Network-based method for drug target discovery at the isoform level

Cell-Type-Specific Gene Regulatory Networks of Pro-Inflammatory and Pro-Resolving Lipid Mediator Biosynthesis in the Immune System

Lipid mediators are important regulators in inflammatory responses, and their biosynthetic pathways are targeted by commonly used anti-inflammatory drugs. Switching from pro-inflammatory lipid mediators (PIMs) to specialized pro-resolving (SPMs) is a critical step toward acute inflammation resolution and preventing chronic inflammation. Although the biosynthetic pathways and enzymes for PIMs and SPMs have now been largely identified, the actual transcriptional profiles underlying the immune cell type-specific transcriptional profiles of these mediators are still unknown. Using the Atlas of Inflammation Resolution, we created a large network of gene regulatory interactions linked to the biosynthesis of SPMs and PIMs. By mapping single-cell sequencing data, we identified cell type-specific gene regulatory networks of the lipid mediator biosynthesis. Using machine learning approaches combined with network features, we identified cell clusters of similar transcriptional regulation and demonstrated how specific immune cell activation affects PIM and SPM profiles. We found substantial differences in regulatory networks in related cells, accounting for network-based preprocessing in functional single-cell analyses. Our results not only provide further insight into the gene regulation of lipid mediators in the immune response but also shed light on the contribution of selected cell types in their biosynthesis.

Vetinformatics from functional genomics to drug discovery: Insights into decoding complex molecular mechanisms of livestock systems in veterinary science

Having played important roles in human growth and development, livestock animals are regarded as integral parts of society. However, industrialization has depleted natural resources and exacerbated climate change worldwide, spurring the emergence of various diseases that reduce livestock productivity. Meanwhile, a growing human population demands sufficient food to meet their needs, necessitating innovations in veterinary sciences that increase productivity both quantitatively and qualitatively. We have been able to address various challenges facing veterinary and farm systems with new scientific and technological advances, which might open new opportunities for research. Recent breakthroughs in multi-omics platforms have produced a wealth of genetic and genomic data for livestock that must be converted into knowledge for breeding, disease prevention and management, productivity, and sustainability. Vetinformatics is regarded as a new bioinformatics research concept or approach that is revolutionizing the field of veterinary science. It employs an interdisciplinary approach to understand the complex molecular mechanisms of animal systems in order to expedite veterinary research, ensuring food and nutritional security. This review article highlights the background, recent advances, challenges, opportunities, and application of vetinformatics for quality veterinary services.

Identification of anticancer drug target genes using an outside competitive dynamics model on cancer signaling networks

Each cancer type has its own molecular signaling network. Analyzing the dynamics of molecular signaling networks can provide useful information for identifying drug target genes. In the present study, we consider an on-network dynamics model—the outside competitive dynamics model—wherein an inside leader and an opponent competitor outside the system have fixed and different states, and each normal agent adjusts its state according to a distributed consensus protocol. If any normal agent links to the external competitor, the state of each normal agent will converge to a stable value, indicating support to the leader against the impact of the competitor. We determined the total support of normal agents to each leader in various networks and observed that the total support correlates with hierarchical closeness, which identifies biomarker genes in a cancer signaling network. Of note, by experimenting on 17 cancer signaling networks from the KEGG database, we observed that 82% of the genes among the top 3 agents with the highest total support are anticancer drug target genes. This result outperforms those of four previous prediction methods of common cancer drug targets. Our study indicates that driver agents with high support from the other agents against the impact of the external opponent agent are most likely to be anticancer drug target genes.

Oncological Ligand-Target Binding Systems and Developmental Approaches for Cancer Theranostics

Targeted treatment of cancer hinges on the identification of specific intracellular molecular receptors on cancer cells to stimulate apoptosis for eventually inhibiting growth; the development of novel ligands to target biomarkers expressed by the cancer cells; and the creation of novel multifunctional carrier systems for targeted delivery of anticancer drugs to specific malignant sites. There are numerous receptors, antigens, and biomarkers that have been discovered as oncological targets (oncotargets) for cancer diagnosis and treatment applications. Oncotargets are critically important to navigate active anticancer drug ingredients to specific disease sites with no/minimal effect on surrounding normal cells. In silico techniques relating to genomics, proteomics, and bioinformatics have catalyzed the discovery of oncotargets for various cancer types. Effective oncotargeting requires high-affinity probes engineered for specific binding of receptors associated with the malignancy. Computational methods such as structural modeling and molecular dynamic (MD) simulations offer opportunities to structurally design novel ligands and optimize binding affinity for specific oncotargets. This article proposes a streamlined approach for the development of ligand-oncotarget bioaffinity systems via integrated structural modeling and MD simulations, making use of proteomics, genomic, and X-ray crystallographic resources, to support targeted diagnosis and treatment of cancers and tumors.

Applications of Machine Learning in Drug Target Discovery

Drug target discovery is a critical step in drug development. It is the basis of modern drug development because it determines the target molecules related to specific diseases in advance. Predicting drug targets by computational methods saves a great deal of financial and material resources compared to in vitro experiments. Therefore, several computational methods for drug target discovery have been designed. Recently, machine learning (ML) methods in biomedicine have developed rapidly. In this paper, we present an overview of drug target discovery methods based on machine learning. Considering that some machine learning methods integrate network analysis to predict drug targets, network-based methods are also introduced in this article. Finally, the challenges and future outlook of drug target discovery are discussed.

Analysis of splice variants of the human protein disulfide isomerase (P4HB) gene

BACKGROUND

Protein Disulfide Isomerases are thiol oxidoreductase chaperones from thioredoxin superfamily with crucial roles in endoplasmic reticulum proteostasis, implicated in many diseases. The family prototype PDIA1 is also involved in vascular redox cell signaling. PDIA1 is coded by the P4HB gene. While forced changes in P4HB gene expression promote physiological effects, little is known about endogenous P4HB gene regulation and, in particular, gene modulation by alternative splicing. This study addressed the P4HB splice variant landscape.

RESULTS

Ten protein coding sequences (Ensembl) of the P4HB gene originating from alternative splicing were characterized. Structural features suggest that except for P4HB-021, other splice variants are unlikely to exert thiol isomerase activity at the endoplasmic reticulum. Extensive analyses using FANTOM5, ENCODE Consortium and GTEx project databases as RNA-seq data sources were performed. These indicated widespread expression but significant variability in the degree of isoform expression among distinct tissues and even among distinct locations of the same cell, e.g., vascular smooth muscle cells from different origins. P4HB-02, P4HB-027 and P4HB-021 were relatively more expressed across each database, the latter particularly in vascular smooth muscle. Expression of such variants was validated by qRT-PCR in some cell types. The most consistently expressed splice variant was P4HB-021 in human mammary artery vascular smooth muscle which, together with canonical P4HB gene, had its expression enhanced by serum starvation.

CONCLUSIONS

Our study details the splice variant landscape of the P4HB gene, indicating their potential role to diversify the functional reach of this crucial gene. P4HB-021 splice variant deserves further investigation in vascular smooth muscle cells.