Database resources of the National Center for Biotechnology Information

Methylobacterium nigriterrae sp. nov., isolated from black soil

An aerobic, Gram-stain-negative, motile rod bacterium, designated as SYSU BS000021T, was isolated from a black soil sample in Harbin, Heilongjiang province, China. Phylogenetic analysis based on 16S rRNA gene sequences indicated that the isolate belongs to the genus Methylobacterium, and showed the highest sequence similarity to Methylobacterium segetis KCTC 62267 T (98.51%) and Methylobacterium oxalidis DSM 24028 T (97.79%). Growth occurred at 20-37℃ (optimum, 28 °C), pH 6.0-8.0 (optimum, pH 7.0) and in the presence of 0% (w/v) NaCl. Polar lipids comprised of phosphatidylcholine, phosphatidylethanolamine, phosphatidylglycerol, diphosphatidylglycerol, one unidentified aminolipid and one unidentified polar lipid. The major cellular fatty acids (> 5%) were C18:0 and C18:1 ω7c and/or C18:1 ω6c. The predominant respiratory quinone was Q-10. The genomic G + C content was 68.36% based on the whole genome analysis. The average nucleotide identity (≤ 83.5%) and digital DNA-DNA hybridization (≤ 27.3%) values between strain SYSU BS000021T and other members of the genus Methylobacterium were all lower than the threshold values recommended for distinguishing novel prokaryotic species. Based on the results of phenotypic, chemotaxonomic and phylogenetic analyses, strain SYSU BS000021T represents a novel species of the genus Methylobacterium, for which the name Methylobacterium nigriterrae sp. nov. is proposed. The type strain of the proposed novel species is SYSU BS000021T (= GDMCC 1.3814 T = KCTC 8051 T).

Integrating taxonomic signals from MAGs and contigs improves read annotation and taxonomic profiling of metagenomes

Metagenomic analysis typically includes read-based taxonomic profiling, assembly, and binning of metagenome-assembled genomes (MAGs). Here we integrate these steps in Read Annotation Tool (RAT), which uses robust taxonomic signals from MAGs and contigs to enhance read annotation. RAT reconstructs taxonomic profiles with high precision and sensitivity, outperforming other state-of-the-art tools. In high-diversity groundwater samples, RAT annotates a large fraction of the metagenomic reads, calling novel taxa at the appropriate, sometimes high taxonomic ranks. Thus, RAT integrative profiling provides an accurate and comprehensive view of the microbiome from shotgun metagenomics data. The package of Contig Annotation Tool (CAT), Bin Annotation Tool (BAT), and RAT is available at https://github.com/MGXlab/CAT_pack (from CAT pack v6.0). The CAT pack now also supports Genome Taxonomy Database (GTDB) annotations.

Advances in phage-host interaction prediction: in silico method enhances the development of phage therapies

Phages can specifically recognize and kill bacteria, which lead to important application value of bacteriophage in bacterial identification and typing, livestock aquaculture and treatment of human bacterial infection. Considering the variety of human-infected bacteria and the continuous discovery of numerous pathogenic bacteria, screening suitable therapeutic phages that are capable of infecting pathogens from massive phage databases has been a principal step in phage therapy design. Experimental methods to identify phage-host interaction (PHI) are time-consuming and expensive; high-throughput computational method to predict PHI is therefore a potential substitute. Here, we systemically review bioinformatic methods for predicting PHI, introduce reference databases and in silico models applied in these methods and highlight the strengths and challenges of current tools. Finally, we discuss the application scope and future research direction of computational prediction methods, which contribute to the performance improvement of prediction models and the development of personalized phage therapy.

Dynamic enhancer landscapes in human craniofacial development

The genetic basis of human facial variation and craniofacial birth defects remains poorly understood. Distant-acting transcriptional enhancers control the fine-tuned spatiotemporal expression of genes during critical stages of craniofacial development. However, a lack of accurate maps of the genomic locations and cell type-resolved activities of craniofacial enhancers prevents their systematic exploration in human genetics studies. Here, we combine histone modification, chromatin accessibility, and gene expression profiling of human craniofacial development with single-cell analyses of the developing mouse face to define the regulatory landscape of facial development at tissue- and single cell-resolution. We provide temporal activity profiles for 14,000 human developmental craniofacial enhancers. We find that 56% of human craniofacial enhancers share chromatin accessibility in the mouse and we provide cell population- and embryonic stage-resolved predictions of their in vivo activity. Taken together, our data provide an expansive resource for genetic and developmental studies of human craniofacial development.

GeneGPT: augmenting large language models with domain tools for improved access to biomedical information

MOTIVATION

While large language models (LLMs) have been successfully applied to various tasks, they still face challenges with hallucinations. Augmenting LLMs with domain-specific tools such as database utilities can facilitate easier and more precise access to specialized knowledge. In this article, we present GeneGPT, a novel method for teaching LLMs to use the Web APIs of the National Center for Biotechnology Information (NCBI) for answering genomics questions. Specifically, we prompt Codex to solve the GeneTuring tests with NCBI Web APIs by in-context learning and an augmented decoding algorithm that can detect and execute API calls.

RESULTS

Experimental results show that GeneGPT achieves state-of-the-art performance on eight tasks in the GeneTuring benchmark with an average score of 0.83, largely surpassing retrieval-augmented LLMs such as the new Bing (0.44), biomedical LLMs such as BioMedLM (0.08) and BioGPT (0.04), as well as GPT-3 (0.16) and ChatGPT (0.12). Our further analyses suggest that: First, API demonstrations have good cross-task generalizability and are more useful than documentations for in-context learning; second, GeneGPT can generalize to longer chains of API calls and answer multi-hop questions in GeneHop, a novel dataset introduced in this work; finally, different types of errors are enriched in different tasks, providing valuable insights for future improvements.

AVAILABILITY AND IMPLEMENTATION

The GeneGPT code and data are publicly available at https://github.com/ncbi/GeneGPT.

The Impact of Patterns in Linkage Disequilibrium and Sequencing Quality on the Imprint of Balancing Selection

Regions under balancing selection are characterized by dense polymorphisms and multiple persistent haplotypes, along with other sequence complexities. Successful identification of these patterns depends on both the statistical approach and the quality of sequencing. To address this challenge, at first, a new statistical method called LD-ABF was developed, employing efficient Bayesian techniques to effectively test for balancing selection. LD-ABF demonstrated the most robust detection of selection in a variety of simulation scenarios, compared against a range of existing tests/tools (Tajima's D, HKA, Dng, BetaScan, and BalLerMix). Furthermore, the impact of the quality of sequencing on detection of balancing selection was explored, as well, using: (i) SNP genotyping and exome data, (ii) targeted high-resolution HLA genotyping (IHIW), and (iii) whole-genome long-read sequencing data (Pangenome). In the analysis of SNP genotyping and exome data, we identified known targets and 38 new selection signatures in genes not previously linked to balancing selection. To further investigate the impact of sequencing quality on detection of balancing selection, a detailed investigation of the MHC was performed with high-resolution HLA typing data. Higher quality sequencing revealed the HLA-DQ genes consistently demonstrated strong selection signatures otherwise not observed from the sparser SNP array and exome data. The HLA-DQ selection signature was also replicated in the Pangenome samples using considerably less samples but, with high-quality long-read sequence data. The improved statistical method, coupled with higher quality sequencing, leads to more consistent identification of selection and enhanced localization of variants under selection, particularly in complex regions.

Robust demarcation of the family Peptostreptococcaceae and its main genera based on phylogenomic studies and taxon-specific molecular markers

The family Peptostreptococcaceae, which contains 15 genera including Clostridioides, presently lacks proper circumscription. Using 52 available genomes for Peptostreptococcaceae species, we report comprehensive phylogenomic and comparative analyses to reliably discern their evolutionary relationships. In phylogenetic trees based on core genome proteins and 16S rRNA gene sequences, the examined species formed a strongly supported clade designated as Peptostreptococcaceae sensu stricto. This clade encompassed the genera Peptostreptococcus (type genus), Asaccharospora, Clostridioides, Intestinibacter, Paeniclostridium, Paraclostridium, Peptacetobacter, Romboutsia and Terrisporobacter, and two misclassified species (viz. Eubacterium tenue and 'Clostridium dakarense'). The distinctness of this clade is strongly supported by eight identified conserved signature indels (CSIs), which are specific for the species from this clade. Based on the robust evidence provided by presented studies, we are proposing the emendment of family Peptostreptococcaceae to only the genera within the Peptostreptococcaceae sensu stricto clade. We also report 67 other novel CSIs, which reliably demarcate different Peptostreptococcaceae species clades and clarify the classification of some misclassified species. Based on the consistent evidence obtained from different presented studies, we are making the following proposals to clarify the classification of Peptostreptococcaceae species: (i) transfer of Eubacterium tenue, Paeniclostridium ghonii and Paeniclostridium sordellii as comb. nov. into the genus Paraclostridium; (ii) transfer of Clostridioides mangenotii as a comb. nov. into Metaclostridioides gen. nov.; (iii) classification of 'Clostridium dakarense' as a novel species Faecalimicrobium dakarense gen. nov., sp. nov. (type strain FF1T; genome and 16S rRNA accession numbers GCA_000499525.1 and KC517358, respectively); (iv) transfer of two misclassified species, Clostridium paradoxum and Clostridium thermoalcaliphilum, into Alkalithermobacter gen. nov.; and (v) proposals for two novel families, Peptoclostridiaceae fam. nov. and Tepidibacteraceae fam. nov., to accommodate remaining unclassified Peptostreptococcaceae genera. The described CSIs specific for different families and genera provide novel and reliable means for the identification, diagnostics and biochemical studies on these bacteria.

