Proteomic Discovery of RNA-Protein Molecular Clamps Using a Thermal Shift Assay with ATP and RNA (TSAR).

Uncompetitive inhibition is an effective strategy for suppressing dysregulated enzymes and their substrates, but discovery of suitable ligands depends on often-unavailable structural knowledge and serendipity. Hence, despite surging interest in mass spectrometry-based target identification, proteomic studies of substrate-dependent target engagement remain sparse. Herein, we describe the Thermal Shift Assay with ATP and RNA (TSAR) as a template for proteome-wide discovery of substrate-dependent ligand binding. Using proteomic thermal shift assays, we show that simple biochemical additives can facilitate detection of target engagement in native cell lysates. We apply our approach to rocaglates, a family of molecules that specifically clamp RNA to eukaryotic translation initiation factor 4A (eIF4A), DEAD-box helicase 3X (DDX3X), and potentially other members of the DEAD-box (DDX) family of RNA helicases. To identify unexpected interactions, we optimized a target class-specific thermal denaturation window and evaluated ATP analog and RNA probe dependencies for key rocaglate-DDX interactions. We report novel DDX targets of the rocaglate clamping spectrum, confirm that DDX3X is a common target of several widely studied analogs, and provide structural insights into divergent DDX3X affinities between synthetic rocaglates. We independently validate novel targets of high-profile rocaglates, including the clinical candidate Zotatifin (eFT226), using limited proteolysis-mass spectrometry and fluorescence polarization experiments. Taken together, our study provides a model for screening uncompetitive inhibitors using a systematic chemical-proteomics approach to uncover actionable DDX targets, clearing a path towards characterization of novel molecular clamps and associated RNA helicase targets.

DESP demixes cell-state profiles from dynamic bulk molecular measurements.

Understanding the dynamic expression of proteins and other key molecules driving phenotypic remodeling in development and pathobiology has garnered widespread interest, yet the exploration of these systems at the foundational resolution of the underlying cell states has been significantly limited by technical constraints. Here, we present DESP, an algorithm designed to leverage independent estimates of cell-state proportions, such as from single-cell RNA sequencing, to resolve the relative contributions of cell states to bulk molecular measurements, most notably quantitative proteomics, recorded in parallel. We applied DESP to an in vitro model of the epithelial-to-mesenchymal transition and demonstrated its ability to accurately reconstruct cell-state signatures from bulk-level measurements of both the proteome and transcriptome, providing insights into transient regulatory mechanisms. DESP provides a generalizable computational framework for modeling the relationship between bulk and single-cell molecular measurements, enabling the study of proteomes and other molecular profiles at the cell-state level using established bulk-level workflows.

DOT1L stimulates MYC/Mondo transcription factor activity by promoting its degradation cycle on chromatin.

The proto-oncogene c-MYC is a key representative of the MYC transcription factor network regulating growth and metabolism. MML-1 (Myc- and Mondo-like) is its homolog in C. elegans. The functional and molecular cooperation between c-MYC and H3 lysine 79 methyltransferase DOT1L was demonstrated in several human cancer types, and we have earlier discovered the connection between C. elegans MML-1 and DOT-1.1. Here, we demonstrate the critical role of DOT1L/DOT-1.1 in regulating c-MYC/MML-1 target genes genome-wide by ensuring the removal of "spent" transcription factors from chromatin by the nuclear proteasome. Moreover, we uncover a previously unrecognized proteolytic activity of DOT1L, which may facilitate c-MYC turnover. This new mechanism of c-MYC regulation by DOT1L may lead to the development of new approaches for cancer treatment.

Corrigendum: Integrated metabolomics and proteomics reveal biomarkers associated with hemodialysis in end-stage kidney disease.

[This corrects the article DOI: 10.3389/fphar.2023.1243505.].

Human pluripotent stem cell modeling of alveolar type 2 cell dysfunction caused by ABCA3 mutations.

Mutations in ATP-binding cassette A3 (ABCA3), a phospholipid transporter critical for surfactant homeostasis in pulmonary alveolar type II epithelial cells (AEC2s), are the most common genetic causes of childhood interstitial lung disease (chILD). Treatments for patients with pathological variants of ABCA3 mutations are limited, in part due to a lack of understanding of disease pathogenesis resulting from an inability to access primary AEC2s from affected children. Here, we report the generation of AEC2s from affected patient induced pluripotent stem cells (iPSCs) carrying homozygous versions of multiple ABCA3 mutations. We generated syngeneic CRISPR/Cas9 gene-corrected and uncorrected iPSCs and ABCA3-mutant knockin ABCA3:GFP fusion reporter lines for in vitro disease modeling. We observed an expected decreased capacity for surfactant secretion in ABCA3-mutant iPSC-derived AEC2s (iAEC2s), but we also found an unexpected epithelial-intrinsic aberrant phenotype in mutant iAEC2s, presenting as diminished progenitor potential, increased NFκB signaling, and the production of pro-inflammatory cytokines. The ABCA3:GFP fusion reporter permitted mutant-specific, quantifiable characterization of lamellar body size and ABCA3 protein trafficking, functional features that are perturbed depending on ABCA3 mutation type. Our disease model provides a platform for understanding ABCA3 mutation-mediated mechanisms of alveolar epithelial cell dysfunction that may trigger chILD pathogenesis.

