Mapping OMIM Disease-Related Variations on Protein Domains Reveals an Association Among Variation Type, Pfam Models, and Disease Classes

Gene and transcript expression patterns, coupled with isoform switching and long non-coding RNA dynamics in adipose tissue, underlie the longevity of Ames dwarf mice

Our study investigates gene expression in adipose tissue of Ames dwarf (df/df) mice, whose deficiency in growth hormone is linked to health and extended lifespan. Recognizing adipose tissue influence on metabolism, aging, and related diseases, we aim to understand its contribution to the health and longevity of df/df mice. We have identified gene and transcript expression patterns associated with critical biological functions, including metabolism, stress response, and resistance to cancer. Intriguingly, we identified genes that, despite maintaining unchanged expression levels, switch between different isoforms, impacting essential cellular functions such as tumor suppression, oncogenic activity, ATP transport, and lipid biosynthesis and storage. The isoform switching is associated with changes in protein domains, retention of introns, initiation of nonsense-mediated decay, and emergence of intrinsically disordered regions. Moreover, we detected various alternative splicing events that may drive these structural alterations. We also found changes in the expression of long non-coding RNAs (lncRNAs) that may be involved in the aging process and disease resistance by regulating crucial genes in survival and metabolism. Through weighted gene co-expression network analysis, we have linked four lncRNAs with 29 genes, which contribute to protein complexes such as the Mili-Tdrd1-Tdrd12 complex. Beyond safeguarding DNA integrity, this complex also has a wider impact on gene regulation, chromatin structure, and metabolic control. Our detailed investigation provides insight into the molecular foundations of the remarkable health and longevity of df/df mice, emphasizing the significance of adipose tissue in aging and identifying new avenues for health-promoting therapeutic strategies.

Variant Effect Prediction in the Age of Machine Learning

Over the years, many computational methods have been created for the analysis of the impact of single amino acid substitutions resulting from single-nucleotide variants in genome coding regions. Historically, all methods have been supervised and thus limited by the inadequate sizes of experimentally curated data sets and by the lack of a standardized definition of variant effect. The emergence of unsupervised, deep learning (DL)-based methods raised an important question: Can machines learn the language of life from the unannotated protein sequence data well enough to identify significant errors in the protein "sentences"? Our analysis suggests that some unsupervised methods perform as well or better than existing supervised methods. Unsupervised methods are also faster and can, thus, be useful in large-scale variant evaluations. For all other methods, however, their performance varies by both evaluation metrics and by the type of variant effect being predicted. We also note that the evaluation of method performance is still lacking on less-studied, nonhuman proteins where unsupervised methods hold the most promise.

Most protein domains exist as variants with distinct functions across cells, tissues and diseases

Protein domains are the active subunits that provide proteins with specific functions through precise three-dimensional structures. Such domains facilitate most protein functions, including molecular interactions and signal transduction. Currently, these protein domains are described and analyzed as invariable molecular building blocks with fixed functions. Here, I show that most human protein domains exist as multiple distinct variants termed 'domain isotypes'. Domain isotypes are used in a cell, tissue and disease-specific manner and have surprisingly different 3D structures. Accordingly, domain isotypes, compared to each other, modulate or abolish the functionality of protein domains. These results challenge the current view of protein domains as invariable building blocks and have significant implications for both wet- and dry-lab workflows. The extensive use of protein domain isotypes within protein isoforms adds to the literature indicating we need to transition to an isoform-centric research paradigm.

Pathogenic variation types in human genes relate to diseases through Pfam and InterPro mapping

Grouping residue variations in a protein according to their physicochemical properties allows a dimensionality reduction of all the possible substitutions in a variant with respect to the wild type. Here, by using a large dataset of proteins with disease-related and benign variations, as derived by merging Humsavar and ClinVar data, we investigate to which extent our physicochemical grouping procedure can help in determining whether patterns of variation types are related to specific groups of diseases and whether they occur in Pfam and/or InterPro gene domains. Here, we download 75,145 germline disease-related and benign variations of 3,605 genes, group them according to physicochemical categories and map them into Pfam and InterPro gene domains. Statistically validated analysis indicates that each cluster of genes associated to Mondo anatomical system categorizations is characterized by a specific variation pattern. Patterns identify specific Pfam and InterPro domain–Mondo category associations. Our data suggest that the association of variation patterns to Mondo categories is unique and may help in associating gene variants to genetic diseases. This work corroborates in a much larger data set previous observations from our group.

A Glance into MTHFR Deficiency at a Molecular Level

MTHFR deficiency still deserves an investigation to associate the phenotype to protein structure variations. To this aim, considering the MTHFR wild type protein structure, with a catalytic and a regulatory domain and taking advantage of state-of-the-art computational tools, we explore the properties of 72 missense variations known to be disease associated. By computing the thermodynamic ΔΔG change according to a consensus method that we recently introduced, we find that 61% of the disease-related variations destabilize the protein, are present both in the catalytic and regulatory domain and correspond to known biochemical deficiencies. The propensity of solvent accessible residues to be involved in protein-protein interaction sites indicates that most of the interacting residues are located in the regulatory domain, and that only three of them, located at the interface of the functional protein homodimer, are both disease-related and destabilizing. Finally, we compute the protein architecture with Hidden Markov Models, one from Pfam for the catalytic domain and the second computed in house for the regulatory domain. We show that patterns of disease-associated, physicochemical variation types, both in the catalytic and regulatory domains, are unique for the MTHFR deficiency when mapped into the protein architecture.

Protonation-Deprotonation Switch Controls the Amyloid-like Misfolding of Nucleic-Acid-Binding Domains of TDP-43

Nutrient starvation stress acidifies the cytosol and leads to the formation of large protein assemblies and misfolded aggregates. However, how starvation stress is sensed at the molecular level and leads to protein misfolding is poorly understood. TDP-43 is a vital protein, which, under stress-like conditions, associates with stress granule proteins via its functional nucleic-acid-binding domains (TDP-43^tRRM) and misfolds to form aberrant aggregates. Here, we show that the monomeric N form of TDP-43^tRRM forms a misfolded amyloid-like protein assembly, β form, in a pH-dependent manner and identified the critical protein side-chain residue whose protonation triggers its misfolding. We systematically mutated the three buried ionizable residues, D105, H166, and H256, to neutral amino acids to block the pH-dependent protonation-deprotonation titration of their side chain and studied their effect on the N-to-β transition. We observed that D105A and H256Q resembled TDP-43^tRRM in their pH-dependent misfolding behavior. However, H166Q retains the N-like secondary structure under low-pH conditions and does not show pH-dependent misfolding to the β form. These results indicate that H166 is the critical side-chain residue whose protonation triggers the misfolding of TDP-43^tRRM and shed light on how stress-induced misfolding of proteins during neurodegeneration could begin from site-specific triggers.