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ABSTRACT Functional annotation and interpretation of genetic variants are a critical step in genetic diagnosis, as it may lead to personalized therapeutic options and genetic counseling. While the number of confirmed pathogenic genetic variants in an individual is relatively low, the number of variants of uncertain significance (VOUS) can be considerably higher, increasing the number of potential carriers of genetic disorders. Thus, reducing uncertainty and assessing the real effect of VOUS are crucial for clinical and medical genetics. In this study, we evaluated the efficacy of genetic screening technologies in accurately predicting pathogenic variants and their corresponding disease prevalence in a cohort of over 6000 healthy individuals involved in assisted reproduction programs. Using data from 305 genes associated with recessive disorders, we determined the frequency of carriers of pathogenic variants and VOUS in our dataset and compared the predicted prevalence based on this information with reported population prevalence data. The higher predicted prevalence in some disorders when considering VOUS suggests a mostly benign effect. [ABSTRACT FROM AUTHOR]

Microsatellites or simple sequence repeats (SSRs) are prevalent across various organisms' genomes. However, their distribution patterns and evolutionary dynamics in reptile genomes are rarely studied systematically. We herein conducted a comprehensive analysis of SSRs in the genomes of 36 reptile species. Our findings revealed that the total number of SSRs ranged from 1,840,965 to 7,664,452, accounting for 2.16%–8.19% of the genomes analyzed. The relative density ranged from 21,567.82 to 81,889.41 bp per megabase (Mbp). The abundance of different SSR categories followed the pattern of imperfect SSR (I‐SSR) > perfect SSR (P‐SSR) > compound SSR (C‐SSR). A significant positive correlation was observed between the number of SSRs and genome size (p = 0.0034), whereas SSR frequency (p = 0.013) or density (p = 0.0099) showed a negative correlation with genome size. Furthermore, no correlation was found between SSR length and genome size. Mononucleotide repeats were the most common P‐SSRs in crocodilians and turtles, whereas mononucleotides, trinucleotides, or tetranucleotides were the most common P‐SSRs in snakes, lizards, and tuatara. P‐SSRs of varying motif sizes showed nonrandom distribution across different genic regions, with AT‐rich repeats being predominant. The genomic SSR content of the squamate lineage ranked the highest in abundance and variability, whereas crocodilians and turtles showed a slowly evolving and reduced microsatellite landscape. Gene ontology enrichment and Kyoto Encyclopedia of Genes and Genomes pathway analyses indicated that genes harboring P‐SSRs in the coding DNA sequence regions were primarily involved in the regulation of transcription and translation processes. The SSR dataset generated in this study provides potential candidates for functional analysis and calls for broader‐scale analyses across the evolutionary spectrum. [ABSTRACT FROM AUTHOR]

Advancements in peptidomics have revealed numerous small open reading frames with coding potential and revealed that some of these micropeptides are closely related to human cancer. However, the systematic analysis and integration from sequence to structure and function remains largely undeveloped. Here, as a solution, we built a workflow for the collection and analysis of proteomic data, transcriptomic data, and clinical outcomes for cancer-associated micropeptides using publicly available datasets from large cohorts. We initially identified 19 586 novel micropeptides by reanalyzing proteomic profile data from 3753 samples across 8 cancer types. Further quantitative analysis of these micropeptides, along with associated clinical data, identified 3065 that were dysregulated in cancer, with 370 of them showing a strong association with prognosis. Moreover, we employed a deep learning framework to construct a micropeptide-protein interaction network for further bioinformatics analysis, revealing that micropeptides are involved in multiple biological processes as bioactive molecules. Taken together, our atlas provides a benchmark for high-throughput prediction and functional exploration of micropeptides, providing new insights into their biological mechanisms in cancer. The HMPA is freely available at http://hmpa.zju.edu.cn. [ABSTRACT FROM AUTHOR]

Wheat powdery mildew (WPM) is one of the most devasting diseases that affects wheat yield worldwide. Few efforts have been made to control such a serious disease. An effective way to control WPM is urgently needed. Biological control is an effective way to conttol plant diseases worldwide. In this study, the efficiency of three different Trichoderma spp. in controlling WPM at the seedling growth stage was tested using 35 highly diverse wheat genotypes. Highly significant differences were found in WPM resistance among the four treatments, confirming the efficiency of Trichoderma in controlling WPM. Of the three species, T. asperellum T34 (T34) was the most effective species in controlling WPM, as it reduced the symptoms by 50.56%. A set of 196 wheat genotypes was used to identify the genetic control of the WPM resistance induced by T34. A total of 39, 27, and 18 gene models were identified to contain the significant markers under Pm, T34, and the improvement in powdery mildew resistance due to T34 (T34_improvement) conditions. Furthermore, no gene model was common between T34 and Pm, suggesting the presence of completely different genetic systems controlling the resistance under T34 and Pm. The functional annotation and biological process pathways of the detected gene models confirm their association with the normal and induced resistance. This study, for the first time, confirms the efficiency of T34 in controlling WPM and provides a deep understanding of the genetic control of induced and normal resistance to WPM. [ABSTRACT FROM AUTHOR]

In order to shed light on the usage of protein language model-based alignment procedures, we attempted the classification of Glutathione S-transferases (GST; EC 2.5.1.18) and compared our results with the ARBA/UNI rule-based annotation in UniProt. GST is a protein superfamily involved in cellular detoxification from harmful xenobiotics and endobiotics, widely distributed in prokaryotes and eukaryotes. What is particularly interesting is that the superfamily is characterized by different classes, comprising proteins from different taxa that can act in different cell locations (cytosolic, mitochondrial and microsomal compartments) with different folds and different levels of sequence identity with remote homologs. For this reason, GST functional annotation in a specific class is problematic: unless a structure is released, the protein can be classified only on the basis of sequence similarity, which excludes the annotation of remote homologs. Here, we adopt an embedding-based alignment to classify 15,061 GST proteins automatically annotated by the UniProt-ARBA/UNI rules. Embedding is based on the Meta ESM2-15b protein language. The embedding-based alignment reaches more than a 99% rate of perfect matching with the UniProt automatic procedure. Data analysis indicates that 46% of the UniProt automatically classified proteins do not conserve the typical length of canonical GSTs, whose structure is known. Therefore, 46% of the classified proteins do not conserve the template/s structure required for their family classification. Our approach finds that 41% of 64,207 GST UniProt proteins not yet assigned to any class can be classified consistently with the structural template length. [ABSTRACT FROM AUTHOR]

