timsTOF HT Improves Protein Identification and Quantitative Reproducibility for Deep Unbiased Plasma Protein Biomarker Discovery

Mass spectrometry (MS) is a valuable tool for plasma proteome profiling and disease biomarker discovery. However, wide-ranging plasma protein concentrations, along with technical and biological variabilities, present significant challenges for deep and reproducible protein quantitation. Here, we evaluated the qualitative and quantitative performance of timsTOF HT and timsTOF Pro 2 mass spectrometers for analysis of neat plasma samples (unfractionated) and plasma samples processed using the Proteograph Product Suite (Proteograph) that enables robust deep proteomics sampling prior to mass spectrometry. Samples were evaluated across a wide range of peptide loading masses and liquid chromatography (LC) gradients. We observed up to a 76% increase in total plasma peptide precursors identified and a >2-fold boost in quantifiable plasma peptide precursors (CV < 20%) with timsTOF HT compared to Pro 2. Additionally, approximately 4.5 fold more plasma peptide precursors were detected by both timsTOF HT and timsTOF Pro 2 in the Proteograph analyzed plasma vs neat plasma. In an exploratory analysis of 20 late-stage lung cancer and 20 control plasma samples with the Proteograph, which were expected to exhibit distinct proteomes, an approximate 50% increase in total and statistically significant plasma peptide precursors (q < 0.05) was observed with timsTOF HT compared to Pro 2. Our data demonstrate the superior performance of timsTOF HT for identifying and quantifying differences between biologically diverse samples, allowing for improved disease biomarker discovery in large cohort studies. Moreover, researchers can leverage data sets from this study to optimize their liquid chromatography-mass spectrometry (LC-MS) workflows for plasma protein profiling and biomarker discovery. (ProteomeXchange identifier: PXD047854 and PXD047839).

Integrated omics analysis unveils a DNA damage response to neurogenic injury

Spinal cord injury (SCI) evokes profound bladder dysfunction. Current treatments are limited by a lack of molecular data to inform novel therapeutic avenues. Previously, we showed systemic inosine treatment improved bladder function following SCI in rats. Here, we applied multi-omics analysis to explore molecular alterations in the bladder and their sensitivity to inosine following SCI. Canonical pathways regulated by SCI included those associated with protein synthesis, neuroplasticity, wound healing, and neurotransmitter degradation. Upstream regulator analysis identified MYC as a key regulator, whereas causal network analysis predicted multiple regulators of DNA damage response signaling following injury, including PARP-1. Staining for both DNA damage (γH2AX) and PARP activity (poly-ADP-ribose) markers in the bladder was increased following SCI, and attenuated in inosine-treated tissues. Proteomics analysis suggested that SCI induced changes in protein synthesis-, neuroplasticity-, and oxidative stress-associated pathways, a subset of which were shown in transcriptomics data to be inosine-sensitive. These findings provide novel insights into the molecular landscape of the bladder following SCI, and highlight a potential role for PARP inhibition to treat neurogenic bladder dysfunction.

Abstract 6606: Biomarker discovery in non-small-cell lung cancer enabled by deep multi-omics profiling of proteins, metabolites, transcripts, and genes in blood

Lung cancer is the leading cause of cancer-related deaths in the United States, with estimates of 236,740 new cases and 118,830 deaths in 2022 secondary to the disease. Blood-based liquid biopsies hold promise to reduce morbidity and mortality from lung cancer by enabling early detection to downstage disease at diagnosis, theragnostic identification of patients most likely to be helped or harmed by therapy, monitoring of therapeutic efficacy, and detection of residual disease. PrognomiQ’s multi-omics platform comprehensively profiles proteins, metabolites, lipids, mRNA, and cfDNA in blood samples which can be used for the development of liquid biopsy tests with high sensitivity and specificity for lung cancer. We conducted a case-control study comprising 1031 subjects: 361 subjects with untreated non-small-cell lung cancer (NSCLC) and 670 matched controls which included 340 subjects with salient pulmonary and gastrointestinal co-morbidities. Blood samples from each subject were processed to provide 7 different `omics readouts. LCMS was used to detect and quantify proteins, metabolites, and lipids. In addition, cfDNA and mRNA were assayed using next-generation sequencing. cfDNA reads were analyzed to estimate fragment-lengths, copy-number variation, and CpG site methylation. All molecular data were normalized using standard methods specific to each assay. Univariate analyses of cases vs controls were performed to identify differentially abundant features on all available samples per assay. We detected 9,868 proteins, 605 lipids, 329 metabolites, and 109,070 mRNA transcripts. Of these, 3,098 proteins, 210 lipids, 57 metabolites, and 30,236 mRNA transcripts were significantly different (FWER &lt; 0.05) in cases versus controls. Gene set enrichment analysis on statistically significant transcripts and proteins identified multiple gene-ontology terms associated with cancer including the Wnt signaling process and IgA immunoglobulin complex, respectively. From cfDNA data, we identified 234 non-contiguous genomic regions associated with the fragment-length disorder, 4,790 with copy-number variation, and 74 differentially methylated genomic regions spanning 184 CpG sites (FWER &lt; 0.05). With the premise that deviations from copy number neutrality are more likely to indicate a tumor contribution, we then focused our examination on those differentially expressed proteins that overlap with differentially expressed mRNA transcripts as well as CNV genomic regions. We identified 52 protein coding genes including E-cadherin (associated with EMT) and related binding proteins such as RAB11B, CAPZB, EPS15, FLNB, MYH9, STK24 and YWHAE. Ongoing machine-learning-based classifier training to distinguish between cancer and non-cancer can serve as the basis for the development of high-sensitivity liquid-biopsy tests for lung cancer.

