Viruses traverse the human proteome through peptide interfaces that can be biomimetically leveraged for drug discovery.

We present a drug design strategy based on structural knowledge of protein-protein interfaces selected through virus-host coevolution and translated into highly potential small molecules. This approach is grounded on Vinland, the most comprehensive atlas of virus-human protein-protein interactions with annotation of interacting domains. From this inspiration, we identified small viral protein domains responsible for interaction with human proteins. These peptides form a library of new chemical entities used to screen for replication modulators of several pathogens. As a proof of concept, a peptide from a KSHV protein, identified as an inhibitor of influenza virus replication, was translated into a small molecule series with low nanomolar antiviral activity. By targeting the NEET proteins, these molecules turn out to be of therapeutic interest in a nonalcoholic steatohepatitis mouse model with kidney lesions. This study provides a biomimetic framework to design original chemistries targeting cellular proteins, with indications going far beyond infectious diseases.

MACSPI enables tissue-selective proteomic and interactomic analyses in multicellular organisms.

Multicellular organisms are composed of many tissue types that have distinct morphologies and functions, which are largely driven by specialized proteomes and interactomes. To define the proteome and interactome of a specific type of tissue in an intact animal, we developed a localized proteomics approach called Methionine Analog-based Cell-Specific Proteomics and Interactomics (MACSPI). This method uses the tissue-specific expression of an engineered methionyl-tRNA synthetase to label proteins with a bifunctional amino acid 2-amino-5-diazirinylnonynoic acid in selected cells. We applied MACSPI in Caenorhabditis elegans, a model multicellular organism, to selectively label, capture, and profile the proteomes of the body wall muscle and the nervous system, which led to the identification of tissue-specific proteins. Using the photo-cross-linker, we successfully profiled HSP90 interactors in muscles and neurons and identified tissue-specific interactors and stress-related interactors. Our study demonstrates that MACSPI can be used to profile tissue-specific proteomes and interactomes in intact multicellular organisms.

CPK28 is a modulator of purinergic signaling in plant growth and defense.

Extracellular ATP (eATP) is a key signaling molecule that plays a pivotal role in plant growth and defense responses. The receptor P2K1 is responsible for perceiving eATP and initiating its signaling cascade. However, the signal transduction mechanisms downstream of P2K1 activation remain incompletely understood. We conducted a comprehensive analysis of the P2K1 interactome using co-immunoprecipitation-coupled tandem mass spectrometry, leading to the identification of 121 candidate proteins interacting with P2K1. In silico analysis narrowed down the candidates to 47 proteins, including Ca2+-binding proteins, ion transport-related proteins, and receptor kinases. To investigate their involvement in eATP signaling, we employed a screening strategy based on changes in gene expression in response to eATP in mutants of the identified interactors. This screening revealed several Ca2+-dependent protein kinases (CPKs) that significantly affected the expression of eATP-responsive genes, suggesting their potential roles in eATP signaling. Notably, CPK28 and CPK6 showed physical interactions with P2K1 both in yeast and plant systems. Calcium influx and gene expression studies demonstrated that CPK28 perturbed eATP-induced Ca2+ mobilization and some early transcriptional responses. Overexpression of CPK28 resulted in an antagonistic physiological response to P2K1-mediated eATP signaling during both plant growth and defense responses to the necrotrophic pathogen Botrytis cinerea. Our findings highlight CPK28, among other CPKs, as a modulator of P2K1-mediated eATP signaling, providing valuable insights into the coordination of eATP signaling in plant growth and immunity.

Mapping protein-protein interactions by mass spectrometry.

Protein-protein interactions (PPIs) are essential for numerous biological activities, including signal transduction, transcription control, and metabolism. They play a pivotal role in the organization and function of the proteome, and their perturbation is associated with various diseases, such as cancer, neurodegeneration, and infectious diseases. Recent advances in mass spectrometry (MS)-based protein interactomics have significantly expanded our understanding of the PPIs in cells, with techniques that continue to improve in terms of sensitivity, and specificity providing new opportunities for the study of PPIs in diverse biological systems. These techniques differ depending on the type of interaction being studied, with each approach having its set of advantages, disadvantages, and applicability. This review highlights recent advances in enrichment methodologies for interactomes before MS analysis and compares their unique features and specifications. It emphasizes prospects for further improvement and their potential applications in advancing our knowledge of PPIs in various biological contexts.

Molecules interact. But how strong and how much?

