Stress adaptation in Tibetan cashmere goats is governed by inherent metabolic differences and manifested through variable cashmere phenotypes.

Tibetan cashmere goats are not only served as a valuable model for studying adaptation to hypoxia and high-altitude conditions but also playing a pivotal role in bolstering local economies through the provision of premium quality cashmere yarn. In this study, we performed an integration and network analysis of metabolomic, transcriptomic and proteomic to elucidate the role of differentially expressed genes, important metabolites, and relevant cellular and metabolic pathways between the fine (average 12.04 ± 0.03 µm of mean fiber diameter) and coarse cashmere (average 14.88 ± 0.05 µm of mean fber diameter) producing by Tibetan cashmere goats. We identified a distinction of 56 and 71 differential metabolites (DMs) between the F and C cashmere groups under positive and negative ion modes, respectively. The KEGG pathway enrichment analysis of these DMs highlighted numerous pathways predominantly involved in amino acid and protein metabolism, as indicated by the finding that the most impactful pathway was the mammalian target of rapamycin (mTOR) signalling pathway. In the F group, we identified a distinctive metabolic profile where amino acid metabolites including serine, histidine, asparagine, glutamic acid, arginine, valine, aspartic acid, tyrosine, and methionine were upregulated, while lysine, isoleucine, glutamine, tryptophan, and threonine were downregulated. The regulatory network and gene co-expression network revealed crucial genes, metabolites, and metabolic pathways. The integrative omics analysis revealed a high enrichment of several pathways, notably encompassing protein digestion and absorption, sphingolipid signalling, and the synaptic vesicle cycle. Within the sphere of our integrative analysis, DNMT3B was identified as a paramount gene, intricately associated with significant proteins such as HMCN1, CPB2, GNG12, and LRP1. Our present study delineated the molecular underpinnings governing the variations in cashmere characteristics by conducting comprehensive analyses across metabolomic, transcriptomic, and proteomic dimensions. This research provided newly insights into the mechanisms regulating cashmere traits and facilitated the advancement of selective breeding programs aimed at cultivating high-quality superfine Tibetan cashmere goats.

Incorporating functional annotation with bilevel continuous shrinkage for polygenic risk prediction.

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

KEGG orthology prediction of bacterial proteins using natural language processing.

BACKGROUND: The advent of high-throughput technologies has led to an exponential increase in uncharacterized bacterial protein sequences, surpassing the capacity of manual curation. A large number of bacterial protein sequences remain unannotated by Kyoto Encyclopedia of Genes and Genomes (KEGG) orthology, making it necessary to use auto annotation tools. These tools are now indispensable in the biological research landscape, bridging the gap between the vastness of unannotated sequences and meaningful biological insights. RESULTS: In this work, we propose a novel pipeline for KEGG orthology annotation of bacterial protein sequences that uses natural language processing and deep learning. To assess the effectiveness of our pipeline, we conducted evaluations using the genomes of two randomly selected species from the KEGG database. In our evaluation, we obtain competitive results on precision, recall, and F1 score, with values of 0.948, 0.947, and 0.947, respectively. CONCLUSIONS: Our experimental results suggest that our pipeline demonstrates performance comparable to traditional methods and excels in identifying distant relatives with low sequence identity. This demonstrates the potential of our pipeline to significantly improve the accuracy and comprehensiveness of KEGG orthology annotation, thereby advancing our understanding of functional relationships within biological systems.

Generic model to unravel the deeper insights of viral infections: an empirical application of evolutionary graph coloring in computational network biology.

