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.

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.

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.

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.

Proteomic and phosphoproteomic profiling in heart failure with preserved ejection fraction (HFpEF).

Although the prevalence of heart failure with preserved ejection fraction (HFpEF) is increasing, evidence-based therapies for HFpEF remain limited, likely due to an incomplete understanding of this disease. This study sought to identify the cardiac-specific features of protein and phosphoprotein changes in a murine model of HFpEF using mass spectrometry. HFpEF mice demonstrated moderate hypertension, left ventricle (LV) hypertrophy, lung congestion and diastolic dysfunction. Proteomics analysis of the LV tissue showed that 897 proteins were differentially expressed between HFpEF and Sham mice. We observed abundant changes in sarcomeric proteins, mitochondrial-related proteins, and NAD-dependent protein deacetylase sirtuin-3 (SIRT3). Upregulated pathways by GSEA analysis were related to immune modulation and muscle contraction, while downregulated pathways were predominantly related to mitochondrial metabolism. Western blot analysis validated SIRT3 downregulated cardiac expression in HFpEF vs. Sham (0.8 ± 0.0 vs. 1.0 ± 0.0; P < 0.001). Phosphoproteomics analysis showed that 72 phosphosites were differentially regulated between HFpEF and Sham LV. Aberrant phosphorylation patterns mostly occurred in sarcomere proteins and nuclear-localized proteins associated with contractile dysfunction and cardiac hypertrophy. Seven aberrant phosphosites were observed at the z-disk binding region of titin. Additional agarose gel analysis showed that while total titin cardiac expression remained unaltered, its stiffer N2B isoform was significantly increased in HFpEF vs. Sham (0.144 ± 0.01 vs. 0.127 ± 0.01; P < 0.05). In summary, this study demonstrates marked changes in proteins related to mitochondrial metabolism and the cardiac contractile apparatus in HFpEF. We propose that SIRT3 may play a role in perpetuating these changes and may be a target for drug development in HFpEF.

Pilot Study Showing Feasibility of Phosphoproteomic Profiling of Pathway-Level Molecular Alterations in Barrett's Esophagus.

(1) Background: Barrett's esophagus is a major risk factor for esophageal adenocarcinoma. In this pilot study, we employed precision mass spectrometry to map global (phospho)protein perturbations in Barrett's esophagus lesions and adjacent normal tissue to glean insights into disease progression. (2) Methods: Biopsies were collected from two small but independent cohorts. Comparative analyses were performed between Barrett's esophagus samples and adjacent matched (normal) tissues from patients with known pathology, while specimens from healthy patients served as additional controls. (3) Results: We identified and quantified 6810 proteins and 6395 phosphosites in the discovery cohort, revealing hundreds of statistically significant differences in protein abundances and phosphorylation states. We identified a robust proteomic signature that accurately classified the disease status of samples from the independent patient cohorts. Pathway-level analysis of the phosphoproteomic profiles revealed the dysregulation of specific cellular processes, including DNA repair, in Barrett's esophagus relative to paired controls. Comparative analysis with previously published transcriptomic profiles provided independent evidence in support of these preliminary findings. (4) Conclusions: This pilot study establishes the feasibility of using unbiased quantitative phosphoproteomics to identify molecular perturbations associated with disease progression in Barrett's esophagus to define potentially clinically actionable targets warranting further assessment.

Multiomic Metabolic Enrichment Network Analysis Reveals Metabolite-Protein Physical Interaction Subnetworks Altered in Cancer.

Metabolism is recognized as an important driver of cancer progression and other complex diseases, but global metabolite profiling remains a challenge. Protein expression profiling is often a poor proxy since existing pathway enrichment models provide an incomplete mapping between the proteome and metabolism. To overcome these gaps, we introduce multiomic metabolic enrichment network analysis (MOMENTA), an integrative multiomic data analysis framework for more accurately deducing metabolic pathway changes from proteomics data alone in a gene set analysis context by leveraging protein interaction networks to extend annotated metabolic models. We apply MOMENTA to proteomic data from diverse cancer cell lines and human tumors to demonstrate its utility at revealing variation in metabolic pathway activity across cancer types, which we verify using independent metabolomics measurements. The novel metabolic networks we uncover in breast cancer and other tumors are linked to clinical outcomes, underscoring the pathophysiological relevance of the findings.

Patient-specific iPSCs carrying an SFTPC mutation reveal the intrinsic alveolar epithelial dysfunction at the inception of interstitial lung disease.

