Schizophrenia interactome derived repurposable drugs and randomized control trials of two candidates.

There is a substantial unmet need for effective and patient-acceptable drugs to treat severe mental illnesses like schizophrenia. Computational analysis of genomic, transcriptomic, and pharmacologic data generated in the past two decades enables repurposing of drugs or compounds with acceptable safety profiles, namely those that are FDA-approved or reached late stages in clinical trials. We developed a rational approach to achieve this computationally for schizophrenia by studying drugs that target the proteins in its protein interaction network ('interactome'). This involved contrasting the transcriptomic modulations observed in the disorder and the drug; our analyses resulted in 12 candidate drugs, 9 of which had additional supportive evidence: their target networks were enriched for pathways relevant to schizophrenia etiology or for genes that had an association with diseases pathogenically similar to schizophrenia. To translate these computational results to the clinic, these shortlisted drugs must be tested empirically through randomized controlled trials (RCT), where their prior safety approvals obviate the need for time-consuming phase I and II studies. We selected two among the shortlisted candidates based on likely adherence and side effect profiles. We are testing them through adjunctive RCTs for patients with schizophrenia or schizoaffective disorder who experienced incomplete resolution of psychotic features with conventional treatment. The integrated computational analysis for identifying and ranking drugs for clinical trials can be iterated as additional data are obtained. Our approach could be expanded to enable disease subtype-specific drug discovery in future and should also be exploited for other psychiatric disorders.

Novel insights into the multifaceted and tissue-specific roles of the endocytic receptor LRP1.

Receptor-mediated endocytosis provides a mechanism for the selective uptake of specific molecules thereby controlling the composition of the extracellular environment and biological processes. The low-density lipoprotein receptor-related protein 1 (LRP1) is a widely expressed endocytic receptor that regulates cellular events by modulating the levels of numerous extracellular molecules via rapid endocytic removal. LRP1 also participates in signalling pathways through this modulation as well as in the interaction with membrane receptors and cytoplasmic adaptor proteins. LRP1 single nucleotide polymorphisms are associated with several diseases and conditions such as migraines, aortic aneurysms, cardiopulmonary dysfunction, corneal clouding, and bone dysmorphology and mineral density. Studies using Lrp1 knockout mice revealed a critical, non-redundant and tissue-specific role of LRP1 in regulating various physiological events. However, exactly how LRP1 functions to regulate so many distinct and specific processes is still not fully clear. Our recent proteomics studies have identified more than 300 secreted proteins that either directly interact with LRP1 or are modulated by LRP1 in various tissues. This review will highlight the remarkable ability of this receptor to regulate secreted molecules in a tissue-specific manner and discuss potential mechanisms underpinning such specificity. Uncovering the depth of these "hidden" specific interactions modulated by LRP1 will provide novel insights into a dynamic and complex extracellular environment that is involved in diverse biological and pathological processes.

CheRRI-Accurate classification of the biological relevance of putative RNA-RNA interaction sites.

BACKGROUND: RNA-RNA interactions are key to a wide range of cellular functions. The detection of potential interactions helps to understand the underlying processes. However, potential interactions identified via in silico or experimental high-throughput methods can lack precision because of a high false-positive rate. RESULTS: We present CheRRI, the first tool to evaluate the biological relevance of putative RNA-RNA interaction sites. CheRRI filters candidates via a machine learning-based model trained on experimental RNA-RNA interactome data. Its unique setup combines interactome data and an established thermodynamic prediction tool to integrate experimental data with state-of-the-art computational models. Applying these data to an automated machine learning approach provides the opportunity to not only filter data for potential false positives but also tailor the underlying interaction site model to specific needs. CONCLUSIONS: CheRRI is a stand-alone postprocessing tool to filter either predicted or experimentally identified potential RNA-RNA interactions on a genomic level to enhance the quality of interaction candidates. It is easy to install (via conda, pip packages), use (via Galaxy), and integrate into existing RNA-RNA interaction pipelines.

trans-Interacting Plasma Membrane Proteins and Binding Partner Identification.

