Mass spectrometry-based protein-protein interaction networks for the study of human diseases

A Biotin Targeting Chimera (BioTAC) System to Map Small Molecule Interactomes in situ

Unbiased chemical biology strategies for direct readout of protein interactome remodelling by small molecules provide advantages over target-focused approaches, including the ability to detect previously unknown targets, and the inclusion of chemical off-compete controls leading to high-confidence identifications. We describe the BioTAC system, a small-molecule guided proximity labelling platform, to rapidly identify both direct and complexed small molecule binding proteins. The BioTAC system overcomes a limitation of current approaches, and supports identification of both inhibitor bound and molecular glue bound complexes.

Exploring Single-Cell Exposomics by Mass Spectrometry

Single-cell exposomics, a revolutionary approach that investigates cell-environment interactions at cellular and subcellular levels, stands distinct from conventional bulk exposomics. Leveraging advancements in mass spectrometry, it provides a detailed perspective on cellular dynamics, interactions, and responses to environmental stimuli and their impacts on human health. This work delves into this innovative realm, highlighting the nuanced interplay between environmental stressors and biological responses at cellular and subcellular levels. The application of spatial mass spectrometry in single-cell exposomics is discussed, revealing the intricate spatial organization and molecular composition within individual cells. Cell-type-specific exposomics, shedding light on distinct susceptibilities and adaptive strategies of various cell types to environmental exposures, is also examined. The Perspective further emphasizes the integration with molecular and cellular biology approaches to validate hypotheses derived from single-cell exposomics in a comprehensive biological context. Looking toward the future, we anticipate continued technological advancements and convergence with other -omics approaches and discuss implications for environmental health research, disease progression studies, and precision medicine. The final emphasis is on the need for robust computational tools and interdisciplinary collaboration to fully leverage the potential of single-cell exposomics, acknowledging the complexities inherent to this paradigm.

Molecular complex detection in protein interaction networks through reinforcement learning

BACKGROUND

Proteins often assemble into higher-order complexes to perform their biological functions. Such protein-protein interactions (PPI) are often experimentally measured for pairs of proteins and summarized in a weighted PPI network, to which community detection algorithms can be applied to define the various higher-order protein complexes. Current methods include unsupervised and supervised approaches, often assuming that protein complexes manifest only as dense subgraphs. Utilizing supervised approaches, the focus is not on how to find them in a network, but only on learning which subgraphs correspond to complexes, currently solved using heuristics. However, learning to walk trajectories on a network to identify protein complexes leads naturally to a reinforcement learning (RL) approach, a strategy not extensively explored for community detection. Here, we develop and evaluate a reinforcement learning pipeline for community detection on weighted protein-protein interaction networks to detect new protein complexes. The algorithm is trained to calculate the value of different subgraphs encountered while walking on the network to reconstruct known complexes. A distributed prediction algorithm then scales the RL pipeline to search for novel protein complexes on large PPI networks.

RESULTS

The reinforcement learning pipeline is applied to a human PPI network consisting of 8k proteins and 60k PPI, which results in 1,157 protein complexes. The method demonstrated competitive accuracy with improved speed compared to previous algorithms. We highlight protein complexes such as C4orf19, C18orf21, and KIAA1522 which are currently minimally characterized. Additionally, the results suggest TMC04 be a putative additional subunit of the KICSTOR complex and confirm the involvement of C15orf41 in a higher-order complex with HIRA, CDAN1, ASF1A, and by 3D structural modeling.

CONCLUSIONS

Reinforcement learning offers several distinct advantages for community detection, including scalability and knowledge of the walk trajectories defining those communities. Applied to currently available human protein interaction networks, this method had comparable accuracy with other algorithms and notable savings in computational time, and in turn, led to clear predictions of protein function and interactions for several uncharacterized human proteins.

Phenotypic heterogeneity in human genetic diseases: ultrasensitivity-mediated threshold effects as a unifying molecular mechanism

Phenotypic heterogeneity is very common in genetic systems and in human diseases and has important consequences for disease diagnosis and treatment. In addition to the many genetic and non-genetic (e.g., epigenetic, environmental) factors reported to account for part of the heterogeneity, we stress the importance of stochastic fluctuation and regulatory network topology in contributing to phenotypic heterogeneity. We argue that a threshold effect is a unifying principle to explain the phenomenon; that ultrasensitivity is the molecular mechanism for this threshold effect; and discuss the three conditions for phenotypic heterogeneity to occur. We suggest that threshold effects occur not only at the cellular level, but also at the organ level. We stress the importance of context-dependence and its relationship to pleiotropy and edgetic mutations. Based on this model, we provide practical strategies to study human genetic diseases. By understanding the network mechanism for ultrasensitivity and identifying the critical factor, we may manipulate the weak spot to gently nudge the system from an ultrasensitive state to a stable non-disease state. Our analysis provides a new insight into the prevention and treatment of genetic diseases.

Site-specific proximity labeling at single residue resolution for identification of protein partners in vitro and on cells

The cell surface proteome, or surfaceome, is encoded by more than 4000 genes, but we are only beginning to understand the complexes they form. Rapid proximity labeling around specific membrane targets allows for capturing weak and transient interactions expected in the crowded and dynamic environment of the surfaceome. Recently, a high-resolution approach called μMap has been described (Geri, J. B., Oakley, J. V., Reyes-Robles, T., Wang, T., McCarver, S. J., White, C. H., Rodriguez-Rivera, F. P., Parker, D. L., Hett, E. C., Fadeyi, O. O., Oslund, R. C., and MacMillan, D. W. C. (2020) Science 367 , 1091-1097) in which an iridium (Ir)-based photocatalyst is attached to a specific antibody to target labeling of neighbors utilizing light-activated generation of carbenes from diazirine compounds via Dexter Energy Transfer (DET). Here we studied and optimized the spatial resolution for the method using an oncoprotein complex between the antibody drug, trastuzumab (Traz), and its target HER2. A set of eight single site-specific Ir-catalytic centers were engineered into Traz to study intra- and inter-molecular labeling in vitro and on cells by mass spectrometry. From this structurally well-characterized complex we observed a maximum distance of ∼110 Å for labeling. Labeling occurred almost uniformly over the full range of amino acids, unlike the residue specific labeling of other techniques. To examine on cell labeling that is specific to HER2 as opposed to simply being on the membrane, we compared the labeling patterns for the eight Traz-catalyst species to random labeling of membrane proteins using a metabolically integrated fatty acid catalyst. Our results identified 20 high confidence HER2 neighbors, many novel, that were more than 6-fold enriched compared to the non-specific membrane tethered catalyst. These studies define distance labeling parameters from single-site catalysts placed directly on the membrane target of interest, and more accurately compare to non-specific labeling to identify membrane complexes with higher confidence.

