How Far Are We from the Completion of the Human Protein Interactome Reconstruction?

PDE4 Gene Family Variants Are Associated with Response to Apremilast Treatment in Psoriasis

Moderate-to-severe psoriasis (Ps) treatment includes systemic drugs and biological agents. Apremilast, a small molecule primarily metabolized by cytochrome CYP3A4, modulates the immune system by specifically inhibiting phosphodiesterase type 4 (PDE4) isoforms and is currently used for the treatment of Ps and psoriatic arthritis (PsA). Clinical trials and real-world data showed variable efficacy in response among Ps patients underlying the need for personalized therapy. This study implements a candidate-gene and a network-based approach to identify genetic markers associated with apremilast response in forty-nine Greek Ps patients. Our data revealed an association of sixty-four SNPs within or near PDE4 and CYP3A4 genes, four SNPs in ncRNAs ANRIL, LINC00941 and miR4706, which influence the abundance or function of PDE4s, and thirty-three SNPs within fourteen genes whose protein products either interact directly with PDE4 proteins or constitute components of the cAMP signaling pathway which is modulated by PDE4s. Notably, fifty-six of the aforementioned SNPs constitute eQTLs for the respective genes in relevant to psoriasis tissues/cells implying that these variants could be causal. Our analysis provides a number of novel genetic variants that, upon validation in larger cohorts, could be utilized as predictive markers regarding the response of Ps patients to apremilast treatment.

Protein-protein interaction network-based integration of GWAS and functional data for blood pressure regulation analysis

BACKGROUND

It is valuable to analyze the genome-wide association studies (GWAS) data for a complex disease phenotype in the context of the protein-protein interaction (PPI) network, as the related pathophysiology results from the function of interacting polyprotein pathways. The analysis may include the design and curation of a phenotype-specific GWAS meta-database incorporating genotypic and eQTL data linking to PPI and other biological datasets, and the development of systematic workflows for PPI network-based data integration toward protein and pathway prioritization. Here, we pursued this analysis for blood pressure (BP) regulation.

METHODS

The relational scheme of the implemented in Microsoft SQL Server BP-GWAS meta-database enabled the combined storage of: GWAS data and attributes mined from GWAS Catalog and the literature, Ensembl-defined SNP-transcript associations, and GTEx eQTL data. The BP-protein interactome was reconstructed from the PICKLE PPI meta-database, extending the GWAS-deduced network with the shortest paths connecting all GWAS-proteins into one component. The shortest-path intermediates were considered as BP-related. For protein prioritization, we combined a new integrated GWAS-based scoring scheme with two network-based criteria: one considering the protein role in the reconstructed by shortest-path (RbSP) interactome and one novel promoting the common neighbors of GWAS-prioritized proteins. Prioritized proteins were ranked by the number of satisfied criteria.

RESULTS

The meta-database includes 6687 variants linked with 1167 BP-associated protein-coding genes. The GWAS-deduced PPI network includes 1065 proteins, with 672 forming a connected component. The RbSP interactome contains 1443 additional, network-deduced proteins and indicated that essentially all BP-GWAS proteins are at most second neighbors. The prioritized BP-protein set was derived from the union of the most BP-significant by any of the GWAS-based or the network-based criteria. It included 335 proteins, with ~ 2/3 deduced from the BP PPI network extension and 126 prioritized by at least two criteria. ESR1 was the only protein satisfying all three criteria, followed in the top-10 by INSR, PTN11, CDK6, CSK, NOS3, SH2B3, ATP2B1, FES and FINC, satisfying two. Pathway analysis of the RbSP interactome revealed numerous bioprocesses, which are indeed functionally supported as BP-associated, extending our understanding about BP regulation.

CONCLUSIONS

The implemented workflow could be used for other multifactorial diseases.

Special Issue "Deployment of Proteomics Approaches in Biomedical Research"

Many angles of personalized medicine, such as diagnostic improvements, systems biology [...].

Network-Based Prediction of Side Effects of Repurposed Antihypertensive Sartans against COVID-19 via Proteome and Drug-Target Interactomes

The potential of targeting the Renin-Angiotensin-Aldosterone System (RAAS) as a treatment for the coronavirus disease 2019 (COVID-19) is currently under investigation. One way to combat this disease involves the repurposing of angiotensin receptor blockers (ARBs), which are antihypertensive drugs, because they bind to angiotensin-converting enzyme 2 (ACE2), which in turn interacts with the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) spike protein. However, there has been no in silico analysis of the potential toxicity risks associated with the use of these drugs for the treatment of COVID-19. To address this, a network-based bioinformatics methodology was used to investigate the potential side effects of known Food and Drug Administration (FDA)-approved antihypertensive drugs, Sartans. This involved identifying the human proteins targeted by these drugs, their first neighbors, and any drugs that bind to them using publicly available experimentally supported data, and subsequently constructing proteomes and protein-drug interactomes. This methodology was also applied to Pfizer's Paxlovid, an antiviral drug approved by the FDA for emergency use in mild-to-moderate COVID-19 treatment. The study compares the results for both drug categories and examines the potential for off-target effects, undesirable involvement in various biological processes and diseases, possible drug interactions, and the potential reduction in drug efficiency resulting from proteoform identification.

