AlphaFold2-guided description of CoBaHMA, a novel family of bacterial domains within the heavy-metal-associated superfamily

Three-dimensional (3D) structure information, now available at the proteome scale, may facilitate the detection of remote evolutionary relationships in protein superfamilies. Here, we illustrate this with the identification of a novel family of protein domains related to the ferredoxin-like superfold, by combining (i) transitive sequence similarity searches, (ii) clustering approaches, and (iii) the use of AlphaFold2 3D structure models. Domains of this family were initially identified in relation with the intracellular biomineralization of calcium carbonates by Cyanobacteria. They are part of the large heavy-metal-associated (HMA) superfamily, departing from the latter by specific sequence and structural features. In particular, most of them share conserved basic amino acids  (hence their name CoBaHMA for Conserved Basic residues HMA), forming a positively charged surface, which is likely to interact with anionic partners. CoBaHMA domains are found in diverse modular organizations in bacteria, existing in the form of monodomain proteins or as part of larger proteins, some of which are membrane proteins involved in transport or lipid metabolism. This suggests that the CoBaHMA domains may exert a regulatory function, involving interactions with anionic lipids. This hypothesis might have a particular resonance in the context of the compartmentalization observed for cyanobacterial intracellular calcium carbonates.

The Landscape and Perspectives of the Human Gut Metaproteomics

The human gut microbiome is closely associated with human health and diseases. Metaproteomics has emerged as a valuable tool for studying the functionality of the gut microbiome by analyzing the entire proteins present in microbial communities. Recent advancements in liquid chromatography and tandem mass spectrometry (LC-MS/MS) techniques have expanded the detection range of metaproteomics. However, the overall coverage of the proteome in metaproteomics is still limited. While metagenomics studies have revealed substantial microbial diversity and functional potential of the human gut microbiome, few studies have summarized and studied the human gut microbiome landscape revealed with metaproteomics. In this article, we present the current landscape of human gut metaproteomics studies by re-analyzing the identification results from 15 published studies. We quantified the limited proteome coverage in metaproteomics and revealed a high proportion of annotation coverage of metaproteomics-identified proteins. We conducted a preliminary comparison between the metaproteomics view and the metagenomics view of the human gut microbiome, identifying key areas of consistency and divergence. Based on the current landscape of human gut metaproteomics, we discuss the feasibility of using metaproteomics to study functionally unknown proteins and propose a whole workflow peptide-centric analysis. Additionally, we suggest enhancing metaproteomics analysis by refining taxonomic classification and calculating confidence scores, as well as developing tools for analyzing the interaction between taxonomy and function.

Genetic patterns and genome-wide association analysis of eggshell quality traits of egg-type chicken across an extended laying period

The industry of egg-type chicken has shown a trend of extending the rearing period, with the goal of breeding chicken breeds capable of producing 500 qualified eggs by 700 d of age. However, the rapid decline in eggshell quality during the late laying period is one of the major challenges. In this study, a total of 3,261 Rhode Island Red chickens were used to measure eggshell quality traits including eggshell strength (ESS), eggshell thickness (EST), eggshell color (ESC) and eggshell gloss (ESG) at seven age points ranging from 36 to 90 wk of age. Phenotypic variations increased with the aging process, especially during the late laying period (> 55 wk), and the heritability during this period decreased by 22.7 to 81.4% compared to the initial and peak laying periods. Then we performed genome-wide association study (GWAS) to identify the genomic variants that associated with eggshell quality, with a custom Illumina 50K BeadChip, named PhenoixChip-I. The results indicated that 2 genomic regions on GGA1(23.24-25.15Mb; 175.95-176.05 Mb) were significantly (P < 4.48E-06) or suggestively (P < 8.97E-05) associated with ESS, which can explain 9.59% and 0.48% of the phenotypic variations of ESS46 and ESS36, respectively. Three genes, FRY, PCNX2, and ENSGALG00000052468, were considered to be the candidate genes for ESS. For other traits, the genome-wide suggestive SNPs were identified at each age point, exhibiting a certain trend with aging process. Additionally, SNP enrichment analysis and functional annotation of cross-tissue regulatory elements to ESS36 revealed a high concentration of enhancer elements specific to shell gland and kidney tissues. This study, deepened our knowledge of eggshells and laying a valued scientific foundation for chicken molecular breeding.

Integrative network analysis of differentially methylated regions to study the impact of gestational weight gain on maternal metabolism and fetal-neonatal growth

Integrative network analysis (INA) is important for identifying gene modules or epigenetically regulated molecular pathways in diseases. This study evaluated the effect of excessive gestational weight gain (EGWG) on INA of differentially methylated regions, maternal metabolism and offspring growth. Brazilian women from "The Araraquara Cohort Study" with adequate pre-pregnancy body mass index were divided into EGWG (n=30) versus adequate gestational weight gain (AGWG, n=45) groups. The methylome analysis was performed on maternal blood using the Illumina MethylationEPIC BeadChip. Fetal-neonatal growth was assessed by ultrasound and anthropometry, respectively. Maternal lipid and glycemic profiles were investigated. Maternal triglycerides-TG (p=0.030) and total cholesterol (p=0.014); fetus occipito-frontal diameter (p=0.005); neonate head circumference-HC (p=0.016) and thoracic perimeter (p=0.020) were greater in the EGWG compared to the AGWG group. Multiple linear regression analysis showed that maternal DNA methylation was associated with maternal TG and fasting insulin, fetal abdominal circumference, and fetal and neonate HC. The DMRs studied were enriched in 142 biological processes, 21 molecular functions,and 17 cellular components with terms directed for the fatty acids metabolism. Three DMGMs were identified:COL3A1, ITGA4 and KLRK1. INA targeted chronic diseases and maternal metabolism contributing to an epigenetic understanding of the involvement of GWG in maternal metabolism and fetal-neonatal growth.

Annotating microbial functions with ProkFunFind

Analyzing microbial genomes has become an essential part of microbiology research, giving valuable insights into the functions and evolution of microbial species. Identifying genes of interest and assigning putative annotations to those genes is a central task in genome analysis, and a plethora of tools and approaches have been developed for this task. The ProkFunFind tool was developed to bridge the gap between these various annotation approaches, providing a flexible and customizable search approach to annotate microbial functions. ProkFunFind is designed around hierarchical definitions of biological functions, where individual genes can be identified using heterogeneous search terms consisting of sequences, profile hidden Markov models, protein domains, and orthology groups. This flexible and customizable search approach allows for searches to be tailored to specific biological functions, and the search results are output in multiple formats to facilitate downstream analyses. The utility of the ProkFunFind search tool was demonstrated through its application in searching for bacterial flagella, which are complex organelles composed of multiple genes. Overall, ProkFunFind provides an accessible and flexible way to integrate multiple types of annotation and sequence data while annotating biological functions in microbial genomes.IMPORTANCEGenome sequencing and analysis are increasingly important parts of microbiology, providing a way to predict metabolic functions, identify virulence factors, and understand the evolution of microbes. The expanded use of genome sequencing has also brought an abundance of search and annotation methods, but integrating the information from these different methods can be challenging and is often done through ad hoc approaches. To bridge the gap between different types of annotations, we developed ProkFunFind, a flexible and customizable search tool incorporating multiple search approaches and annotation types to annotate microbial functions. We demonstrated the utility of ProkFunFind by searching for gene clusters encoding flagellar genes using a combination of different annotation types and searches. Overall, ProkFunFind provides a reproducible and flexible way to identify gene clusters of interest, facilitating the meaningful analysis of new and existing microbial genomes.

Analyzing the Diversity of MYB Family Response Strategies to Drought Stress in Different Flax Varieties Based on Transcriptome Data

The MYB transcription factor family has numerous members, and is involved in biological activities, such as ABA signaling, which plays an important role in a plant's resistance to abiotic stresses such as drought. However, the diversity of MYB members that respond to drought stress and their regulatory mechanisms in different flax varieties were unclear. In this study, we obtained 855.69 Gb of clean data from 120 flax root samples from 20 flax (Linum usitatissimum L.) varieties, assembled 92,861 transcripts, and identified 434 MYB family members in each variety. The expression profiles of the MYB transcription factor family from 20 flax varieties under drought stress were analyzed. The results indicated that there are four strategies by which the MYB family responds to drought stress in these 20 flax varieties, each of which has its own specific processes, such as development, reproduction, and localization processes. The four strategies also include common biological processes, such as stimulus responses, metabolic processes, and biological regulation. The WGCNA method was subsequently employed to identify key members of the MYB family involved in response strategies to drought stress. The results demonstrated that a 1R-MYB subfamily gene co-expression network is significantly related to the gibberellin response and cytokinin-activated signaling pathway processes in the 'Strategy 4' for MYB family response to drought, identifying core genes such as Lus.scaffold70.240. Our results showed a diversity of MYB family responses to drought stress within flax varieties, and these results contribute to deciphering the mechanisms of the MYB family regulation of drought resistance. This will promote the more accurate breeding development of flax to adapt to agricultural production under drought conditions.

