High-throughput Proteomics Research Articles

Aortic valve disease augments vesicular microRNA-145-5p to regulate the calcification of valvular interstitial cells via cellular crosstalk

Abstract Rationale: Aortic valve stenosis (AVS) is a major contributor to cardiovascular death in the elderly population worldwide. MicroRNAs (miRNAs) are highly dysregulated in patients with AVS undergoing surgical aortic valve replacement (SAVR). However, miRNA-dependent mechanisms regulating inflammation and calcification or miRNA-mediated cell-cell crossstalk during the pathogenesis of AVS are still poorly understood. Here, we explored the role of extracellular vesicles (EV)-associated miR-145-5p, which we showed to be highly upregulated upon valvular calcification in AVS in mice and humans. Methods Human TaqMan miRNA arrays identified dysregulated miRNAs in aortic valve tissue explants from AVS patients compared to non-calcified valvular tissue explants of patients undergoing SAVR. Echocardiographic parameters were measured in association with the quantification of dysregulated miRNAs in a murine AVS model. In vitro calcification experiments were performed to explore the effects of EV-miR-145-5p on calcification and crosstalk in valvular cells. To dissect molecular miRNA signatures and their effect on signaling pathways, integrated OMICS analyses were performed. RNA sequencing (RNA-seq), high-throughput transcription factor (TF) and proteome arrays showed that a number of genes, miRNAs, TFs, and proteins are crucial for calcification and apoptosis, which are involved in the pathogenesis of AVS. Results Among several miRNAs dysregulated in valve explants of AVS patients, miR-145-5p was the most highly gender-independently dysregulated miRNA (AUC, 0.780, p-value, 0.01). MiRNA arrays utilizing patient-derived- and murine aortic-stenosis samples demonstrated that the expression of miR-145-5p is significantly upregulated and correlates positively with cardiac function based on echocardiography. In vitro experiments confirmed that miR-145-5p is encapsulated into EVs and shuttled into valvular interstitial cells. Based on the integrated OMICs results, miR-145-5p interrelates with markers of inflammation, calcification, and apoptosis. In vitro calcification experiments demonstrated that miR-145-5p regulates the ALPL gene, a hallmark of calcification in vascular and valvular cells. EV-mediated shuttling of miR-145-5p suppressed the expression of ZEB2, a negative regulator of the ALPL gene, by binding to its 3' untranslated region to inhibit its translation, thereby diminishing the calcification of target valvular interstitial cells. Conclusion Elevated levels of pro-calcific and pro-apoptotic EV-associated miR-145-5p contribute to the progression of AVS via the ZEB2-ALPL axis, which could potentially be therapeutically targeted to minimize the burden of AVS.

Deciphering lipoprotein(a) atherogenicity with use of high-throughput proteomics

Abstract Background Accumulating observational and genetic evidence suggests that lipoprotein(a) [Lp(a)], is significantly more atherogenic than other apolipoprotein-B containing particles. Systemic inflammation is thought to mediate the atherogenic effects of Lp(a). Previous studies have predominantly focused on single inflammatory biomarkers, an approach that fails to capture the broader molecular response to elevated Lp(a) levels. Purpose The aim of this study was to derive a proteome-wide signature of plasma Lp(a) concentrations and subsequently identify the pathways through which Lp(a) influences the risk of coronary artery disease (CAD). Methods This study included 41,123 UK Biobank participants with baseline Lp(a) measurements who also underwent a proteomic assessment. Relative concentration of 2,920 proteins was quantified in EDTA plasma samples using a high-throughput proximity extension assay. Lp(a)-associated proteins were identified in linear regression models, adjusting for age, sex, body mass index, race, smoking status, estimated glomerular filtration rate, medication use, and apolipoprotein B. Proteins linked with Lp(a) concentration were then evaluated for their association with incident CAD risk using Cox proportional hazards models. Significant traits were further included in pathway enrichment analysis. Results Out of 2,920 analyzed proteins (Figure 1), 140 showed a positive association with Lp(a) concentration, while 336 exhibited a negative association after adjustment for multiple testing (False discovery rate < 0.05). The strongest positive associations were observed for apolipoprotein F, group 10 secretory phospholipase A, and C4b-binding protein beta chain. The strongest negative associations were observed for asporin, aminoacylase-1, and retinol-binding protein 5. Among 140 proteins positively associated with Lp(a), 49 were associated with an increased risk of CAD. Conversely, among the 336 proteins negatively associated with Lp(a), 10 were associated with a decreased risk of CAD. Pathway enrichment analysis (Figure 2) indicated that proteins linked with both Lp(a) levels and CAD risk predominantly upregulated pathways related to peptide-ligand receptor signalling, platelet degranulation, neutrophil degranulation, and cytosolic calcium response. Conversely, pathways related to HDL remodeling, PI3K/AKT, KIT, and nuclear receptor signalling were downregulated. Conclusions The greater atherogenic potential of Lp(a) may be attributable to the modulation of several molecular pathways, particularly those involving platelet and neutrophil responses, cell signalling, and lipoprotein metabolism.

