including one novel genus/species combination (Kalamiella piersonii) and one novel bacterial species (Methylobacterium ajmalii). In addition, four bins exhibited fungal genomes; this is the first-time fungal genomes were assembled from ISS metagenomes. Phylogenetic analyses of five bacterial species showed ISS-specific evolution. The genes pertaining to cell membranes, such as transmembrane transport, cell wall organization, and regulation of cell shape, were enriched. Variations in the antimicrobial-resistant (AMR) and virulence genes of the selected 20 MAGs were characterized to predict the ecology and evolution of biosafety level (BSL) 2 microorganisms in space. Since microbial virulence increases in microgravity, AMR gene sequences of MAGs were compared with genomes of respective ISS isolates and corresponding type strains. Among these 20 MAGs characterized, AMR genes were more prevalent in the Enterobacter bugandensis MAG, which has been predominantly isolated from clinical samples. MAGs were further used to analyze if genes involved in AMR and biofilm formation of viable microbes in ISS have variation due to generational evolution in microgravity and radiation pressure.

CONCLUSIONS

Comparative analyses of MAGs and whole-genome sequences of related ISS isolates and their type strains were characterized to understand the variation related to the microbial evolution under microgravity. The Pantoea/Kalamiella strains have the maximum single-nucleotide polymorphisms found within the ISS strains examined. This may suggest that Pantoea/Kalamiella strains are much more subjective to microgravity changes. The reconstructed genomes will enable researchers to study the evolution of genomes under microgravity and low-dose irradiation compared to the evolution of microbes here on Earth. Video Abstract.

Genomic attributes of Vibrio cholerae O1 responsible for 2022 massive cholera outbreak in Bangladesh

In 2022, one of its worst cholera outbreaks began in Bangladesh and the icddr,b Dhaka hospital treated more than 1300 patients and ca. 42,000 diarrheal cases from March-1 to April-10, 20221. Here, we present genomic attributes of V. cholerae O1 responsible for the 2022 Dhaka outbreak and 960 7th pandemic El Tor (7PET) strains from 88 countries. Results show strains isolated during the Dhaka outbreak cluster with 7PET wave-3 global clade strains, but comprise subclade BD-1.2, for which the most recent common ancestor appears to be that responsible for recent endemic cholera in India. BD-1.2 strains are present in Bangladesh since 2016, but not establishing dominance over BD-2 lineage strains2 until 2018 and predominantly associated with endemic cholera. In conclusion, the recent shift in lineage and genetic attributes, including serotype switching of BD-1.2 from Ogawa to Inaba, may explain the increasing number of cholera cases in Bangladesh.

A systematic review of state-of-the-art strategies for machine learning-based protein function prediction

New drug discovery is inseparable from the discovery of drug targets, and the vast majority of the known targets are proteins. At the same time, proteins are essential structural and functional elements of living cells necessary for the maintenance of all forms of life. Therefore, protein functions have become the focus of many pharmacological and biological studies. Traditional experimental techniques are no longer adequate for rapidly growing annotation of protein sequences, and approaches to protein function prediction using computational methods have emerged and flourished. A significant trend has been to use machine learning to achieve this goal. In this review, approaches to protein function prediction based on the sequence, structure, protein-protein interaction (PPI) networks, and fusion of multi-information sources are discussed. The current status of research on protein function prediction using machine learning is considered, and existing challenges and prominent breakthroughs are discussed to provide ideas and methods for future studies.

Enhanced Viral Metagenomics with Lazypipe 2

Viruses are the main agents causing emerging and re-emerging infectious diseases. It is therefore important to screen for and detect them and uncover the evolutionary processes that support their ability to jump species boundaries and establish themselves in new hosts. Metagenomic next-generation sequencing (mNGS) is a high-throughput, impartial technology that has enabled virologists to detect either known or novel, divergent viruses from clinical, animal, wildlife and environmental samples, with little a priori assumptions. mNGS is heavily dependent on bioinformatic analysis, with an emerging demand for integrated bioinformatic workflows. Here, we present Lazypipe 2, an updated mNGS pipeline with, as compared to Lazypipe1, significant improvements in code stability and transparency, with added functionality and support for new software components. We also present extensive benchmarking results, including evaluation of a novel canine simulated metagenome, precision and recall of virus detection at varying sequencing depth, and a low to extremely low proportion of viral genetic material. Additionally, we report accuracy of virus detection with two strategies: homology searches using nucleotide or amino acid sequences. We show that Lazypipe 2 with nucleotide-based annotation approaches near perfect detection for eukaryotic viruses and, in terms of accuracy, outperforms the compared pipelines. We also discuss the importance of homology searches with amino acid sequences for the detection of highly divergent novel viruses.