Plasma membrane proteins (PMPs) play critical roles in a myriad of physiological and disease conditions. A unique subset of PMPs functions through interacting with each other in trans at the interface between two contacting cells. These trans-interacting PMPs (tiPMPs) include adhesion molecules and ligands/receptors that facilitate cell-cell contact and direct communication between cells. Among the tiPMPs, a significant number have apparent extracellular binding domains but remain orphans with no known binding partners. Identification of their potential binding partners is therefore important for the understanding of processes such as organismal development and immune cell activation. While a number of methods have been developed for the identification of protein binding partners in general, very few are applicable to tiPMPs, which interact in a two-dimensional fashion with low intrinsic binding affinities. In this review, we present the significance of tiPMP interactions, the challenges of identifying binding partners for tiPMPs, and the landscape of method development. We describe current avidity-based screening approaches for identifying novel tiPMP binding partners and discuss their advantages and limitations. We conclude by highlighting the importance of developing novel methods of identifying new tiPMP interactions for deciphering the complex protein interactome and developing targeted therapeutics for diseases.

Extra Tree Classifier Predicts an Interactome Hub Gene as HSPB1 in Oral Cancer: A Bioinformatics Analysis.

Introduction Oral cancer is a significant global health issue that is mainly caused by factors, such as smoking, alcohol consumption, poor oral hygiene, age, and the human papillomavirus. Unfortunately, delayed diagnosis contributes to high rates of illness and mortality. However, saliva shows promise as a potential source for early detection, prognosis, and treatment. By analyzing the proteins and their interactions in saliva, we can gain insights that can assist in early detection and prediction. In this study, we aim to identify and predict the key genes, known as hub genes, in the salivary transcriptomics data of oral cancer patients and healthy individuals. Methods The data used for the analysis were obtained from salivaryproteome.org (https://salivaryproteome.org/) . The retrieved data consisted of individuals with oral cancer who were assigned unique identification numbers (IDs) 1025, 1030, 1027, and 1029, while the healthy individuals were assigned IDs 4256, 4257, 4255, and 4258, respectively. Differential gene expression analysis was used to identify genes that showed significant differences between the two groups. Uniformity and clustering were assessed through heatmaps and principal component analysis. Protein-protein interactions were investigated using the STRING database and Cytoscape. In addition, machine learning algorithms were employed to identify key genes involved in the interatomic interactions by analyzing transcriptomics data generated from the differential gene expression analysis. Results The accuracy and class accuracy of the extra tree classifier showed 98% and 97% in predicting interactomic hub genes, and HSPB1 was identified as a hub gene using Cytohubba from Cytoscape. Conclusion The predictive extra tree classifier, with its high accuracy in analysing interactomic hub genes in oral cancer, can potentially improve diagnosis and treatment strategies.

Revealing the Arabidopsis AtGRP7 mRNA binding proteome by specific enhanced RNA interactome capture.

BACKGROUND: The interaction of proteins with RNA in the cell is crucial to orchestrate all steps of RNA processing. RNA interactome capture (RIC) techniques have been implemented to catalogue RNA- binding proteins in the cell. In RIC, RNA-protein complexes are stabilized by UV crosslinking in vivo. Polyadenylated RNAs and associated proteins are pulled down from cell lysates using oligo(dT) beads and the RNA-binding proteome is identified by quantitative mass spectrometry. However, insights into the RNA-binding proteome of a single RNA that would yield mechanistic information on how RNA expression patterns are orchestrated, are scarce. RESULTS: Here, we explored RIC in Arabidopsis to identify proteins interacting with a single mRNA, using the circadian clock-regulated Arabidopsis thaliana GLYCINE-RICH RNA-BINDING PROTEIN 7 (AtGRP7) transcript, one of the most abundant transcripts in Arabidopsis, as a showcase. Seedlings were treated with UV light to covalently crosslink RNA and proteins. The AtGRP7 transcript was captured from cell lysates with antisense oligonucleotides directed against the 5'untranslated region (UTR). The efficiency of RNA capture was greatly improved by using locked nucleic acid (LNA)/DNA oligonucleotides, as done in the enhanced RIC protocol. Furthermore, performing a tandem capture with two rounds of pulldown with the 5'UTR oligonucleotide increased the yield. In total, we identified 356 proteins enriched relative to a pulldown from atgrp7 mutant plants. These were benchmarked against proteins pulled down from nuclear lysates by AtGRP7 in vitro transcripts immobilized on beads. Among the proteins validated by in vitro interaction we found the family of Acetylation Lowers Binding Affinity (ALBA) proteins. Interaction of ALBA4 with the AtGRP7 RNA was independently validated via individual-nucleotide resolution crosslinking and immunoprecipitation (iCLIP). The expression of the AtGRP7 transcript in an alba loss-of-function mutant was slightly changed compared to wild-type, demonstrating the functional relevance of the interaction. CONCLUSION: We adapted specific RNA interactome capture with LNA/DNA oligonucleotides for use in plants using AtGRP7 as a showcase. We anticipate that with further optimization and up scaling the protocol should be applicable for less abundant transcripts.