PURPOSE: Graph coloring approach has emerged as a valuable problem-solving tool for both theoretical and practical aspects across various scientific disciplines, including biology. In this study, we demonstrate the graph coloring's effectiveness in computational network biology, more precisely in analyzing protein-protein interaction (PPI) networks to gain insights about the viral infections and its consequences on human health. Accordingly, we propose a generic model that can highlight important hub proteins of virus-associated disease manifestations, changes in disease-associated biological pathways, potential drug targets and respective drugs. We test our model on SARS-CoV-2 infection, a highly transmissible virus responsible for the COVID-19 pandemic. The pandemic took significant human lives, causing severe respiratory illnesses and exhibiting various symptoms ranging from fever and cough to gastrointestinal, cardiac, renal, neurological, and other manifestations. METHODS: To investigate the underlying mechanisms of SARS-CoV-2 infection-induced dysregulation of human pathobiology, we construct a two-level PPI network and employed a differential evolution-based graph coloring (DEGCP) algorithm to identify critical hub proteins that might serve as potential targets for resolving the associated issues. Initially, we concentrate on the direct human interactors of SARS-CoV-2 proteins to construct the first-level PPI network and subsequently applied the DEGCP algorithm to identify essential hub proteins within this network. We then build a second-level PPI network by incorporating the next-level human interactors of the first-level hub proteins and use the DEGCP algorithm to predict the second level of hub proteins. RESULTS: We first identify the potential crucial hub proteins associated with SARS-CoV-2 infection at different levels. Through comprehensive analysis, we then investigate the cellular localization, interactions with other viral families, involvement in biological pathways and processes, functional attributes, gene regulation capabilities as transcription factors, and their associations with disease-associated symptoms of these identified hub proteins. Our findings highlight the significance of these hub proteins and their intricate connections with disease pathophysiology. Furthermore, we predict potential drug targets among the hub proteins and identify specific drugs that hold promise in preventing or treating SARS-CoV-2 infection and its consequences. CONCLUSION: Our generic model demonstrates the effectiveness of DEGCP algorithm in analyzing biological PPI networks, provides valuable insights into disease biology, and offers a basis for developing novel therapeutic strategies for other viral infections that may cause future pandemic.

In Silico and In Vitro Study of Isoquercitrin against Kidney Cancer and Inflammation by Triggering Potential Gene Targets.

Kidney cancer has emerged as a major medical problem in recent times. Multiple compounds are used to treat kidney cancer by triggering cancer-causing gene targets. For instance, isoquercitrin (quercetin-3-O-ß-d-glucopyranoside) is frequently present in fruits, vegetables, medicinal herbs, and foods and drinks made from plants. Our previous study predicted using protein-protein interaction (PPI) and molecular docking analysis that the isoquercitrin compound can control kidney cancer and inflammation by triggering potential gene targets of IGF1R, PIK3CA, IL6, and PTGS2. So, the present study is about further in silico and in vitro validation. We performed molecular dynamic (MD) simulation, gene ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis, cytotoxicity assay, and RT-PCR and qRT-PCR validation. According to the MD simulation (250 ns), we found that IGF1R, PIK3CA, and PTGS2, except for IL6 gene targets, show stable binding energy with a stable complex with isoquercitrin. We also performed gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analyses of the final targets to determine their regulatory functions and signaling pathways. Furthermore, we checked the cytotoxicity effect of isoquercitrin (IQ) and found that 5 µg/mL and 10 µg/mL doses showed higher cell viability in a normal kidney cell line (HEK 293) and also inversely showed an inhibition of cell growth at 35% and 45%, respectively, in the kidney cancer cell line (A498). Lastly, the RT-PCR and qRT-PCR findings showed a significant decrease in PTGS2, PIK3CA, and IGF1R gene expression, except for IL6 expression, following dose-dependent treatments with IQ. Thus, we can conclude that isoquercitrin inhibits the expression of PTGS2, PIK3CA, and IGF1R gene targets, which in turn controls kidney cancer and inflammation.

Comparative transcriptome and coexpression network analysis reveals key pathways and hub candidate genes associated with sunflower (Helianthus annuus L.) drought tolerance.