Beyond Glycolysis: Aldolase A is a Novel Effector in Reelin Mediated Dendritic Development.

Reelin, a secreted glycoprotein, plays a crucial role in guiding neocortical neuronal migration, dendritic outgrowth and arborization, and synaptic plasticity in the adult brain. Reelin primarily operates through the canonical lipoprotein receptors apolipoprotein E receptor 2 (Apoer2) and very low-density lipoprotein receptor (Vldlr). Reelin also engages with non-canonical receptors and unidentified co-receptors; however, the effects of which are less understood. Using high-throughput tandem mass tag LC-MS/MS-based proteomics and gene set enrichment analysis, we identified both shared and unique intracellular pathways activated by Reelin through its canonical and non-canonical signaling in primary murine neurons during dendritic growth and arborization. We observed pathway crosstalk related to regulation of cytoskeleton, neuron projection development, protein transport, and actin filament-based process. We also found enriched gene sets exclusively by the non-canonical Reelin pathway including protein translation, mRNA metabolic process and ribonucleoprotein complex biogenesis suggesting Reelin fine-tunes neuronal structure through distinct signaling pathways. A key discovery is the identification of aldolase A, a glycolytic enzyme and actin binding protein, as a novel effector of Reelin signaling. Reelin induced de novo translation and mobilization of aldolase A from the actin cytoskeleton. We demonstrated that aldolase A is necessary for Reelin-mediated dendrite growth and arborization in primary murine neurons and mouse brain cortical neurons. Interestingly, the function of aldolase A in dendrite development is independent of its known role in glycolysis. Altogether, our findings provide new insights into the Reelin-dependent signaling pathways and effector proteins that are crucial for actin remodeling and dendritic development. Significance: Reelin is an extracellular glycoprotein and exerts its function primarily by binding to the canonical lipoprotein receptors Apoer2 and Vldlr. Reelin is best known for its role in neuronal migration during prenatal brain development. Reelin also signals through a non-canonical pathway outside of Apoer2/Vldlr; however, these receptors and signal transduction pathways are less defined. Here, we examined Reelin's role during dendritic outgrowth in primary murine neurons and identified shared and distinct pathways activated by canonical and non-canonical Reelin signaling. We also found aldolase A as a novel effector of Reelin signaling, that functions independently of its known metabolic role, highlighting Reelin's influence on actin dynamics and neuronal structure and growth.

Integrated metabolomics and proteomics reveal biomarkers associated with hemodialysis in end-stage kidney disease.

Background: We hypothesize that the poor survival outcomes of end-stage kidney disease (ESKD) patients undergoing hemodialysis are associated with a low filtering efficiency and selectivity. The current gold standard criteria using single or several markers show an inability to predict or disclose the treatment effect and disease progression accurately. Methods: We performed an integrated mass spectrometry-based metabolomic and proteomic workflow capable of detecting and quantifying circulating small molecules and proteins in the serum of ESKD patients. Markers linked to cardiovascular disease (CVD) were validated on human induced pluripotent stem cell (iPSC)-derived cardiomyocytes. Results: We identified dozens of elevated molecules in the serum of patients compared with healthy controls. Surprisingly, many metabolites, including lipids, remained at an elevated blood concentration despite dialysis. These molecules and their associated physical interaction networks are correlated with clinical complications in chronic kidney disease. This study confirmed two uremic toxins associated with CVD, a major risk for patients with ESKD. Conclusion: The retained molecules and metabolite-protein interaction network address a knowledge gap of candidate uremic toxins associated with clinical complications in patients undergoing dialysis, providing mechanistic insights and potential drug discovery strategies for ESKD.

SND1 binds to ERG and promotes tumor growth in genetic mouse models of prostate cancer.