Predicting herb-disease associations using network-based measures in human protein interactome.

BACKGROUND: Natural herbs are frequently used to treat diseases or to relieve symptoms in many countries. Moreover, as their safety has been proven for a long time, they are considered as main sources of new drug development. However, in many cases, the herbs are still prescribed relying on ancient records and/or traditional practices without scientific evidences. More importantly, the medicinal efficacy of the herbs has to be evaluated in the perspective of MCMT (multi-compound multi-target) effects, but most efforts focus on identifying and analyzing a single compound experimentally. To overcome these hurdles, computational approaches which are based on the scientific evidences and are able to handle the MCMT effects are needed to predict the herb-disease associations. RESULTS: In this study, we proposed a network-based in silico method to predict the herb-disease associations. To this end, we devised a new network-based measure, WACP (weighted average closest path length), which not only quantifies proximity between herb-related genes and disease-related genes but also considers compound compositions of each herb. As a result, we confirmed that our method successfully predicts the herb-disease associations in the human protein interactome (AUROC = 0.777). In addition, we observed that our method is superior than the other simple network-based proximity measures (e.g. average shortest and closest path length). Additionally, we analyzed the associations between Brassica oleracea var. italica and its known associated diseases more specifically as case studies. Finally, based on the prediction results of the WACP, we suggested novel herb-disease pairs which are expected to have potential relations and their literature evidences. CONCLUSIONS: This method could be a promising solution to modernize the use of the natural herbs by providing the scientific evidences about the molecular associations between the herb-related genes targeted by multiple compounds and the disease-related genes in the human protein interactome.

Proteome-wide analysis reveals G protein-coupled receptor-like proteins in rice (Oryza sativa).

G protein-coupled receptors (GPCRs) constitute the largest family of transmembrane proteins in metazoans that mediate the regulation of various physiological responses to discrete ligands through heterotrimeric G protein subunits. The existence of GPCRs in plant is contentious, but their comparable crucial role in various signaling pathways necessitates the identification of novel remote GPCR-like proteins that essentially interact with the plant G protein α subunit and facilitate the transduction of various stimuli. In this study, we identified three putative GPCR-like proteins (OsGPCRLPs) (LOC_Os06g09930.1, LOC_Os04g36630.1, and LOC_Os01g54784.1) in the rice proteome using a stringent bioinformatics workflow. The identified OsGPCRLPs exhibited a canonical GPCR 'type I' 7TM topology, patterns, and biologically significant sites for membrane anchorage and desensitization. Cluster-based interactome mapping revealed that the identified proteins interact with the G protein α subunit which is a characteristic feature of GPCRs. Computational results showing the interaction of identified GPCR-like proteins with G protein α subunit and its further validation by the membrane yeast-two-hybrid assay strongly suggest the presence of GPCR-like 7TM proteins in the rice proteome. The absence of a regulator of G protein signaling (RGS) box in the C- terminal domain, and the presence of signature motifs of canonical GPCR in the identified OsGPCRLPs strongly suggest that the rice proteome contains GPCR-like proteins that might be involved in signal transduction.

Investigating SARS-CoV-2 virus-host interactions and mRNA expression: Insights using three models of D. melanogaster.