Proteome-wide copy-number estimation from transcriptomics

Protein copy numbers constrain systems-level properties of regulatory networks, but absolute proteomic data remain scarce compared to transcriptomics obtained by RNA sequencing. We addressed this persistent gap by relating mRNA to protein statistically using best-available data from quantitative proteomics-transcriptomics for 4366 genes in 369 cell lines. The approach starts with a central estimate of protein copy number and hierarchically appends mRNA-protein and mRNA-mRNA dependencies to define an optimal gene-specific model that links mRNAs to protein. For dozens of independent cell lines and primary prostate samples, these protein inferences from mRNA outmatch stringent null models, a count-based protein-abundance repository, and empirical protein-to-mRNA ratios. The optimal mRNA-to-protein relationships capture biological processes along with hundreds of known protein-protein interaction complexes, suggesting mechanistic relationships are embedded. We use the method to estimate viral-receptor abundances of CD55-CXADR from human heart transcriptomes and build 1489 systems-biology models of coxsackievirus B3 infection susceptibility. When applied to 796 RNA sequencing profiles of breast cancer from The Cancer Genome Atlas, inferred copy-number estimates collectively reclassify 26% of Luminal A and 29% of Luminal B tumors. Protein-based reassignments strongly involve a pharmacologic target for luminal breast cancer (CDK4) and an α-catenin that is often undetectable at the mRNA level (CTTNA2). Thus, by adopting a gene-centered perspective of mRNA-protein covariation across different biological contexts, we achieve accuracies comparable to the technical reproducibility limits of contemporary proteomics. The collection of gene-specific models is assembled as a web tool for users seeking mRNA-guided predictions of absolute protein abundance (http://janeslab.shinyapps.io/Pinferna).

Recent Advances in Deep Learning for Protein-Protein Interaction Analysis: A Comprehensive Review

Deep learning, a potent branch of artificial intelligence, is steadily leaving its transformative imprint across multiple disciplines. Within computational biology, it is expediting progress in the understanding of Protein-Protein Interactions (PPIs), key components governing a wide array of biological functionalities. Hence, an in-depth exploration of PPIs is crucial for decoding the intricate biological system dynamics and unveiling potential avenues for therapeutic interventions. As the deployment of deep learning techniques in PPI analysis proliferates at an accelerated pace, there exists an immediate demand for an exhaustive review that encapsulates and critically assesses these novel developments. Addressing this requirement, this review offers a detailed analysis of the literature from 2021 to 2023, highlighting the cutting-edge deep learning methodologies harnessed for PPI analysis. Thus, this review stands as a crucial reference for researchers in the discipline, presenting an overview of the recent studies in the field. This consolidation helps elucidate the dynamic paradigm of PPI analysis, the evolution of deep learning techniques, and their interdependent dynamics. This scrutiny is expected to serve as a vital aid for researchers, both well-established and newcomers, assisting them in maneuvering the rapidly shifting terrain of deep learning applications in PPI analysis.

"Common-Precursor" Protein Mimetic Approach to Rescue Aβ Aggregation-Mediated Alzheimer's Phenotypes

Abberent protein-protein interactions (aPPIs) are associated with an array of pathological conditions, which make them important therapeutic targets. The aPPIs are mediated via specific chemical interactions that spread over a large and hydrophobic surface. Therefore, ligands that can complement the surface topography and chemical fingerprints could manipulate aPPIs. Oligopyridylamides (OPs) are synthetic protein mimetics that have been shown to manipulate aPPIs. However, the previous OP library used to disrupt these aPPIs was moderate in number (∼30 OPs) with very limited chemical diversity. The onus is on the laborious and time-consuming synthetic pathways with multiple chromatography steps. We have developed a novel chromatography-free technique to synthesize a highly diverse chemical library of OPs using a "common-precursor" approach. We significantly expanded the chemical diversity of OPs using a chromatography-free high-yielding method. To validate our novel approach, we have synthesized an OP with identical chemical diversity to a pre-existing OP-based potent inhibitor of Aβ aggregation, a process central to Alzheimer's disease (AD). The newly synthesized OP ligand (RD242) was very potent in inhibiting Aβ aggregation and rescuing AD phenotypes in an in vivo model. Moreover, RD242 was very effective in rescuing AD phenotypes in a post-disease onset AD model. We envision that our "common-precursor" synthetic approach will have tremendous potential as it is expandable for other oligoamide scaffolds to enhance affinity for disease-relevant targets.

Structure Relaxation Approximation (SRA) for Elucidation of Protein Structures from Ion Mobility Measurements (II). Protein Complexes

Characterizing structures of protein complexes and their disease-related aberrations is essential to understanding molecular mechanisms of many biological processes. Electrospray ionization coupled with hybrid ion mobility/mass spectrometry (ESI-IM/MS) methods offer sufficient sensitivity, sample throughput, and dynamic range to enable systematic structural characterization of proteomes. However, because ESI-IM/MS characterizes ionized protein systems in the gas phase, it generally remains unclear to what extent the protein ions characterized by IM/MS have retained their solution structures. Here, we discuss the first application of our computational structure relaxation approximation [Bleiholder, C.; et al. J. Phys. Chem. B 2019, 123 (13), 2756-2769] to assign structures of protein complexes in the range from ∼16 to ∼60 kDa from their "native" IM/MS spectra. Our analysis shows that the computed IM/MS spectra agree with the experimental spectra within the errors of the methods. The structure relaxation approximation (SRA) indicates that native backbone contacts appear largely retained in the absence of solvent for the investigated protein complexes and charge states. Native contacts between polypeptide chains of the protein complex appear to be retained to a comparable extent as contacts within a folded polypeptide chain. Our computations also indicate that the hallmark "compaction" often observed for protein systems in native IM/MS measurements appears to be a poor indicator of the extent to which native residue-residue interactions are lost in the absence of solvent. Further, the SRA indicates that structural reorganization of the protein systems in IM/MS measurements appears driven largely by remodeling of the protein surface that increases its hydrophobic content by approximately 10%. For the systems studied here, this remodeling of the protein surface appears to occur mainly by structural reorganization of surface-associated hydrophilic amino acid residues not associated with β-strand secondary structure elements. Properties related to the internal protein structure, as assessed by void volume or packing density, appear unaffected by remodeling of the surface. Taken together, the structural reorganization of the protein surface appears to be generic in nature and to sufficiently stabilize protein structures to render them metastable on the time scale of IM/MS measurements.

Analysis of context-specific KRAS-effector (sub)complexes in Caco-2 cells

Ras is a key switch controlling cell behavior. In the GTP-bound form, Ras interacts with numerous effectors in a mutually exclusive manner, where individual Ras-effectors are likely part of larger cellular (sub)complexes. The molecular details of these (sub)complexes and their alteration in specific contexts are not understood. Focusing on KRAS, we performed affinity purification (AP)-mass spectrometry (MS) experiments of exogenously expressed FLAG-KRAS WT and three oncogenic mutants ("genetic contexts") in the human Caco-2 cell line, each exposed to 11 different culture media ("culture contexts") that mimic conditions relevant in the colon and colorectal cancer. We identified four effectors present in complex with KRAS in all genetic and growth contexts ("context-general effectors"). Seven effectors are found in KRAS complexes in only some contexts ("context-specific effectors"). Analyzing all interactors in complex with KRAS per condition, we find that the culture contexts had a larger impact on interaction rewiring than genetic contexts. We investigated how changes in the interactome impact functional outcomes and created a Shiny app for interactive visualization. We validated some of the functional differences in metabolism and proliferation. Finally, we used networks to evaluate how KRAS-effectors are involved in the modulation of functions by random walk analyses of effector-mediated (sub)complexes. Altogether, our work shows the impact of environmental contexts on network rewiring, which provides insights into tissue-specific signaling mechanisms. This may also explain why KRAS oncogenic mutants may be causing cancer only in specific tissues despite KRAS being expressed in most cells and tissues.

