Z-DNA binding protein 1 orchestrates innate immunity and inflammatory cell death

Nucleic Acid Sensor-Mediated PANoptosis in Viral Infection

Innate immunity, the first line of host defense against viral infections, recognizes viral components through different pattern-recognition receptors. Nucleic acids derived from viruses are mainly recognized by Toll-like receptors, nucleotide-binding domain leucine-rich repeat-containing receptors, absent in melanoma 2-like receptors, and cytosolic DNA sensors (e.g., Z-DNA-binding protein 1 and cyclic GMP-AMP synthase). Different types of nucleic acid sensors can recognize specific viruses due to their unique structures. PANoptosis is a unique form of inflammatory cell death pathway that is triggered by innate immune sensors and driven by caspases and receptor-interacting serine/threonine kinases through PANoptosome complexes. Nucleic acid sensors (e.g., Z-DNA-binding protein 1 and absent in melanoma 2) not only detect viruses, but also mediate PANoptosis through providing scaffold for the assembly of PANoptosomes. This review summarizes the structures of different nucleic acid sensors, discusses their roles in viral infections by driving PANoptosis, and highlights the crosstalk between different nucleic acid sensors. It also underscores the promising prospect of manipulating nucleic acid sensors as a therapeutic approach for viral infections.

Comprehensive machine learning models for predicting therapeutic targets in type 2 diabetes utilizing molecular and biochemical features in rats

Introduction

With the increasing prevalence of type 2 diabetes mellitus (T2DM), there is an urgent need to discover effective therapeutic targets for this complex condition. Coding and non-coding RNAs, with traditional biochemical parameters, have shown promise as viable targets for therapy. Machine learning (ML) techniques have emerged as powerful tools for predicting drug responses.

Method

In this study, we developed an ML-based model to identify the most influential features for drug response in the treatment of type 2 diabetes using three medicinal plant-based drugs (Rosavin, Caffeic acid, and Isorhamnetin), and a probiotics drug (Z-biotic), at different doses. A hundred rats were randomly assigned to ten groups, including a normal group, a streptozotocin-induced diabetic group, and eight treated groups. Serum samples were collected for biochemical analysis, while liver tissues (L) and adipose tissues (A) underwent histopathological examination and molecular biomarker extraction using quantitative PCR. Utilizing five machine learning algorithms, we integrated 32 molecular features and 12 biochemical features to select the most predictive targets for each model and the combined model.

Results and discussion

Our results indicated that high doses of the selected drugs effectively mitigated liver inflammation, reduced insulin resistance, and improved lipid profiles and renal function biomarkers. The machine learning model identified 13 molecular features, 10 biochemical features, and 20 combined features with an accuracy of 80% and AUC (0.894, 0.93, and 0.896), respectively. This study presents an ML model that accurately identifies effective therapeutic targets implicated in the molecular pathways associated with T2DM pathogenesis.

Special Issue "Bioinformatics of Unusual DNA and RNA Structures"

Nucleic acids are not only static carriers of genetic information but also play vital roles in controlling cellular lifecycles through their fascinating structural diversity [...].