Functional Annotation Of Genomes Research Articles

Harnessing transcriptomic signals for amyotrophic lateral sclerosis to identify novel drugs and enhance risk prediction

IntroductionAmyotrophic lateral sclerosis (ALS) is a fatal neurodegenerative disease. This study integrates common genetic association results from the latest ALS genome-wide association study (GWAS) summary statistics with functional genomic annotations with the aim of providing mechanistic insights into ALS risk loci, inferring drug repurposing opportunities, and enhancing prediction of ALS risk and clinical characteristics. MethodsGenes associated with ALS were identified using GWAS summary statistic methodology including SuSiE SNP-based fine-mapping, and transcriptome- and proteome-wide association study (TWAS/PWAS) analyses. Using several approaches, gene associations were integrated with the DrugTargetor drug-gene interaction database to identify drugs that could be repurposed for the treatment of ALS. Furthermore, ALS gene associations from TWAS were combined with observed blood expression in two external ALS case-control datasets to calculate polytranscriptomic scores and evaluate their utility for prediction of ALS risk and clinical characteristics, including site of onset, age at onset, and survival. ResultsSNP-based fine-mapping, TWAS and PWAS identified 118 genes associated with ALS, with TWAS and PWAS providing novel mechanistic insights. Drug repurposing analyses identified six drugs significantly enriched for interactions with ALS associated genes, though directionality could not be determined. Additionally, drug class enrichment analysis showed gene signatures linked to calcium channel blockers may reduce ALS risk, whereas antiepileptic drugs may increase ALS risk. Across the two observed expression target samples, ALS polytranscriptomic scores significantly predicted ALS risk (R2 = 5.1 %; p-value = 3.2 × 10−27) and clinical characteristics. ConclusionsFunctionally-informed analyses of ALS GWAS summary statistics identified novel mechanistic insights into ALS aetiology, highlighted several therapeutic research avenues, and enabled statistically significant prediction of ALS risk.

Network pharmacology- and molecular docking-based exploration of the molecular mechanism underlying Jianpi Yiwei Recipe treatment of gastric cancer.

Traditional Chinese medicine (TCM) is widely used as an important complementary and alternative healthcare system for cancer treatment in Asian countries. Network pharmacology, which utilizes various database platforms and computer software to study the interactions between complex drug components in vivo, is particularly useful for studying the pharmacodynamic mechanisms of multi-pathway and multi-target Chinese medicines. To explore the potential targets and function of Jianpi Yiwei Recipe treatment of gastric cancer (GC) through network pharmacology and molecular docking. Data on the components of Jianpi Yiwei Recipe (Radix Astragali, Radix Codonopsis, Agrimonia eupatoria, Atractylodes macrocephala Koidz., Poria cocos, stir-baked rhizoma dioscoreae, Amomum villosum Lour., fried Fructus Aurantii, pericarpium citri reticulatae, Rhizoma Pinelliae Preparata, and Radix Glycyrrhizae Preparata) were collected and screened by using the TCM systems pharmacology database and analysis platform (TCMSP). Then the targets of these compounds were predicted. GC-related targets were screened using the GeneCards database. Venn diagram was used to identify common targets. An active ingredient-core target interaction network and a protein-protein interaction (PPI) network were built. Moreover, we performed gene ontology (GO) functional annotation and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses on the core targets and validated them by molecular docking. TCMSP screening revealed 11 active components and 184 targets, whereas GeneCards found 10118 disease-related targets, with 180 shared targets between them. Topology analysis of the PPI network identified 38 targets, including ATK1, TP53, and tumor necrosis factor, as key targets for the treatment of GC by Jianpi Yiwei Recipe. Quercetin, naringenin, luteolin, etc., may be the main active components of Jianpi Yiwei Recipe. GO enrichment analysis identified 2809, 1218, and 553 functions related to biological process, molecular function, and cellular component, respectively. KEGG pathway enrichment analysis revealed 167 related pathways, mainly involved in cancer, endocrine resistance, and AGE-RAGE signaling in diabetic complication. Validation with molecular docking analysis showed docking of key active components with core targets. Jianpi Yiwei Recipe plays a therapeutic role in GC through multiple components, targets, and pathways. These findings form a basis for follow-up exploration of Jianpi Yiwei Recipe in the treatment of GC.

Expanding the Pseudomonas diversity of the wheat rhizosphere: four novel species antagonizing fungal phytopathogens and with plant-beneficial properties.

