Systems Biology

Pinpointing causal genes for complex traits from genome-wide association studies (GWAS) remains a central challenge in crop genetics, particularly in species with extensive linkage disequilibrium (LD) such as rice. Here, we present CisTrans-ECAS, a computational protocol that overcomes this limitation by integrating population genomics and transcriptomics. The method’s core principle is the decomposition of gene expression into two distinct components: a cis-expression component (cis-EC), regulated by local genetic variants, and a trans-expression component (trans-EC), influenced by distal genetic factors. By testing the association of both components with a phenotype, CisTrans-ECAS establishes a dual-evidence framework that substantially improves the reliability of causal inference. This protocol details the complete workflow, demonstrating its power not only to identify causal genes at loci with weak GWAS signals but also to systematically reconstruct gene regulatory networks. It provides a robust and powerful tool for advancing crop functional genomics and molecular breeding.

In neuropharmacology and drug development, in silico methods have become increasingly vital, particularly for studying receptor–ligand interactions at the molecular level. Membrane proteins such as GABA (A) receptors play a central role in neuronal signaling and are key targets for therapeutic intervention. While experimental techniques like electrophysiology and radioligand binding provide valuable functional data, they often fall short in resolving the structural complexity of membrane proteins and can be time-consuming, costly, and inaccessible in many research settings. This study presents a comprehensive computational workflow for investigating membrane protein–ligand interactions, demonstrated using the GABA (A) receptor α5β2γ2 subtype and mitragynine, an alkaloid from Mitragyna speciosa (Kratom), as a case study. The protocol includes homology modeling of the receptor based on a high-resolution template, followed by structure optimization and validation. Ligand docking is then used to predict binding sites and affinities at known modulatory interfaces. Finally, molecular dynamics (MD) simulations assess the stability and conformational dynamics of receptor–ligand complexes over time. Overall, this workflow offers a robust, reproducible approach for structural analysis of membrane protein–ligand interactions, supporting early-stage drug discovery and mechanistic studies across diverse membrane protein targets.

Protein–protein interactions facilitate cellular functions through the creation of networks and multi-protein complexes. Mapping the interactions within and between protein networks and elucidating the composition of protein complexes provides critical insight into biological processes. Interactions among soluble cytoplasmic proteins have been extensively investigated through the application of immunoaffinity capture as well as conventional nuclear two-hybrid testing. The integrated membrane yeast two-hybrid provides a method to investigate protein–protein interactions between integral membrane proteins in their native membrane environment. This procedure makes use of the ability of the amino-terminal fragment of ubiquitin (Nub) and the carboxyl-terminal fragment of ubiquitin (Cub) to refold reconstituting functional ubiquitin, which can be recognized by a ubiquitin peptidase. Appending a fusion protein composed of Cub fused to LexA and VP16 (CLV) to a candidate "bait" protein and Nub to candidate "prey" proteins allows a test of their interaction. If the two proteins interact closely, the CLV fragment is cleaved and enters the nucleus to activate the expression of reporter genes, signaling the interaction. When the bait and prey proteins are tagged with CLV and NubG, respectively, at their genomic loci, they are only copies of the bait and prey in the cell and are expressed under the regulation of their native promoters. This avoids overexpression artifacts that can occur if the tagged proteins are expressed from plasmids while the untagged chromosomally encoded copies of the bait and prey continue to be expressed.

Non-small cell lung cancer (NSCLC) is the most common type of lung cancer. According to 2020 reports, globally, 2.2 million cases are reported every year, with the mortality number being as high as 1.8 million patients. To study NSCLC, systems biology offers mathematical modeling as a tool to understand complex pathways and provide insights into the identification of biomarkers and potential therapeutic targets, which aids precision therapy. Mathematical modeling, specifically ordinary differential equations (ODEs), is used to better understand the dynamics of cancer growth and immunological interactions in the tumor microenvironment. This study highlighted the dual role of the cyclic GMP-AMP synthase–stimulator of interferon genes (cGAS/STING) pathway's classical involvement in regulating type 1 interferon (IFN I) and pro-inflammatory responses to promote tumor regression through senescence and apoptosis. Alternative signaling was induced by nuclear factor kappa B (NF-κB), mutated tumor protein p53 (p53), and programmed death-ligand1 (PD-L1), which lead to tumor growth. We identified key regulators in cancer progression by simulating the model and validating it with the following model estimation parameters: local sensitivity analysis, principal component analysis, rate of flow of metabolites, and model reduction. Integration of multiple signaling axes revealed that cGAS-STING, phosphoinositide 3-kinases (PI3K), and Ak strain transforming (AKT) may be potential targets that can be validated for cancer therapy.

Genome-wide screens using yeast or phage displays are powerful tools for identifying protein–ligand interactions, including drug or vaccine targets, ligand receptors, or protein–protein interactions. However, assembling libraries for genome-wide screens can be challenging and often requires unbiased cloning of 10⁵–10⁷ DNA fragments for a complete representation of a eukaryote genome. A sub-optimal genomic library can miss key genomic sequences and thus result in biased screens. Here, we describe an efficient method to generate genome-wide libraries for yeast surface display using Gibson assembly. The protocol entails genome fragmentation, ligation of adapters, library cloning using Gibson assembly, library transformation, library DNA recovery, and a streamlined Oxford nanopore library sequencing procedure that covers the length of the cloned DNA fragments. We also describe a computational pipeline to analyze the library coverage of the genome and predict the proportion of expressed proteins. The method allows seamless library transfer among multiple vectors and can be easily adapted to any expression system.