SND1 and MTDH are known to promote cancer and therapy resistance, but their mechanisms and interactions with other oncogenes remain unclear. Here, we show that oncoprotein ERG interacts with SND1/MTDH complex through SND1's Tudor domain. ERG, an ETS-domain transcription factor, is overexpressed in many prostate cancers. Knocking down SND1 in human prostate epithelial cells, especially those overexpressing ERG, negatively impacts cell proliferation. Transcriptional analysis shows substantial overlap in genes regulated by ERG and SND1. Mechanistically, we show that ERG promotes nuclear localization of SND1/MTDH. Forced nuclear localization of SND1 prominently increases its growth promoting function irrespective of ERG expression. In mice, prostate-specific Snd1 deletion reduces cancer growth and tumor burden in a prostate cancer model (PB-Cre/Ptenflox/flox/ERG mice), Moreover, we find a significant overlap between prostate transcriptional signatures of ERG and SND1. These findings highlight SND1's crucial role in prostate tumorigenesis, suggesting SND1 as a potential therapeutic target in prostate cancer.

Poly(GR) interacts with key stress granule factors promoting its assembly into cytoplasmic inclusions.

C9orf72 repeat expansions are the most common genetic cause of frontotemporal dementia (FTD) and amyotrophic lateral sclerosis (ALS). Poly(GR) proteins are toxic to neurons by forming cytoplasmic inclusions that sequester RNA-binding proteins including stress granule (SG) proteins. However, little is known of the factors governing poly(GR) inclusion formation. Here, we show that poly(GR) infiltrates a finely tuned network of protein-RNA interactions underpinning SG formation. It interacts with G3BP1, the key driver of SG assembly and a protein we found is critical for poly(GR) inclusion formation. Moreover, we discovered that N6-methyladenosine (m6A)-modified mRNAs and m6A-binding YTHDF proteins not only co-localize with poly(GR) inclusions in brains of c9FTD/ALS mouse models and patients with c9FTD, they promote poly(GR) inclusion formation via the incorporation of RNA into the inclusions. Our findings thus suggest that interrupting interactions between poly(GR) and G3BP1 or YTHDF1 proteins or decreasing poly(GR) altogether represent promising therapeutic strategies to combat c9FTD/ALS pathogenesis.

Human herpesvirus 8 ORF57 protein is able to reduce TDP-43 pathology: network analysis identifies interacting pathways.

Aggregation of TAR DNA-binding protein 43 kDa (TDP-43) is thought to drive the pathophysiology of amyotrophic lateral sclerosis and some frontotemporal dementias. TDP-43 is normally a nuclear protein that in neurons translocates to the cytoplasm and can form insoluble aggregates upon activation of the integrated stress response (ISR). Viruses evolved to control the ISR. In the case of Herpesvirus 8, the protein ORF57 acts to bind protein kinase R, inhibit phosphorylation of eIF2α and reduce activation of the ISR. We hypothesized that ORF57 might also possess the ability to inhibit aggregation of TDP-43. ORF57 was expressed in the neuronal SH-SY5Y line and its effects on TDP-43 aggregation characterized. We report that ORF57 inhibits TDP-43 aggregation by 55% and elicits a 2.45-fold increase in the rate of dispersion of existing TDP-43 granules. These changes were associated with a 50% decrease in cell death. Proteomic studies were carried out to identify the protein interaction network of ORF57. We observed that ORF57 directly binds to TDP-43 as well as interacts with many components of the ISR, including elements of the proteostasis machinery known to reduce TDP-43 aggregation. We propose that viral proteins designed to inhibit a chronic ISR can be engineered to remove aggregated proteins and dampen a chronic ISR.

High-resolution photocatalytic mapping of SARS-CoV-2 spike interactions on the cell surface.

Identifying virus-host interactions on the cell surface can improve our understanding of viral entry and pathogenesis. SARS-CoV-2, the causative agent of the COVID-19 disease, uses ACE2 as a receptor to enter cells. Yet the full repertoire of cell surface proteins that contribute to viral entry is unknown. We developed a photocatalyst-based viral-host protein microenvironment mapping platform (ViraMap) to probe the molecular neighborhood of the SARS-CoV-2 spike protein on the human cell surface. Application of ViraMap to ACE2-expressing cells captured ACE2, the established co-receptor NRP1, and several novel cell surface proteins. We systematically analyzed the relevance of these candidate proteins to SARS-CoV-2 entry by knockdown and overexpression approaches in pseudovirus and authentic infection models and identified PTGFRN and EFNB1 as bona fide viral entry factors. Our results highlight additional host targets that participate in SARS-CoV-2 infection and showcase ViraMap as a powerful platform for defining viral interactions on the cell surface.

State-of-the-art computational methods to predict protein-protein interactions with high accuracy and coverage.