Plant-beneficial Pseudomonas bacteria hold the potential to be used as inoculants in agriculture to promote plant growth and health through various mechanisms. The discovery of new strains tailored to specific agricultural needs remains an open area of research. In this study, we report the isolation and characterization of four novel Pseudomonas species associated with the wheat rhizosphere. Comparative genomic analysis with all available Pseudomonas type strains revealed species-level differences, substantiated by both digital DNA-DNA hybridization and average nucleotide identity, underscoring their status as novel species. This was further validated by the phenotypic differences observed when compared to their closest relatives. Three of the novel species belong to the P. fluorescens species complex, with two representing a novel lineage in the Pseudomonas phylogeny. Functional genome annotation revealed the presence of specific features contributing to rhizosphere colonization, including flagella and components for biofilm formation. The novel species have the genetic potential to solubilize nutrients by acidifying the environment, releasing alkaline phosphatases and their metabolism of nitrogen species, indicating potential as biofertilizers. Additionally, the novel species possess traits that may facilitate direct promotion of plant growth through the modulation of the plant hormone balance, including the ACC deaminase enzyme and auxin metabolism. The presence of biosynthetic clusters for toxins such as hydrogen cyanide and non-ribosomal peptides suggests their ability to compete with other microorganisms, including plant pathogens. Direct inoculation of wheat roots significantly enhanced plant growth, with two strains doubling shoot biomass. Three of the strains effectively antagonized fungal phytopathogens (Thielaviopsis basicola, Fusarium oxysporum, and Botrytis cinerea), demonstrating their potential as biocontrol agents. Based on the observed genetic and phenotypic differences from closely related species, we propose the following names for the four novel species: Pseudomonas grandcourensis sp. nov., type strain DGS24T ( = DSM 117501T = CECT 31011T), Pseudomonas purpurea sp. nov., type strain DGS26T ( = DSM 117502T = CECT 31012T), Pseudomonas helvetica sp. nov., type strain DGS28T ( = DSM 117503T = CECT 31013T) and Pseudomonas aestiva sp. nov., type strain DGS32T ( = DSM 117504T = CECT 31014T).

Identification and Characterization of four Bacillus species from the intestine of hybrid grouper (Epinephelus fuscoguttatus♀ × E. lanceolatus♂), their antagonistic role on common pathogenic bacteria, and effects on intestinal health

As an alternative to the criticized antibiotics, probiotics have been adopted for their eco-friendly nature and ability to enhance host growth and immunity. Nevertheless, reports suggest ineffectiveness in commercially available probiotics since most are from non-fish sources; thus, this study was envisaged to isolate and characterize new Bacillus spp. from the gut of hybrid grouper (Epinephelus fuscoguttatus♀×Epinephelus lanceolatus♂) as potential probiotics. The isolation and characterization were performed based on their morphological and biochemical properties and 16S rRNA sequencing homology analysis, and a subsequent 4-week in vivo biosafety feeding trial was conducted to ascertain the effects on isolates’ non-pathogenicity, growth, and intestinal mucosal microvilli via scanning electron microscopy (SEM) analysis. Four Bacillus spp. strains, namely, B. velezensis strain PGSAK01 (accession number OQ726606), B. stercoris strain PGSAK05 (accession number OQ726607), B. velezensis strain PGSAK17 (accession number OQ726601), and B. subtilis strain PGSAK19 (accession number OQ726605), were identified and characterized in the current study. The strains showed promising probiotic properties such as showing higher adhesion capability, higher thermotolerance, displaying higher survivability to 0.5% bile, lower pH tolerance, γ-haemolytic activity, and multispecies characteristics. Among the 24 antibiotics tested, isolates were susceptible to 21, whereas the PGSAK01 strain showed resistance to furazolidone antibiotics. None of the isolates showed possession of i) virulence factor genes encoding enterotoxigenic (hblA, hblC, hblD, nheA, nheB, and entFM) and emetic (cereulide synthetase gene, ces) genes, and ii) streptomycin resistance gene (vat c), ampicillin-resistant genes (mecA and bla), and vancomycin-resistant gene (van B). Nevertheless, the PGSAK01 and PGSAK17 strains showed possession of tek K, cat, and ant(4′)-Ia (adenylyltransferase) (except the PGSAK01) resistant genes. All isolates displayed better antimicrobial effects against pathogenic bacteria Streptococcus agalactiae, S. iniae, Vibrio harveyi, and V. alginolyticus. The vivo biosafety trial involved hybrid grouper fish being grouped into five (average weight 32±0.94 g), namely, the group fed the basal diet void of isolate’s supplementation (control), and the remaining four groups fed the basal diet with 1×108 CFU/g diet of individual strain PGSAK01, PGSAK05, PGSAK17, and PGSAK19 supplementation. At the end of the study, a significantly higher WGR, K (except the PGSAK01 group), VSI; lysozyme (except PGSAK01 group), superoxide dismutase, total antioxidant activity, alkaline phosphatase enzyme activities; highly dense intestinal mucosal villi (based on the scanning electron microscopy analysis); and significantly lower malondialdehyde levels were witnessed in the isolated treated groups compared to the control, supporting the results obtained in the auto-aggregation and cell-surface hydrophobicity test. This work’s results have provided thought-provoking targets; thus, studies involving extensive genome sequencing and functional annotation analysis will be explored to offer unfathomable insights into their mechanisms of action and potential health benefits, further establishing the four Bacillus strains PGSAK01, PGSAK05, PGSAK17, and PGSAK19 potential role in probiotic fields and functional foods.