Excretory/Secretory Proteome of Females and Males of the Hookworm Ancylostoma ceylanicum

The dynamic host-parasite mechanisms underlying hookworm infection establishment and maintenance in mammalian hosts remain poorly understood but are primarily mediated by hookworm's excretory/secretory products (ESPs), which have a wide spectrum of biological functions. We used ultra-high performance mass spectrometry to comprehensively profile and compare female and male ESPs from the zoonotic human hookworm Ancylostoma ceylanicum, which is a natural parasite of dogs, cats, and humans. We improved the genome annotation, decreasing the number of protein-coding genes by 49% while improving completeness from 92 to 96%. Compared to the previous genome annotation, we detected 11% and 10% more spectra in female and male ESPs, respectively, using this improved version, identifying a total of 795 ESPs (70% in both sexes, with the remaining sex-specific). Using functional databases (KEGG, GO and Interpro), common and sex-specific enriched functions were identified. Comparisons with the exclusively human-infective hookworm Necator americanus identified species-specific and conserved ESPs. This is the first study identifying ESPs from female and male A. ceylanicum. The findings provide a deeper understanding of hookworm protein functions that assure long-term host survival and facilitate future engineering of transgenic hookworms and analysis of regulatory elements mediating the high-level expression of ESPs. Furthermore, the findings expand the list of potential vaccine and diagnostic targets and identify biologics that can be explored for anti-inflammatory potential.

Whole genome sequence characterization of Aspergillus terreus ATCC 20541 and genome comparison of the fungi A. terreus

Aspergillus terreus is well-known for lovastatin and itaconic acid production with biomedical and commercial importance. The mechanisms of metabolite formation have been extensively studied to improve their yield through genetic engineering. However, the combined repertoire of carbohydrate-active enzymes (CAZymes), cytochrome P450s (CYP) enzymes, and secondary metabolites (SMs) in the different A. terreus strains has not been well studied yet, especially with respect to the presence of biosynthetic gene clusters (BGCs). Here we present a 30 Mb whole genome sequence of A. terreus ATCC 20541 in which we predicted 10,410 protein-coding genes. We compared the CAZymes, CYPs enzyme, and SMs across eleven A. terreus strains, and the results indicate that all strains have rich pectin degradation enzyme and CYP52 families. The lovastatin BGC of lovI was linked with lovF in A. terreus ATCC 20541, and the phenomenon was not found in the other strains. A. terreus ATCC 20541 lacked a non-ribosomal peptide synthetase (AnaPS) participating in acetylaszonalenin production, which was a conserved protein in the ten other strains. Our results present a comprehensive analysis of CAZymes, CYPs enzyme, and SM diversities in A. terreus strains and will facilitate further research in the function of BGCs associated with valuable SMs.

AnnotaPipeline: An integrated tool to annotate eukaryotic proteins using multi-omics data

Assignment of gene function has been a crucial, laborious, and time-consuming step in genomics. Due to a variety of sequencing platforms that generates increasing amounts of data, manual annotation is no longer feasible. Thus, the need for an integrated, automated pipeline allowing the use of experimental data towards validation of in silico prediction of gene function is of utmost relevance. Here, we present a computational workflow named AnnotaPipeline that integrates distinct software and data types on a proteogenomic approach to annotate and validate predicted features in genomic sequences. Based on FASTA (i) nucleotide or (ii) protein sequences or (iii) structural annotation files (GFF3), users can input FASTQ RNA-seq data, MS/MS data from mzXML or similar formats, as the pipeline uses both transcriptomic and proteomic information to corroborate annotations and validate gene prediction, providing transcription and expression evidence for functional annotation. Reannotation of the available Arabidopsis thaliana, Caenorhabditis elegans, Candida albicans, Trypanosoma cruzi, and Trypanosoma rangeli genomes was performed using the AnnotaPipeline, resulting in a higher proportion of annotated proteins and a reduced proportion of hypothetical proteins when compared to the annotations publicly available for these organisms. AnnotaPipeline is a Unix-based pipeline developed using Python and is available at: https://github.com/bioinformatics-ufsc/AnnotaPipeline.

Making the Most of Its Short Reads: A Bioinformatics Workflow for Analysing the Short-Read-Only Data of Leishmania orientalis (Formerly Named Leishmania siamensis) Isolate PCM2 in Thailand

Simple Summary

Leishmaniasis is a parasitic disease caused by flagellated protozoa of the genus Leishmania. Multiple genome sequencing platforms have been employed to complete Leishmania genomes at the expense of high cost. This study proposes an integrative bioinformatic workflow for assembling only the short-read data of Leishmania orientalis isolate PCM2 from Thailand and produce an acceptable-quality genome for further genomic analysis. This workflow gives extensive information required for identifying strain-specific markers and virulence-associated genes useful for drug and vaccine development before a more exhaustive and expensive investigation.