PB-LKS: a python package for predicting phage-bacteria interaction through local K-mer strategy

Bacteriophages can help the treatment of bacterial infections yet require in-silico models to deal with the great genetic diversity between phages and bacteria. Despite the tolerable prediction performance, the application scope of current approaches is limited to the prediction at the species level, which cannot accurately predict the relationship of phages across strain mutants. This has hindered the development of phage therapeutics based on the prediction of phage-bacteria relationships. In this paper, we present, PB-LKS, to predict the phage-bacteria interaction based on local K-mer strategy with higher performance and wider applicability. The utility of PB-LKS is rigorously validated through (i) large-scale historical screening, (ii) case study at the class level and (iii) in vitro simulation of bacterial antiphage resistance at the strain mutant level. The PB-LKS approach could outperform the current state-of-the-art methods and illustrate potential clinical utility in pre-optimized phage therapy design.

Phylogenomics studies and molecular markers reliably demarcate genus Pseudomonas sensu stricto and twelve other Pseudomonadaceae species clades representing novel and emended genera

Genus Pseudomonas is a large assemblage of diverse microorganisms, not sharing a common evolutionary history. To clarify their evolutionary relationships and classification, we have conducted comprehensive phylogenomic and comparative analyses on 388 Pseudomonadaceae genomes. In phylogenomic trees, Pseudomonas species formed 12 main clusters, apart from the "Aeruginosa clade" containing its type species, P. aeruginosa. In parallel, our detailed analyses on protein sequences from Pseudomonadaceae genomes have identified 98 novel conserved signature indels (CSIs), which are uniquely shared by the species from different observed clades/groups. Six CSIs, which are exclusively shared by species from the "Aeruginosa clade," provide reliable demarcation of this clade corresponding to the genus Pseudomonas sensu stricto in molecular terms. The remaining 92 identified CSIs are specific for nine other Pseudomonas species clades and the genera Azomonas and Azotobacter which branch in between them. The identified CSIs provide strong independent evidence of the genetic cohesiveness of these species clades and offer reliable means for their demarcation/circumscription. Based on the robust phylogenetic and molecular evidence presented here supporting the distinctness of the observed Pseudomonas species clades, we are proposing the transfer of species from the following clades into the indicated novel genera: Alcaligenes clade - Aquipseudomonas gen. nov.; Fluvialis clade - Caenipseudomonas gen. nov.; Linyingensis clade - Geopseudomonas gen. nov.; Oleovorans clade - Ectopseudomonas gen. nov.; Resinovorans clade - Metapseudomonas gen. nov.; Straminea clade - Phytopseudomonas gen. nov.; and Thermotolerans clade - Zestomonas gen. nov. In addition, descriptions of the genera Azomonas, Azotobacter, Chryseomonas, Serpens, and Stutzerimonas are emended to include information for the CSIs specific for them. The results presented here should aid in the development of a more reliable classification scheme for Pseudomonas species.

The IUPHAR/BPS Guide to PHARMACOLOGY in 2024

The IUPHAR/BPS Guide to PHARMACOLOGY (GtoPdb; https://www.guidetopharmacology.org) is an open-access, expert-curated, online database that provides succinct overviews and key references for pharmacological targets and their recommended experimental ligands. It includes over 3039 protein targets and 12 163 ligand molecules, including approved drugs, small molecules, peptides and antibodies. Here, we report recent developments to the resource and describe expansion in content over the six database releases made during the last two years. The database update section of this paper focuses on two areas relating to important global health challenges. The first, SARS-CoV-2 COVID-19, remains a major concern and we describe our efforts to expand the database to include a new family of coronavirus proteins. The second area is antimicrobial resistance, for which we have extended our coverage of antibacterials in partnership with AntibioticDB, a collaboration that has continued through support from GARDP. We discuss other areas of curation and also focus on our external links to resources such as PubChem that bring important synergies to the resources.

CLEC19A overexpression inhibits tumor cell proliferation/migration and promotes apoptosis concomitant suppression of PI3K/AKT/NF-κB signaling pathway in glioblastoma multiforme

BACKGROUND

GBM is the most frequent malignant primary brain tumor in humans. The CLEC19A is a member of the C-type lectin family, which has a high expression in brain tissue. Herein, we sought to carry out an in-depth analysis to pinpoint the role of CLEC19A expression in GBM.

METHODS

To determine the localization of CLEC19A, this protein was detected using Western blot, Immunocytochemistry/Immunofluorescence, and confocal microscopy imaging. CLEC19A expression in glioma cells and tissues was evaluated by qRT-PCR. Cell viability, proliferation, migration, and apoptosis were examined through MTT assay, CFSE assay, colony formation, wound healing assay, transwell test, and flow cytometry respectively after CLEC19A overexpression. The effect of CLEC19A overexpression on the PI3K/AKT/NF-κB signaling pathway was investigated using Western blot. An in vivo experiment substantiated the in vitro results using the glioblastoma rat models.

RESULTS

Our in-silico analysis using TCGA data and measuring CLEC19A expression level by qRT-PCR determined significantly lower expression of CLEC19A in human glioma tissues compared to healthy brain tissues. By employment of ICC/IF, confocal microscopy imaging, and Western blot we could show that CLEC19A is plausibly a secreted protein. Results obtained from several in vitro readouts showed that CLEC19A overexpression in U87 and C6 glioma cell lines is associated with the inhibition of cell proliferation, viability, and migration. Further, qRT-PCR and Western blot analysis showed CLEC19A overexpression could reduce the expression levels of PI3K, VEGFα, MMP2, and NF-κB and increase PTEN, TIMP3, RECK, and PDCD4 expression levels in glioma cell lines. Furthermore, flow cytometry results revealed that CLEC19A overexpression was associated with significant cell cycle arrest and promotion of apoptosis in glioma cell lines. Interestingly, using a glioma rat model we could substantiate that CLEC19A overexpression suppresses glioma tumor growth.

CONCLUSIONS

To our knowledge, this is the first report providing in-silico, molecular, cellular, and in vivo evidences on the role of CLEC19A as a putative tumor suppressor gene in GBM. These results enhance our understanding of the role of CLEC19A in glioma and warrant further exploration of CLEC19A as a potential therapeutic target for GBM.

Multicenter evaluation of gut microbiome profiling by next-generation sequencing reveals major biases in partial-length metabarcoding approach

Next-generation sequencing workflows, using either metabarcoding or metagenomic approaches, have massively contributed to expanding knowledge of the human gut microbiota, but methodological bias compromises reproducibility across studies. Where these biases have been quantified within several comparative analyses on their own, none have measured inter-laboratory reproducibility using similar DNA material. Here, we designed a multicenter study involving seven participating laboratories dedicated to partial- (P1 to P5), full-length (P6) metabarcoding, or metagenomic profiling (MGP) using DNA from a mock microbial community or extracted from 10 fecal samples collected at two time points from five donors. Fecal material was collected, and the DNA was extracted according to the IHMS protocols. The mock and isolated DNA were then provided to the participating laboratories for sequencing. Following sequencing analysis according to the laboratories' routine pipelines, relative taxonomic-count tables defined at the genus level were provided and analyzed. Large variations in alpha-diversity between laboratories, uncorrelated with sequencing depth, were detected among the profiles. Half of the genera identified by P1 were unique to this partner and two-thirds of the genera identified by MGP were not detected by P3. Analysis of beta-diversity revealed lower inter-individual variance than inter-laboratory variances. The taxonomic profiles of P5 and P6 were more similar to those of MGP than those obtained by P1, P2, P3, and P4. Reanalysis of the raw sequences obtained by partial-length metabarcoding profiling, using a single bioinformatic pipeline, harmonized the description of the bacterial profiles, which were more similar to each other, except for P3, and closer to the profiles obtained by MGP. This study highlights the major impact of the bioinformatics pipeline, and primarily the database used for taxonomic annotation. Laboratories need to benchmark and optimize their bioinformatic pipelines using standards to monitor their effectiveness in accurately detecting taxa present in gut microbiota.