BACKGROUND: Drought severely limits sunflower production especially at the seedling stage. To investigate the response mechanism of sunflowers to drought stress, we utilized two genotypes of sunflower materials with different drought resistances as test materials. The physiological responses were investigated under well-watered (0 h) and drought-stressed conditions (24 h, 48 h, and 72 h). RESULTS: ANOVA revealed the greatest differences in physiological indices between 72 h of drought stress and 0 h of drought stress. Transcriptome analysis was performed after 72 h of drought stress. At 0 h, there were 7482 and 5627 differentially expressed genes (DEGs) in the leaves of K55 and K58, respectively, and 2150 and 2527 DEGs in the roots of K55 and K58, respectively. A total of 870 transcription factors (TFs) were identified among theDEGs, among which the high-abundance TF families included AP2/ERF, MYB, bHLH,and WRKY. Five modules were screened using weighted gene coexpressionnetwork analysis (WGCNA), three and two of which were positively and negatively, respectively, related to physiological traits. KEGG analysis revealedthat under drought stress, "photosynthesis", "carotenoid biosynthesis", "starch and sucrose metabolism", "ribosome", "carotenoid biosynthesis", "starch and sucrose metabolism", "protein phosphorylation" and "phytohormone signaling" are six important metabolic pathways involved in the response of sunflower to drought stress. Cytoscape software was used to visualize the three key modules, and the hub genes were screened. Finally, a total of 99 important candidate genes that may be associated with the drought response in sunflower plants were obtained, and the homology of these genes was compared with that in Arabidopsis thaliana. CONCLUSIONS: Taken together, our findings could lead to a better understanding of drought tolerance in sunflowers and facilitate the selection of drought-tolerant sunflower varieties.

Unravelling the metabolomic diversity of pigmented and non-pigmented traditional rice from Tamil Nadu, India.

Rice metabolomics is widely used for biomarker research in the fields of pharmacology. As a consequence, characterization of the variations of the pigmented and non-pigmented traditional rice varieties of Tamil Nadu is crucial. These varieties possess fatty acids, sugars, terpenoids, plant sterols, phenols, carotenoids and other compounds that plays a major role in achieving sustainable development goal 2 (SDG 2). Gas-chromatography coupled with mass spectrometry was used to profile complete untargeted metabolomics of Kullkar (red colour) and Milagu Samba (white colour) for the first time and a total of 168 metabolites were identified. The metabolite profiles were subjected to data mining processes, including principal component analysis (PCA), Orthogonal Partial Least Square Discrimination Analysis (OPLS-DA) and Heat map analysis. OPLS-DA identified 144 differential metabolites between the 2 rice groups, variable importance in projection (VIP) ≥ 1 and fold change (FC) ≥ 2 or FC ≤ 0.5. Volcano plot (64 down regulated, 80 up regulated) was used to illustrate the differential metabolites. OPLS-DA predictive model showed good fit (R2X = 0.687) and predictability (Q2 = 0.977). The pathway enrichment analysis revealed the presence of three distinct pathways that were enriched. These findings serve as a foundation for further investigation into the function and nutritional significance of both pigmented and non-pigmented rice grains thereby can achieve the SDG 2.

Comparative genome analysis of the genus Marivirga and proposal of two novel marine species: Marivirga arenosa sp. nov., and Marivirga salinae sp. nov.

BACKGROUND: The phylum Bacteroidota represents a significant proportion of heterotrophic bacteria found in marine ecosystems. Members of the phylum Bacteroidota are actively involved in the degradation of biopolymers such as polysaccharides and proteins. Bacteroidota genomes exhibit a significant enrichment of various enzymes, including carbohydrate-active enzymes (CAZymes), carboxypeptidases, esterases, isomerases, peptidases, phosphatases, and sulfatases. The genus Marivirga, a member of the family Marivirgaceae within the phylum Bacteroidota, comprises six documented species. During a microbial diversity study, three novel Marivirga strains (BKB1-2 T, ABR2-2, and BDSF4-3 T) were isolated from the West Sea, Republic of Korea. RESULTS: To explore the taxonomic status and genomic characteristics of the novel isolates, we employed a polyphasic taxonomic approach, which included phylogenetic, chemotaxonomic and comprehensive genome analysis. The three isolates were Gram-stain-negative, aerobic, rod-shaped, moderately halophilic, and had a gliding motility. The average nucleotide identity (ANI) and digital DNA-DNA hybridization (dDDH) values among the two isolates, BKB1-2 T and BDSF4-3 T, and the six reference strains were 70.5-76.5% for ANI and 18.1-25.7% for dDDH. Interestingly, the Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis showed that the strains harbor genes for a comprehensive pathway for dissimilatory nitrate reduction to ammonium (DNRA), as well as other nitrogen pathways for the reduction of nitrite, nitric oxide, and nitrous oxide. Additionally, the antiSMASH analysis indicated that the strains contained three to eight biosynthetic gene clusters (BGCs) associated with the synthesis of secondary metabolites. Furthermore, the strains carried a high number of CAZyme ranging from 53 to 152, which was also demonstrated by an in vitro analysis of degradation of the polysaccharide cellulose, chitin, laminarin, starch, and xylan. Additionally, all the strains carried genes for the metabolism of heavy metals, and exhibited tolerance to heavy metals, with minimum inhibitory concentrations (MICs) in millimoles (mM) in ranges of Co2+ (3-6), Cu2+ (0.2-0.4), Ni2+ (3-5), Zn2+ (2-4), Mn2+ (20-50), and Hg2+ (0.3). CONCLUSIONS: Based on polyphasic taxonomic approach, the three isolated strains represent two novel species names Marivirga arenosa sp. nov. (BKB1-2 T = KCTC 82989 T = InaCC B1618T), and Marivirga salinae sp. nov. (BDSF4-3 T = KCTC 82973 T = InaCC B1619T).

