Integration of multi-omics provides insights into thermogenic mechanisms
Ontology highlight
ABSTRACT:
ORGANISM(S): Alocasia Odora
SUBMITTER: 
Fuhua Bian
PROVIDER: PXD038356 | iProX | Fri Nov 25 00:00:00 GMT 2022
REPOSITORIES: iProX
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Project description:Multi-omics has the promise to provide a detailed molecular picture for biological systems. Although obtaining multi-omics data is relatively easy, methods that analyze such data have been lagging. In this paper, we present an algorithm that uses probabilistic graph representations and external knowledge to perform optimum structure learning and deduce a multifarious interaction network for multi-omics data from a bacterial community. Kefir grain, a microbial community that ferments milk and creates kefir, represents a self-renewing, stable, natural microbial community. Kefir has been shown to associate with a wide range of health benefits. We obtained a controlled bacterial community using the two most abundant and well-studied species in kefir grains: Lentilactobacillus kefiri and Lactobacillus kefiranofaciens. We applied growth temperatures of 30°C and 37°C, and ob-tained transcriptomic, metabolomic, and proteomic data for the same 20 samples (10 samples per temperature). We obtained a multi-omics interaction network, which generated insights that would not have been possible with single-omics analysis. We identified interactions among transcripts, proteins, and metabolites suggesting active toxin/antitoxin systems. We also observed multifarious interactions that involved the shikimate pathway. These observations helped explain bacterial adaptation to different stress conditions, co-aggregation, and increased activation of L. kefiranofa-ciens at 37°C.
2023-08-18 | PXD042954 | Pride
Project description:Running title: More insights into ovulation Purpose:Ovulation is the process by which mature follicles release fertilized oocytes under the stimulation of gonadotropin. This involves the precise interaction and destiny of various cells in the follicle. Granulosa cells (GCs) are the only cells that recognize and respond to ovulation LH (or HCG) signals. Therefore, the classification and deep interpretation of genes in GCs under LH surge stimulation is the key to understand ovulation. Methods: In this study, RNA-seq, Q-PCR, Western blot, gene knockout, gene knockout and other experimental techniques were used to conduct trend and classification analysis of the genes regulated by ovulation signal of LH at the cellular and individual levels. Genes that may be involved in ovulation were screened, identified and were selected for functional verification. Results:(1) Firstly, 7104 and 347 mRNA and Long noncoding RNA (LncRNA) that may be involved in ovulation activity were screened at the gene level by transcriptome sequencing technology, respectively. According to gene expression, they were divided into activated, inhibited and transient activated gene expression patterns. 44 secreted proteins, 62 transcription factors, 22 periodic expression characteristic genes, 10 ovulation process unknown genes, 16 LncRNA-target gene cis pair combinations and 41 highly expressed LncRNA were identified and listed in the three models;(2) The functions of LncRNA Gm12840 and Gm20186 were studied at the cellular level. The results showed that knockdown Gm12840/20186 not only inhibited the proliferation and hormone synthesis of GCs, but also induced cell apoptosis;(3) The function of LncRNA Gm12840 was further studied in mice, and Gm12840 knockout mice were constructed by CRISPR/CAS9 technology: Results It was found that Gm12840 knockout significantly increased the number of antral follicles and ovulation, and significantly inhibited the number of atretic follicles and antral follicles not discharged after ovulation. Gm12840 knockout significantly increased litter size at 2-3 months of age, but also significantly inhibited litter size at 7-9 months of age. Meanwhile, Gm12840 knockout also significantly inhibited the number of follicles growing in the ovary of 11-month-old mice;(4) Finally, transcriptomic sequencing analysis of wilt-type and Gm12840-/- mouse GCs showed 349 genes were differentially expressed. KEGG enrichment analysis and WB results showed that Gm12840 knockout enhanced mTOR and MAPK signals. Conclusions:The above results screened and identified genes with potential research value during ovulation process, and took LncRNA Gm12840 as an example to verify their functions at the cell and individual levels, providing reference for further revealing the ovulation process.
