Biology subjects

Aloy, P.

Publications and source records attributed to Aloy, P..

27 records · Page 2Linked to original sources

bioRxiv2020

Analysing the Yeast Complexome - The Complex Portal rising to the challenge

The EMBL-EBI Complex Portal is a knowledgebase of macromolecular complexes providing persistent stable identifiers. Entries are linked to literature evidence and provide details of complex membership, function, structure and complex-specific Gene Ontology annotations. Data is freely available and downloadable in HUPO-PSI community standards and missing entries can be requested for curation. In collaboration with Saccharomyces Genome Database and UniProt, the yeast complexome, a compendium of all known heteromeric assemblies from the model organism Saccharomyces cerevisiae, was curated. This expansion of knowledge and scope has led to a 50% increase in curated complexes compared to the previously published dataset, CYC2008. The yeast complexome is used as a reference resource for the analysis of complexes from large-scale experiments. Our analysis showed that genes coding for proteins in complexes tend to have more genetic interactions, are co-expressed with more genes, are multifunctional, localize more often in the nucleus, and are more often involved in nucleic acid-related metabolic processes and processes where large machineries are the predominant functional drivers. A comparison to genetic interactions showed that about 40% of expanded co-complex pairs also have genetic interactions, suggesting strong functional links between complex members.

systems biology↗

bioRxiv2020

Global BioID-based SARS-CoV-2 proteins proximal interactome unveils novel ties between viral polypeptides and host factors involved in multiple COVID19-associated mechanisms

The worldwide SARS-CoV-2 outbreak poses a serious challenge to human societies and economies. SARS-CoV-2 proteins orchestrate complex pathogenic mechanisms that underlie COVID-19 disease. Thus, understanding how viral polypeptides rewire host protein networks enables better-founded therapeutic research. In complement to existing proteomic studies, in this study we define the first proximal interaction network of SARS-CoV-2 proteins, at the whole proteome level in human cells. Applying a proximity-dependent biotinylation (BioID)-based approach greatly expanded the current knowledge by detecting interactions within poorly soluble compartments, transient, and/or of weak affinity in living cells. Our BioID study was complemented by a stringent filtering and uncovered 2,128 unique cellular targets (1,717 not previously associated with SARS-CoV-1 or 2 proteins) connected to the N- and C-ter BioID-tagged 28 SARS-CoV-2 proteins by a total of 5,415 (5,236 new) proximal interactions. In order to facilitate data exploitation, an innovative interactive 3D web interface was developed to allow customized analysis and exploration of the landscape of interactions (accessible at http://www.sars-cov-2-interactome.org/). Interestingly, 342 membrane proteins including interferon and interleukin pathways factors, were associated with specific viral proteins. We uncovered ORF7a and ORF7b protein proximal partners that could be related to anosmia and ageusia symptoms. Moreover, comparing proximal interactomes in basal and infection-mimicking conditions (poly(I:C) treatment) allowed us to detect novel links with major antiviral response pathway components, such as ORF9b with MAVS and ISG20; N with PKR and TARB2; NSP2 with RIG-I and STAT1; NSP16 with PARP9-DTX3L. Altogether, our study provides an unprecedented comprehensive resource for understanding how SARS-CoV-2 proteins orchestrate host proteome remodeling and innate immune response evasion, which can inform development of targeted therapeutic strategies.

systems biology↗

bioRxiv2020

Bioactivity descriptors for uncharacterized compounds

Chemical descriptors encode the physicochemical and structural properties of small molecules, and they are at the core of chemoinformatics. The broad release of bioactivity data has prompted enriched representations of compounds, reaching beyond chemical structures and capturing their known biological properties. Unfortunately, bioactivity descriptors are not available for most small molecules, which limits their applicability to a few thousand well characterized compounds. Here we present a collection of deep neural networks able to infer bioactivity signatures for any compound of interest, even when little or no experimental information is available for them. Our signaturizers relate to bioactivities of 25 different types (including target profiles, cellular response and clinical outcomes) and can be used as drop-in replacements for chemical descriptors in day-to-day chemoinformatics tasks. Indeed, we illustrate how inferred bioactivity signatures are useful to navigate the chemical space in a biologically relevant manner, unveiling higher-order organization in natural product collections, and to enrich mostly uncharacterized chemical libraries for activity against the drug-orphan target Snail1. Moreover, we implement a battery of signature-activity relationship (SigAR) models and show a substantial improvement in performance, with respect to chemistry-based classifiers, across a series of biophysics and physiology activity prediction benchmarks.

bioinformatics↗

bioRxiv2019

Personalized Cancer Therapy Prioritization Based on Driver Alteration Co-occurrence Patterns

Identification of actionable genomic vulnerabilities is the cornerstone of precision oncology. Based on a large-scale drug screening in patient derived-xenografts, we uncover connections between driver gene alterations, derive Driver Co-Occurrence (DCO) networks, and relate these to drug sensitivity. Our collection of 53 drug response predictors attained an average balanced accuracy of 58% in a cross-validation setting, which rose to a 66% for the subset of high-confidence predictions. Morevover, we experimentally validated 12 out of 14 de novo predictions in mice. Finally, we adapted our strategy to obtain drug-response models from patients progression free survival data. By revealing unexpected links between oncogenic alterations, our strategy can increase the clinical impact of genomic profiling.

cancer biology↗

bioRxiv2019

Extending the small molecule similarity principle to all levels of biology

We present the Chemical Checker (CC), a resource that provides processed, harmonized and integrated bioactivity data on 800,000 small molecules. The CC divides data into five levels of increasing complexity, ranging from the chemical properties of compounds to their clinical outcomes. In between, it considers targets, off-targets, perturbed biological networks and several cell-based assays such as gene expression, growth inhibition and morphological profilings. In the CC, bioactivity data are expressed in a vector format, which naturally extends the notion of chemical similarity between compounds to similarities between bioactivity signatures of different kinds. We show how CC signatures can boost the performance of drug discovery tasks that typically capitalize on chemical descriptors, including target identification and library characterization. Moreover, we demonstrate and experimentally validate that CC signatures can be used to reverse and mimic biological signatures of disease models and genetic perturbations, options that are otherwise impossible using chemical information alone.

bioinformatics↗

bioRxiv2019

Exploring endocytic compartment morphology with systematic genetics and single cell image analysis

Endocytosis is a conserved process that mediates the internalization of nutrients and plasma membrane components, including receptors, for sorting to endosomes and the vacuole (lysosome). We combined systematic yeast genetics, high-content screening, and neural network-based image analysis of single cells to screen for genes that influence the morphology of four main endocytic compartments: coat proteins, actin patches, late endosome, and vacuole. This unbiased approach identified 17 mutant phenotypes and [~]1600 genes whose perturbation affected at least one of the four compartments. Numerous mutants were associated with multiple phenotypes, indicating that morphological pleiotropy is often seen within the endocytic pathway. Morphological profiles based on the 17 aberrant phenotypes were highly correlated for functionally related genes, enabling prediction of gene function. Incomplete penetrance was prevalent, and single-cell analysis enabled exploration of the mechanisms underlying cellular heterogeneity, which include replicative age, organelle inheritance, and stress response.

cell biology↗

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↗