An atlas of protein-protein interactions across mouse tissues.

Cellular processes arise from the dynamic organization of proteins in networks of physical interactions. Mapping the interactome has therefore been a central objective of high-throughput biology. However, the dynamics of protein interactions across physiological contexts remain poorly understood. Here, we develop a quantitative proteomic approach combining protein correlation profiling with stable isotope labeling of mammals (PCP-SILAM) to map the interactomes of seven mouse tissues. The resulting maps provide a proteome-scale survey of interactome rewiring across mammalian tissues, revealing more than 125,000 unique interactions at a quality comparable to the highest-quality human screens. We identify systematic suppression of cross-talk between the evolutionarily ancient housekeeping interactome and younger, tissue-specific modules. Rewired proteins are tightly regulated by multiple cellular mechanisms and are implicated in disease. Our study opens up new avenues to uncover regulatory mechanisms that shape in vivo interactome responses to physiological and pathophysiological stimuli in mammalian systems.

Polarization of the endoplasmic reticulum by ER-septin tethering.

Polarization of the plasma membrane (PM) into domains is an important mechanism to compartmentalize cellular activities and to establish cell polarity. Polarization requires formation of diffusion barriers that prevent mixing of proteins between domains. Recent studies have uncovered that the endoplasmic reticulum (ER) of budding yeast and neurons is polarized by diffusion barriers, which in neurons controls glutamate signaling in dendritic spines. The molecular identity of these barriers is currently unknown. Here, we show that a direct interaction between the ER protein Scs2 and the septin Shs1 creates the ER diffusion barrier in yeast. Barrier formation requires Epo1, a novel ER-associated subunit of the polarisome that interacts with Scs2 and Shs1. ER-septin tethering polarizes the ER into separate mother and bud domains, one function of which is to position the spindle in the mother until M phase by confining the spindle capture protein Num1 to the mother ER.

Breaking spore dormancy in budding yeast transforms the cytoplasm and the solubility of the proteome.

The biophysical properties of the cytoplasm are major determinants of key cellular processes and adaptation. Many yeasts produce dormant spores that can withstand extreme conditions. We show that spores of Saccharomyces cerevisiae exhibit extraordinary biophysical properties, including a highly viscous and acidic cytosol. These conditions alter the solubility of more than 100 proteins such as metabolic enzymes that become more soluble as spores transit to active cell proliferation upon nutrient repletion. A key regulator of this transition is the heat shock protein, Hsp42, which shows transient solubilization and phosphorylation, and is essential for the transformation of the cytoplasm during germination. Germinating spores therefore return to growth through the dissolution of protein assemblies, orchestrated in part by Hsp42 activity. The modulation of spores' molecular properties are likely key adaptive features of their exceptional survival capacities.

Global Interactome Mapping Reveals Pro-tumorigenic Interactions of NF-κB in Breast Cancer.

NF-κB pathway is involved in inflammation; however, recent data shows its role also in cancer development and progression, including metastasis. To understand the role of NF-κB interactome dynamics in cancer, we study the complexity of breast cancer interactome in luminal A breast cancer model and its rearrangement associated with NF-κB modulation. Liquid chromatography-mass spectrometry measurement of 160 size-exclusion chromatography fractions identifies 5460 protein groups. Seven thousand five hundred sixty eight interactions among these proteins have been reconstructed by PrInCE algorithm, of which 2564 have been validated in independent datasets. NF-κB modulation leads to rearrangement of protein complexes involved in NF-κB signaling and immune response, cell cycle regulation, and DNA replication. Central NF-κB transcription regulator RELA co-elutes with interactors of NF-κB activator PRMT5, and these complexes are confirmed by AlphaPulldown prediction. A complementary immunoprecipitation experiment recapitulates RELA interactions with other NF-κB factors, associating NF-κB inhibition with lower binding of NF-κB activators to RELA. This study describes a network of pro-tumorigenic protein interactions and their rearrangement upon NF-κB inhibition with potential therapeutic implications in tumors with high NF-κB activity.