A new chromosome-level genome assembly and annotation of Cryptosporidium meleagridis

Cryptosporidium spp. are medically and scientifically relevant protozoan parasites that cause severe diarrheal illness in infants and immunosuppressed populations as well as animals. Although most human Cryptosporidium infections are caused by C. parvum and C. hominis, there are several other human-infecting species including C. meleagridis, which is commonly observed in developing countries. Here, we polished and annotated a long-read genome sequence assembly for C. meleagridis TU1867, a species which infects birds and humans. The genome sequence was generated using a combination of whole genome amplification (WGA) and long-read Oxford Nanopore Technologies sequencing. The assembly was then polished with Illumina data. The chromosome-level genome assembly is 9.2 Mbp with a contig N50 of 1.1 Mb. Annotation revealed 3,923 protein-coding genes. A BUSCO analysis indicates a completeness of 96.6% (n=446), including 430 (96.4%) single-copy and 1 (0.224%) duplicated apicomplexan conserved gene(s). The new C. meleagridis genome assembly is nearly gap-free and provides a valuable new resource for the Cryptosporidium community and future studies on evolution and host-specificity.

Charge cluster occurrence in land plants' mitochondrial proteomes with functional and structural insights

The Charge Clusters (CCs) are involved in key functions and are distributed according to the organism, the protein's type, and the charge of amino acids. In the present study, we have explored the occurrence, position, and annotation as a first large-scale study of the CCs in land plants mitochondrial proteomes. A new python script was used for data curation. The Finding Clusters Charge in Protein Sequences Program was performed after adjusting the reading window size. A 44316 protein sequences belonging to 52 species of land plants were analysed. The occurrence of Negative Charge Clusters (NCCs) (1.2%) is two times more frequent than the Positive Charge Clusters (PCCs) (0.64%). Moreover, 39 and 30 NCCs were conserved in 88 and 41 proteins in intra and in inter proteomes respectively, while 14 and 21 PCCs were conserved in 53 and 85 protein sequences in intra and inter proteomes consecutively. Sequences carrying mixed CCs are rare (0.12%). Despite this low abundance, CCs play a crucial role in protein function. The CCs tend to be located mainly in the terminal regions of proteins which guarantees specific protein targeting and import into the mitochondria. In addition, the functional annotation of CCs according to Gene Ontology shows that CCs are involved in binding functions of either proteins or macromolecules which are deployed in different metabolic and cellular processes such as RNA editing and transcription. This study may provide valuable information while considering the CCs in understanding the environmental adaptation of plants.Communicated by Ramaswamy H. Sarma.

Full-length transcriptome characterization of Platycladus orientalis based on the PacBio platform

As a unique and native conifer in China, Platycladus orientalis is widely used in soil erosion control, garden landscapes, timber, and traditional Chinese medicine. However, due to the lack of reference genome and transcriptome, it is limited to the further molecular mechanism research and gene function mining. To develop a full-length reference transcriptome, tissues from five different parts of P. orientalis and four cone developmental stages were sequenced and analyzed by single-molecule real-time (SMRT) sequencing through the PacBio platform in this study. Overall, 37,111 isoforms were detected by PacBio with an N50 length of 2,317 nt, an average length of 1,999 bp, and the GC content of 41.81%. Meanwhile, 36,120 coding sequences, 5,645 simple sequence repeats (SSRs), 1,201 non-coding RNAs (lncRNAs), and 182 alternative splicing (AS) events with five types were identified using the results obtained from the PacBio transcript isoforms. Furthermore, 1,659 transcription factors (TFs) were detected and belonged to 51 TF families. A total of 35,689 transcripts (96.17%) were annotated through the NCBI nr, KOG, Swiss-Prot and KEGG databases, and 385 transcript isoforms related to 8 types of hormones were identified incorporated into plant hormone signal transduction pathways. The assembly and revelation of the full-length transcriptome of P. orientalis offer a pioneering insight for future investigations into gene function and genetic breeding within Platycladus species.

Whole-genome sequencing and comparative genome analysis of Xanthomonas fragariae YM2 causing angular leaf spot disease in strawberry

Background

Angular leaf spot disease caused by plant pathogenic bacterium Xanthomonas fragariae seriously threatens strawberry crop production globally.

Methods

In this study, we sequenced the whole genome of X. fragariae YM2, isolated from Yunnan Province, China. In addition, we performed a comparative genome analysis of X. fragariae YM2 with two existing strains of X. fragariae YL19 and SHQP01 isolated from Liaoning and Shanghai, respectively.

Results

The results of Nanopore sequencing showed that X. fragariae YM2 comprises one single chromosome with a contig size of 4,263,697 bp, one plasmid contig size of 0.39 Mb, a GC content ratio of 62.27%, and 3,958 predicted coding genes. The genome of YM2 comprises gum, hrp, rpf, and xps gene clusters and lipopolysaccharide (LPS), which are typical virulence factors in Xanthomonas species. By performing a comparative genomic analysis between X. fragariae strains YM2, YL19, and SHQP01, we found that strain YM2 is similar to YL19 and SHQP01 regarding genome size and GC contents. However, there are minor differences in the composition of major virulence factors and homologous gene clusters. Furthermore, the results of collinearity analysis demonstrated that YM2 has lower similarity and longer evolutionary distance with YL19 and SHQP01, but YL19 is more closely related to SHQP01.

Conclusions

The availability of this high-quality genetic resource will serve as a basic tool for investigating the biology, molecular pathogenesis, and virulence of X. fragariae YM2. In addition, unraveling the potential vulnerabilities in its genetic makeup will aid in developing more effective disease suppression control measures.

Identification and preliminary characterization of conserved uncharacterized proteins from Chlamydomonas reinhardtii, Arabidopsis thaliana, and Setaria viridis

The rapid accumulation of sequenced plant genomes in the past decade has outpaced the still difficult problem of genome-wide protein-coding gene annotation. A substantial fraction of protein-coding genes in all plant genomes are poorly annotated or unannotated and remain functionally uncharacterized. We identified unannotated proteins in three model organisms representing distinct branches of the green lineage (Viridiplantae): Arabidopsis thaliana (eudicot), Setaria viridis (monocot), and Chlamydomonas reinhardtii (Chlorophyte alga). Using similarity searching, we identified a subset of unannotated proteins that were conserved between these species and defined them as Deep Green proteins. Bioinformatic, genomic, and structural predictions were performed to begin classifying Deep Green genes and proteins. Compared to whole proteomes for each species, the Deep Green set was enriched for proteins with predicted chloroplast targeting signals predictive of photosynthetic or plastid functions, a result that was consistent with enrichment for daylight phase diurnal expression patterning. Structural predictions using AlphaFold and comparisons to known structures showed that a significant proportion of Deep Green proteins may possess novel folds. Though only available for three organisms, the Deep Green genes and proteins provide a starting resource of high-value targets for further investigation of potentially new protein structures and functions conserved across the green lineage.

Comparative genomic diversity analysis of copy number variations (CNV) in indicine and taurine cattle thriving in Europe and Indian subcontinent

Copy number variations (CNVs) include deletions, duplications, and insertions that are larger than 50 bp in size causing structural variation responsible for diversity, adaptation, and breed development. Indian cattle breeds are highly diverse from the taurine breeds. The pattern of CNVRs in 191 animals belonging to 39 cattle breeds (four Indicine and 35 Taurine) was studied based on Illumina 777K BovineHD chip data. The Indicine breeds revealed 2590 CNVs and 335 copy number variation regions (CNVRs) in autosomes. Out of the identified CNVs, 50 were found to be novel. Structure analysis revealed admixed nature of Siri. Neighbor joining tree from CNVR data showed that hot (Kankrej and Hallikar) and cold (Ladakhi and Siri) adapted cattle breeds clustered separately. CNVR of Indian and European breeds revealed that Balkan and Italian breeds of Podolian group are admixed with Indian cattle breeds corroborating indicine introgression (6.1-13.5%). CNVRs spanning the regions of olfactory receptors and immune system genes were identified. AMOVA revealed 9% variation among populations which is 2% greater than SNP based studies showing higher inclusion of variation by CNVR. Detailed analysis of CNVs/CNVRs in Indian cattle adapted to hot and cold climate, and their diversity among worldwide cattle is presented in this study.

A Novel Subcluster of Closely Related Bacillus Phages with Distinct Tail Fiber/Lysin Gene Combinations

Bacteriophages (phages) are the most numerous entities on Earth, but we have only scratched the surface of describing phage diversity. We isolated seven Bacillus subtilis phages from desert soil in the southwest United States and then sequenced and characterized their genomes. Comparative analyses revealed high nucleotide and amino acid similarity between these seven phages, which constitute a novel subcluster. Interestingly, the tail fiber and lysin genes of these phages seem to come from different origins and carry out slightly different functions. These genes were likely acquired by this subcluster of phages via horizontal gene transfer. In conjunction with host range assays, our data suggest that these phages are adapting to hosts with different cell walls.