Prediction of protein-protein interactions (PPIs) commonly involves a significant computational component. Rapid recent advances in the power of computational methods for protein interaction prediction motivate a review of the state-of-the-art. We review the major approaches, organized according to the primary source of data utilized: protein sequence, protein structure, and protein co-abundance. The advent of deep learning (DL) has brought with it significant advances in interaction prediction, and we show how DL is used for each source data type. We review the literature taxonomically, present example case studies in each category, and conclude with observations about the strengths and weaknesses of machine learning methods in the context of the principal sources of data for protein interaction prediction.

Dynamic remodeling of Escherichia coli interactome in response to environmental perturbations.

Proteins play an essential role in the vital biological processes governing cellular functions. Most proteins function as members of macromolecular machines, with the network of interacting proteins revealing the molecular mechanisms driving the formation of these complexes. Profiling the physiology-driven remodeling of these interactions within different contexts constitutes a crucial component to achieving a comprehensive systems-level understanding of interactome dynamics. Here, we apply co-fractionation mass spectrometry and computational modeling to quantify and profile the interactions of ∼2000 proteins in the bacterium Escherichia coli cultured under 10 distinct culture conditions. The resulting quantitative co-elution patterns revealed large-scale condition-dependent interaction remodeling among protein complexes involved in diverse biochemical pathways in response to the unique environmental challenges. The network-level analysis highlighted interactome-wide biophysical properties and structural patterns governing interaction remodeling. Our results provide evidence of the local and global plasticity of the E. coli interactome along with a rigorous generalizable framework to define protein interaction specificity. We provide an accompanying interactive web application to facilitate the exploration of these rewired networks.

Integration of Metabolomic and Proteomic Data to Uncover Actionable Metabolic Pathways.

Mass spectrometry (MS) is an important tool for biological studies because it is capable of interrogating a diversity of biomolecules (proteins, drugs, metabolites) not captured via alternate genomic platforms. Unfortunately, downstream data analysis becomes complicated when attempting to evaluate and integrate measurements of different molecular classes and requires the aggregation of expertise from different relevant disciplines. This complexity represents a significant bottleneck that limits the routine deployment of MS-based multi-omic methods, despite the unmatched biological and functional insight the data can provide. To address this unmet need, our group introduced Omics Notebook as an open-source framework for facilitating exploratory analysis, reporting and integrating MS-based multi-omic data in a way that is automated, reproducible and customizable. By deploying this pipeline, we have devised a framework for researchers to more rapidly identify functional patterns across complex data types and focus on statistically significant and biologically interesting aspects of their multi-omic profiling experiments. This chapter aims to describe a protocol which leverages our publicly accessible tools to analyze and integrate data from high-throughput proteomics and metabolomics experiments and produce reports that will facilitate more impactful research, cross-institutional collaborations, and wider data dissemination.

Proteomics and phosphoproteomics of failing human left ventricle identifies dilated cardiomyopathy-associated phosphorylation of CTNNA3.

The prognosis and treatment outcomes of heart failure (HF) patients rely heavily on disease etiology, yet the majority of underlying signaling mechanisms are complex and not fully elucidated. Phosphorylation is a major point of protein regulation with rapid and profound effects on the function and activity of protein networks. Currently, there is a lack of comprehensive proteomic and phosphoproteomic studies examining cardiac tissue from HF patients with either dilated dilated cardiomyopathy (DCM) or ischemic cardiomyopathy (ICM). Here, we used a combined proteomic and phosphoproteomic approach to identify and quantify more than 5,000 total proteins with greater than 13,000 corresponding phosphorylation sites across explanted left ventricle (LV) tissue samples, including HF patients with DCM vs. nonfailing controls (NFC), and left ventricular infarct vs. noninfarct, and periinfarct vs. noninfarct regions of HF patients with ICM. Each pair-wise comparison revealed unique global proteomic and phosphoproteomic profiles with both shared and etiology-specific perturbations. With this approach, we identified a DCM-associated hyperphosphorylation cluster in the cardiomyocyte intercalated disc (ICD) protein, αT-catenin (CTNNA3). We demonstrate using both ex vivo isolated cardiomyocytes and in vivo using an AAV9-mediated overexpression mouse model, that CTNNA3 phosphorylation at these residues plays a key role in maintaining protein localization at the cardiomyocyte ICD to regulate conductance and cell-cell adhesion. Collectively, this integrative proteomic/phosphoproteomic approach identifies region- and etiology-associated signaling pathways in human HF and describes a role for CTNNA3 phosphorylation in the pathophysiology of DCM.

Multi-Omics Profiling of Hypertrophic Cardiomyopathy Reveals Altered Mechanisms in Mitochondrial Dynamics and Excitation-Contraction Coupling.

