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Gene coexpression network comparison in healthy and tumoral tissues identifies gains and losses of connections between genes. This phenomenon is called rewiring. Such behavior reflects disruptions in molecular mechanisms controlling cell phenotype. Hence, the study of rewiring processes in complex diseases is essential to find malignancy-specific patterns and to classify genes as potential biomarkers for disorders.
 
Herein, we deepen insight into the rewiring of intronless genes (IGs) in breast cancer compared to the behavior of multi-exonic genes (MEGs). We aim to elucidate how likely are IGs to drive the disease. The interest in IGs is due to their conjectured high transcriptional fidelity attributed to the absence of splicing.
 
We built a gene co-expression network utilizing breast cancer adenocarcinoma (BRCA) samples obtained from TCGA, having 1102 tumor samples and 113 adjacent normal tissue samples. This data set was processed using ARACNe-AP, a mutual information-based tool for gene regulatory network deconvolution.
 
During the transition from healthy to tumoral tissue, each gene will change its coexpression links. some of them will be lost, others will be gained, and others will remain in both conditions. This defines a rewiring profile, which allows us to group genes by their behavior in the network: turn-off genes tend to lose connections in tumor tissue, in contrast with turn-on genes whose connections in healthy tissue are considerably less than in tumor tissue. Finally, essential genes have almost the same links in both tissues.
 
We found that IGs are biased towards turn-on behavior, which contrasts with the MEG group, whose members are homogeneously distributed across turn-on and turn-off classes. Thus, breast cancer prompts a gain of new links with IGs. Such behavior presumably shows that these genes are highly involved in the disease progress and have a great potential for biomarkers in breast cancer.
 
Remarkably there is a current need to increase the number of samples available in public datasets to infer statistically significant co-expression networks for other cancer types. In the future, this analysis could be performed to compare the rewiring patterns across complex malignancies.

A better understanding of the pathophysiology underlying COVID-19 across tissues and within cells upon SARS-CoV-2 infection is necessary. While main characters in this process have been identified (receptor, cofactors, interferon, and the cytokine cascade), a consensus over how these elements ensemble into gene regulatory networks (GRNs) and change across different tissues hasn’t been reached. Moreover, discovered networks should be compared across studies to evaluate reproducibility. Here, we perform an integrative and robust GRN analysis, simultaneously exploring SARS-CoV-2 infection compared to other viruses and COVID-19 across tissues. For each condition, we searched for enrichment and differential activation of regulons, therefore identifying the specific sets of critical transcription factors and gene targets. As expected, we find that the most enriched upregulated regulons drive a pro-inflammatory response. Furthermore, we observe an enrichment of novel regulons: ZNF595 and POU2F3, and uniquely activated SMARCA5 and RXRG for SARS-CoV-2 infection. Our tissue analysis showed that 22 regulons are shared in the most COVID-affected tissues: lung, liver and heart, where tissue-specific interactions could explain extrapulmonary manifestations of COVID-19. Our study corroborates previous findings and brings further insight into the regulatory mechanisms instigating COVID-19 that could be relevant for alternative treatment options development for patients.

Aging is one of the more complex processes in biological systems. The discovery of long-lived mutants in C. elegans there has generated a lot of data about molecular and cellular interactions. The long-lived mutant clk-1 has shown to be an interesting example in the study of aging due to multiple characteristics such as slow rate behaviors, high levels of mitochondrial ROS, induction of autophagy, and changes in metabolism, among others. However, the complex relationship between these molecular changes and the phenotype (in neuromuscular behaviors and lifespan extension) is unclear. We replicated and then analyzed these interactions in this work through a novel boolean network. We have seen that aak-2 is a critical gene for the long lifespan induction of clk-1 because of its mediation in multiple processes and its changes in the network attractors if this gene is deleted. We found a cyclic attractor, which is explained very well by the loop of the interactions between ROS, aak-2 and hif-1, in the network. We expect to corroborate more of the attractors’ predictions by combining the double mutant clk-1:aak-2 with transgenic strains expressing GFP for some crucial genes.