Multiple-microarray analysis for identification of key genes involved in diabetic nephropathy

The purpose of our study was to discover genes with significantly aberrant expression in diabetic nephropathy (DN) and to determine their potential mechanism. We acquired renal tubules, glomerulus and blood samples data from DN patients and controls from the GEO database. The differentially expressed genes (DEGs) in renal tubules, glomerulus and blood samples between DN patients and controls were studied. Based on these DEGs, we carried out the functional annotation and constructed protein-protein interaction (PPI) network. By comparing DN patients and controls of DEGs, we acquired the shared DGEs in renal tubules, glomerulus and blood samples of DN patients and controls. DN patients compared to controls, we obtained 3000 DEGs, 3064 DEGs, and 2296 DEGs in renal tubules, glomerulus and blood samples, respectively. The PPI networks of top 40 DEGs in renal tubules, glomerulus and blood samples was consisted of 229 nodes and 229 edges, 540 nodes and 606 edges, and 132 nodes and 124 edges, respectively. In total, 21 shared genes were finally found, including CASP3, DHCR24, CXCL1, GYPC, INHBA, LTF, MT1G, MUC1, NINJ1, PFKFB3, PPP1R3C, CCL5, SRSF7, PHLDA2, RBM39, WTAP, BASP1, PLK2, PDK2, PNPLA4, and SNED1. These genes may be associated with the DN process. Our study provides a basis to explore the potential mechanism and identify novel therapeutic targets for DN.

Linc2function: A Comprehensive Pipeline and Webserver for Long Non-Coding RNA (lncRNA) Identification and Functional Predictions Using Deep Learning Approaches

Long non-coding RNAs (lncRNAs), comprising a significant portion of the human transcriptome, serve as vital regulators of cellular processes and potential disease biomarkers. However, the function of most lncRNAs remains unknown, and furthermore, existing approaches have focused on gene-level investigation. Our work emphasizes the importance of transcript-level annotation to uncover the roles of specific transcript isoforms. We propose that understanding the mechanisms of lncRNA in pathological processes requires solving their structural motifs and interactomes. A complete lncRNA annotation first involves discriminating them from their coding counterparts and then predicting their functional motifs and target bio-molecules. Current in silico methods mainly perform primary-sequence-based discrimination using a reference model, limiting their comprehensiveness and generalizability. We demonstrate that integrating secondary structure and interactome information, in addition to using transcript sequence, enables a comprehensive functional annotation. Annotating lncRNA for newly sequenced species is challenging due to inconsistencies in functional annotations, specialized computational techniques, limited accessibility to source code, and the shortcomings of reference-based methods for cross-species predictions. To address these challenges, we developed a pipeline for identifying and annotating transcript sequences at the isoform level. We demonstrate the effectiveness of the pipeline by comprehensively annotating the lncRNA associated with two specific disease groups. The source code of our pipeline is available under the MIT licensefor local use by researchers to make new predictions using the pre-trained models or to re-train models on new sequence datasets. Non-technical users can access the pipeline through a web server setup.

Evaluating restriction enzyme selection for reduced representation sequencing in conservation genomics

Conservation genomic studies in non-model organisms generally rely on reduced representation sequencing techniques based on restriction enzymes to identify population structure as well as candidate loci for local adaptation. While the expectation is that the reduced representation of the genome is randomly distributed, the proportion of the genome sampled might depend on the GC content of the recognition site of the restriction enzyme used. Here, we evaluated the distribution and functional composition of loci obtained after a reduced representation approach using Genotyping-by-Sequencing (GBS). To do so, we compared experimental data from two endemic fish species (Symphodus ocellatus and Symphodus tinca, EcoT22I enzyme) and two ecosystem engineer sea urchins (Paracentrotus lividus and Arbacia lixula, ApeKI enzyme). In brief, we mapped the sequenced loci to the phylogenetically closest reference genome available (Labrus bergylta in the fish and Strongylocentrotus purpuratus in the sea urchin datasets), classified them as exonic, intronic and intergenic, and studied their function by using Gene Ontology (GO) terms. We also simulated the effect of using both enzymes in the two reference genomes. In both simulated and experimental data, we detected an enrichment towards exonic or intergenic regions depending on the restriction enzyme used and failed to detect differences between total loci and candidate loci for adaptation in the empirical dataset. Most of the functions assigned to the mapped loci were shared between the four species and involved a myriad of general functions. Our results highlight the importance of restriction enzyme selection and the need for high-quality annotated genomes in conservation genomic studies.

Chromosome-level assembly of Dictyophora rubrovolvata genome using third-generation DNA sequencing and Hi-C analysis

Dictyophora rubrovolvata, a rare edible mushroom with both nutritional and medicinal values, was regarded as the "queen of the mushroom" for its attractive appearance. Dictyophora rubrovolvata has been widely cultivated in China in recent years, and many researchers were focusing on its nutrition, culture condition, and artificial cultivation. Due to a lack of genomic information, research on bioactive substances, cross breeding, lignocellulose degradation, and molecular biology is limited. In this study, we report a chromosome-level reference genome of D. rubrovolvata using the PacBio single-molecule real-time-sequencing technique and high-throughput chromosome conformation capture (Hi-C) technologies. A total of 1.83 Gb circular consensus sequencing reads representing ∼983.34 coverage of the D. rubrovolvata genome were generated. The final genome was assembled into 136 contigs with a total length of 32.89 Mb. The scaffold and contig N50 length were 2.71 and 2.48 Mb, respectively. After chromosome-level scaffolding, 11 chromosomes with a total length of 28.24 Mb were constructed. Genome annotation further revealed that 9.86% of the genome was composed of repetitive sequences, and a total of 508 noncoding RNA (rRNA: 329, tRNA: 150, ncRNA: 29) were annotated. In addition, 9,725 protein-coding genes were predicted, among which 8,830 (90.79%) genes were predicted using homology or RNA-seq. Benchmarking Universal Single-Copy Orthologs results further revealed that there were 80.34% complete single-copy fungal orthologs. In this study, a total of 360 genes were annotated as belonging to the carbohydrate-active enzymes family. Further analysis also predicted 425 cytochromes P450 genes, which can be classified into 41 families. This highly accurate, chromosome-level reference genome of D. rubrovolvata will provide essential genomic information for understanding the molecular mechanism in its fruiting body formation during morphological development and facilitate the exploitation of medicinal compounds produced by this mushroom.

Genomic comparison between two Inonotus hispidus strains isolated from growing in different tree species

Inonotus hispidus mainly growing in broad-leaved trees, including Morus alba, Fraxinus mandshurica, and Ulmus macrocarpa etc. The fruiting body of I. hispidus growing in M. alba (hereafter as MA) is used as a traditional Chinese medicine "Sanghuang". However, differences between the genetic material basis of I. hispidus growing in other tree species have not been reported. Therefore, in this paper, the genomic comparison between MA and I. hispidus growing in F. mandshurica (hereafter as FM) were studied. The whole genome of MA monokaryon was sequenced by Illumina combined with Pac Bio platform. Next, genome assembly, genome component prediction and genome functional annotation were performed. Comparative genomics analysis was performed between FM monokaryon and MA monokaryon, using MA as the reference. The results showed that, MA had 24 contigs with a N50 length of 2.6 Mb. Specifically, 5,342, 6,564, 1,595, 383 and 123 genes were annotated from GO, KEGG, KOG, CAZymes and CYP450, respectively. Moreover, comparative genomics showed that, the coding genes and total number of genes annotated in different databases of FM were higher than that of MA. This study provides a foundation for the medicinal application of FM as MA from the perspective of genetic composition.

graph-GPA 2.0: improving multi-disease genetic analysis with integration of functional annotation data

Genome-wide association studies (GWAS) have successfully identified a large number of genetic variants associated with traits and diseases. However, it still remains challenging to fully understand the functional mechanisms underlying many associated variants. This is especially the case when we are interested in variants shared across multiple phenotypes. To address this challenge, we propose graph-GPA 2.0 (GGPA 2.0), a statistical framework to integrate GWAS datasets for multiple phenotypes and incorporate functional annotations within a unified framework. Our simulation studies showed that incorporating functional annotation data using GGPA 2.0 not only improves the detection of disease-associated variants, but also provides a more accurate estimation of relationships among diseases. Next, we analyzed five autoimmune diseases and five psychiatric disorders with the functional annotations derived from GenoSkyline and GenoSkyline-Plus, along with the prior disease graph generated by biomedical literature mining. For autoimmune diseases, GGPA 2.0 identified enrichment for blood-related epigenetic marks, especially B cells and regulatory T cells, across multiple diseases. Psychiatric disorders were enriched for brain-related epigenetic marks, especially the prefrontal cortex and the inferior temporal lobe for bipolar disorder and schizophrenia, respectively. In addition, the pleiotropy between bipolar disorder and schizophrenia was also detected. Finally, we found that GGPA 2.0 is robust to the use of irrelevant and/or incorrect functional annotations. These results demonstrate that GGPA 2.0 can be a powerful tool to identify genetic variants associated with each phenotype or those shared across multiple phenotypes, while also promoting an understanding of functional mechanisms underlying the associated variants.

SMRT Sequencing Technology Was Used to Construct the Batocera horsfieldi (Hope) Transcriptome and Reveal Its Features

Batocera horsfieldi (Hope) (Coleoptera: Cerambycidae) is an important forest pest in China that mainly infests timber and economic forests. This pest primarily causes plant tissue to necrotize, rot, and eventually die by feeding on the woody parts of tree trunks. To gain a deeper understanding of the genetic mechanism of B. horsfieldi, this study employed single-molecule real-time sequencing (SMRT) and Illumina RNA-seq technologies to conduct full-length transcriptome sequencing of the insect. Total RNA extracted from male and female adults was mixed and subjected to SMRT sequencing, generating a complete transcriptome. Transcriptome analysis, prediction of long non-coding RNA (lncRNA), coding sequences (CDs), analysis of simple sequence repeats (SSR), prediction of transcription factors, and functional annotation of transcripts were performed in this study. The collective 20,356,793 subreads (38.26 G, clean reads) were generated, including 432,091 circular consensus sequences and 395,851 full-length non-chimera reads. The full-length non-chimera reads (FLNC) were clustered and redundancies were removed, resulting in 39,912 consensus reads. SSR and ANGEL software v3.0 were used for predicting SSR and CDs. In addition, four tools were used for annotating 6058 lncRNAs, identifying 636 transcription factors. Furthermore, a total of 84,650 transcripts were functionally annotated in seven different databases. This is the first time that the full-length transcriptome of B. horsfieldi has been obtained using SMRT sequencing. This provides an important foundation for investigating the gene regulation underlying the interaction between B. horsfieldi and its host plants through gene editing in the future and provides a scientific basis for the prevention and control of B. horsfieldi.