Taxonomic and evolutionary insights from comparative genomics of hydrocarbon degrading bacteria isolated from petroleum reservoirs

Petroleum hydrocarbons and their derivatives are compounds widely used in several industries worldwide. Inappropriate disposal or handling of hydrocarbon-derived products may generate environmental and/or health negative impacts. Understanding the genomic traits underlying hydrocarbon degradation by microorganisms may provide valuable information to improve the use of bioremediation-based strategies for the recovery of impacted areas. In this sense, this study aimed to characterize and compare hydrocarbon-degradation genes and pathways of four bacteria isolated from deep oil reservoirs: Micrococcus sp. CBMAI 636, Dietzia maris CBMAI 705, Bacillus subtilis CBMAI 707, Achromobacter xylosoxidans CBMAI 709, via complete genome sequencing and functional annotation. In addition, phylogenetic analyses were carried out seeking to unravel the evolutionary relatedness of such functional genes to those found in different taxa and environments. Genomic analyses confirmed a high genetic potential for hydrocarbon degradation in the studied strains. Comparative genomics indicated the presence of hydrocarbon degradation genes across all strains, suggesting adaptive evolutionary convergence to hydrocarbon-affected environments. Interestingly, Achromobacter xylosoxidans CBMAI 709 exhibited unique orthologous genes that play a crucial role in the capture, uptake and/or breakdown of petroleum compounds, enhancing its adaptability to hydrocarbon-contaminated environments. These are innovative results that provide novel evolutionary insights into the diversity of hydrocarbon-degrading genes and pathways, enriching our understanding of microbial adaptation to hydrocarbon-rich habitats. The findings gathered in this study underscore the potential of these organisms for bioremediation endeavors, paving the way for future applications in environmental polluting restoration.

Transcriptomics reveals dynamic changes in the “gene profiles” of rat supraspinatus tendon at three different time points after diabetes induction

ObjectiveThere is increasing evidence that type 2 diabetes mellitus (T2DM) is an independent risk factor for the occur of tendinopathy. Therefore, this study is the first to explore the dynamic changes of the “gene profile” of supraspinatus tendon in rats at different time points after T2DM induction through transcriptomics, providing potential molecular markers for exploring the pathogenesis of diabetic tendinopathy.MethodsA total of 40 Sprague-Dawley rats were randomly divided into normal (NG, n = 10) and T2DM groups (T2DM, n = 30) and subdivided into three groups according to the duration of diabetes: T2DM-4w, T2DM-8w, and T2DM-12w groups; the duration was calculated from the time point of T2DM rat model establishment. The three comparison groups were set up in this study, T2DM-4w group vs. NG, T2DM-8w group vs. NG, and T2DM-12w group vs. NG. Differentially expressed genes (DEGs) in 3 comparison groups were screened. The intersection of the three comparison groups’ DEGs was defined as key genes that changed consistently in the supraspinatus tendon after diabetes induction. Cluster analysis, gene ontology (GO) functional annotation analysis and Kyoto encyclopedia of genes and genomes (KEGG) functional annotation and enrichment analysis were performed for DEGs.ResultsT2DM-4w group vs. NG, T2DM-8w group vs. NG, and T2DM-12w group vs. NG detected 519 (251 up-regulated and 268 down-regulated), 459 (342 up-regulated and 117 down-regulated) and 328 (255 up-regulated and 73 down-regulated) DEGs, respectively. 103 key genes of sustained changes in the supraspinatus tendon following induction of diabetes, which are the first identified biomarkers of the supraspinatus tendon as it progresses through the course of diabetes.The GO analysis results showed that the most significant enrichment in biological processes was calcium ion transmembrane import into cytosol (3 DEGs). The most significant enrichment in cellular component was extracellular matrix (9 DEGs). The most significant enrichment in molecular function was glutamate-gated calcium ion channel activity (3 DEGs). The results of KEGG pathway enrichment analysis showed that there were 17 major pathways (p < 0.05) that diabetes affected supratinusculus tendinopathy, including cAMP signaling pathway and Calcium signaling pathway.ConclusionsTranscriptomics reveals dynamic changes in the“gene profiles”of rat supraspinatus tendon at three different time points after diabetes induction. The 103 DEGs identified in this study may provide potential molecular markers for exploring the pathogenesis of diabetic tendinopathy, and the 17 major pathways enriched in KEGG may provide new ideas for exploring the pathogenesis of diabetic tendinopathy.