Mechanisms of Pathogen and Pesticide Resistance in Honey Bees.

Bees are the most important insect pollinators of the crops humans grow, and Apis mellifera, the Western honey bee, is the most commonly managed species for this purpose. In addition to providing agricultural services, the complex biology of honey bees has been the subject of scientific study since the 18th century, and the intricate behaviors of honey bees and ants, fellow hymenopterans, inspired much sociobiological inquest. Unfortunately, honey bees are constantly exposed to parasites, pathogens, and xenobiotics, all of which pose threats to their health. Despite our curiosity about and dependence on honey bees, defining the molecular mechanisms underlying their interactions with biotic and abiotic stressors has been challenging. The very aspects of their physiology and behavior that make them so important to agriculture also make them challenging to study, relative to canonical model organisms. However, because we rely on A. mellifera so much for pollination, we must continue our efforts to understand what ails them. Here, we review major advancements in our knowledge of honey bee physiology, focusing on immunity and detoxification, and highlight some challenges that remain.

Ribosomes lacking bS21 gain function to regulate protein synthesis in Flavobacterium johnsoniae.

Ribosomes of Bacteroidia (formerly Bacteroidetes) fail to recognize Shine-Dalgarno (SD) sequences even though they harbor the anti-SD (ASD) of 16S rRNA. Inhibition of SD-ASD pairing is due to sequestration of the 3' tail of 16S rRNA in a pocket formed by bS21, bS18, and bS6 on the 30S platform. Interestingly, in many Flavobacteriales, the gene encoding bS21, rpsU, contains an extended SD sequence. In this work, we present genetic and biochemical evidence that bS21 synthesis in Flavobacterium johnsoniae is autoregulated via a subpopulation of ribosomes that specifically lack bS21. Mutation or depletion of bS21 in the cell increases translation of reporters with strong SD sequences, such as rpsU'-gfp, but has no effect on other reporters. Purified ribosomes lacking bS21 (or its C-terminal region) exhibit higher rates of initiation on rpsU mRNA and lower rates of initiation on other (SD-less) mRNAs than control ribosomes. The mechanism of autoregulation depends on extensive pairing between mRNA and 16S rRNA, and exceptionally strong SD sequences, with predicted pairing free energies of < -13 kcal/mol, are characteristic of rpsU across the Bacteroidota. This work uncovers a clear example of specialized ribosomes in bacteria.

What proteomics has taught us about honey bee (Apis mellifera) health and disease.

The Western honey bee, Apis mellifera, is currently navigating a gauntlet of environmental pressures, including the persistent threat of parasites, pathogens, and climate change - all of which compromise the vitality of honey bee colonies. The repercussions of their declining health extend beyond the immediate concerns of apiarists, potentially imposing economic burdens on society through diminished agricultural productivity. Hence, there is an imperative to devise innovative monitoring techniques for assessing the health of honey bee populations. Proteomics, recognized for its proficiency in biomarker identification and protein-protein interactions, is poised to play a pivotal role in this regard. It offers a promising avenue for monitoring and enhancing the resilience of honey bee colonies, thereby contributing to the stability of global food supplies. This review delves into the recent proteomic studies of A. mellifera, highlighting specific proteins of interest and envisioning the potential of proteomics to improve sustainable beekeeping practices amidst the challenges of a changing planet.

Investigating trade-offs between ovary activation and immune protein expression in bumble bee (Bombus impatiens) workers and queens.

Evidence for a trade-off between reproduction and immunity has manifested in many animal species, including social insects. However, investigations in social insect queens present a conundrum: new gynes of many social hymenopterans, such as bumble bees and ants, must first mate, then transition from being solitary to social as they establish their nests, thus experiencing confounding shifts in environmental conditions. Worker bumble bees offer an opportunity to investigate patterns of immune protein expression associated with ovary activation while minimizing extraneous environmental factors and genetic differences. Here, we use proteomics to interrogate the patterns of immune protein expression of female bumble bees (Bombus impatiens) by (i) sampling queens at different stages of their life cycle, then (ii) by sampling workers with different degrees of ovary activation. Patterns of immune protein expression in the haemolymph of queens are consistent with a reproduction-immunity trade-off, but equivalent samples from workers are not. This brings into question whether queen bumble bees really experience a reproduction-immunity trade-off, or if patterns of immune protein expression may actually be due to the selective pressure of the different environmental conditions they are exposed to during their life cycle.