Diagnosis of invasive encapsulated follicular variant papillary thyroid carcinoma by protein-based machine learning.

Although the criteria for follicular-pattern thyroid tumors are well-established, diagnosing these lesions remains challenging in some cases. In the recent World Health Organization Classification of Endocrine and Neuroendocrine Tumors (5th edition), the invasive encapsulated follicular variant of papillary thyroid carcinoma was reclassified as its own entity. It is crucial to differentiate this variant of papillary thyroid carcinoma from low-risk follicular pattern tumors due to their shared morphological characteristics. Proteomics holds significant promise for detecting and quantifying protein biomarkers. We investigated the potential value of a protein biomarker panel defined by machine learning for identifying the invasive encapsulated follicular variant of papillary thyroid carcinoma, initially using formalin- fixed paraffin-embedded samples. We developed a supervised machine-learning model and tested its performance using proteomics data from 46 thyroid tissue samples. We applied a random forest classifier utilizing five protein biomarkers (ZEB1, NUP98, C2C2L, NPAP1, and KCNJ3). This classifier achieved areas under the curve (AUCs) of 1.00 and accuracy rates of 1.00 in training samples for distinguishing the invasive encapsulated follicular variant of papillary thyroid carcinoma from non-malignant samples. Additionally, we analyzed the performance of single-protein/gene receiver operating characteristic in differentiating the invasive encapsulated follicular variant of papillary thyroid carcinoma from others within The Cancer Genome Atlas projects, which yielded an AUC > 0.5. We demonstrated that integration of high-throughput proteomics with machine learning can effectively differentiate the invasive encapsulated follicular variant of papillary thyroid carcinoma from other follicular pattern thyroid tumors.

Simple and robust high-throughput serum proteomics workflow with low-microflow LC-MS/MS.

Clinical proteomics has substantially advanced in identifying and quantifying proteins from biofluids, such as blood, contributing to the discovery of biomarkers. The throughput and reproducibility of serum proteomics for large-scale clinical sample analyses require improvements. High-throughput analysis typically relies on automated equipment, which can be costly and has limited accessibility. In this study, we present a rapid, high-throughput workflow low-microflow LC-MS/MS method without automation. This workflow was optimized to minimize the preparation time and costs by omitting the depletion and desalting steps. The developed method was applied to data-independent acquisition (DIA) analysis of 235 samples, and it consistently yielded approximately 6000 peptides and 600 protein groups, including 33 FDA-approved biomarkers. Our results demonstrate that an 18-min DIA high-throughput workflow, assessed through intermittently collected quality control samples, ensures reproducibility and stability even with 2 µL of serum. It was successfully used to analyze serum samples from patients with diabetes having chronic kidney disease (CKD), and could identify five dysregulated proteins across various CKD stages.

CCL2-mediated endothelial injury drives cardiac dysfunction in long COVID.