Leveraging functional genomic annotations and genome coverage to improve polygenic prediction of complex traits within and between ancestries.

We develop a method, SBayesRC, that integrates genome-wide association study (GWAS) summary statistics with functional genomic annotations to improve polygenic prediction of complex traits. Our method is scalable to whole-genome variant analysis and refines signals from functional annotations by allowing them to affect both causal variant probability and causal effect distribution. We analyze 50 complex traits and diseases using ∼7 million common single-nucleotide polymorphisms (SNPs) and 96 annotations. SBayesRC improves prediction accuracy by 14% in European ancestry and up to 34% in cross-ancestry prediction compared to the baseline method SBayesR, which does not use annotations, and outperforms other methods, including LDpred2, LDpred-funct, MegaPRS, PolyPred-S and PRS-CSx. Investigation of factors affecting prediction accuracy identifies a significant interaction between SNP density and annotation information, suggesting whole-genome sequence variants with annotations may further improve prediction. Functional partitioning analysis highlights a major contribution of evolutionary constrained regions to prediction accuracy and the largest per-SNP contribution from nonsynonymous SNPs.

Research Progress and Applications of Bovine Genome in the Tribe Bovini.

Various bovine species have been domesticated and bred for thousands of years, and they provide adequate animal-derived products, including meat, milk, and leather, to meet human requirements. Despite the review studies on economic traits in cattle, the genetic basis of traits has only been partially explained by phenotype and pedigree breeding methods, due to the complexity of genomic regulation during animal development and growth. With the advent of next-generation sequencing technology, genomics projects, such as the 1000 Bull Genomes Project, Functional Annotation of Animal Genomes project, and Bovine Pangenome Consortium, have advanced bovine genomic research. These large-scale genomics projects gave us a comprehensive concept, technology, and public resources. In this review, we summarize the genomics research progress of the main bovine species during the past decade, including cattle (Bos taurus), yak (Bos grunniens), water buffalo (Bubalus bubalis), zebu (Bos indicus), and gayal (Bos frontalis). We mainly discuss the development of genome sequencing and functional annotation, focusing on how genomic analysis reveals genetic variation and its impact on phenotypes in several bovine species.

Mechanism of Shexiang Tongxin Dropping Pills in treating diabetic cardiomyopathy based on network pharmacology and animal experiments

This study aimed to explore the mechanism of Shexiang Tongxin Dropping Pills(STDP) in treating diabetic cardiomyopathy(DCM) based on network pharmacology, molecular docking, and animal experiments. BATMAN, TCMSP, and GeneCards were searched for the active ingredients and targets of STDP against DCM. STRING and Cytoscape were used to build the protein-protein interaction(PPI) network and &quot;drug-active ingredient-target&quot; network. Gene Ontology(GO) functional annotation and Kyoto Encyclopedia of Genes and Genomes(KEGG) enrichment analysis of the targets were carried out based on DAVID. The molecular docking of key receptor proteins with corresponding active ingredients was performed using AutoDock Vina. The rat model of DCM was established by a high-fat diet combined with intraperitoneal injection of streptozotocin. Rats were assigned into control, model, low-(20 mg·kg~(-1)) and high-dose(40 mg·kg~(-1)) STDP, and metformin(200 mg·kg~(-1)) groups. After 8 weeks of continuous administration, the cardiac function, myocardial pathological changes, and myocardial collagen fiber deposition of rats in each group were detected by echocardiography, hematoxylin-eosin(HE) staining, and Sirius red staining, respectively. The myocardial hypertrophy was detected by WGA staining. The expression levels of p38 mitogen-activated protein kinase(p38), phosphorylation-p38(p-p38), c-Jun N-terminal kinase(JNK), phosphorylation-JNK(p-JNK), caspase-3, and C-caspase-3 in the myocardial tissue of rats in each group were measured by Western blot. The network pharmacology predicted 199 active ingredients and 1 655 targets of STDP and 463 targets of DCM. One hundred and thirty-four potential targets of STDP for treating DCM were obtained, and the AGE-RAGE signaling pathway in diabetic complications was screened out. Molecular docking results showed that miltirone, dehydromiltirone, and tryptanthrin had strong binding affinity with RAGE. The results of animal experiments confirmed that STDP effectively protected the cardiac function of DCM rats. Compared with the DCM model group, the STDP groups showed significantly down-regulated protein levels of p-p38, p-JNK, and C-caspase-3. To sum up, STDP may protect the cardiac function of DCM rats by regulating the AGE-RAGE signaling pathway.