Responsible for COVID-19, SARS-CoV-2 is a coronavirus in which contagious variants continue to appear. Therefore, some population groups have demonstrated greater susceptibility to contagion and disease progression. For these reasons, several researchers have been studying the SARS-CoV-2/human interactome to understand the pathophysiology of COVID-19 and develop new pharmacological strategies. D. melanogaster is a versatile animal model with approximately 90 % human protein orthology related to SARS-CoV-2/human interactome and is widely used in metabolic studies. In this context, our work assessed the potential interaction between human proteins (ZNF10, NUP88, BCL2L1, UBC9, and RBX1) and their orthologous proteins in D. melanogaster (gl, Nup88, Buffy, ubc9, and Rbx1a) with proteins from SARS-CoV-2 (nsp3, nsp9, E, ORF7a, N, and ORF10) using computational approaches. Our results demonstrated that all the proteins have the potential to interact, and we compared the binding sites between humans and fruit flies. The stability and consistency in the structure of the gl_nsp3 complex, specifically, could be crucial for its specific biological functions. Lastly, to enhance the understanding of the influence of host factors on coronavirus infection, we also analyse the mRNA expression of the five genes (mbo, gl, lwr, Buffy, and Roc1a) responsible for encoding the fruit fly proteins. Briefly, we demonstrated that those genes were differentially regulated according to diets, sex, and age. Two groups showed higher positive gene regulation than others: females in the HSD group and males in the aging group, which could imply a higher virus-host susceptibility. Overall, while preliminary, our work contributes to the understanding of host defense mechanisms and potentially identifies candidate proteins and genes for in vivo viral studies against SARS-CoV-2.

Next-generation Drosophila protein interactome map and its functional implications.

We describe a next-generation Drosophila protein interaction map-"DPIM2"-established from affinity purification-mass spectrometry of 5,805 baits, covering the largest fraction of the Drosophila proteome. The network contains 32,668 interactions among 3,644 proteins, organized into 632 clusters representing putative functional modules. Our analysis expands the pool of known protein interactions in Drosophila, provides annotation for poorly studied genes, and postulates previously undescribed protein interaction relationships. The predictive power and functional relevance of this network are probed through the lens of the Notch signaling pathway, and we find that newly identified members of complexes that include known Notch modifiers can also modulate Notch signaling. DPIM2 allows direct comparisons with a recently published human protein interaction network, defining the existence of functional interactions conserved across species. Thus, DPIM2 defines a valuable resource for predicting protein co-complex memberships and functional associations as well as generates functional hypotheses regarding specific protein interactions.

Combining quantitative proteomics and interactomics for a deeper insight into molecular differences between human cell lines.

Cellular functional pathways have evolved through selection based on fitness benefits conferred through protein intra- and inter-molecular interactions that comprise all protein conformational features and protein-protein interactions, collectively referred to as the interactome. While the interactome is regulated by proteome levels, it is also regulated independently by, post translational modification, co-factor, and ligand levels, as well as local protein environmental factors, such as osmolyte concentration, pH, ionic strength, temperature and others. In modern biomedical research, cultivatable cell lines have become an indispensable tool, with selection of optimal cell lines that exhibit specific functional profiles being critical for success in many cases. While it is clear that cell lines derived from different cell types have differential proteome levels, increased understanding of large-scale functional differences requires additional information beyond abundance level measurements, including how protein conformations and interactions are altered in certain cell types to shape functional landscapes. Here, we employed quantitative in vivo protein cross-linking coupled to mass spectrometry to probe large-scale protein conformational and interaction changes among three commonly employed human cell lines, HEK293, MCF-7, and HeLa cells. Isobaric quantitative Protein Interaction Reporter (iqPIR) technologies were used to obtain quantitative values of cross-linked peptides across three cell lines. These data illustrated highly reproducible (R2 values larger than 0.8 for all biological replicates) quantitative interactome levels across multiple biological replicates. We also measured protein abundance levels in these cells using data independent acquisition quantitative proteomics methods. Combining quantitative interactome and proteomics information allowed visualization of cell type-specific interactome changes mediated by proteome level adaptations as well as independently regulated interactome changes to gain deeper insight into possible drivers of these changes. Among the biggest detected alterations in protein interactions and conformations are changes in cytoskeletal proteins, RNA-binding proteins, chromatin remodeling complexes, mitochondrial proteins, and others. Overall, these data demonstrate the utility and reproducibility of quantitative cross-linking to study systems-level interactome variations. Moreover, these results illustrate how combined quantitative interactomics and proteomics can provide unique insight on cellular functional landscapes.