Lessons on fruiting body morphogenesis from genomes and transcriptomes of Agaricomycetes

Fruiting bodies (sporocarps, sporophores or basidiomata) of mushroom-forming fungi (Agaricomycetes) are among the most complex structures produced by fungi. Unlike vegetative hyphae, fruiting bodies grow determinately and follow a genetically encoded developmental program that orchestrates their growth, tissue differentiation and sexual sporulation. In spite of more than a century of research, our understanding of the molecular details of fruiting body morphogenesis is still limited and a general synthesis on the genetics of this complex process is lacking. In this paper, we aim at a comprehensive identification of conserved genes related to fruiting body morphogenesis and distil novel functional hypotheses for functionally poorly characterised ones. As a result of this analysis, we report 921 conserved developmentally expressed gene families, only a few dozens of which have previously been reported to be involved in fruiting body development. Based on literature data, conserved expression patterns and functional annotations, we provide hypotheses on the potential role of these gene families in fruiting body development, yielding the most complete description of molecular processes in fruiting body morphogenesis to date. We discuss genes related to the initiation of fruiting, differentiation, growth, cell surface and cell wall, defence, transcriptional regulation as well as signal transduction. Based on these data we derive a general model of fruiting body development, which includes an early, proliferative phase that is mostly concerned with laying out the mushroom body plan (via cell division and differentiation), and a second phase of growth via cell expansion as well as meiotic events and sporulation. Altogether, our discussions cover 1 480 genes of Coprinopsis cinerea, and their orthologs in Agaricus bisporus, Cyclocybe aegerita, Armillaria ostoyae, Auriculariopsis ampla, Laccaria bicolor, Lentinula edodes, Lentinus tigrinus, Mycena kentingensis, Phanerochaete chrysosporium, Pleurotus ostreatus, and Schizophyllum commune, providing functional hypotheses for ~10 % of genes in the genomes of these species. Although experimental evidence for the role of these genes will need to be established in the future, our data provide a roadmap for guiding functional analyses of fruiting related genes in the Agaricomycetes. We anticipate that the gene compendium presented here, combined with developments in functional genomics approaches will contribute to uncovering the genetic bases of one of the most spectacular multicellular developmental processes in fungi. Citation: Nagy LG, Vonk PJ, Künzler M, Földi C, Virágh M, Ohm RA, Hennicke F, Bálint B, Csernetics Á, Hegedüs B, Hou Z, Liu XB, Nan S, M. Pareek M, Sahu N, Szathmári B, Varga T, Wu W, Yang X, Merényi Z (2023). Lessons on fruiting body morphogenesis from genomes and transcriptomes of Agaricomycetes. Studies in Mycology 104: 1-85. doi: 10.3114/sim.2022.104.01.

Comparison of the Strengths and Weaknesses of Machine Learning Algorithms and Feature Selection on KEGG Database Microbial Gene Pathway Annotation and Its Effects on Reconstructed Network Topology

The development of tools for the annotation of genes from newly sequenced species has not evolved much from homologous alignment to prior annotated species. While the quality of gene annotations continues to decline as we sequence and assemble more evolutionary distant gut microbiome species, machine learning presents a high quality alternative to traditional techniques. In this study, we investigate the relative performance of common classical and nonclassical machine learning algorithms in the problem of gene annotation using human microbiome-associated species genes from the KEGG database. The majority of the ensemble, clustering, and deep learning algorithms that we investigated showed higher prediction accuracy than CD-Hit in predicting partial KEGG function. Motif-based, machine-learning methods of annotation in new species were faster and had higher precision-recall than methods of homologous alignment or orthologous gene clustering. Gradient boosted ensemble methods and neural networks also predicted higher connectivity in reconstructed KEGG pathways, finding twice as many new pathway interactions than blast alignment. The use of motif-based, machine-learning algorithms in annotation software will allow researchers to develop powerful tools to interact with bacterial microbiomes in ways previously unachievable through homologous sequence alignment alone.

Structural and functional annotation of solute carrier transporters: implication for drug discovery

INTRODUCTION

Solute carriers (SLCs) represent the largest group of membrane transporters in the human genome. They play a central role in controlling the compartmentalization of metabolism and most of this superfamily is linked to human disease. Despite being in general considered druggable and attractive therapeutic targets, many SLCs remain poorly annotated, both functionally and structurally.

AREAS COVERED

The aim of this review is to provide an overview of functional and structural parameters of SLCs that play important roles in their druggability. To do this, the authors provide an overview of experimentally solved structures of human SLCs, with emphasis on structures solved in complex with chemical modulators. From the functional annotations, the authors focus on SLC localization and SLC substrate annotations.

EXPERT OPINION

Recent progress in the structural and functional annotations allows to refine the SLC druggability index. Particularly the increasing number of experimentally solved structures of SLCs provides insights into mode-of-action of a significant number of chemical modulators of SLCs.

RoseAP: an analytical platform for gene function of Rosa rugosa

Rosa rugosa, a perennial shrub belonging to family Rosaceae, is a well-known ornamental plant. Its petals contain an abundance of essential oils and anthocyanins with enormous economic and health benefits when used as edible or cosmetic ingredients. The whole genome of R. rugosa was sequenced in 2021, which provided opportunities and challenges for gene regulation. However, many gene functions remain unknown. Therefore, an analytical platform named RoseAP (http://www.gzybioinformatics.cn/RoseAP/index.php) for the functional analysis of R. rugosa genes was constructed. It improved the gene annotation rate by integrating and analyzing genomic and transcriptomic datasets. First, 38,815 genes, covering 97.76% of the coding genes, were annotated functionally and structurally using a variety of algorithms and rules. Second, a total of 33 transcriptome samples were integrated, including 23 samples from our lab and 10 samples from the SRA database. A co-expression network containing approximately 29,657 positive or negative gene pairs, covering 74.7% of the coding genes, was constructed based on PCC and MR algorithms. Network analysis revealed that the DFR function was closely related to anthocyanin metabolism. It demonstrated the reliability of the network. Several SAUR genes of R. rugosa shared similar expression patterns. RoseAP was used to determine the sequence, structure, functional annotation, expression profile, regulatory network, and functional modules at the transcriptional and protein levels by inputting gene IDs. In addition, auxiliary analytical tools, including BLAST, gene set enrichment, orthologue conversion, gene sequence extraction, gene expression value extraction, and JBrowse, were utilized. Regular updates to RoseAP are expected to facilitate mining of gene function and promote genetic improvement in R. rugosa.

Shared genetics and causal relationships between major depressive disorder and COVID-19 related traits: a large-scale genome-wide cross-trait meta-analysis

Introduction

The comorbidity between major depressive disorder (MDD) and coronavirus disease of 2019 (COVID-19) related traits have long been identified in clinical settings, but their shared genetic foundation and causal relationships are unknown. Here, we investigated the genetic mechanisms behind COVID-19 related traits and MDD using the cross-trait meta-analysis, and evaluated the underlying causal relationships between MDD and 3 different COVID-19 outcomes (severe COVID-19, hospitalized COVID-19, and COVID-19 infection).

Methods

In this study, we conducted a comprehensive analysis using the most up-to-date and publicly available GWAS summary statistics to explore shared genetic etiology and the causality between MDD and COVID-19 outcomes. We first used genome-wide cross-trait meta-analysis to identify the pleiotropic genomic SNPs and the genes shared by MDD and COVID-19 outcomes, and then explore the potential bidirectional causal relationships between MDD and COVID-19 outcomes by implementing a bidirectional MR study design. We further conducted functional annotations analyses to obtain biological insight for shared genes from the results of cross-trait meta-analysis.

Results

We have identified 71 SNPs located on 25 different genes are shared between MDD and COVID-19 outcomes. We have also found that genetic liability to MDD is a causal factor for COVID-19 outcomes. In particular, we found that MDD has causal effect on severe COVID-19 (OR = 1.832, 95% CI = 1.037-3.236) and hospitalized COVID-19 (OR = 1.412, 95% CI = 1.021-1.953). Functional analysis suggested that the shared genes are enriched in Cushing syndrome, neuroactive ligand-receptor interaction.

Discussion

Our findings provide convincing evidence on shared genetic etiology and causal relationships between MDD and COVID-19 outcomes, which is crucial to prevention, and therapeutic treatment of MDD and COVID-19.