Meta-analysis defines principles for the design and analysis of co-fractionation mass spectrometry experiments.

Co-fractionation mass spectrometry (CF-MS) has emerged as a powerful technique for interactome mapping. However, there is little consensus on optimal strategies for the design of CF-MS experiments or their computational analysis. Here, we reanalyzed a total of 206 CF-MS experiments to generate a uniformly processed resource containing over 11 million measurements of protein abundance. We used this resource to benchmark experimental designs for CF-MS studies and systematically optimize computational approaches to network inference. We then applied this optimized methodology to reconstruct a draft-quality human interactome by CF-MS and predict over 700,000 protein-protein interactions across 27 eukaryotic species or clades. Our work defines new resources to illuminate proteome organization over evolutionary timescales and establishes best practices for the design and analysis of CF-MS studies.

Cleavage of 14-3-3ε by the enteroviral 3C protease dampens RIG-I-mediated antiviral signaling.

Viruses have evolved diverse strategies to evade the host innate immune response and promote infection. The retinoic acid-inducible gene I (RIG-I)-like receptors RIG-I and MDA5 are antiviral factors that sense viral RNA and trigger downstream signal via mitochondrial antiviral-signaling protein (MAVS) to activate type I interferon expression. 14-3-3ε is a key component of the RIG-I translocon complex that interacts with MAVS at the mitochondrial membrane; however, the exact role of 14-3-3ε in this pathway is not well understood. In this study, we demonstrate that 14-3-3ε is a direct substrate of both the poliovirus and coxsackievirus B3 (CVB3) 3C proteases (3Cpro) and that it is cleaved at Q236↓G237, resulting in the generation of N- and C-terminal fragments of 27.0 and 2.1 kDa, respectively. While the exogenous expression of wild-type 14-3-3ε enhances IFNB mRNA production during poly(I:C) stimulation, expression of the truncated N-terminal fragment does not. The N-terminal 14-3-3ε fragment does not interact with RIG-I in co-immunoprecipitation assays, nor can it facilitate RIG-I translocation to the mitochondria. Probing the intrinsically disordered C-terminal region identifies key residues responsible for the interaction between 14-3-3ε and RIG-I. Finally, overexpression of the N-terminal fragment promotes CVB3 infection in mammalian cells. The strategic enterovirus 3Cpro-mediated cleavage of 14-3-3ε antagonizes RIG-I signaling by disrupting critical interactions within the RIG-I translocon complex, thus contributing to evasion of the host antiviral response. IMPORTANCE Host antiviral factors work to sense virus infection through various mechanisms, including a complex signaling pathway known as the retinoic acid-inducible gene I (RIG-I)-like receptor pathway. This pathway drives the production of antiviral molecules known as interferons, which are necessary to establish an antiviral state in the cellular environment. Key to this antiviral signaling pathway is the small chaperone protein 14-3-3ε, which facilitates the delivery of a viral sensor protein, RIG-I, to the mitochondria. In this study, we show that the enteroviral 3C protease cleaves 14-3-3ε during infection, rendering it incapable of facilitating this antiviral response. We also find that the resulting N-terminal cleavage fragment dampens RIG-I signaling and promotes virus infection. Our findings reveal a novel viral strategy that restricts the antiviral host response and provides insights into the mechanisms underlying 14-3-3ε function in RIG-I antiviral signaling.

The Effect of Nutrient Deprivation on Early Life Small Intestinal Mucosal Microbiome and Host Proteome.