Recent advances in microfluidics for single-cell functional proteomics

Single-cell proteomics (SCP) reveals phenotypic heterogeneity by profiling individual cells, their biological states and functional outcomes upon signaling activation that can hardly be probed via other omics characterizations. This has become appealing to researchers as it enables an overall more holistic view of biological details underlying cellular processes, disease onset and progression, as well as facilitates unique biomarker identification from individual cells. Microfluidic-based strategies have become methods of choice for single-cell analysis because they allow facile assay integrations, such as cell sorting, manipulation, and content analysis. Notably, they have been serving as an enabling technology to improve the sensitivity, robustness, and reproducibility of recently developed SCP methods. Critical roles of microfluidics technologies are expected to further expand rapidly in advancing the next phase of SCP analysis to reveal more biological and clinical insights. In this review, we will capture the excitement of the recent achievements of microfluidics methods for both targeted and global SCP, including efforts to enhance the proteomic coverage, minimize sample loss, and increase multiplexity and throughput. Furthermore, we will discuss the advantages, challenges, applications, and future prospects of SCP.

CyanoMapDB: a database integrating experimentally validated protein-protein interactions in cyanobacteria

As one of the essential life forms in the biosphere, research on cyanobacteria has been growing remarkably for decades. Biological functions in organisms are often accomplished through protein-protein interactions (PPIs), which help to regulate interacting proteins or organize them into an integral machine. However, the study of PPIs in cyanobacteria falls far behind that in mammals and has not been integrated for ease of use. Thus, we built CyanoMapDB (http://www.cyanomapdb.msbio.pro/), a database providing cyanobacterial PPIs with experimental evidence, consisting of 52,304 PPIs among 6,789 proteins from 23 cyanobacterial species. We collected available data in UniProt, STRING, and IntAct, and mined numerous PPIs from co-fractionation MS data in cyanobacteria. The integrated data are accessible in CyanoMapDB (http://www.cyanomapdb.msbio.pro/), enabling users to easily query proteins of interest, investigate interacting proteins with evidence from different sources, and acquire a visual network of the target protein. We believe that CyanoMapDB will promote research involved with cyanobacteria and plants.

10 years of extracellular matrix proteomics: Accomplishments, challenges, and future perspectives

The extracellular matrix (ECM) is a complex assembly of hundreds of proteins forming the architectural scaffold of multicellular organisms. In addition to its structural role, the ECM conveys signals orchestrating cellular phenotypes. Alterations of ECM composition, abundance, structure, or mechanics, have been linked to diseases and disorders affecting all physiological systems, including fibrosis and cancer. Deciphering the protein composition of the ECM and how it changes in pathophysiological contexts is thus the first step toward understanding the roles of the ECM in health and disease and toward the development of therapeutic strategies to correct disease-causing ECM alterations. Potentially, the ECM also represents a vast, yet untapped reservoir of disease biomarkers. ECM proteins are characterized by unique biochemical properties that have hindered their study: they are large, heavily and uniquely post-translationally modified, and highly insoluble. Overcoming these challenges, we and others have devised mass-spectrometry-based proteomic approaches to define the ECM composition, or "matrisome", of tissues. This review provides a historical overview of ECM proteomics research and presents the latest advances that now allow the profiling of the ECM of healthy and diseased tissues. The second part highlights recent examples illustrating how ECM proteomics has emerged as a powerful discovery pipeline to identify prognostic cancer biomarkers. The third part discusses remaining challenges limiting our ability to translate findings to clinical application and proposes approaches to overcome them. Last, the review introduces readers to resources available to facilitate the interpretation of ECM proteomics datasets. The ECM was once thought to be impenetrable. MS-based proteomics has proven to be a powerful tool to decode the ECM. In light of the progress made over the past decade, there are reasons to believe that the in-depth exploration of the matrisome is within reach and that we may soon witness the first translational application of ECM proteomics.

Characterization of Cystatin B Interactome in Saliva from Healthy Elderly and Alzheimer’s Disease Patients

Cystatin B is a small, multifunctional protein involved in the regulation of inflammation, innate immune response, and neuronal protection and found highly abundant in the brains of patients with Alzheimer’s disease (AD). Recently, our study demonstrated a significant association between the level of salivary cystatin B and AD. Since the protein is able to establish protein-protein interaction (PPI) in different contexts and aggregation-prone proteins and the PPI networks are relevant for AD pathogenesis, and due to the relevance of finding new AD markers in peripheral biofluids, we thought it was interesting to study the possible involvement of cystatin B in PPIs in saliva and to evaluate differences and similarities between AD and age-matched elderly healthy controls (HC). For this purpose, we applied a co-immunoprecipitation procedure and a bottom-up proteomics analysis to purify, identify, and quantify cystatin B interactors. Results demonstrated for the first time the existence of a salivary cystatin B-linked multi-protein complex composed by 82 interactors and largely expressed in the body. Interactors are involved in neutrophil activation, antimicrobial activity, modulation of the cytoskeleton and extra-cellular matrix (ECM), and glucose metabolism. Preliminary quantitative data showed significantly lower levels of triosophosphate isomerase 1 and higher levels of mucin 7, BPI, and matrix Gla protein in AD with respect to HC, suggesting implications associated with AD of altered glucose metabolism, antibacterial activities, and calcification-associated processes. Data are available via ProteomeXchange with identifiers PXD039286 and PXD030679.

Multiplexed kinase interactome profiling quantifies cellular network activity and plasticity

Dynamic changes in protein-protein interaction (PPI) networks underlie all physiological cellular functions and drive devastating human diseases. Profiling PPI networks can, therefore, provide critical insight into disease mechanisms and identify new drug targets. Kinases are regulatory nodes in many PPI networks; yet, facile methods to systematically study kinase interactome dynamics are lacking. We describe kinobead competition and correlation analysis (kiCCA), a quantitative mass spectrometry-based chemoproteomic method for rapid and highly multiplexed profiling of endogenous kinase interactomes. Using kiCCA, we identified 1,154 PPIs of 238 kinases across 18 diverse cancer lines, quantifying context-dependent kinase interactome changes linked to cancer type, plasticity, and signaling states, thereby assembling an extensive knowledgebase for cell signaling research. We discovered drug target candidates, including an endocytic adapter-associated kinase (AAK1) complex that promotes cancer cell epithelial-mesenchymal plasticity and drug resistance. Our data demonstrate the importance of kinase interactome dynamics for cellular signaling in health and disease.

New insights into extracellular and intracellular redox status in COVID-19 patients

BACKGROUND

The imbalance of redox homeostasis induces hyper-inflammation in viral infections. In this study, we explored the redox system signature in response to SARS-COV-2 infection and examined the status of these extracellular and intracellular signatures in COVID-19 patients.

