From single- to multi-omics: future research trends in medicinal plants.

Medicinal plants are the main source of natural metabolites with specialised pharmacological activities and have been widely examined by plant researchers. Numerous omics studies of medicinal plants have been performed to identify molecular markers of species and functional genes controlling key biological traits, as well as to understand biosynthetic pathways of bioactive metabolites and the regulatory mechanisms of environmental responses. Omics technologies have been widely applied to medicinal plants, including as taxonomics, transcriptomics, metabolomics, proteomics, genomics, pangenomics, epigenomics and mutagenomics. However, because of the complex biological regulation network, single omics usually fail to explain the specific biological phenomena. In recent years, reports of integrated multi-omics studies of medicinal plants have increased. Until now, there have few assessments of recent developments and upcoming trends in omics studies of medicinal plants. We highlight recent developments in omics research of medicinal plants, summarise the typical bioinformatics resources available for analysing omics datasets, and discuss related future directions and challenges. This information facilitates further studies of medicinal plants, refinement of current approaches and leads to new ideas.

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.

Comprehensive multi-omics analysis and experimental verification reveal PFDN5 is a novel prognostic and therapeutic biomarker for gastric cancer.

Prefoldin Subunit 5 (PFDN5) plays a critical role as a member of the prefoldins (PFDNs) in maintaining a finely tuned equilibrium between protein production and degradation. However, there has been no comprehensive analysis specifically focused on PFDN5 thus far. Here, a comprehensive multi-omics (transcriptomics, genomics, and proteomics) analysis, systematic molecular biology experiments (in vitro and in vivo), transcriptome sequencing and PCR Array were performed for identifying the value of PFDN5 in pan-cancer, especially in Gastric Cancer (GC). We found PFDN5 had the potential to serve as a prognostic and therapeutic biomarker in GC. And PFDN5 could promote the proliferation of GC cells, primarily by affecting the cell cycle, cell death and immune process etc. These findings provide novel insights into the molecular mechanisms and precise treatments of in GC.

Identification and characterization of structural variants related to meat quality in pigs using chromosome-level genome assemblies.

BACKGROUND: Many studies have been performed to identify various genomic loci and genes associated with the meat quality in pigs. However, the full genetic architecture of the trait still remains unclear in part because of the lack of accurate identification of related structural variations (SVs) which resulted from the shortage of target breeds, the limitations of sequencing data, and the incompleteness of genome assemblies. The recent generation of a new pig breed with superior meat quality, called Nanchukmacdon, and its chromosome-level genome assembly (the NCMD assembly) has provided new opportunities. RESULTS: By applying assembly-based SV calling approaches to various genome assemblies of pigs including Nanchukmacdon, the impact of SVs on meat quality was investigated. Especially, by checking the commonality of SVs with other pig breeds, a total of 13,819 Nanchukmacdon-specific SVs (NSVs) were identified, which have a potential effect on the unique meat quality of Nanchukmacdon. The regulatory potentials of NSVs for the expression of nearby genes were further examined using transcriptome- and epigenome-based analyses in different tissues. CONCLUSIONS: Whole-genome comparisons based on chromosome-level genome assemblies have led to the discovery of SVs affecting meat quality in pigs, and their regulatory potentials were analyzed. The identified NSVs will provide new insights regarding genetic architectures underlying the meat quality in pigs. Finally, this study confirms the utility of chromosome-level genome assemblies and multi-omics analysis to enhance the understanding of unique phenotypes.

Multi-omics and clustering analyses reveal the mechanisms underlying unmet needs for patients with lung adenocarcinoma and identify potential therapeutic targets.

