Biology subjects

Aloy, P.

Publications and source records attributed to Aloy, P..

3 recordsLinked to original sources

bioRxiv2018

Truncating Variant Burden in High Functioning Autism and Pleiotropic Effects of LRP1 Across Psychiatric Phenotypes

Previous research has implicated de novo (DN) and inherited truncating mutations in autism spectrum disorder (ASD). We aim to investigate whether the load of inherited truncating mutations contribute similarly to high functioning autism (HFA), and to characterise genes harbouring DN variants in HFA.\n\nWe performed whole-exome sequencing (WES) in 20 HFA families (average IQ = 100). No difference was observed in the number of transmitted versus non-transmitted truncating alleles to HFA (117 vs 130, P = 0.32). Transmitted truncating and DN variants in HFA were not enriched in GO or KEGG categories, nor autism-related gene sets. However, in a HFA patient we identified a DN variant in a canonical splice site of LRP1, a post-synaptic density gene that is a target for the FMRP. This DN leads to in-frame skipping of exon-29, removing 2 of 6 blades of the {beta}-propeller domain-4 of LRP1, with putative functional consequences. Results using large datasets implicate LRP1 across psychiatric diseases: i) DN are associated with ASD (P = 0.039) and schizophrenia (P = 0.008) from combined sequencing projects; ii) Common variants using Psychiatric Genomics Consortium GWAS datasets show gene-based association in schizophrenia (P = 6.6E-07) and across six psychiatric diseases (meta-analysis P = 8.1E-05); and iii) burden of ultra-rare pathogenic variants is higher in ASD (P = 1.2E-05), using WES from 6,135 schizophrenia patients, 1,778 ASD patients and 6,245 controls. Previous and current studies suggest an impact of truncating mutations restricted to severe ASD phenotypes associated with intellectual disability. We provide evidence for pleiotropic effects of common and rare variants in the LRP1 gene across psychiatric phenotypes.

genomics↗

bioRxiv2018

Encircling the regions of the pharmacogenomic landscape that determine drug response

The integration of large-scale drug sensitivity screens and genome-wide experiments is changing the field of pharmacogenomics, revealing molecular determinants of drug response without the need for a priori, hypothesis-driven assumptions about drug action. In particular, transcriptomic signatures of drug sensitivity may guide drug repositioning, the discovery of synergistic drug combinations and suggest new therapeutic biomarkers. However, the inherent complexity of transcriptomic signatures, with thousands of genes differentially expressed, makes them hard to interpret, giving poor mechanistic insights and hampering translation to the clinics. Here we show how network biology can help simplify transcriptomic drug signatures, filtering out irrelevant genes, accounting for tissue-specific biases and ultimately yielding functionally-coherent, less noisy drug modules. We successfully analyzed 170 drugs tested in 637 cancer cell lines, proving a broad applicability of our approach and evincing an intimate relationship between modules gene expression levels and drugs mechanisms of action. Further, we have characterized multiple aspects of our transcriptomic modules. As a result, the drugs included in this study are now annotated well beyond the reductionist (target-centered) view.\n\nAuthor SummaryLarge scale pharmacogenomics studies performed with hundreds of cell lines offer a means to link the molecular features of the cells to their response to drug treatments. Unfortunately, simple drug-gene correlations are usually not enough to consistently identify what gene expression patterns will determine drug sensitivity, as the tissue of origin of the cells, together with the expression of e.g. membrane transporters can greatly confound the analysis. To ameliorate these biases, we have devised a network-based strategy that selects genes that are both well correlated to drug response and closeby in the human protein interaction network. Reassuringly, we have confirmed that our identified drug sensitivity modules are tightly connected to the mechanisms of action of the drugs. Moreover, while our modules have no more than 100 genes, they retain the predictive power of the much larger gene signatures that are typically obtained by drug-gene correlations alone. Here, we release the characteristic modules for almost 200 drugs, in a format that is suitable for downstream bioinformatics analyses such as gene-set enrichment analysis.

bioinformatics↗

bioRxiv2017

Expanding the Atlas of Functional Missense Variation for Human Genes

Although we now routinely sequence human genomes, we can confidently identify only a fraction of the sequence variants that have a functional impact. Here we developed a deep mutational scanning framework that produces exhaustive maps for human missense variants by combining random codon-mutagenesis and multiplexed functional variation assays with computational imputation and refinement. We applied this framework to four proteins corresponding to six human genes: UBE2I (encoding SUMO E2 conjugase), SUMO1 (small ubiquitin-like modifier), TPK1 (thiamin pyrophosphokinase), and CALM1/2/3 (three genes encoding the protein calmodulin). The resulting maps recapitulate known protein features, and confidently identify pathogenic variation. Assays potentially amenable to deep mutational scanning are already available for 57% of human disease genes, suggesting that DMS could ultimately map functional variation for all human disease genes.

molecular biology↗