Alveolar epithelial type 2 cell (AEC2) dysfunction is implicated in the pathogenesis of adult and pediatric interstitial lung disease (ILD), including idiopathic pulmonary fibrosis (IPF); however, identification of disease-initiating mechanisms has been impeded by inability to access primary AEC2s early on. Here, we present a human in vitro model permitting investigation of epithelial-intrinsic events culminating in AEC2 dysfunction, using patient-specific induced pluripotent stem cells (iPSCs) carrying an AEC2-exclusive disease-associated variant (SFTPCI73T). Comparing syngeneic mutant versus gene-corrected iPSCs after differentiation into AEC2s (iAEC2s), we find that mutant iAEC2s accumulate large amounts of misprocessed and mistrafficked pro-SFTPC protein, similar to in vivo changes, resulting in diminished AEC2 progenitor capacity, perturbed proteostasis, altered bioenergetic programs, time-dependent metabolic reprogramming, and nuclear factor κB (NF-κB) pathway activation. Treatment of SFTPCI73T-expressing iAEC2s with hydroxychloroquine, a medication used in pediatric ILD, aggravates the observed perturbations. Thus, iAEC2s provide a patient-specific preclinical platform for modeling the epithelial-intrinsic dysfunction at ILD inception.

Omics Notebook: robust, reproducible and flexible automated multiomics exploratory analysis and reporting.

Summary: Mass spectrometry is an increasingly important tool for the global interrogation of diverse biomolecules. Unfortunately, the complexity of downstream data analysis is a major challenge for the routine use of these data by investigators from broader training backgrounds. Omics Notebook is an open-source framework for exploratory analysis, reporting and integrating multiomic data that are automated, reproducible and customizable. Built-in functions allow the processing of proteomic data from MaxQuant and metabolomic data from XCMS, along with other omics data in standardized input formats as specified in the documentation. In addition, the use of containerization manages R package installation requirements and is tailored for shared high-performance computing or cloud environments. Availability and implementation: Omics Notebook is implemented in Python and R and is available for download from https://github.com/cnsb-boston/Omics_Notebook with additional documentation under a GNU GPLv3 license. Supplementary information: Supplementary data are available at Bioinformatics Advances online.

Actionable Cytopathogenic Host Responses of Human Alveolar Type 2 Cells to SARS-CoV-2.

Human transmission of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), causative pathogen of the COVID-19 pandemic, exerts a massive health and socioeconomic crisis. The virus infects alveolar epithelial type 2 cells (AT2s), leading to lung injury and impaired gas exchange, but the mechanisms driving infection and pathology are unclear. We performed a quantitative phosphoproteomic survey of induced pluripotent stem cell-derived AT2s (iAT2s) infected with SARS-CoV-2 at air-liquid interface (ALI). Time course analysis revealed rapid remodeling of diverse host systems, including signaling, RNA processing, translation, metabolism, nuclear integrity, protein trafficking, and cytoskeletal-microtubule organization, leading to cell cycle arrest, genotoxic stress, and innate immunity. Comparison to analogous data from transformed cell lines revealed respiratory-specific processes hijacked by SARS-CoV-2, highlighting potential novel therapeutic avenues that were validated by a high hit rate in a targeted small molecule screen in our iAT2 ALI system.

Organoids Model Transcriptional Hallmarks of Oncogenic KRAS Activation in Lung Epithelial Progenitor Cells.

Mutant KRAS is a common driver in epithelial cancers. Nevertheless, molecular changes occurring early after activation of oncogenic KRAS in epithelial cells remain poorly understood. We compared transcriptional changes at single-cell resolution after KRAS activation in four sample sets. In addition to patient samples and genetically engineered mouse models, we developed organoid systems from primary mouse and human induced pluripotent stem cell-derived lung epithelial cells to model early-stage lung adenocarcinoma. In all four settings, alveolar epithelial progenitor (AT2) cells expressing oncogenic KRAS had reduced expression of mature lineage identity genes. These findings demonstrate the utility of our in vitro organoid approaches for uncovering the early consequences of oncogenic KRAS expression. This resource provides an extensive collection of datasets and describes organoid tools to study the transcriptional and proteomic changes that distinguish normal epithelial progenitor cells from early-stage lung cancer, facilitating the search for targets for KRAS-driven tumors.

Triphenyl phosphate is a selective PPARγ modulator that does not induce brite adipogenesis in vitro and in vivo.