Characterization and Genome Study of a Newly Isolated Temperate Phage Belonging to a New Genus Targeting Alicyclobacillus acidoterrestris

The spoilage of juices by Alicyclobacillus spp. remains a serious problem in industry and leads to economic losses. Compounds such as guaiacol and halophenols, which are produced by Alicyclobacillus, create undesirable flavors and odors and, thus, decrease the quality of juices. The inactivation of Alicyclobacillus spp. constitutes a challenge because it is resistant to environmental factors, such as high temperatures, and active acidity. However, the use of bacteriophages seems to be a promising approach. In this study, we aimed to isolate and comprehensively characterize a novel bacteriophage targeting Alicyclobacillus spp. The Alicyclobacillus phage strain KKP 3916 was isolated from orchard soil against the Alicyclobacillus acidoterrestris strain KKP 3133. The bacterial host's range and the effect of phage addition at different rates of multiplicity of infections (MOIs) on the host's growth kinetics were determined using a Bioscreen C Pro growth analyzer. The Alicyclobacillus phage strain KKP 3916, retained its activity in a wide range of temperatures (from 4 °C to 30 °C) and active acidity values (pH from 3 to 11). At 70 °C, the activity of the phage decreased by 99.9%. In turn, at 80 °C, no activity against the bacterial host was observed. Thirty minutes of exposure to UV reduced the activity of the phages by almost 99.99%. Based on transmission-electron microscopy (TEM) and whole-genome sequencing (WGS) analyses, the Alicyclobacillus phage strain KKP 3916 was classified as a tailed bacteriophage. The genomic sequencing revealed that the newly isolated phage had linear double-stranded DNA (dsDNA) with sizes of 120 bp and 131 bp and 40.3% G+C content. Of the 204 predicted proteins, 134 were of unknown function, while the remainder were annotated as structural, replication, and lysis proteins. No genes associated with antibiotic resistance were found in the genome of the newly isolated phage. However, several regions, including four associated with integration into the bacterial host genome and excisionase, were identified, which indicates the temperate (lysogenic) life cycle of the bacteriophage. Due to the risk of its potential involvement in horizontal gene transfer, this phage is not an appropriate candidate for further research on its use in food biocontrol. To the best of our knowledge, this is the first article on the isolation and whole-genome analysis of the Alicyclobacillus-specific phage.

Newly Isolated Virulent Salmophages for Biocontrol of Multidrug-Resistant Salmonella in Ready-to-Eat Plant-Based Food

Due to irrational antibiotic stewardship, an increase in the incidence of multidrug resistance of bacteria has been observed recently. Therefore, the search for new therapeutic methods for pathogen infection treatment seems to be necessary. One of the possibilities is the utilization of bacteriophages (phages)—the natural enemies of bacteria. Thus, this study is aimed at the genomic and functional characterization of two newly isolated phages targeting MDR Salmonella enterica strains and their efficacy in salmonellosis biocontrol in raw carrot–apple juice. The Salmonella phage vB_Sen-IAFB3829 (Salmonella phage strain KKP 3829) and Salmonella phage vB_Sen-IAFB3830 (Salmonella phage strain KKP 3830) were isolated against S. I (6,8:l,-:1,7) strain KKP 1762 and S. Typhimurium strain KKP 3080 host strains, respectively. Based on the transmission electron microscopy (TEM) and whole-genome sequencing (WGS) analyses, the viruses were identified as members of tailed bacteriophages from the Caudoviricetes class. Genome sequencing revealed that these phages have linear double-stranded DNA and sizes of 58,992 bp (vB_Sen-IAFB3829) and 50,514 bp (vB_Sen-IAFB3830). Phages retained their activity in a wide range of temperatures (from −20 °C to 60 °C) and active acidity values (pH from 3 to 11). The exposure of phages to UV radiation significantly decreased their activity in proportion to the exposure time. The application of phages to the food matrices significantly reduced the level of Salmonella contamination compared to the control. Genome analysis showed that both phages do not encode virulence or toxin genes and can be classified as virulent bacteriophages. Virulent characteristics and no possible pathogen factors make examined phages feasible to be potential candidates for food biocontrol.

Functional annotation of uncharacterized proteins from Fusobacterium nucleatum: identification of virulence factors

Fusobacterium nucleatum is a gram-negative bacteria associated with diverse infections like appendicitis and colorectal cancer. It mainly attacks the epithelial cells in the oral cavity and throat of the infected individual. It has a single circular genome of 2.7 Mb. Many proteins in F. nucleatum genome are listed as "Uncharacterized." Annotation of these proteins is crucial for obtaining new facts about the pathogen and deciphering the gene regulation, functions, and pathways along with discovery of novel target proteins. In the light of new genomic information, an armoury of bioinformatic tools were used for predicting the physicochemical parameters, domain and motif search, pattern search, and localization of the uncharacterized proteins. The programs such as receiver operating characteristics determine the efficacy of the databases that have been employed for prediction of different parameters at 83.6%. Functions were successfully assigned to 46 uncharacterized proteins which included enzymes, transporter proteins, membrane proteins, binding proteins, etc. Apart from the function prediction, the proteins were also subjected to string analysis to reveal the interacting partners. The annotated proteins were also put through homology-based structure prediction and modeling using Swiss PDB and Phyre2 servers. Two probable virulent factors were also identified which could be investigated further for potential drug-related studies. The assigning of functions to uncharacterized proteins has shown that some of these proteins are important for cell survival inside the host and can act as effective drug targets.

Shared Genetic Architecture between Muscle and Bone: Identification and Functional Implications of EPDR1, PKDCC, and SPTBN1

Recent studies suggest a shared genetic architecture between muscle and bone, yet the underlying molecular mechanisms remain elusive. This study aims to identify the functionally annotated genes with shared genetic architecture between muscle and bone using the most up-to-date genome-wide association study (GWAS) summary statistics from bone mineral density (BMD) and fracture-related genetic variants. We employed an advanced statistical functional mapping method to investigate shared genetic architecture between muscle and bone, focusing on genes highly expressed in muscle tissue. Our analysis identified three genes, EPDR1, PKDCC, and SPTBN1, highly expressed in muscle tissue and previously unlinked to bone metabolism. About 90% and 85% of filtered Single-Nucleotide Polymorphisms were located in the intronic and intergenic regions for the threshold at P ≤ 5 × 10 - 8 and P ≤ 5 × 10 - 100 , respectively. EPDR1 was highly expressed in multiple tissues, including muscle, adrenal gland, blood vessels, and thyroid. SPTBN1 was highly expressed in all 30 tissue types except blood, while PKDCC was highly expressed in all 30 tissue types except the brain, pancreas, and skin. Our study provides a framework for using GWAS findings to highlight functional evidence of crosstalk between multiple tissues based on shared genetic architecture between muscle and bone. Further research should focus on functional validation, multi-omics data integration, gene-environment interactions, and clinical relevance in musculoskeletal disorders.

Functional Profiling and Evolutionary Analysis of a Marine Microalgal Virus Pangenome

Phycodnaviridae are large double-stranded DNA viruses, which facilitate studies of host-virus interactions and co-evolution due to their prominence in algal infection and their role in the life cycle of algal blooms. However, the genomic interpretation of these viruses is hampered by a lack of functional information, stemming from the surprising number of hypothetical genes of unknown function. It is also unclear how many of these genes are widely shared within the clade. Using one of the most extensively characterized genera, Coccolithovirus, as a case study, we combined pangenome analysis, multiple functional annotation tools, AlphaFold structural modeling, and literature analysis to compare the core and accessory pangenome and assess support for novel functional predictions. We determined that the Coccolithovirus pangenome shares 30% of its genes with all 14 strains, making up the core. Notably, 34% of its genes were found in at most three strains. Core genes were enriched in early expression based on a transcriptomic dataset of Coccolithovirus EhV-201 algal infection, were more likely to be similar to host proteins than the non-core set, and were more likely to be involved in vital functions such as replication, recombination, and repair. In addition, we generated and collated annotations for the EhV representative EhV-86 from 12 different annotation sources, building up information for 142 previously hypothetical and putative membrane proteins. AlphaFold was further able to predict structures for 204 EhV-86 proteins with a modelling accuracy of good-high. These functional clues, combined with generated AlphaFold structures, provide a foundational framework for the future characterization of this model genus (and other giant viruses) and a further look into the evolution of the Coccolithovirus proteome.

Meta4P: A User-Friendly Tool to Parse Label-Free Quantitative Metaproteomic Data and Taxonomic/Functional Annotations

We present Meta4P (MetaProteins-Peptides-PSMs Parser), an easy-to-use bioinformatic application designed to integrate label-free quantitative metaproteomic data with taxonomic and functional annotations. Meta4P can retrieve, filter, and process identification and quantification data from three levels of inputs (proteins, peptides, PSMs) in different file formats. Abundance data can be combined with taxonomic and functional information and aggregated at different and customizable levels, including taxon-specific functions and pathways. Meta4P output tables, available in various formats, are ready to be used as inputs for downstream statistical analyses. This user-friendly tool is expected to provide a useful contribution to the field of metaproteomic data analysis, helping make it more manageable and straightforward.

