The specific NLRP3 N-terminal domain is essential for NLRP3 activation and inflammasome formation in response to K ⁺ efflux.

Complex genetic disorders often involve products of multiple genes acting cooperatively. Hence, the pathophenotype is the outcome of the perturbations in the underlying pathways, where gene products cooperate through various mechanisms such as protein-protein interactions. Pinpointing the decisive elements of such disease pathways is still challenging. Over the last years, computational approaches exploiting interaction network topology have been successfully applied to prioritize individual genes involved in diseases. Although linkage intervals provide a list of disease-gene candidates, recent genome-wide studies demonstrate that genes not associated with any known linkage interval may also contribute to the disease phenotype. Network based prioritization methods help highlighting such associations. Still, there is a need for robust methods that capture the interplay among disease-associated genes mediated by the topology of the network. Here, we propose a genome-wide network-based prioritization framework named GUILD. This framework implements four network-based disease-gene prioritization algorithms. We analyze the performance of these algorithms in dozens of disease phenotypes. The algorithms in GUILD are compared to state-of-the-art network topology based algorithms for prioritization of genes. As a proof of principle, we investigate top-ranking genes in Alzheimer's disease (AD), diabetes and AIDS using disease-gene associations from various sources. We show that GUILD is able to significantly highlight disease-gene associations that are not used a priori. Our findings suggest that GUILD helps to identify genes implicated in the pathology of human disorders independent of the loci associated with the disorders.

Alzheimer's disease (AD) is a neurodegenerative disorder characterized by the pathological aggregation of the amyloid-β peptide (Aβ). Monomeric soluble Aβ can switch from helicoidal to β-sheet conformation, promoting its assembly into oligomers and subsequently to amyloid fibrils. Oligomers are highly toxic to neurons and have been reported to induce synaptic transmission impairments. The progression from oligomers to fibrils forming senile plaques is currently considered a protective mechanism to avoid the presence of the highly toxic oligomers. Protein nitration is a frequent post-translational modification under AD nitrative stress conditions. Aβ can be nitrated at tyrosine 10 (Y10) by peroxynitrite. Based on our analysis of ThT binding, Western blot and electron and atomic force microscopy, we report that Aβ nitration stabilizes soluble, highly toxic oligomers and impairs the formation of fibrils. We propose a mechanism by which fibril elongation is interrupted upon Y10 nitration: Nitration disrupts fibril-forming folds by preventing H14-mediated bridging, as shown with an Aβ analog containing a single residue (H to E) replacement that mimics the behavior of nitrated Aβ related to fibril formation and neuronal toxicity. The pathophysiological role of our findings in AD was highlighted by the study of these nitrated oligomers on mouse hippocampal neurons, where an increased NMDAR-dependent toxicity of nitrated Aβ oligomers was observed. Our results show that Aβ nitrotyrosination is a post-translational modification that increases Aβ synaptotoxicity.SIGNIFICANCE STATEMENTWe report that nitration (i.e., the irreversible addition of a nitro group) of the Alzheimer-related peptide amyloid-β (Aβ) favors the stabilization of highly toxic oligomers and inhibits the formation of Aβ fibrils. The nitrated Aβ oligomers are more toxic to neurons due to increased cytosolic calcium levels throughout their action on NMDA receptors. Sustained elevated calcium levels trigger excitotoxicity, a characteristic event in Alzheimer's disease.

Protein–protein interactions (PPIs) play a crucial role in biology, and high-throughput experiments have greatly increased the coverage of known interactions. Still, identification of complete inter- and intraspecies interactomes is far from being complete. Experimental data can be complemented by the prediction of PPIs within an organism or between two organisms based on the known interactions of the orthologous genes of other organisms (interologs). Here, we present the BIANA (Biologic Interactions and Network Analysis) Interolog Prediction Server (BIPS), which offers a web-based interface to facilitate PPI predictions based on interolog information. BIPS benefits from the capabilities of the framework BIANA to integrate the several PPI-related databases. Additional metadata can be used to improve the reliability of the predicted interactions. Sensitivity and specificity of the server have been calculated using known PPIs from different interactomes using a leave-one-out approach. The specificity is between 72 and 98%, whereas sensitivity varies between 1 and 59%, depending on the sequence identity cut-off used to calculate similarities between sequences. BIPS is freely accessible at http://sbi.imim.es/BIPS.php.

The highly conserved 5’–3’ exonuclease Xrn1 regulates gene expression in eukaryotes by coupling nuclear DNA transcription to cytosolic mRNA decay. By integrating transcriptome-wide analyses of translation with biochemical and functional studies, we demonstrate an unanticipated regulatory role of Xrn1 in protein synthesis. Xrn1 promotes translation of a specific group of transcripts encoding membrane proteins. Xrn1-dependence for translation is linked to poor structural RNA contexts for translation initiation, is mediated by interactions with components of the translation initiation machinery and correlates with an Xrn1-dependence for mRNA localization at the endoplasmic reticulum, the translation compartment of membrane proteins. Importantly, for this group of mRNAs, Xrn1 stimulates transcription, mRNA translation and decay. Our results uncover a crosstalk between the three major stages of gene expression coordinated by Xrn1 to maintain appropriate levels of membrane proteins.

From January 2020, COVID-19 is spreading around the world producing serious respiratory symptoms in infected patients that in some cases can be complicated by the severe acute respiratory syndrome, sepsis and septic shock, multiorgan failure, including acute kidney injury and cardiac injury. Cost and time efficient approaches to reduce the burthen of the disease are needed. To find potential COVID-19 treatments among the whole arsenal of existing drugs, we combined system biology and artificial intelligence-based approaches. The drug combination of pirfenidone and melatonin has been identified as a candidate treatment that may contribute to reduce the virus infection. Starting from different drug targets the effect of the drugs converges on human proteins with a known role in SARS-CoV-2 infection cycle. Simultaneously, GUILDify v2.0 web server has been used as an alternative method to corroborate the effect of pirfenidone and melatonin against the infection of SARS-CoV-2. We have also predicted a potential therapeutic effect of the drug combination over the respiratory associated pathology, thus tackling at the same time two important issues in COVID-19. These evidences, together with the fact that from a medical point of view both drugs are considered safe and can be combined with the current standard of care treatments for COVID-19 makes this combination very attractive for treating patients at stage II, non-severe symptomatic patients with the presence of virus and those patients who are at risk of developing severe pulmonary complications.