Fecal Metagenomics to Identify Biomarkers of Food Intake in Healthy Adults: Findings from Randomized, Controlled, Nutrition Trials.

BACKGROUND: Undigested components of the human diet affect the composition and function of the microorganisms present in the gastrointestinal tract. Techniques like metagenomic analyses allow researchers to study functional capacity, thus revealing the potential of using metagenomic data for developing objective biomarkers of food intake. OBJECTIVES: As a continuation of our previous work using 16S and metabolomic datasets, we aimed to utilize a computationally intensive, multivariate, machine-learning approach to identify fecal KEGG (Kyoto encyclopedia of genes and genomes) Orthology (KO) categories as biomarkers that accurately classify food intake. METHODS: Data were aggregated from 5 controlled feeding studies that studied the individual impact of almonds, avocados, broccoli, walnuts, barley, and oats on the adult gastrointestinal microbiota. Deoxyribonucleic acid from preintervention and postintervention fecal samples underwent shotgun genomic sequencing. After preprocessing, sequences were aligned and functionally annotated with Double Index AlignMent Of Next-generation sequencing Data v2.0.11.149 and MEtaGenome ANalyzer v6.12.2, respectively. After the count normalization, the log of the fold change ratio for resulting KOs between pre- and postintervention of the treatment group against its corresponding control was utilized to conduct differential abundance analysis. Differentially abundant KOs were used to train machine-learning models examining potential biomarkers in both single-food and multi-food models. RESULTS: We identified differentially abundant KOs in the almond (n = 54), broccoli (n = 2474), and walnut (n = 732) groups (q < 0.20), which demonstrated classification accuracies of 80%, 87%, and 86% for the almond, broccoli, and walnut groups using a random forest model to classify food intake into each food group's respective treatment and control arms, respectively. The mixed-food random forest achieved 81% accuracy. CONCLUSIONS: Our findings reveal promise in utilizing fecal metagenomics to objectively complement self-reported measures of food intake. Future research on various foods and dietary patterns will expand these exploratory analyses for eventual use in feeding study compliance and clinical settings.

Placenta-derived mesenchymal stem cells promote diabetic wound healing via exosomal protein interaction networks.

There is a lack of effective treatment options for diabetic refractory wounds, which presents a critical clinical issue that needs to be addressed urgently. Our research has demonstrated that human placenta-derived mesenchymal stem cells (plaMSCs) facilitate the migration and proliferation of HaCat cells, thereby enhancing diabetic wound healing primarily via the exosomes derived from plaMSCs (plaMSCs-Ex). Using label-free proteomics, plaMSCs and their exosomes were analysed for proteome taxonomic content in order to explore the underlying effective components mechanism of plaMSCs-Ex in diabetic wound healing. Differentially expressed proteins enriched in plaMSCs-Ex were identified and underwent bioinformatics analysis including GO annotation, KEGG pathway enrichment, gene set enrichment analysis (GSEA) and protein-protein interaction analysis (PPI). Results showed that the proteins enriched in plaMSCs-Ex are significantly involved in extracellular matrix organisation, epithelium morphogenesis, cell growth, adhesion, proliferation and angiogenesis. PPI analysis filtered 2 wound healing-related clusters characterised by hub proteins such as POSTN, FN1, SPARC, TIMP1, SERPINE1, LRP1 and multiple collagens. In brief, the exosomal proteins derived from plaMSCs reveal diverse functions of regeneration and tissue remodelling based on proteomics analysis and potentially play a role in diabetic wound healing.

