scholarly journals Multi-omics data analysis implicating epigenetic inheritance in evolution and disease

2019 ◽  
Author(s):  
Abhay Sharma

AbstractRecent evidence surprisingly suggests existence of germline mediated epigenetic inheritance in diverse species including mammals. The evolutionary and health implications as well as the mechanistic plausibility of epigenetic inheritance are subjects of immense current interest and controversy, with integrative analysis expected to provide valuable insights. Here, an unbiased gene set enrichment analysis of existing multi-omics data is presented that readily supports a role of sperm DNA methylome in evolution and disease, as also in developmental mechanisms. In mice, differentially methylated sperm genes in cold induced inheritance specifically overrepresent genes associated with cold adaptation. Similarly, in humans, differentially methylated sperm genes associate with disease and adaptation in general, with specific disease association supported by prior evidence. Further, the sperm genes, like disease and adaptation genes, overrepresent genes known to exhibit higher mutability, loss-of-function intolerance, and haploinsufficiency. Finally, both mouse and human sperm genes show enrichment for genes that retain sperm methylation during development and are developmentally expressed. Together, the present analysis provides one-stop evidence to suggest that sperm DNA methylome acts as a melting pot of gene-environment interaction, inheritance, evolution, and health and disease.

2021 ◽  
Vol 22 (1) ◽  
Author(s):  
Jovana Maksimovic ◽  
Alicia Oshlack ◽  
Belinda Phipson

AbstractDNA methylation is one of the most commonly studied epigenetic marks, due to its role in disease and development. Illumina methylation arrays have been extensively used to measure methylation across the human genome. Methylation array analysis has primarily focused on preprocessing, normalization, and identification of differentially methylated CpGs and regions. GOmeth and GOregion are new methods for performing unbiased gene set testing following differential methylation analysis. Benchmarking analyses demonstrate GOmeth outperforms other approaches, and GOregion is the first method for gene set testing of differentially methylated regions. Both methods are publicly available in the missMethyl Bioconductor R package.


2020 ◽  
Vol 26 (6) ◽  
pp. 841-873 ◽  
Author(s):  
Fredrika Åsenius ◽  
Amy F Danson ◽  
Sarah J Marzi

Abstract BACKGROUND Studies in non-human mammals suggest that environmental factors can influence spermatozoal DNA methylation, and some research suggests that spermatozoal DNA methylation is also implicated in conditions such as subfertility and imprinting disorders in the offspring. Together with an increased availability of cost-effective methods of interrogating DNA methylation, this premise has led to an increasing number of studies investigating the DNA methylation landscape of human spermatozoa. However, how the human spermatozoal DNA methylome is influenced by environmental factors is still unclear, as is the role of human spermatozoal DNA methylation in subfertility and in influencing offspring health. OBJECTIVE AND RATIONALE The aim of this systematic review was to critically appraise the quality of the current body of literature on DNA methylation in human spermatozoa, summarize current knowledge and generate recommendations for future research. SEARCH METHODS A comprehensive literature search of the PubMed, Web of Science and Cochrane Library databases was conducted using the search terms ‘semen’ OR ‘sperm’ AND ‘DNA methylation’. Publications from 1 January 2003 to 2 March 2020 that studied human sperm and were written in English were included. Studies that used sperm DNA methylation to develop methodologies or forensically identify semen were excluded, as were reviews, commentaries, meta-analyses or editorial texts. The Grading of Recommendations, Assessment, Development and Evaluations (GRADE) criteria were used to objectively evaluate quality of evidence in each included publication. OUTCOMES The search identified 446 records, of which 135 were included in the systematic review. These 135 studies were divided into three groups according to area of research; 56 studies investigated the influence of spermatozoal DNA methylation on male fertility and abnormal semen parameters, 20 studies investigated spermatozoal DNA methylation in pregnancy outcomes including offspring health and 59 studies assessed the influence of environmental factors on spermatozoal DNA methylation. Findings from studies that scored as ‘high’ and ‘moderate’ quality of evidence according to GRADE criteria were summarized. We found that male subfertility and abnormal semen parameters, in particular oligozoospermia, appear to be associated with abnormal spermatozoal DNA methylation of imprinted regions. However, no specific DNA methylation signature of either subfertility or abnormal semen parameters has been convincingly replicated in genome-scale, unbiased analyses. Furthermore, although findings require independent replication, current evidence suggests that the spermatozoal DNA methylome is influenced by cigarette smoking, advanced age and environmental pollutants. Importantly however, from a clinical point of view, there is no convincing evidence that changes in spermatozoal DNA methylation influence pregnancy outcomes or offspring health. WIDER IMPLICATIONS Although it appears that the human sperm DNA methylome can be influenced by certain environmental and physiological traits, no findings have been robustly replicated between studies. We have generated a set of recommendations that would enhance the reliability and robustness of findings of future analyses of the human sperm methylome. Such studies will likely require multicentre collaborations to reach appropriate sample sizes, and should incorporate phenotype data in more complex statistical models.