BACKGROUND: The cancer genome contains several driver mutations. However, in some cases, no known drivers have been identified; these remaining areas of unmet needs, leading to limited progress in cancer therapy. Whole-genome sequencing (WGS) can identify non-coding alterations associated with the disease. Consequently, exploration of non-coding regions using WGS and other omics data such as ChIP-sequencing (ChIP-seq) to discern novel alterations and mechanisms related to tumorigenesis have been attractive these days. METHODS: Integrated multi-omics analyses, including WGS, ChIP-seq, DNA methylation, and RNA-sequencing (RNA-seq), were conducted on samples from patients with non-clinically actionable genetic alterations (non-CAGAs) in lung adenocarcinoma (LUAD). Second-level cluster analysis was performed to reinforce the correlations associated with patient survival, as identified by RNA-seq. Subsequent differential gene expression analysis was performed to identify potential druggable targets. RESULTS: Differences in H3K27ac marks in non-CAGAs LUAD were found and confirmed by analyzing RNA-seq data, in which mastermind-like transcriptional coactivator 2 (MAML2) was suppressed. The down-regulated genes whose expression was correlated to MAML2 expression were associated with patient prognosis. WGS analysis revealed somatic mutations associated with the H3K27ac marks in the MAML2 region and high levels of DNA methylation in MAML2 were observed in tumor samples. The second-level cluster analysis enabled patient stratification and subsequent analyses identified potential therapeutic target genes and treatment options. CONCLUSIONS: We overcome the persistent challenges of identifying alterations or driver mutations in coding regions related to tumorigenesis through a novel approach combining multi-omics data with clinical information to reveal the molecular mechanisms underlying non-CAGAs LUAD, stratify patients to improve patient prognosis, and identify potential therapeutic targets. This approach may be applicable to studies of other cancers with unmet needs.

Multi-omics analysis revealed the mechanism underlying flavonol biosynthesis during petal color formation in Camellia Nitidissima.

BACKGROUND: Camellia nitidissima is a rare, prized camellia species with golden-yellow flowers. It has a high ornamental, medicinal, and economic value. Previous studies have shown substantial flavonol accumulation in C. nitidissima petals during flower formation. However, the mechanisms underlying the golden flower formation in C. nitidissima remain largely unknown. RESULTS: We performed an integrative analysis of the transcriptome, proteome, and metabolome of the petals at five flower developmental stages to construct the regulatory network underlying golden flower formation in C. nitidissima. Metabolome analysis revealed the presence of 323 flavonoids, and two flavonols, quercetin glycosides and kaempferol glycosides, were highly accumulated in the golden petals. Transcriptome and proteome sequencing suggested that the flavonol biosynthesis-related genes and proteins upregulated and the anthocyanin and proanthocyanidin biosynthesis-related genes and proteins downregulated in the golden petal stage. Further investigation revealed the involvement of MYBs and bHLHs in flavonoid biosynthesis. Expression analysis showed that flavonol synthase 2 (CnFLS2) was highly expressed in the petals, and its expression positively correlated with flavonol content at all flower developmental stages. Transient overexpression of CnFLS2 in the petals increased flavonol content. Furthermore, correlation analysis showed that the jasmonate (JA) pathways positively correlated with flavonol biosynthesis, and exogenous methyl jasmonate (MeJA) treatment promoted CnFLS2 expression and flavonol accumulation. CONCLUSIONS: Our findings showed that the JA-CnFLS2 module regulates flavonol biosynthesis during golden petal formation in C. nitidissima.

Bibliometric review of ATAC-Seq and its application in gene expression.

With recent advances in high-throughput next-generation sequencing, it is possible to describe the regulation and expression of genes at multiple levels. An assay for transposase-accessible chromatin using sequencing (ATAC-seq), which uses Tn5 transposase to sequence protein-free binding regions of the genome, can be combined with chromatin immunoprecipitation coupled with deep sequencing (ChIP-seq) and ribonucleic acid sequencing (RNA-seq) to provide a detailed description of gene expression. Here, we reviewed the literature on ATAC-seq and described the characteristics of ATAC-seq publications. We then briefly introduced the principles of RNA-seq, ChIP-seq and ATAC-seq, focusing on the main features of the techniques. We built a phylogenetic tree from species that had been previously studied by using ATAC-seq. Studies of Mus musculus and Homo sapiens account for approximately 90% of the total ATAC-seq data, while other species are still in the process of accumulating data. We summarized the findings from human diseases and other species, illustrating the cutting-edge discoveries and the role of multi-omics data analysis in current research. Moreover, we collected and compared ATAC-seq analysis pipelines, which allowed biological researchers who lack programming skills to better analyze and explore ATAC-seq data. Through this review, it is clear that multi-omics analysis and single-cell sequencing technology will become the mainstream approach in future research.