Andrographis paniculata is known for its diterpenoid medicinal compounds with antibacterial and anti-inflammatory properties. However, it faces production and cultivation challenges due to low temperatures (LTs). Cytochrome P450 monooxygenases (CYPs) are key enzymes in diterpenoid accumulation. Nevertheless, the functions and LT-related expression patterns of diterpenoid pathway CYPs in Andrographis paniculata remain poorly understood. In this study, 346 CYPs were discovered in Andrographis paniculata. Among them, 328 CYPs belonged to 42 known subfamilies. The remaining 17 CYPs might have represented novel subfamilies unique to this species. A total of 65 candidate CYPs associated with diterpenoid modification were identified. Of these, 50 were transmembrane proteins, and 57 were localized to chloroplasts. The CYP71 subfamily was the most abundant and had the highest motif diversity. Promoters of all candidate CYPs commonly contained elements responsive to gibberellins (GAs), methyl jasmonate (MeJA), and abiotic stresses. Notably, the XP_051152769 protein, corresponding to a CYP gene over 40,000 bp in length, featured an extraordinarily long intron (40,751 nts). Functional elements within this intron were related to LT, GAs, and dehydration pathways. Based on the promoter element arrangement and subfamily classification, 10 representative candidate CYPs were selected. Under LT stress, significant expression changes were observed in three representative CYPs: CYP71D, ent-kaurenoic acid oxidase (KAO), and ent-kaurene oxidase (KO). KAO and KO were significantly upregulated during early LT stress. KAO and KO interacted with each other and jointly interacted with GA20OX2-like. CYP71D acted as a negative response factor to LT stress. Among the 37 proteins interacting with CYP71D, 95% were CYPs. This study provides a critical preliminary foundation for investigating the functions of diterpenoid pathway CYPs in Andrographis paniculata, thereby facilitating the development of LT-tolerant cultivars. [ABSTRACT FROM AUTHOR]

Plant–microbe interactions significantly influence plant growth dynamics and adaptability. This study explores the impact of metabolites on microbial biodiversity in shoot tips and wood of Populus nigra under greenhouse conditions, using high‐throughput sequencing and metabolite profiling. Branches from P. nigra were harvested, rooted, and transplanted into pots for growth. After 3 months, tissue samples from shoot tips and wood were collected, and metabolites extracted and analysed using GC‐MS and LC‐MS. Genomic DNA was extracted and subjected to high‐throughput sequencing for bacterial biodiversity profiling. Both datasets were analysed using bioinformatic and statistical pipelines. Metabolite profiling indicated that shoot tips had a higher relative abundance of primary and secondary metabolites, including sugars, fatty acids, organic acids, phenolic acid derivatives and salicinoids, while wood was enriched in flavonoids. Bacterial biodiversity also differed significantly between these tissues, with Clostridiales, Bacteroidales and Bacillales dominating in shoot tips, associated with rapid growth and anaerobic fermentation, while wood tissues were characterized by diazotrophs from Rhizobiales, Sphingomonadales and Frankiales. PCoA clustering confirmed tissue‐specific microbial differences. Functional analysis revealed an enrichment of fundamental cellular processes in shoot tips, while wood exhibited pathways related to degradation and mortality. Metabolite profiling revealed significant variations in primary and secondary metabolites, highlighting their influence on microbial biodiversity across plant tissues. The dominance of specific bacterial orders and distinct functional pathways in each tissue suggests a tailored microbial response to the unique environments of shoot tips and wood. [ABSTRACT FROM AUTHOR]

Transcription factors (TFs) are major contributors to gene transcription, especially in controlling cell-specific gene expression and disease occurrence and development. Uncovering the relationship between TFs and their target genes is critical to understanding the mechanism of action of TFs. With the development of high-throughput sequencing techniques, a large amount of TF-related data has accumulated, which can be used to identify their target genes. In this study, we developed TFTG (Transcription Factor and Target Genes) database (http://tf.liclab.net/TFTG), which aimed to provide a large number of available human TF-target gene resources by multiple strategies, besides performing a comprehensive functional and epigenetic annotations and regulatory analyses of TFs. We identified extensive available TF-target genes by collecting and processing TF-associated ChIP-seq datasets, perturbation RNA-seq datasets and motifs. We also obtained experimentally confirmed relationships between TF and target genes from available resources. Overall, the target genes of TFs were obtained through integrating the relevant data of various TFs as well as fourteen identification strategies. Meanwhile, TFTG was embedded with user-friendly search, analysis, browsing, downloading and visualization functions. TFTG is designed to be a convenient resource for exploring human TF-target gene regulations, which will be useful for most users in the TF and gene expression regulation research.

To dissect the bacteriocin gene clusters of Lactiplantibacillus plantarum CHEN1, which has a significant inhibitory effect on methicillin-resistant Staphylococcus aureus (MRSA), the whole genome of CHEN1 was sequenced using PacBio RS and Illumina platforms. antiSMASH and BAGEL4 were used to predict bacteriocin gene clusters and explore their potential action mechanisms. The whole genome sequencing results revealed that the genome of L. plantarum CHEN1 was 3 330 435 bp in size, with a GC content of 44.34%, including one chromosome sequence and eight plasmids. It contained 3 196 protein-coding genes, with 704, 2 317 and 2 775 genes being annotated in the Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG) and Cluster of Orthologous Groups of proteins (COG) databases, respectively. The genome sequencing data were submitted to NCBI under GenBank accession number PRJNA1014938. Three bacteriocin-related gene clusters, T3PKS, RiPPs and Class IIb bacteriocins, were predicted by antiSMASH and BAGEL4, meeting the prerequisites for bacteriocin expression. This study provides a bioinformatic foundation for the development and application of CHEN1 and its MRSA-inhibiting bacteriocin.

Abstract Background The extreme environment of the International Space Station (ISS) puts selective pressure on microorganisms unintentionally introduced during its 20+ years of service as a low-orbit science platform and human habitat. Such pressure leads to the development of new features not found in the Earth-bound relatives, which enable them to adapt to unfavorable conditions. Results In this study, we generated the functional annotation of the genomes of five newly identified species of Gram-positive bacteria, four of which are non-spore-forming and one spore-forming, all isolated from the ISS. Using a deep-learning based tool—deepFRI—we were able to functionally annotate close to 100% of protein-coding genes in all studied species, overcoming other annotation tools. Our comparative genomic analysis highlights common characteristics across all five species and specific genetic traits that appear unique to these ISS microorganisms. Proteome analysis mirrored these genomic patterns, revealing similar traits. The collective annotations suggest adaptations to life in space, including the management of hypoosmotic stress related to microgravity via mechanosensitive channel proteins, increased DNA repair activity to counteract heightened radiation exposure, and the presence of mobile genetic elements enhancing metabolism. In addition, our findings suggest the evolution of certain genetic traits indicative of potential pathogenic capabilities, such as small molecule and peptide synthesis and ATP-dependent transporters. These traits, exclusive to the ISS microorganisms, further substantiate previous reports explaining why microbes exposed to space conditions demonstrate enhanced antibiotic resistance and pathogenicity. Conclusion Our findings indicate that the microorganisms isolated from ISS we studied have adapted to life in space. Evidence such as mechanosensitive channel proteins, increased DNA repair activity, as well as metallopeptidases and novel S-layer oxidoreductases suggest a convergent adaptation among these diverse microorganisms, potentially complementing one another within the context of the microbiome. The common genes that facilitate adaptation to the ISS environment may enable bioproduction of essential biomolecules need during future space missions, or serve as potential drug targets, if these microorganisms pose health risks. Video Abstract

