Biology subjects

Gaujoux, R.

Publications and source records attributed to Gaujoux, R..

3 recordsLinked to original sources

bioRxiv2021

CytoPro: A computational platform for accurate and robust assessment of cell contributions in bulk expression from diverse tissues and conditions

Cells are the quanta unit of biology and their relative composition in a tissue is the prime driver of bulk tissue gene expression variation. When there is no cell information, deconvolution is an effective tool to achieve cell resolution, which provides important information for learning disease complexity and its interactions with treatments, drugs and/or the environment in a wide variety of contexts. Here we present CytoPro, a production-level tissue and condition-specific deconvolution platform, based on a large collection of human tissue-specific signatures derived from single and sorted cells. CytoPro infer per-sample multiple cell-type composition, given input bulk gene expression. CytoPro includes a rigorous QC pipeline for learning, generating and selecting signatures and performs internal automated validation using multiple QC test criteria including: Comparison to ground truth cytometry and pure sorted cells data, performance evaluation using simulated data including robustness to noise as well as agreement with biological expectations in validation datasets regarding genes and cells. We demonstrate that CytoPro outperforms existing deconvolution tools, in both accuracy and robustness. By exploring multiple datasets with predefined disease phenotypes, and analyzing a use-case of biological treatment response, we show the ability of CytoPro to flush out relevant cell biology in real pathological conditions.

bioinformatics↗

bioRxiv2021

A personalized network framework reveals predictive axis of anti-TNF response across diseases

Personalized treatment of complex diseases has been mostly predicated on biomarker identification of one drug-disease combination at a time. Here, we used a novel computational approach termed Disruption Networks to generate a new data type, contextualized by cell-centered individual-level networks, that captures biology otherwise overlooked when performing standard statistics. The new data-type extends beyond the feature level space, to the relations space, by quantifying individual-level breaking or rewiring of cross-feature relations. Applying disruption network to dissect high-dimensional blood data, we discover and validate that the RAC1-PAK1 axis is predictive of anti-TNF response in inflammatory bowel disease. Intermediate monocytes, which correlate with the inflammatory state, play a key role in the RAC1-PAK1 responses, supporting their modulation as a therapeutic target. This axis also predicts response in rheumatoid arthritis, validated in three public cohorts. Our findings support blood-based drug response diagnostics across immune-mediated diseases, implicating common mechanisms of non-response.

systems biology↗

bioRxiv2018

Genome-wide DNA methylation patterns in Autism Spectrum Disorder and mitochondrial function.

Autism Spectrum Disorder (ASD) is a neurodevelopmental disorder characterised by phenotypic heterogeneity and overlapping co-morbidities. The genetic architecture of ASD is complex, with 100s of risk genes cumulatively contributing to the aetiology of ASD. Epigenetic mechanisms, particularly DNA methylation, have been associated with ASD. The vast majority of ASD molecular research has focused on Northern European populations, with a paucity of data from Africa. This study examines genome-wide DNA methylation patterns in a novel cohort of South African children with ASD and matched, unrelated controls. We performed a whole-genome DNA methylation screen using the Illumina 450K Human Methylation Array. We identify differentially methylated loci associated with ASD across 898 genes (p-value [≤] 0.05). Using a pathway analysis framework, we find nine enriched canonical pathways implicating 32 of the significant genes in our ASD cohort. These pathways converge on two crucial biological processes: mitochondrial metabolism and protein ubiquitination, both hallmarks of mitochondrial function. The involvement of mitochondrial function in ASD aetiology is in line with the recently reported transcriptomic dysregulation associated with the disorder. The differentially methylated genes in our cohort overlap with the gene co-expression modules identified in brain tissue from five major neurological disorders, including ASD. We find significant enrichment of three gene modules, two of which are classified as Mitochondrial and were significantly downregulated in ASD brains. Furthermore, we find significant overlap between differentially methylated and differentially expressed genes from our dataset with a RNA seq dataset from ASD brain tissue. This overlap is particularly significant across the Occipital brain region (padj= 0.0002) which has known association to ASD. Our differential methylation data recapitulate the expression differences of genes and co-expression module functions observed in ASD brain tissue which is consistent with a central role for DNA-methylation leading to mitochondrial dysfunction in the aetiology of ASD.

genetics↗