Intestinal microbiota and metabolome perturbations in ischemic and idiopathic dilated cardiomyopathy.

BACKGROUND: Various clinical similarities are present in ischemic (ICM) and idiopathic dilated cardiomyopathy (IDCM), leading to ambiguity on some occasions. Previous studies have reported that intestinal microbiota appeared dysbiosis in ICM, whether implicating in the IDCM remains unclear. The aim of this study was to assess the alterations in intestinal microbiota and fecal metabolites in ICM and IDCM. METHODS: ICM (n = 20), IDCM (n = 22), and healthy controls (HC, n = 20) were enrolled in this study. Stool samples were collected for 16S rRNA gene sequencing and gas chromatography-mass spectrometry (GC-MS) analysis. RESULTS: Both ICM and IDCM exhibited reduced alpha diversity and altered microbial community structure compared to HC. At the genus level, nine taxa including Blautia, [Ruminococcus]_torques_group, Christensenellaceae_R-7_group, UCG-002, Corynebacterium, Oceanobacillus, Gracilibacillus, Klebsiella and Citrobacter was specific to ICM, whereas one taxa Alistipes uniquely altered in IDCM. Likewise, these changes were accompanied by significant metabolic differences. Further differential analysis displayed that 18 and 14 specific metabolites uniquely changed in ICM and IDCM, respectively. The heatmap was generated to display the association between genera and metabolites. Receiver operating characteristic curve (ROC) analysis confirmed the predictive value of the distinct microbial-metabolite features in disease status. The results showed that microbial (area under curve, AUC = 0.95) and metabolic signatures (AUC = 0.84) were effective in discriminating ICM from HC. Based on the specific microbial and metabolic features, the patients with IDCM could be separated from HC with an AUC of 0.80 and 0.87, respectively. Furthermore, the gut microbial genus (AUC = 0.88) and metabolite model (AUC = 0.89) were comparable in predicting IDCM from ICM. Especially, the combination of fecal microbial-metabolic features improved the ability to differentiate IDCM from ICM with an AUC of 0.96. CONCLUSION: Our findings highlighted the alterations of gut microbiota and metabolites in different types of cardiomyopathies, providing insights into the pathophysiological mechanisms of myocardial diseases. Moreover, multi-omics analysis of fecal samples holds promise as a non-invasive tool for distinguishing disease status.

Characterizing neuroinflammation and identifying prenatal diagnostic markers for neural tube defects through integrated multi-omics analysis.