Ovule Transcriptome Analysis Discloses Deregulation of Genes and Pathways in Sexual and Apomictic Limonium Species (Plumbaginaceae)

The genus Limonium Mill. (sea lavenders) includes species with sexual and apomixis reproductive strategies, although the genes involved in these processes are unknown. To explore the mechanisms beyond these reproduction modes, transcriptome profiling of sexual, male sterile, and facultative apomictic species was carried out using ovules from different developmental stages. In total, 15,166 unigenes were found to be differentially expressed with apomictic vs. sexual reproduction, of which 4275 were uniquely annotated using an Arabidopsis thaliana database, with different regulations according to each stage and/or species compared. Gene ontology (GO) enrichment analysis indicated that genes related to tubulin, actin, the ubiquitin degradation process, reactive oxygen species scavenging, hormone signaling such as the ethylene signaling pathway and gibberellic acid-dependent signal, and transcription factors were found among differentially expressed genes (DEGs) between apomictic and sexual plants. We found that 24% of uniquely annotated DEGs were likely to be implicated in flower development, male sterility, pollen formation, pollen-stigma interactions, and pollen tube formation. The present study identifies candidate genes that are highly associated with distinct reproductive modes and sheds light on the molecular mechanisms of apomixis expression in Limonium sp.

Comprehensive Functional Annotation of Metagenomes and Microbial Genomes Using a Deep Learning-Based Method

Comprehensive protein function annotation is essential for understanding microbiome-related disease mechanisms in the host organisms. However, a large portion of human gut microbial proteins lack functional annotation. Here, we have developed a new metagenome analysis workflow integrating de novo genome reconstruction, taxonomic profiling, and deep learning-based functional annotations from DeepFRI. This is the first approach to apply deep learning-based functional annotations in metagenomics. We validate DeepFRI functional annotations by comparing them to orthology-based annotations from eggNOG on a set of 1,070 infant metagenomes from the DIABIMMUNE cohort. Using this workflow, we generated a sequence catalogue of 1.9 million nonredundant microbial genes. The functional annotations revealed 70% concordance between Gene Ontology annotations predicted by DeepFRI and eggNOG. DeepFRI improved the annotation coverage, with 99% of the gene catalogue obtaining Gene Ontology molecular function annotations, although they are less specific than those from eggNOG. Additionally, we constructed pangenomes in a reference-free manner using high-quality metagenome-assembled genomes (MAGs) and analyzed the associated annotations. eggNOG annotated more genes on well-studied organisms, such as Escherichia coli, while DeepFRI was less sensitive to taxa. Further, we show that DeepFRI provides additional annotations in comparison to the previous DIABIMMUNE studies. This workflow will contribute to novel understanding of the functional signature of the human gut microbiome in health and disease as well as guiding future metagenomics studies. IMPORTANCE The past decade has seen advancement in high-throughput sequencing technologies resulting in rapid accumulation of genomic data from microbial communities. While this growth in sequence data and gene discovery is impressive, the majority of microbial gene functions remain uncharacterized. The coverage of functional information coming from either experimental sources or inferences is low. To solve these challenges, we have developed a new workflow to computationally assemble microbial genomes and annotate the genes using a deep learning-based model DeepFRI. This improved microbial gene annotation coverage to 1.9 million metagenome-assembled genes, representing 99% of the assembled genes, which is a significant improvement compared to 12% Gene Ontology term annotation coverage by commonly used orthology-based approaches. Importantly, the workflow supports pangenome reconstruction in a reference-free manner, allowing us to analyze the functional potential of individual bacterial species. We therefore propose this alternative approach combining deep-learning functional predictions with the commonly used orthology-based annotations as one that could help us uncover novel functions observed in metagenomic microbiome studies.

In-silico identification and characterization of O-methyltransferase gene family in peanut (Arachis hypogaea L.) reveals their putative roles in development and stress tolerance

Cultivated peanut (Arachis hypogaea) is a leading protein and oil-providing crop and food source in many countries. At the same time, it is affected by a number of biotic and abiotic stresses. O-methyltransferases (OMTs) play important roles in secondary metabolism, biotic and abiotic stress tolerance. However, the OMT genes have not been comprehensively analyzed in peanut. In this study, we performed a genome-wide investigation of A. hypogaea OMT genes (AhOMTs). Gene structure, motifs distribution, phylogenetic history, genome collinearity and duplication of AhOMTs were studied in detail. Promoter cis-elements, protein-protein interactions, and micro-RNAs targeting AhOMTs were also predicted. We also comprehensively studied their expression in different tissues and under different stresses. We identified 116 OMT genes in the genome of cultivated peanut. Phylogenetically, AhOMTs were divided into three groups. Tandem and segmental duplication events played a role in the evolution of AhOMTs, and purifying selection pressure drove the duplication process. AhOMT promoters were enriched in several key cis-elements involved in growth and development, hormones, light, and defense-related activities. Micro-RNAs from 12 different families targeted 35 AhOMTs. GO enrichment analysis indicated that AhOMTs are highly enriched in transferase and catalytic activities, cellular metabolic and biosynthesis processes. Transcriptome datasets revealed that AhOMTs possessed varying expression levels in different tissues and under hormones, water, and temperature stress. Expression profiling based on qRT-PCR results also supported the transcriptome results. This study provides the theoretical basis for further work on the biological roles of AhOMT genes for developmental and stress responses.

ProsmORF-pred: a machine learning-based method for the identification of small ORFs in prokaryotic genomes

Small open reading frames (smORFs) encoding proteins less than 100 amino acids (aa) are known to be important regulators of key cellular processes. However, their computational identification remains a challenge. Based on a comprehensive analysis of known prokaryotic small ORFs, we have developed the ProsmORF-pred resource which uses a machine learning (ML)-based method for prediction of smORFs in the prokaryotic genome sequences. ProsmORF-pred consists of two ML models, one for initiation site recognition in nucleic acid sequences upstream of putative start codons and the other uses translated amino acid sequences to decipher functional protein like sequences. The nucleotide sequence-based initiation site recognition model has been trained using longer ORFs (>100 aa) in the same genome while the ML model for identification of protein like sequences has been trained using annotated smORFs from Escherichia coli. Comprehensive benchmarking of ProsmORF-pred reveals that its performance is comparable to other state-of-the-art approaches on the annotated smORF set derived from 32 prokaryotic genomes. Its performance is distinctly superior to other tools like PRODIGAL and RANSEPS for prediction of newly identified smORFs which have a length range of 10-30 aa, where prediction of smORFs has been a major challenge. Apart from identification of smORFs in genomic sequences, ProsmORF-pred can also aid in functional annotation of the predicted smORFs based on sequence similarity and genomic neighbourhood similarity searches in ProsmORFDB, a well-curated database of known smORFs. ProsmORF-pred along with its backend database ProsmORFDB is available as a user-friendly web server (http://www.nii.ac.in/prosmorfpred.html).

LMTdb: A comprehensive transcriptome database for climate-resilient, nutritionally rich little millet (Panicum sumatrense)

Little millet (Panicum sumatrense) a native of Chhattisgarh, belongs to the minor millet group and is primarily known as a climate-resilient and nutritionally rich crop. However, due to the lack of enough Omic studies on the crop, the scientific community has largely remained unaware of the potential of this crop, resulting in less scope for its utilization in crop improvement programs. Looking at global warming, erratic climate change, nutritional security, and limited genetic information available, the Little Millet Transcriptome Database (LMT^db) (https://igkv.ac.in/xenom/index.aspx) was conceptualized upon completion of the transcriptome sequencing of little millet with the aim of deciphering the genetic signatures of this largely unknown crop. The database was developed with the view of providing information about the most comprehensive part of the genome, the 'Transcriptome'. The database includes transcriptome sequence information, functional annotation, microsatellite markers, DEGs, and pathway information. The database is a freely available resource that provides breeders and scientists a portal to search, browse, and query data to facilitate functional and applied Omic studies in millet crops.

Genome-Wide Associations and Confirmatory Meta-Analyses in Diabetic Retinopathy

The present study aimed to summarize and validate the genomic association signals for diabetic retinopathy (DR), proliferative DR, and diabetic macular edema/diabetic maculopathy. A systematic search of the genome-wide association study (GWAS) catalog and PubMed/MELINE databases was conducted to curate a comprehensive list of significant GWAS discoveries. The top signals were then subjected to meta-analysis using established protocols. The results indicate the need for improved consensus among DR GWASs, highlighting the importance of validation efforts. A subsequent meta-analysis confirmed the association of two SNPs, rs4462262 (ZWINT-MRPS35P3) (odds ratio = 1.38, p = 0.001) and rs7903146 (TCF7L2) (odd ratio = 1.30, p < 0.001), with DR in independent populations, strengthening the evidence of their true association. We also compiled a list of candidate SNPs for further validation. This study highlights the importance of consistent validation and replication efforts in the field of DR genetics. The two identified gene loci warrant further functional investigation to understand their role in DR pathogenesis.

A bioinformatics approach to characterize a hypothetical protein Q6S8D9_SARS of SARS-CoV

Characterization as well as prediction of the secondary and tertiary structure of hypothetical proteins from their amino acid sequences uploaded in databases by in silico approach are the critical issues in computational biology. Severe acute respiratory syndrome-associated coronavirus (SARS-CoV), which is responsible for pneumonia alike diseases, possesses a wide range of proteins of which many are still uncharacterized. The current study was conducted to reveal the physicochemical characteristics and structures of an uncharacterized protein Q6S8D9_SARS of SARS-CoV. Following the common flowchart of characterizing a hypothetical protein, several sophisticated computerized tools e.g., ExPASy Protparam, CD Search, SOPMA, PSIPRED, HHpred, etc. were employed to discover the functions and structures of Q6S8D9_SARS. After delineating the secondary and tertiary structures of the protein, some quality evaluating tools e.g., PROCHECK, ProSA-web etc. were performed to assess the structures and later the active site was identified also by CASTp v.3.0. The protein contains more negatively charged residues than positively charged residues and a high aliphatic index value which make the protein more stable. The 2D and 3D structures modeled by several bioinformatics tools ensured that the proteins had domain in it which indicated it was functional protein having the ability to trouble host antiviral inflammatory cytokine and interferon production pathways. Moreover, active site was found in the protein where ligand could bind. The study was aimed to unveil the features and structures of an uncharacterized protein of SARS-CoV which can be a therapeutic target for development of vaccines against the virus. Further research are needed to accomplish the task.