Transcriptome sequencing of medical herb Salvia Rosmarinus (Rosemary) revealed the phenylpropanoid biosynthesis pathway genes and their phylogenetic relationships.

BACKGROUND: The Salvia rosmarinus spenn. (rosemary) is considered an economically important ornamental and medicinal plant and is widely utilized in culinary and for treating several diseases. However, the procedure behind synthesizing secondary metabolites-based bioactive compounds at the molecular level in S. rosmarinus is not explored completely. METHODS AND RESULTS: We performed transcriptomic sequencing of the pooled sample from leaf and stem tissues on the Illumina HiSeqTM X10 platform. The transcriptomics analysis led to the generation of 29,523,608 raw reads, followed by data pre-processing which generated 23,208,592 clean reads, and de novo assembly of S. rosmarinus obtained 166,849 unigenes. Among them, nearly 75.1% of unigenes i.e., 28,757 were interpreted against a non-redundant protein database. The gene ontology-based annotation classified them into 3 main categories and 55 sub-categories, and clusters of orthologous genes annotation categorized them into 23 functional categories. The Kyoto Encyclopedia of Genes and Genomes database-based pathway analysis confirmed the involvement of 13,402 unigenes in 183 biochemical pathways, among these unigenes, 1,186 are involved in the 17 secondary metabolite production pathways. Several key enzymes involved in producing aromatic amino acids and phenylpropanoids were identified from the transcriptome database. Among the identified 48 families of transcription factors from coding unigenes, bHLH, MYB, WRKYs, NAC, C2H2, C3H, and ERF are involved in flavonoids and other secondary metabolites biosynthesis. CONCLUSION: The phylogenetic analysis revealed the evolutionary relationship between the phenylpropanoid pathway genes of rosemary with other members of Lamiaceae. Our work reveals a new molecular mechanism behind the biosynthesis of phenylpropanoids and their regulation in rosemary plants.

Tissue-specific toxic effects of nano-copper on zebrafish.

Understanding the behavior and potential toxicity of copper nanoparticles (nano-Cu) in the aquatic environment is a primary way to assess their environmental risks. In this study, RNA-seq was performed on three different tissues (gills, intestines, and muscles) of zebrafish exposed to nano-Cu, to explore the potential toxic mechanism of nano-Cu on zebrafish. The results indicated that the toxic mechanism of nano-Cu on zebrafish was tissue-specific. Nano-Cu enables the CB1 receptor of the presynaptic membrane of gill cells to affect short-term synaptic plasticity or long-term synaptic changes (ECB-LTD) through DSI and DSE, causing dysfunction of intercellular signal transmission. Imbalance of de novo synthesis of UMP in intestinal cells and its transformation to UDP, UTP, uridine, and uracil, resulted in many functions involved in the pyrimidine metabolic pathway being blocked. Meanwhile, the toxicity of nano-Cu caused abnormal expression of RAD51 gene in muscle cells, which affects the repair of damaged DNA through Fanconi anemia and homologous recombination pathway, thus causing cell cycle disorder. These results provide insights for us to better understand the differences in toxicity of nano-Cu on zebrafish tissues and are helpful for a comprehensive assessment of nano-Cu's effects on aquatic organisms.

Microflora structure and functional capacity in Tibetan kefir grains and selenium-enriched Tibetan kefir grains: A metagenomic analysis.