Interactomics aims to characterize all interactions formed between molecules that comprise our body. Although it emerged from quantitative biophysics, it has devolved into a predominantly qualitative field of science over the past decades. Due to technical limitations at its onset, almost all tools in interactomics are qualitative, which persists in defining the discipline. Here, we argue that interactomics needs to return to a quantitative direction because the technical achievements of the last decade have overcome the original limitations that forced its current path. In contrast to qualitative interactomics which is constrained to charting lists of observed interactions, quantitative interactomics can also uncover answers to key questions such as the strength of interactions or how many of certain complexes can form in cells, thus providing researchers with more immediate proxies for understanding and predicting biological processes.

Proteasome complexes experience profound structural and functional rearrangements throughout mammalian spermatogenesis.

During spermatogenesis, spermatogonia undergo a series of mitotic and meiotic divisions on their path to spermatozoa. To achieve this, a succession of processes requiring high proteolytic activity are in part orchestrated by the proteasome. The spermatoproteasome (s20S) is specific to the developing gametes, in which the gamete-specific α4s subunit replaces the α4 isoform found in the constitutive proteasome (c20S). Although the s20S is conserved across species and was shown to be crucial for germ cell development, its mechanism, function, and structure remain incompletely characterized. Here, we used advanced mass spectrometry (MS) methods to map the composition of proteasome complexes and their interactomes throughout spermatogenesis. We observed that the s20S becomes highly activated as germ cells enter meiosis, mainly through a particularly extensive 19S activation and, to a lesser extent, PA200 binding. Additionally, the proteasome population shifts from c20S (98%) to s20S (>82 to 92%) during differentiation, presumably due to the shift from α4 to α4s expression. We demonstrated that s20S, but not c20S, interacts with components of the meiotic synaptonemal complex, where it may localize via association with the PI31 adaptor protein. In vitro, s20S preferentially binds to 19S and displays higher trypsin- and chymotrypsin-like activities, both with and without PA200 activation. Moreover, using MS methods to monitor protein dynamics, we identified significant differences in domain flexibility between α4 and α4s. We propose that these differences induced by α4s incorporation result in significant changes in the way the s20S interacts with its partners and dictate its role in germ cell differentiation.

Elexacaftor/VX-445-mediated CFTR interactome remodeling reveals differential correction driven by mutation-specific translational dynamics.

Cystic fibrosis (CF) is one of the most prevalent lethal genetic diseases with over 2000 identified mutations in the cystic fibrosis transmembrane conductance regulator (CFTR) gene. Pharmacological chaperones such as lumacaftor (VX-809), tezacaftor (VX-661), and elexacaftor (VX-445) treat mutation-induced defects by stabilizing CFTR and are called correctors. These correctors improve proper folding and thus facilitate processing and trafficking to increase the amount of functional CFTR on the cell surface. Yet, CFTR variants display differential responses to each corrector. Here, we report that variants P67L and L206W respond similarly to VX-809 but divergently to VX-445 with P67L exhibiting little rescue when treated with VX-445. We investigate the underlying cellular mechanisms of how CFTR biogenesis is altered by correctors in these variants. Affinity purification-mass spectrometry multiplexed with isobaric tandem mass tags was used to quantify CFTR protein-protein interaction changes between variants P67L and L206W. VX-445 facilitates unique proteostasis factor interactions especially in translation, folding, and degradation pathways in a CFTR variant-dependent manner. A number of these interacting proteins knocked down by siRNA, such as ribosomal subunit proteins, moderately rescued fully glycosylated P67L. Importantly, these knockdowns sensitize P67L to VX-445 and further enhance the trafficking correction of this variant. Partial inhibition of protein translation also mildly sensitizes P67L CFTR to VX-445 correction, supporting a role for translational dynamics in the rescue mechanism of VX-445. Our results provide a better understanding of VX-445 biological mechanism of action and reveal cellular targets that may sensitize nonresponsive CFTR variants to known and available correctors.

Engineering the plant metabolic system by exploiting metabolic regulation.

Plants are the most sophisticated biofactories and sources of food and biofuels present in nature. By engineering plant metabolism, the production of desired compounds can be increased and the nutritional or commercial value of the plant species can be improved. However, this can be challenging because of the complexity of the regulation of multiple genes and the involvement of different protein interactions. To improve metabolic engineering (ME) capabilities, different tools and strategies for rerouting the metabolic pathways have been developed, including genome editing and transcriptional regulation approaches. In addition, cutting-edge technologies have provided new methods for understanding uncharacterized biosynthetic pathways, protein degradation mechanisms, protein-protein interactions, or allosteric feedback, enabling the design of novel ME approaches.