Triphenyl phosphate (TPhP) is an environmental PPARγ ligand, and growing evidence suggests that it is a metabolic disruptor. We have shown previously that the structurally similar ligand, tributyltin, does not induce brite adipocyte gene expression. Here, using in vivo and in vitro models, we tested the hypothesis that TPhP is a selective PPARγ ligand, which fails to induce brite adipogenesis. C57BL/6 J male mice were fed either a low or very high-fat diet for 13 weeks. From weeks 7-13, mice were injected intraperitoneally, daily, with vehicle, rosiglitazone (Rosi), or TPhP (10 mg/kg). Compared to Rosi, TPhP did not induce expression of browning-related genes (e.g. Elovl3, Cidea, Acaa2, CoxIV) in mature adipocytes isolated from inguinal adipose. To determine if this resulted from an effect directly on the adipocytes, 3T3-L1 cells and primary human preadipocytes were differentiated into adipocytes in the presence of Rosi or TPhP. Rosi, but not TPhP, induced expression of brite adipocyte genes, mitochondrial biogenesis and cellular respiration. Further, Rosi and TPhP-induced distinct proteomes and phosphoproteomes; Rosi enriched more regulatory pathways related to fatty acid oxidation and mitochondrial proteins. We assessed the role of phosphorylation of PPARγ in these differences in 3T3-L1 cells. Only Rosi protected PPARγ from phosphorylation at Ser273. TPhP gained the ability to stimulate brite adipocyte gene expression in the presence of the CDK5 inhibitor and in 3T3-L1 cells expressing alanine at position 273. We conclude that TPhP is a selective PPARγ modulator that fails to protect PPARγ from phosphorylation at ser273.

BraInMap Elucidates the Macromolecular Connectivity Landscape of Mammalian Brain.

Connectivity webs mediate the unique biology of the mammalian brain. Yet, while cell circuit maps are increasingly available, knowledge of their underlying molecular networks remains limited. Here, we applied multi-dimensional biochemical fractionation with mass spectrometry and machine learning to survey endogenous macromolecules across the adult mouse brain. We defined a global "interactome" comprising over one thousand multi-protein complexes. These include hundreds of brain-selective assemblies that have distinct physical and functional attributes, show regional and cell-type specificity, and have links to core neurological processes and disorders. Using reciprocal pull-downs and a transgenic model, we validated a putative 28-member RNA-binding protein complex associated with amyotrophic lateral sclerosis, suggesting a coordinated function in alternative splicing in disease progression. This brain interaction map (BraInMap) resource facilitates mechanistic exploration of the unique molecular machinery driving core cellular processes of the central nervous system. It is publicly available and can be explored here https://www.bu.edu/dbin/cnsb/mousebrain/.

Loss of G-Protein Pathway Suppressor 2 Promotes Tumor Growth Through Activation of AKT Signaling.

G Protein Suppressor 2 (GPS2) is a multifunctional protein that exerts important roles in inflammation and metabolism in adipose, liver, and immune cells. GPS2 has recently been identified as a significantly mutated gene in breast cancer and other malignancies and proposed to work as a putative tumor suppressor. However, molecular mechanisms by which GPS2 prevents cancer development and/or progression are largely unknown. Here, we have profiled the phenotypic changes induced by GPS2 depletion in MDA-MB-231 triple negative breast cancer cells and investigated the underlying molecular mechanisms. We found that GPS2-deleted MDA-MB-231 cells exhibited increased proliferative, migratory, and invasive properties in vitro, and conferred greater tumor burden in vivo in an orthotopic xenograft mouse model. Transcriptomic, proteomic and phospho-proteomic profiling of GPS2-deleted MBA-MB-231 revealed a network of altered signals that relate to cell growth and PI3K/AKT signaling. Overlay of GPS2-regulated gene expression with MDA-MB-231 cells modified to express constitutively active AKT showed significant overlap, suggesting that sustained AKT activation is associated with loss of GPS2. Accordingly, we demonstrate that the pro-oncogenic phenotypes associated with GPS2 deletion are rescued by pharmacological inhibition of AKT with MK2206. Collectively, these observations confirm a tumor suppressor role for GPS2 and reveal that loss of GPS2 promotes breast cancer cell proliferation and tumor growth through uncontrolled activation of AKT signaling. Moreover, our study points to GPS2 as a potential biomarker for a subclass of breast cancers that would be responsive to PI3K-class inhibitor drugs.