Chromosome-Level Assembly and Comparative Genomic Analysis of Suillus bovinus Provides Insights into the Mechanism of Mycorrhizal Symbiosis.

Suillus bovinus is a wild edible ectomycorrhizal fungus with important economic and ecological value, which often forms an ectomycorrhiza with pine trees. We know little about the mechanisms associated with the metabolism and symbiosis of S. bovinus and its effects on the nutritional value. In this study, the whole-genome sequencing of S. bovinus was performed using Illumina, HiFi, and Hi-C technologies, and the sequencing data were subjected to genome assembly, gene prediction, and functional annotation to obtain a high-quality chromosome-level genome of S. bovinus. The final assembly of the S. bovinus genome includes 12 chromosomes, with a total length of 43.03 Mb, a GC content of 46.58%, and a contig N50 size of 3.78 Mb. A total of 11,199 coding protein sequences were predicted from genome annotation. The S. bovinus genome contains a large number of small secreted proteins (SSPs) and genes that encode enzymes related to carbohydrates, as well as genes related to terpenoids, auxin, and lipochitooligosaccharides. These genes may contribute to symbiotic processes. The whole-genome sequencing and genetic information provide a theoretical basis for a deeper understanding of the mechanism of the mycorrhizal symbiosis of S. bovinus and can serve as a reference for comparative genomics of ectomycorrhizal fungi.

Study of the intestinal microbiota composition and the effect of treatment with intensive chemotherapy in patients recovered from acute leukemia

A dataset comprising metagenomes of outpatients (n = 28) with acute leukemia (AL) and healthy controls (n = 14) was analysed to investigate the associations between gut microbiota composition and metabolic activity and AL. According to the results obtained, no significant differences in the microbial diversity between AL outpatients and healthy controls were found. However, significant differences in the abundance of specific microbial clades of healthy controls and AL outpatients were found. We found some differences at taxa level. The relative abundance of Enterobacteriaceae, Prevotellaceae and Rikenellaceae was increased in AL outpatients, while Bacteirodaceae, Bifidobacteriaceae and Lachnospiraceae was decreased. Interestingly, the abundances of several taxa including Bacteroides and Faecalibacterium species showed variations based on recovery time from the last cycle of chemotherapy. Functional annotation of metagenome-assembled genomes (MAGs) revealed the presence of functional domains corresponding to therapeutic enzymes including l-asparaginase in a wide range of genera including Prevotella, Ruminococcus, Faecalibacterium, Alistipes, Akkermansia. Metabolic network modelling revealed potential symbiotic relationships between Veillonella parvula and Levyella massiliensis and several species found in the microbiota of AL outpatients. These results may contribute to develop strategies for the recovery of microbiota composition profiles in the treatment of patients with AL.

A Comprehensive Review on Machine Learning Techniques for Protein Family Prediction.

Proteomics is a field dedicated to the analysis of proteins in cells, tissues, and organisms, aiming to gain insights into their structures, functions, and interactions. A crucial aspect within proteomics is protein family prediction, which involves identifying evolutionary relationships between proteins by examining similarities in their sequences or structures. This approach holds great potential for applications such as drug discovery and functional annotation of genomes. However, current methods for protein family prediction have certain limitations, including limited accuracy, high false positive rates, and challenges in handling large datasets. Some methods also rely on homologous sequences or protein structures, which introduce biases and restrict their applicability to specific protein families or structures. To overcome these limitations, researchers have turned to machine learning (ML) approaches that can identify connections between protein features and simplify complex high-dimensional datasets. This paper presents a comprehensive survey of articles that employ various ML techniques for predicting protein families. The primary objective is to explore and improve ML techniques specifically for protein family prediction, thus advancing future research in the field. Through qualitative and quantitative analyses of ML techniques, it is evident that multiple methods utilizing a range of classifiers have been applied for protein family prediction. However, there has been limited focus on developing novel classifiers for protein family classification, highlighting the urgent need for improved approaches in this area. By addressing these challenges, this research aims to enhance the accuracy and effectiveness of protein family prediction, ultimately facilitating advancements in proteomics and its diverse applications.