Abstract Coxiella burnetii, the causative agent of Q fever, is an intracellular pathogen posing a significant global public health threat. There is a pressing need for dependable and effective treatments, alongside an urgency for further research into the molecular characterization of its genome. Within the genomic landscape of Coxiella burnetii, numerous hypothetical proteins remain unidentified, underscoring the necessity for in-depth study. In this study, we conducted comprehensive in silico analyses to identify and prioritize potential hypothetical protein of Coxiella burnetii, aiming to elucidate the structure and function of uncharacterized protein. Furthermore, we delved into the physicochemical properties, localization, and molecular dynamics and simulations, and assessed the primary, secondary, and tertiary structures employing a variety of bioinformatics tools. The in-silico analysis revealed that the uncharacterized protein contains a conserved Mth938-like domain, suggesting a role in preadipocyte differentiation and adipogenesis. Subcellular localization predictions indicated its presence in the cytoplasm, implicating a significant role in cellular processes. Virtual screening identified ligands with high binding affinities, suggesting the protein’s potential as a drug target against Q fever. Molecular dynamics simulations confirmed the stability of these complexes, indicating their therapeutic relevance. The findings provide a structural and functional overview of an uncharacterized protein from C. burnetii, implicating it in adipogenesis. This study underscores the power of in-silico approaches in uncovering the biological roles of uncharacterized proteins and facilitating the discovery of new therapeutic strategies. The findings provide valuable preliminary data for further investigation into the protein’s role in adipogenesis.

Abstract Long noncoding RNAs (lncRNAs) are RNA molecules with a length greater than 200 nucleotides that do not code for functional proteins. Although, genes play a vital role in immune response against a disease, it is less known that lncRNAs also contribute through gene regulation. Bovine tuberculosis is a significant zoonotic disease caused by Mycobacterium bovis (M. bovis) in cattle. Here, we report the in-silico analysis of the publicly available transcriptomic data of calves infected with M. bovis. A total of 51,812 lncRNAs were extracted across all the samples. A total of 216 genes and 260 lncRNAs were found to be differentially expressed across all the 4 conditions—infected vs uninfected at 8- and 20-week post-infection (WPI), 8 vs 20-WPI of both infected and uninfected. Gene Ontology and Functional annotation showed that 8 DEGs were annotated with immune system GOs and 2 DEGs with REACTOME immune system pathways. Co-expression analysis of DElncRNAs with DEGs revealed the involvement of lncRNAs with the genes annotated with Immune related GOs and pathways. Overall, our study sheds light on the dynamic transcriptomic changes in response to M. bovis infection, particularly highlighting the involvement of lncRNAs with immune-related genes. The identified immune pathways and gene–lncRNA interactions offer valuable insights for further research in understanding host–pathogen interactions and potential avenues for genetic improvement strategies in cattle.

Abstract Background Heritability partitioning approaches estimate the contribution of different functional classes, such as coding or regulatory variants, to the genetic variance. This information allows a better understanding of the genetic architecture of complex traits, including complex diseases, but can also help improve the accuracy of genomic selection in livestock species. However, methods have mainly been tested on human genomic data, whereas livestock populations have specific characteristics, such as high levels of relatedness, small effective population size or long-range levels of linkage disequilibrium. Results Here, we used data from 14,762 cows, imputed at the whole-genome sequence level for 11,537,240 variants, to simulate traits in a typical livestock population and evaluate the accuracy of two state-of-the-art heritability partitioning methods, GREML and a Bayesian mixture model. In simulations where a single functional class had increased contribution to heritability, we observed that the estimators were unbiased but had low precision. When causal variants were enriched in variants with low ( 0.20) minor allele frequency or low (below 1st quartile) or high (above 3rd quartile) linkage disequilibrium scores, it was necessary to partition the genetic variance into multiple classes defined on the basis of allele frequencies or LD scores to obtain unbiased results. When multiple functional classes had variable contributions to heritability, estimators showed higher levels of variation and confounding between certain categories was observed. In addition, estimators from small categories were particularly imprecise. However, the estimates and their ranking were still informative about the contribution of the classes. We also demonstrated that using methods that estimate the contribution of a single category at a time, a commonly used approach, results in an overestimation. Finally, we applied the methods to phenotypes for muscular development and height and estimated that, on average, variants in open chromatin regions had a higher contribution to the genetic variance (> 45%), while variants in coding regions had the strongest individual effects (> 25-fold enrichment on average). Conversely, variants in intergenic or intronic regions showed lower levels of enrichment (0.2 and 0.6-fold on average, respectively). Conclusions Heritability partitioning approaches should be used cautiously in livestock populations, in particular for small categories. Two-component approaches that fit only one functional category at a time lead to biased estimators and should not be used.

Coxiella burnetii, the causative agent of Q fever, is an intracellular pathogen posing a significant global public health threat. There is a pressing need for dependable and effective treatments, alongside an urgency for further research into the molecular characterization of its genome. Within the genomic landscape of Coxiella burnetii, numerous hypothetical proteins remain unidentified, underscoring the necessity for in-depth study. In this study, we conducted comprehensive in silico analyses to identify and prioritize potential hypothetical protein of Coxiella burnetii, aiming to elucidate the structure and function of uncharacterized protein. Furthermore, we delved into the physicochemical properties, localization, and molecular dynamics and simulations, and assessed the primary, secondary, and tertiary structures employing a variety of bioinformatics tools. The in-silico analysis revealed that the uncharacterized protein contains a conserved Mth938-like domain, suggesting a role in preadipocyte differentiation and adipogenesis. Subcellular localization predictions indicated its presence in the cytoplasm, implicating a significant role in cellular processes. Virtual screening identified ligands with high binding affinities, suggesting the protein's potential as a drug target against Q fever. Molecular dynamics simulations confirmed the stability of these complexes, indicating their therapeutic relevance. The findings provide a structural and functional overview of an uncharacterized protein from C. burnetii, implicating it in adipogenesis. This study underscores the power of in-silico approaches in uncovering the biological roles of uncharacterized proteins and facilitating the discovery of new therapeutic strategies. The findings provide valuable preliminary data for further investigation into the protein's role in adipogenesis. [ABSTRACT FROM AUTHOR]

Highly acidic citrus pomace (CP) is a byproduct of Pericarpium Citri Reticulatae production and causes significant environmental damage. In this study, a newly isolated acid-tolerant strain of Serratia sp. JS-043 was used to treat CP and evaluate the effect of reduced acid citrus pomace (RACP) in passivating heavy metals. The results showed that biological treatment could remove 97.56% of citric acid in CP, the organic matter in the soil increased by 202.60% and the catalase activity in the soil increased from 0 to 0.117 U g−1. Adding RACP into soil can increase the stabilization of Cu, Zn, As, Co, and Pb. Specifically, through the metabolism of strain JS-043, RACP was also involved in the stabilization of Zn and Pb, and Residual Fraction in the total pool of these metals increased by 10.73% and 10.54%, respectively. Finally, the genome sequence of Serratia sp. JS-043 was completed, and the genetic basis of its acid-resistant and acid-reducing characteristics was preliminarily revealed. JS-043 also contains many genes encoding proteins associated with heavy metal ion tolerance and transport. These findings suggest that JS-043 may be a high-potential strain to improve the quality of acidic organic wastes that can then be useful for soil bioremediation. [ABSTRACT FROM AUTHOR]