BACKGROUND: Nutrition plays a vital role in shaping the intestinal microbiome. However, many hospitalized children undergo periods of fasting during medical treatment. Changes to the small intestinal microbiota in early life in the setting of enteral deprivation have not been well described. OBJECTIVE: The aim of this study was to investigate the impact of enteral deprivation on the small intestinal mucosal microbiome and to identify factors that shape this interaction in infancy. METHODS: Intestinal biopsies were collected from proximal (fed) and distal (unfed) small bowel at the time of ostomy closure in children with a small intestinal enterostomy. Mucosal and luminal microbiome comparisons were performed including ß-diversity and differential abundance and correlations with clinical factors were analyzed. Host proteomics were compared between fed and unfed samples and correlated with microbiome parameters. Finally, microbial results were validated in another cohort of pediatric patients. RESULTS: Samples from 13 children (median age 84 d) were collected. Mucosal microbiome communities in the fed and unfed segments were strikingly similar [paired UniFrac distance (ß-diversity)], whereas luminal effluent differed significantly from fed samples (PERMANOVA, P = 0.003). Multivariate analysis revealed patient as the strongest predictor of the UniFrac distance. Environmental variables did not influence the intrapatient microbial dissimilarity. Host proteomics were similar intrapatient (paired fed-unfed Euclidian distance) and showed a correlation with the UniFrac distance (Spearman rho = 0.71, P < 0.001). Specific proteins and functional clusters were significantly different between paired samples, including lipid metabolism and intracellular trafficking, whereas no difference was seen in innate immune proteins. The microbiome results were validated in a different cohort with similar characteristics. CONCLUSION: We found the host to be the most dominant factor in the structure of the early life small intestinal mucosal microbiome. Nutrient deprivation was associated with specific changes in the host proteome. Further research is needed to better understand this host-microbe-nutrition interaction.

Pivotal functions and impact of long con-coding RNAs on cellular processes and genome integrity.

Recent advances in uncovering the mysteries of the human genome suggest that long non-coding RNAs (lncRNAs) are important regulatory components. Although lncRNAs are known to affect gene transcription, their mechanisms and biological implications are still unclear. Experimental research has shown that lncRNA synthesis, subcellular localization, and interactions with macromolecules like DNA, other RNAs, or proteins can all have an impact on gene expression in various biological processes. In this review, we highlight and discuss the major mechanisms through which lncRNAs function as master regulators of the human genome. Specifically, the objective of our review is to examine how lncRNAs regulate different processes like cell division, cell cycle, and immune responses, and unravel their roles in maintaining genomic architecture and integrity.

Truncated TDP-43 proteoforms diagnostic of frontotemporal dementia with TDP-43 pathology.

INTRODUCTION: Biomarkers of TDP-43 pathology are needed to distinguish frontotemporal lobar degeneration with TDP-43 pathology (FTLD-TDP) from phenotypically related disorders. While normal physiological TDP-43 is not a promising biomarker, low-resolution techniques have suggested truncated forms of TDP-43 may be specific to TDP-43 pathology. To advance biomarker efforts for FTLD-TDP, we employed a high-resolution structural technique to characterize TDP-43 post-translational modifications in FTLD-TDP. METHODS: High-resolution mass spectrometry was used to characterize TDP-43 proteoforms in brain tissue from FTLD-TDP, non-TDP-43 dementias and neuropathologically unaffected cases. Findings were then verified in a larger cohort of FTLD-TDP and non-TDP-43 dementias via targeted quantitative mass spectrometry. RESULTS: In the discovery phase, truncated TDP-43 identified FTLD-TDP with 85% sensitivity and 100% specificity. The verification phase revealed similar findings, with 83% sensitivity and 89% specificity. DISCUSSION: The concentration of truncated TDP-43 proteoforms-in particular, in vivo generated C-terminal fragments-have high diagnostic accuracy for FTLD-TDP. HIGHLIGHTS: Discovery: Truncated TDP-43 differentiates FTLD-TDP from related dementias. Verification: Truncated TDP-43 concentration has high accuracy for FTLD-TDP. TDP-43 proteoforms <28 kDa have highest discriminatory power for TDP-43 pathology.