Evidence linking the endothelium to cardiac injury in long coronavirus disease (COVID) is well documented, but the underlying mechanisms remain unknown. Here we show that cytokines released by endothelial cells (ECs) contribute to long-COVID-associated cardiac dysfunction. Using thrombotic vascular tissues from patients with long COVID and induced pluripotent stem cell-derived ECs (iPSC-ECs), we modeled endotheliitis and observed similar dysfunction and cytokine upregulation, notably CCL2. Cardiac organoids comprising iPSC-ECs and iPSC-derived cardiomyocytes showed cardiac dysfunction after severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) exposure, driven by CCL2. Profiling of chromatin accessibility and gene expression at a single-cell resolution linked CCL2 to 'phenotype switching' and cardiac dysfunction, validated by high-throughput proteomics. Disease modeling of cardiac organoids and exposure of human ACE2 transgenic mice to SARS-CoV-2 spike proteins revealed that CCL2-induced oxidative stress promoted post-translational modification of cardiac proteins, leading to cardiac dysfunction. These findings suggest that EC-released cytokines contribute to cardiac dysfunction in long COVID, highlighting the importance of early vascular health monitoring in patients with long COVID.

Protein biomarker signatures of preeclampsia - a longitudinal 5000-multiplex proteomics study

We aimed to explore novel biomarker candidates and biomarker signatures of late-onset preeclampsia (LOPE) by profiling samples collected in a longitudinal discovery cohort with a high-throughput proteomics platform. Using the Somalogic 5000-plex platform, we analyzed proteins in plasma samples collected at three visits (gestational weeks (GW) 12–19, 20–26 and 28–34 in 35 women with LOPE (birth ≥ 34 GW) and 70 healthy pregnant women). To identify biomarker signatures, we combined Elastic Net with Stability Selection for stable variable selection and validated their predictive performance in a validation cohort. The biomarker signature with the highest predictive performance (AUC 0.88 (95% CI 0.85–0.97)) was identified in the last trimester of pregnancy (GW 28–34) and included the Fatty acid amid hydrolase 2 (FAAH2), HtrA serine peptidase 1 (HTRA1) and Interleukin-17 receptor C (IL17RC) together with sFLT1 and maternal age, BMI and nulliparity. Our biomarker signature showed increased or similar predictive performance to the sFLT1/PGF-ratio within our data set, and we were able to validate the biomarker signature in a validation cohort (AUC ≥ 0.90). Further validation of these candidates should be performed using another protein quantification platform in an independent cohort where the negative and positive predictive values can be validly calculated.

Development and Validation of RoboCap, a Robotic Capillary Platform to Automate Capillary Electrophoresis Mass Spectrometry En Route to High-Throughput Single-Cell Proteomics.

Current developments in single-cell mass spectrometry (MS) aim to deepen proteome coverage while enhancing analytical speed to study entire cell populations, one cell at a time. Custom-built microanalytical capillary electrophoresis (μCE) played a critical role in the foundation of discovery single-cell MS proteomics. However, requirements for manual operation, substantial expertise, and low measurement throughput have so far hindered μCE-based single-cell studies on large numbers of cells. Here, we design and construct a robotic capillary (RoboCap) platform that grants single-cell CE-MS with automation for proteomes limited to less than ∼100 nL. RoboCap remotely controls precision actuators to translate the sample to the fused silica separation capillary, using vials in this work. The platform is hermetically enclosed and actively pressurized to inject ∼1-250 nL of the sample into a CE separation capillary, with errors below ∼5% relative standard deviation (RSD). The platform and supporting equipment were operated and monitored remotely on a custom-written Virtual Instrument (LabView). Detection performance was validated empirically on ∼5-250 nL portions of the HeLa proteome digest using a trapped ion mobility mass spectrometer (timsTOF PRO). RoboCap improved CE-ESI sample utilization to ∼20% from ∼3% on the manual μCE, the closest reference technology. Proof-of-principle experiments found proteome identification and quantification to robustly return ∼1,800 proteins (∼13% RSD) from ∼20 ng of the HeLa proteome digest on this earlier-generation detector. RoboCap automates CE-MS for limited sample amounts, paving the way to electrophoresis-based high-throughput single-cell proteomics.