Tibetan kefir grains (TKGs) are a complex protein-lipid-polysaccharide matrix composed of various microorganisms. Microorganisms have the benefit of being effective, secure, and controllable when used for selenium enrichment. In this study, selenium-enriched Tibetan kefir grains (Se-TKGs) were made, and the microbiology composition was analyzed through a metagenomic analysis, to explore the influence of selenium enrichment. The microbial composition of TKGs and Se-TKGs, as well as the probiotic species, quorum sensing system (QS) and functional genes were compared and evaluated. Lactobacillus kefiranofaciens was the most abundant microbial species in both communities. Compared with TKGs, Se-TKGs had a much higher relative abundance of acetic acid bacteria. Lactobacillus helveticus was the most common probiotic species both in TKGs and Se-TKGs. Probiotics with antibacterial and anti-inflammatory properties were more abundant in Se-TKGs. QS analysis revealed that Se-TKGs contained more QS system-associated genes than TKGs. Moreover, Kyoto Encyclopedia of Genes and Genomes analysis revealed that the pathway for human disease ko01501 had the greatest relative abundance in both TKGs and Se-TKGs. Compared with TKGs, Se-TKGs demonstrated a greater relative abundance of different drug resistance-related metabolic pathways. Additionally, linear discriminant analysis effect size was used to examine the biomarkers responsible for the difference between the two groups. In this study, we focused on the microbiological structure of TKGs and Se-TKGs, with the aim of establishing a foundation for a more thorough investigation of Se-TKGs and providing a basis for exploring potential future use.

Identifying Key Genes and Functionally Enriched Pathways in Osteoporotic Patients by Weighted Gene Co-Expression Network Analysis.

Osteoporosis is a systemic bone disease characterized by low bone mineral density and bone microstructure damage, resulting in increased bone fragility and fracture risk. The present study aimed to identify key genes and functionally enriched pathways in osteoporotic patients. Weighted Gene Co-expression Network Analysis (WGCNA) was applied to microarray datasets of blood samples of osteoporotic patients from the Sao Paulo Ageing & Health [SPAH] study (26 osteoporotic samples and 31 normal samples) to construct co-expression networks and identify hub gene. The results showed that HDGF, AP2M1, DNAJC6, TMEM183B, MFSD2B, IGKV1-5, IGKV1-8, IGKV3-7, IGKV3D-11, and IGKV1D-42 are genes which were associated with the disease status of osteoporosis. Differentially expressed genes are enriched in proteasomal protein catabolic process, ubiquitin ligase complex, and ubiquitin-like protein transferase activity. Functional enrichment analysis demonstrated that genes in the tan module were enriched in immune-related functions, indicating that the immune system plays a critical role in osteoporosis. Validation assay demonstrated that the HDGF, AP2M1, TMEM183B, and MFSD2B levels were decreased in osteoporosis samples compared with healthy controls, while the levels of IGKV1-5, IGKV1-8, and IGKV1D-42 were increased in osteoporosis samples compared with healthy controls. In conclusion, our data identified and validated the association of HDGF, AP2M1, TMEM183B, MFSD2B, IGKV1-5, IGKV1-8, and IGKV1D-42 with osteoporosis in elderly women. These results suggest that these transcripts have potential clinical significance and may help to explain the molecular mechanisms and biological functions of osteoporosis.

Analysis of key circRNA events in the AOP framework of TCS acting on zebrafish based on the data-driven.

Triclosan (TCS) is a broad-spectrum antibiotic widely used in various personal care products. Research has found that exposure to TCS can cause toxic effects on organisms including neurotoxicity, cardiotoxicity, disorders of lipid metabolism, and abnormal vascular development, and the corresponding toxic mechanisms are gradually delving into the level of abnormal expression of miRNA regulating gene expression. Although the downstream mechanism of TCS targeting miRNA abnormal expression to induce toxicity is gradually improving, its upstream mechanism is still in a fog. Starting from the abnormal expression data of circRNA in zebrafish larvae induced by TCS, this study conducted a hierarchical analysis of the expression levels of all circRNAs, differential circRNAs, and trend circRNAs, and identified 29 key circRNA events regulating miRNA abnormal expression. In combination with GO and KEGG, the effects of TCS exposure were analyzed from the function and signaling pathway of the corresponding circRNA host gene. Furthermore, based on existing literature evidence about the biological toxicity induced by TCS targeting miRNA as data support, a competing endogenous RNAs (ceRNA) network characterizing the regulatory relationship between circRNA and miRNA was constructed and optimized. Finally, a comprehensive Adverse Outcome Pathway (AOP) framework of multiple levels of events including circRNA, miRNA, mRNA, pathway, and toxicity endpoints was established to systematically elucidate the toxic mechanism of TCS. Moreover, the rationality of the AOP framework was verified from the expression level of miRNA and adverse outcomes such as neurotoxicity, cardiotoxicity, oxidative stress, and inflammatory response by knockdown of circRNA48. This paper not only provides the key circRNA events for exploring the upstream mechanism of miRNA regulating gene expression but also provides an AOP framework for comprehensively demonstrating the toxicity mechanism of TCS on zebrafish, which is a theoretical basis for subsequent hazard assessment and prevention and control of TCS.