Impact of contrasting poultry exposures on human, poultry, and wastewater antibiotic resistomes in Bangladesh

IMPORTANCE

Through the use of DNA sequencing, our study shows that there is no significant difference in the antibiotic resistance genes found in stool samples taken from individuals with high exposure to poultry routinely fed antibiotics and those without such exposure. This finding is significant as it suggests limited transmission of antibiotic resistance genes between poultry and humans in these circumstances. However, our research also demonstrates that commercially reared poultry are more likely to possess resistance genes to antibiotics commonly administered on medium-sized farms. Additionally, our study highlights the under-explored potential of wastewater as a source of various antibiotic resistance genes, some of which are clinically relevant.

Resin acids play key roles in shaping microbial communities during degradation of spruce bark

The bark is the outermost defense of trees against microbial attack, largely thanks to toxicity and prevalence of extractive compounds. Nevertheless, bark decomposes in nature, though by which species and mechanisms remains unknown. Here, we have followed the development of microbial enrichments growing on spruce bark over six months, by monitoring both chemical changes in the material and performing community and metagenomic analyses. Carbohydrate metabolism was unexpectedly limited, and instead a key activity was metabolism of extractives. Resin acid degradation was principally linked to community diversification with specific bacteria revealed to dominate the process. Metagenome-guided isolation facilitated the recovery of the dominant enrichment strain in pure culture, which represents a new species (Pseudomonas abieticivorans sp. nov.), that can grow on resin acids as a sole carbon source. Our results illuminate key stages in degradation of an abundant renewable resource, and how defensive extractive compounds have major roles in shaping microbiomes.

Examining the efficacy of a cardio-dance intervention on brain health and the moderating role of ABCA7 in older African Americans: a protocol for a randomized controlled trial

Introduction

African Americans are two to three times more likely to be diagnosed with Alzheimer's disease (AD) compared to White Americans. Exercise is a lifestyle behavior associated with neuroprotection and decreased AD risk, although most African Americans, especially older adults, perform less than the recommended 150 min/week of moderate-to-vigorous intensity exercise. This article describes the protocol for a Phase III randomized controlled trial that will examine the effects of cardio-dance aerobic exercise on novel AD cognitive and neural markers of hippocampal-dependent function (Aims #1 and #2) and whether exercise-induced neuroprotective benefits may be modulated by an AD genetic risk factor, ABCA7 rs3764650 (Aim #3). We will also explore the effects of exercise on blood-based biomarkers for AD.

Methods and analysis

This 6-month trial will include 280 African Americans (≥ 60 years), who will be randomly assigned to 3 days/week of either: (1) a moderate-to-vigorous cardio-dance fitness condition or (2) a low-intensity strength, flexibility, and balance condition for 60 min/session. Participants will complete health and behavioral surveys, neuropsychological testing, saliva and venipuncture, aerobic fitness, anthropometrics and resting-state structural and functional neuroimaging at study entry and 6 months.

Discussion

Results from this investigation will inform future exercise trials and the development of prescribed interventions that aim to reduce the risk of AD in African Americans.

Genomic Divergence and the Evolution of Ecotypes in Bottlenose Dolphins (Genus Tursiops)

Climatic changes have caused major environmental restructuring throughout the world's oceans. Marine organisms have responded to novel conditions through various biological systems, including genomic adaptation. Growing accessibility of next-generation DNA sequencing methods to study nonmodel species has recently allowed genomic changes underlying environmental adaptations to be investigated. This study used double-digest restriction-site associated DNA (ddRAD) sequence data to investigate the genomic basis of ecotype formation across currently recognized species and subspecies of bottlenose dolphins (genus Tursiops) in the Southern Hemisphere. Subspecies-level genomic divergence was confirmed between the offshore common bottlenose dolphin (T. truncatus truncatus) and the inshore Lahille's bottlenose dolphin (T. t. gephyreus) from the southwestern Atlantic Ocean (SWAO). Similarly, subspecies-level divergence is suggested between inshore (eastern Australia) Indo-Pacific bottlenose dolphin (T. aduncus) and the proposed Burrunan dolphin (T. australis) from southern Australia. Inshore bottlenose dolphin lineages generally had lower genomic diversity than offshore lineages, a pattern particularly evident for T. t. gephyreus, which showed exceptionally low diversity. Genomic regions associated with cardiovascular, musculoskeletal, and energy production systems appear to have undergone repeated adaptive evolution in inshore lineages across the Southern Hemisphere. We hypothesize that comparable selective pressures in the inshore environment drove similar adaptive responses in each lineage, supporting parallel evolution of inshore bottlenose dolphins. With climate change altering marine ecosystems worldwide, it is crucial to gain an understanding of the adaptive capacity of local species and populations. Our study provides insights into key adaptive pathways that may be important for the long-term survival of cetaceans and other organisms in a changing marine environment.

Analyzing microbial evolution through gene and genome phylogenies

Microbiome scientists critically need modern tools to explore and analyze microbial evolution. Often this involves studying the evolution of microbial genomes as a whole. However, different genes in a single genome can be subject to different evolutionary pressures, which can result in distinct gene-level evolutionary histories. To address this challenge, we propose to treat estimated gene-level phylogenies as data objects, and present an interactive method for the analysis of a collection of gene phylogenies. We use a local linear approximation of phylogenetic tree space to visualize estimated gene trees as points in low-dimensional Euclidean space, and address important practical limitations of existing related approaches, allowing an intuitive visualization of complex data objects. We demonstrate the utility of our proposed approach through microbial data analyses, including by identifying outlying gene histories in strains of Prevotella, and by contrasting Streptococcus phylogenies estimated using different gene sets. Our method is available as an open-source R package, and assists with estimating, visualizing, and interacting with a collection of bacterial gene phylogenies.

Computer-assisted stabilization of fibroblast growth factor FGF-18

The fibroblast growth factors (FGF) family holds significant potential for addressing chronic diseases. Specifically, recombinant FGF18 shows promise in treating osteoarthritis by stimulating cartilage formation. However, recent phase 2 clinical trial results of sprifermin (recombinant FGF18) indicate insufficient efficacy. Leveraging our expertise in rational protein engineering, we conducted a study to enhance the stability of FGF18. As a result, we obtained a stabilized variant called FGF18-E4, which exhibited improved stability with 16 °C higher melting temperature, resistance to trypsin and a 2.5-fold increase in production yields. Moreover, the FGF18-E4 maintained mitogenic activity after 1-week incubation at 37 °C and 1-day at 50 °C. Additionally, the inserted mutations did not affect its binding to the fibroblast growth factor receptors, making FGF18-E4 a promising candidate for advancing FGF-based osteoarthritis treatment.

GNorm2: an improved gene name recognition and normalization system

MOTIVATION

Gene name normalization is an important yet highly complex task in biomedical text mining research, as gene names can be highly ambiguous and may refer to different genes in different species or share similar names with other bioconcepts. This poses a challenge for accurately identifying and linking gene mentions to their corresponding entries in databases such as NCBI Gene or UniProt. While there has been a body of literature on the gene normalization task, few have addressed all of these challenges or make their solutions publicly available to the scientific community.

RESULTS

Building on the success of GNormPlus, we have created GNorm2: a more advanced tool with optimized functions and improved performance. GNorm2 integrates a range of advanced deep learning-based methods, resulting in the highest levels of accuracy and efficiency for gene recognition and normalization to date. Our tool is freely available for download.

AVAILABILITY AND IMPLEMENTATION

https://github.com/ncbi/GNorm2.

A clathrin-related protein FaRRP1/SCD2 integrates ABA trafficking and signaling to regulate strawberry fruit ripening

Abscisic acid (ABA) is a critical regulator for nonclimacteric fruit ripening such as in the model plant of strawberry (Fragaria × ananassa). Although FaRRP1 is proposed to participate in clathrin-mediated endocytosis of ABA, its action molecular mechanisms in ABA signaling are not fully understood. Here, using our isolated FaRRP1 (ripening-regulation protein) and candidate ABA receptor FaPYL2 and FaABAR from strawberry fruit, a series of silico and molecular interaction analyses demonstrate that they all bind to ABA, and FaRRP1 binds both FaPYL2 and FaABAR; by contrast, the binding affinity of FaRRP1 to FaPYL2 is relatively higher. Interestingly, the binding of FaRRP1 to FaPYL2 and FaABAR affects the perception affinity to ABA. Furthermore, exogenous ABA application and FaRRP1 transgenic analyses confirm that FaRRP1 participates in clathrin-mediated endocytosis and vesicle transport. Importantly, FaRRP1, FaPYL2, and FaABAR all trigger the initiation of strawberry fruit ripening at physiological and molecular levels. In conclusion, FaRRP1 not only binds to ABA but also affects the binding affinity of FaPYL2 and FaABAR to ABA, thus promoting strawberry fruit ripening. Our findings provide novel insights into the role of FaRRP1 in ABA trafficking and signaling, at least in strawberry, a model plant for nonclimacteric fruit ripening.