Phosphorylation strongly affects the inhibition of human carbonic anhydrase I CO2 hydration activity

Human carbonic anhydrases (hCAs) have essential roles in respiration, acid-base balance, and fluid secretion, with implications in diseases such as glaucoma, epilepsy, obesity, and cancer. Of the fifteen known hCAs, human CA I (hCA I) is particularly abundant in erythrocytes, playing a critical role in CO2 transport. Despite extensive research on hCA I, the impact of post-translational modifications (PTMs), particularly phosphorylation, on its catalytic activity and inhibitor binding remains poorly understood. Although multiple phosphorylation sites have been identified in hCA I in vivo through high-throughput proteomics studies including at the highly conserved Ser51 residue, the functional consequences of these modifications are not well characterized. We investigated the effects of a phosphomimetic mutation at Ser51 on hCA I, examining its catalytic efficiency and susceptibility to inhibition by sulfonamides and anions. Using a recombinant expression system and a stopped-flow kinetic assay, we characterized the CO2 hydration activity and inhibition profiles of S51E hCA I compared to the wild type enzyme. Our results demonstrate that the S51E mutation increases the catalytic turnover rate (kcat) from 2.0 × 105 s−1 to 2.6 × 105 s−1 but significantly decreases substrate affinity, raising the Michaelis constant (KM) from 4.0 mM to 13.9 mM, reducing overall catalytic efficiency by over 50 %. Inhibition studies with a panel of 41 sulfonamides revealed that the S51E mutation dramatically alters inhibitor sensitivity, particularly for the most effective inhibitors. For example, 15 of the 16 most effective sulfonamide inhibitors for hCA I (with KIs <350 nM) were an average of over 35-fold less effective in inhibiting S51E hCA I than the wild type. The KI of the anticonvulsant zonisamide increased from 31 nM for the wild type hCA I to 4.0 μM. The inhibition profile with a panel of 37 small anions further indicated that the S51E mutant exhibited significantly reduced susceptibility to inhibition by 24 out of 37 tested anions, with some KI values increasing by up to 11,000-fold for inhibitors like hydrogen sulfide. This study underscores the significant impact that phosphorylation may have on hCA I function and inhibition. By characterizing the effects of phosphorylation on the CO2 hydration activity and inhibitor sensitivity of hCA I, these findings represent early steps in developing more selective proteoform-specific inhibitors, which could lead to more effective treatments for diseases involving carbonic anhydrases.

The revolution in high-throughput proteomics and AI.

The recent capability to measure thousands of plasma proteins from a tiny blood sample has provided a new dimension of expansive data that can advance our understanding of human health. For example, the company SomaLogic has developed the means to measure more than 10,000 proteins and Thermo Fisher's Olink assays over 5400 proteins from as little as 2 μl. When these rich data are integrated with other layers of information from large patient cohorts, such as the UK Biobank's genetic, health, and lifestyle information from half a million participants, we get new insights about the underpinnings of disease, the aging process, and the potential ability to forecast an individual's health trajectory.

Cytotoxicity and mechanisms of perfluorobutane sulfonate (PFBS) in umbilical cord fibroblast cells of Yangtze finless porpoise

