Unravelling the genetic determinants of sinusoidal obstruction syndrome: an integrative multi-omics approach
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ABSTRACT:
ORGANISM(S): Homo sapiens
PROVIDER: GSE178497 | GEO | 2023/06/15
REPOSITORIES: GEO
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Project description:Unravelling the genetic determinants of sinusoidal obstruction syndrome: an integrative multi-omics approach
| PRJNA739371 | ENA
Project description:BackgroundGenotype-phenotype analyses of rare diseases often suffer from a lack of power, due to small sample size, which makes identifying significant associations difficult. Sinusoidal obstruction syndrome (SOS) of the liver is a rare but life-threatening complication of hematopoietic stem cell transplantation (HSCT). The alkylating agent busulfan is commonly used in HSCT and known to trigger SOS. We developed a novel pipeline to identify genetic determinants in rare diseases by combining in vitro information with clinical whole-exome sequencing (WES) data and applied it in SOS patients and controls.MethodsFirst, we analysed differential gene expression in six lymphoblastoid cell lines (LCLs) before and after incubation with busulfan. Second, we used WES data from 87 HSCT patients and estimated the association with SOS at the SNP and the gene levels. We then combined the results of the expression and the association analyses into an association statistic at the gene level. We used an over-representation analysis to functionally characterize the genes that were associated with a significant combined test statistic.ResultsAfter treatment of LCLs with busulfan, 1708 genes were significantly up-, and 1385 down-regulated. The combination of the expression experiment and the association analysis of WES data into a single test statistic revealed 35 genes associated with the outcome. These genes are involved in various biological functions and processes, such as "Cell growth and death", "Signalling molecules and interaction", "Cancer", and "Infectious disease".ConclusionsThis novel data analysis pipeline integrates two independent omics datasets and increases statistical power for identifying genotype-phenotype associations. The analysis of the transcriptomics profile of cell lines treated with busulfan and WES data from HSCT patients allowed us to identify potential genetic contributors to SOS. Our pipeline could be useful for identifying genetic contributors to other rare diseases where limited power renders genome-wide analyses unpromising.Trial registrationFor the clinical dataset: Clinicaltrials.gov: NCT01257854. https://clinicaltrials.gov/ct2/history/NCT01257854.
| S-EPMC10075428 | biostudies-literature
Project description:Biological mechanisms related to cancer development can leave distinct molecular fingerprints in tumours. By leveraging multi-omics and epidemiological information, we can unveil relationships between carcinogenesis processes that would otherwise remain hidden. Our integrative analysis of DNA methylome, transcriptome, and somatic mutation profiles of kidney tumours linked ageing, epithelial-mesenchymal transition (EMT), and xenobiotic metabolism to kidney carcinogenesis. Ageing process was represented by associations with cellular mitotic clocks such as epiTOC2, SBS1, telomere length, and PBRM1 and SETD2 mutations, which ticked faster as tumours progressed. We identified a relationship between BAP1 driver mutations and the epigenetic upregulation of EMT genes (IL20RB and WT1), correlating with increased tumour immune infiltration, advanced stage, and poorer patient survival. We also observed an interaction between epigenetic silencing of the xenobiotic metabolism gene GSTP1 and tobacco use, suggesting a link to genotoxic effects and impaired xenobiotic metabolism. Our pan-cancer analysis showed these relationships in other tumour types. Our study enhances the understanding of kidney carcinogenesis and its relation to risk factors and progression, with implications for other tumour types.
| S-SCDT-10_1038-S44320-024-00072-3 | biostudies-other
Project description:Rationale: GWAS (Genome-Wide Association Studies) have identified hundreds of genetic loci associated with atrial fibrillation (AF). However, these loci explain only a small proportion of AF heritability. Objective: To develop an approach to identify additional AF-related genes by integrating multiple omics data. Methods and Results: Three types of omics data were integrated: (1) summary statistics from the AFGen 2017 GWAS; (2) a whole blood EWAS (Epigenome-Wide Association Study) of AF; and (3) a whole blood TWAS (Transcriptome-Wide Association Study) of AF. The variant-level GWAS results were collapsed into gene-level associations using fast set-based association analysis. The CpG-level EWAS results were also collapsed into gene-level associations by an adapted SNP-set Kernel Association Test approach. Both GWAS and EWAS gene-based associations were then meta-analyzed with TWAS using a fixed-effects model weighted by the sample size of each data set. A tissue-specific network was subsequently constructed using the NetWAS (Network-Wide Association Study). The identified genes were then compared with the AFGen 2018 GWAS that contained more than triple the number of AF cases compared with AFGen 2017 GWAS. We observed that the multiomics approach identified many more relevant AF-related genes than using AFGen 2018 GWAS alone (1931 versus 206 genes). Many of these genes are involved in the development and regulation of heart- and muscle-related biological processes. Moreover, the gene set identified by multiomics approach explained much more AF variance than those identified by GWAS alone (10.4% versus 3.5%). Conclusions: We developed a strategy to integrate multiple omics data to identify AF-related genes. Our integrative approach may be useful to improve the power of traditional GWAS, which might be particularly useful for rare traits and diseases with limited sample size.