Baicalin induces ferroptosis in HepG2 cells by inhibiting ROS-mediated PI3K/Akt/FoxO3a signaling pathway

This study aims to investigate whether baicalin induces ferroptosis in HepG2 cells and decipher the underlying mechanisms based on network pharmacology and cell experiments. HepG2 cells were cultured in vitro and the cell viability was detected by the cell counting kit-8(CCK-8). The transcriptome data of hepatocellular carcinoma were obtained from the Cancer Genome Atlas(TCGA), and the ferroptosis gene data from FerrDb V2. The DEG2 package was used to screen the differentially expressed genes(DEGs), and the common genes between DEGs and ferroptosis genes were selected as the target genes that mediate ferroptosis to regulate hepatocellular carcinoma progression. The functions and structures of the target genes were analyzed by Gene Ontology(GO) functional annotation and Kyoto Encyclopedia of Genes and Genomes(KEGG) pathway enrichment with the thresholds of P&lt;0.05 and |log_2(fold change)|&gt;0.5. DCFH-DA probe was used to detect the changes in the levels of cellular reactive oxygen species(ROS) in each group. The reduced glutathione(GSH) assay kit was used to measure the cellular GSH level, and Fe~(2+) assay kit to determine the Fe~(2+) level. Real-time quantitative PCR(RT-PCR) was employed to measure the mRNA levels of glutathione peroxidase 4(GPX4) and solute carrier family 7 member 11(SLC7A11) in each group. Western blot was employed to determine the protein levels of GPX4, SLC7A11, phosphatidylinositol 3-kinase(PI3K), p-PI3K, protein kinase B(Akt), p-Akt, forkhead box protein O3a(FoxO3a), and p-FoxO3a in each group. The results showed that treatment with 200 μmol·L~(-1) baicalin for 48 h significantly inhibited the viability of HepG2 cells. Ferroptosis in hepatocellular carcinoma could be regulated via the PI3K/Akt signaling pathway. The cell experiments showed that baicalin down-regulated the expression of SLC7A11 and GPX4, lowered the GSH level, and increased ROS accumulation and Fe~(2+) production in HepG2 cells. However, ferrostatin-1, an ferroptosis inhibitor, reduced baicalin-induced ROS accumulation, up-regulated the expression of SLC7A11 and GPX4, elevated the GSH level, and decreased PI3K, Akt, and FoxO3a phosphorylation. In summary, baicalin can induce ferroptosis in HepG2 cells by inhibiting the ROS-mediated PI3K/Akt/FoxO3a pathway.

Isolation, Characterization, Genome Annotation, and Evaluation of Tyrosinase Inhibitory Activity in Secondary Metabolites of Paenibacillus sp. JNUCC32: A Comprehensive Analysis through Molecular Docking and Molecular Dynamics Simulation.

A potential strain, Paenibacillus sp. JNUCC32, was isolated and subjected to whole-genome sequencing. Genome functional annotation revealed its active metabolic capabilities. This study aimed to investigate the pivotal secondary metabolites in the biological system. Fermentation and extraction were performed, resulting in the isolation of seven known compounds: tryptophol (1), 3-(4-hydroxyphenyl)propionic acid (2), ferulic acid (3), maculosin (4), brevianamide F (5), indole-3-acetic acid (6), and butyric acid (7). Tryptophol exhibited favorable pharmacokinetic properties and demonstrated certain tyrosinase inhibitory activity (IC50 = 999 μM). For further analysis of its inhibition mechanism through molecular docking and molecular dynamics (MD) simulation, tryptophol formed three hydrogen bonds and a pro-Michaelis complex with tyrosinase (binding energy = -5.3 kcal/mol). The MD simulation indicated favorable stability for the tryptophol-mushroom tyrosinase complex, primarily governed by hydrogen bond interactions. The crucial residues VAL-283 and HIS-263 in the docking were also validated. This study suggests tryptophol as a potential candidate for antibrowning agents and dermatological research.

Transcriptomic analyses and machine-learning methods reveal dysregulated key genes and potential pathogenesis in human osteoarthritic cartilage.