Sex-Based Mechanisms of Cardiac Development and Function: Applications for Induced-Pluripotent Stem Cell Derived-Cardiomyocytes.

Males and females exhibit intrinsic differences in the structure and function of the heart, while the prevalence and severity of cardiovascular disease vary in the two sexes. However, the mechanisms of this sex-based dimorphism are yet to be elucidated. Sex chromosomes and sex hormones are the main contributors to sex-based differences in cardiac physiology and pathophysiology. In recent years, the advances in induced pluripotent stem cell-derived cardiac models and multi-omic approaches have enabled a more comprehensive understanding of the sex-specific differences in the human heart. Here, we provide an overview of the roles of these two factors throughout cardiac development and explore the sex hormone signaling pathways involved. We will also discuss how the employment of stem cell-based cardiac models and single-cell RNA sequencing help us further investigate sex differences in healthy and diseased hearts.

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.

Identification of Emerging Novel Psychoactive Substances by Retrospective Analysis of Population-Scale Mass Spectrometry Data Sets.

Over the last two decades, hundreds of new psychoactive substances (NPSs), also known as "designer drugs", have emerged on the illicit drug market. The toxic and potentially fatal effects of these compounds oblige laboratories around the world to screen for NPS in seized materials and biological samples, commonly using high-resolution mass spectrometry. However, unambiguous identification of a NPS by mass spectrometry requires comparison to data from analytical reference materials, acquired on the same instrument. The sheer number of NPSs that are available on the illicit market, and the pace at which new compounds are introduced, means that forensic laboratories must make difficult decisions about which reference materials to acquire. Here, we asked whether retrospective suspect screening of population-scale mass spectrometry data could provide a data-driven platform to prioritize emerging NPSs for assay development. We curated a suspect database of precursor and diagnostic fragment ion masses for 83 emerging NPSs and used this database to retrospectively screen mass spectrometry data from 12,727 urine drug screens from one Canadian province. We developed integrative computational strategies to prioritize the most reliable identifications and tracked the frequency of these identifications over a 3 year study period between August 2019 and August 2022. The resulting data were used to guide the acquisition of new reference materials, which were in turn used to validate a subset of the retrospective identifications. Last, we took advantage of matching clinical reports for all 12,727 samples to systematically benchmark the accuracy of our retrospective data analysis approach. Our work opens up new avenues to enable the rapid detection of emerging illicit drugs through large-scale reanalysis of mass spectrometry data.

Deep Learning-Enabled MS/MS Spectrum Prediction Facilitates Automated Identification Of Novel Psychoactive Substances.

The market for illicit drugs has been reshaped by the emergence of more than 1100 new psychoactive substances (NPS) over the past decade, posing a major challenge to the forensic and toxicological laboratories tasked with detecting and identifying them. Tandem mass spectrometry (MS/MS) is the primary method used to screen for NPS within seized materials or biological samples. The most contemporary workflows necessitate labor-intensive and expensive MS/MS reference standards, which may not be available for recently emerged NPS on the illicit market. Here, we present NPS-MS, a deep learning method capable of accurately predicting the MS/MS spectra of known and hypothesized NPS from their chemical structures alone. NPS-MS is trained by transfer learning from a generic MS/MS prediction model on a large data set of MS/MS spectra. We show that this approach enables a more accurate identification of NPS from experimentally acquired MS/MS spectra than any existing method. We demonstrate the application of NPS-MS to identify a novel derivative of phencyclidine (PCP) within an unknown powder seized in Denmark without the use of any reference standards. We anticipate that NPS-MS will allow forensic laboratories to identify more rapidly both known and newly emerging NPS. NPS-MS is available as a web server at https://nps-ms.ca/, which provides MS/MS spectra prediction capabilities for given NPS compounds. Additionally, it offers MS/MS spectra identification against a vast database comprising approximately 8.7 million predicted NPS compounds from DarkNPS and 24.5 million predicted ESI-QToF-MS/MS spectra for these compounds.