Identification of an FMNL2 Interactome by Quantitative Mass Spectrometry.

Formin Homology Proteins (Formins) are a highly conserved family of cytoskeletal regulatory proteins that participate in a diverse range of cellular processes. FMNL2 is a member of the Diaphanous-Related Formin sub-group, and previous reports suggest FMNL2's role in filopodia assembly, force generation at lamellipodia, subcellular trafficking, cell-cell junction assembly, and focal adhesion formation. How FMNL2 is recruited to these sites of action is not well understood. To shed light on how FMNL2 activity is partitioned between subcellular locations, we used biotin proximity labeling and proteomic analysis to identify an FMNL2 interactome. The interactome identified known and new FMNL2 interacting proteins with functions related to previously described FMNL2 activities. In addition, our interactome predicts a novel connection between FMNL2 and extracellular vesicle assembly. We show directly that FMNL2 protein is present in exosomes.

The proteostasis interactomes of trafficking-deficient variants of the voltage-gated potassium channel KV11.1 associated with long QT syndrome.

The voltage-gated potassium ion channel KV11.1 plays a critical role in cardiac repolarization. Genetic variants that render Kv11.1 dysfunctional cause long QT syndrome (LQTS), which is associated with fatal arrhythmias. Approximately 90% of LQTS-associated variants cause intracellular protein transport (trafficking) dysfunction, which pharmacological chaperones like E-4031 can rescue. Protein folding and trafficking decisions are regulated by chaperones, protein quality control factors, and trafficking machinery comprising the cellular proteostasis network. Here, we test whether trafficking dysfunction is associated with alterations in the proteostasis network of pathogenic Kv11.1 variants and whether pharmacological chaperones can normalize the proteostasis network of responsive variants. We used affinity-purification coupled with tandem mass tag-based quantitative mass spectrometry to assess protein interaction changes of WT KV11.1 or trafficking-deficient channel variants in the presence or absence of E-4031. We identified 572 core KV11.1 protein interactors. Trafficking-deficient variants KV11.1-G601S and KV11.1-G601S-G965∗ had significantly increased interactions with proteins responsible for folding, trafficking, and degradation compared to WT. We confirmed previous findings that the proteasome is critical for KV11.1 degradation. Our report provides the first comprehensive characterization of protein quality control mechanisms of KV11.1. We find extensive interactome remodeling associated with trafficking-deficient KV11.1 variants and with pharmacological chaperone rescue of KV11.1 cell surface expression. The identified protein interactions could be targeted therapeutically to improve KV11.1 trafficking and treat LQTS.

Neurodegenerative pathways and metabolic changes in the hippocampus and cortex of mice exposed to urban particulate matter: Insights from an integrated interactome analysis.

Recently, urban particulate matter (UPM) exposure has been associated with the development of brain disorders. This study uses bioinformatic analyses to elucidate the molecular unexplored mechanisms underlying the effects of UPM exposure on the brain. Mice are exposed to UPM (from 3 days to 20 weeks), and their behavioral patterns measured. We measure pathology and gene expression in the hippocampus and cortical regions of the brain. An integrated interactome of genes is established, which enriches information on metabolic processes. Using this network, we isolate the core genes that are differentially expressed in the samples. We observe cognitive loss and pathological changes in the brains of mice at 16 or 20 weeks of exposure. Through network analysis of core-differential genes and measurement of pathway activity, we identify differences in the response to UPM exposure between the hippocampus and cortex. However, neurodegenerative disease pathways are implicated in both tissues following short-term exposure to UPM. There were also significant changes in metabolic function in both tissues depending on UPM exposure time. Additionally, the cortex of UPM-exposed mice shows more similarities with psychiatric disorders than with neurodegenerative diseases. The connectivity map database is used to isolate genes contributing to changes in expression due to UPM exposure. New approaches for inhibiting or preventing the brain damage caused by UPM exposure can be developed by targeting the functions and selected genes identified in this study.

