Biology subjects

Cuomo, F.

Publications and source records attributed to Cuomo, F..

4 recordsLinked to original sources

bioRxiv2026

Loss of the Y chromosome is transcriptionally silent in clear cell renal cell carcinoma and across cancer entities

Tumors in men often lose the Y chromosome. In some tumor types, this is predictive of poor outcome. Given the male predominance and high LOY frequency in clear cell renal cell carcinoma (ccRCC), we asked whether LOY might impact the transcriptome of ccRCC. We determined somatic Y chromosome status and performed differential expression analysis on tumor RNA-sequencing samples of two human ccRCC cohorts (TCGA-KIRC, CPTAC-ccRCC) and in an autochthonous murine ccRCC model with renal epithelium-specific deletion of Vhl, Trp53 and Rb1. We additionally derived five isogenic Y-deficient and five Y chromosome-retaining clones of the human ccRCC cell line 786-O. Across all three settings, transcriptional changes were largely restricted to the expected gene dosage effects of male-specific Y (MSY) and pseudoautosomal region (PAR) genes, without large effects on autosomal genes. At the pathway level, we identified a modest but reproducible response in the human cohorts, converging on cell cycle, ribosomal biogenesis and DNA repair genes. A pan-cancer meta-analysis across TCGA entities recovered the same pattern, with no autosomal gene showing a large consistent effect. In some entities, but not in ccRCC, cancer/testis antigen expression was higher in LOY tumors. We conclude that, apart from direct gene dosage, modest pathway-level shifts and altered silencing of cancer/testis antigens, LOY is transcriptionally silent in adult humans and mice. Y-chromosomal genes are therefore unlikely to act as transcriptional drivers of ccRCC and other cancer entities.

cancer biology↗

bioRxiv2026

Sexually dimorphic effects of mutations in ccRCC driver genes in renal proximal tubule cells

Clear cell renal cell carcinoma (ccRCC) arises more frequently in men than in women, but it remains unclear why this is the case. Mouse models that mimic the truncal mutation of the two most commonly affected tumor suppressor genes in human ccRCC revealed that the transcriptional effects of Vhl or Vhl/Pbrm1 mutation in male and female proximal tubule cells only partially overlap. Cellular responses to mutations in these genes were also sex-specific. Vhl and Vhl/Pbrm1 mutation in females, but only Vhl/Pbrm1 mutation in males, induced the accumulation of PLIN2-coated lipid droplets. The accumulation of optically-clear lipid droplets, reflecting the hallmark clear-cell histological feature of ccRCC, only arose in Vhl/Pbrm1 mutant male mice. Consistent with lipotoxic tubular damage, markers of tubular repair and ongoing epithelial cell proliferation correlated with the lipid accumulation phenotypes. Vhl/Pbrm1 mutation caused stronger clonal expansion than Vhl mutation in males, but not in females. These findings suggest that there are likely to be sexual dimorphisms at the very earliest stages of ccRCC evolution that result from the interplay of intrinsic and mutation-induced transcriptional differences between male and female proximal tubule cells.

cancer biology↗

bioRxiv2021

Assessment of community efforts to advance computational prediction of protein-protein interactions

Comprehensive insights from the human protein-protein interaction (PPI) network, known as the human interactome, can provide important insights into the molecular mechanisms of complex biological processes and diseases. Despite the remarkable experimental efforts undertaken to date to determine the structure of the human interactome, many PPIs remain unmapped. Computational approaches, especially network-based methods, can facilitate the identification of new PPIs. Many such approaches have been proposed. However, a systematic evaluation of existing network-based methods in predicting PPIs is still lacking. Here, we report community efforts initiated by the International Network Medicine Consortium to benchmark the ability of 24 representative network-based methods to predict PPIs across five different interactomes, including a synthetic interactome generated by the duplication-mutation-complementation model, and the interactomes of four different organisms: A. thaliana, C. elegans, S. cerevisiae, and H. sapiens. We selected the top-seven methods through a computational validation on the human interactome. We next experimentally validated their top-500 predicted PPIs (in total 3,276 predicted PPIs) using the yeast two-hybrid assay, finding 1,177 new human PPIs (involving 633 proteins). Our results indicate that task-tailored similarity-based methods, which leverage the underlying network characteristics of PPIs, show superior performance over other general link prediction methods. Through experimental validation, we confirmed that the top-ranking methods show promising performance externally. For example, from the top 500 PPIs predicted by an advanced similarity-base method [MPS(B&T)], 430 were successfully tested by Y2H with 376 testing positive, yielding a precision of 87.4%. These results establish advanced similarity-based methods as powerful tools for the prediction of human PPIs.

systems biology↗

bioRxiv2020

CRIMSON: An Open-Source Software Framework for Cardiovascular Integrated Modelling and Simulation

In this work, we describe the CRIMSON (CardiovasculaR Integrated Modelling and SimulatiON) software environment. CRIMSON provides a powerful, customizable and user-friendly system for performing three-dimensional and reduced-order computational haemodynamics studies via a pipeline which involves: 1) segmenting vascular structures from medical images; 2) constructing analytic arterial and venous geometric models; 3) performing finite element mesh generation; 4) designing, and 5) applying boundary conditions; 6) running incompressible Navier-Stokes simulations of blood flow with fluid-structure interaction capabilities; and 7) post-processing and visualizing the results, including velocity, pressure and wall shear stress fields. A key aim of CRIMSON is to create a software environment that makes powerful computational haemodynamics tools accessible to a wide audience, including clinicians and students, both within our research laboratories and throughout the community. The overall philosophy is to leverage best-in-class open source standards for medical image processing, parallel flow computation, geometric solid modelling, data assimilation, and mesh generation. It is actively used by researchers in Europe, North and South America, Asia, and Australia. It has been applied to numerous clinical problems; we illustrate applications of CRIMSON to real-world problems using examples ranging from pre-operative surgical planning to medical device design optimization. CRIMSON binaries for Microsoft Windows 10, documentation and example input files are freely available for download from www.crimson.software, and the source code with compilation instructions is available on GitHub https://github.com/carthurs/CRIMSONFlowsolver (CRIMSON Flowsolver) under the GPL v3.0 license, and https://github.com/carthurs/CRIMSONGUI (CRIMSON GUI), under the AGPL v3.0 license. Support is available on the CRIMSON Google Groups forum, located at https://groups.google.com/forum/#!forum/crimson-users.

bioengineering↗