The ever-increasing availability of genome sequencing data has revealed a substantial number of uncharacterized genes without known functions across various organisms. The first comprehensive genome sequencing of E. coli K12 revealed that more than 50% of its open reading frames corresponded to transcripts with no known functions. The group of protein-coding genes without a functional description and/or a recognized pathway, beginning with the letter "Y", is classified as the "y-ome". Several efforts have been made to elucidate the functions of these genes and to recognize their role in biological processes. This review provides a brief update on various strategies employed when studying the y-ome, such as high-throughput experimental approaches, comparative omics, metabolic engineering, gene expression analysis, and data integration techniques. Additionally, we highlight recent advancements in functional annotation methods, including the use of machine learning, network analysis, and functional genomics approaches. Novel approaches are required to produce more precise functional annotations across the genome to reduce the number of genes with unknown functions. [ABSTRACT FROM AUTHOR]

Long noncoding RNAs (lncRNAs) are RNA molecules with a length greater than 200 nucleotides that do not code for functional proteins. Although, genes play a vital role in immune response against a disease, it is less known that lncRNAs also contribute through gene regulation. Bovine tuberculosis is a significant zoonotic disease caused by Mycobacterium bovis (M. bovis) in cattle. Here, we report the in-silico analysis of the publicly available transcriptomic data of calves infected with M. bovis. A total of 51,812 lncRNAs were extracted across all the samples. A total of 216 genes and 260 lncRNAs were found to be differentially expressed across all the 4 conditions—infected vs uninfected at 8- and 20-week post-infection (WPI), 8 vs 20-WPI of both infected and uninfected. Gene Ontology and Functional annotation showed that 8 DEGs were annotated with immune system GOs and 2 DEGs with REACTOME immune system pathways. Co-expression analysis of DElncRNAs with DEGs revealed the involvement of lncRNAs with the genes annotated with Immune related GOs and pathways. Overall, our study sheds light on the dynamic transcriptomic changes in response to M. bovis infection, particularly highlighting the involvement of lncRNAs with immune-related genes. The identified immune pathways and gene–lncRNA interactions offer valuable insights for further research in understanding host–pathogen interactions and potential avenues for genetic improvement strategies in cattle. [ABSTRACT FROM AUTHOR]

Background: Heritability partitioning approaches estimate the contribution of different functional classes, such as coding or regulatory variants, to the genetic variance. This information allows a better understanding of the genetic architecture of complex traits, including complex diseases, but can also help improve the accuracy of genomic selection in livestock species. However, methods have mainly been tested on human genomic data, whereas livestock populations have specific characteristics, such as high levels of relatedness, small effective population size or long-range levels of linkage disequilibrium. Results: Here, we used data from 14,762 cows, imputed at the whole-genome sequence level for 11,537,240 variants, to simulate traits in a typical livestock population and evaluate the accuracy of two state-of-the-art heritability partitioning methods, GREML and a Bayesian mixture model. In simulations where a single functional class had increased contribution to heritability, we observed that the estimators were unbiased but had low precision. When causal variants were enriched in variants with low (< 0.05) or high (> 0.20) minor allele frequency or low (below 1st quartile) or high (above 3rd quartile) linkage disequilibrium scores, it was necessary to partition the genetic variance into multiple classes defined on the basis of allele frequencies or LD scores to obtain unbiased results. When multiple functional classes had variable contributions to heritability, estimators showed higher levels of variation and confounding between certain categories was observed. In addition, estimators from small categories were particularly imprecise. However, the estimates and their ranking were still informative about the contribution of the classes. We also demonstrated that using methods that estimate the contribution of a single category at a time, a commonly used approach, results in an overestimation. Finally, we applied the methods to phenotypes for muscular development and height and estimated that, on average, variants in open chromatin regions had a higher contribution to the genetic variance (> 45%), while variants in coding regions had the strongest individual effects (> 25-fold enrichment on average). Conversely, variants in intergenic or intronic regions showed lower levels of enrichment (0.2 and 0.6-fold on average, respectively). Conclusions: Heritability partitioning approaches should be used cautiously in livestock populations, in particular for small categories. Two-component approaches that fit only one functional category at a time lead to biased estimators and should not be used. [ABSTRACT FROM AUTHOR]

One of the main signal transduction pathways that modulate plant growth and stress responses, including drought, is the action of phytohormones. Recent advances in omics approaches have facilitated the exploration of plant genomes. However, the molecular mechanisms underlying the response in the crown of barley, which plays an essential role in plant performance under stress conditions and regeneration after stress treatment, remain largely unclear. The objective of the present study was the elucidation of drought-induced molecular reactions in the crowns of different barley phytohormone mutants. We verified the hypothesis that defects of gibberellins, brassinosteroids, and strigolactones action affect the transcriptomic, proteomic, and hormonal response of barley crown to the transitory drought influencing plant development under stress. Moreover, we assumed that due to the strong connection between strigolactones and branching the hvdwarf14.d mutant, with dysfunctional receptor of strigolactones, manifests the most abundant alternations in crowns and phenotype under drought. Finally, we expected to identify components underlying the core response to drought which are independent of the genetic background. Large-scale analyses were conducted using gibberellins-biosynthesis, brassinosteroids-signaling, and strigolactones-signaling mutants, as well as reference genotypes. Detailed phenotypic evaluation was also conducted. The obtained results clearly demonstrated that hormonal disorders caused by mutations in the HvGA20ox2, HvBRI1, and HvD14 genes affected the multifaceted reaction of crowns to drought, although the expression of these genes was not induced by stress. The study further detected not only genes and proteins that were involved in the drought response and reacted specifically in mutants compared to the reaction of reference genotypes and vice versa, but also the candidates that may underlie the genotype-universal stress response. Furthermore, candidate genes involved in phytohormonal interactions during the drought response were identified. We also found that the interplay between hormones, especially gibberellins and auxins, as well as strigolactones and cytokinins may be associated with the regulation of branching in crowns exposed to drought. Overall, the present study provides novel insights into the molecular drought-induced responses that occur in barley crowns.

The kiwifruit JD (Jindun No. 1) and HY (Hongyang) are two species of Actinidia chinensis, a major horticultural crop growing worldwide. Fruity texture and taste of HY are better compared with that of JD. However, the fruit transcriptome differences at the ripening stage between JD and HY are poorly understood. In this study, we identified a total of 2,565 differentially expressed genes (DEGs), including 1,336 upregulated and 1,229 downregulated DEGs between the two cultivars. Among the total DEGs, 2,329 matched at least one database. Meanwhile, 1,915 could be aligned to the GO (Gene Ontology), 551 could be aligned to the KEGG (Kyoto Encyclopedia of Genes and Genomes Pathway), and 2,187 could be aligned to SWISS-PROT (SWISS-PROT Protein Sequence) database, respectively. To analyze the DEGs using functional categorization, some regulated processes were significantly changed, and 52 TF (transcription factor) families were detected. This study revealed the change in gene expression level at maturity through the analysis of DEGs data, and these data can provide valuable clues for exploring the molecular mechanism between two kiwifruit varieties, and will be helpful for molecular breeding in Actinidia plants.