Phylogenomic and molecular markers based studies on Staphylococcaceae and Gemella species. Proposals for an emended family Staphylococcaceae and three new families (Abyssicoccaceae fam. nov., Salinicoccaceae fam. nov. and Gemellaceae fam. nov.) harboring four new genera, Lacicoccus gen. nov., Macrococcoides gen. nov., Gemelliphila gen. nov., and Phocicoccus gen. nov

The family Staphylococcacae and genus Gemella contain several organisms of clinical or biotechnological importance. We report here comprehensive phylogenomic and comparative analyses on 112 available genomes from species in these taxa to clarify their evolutionary relationships and classification. In a phylogenomic tree based on 678 core proteins, Gemella species were separated from Staphylococcacae by a long branch indicating that they constitute a distinct family (Gemellaceae fam. nov.). In this tree, Staphylococcacae species formed two main clades, one encompassing the genera Aliicoccus, Jeotgalicoccus, Nosocomiicoccus and Salinicoccus (Family "Salinicoccaceae"), while the other clade consisted of the genera Macrococcus, Mammaliicoccus and Staphylococcus (Family Staphylococcaceae emend.). In this tree, species from the genera Gemella, Jeotgalicoccus, Macrococcus and Salinicoccus each formed two distinct clades. Two species clades for these genera are also observed in 16S rRNA gene trees and supported by average amino acid identity analysis. We also report here detailed analyses on protein sequences from Staphylococcaceae and Gemella genomes to identify conserved signature indels (CSIs) which are specific for different genus and family-level clades. These analyses have identified 120 novel CSIs robustly demarcating different proposed families and genera. The identified CSIs provide independent evidence that the genera Gemella, Jeotgalicoccus, Macrococcus and Salinicoccus consist of two distinct clades, which can be reliably distinguished based on multiple exclusively shared CSIs. We are proposing transfers of the species from the novel clades of the above four genera into the genera Gemelliphila gen. nov., Phocicoccus gen. nov., Macrococcoides gen. nov. and Lacicoccus gen. nov., respectively. The identified CSIs also provide strong evidence for division of Staphylococcaceae into an emended family Staphylococcaceae and two new families, Abyssicoccaceae fam. nov. and Salinicoccaceae fam. nov. All of these families can be reliably demarcated based on several exclusively shared CSIs.

ARGem: a new metagenomics pipeline for antibiotic resistance genes: metadata, analysis, and visualization

Antibiotic resistance is of crucial interest to both human and animal medicine. It has been recognized that increased environmental monitoring of antibiotic resistance is needed. Metagenomic DNA sequencing is becoming an attractive method to profile antibiotic resistance genes (ARGs), including a special focus on pathogens. A number of computational pipelines are available and under development to support environmental ARG monitoring; the pipeline we present here is promising for general adoption for the purpose of harmonized global monitoring. Specifically, ARGem is a user-friendly pipeline that provides full-service analysis, from the initial DNA short reads to the final visualization of results. The capture of extensive metadata is also facilitated to support comparability across projects and broader monitoring goals. The ARGem pipeline offers efficient analysis of a modest number of samples along with affordable computational components, though the throughput could be increased through cloud resources, based on the user's configuration. The pipeline components were carefully assessed and selected to satisfy tradeoffs, balancing efficiency and flexibility. It was essential to provide a step to perform short read assembly in a reasonable time frame to ensure accurate annotation of identified ARGs. Comprehensive ARG and mobile genetic element databases are included in ARGem for annotation support. ARGem further includes an expandable set of analysis tools that include statistical and network analysis and supports various useful visualization techniques, including Cytoscape visualization of co-occurrence and correlation networks. The performance and flexibility of the ARGem pipeline is demonstrated with analysis of aquatic metagenomes. The pipeline is freely available at https://github.com/xlxlxlx/ARGem.

Vaccinomics-based next-generation multi-epitope chimeric vaccine models prediction against Leishmania tropica - a hierarchical subtractive proteomics and immunoinformatics approach

Leishmania tropica is a vector-borne parasitic protozoa that is the leading cause of leishmaniasis throughout the global tropics and subtropics. L. tropica is a multidrug-resistant parasite with a diverse set of serological, biochemical, and genomic features. There are currently no particular vaccines available to combat leishmaniasis. The present study prioritized potential vaccine candidate proteins of L. tropica using subtractive proteomics and vaccinomics approaches. These vaccine candidate proteins were downstream analyzed to predict B- and T-cell epitopes based on high antigenicity, non-allergenic, and non-toxic characteristics. The top-ranked overlapping MHC-I, MHC-II, and linear B-cell epitopes were prioritized for model vaccine designing. The lead epitopes were linked together by suitable linker sequences to design multi-epitope constructs. Immunogenic adjuvant sequences were incorporated at the N-terminus of the model vaccine constructs to enhance their immunological potential. Among different combinations of constructs, four vaccine designs were selected based on their physicochemical and immunological features. The tertiary structure models of the designed vaccine constructs were predicted and verified. The molecular docking and molecular dynamic (MD) simulation analyses indicated that the vaccine design V1 demonstrated robust and stable molecular interactions with toll-like receptor 4 (TLR4). The top-ranked vaccine construct model-IV demonstrated significant expressive capability in the E. coli expression system during in-silico restriction cloning analysis. The results of the present study are intriguing; nevertheless, experimental bioassays are required to validate the efficacy of the predicted model chimeric vaccine.

Analyzing microbial evolution through gene and genome phylogenies

Microbiome scientists critically need modern tools to explore and analyze microbial evolution. Often this involves studying the evolution of microbial genomes as a whole. However, different genes in a single genome can be subject to different evolutionary pressures, which can result in distinct gene-level evolutionary histories. To address this challenge, we propose to treat estimated gene-level phylogenies as data objects, and present an interactive method for the analysis of a collection of gene phylogenies. We use a local linear approximation of phylogenetic tree space to visualize estimated gene trees as points in low-dimensional Euclidean space, and address important practical limitations of existing related approaches, allowing an intuitive visualization of complex data objects. We demonstrate the utility of our proposed approach through microbial data analyses, including by identifying outlying gene histories in strains of Prevotella, and by contrasting Streptococcus phylogenies estimated using different gene sets. Our method is available as an open-source R package, and assists with estimating, visualizing and interacting with a collection of bacterial gene phylogenies. dimension reduction, microbiome, non-Euclidean, statistical genetics, visualization.

Tracing the Sources and Prevalence of Class 1 Integrons, Antimicrobial Resistance, and Trace Elements Using European Honey Bees

Surveillance of antimicrobial resistance is essential for an effective One Health response. This study explores the efficacy of European honey bees (Apis mellifera) for biomonitoring antimicrobial resistance (AMR) in urban areas. Class 1 integrons (intI1) are investigated as a universal AMR indicator, as well as associated cassette arrays and trace element contaminants at a city-wide scale. Class 1 integrons were found to be pervasive across the urban environment, occurring in 52% (75/144) of the honey bees assessed. The area of waterbodies within the honey bee's foraging radius was associated with intI1 prevalence, indicating an exposure pathway for future investigation to address. Trace element concentrations in honey bees reflected urban sources, supporting the application of this biomonitoring approach. As the first study of intI1 in honey bees, we provide insights into the environmental transfer of bacterial DNA to a keystone species and demonstrate how intI1 biomonitoring can support the surveillance of AMR.

Protein embeddings improve phage-host interaction prediction

With the growing interest in using phages to combat antimicrobial resistance, computational methods for predicting phage-host interactions have been explored to help shortlist candidate phages. Most existing models consider entire proteomes and rely on manual feature engineering, which poses difficulty in selecting the most informative sequence properties to serve as input to the model. In this paper, we framed phage-host interaction prediction as a multiclass classification problem that takes as input the embeddings of a phage's receptor-binding proteins, which are known to be the key machinery for host recognition, and predicts the host genus. We explored different protein language models to automatically encode these protein sequences into dense embeddings without the need for additional alignment or structural information. We show that the use of embeddings of receptor-binding proteins presents improvements over handcrafted genomic and protein sequence features. The highest performance was obtained using the transformer-based protein language model ProtT5, resulting in a 3% to 4% increase in weighted F1 and recall scores across different prediction confidence thresholds, compared to using selected handcrafted sequence features.