Qualitative and Untargeted Volatilome Fingerprinting of Aspergillus sp. and Bulbithecium sp. by HS-SPME-GCMS and Functional Interactions.

Research on fungal volatile organic compounds (VOCs) has increased worldwide in the last 10 years, but marine fungal volatilomes remain underexplored. Similarly, the hormone-signaling pathways, agronomic significance, and biocontrol potential of VOCs in plant-associated fungi make the area of research extremely promising. In the current investigation, VOCs of the isolates-Aspergillus sp. GSBT S13 and GSBT S14 from marine sediments, and Bulbithecium sp. GSBT E3 from Eucalyptus foliage were extracted using Head Space solid phase microextraction, followed by gas chromatography-mass spectrometry, identification, statistical analyses, and prediction of functions by KEGG COMPOUND and STITCH 5.0 databases. The significance of this research is fingerprinting VOCs of the isolates from distinct origins, identification of compounds using three libraries (NIST02, NIST14, and W9N11), and using bioinformatic tools to perform functional analysis. The most important findings include the identification of previously unreported compounds in fungi-1-methoxy naphthalene, diethyl phthalate, pentadecane, pristane, and nonanal; the prediction of the involvement of small molecules in the degradation of aromatic compound pathways and activation, inhibition, binding, and catalysis of metabolites with predicted protein partners. This study has ample opportunity to validate the findings and understand the mechanism or mode of action, the interspecies interactions, and the role of the metabolites in geochemical cycles.

Genome-Wide Association Studies on the Kernel Row Number in a Multi-Parent Maize Population.

Kernel row number (KRN) is a crucial trait in maize that directly influences yield; hence, understanding the mechanisms underlying KRN is vital for the development of high-yielding inbred lines and hybrids. We crossed four excellent panicle inbred lines (CML312, CML444, YML46, and YML32) with Ye107, and after eight generations of selfing, a multi-parent population was developed comprising four subpopulations, each consisting of 200 lines. KRN was accessed in five environments in Yunnan province over three years (2019, 2021, and 2022). The objectives of this study were to (1) identify quantitative trait loci and single nucleotide polymorphisms associated with KRN through linkage and genome-wide association analyses using high-quality genotypic data, (2) identify candidate genes regulating KRN by identifying co-localized QTLs and SNPs, and (3) explore the pathways involved in KRN formation and identify key candidate genes through Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses. Our study successfully identified 277 significant Quantitative trait locus (QTLs) and 53 significant Single Nucleotide Polymorphism (SNPs) related to KRN. Based on gene expression, GO, and KEGG analyses, SNP-177304649, SNP-150393177, SNP-135283055, SNP-138554600, and SNP-120370778, which were highly likely to be associated with KRN, were identified. Seven novel candidate genes at this locus (Zm00001d022420, Zm00001d022421, Zm00001d016202, Zm00001d050984, Zm00001d050985, Zm00001d016000, and Zm00014a012929) are associated with KRN. Among these, Zm00014a012929 was identified using the reference genome Mo17. The remaining six genes were identified using the reference genome B73. To our knowledge, this is the first report on the association of these genes with KRN in maize. These findings provide a theoretical foundation and valuable insights into the genetic mechanisms underlying maize KRN and the development of high-yielding hybrids through heterosis.

Transcriptome Analysis of White- and Red-Fleshed Apple Fruits Uncovered Novel Genes Related to the Regulation of Anthocyanin Biosynthesis.

