Project description:BackgroundGlioblastoma (GBM) is the most common and lethal primary brain tumor in adults, with limited treatment modalities and poor prognosis. Recent studies have highlighted the importance of considering sex differences in cancer incidence, prognosis, molecular disparities, and treatment outcomes across various tumor types, including colorectal adenocarcinoma, lung adenocarcinoma, and GBM.MethodsWe performed comprehensive analyses of large-scale multi-omics data (genomic, transcriptomic, and proteomic data) from TCGA, GLASS, and CPTAC to investigate the genetic and molecular determinants that contribute to the unique clinical properties of male and female GBM patients.ResultsOur results revealed several key differences, including enrichments of MGMT promoter methylation, which correlated with increased overall and post-recurrence survival and improved response to chemotherapy in female patients. Moreover, female GBM exhibited a higher degree of genomic instability, including aneuploidy and tumor mutational burden. Integrative proteomic and phosphor-proteomic characterization uncovered sex-specific protein abundance and phosphorylation activities, including EGFR activation in males and SPP1 hyperphosphorylation in female patients. Lastly, the identified sex-specific biomarkers demonstrated prognostic significance, suggesting their potential as therapeutic targets.ConclusionsCollectively, our study provides unprecedented insights into the fundamental modulators of tumor progression and clinical outcomes between male and female GBM patients and facilitates sex-specific treatment interventions. Highlights Female GBM patients were characterized by increased MGMT promoter methylation and favorable clinical outcomes compared to male patients. Female GBMs exhibited higher levels of genomic instability, including aneuploidy and TMB. Each sex-specific GBM is characterized by unique pathway dysregulations and molecular subtypes. EGFR activation is prevalent in male patients, while female patients are marked by SPP1 hyperphosphorylation.
Project description:Beef flavor plays a crucial role in consumer preference, yet research on this trait has been limited by past technological constraints. Intramuscular fat (IMF) is a key determinant of beef quality, influencing taste, marbling, and overall flavor. Xinjiang brown cattle (XBC), an indigenous breed from northern Xinjiang, China, presents significant variation in meat quality, with IMF content ranging from 0.2 % to 4.3 % within the population. This variation suggests strong potential for breeding improvement. In this study, we selected 82 XBC for slaughter and meat quality analysis, categorizing them based on IMF content. Using two-dimensional gas chromatography-time-of-flight mass spectrometry (GC×GC-TOF MS), we analyzed volatile flavor compounds across different beef cuts (Longissimus dorsi, Semitendinosus, Supraspinatus). Our results showed that beef with higher IMF levels exhibited enhanced flavor profiles, characterized by sweet, green, fruity, and waxy notes, while castrated bulls displayed the weakest flavor intensity. Metabolomic analysis further revealed significant differences in flavor substances between high and low IMF content beef. RNA-Seq analysis identified key genes (AQP4, FZD2, FADS1, BPG1, CEBPD, FABP4) associated with flavor formation, offering valuable insights for breeding strategies aimed at improving XBC meat quality. This comprehensive study provides a robust theoretical foundation for advancing the genetic improvement of XBC.
Project description:BackgroundOld age is associated with a significantly increased mortality in COVID-19 patients exposed to long-term controlled mechanical ventilation (CMV) and suggested to be due to the hyperinflammatory response associated with the viral infection. However, our understanding of age-related differences in the response to CMV in the absence of a viral infection remains insufficient.MethodsYoung (7-8 months) and old (28-32 months) F344 BN hybrid rats were exposed to the ICU condition for 5 days, i.e., complete immobilization, mechanical ventilation, and extensive monitoring. Transcriptomic (RNA-Seq) and proteomics (Proximity Extension Assay) analyses of the diaphragm and proteomics analysis of plasma were conducted to investigate the molecular differences between young and old rats exposed to the ICU condition.ResultsAccording to multi-omics analyses, significant differences were observed in the diaphragm between young and old rats in response to 5 days CMV and immobilization. In young rats, metabolic pathways were primarily downregulated in response to immobilization (post-synaptic blockade of neuromuscular transmission). In old rats, on the other hand, dramatic immune and inflammatory responses were observed, i.e., an upregulation of specific related pathways such as "IL-17 signaling pathway", along with a higher level of inflammatory factors and cytokine/chemokine in plasma.ConclusionsThe dramatically increased mortality in old ICU patients with COVID-19-associated hyperinflammation and cytokine storm need not only reflect the viral infection but may also be associated with the ventilator induced diaphragm dysfunction (VIDD) and hyperinflammatory responses induced by long-term CMV per se. Although mechanical ventilation is a life-saving intervention in COVID-19 ICU patients, CMV should be cautiously used especially in old age and other means of respiratory support may be considered, such as negative pressure ventilation.
