Leveraging pleiotropic association using sparse group variable selection in genomics data

Background Genome-wide association studies (GWAS) have identified genetic variants associated with multiple complex diseases. We can leverage this phenomenon, known as pleiotropy, to integrate multiple data sources in a joint analysis. Often integrating additional information such as gene pathway knowledge can improve statistical efficiency and biological interpretation. In this article, we propose statistical methods which incorporate both gene pathway and pleiotropy knowledge to increase statistical power and identify important risk variants affecting multiple traits. Methods We propose novel feature selection methods for the group variable selection in multi-task regression problem. We develop penalised likelihood methods exploiting different penalties to induce structured sparsity at a gene (or pathway) and SNP level across all studies. We implement an alternating direction method of multipliers (ADMM) algorithm for our penalised regression methods. The performance of our approaches are compared to a subset based meta analysis approach on simulated data sets. A bootstrap sampling strategy is provided to explore the stability of the penalised methods. Results Our methods are applied to identify potential pleiotropy in an application considering the joint analysis of thyroid and breast cancers. The methods were able to detect eleven potential pleiotropic SNPs and six pathways. A simulation study found that our method was able to detect more true signals than a popular competing method while retaining a similar false discovery rate. Conclusion We developed feature selection methods for jointly analysing multiple logistic regression tasks where prior grouping knowledge is available. Our method performed well on both simulation studies and when applied to a real data analysis of multiple cancers.

Investigating the Mechanisms of Pollen Typhae in the Treatment of Diabetic Retinopathy Based on Network Pharmacology and Molecular Docking

Objective. To explore the main bioactive compounds and investigate the underlying mechanism of Pollen Typhae (PT) against diabetic retinopathy (DR) by network pharmacology and molecular docking analysis. Methods. Bioactive ingredients and the target proteins of PT were obtained from TCMSP, and the related target genes were acquired from the SwissTargetPrediction database. The target genes of DR were obtained from GeneCards, TTD database, DisGeNET database, and DrugBank. The compound-target interaction network was established based on Cytoscape 3.7.2. The protein-protein interaction (PPI) network was constructed via STRING database and Cytoscape 3.7.2. Gene ontology (GO) analysis and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis were visualized through DAVID database and Bioinformatics. Ingredient-gene-pathway network analysis was conducted to further screen the ingredients, target proteins, and pathways closely related to the biological mechanism on PT for DR, and molecular docking analysis was performed by SYBYL-X 2.1.1 software. Finally, the mechanism and underlying targets of PT in the treatment of DR were predicted. Results. A total of 8 compounds and 171 intersection targets were obtained based on the online network database. 7 main compounds were screened from compound-target network, and 53 targets including the top six key targets (PTGS2, AKT1, VEGFA, MAPK3, TNF, and EGFR) were further acquired from PPI analysis. The 53 key targets covered 80 signaling pathways, among which PI3K-Akt signaling pathway, focal adhesion, Rap1 signaling pathway, VEGF signaling pathway, and HIF-1 signaling pathway were closely connected with the biological mechanism involved in the alleviation of DR by PT. Ingredient-gene-pathway network shows that AKTI, EGFR, and VEGFA were core genes, kaempferol and isorhamnetin were pivotal ingredients, and VEGF signaling pathway and Rap1 signaling pathway were closely involved in anti-DR. The docking results indicated that five main compounds (arachidonic acid, isorhamnetin, quercetin, kaempferol, and (2R)-5,7-dihydroxy-2-(4-hydroxyphenyl)chroman-4-one) had good binding activity with EGFR and AKT1 targets. Conclusion. The active ingredients in PT may regulate the levels of inflammatory factors, suppress the oxidative stress, and inhibit the proliferation, migration, and invasion of retinal pericytes by acting on PTGS2, AKT1, VEGFA, MAPK3, TNF, and EGFR targets through VEGF signaling pathway, PI3K-Akt signaling pathway, Rap1 signaling pathway, and HIF-1 signaling pathway to play a therapeutic role in diabetic retinopathy.

Regeneration in Echinoderms: Molecular Advancements

Which genes and gene signaling pathways mediate regenerative processes? In recent years, multiple studies, using a variety of animal models, have aimed to answer this question. Some answers have been obtained from transcriptomic and genomic studies where possible gene and gene pathway candidates thought to be involved in tissue and organ regeneration have been identified. Several of these studies have been done in echinoderms, an animal group that forms part of the deuterostomes along with vertebrates. Echinoderms, with their outstanding regenerative abilities, can provide important insights into the molecular basis of regeneration. Here we review the available data to determine the genes and signaling pathways that have been proposed to be involved in regenerative processes. Our analyses provide a curated list of genes and gene signaling pathways and match them with the different cellular processes of the regenerative response. In this way, the molecular basis of echinoderm regenerative potential is revealed, and is available for comparisons with other animal taxa.