METHOD

The multi-level network was constructed using multi-level data of oxidative stress-related biological processes, protein-protein interactions, transcription factors, and co-expression coefficients obtained from GSE164805, which included gene expression profiles of peripheral blood mononuclear cells (PBMCs) from COVID-19 patients and healthy controls. Top genes were designated based on the degree and closeness centralities. The expression of high-ranked genes was evaluated in PBMCs and nasopharyngeal (NP) samples of 30 COVID-19 patients and 30 healthy controls. The intracellular levels of GSH and ROS/O₂^• - and extracellular oxidative stress markers were assayed in PBMCs and plasma samples by flow cytometry and ELISA. ELISA results were applied to construct a classification model using logistic regression to differentiate COVID-19 patients from healthy controls.

RESULTS

CAT, NFE2L2, SOD1, SOD2 and CYBB were 5 top genes in the network analysis. The expression of these genes and intracellular levels of ROS/O₂^• - were increased in PBMCs of COVID-19 patients while the GSH level decreased. The expression of high-ranked genes was lower in NP samples of COVID-19 patients compared to control group. The activity of extracellular enzymes CAT and SOD, and the total oxidant status (TOS) level were increased in plasma samples of COVID-19 patients. Also, the 2-marker panel of CAT and TOS and 3-marker panel showed the best performance.

CONCLUSION

SARS-COV-2 disrupts the redox equilibrium in immune cells and the upper respiratory tract, leading to exacerbated inflammation and increased replication and entrance of SARS-COV-2 into host cells. Furthermore, utilizing markers of oxidative stress as a complementary validation to discriminate COVID-19 from healthy controls, seems promising.

Perspective on the potential of tandem-ion mobility /mass spectrometry methods for structural proteomics applications

Cellular processes are usually carried out collectively by the entirety of all proteins present in a biological cell, i.e. the proteome. Mass spectrometry-based methods have proven particularly successful in identifying and quantifying the constituent proteins of proteomes, including different molecular forms of a protein. Nevertheless, protein sequences alone do not reveal the function or dysfunction of the identified proteins. A straightforward way to assign function or dysfunction to proteins is characterization of their structures and dynamics. However, a method capable to characterize detailed structures of proteins and protein complexes in a large-scale, systematic manner within the context of cellular processes does not yet exist. Here, we discuss the potential of tandem-ion mobility / mass spectrometry (tandem-IM/MS) methods to provide such ability. We highlight the capability of these methods using two case studies on the protein systems ubiquitin and avidin using the tandem-TIMS/MS technology developed in our laboratory and discuss these results in the context of other developments in the broader field of tandem-IM/MS.

Predicting the Likelihood of Molecules to Act as Modulators of Protein-Protein Interactions

Targeting protein-protein interactions (PPIs) by small molecule modulators (iPPIs) is an attractive strategy for drug therapy, and some iPPIs have already been introduced into the clinic. Blocking PPIs is however considered to be a more difficult task than inhibiting enzymes or antagonizing receptor activity. In this paper, we examine whether it is possible to predict the likelihood of molecules to act as iPPIs. Using our in-house iterative stochastic elimination (ISE) algorithm, we constructed two classification models that successfully distinguish between iPPIs from the iPPI-DB database and decoy molecules from either the Enamine HTS collection (ISE 1) or the ZINC database (ISE 2). External test sets of iPPIs taken from the TIMBAL database and decoys from Enamine HTS or ZINC were screened by the models: the area under the curve for the receiver operating characteristic curve was 0.85-0.89, and the Enrichment Factor increased from an initial 1 to as much as 66 for ISE 1 and 57 for ISE 2. Screening of the Enamine HTS and ZINC data sets through both models results in a library of ∼1.3 million molecules that pass either one of the models. This library is enriched with iPPI candidates that are structurally different from known iPPIs, and thus, it is useful for target-specific screenings and should accelerate the discovery of iPPI drug candidates. The entire library is available in Table S6.

Hepatocyte-derived MANF mitigates ethanol-induced liver steatosis in mice via enhancing ASS1 activity and activating AMPK pathway

Hepatic steatosis plays a detrimental role in the onset and progression of alcohol-associated liver disease (ALD). Mesencephalic astrocyte-derived neurotrophic factor (MANF) is an evolutionarily conserved protein related to the unfolded protein response. Recent studies have demonstrated that MANF plays an important role in liver diseases. In this study, we investigated the role of MANF in ethanol-induced steatosis and the underlying mechanisms. We showed that the hepatic MANF expression was markedly upregulated in mouse model of ALD by chronic-plus-single-binge ethanol feeding. Moreover, after chronic-plus-binge ethanol feeding, hepatocyte-specific MANF knockout (HKO) mice displayed more severe hepatic steatosis and liver injury than wild-type (WT) control mice. Immunoprecipitation-coupled MS proteomic analysis revealed that arginosuccinate synthase 1 (ASS1), a rate-limiting enzyme in the urea cycle, resided in the same immunoprecipitated complex with MANF. Hepatocyte-specific MANF knockout led to decreased ASS1 activity, whereas overexpression of MANF contributed to enhanced ASS1 activity in vitro. In addition, HKO mice displayed unique urea cycle metabolite patterns in the liver with elevated ammonia accumulation after ethanol feeding. ASS1 is known to activate AMPK by generating an intracellular pool of AMP from the urea cycle. We also found that MANF supplementation significantly ameliorated ethanol-induced steatosis in vivo and in vitro by activating the AMPK signaling pathway, which was partly ASS1 dependent. This study demonstrates a new mechanism in which MANF acts as a key molecule in maintaining hepatic lipid homeostasis by enhancing ASS1 activity and uncovers an interesting link between lipid metabolism and the hepatic urea cycle under excessive alcohol exposure.

Native holdup (nHU) to measure binding affinities from cell extracts

Characterizing macromolecular interactions is essential for understanding cellular processes, yet most methods currently used to detect protein interactions from cells are qualitative. Here, we introduce the native holdup (nHU) approach to estimate equilibrium binding constants of protein interactions directly from cell extracts. Compared to other pull-down-based assays, nHU requires less sample preparation and can be coupled to any analytical methods as readouts, such as Western blotting or mass spectrometry. We use nHU to explore interactions of SNX27, a cargo adaptor of the retromer complex and find good agreement between in vitro affinities and those measured directly from cell extracts using nHU. We discuss the strengths and limitations of nHU and provide simple protocols that can be implemented in most laboratories.