BACKGROUND: Neural Tube Defects (NTDs) are congenital malformations of the central nervous system resulting from the incomplete closure of the neural tube during early embryonic development. Neuroinflammation refers to the inflammatory response in the nervous system, typically resulting from damage to neural tissue. Immune-related processes have been identified in NTDs, however, the detailed relationship and underlying mechanisms between neuroinflammation and NTDs remain largely unclear. In this study, we utilized integrated multi-omics analysis to explore the role of neuroinflammation in NTDs and identify potential prenatal diagnostic markers using a murine model. METHODS: Nine public datasets from Gene Expression Omnibus (GEO) and ArrayExpress were mined using integrated multi-omics analysis to characterize the molecular landscape associated with neuroinflammation in NTDs. Special attention was given to the involvement of macrophages in neuroinflammation within amniotic fluid, as well as the dynamics of macrophage polarization and their interactions with neural cells at single-cell resolution. We also used qPCR assay to validate the key TFs and candidate prenatal diagnostic genes identified through the integrated analysis in a retinoic acid-induced NTDs mouse model. RESULTS: Our analysis indicated that neuroinflammation is a critical pathological feature of NTDs, regulated both transcriptionally and epigenetically within central nervous system tissues. Key alterations in gene expression and pathways highlighted the crucial role of STATs molecules in the JAK-STAT signaling pathway in regulating NTDs-associated neuroinflammation. Furthermore, single-cell resolution analysis revealed significant polarization of macrophages and their interaction with neural cells in amniotic fluid, underscoring their central role in mediating neuroinflammation associated with NTDs. Finally, we identified a set of six potential prenatal diagnostic genes, including FABP7, CRMP1, SCG3, SLC16A10, RNASE6 and RNASE1, which were subsequently validated in a murine NTDs model, indicating their promise as prospective markers for prenatal diagnosis of NTDs. CONCLUSIONS: Our study emphasizes the pivotal role of neuroinflammation in the progression of NTDs and underlines the potential of specific inflammatory and neural markers as novel prenatal diagnostic tools. These findings provide important clues for further understanding the underlying mechanisms between neuroinflammation and NTDs, and offer valuable insights for the future development of prenatal diagnostics.

Multi-omic characterization of antibody-producing CHO cell lines elucidates metabolic reprogramming and nutrient uptake bottlenecks.

Characterizing the phenotypic diversity and metabolic capabilities of industrially relevant manufacturing cell lines is critical to bioprocess optimization and cell line development. Metabolic capabilities of production hosts limit nutrient and resource channeling into desired cellular processes and can have a profound impact on productivity. These limitations cannot be directly inferred from measured data such as spent media concentrations or transcriptomics. Here, we present an integrated multi-omic analysis pipeline combining exo-metabolomics, transcriptomics, and genome-scale metabolic network analysis and apply it to three antibody-producing Chinese Hamster Ovary cell lines to identify reprogramming features associated with high-producing clones and metabolic bottlenecks limiting product formation in an industrial bioprocess. Analysis of individual datatypes revealed a decreased nitrogenous byproduct secretion in high-producing clones and the topological changes in peripheral metabolic pathway expression associated with phase shifts. An integrated omics analysis in the context of the genome-scale metabolic model elucidated the differences in central metabolism and identified amino acid utilization bottlenecks limiting cell growth and antibody production that were not evident from exo-metabolomics or transcriptomics alone. Thus, we demonstrate the utility of a multi-omics characterization in providing an in-depth understanding of cellular metabolism, which is critical to efforts in cell engineering and bioprocess optimization.

Multi-omics analysis reveals genes and metabolites involved in Streptococcus suis biofilm formation.

BACKGROUND: Streptococcus suis is an important zoonotic pathogen. Biofilm formation largely explains the difficulty in preventing and controlling S. suis. However, little is known about the molecular mechanism of S. suis biofilm formation. RESULTS: In this study, transcriptomic and metabolomic analyses of S. suis in biofilm and planktonic states were performed to identify key genes and metabolites involved in biofilm formation. A total of 789 differential genes and 365 differential metabolites were identified. By integrating transcriptomics and metabolomics, five main metabolic pathways were identified, including amino acid pathway, nucleotide metabolism pathway, carbon metabolism pathway, vitamin and cofactor metabolism pathway, and aminoacyl-tRNA biosynthesis metabolic pathway. CONCLUSIONS: These results provide new insights for exploring the molecular mechanism of S. suis biofilm formation.

Identification of tumor-specific T cell signature predicting cancer immunotherapy response in bladder cancer by multi-omics analysis and experimental verification.