2021 ◽  
Vol 2021 ◽  
pp. 1-12
Author(s):  
Qian Yang ◽  
Guowei Huang ◽  
Liyan Li ◽  
Enmin Li ◽  
Liyan Xu

Colorectal cancer (CRC) has two major subtypes, microsatellite instability (MSI) and microsatellite stability (MSS) based on the genomic instability. In this study, using computational programs, we identified 9 master transcription factors (TFs) based on epigenomic profiling in MSS CRC samples. Notably, unbiased gene set enrichment analysis (GSEA) showed that several master TFs were strongly associated with immune-related functions in TCGA MSS CRC tissues, such as interferon gamma (IFN-γ) and interferon alpha (IFN-α) responses. Focusing to the top candidate, ASCL2, we found that CD8+ T cell infiltration was low in ASCL2 overexpressed MSS CRC samples. Compared with other gastrointestinal (GI) cancers (gastric cancer, MSI CRC, and esophageal cancer), ASCL2 is specifically upregulated in MSS CRC. Moreover, we identified 28 candidate genes in IFN-γ and IFN-α response pathways which were negatively correlated with ASCL2. Together, these results link transcriptional dysregulation with the immune evasion in MSS CRC, which may advance the understanding of immune resistance and contribute to developing novel treatments of MSS CRC.


2018 ◽  
Vol 29 (1) ◽  
pp. 42-52 ◽  
Author(s):  
Jan Baumann ◽  
Tatiana I. Ignashkova ◽  
Sridhar R. Chirasani ◽  
Silvia Ramírez-Peinado ◽  
Hamed Alborzinia ◽  
...  

The secretory pathway is a major determinant of cellular homoeostasis. While research into secretory stress signaling has so far mostly focused on the endoplasmic reticulum (ER), emerging data suggest that the Golgi itself serves as an important signaling hub capable of initiating stress responses. To systematically identify novel Golgi stress mediators, we performed a transcriptomic analysis of cells exposed to three different pharmacological compounds known to elicit Golgi fragmentation: brefeldin A, golgicide A, and monensin. Subsequent gene-set enrichment analysis revealed a significant contribution of the ETS family transcription factors ELK1, GABPA/B, and ETS1 to the control of gene expression following compound treatment. Induction of Golgi stress leads to a late activation of the ETS upstream kinases MEK1/2 and ERK1/2, resulting in enhanced ETS factor activity and the transcription of ETS family target genes related to spliceosome function and cell death induction via alternate MCL1 splicing. Further genetic analyses using loss-of-function and gain-of-function experiments suggest that these transcription factors operate in parallel.


Cancers ◽  
2020 ◽  
Vol 12 (7) ◽  
pp. 1943
Author(s):  
Carol M. Amato ◽  
Jennifer D. Hintzsche ◽  
Keith Wells ◽  
Allison Applegate ◽  
Nicholas T. Gorden ◽  
...  

Immunotherapy, such as anti-PD1, has improved the survival of patients with metastatic melanoma. However, predicting which patients will respond to immunotherapy remains a significant knowledge gap. In this study we analyzed pre-immunotherapy treated tumors from 52 patients with metastatic melanoma and monitored their response based on RECIST 1.1 criteria. The responders group contained 21 patients that had a complete or partial response, while the 31 non-responders had stable or progressive disease. Whole exome sequencing (WES) was used to identify biomarkers of anti-PD1 response from somatic mutations between the two groups. Variants in codons G34 and G41 in NFKBIE, a negative regulator of NFkB, were found exclusively in the responders. Mutations in NKBIE-related genes were also enriched in the responder group compared to the non-responders. Patients that harbored NFKBIE-related gene mutations also had a higher mutational burden, decreased tumor volume with treatment, and increased progression-free survival. RNA sequencing on a subset of tumor samples identified that CD83 was highly expressed in our responder group. Additionally, Gene Set Enrichment Analysis showed that the TNFalpha signaling via NFkB pathway was one of the top pathways with differential expression in responders vs. non-responders. In vitro NFkB activity assays indicated that the G34E variant caused loss-of-function of NFKBIE, and resulted in activation of NFkB signaling. Flow cytometry assays indicated that G34E variant was associated with upregulation of CD83 in human melanoma cell lines. These results suggest that NFkB activation and signaling in tumor cells contributes to a favorable anti-PD1 treatment response, and clinical screening to include aberrations in NFkB-related genes should be considered.


2016 ◽  
Vol 32 (2) ◽  
pp. 272-283 ◽  
Author(s):  
D. Chan ◽  
S. McGraw ◽  
K. Klein ◽  
L.M. Wallock ◽  
C. Konermann ◽  
...  