Genome-Wide Investigation of Apyrase (APY) Genes in Peanut (Arachis hypogaea L.) and Functional Characterization of a Pod-Abundant Expression Promoter AhAPY2-1p

Peanut (Arachis hypogaea L.) is an important food and feed crop worldwide and is affected by various biotic and abiotic stresses. The cellular ATP levels decrease significantly during stress as ATP molecules move to extracellular spaces, resulting in increased ROS production and cell apoptosis. Apyrases (APYs) are the nucleoside phosphatase (NPTs) superfamily members and play an important role in regulating cellular ATP levels under stress. We identified 17 APY homologs in A. hypogaea (AhAPYs), and their phylogenetic relationships, conserved motifs, putative miRNAs targeting different AhAPYs, cis-regulatory elements, etc., were studied in detail. The transcriptome expression data were used to observe the expression patterns in different tissues and under stress conditions. We found that the AhAPY2-1 gene showed abundant expression in the pericarp. As the pericarp is a key defense organ against environmental stress and promoters are the key elements regulating gene expression, we functionally characterized the AhAPY2-1 promoter for its possible use in future breeding programs. The functional characterization of AhAPY2-1P in transgenic Arabidopsis plants showed that it effectively regulated GUS gene expression in the pericarp. GUS expression was also detected in flowers of transgenic Arabidopsis plants. Overall, these results strongly suggest that APYs are an important future research subject for peanut and other crops, and AhPAY2-1P can be used to drive the resistance-related genes in a pericarp-specific manner to enhance the defensive abilities of the pericarp.

Metagenomes from Coastal Sediments of Kuwait: Insights into the Microbiome, Metabolic Functions and Resistome

Coastal sediments in the proximity of wastewater and emergency outfalls are often sinks of pharmaceutical compounds and other organic and inorganic contaminants that are likely to affect the microbial community. The metabolites of these contaminants affect microbial diversity and their metabolic processes, resulting in undesirable effects on ecosystem functioning, thus necessitating the need to understand their composition and functions. In the present investigation, we studied the metagenomes of 12 coastal surface sediments through whole genome shot-gun sequencing. Taxonomic binning of the genes predicted about 86% as bacteria, 1% as archaea, >0.001% as viruses and Eukaryota, and 12% as other communities. The dominant bacterial, archaeal, and fungal genera were Woeseia, Nitrosopumilus, and Rhizophagus, respectively. The most prevalent viral families were Myoviridae and Siphoviridae, and the T4 virus was the most dominant bacteriophage. The unigenes further aligned to 26 clusters of orthologous genes (COGs) and five carbohydrate-active enzymes (CAZy) classes. Glycoside hydrolases (GH) and glycoside transferase (GT) were the highest-recorded CAzymes. The Kyoto Encyclopedia of Genes and Genomes (KEGG) level 3 functions were subjugated by purine metabolism > ABC transporters > oxidative phosphorylation > two-component system > pyrimidine metabolism > pyruvate metabolism > quorum sensing > carbon fixation pathways > ribosomes > and glyoxalate and dicarboxylate metabolism. Sequences allying with plasmids, integrons, insertion sequences and antibiotic-resistance genes were also observed. Both the taxonomies and functional abundances exhibited variation in relative abundances, with limited spatial variability (ANOVA p > 0.05; ANOSIM-0.05, p > 0.05). This study underlines the dominant microbial communities and functional genes in the marine sediments of Kuwait as a baseline for future biomonitoring programs.

Atypical hemispheric lateralization of brain function and structure in autism: a comprehensive meta-analysis study

BACKGROUND

Characteristic changes in the asymmetric nature of the human brain are associated with neurodevelopmental differences related to autism. In people with autism, these differences are thought to affect brain structure and function, although the structural and functional bases of these defects are yet to be fully characterized.

METHODS

We applied a comprehensive meta-analysis to resting-state functional and structural magnetic resonance imaging datasets from 370 people with autism and 498 non-autistic controls using seven datasets of the Autism Brain Imaging Data Exchange Project. We studied the meta-effect sizes based on standardized mean differences and standard deviations (s.d.) for lateralization of gray matter volume (GMV), fractional amplitude of low-frequency fluctuation (fALFF), and regional homogeneity (ReHo). We examined the functional correlates of atypical laterality through an indirect annotation approach followed by a direct correlation analysis with symptom scores.

RESULTS

In people with autism, 85, 51, and 51% of brain regions showed a significant diagnostic effect for lateralization in GMV, fALFF, and ReHo, respectively. Among these regions, 35.7% showed overlapping differences in lateralization in GMV, fALFF, and ReHo, particularly in regions with functional annotations for language, motor, and perceptual functions. These differences were associated with clinical measures of reciprocal social interaction, communication, and repetitive behaviors. A meta-analysis based on s.d. showed that people with autism had lower variability in structural lateralization but higher variability in functional lateralization.

CONCLUSION

These findings highlight that atypical hemispheric lateralization is a consistent feature in autism across different sites and may be used as a neurobiological marker for autism.

Comprehensive Comparative Analysis Sheds Light on the Patterns of Microsatellite Distribution across Birds Based on the Chromosome-Level Genomes

Microsatellites (SSRs) are widely distributed in the genomes of organisms and are an important genetic basis for genome evolution and phenotypic adaptation. Although the distribution patterns of microsatellites have been investigated in many phylogenetic lineages, they remain unclear within the morphologically and physiologically diverse avian clades. Here, based on high-quality chromosome-level genomes, we examined the microsatellite distribution patterns for 53 birds from 16 orders. The results demonstrated that each type of SSR had the same ratio between taxa. For example, the frequency of imperfect SSRs (I-SSRs) was 69.90-84.61%, while perfect SSRs (P-SSRs) were 14.86-28.13% and compound SSRs (C-SSRs) were 0.39-2.24%. Mononucleotide SSRs were dominant for perfect SSRs (32.66-76.48%) in most bird species (98.11%), and A(n) was the most abundant repeat motifs of P-SSRs in all birds (5.42-68.22%). Our study further confirmed that the abundance and diversity of microsatellites were less effected by evolutionary history but its length. The number of P-SSRs decreased with increasing repeat times, and longer P-SSRs motifs had a higher variability coefficient of the repeat copy number and lower diversity, indicating that longer motifs tended to have more stable preferences in avian genomes. We also found that P-SSRs were mainly distributed at the gene ends, and the functional annotation for these genes demonstrated that they were related to signal transduction and cellular process. In conclusion, our research provided avian SSR distribution patterns, which will help to explore the genetic basis for phenotypic diversity in birds.

N2 -fixation can sustain wastewater treatment performance of photogranules under nitrogen-limiting conditions

Wastewater characteristics can vary significantly, and in some municipal wastewaters the N:P ratio is as low as 5 resulting in nitrogen-limiting conditions. In this study, the microbial community, function, and morphology of photogranules under nitrogen-replete (N+) and limiting (N-) conditions was assessed in sequencing batch reactors. Photogranules under N- condition were nitrogen deprived 2/3 of a batch cycle duration. Surprisingly, this nitrogen limitation had no adverse effect on biomass productivity. Moreover, phosphorus and chemical oxygen demand removal were similar to their removal under N+ conditions. Although performance was similar, the difference in granule morphology was obvious. While N+ photogranules were dense and structurally confined, N- photogranules showed loose structures with occasional voids. Microbial community analysis revealed high abundance of cyanobacteria capable of N₂ -fixation. These were higher at N- (38%) than N+ (29%) treatments, showing that photogranules could adjust and maintain treatment performance and high biomass productivity by means of N₂ -fixation.

A long-read and short-read transcriptomics approach provides the first high-quality reference transcriptome and genome annotation for Pseudotsuga menziesii (Douglas-fir)

Douglas-fir (Pseudotsuga menziesii) is native to western North America. It grows in a wide range of environmental conditions and is an important timber tree. Although there are several studies on the gene expression responses of Douglas-fir to abiotic cues, the absence of high-quality transcriptome and genome data is a barrier to further investigation. Like for most conifers, the available transcriptome and genome reference dataset for Douglas-fir remains fragmented and requires refinement. We aimed to generate a highly accurate, and complete reference transcriptome and genome annotation. We deep-sequenced the transcriptome of Douglas-fir needles from seedlings that were grown under nonstress control conditions or a combination of heat and drought stress conditions using long-read (LR) and short-read (SR) sequencing platforms. We used 2 computational approaches, namely de novo and genome-guided LR transcriptome assembly. Using the LR de novo assembly, we identified 1.3X more high-quality transcripts, 1.85X more "complete" genes, and 2.7X more functionally annotated genes compared to the genome-guided assembly approach. We predicted 666 long noncoding RNAs and 12,778 unique protein-coding transcripts including 2,016 putative transcription factors. We leveraged the LR de novo assembled transcriptome with paired-end SR and a published single-end SR transcriptome to generate an improved genome annotation. This was conducted with BRAKER2 and refined based on functional annotation, repetitive content, and transcriptome alignment. This high-quality genome annotation has 51,419 unique gene models derived from 322,631 initial predictions. Overall, our informatics approach provides a new reference Douglas-fir transcriptome assembly and genome annotation with considerably improved completeness and functional annotation.