BACKGROUND: Numerous gene signatures predicting the prognosis of bladder cancer have been identified. However, a tumor-specific T cell signature related to immunotherapy response in bladder cancer remains under investigation. METHODS: Single-cell RNA and TCR sequencing from the Gene expression omnibus (GEO) database were used to identify tumor-specific T cell-related genes in bladder cancer. Subsequently, we constructed a tumor-specific T cell signature (TstcSig) and validated its clinical relevance for predicting immunotherapy response in multiple immunotherapy cohorts. Further analyses explored the immune characteristics of TstcSig in bladder cancer patients from other cohorts in the TCGA and GEO databases. Western blot (WB), multicolor immunofluorescence (MIF), qRT-PCR and flow cytometry assays were performed to validate the results of bioinformatics analysis. RESULTS: The established TstcSig, based on five tumor-specific T cell-related genes, could predict outcomes in a bladder cancer immunotherapy cohort. This was verified using two additional immunotherapy cohorts and showed better predictive performance compared to 109 published T cell signatures. TstcSig was strongly correlated with immune characteristics such as immune checkpoint gene expression, tumor mutation burden, and T cell infiltration, as validated by single-cell and spatial transcriptomics datasets. Notably, the positive correlation between TstcSig and T cell infiltration was confirmed in the TCGA cohort. Furthermore, pan-cancer analysis demonstrated the heterogeneity of the prognostic value of TstcSig. Tumor-specific T cells highly expressed CD27, IFNG, GZMB and CXCL13 and secreted more effector cytokines for tumor cell killing, as validated experimentally. CONCLUSION: We developed a five-gene signature (including VAMP5, TIGIT, LCK, CD27 and CACYBP) based on tumor-specific T cell-related genes to predict the immunotherapy response in bladder cancer patients.

Integrated multi-omics analyses revealed the association between rheumatoid arthritis and colorectal cancer: MYO9A as a shared gene signature and an immune-related therapeutic target.

BACKGROUND: Our study aims to explore the relationship, shared gene signature, and the underlying mechanisms that connect rheumatoid arthritis (RA) to colorectal cancer (CRC). METHODS: Mendelian randomization (MR) analysis was conducted to assess the causality between RA and CRC. Summary statistic data-based Mendelian randomization (SMR) leveraging eQTL data was employed to identify the CRC-related causal genes. Integrated analyses of single-cell RNA sequencing and bulk RNA sequencing were employed to comprehensively investigate the shared gene signature and potential mechanisms underlying the pathogenesis of both RA and CRC. Predictive analysis of the shared hub gene in CRC immunotherapy response was performed. Pan-cancer analyses were conducted to explore the potential role of MYO9A in 33 types of human tumors. RESULTS: MR analysis suggested that RA might be associated with a slight increased risk of CRC (Odds Ratio = 1.04, 95% Confidence Interval = 1.01-1.07, P = 0.005). SMR analysis combining transcriptome analyses identified MYO9A as a causal gene in CRC and a shared gene signature in both RA and CRC. MYO9A may contribute to tumor suppression, while downregulation of MYO9A may impact CRC tumorigenesis by disrupting epithelial polarity and architecture, resulting in a worse prognosis in CRC. Additionally, MYO9A shows promise as a powerful predictive biomarker for cancer prognosis and immunotherapy response in CRC. Pan-cancer analyses demonstrated MYO9A may have a protective role in the occurrence and progression of various human cancers. CONCLUSION: RA might be associated with a slight increased risk of CRC. MYO9A is a shared gene signature and a potential immune-related therapeutic target for both CRC and RA. Targeting the MYO9A-mediated loss of polarity and epithelial architecture could be a novel therapeutic approach for CRC.

Genomic and Transcriptomic Diversification of Flagellin Genes Provides Insight into Environmental Adaptation and Phylogeographic Characteristics in Aeromonas hydrophila.