Bioinformatics for wet-lab scientists: practical application in sequencing analysis

BACKGROUND

Genomics data is available to the scientific community after publication of research projects and can be investigated for a multitude of research questions. However, in many cases deposited data is only assessed and used for the initial publication, resulting in valuable resources not being exploited to their full depth. MAIN: A likely reason for this is that many wetlab-based researchers are not formally trained to apply bioinformatic tools and may therefore assume that they lack the necessary experience to do so themselves. In this article, we present a series of freely available, predominantly web-based platforms and bioinformatic tools that can be combined in analysis pipelines to interrogate different types of next-generation sequencing data. Additionally to the presented exemplary route, we also list a number of alternative tools that can be combined in a mix-and-match fashion. We place special emphasis on tools that can be followed and used correctly without extensive prior knowledge in programming. Such analysis pipelines can be applied to existing data downloaded from the public domain or be compared to the results of own experiments.

CONCLUSION

Integrating transcription factor binding to chromatin (ChIP-seq) with transcriptional output (RNA-seq) and chromatin accessibility (ATAC-seq) can not only assist to form a deeper understanding of the molecular interactions underlying transcriptional regulation but will also help establishing new hypotheses and pre-testing them in silico.

Identification of five novel variants of ADAR1 in dyschromatosis symmetrica hereditaria by next-generation sequencing

Background

Dyschromatosis symmetrica hereditaria (DSH) is a rare autosomal dominant inherited pigmentary dermatosis characterized by a mixture of hyperpigmented and hypopigmented freckles on the dorsal aspect of the distal extremities. To date, pathogenic mutations causing DSH have been identified in the adenosine deaminase acting on RNA1 gene (ADAR1), which is mapped to chromosome 1q21.

Objective

The present study aimed to investigate the underlying pathological mechanism in 14 patients with DSH from five unrelated Chinese families. Next-generation sequencing (NGS) and direct sequencing were performed on a proband with DSH to identify causative mutations. All coding, adjacent intronic, and 5'- and 3'-untranslated regions of ADAR1 were screened, and variants were identified.

Result

These mutations consisted of three missense mutations (NM_001025107: c.716G>A, NM_001111.5: c.3384G>C, and NM_001111.5: c.3385T>G), one nonsense mutation (NM_001111.5:c.511G>T), and one splice-site mutation (NM_001111.5: c.2080-1G>T) located in exon 2, exon 14, and the adjacent intronic region according to recommended Human Genome Variation Society (HGVS) nomenclature. Moreover, using polymerase chain reaction and Sanger sequencing, we identified five novel ADAR1 variants, which can be predicted to be pathogenic by in silico genome sequence analysis. Among the mutations, the missense mutations had no significant effect on the spatial structure of the protein, while the stop codon introduced by the nonsense mutation truncated the protein.

Conclusion

Our results highlighted that the advent of NGS has facilitated high-throughput screening for the identification of disease-causing mutations with high accuracy, stability, and specificity. Five novel genetic mutations were found in five unrelated families, thereby extending the pathogenic mutational spectrum of ADAR1 in DSH and providing new insights into this complex genetic disorder.

Exploring the potential mechanisms of Shiwei Hezi pill against nephritis based on the method of network pharmacology

Objective: We aimed to reveal the potential active ingredients, targets and pathways of Shiwei Hezi pill (SHP) in the treatment of nephritis based on systematic network pharmacology. Methods: The online database was used to screen the common targets of SHP and nephritis, and the interaction between targets was analyzed. Gene Ontology (GO) functional annotation and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis were performed using the Bioinformatics website. Molecular docking was carried out to verify the correlation between core ingredients and key targets. Cytoscape 3.6.1 was applied to perform protein-protein interactions (PPT) network construction and data visualization. Results: A total of 82 active ingredients in SHP were screened, and 140 common targets of SHP and nephritis were obtained. Our results demonstrated that TNF, AKT1 and PTGS2 might be the key targets of SHP in the treatment of nephritis. GO enrichment analysis yielded 2163 GO entries (p < 0.05), including 2,014 entries of the biological process (BP) category, 61 entries of the cell composition (CC) category and 143 entries of the molecular function (MF) category. KEGG pathway enrichment analysis produced 186 signaling pathways (p < 0.05), involving the AGE-RAGE, IL-17and TNF signaling pathways. The results of molecular docking showed that three active ingredients in SHP (quercetin, kaempferol and luteolin) could effectively bind to the TNF, AKT1 and PTGS2 targets. Conclusion: The effective active ingredients in SHP may regulate multiple signaling pathways through multiple targets, thereby exhibiting a therapeutic effect on nephritis.

Noise reduction strategies in metagenomic chromosome confirmation capture to link antibiotic resistance genes to microbial hosts

The gut microbiota is a reservoir for antimicrobial resistance genes (ARGs). With current sequencing methods, it is difficult to assign ARGs to their microbial hosts, particularly if these ARGs are located on plasmids. Metagenomic chromosome conformation capture approaches (meta3C and Hi-C) have recently been developed to link bacterial genes to phylogenetic markers, thus potentially allowing the assignment of ARGs to their hosts on a microbiome-wide scale. Here, we generated a meta3C dataset of a human stool sample and used previously published meta3C and Hi-C datasets to investigate bacterial hosts of ARGs in the human gut microbiome. Sequence reads mapping to repetitive elements were found to cause problematic noise in, and may importantly skew interpretation of, meta3C and Hi-C data. We provide a strategy to improve the signal-to-noise ratio by discarding reads that map to insertion sequence elements and to the end of contigs. We also show the importance of using spike-in controls to quantify whether the cross-linking step in meta3C and Hi-C protocols has been successful. After filtering to remove artefactual links, 87 ARGs were assigned to their bacterial hosts across all datasets, including 27 ARGs in the meta3C dataset we generated. We show that commensal gut bacteria are an important reservoir for ARGs, with genes coding for aminoglycoside and tetracycline resistance being widespread in anaerobic commensals of the human gut.

A local-scale One Health genomic surveillance of Clostridioides difficile demonstrates highly related strains from humans, canines, and the environment

Although infections caused by Clostridioides difficile have historically been attributed to hospital acquisition, growing evidence supports the role of community acquisition in C. difficile infection (CDI). Symptoms of CDI can range from mild, self-resolving diarrhoea to toxic megacolon, pseudomembranous colitis, and death. In this study, we sampled C. difficile from clinical, environmental, and canine reservoirs in Flagstaff, Arizona, USA, to understand the distribution and transmission of the pathogen in a One Health framework; Flagstaff is a medium-sized, geographically isolated city with a single hospital system, making it an ideal site to characterize genomic overlap between sequenced C. difficile isolates across reservoirs. An analysis of 562 genomes from Flagstaff isolates identified 65 sequence types (STs), with eight STs being found across all three reservoirs and another nine found across two reservoirs. A screen of toxin genes in the pathogenicity locus identified nine STs where all isolates lost the toxin genes needed for CDI manifestation (tcdB, tcdA), demonstrating the widespread distribution of non-toxigenic C. difficile (NTCD) isolates in all three reservoirs; 15 NTCD genomes were sequenced from symptomatic, clinical samples, including two from mixed infections that contained both tcdB+ and tcdB- isolates. A comparative single nucleotide polymorphism (SNP) analysis of clinically derived isolates identified 78 genomes falling within clusters separated by ≤2 SNPs, indicating that ~19 % of clinical isolates are associated with potential healthcare-associated transmission clusters; only symptomatic cases were sampled in this study, and we did not sample asymptomatic transmission. Using this same SNP threshold, we identified genomic overlap between canine and soil isolates, as well as putative transmission between environmental and human reservoirs. The core genome of isolates sequenced in this study plus a representative set of public C. difficile genomes (n=136), was 2690 coding region sequences, which constitutes ~70 % of an individual C. difficile genome; this number is significantly higher than has been published in some other studies, suggesting that genome data quality is important in understanding the minimal number of genes needed by C. difficile. This study demonstrates the close genomic overlap among isolates sampled across reservoirs, which was facilitated by maximizing the genomic search space used for comprehensive identification of potential transmission events. Understanding the distribution of toxigenic and non-toxigenic C. difficile across reservoirs has implications for surveillance sampling strategies, characterizing routes of infections, and implementing mitigation measures to limit human infection.

Erosion of heterogeneous rock drives diversification of Appalachian fishes

The high levels of biodiversity supported by mountains suggest a possible link between geologic processes and biological evolution. Freshwater biodiversity is high not only in tectonically active settings but also in tectonically quiescent montane regions such as the Appalachian Mountains. We show that erosion through different rock types drove allopatric divergence between lineages of the Greenfin Darter (Nothonotus chlorobranchius), a fish species endemic to rivers draining metamorphic rocks in the Tennessee River basin in the United States. In the past, metamorphic rock preferred by N. chlorobranchius was more widespread, but as erosion exposed other rock types, lineages of this species were progressively isolated in tributaries farther upstream, where metamorphic rock remained. Our results suggest a geologic mechanism for initiating allopatric diversification in mountains long after tectonic activity ceases.

