Evolutionary rate heterogeneity between multi- and single-interface hubs across human housekeeping and tissue-specific protein interaction network: Insights from proteins' and its partners' properties

Resolving the current controversy of use and reuse of housekeeping proteins in ageing research: Focus on saving people's tax dollars

The use of housekeeping genes and proteins to normalize mRNA and protein levels in biomedical research has faced growing scrutiny. Researchers encounter challenges in determining the optimal frequency for running housekeeping proteins such as β-actin, Tubulin, and GAPDH for nuclear-encoded proteins, and Porin, HSP60, and TOM20 for mitochondrial proteins alongside experimental proteins. The regulation of these proteins varies with age, gender, disease progression, epitope nature, gel running conditions, and their reported sizes can differ among antibody suppliers. Additionally, anonymous readers have raised concerns about peer-reviewed and published articles, creating confusion and concern within the research and academic institutions. To clarify these matters, this minireview discusses the role of reference housekeeping proteins in Western blot analysis and outlines key considerations for their use as normalization controls. Instead of Western blotting of housekeeping proteins, staining of total proteins, using Amido Black and Coomassie Blue can be visualized the total protein content on a membrane. The reducing repeated Western blotting analysis of housekeeping proteins, will save resources, time and efforts and in turn increase the number of competitive grants from NIH and funding agencies. We also discussed the use of dot blots over traditional Western blots, when protein levels are low in rare tissues/specimens and cell lines. We sincerely hope that the facts, figures, and discussions presented in this article will clarify the current controversy regarding housekeeping protein(s) use, reuse, and functional aspects of housekeeping proteins. The contents presented in our article will be useful to students, scholars and researchers of all levels in cell biology, protein chemistry and mitochondrial research.

Hubs and Bottlenecks in Protein-Protein Interaction Networks

Protein-protein interaction networks (PPINs) represent the physical interactions among proteins in a cell. These interactions are critical in all cellular processes, including signal transduction, metabolic regulation, and gene expression. In PPINs, centrality measures are widely used to identify the most critical nodes. The two most commonly used centrality measures in networks are degree and betweenness centralities. Degree centrality is the number of connections a node has in the network, and betweenness centrality is the measure of the extent to which a node lies on the shortest paths between pairs of other nodes in the network. In PPINs, proteins with high degree and betweenness centrality are referred to as hubs and bottlenecks respectively. Hubs and bottlenecks are topologically and functionally essential proteins that play crucial roles in maintaining the network's structure and function. This article comprehensively reviews essential literature on hubs and bottlenecks, including their properties and functions.

Role of genetic architecture in phenotypic plasticity

Phenotypic plasticity, the ability of an organism to display different phenotypes across environments, is widespread in nature. Plasticity aids survival in novel environments. Herein, we review studies from yeast that allow us to start uncovering the genetic architecture of phenotypic plasticity. Genetic variants and their interactions impact the phenotype in different environments, and distinct environments modulate the impact of genetic variants and their interactions on the phenotype. Because of this, certain hidden genetic variation is expressed in specific genetic and environmental backgrounds. A better understanding of the genetic mechanisms of phenotypic plasticity will help to determine short- and long-term responses to selection and how wide variation in disease manifestation occurs in human populations.

Dependency Between Protein-Protein Interactions and Protein Variability and Evolutionary Rates in Vertebrates: Observed Relationships and Stochastic Modeling

Recent developments in sequencing and growth of bioinformatics resources provide us with vast depositories of protein network and single nucleotide polymorphism data. It allows us to re-examine, on a larger and more comprehensive scale, the relationship between protein–protein interactions and protein variability and evolutionary rates. This relationship has remained far from unambiguously resolved for quite a long time, reflecting shifting analysis approaches in the literature, and growing data availability. In this study, we utilized several public genomic databases to investigate this relationship in human, mouse, pig, chicken, and zebrafish. We observed strong non-linear relationship patterns (tending towards convex decreasing function shapes) between protein variability and the density of corresponding protein–protein interactions across all five species. To investigate further, we carried out stochastic simulations, modeling the interplay between protein connectivity and variability. Our results indicate that a simple negative linear correlation model, often suggested (or tacitly assumed) in the literature, as either a null or an alternative hypothesis, is not a good fit with the observed data. After considering different (but still relatively simple, and not overfitting) simulation models, we found that a convex decreasing protein variability–connectivity function (specifically, exponential decay) led to a much better fit with the real data. We conclude that simple correlation models might be inadequate for describing protein variability–connectivity interplay in vertebrates; they often tend towards false negatives (showing no more than marginal linear or rank correlation where there are in fact strong non-random patterns).

Integrating molecular networks with genetic variant interpretation for precision medicine

More reliable and cheaper sequencing technologies have revealed the vast mutational landscapes characteristic of many phenotypes. The analysis of such genetic variants has led to successful identification of altered proteins underlying many Mendelian disorders. Nevertheless the simple one‐variant one‐phenotype model valid for many monogenic diseases does not capture the complexity of polygenic traits and disorders. Although experimental and computational approaches have improved detection of functionally deleterious variants and important interactions between gene products, the development of comprehensive models relating genotype and phenotypes remains a challenge in the field of genomic medicine. In this context, a new view of the pathologic state as significant perturbation of the network of interactions between biomolecules is crucial for the identification of biochemical pathways associated with complex phenotypes. Seminal studies in systems biology combined the analysis of genetic variation with protein–protein interaction networks to demonstrate that even as biological systems evolve to be robust to genetic variation, their topologies create disease vulnerabilities. More recent analyses model the impact of genetic variants as changes to the “wiring” of the interactome to better capture heterogeneity in genotype–phenotype relationships. These studies lay the foundation for using networks to predict variant effects at scale using machine‐learning or algorithmic approaches. A wealth of databases and resources for the annotation of genotype–phenotype relationships have been developed to support developments in this area. This overview describes how study of the molecular interactome has generated insights linking the organization of biological systems to disease mechanism, and how this information can enable precision medicine.

This article is categorized under:

Translational, Genomic, and Systems Medicine > Translational Medicine

Biological Mechanisms > Cell Signaling

Models of Systems Properties and Processes > Mechanistic Models

Analytical and Computational Methods > Computational Methods