Citation Format: Jinlyung Choi, Ajinkya Kokate, Ehdieh Khaledian, Manway Liu, Preethi Prasad, John Blume, Jessica Chan, Rea Cuaresma, Kevin Dai, Manoj Khadka, Thidar Khin, Yuya Kodama, Joon-Yong Lee, Hoda Malekpour, Megan Mora, Nithya Mudaliar, Sara Nouri Golmaei, Madhuvanthi Ramaiah, Saividya Ramaswamy, Peter Spiro, Dijana Vitko, Kavya Swaminathan, James Yee, Brian Young, Chinmay Belthangady, Bruce Wilcox, Brian Koh, Philip Ma. Biomarker discovery in non-small-cell lung cancer enabled by deep multi-omics profiling of proteins, metabolites, transcripts, and genes in blood. [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2023; Part 1 (Regular and Invited Abstracts); 2023 Apr 14-19; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2023;83(7_Suppl):Abstract nr 6606.

Abstract 6597: A multi-omics classifier achieves high sensitivity and specificity for pancreatic ductal adenocarcinoma in a case-control study of 146 subjects

Pancreatic ductal adenocarcinoma (PDAC) is currently the 3rd leading cause of cancer-related deaths in the US. Although the all-stage 5-year survival rate is ~10%, early-stage 5-year survival is markedly superior and in excess of 40%. Hence, early detection of PDAC via blood-based liquid biopsies holds promise to reduce morbidity and mortality. PrognomiQ’s multi-omics platform performs deep and unbiased molecular profiling of blood samples to detect proteins, metabolites, lipids, mRNA, miRNA, cfDNA fragmentation and copy-number, and CpG methylation. Here we report results from training and validation of a classifier on a subset of that multi-omic data with the potential to enable the development of high sensitivity and specificity tests for early detection of PDAC.We conducted a case-control study comprising 146 subjects across 16 clinical sites, including 63 pathology-confirmed, untreated PDAC cases (12 stage I, 8 stage II, 4 stage III, 36 stage IV, and 3 stage unknown) and 83 age- and gender- matched controls without any known cancer. For each subject, venous blood samples including plasma were collected. Unbiased LCMS was used to detect and quantify proteins, and targeted, multiplexed MRM-LCMS assays were used for both metabolites and lipids. After data processing, we detected 54,114 proteomic features, 898 lipids, and 373 metabolites. 445 proteomic features, 170 lipids, and 37 metabolites were found to be significantly different as determined by Bonferroni-corrected Wilcoxon tests with FWER &lt; 0.05. For classification, the dataset was split into training (37 cases and 37 controls) and validation (26 cases and 46 controls) sets, with control for collection site and date, age, and gender. XGBoost models were constructed for each analyte class using ten repeats of 10-fold cross-validation. To improve specificity to PDAC, all proteomic features which mapped to GOBP terms associated with acute-phase response, inflammation, and immune response were excluded prior to training. The best-performing hyperparameters were used for a final model built on the full training set and then used for inference on the validation set. At 99% specificity, the proteomic classifier had sensitivities of 77%, 57%, and 88% for Stages 1-4, Stages 1-2, and Stages 3-4, respectively, estimated by bootstrap re-sampling of the validation results. Metabolomics had sensitivities of 81%, 71%, and 88%. Lipidomics had sensitivities of 65%, 71%, and 65%. A joint, multi-omic model was constructed by averaging the scaled probabilities of all models. This joint model improved performance at 99% specificity with sensitivities of 92%, 86%, and 94%, highlighting the synergy of multi-omics data, particularly phenotypically related omics such as those described here. Multi-omic classifiers such as these can serve as the foundation for blood-based liquid biopsies for the early detection of PDAC.