AppIndels.com server: a web-based tool for the identification of known taxon-specific conserved signature indels in genome sequences. Validation of its usefulness by predicting the taxonomic affiliation of >700 unclassified strains of Bacillus species

Taxon-specific conserved signature indels (CSIs) in genes/proteins provide reliable molecular markers (synapomorphies) for unambiguous demarcation of taxa of different ranks in molecular terms and for genetic, biochemical and diagnostic studies. Because of their predictive abilities, the shared presence of known taxon-specific CSIs in genome sequences has proven useful for taxonomic purposes. However, the lack of a convenient method for identifying the presence of known CSIs in genome sequences has limited their utility for taxonomic and other studies. We describe here a web-based tool/server (AppIndels.com) that identifies the presence of known and validated CSIs in genome sequences and uses this information for predicting taxonomic affiliation. The utility of this server was tested by using a database of 585 validated CSIs, which included 350 CSIs specific for ≈45 Bacillales genera, with the remaining CSIs being specific for members of the orders Neisseriales, Legionellales and Chlorobiales, family Borreliaceae, and some Pseudomonadaceae species/genera. Using this server, genome sequences were analysed for 721 Bacillus strains of unknown taxonomic affiliation. Results obtained showed that 651 of these genomes contained significant numbers of CSIs specific for the following Bacillales genera/families: Alkalicoccus, 'Alkalihalobacillaceae', Alteribacter, Bacillus Cereus clade, Bacillus Subtilis clade, Caldalkalibacillus, Caldibacillus, Cytobacillus, Ferdinandcohnia, Gottfriedia, Heyndrickxia, Lederbergia, Litchfieldia, Margalitia, Mesobacillus, Metabacillus, Neobacillus, Niallia, Peribacillus, Priestia, Pseudalkalibacillus, Robertmurraya, Rossellomorea, Schinkia, Siminovitchia, Sporosarcina, Sutcliffiella, Weizmannia and Caryophanaceae. Validity of the taxon assignment made by the server was examined by reconstructing phylogenomic trees. In these trees, all Bacillus strains for which taxonomic predictions were made correctly branched with the indicated taxa. The unassigned strains likely correspond to taxa for which CSIs are lacking in our database. Results presented here show that the AppIndels server provides a useful new tool for predicting taxonomic affiliation based on shared presence of the taxon-specific CSIs. Some caveats in using this server are discussed.

The Evolution of Post-Vaccine G8P[4] Group a Rotavirus Strains in Rwanda; Notable Variance at the Neutralization Epitope Sites

Africa has a high level of genetic diversity of rotavirus strains, which is suggested to be a possible reason contributing to the suboptimal effectiveness of rotavirus vaccines in this region. One strain that contributes to this rotavirus diversity in Africa is the G8P[4]. This study aimed to elucidate the entire genome and evolution of Rwandan G8P[4] strains. Illumina sequencing was performed for twenty-one Rwandan G8P[4] rotavirus strains. Twenty of the Rwandan G8P[4] strains had a pure DS-1-like genotype constellation, and one strain had a reassortant genotype constellation. Notable radical amino acid differences were observed at the neutralization sites when compared with cognate regions in vaccine strains potentially playing a role in neutralization escape. Phylogenetic analysis revealed that the closest relationship was with East African human group A rotavirus (RVA) strains for five of the genome segments. Two genome sequences of the NSP4 genome segment were closely related to bovine members of the DS-1-like family. Fourteen VP1 and eleven VP3 sequences had the closest relationships with the RotaTeq™ vaccine WC3 bovine genes. These findings suggest that the evolution of VP1 and VP3 might have resulted from reassortment events with RotaTeq™ vaccine WC3 bovine genes. The close phylogenetic relationship with East African G8P[4] strains from Kenya and Uganda suggests co-circulation in these countries. These findings highlight the need for continued whole-genomic surveillance to elucidate the evolution of G8P[4] strains, especially after the introduction of rotavirus vaccination.

Comparison of relative areas of interest between major depression disorder and postpartum depression

INTRODUCTION

Postpartum depression (PPD) is defined as a major depressive disorder (MDD) beginning after childbirth. Wide debates aim to better understand PPD's specificities compared with MDD. One of the keys in differentiating PPD from MDD is to systematically study scientific "Areas Of Interest" (AOIs) of these disorders.

METHODS

In November 2021, we performed an extraction and textual computational analysis of associated terms for PPD and MDD, using the biomedical database PubMed. We performed an undirected lexical network analysis to map the 150 first terms in space. Then, we used an unsupervised machine learning technique to detect word patterns and automatically cluster AOIs with a topic-modeling analysis.

RESULTS

We identified 30,000 articles of the 554,724 articles for MDD and 15,642 articles for PPD. Four AOIs were detected in the MDD network: mood disorders and their treatments, risk factors, consequences and quality of life, and mental health and comorbidities. Five AOIs were detected in the PPD network: mood disorders and treatments, risk factors, consequences and child health, patient's background, and the challenges of screening.

DISCUSSION AND CONCLUSION

Limitations are both methodological, in particular due to the qualitative interpretation of AOIs, and are also related to the difficult transferability of these research results to the clinical practice. The partial overlap between AOIs for MDD and for PPD suggest that the latter is a particular form of the former.

How Deepbics Quantifies Intensities of Transcription Factor-DNA Binding and Facilitates Prediction of Single Nucleotide Variant Pathogenicity With a Deep Learning Model Trained On ChIP-Seq Data Sets

The binding of DNA sequences to cell type-specific transcription factors is essential for regulating gene expression in all organisms. Many variants occurring in these binding regions play crucial roles in human disease by disrupting the cis-regulation of gene expression. We first implemented a sequence-based deep learning model called deepBICS to quantify the intensity of transcription factors-DNA binding. The experimental results not only showed the superiority of deepBICS on ChIP-seq data sets but also suggested deepBICS as a language model could help the classification of disease-related and neutral variants. We then built a language model-based method called deepBICS4SNV to predict the pathogenicity of single nucleotide variants. The good performance of deepBICS4SNV on 2 tests related to Mendelian disorders and viral diseases shows the sequence contextual information derived from language models can improve prediction accuracy and generalization capability.

Identification of probable inhibitors for the DNA polymerase of the Monkeypox virus through the virtual screening approach

Given the paucity of antiviral treatments for monkeypox disease, caused by the Monkeypox virus (MPXV), there is a pressing need for the development/identification of new drugs to treat the infection. MPXV possesses a linear dsDNA genome that is replicated by a DNA replication complex of which DNA polymerase (DPol) forms an important component. Owing to the importance of DPol in the viral life cycle, identifying/designing small molecules abolishing its function could yield new antivirals. In this study, we first used the AlphaFold artificial intelligence program to model the 3D structure of the MPXV DPol; like the fold of DPol from other organisms, the MPXV DPol structure has the characteristic exonuclease, thumb, palm, and fingers sub-domains arrangement. Subsequently, we have identified several inhibitors through virtual screening of ZINC and antiviral libraries. Molecules with phenyl scaffold along with alanine-based and tetrazole-based molecules showed the best docking score of -8 to -10 kcal/mol. These molecules bind in the palm and fingers sub-domains interface region, which partially overlaps with the DNA binding path. The delineation of DPol/inhibitor interactions showed that majorly active site residues ASP549, ASP753, TYR550, ASN551, SER552, and ASN665 interact with the inhibitors. These compounds exhibit good Absorption, Distribution, Metabolism and Excretion properties.

Genomic Analysis of G2P[4] Group A Rotaviruses in Zambia Reveals Positive Selection in Amino Acid Site 7 of Viral Protein 3

The G2P[4] genotype is among the rotavirus strains that circulate commonly in humans. Several countries have reported its immediate upsurge after the introduction of rotavirus vaccination, raising concern about sub-optimal vaccine effectiveness against this genotype in the long term. This study aimed to gain insight into the evolution of post-vaccine Zambian G2P[4] group A rotavirus (RVA) strains and their overall genetic make-up by analysis of sequence alignments at the amino acid (AA) level. Twenty-nine Zambian G2P[4] rotavirus strains were subjected to whole-genome sequencing using the Illumina MiSeq^® platform. All the strains exhibited the typical DS-1-like genotype constellation, and the nucleotide sequences of the 11 genome segments showed high nucleotide similarities (>97%). Phylogenetic analyses together with representative global G2P[4] RVA showed that Zambian strains clustered into human lineages IV (for VP2, VP4, VP7, NSP1, and NSP5), V (for VP1, VP3, VP6, NSP2, and NSP3), and XXIII (for NSP4). The AA differences between the lineages where the study strains clustered and lineages of global reference strains were identified and analyzed. Selection pressure analysis revealed that AA site seven in the Viral Protein 3 (VP3) genome segment was under positive selection. This site occurs in the region of intrinsic disorder in the VP3 protein, and Zambian G2P[4] strains could potentially be utilizing this intrinsically disordered region to survive immune pressure. The Zambian G2P[4] strains from 2012 to 2016 comprised the G2P[4] strains that have been circulating globally since the early 2000s, highlighting the epidemiological fitness of these contemporary G2P[4] strains. Continuous whole-genome surveillance of G2P[4] strains remains imperative to understand their evolution during the post-vaccination period.