| S-EPMC7004281 | biostudies-literature
Project description:Recent technological advances and international efforts, such as The Cancer Genome Atlas (TCGA), have made available several pan-cancer datasets encompassing multiple omics layers with detailed clinical information in large collection of samples. The need has thus arisen for the development of computational methods aimed at improving cancer subtyping and biomarker identification from multi-modal data. Here we apply the Integrative Network Fusion (INF) pipeline, which combines multiple omics layers exploiting Similarity Network Fusion (SNF) within a machine learning predictive framework. INF includes a feature ranking scheme (rSNF) on SNF-integrated features, used by a classifier over juxtaposed multi-omics features (juXT). In particular, we show instances of INF implementing Random Forest (RF) and linear Support Vector Machine (LSVM) as the classifier, and two baseline RF and LSVM models are also trained on juXT. A compact RF model, called rSNFi, trained on the intersection of top-ranked biomarkers from the two approaches juXT and rSNF is finally derived. All the classifiers are run in a 10x5-fold cross-validation schema to warrant reproducibility, following the guidelines for an unbiased Data Analysis Plan by the US FDA-led initiatives MAQC/SEQC. INF is demonstrated on four classification tasks on three multi-modal TCGA oncogenomics datasets. Gene expression, protein expression and copy number variants are used to predict estrogen receptor status (BRCA-ER, N = 381) and breast invasive carcinoma subtypes (BRCA-subtypes, N = 305), while gene expression, miRNA expression and methylation data is used as predictor layers for acute myeloid leukemia and renal clear cell carcinoma survival (AML-OS, N = 157; KIRC-OS, N = 181). In test, INF achieved similar Matthews Correlation Coefficient (MCC) values and 97% to 83% smaller feature sizes (FS), compared with juXT for BRCA-ER (MCC: 0.83 vs. 0.80; FS: 56 vs. 1801) and BRCA-subtypes (0.84 vs. 0.80; 302 vs. 1801), improving KIRC-OS performance (0.38 vs. 0.31; 111 vs. 2319). INF predictions are generally more accurate in test than one-dimensional omics models, with smaller signatures too, where transcriptomics consistently play the leading role. Overall, the INF framework effectively integrates multiple data levels in oncogenomics classification tasks, improving over the performance of single layers alone and naive juxtaposition, and provides compact signature sizes.
| S-EPMC7340129 | biostudies-literature
Project description:Congenital heart disease (CHD)-associated aortopathy is a very heterogeneous entity with a wide spectrum of clinical presentations. The pathogenesis of aortopathy is still incompletely understood, and, therefore, the best prevention and management strategy is currently unknown. The most common entity of CHD-associated aortopathies is bicuspid aortic valve (BAV)-associated aortic disease (so called bicuspid aortopathy) that is found in 50%-60% of BAV individuals. BAV aortopathy has been reported in association with an increased risk of aortic events, especially aortic dissection and sudden cardiac death. Risk stratification of adverse aortic events is still very rudimentary and considers only the maximal aortic diameter, which makes it unsuitable for an individual risk prediction. This introductory Editorial highlights the unmet clinical need for more integrative and translational research to unravel pathogenetic pathways in the development of CHD-associated aortopathies, integrating recently identified genetic lesions and knowledge on circulating biomarkers and microstructural changes in the diseased aorta.
| S-EPMC7019613 | biostudies-literature
Project description:Sinusoidal obstruction syndrome (SOS) is a potentially life-threatening complication after hematopoietic stem cell transplantation (HSCT) or antineoplastic treatment without HSCT. Genetic variants were investigated for their association with SOS, but the evidence is inconclusive. We performed a systematic literature review to identify genes, gene variants, and methods of association analyses of genetic markers with SOS. We identified 23 studies after HSCT and 4 studies after antineoplastic treatment without HSCT. One study (4%) performed whole-exome sequencing (WES) and replicated the analysis in an independent cohort, 26 used a candidate-gene approach. Three studies included >200 participants (11%), and six were of high quality (22%). Variants in 34 genes were tested in candidate gene studies after HSCT. Variants in GSTA1 were associated with SOS in three studies, MTHFR in two, and CPS1, CTH, CYP2B6, GSTM1, GSTP1, HFE, and HPSE in one study each. UGT2B10 and LNPK variants were identified in a WES analysis. After exposure to antineoplastic agents without HSCT, variants in six genes were tested and only GSTM1 was associated with SOS. There was a substantial heterogeneity of populations within and between studies. Future research should be based on sufficiently large homogenous samples, adjust for covariates, and replicate findings in independent cohorts.