2020 ◽  
Vol 21 (1) ◽  
Author(s):  
Sebastian Canzler ◽  
Jörg Hackermüller

Abstract Background Gaining biological insights into molecular responses to treatments or diseases from omics data can be accomplished by gene set or pathway enrichment methods. A plethora of different tools and algorithms have been developed so far. Among those, the gene set enrichment analysis (GSEA) proved to control both type I and II errors well. In recent years the call for a combined analysis of multiple omics layers became prominent, giving rise to a few multi-omics enrichment tools. Each of these has its own drawbacks and restrictions regarding its universal application. Results Here, we present the package aiding to calculate a combined GSEA-based pathway enrichment on multiple omics layers. The package queries 8 different pathway databases and relies on the robust GSEA algorithm for a single-omics enrichment analysis. In a final step, those scores will be combined to create a robust composite multi-omics pathway enrichment measure. supports 11 different organisms and includes a comprehensive mapping of transcripts, proteins, and metabolite IDs. Conclusions With we introduce a highly versatile tool for multi-omics pathway integration that minimizes previous restrictions in terms of omics layer selection, pathway database availability, organism selection and the mapping of omics feature identifiers. is publicly available under the GPL-3 license at https://github.com/yigbt/multiGSEA and at bioconductor: https://bioconductor.org/packages/multiGSEA.


Author(s):  
Jovana Maksimovic ◽  
Alicia Oshlack ◽  
Belinda Phipson

AbstractDNA methylation is one of the most commonly studied epigenetic marks, due to its role in disease and development. Illumina methylation arrays have been extensively used to measure methylation across the human genome. Methylation array analysis has primarily focused on preprocessing, normalisation and identification of differentially methylated CpGs and regions. GOmeth and GOregion are new methods for performing unbiased gene set testing following differential methylation analysis. Benchmarking analyses demonstrate GOmeth outperforms other approaches and GOregion is the first method for gene set testing of differentially methylated regions. Both methods are publicly available in the missMethyl Bioconductor R package.


2021 ◽  
Author(s):  
Xianyu Hu ◽  
Zhenglin Wang ◽  
Qing Wang ◽  
Ke Chen ◽  
Qijun Han ◽  
...  

Background: Gastric cancer (GC) is the fifth most common tumor around the world, it is necessary to reveal novel molecular subtypes to guide the selection of patients who may benefit from specific target therapy. Methods: Multi-omics data, including RNA-sequence of transcriptomics (mRNA, LncRNA, miRNA), DNA methylation and gene mutation of TCGA-STAD cohort was used for the clustering. Ten classical clustering algorithms were applied to recognize patients with different molecular features via the R package MOVICS. The activated signaling pathways were evaluated using the single-sample gene set enrichment analysis. The difference distribution of gene mutations, copy number alterations and tumor mutation burden was compared, and potential response to immunotherapy and chemotherapy was assessed as well. Results: Two molecular subtypes (CS1 and CS2) were recognized by ten clustering algorithms with further consensus ensembles. Patients in the CS1 group were found to contain a shorter average overall survival time (28.5 vs. 68.9 months, P = 0.016), and progression-free survival (19.0 vs. 63.9 months, P = 0.008) compared to the CS2 group. CS1 group contained more activation of extracellular associated biological process, while CS2 group displayed the activation of cell cycle associated pathways. The significantly higher total mutation numbers and neo antigens were observed in CS2 group, along with the specific mutation of TTN, MUC16 and ARID1A. Higher infiltration of immunocytes were also observed in CS2 group, reflected to the potential benefit from immunotherapy. Moreover, CS2 group also can response to 5-fluorouracil, cisplatin, and paclitaxel. The similar diverse of clinical outcome of CS1 and CS2 groups were successfully validation in external cohorts of GSE62254, GSE26253, GSE15459, and GSE84437. Conclusion: Novel insight into the GC subtypes was obtained via integrative analysis of five omics data by ten clustering algorithms, which can provide the idea to the clinical target therapy based on the specific molecular features.


Author(s):  
Sebastian Canzler ◽  
Jörg Hackermüller

AbstractGaining biological insights into molecular responses to treatments or diseases from omics data can be accomplished by gene set or pathway enrichment methods. A plethora of different tools and algorithms have been developed so far. Among those, the gene set enrichment analysis (GSEA) proved to control both type I and II errors well.In recent years the call for a combined analysis of multiple omics layer became prominent, giving rise to a few multi-omics enrichment tools. Each of which has its own drawbacks and restrictions regarding its universal application.Here, we present the multiGSEA package aiding to calculate a combined GSEA-based pathway enrichment on multiple omics layer. The package queries 8 different pathway databases and relies on the robust GSEA algorithm for a single-omics enrichment analysis. In a final step, those scores will be combined to create a robust composite multi-omics pathway enrichment measure. multiGSEA supports 11 different organisms and includes a comprehensive mapping of transcripts, proteins, and metabolite IDs. It is publicly available under the GPL-3 license at https://github.com/yigbt/multiGSEA and at Bioconductor: https://bioconductor.org/packages/multiGSEA.


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