A novel vision into the binding behavior of curcumin with human serum albumin-holo transferrin complex: molecular dynamic simulation and multi-spectroscopic perspectives

In this work, we investigated the simultaneous binding of curcumin (CUR) to human serum albumin (HSA) and human-holo transferrin (HTF) in the roles of binary and ternary systems. The binding affinity and binding site of protein-protein interaction were studied by the methods of multiple spectroscopic and molecular dynamics (MD) simulation. According to the results, the measurements for binding constant of HSA-CUR, HTF-CUR and (HSA-HTF) CUR complexes were observed to be 1.51 × 10⁵, 7.93 × 10⁴ and 1.44 × 10⁵ M^-1 respectively. Thermodynamic parameters were considered to be set at three varying temperatures including 298, 303, and 308 K. In conformity to the negative values of ΔH⁰ and ΔS⁰ the significant roles of hydrogen binding and van der-Waals forces in the formation of complexes are quiet evident. The binding distance between Trp residues of HSA, HTF and HSA-HTF upon interaction with CUR, were acquired by applying the Förster's theory of non-radioactive energy transfer and reported to be 2.04 nm, 1.78 nm, and 1.86 nm, respectively. In accordance with the conductometry and Resonance light scattering (RLS) results, there were different interaction behaviors among the HSA-HTF complex and CUR in ternary system when being compared to the outcomes of binary system. The secondary structure of all three cases increased as the CUR concentration was intensified, which confirmed the inducement of proteins conformational changes through the application of circular dichroism (CD) technique. The experimental results that were acquired throughout the binding of HSA-CUR, HTF-CUR, and (HSA-HTF) CUR complexes were approved by molecular modeling.Communicated by Ramaswamy H. Sarma.

Protein Interactome Profiling of Stable Molecular Complexes in Biomaterial Lysate

Most proteins function as part of various complexes, forming via stable and dynamic protein-protein interactions (PPIs). The profiling of PPIs expands the fundamental knowledge about the structures, functions, and regulation patterns of protein complexes and intracellular molecular machineries. Protein interactomics aims at solving three main tasks: (1) identification of protein partners and parts of complex intracellular structures; (2) analysis of PPIs parameters (affinity, molecular-recognition specificity, kinetic rate constants, and thermodynamic-parameters determination); (3) the study of the functional role of novel PPIs. The purpose of this work is to update the current state and prospects of multi-omics approaches to profiling of proteins involved in the formation of stable complexes. Methodological paradigm includes a development of protein-extraction and -separation techniques from tissues or cellular lysates and subsequent identification of proteins using mass-spectrometry analysis. In addition, some aspects of authors' experimental platforms, based on high-performance size-exclusion chromatography, procedures of molecular fishing, and protein identification, as well as the possibilities of interactomic taxonomy of each protein, are discussed.

Oncogenic functions of the FOXC2 transcription factor: a hallmarks of cancer perspective

Epigenetic regulation of gene expression is a fundamental determinant of molecular and cellular function, and epigenetic reprogramming in the context of cancer has emerged as one of the key enabling characteristics associated with acquisition of the core hallmarks of this disease. As such, there has been renewed interest in studying the role of transcription factors as epigenetic regulators of gene expression in cancer. In this review, we discuss the current state of knowledge surrounding the oncogenic functions of FOXC2, a transcription factor that frequently becomes dysregulated in a variety of cancer types. In addition to highlighting the clinical impact of aberrant FOXC2 activity in cancer, we discuss mechanisms by which this transcription factor becomes dysregulated in both tumor and tumor-associated cells, placing particular emphasis on the ways in which FOXC2 promotes key hallmarks of cancer progression. Finally, we bring attention to important issues related to the oncogenic dysregulation of FOXC2 that must be addressed going forward in order to improve our understanding of FOXC2-mediated cancer progression and to guide prognostic and therapeutic applications of this knowledge in clinical settings.

Comparative proteomic analysis of mustard lung as a complicated disease using systems biology approach

During Iraq-Iran conflict, chemical weapons, particularly SM gas, were used numerous times, whose aftereffects are still present. This study aimed to compare serum proteome in the chronic ML (n = 10) and HC (n = 10). TMT label-based quantitative proteomics was used to examine serums from two groups. Among total significant proteins, 14 proteins were upregulated (log₂ ≥ FC 0.5, p 0.05), and 6 proteins were downregulated (log₂ ≤ FC - 0.5, p 0.05). By helping PPI network, and EA, 11 main pathways connected to significantly different protein expression levels were discovered, including inflammatory and cell adhesion signaling pathways. It may be deduced that the wounded organs of exposed individuals experience poor repair cycles of cell degeneration and regeneration because certain repair signals were elevated while other structural and adhesion molecules were downregulated. The systems biology approach can help enhance our basic knowledge of biological processes, and contribute to a deeper understanding of pathophysiological mechanisms, as well as the identification of potential biomarkers of disease.

Quantitative trait locus (xQTL) approaches identify risk genes and drug targets from human non-coding genomes

Advances and reduction of costs in various sequencing technologies allow for a closer look at variations present in the non-coding regions of the human genome. Correlating non-coding variants with large-scale multi-omic data holds the promise not only of a better understanding of likely causal connections between non-coding DNA and expression of traits but also identifying potential disease-modifying medicines. Genome-phenome association studies have created large datasets of DNA variants that are associated with multiple traits or diseases, such as Alzheimer's disease; yet, the functional consequences of variants, in particular of non-coding variants, remain largely unknown. Recent advances in functional genomics and computational approaches have led to the identification of potential roles of DNA variants, such as various quantitative trait locus (xQTL) techniques. Multi-omic assays and analytic approaches toward xQTL have identified links between genetic loci and human transcriptomic, epigenomic, proteomic and metabolomic data. In this review, we first discuss the recent development of xQTL from multi-omic findings. We then highlight multimodal analysis of xQTL and genetic data for identification of risk genes and drug targets using Alzheimer's disease as an example. We finally discuss challenges and future research directions (e.g. artificial intelligence) for annotation of non-coding variants in complex diseases.

The human milk proteome and allergy of mother and child: Exploring associations with protein abundances and protein network connectivity

Background

The human milk proteome comprises a vast number of proteins with immunomodulatory functions, but it is not clear how this relates to allergy of the mother or allergy development in the breastfed infant. This study aimed to explore the relation between the human milk proteome and allergy of both mother and child.

Methods

Proteins were analyzed in milk samples from a subset of 300 mother-child dyads from the Canadian CHILD Cohort Study, selected based on maternal and child allergy phenotypes. For this selection, the definition of "allergy" included food allergy, eczema, allergic rhinitis, and asthma. Proteins were analyzed with non-targeted shotgun proteomics using filter-aided sample preparation (FASP) and nanoLC-Orbitrap-MS/MS. Protein abundances, based on label-free quantification, were compared using multiple statistical approaches, including univariate, multivariate, and network analyses.

Results

Using univariate analysis, we observed a trend that milk for infants who develop an allergy by 3 years of age contains higher abundances of immunoglobulin chains, irrespective of the allergy status of the mother. This observation suggests a difference in the milk's immunological potential, which might be related to the development of the infant's immune system. Furthermore, network analysis showed overall increased connectivity of proteins in the milk of allergic mothers and milk for infants who ultimately develop an allergy. This difference in connectivity was especially noted for proteins involved in the protein translation machinery and may be due to the physiological status of the mother, which is reflected in the interconnectedness of proteins in her milk. In addition, it was shown that network analysis complements the other methods for data analysis by revealing complex associations between the milk proteome and mother-child allergy status.

Conclusion

Together, these findings give new insights into how the human milk proteome, through differences in the abundance of individual proteins and protein-protein associations, relates to the allergy status of mother and child. In addition, these results inspire new research directions into the complex interplay of the mother-milk-infant triad and allergy.