Abstract

Background: Leishmania orientalis (formerly named Leishmania siamensis) has been neglected for years in Thailand. The genomic study of L. orientalis has gained much attention recently after the release of the first high-quality reference genome of the isolate LSCM4. The integrative approach of multiple sequencing platforms for whole-genome sequencing has proven effective at the expense of considerably expensive costs. This study presents a preliminary bioinformatic workflow including the use of multi-step de novo assembly coupled with the reference-based assembly method to produce high-quality genomic drafts from the short-read Illumina sequence data of L. orientalis isolate PCM2. Results: The integrating multi-step de novo assembly by MEGAHIT and SPAdes with the reference-based method using the L. enriettii genome and salvaging the unmapped reads resulted in the 30.27 Mb genomic draft of L. orientalis isolate PCM2 with 3367 contigs and 8887 predicted genes. The results from the integrated approach showed the best integrity, coverage, and contig alignment when compared to the genome of L. orientalis isolate LSCM4 collected from the northern province of Thailand. Similar patterns of gene ratios and frequency were observed from the GO biological process annotation. Fifty GO terms were assigned to the assembled genomes, and 23 of these (accounting for 61.6% of the annotated genes) showed higher gene counts and ratios when results from our workflow were compared to those of the LSCM4 isolate. Conclusions: These results indicated that our proposed bioinformatic workflow produced an acceptable-quality genome of L. orientalis strain PCM2 for functional genomic analysis, maximising the usage of the short-read data. This workflow would give extensive information required for identifying strain-specific markers and virulence-associated genes useful for drug and vaccine development before a more exhaustive and expensive investigation.

Identification and Characterization of Jasmonic Acid Biosynthetic Genes in Salvia miltiorrhiza Bunge

Jasmonic acid (JA) is a vital plant hormone that performs a variety of critical functions for plants. Salvia miltiorrhiza Bunge (S. miltiorrhiza), also known as Danshen, is a renowned traditional Chinese medicinal herb. However, no thorough and systematic analysis of JA biosynthesis genes in S. miltiorrhiza exists. Through genome-wide prediction and molecular cloning, 23 candidate genes related to JA biosynthesis were identified in S. miltiorrhiza. These genes belong to four families that encode lipoxygenase (LOX), allene oxide synthase (AOS), allene oxide cyclase (AOC), and 12-OPDA reductase3 (OPR3). It was discovered that the candidate genes for JA synthesis of S. miltiorrhiza were distinct and conserved, in contrast to related genes in other plants, by evaluating their genetic structures, protein characteristics, and phylogenetic trees. These genes displayed tissue-specific expression patterns concerning to methyl jasmonate (MeJA) and wound tests. Overall, the results of this study provide valuable information for elucidating the JA biosynthesis pathway in S. miltiorrhiza by comprehensive and methodical examination.

Identification of QTNs Associated With Flowering Time, Maturity, and Plant Height Traits in Linum usitatissimum L. Using Genome-Wide Association Study

Early flowering, maturity, and plant height are important traits for linseed to fit in rice fallows, for rainfed agriculture, and for economically viable cultivation. Here, Multi-Locus Genome-Wide Association Study (ML-GWAS) was undertaken in an association mapping panel of 131 accessions, genotyped using 68,925 SNPs identified by genotyping by sequencing approach. Phenotypic evaluation data of five environments comprising 3 years and two locations were used. GWAS was performed for three flowering time traits including days to 5%, 50%, and 95% flowering, days to maturity, and plant height by employing five ML-GWAS methods: FASTmrEMMA, FASTmrMLM, ISIS EM-BLASSO, mrMLM, and pLARmEB. A total of 335 unique QTNs have been identified for five traits across five environments. 109 QTNs were stable as observed in ≥2 methods and/or environments, explaining up to 36.6% phenotypic variance. For three flowering time traits, days to maturity, and plant height, 53, 30, and 27 stable QTNs, respectively, were identified. Candidate genes having roles in flower, pollen, embryo, seed and fruit development, and xylem/phloem histogenesis have been identified. Gene expression of candidate genes for flowering and plant height were studied using transcriptome of an early maturing variety Sharda (IC0523807). The present study unravels QTNs/candidate genes underlying complex flowering, days to maturity, and plant height traits in linseed.