Leaf gene expression trajectories during the growing season are consistent between sites and years in American beech.

Transcriptomics provides a versatile tool for ecological monitoring. Here, through genome-guided profiling of transcripts mapping to 33 042 gene models, expression differences can be discerned among multi-year and seasonal leaf samples collected from American beech trees at two latitudinally separated sites. Despite a bottleneck due to post-Columbian deforestation, the single nucleotide polymorphism-based population genetic background analysis has yielded sufficient variation to account for differences between populations and among individuals. Our expression analyses during spring-summer and summer-autumn transitions for two consecutive years involved 4197 differentially expressed protein coding genes. Using Populus orthologues we reconstructed a protein-protein interactome representing leaf physiological states of trees during the seasonal transitions. Gene set enrichment analysis revealed gene ontology terms that highlight molecular functions and biological processes possibly influenced by abiotic forcings such as recovery from drought and response to excess precipitation. Further, based on 324 co-regulated transcripts, we focused on a subset of GO terms that could be putatively attributed to late spring phenological shifts. Our conservative results indicate that extended transcriptome-based monitoring of forests can capture diverse ranges of responses including air quality, chronic disease, as well as herbivore outbreaks that require activation and/or downregulation of genes collectively tuning reaction norms maintaining the survival of long living trees such as the American beech.

Differential Proteome and Interactome Analysis Reveal the Basis of Pleiotropy Associated With the Histidine Methyltransferase Hpm1p.

The methylation of histidine is a post-translational modification whose function is poorly understood. Methyltransferase histidine protein methyltransferase 1 (Hpm1p) monomethylates H243 in the ribosomal protein Rpl3p and represents the only known histidine methyltransferase in Saccharomyces cerevisiae. Interestingly, the hpm1 deletion strain is highly pleiotropic, with many extraribosomal phenotypes including improved growth rates in alternative carbon sources. Here, we investigate how the loss of histidine methyltransferase Hpm1p results in diverse phenotypes, through use of targeted mass spectrometry (MS), growth assays, quantitative proteomics, and differential crosslinking MS. We confirmed the localization and stoichiometry of the H243 methylation site, found unreported sensitivities of Δhpm1 yeast to nonribosomal stressors, and identified differentially abundant proteins upon hpm1 knockout with clear links to the coordination of sugar metabolism. We adapted the emerging technique of quantitative large-scale stable isotope labeling of amino acids in cell culture crosslinking MS for yeast, which resulted in the identification of 1267 unique in vivo lysine-lysine crosslinks. By reproducibly monitoring over 350 of these in WT and Δhpm1, we detected changes to protein structure or protein-protein interactions in the ribosome, membrane proteins, chromatin, and mitochondria. Importantly, these occurred independently of changes in protein abundance and could explain a number of phenotypes of Δhpm1, not addressed by expression analysis. Further to this, some phenotypes were predicted solely from changes in protein structure or interactions and could be validated by orthogonal techniques. Taken together, these studies reveal a broad role for Hpm1p in yeast and illustrate how crosslinking MS will be an essential tool for understanding complex phenotypes.

Integration of data-independent acquisition (DIA) with co-fractionation mass spectrometry (CF-MS) to enhance interactome mapping capabilities.

Proteomics technologies are continually advancing, providing opportunities to develop stronger and more robust protein interaction networks (PINs). In part, this is due to the ever-growing number of high-throughput proteomics methods that are available. This review discusses how data-independent acquisition (DIA) and co-fractionation mass spectrometry (CF-MS) can be integrated to enhance interactome mapping abilities. Furthermore, integrating these two techniques can improve data quality and network generation through extended protein coverage, less missing data, and reduced noise. CF-DIA-MS shows promise in expanding our knowledge of interactomes, notably for non-model organisms (NMOs). CF-MS is a valuable technique on its own, but upon the integration of DIA, the potential to develop robust PINs increases, offering a unique approach for researchers to gain an in-depth understanding into the dynamics of numerous biological processes.

Intrinsically disordered proteins and liquid-liquid phase separation in SARS-CoV-2 interactomes.