Yangtze finless porpoises (YFP) accumulate high levels of per- and polyfluoroalkyl substances (PFASs). However, the health impacts of PFASs to YFP are still unknown because it is technically and ethically unfeasible to use the critically endangered YFP in toxicological exposures. To uncover the potential toxicities of PFASs to YFP, this study exposed a YFP umbilical cord fibroblast cell line to perfluorobutane sulfonate (PFBS), an emerging PFASs pollutant in the aquatic environments. After exposure, the cytotoxicity and mechanisms of PFBS were explored. Our preliminary experiments found that PFBS compromised the cell viability in a concentration and duration dependent manner. In an exposure of 48-h duration, the maximum no observed effect concentration (NOEC) of PFBS was determined to be 400 µM. High-throughput proteomics were then conducted to identify the differentially expressed proteins in YFP cells exposed to 400 µM PFBS for 48 h. The results found that PFBS exposure significantly perturbed the proteome fingerprints of YFP umbilical cord fibroblast cells. Functional annotation of differential proteins showed that PFBS had the potential to impair a variety of biological processes associated with the immunity, oxidative stress, metabolism, and proteolysis. Consistently, the intracellular levels of reactive oxygen species (ROS) and proinflammatory cytokine IL-1β were significantly increased by PFBS in YFP umbilical cord fibroblast cells. Overall, this study highlights the toxic effects of emerging PFASs on YFP and provides reference data to evaluate the health risks of aquatic pollution under the context of national YFP protection. To our knowledge, this is the first omics study using YFP umbilical cord fibroblast cells in ecotoxicology of PFASs, which is applicable to various cetacean species and pollutants.

ToxDAR: A Workflow Software for Analyzing Toxicologically Relevant Proteomic and Transcriptomic Data, from Data Preparation to Toxicological Mechanism Elucidation.

Exploration of toxicological mechanisms is imperative for the assessment of potential adverse reactions to chemicals and pharmaceutical agents, the engineering of safer compounds, and the preservation of public health. It forms the foundation of drug development and disease treatment. High-throughput proteomics and transcriptomics can accurately capture the body's response to toxins and have become key tools for revealing complex toxicological mechanisms. Recently, a vast amount of omics data related to toxicological mechanisms have been accumulated. However, analyzing and utilizing these data remains a major challenge for researchers, especially as there is a lack of a knowledge-based analysis system to identify relevant biological pathways associated with toxicity from the data and to establish connections between omics data and existing toxicological knowledge. To address this, we have developed ToxDAR, a workflow-oriented R package for preprocessing and analyzing toxicological multi-omics data. ToxDAR integrates packages like NormExpression, DESeq2, and igraph, and utilizes R functions such as prcomp and phyper. It supports data preparation, quality control, differential expression analysis, functional analysis, and network analysis. ToxDAR's architecture also includes a knowledge graph with five major categories of mechanism-related biological entities and details fifteen types of interactions among them, providing comprehensive knowledge annotation for omics data analysis results. As a case study, we used ToxDAR to analyze a transcriptomic dataset on the toxicology of triphenyl phosphate (TPP). The results indicate that TPP may impair thyroid function by activating thyroid hormone receptor β (THRB), impacting pathways related to programmed cell death and inflammation. As a workflow-oriented data analysis tool, ToxDAR is expected to be crucial for understanding toxic mechanisms from omics data, discovering new therapeutic targets, and evaluating chemical safety.

Serum proteomics reveal APOE-ε4-dependent and APOE-ε4-independent protein signatures in Alzheimer's disease.

A deeper understanding of the molecular processes underlying late-onset Alzheimer's disease (LOAD) could aid in biomarker and drug target discovery. Using high-throughput serum proteomics in the prospective population-based Age, Gene/Environment Susceptibility-Reykjavik Study (AGES) cohort of 5,127 older Icelandic adults (mean age, 76.6 ± 5.6 years), we identified 303 proteins associated with incident LOAD over a median follow-up of 12.8 years. Over 40% of these proteins were associated with LOAD independently of APOE-ε4 carrier status, were implicated in neuronal processes and overlapped with LOAD protein signatures in brain and cerebrospinal fluid. We identified 17 proteins whose associations with LOAD were strongly dependent on APOE-ε4 carrier status, with mostly consistent associations in cerebrospinal fluid. Remarkably, four of these proteins (TBCA, ARL2, S100A13 and IRF6) were downregulated by APOE-ε4 yet upregulated due to LOAD, a finding replicated in external cohorts and possibly reflecting a response to disease onset. These findings highlight dysregulated pathways at the preclinical stages of LOAD, including those both independent of and dependent on APOE-ε4 status.