ABSTRACT Microsatellites or simple sequence repeats (SSRs) are prevalent across various organisms' genomes. However, their distribution patterns and evolutionary dynamics in reptile genomes are rarely studied systematically. We herein conducted a comprehensive analysis of SSRs in the genomes of 36 reptile species. Our findings revealed that the total number of SSRs ranged from 1,840,965 to 7,664,452, accounting for 2.16%–8.19% of the genomes analyzed. The relative density ranged from 21,567.82 to 81,889.41 bp per megabase (Mbp). The abundance of different SSR categories followed the pattern of imperfect SSR (I‐SSR) > perfect SSR (P‐SSR) > compound SSR (C‐SSR). A significant positive correlation was observed between the number of SSRs and genome size (p = 0.0034), whereas SSR frequency (p = 0.013) or density (p = 0.0099) showed a negative correlation with genome size. Furthermore, no correlation was found between SSR length and genome size. Mononucleotide repeats were the most common P‐SSRs in crocodilians and turtles, whereas mononucleotides, trinucleotides, or tetranucleotides were the most common P‐SSRs in snakes, lizards, and tuatara. P‐SSRs of varying motif sizes showed nonrandom distribution across different genic regions, with AT‐rich repeats being predominant. The genomic SSR content of the squamate lineage ranked the highest in abundance and variability, whereas crocodilians and turtles showed a slowly evolving and reduced microsatellite landscape. Gene ontology enrichment and Kyoto Encyclopedia of Genes and Genomes pathway analyses indicated that genes harboring P‐SSRs in the coding DNA sequence regions were primarily involved in the regulation of transcription and translation processes. The SSR dataset generated in this study provides potential candidates for functional analysis and calls for broader‐scale analyses across the evolutionary spectrum.

The Critical Assessment of Massive Data Analysis (CAMDA) addresses the complexities of harnessing Big Data in life sciences by hosting annual competitions that inspire research groups to develop innovative solutions. In 2023, the Forensic Challenge focused on identifying the city of origin for 365 metagenomic samples collected from public transportation systems and identifying associations between bacterial distribution and other covariates. For microbiome classification, we incorporated both taxonomic and functional annotations as features. To identify the most informative Operational Taxonomic Units, we selected features by fitting negative binomial models. We then implemented supervised models conducting 5-fold cross-validation (CV) with a 4:1 training-to-validation ratio. After variable selection, which reduced the dataset to fewer than 300 OTUs, the Support Vector Classifier achieved the highest F1 score (0.96). When using functional features from MIFASER, the Neural Network model outperformed other models. When considering climatic and demographic variables of the cities, Dirichlet regression over Escherichia, Enterobacter, and Klebsiella bacteria abundances suggests that population increase is indeed associated with a rise in the mean of Escherichia while decreasing temperature is linked to higher proportions of Klebsiella. This study validates microbiome classification using taxonomic features and, to a lesser extent, functional features. It shows that demographic and climatic factors influence urban microbial distribution. A Docker container and a Conda environment are available at the repository: GitHub facilitating broader adoption and validation of these methods by the scientific community.

IntroductionThe gut microbiome (GM) has been implicated in cancer pathogenesis and treatment, including head and neck cancers (HNC). However, the specific microbial compositions influencing HNC and the underlying mechanisms remain largely unknown.MethodsThis study utilized published genome-wide association studies (GWAS) summary data-based two-sample Mendelian randomization (MR) to uncover the GM compositions that exert significant causal effects on HNC. Functional annotation and enrichment analysis were conducted to better understand the significant genetic variables and their connection with HNC. The HNC dataset included 2,281 cases and 314,193 controls. The GM GWAS data of 211 gut taxa (35 families, 20 orders, 16 classes, 9 phyla, and 131 genera) were obtained from the MibioGen consortium, involving 18,340 participants.ResultsMR analysis revealed four GM compositions exerting causal effects on HNC. Specifically, family Peptococcaceae.id.2024 was significantly associated with a 35% reduced risk of HNC (OR=0.65; 95%CI=0.48-0.90; P=0.0080). In contrast, genus DefluviitaleaceaeUCG-011.id.11287 (OR=1.54; 95%CI=1.13-2.09; P=0.0060), genus Gordonibacter.id.821 (OR=1.23; 95%CI=1.05-1.45; P=0.012), and genus Methanobrevibacter.id.123 (OR=1.28; 95%CI=1.01-1.62; P=0.040) showed a significant association with an increased risk of HNC. These GMs interact with genes and genetic variants involved in signaling pathways, such as GTPase regulation, influencing tumor progression and disease prognosis.ConclusionsOur study demonstrates, for the first time, the causal influence of specific gut microbiome compositions on HNC, offering significant insights for advancing clinical research and personalized treatments. The identified GMs may serve as potential biomarkers or therapeutic targets, paving the way for innovative approaches in HNC diagnosis, prevention, and therapy.

Trypanosomatids are the causative agents of deadly diseases in humans and livestock. Given the high phylogenetic distance of trypanosomatids from model organisms, these organisms have ample unannotated genes. Manual functional annotation is time-consuming, highlighting the importance of automated functional annotation tools. The development of automated functional tools is a hot research topic, and multiple tools have been developed for the task. PANNZER2 is an automated functional annotation tool that merely relies on the sequence similarity of the query to the annotated proteins. We tried PANNZER2 on Trypanosoma brucei, the most studied organism among trypanosomatids, to see if it could improve our knowledge of the functions of the genes.Even with the availability of automated annotation tools like InterPro2GO in databases such as TriTrypDB, PANNZER2 has made surprisingly confident predictions for some hypothetical proteins in T. brucei. In this study, we identify gaps in such annotations because of not employing pairwise sequence alignment tools in TriTrypDB's automated annotation process. Our findings demonstrate that even the use of stringent cutoffs can successfully annotate a significant number of proteins. Additionally, we discovered that adjusting the open reading frames in certain genes leads to sequences with increased sequence signature coverage—characterized by the length covered by at least one sequence signature—compared to the original sequences. This enhanced sequence signature coverage suggests these genomic fragments could be pseudogenes. To facilitate further exploration, we developed a script to help identify potential pseudogenes within an organism's genome, offering researchers a new tool for genomic analysis and understanding. We extended all our analysis to Trypanosoma cruzi and Leishmania major to assess the impact of this approach across different species.Our study demonstrates that by utilizing pairwise sequence similarity alignment, even with stringent cutoffs, we can attribute 2986, 3953, and 3798 new GO terms to the genomes of T. brucei, T. cruzi, and L. major. Additionally, we found that 210, 239, and 29 genes exhibit increased sequence signature coverage following frame correction, suggesting the presence of pseudogenes.