Elucidation of the Mechanisms and Molecular Targets of Lianhuaqingwen for Treatment of COVID-19 Based on Network Pharmacology

Lianhuaqingwen (LH) is the widely used in the treatment of Coronavirus disease 2019 (COVID-19). However, its mechanisms of action and molecular targets for treatment of COVID-19 are not clear. The active compounds of LH were collected and their targets were identified through the network pharmacology. The mechanism of compound multi components and multi targets and its relationship with disease are analyzed. COVID-19 targets were obtained by analyzing with TCMSP. In total, 282 active ingredients and 510 targets of LH were identified. Twenty-one target genes associated with LH and COVID-19. Protein-protein interaction (PPI) data were then obtained and PPI networks of LH putative targets and COVID-19-related targets were visualized and merged to identify the candidate targets for LH against COVID-19. Gene ontology and Kyoto Encyclopedia of Genes and Genomes pathway analysis were carried out. The gene-pathway network was constructed to screen the crucial target genes. The functional annotations of target genes were found to be related to immune regulation, host defense, inflammatory reaction and autoimmune diseases and so on. Twenty pathways including immunology, cancer, and cell processing were significantly enriched. Quercetin and luteolin might be the crucial ingredients. IL6 was the core gene and other several genes including IL1B, STAT1, IFNGR1, and NCF1 were the key genes in the gene-pathway network of LH for treatment of COVID-19. The results indicated that LH’s effects against COVID-19 might relate to regulation of immunological function through the specific biological processes and the related pathways. This study demonstrates the application of network pharmacology in evaluating mechanisms of action and molecular targets of complex herbal formulations.

The Shh/Gli3 gene regulatory network precedes the origin of paired fins and reveals the deep homology between distal fins and digits

One of the central problems of vertebrate evolution is understanding the relationship among the distal portions of fins and limbs. Lacking comparable morphological markers of these regions in fish and tetrapods, these relationships have remained uncertain for the past century and a half. Here we show that Gli3 functions in controlling the proliferative expansion of distal progenitors are shared among dorsal and paired fins as well as tetrapod limbs. Mutant knockout gli3 fins in medaka (Oryzias latipes) form multiple radials and rays, in a pattern reminiscent of the polydactyly observed in Gli3-null mutant mice. In limbs, Gli3 controls both anterior–posterior patterning and cell proliferation, two processes that can be genetically uncoupled. In situ hybridization, quantification of proliferation markers, and analysis of regulatory regions reveal that in paired and dorsal fins, gli3 plays a main role in controlling proliferation but not in patterning. Moreover, gli3 down-regulation in shh mutant fins rescues fin loss in a manner similar to how Gli3 deficiency restores digits in the limbs of Shh mutant mouse embryos. We hypothesize that the Gli3/Shh gene pathway preceded the origin of paired appendages and was originally involved in modulating cell proliferation. Accordingly, the distal regions of dorsal fins, paired fins, and limbs retain a deep regulatory and functional homology that predates the origin of paired appendages.

COMPUTATIONAL APPROACHES FOR DRUG DISCOVERY FROM MEDICINAL PLANTS IN THE ERA OF DATA DRIVEN RESEARCH

The significant scientific work on the development of bio-active compound databases, computational technologies, and the integration of Information Technology with Biotechnology has brought a revolution in the domain of drug discovery. These tools facilitate the medicinal plant-based in silico drug discovery, which has become the frontier of pharmacological science. In this review article, we elucidate the methodology of in silico drug discovery for the medicinal plants and present an outlook on recent tools and technologies. Further, we explore the multi-component, multi-target, and multi-pathway mechanism of the bio-active compounds with the help of Network Pharmacology, which enables us to create a topological network between drug, target, gene, pathway, and disease.

Ratio of Electron Donor to Acceptor Influences Metabolic Specialization and Denitrification Dynamics in Pseudomonas aeruginosa in a Mixed Carbon Medium

Denitrifying microbes sequentially reduce nitrate (NO3–) to nitrite (NO2–), NO, N2O, and N2 through enzymes encoded by nar, nir, nor, and nos. Some denitrifiers maintain the whole four-gene pathway, but others possess partial pathways. Partial denitrifiers may evolve through metabolic specialization whereas complete denitrifiers may adapt toward greater metabolic flexibility in nitrogen oxide (NOx–) utilization. Both exist within natural environments, but we lack an understanding of selective pressures driving the evolution toward each lifestyle. Here we investigate differences in growth rate, growth yield, denitrification dynamics, and the extent of intermediate metabolite accumulation under varying nutrient conditions between the model complete denitrifier Pseudomonas aeruginosa and a community of engineered specialists with deletions in the denitrification genes nar or nir. Our results in a mixed carbon medium indicate a growth rate vs. yield tradeoff between complete and partial denitrifiers, which varies with total nutrient availability and ratios of organic carbon to NOx–. We found that the cultures of both complete and partial denitrifiers accumulated nitrite and that the metabolic lifestyle coupled with nutrient conditions are responsible for the extent of nitrite accumulation.

AI for predicting chemical-effect associations at the universe level - deepFPlearn

A plethora of chemical substances is out there in our environment, and all living species, including us humans, are exposed to various mixtures of these. Our society is accustomed to developing, producing, using and dispersing a diverse and vast amount of chemicals with the original intention to improve our standard of living. However, many chemicals pose risks, for example of developing severe diseases, if they occur at the wrong time in the wrong place. For the majority of the chemicals these risks are not known. Chemical risk assessment and subsequent regulation of use requires efficient and systematic strategies, which are not available so far. Experimental methods, even those of high-throughput, are still lab based and therefore too slow to keep up with the pace of chemical innovation.Existing computational approaches, e.g. ML based, are powerful on specific chemical classes, or sub-problems, but not applicable on a large scale. Their main limitation is the lack of applicability to chemicals outside the training data and the availability of sufficient amounts of training data. Here, we present the ready-to-use and stand-alone program deepFPlearn that predicts the association between chemical structures and effects on the gene/pathway level using deep learning. We show good performance values for our trained models, and demonstrate that our program can predict meaningful associations of chemicals and effects beyond the training range due to the application of a sophisticated feature compression approach using a deep autoencoder. Further, it can be applied to hundreds of thousands of chemicals in seconds. We provide deepFPlearn as open source and flexible tool that can be easily retrained and customized to different application settings at https://github.com/yigbt/deepFPlearn.