K128 ubiquitination constrains RAS activity by expanding its binding interface with GAP proteins.

The RAS pathway is among the most frequently activated signaling nodes in cancer. However, the mechanisms that alter RAS activity in human pathologies are not entirely understood. The most prevalent post-translational modification within the GTPase core domain of NRAS and KRAS is ubiquitination at lysine 128 (K128), which is significantly decreased in cancer samples compared to normal tissue. Here, we found that K128 ubiquitination creates an additional binding interface for RAS GTPase-activating proteins (GAPs), NF1 and RASA1, thus increasing RAS binding to GAP proteins and promoting GAP-mediated GTP hydrolysis. Stimulation of cultured cancer cells with growth factors or cytokines transiently induces K128 ubiquitination and restricts the extent of wild-type RAS activation in a GAP-dependent manner. In KRAS mutant cells, K128 ubiquitination limits tumor growth by restricting RAL/ TBK1 signaling and negatively regulating the autocrine circuit induced by mutant KRAS. Reduction of K128 ubiquitination activates both wild-type and mutant RAS signaling and elicits a senescence-associated secretory phenotype, promoting RAS-driven pancreatic tumorigenesis.

Aerobic anoxygenic phototrophs play important roles in nutrient cycling within cyanobacterial Microcystis bloom microbiomes.

BACKGROUND: During the bloom season, the colonial cyanobacterium Microcystis forms complex aggregates which include a diverse microbiome within an exopolymer matrix. Early research postulated a simple mutualism existing with bacteria benefitting from the rich source of fixed carbon and Microcystis receiving recycled nutrients. Researchers have since hypothesized that Microcystis aggregates represent a community of synergistic and interacting species, an interactome, each with unique metabolic capabilities that are critical to the growth, maintenance, and demise of Microcystis blooms. Research has also shown that aggregate-associated bacteria are taxonomically different from free-living bacteria in the surrounding water. Moreover, research has identified little overlap in functional potential between Microcystis and members of its microbiome, further supporting the interactome concept. However, we still lack verification of general interaction and know little about the taxa and metabolic pathways supporting nutrient and metabolite cycling within Microcystis aggregates. RESULTS: During a 7-month study of bacterial communities comparing free-living and aggregate-associated bacteria in Lake Taihu, China, we found that aerobic anoxygenic phototrophic (AAP) bacteria were significantly more abundant within Microcystis aggregates than in free-living samples, suggesting a possible functional role for AAP bacteria in overall aggregate community function. We then analyzed gene composition in 102 high-quality metagenome-assembled genomes (MAGs) of bloom-microbiome bacteria from 10 lakes spanning four continents, compared with 12 complete Microcystis genomes which revealed that microbiome bacteria and Microcystis possessed complementary biochemical pathways that could serve in C, N, S, and P cycling. Mapping published transcripts from Microcystis blooms onto a comprehensive AAP and non-AAP bacteria MAG database (226 MAGs) indicated that observed high levels of expression of genes involved in nutrient cycling pathways were in AAP bacteria. CONCLUSIONS: Our results provide strong corroboration of the hypothesized Microcystis interactome and the first evidence that AAP bacteria may play an important role in nutrient cycling within Microcystis aggregate microbiomes. Video Abstract.

NMDA receptor autoantibodies primarily impair the extrasynaptic compartment.