This study aimed to explore the biological and clinical importance of dysregulated key genes in osteoarthritis (OA) patients at the cartilage level to find potential biomarkers and targets for diagnosing and treating OA. Six sets of gene expression profiles were obtained from the Gene Expression Omnibus database. Differential expression analysis, weighted gene coexpression network analysis (WGCNA), and multiple machine-learning algorithms were used to screen crucial genes in osteoarthritic cartilage, and genome enrichment and functional annotation analyses were used to decipher the related categories of gene function. Single-sample gene set enrichment analysis was performed to analyze immune cell infiltration. Correlation analysis was used to explore the relationship among the hub genes and immune cells, as well as markers related to articular cartilage degradation and bone mineralization. A total of 46 genes were obtained from the intersection of significantly upregulated genes in osteoarthritic cartilage and the key module genes screened by WGCNA. Functional annotation analysis revealed that these genes were closely related to pathological responses associated with OA, such as inflammation and immunity. Four key dysregulated genes (cartilage acidic protein 1 (CRTAC1), iodothyronine deiodinase 2 (DIO2), angiopoietin-related protein 2 (ANGPTL2), and MAGE family member D1 (MAGED1)) were identified after using machine-learning algorithms. These genes had high diagnostic value in both the training cohort and external validation cohort (receiver operating characteristic > 0.8). The upregulated expression of these hub genes in osteoarthritic cartilage signified higher levels of immune infiltration as well as the expression of metalloproteinases and mineralization markers, suggesting harmful biological alterations and indicating that these hub genes play an important role in the pathogenesis of OA. A competing endogenous RNA network was constructed to reveal the underlying post-transcriptional regulatory mechanisms. The current study explores and validates a dysregulated key gene set in osteoarthritic cartilage that is capable of accurately diagnosing OA and characterizing the biological alterations in osteoarthritic cartilage; this may become a promising indicator in clinical decision-making. This study indicates that dysregulated key genes play an important role in the development and progression of OA, and may be potential therapeutic targets.

Multi-omics reveals the testosterone promotion effect mechanism of Cordyceps Sobolifera on Leydig cells

Ethnopharmacological relevanceCordyceps sobolifera (CS) has been traditionally utilized as an ethnic remedy for various health conditions, including chronic kidney diseases, anti-fatigue interventions, and management of chronic inflammation. Notably, CS is recognized for its substantial content of bioactive compounds, among which nucleosides prominently feature as constituents with diverse therapeutic advantages.Aim of the study: This study aims to investigate the effects of CS on testosterone secretion in Leydig cells and explore the underlying mechanism. Materials and methodsLeydig cells were isolated from rat testes to establish a primary rat Leydig cells model. Cell proliferation and testosterone secretion were assessed via the methyl-piperidino-pyrazole (MTT) assay and enzyme-linked immunosorbent assay (ELISA), respectively. Samples earmarked for RNA sequencing (RNA-Seq) analysis facilitated the identification of significantly differentially expressed genes (DEGs), and we conducted Gene Ontology (GO)/Kyoto Encyclopedia of Genes and Genomes (KEGG) functional annotation and enrichment analyses. The veracity of our findings was validated through quantitative real time polymerase chain reaction (qRT-PCR) and western blotting. ResultsThe results showed that CS and guanosine could promote Leydig cell proliferation and bolster testosterone secretion. Our integrative analysis of metabolomics and transcriptomics has unveiled the potential mechanisms governing testosterone synthesis. Specifically, metabolomics has illuminated striking correlations within cholesterol metabolism, and bile secretion. Concurrently, transcriptomics has underscored the pivotal roles played by the cyclic adenosine monophosphate (cAMP) signaling pathway and steroid hormone biosynthesis. Furthermore, our investigation has demonstrated CS's aptitude in elevating the expression of proteins and genes. Notably, our findings have elucidated that these effects can be mitigated by protein kinase A (PKA) and adenylate cyclase (AC) specific inhibitors. ConclusionThis study delineates the cAMP-PKA pathways as plausible mechanisms underpinning the testosterone-enhancing properties of CS, with guanosine emerging as a fundamental bioactive constituent.

Comparison of the DNBSEQ platform and Illumina HiSeq 2000 for bacterial genome assembly

The Illumina HiSeq platform has been a commonly used option for bacterial genome sequencing. Now the BGI DNA nanoball (DNB) nanoarrays platform may provide an alternative platform for sequencing of bacterial genomes. To explore the impact of sequencing platforms on bacterial genome assembly, quality assessment, sequence alignment, functional annotation, mutation detection, and metagenome mapping, we compared genome assemblies based on sequencing of cultured bacterial species using the HiSeq 2000 and BGISEQ-500 platforms. In addition, simulated reads were used to evaluate the impact of insert size on genome assembly. Genome assemblies based on BGISEQ-500 sequencing exhibited higher completeness and fewer N bases in high GC genomes, whereas HiSeq 2000 assemblies exhibited higher N50. The majority of assembly assessment parameters, sequences of 16S rRNA genes and genomes, numbers of single nucleotide variants (SNV), and mapping to metagenome data did not differ significantly between platforms. More insertions were detected in HiSeq 2000 genome assemblies, whereas more deletions were detected in BGISEQ-500 genome assemblies. Insert size had no significant impact on genome assembly. Taken together, our results suggest that DNBSEQ platforms would be a valid substitute for HiSeq 2000 for bacterial genome sequencing.