Interactomics in plant defence: progress and opportunities

Interactomics is a branch of systems biology that deals with the study of protein-protein interactions and how these interactions influence phenotypes. Identifying the interactomes involved during host-pathogen interaction events may bring us a step closer to deciphering the molecular mechanisms underlying plant defence. Here, we conducted a systematic review of plant interactomics studies over the last two decades and found that while a substantial progress has been made in the field, plant-pathogen interactomics remains a less-travelled route. As an effort to facilitate the progress in this field, we provide here a comprehensive research pipeline for an in planta plant-pathogen interactomics study that encompasses the in silico prediction step to the validation step, unconfined to model plants. We also highlight four challenges in plant-pathogen interactomics with plausible solution(s) for each.

Reconstruction of a Comprehensive Interactome and Experimental Data Analysis of FRA10AC1 May Provide Insights into Its Biological Role in Health and Disease

FRA10AC1, the causative gene for the manifestation of the FRA10A fragile site, encodes a well-conserved nuclear protein characterized as a non-core spliceosomal component. Pre-mRNA splicing perturbations have been linked with neurodevelopmental diseases. FRA10AC1 variants have been, recently, causally linked with severe neuropathological and growth retardation phenotypes. To further elucidate the participation of FRA10AC1 in spliceosomal multiprotein complexes and its involvement in neurological phenotypes related to splicing, we exploited protein–protein interaction experimental data and explored network information and information deduced from transcriptomics. We confirmed the direct interaction of FRA10AC1with ESS2, a non-core spliceosomal protein, mapped their interacting domains, and documented their tissue co-localization and physical interaction at the level of intracellular protein stoichiometries. Although FRA10AC1 and SF3B2, a major core spliceosomal protein, were shown to interact under in vitro conditions, the endogenous proteins failed to co-immunoprecipitate. A reconstruction of a comprehensive, strictly binary, protein–protein interaction network of FRA10AC1 revealed dense interconnectivity with many disease-associated spliceosomal components and several non-spliceosomal regulatory proteins. The topological neighborhood of FRA10AC1 depicts an interactome associated with multiple severe monogenic and multifactorial neurodevelopmental diseases mainly referring to spliceosomopathies. Our results suggest that FRA10AC1 involvement in pre-mRNA processing might be strengthened by interconnecting splicing with transcription and mRNA export, and they propose the broader role(s) of FRA10AC1 in cell pathophysiology.

Protein network and pathway analysis in a pharmacogenetic study of cyclosporine treatment response in Greek patients with psoriasis

Although cyclosporine comprises a well-established systemic therapy for psoriasis, patients show important heterogeneity in their treatment response. The aim of our study was the pharmacogenetic analysis of 200 Greek patients with psoriasis based on the cyclosporine pathway related protein-protein interaction (PPI) network, reconstructed through the PICKLE meta-database. We genotyped 27 single nucleotide polymorphisms, mapped to 22 key protein nodes of the cyclosporine pathway, via the utilization of the iPLEX®GOLD panel of the MassARRAY® System. Single-SNP analyses showed statistically significant associations between CALM1 rs12885713 (P = 0.0108) and MALT1 rs2874116 (P = 0.0006) polymorphisms with positive response to cyclosporine therapy after correction for multiple comparisons, with the haplotype analyses further enhancing the predictive value of rs12885713 as a pharmacogenetic biomarker for cyclosporine therapy (P = 0.0173). Our findings have the potential to improve our prediction of cyclosporine efficacy and safety in psoriasis patients, as well as provide the framework for the pharmacogenetics of biological therapies in complex diseases.

Data Incompleteness May form a Hard-to-Overcome Barrier to Decoding Life’s Mechanism

Simple Summary

The influence of data incompleteness on the correctness of conclusions about the structure and functions of the objects under study is widely discussed in the literature. It was noted that even a small percentage of missing data can lead to incorrect conclusions and imperfect knowledge. In particular, incompleteness can lead to critical errors in the qualitative and quantitative assessments of interactions in biological systems and a distorted understanding of the functioning mechanisms of living systems. In this brief review, we attempt to demonstrate the extent of this incompleteness in functional information about living systems using the best-studied examples. We suggest that this incompleteness may form seemingly insurmountable barriers in deciphering the mechanisms of the functioning of complex systems with unpredictable properties arising from the interaction of the system components.

Abstract

In this brief review, we attempt to demonstrate that the incompleteness of data, as well as the intrinsic heterogeneity of biological systems, may form very strong and possibly insurmountable barriers for researchers trying to decipher the mechanisms of the functioning of live systems. We illustrate this challenge using the two most studied organisms: E. coli, with 34.6% genes lacking experimental evidence of function, and C. elegans, with identified proteins for approximately 50% of its genes. Another striking example is an artificial unicellular entity named JCVI-syn3.0, with a minimal set of genes. A total of 31.5% of the genes of JCVI-syn3.0 cannot be ascribed a specific biological function. The human interactome mapping project identified only 5–10% of all protein interactions in humans. In addition, most of the available data are static snapshots, and it is barely possible to generate realistic models of the dynamic processes within cells. Moreover, the existing interactomes reflect the de facto interaction but not its functional result, which is an unpredictable emerging property. Perhaps the completeness of molecular data on any living organism is beyond our reach and represents an unsolvable problem in biology.