Small-molecule dual inhibitors targeting heat shock protein 90 for cancer targeted therapy.

Heat shock protein 90, also known as Hsp90, is an extensively preserved molecular chaperone that performs a critical function in organizing various biological pathways and cellular operations. As a potential drug target, Hsp90 is closely linked to cancer. Hsp90 inhibitors are a class of drugs that have been extensively studied in preclinical models and have shown promise in a variety of diseases, especially cancer. However, Hsp90 inhibitors have encountered several challenges in clinical development, such as low efficacy, toxicity, or drug resistance, few Hsp90 small molecule inhibitors have been approved worldwide. Nonetheless, combining Hsp90 inhibitors with other tumor inhibitors, such as HDAC inhibitors, tubulin inhibitors, and Topo II inhibitors, has been shown to have synergistic antitumor effects. Consequently, the development of Hsp90 dual-target inhibitors is an effective strategy in cancer treatment, as it enhances potency while reducing drug resistance. This article provides an overview of Hsp90's domain structure and biological functions, as well as a discussion of the design, discovery, and structure-activity relationships of Hsp90 dual inhibitors, aiming to provide insights into clinical drug research from a medicinal chemistry perspective and discover novel Hsp90 dual inhibitors.

On the Robustness of Graph-Based Clustering to Random Network Alterations.

Biological functions emerge from complex and dynamic networks of protein-protein interactions. Because these protein-protein interaction networks, or interactomes, represent pairwise connections within a hierarchically organized system, it is often useful to identify higher-order associations embedded within them, such as multimember protein complexes. Graph-based clustering techniques are widely used to accomplish this goal, and dozens of field-specific and general clustering algorithms exist. However, interactomes can be prone to errors, especially when inferred from high-throughput biochemical assays. Therefore, robustness to network-level noise is an important criterion. Here, we tested the robustness of a range of graph-based clustering algorithms in the presence of noise, including algorithms common across domains and those specific to protein networks. Strikingly, we found that all of the clustering algorithms tested here markedly amplified network-level noise. Randomly rewiring only 1% of network edges yielded more than a 50% change in clustering results. Moreover, we found the impact of network noise on individual clusters was not uniform: some clusters were consistently robust to injected noise, whereas others were not. Therefore we developed the clust.perturb R package and Shiny web application to measure the reproducibility of clusters by randomly perturbing the network. We show that clust.perturb results are predictive of real-world cluster stability: poorly reproducible clusters as identified by clust.perturb are significantly less likely to be reclustered across experiments. We conclude that graph-based clustering amplifies noise in protein interaction networks, but quantifying the robustness of a cluster to network noise can separate stable protein complexes from spurious associations.

Proteomic Portraits Reveal Evolutionarily Conserved and Divergent Responses to Spinal Cord Injury.

Despite the emergence of promising therapeutic approaches in preclinical studies, the failure of large-scale clinical trials leaves clinicians without effective treatments for acute spinal cord injury (SCI). These trials are hindered by their reliance on detailed neurological examinations to establish outcomes, which inflate the time and resources required for completion. Moreover, therapeutic development takes place in animal models whose relevance to human injury remains unclear. Here, we address these challenges through targeted proteomic analyses of cerebrospinal fluid and serum samples from 111 patients with acute SCI and, in parallel, a large animal (porcine) model of SCI. We develop protein biomarkers of injury severity and recovery, including a prognostic model of neurological improvement at 6 months with an area under the receiver operating characteristic curve of 0.91, and validate these in an independent cohort. Through cross-species proteomic analyses, we dissect evolutionarily conserved and divergent aspects of the SCI response and establish the cerebrospinal fluid abundance of glial fibrillary acidic protein as a biochemical outcome measure in both humans and pigs. Our work opens up new avenues to catalyze translation by facilitating the evaluation of novel SCI therapies, while also providing a resource from which to direct future preclinical efforts.