Aeromonas hydrophila is an opportunistic motile pathogen with a broad host range, infecting both terrestrial and aquatic animals. Environmental and geographical conditions exert selective pressure on both geno- and phenotypes of pathogens. Flagellin, directly exposed to external environments and containing important immunogenic epitopes, may display significant variability in response to external conditions. In this study, we conducted a comparative analysis of ~ 150 A. hydrophila genomes, leading to the identification of six subunits of the flagellin gene (fla-1 to fla-4, flaA, and flaB). Individual strains harbored different composition of flagellin subunits and copies. The composition of subunits showed distinct patterns depending on environmental sources. Strains from aquatic environments were mainly comprised of fla-1 to fla-4 subunits, while terrestrial strains predominated in groups harboring flaA and flaB subunits. Each flagellin showed varying levels of expression, with flaA and flaB demonstrating significantly higher expression compared to others. One of the chemotaxis pathways that control flagellin movement through a two-component system was significantly upregulated in flaA(+ 1)/flaB(+ 1) group, whereas flaA and flaB showed different transcriptomic expressions. The genes positively correlated with flaA expression were relevant to biofilm formation and bacterial chemotaxis, but flaB showed a negative correlation with the genes in ABC transporters and quorum sensing pathway. However, the expression patterns of fla-2 to fla-4 were identical. This suggests various types of flagellin subunits may have different biological functions. The composition and expression levels of flagellin subunits could provide valuable insights into the adaptation of A. hydrophila and the differences among strains in response to various external environments.

Microcystin-RR promote lipid accumulation through CD36 mediated signal pathway and fatty acid uptake in HepG2 cells.

Microcystins (MC)-RR is a significant analogue of MC-LR, which has been identified as a hepatotoxin capable of influencing lipid metabolism and promoting the progression of liver-related metabolic diseases. However, the toxicity and biological function of MC-RR are still not well understood. In this study, the toxic effects and its role in lipid metabolism of MC-RR were investigated in hepatoblastoma cells (HepG2cells). The results demonstrated that MC-RR dose-dependently reduced cell viability and induced apoptosis. Additionally, even at low concentrations, MC-RR promoted lipid accumulation through up-regulating levels of triglyceride, total cholesterol, phosphatidylcholines and phosphatidylethaolamine in HepG2 cells, with no impact on cell viability. Proteomics and transcriptomics analysis further revealed significant alterations in the protein and gene expression profiles in HepG2 cells treated with MC-RR. Bioinformatic analysis, along with subsequent validation, indicated the upregulation of CD36 and activation of the AMPK and PI3K/AKT/mTOR in response to MC-RR exposure. Finally, knockdown of CD36 markedly ameliorated MC-RR-induced lipid accumulation in HepG2 cells. These findings collectively suggest that MC-RR promotes lipid accumulation in HepG2 cells through CD36-mediated signal pathway and fatty acid uptake. Our findings provide new insights into the hepatotoxic mechanism of MC-RR.

Multi-omics analysis of Streptomyces djakartensis strain MEPS155 reveal a molecular response strategy combating Ceratocystis fimbriata causing sweet potato black rot.