Hypertrophic cardiomyopathy is one of the most common inherited cardiomyopathies and a leading cause of sudden cardiac death in young adults. Despite profound insights into the genetics, there is imperfect correlation between mutation and clinical prognosis, suggesting complex molecular cascades driving pathogenesis. To investigate this, we performed an integrated quantitative multi-omics (proteomic, phosphoproteomic, and metabolomic) analysis to illuminate the early and direct consequences of mutations in myosin heavy chain in engineered human induced pluripotent stem-cell-derived cardiomyocytes relative to late-stage disease using patient myectomies. We captured hundreds of differential features, which map to distinct molecular mechanisms modulating mitochondrial homeostasis at the earliest stages of pathobiology, as well as stage-specific metabolic and excitation-coupling maladaptation. Collectively, this study fills in gaps from previous studies by expanding knowledge of the initial responses to mutations that protect cells against the early stress prior to contractile dysfunction and overt disease.

GPS2-mediated regulation of the adipocyte secretome modulates adipose tissue remodeling at the onset of diet-induced obesity.

OBJECTIVE: Dysfunctional, unhealthy expansion of white adipose tissue due to excess dietary intake is a process at the root of obesity and Type 2 Diabetes development. The objective of this study is to contribute to a better understanding of the underlying mechanism(s) regulating the early stages of adipose tissue expansion and adaptation to dietary stress due to an acute, high-fat diet (HFD) challenge, with a focus on the communication between adipocytes and other stromal cells. METHODS: We profiled the early response to high-fat diet exposure in wildtype and adipocyte-specific GPS2-KO (GPS2-AKO) mice at the cellular, tissue and organismal level. A multi-pronged approach was employed to disentangle the complex cellular interactions dictating tissue remodeling, via single-cell RNA sequencing and FACS profiling of the stromal fraction, and semi-quantitative proteomics of the adipocyte-derived exosomal cargo after 5 weeks of HFD feeding. RESULTS: Our results indicate that loss of GPS2 in mature adipocytes leads to impaired adaptation to the metabolic stress imposed by HFD feeding. GPS2-AKO mice are significantly more inflamed, insulin resistant, and obese, compared to the WT counterparts. At the cellular level, lack of GPS2 in adipocytes impacts upon other stromal populations, with both the eWAT and scWAT depots exhibiting changes in the immune and non-immune compartments that contribute to an increase in inflammatory and anti-adipogenic cell types. Our studies also revealed that adipocyte to stromal cell communication is facilitated by exosomes, and that transcriptional rewiring of the exosomal cargo is crucial for tissue remodeling. Loss of GPS2 results in increased expression of secreted factors promoting a TGFß-driven fibrotic microenvironment favoring unhealthy tissue remodeling and expansion. CONCLUSIONS: Adipocytes serve as an intercellular signaling hub, communicating with the stromal compartment via paracrine signaling. Our study highlights the importance of proper regulation of the 'secretome' released by energetically stressed adipocytes at the onset of obesity. Altered transcriptional regulation of factors secreted via adipocyte-derived exosomes (AdExos), in the absence of GPS2, contributes to the establishment of an anti-adipogenic, pro-fibrotic adipose tissue environment, and to hastened progression towards a metabolically dysfunctional phenotype.

Parallelized multidimensional analytic framework applied to mammary epithelial cells uncovers regulatory principles in EMT.

A proper understanding of disease etiology will require longitudinal systems-scale reconstruction of the multitiered architecture of eukaryotic signaling. Here we combine state-of-the-art data acquisition platforms and bioinformatics tools to devise PAMAF, a workflow that simultaneously examines twelve omics modalities, i.e., protein abundance from whole-cells, nucleus, exosomes, secretome and membrane; N-glycosylation, phosphorylation; metabolites; mRNA, miRNA; and, in parallel, single-cell transcriptomes. We apply PAMAF in an established in vitro model of TGFß-induced epithelial to mesenchymal transition (EMT) to quantify >61,000 molecules from 12 omics and 10 timepoints over 12 days. Bioinformatics analysis of this EMT-ExMap resource allowed us to identify; -topological coupling between omics, -four distinct cell states during EMT, -omics-specific kinetic paths, -stage-specific multi-omics characteristics, -distinct regulatory classes of genes, -ligand-receptor mediated intercellular crosstalk by integrating scRNAseq and subcellular proteomics, and -combinatorial drug targets (e.g., Hedgehog signaling and CAMK-II) to inhibit EMT, which we validate using a 3D mammary duct-on-a-chip platform. Overall, this study provides a resource on TGFß signaling and EMT.