Functional annotation of a divergent genome using sequence and structure-based similarity

BackgroundMicrosporidia 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.ResultsIn 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.ConclusionWe 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.

Discussion on the mechanism of Lingguizhugan Decoction in treating hypertension based on network pharmacology and molecular simulation technology

To explore the mechanism of Lingguizhugan Decoction in treating hypertension based on network pharmacology and molecular simulation. The active ingredients and potential targets were screened by the Systematic Pharmacological Analysis Platform of Traditional Chinese Medicine (TCMSP). Hypertension-related targets were obtained from OMIM and GeneCards databases. Common targets between drug and hypertension were screened in the Venny platform. A protein-protein interaction (PPI) network was constructed in the STRING database using intersection targets. Key targets in PPI network were analyzed by Cytoscape. R language program was used for Gene Ontology (GO) functional annotation and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis. Finally, the binding abilities of the main active ingredients to critical targets were verified by molecular simulation. Naringenin, quercetin, kaempferol, and β-sitosterol in Lingguizhugan Decoction, and potential targets such as STAT3, AKT1, TNF, IL6, JUN, PTGS2, MMP9, CASP3, TP53, and MAPK3, were screened out. KEGG Enrichment analysis revealed that the common targets of Lingguizhugan Decoction and hypertension are mainly involved in the lipid and atherosclerosis signaling pathway, AGE-RAGE signaling pathway in diabetic complications, fluid shear stress and atherosclerosis, and IL17 signaling pathway. The molecular simulation results showed that naringenin-MAPK3, quercetin-MMP9, quercetin-PTGS2, and quercetin-TP53 were the top four in the docking scores. Naringenin-MAPK3 and quercetin-MMP9 were stable, with binding free energies of −27.97 ± 1.41 kcal/mol and −21.15 ± 3.17 kcal/mol, respectively. The possible mechanism of Lingguizhugan Decoction in treating hypertension is characterized of multi-component, multi-target, and multi-pathway. Communicated by Ramaswamy H. Sarma

Comamonas-dominant microbial community in carbon poor aquitard sediments revealed by metagenomic-based growth rate investigation

The microbiological ecology of a low-nutrient shallow aquifer with high arsenic content in the Yinchuan Plain was investigated in this study. Amplicon sequencing data from five samples (depths: 1.5 m, 3.5 m, 11.2 m, 19.3 m, and 25.5 m) revealed diverse and adaptable microbial community. Among the microbial community, Comamonas was the most prominent, accounting for 10.52 % of the total. This genus displayed high growth rates, with a maximum growth rate of 12.06 d−1 and a corresponding doubling time of 1.38 days, as determined through an analysis of codon usage bias. Functional annotation of Metagenome-Assembled Genomes (MAGs) for samples at 1.5 m and 11.2 m depths revealed Comamonas' metabolic versatility, including various carbon pathways, assimilative sulfate reduction (ASR), and dissimilatory reduction to ammonium (DNRA). The TPM (Transcripts Per Kilobase of exon model per Million mapped reads) of MAGs at 11.2 m sample was 15.7 and 12.3. The presence of arsenic resistance genes in Comamonas aligns with sediment arsenic levels (65.8 mg/kg for 1.5 m depth, 32.8 mg/kg for 11.2 m depth). This study highlights the role of Comamonas as a ‘generalist’ bacteria in challenging oligotrophic sediments, emphasizing the significance of such organisms in community stability and ecological functions. Environmental implicationLow-biomass limits the microbial activity and biogeochemical study in oligotrophic environments, which is the typical condition for underground aquatic ecosystems. Facilitated by growth rate estimation, our research focuses on active functional microorganisms and their biogeochemical metabolic in oligotrophic aquifer sediments, revealing their impact on the environment and response to arsenic threats. Findings illuminate the metabolic advantage of a ‘generalist life-style’ in carbon-scarce environments and contribute to a broader understanding of bacterial ecosystems and environmental impacts in oligotrophic aquifer sediments worldwide.