2024-08-27 | GSE182965 | GEO
Project description:Running title: More insights into ovulation Purpose:Ovulation is the process by which mature follicles release fertilized oocytes under the stimulation of gonadotropin. This involves the precise interaction and destiny of various cells in the follicle. Granulosa cells (GCs) are the only cells that recognize and respond to ovulation LH (or HCG) signals. Therefore, the classification and deep interpretation of genes in GCs under LH surge stimulation is the key to understand ovulation. Methods: In this study, RNA-seq, Q-PCR, Western blot, gene knockout, gene knockout and other experimental techniques were used to conduct trend and classification analysis of the genes regulated by ovulation signal of LH at the cellular and individual levels. Genes that may be involved in ovulation were screened, identified and were selected for functional verification. Results:(1) Firstly, 7104 and 347 mRNA and Long noncoding RNA (LncRNA) that may be involved in ovulation activity were screened at the gene level by transcriptome sequencing technology, respectively. According to gene expression, they were divided into activated, inhibited and transient activated gene expression patterns. 44 secreted proteins, 62 transcription factors, 22 periodic expression characteristic genes, 10 ovulation process unknown genes, 16 LncRNA-target gene cis pair combinations and 41 highly expressed LncRNA were identified and listed in the three models;(2) The functions of LncRNA Gm12840 and Gm20186 were studied at the cellular level. The results showed that knockdown Gm12840/20186 not only inhibited the proliferation and hormone synthesis of GCs, but also induced cell apoptosis;(3) The function of LncRNA Gm12840 was further studied in mice, and Gm12840 knockout mice were constructed by CRISPR/CAS9 technology: Results It was found that Gm12840 knockout significantly increased the number of antral follicles and ovulation, and significantly inhibited the number of atretic follicles and antral follicles not discharged after ovulation. Gm12840 knockout significantly increased litter size at 2-3 months of age, but also significantly inhibited litter size at 7-9 months of age. Meanwhile, Gm12840 knockout also significantly inhibited the number of follicles growing in the ovary of 11-month-old mice;(4) Finally, transcriptomic sequencing analysis of wilt-type and Gm12840-/- mouse GCs showed 349 genes were differentially expressed. KEGG enrichment analysis and WB results showed that Gm12840 knockout enhanced mTOR and MAPK signals. Conclusions:The above results screened and identified genes with potential research value during ovulation process, and took LncRNA Gm12840 as an example to verify their functions at the cell and individual levels, providing reference for further revealing the ovulation process.
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Project description:Transcriptomic and metabolomic analysis provides insights into anthocyanin and procyanidin accumulation in pear
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Project description:Background: Vascular cognitive impairment and dementia (VCID) represents a spectrum of cognitive disorders linked to cerebrovascular pathology, yet its molecular underpinnings remain misunderstood. Methods: We conducted a multi-omics analysis of post-mortem brain tissue from the superior parietal lobe (Brodmann area 7) in 19 individuals with neuropathologically confirmed VCID and 21 age-matched controls. Whole genome sequencing, genome-wide DNA methylation profiling, transcriptomic analysis, and metabolomics profiling were performed to identify molecular signatures and integrated pathways involved in VCID pathogenesis. Results: Epigenome-wide association analysis revealed widespread hypermethylation in VCID, with significant enrichment of genes involved in the Rac/Rho GTPase cycle and cytoskeletal remodeling. Transcriptomic analysis confirmed dysregulation of small GTPase signaling, oxidative stress responses, and lipid metabolism. Metabolomic profiling identified altered levels of diacylglycerols (DAGs) and phosphatidylethanolamines (PEs), which showed strengthened associations with Rac/Rho pathway genes in VCID compared to controls. Conclusions: Our integrative multi-omics study identifies the Rac/Rho GTPase cycle as a convergent pathway disrupted at the genomic, epigenomic, transcriptomic, and metabolic levels in VCID. Lipid metabolism, particularly involving DAGs and PEs, emerged as a key downstream effector contributing to VCID. These findings offer mechanistic insights into VCID pathogenesis and suggest lipid signaling pathways as promising therapeutic targets for intervention.