A Mutation in Hnrnph1 That Decreases Methamphetamine-Induced Reinforcement, Reward, and Dopamine Release and Increases Synaptosomal hnRNP H and Mitochondrial Proteins.

Individual variation in the addiction liability of amphetamines has a heritable genetic component. We previously identified Hnrnph1 (heterogeneous nuclear ribonucleoprotein H1) as a quantitative trait gene underlying decreased methamphetamine-induced locomotor activity in mice. Here, we showed that mice (both females and males) with a heterozygous mutation in the first coding exon of Hnrnph1 (H1+/-) showed reduced methamphetamine reinforcement and intake and dose-dependent changes in methamphetamine reward as measured via conditioned place preference. Furthermore, H1+/- mice showed a robust decrease in methamphetamine-induced dopamine release in the NAc with no change in baseline extracellular dopamine, striatal whole-tissue dopamine, dopamine transporter protein, dopamine uptake, or striatal methamphetamine and amphetamine metabolite levels. Immunohistochemical and immunoblot staining of midbrain dopaminergic neurons and their forebrain projections for TH did not reveal any major changes in staining intensity, cell number, or forebrain puncta counts. Surprisingly, there was a twofold increase in hnRNP H protein in the striatal synaptosome of H1+/- mice with no change in whole-tissue levels. To gain insight into the mechanisms linking increased synaptic hnRNP H with decreased methamphetamine-induced dopamine release and behaviors, synaptosomal proteomic analysis identified an increased baseline abundance of several mitochondrial complex I and V proteins that rapidly decreased at 30 min after methamphetamine administration in H1+/- mice. In contrast, the much lower level of basal synaptosomal mitochondrial proteins in WT mice showed a rapid increase. We conclude that H1+/- decreases methamphetamine-induced dopamine release, reward, and reinforcement and induces dynamic changes in basal and methamphetamine-induced synaptic mitochondrial function.SIGNIFICANCE STATEMENT Methamphetamine dependence is a significant public health concern with no FDA-approved treatment. We discovered a role for the RNA binding protein hnRNP H in methamphetamine reward and reinforcement. Hnrnph1 mutation also blunted methamphetamine-induced dopamine release in the NAc, a key neurochemical event contributing to methamphetamine addiction liability. Finally, Hnrnph1 mutants showed a marked increase in basal level of synaptosomal hnRNP H and mitochondrial proteins that decreased in response to methamphetamine, whereas WT mice showed a methamphetamine-induced increase in synaptosomal mitochondrial proteins. Thus, we identified a potential role for hnRNP H in basal and dynamic mitochondrial function that informs methamphetamine-induced cellular adaptations associated with reduced addiction liability.

Single-platform 'multi-omic' profiling: unified mass spectrometry and computational workflows for integrative proteomics-metabolomics analysis.

The objective of omics studies is to globally measure the different classes of cellular biomolecules present in a biological specimen (e.g. proteins, metabolites) as accurately as possible in order to investigate the corresponding 'states' of biological systems. High throughput omics technologies are emerging as an increasingly powerful toolkit in the rapidly advancing field of systems biology, enabling the systematic study of dynamic molecular processes that drive core cell functions like growth, sensing, and environmental adaptation. Advances in high resolution mass spectrometry, in particular, now allow for the near comprehensive study of cellular proteins and metabolites that underlie physiological homeostasis and disease pathogenesis. Yet while the expression levels, modification states, and functional associations of diverse molecular species are now measurable, existing proteomic and metabolomic data generation and analysis workflows are often specialized and incompatible. Hence, while there are now many reports of ad hoc combinations of unimolecular proteomic and metabolomic workflows, only a limited number of multi-omic profiling approaches have been reported for obtaining different molecular measurements (proteins, metabolites, nucleic acids) in parallel from a single biological sample. Moreover, elucidating how the myriad of measured cellular components are linked together functionally within the metabolic processes, signal transduction pathways, and macromolecular interaction networks central to living systems remains a massive, complicated, and uncertain endeavor. Presented here is a review of convergent mass spectrometry-based multi-omic methodologies, with a focus on notable recent advances and remaining challenges in terms of efficient sample preparation, biochemical separations, data acquisition, and integrative computational strategies. We outline a unifying network-based integrative framework to better derive biological knowledge from integrated profiling studies with the goal of realizing the full potential of multi-omic data sets.