Structure-function analysis of enterovirus protease 2A in complex with its essential host factor SETD3

Enteroviruses cause a number of medically relevant and widespread human diseases with no approved antiviral therapies currently available. Host-directed therapies present an enticing option for this diverse genus of viruses. We have previously identified the actin histidine methyltransferase SETD3 as a critical host factor physically interacting with the viral protease 2A. Here, we report the 3.5 Å cryo-EM structure of SETD3 interacting with coxsackievirus B3 2A at two distinct interfaces, including the substrate-binding surface within the SET domain. Structure-function analysis revealed that mutations of key residues in the SET domain resulted in severely reduced binding to 2A and complete protection from enteroviral infection. Our findings provide insight into the molecular basis of the SETD3-2A interaction and a framework for the rational design of host-directed therapeutics against enteroviruses.

Uncover New Reactivity of Genetically Encoded Alkyl Bromide Non-Canonical Amino Acids

Genetically encoded non-canonical amino acids (ncAAs) with electrophilic moieties are excellent tools to investigate protein-protein interactions (PPIs) both in vitro and in vivo. These ncAAs, including a series of alkyl bromide-based ncAAs, mainly target cysteine residues to form protein-protein cross-links. Although some reactivities towards lysine and tyrosine residues have been reported, a comprehensive understanding of their reactivity towards a broad range of nucleophilic amino acids is lacking. Here we used a recently developed OpenUaa search engine to perform an in-depth analysis of mass spec data generated for Thioredoxin and its direct binding proteins cross-linked with an alkyl bromide-based ncAA, BprY. The analysis showed that, besides cysteine residues, BprY also targeted a broad range of nucleophilic amino acids. We validated this broad reactivity of BprY with Affibody/Z protein complex. We then successfully applied BprY to map a binding interface between SUMO2 and SUMO-interacting motifs (SIMs). BprY was further applied to probe SUMO2 interaction partners. We identified 264 SUMO2 binders, including several validated SUMO2 binders and many new binders. Our data demonstrated that BprY can be effectively used to probe protein-protein interaction interfaces even without cysteine residues, which will greatly expand the power of BprY in studying PPIs.

Mapping oncogenic protein interactions for precision medicine

Normal protein‐protein interactions (normPPIs) occur with high fidelity to regulate almost every physiological process. In cancer, this highly organised and precisely regulated network is disrupted, hijacked or reprogrammed resulting in oncogenic protein‐protein interactions (oncoPPIs). OncoPPIs, which can result from genomic alterations, are a hallmark of many types of cancers. Recent technological advances in the field of mass spectrometry (MS)‐based interactomics, structural biology and drug discovery have prompted scientists to identify and characterise oncoPPIs. Disruption of oncoPPI interfaces has become a major focus of drug discovery programs and has resulted in the use of PPI‐specific drugs clinically. However, due to several technical hurdles, studies to build a reference oncoPPI map for various cancer types have not been undertaken. Therefore, there is an urgent need for experimental workflows to overcome the existing challenges in studying oncoPPIs in various cancers and to build comprehensive reference maps. Here, we discuss the important hurdles for characterising oncoPPIs and propose a three‐phase multidisciplinary workflow to identify and characterise oncoPPIs. Systematic identification of cancer‐type‐specific oncogenic interactions will spur new opportunities for PPI‐focused drug discovery projects and precision medicine.

A Practical Guide to Small Protein Discovery and Characterization Using Mass Spectrometry

Small proteins of up to ∼50 amino acids are an abundant class of biomolecules across all domains of life. Yet due to the challenges inherent in their size, they are often missed in genome annotations, and are difficult to identify and characterize using standard experimental approaches. Consequently, we still know few small proteins even in well-studied prokaryotic model organisms. Mass spectrometry (MS) has great potential for the discovery, validation, and functional characterization of small proteins. However, standard MS approaches are poorly suited to the identification of both known and novel small proteins due to limitations at each step of a typical proteomics workflow, i.e., sample preparation, protease digestion, liquid chromatography, MS data acquisition, and data analysis. Here, we outline the major MS-based workflows and bioinformatic pipelines used for small protein discovery and validation. Special emphasis is placed on highlighting the adjustments required to improve detection and data quality for small proteins. We discuss both the unbiased detection of small proteins and the targeted analysis of small proteins of interest. Finally, we provide guidelines to prioritize novel small proteins, and an outlook on methods with particular potential to further improve comprehensive discovery and characterization of small proteins.

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

After more than fifteen years from the first high-throughput experiments for human protein–protein interaction (PPI) detection, we are still wondering how close the completion of the genome-scale human PPI network reconstruction is, what needs to be further explored and whether the biological insights gained from the holistic investigation of the current network are valid and useful. The unique structure of PICKLE, a meta-database of the human experimentally determined direct PPI network developed by our group, presently covering ~80% of the UniProtKB/Swiss-Prot reviewed human complete proteome, enables the evaluation of the interactome expansion by comparing the successive PICKLE releases since 2013. We observe a gradual overall increase of 39%, 182%, and 67% in protein nodes, PPIs, and supporting references, respectively. Our results indicate that, in recent years, (a) the PPI addition rate has decreased, (b) the new PPIs are largely determined by high-throughput experiments and mainly concern existing protein nodes and (c), as we had predicted earlier, most of the newly added protein nodes have a low degree. These observations, combined with a largely overlapping k-core between PICKLE releases and a network density increase, imply that an almost complete picture of a structurally defined network has been reached. The comparative unsupervised application of two clustering algorithms indicated that exploring the full interactome topology can reveal the protein neighborhoods involved in closely related biological processes as transcriptional regulation, cell signaling and multiprotein complexes such as the connexon complex associated with cancers. A well-reconstructed human protein interactome is a powerful tool in network biology and medicine research forming the basis for multi-omic and dynamic analyses.

Mass-Spectrometry-Based Functional Proteomic and Phosphoproteomic Technologies and Their Application for Analyzing Ex Vivo and In Vitro Models of Hypertrophic Cardiomyopathy

Hypertrophic cardiomyopathy (HCM) is an autosomal dominant disease thought to be principally caused by mutations in sarcomeric proteins. Despite extensive genetic analysis, there are no comprehensive molecular frameworks for how single mutations in contractile proteins result in the diverse assortment of cellular, phenotypic, and pathobiological cascades seen in HCM. Molecular profiling and system biology approaches are powerful tools for elucidating, quantifying, and interpreting dynamic signaling pathways and differential macromolecule expression profiles for a wide range of sample types, including cardiomyopathy. Cutting-edge approaches combine high-performance analytical instrumentation (e.g., mass spectrometry) with computational methods (e.g., bioinformatics) to study the comparative activity of biochemical pathways based on relative abundances of functionally linked proteins of interest. Cardiac research is poised to benefit enormously from the application of this toolkit to cardiac tissue models, which recapitulate key aspects of pathogenesis. In this review, we evaluate state-of-the-art mass-spectrometry-based proteomic and phosphoproteomic technologies and their application to in vitro and ex vivo models of HCM for global mapping of macromolecular alterations driving disease progression, emphasizing their potential for defining the components of basic biological systems, the fundamental mechanistic basis of HCM pathogenesis, and treating the ensuing varied clinical outcomes seen among affected patient cohorts.

