Enumerateblood - an R package to estimate the cellular composition of whole blood from Affymetrix Gene ST gene expression profiles

Mitochondrial dysfunction in rheumatoid arthritis: A comprehensive analysis by integrating gene expression, protein-protein interactions and gene ontology data

Several studies have reported mitochondrial dysfunction in rheumatoid arthritis (RA). Many nuclear DNA (nDNA) encoded proteins translocate to mitochondria, but their participation in the dysfunction of this cell organelle during RA is quite unclear. In this study, we have carried out an integrative analysis of gene expression, protein-protein interactions (PPI) and gene ontology data. The analysis has identified potential implications of the nDNA encoded proteins in RA mitochondrial dysfunction. Firstly, by analysing six synovial microarray datasets of RA patients and healthy controls obtained from the gene expression omnibus (GEO) database, we found differentially expressed nDNA genes that encode mitochondrial proteins. We uncovered some of the roles of these genes in RA mitochondrial dysfunction using literature search and gene ontology analysis. Secondly, by employing gene co-expression from microarrays and collating reliable PPI from seven databases, we created the first mitochondrial PPI network that is specific to the RA synovial joint tissue. Further, we identified hubs of this network, and moreover, by integrating gene expression and network analysis, we found differentially expressed neighbours of the hub proteins. The results demonstrate that nDNA encoded proteins are (i) crucial for the elevation of mitochondrial reactive oxygen species (ROS) and (ii) involved in membrane potential, transport processes, metabolism and intrinsic apoptosis during RA. Additionally, we proposed a model relating to mitochondrial dysfunction and inflammation in the disease. Our analysis presents a novel perspective on the roles of nDNA encoded proteins in mitochondrial dysfunction, especially in apoptosis, oxidative stress-related processes and their relation to inflammation in RA. These findings provide a plethora of information for further research.

Computational deconvolution of transcriptomics data from mixed cell populations

Summary

Gene expression analyses of bulk tissues often ignore cell type composition as an important confounding factor, resulting in a loss of signal from lowly abundant cell types. In this review, we highlight the importance and value of computational deconvolution methods to infer the abundance of different cell types and/or cell type-specific expression profiles in heterogeneous samples without performing physical cell sorting. We also explain the various deconvolution scenarios, the mathematical approaches used to solve them and the effect of data processing and different confounding factors on the accuracy of the deconvolution results.

Contact

katleen.depreter@ugent.be.

Supplementary information

Supplementary data are available at Bioinformatics online.

Insights from deconvolution of cell subtype proportions enhance the interpretation of functional genomic data

Cell subtype proportion variability between samples contributes significantly to the variation of functional genomic properties such as gene expression or DNA methylation. Although the impact of the variation of cell subtype composition on measured genomic quantities is recognized, and some innovative tools have been developed for the analysis of heterogeneous samples, most functional genomics studies using samples with mixed cell types still ignore the influence of cell subtype proportion variation, or just deal with it as a nuisance variable to be eliminated. Here we demonstrate how harvesting information about cell subtype proportions from functional genomics data can provide insights into cellular changes associated with phenotypes. We focused on two types of mixed cell populations, human blood and mouse kidney. Cell type prediction is well developed in the former, but not currently in the latter. Estimating the cellular repertoire is easier when a reference dataset from purified samples of all cell types in the tissue is available, as is the case for blood. However, reference datasets are not available for most other tissues, such as the kidney. In this study, we showed that the proportion of alterations attributable to changes in the cellular composition varies strikingly in the two disorders (asthma and systemic lupus erythematosus), suggesting that the contribution of cell subtype proportion changes to functional genomic properties can be disease-specific. We also showed that a reference dataset from a single-cell RNA-seq study successfully estimated the cell subtype proportions in mouse kidney and allowed us to distinguish altered cell subtype differences between two different knock-out mouse models, both of which had reported a reduced number of glomeruli compared to their wild-type counterparts. These findings demonstrate that testing for changes in cell subtype proportions between conditions can yield important insights in functional genomics studies.

Cross-Laboratory Analysis of Brain Cell Type Transcriptomes with Applications to Interpretation of Bulk Tissue Data

Establishing the molecular diversity of cell types is crucial for the study of the nervous system. We compiled a cross-laboratory database of mouse brain cell type-specific transcriptomes from 36 major cell types from across the mammalian brain using rigorously curated published data from pooled cell type microarray and single-cell RNA-sequencing (RNA-seq) studies. We used these data to identify cell type-specific marker genes, discovering a substantial number of novel markers, many of which we validated using computational and experimental approaches. We further demonstrate that summarized expression of marker gene sets (MGSs) in bulk tissue data can be used to estimate the relative cell type abundance across samples. To facilitate use of this expanding resource, we provide a user-friendly web interface at www.neuroexpresso.org.

Epigenetic Pathways in Human Disease: The Impact of DNA Methylation on Stress-Related Pathogenesis and Current Challenges in Biomarker Development

HPA axis genes implicated in glucocorticoid regulation play an important role in regulating the physiological impact of social and environmental stress, and have become a focal point for investigating the role of glucocorticoid regulation in the etiology of disease. We conducted a systematic review to critically assess the full range of clinical associations that have been reported in relation to DNA methylation of CRH, CRH-R1/2, CRH-BP, AVP, POMC, ACTH, ACTH-R, NR3C1, FKBP5, and HSD11β1/2 genes in adults. A total of 32 studies were identified. There is prospective evidence for an association between HSD11β2 methylation and hypertension, and functional evidence of an association between NR3C1 methylation and both small cell lung cancer (SCLC) and breast cancer. Strong associations have been reported between FKBP5 and NR3C1 methylation and PTSD, and biologically-plausible associations have been reported between FKBP5 methylation and Alzheimer's Disease. Mixed associations between NR3C1 methylation and mental health outcomes have been reported according to different social and environmental exposures, and according to varying gene regions investigated. We conclude by highlighting key challenges and future research directions that will need to be addressed in order to develop both clinically meaningful prognostic biomarkers and an evidence base that can inform public policy practice.