This paper discusses the properties of proteins and their relations in the interactomes of the selected subsets of SARS-CoV-2 proteome-the membrane protein, nonstructural proteins, and, finally, full proteome. Protein disorder according to several measures, liquid-liquid phase separation probabilities, and protein node degrees in the interaction networks were singled out as the features of interest. Additionally, viral interactomes were combined with the interactome of human lung tissue so as to examine if the new connections in the resulting viral-host interactome are linked to protein disorder. Correlation analysis shows that there is no clear relationship between raw features of interest, whereas there is a positive correlation between the protein disorder and its neighborhood mean disorder. There are also indications that highly connected viral hubs tend to be on average more ordered than proteins with a small number of connections. This is in contrast to previous similar studies conducted on eukaryotic interactomes and possibly raises new questions in research on viral interactomes.

Web tools to fight pandemics: the COVID-19 experience.

The current outbreak of COVID-19 has generated an unprecedented scientific response worldwide, with the generation of vast amounts of publicly available epidemiological, biological and clinical data. Bioinformatics scientists have quickly produced online methods to provide non-computational users with the opportunity of analyzing such data. In this review, we report the results of this effort, by cataloguing the currently most popular web tools for COVID-19 research and analysis. Our focus was driven on tools drawing data from the fields of epidemiology, genomics, interactomics and pharmacology, in order to provide a meaningful depiction of the current state of the art of COVID-19 online resources.

Proteomic profiling and network biology of colorectal cancer liver metastasis.

INTRODUCTION: Tissue-based proteomic studies of colorectal cancer (CRC) metastasis have delivered fragmented results, with very few therapeutic targets and prognostic biomarkers moving beyond the discovery phase. This situation is likely due to the difficulties in obtaining and analyzing large numbers of patient-derived metastatic samples, the own heterogeneity of CRC, and technical limitations in proteomics discovery. As an alternative, metastatic CRC cell lines provide a flexible framework to investigate the underlying mechanisms and network biology of metastasis for target discovery. AREAS COVERED: In this perspective, we comment on different in-depth proteomic studies of metastatic versus non-metastatic CRC cell lines. Identified metastasis-related proteins are introduced and discussed according to the spatial location in different cellular fractions, with special emphasis on membrane/adhesion proteins, secreted proteins, and nuclear factors, including miRNAs associated with liver metastasis. Moreover, we analyze the biological significance and potential therapeutic applications of the identified liver metastasis-related proteins. EXPERT OPINION: The combination of protein discovery and functional analysis is the only way to accelerate the progress to clinical translation of the proteomic-derived findings in a relatively fast pace. Patient-derived organoids represent a promising alternative to patient tissues and cell lines, but further optimizations are still required for achieving solid and reproducible results.

The HBV web: An insight into molecular interactomes between the hepatitis B virus and its host en route to hepatocellular carcinoma.

Hepatitis B virus (HBV) is a major aetiology associated with the development and progression of hepatocellular carcinoma (HCC), the most common primary liver malignancy. Over the past few decades, direct and indirect mechanisms have been identified in the pathogenesis of HBV-associated HCC which include altered signaling pathways, genome integration, mutation-induced genomic instability, chromosomal deletions and rearrangements. Intertwining of the HBV counterparts with the host cellular factors, though well established, needs to be systemized to understand the dynamics of host-HBV crosstalk and its consequences on HCC progression. Existence of a vast array of protein-protein and protein-nucleic acid interaction databases has led to the uncoiling of the compendia of genes/gene products associated with these interactions. This review covers the existing knowledge about the HBV-host interplay and brings it down under one canopy emphasizing on the HBV-host interactomics; and thereby highlights new strategies for therapeutic advancements against HBV-induced HCC.

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.

The Knowns and Unknowns in Protein-Metabolite Interactions.

Increasing attention has been focused on the study of protein-metabolite interactions (PMI), which play a key role in regulating protein functions and directing an orchestra of cellular processes. The investigation of PMIs is complicated by the fact that many such interactions are extremely short-lived, which requires very high resolution in order to detect them. As in the case of protein-protein interactions, protein-metabolite interactions are still not clearly defined. Existing assays for detecting protein-metabolite interactions have an additional limitation in the form of a limited capacity to identify interacting metabolites. Thus, although recent advances in mass spectrometry allow the routine identification and quantification of thousands of proteins and metabolites today, they still need to be improved to provide a complete inventory of biological molecules, as well as all interactions between them. Multiomic studies aimed at deciphering the implementation of genetic information often end with the analysis of changes in metabolic pathways, as they constitute one of the most informative phenotypic layers. In this approach, the quantity and quality of knowledge about PMIs become vital to establishing the full scope of crosstalk between the proteome and the metabolome in a biological object of interest. In this review, we analyze the current state of investigation into the detection and annotation of protein-metabolite interactions, describe the recent progress in developing associated research methods, and attempt to deconstruct the very term "interaction" to advance the field of interactomics further.