2025-07-28 | GSE303449 | GEO
Project description:The integration of multi-omic data sets can provide unique information about molecular processes in a cell. Despite the development of many tools to extract information from such data sets, there are limited strategies to systematically extract mechanistic hypotheses from them. We here present COSMOS (Causal Oriented Search of Multi-Omic Space), a method that integrates cell signaling pathways, transcriptional, and metabolics data sets. COSMOS leverages extensive prior knowledge of interactions between biomolecules with computational methods to estimate activities of transcription factors and kinases as well as network-level causal reasoning. COSMOS can provide mechanistic explanations for experimental observations across multiple omic data sets. We applied COSMOS to a dataset comprising transcriptomic, phosphoproteomic, and metabolomic data from nine renal cell carcinoma patients comparing healthy non affected kidney tissue and kidney cancer. We used COSMOS to generate novel hypotheses such as the impact of CDK7 on nucleoside metabolism and its influence on citrulline production, that we validated experimentally. We expect that our freely available method will be broadly useful to extract mechanistic insights from multi-omic studies.
2021-02-02 | PXD018218 | Pride
Project description:Colorectal cancer (CRC) is one of the most malignant cancers and results in a substantial rate of morbidity and mortality. Diagnosis of this malignancy in early stages increases the chance of effective treatment. High-throughput data analyses reveal omics signatures and also provide the possibility of developing computational models for early detection of this disease. Such models would be able to use as complementary tools for early detection of different types of cancers including CRC. In this study, using gene expression data, the Flux balance analysis (FBA) applied to decode metabolic fluxes in cancer and normal cells. Moreover, transcriptome and genome analyses revealed driver agents of CRC in a biological network scheme. By applying comprehensive publicly available data from TCGA, different aspect of CRC regulome including the regulatory effect of gene expression, methylation, microRNA, copy number aberration and point mutation profile over protein levels investigated and the results provide a regulatory picture underlying CRC. Compiling omics profiles indicated snapshots of changes in different omics levels and flux rate of CRC. In conclusion, considering obtained CRC signatures and their role in biological operating systems of cells, the results suggest reliable driver regulatory modules that could potentially serve as biomarkers and therapeutic targets and furthermore expand our understanding of driving mechanisms of this disease..
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Project description:INTRODUCTION: Chronic antibody-mediated rejection (CAMR) is currently the leading cause of long-term allograft loss, affecting approximately 50-60% of renal transplant recipients after a decade. Although in the last years many studies have performed to define clinical/histological characteristics and to identify the leading causes and triggers of this condition, at the moment, the complete biological machinery associated with this complex complication has been only partially defined. METHODS: We performed mass-spectrometry-based serum proteomics of 19 patients with CAMR and 26 kidney transplant recipients without any functional alteration (CTR). The results were compared with a previously obtained transcriptomic profile of peripheral blood mononuclear cells (PBMC) isolated from 10 CAMR and 8 CTR. The biological elements identified by the statistical analysis were then validated through ELISA in all patients. RESULTS: Proteomics demonstrated that 18 proteins were able to discriminate CAMR from CTR (p<0.01): five proteins resulted more abundant (CHI3L1, LYZ, PRSS2, CPQ, IGLV3-32) while 13 were less abundant (SERPINA5, SERPING1, KNG1, CAMP, VNN1, BTD, WDR1, PON3, AHNAK2, MELTF, CA1, CD44, CUL1) in CAMR compared with CTR patients. The innovative bioinformatic analysis (SVM, PLS-DA) applied to transcriptomic data revealed that 6 genes were down-regulated, and 33 genes were up-regulated in CAMR compared with CTR patients (p<0.01). Notably, only 2 biological elements resulted significantly deregulated in both omics analyses: chitinase-3-like protein 1 (CHI3L1) and plasma protease inhibitor C1 (SERPING1). CHI3L1 was up-regulated in CAMR in both transcriptomics and proteomics, while SERPING1 was up-regulated in CAMR in transcriptomics, but down-regulated in proteomics. ELISA validated the omics data and ROC curve indicated that CHI3L1 had good discrimination power between CAMR and CTR. CONCLUSIONS: The results of the multi-omics analysis confirmed the evident systemic immunological perturbation of patients with CAMR and identified CHI3L1 as a new potential biomarker and therapeutic target for this important clinical complication.
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