Autoantibodies directed against the N-methyl-D-aspartate receptor (NMDAR-Ab) are pathogenic immunoglobulins detected in patients suffering from NMDAR encephalitis. NMDAR-Ab alter the receptor membrane trafficking, synaptic transmission and neuronal network properties, leading to patients' neurological and psychiatric symptoms. Patients often have very little neuronal damage but rapid and massive (treatment-responsive) brain dysfunctions related to unknown early mechanism of NMDAR-Ab. Our understanding of this early molecular cascade remains surprisingly fragmented. Here, we used a combination of single molecule-based imaging of membrane proteins to unveil the spatio-temporal action of NMDAR-Ab onto live hippocampal neurons. We first demonstrate that different clones of NMDAR-Ab primarily affect extrasynaptic -and not synaptic- NMDAR. In the first minutes, NMDAR-Ab increase extrasynaptic NMDAR membrane dynamics, de-clustering its surface interactome. NMDAR-Ab also rapidly reshuffle all membrane proteins located at the extrasynaptic compartment. Consistent with this alteration of multiple proteins, NMDAR-Ab effects were not mediated through the sole interaction between NMDAR and EphB2 receptor. At the long-term, NMDAR-Ab reduce NMDAR synaptic pool by slowing down receptor membrane dynamics in a cross-linking independent manner. Remarkably, exposing only extrasynaptic NMDAR to NMDAR-Ab was sufficient to produce their full-blown effect on synaptic receptors. Collectively, we demonstrate that NMDAR-Ab first impair extrasynaptic proteins, and then the synaptic ones. These data shed thus new, and unsuspected, lights on the mode of action of NMDAR-Ab and likely to our understanding of (extra)synaptopathies.

Emerging paradigms and recent progress in targeting ErbB in cancers.

The epidermal growth factor receptor (EGFR) family is a class of transmembrane proteins, highly regarded as anticancer targets due to their pivotal role in various malignancies. Standard cancer treatments targeting the ErbB receptors include tyrosine kinase inhibitors (TKIs) and monoclonal antibodies (mAbs). Despite their substantial survival benefits, the achievement of curative outcomes is hindered by acquired resistance. Recent advancements in anti-ErbB approaches, such as inhibitory peptides, nanobodies, targeted-protein degradation strategies, and bispecific antibodies (BsAbs), aim to overcome such resistance. More recently, emerging insights into the cell surface interactome of the ErbB family open new avenues for modulating ErbB signaling by targeting specific domains of ErbB partners. Here, we review recent progress in ErbB targeting and elucidate emerging paradigms that underscore the significance of EGF domain-containing proteins (EDCPs) as new ErbB-targeting pathways.

The influence of APOEε4 on the pTau interactome in sporadic Alzheimer's disease.

APOEε4 is the major genetic risk factor for sporadic Alzheimer's disease (AD). Although APOEε4 is known to promote Aß pathology, recent data also support an effect of APOE polymorphism on phosphorylated Tau (pTau) pathology. To elucidate these potential effects, the pTau interactome was analyzed across APOE genotypes in the frontal cortex of 10 advanced AD cases (n = 5 APOEε3/ε3 and n = 5 APOEε4/ε4), using a combination of anti-pTau pS396/pS404 (PHF1) immunoprecipitation (IP) and mass spectrometry (MS). This proteomic approach was complemented by an analysis of anti-pTau PHF1 and anti-Aß 4G8 immunohistochemistry, performed in the frontal cortex of 21 advanced AD cases (n = 11 APOEε3/ε3 and n = 10 APOEε4/ε4). Our dataset includes 1130 and 1330 proteins enriched in IPPHF1 samples from APOEε3/ε3 and APOEε4/ε4 groups (fold change ≥ 1.50, IPPHF1 vs IPIgG ctrl). We identified 80 and 68 proteins as probable pTau interactors in APOEε3/ε3 and APOEε4/ε4 groups, respectively (SAINT score ≥ 0.80; false discovery rate (FDR) ≤ 5%). A total of 47/80 proteins were identified as more likely to interact with pTau in APOEε3/ε3 vs APOEε4/ε4 cases. Functional enrichment analyses showed that they were significantly associated with the nucleoplasm compartment and involved in RNA processing. In contrast, 35/68 proteins were identified as more likely to interact with pTau in APOEε4/ε4 vs APOEε3/ε3 cases. They were significantly associated with the synaptic compartment and involved in cellular transport. A characterization of Tau pathology in the frontal cortex showed a higher density of plaque-associated neuritic crowns, made of dystrophic axons and synapses, in APOEε4 carriers. Cerebral amyloid angiopathy was more frequent and severe in APOEε4/ε4 cases. Our study supports an influence of APOE genotype on pTau-subcellular location in AD. These results suggest a facilitation of pTau progression to Aß-affected brain regions in APOEε4 carriers, paving the way to the identification of new therapeutic targets.