To investigate the potential antifungal mechanisms of rhizosphere Actinobacteria against Ceratocystis fimbriata in sweet potato, a comprehensive approach combining biochemical analyses and multi-omics techniques was employed in this study. A total of 163 bacterial strains were isolated from the rhizosphere soil of sweet potato. Among them, strain MEPS155, identified as Streptomyces djakartensis, exhibited robust and consistent inhibition of C. fimbriata mycelial growth in in vitro dual culture assays, attributed to both cell-free supernatant and volatile organic compounds. Moreover, strain MEPS155 demonstrated diverse plant growth-promoting attributes, including the production of indole-3-acetic acid, 1-aminocyclopropane-1-carboxylate deaminase, phosphorus solubilization, nitrogen fixation, and enzymatic activities such as cellulase, chitinase, and protease. Notably, strain MEPS155 exhibited efficacy against various sweet potato pathogenic fungi. Following the inoculation of strain MEPS155, a significant reduction (P < 0.05) in malondialdehyde content was observed in sweet potato slices, indicating a potential protective effect. The whole genome of MEPS155 was characterized by a size of 8,030,375 bp, encompassing 7234 coding DNA sequences and 32 secondary metabolite biosynthetic gene clusters. Transcriptomic analysis revealed 1869 differentially expressed genes in the treated group that cultured with C. fimbriata, notably influencing pathways associated with porphyrin metabolism, fatty acid biosynthesis, and biosynthesis of type II polyketide products. These alterations in gene expression are hypothesized to be linked to the production of secondary metabolites contributing to the inhibition of C. fimbriata. Metabolomic analysis identified 1469 potential differently accumulated metabolites (PDAMs) when comparing MEPS155 and the control group. The up-regulated PDAMs were predominantly associated with the biosynthesis of various secondary metabolites, including vanillin, myristic acid, and protocatechuic acid, suggesting potential inhibitory effects on plant pathogenic fungi. Our study underscores the ability of strain S. djakartensis MEPS155 to inhibit C. fimbriata growth through the production of secretory enzymes or secondary metabolites. The findings contribute to a theoretical foundation for future investigations into the role of MEPS155 in postharvest black rot prevention in sweet potato.

miR-185-5p May Modulate the Chemosensitivity of LUSC to Cisplatin via Targeting PCDHA11: Multi-omics Analysis and Experimental Validation.

Drug resistance is the major difficulty in treatment of lung squamous cell carcinoma (LUSC). This study aims to explore drug response-related miRNAs (DRmiRNAs) based on multi-omics research. We identified DRmiRNAs of LUSC with a multi-omics integrated system that combines expression data of microRNA, lncRNA, mRNA, methylation levels, somatic mutations. After identifying DRmiRNAs, we screened and validated of the target mRNAs of DRmiRNAs through Targetscan and the miRDB database. Then, Real-time PCR and Western blot assays were used to estimate the expression of DRmiRNAs and target protein, and the dual-luciferase assays were used to confirm the interaction of DRmiRNAs and target mRNA. Furthermore, CCK-8 (Cell Counting Kit-8) assays were used to evaluate cell proliferation and drug sensitivity. After integrated analysis, hsa-miR-185-5p was identified as DRmiRNA based on multi-omics data. Through Targetscan and miRDB database, the possible target mRNAs were obtained and PCDHA11 was validated as a target mRNA of miR-185-5p by real-time PCR, Western blot assays and dual-luciferase assays. CCK-8 assays and clone formation assays showed that the proliferation of miR-185-5p mimics was significantly slower than that of miR-185-5p inhibitors, which means overexpression of miR-185-5p enhanced the anticancer effects of cisplatin, whereas the downregulation of miR-185-5p reduced the effects. Furthermore, the proliferation of silencing PCDHA11 was significantly slower than that of overexpression of PCDHA11, which means PCDHA11 overexpression weakened the anticancer effects of cisplatin, and silencing PCDHA11 enhanced the effects. This study demonstrated that miR-185-5p was involved in chemoresistance of LUSC cells to cisplatin partly via down-regulating PCDHA11, which may promote understanding the underlying molecular mechanisms of drug response.

Impact of imidacloprid exposure on gestational hyperglycemia: A multi-omics analysis.