Pathogenesis and virulence of flavivirus infections

The Flavivirus genus consists of >70 members including several that are considered significant human pathogens. Flaviviruses display a broad spectrum of diseases that can be roughly categorised into two phenotypes - systemic disease involving haemorrhage exemplified by dengue and yellow Fever virus, and neurological complications associated with the likes of West Nile and Zika viruses. Attempts to develop vaccines have been variably successful against some. Besides, mosquito-borne flaviviruses can be vertically transmitted in the arthropods, enabling long term persistence and the possibility of re-emergence. Therefore, developing strategies to combat disease is imperative even if vaccines become available. The cellular interactions of flaviviruses with their human hosts are key to establishing the viral lifecycle on the one hand, and activation of host immunity on the other. The latter should ideally eradicate infection, but often leads to immunopathological and neurological consequences. In this review, we use Dengue and Zika viruses to discuss what we have learned about the cellular and molecular determinants of the viral lifecycle and the accompanying immunopathology, while highlighting current knowledge gaps which need to be addressed in future studies.

Chemical biology approaches to study histone interactors

Protein-protein interactions (PPIs) in the nucleus play key roles in transcriptional regulation and ensure genomic stability. Critical to this are histone-mediated PPI networks, which are further fine-tuned through dynamic post-translational modification. Perturbation to these networks leads to genomic instability and disease, presenting epigenetic proteins as key therapeutic targets. This mini-review will describe progress in mapping the combinatorial histone PTM landscape, and recent chemical biology approaches to map histone interactors. Recent advances in mapping direct interactors of histone PTMs as well as local chromatin interactomes will be highlighted, with a focus on mass-spectrometry based workflows that continue to illuminate histone-mediated PPIs in unprecedented detail.

Target Discovery for Host-Directed Antiviral Therapies: Application of Proteomics Approaches

Current epidemics, such as AIDS or flu, and the emergence of new threatening pathogens, such as the one causing the current coronavirus disease 2019 (COVID-19) pandemic, represent major global health challenges. While vaccination is an important part of the arsenal to counter the spread of viral diseases, it presents limitations and needs to be complemented by efficient therapeutic solutions. Intricate knowledge of host-pathogen interactions is a powerful tool to identify host-dependent vulnerabilities that can be exploited to dampen viral replication. Such host-directed antiviral therapies are promising and are less prone to the development of drug-resistant viral strains. Here, we first describe proteomics-based strategies that allow the rapid characterization of host-pathogen interactions. We then discuss how such data can be exploited to help prioritize compounds with potential host-directed antiviral activity that can be tested in preclinical models.

Developing a Bimolecular Affinity Purification Strategy to Isolate 26S Proteasome Holocomplexes for Complex-Centric Proteomic Analysis

The 26S proteasome is a mega-dalton protein complex responsible for intracellular degradation in eukaryotes. It is composed of two subcomplexes: the 20S core particle and the 19S regulatory particle, which form compositionally and structurally heterogeneous proteasome complexes in cells. To fully characterize the 26S proteasome, it is necessary to understand its structural and functional diversities. Multiple mass spectrometry (MS) methodologies have been developed in recent years for the study of proteasome structural dynamics in which biochemically isolated complexes are subjected to analysis. Due to the inherent heterogeneity of proteasome complexes, single-bait affinity purification typically results in a mixture of compositionally heterogeneous complexes regardless of the baits, making accurate assessment of complex-specific conformations and functions challenging. To facilitate complex-centric analysis, we have adopted a bimolecular affinity purification method utilizing a dual-bait cell line expressing tagged 19S and tagged 20S subunits to improve the homogeneity of the resulting 26S holocomplexes. To establish the method, four types of purifications were performed and the resulting samples were extensively examined by biochemical analysis and two label-free quantitative MS methods. Our results have demonstrated the effectiveness of this purification strategy in improving the complex homogeneity for downstream biochemical and MS characterizations. This strategy will be valuable for facilitating detailed quantitative assessments of complex-specific molecular details under different conditions and can be directly adopted for studying other complexes.

Identifying cancer drivers

[Figure: see text].

Interactome Networks of FOSL1 and FOSL2 in Human Th17 Cells

Dysregulated function of Th17 cells has implications in immunodeficiencies and autoimmune disorders. Th17 cell differentiation is orchestrated by a complex network of transcription factors, including several members of the activator protein (AP-1) family. Among the latter, FOSL1 and FOSL2 modulate the effector functions of Th17 cells. However, the molecular mechanisms underlying these effects are unclear, owing to the poorly characterized protein interaction networks of FOSL factors. Here, we establish the first interactomes of FOSL1 and FOSL2 in human Th17 cells, using affinity purification-mass spectrometry analysis. In addition to the known JUN proteins, we identified several novel binding partners of FOSL1 and FOSL2. Gene ontology analysis found a significant fraction of these interactors to be associated with RNA-binding activity, which suggests new mechanistic links. Intriguingly, 29 proteins were found to share interactions with FOSL1 and FOSL2, and these included key regulators of Th17 fate. We further validated the binding partners identified in this study by using parallel reaction monitoring targeted mass spectrometry and other methods. Our study provides key insights into the interaction-based signaling mechanisms of FOSL proteins that potentially govern Th17 cell differentiation and associated pathologies.

Mechanically transduced immunosorbent assay to measure protein-protein interactions

Measuring protein-protein interaction (PPI) affinities is fundamental to biochemistry. Yet, conventional methods rely upon the law of mass action and cannot measure many PPIs due to a scarcity of reagents and limitations in the measurable affinity ranges. Here, we present a novel technique that leverages the fundamental concept of friction to produce a mechanical signal that correlates to binding potential. The mechanically transduced immunosorbent (METRIS) assay utilizes rolling magnetic probes to measure PPI interaction affinities. METRIS measures the translational displacement of protein-coated particles on a protein-functionalized substrate. The translational displacement scales with the effective friction induced by a PPI, thus producing a mechanical signal when a binding event occurs. The METRIS assay uses as little as 20 pmols of reagents to measure a wide range of affinities while exhibiting a high resolution and sensitivity. We use METRIS to measure several PPIs that were previously inaccessible using traditional methods, providing new insights into epigenetic recognition.

Thiol-Cleavable Biotin for Chemical and Enzymatic Biotinylation and Its Application to Mitochondrial TurboID Proteomics

Protein biotinylation via chemical or enzymatic reactions is often coupled with streptavidin-based enrichment and on-bead digestion in numerous biological applications. However, the popular on-bead digestion method faces major challenges of streptavidin contamination, overwhelming signals from endogenous biotinylated proteins, the lost information on biotinylation sites, and limited sequence coverage of enriched proteins. Here, we explored thiol-cleavable biotin as an alternative approach to elute biotinylated proteins from streptavidin-coated beads for both chemical biotinylation and biotin ligase-based proximity labeling. All possible amino acid sites for biotinylation were thoroughly evaluated in addition to the primary lysine residue. We found that biotinylation at lysine residues notably reduces the trypsin digestion efficiency, which can be mitigated by the thiol-cleavable biotinylation method. We then evaluated the applicability of thiol-cleavable biotin as a substrate for proximity labeling in living cells, where TurboID biotin ligase was engineered onto the mitochondrial inner membrane facing the mitochondrial matrix. As a proof-of-principle study, thiol-cleavable biotin-assisted TurboID proteomics achieved remarkable intraorganelle spatial resolution with significantly enriched proteins localized in the mitochondrial inner membrane and mitochondrial matrix.