Complex polyploid and hybrid species in an apomictic and sexual tropical forage grass group: genomic composition and evolution in Urochloa (Brachiaria) species

BACKGROUND AND AIMS

Diploid and polyploid Urochloa (including Brachiaria, Panicum and Megathyrsus species) C4 tropical forage grasses originating from Africa are important for food security and the environment, often being planted in marginal lands worldwide. We aimed to characterize the nature of their genomes, the repetitive DNA and the genome composition of polyploids, leading to a model of the evolutionary pathways within the group including many apomictic species.

METHODS

Some 362 forage grass accessions from international germplasm collections were studied, and ploidy was determined using an optimized flow cytometry method. Whole-genome survey sequencing and molecular cytogenetic analysis were used to identify chromosomes and genomes in Urochloa accessions belonging to the 'brizantha' and 'humidicola' agamic complexes and U. maxima.

KEY RESULTS

Genome structures are complex and variable, with multiple ploidies and genome compositions within the species, and no clear geographical patterns. Sequence analysis of nine diploid and polyploid accessions enabled identification of abundant genome-specific repetitive DNA motifs. In situ hybridization with a combination of repetitive DNA and genomic DNA probes identified evolutionary divergence and allowed us to discriminate the different genomes present in polyploids.

CONCLUSIONS

We suggest a new coherent nomenclature for the genomes present. We develop a model of evolution at the whole-genome level in diploid and polyploid accessions showing processes of grass evolution. We support the retention of narrow species concepts for Urochloa brizantha, U. decumbens and U. ruziziensis, and do not consider diploids and polyploids of single species as cytotypes. The results and model will be valuable in making rational choices of parents for new hybrids, assist in use of the germplasm for breeding and selection of Urochloa with improved sustainability and agronomic potential, and assist in measuring and conserving biodiversity in grasslands.

TIVAN-indel: a computational framework for annotating and predicting non-coding regulatory small insertions and deletions

MOTIVATION

Small insertion and deletion (sindel) of human genome has an important implication for human disease. One important mechanism for non-coding sindel (nc-sindel) to have an impact on human diseases and phenotypes is through the regulation of gene expression. Nevertheless, current sequencing experiments may lack statistical power and resolution to pinpoint the functional sindel due to lower minor allele frequency or small effect size. As an alternative strategy, a supervised machine learning method can identify the otherwise masked functional sindels by predicting their regulatory potential directly. However, computational methods for annotating and predicting the regulatory sindels, especially in the non-coding regions, are underdeveloped.

RESULTS

By leveraging labeled nc-sindels identified by cis-expression quantitative trait loci analyses across 44 tissues in Genotype-Tissue Expression (GTEx), and a compilation of both generic functional annotations and large-scale epigenomic profiles, we develop TIssue-specific Variant Annotation for Non-coding indel (TIVAN-indel), which is a supervised computational framework for predicting non-coding regulatory sindels. As a result, we demonstrate that TIVAN-indel achieves the best prediction performance in both with-tissue prediction and cross-tissue prediction. As an independent evaluation, we train TIVAN-indel from the 'Whole Blood' tissue in GTEx and test the model using 15 immune cell types from an independent study named Database of Immune Cell Expression. Lastly, we perform an enrichment analysis for both true and predicted sindels in key regulatory regions such as chromatin interactions, open chromatin regions and histone modification sites, and find biologically meaningful enrichment patterns.

AVAILABILITY AND IMPLEMENTATION

https://github.com/lichen-lab/TIVAN-indel.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Autopolyploid inheritance and a heterozygous reciprocal translocation shape chromosome genetic behavior in tetraploid blueberry (Vaccinium corymbosum)

Understanding chromosome recombination behavior in polyploidy species is key to advancing genetic discoveries. In blueberry, a tetraploid species, the line of evidences about its genetic behavior still remain poorly understood, owing to the inter-specific, and inter-ploidy admixture of its genome and lack of in depth genome-wide inheritance and comparative structural studies. Here we describe a new high-quality, phased, chromosome-scale genome of a diploid blueberry, clone W85. The genome was integrated with cytogenetics and high-density, genetic maps representing six tetraploid blueberry cultivars, harboring different levels of wild genome admixture, to uncover recombination behavior and structural genome divergence across tetraploid and wild diploid species. Analysis of chromosome inheritance and pairing demonstrated that tetraploid blueberry behaves as an autotetraploid with tetrasomic inheritance. Comparative analysis demonstrated the presence of a reciprocal, heterozygous, translocation spanning one homolog of chr-6 and one of chr-10 in the cultivar Draper. The translocation affects pairing and recombination of chromosomes 6 and 10. Besides the translocation detected in Draper, no other structural genomic divergences were detected across tetraploid cultivars and highly inter-crossable wild diploid species. These findings and resources will facilitate new genetic and comparative genomic studies in Vaccinium and the development of genomic assisted selection strategy for this crop.

A systematic drug repurposing approach to identify promising inhibitors from FDA-approved drugs against Nsp4 protein of SARS-CoV-2

COVID-19 is caused by SARS-CoV-2 and responsible for the ongoing global pandemic in the world. After more than a year, we are still in lurch to combat and control the situation. Therefore, new therapeutic options to control the ongoing COVID-19 are urgently in need. In our study, we found that nonstructural protein 4 (Nsp4) of SARS-CoV-2 could be a potential target for drug repurposing. Due to availability of only the crystal structure of C-terminal domain of Nsp4 (Ct-Nsp4) and its crucial participation in viral RNA synthesis, we have chosen Ct-Nsp4 as a target for screening the 1600 FDA-approved drugs using molecular docking. Top 102 drugs were found to have the binding energy equal or less than -7.0 kcal/mol. Eribulin and Suvorexant were identified as the two most promising drug molecules based on the docking score. The dynamics of Ct-Nsp4-drug binding was monitored using 100 ns molecular dynamics simulations. From binding free energy calculation over the simulation, both the drugs were found to have considerable binding energy. The present study has identified Eribulin and Suvorexant as promising drug candidates. This finding will be helpful to accelerate the drug discovery process against COVID-19 disease.Communicated by Ramaswamy H. Sarma.

Client Applications and Server-Side Docker for Management of RNASeq and/or VariantSeq Workflows and Pipelines of the GPRO Suite

The GPRO suite is an in-progress bioinformatic project for -omics data analysis. As part of the continued growth of this project, we introduce a client- and server-side solution for comparative transcriptomics and analysis of variants. The client-side consists of two Java applications called "RNASeq" and "VariantSeq" to manage pipelines and workflows based on the most common command line interface tools for RNA-seq and Variant-seq analysis, respectively. As such, "RNASeq" and "VariantSeq" are coupled with a Linux server infrastructure (named GPRO Server-Side) that hosts all dependencies of each application (scripts, databases, and command line interface software). Implementation of the Server-Side requires a Linux operating system, PHP, SQL, Python, bash scripting, and third-party software. The GPRO Server-Side can be installed, via a Docker container, in the user's PC under any operating system or on remote servers, as a cloud solution. "RNASeq" and "VariantSeq" are both available as desktop (RCP compilation) and web (RAP compilation) applications. Each application has two execution modes: a step-by-step mode enables each step of the workflow to be executed independently, and a pipeline mode allows all steps to be run sequentially. "RNASeq" and "VariantSeq" also feature an experimental, online support system called GENIE that consists of a virtual (chatbot) assistant and a pipeline jobs panel coupled with an expert system. The chatbot can troubleshoot issues with the usage of each tool, the pipeline jobs panel provides information about the status of each computational job executed in the GPRO Server-Side, while the expert system provides the user with a potential recommendation to identify or fix failed analyses. Our solution is a ready-to-use topic specific platform that combines the user-friendliness, robustness, and security of desktop software, with the efficiency of cloud/web applications to manage pipelines and workflows based on command line interface software.

