Ribonucleotide reductase inhibition improves the symptoms of a Caenorhabditis elegans model of Alzheimer's Disease.

Alzheimer's disease is the main cause of aging-associated dementia, for which there is no effective treatment. In this work, we reanalyze the information of a previous Genome Wide Association Study, using a new pipeline design to identify novel potential drugs. With this approach, ribonucleoside-diphosphate reductase gene (RRM2B) emerged as a candidate target and its inhibitor, 2', 2'-difluoro 2'deoxycytidine (Gemcitabine), as a potential pharmaceutical drug against Alzheimer's disease. We functionally verified the effect of inhibiting the RRM2B homologue, rnr-2, in an Alzheimer's model of Caenorhabditis elegans, which accumulates human Aß1-42 peptide to an irreversible paralysis. RNA interference against rnr-2 and also treatment with 200 ng/ml of Gemcitabine, showed animprovement of the phenotype. Gemcitabine treatment increased the intracellular ATP level 3.03 times, which may point to its mechanism of action. Gemcitabine has been extensively used in humans for cancer treatment but at higher concentration. The 200 ng/ml concentration did not exert a significant effect over cell cycle, or affected cell viability when assayed in microglia N13 cell line. Thus, inhibitory drug of the RRM2B activity could be of potential use to treat Alzheimer's disease and particularly Gemcitabine might be considered as a promising candidate to be repurposed for its treatment.

ASACO: Automatic and Serial Analysis of CO-expression to discover gene modifiers with potential use in drug repurposing.

Massive gene expression analyses are widely used to find differentially expressed genes under specific conditions. The results of these experiments are often available in public databases that are undergoing a growth similar to that of molecular sequence databases in the past. This now allows novel secondary computational tools to emerge that use such information to gain new knowledge. If several genes have a similar expression profile across heterogeneous transcriptomics experiments, they could be functionally related. These associations are usually useful for the annotation of uncharacterized genes. In addition, the search for genes with opposite expression profiles is useful for finding negative regulators and proposing inhibitory compounds in drug repurposing projects. Here we present a new web application, Automatic and Serial Analysis of CO-expression (ASACO), which has the potential to discover positive and negative correlator genes to a given query gene, based on thousands of public transcriptomics experiments. In addition, examples of use are presented, comparing with previous contrasted knowledge. The results obtained propose ASACO as a useful tool to improve knowledge about genes associated with human diseases and noncoding genes. ASACO is available at http://www.bioinfocabd.upo.es/asaco/.

Serial co-expression analysis of host factors from SARS-CoV viruses highly converges with former high-throughput screenings and proposes key regulators.

The current genomics era is bringing an unprecedented growth in the amount of gene expression data, only comparable to the exponential growth of sequences in databases during the last decades. This data allow the design of secondary analyses that take advantage of this information to create new knowledge. One of these feasible analyses is the evaluation of the expression level for a gene through a series of different conditions or cell types. Based on this idea, we have developed Automatic and Serial Analysis of CO-expression, which performs expression profiles for a given gene along hundreds of heterogeneous and normalized transcriptomics experiments and discover other genes that show either a similar or an inverse behavior. It might help to discover co-regulated genes, and common transcriptional regulators in any biological model. The present severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) pandemic is an opportunity to test this novel approach due to the wealth of data that are being generated, which could be used for validating results. Thus, we have identified 35 host factors in the literature putatively involved in the infectious cycle of SARS-CoV viruses and searched for genes tightly co-expressed with them. We have found 1899 co-expressed genes whose assigned functions are strongly related to viral cycles. Moreover, this set of genes heavily overlaps with those identified by former laboratory high-throughput screenings (with P-value near 0). Our results reveal a series of common regulators, involved in immune and inflammatory responses that might be key virus targets to induce the coordinated expression of SARS-CoV-2 host factors.

Serial co-expression analysis of host factors from SARS-CoV viruses highly converges with former high-throughput screenings and proposes key regulators and co-option of cellular pathways.

