Biology subjects

Goncalves, E.

Publications and source records attributed to Goncalves, E..

6 recordsLinked to original sources

bioRxiv2018

Tandem duplications lead to loss of fitness effects in CRISPR-Cas9 data

CRISPR-Cas9 gene-editing is widely used to study gene function and is being advanced for therapeutic applications. Structural rearrangements are a ubiquitous feature of cancers and their impact on CRISPR-Cas9 gene-editing has not yet been systematically assessed. Utilising CRISPR-Cas9 knockout screens for 163 cancer cell lines, we demonstrate that targeting tandem amplified regions is highly detrimental to cellular fitness, in contrast to amplifications caused by chromosomal duplications which have little to no effect. Genomically clustered Cas9 double-strand DNA breaks are associated with a strong gene-independent decrease in cell fitness. We systematically identified collateral vulnerabilities in 25% of cancer cells, introduced by tandem amplifications of tissue non-expressed genes. Our analysis demonstrates the importance of structural rearrangements in mediating the effect of CRISPR-Cas9-induced DNA damage, with implications for the use of CRISPR-Cas9 gene-editing technology, and how resulting collateral vulnerabilities are a generalisable strategy to target cancer cells.

genomics↗

bioRxiv2017

Unsupervised correction of gene-independent cell responses to CRISPR-Cas9 targeting

Background: Genome editing by CRISPR-Cas9 technology allows large-scale screening of gene essentiality in cancer. A confounding factor when interpreting CRISPR-Cas9 screens is the high false-positive rate in detecting essential genes within copy number amplified regions of the genome. We have developed the computational tool CRISPRcleanR which is capable of identifying and correcting gene-independent responses to CRISPR-Cas9 targeting. CRISPRcleanR uses an unsupervised approach based on the segmentation of single-guide RNA fold change values across the genome, without making any assumption about the copy number status of the targeted genes.\n\nResults Applying our method to existing and newly generated genome-wide essentiality profiles from 15 cancer cell lines, we demonstrate that CRISPRcleanR reduces false positives when calling essential genes, correcting biases within and outside of amplified regions, while maintaining true positive rates. Established cancer dependencies and essentiality signals of amplified cancer driver genes are detectable post-correction. CRISPRcleanR reports sgRNA fold changes and normalised read counts, is therefore compatible with downstream analysis tools, and works with multiple sgRNA libraries.\n\nConclusions CRISPRcleanR is a versatile open-source tool for the analysis of CRISPR-Cas9 knockout screens to identify essential genes.

bioinformatics↗

bioRxiv2017

Post-translational regulation of metabolism in fumarate hydratase deficient cancer cells

Deregulated signal transduction pathways and energy metabolism are hallmarks of cancer and both play a fundamental role in the process of tumorigenesis. While it is increasingly recognised that signalling and metabolism are highly interconnected, the underpinning mechanisms of their co-regulation are still largely unknown. Here we designed and acquired proteomics, phosphoproteomics, and metabolomics experiments in fumarate hydratase (FH) deficient cells and developed a computational modelling approach to identify putative regulatory phosphorylation-sites of metabolic enzymes. We identified previously reported functionally relevant phosphosites and potentially novel regulatory residues in enzymes of the central carbon metabolism. In particular, we show that pyruvate dehydrogenase (PDHA1) enzymatic activity is inhibited by increased phosphorylation in FH-deficient cells. Our work provides a novel approach to investigate how post-translational modifications of enzymes regulate metabolism and could have important implications for understanding the metabolic transformation of FH-deficient cancers.

systems biology↗

bioRxiv2016

Chromosomal rearrangements are commonly post-transcriptionally attenuated in cancer

Chromosomal rearrangements, despite being detrimental, are ubiquitous in cancer and often act as driver events. The effect of copy number variations (CNVs) on the cellular proteome of tumours is poorly understood. Therefore, we have analysed recently generated proteogenomic data-sets on 282 tumour samples to investigate the impact of CNVs in the proteome of these cells. We found that CNVs are post-transcriptionally attenuated in 23-33% of proteins with an enrichment for protein complexes. Complex subunits are highly co-regulated and some act as rate-limiting steps of complex assembly, indirectly controlling the abundance of other complex members. We identified 48 such regulatory interactions and experimentally validated AP3B1 and GTF2E2 as controlling subunits. Lastly, we found that a gene-signature of protein attenuation is associated with increased resistance to chaperone and proteasome inhibitors. This study highlights the importance of post-transcriptional mechanisms in cancer which allow cells to cope with their altered genomes.

genomics↗

bioRxiv2016

Genomic determinants of protein abundance variation in colorectal cancer cells

Assessing the extent to which genomic alterations compromise the integrity of the proteome is fundamental in identifying the mechanisms that shape cancer heterogeneity. We have used isobaric labelling and tribrid mass spectrometry to characterize the proteomic landscapes of 50 colorectal cancer cell lines and to decipher the relationships between genomic and proteomic variation. The robust quantification of 12,000 proteins and 27,000 phosphopeptides revealed how protein symbiosis translates to a co-variome which is subjected to a hierarchical order and exposes the collateral effects of somatic mutations on protein complexes. Targeted depletion of key chromatin modifiers confirmed the transmission of variation and the directionality as characteristics of protein interactions. Protein level variation was leveraged to build drug response predictive models towards a better understanding of pharmacoproteomic interactions in colorectal cancer. Overall, we provide a deep integrative view of the molecular structure underlying the variation of colorectal cancer cells.\n\nHighlightsO_LIThe cancer cell functional \"co-variome\" is a strong attribute of the proteome.\nC_LIO_LIMutations can have a direct impact on protein levels of chromatin modifiers.\nC_LIO_LITransmission of genomic variation is a characteristic of protein interactions.\nC_LIO_LIPharmacoproteomic models are strong predictors of response to DNA damaging agents.\nC_LI\n\nAbbreviations

systems biology↗

bioRxiv2016

Benchmarking substrate-based kinase activity inference using phosphoproteomic data

MotivationPhosphoproteomic experiments are increasingly used to study the changes in signalling occurring across different conditions. It has been proposed that changes in phosphorylation of kinase target sites can be used to infer when a kinase activity is under regulation. However, these approaches have not yet been benchmarked due to a lack of appropriate benchmarking strategies.\n\nResultsWe curated public phosphoproteomic experiments to identify a gold standard dataset containing a total of 184 kinase-condition pairs where regulation is expected to occur. A list of kinase substrates was compiled and used to estimate changes in kinase activities using the following methods: Z-test, Kolmogorov Smirnov test, Wilcoxon rank sum test, gene set enrichment analysis (GSEA), and a multiple linear regression model (MLR). We also tested weighted variants of the Z-test, and GSEA that include information on kinase sequence specificity as proxy for affinity. Finally, we tested how the number of known substrates and the type of evidence (in vivo, in vitro or in silico) supporting these influence the predictions.\n\nConclusionsMost models performed well with the Z-test and the GSEA performing best as determined by the area under the ROc curve (Mean AUC=0.722). Weighting kinase targets by the kinase target sequence preference improves the results only marginally. However, the number of known substrates and the evidence supporting the interactions has a strong effect on the predictions.

bioinformatics↗