Project description:The recent development of high-throughput technology has allowed us to accumulate vast amounts of multi-omics data. Because even single omics data have a large number of variables, integrated analysis of multi-omics data suffers from problems such as computational instability and variable redundancy. Most multi-omics data analyses apply single supervised analysis, repeatedly, for dimensional reduction and variable selection. However, these approaches cannot avoid the problems of redundancy and collinearity of variables. In this study, we propose a novel approach using blockwise component analysis. This would solve the limitations of current methods by applying variable clustering and sparse principal component (sPC) analysis. Our approach consists of two stages. The first stage identifies homogeneous variable blocks, and then extracts sPCs, for each omics dataset. The second stage merges sPCs from each omics dataset, and then constructs a prediction model. We also propose a graphical method showing the results of sparse PCA and model fitting, simultaneously. We applied the proposed methodology to glioblastoma multiforme data from The Cancer Genome Atlas. The comparison with other existing approaches showed that our proposed methodology is more easily interpretable than other approaches, and has comparable predictive power, with a much smaller number of variables.
Project description:To realize the full potential of immunotherapy, it is critical to understand the drivers of tumor infiltration by immune cells. Previous studies have linked immune infiltration with tumor neoantigen levels, but the broad applicability of this concept remains unknown. Here, we find that while this observation is true across cancers characterized by recurrent mutations, it does not hold for cancers driven by recurrent copy number alterations, such as breast and pancreatic tumors. To understand immune invasion in these cancers, we developed an integrative multi-omics framework, identifying the DNA damage response protein ATM as a driver of cytokine production leading to increased immune infiltration. This prediction was validated in numerous orthogonal datasets, as well as experimentally in vitro and in vivo by cytokine release and immune cell migration. These findings demonstrate diverse drivers of immune cell infiltration across cancer lineages and may facilitate the clinical adaption of immunotherapies across diverse malignancies.
Project description:During development, precursor cells are continuously and intimately interacting with their extracellular environment, which guides their ability to generate functional tissues and organs. Much is known about the development of the neocortex in mammals. This information has largely been derived from histological analyses, heterochronic cell transplants, and genetic manipulations in mice, and to a lesser extent from transcriptomic and histological analyses in humans. However, these approaches have not led to a characterization of the extracellular composition of the developing neocortex in any species. Here, using a combination of single-cell transcriptomic analyses from published datasets, and our proteomics and immunohistofluorescence analyses, we provide a more comprehensive and unbiased picture of the early developing fetal neocortex in humans and non-human primates. Our findings provide a starting point for further hypothesis-driven studies on structural and signaling components in the developing cortex that had previously not been identified.
Project description:BackgroundMicroRNAs (miRNAs) play a key role in mediating the action of insulin on cell growth and the development of diabetes. However, few studies have been conducted to provide a comprehensive overview of the miRNA-mediated signaling network in response to glucose in pancreatic beta cells. In our study, we established a computational framework integrating multi-omics profiles analyses, including RNA sequencing (RNA-seq) and small RNA sequencing (sRNA-seq) data analysis, inverse expression pattern analysis, public data integration, and miRNA targets prediction to illustrate the miRNA-mediated regulatory network at different glucose concentrations in INS-1 pancreatic beta cells (INS-1), which display important characteristics of the pancreatic beta cells.ResultsWe applied our computational framework to the expression profiles of miRNA/mRNA of INS-1, at different glucose concentrations. A total of 1437 differentially expressed genes (DEGs) and 153 differentially expressed miRNAs (DEmiRs) were identified from multi-omics profiles. In particular, 121 DEmiRs putatively regulated a total of 237 DEGs involved in glucose metabolism, fatty acid oxidation, ion channels, exocytosis, homeostasis, and insulin gene regulation. Moreover, Argonaute 2 immunoprecipitation sequencing, qRT-PCR, and luciferase assay identified Crem, Fn1, and Stc1 are direct targets of miR-146b and elucidated that miR-146b acted as a potential regulator and promising target to understand the insulin signaling network.ConclusionsIn this study, the integration of experimentally verified data with system biology framework extracts the miRNA network for exploring potential insulin-associated miRNA and their target genes. The findings offer a potentially significant effect on the understanding of miRNA-mediated insulin signaling network in the development and progression of pancreatic diabetes.