The current genomics era is bringing an unprecedented growth in the amount of gene expression data, only comparable to the exponential growth of sequences in databases during the last decades. This data now allows the design of secondary analyses that take advantage of this information to create new knowledge through specific computational approaches. One of these feasible analyses is the evaluation of the expression level for a gene through a series of different conditions or cell types. Based on this idea, we have developed ASACO, Automatic and Serial Analysis of CO-expression, which performs expression profiles for a given gene along hundreds of normalized and heterogeneous transcriptomics experiments and discover other genes that show either a similar or an inverse behavior. It might help to discover co-regulated genes, and even common transcriptional regulators in any biological model, including human diseases or microbial infections. The present SARS-CoV-2 pandemic is an opportunity to test this novel approach due to the wealth of data that is being generated, which could be used for validating results. In addition, new cell mechanisms identified could become new therapeutic targets. Thus, we have identified 35 host factors in the literature putatively involved in the infectious cycle of SARS-CoV and/or SARS-CoV-2 and searched for genes tightly co-expressed with them. We have found around 1900 co-expressed genes whose assigned functions are strongly related to viral cycles. Moreover, this set of genes heavily overlap with those identified by former laboratory high-throughput screenings (with p-value near 0). Some of these genes aim to cellular structures such as the stress granules, which could be essential for the virus replication and thereby could constitute potential targets in the current fight against the virus. Additionally, our results reveal a series of common transcription regulators, involved in immune and inflammatory responses, that might be key virus targets to induce the coordinated expression of SARS-CoV-2 host factors. All of this proves that ASACO can discover gene co-regulation networks with potential for proposing new genes, pathways and regulators participating in particular biological systems. Highlights: ASACO identifies regulatory associations of genes using public transcriptomics data.ASACO highlights new cell functions likely involved in the infection of coronavirus.Comparison with high-throughput screenings validates candidates proposed by ASACO.Genes co-expressed with host's genes used by SARS-CoV-2 are related to stress granules.

Developmental defects in a Caenorhabditis elegans model for type III galactosemia.

Type III galactosemia is a metabolic disorder caused by reduced activity of UDP-galactose-4-epimerase, which participates in galactose metabolism and the generation of various UDP-sugar species. We characterized gale-1 in Caenorhabditis elegans and found that a complete loss-of-function mutation is lethal, as has been hypothesized for humans, whereas a nonlethal partial loss-of-function allele causes a variety of developmental abnormalities, likely resulting from the impairment of the glycosylation process. We also observed that gale-1 mutants are hypersensitive to galactose as well as to infections. Interestingly, we found interactions between gale-1 and the unfolded protein response.

pkc-1 regulates daf-2 insulin/IGF signalling-dependent control of dauer formation in Caenorhabditis elegans.

In Caenorhabditis elegans, the insulin/IGF pathway participates in the decision to initiate dauer development. Dauer is a diapause stage that is triggered by environmental stresses, such as a lack of nutrients. Insulin/IGF receptor mutants arrest constitutively in dauer, an effect that can be suppressed by mutations in other elements of the insulin/IGF pathway or by a reduction in the activity of the nuclear hormone receptor daf-12. We have isolated a pkc-1 mutant that acts as a novel suppressor of the dauer phenotypes caused by insulin/IGF receptor mutations. Interactions between insulin/IGF mutants and the pkc-1 suppressor mutant are similar to those described for daf-12 or the DAF-12 coregulator din-1. Moreover, we show that the expression of the DAF-12 target daf-9, which is normally elevated upon a reduction in insulin/IGF receptor activity, is suppressed in a pkc-1 mutant background, suggesting that pkc-1 could link the daf-12 and insulin/IGF pathways. pkc-1 has been implicated in the regulation of peptide neurosecretion in C. elegans. Although we demonstrate that pkc-1 expression in the nervous system regulates dauer formation, our results suggest that the requirement for pkc-1 in neurosecretion is independent of its role in modulating insulin/IGF signalling. pkc-1 belongs to the novel protein kinase C (nPKC) family, members of which have been implicated in insulin resistance and diabetes in mammals, suggesting a conserved role for pkc-1 in the regulation of the insulin/IGF pathway.