Abstract Background Genome-wide functional screening using the CRISPR-Cas9 system is a powerful tool to uncover tumor-specific and common genetic dependencies across cancer cell lines. Current CRISPR-Cas9 knockout libraries, however, primarily target protein-coding genes. This limits functional genomics-based investigations of miRNA function. Methods We designed a novel CRISPR-Cas9 knockout library (lentiG-miR) of 8107 distinct sgRNAs targeting a total of 1769 human miRNAs and benchmarked its single guide RNA (sgRNA) composition, predicted on- and off-target activity, and screening performance against previous libraries. Using a total of 45 human cancer cell lines, representing 16 different tumor entities, we performed negative selection screens to identify miRNA fitness genes. Fitness miRNAs in each cell line were scored using a combination of supervised and unsupervised essentiality classifiers. Common essential miRNAs across distinct cancer cell lines were determined using the 90th percentile method. For subsequent validation, we performed knockout experiments for selected common essential miRNAs in distinct cancer cell lines and gene expression profiling. Results We found significantly lower off-target activity for protein-coding genes and a higher miRNA gene coverage for lentiG-miR as compared to previously described miRNA-targeting libraries, while preserving high on-target activity. A minor fraction of miRNAs displayed robust depletion of targeting sgRNAs, and we observed a high level of consistency between redundant sgRNAs targeting the same miRNA gene. Across 45 human cancer cell lines, only 217 (12%) of all targeted human miRNAs scored as a fitness gene in at least one model, and fitness effects for most miRNAs were confined to small subsets of cell lines. In contrast, we identified 49 common essential miRNAs with a homogenous fitness profile across the vast majority of all cell lines. Transcriptional profiling verified highly consistent gene expression changes in response to knockout of individual common essential miRNAs across a diverse set of cancer cell lines. Conclusions Our study presents a miRNA-targeting CRISPR-Cas9 knockout library with high gene coverage and optimized on- and off-target activities. Taking advantage of the lentiG-miR library, we define a catalogue of miRNA fitness genes in human cancer cell lines, providing the foundation for further investigation of miRNAs in human cancer.

Abstract Background The field of bee genomics has considerably advanced in recent years, however, the most diverse group of honey producers on the planet, the stingless bees, are still largely neglected. In fact, only eleven of the ~ 600 described stingless bee species have been sequenced, and only three using a long-read (LR) sequencing technology. Here, we sequenced the nuclear and mitochondrial genomes of the most common, widespread and broadly reared stingless bee in Brazil and other neotropical countries—Tetragonisca angustula (popularly known in Brazil as jataí). Results A total of 48.01 Gb of DNA data were generated, including 2.31 Gb of Pacific Bioscience HiFi reads and 45.70 Gb of Illumina short reads (SRs). Our preferred assembly comprised 683 contigs encompassing 284.49 Mb, 62.84 Mb of which (22.09%) corresponded to 445,793 repetitive elements. N50, L50 and complete BUSCOs reached 1.02 Mb, 91 contigs and 97.1%, respectively. We predicted that the genome of T. angustula comprises 17,459 protein-coding genes and 4,108 non-coding RNAs. The mitogenome consisted of 17,410 bp, and all 37 genes were found to be on the positive strand, an unusual feature among bees. A phylogenomic analysis of 26 hymenopteran species revealed that six odorant receptor orthogroups of T. angustula were found to be experiencing rapid evolution, four of them undergoing significant contractions. Conclusions Here, we provided the first nuclear and mitochondrial genome assemblies for the ecologically and economically important T. angustula, the fourth stingless bee species to be sequenced with LR technology thus far. We demonstrated that even relatively small amounts of LR data in combination with sufficient SR data can yield high-quality genome assemblies for bees.

Abstract Rosa roxburghii Tratt, a valuable plant in China with long history, is famous for its fruit. It possesses various secondary metabolites, such as L-ascorbic acid (vitamin C), alkaloids and poly saccharides, which make it a high nutritional and medicinal value. Here we characterized the chromosome-level genome sequence of R. roxburghii, comprising seven pseudo-chromosomes with a total size of 531 Mb and a heterozygosity of 0.25%. We also annotated 45,226 coding gene loci after masking repeat elements. Orthologs for 90.1% of the Complete Single-Copy BUSCOs were found in the R. roxburghii annotation. By aligning with protein sequences from public platform, we annotated 85.89% genes from R. roxburghii. Comparative genomic analysis revealed that R. roxburghii diverged from Rosa chinensis approximately 5.58 to 13.17 million years ago, and no whole-genome duplication event occurred after the divergence from eudicots. To fully utilize this genomic resource, we constructed a genomic database RroFGD with various analysis tools. Otherwise, 69 enzyme genes involved in L-ascorbate biosynthesis were identified and a key enzyme in the biosynthesis of vitamin C, GDH (L-Gal-1-dehydrogenase), is used as an example to introduce the functions of the database. This genome and database will facilitate the future investigations into gene function and molecular breeding in R. roxburghii.

Background: Genome-wide functional screening using the CRISPR-Cas9 system is a powerful tool to uncover tumor-specific and common genetic dependencies across cancer cell lines. Current CRISPR-Cas9 knockout libraries, however, primarily target protein-coding genes. This limits functional genomics-based investigations of miRNA function. Methods: We designed a novel CRISPR-Cas9 knockout library (lentiG-miR) of 8107 distinct sgRNAs targeting a total of 1769 human miRNAs and benchmarked its single guide RNA (sgRNA) composition, predicted on- and off-target activity, and screening performance against previous libraries. Using a total of 45 human cancer cell lines, representing 16 different tumor entities, we performed negative selection screens to identify miRNA fitness genes. Fitness miRNAs in each cell line were scored using a combination of supervised and unsupervised essentiality classifiers. Common essential miRNAs across distinct cancer cell lines were determined using the 90th percentile method. For subsequent validation, we performed knockout experiments for selected common essential miRNAs in distinct cancer cell lines and gene expression profiling. Results: We found significantly lower off-target activity for protein-coding genes and a higher miRNA gene coverage for lentiG-miR as compared to previously described miRNA-targeting libraries, while preserving high on-target activity. A minor fraction of miRNAs displayed robust depletion of targeting sgRNAs, and we observed a high level of consistency between redundant sgRNAs targeting the same miRNA gene. Across 45 human cancer cell lines, only 217 (12%) of all targeted human miRNAs scored as a fitness gene in at least one model, and fitness effects for most miRNAs were confined to small subsets of cell lines. In contrast, we identified 49 common essential miRNAs with a homogenous fitness profile across the vast majority of all cell lines. Transcriptional profiling verified highly consistent gene expression changes in response to knockout of individual common essential miRNAs across a diverse set of cancer cell lines. Conclusions: Our study presents a miRNA-targeting CRISPR-Cas9 knockout library with high gene coverage and optimized on- and off-target activities. Taking advantage of the lentiG-miR library, we define a catalogue of miRNA fitness genes in human cancer cell lines, providing the foundation for further investigation of miRNAs in human cancer. [ABSTRACT FROM AUTHOR]

Due to the high microbiological contamination of raw food materials and the increase in the incidence of multidrug-resistant bacteria, new methods of ensuring microbiological food safety are being sought. One solution may be to use bacteriophages (so-called phages) as natural bacterial enemies. Therefore, the aim of this study was the biological and genomic characterization of three newly isolated Serratia- and Enterobacter-specific virulent bacteriophages as potential candidates for food biocontrol. Serratia phage KKP_3708 (vB_Sli-IAFB_3708), Serratia phage KKP_3709 (vB_Sma-IAFB_3709), and Enterobacter phage KKP_3711 (vB_Ecl-IAFB_3711) were isolated from municipal sewage against Serratia liquefaciens strain KKP 3654, Serratia marcescens strain KKP 3687, and Enterobacter cloacae strain KKP 3684, respectively. The effect of phage addition at different multiplicity of infection (MOI) rates on the growth kinetics of the bacterial hosts was determined using a Bioscreen C Pro growth analyzer. The phages retained high activity in a wide temperature range (from −20 °C to 60 °C) and active acidity values (pH from 3 to 12). Based on transmission electron microscopy (TEM) imaging and whole-genome sequencing (WGS), the isolated bacteriophages belong to the tailed bacteriophages from the Caudoviricetes class. Genomic analysis revealed that the phages have linear double-stranded DNA of size 40,461 bp (Serratia phage KKP_3708), 67,890 bp (Serratia phage KKP_3709), and 113,711 bp (Enterobacter phage KKP_3711). No virulence, toxins, or antibiotic resistance genes were detected in the phage genomes. The lack of lysogenic markers indicates that all three bacteriophages may be potential candidates for food biocontrol. [ABSTRACT FROM AUTHOR]