An Inflection Point in High-Throughput Proteomics with Orbitrap Astral: Analysis of Biofluids, Cells, and Tissues.

This Technical Note presents a comprehensive proteomics workflow for the new combination of Orbitrap and Astral mass analyzers across biofluids, cells, and tissues. Central to our workflow is the integration of Adaptive Focused Acoustics (AFA) technology for cells and tissue lysis to ensure robust and reproducible sample preparation in a high-throughput manner. Furthermore, we automated the detergent-compatible single-pot, solid-phase-enhanced sample Preparation (SP3) method for protein digestion. The synergy of these advanced methodologies facilitates a robust and high-throughput approach for cell and tissue analysis, an important consideration in translational research. This work disseminates our platform workflow, analyzes the effectiveness, demonstrates the reproducibility of the results, and highlights the potential of these technologies in biomarker discovery and disease pathology. For cells and tissues (heart, liver, lung, and intestine) proteomics analysis by data-independent acquisition mode, identifications exceeding 10,000 proteins can be achieved with a 24 min active gradient. In 200 ng injections of HeLa digest across multiple gradients, an average of more than 80% of proteins have a CV less than 20%, and a 45 min run covers ∼90% of the expressed proteome. This complete workflow allows for large swaths of the proteome to be identified and is compatible with diverse sample types.

Oral secretions from striped stem borer (Chilo suppressalis) induce defenses in rice.

The striped stem borer (SSB, Chilo suppressalis) is one of the most destructive insect pests on rice. As a chewing insect, SSB larval feeding causes a dramatic increase in rice defense responses. However, the effects of oral secretions (OSs) during SSB feeding on rice defense remain largely unexplored. In this study, based on transcriptome analysis results, treatment with SSB OSs regulated the expression of genes involved in the plant defense-related pathways of calcium, mitogen-activated protein kinases, reactive oxygen species, jasmonic acid (JA), herbivore-induced plant volatiles (HIPVs), and protease inhibitors. Unsurprisingly, treatment with SSB OSs elicited the accumulation of JA and JA-isoleucine in rice. The defense mechanisms activated by the cascade not only induced the expression of trypsin inhibitors, inhibiting the normal growth of SSB larvae but also induced HIPVs emission, rendering rice attractive to a common larval parasitoid. High-throughput proteome sequencing of SSB OSs led to 534 proteins being identified and 343 proteins with two or more unique peptides being detected. The study demonstrates that SSB OSs trigger both direct and indirect defense mechanisms in rice, akin to the effects of SSB feeding. It identifies specific proteins in SSB OSs that may influence the interactions between SSB and rice during feeding, providing valuable insights for effectors research. © 2024 Society of Chemical Industry.

Proteomic and Phosphoproteomic Analyses during Plant Regeneration Initiation in Cotton (Gossypium hirsutum L.).