| S-EPMC8145271 | biostudies-literature
Project description:Sinusoidal obstruction syndrome/veno-occlusive disease (SOS/VOD) detected in the liver has been considered a severe complication of hematopoietic stem cell transplantation (HSCT). SOS/VOD is characterized by hepatomegaly, right upper quadrant pain, jaundice, and ascites. The severe forms of the disease may result in multi-organ dysfunction (MOD) with a high mortality rate (>80%). The development of SOS/VOD can be rapid and unpredictable. Therefore, early identification and severity assessment is crucial in facilitating prompt diagnosis and timely treatment. Effective treatment and potential prophylaxis with defibrotide highlight the need for characterizing a sub-group of patients at high risk for SOS/VOD. Moreover, antibodies that are conjugated with calicheamicin, gemtuzumab, and inotuzumab ozogamicin, have led to renewed interest in this syndrome. Evaluation and management of serious adverse events associated with gemtuzumab and inotuzumab ozogamicin are recommended. We review hepatic-, transplant- and patient-related risk factors, criteria for diagnosis and grading classification, and SOS/VOD potential biomarkers. Furthermore, we examine pathogenesis, clinical presentation, diagnostic criteria, risk factors, prophylaxis, and treatment of SOS/VOD occurring post HSCT. Moreover, we aim to provide an up-to-date summary of molecular advances in the diagnosis and management of SOS/VOD. We performed a comprehensive review of the literature and examined the recently available data, mostly using the PubMed and Medline search engines for original articles published over the last decade. In the era of precision medicine, our review provides up-to-date knowledge of genetic or sera markers for SOS/VOD with the goal of identifying a subset of high-risk patients.
| S-EPMC10051970 | biostudies-literature
Project description:The wealth of high-throughput data has opened up new opportunities to analyze and describe biological processes at higher resolution, ultimately leading to a significant acceleration of scientific output using high-throughput data from the different omics layers and the generation of databases to store and report raw datasets. The great variability among the techniques and the heterogeneous methodologies used to produce this data have placed meta-analysis methods as one of the approaches of choice to correlate the resultant large-scale datasets from different research groups. Through multi-study meta-analyses, it is possible to generate results with greater statistical power compared to individual analyses. Gene signatures, biomarkers and pathways that provide new insights of a phenotype of interest have been identified by the analysis of large-scale datasets in several fields of science. However, despite all the efforts, a standardized regulation to report large-scale data and to identify the molecular targets and signaling networks is still lacking. Integrative analyses have also been introduced as complementation and augmentation for meta-analysis methodologies to generate novel hypotheses. Currently, there is no universal method established and the different methods available follow different purposes. Herein we describe a new unifying, scalable and straightforward methodology to meta-analyze different omics outputs, but also to integrate the significant outcomes into novel pathways describing biological processes of interest. The significance of using proper molecular identifiers is highlighted as well as the potential to further correlate molecules from different regulatory levels. To show the methodology's potential, a set of transcriptomic datasets are meta-analyzed as an example.
| S-EPMC8855827 | biostudies-literature
Project description:Interleukin-16 (IL-16) is a multifunctional cytokine that has been associated with autoimmune and allergic diseases. To investigate comprehensively whether IL-16 is also associated with chronic obstructive pulmonary disease (COPD) and emphysema, we performed an integrated analysis of multiple "omics" data. Over 500 subjects participating in the COPDGene® study donated blood and were clinically characterized and genetically profiled. IL-16 mRNA levels were measured in peripheral blood mononuclear cells (PBMC), and protein levels were measured in fresh frozen plasma. A multivariate analysis found plasma IL-16 positively associated with age and body mass index, and negatively associated with current smoking and emphysema in the upper lobes. PBMC IL-16 expression was positively associated with gender and a composite score for airflow obstruction, emphysema, and gas trapping. Whole-genome expression quantitative trait locus (eQTL) analysis identified a novel IL-16 missense SNP (rs11556218) associated with lower IL-16 in plasma. In summary, an integrated "omics" analysis in a very large cohort identified an association between decreased IL-16 and emphysema and discovered a novel IL-16 cis-eQTL. Thus IL-16 plasma levels and IL-16 genotyping may be useful in a personalized medicine approach for lung disease.
| S-EPMC3837437 | biostudies-literature