Large-scale analysis of the genome of the rare alkaline-halophilic Stachybotrys microspora reveals 46 cellulase genes

Fungi are of great importance in biotechnology, for example in the production of enzymes and metabolites. The main goal of this study was to obtain a high-coverage draft of the Stachybotrys microspora genome and to annotate and analyze the genome sequence data. The rare fungus S. microspora N1 strain is distinguished by its ability to grow in an alkaline halophilic environment and to efficiently secrete cellulolytic enzymes. Here we report the draft genome sequence composed of 3715 contigs, a genome size of 35 343 854 bp, with a GC content of 53.31% and a coverage around 20.5×. The identification of cellulolytic genes and of their corresponding functions was carried out through analysis and annotation of the whole genome sequence. Forty-six cellulases were identified using the fungicompanion bioinformatic tool. Interestingly, an S. microspora endoglucanase selected from those with a low isoelectric point was predicted to have a halophilic profile and share significant homology with a well-known bacterial halophilic cellulase. These results confirm previous biochemical studies revealing a halophilic character, which is a very rare feature among fungal cellulases. All these properties suggest that cellulases of S. microspora may have potential for use in the biofuel, textile, and detergent industries.

Genomic Characteristics and Phylogenetic Analyses of a Multiple Drug-Resistant Klebsiella pneumoniae Harboring Plasmid-Mediated MCR-1 Isolated from Tai'an City, China

Klebsiella pneumoniae is a clinically common opportunistic pathogen that causes pneumonia and upper respiratory tract infection in humans as well as community-and hospital-acquired infections, posing significant threats to public health. Moreover, the insertion of a plasmid carrying the mobile colistin resistance (MCR) genes brings obstacles to the clinical treatment of K. pneumoniae infection. In this study, a strain of colistin-resistant K. pneumoniae (CRKP) was isolated from sputum samples of a patient who was admitted to a tertiary hospital in Tai'an city, China, and tested for drug sensitivity. The results showed that KPTA-2108 was multidrug-resistant (MDR), being resistant to 21 of 26 selected antibiotics, such as cefazolin, amikacin, tigecycline and colistin but sensitive to carbapenems via antibiotic resistance assays. The chromosome and plasmid sequences of the isolated strain KPTA-2108 were obtained using whole-genome sequencing technology and then were analyzed deeply using bioinformatics methods. The whole-genome sequencing analysis showed that the length of KPTA-2108 was 5,306,347 bp and carried four plasmids, pMJ4-1, pMJ4-2, pMJ4-3, and pMJ4-4-MCR. The plasmid pMJ4-4-MCR contained 30,124 bp and was found to be an IncX4 type. It was the smallest plasmid in the KPTA-2108 strain and carried only one resistance gene MCR-1. Successful conjugation tests demonstrated that pMJ4-4-MCR carrying MCR-1 could be horizontally transmitted through conjugation between bacteria. In conclusion, the acquisition and genome-wide characterization of a clinical MDR strain of CRKP may provide a scientific basis for the treatment of K. pneumoniae infection and epidemiological data for the surveillance of CRKP.

Best Practices for Comprehensive Annotation of Neuropeptides of Gryllus bimaculatus

Genome annotation is critically important data that can support research. Draft genome annotations cover representative genes; however, they often do not include genes that are expressed only in limited tissues and stages, or genes with low expression levels. Neuropeptides are responsible for regulation of various physiological and biological processes. A recent study disclosed the genome draft of the two-spotted cricket Gryllus bimaculatus, which was utilized to understand the intriguing physiology and biology of crickets. Thus far, only two of the nine reported neuropeptides in G. bimaculatus were annotated in the draft genome. Even though de novo assembly using transcriptomic analyses can comprehensively identify neuropeptides, this method does not follow those annotations on the genome locus. In this study, we performed the annotations based on the reference mapping, de novo transcriptome assembly, and manual curation. Consequently, we identified 41 neuropeptides out of 43 neuropeptides, which were reported in the insects. Further, 32 of the identified neuropeptides on the genomic loci in G. bimaculatus were annotated. The present annotation methods can be applicable for the neuropeptide annotation of other insects. Furthermore, the methods will help to generate useful infrastructures for studies relevant to neuropeptides.

Genome-wide identification of germin-like proteins in peanut (Arachis hypogea L.) and expression analysis under different abiotic stresses

Peanut is an important food and feed crop, providing oil and protein nutrients. Germins and germin-like proteins (GLPs) are ubiquitously present in plants playing numerous roles in defense, growth and development, and different signaling pathways. However, the GLP members have not been comprehensively studied in peanut at the genome-wide scale. We carried out a genome-wide identification of the GLP genes in peanut genome. GLP members were identified comprehensively, and gene structure, genomic positions, motifs/domains distribution patterns, and phylogenetic history were studied in detail. Promoter Cis-elements, gene duplication, collinearity, miRNAs, protein-protein interactions, and expression were determined. A total of 84 GLPs (AhGLPs ) were found in the genome of cultivated peanut. These GLP genes were clustered into six groups. Segmental duplication events played a key role in the evolution of AhGLPs, and purifying selection pressure was underlying the duplication process. Most AhGLPs possessed a well-maintained gene structure and motif organization within the same group. The promoter regions of AhGLPs contained several key cis-elements responsive to 'phytohormones', 'growth and development', defense, and 'light induction'. Seven microRNAs (miRNAs) from six families were found targeting 25 AhGLPs. Gene Ontology (GO) enrichment analysis showed that AhGLPs are highly enriched in nutrient reservoir activity, aleurone grain, external encapsulating structure, multicellular organismal reproductive process, and response to acid chemicals, indicating their important biological roles. AhGLP14, AhGLP38, AhGLP54, and AhGLP76 were expressed in most tissues, while AhGLP26, AhGLP29, and AhGLP62 showed abundant expression in the pericarp. AhGLP7, AhGLP20, and AhGLP21, etc., showed specifically high expression in embryo, while AhGLP12, AhGLP18, AhGLP40, AhGLP78, and AhGLP82 were highly expressed under different hormones, water, and temperature stress. The qRT-PCR results were in accordance with the transcriptome expression data. In short, these findings provided a foundation for future functional investigations on the AhGLPs for peanut breeding programs.

A Network-Based Approach for Improving Annotation of Transcription Factor Functions and Binding Sites in Arabidopsis thaliana

Transcription factors are an integral component of the cellular machinery responsible for regulating many biological processes, and they recognize distinct DNA sequence patterns as well as internal/external signals to mediate target gene expression. The functional roles of an individual transcription factor can be traced back to the functions of its target genes. While such functional associations can be inferred through the use of binding evidence from high-throughput sequencing technologies available today, including chromatin immunoprecipitation sequencing, such experiments can be resource-consuming. On the other hand, exploratory analysis driven by computational techniques can alleviate this burden by narrowing the search scope, but the results are often deemed low-quality or non-specific by biologists. In this paper, we introduce a data-driven, statistics-based strategy to predict novel functional associations for transcription factors in the model plant Arabidopsis thaliana. To achieve this, we leverage one of the largest available gene expression compendia to build a genome-wide transcriptional regulatory network and infer regulatory relationships among transcription factors and their targets. We then use this network to build a pool of likely downstream targets for each transcription factor and query each target pool for functionally enriched gene ontology terms. The results exhibited sufficient statistical significance to annotate most of the transcription factors in Arabidopsis with highly specific biological processes. We also perform DNA binding motif discovery for transcription factors based on their target pool. We show that the predicted functions and motifs strongly agree with curated databases constructed from experimental evidence. In addition, statistical analysis of the network revealed interesting patterns and connections between network topology and system-level transcriptional regulation properties. We believe that the methods demonstrated in this work can be extended to other species to improve the annotation of transcription factors and understand transcriptional regulation on a system level.

The Clostridium-infecting filamentous phage CAK1 genome analysis allows to define a new potential clade of Tubulavirales

What we know about Tubulavirales, i.e. filamentous phages, essentially comes from Gram-negative-infecting Inoviridae. However, metagenomics recently suggests filamentous phages are much more widespread and diverse. Here, we report the complete sequence and functional annotation of CAK1, a 6.6 kb filamentous phage that was shown to chronically infect Clostridium beijerinckii 30 years ago and only represents the second filamentous phage cultivated on a Gram-positive bacterium. CAK1 has a typical filamentous phage modular genome with no homologs in databases and we were interested to compare it with a pig gut filamentous phage metagenomics dataset that we previously assembled and for which many filamentous phages were predicted to infect Clostridium species by bioinformatics means. CAK1 is distantly related to nine of these sequences, two of which have been predicted as Clostridium-associated. In itself, this small cluster of CAK1-connected sequences sheds light on the diversity of filamentous phages that putatively infect Clostridium species, and probably many other Gram-positive genera.