Imidacloprid (IMI), a commonly utilized neonicotinoid insecticide, has been identified to adversely impact glucose homeostasis. Pregnant women are believed to be more sensitive to toxins than non-pregnant women, and the impact of IMI exposure on gestational hyperglycemia remain unclear. To explore the impact, pregnant mice fed a high-fat diet were exposed to different doses (0.06, 0.6, 6 mg/kg bw/day) of IMI by gavage. Glucose homeostasis-related parameters were measured. The glucose homeostasis influenced by IMI treatment was explored through integrating gut microbiota, metabolomic and transcriptomic analysis. Results showed that IMI-H (6 mg/kg bw/day) exposure notably restricted gestational weight gain and perturbed glucose homeostasis characterized by reduced glucose tolerance and insulin sensitivity, alongside elevated levels of fasting blood glucose and insulin. Multi-omics analysis revealed that IMI-H exposure induced significant changes in the richness and composition of the gut microbiome. The metabolite profiles of serum samples and cecal contents, and transcriptome of liver and ileum were all affected by IMI-H treatment. The altered gut microbiota, metabolites and genes exhibited significant correlations with glucose homeostasis-related parameters. These differential metabolites and genes were implicated in various metabolic pathways including bile secretion, glucagon signaling pathway, lipid metabolism, fatty acid metabolism. Significant correlations were observed between the altered gut microbiota and caecum metabolome as well as liver transcriptome. For example, the abundance of Oscillibacter was strongly correlated with gut microflora-related metabolites (Icosenoic acid, Lysosulfatide, and fluticasone) and liver differential genes (Grin3b, Lifr, and Spta1). Together, IMI exposure resulted in significant changes in microbial composition, along with alterations in certain metabolites and genes associated with metabolic process, which may promote gestational hyperglycemia.

Multi-Omics Characterization of E3 Regulatory Patterns in Different Cancer Types.

Ubiquitination, a post-translational modification, refers to the covalent attachment of ubiquitin molecules to substrates. This modification plays a critical role in diverse cellular processes such as protein degradation. The specificity of ubiquitination for substrates is regulated by E3 ubiquitin ligases. Dysregulation of ubiquitination has been associated with numerous diseases, including cancers. In our study, we first investigated the protein expression patterns of E3 ligases across 12 cancer types. Our findings indicated that E3 ligases tend to be up-regulated and exhibit reduced tissue specificity in tumors. Moreover, the correlation of protein expression between E3 ligases and substrates demonstrated significant changes in cancers, suggesting that E3-substrate specificity alters in tumors compared to normal tissues. By integrating transcriptome, proteome, and ubiquitylome data, we further characterized the E3-substrate regulatory patterns in lung squamous cell carcinoma. Our analysis revealed that the upregulation of the SKP2 E3 ligase leads to excessive degradation of BRCA2, potentially promoting tumor cell proliferation and metastasis. Furthermore, the upregulation of E3 ubiquitin-protein ligase TRIM33 was identified as a biomarker associated with a favorable prognosis by inhibiting the cell cycle. This work exemplifies how leveraging multi-omics data to analyze E3 ligases across various cancers can unveil prognosis biomarkers and facilitate the identification of potential drug targets for cancer therapy.

Multi-Omics Analysis of a Chromosome Segment Substitution Line Reveals a New Regulation Network for Soybean Seed Storage Profile.

Soybean, a major source of oil and protein, has seen an annual increase in consumption when used in soybean-derived products and the broadening of its cultivation range. The demand for soybean necessitates a better understanding of the regulatory networks driving storage protein accumulation and oil biosynthesis to broaden its positive impact on human health. In this study, we selected a chromosome segment substitution line (CSSL) with high protein and low oil contents to investigate the underlying effect of donor introgression on seed storage through multi-omics analysis. In total, 1479 differentially expressed genes (DEGs), 82 differentially expressed proteins (DEPs), and 34 differentially expressed metabolites (DEMs) were identified in the CSSL compared to the recurrent parent. Based on Gene Ontology (GO) term analysis and the Kyoto Encyclopedia of Genes and Genomes enrichment (KEGG), integrated analysis indicated that 31 DEGs, 24 DEPs, and 13 DEMs were related to seed storage functionality. Integrated analysis further showed a significant decrease in the contents of the seed storage lipids LysoPG 16:0 and LysoPC 18:4 as well as an increase in the contents of organic acids such as L-malic acid. Taken together, these results offer new insights into the molecular mechanisms of seed storage and provide guidance for the molecular breeding of new favorable soybean varieties.