The red flesh coloration of apples is a result of a biochemical pathway involved in the biosynthesis of anthocyanins and anthocyanidins. Based on apple genome analysis, a high number of regulatory genes, mainly transcription factors such as MYB, which are components of regulatory complex MYB-bHLH-WD40, and several structural genes (PAL, 4CL, CHS, CHI, F3H, DFR, ANS, UFGT) involved in anthocyanin biosynthesis, have been identified. In this study, we investigated novel genes related to the red-flesh apple phenotype. These genes could be deemed molecular markers for the early selection of new apple cultivars. Based on a comparative transcriptome analysis of apples with different fruit-flesh coloration, we successfully identified and characterized ten potential genes from the plant hormone transduction pathway of auxin (GH3); cytokinins (B-ARR); gibberellins (DELLA); abscisic acid (SnRK2 and ABF); brassinosteroids (BRI1, BZR1 and TCH4); jasmonic acid (MYC2); and salicylic acid (NPR1). An analysis of expression profiles was performed in immature and ripe fruits of red-fleshed cultivars. We have uncovered genes mediating the regulation of abscisic acid, salicylic acid, cytokinin, and jasmonic acid signaling and described their role in anthocyanin biosynthesis, accumulation, and degradation. The presented results underline the relationship between genes from the hormone signal transduction pathway and UFGT genes, which are directly responsible for anthocyanin color transformation as well as anthocyanin accumulation during apple-fruit ripening.

Probing Liver Injuries Induced by Thioacetamide in Human In Vitro Pooled Hepatocyte Experiments.

Animal studies are typically utilized to understand the complex mechanisms associated with toxicant-induced hepatotoxicity. Among the alternative approaches to animal studies, in vitro pooled human hepatocytes have the potential to capture population variability. Here, we examined the effect of the hepatotoxicant thioacetamide on pooled human hepatocytes, divided into five lots, obtained from forty diverse donors. For 24 h, pooled human hepatocytes were exposed to vehicle, 1.33 mM (low dose), and 12 mM (high dose) thioacetamide, followed by RNA-seq analysis. We assessed gene expression variability using heat maps, correlation plots, and statistical variance. We used KEGG pathways and co-expression modules to identify underlying physiological processes/pathways. The co-expression module analysis showed that the majority of the lots exhibited activation for the bile duct proliferation module. Despite lot-to-lot variability, we identified a set of common differentially expressed genes across the lots with similarities in their response to amino acid, lipid, and carbohydrate metabolism. We also examined efflux transporters and found larger lot-to-lot variability in their expression patterns, indicating a potential for alteration in toxicant bioavailability within the cells, which could in turn affect the gene expression patterns between the lots. Overall, our analysis highlights the challenges in using pooled hepatocytes to understand mechanisms of toxicity.

The Relevance of Reperfusion Stroke Therapy for miR-9-3p and miR-9-5p Expression in Acute Stroke-A Preliminary Study.

Reperfusion stroke therapy is a modern treatment that involves thrombolysis and the mechanical removal of thrombus from the extracranial and/or cerebral arteries, thereby increasing penumbra reperfusion. After reperfusion therapy, 46% of patients are able to live independently 3 months after stroke onset. MicroRNAs (miRNAs) are essential regulators in the development of cerebral ischemia/reperfusion injury and the efficacy of the applied treatment. The first aim of this study was to examine the change in serum miRNA levels via next-generation sequencing (NGS) 10 days after the onset of acute stroke and reperfusion treatment. Next, the predictive values of the bioinformatics analysis of miRNA gene targets for the assessment of brain ischemic response to reperfusion treatment were explored. Human serum samples were collected from patients on days 1 and 10 after stroke onset and reperfusion treatment. The samples were subjected to NGS and then validated using qRT-PCR. Differentially expressed miRNAs (DEmiRNAs) were used for enrichment analysis. Hsa-miR-9-3p and hsa-miR-9-5p expression were downregulated on day 10 compared to reperfusion treatment on day 1 after stroke. The functional analysis of miRNA target genes revealed a strong association between the identified miRNA and stroke-related biological processes related to neuroregeneration signaling pathways. Hsa-miR-9-3p and hsa-miR-9-5p are potential candidates for the further exploration of reperfusion treatment efficacy in stroke patients.