Somatic embryogenesis (SE) is a biotechnological tool used to generate new individuals and is the preferred method for rapid plant regeneration. However, the molecular basis underlying somatic cell regeneration through SE is not yet fully understood, particularly regarding interactions between the proteome and post-translational modifications. Here, we performed association analysis of high-throughput proteomics and phosphoproteomics in three representative samples (non-embryogenic calli, NEC; primary embryogenic calli, PEC; globular embryos, GE) during the initiation of plant regeneration in cotton, a pioneer crop for genetic biotechnology applications. Our results showed that protein accumulation is positively regulated by phosphorylation during SE, as revealed by correlation analyses. Of the 1418 proteins that were differentially accumulated in the proteome and the 1106 phosphoproteins that were differentially regulated in the phosphoproteome, 115 proteins with 229 phosphorylation sites overlapped (co-differential). Furthermore, seven dynamic trajectory patterns of differentially accumulated proteins (DAPs) and the correlated differentially regulated phosphoproteins (DRPPs) pairs with enrichment features were observed. During the initiation of plant regeneration, functional enrichment analysis revealed that the overlapping proteins (DAPs-DRPPs) were considerably enriched in cellular nitrogen metabolism, spliceosome formation, and reproductive structure development. Moreover, 198 DRPPs (387 phosphorylation sites) were specifically regulated at the phosphorylation level and showed four patterns of stage-enriched phosphorylation susceptibility. Furthermore, enrichment annotation analysis revealed that these phosphoproteins were significantly enriched in endosomal transport and nucleus organization processes. During embryogenic differentiation, we identified five DAPs-DRPPs with significantly enriched characteristic patterns. These proteins may play essential roles in transcriptional regulation and signaling events that initiate plant regeneration through protein accumulation and/or phosphorylation modification. This study enriched the understanding of key proteins and their correlated phosphorylation patterns during plant regeneration, and also provided a reference for improving plant regeneration efficiency.

Branched-chain amino acid transaminase 1 confers EGFR-TKI resistance through epigenetic glycolytic activation

Third-generation EGFR tyrosine kinase inhibitors (TKIs), exemplified by osimertinib, have demonstrated promising clinical efficacy in the treatment of non-small cell lung cancer (NSCLC). Our previous work has identified ASK120067 as a novel third-generation EGFR TKI with remarkable antitumor effects that has undergone New Drug Application (NDA) submission in China. Despite substantial progress, acquired resistance to EGFR-TKIs remains a significant challenge, impeding the long-term effectiveness of therapeutic approaches. In this study, we conducted a comprehensive investigation utilizing high-throughput proteomics analysis on established TKI-resistant tumor models, and found a notable upregulation of branched-chain amino acid transaminase 1 (BCAT1) expression in both osimertinib- and ASK120067-resistant tumors compared with the parental TKI-sensitive NSCLC tumors. Genetic depletion or pharmacological inhibition of BCAT1 impaired the growth of resistant cells and partially re-sensitized tumor cells to EGFR TKIs. Mechanistically, upregulated BCAT1 in resistant cells reprogrammed branched-chain amino acid (BCAA) metabolism and promoted alpha ketoglutarate (α-KG)-dependent demethylation of lysine 27 on histone H3 (H3K27) and subsequent transcriptional derepression of glycolysis-related genes, thereby enhancing glycolysis and promoting tumor progression. Moreover, we identified WQQ-345 as a novel BCAT1 inhibitor exhibiting antitumor activity both in vitro and in vivo against TKI-resistant lung cancer with high BCAT1 expression. In summary, our study highlighted the crucial role of BCAT1 in mediating resistance to third-generation EGFR-TKIs through epigenetic activation of glycolysis in NSCLC, thereby supporting BCAT1 as a promising therapeutic target for the treatment of TKI-resistant NSCLC.

Insights From Omics in Lyme Disease.

Lyme disease is a zoonotic infection due to Ixodes tick-transmitted Borrelia burgdorferi sensu lato spirochetes and the most common vector-borne disease in the Northern Hemisphere. Despite nearly 50 years of investigation, the pathogenesis of this infection and its 2 main adverse outcomes-postinfectious Lyme arthritis and posttreatment Lyme disease syndrome-are incompletely understood. Advancement in sequencing and mass spectrometry have led to the rapid expansion of high-throughput omics technologies, including transcriptomics, metabolomics, and proteomics, which are now being applied to human diseases. This review summarizes findings of omics studies conducted on blood and tissue samples of people with acute Lyme disease and its postinfectious outcomes.

Multifaceted Proteome Analysis at Solubility, Redox, and Expression Dimensions for Target Identification.

