PANAMA-enabled high-sensitivity dual nanoflow LC-MS metabolomics and proteomics analysis.

High-sensitivity nanoflow liquid chromatography (nLC) is seldom employed in untargeted metabolomics because current sample preparation techniques are inefficient at preventing nanocapillary column performance degradation. Here, we describe an nLC-based tandem mass spectrometry workflow that enables seamless joint analysis and integration of metabolomics (including lipidomics) and proteomics from the same samples without instrument duplication. This workflow is based on a robust solid-phase micro-extraction step for routine sample cleanup and bioactive molecule enrichment. Our method, termed proteomic and nanoflow metabolomic analysis (PANAMA), improves compound resolution and detection sensitivity without compromising the depth of coverage as compared with existing widely used analytical procedures. Notably, PANAMA can be applied to a broad array of specimens, including biofluids, cell lines, and tissue samples. It generates high-quality, information-rich metabolite-protein datasets while bypassing the need for specialized instrumentation.

Lead toxicity regulation via protein degradation and tetrapyrrole biosynthesis pathways in Brassica species: A comparative quantitative analysis of proteomic study.

Understanding the heavy metals (HMs) tolerance mechanism is crucial for improving plant growth in metal-contaminated soil. In order to evaluate the lead (Pb) tolerance mechanism in Brassica species, a comparative proteomic study was used. Thirteen-day-old seedlings of B. juncea and B. napus were treated with different Pb(NO3)2 concentrations at 0, 3, 30, and 300 mg/L. Under 300 mg/L Pb(NO3)2 concentration, B. napus growth was significantly decreased, while B. juncea maintained normal growth similar to the control. The Pb accumulation was also higher in B. napus root and shoot compared to B. juncea. Gel-free proteomic analysis of roots revealed a total of 68 and 37 differentially abundant proteins (DAPs) in B. juncea and B. napus-specifically, after 300 mg/L Pb exposure. The majority of these proteins are associated with protein degradation, cellular respiration, and enzyme classification. The upregulated RPT2 and tetrapyrrole biosynthesis pathway-associated proteins maintain the cellular homeostasis and photosynthetic rate in B. juncea. Among the 55 common DAPs, S-adenosyl methionine and TCA cycle proteins were upregulated in B. juncea and down-regulated in B. napus after Pb exposure. Furthermore, higher oxidative stress also reduced the antioxidant enzyme activity in B. napus. The current finding suggests that B. juncea is more Pb tolerant than B. napus, possibly due to the upregulation of proteins involved in protein recycling, degradation, and tetrapyrrole biosynthesis pathway.

ToxoNet: A high confidence map of protein-protein interactions in Toxoplasma gondii.

The apicomplexan intracellular parasite Toxoplasma gondii is a major food borne pathogen that is highly prevalent in the global population. The majority of the T. gondii proteome remains uncharacterized and the organization of proteins into complexes is unclear. To overcome this knowledge gap, we used a biochemical fractionation strategy to predict interactions by correlation profiling. To overcome the deficit of high-quality training data in non-model organisms, we complemented a supervised machine learning strategy, with an unsupervised approach, based on similarity network fusion. The resulting combined high confidence network, ToxoNet, comprises 2,063 interactions connecting 652 proteins. Clustering identifies 93 protein complexes. We identified clusters enriched in mitochondrial machinery that include previously uncharacterized proteins that likely represent novel adaptations to oxidative phosphorylation. Furthermore, complexes enriched in proteins localized to secretory organelles and the inner membrane complex, predict additional novel components representing novel targets for detailed functional characterization. We present ToxoNet as a publicly available resource with the expectation that it will help drive future hypotheses within the research community.

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.

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.].

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.

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.

Proximity labeling reveals dynamic changes in the SQSTM1 protein network.

Sequestosome1 (SQSTM1) is an autophagy receptor that mediates degradation of intracellular cargo, including protein aggregates, through multiple protein interactions. These interactions form the SQSTM1 protein network, and these interactions are mediated by SQSTM1 functional interaction domains, which include LIR, PB1, UBA and KIR. Technological advances in cell biology continue to expand our knowledge of the SQSTM1 protein network and of the relationship of the actions of the SQSTM1 protein network in cellular physiology and disease states. Here we apply proximity profile labeling to investigate the SQSTM1 protein interaction network by fusing TurboID with the human protein SQSTM1 (TurboID::SQSTM1). This chimeric protein displayed well-established SQSTM1 features including production of SQSTM1 intracellular bodies, binding to known SQSTM1 interacting partners, and capture of novel SQSTM1 protein interactors. Strikingly, aggregated tau protein altered the protein interaction network of SQSTM1 to include many stress-associated proteins. We demonstrate the importance of the PB1 and/or UBA domains for binding network members, including the K18 domain of tau. Overall, our work reveals the dynamic landscape of the SQSTM1 protein network and offers a resource to study SQSTM1 function in cellular physiology and disease state.

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.

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.