Characterization of Arbuscular Mycorrhizal Effector Proteins.

Plants are colonized by various fungi with both pathogenic and beneficial lifestyles. One type of colonization strategy is through the secretion of effector proteins that alter the plant's physiology to accommodate the fungus. The oldest plant symbionts, the arbuscular mycorrhizal fungi (AMF), may exploit effectors to their benefit. Genome analysis coupled with transcriptomic studies in different AMFs has intensified research on the effector function, evolution, and diversification of AMF. However, of the current 338 predicted effector proteins from the AM fungus Rhizophagus irregularis, only five have been characterized, of which merely two have been studied in detail to understand which plant proteins they associate with to affect the host physiology. Here, we review the most recent findings in AMF effector research and discuss the techniques used for the functional characterization of effector proteins, from their in silico prediction to their mode of action, with an emphasis on high-throughput approaches for the identification of plant targets of the effectors through which they manipulate their hosts.

Co-Expression of Podoplanin and CD44 in Proliferative Vitreoretinopathy Epiretinal Membranes.

Epiretinal membranes (ERMs) are sheets of tissue that pathologically develop in the vitreoretinal interface leading to progressive vision loss. They are formed by different cell types and by an exuberant deposition of extracellular matrix proteins. Recently, we reviewed ERMs' extracellular matrix components to better understand molecular dysfunctions that trigger and fuel the onset and development of this disease. The bioinformatics approach we applied delineated a comprehensive overview on this fibrocellular tissue and on critical proteins that could really impact ERM physiopathology. Our interactomic analysis proposed the hyaluronic-acid-receptor cluster of differentiation 44 (CD44) as a central regulator of ERM aberrant dynamics and progression. Interestingly, the interaction between CD44 and podoplanin (PDPN) was shown to promote directional migration in epithelial cells. PDPN is a glycoprotein overexpressed in various cancers and a growing body of evidence indicates its relevant function in several fibrotic and inflammatory pathologies. The binding of PDPN to partner proteins and/or its ligand results in the modulation of signaling pathways regulating proliferation, contractility, migration, epithelial-mesenchymal transition, and extracellular matrix remodeling, all processes that are vital in ERM formation. In this context, the understanding of the PDPN role can help to modulate signaling during fibrosis, hence opening a new line of therapy.

Study of MicroRNA (miR-221-3p, miR-133a-3p, and miR-9-5p) Expressions in Oral Submucous Fibrosis and Squamous Cell Carcinoma.

Oral squamous cell carcinoma (OSCC) is one of the common types of cancer. Its progression follows a transition from oral potentially malignant disorders (OPMDs) such as oral submucous fibrosis (OSMF). Epigenetic modifiers, especially microRNAs (miRNAs), have an appreciable role in the regulation of various carcinogenic pathways which are being used as biomarkers. miRNAs may also be helpful in the differentiation of oral submucous fibrosis from oral squamous cell carcinoma. Three miRNAs, miR-221-3p, miR133a-3p, and miR-9-5p, were found differentially expressed in many cancers in the literature search supported by our preliminary database search-based screening. The literature and our functional enrichment analysis in an earlier study have reported these miRNAs to regulate carcinogenesis at various steps. In the present study, the expression of these miRNAs was examined in 34 histopathologically confirmed OSCC, 30 OSMF, and 29 control (healthy volunteers) human samples. There was a significant downregulation of miRNA-133a-3p in OSCC compared to OSMF and controls, whereas there was up-regulation in oral submucous fibrosis compared to controls. There was no significant difference in the expression of miR-221-3p between OSCC and OSMF, but an upregulation in OSCC compared to controls. miR-9-5p was also found upregulated in both OSCC and OSMF. Further, miR-133a-3p expression was negatively correlated with age, smoking, drinking status, and AJCC staging, whereas miR-9-5p expression was only positively associated with tobacco/ areca nut chewing. The ROC plots, logistic regression model generated, and the correlation between the expression of miR-9-5p and miR-133a-3p in blood and tissue suggests that these could be used as risk stratification biomarkers.