Chokkalingam, M. | Search bioRxiv

bioRxiv2024

A VHL-1/HIF-1/SQRD1/COL-88 axis links extracellular matrix formation with longevity in Caenorhabditis elegans

The extracellular matrix (ECM) is a pivotal three-dimensional network crucial for tissue organization, cellular communication, and fundamental cellular processes, where collagens are the major chemical entity in amount. ECM deregulation is directly involved with several pathologies, such as tumour growth and invasiveness, atherosclerosis, and diabetic nephropathy. Mutations in the von Hippel-Lindau tumour suppressor (pVHL) cause VHL syndrome, a multi-tumour syndrome commonly associated with clear cell renal carcinoma (ccRCC). Loss of pVHL is associated with the activation of hypoxia-inducible factor (HIF) signaling. Mutation of VHL-1 in the nematode Canorhabditis elegans has been shown to increase lifespan and stress resistance. Interestingly, considering recent findings on the involvement of collagens in the regulation of lifespan, we also observed these animals to show defects in body morphology in a HIF-1 dependent manner. Based on this finding, we established a link between HIF-1 activation upon loss of VHL-1 and ECM defects associated with alterations in collagen expression. An RNAi screen examining genes upregulated in vhl-1 mutant worms revealed the sulfide quinone oxidoreductase sqrd-1 to mediate the change in body morphology. SQRD-1 is essential to the HIF-1 dependent increase in several collagen genes. One of these genes, col-88, partly mediates both the impact of loss of VHL-1 on lifespan extension and body length. The downregulation of the uncharacterised col-88 partially restores lifespan extension and reduces body size of vhl-1/sqrd-1 to vhl-1(ok161) single mutant. This study contributes to the increasing body of evidence linking lifespan extension and the ECM and now implicates this axis in hypoxia-signaling. These findings are of special interest considering the role of ECM integrity in tumour growth and metastasis. Author SummaryThe extracellular matrix and its composing collagens are associated with a wide number of diseases, including cancer. The von Hippel-Lindau tumour suppressor (pVHL) is known to work by regulating the Hypoxia Inducible Factor (HIF) to help the organism to adapt to lack of oxygen. Mutations in pVHL are associated with clear cell renal carcinoma (ccRCC). Interestingly, a small number of studies have shown that pVHL can be directly associated with collagens, a function that is independent of its classical role regulating HIF. However, there is no further knowledge about which role the hypoxia pathway has when it comes to extracellular matrix formation and function, what would be useful since the invasiveness of cancers, such as ccRCC, are directly connected to their matrix/collagen composition. Here we observed that the model organism C. elegans has drastically different collagen composition and body size upon a mutation on its vhl-1 gene. Furthermore, a protein previously only known to be involved in sulfide metabolism, SQRD-1, connects body size and lifespan in this animal model, revealing a surprising link between the hypoxia pathway and sulfur metabolism to control lifespan. Further studies could target sulfur metabolism in ccRCC to modulate collagen production and tumour invasiveness.

genetics↗

bioRxiv2021

Optimizing Network Propagation for Multi-Omics Data Integration

Network propagation refers to a class of algorithms that integrate information from input data across connected nodes in a given network. These algorithms have wide applications in systems biology, protein function prediction, inferring condition-specifically altered sub-networks, and prioritizing disease genes. Despite the popularity of network propagation, there is a lack of comparative analyses of different algorithms on real data and little guidance on how to select and parameterize the various algorithms. Here, we address this problem by analyzing different combinations of network normalization and propagation methods and by demonstrating schemes for the identification of optimal parameter settings on real proteome and transcriptome data. Our work highlights the risk of a topology bias caused by the incorrect use of network normalization approaches. Capitalizing on the fact that network propagation is a regularization approach, we show that minimizing the bias-variance tradeoff can be utilized for selecting optimal parameters. The application to real multi-omics data demonstrated that optimal parameters could also be obtained by either maximizing the agreement between different omics layers (e.g. proteome and transcriptome) or by maximizing the consistency between biological replicates. Furthermore, we exemplified the utility and robustness of network propagation on multi-omics datasets for identifying ageing-associated genes in brain and liver tissues of rats and for elucidating molecular mechanisms underlying prostate cancer progression. Overall, this work compares different network propagation approaches and it presents strategies for how to use network propagation algorithms to optimally address a specific research question at hand.

bioinformatics↗