Parameterization of asymmetric sigmoid functions in weighted gene co-expression network analysis.

In most the biological contexts, examining gene expressions at the genomic level gives more accurate results than examining genes individually. It can improve understanding of the molecular mechanisms that cause molecular alterations. Weighted gene co-expression network analysis (WGCNA), which has recently been widely used to cluster transcriptomic datasets, implements a soft thresholding procedure using power function. However, these functions may sometimes exaggerate minor differences in expression correlations. We have previously proposed to use asymmetric sigmoid functions in soft thresholding as an alternative solution. However, the number of variables in asymmetric sigmoid functions may vary and parameterization can be problematic. In this study, we have introduced a systematic procedure for parameterizing asymmetric sigmoid function to ease using it as an alternative soft-thresholding solution in WGCNA. The efficiency of the employment was shown on four different COVID-19 datasets, on a yeast dataset, and on an E.Coli dataset. The results indicate that this approach provides biologically plausible associations for the resulting modules.

Data Ethics in Digital Health and Genomics.

The digital revolution has disruptively reshaped the way health services are provided and how research is conducted. This transformation has produced novel ethical challenges. The digitalization of health records, bioinformatics, molecular medicine, wearable biomedical technologies, biotechnology, and synthetic biology has created new biological data niches. How these data are shared, stored, distributed, and analyzed has created ethical problems regarding privacy, trust, accountability, fairness, and justice. This study investigates issues related to data-sharing permissions, fairness in secondary data distribution, and commercial and political conflicts of interest among individuals, companies, and states. In conclusion, establishing an agency to act as deputy trustee on behalf of individuals is recommended to intermediate the complex nature of informed consent. Focusing on decentralized digital technologies is recommended in order to catalyze the utilization of data and prevent discrimination without circulating data unnecessarily.

Time-dependent re-organization of biological processes by the analysis of the dynamic transcriptional response of yeast cells to doxorubicin.

Doxorubicin is an efficient chemotherapeutic reagent in the treatment of a variety of cancers. However, its underlying molecular mechanism is not fully understood and several severe side effects limit its application. In this study, the dynamic transcriptomic response of Saccharomyces cerevisiae cells to a doxorubicin pulse in a chemostat system was investigated to reveal the underlying molecular mechanism of this drug. The clustering of differentially and significantly expressed genes (DEGs) indicated that the response of yeast cells to doxorubicin is time dependent and may be classified as short-term, mid-term and long-term responses. The cells have started to reorganize their response after the first minute following the injection of the pulse. A modified version of Weighted Gene Co-expression Network Analysis (WGCNA) was used to cluster the positively correlated co-expression profiles, and functional enrichment analysis of these clusters was carried out. DNA replication and DNA repair processes were significantly affected and induced 60 minutes after exposure to doxorubicin. The response to oxidative stress was not identified as a significant term. A transcriptional re-organization of the metabolic pathways seems to be an early event and persists afterwards. The present study reveals for the first time that the RNA surveillance pathway, which is a post-transcriptional regulatory pathway, may be implicated in the short-term reaction of yeast cells to doxorubicin. Integration with regulome revealed the dynamic re-organization of the transcriptomic landscape. Fhl1p, Mbp1p, and Mcm1p were identified as primary regulatory factors responsible for tuning the differentially expressed genes.

Doxorubicin induces an extensive transcriptional and metabolic rewiring in yeast cells.

Doxorubicin is one of the most effective chemotherapy drugs used against solid tumors in the treatment of several cancer types. Two different mechanisms, (i) intercalation of doxorubicin into DNA and inhibition of topoisomerase II leading to changes in chromatin structure, (ii) generation of free radicals and oxidative damage to biomolecules, have been proposed to explain the mode of action of this drug in cancer cells. A genome-wide integrative systems biology approach used in the present study to investigate the long-term effect of doxorubicin in Saccharomyces cerevisiae cells indicated the up-regulation of genes involved in response to oxidative stress as well as in Rad53 checkpoint sensing and signaling pathway. Modular analysis of the active sub-network has also revealed the induction of the genes significantly associated with nucleosome assembly/disassembly and DNA repair in response to doxorubicin. Furthermore, an extensive re-wiring of the metabolism was observed. In addition to glycolysis, and sulfate assimilation, several pathways related to ribosome biogenesis/translation, amino acid biosynthesis, nucleotide biosynthesis, de novo IMP biosynthesis and one-carbon metabolism were significantly repressed. Pentose phosphate pathway, MAPK signaling pathway biological processes associated with meiosis and sporulation were found to be induced in response to long-term exposure to doxorubicin in yeast cells.

A novel topological centrality measure capturing biologically important proteins.

Topological centrality in protein interaction networks and its biological implications have widely been investigated in the past. In the present study, a novel metric of centrality-weighted sum of loads eigenvector centrality (WSL-EC)-based on graph spectra is defined and its performance in identifying topologically and biologically important nodes is comparatively investigated with common metrics of centrality in a human protein-protein interaction network. The metric can capture nodes from peripherals of the network differently from conventional eigenvector centrality. Different metrics were found to selectively identify hub sets that are significantly associated with different biological processes. The widely accepted metrics degree centrality, betweenness centrality, subgraph centrality and eigenvector centrality are subject to a bias towards super-hubs, whereas WSL-EC is not affected by the presence of super-hubs. WSL-EC outperforms other metrics of centrality in detecting biologically central nodes such as pathogen-interacting, cancer, ageing, HIV-1 or disease-related proteins and proteins involved in immune system processes and autoimmune diseases in the human interactome.