The present study identified 151 proteins from the salivary gland of cowpea aphid Aphis craccivora Koch, using LC-MS/MS analysis. These included enzymes mainly involved in the digestion and detoxification of secondary metabolites and proteins related to cell development and molecular function. Enzymes like peroxidase, trehalase, cytochrome P450 monooxygenase, glutathione peroxidase, esterase, peptidase, carboxypeptidase, maltase, and beta-galactosidase were prevalent in the proteome. Additionally, several proteins were assigned to cellular and molecular functions of salivary gland. These proteins may be involved in host-plant interactions. Comprehensively, these results provide a database for elucidating aphid-plant interactions at the molecular level in the future. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

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

Metagenomics has now evolved as a promising technology for understanding the microbial population in the environment. By metagenomics, a number of extreme and complex environment has been explored for their microbial population. Using this technology, researchers have brought out novel genes and their potential characteristics, which have robust applications in food, pharmaceutical, scientific research, and other biotechnological fields. A sequencing platform can provide a sequence of microbial populations in any given environment. The sequence needs to be analysed computationally to derive meaningful information. It is presumed that only bioinformaticians with extensive computational skills can process the sequencing data till the downstream end. However, numerous open-source software and online servers are available to analyse the metagenomic data developed for a biologist with less computational skills. This review is focused on bioinformatics tools such as Galaxy, CSI-NGS portal, ANASTASIA and SHAMAN, EBI- metagenomics, IDseq, and MG-RAST for analysing metagenomic data. [ABSTRACT FROM AUTHOR]

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Neisseria gonorrhoeae is the causative agent of the sexually transmitted disease gonorrhea. The increasing prevalence of this disease worldwide, the rise of antibiotic-resistant strains, and the difficulties in treatment necessitate the development of a vaccine, highlighting the significance of preventative measures to control and eradicate the infection. Currently, there is no widely available vaccine, partly due to the bacterium’s ability to evade natural immunity and the limited research investment in gonorrhea compared to other diseases. To identify distinct vaccine candidates, we chose to focus on the uncharacterized, hypothetical proteins (HPs) as our initial approach. Using the in silico method, we first carried out a comprehensive assessment of hypothetical proteins of Neisseria gonorrhoeae, encompassing assessments of physicochemical properties, cellular localization, secretary pathways, transmembrane regions, antigenicity, toxicity, and prediction of B-cell and T-cell epitopes, among other analyses. Detailed analysis of all HPs resulted in the functional annotation of twenty proteins with a great degree of confidence. Further, using the immuno-informatics approach, the prediction pipeline identified one CD8+ restricted T-cell epitope, seven linear B-cell epitopes, and seven conformational B-cell epitopes as putative epitope-based peptide vaccine candidates which certainly require further validation in laboratory settings. The study accentuates the promise of functional annotation and immuno-informatics in the systematic design of epitope-based peptide vaccines targeting Neisseria gonorrhoeae.

Abstract 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.

Abstract Background Genetic variants can contribute differently to trait heritability by their functional categories, and recent studies have shown that incorporating functional annotation can improve the predictive performance of polygenic risk scores (PRSs). In addition, when only a small proportion of variants are causal variants, PRS methods that employ a Bayesian framework with shrinkage can account for such sparsity. It is possible that the annotation group level effect is also sparse. However, the number of PRS methods that incorporate both annotation information and shrinkage on effect sizes is limited. We propose a PRS method, PRSbils, which utilizes the functional annotation information with a bilevel continuous shrinkage prior to accommodate the varying genetic architectures both on the variant-specific level and on the functional annotation level. Results We conducted simulation studies and investigated the predictive performance in settings with different genetic architectures. Results indicated that when there was a relatively large variability of group-wise heritability contribution, the gain in prediction performance from the proposed method was on average 8.0% higher AUC compared to the benchmark method PRS-CS. The proposed method also yielded higher predictive performance compared to PRS-CS in settings with different overlapping patterns of annotation groups and obtained on average 6.4% higher AUC. We applied PRSbils to binary and quantitative traits in three real world data sources (the UK Biobank, the Michigan Genomics Initiative (MGI), and the Korean Genome and Epidemiology Study (KoGES)), and two sources of annotations: ANNOVAR, and pathway information from the Kyoto Encyclopedia of Genes and Genomes (KEGG), and demonstrated that the proposed method holds the potential for improving predictive performance by incorporating functional annotations. Conclusions By utilizing a bilevel shrinkage framework, PRSbils enables the incorporation of both overlapping and non-overlapping annotations into PRS construction to improve the performance of genetic risk prediction. The software is available at https://github.com/styvon/PRSbils .

Objective: To investigate the effect of the addition of 2.5% and 5% mass fractions of Polygonatum sibiricum on the microbial community structure and functional genes of wheat Qu, and on this basis to provide theoretical reference for future selection of Polygonatum sibiricum wheat Qu. Methods: The microbial composition and functional genes in Polygonatum sibiricum wheat Qu were annotated by macrogenomics techniques, and the effects of Polygonatum sibiricum on the microbial community structure and functional genes of wheat Qu were analyzed using principal component analysis, stacked histogram and cluster heat map. Results: The species richness and homogeneity of wheat Qu (HJ100) made by adding 2.5% mass fraction of Polygonatum sibiricum were higher than that of wheat Qu (HJ200) with 5% mass fraction and without the addition（PTMQ）. All there were differed at the genus and species levels. The dominant genera of HJ100 were Aspergillus, Paecilomyces, Rasamsonia, Limosilactobacillus, Rhizopus, Klebsiella, Pantoea and Puccinia, and the dominant genera of both HJ200 and PTMQ were Aspergillus, Paecilomyces and Rasamsonia. The dominant species of HJ100 were Paecilomyces varioti, Rasamsonia emersonii, Limosilactobacillus pontis, Pantoea agglomerans, Rhizopus microspores and Puccinia striiformis, and the dominant species of HJ200 and PTMQ were Paecilomyces varioti and Rasamsonia emersonii. Compared to HJ200 and PTMQ, the relative abundance of functional genes annotated by HJ100 showed higher homogeneity, there were a higher abundance of functional genes related to carbohydrate metabolism, protein and amino acid metabolism, which were directly associated with the quality of Huang Jiu. The brewing experiments showed that the Huang Jiu brewed by HJ100 was better than HJ200 and PTMQ in terms of total sugar and polysaccharide content, which could guarantee the functionality and quality of Huang Jiu, and HJ100 had a same fermentation capacity as PTMQ and HJ200. Conclusion: The addition of 2.5% mass fraction of Polygonatum sibiricum could improve the species richness and uniformity and quality of wheat Qu, and could be used for brewing Hung Jiu in the future.