Project description:MicroRNAs (miRNAs) are non-coding RNA molecules that regulate gene expression. Extensive research has explored the role of miRNAs in the risk for type 2 diabetes (T2D) and coronary heart disease (CHD) using single-omics data, but much less by leveraging population-based omics data. Here we aimed to conduct a multi-omics analysis to identify miRNAs associated with cardiometabolic risk factors and diseases. First, we used publicly available summary statistics from large-scale genome-wide association studies to find genetic variants in miRNA-related sequences associated with various cardiometabolic traits, including lipid and obesity-related traits, glycemic indices, blood pressure, and disease prevalence of T2D and CHD. Then, we used DNA methylation and miRNA expression data from participants of the Rotterdam Study to further investigate the link between associated miRNAs and cardiometabolic traits. After correcting for multiple testing, 180 genetic variants annotated to 67 independent miRNAs were associated with the studied traits. Alterations in DNA methylation levels of CpG sites annotated to 38 of these miRNAs were associated with the same trait(s). Moreover, we found that plasma expression levels of 8 of the 67 identified miRNAs were also associated with the same trait. Integrating the results of different omics data showed miR-10b-5p, miR-148a-3p, miR-125b-5p, and miR-100-5p to be strongly linked to lipid traits. Collectively, our multi-omics analysis revealed multiple miRNAs that could be considered as potential biomarkers for early diagnosis and progression of cardiometabolic diseases.
Project description:Triple negative breast cancer is an aggressive type of breast cancer with very little treatment options. TNBC is very heterogeneous with large alterations in the genomic, transcriptomic, and proteomic landscapes leading to various subtypes with differing responses to therapeutic treatments. We applied a multi-omics data integration method to evaluate the correlation of important regulatory features in TNBC BRCA1 wild-type MDA-MB-231 and TNBC BRCA1 5382insC mutated HCC1937 cells compared with normal epithelial breast MCF10A cells. The data includes DNA methylation, RNAseq, protein, phosphoproteomics, and histone post-translational modification. Data integration methods identified regulatory features from each omics method had greater than 80% positive correlation within each TNBC subtype. Key regulatory features at each omics level were identified distinguishing the three cell lines and were involved in important cancer related pathways such as TGFbeta signaling, PI3K/AKT/mTOR, and Wnt/beta-catenin signaling. EPIC Bead Chip
Project description:The root of Achyranthes bidentata Blume (AB) is a well-known traditional Chinese medicine for treating osteoporosis. Plenty of studies focused on the pharmacological mechanism of the whole extract; however, the contribution of different components to the anti-osteoporosis effect remains unknown. The aim of this study is to explore the anti-osteoporosis mechanism of different components of crude and salt-processed AB under the guidance of network pharmacology, metabolomics, and microbiomics. First, network pharmacology analysis was applied to constructing the compound-target-disease network of AB to provide a holistic view. Second, the anti-osteoporosis effects of the four components were evaluated in female Wistar rats. The subjects were divided into a normal group, a model group, a 17α-estradiol (E2)-treated group, a polysaccharide-component-treated groups, and a polysaccharide-knockout-component-treated groups. All the serum, urine, and feces samples of the six groups were collected after 16 weeks of treatment. Biochemical and microcomputed tomography (μCT) parameters were also acquired. Coupled with orthogonal partial least-squares discrimination analysis, one dimensional nuclear magnetic resonance (NMR) was used to monitor serum metabolic alterations. A total of twenty-two biomarkers, including lipids, amino acids, polyunsaturated fatty acids, glucose, and so on were identified for the different components-treated groups. Through pathway analysis, it is indicated that glyoxylate and dicarboxylate metabolism, glycine, serine, and threonine metabolism, alanine, aspartate, and glutamate metabolism, d-glutamine, and d-glutamate metabolism were the major intervened pathways. Levels of these biomarkers shifted away from the model group and were restored to normal after treatment with the four components. In addition, 16S rDNA sequencing demonstrated that the abundance of Anaerofilum, Rothia, and Turicibacter bacteria was positively correlated with an anti-osteoporosis effect, whereas the abundance of Oscillospira was negatively correlated. The osteoprotective effect of the polysaccharide components of crude and salt-processed AB is related to the regulation of the abundance of these gut microbiota.