A kinetic model of the evolution of a protein interaction network

Key interaction networks: Identifying evolutionarily conserved non-covalent interaction networks across protein families

Protein structure (and thus function) is dictated by non-covalent interaction networks. These can be highly evolutionarily conserved across protein families, the members of which can diverge in sequence and evolutionary history. Here we present KIN, a tool to identify and analyze conserved non-covalent interaction networks across evolutionarily related groups of proteins. KIN is available for download under a GNU General Public License, version 2, from https://www.github.com/kamerlinlab/KIN. KIN can operate on experimentally determined structures, predicted structures, or molecular dynamics trajectories, providing insight into both conserved and missing interactions across evolutionarily related proteins. This provides useful insight both into protein evolution, as well as a tool that can be exploited for protein engineering efforts. As a showcase system, we demonstrate applications of this tool to understanding the evolutionary-relevant conserved interaction networks across the class A β-lactamases.

Multiorgan locked-state model of chronic diseases and systems pharmacology opportunities

With increasing human life expectancy, the global medical burden of chronic diseases is growing. Hence, chronic diseases are a pressing health concern and will continue to be in decades to come. Chronic diseases often involve multiple malfunctioning organs in the body. An imminent question is how interorgan crosstalk contributes to the etiology of chronic diseases. We conceived the locked-state model (LoSM), which illustrates how interorgan communication can give rise to body-wide memory-like properties that 'lock' healthy or pathological conditions. Next, we propose cutting-edge systems biology and artificial intelligence strategies to decipher chronic multiorgan locked states. Finally, we discuss the clinical implications of the LoSM and assess the power of systems-based therapies to dismantle pathological multiorgan locked states while improving treatments for chronic diseases.

Integrated Genomic and Transcriptomic Analysis reveals key genes for predicting dual-phenotype Hepatocellular Carcinoma Prognosis

Dual-phenotype hepatocellular carcinoma (DPHCC) expresses both hepatocyte and cholangiocyte markers, and is characterized by high recurrence and low survival rates. The underlying molecular mechanisms of DPHCC pathogenesis are unclear.

We performed whole exome sequencing and RNA sequencing of three subtypes of HCC (10 DPHCC, 10 CK19-positive HCC, and 14 CK19-negative HCC), followed by integrated bioinformatics analysis, including somatic mutation analysis, mutation signal analysis, differential gene expression analysis, and pathway enrichment analysis. Cox proportional hazard regression analyses were applied for exploring survival related characteristics.

We found that mutated genes in DPHCC patients were associated with carcinogenesis and immunity, and the up-regulated genes were mainly enriched in transcription-related and cancer-related pathways, and the down-regulated genes were mainly enriched in immune-related pathways. CXCL9 was selected as the hub gene, which is associated with immune cells and survival prognosis. Our results showed that low CXCL9 expression was significantly associated with poor prognosis, and its expression was significantly reduced in DPHCC samples.

In conclusion, we explored the molecular mechanisms governing DPHCC development and progression and identified CXCL9, which influences the immune microenvironment and prognosis of DPHCC and might be new clinically significant biomarkers for predicting prognosis.

Towards a Dynamic Interaction Network of Life to unify and expand the evolutionary theory

The classic Darwinian theory and the Synthetic evolutionary theory and their linear models, while invaluable to study the origins and evolution of species, are not primarily designed to model the evolution of organisations, typically that of ecosystems, nor that of processes. How could evolutionary theory better explain the evolution of biological complexity and diversity? Inclusive network-based analyses of dynamic systems could retrace interactions between (related or unrelated) components. This theoretical shift from a Tree of Life to a Dynamic Interaction Network of Life, which is supported by diverse molecular, cellular, microbiological, organismal, ecological and evolutionary studies, would further unify evolutionary biology.