Abstract Background Microsporidia are a large taxon of intracellular pathogens characterized by extraordinarily streamlined genomes with unusually high sequence divergence and many species-specific adaptations. These unique factors pose challenges for traditional genome annotation methods based on sequence similarity. As a result, many of the microsporidian genomes sequenced to date contain numerous genes of unknown function. Recent innovations in rapid and accurate structure prediction and comparison, together with the growing amount of data in structural databases, provide new opportunities to assist in the functional annotation of newly sequenced genomes. Results In this study, we established a workflow that combines sequence and structure-based functional gene annotation approaches employing a ChimeraX plugin named ANNOTEX (Annotation Extension for ChimeraX), allowing for visual inspection and manual curation. We employed this workflow on a high-quality telomere-to-telomere sequenced tetraploid genome of Vairimorpha necatrix. First, the 3080 predicted protein-coding DNA sequences, of which 89% were confirmed with RNA sequencing data, were used as input. Next, ColabFold was used to create protein structure predictions, followed by a Foldseek search for structural matching to the PDB and AlphaFold databases. The subsequent manual curation, using sequence and structure-based hits, increased the accuracy and quality of the functional genome annotation compared to results using only traditional annotation tools. Our workflow resulted in a comprehensive description of the V. necatrix genome, along with a structural summary of the most prevalent protein groups, such as the ricin B lectin family. In addition, and to test our tool, we identified the functions of several previously uncharacterized Encephalitozoon cuniculi genes. Conclusion We provide a new functional annotation tool for divergent organisms and employ it on a newly sequenced, high-quality microsporidian genome to shed light on this uncharacterized intracellular pathogen of Lepidoptera. The addition of a structure-based annotation approach can serve as a valuable template for studying other microsporidian or similarly divergent species.

Abstract The majority of disease-associated variants identified through genome-wide association studies are located outside of protein-coding regions. Prioritizing candidate regulatory variants and gene targets to identify potential biological mechanisms for further functional experiments can be challenging. To address this challenge, we developed FORGEdb ( https://forgedb.cancer.gov/ ; https://forge2.altiusinstitute.org/files/forgedb.html ; and https://doi.org/10.5281/zenodo.10067458 ), a standalone and web-based tool that integrates multiple datasets, delivering information on associated regulatory elements, transcription factor binding sites, and target genes for over 37 million variants. FORGEdb scores provide researchers with a quantitative assessment of the relative importance of each variant for targeted functional experiments.

Abstract Background The anaerobic digestion process degrades organic matter into simpler compounds and occurs in strictly anaerobic and microaerophilic environments. The process is carried out by a diverse community of microorganisms where each species has a unique role and it has relevant biotechnological applications since it is used for biogas production. Some aspects of the microbiome, including its interaction with phages, remains still unclear: a better comprehension of the community composition and role of each species is crucial for a cured understanding of the carbon cycle in anaerobic systems and improving biogas production. Results The primary objective of this study was to expand our understanding on the anaerobic digestion microbiome by jointly analyzing its prokaryotic and viral components. By integrating 192 additional datasets into a previous metagenomic database, the binning process generated 11,831 metagenome-assembled genomes from 314 metagenome samples published between 2014 and 2022, belonging to 4,568 non-redundant species based on ANI calculation and quality verification. CRISPR analysis on these genomes identified 76 archaeal genomes with active phage interactions. Moreover, single-nucleotide variants further pointed to archaea as the most critical members of the community. Among the MAGs, two methanogenic archaea, Methanothrix sp. 43zhSC_152 and Methanoculleus sp. 52maCN_3230, had the highest number of SNVs, with the latter having almost double the density of most other MAGs. Conclusions This study offers a more comprehensive understanding of microbial community structures that thrive at different temperatures. The findings revealed that the fraction of archaeal species characterized at the genome level and reported in public databases is higher than that of bacteria, although still quite limited. The identification of shared spacers between phages and microbes implies a history of phage-bacterial interactions, and specifically lysogenic infections. A significant number of SNVs were identified, primarily comprising synonymous and nonsynonymous variants. Together, the findings indicate that methanogenic archaea are subject to intense selective pressure and suggest that genomic variants play a critical role in the anaerobic digestion process. Overall, this study provides a more balanced and diverse representation of the anaerobic digestion microbiota in terms of geographic location, temperature range and feedstock utilization.

Abstract Background Angelica sinensis (Danggui), a renowned medicinal orchid, has gained significant recognition for its therapeutic effects in treating a wide range of ailments. Genome information serves as a valuable resource, enabling researchers to gain a deeper understanding of gene function. In recent times, the availability of chromosome-level genomes for A. sinensis has opened up vast opportunities for exploring gene functionality. Integrating multiomics data can allow researchers to unravel the intricate mechanisms underlying gene function in A. sinensis and further enhance our knowledge of its medicinal properties. Results In this study, we utilized genomic and transcriptomic data to construct a coexpression network for A. sinensis. To annotate genes, we aligned them with sequences from various databases, such as the NR, TAIR, trEMBL, UniProt, and SwissProt databases. For GO and KEGG annotations, we employed InterProScan and GhostKOALA software. Additionally, gene families were predicted using iTAK, HMMER, OrholoFinder, and KEGG annotation. To facilitate gene functional analysis in A. sinensis, we developed a comprehensive platform that integrates genomic and transcriptomic data with processed functional annotations. The platform includes several tools, such as BLAST, GSEA, Heatmap, JBrowse, and Sequence Extraction. This integrated resource and approach will enable researchers to explore the functional aspects of genes in A. sinensis more effectively. Conclusion We developed a platform, named ASAP, to facilitate gene functional analysis in A. sinensis. ASAP ( www.gzybioinformatics.cn/ASAP ) offers a comprehensive collection of genome data, transcriptome resources, and analysis tools. This platform serves as a valuable resource for researchers conducting gene functional research in their projects, providing them with the necessary data and tools to enhance their studies.

Kongshan Cattle, indigenous to Sichuan Province and recognized as China’s 56th local cattle breed in 2024, exhibit unique adaptations including superior resistance to harsh conditions. Despite a declining population due to the influx of foreign breeds, there is a significant focus on preserving their genetic diversity through advanced genomic techniques. This study utilized whole-genome resequencing, a cost-effective and information-rich method, to perform a comprehensive genetic assessment of the Kongshan Cattle. High-quality resequencing data yielded an average of 17.5 billion clean bases per sample, with high proportions of Q20 and Q30 bases and a balanced GC content. SNP analysis revealed an average of 14 million SNPs per sample, with a notable transition-to-transversion ratio and a significant portion of heterozygosity. Further analysis of genomic and coding regions identified substantial insertions and deletions, particularly in coding sequences affecting gene functionality. A detailed examination of these genetic variations highlighted genes, including NEIL2 and PNKP, which are integral to stress resistance pathways, indicating potential adaptive advantages. This study not only underscores the genetic diversity of Kongshan Cattle but also contributes to broader efforts in germplasm conservation.