Dali server: structural unification of protein families

Protein structure is key to understanding biological function. Structure comparison deciphers deep phylogenies, providing insight into functional conservation and functional shifts during evolution. Until recently, structural coverage of the protein universe was limited by the cost and labour involved in experimental structure determination. Recent breakthroughs in deep learning revolutionized structural bioinformatics by providing accurate structural models of numerous protein families for which no structural information existed. The Dali server for 3D protein structure comparison is widely used by crystallographers to relate new structures to pre-existing ones. Here, we report two most recent upgrades to the web server: (i) the foldomes of key organisms in the AlphaFold Database (version 1) are searchable by Dali, (ii) structural alignments are annotated with protein families. Using these new features, we discovered a novel functionally diverse subgroup within the WRKY/GCM1 clan. This was accomplished by linking the structurally characterized SWI/SNF and NAM families as well as the structural models of the CG-1 family and uncharacterized proteins to the structure of Gti1/Pac2, a previously known member of the WRKY/GCM1 clan. The Dali server is available at http://ekhidna2.biocenter.helsinki.fi/dali. This website is free and open to all users and there is no login requirement.

The value of genotype-specific reference for transcriptome analyses in barley

We demonstrate in this study that using a common reference genome may lead to loss of genotype-specific information in the assembled Reference Transcript Dataset (RTD) and the generation of erroneous, incomplete, or misleading transcriptomics analysis results in barley.

It is increasingly apparent that although different genotypes within a species share “core” genes, they also contain variable numbers of “specific” genes and different structures of “core” genes that are only present in a subset of individuals. Using a common reference genome may thus lead to a loss of genotype-specific information in the assembled Reference Transcript Dataset (RTD) and the generation of erroneous, incomplete or misleading transcriptomics analysis results. In this study, we assembled genotype-specific RTD (sRTD) and common reference–based RTD (cRTD) from RNA-seq data of cultivated Barke and Morex barley, respectively. Our quantitative evaluation showed that the sRTD has a significantly higher diversity of transcripts and alternative splicing events, whereas the cRTD missed 40% of transcripts present in the sRTD and it only has ∼70% accurate transcript assemblies. We found that the sRTD is more accurate for transcript quantification as well as differential expression analysis. However, gene-level quantification is less affected, which may be a reasonable compromise when a high-quality genotype-specific reference is not available.

EnsembleFam: towards more accurate protein family prediction in the twilight zone

Background

Current protein family modeling methods like profile Hidden Markov Model (pHMM), k-mer based methods, and deep learning-based methods do not provide very accurate protein function prediction for proteins in the twilight zone, due to low sequence similarity to reference proteins with known functions.

Results

We present a novel method EnsembleFam, aiming at better function prediction for proteins in the twilight zone. EnsembleFam extracts the core characteristics of a protein family using similarity and dissimilarity features calculated from sequence homology relations. EnsembleFam trains three separate Support Vector Machine (SVM) classifiers for each family using these features, and an ensemble prediction is made to classify novel proteins into these families. Extensive experiments are conducted using the Clusters of Orthologous Groups (COG) dataset and G Protein-Coupled Receptor (GPCR) dataset. EnsembleFam not only outperforms state-of-the-art methods on the overall dataset but also provides a much more accurate prediction for twilight zone proteins.

Conclusions

EnsembleFam, a machine learning method to model protein families, can be used to better identify members with very low sequence homology. Using EnsembleFam protein functions can be predicted  using just sequence information with better accuracy than state-of-the-art methods.

Functional Analysis of Hypothetical Proteins of Vibrio parahaemolyticus Reveals the Presence of Virulence Factors and Growth-Related Enzymes With Therapeutic Potential

Vibrio parahaemolyticus, an aquatic pathogen, is a major concern in the shrimp aquaculture industry. Several strains of this pathogen are responsible for causing acute hepatopancreatic necrosis disease as well as other serious illness, both of which result in severe economic losses. The genome sequence of two pathogenic strains of V. parahaemolyticus, MSR16 and MSR17, isolated from Bangladesh, have been reported to gain a better understanding of their diversity and virulence. However, the prevalence of hypothetical proteins (HPs) makes it challenging to obtain a comprehensive understanding of the pathogenesis of V. parahaemolyticus. The aim of the present study is to provide a functional annotation of the HPs to elucidate their role in pathogenesis employing several in silico tools. The exploration of protein domains and families, similarity searches against proteins with known function, gene ontology enrichment, along with protein-protein interaction analysis of the HPs led to the functional assignment with a high level of confidence for 656 proteins out of a pool of 2631 proteins. The in silico approach used in this study was important for accurately assigning function to HPs and inferring interactions with proteins with previously described functions. The HPs with function predicted were categorized into various groups such as enzymes involved in small-compound biosynthesis pathway, iron binding proteins, antibiotics resistance proteins, and other proteins. Several proteins with potential druggability were identified among them. In addition, the HPs were investigated in search of virulent factors, which led to the identification of proteins that have the potential to be exploited as vaccine candidate. The findings of the study will be effective in gaining a better understanding of the molecular mechanisms of bacterial pathogenesis. They may also provide an insight into the process of evaluating promising targets for the development of drugs and vaccines against V. parahaemolyticus.