Comparative Proteomics and Genome-Wide Druggability Analyses Prioritized Promising Therapeutic Targets against Drug-Resistant Leishmania tropica

Leishmania tropica is a tropical parasite causing cutaneous leishmaniasis (CL) in humans. Leishmaniasis is a serious public health threat, affecting an estimated 350 million people in 98 countries. The global rise in antileishmanial drug resistance has triggered the need to explore novel therapeutic strategies against this parasite. In the present study, we utilized the recently available multidrug resistant L. tropica strain proteome data repository to identify alternative therapeutic drug targets based on comparative subtractive proteomic and druggability analyses. Additionally, small drug-like compounds were scanned against novel targets based on virtual screening and ADME profiling. The analysis unveiled 496 essential cellular proteins of L. tropica that were nonhomologous to the human proteome set. The druggability analyses prioritized nine parasite-specific druggable proteins essential for the parasite's basic cellular survival, growth, and virulence. These prioritized proteins were identified to have appropriate binding pockets to anchor small drug-like compounds. Among these, UDPase and PCNA were prioritized as the top-ranked druggable proteins. The pharmacophore-based virtual screening and ADME profiling predicted MolPort-000-730-162 and MolPort-020-232-354 as the top hit drug-like compounds from the Pharmit resource to inhibit L. tropica UDPase and PCNA, respectively. The alternative drug targets and drug-like molecules predicted in the current study lay the groundwork for developing novel antileishmanial therapies.

De Novo Genome Sequencing, Annotation, and Taxonomy of Unknown Bacteria

Whole-genome sequencing of viruses and bacteria has become routine thanks to advances in DNA-sequencing technologies. Parallel advances in computing power and software design allow for billions of base pairs of sequence information to be analyzed in hours to minutes. Here, I describe methods to isolate known as well as new species of bacteria from the environment; to purify, sequence, assemble, and bioinformatically annotate their genomes; and to determine their place in the tree of life by phylogenetic analysis. The protocol introduced here was developed as part of Cold Spring Harbor's Advanced Bacterial Genetics course.

The Plant Phenomics and Genomics Research Data Repository: An On-Premise Approach for FAIR-Compliant Data Acquisition

The FAIR data principle as a commitment to support long-term research data management is widely accepted in the scientific community. However, although many established infrastructures provide comprehensive and long-term stable services and platforms, a large quantity of research data is still hidden. Currently, high-throughput plant genomics and phenomics technologies are producing research data in abundance, the storage of which is not covered by established core databases. This concerns the data volume, for example, time series of images or high-resolution hyperspectral data; the quality of data formatting and annotation, e.g., with regard to structure and annotation specifications of core databases; uncovered data domains; or organizational constraints prohibiting primary data storage outside institutional boundaries. To share these potentially dark data in a FAIR way and master these challenges the ELIXIR Germany/de.NBI service Plant Genomic and Phenomics Research Data Repository (PGP) implements an on-premise approach, which allows research data to be kept in place and wrapped in FAIR-aware software infrastructure. In this chapter, the e!DAL infrastructure software and the PGP repository are presented as best practice on how to easily setup FAIR-compliant and intuitive research data services.

Discovering potential inhibitors against SARS-CoV-2 by targeting Nsp13 Helicase

The rise in the incidence of COVID-19 as a result of SARS-CoV-2 infection has threatened public health globally. Till now, there have been no proper prophylactics available to fight COVID-19, necessitating the advancement and evolution of effective curative against SARS-CoV-2. This study aimed at the nonstructural protein 13 (nsp13) helicase as a promising target for drug development against COVID-19. A unique collection of nucleoside analogs was screened against the SARS-CoV-2 helicase protein, for which a molecular docking experiment was executed to depict the selected ligand's binding affinity with the SARS-CoV-2 helicase proteins. Simultaneously, molecular dynamic simulations were performed to examine the protein's binding site's conformational stability, flexibility, and interaction with the ligands. Key nucleoside ligands were selected for pharmacokinetic analysis based on their docking scores. Selected ligands (cordycepin and pritelivir) showed excellent pharmacokinetics and were well stabilized at the proteins' binding site throughout the MD simulation. We have also performed binding free energy analysis or the binding characteristics of ligands with Nsp13 by using MM-PBSA and MM-GBSA. Free energy calculation by MM-PBSA and MM-GBSA analysis suggests that pritelivir may work as viable therapeutics for efficient drug advancement against SARS-CoV-2 Nsp13 helicase, potentially arresting the SARS-CoV-2 replication.Communicated by Ramaswamy H. Sarma.

Drug and Anti-Viral Peptide Design to Inhibit the Monkeypox Virus by Restricting A36R Protein

Most recently, monkeypox virus (MPXV) has emanated as a global public health threat. Unavailability of effective medicament against MPXV escalates demand for new therapeutic agent. In this study, in silico strategies were conducted to identify novel drug against the A36R protein of MPXV. The A36R protein of MPXV is responsible for the viral migration, adhesion, and vesicle trafficking to the host cell. To block the A36R protein, 4893 potential antiviral peptides (AVPs) were retrieved from DRAMP and SATPdb databases. Finally, 57 sequences were screened based on peptide filtering criteria, which were then modeled. Likewise, 31 monkeypox virus A36R protein sequences were collected from NCBI protein database to find consensus sequence and to predict 3D protein model. The refined and validated models of the A36R protein and AVP peptides were used to predict receptor-ligand interactions using DINC 2 server. Three peptides that showed best interactions were SATPdb10193, SATPdb21850, and SATPdb26811 with binding energies -6.10, -6.10, and -6.30 kcal/mol, respectively. Small molecules from drug databases were also used to perform virtual screening against the A36R protein. Among databases, Enamine-HTSC showed strong affinity with docking scores ranging from -8.8 to 9.8 kcal/mol. Interaction of target protein A36R with the top 3 peptides and the most probable drug (Z55287118) examined by molecular dynamic (MD) simulation. Trajectory analyses (RMSD, RMSF, SASA, and Rg) confirmed the stable nature of protein-ligand and protein-peptide complexes. This work suggests that identified top AVPs and small molecules might interfere with the function of the A36R protein of MPXV.

Sex-specific expression of pheromones and other signals in gravid starfish

BACKGROUND

Many echinoderms form seasonal aggregations prior to spawning. In some fecund species, a spawning event can lead to population outbreaks with detrimental ecosystem impacts. For instance, outbreaks of crown-of-thorns starfish (COTS), a corallivore, can destroy coral reefs. Here, we examine the gene expression in gravid male and female COTS prior to spawning in the wild, to identify genome-encoded factors that may regulate aggregation and spawning. This study is informed by a previously identified exoproteome that attracts conspecifics. To capture the natural gene expression profiles, we isolated RNAs from gravid female and male COTS immediately after they were removed from the Great Barrier Reef.  RESULTS: Sexually dimorphic gene expression is present in all seven somatic tissues and organs that we surveyed and in the gonads. Approximately 40% of the exoproteome transcripts are differentially expressed between sexes. Males uniquely upregulate an additional 68 secreted factors in their testes. A suite of neuropeptides in sensory organs, coelomocytes and gonads is differentially expressed between sexes, including the relaxin-like gonad-stimulating peptide and gonadotropin-releasing hormones. Female sensory tentacles-chemosensory organs at the distal tips of the starfish arms-uniquely upregulate diverse receptors and signalling molecules, including chemosensory G-protein-coupled receptors and several neuropeptides, including kisspeptin, SALMFamide and orexin.

CONCLUSIONS

Analysis of 103 tissue/organ transcriptomes from 13 wild COTS has revealed genes that are consistently differentially expressed between gravid females and males and that all tissues surveyed are sexually dimorphic at the molecular level. This finding is consistent with female and male COTS using sex-specific pheromones to regulate reproductive aggregations and synchronised spawning events. These pheromones appear to be received primarily by the sensory tentacles, which express a range of receptors and signalling molecules in a sex-specific manner. Furthermore, coelomocytes and gonads differentially express signalling and regulatory factors that control gametogenesis and spawning in other echinoderms.

Pan-kinome of Legionella expanded by a bioinformatics survey

The pathogenic Legionella bacteria are notorious for delivering numerous effector proteins into the host cell with the aim of disturbing and hijacking cellular processes for their benefit. Despite intensive studies, many effectors remain uncharacterized. Motivated by the richness of Legionella effector repertoires and their oftentimes atypical biochemistry, also by several known atypical Legionella effector kinases and pseudokinases discovered recently, we undertook an in silico survey and exploration of the pan-kinome of the Legionella genus, i.e., the union of the kinomes of individual species. In this study, we discovered 13 novel (pseudo)kinase families (all are potential effectors) with the use of non-standard bioinformatic approaches. Together with 16 known families, we present a catalog of effector and non-effector protein kinase-like families within Legionella, available at http://bioinfo.sggw.edu.pl/kintaro/ . We analyze and discuss the likely functional roles of the novel predicted kinases. Notably, some of the kinase families are also present in other bacterial taxa, including other pathogens, often phylogenetically very distant from Legionella. This work highlights Nature's ingeniousness in the pathogen-host arms race and offers a useful resource for the study of infection mechanisms.