Protein interaction landscapes revealed by advanced in vivo cross-linking-mass spectrometry

Defining protein-protein interactions (PPIs) in their native environment is crucial to understanding protein structure and function. Cross-linking-mass spectrometry (XL-MS) has proven effective in capturing PPIs in living cells; however, the proteome coverage remains limited. Here, we have developed a robust in vivo XL-MS platform to facilitate in-depth PPI mapping by integrating a multifunctional MS-cleavable cross-linker with sample preparation strategies and high-resolution MS. The advancement of click chemistry-based enrichment significantly enhanced the detection of cross-linked peptides for proteome-wide analyses. This platform enabled the identification of 13,904 unique lysine-lysine linkages from in vivo cross-linked HEK 293 cells, permitting construction of the largest in vivo PPI network to date, comprising 6,439 interactions among 2,484 proteins. These results allowed us to generate a highly detailed yet panoramic portrait of human interactomes associated with diverse cellular pathways. The strategy presented here signifies a technological advancement for in vivo PPI mapping at the systems level and can be generalized for charting protein interaction landscapes in any organisms.

Computational and Mass Spectrometry-Based Approach Identify Deleterious Non-Synonymous Single Nucleotide Polymorphisms (nsSNPs) in JMJD6

The jumonji domain-containing protein 6 (JMJD6) gene catalyzes the arginine demethylation and lysine hydroxylation of histone and a growing list of its known substrate molecules, including p53 and U2AF65, suggesting a possible role in mRNA splicing and transcription in cancer progression. Mass spectrometry-based technology offers the opportunity to detect SNP variants accurately and effectively. In our study, we conducted a combined computational and filtration workflow to predict the nonsynonymous single nucleotide polymorphisms (nsSNPs) present in JMJD6, followed by a liquid chromatography-tandem mass spectrometry (LC-MS/MS) analysis and validation. The computational approaches SIFT, PolyPhen-2, SNAP, I-Mutant 2.0, PhD-SNP, PANTHER, and SNPS&GO were integrated to screen out the predicted damaging/deleterious nsSNPs. Through the three-dimensional structure of JMJD6, H187R (rs1159480887) was selected as a candidate for validation. The validation experiments showed that the mutation of this nsSNP in JMJD6 obviously affected mRNA splicing or the transcription of downstream genes through the reduced lysyl-hydroxylase activity of its substrates, U2AF65 and p53, further indicating the accuracy of this prediction method. This research provides an effective computational workflow for researchers with an opportunity to select prominent deleterious nsSNPs and, thus, remains promising for examining the dysfunction of proteins.

Concepts to Reveal Parvovirus-Nucleus Interactions

Parvoviruses are small single-stranded (ss) DNA viruses, which replicate in the nucleoplasm and affect both the structure and function of the nucleus. The nuclear stage of the parvovirus life cycle starts at the nuclear entry of incoming capsids and culminates in the successful passage of progeny capsids out of the nucleus. In this review, we will present past, current, and future microscopy and biochemical techniques and demonstrate their potential in revealing the dynamics and molecular interactions in the intranuclear processes of parvovirus infection. In particular, a number of advanced techniques will be presented for the detection of infection-induced changes, such as DNA modification and damage, as well as protein-chromatin interactions.

PPI-MASS: An Interactive Web Server to Identify Protein-Protein Interactions From Mass Spectrometry-Based Proteomics Data

Mass spectrometry-based proteomics methods are widely used to identify and quantify protein complexes involved in diverse biological processes. Specifically, tandem mass spectrometry methods represent an accurate and sensitive strategy for identifying protein-protein interactions. However, most of these approaches provide only lists of peptide fragments associated with a target protein, without performing further analyses to discriminate physical or functional protein-protein interactions. Here, we present the PPI-MASS web server, which provides an interactive analytics platform to identify protein-protein interactions with pharmacological potential by filtering a large protein set according to different biological features. Starting from a list of proteins detected by MS-based methods, PPI-MASS integrates an automatized pipeline to obtain information of each protein from freely accessible databases. The collected data include protein sequence, functional and structural properties, associated pathologies and drugs, as well as location and expression in human tissues. Based on this information, users can manipulate different filters in the web platform to identify candidate proteins to establish physical contacts with a target protein. Thus, our server offers a simple but powerful tool to detect novel protein-protein interactions, avoiding tedious and time-consuming data postprocessing. To test the web server, we employed the interactome of the TRPM4 and TMPRSS11a proteins as a use case. From these data, protein-protein interactions were identified, which have been validated through biochemical and bioinformatic studies. Accordingly, our web platform provides a comprehensive and complementary tool for identifying protein-protein complexes assisting the future design of associated therapies.

Mammalian Neuronal mRNA Transport Complexes: The Few Knowns and the Many Unknowns

Hundreds of messenger RNAs (mRNAs) are transported into neurites to provide templates for the assembly of local protein networks. These networks enable a neuron to configure different cellular domains for specialized functions. According to current evidence, mRNAs are mostly transported in rather small packages of one to three copies, rarely containing different transcripts. This opens up fascinating logistic problems: how are hundreds of different mRNA cargoes sorted into distinct packages and how are they coupled to and released from motor proteins to produce the observed mRNA distributions? Are all mRNAs transported by the same transport machinery, or are there different adaptors or motors for different transcripts or classes of mRNAs? A variety of often indirect evidence exists for the involvement of proteins in mRNA localization, but relatively little is known about the essential activities required for the actual transport process. Here, we summarize the different types of available evidence for interactions that connect mammalian mRNAs to motor proteins to highlight at which point further research is needed to uncover critical missing links. We further argue that a combination of discovery approaches reporting direct interactions, in vitro reconstitution, and fast perturbations in cells is an ideal future strategy to unravel essential interactions and specific functions of proteins in mRNA transport processes.

Joint cell segmentation and cell type annotation for spatial transcriptomics

RNA hybridization-based spatial transcriptomics provides unparalleled detection sensitivity. However, inaccuracies in segmentation of image volumes into cells cause misassignment of mRNAs which is a major source of errors. Here, we develop JSTA, a computational framework for joint cell segmentation and cell type annotation that utilizes prior knowledge of cell type-specific gene expression. Simulation results show that leveraging existing cell type taxonomy increases RNA assignment accuracy by more than 45%. Using JSTA, we were able to classify cells in the mouse hippocampus into 133 (sub)types revealing the spatial organization of CA1, CA3, and Sst neuron subtypes. Analysis of within cell subtype spatial differential gene expression of 80 candidate genes identified 63 with statistically significant spatial differential gene expression across 61 (sub)types. Overall, our work demonstrates that known cell type expression patterns can be leveraged to improve the accuracy of RNA hybridization-based spatial transcriptomics while providing highly granular cell (sub)type information. The large number of newly discovered spatial gene expression patterns substantiates the need for accurate spatial transcriptomic measurements that can provide information beyond cell (sub)type labels.