Multifaceted interrogation of the proteome deepens the system-wide understanding of biological systems; however, mapping the redox changes in the proteome has so far been significantly more challenging than expression and solubility/stability analyses. Here, the first high-throughput redox proteomics approach integrated with expression analysis (REX) is devised and combined with the Proteome Integral Solubility Alteration (PISA) assay. The whole PISA-REX experiment with up to four biological replicates can be multiplexed into a single tandem mass tag TMTpro set. For benchmarking this compact tool, HCT116 cells treated with auranofin are analyzed, showing great improvement compared with previous studies. PISA-REX is then applied to study proteome remodeling upon stimulation of human monocytes by interferon α (IFN-α). Applying this tool to study the proteome changes in plasmacytoid dendritic cells (pDCs) isolated from wild-type versus Ncf1-mutant mice treated with interferon α, shows that NCF1 deficiency enhances the STAT1 pathway and modulates the expression, solubility, and redox state of interferon-induced proteins. Providing comprehensive multifaceted information on the proteome, the compact PISA-REX has the potential to become an industry standard in proteomics and to open new windows into the biology of health and disease.

Gel-Assisted Proteome Position Integral Shift Assay Returns Molecular Weight to Shotgun Proteomics and Identifies Caspase 3 Substrates.

Here, we present a high-throughput virtual top-down proteomics approach that restores the molecular weight (MW) information in shotgun proteomics and demonstrates its utility in studying proteolytic events in programmed cell death. With gel-assisted proteome position integral shift (GAPPIS), we quantified over 7000 proteins in staurosporine-induced apoptotic HeLa cells and identified 84 proteins exhibiting in a statistically significant manner at least two of the following features: (i) a negative MW shift; (ii) an elevated ratio in a pair of a semitryptic and tryptic peptide, (iii) a negative shift in the standard deviation of MW estimated for different peptides, and (iv) a negative shift in skewness of the same data. Of these proteins, 58 molecules were previously unreported caspase 3 substrates. Further analysis identified the preferred cleavage sites consistent with the known caspase cleavages after the DXXD motif. As a powerful tool for high-throughput MW analysis simultaneously with the conventional expression analysis, the GAPPIS assay can prove useful in studying a broad range of biological processes involving proteolytic events.

Combined High-Throughput Proteomics and Random Forest Machine-Learning Approach Differentiates and Classifies Metabolic, Immune, Signaling and ECM Intra-Tumor Heterogeneity of Colorectal Cancer.

Colorectal cancer (CRC) is a frequent, worldwide tumor described for its huge complexity, including inter-/intra-heterogeneity and tumor microenvironment (TME) variability. Intra-tumor heterogeneity and its connections with metabolic reprogramming and epithelial-mesenchymal transition (EMT) were investigated with explorative shotgun proteomics complemented by a Random Forest (RF) machine-learning approach. Deep and superficial tumor regions and distant-site non-tumor samples from the same patients (n = 16) were analyzed. Among the 2009 proteins analyzed, 91 proteins, including 23 novel potential CRC hallmarks, showed significant quantitative changes. In addition, a 98.4% accurate classification of the three analyzed tissues was obtained by RF using a set of 21 proteins. Subunit E1 of 2-oxoglutarate dehydrogenase (OGDH-E1) was the best classifying factor for the superficial tumor region, while sorting nexin-18 and coatomer-beta protein (beta-COP), implicated in protein trafficking, classified the deep region. Down- and up-regulations of metabolic checkpoints involved different proteins in superficial and deep tumors. Analogously to immune checkpoints affecting the TME, cytoskeleton and extracellular matrix (ECM) dynamics were crucial for EMT. Galectin-3, basigin, S100A9, and fibronectin involved in TME-CRC-ECM crosstalk were found to be differently variated in both tumor regions. Different metabolic strategies appeared to be adopted by the two CRC regions to uncouple the Krebs cycle and cytosolic glucose metabolism, promote lipogenesis, promote amino acid synthesis, down-regulate bioenergetics in mitochondria, and up-regulate oxidative stress. Finally, correlations with the Dukes stage and budding supported the finding of novel potential CRC hallmarks and therapeutic targets.