Citation Format: John Blume, Ghristine Bundalian, Jessica Chan, Connie Chao-Shern, Jinlyung Choi, Rea Cuaresma, Kevin Dai, Sara N. Golmaei, Jun Heok Jang, Manoj Khadka, Ehdieh Khaledian, Thidar Khin, Yuya Kodama, Ajinkya Kokate, Joon-Yong Lee, Manway Liu, Hoda Malekpour, Megan Mora, Nithya Mudaliar, Preethi Prasad, Madhuvanthi Ramaiah, Saividya Ramaswamy, Peter Spiro, Kavya Swaminathan, Dijana Vitko, James Yee, Brian Young, Susan Zhang, Chinmay Belthangady, Bruce Wilcox, Brian Koh, Philip Ma. A multi-omics classifier achieves high sensitivity and specificity for pancreatic ductal adenocarcinoma in a case-control study of 146 subjects [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2023; Part 1 (Regular and Invited Abstracts); 2023 Apr 14-19; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2023;83(7_Suppl):Abstract nr 6597.

TMBIM5 is the Ca2+ /H+ antiporter of mammalian mitochondria

Mitochondrial Ca2+ ions are crucial regulators of bioenergetics and cell death pathways. Mitochondrial Ca2+ content and cytosolic Ca2+ homeostasis strictly depend on Ca2+ transporters. In recent decades, the major players responsible for mitochondrial Ca2+ uptake and release have been identified, except the mitochondrial Ca2+ /H+ exchanger (CHE). Originally identified as the mitochondrial K+ /H+ exchanger, LETM1 was also considered as a candidate for the mitochondrial CHE. Defining the mitochondrial interactome of LETM1, we identify TMBIM5/MICS1, the only mitochondrial member of the TMBIM family, and validate the physical interaction of TMBIM5 and LETM1. Cell-based and cell-free biochemical assays demonstrate the absence or greatly reduced Na+ -independent mitochondrial Ca2+ release in TMBIM5 knockout or pH-sensing site mutants, respectively, and pH-dependent Ca2+ transport by recombinant TMBIM5. Taken together, we demonstrate that TMBIM5, but not LETM1, is the long-sought mitochondrial CHE, involved in setting and regulating the mitochondrial proton gradient. This finding provides the final piece of the puzzle of mitochondrial Ca2+ transporters and opens the door to exploring its importance in health and disease, and to developing drugs modulating Ca2+ exchange.

Urinary Tract Infections in Children with Vesicoureteral Reflux Are Accompanied by Alterations in Urinary Microbiota and Metabolome Profiles

Children with vesicoureteral reflux (VUR) are at an increased risk of recurrent urinary tract infections (UTIs) and renal scarring. Gut microbiota are associated with disease phenotypes, but there has been no study that associates urinary microbiota (uMB) and metabolic profiles with VUR pathology. To identify dominant uMB genera and metabolites associated with UTIs in VUR, urine samples collected under sterile conditions underwent 16S ribosomal RNA sequencing (n = 49) and metabolomic analysis by mass spectrometry (n = 96). Alterations in uMB and metabolomic profiles in VUR patients suggest remodeling of urinary bacterial communities after UTIs: Dorea- and Escherichia-dominant uMB profiles were more frequently identified in participants with VUR. Prevotella- and Lactobacillus-dominant uMB profiles were more prevalent in controls (p < 0.001). Microbial composition varied based on recurrent febrile UTI status (p = 0.001). A total of 243 urinary metabolites involved in energy, amino acid, nucleotide, and lipid metabolism were altered in VUR patients with UTIs (p < 0.05). Importantly, VUR specimens revealed changes in the bacteria-associated metabolic pathways such as glutamate degradation, methyl-citrate cycle, and bile acid metabolism. PATIENT SUMMARY: Differences in urinary commensal bacteria and metabolites exist between children with and without vesicoureteral reflux (VUR). These changes may be utilized to identify patients at risk of VUR-associated kidney damage.

Characterizing Patients with Recurrent Urinary Tract Infections in Vesicoureteral Reflux: A Pilot Study of the Urinary Proteome

Recurrent urinary tract infections (UTIs) pose a significant burden on the health care system. Underlying mechanisms predisposing children to UTIs and associated changes in the urinary proteome are not well understood. We aimed to investigate the urinary proteome of a subset of children who have vesicoureteral reflux (VUR) and recurrent UTIs because of their risk of developing infection-related renal damage. Improving diagnostic modalities to identify UTI risk factors would significantly alter the clinical management of children with VUR. We profiled the urinary proteomes of 22 VUR patients with low grade VUR (1-3 out of 5), a history of recurrent UTIs, and renal scarring, comparing them to those obtained from 22 age-matched controls. Urinary proteins were analyzed by mass spectrometry followed by protein quantitation based on spectral counting. Of the 2,551 proteins identified across both cohorts, 964 were robustly quantified, as defined by meeting criteria with spectral count (SC) ≥2 in at least 7 patients in either VUR or control cohort. Eighty proteins had differential expression between the two cohorts, with 44 proteins significantly up-regulated and 36 downregulated (q <0.075, FC ≥1.2). Urinary proteins involved in inflammation, acute phase response (APR), modulation of extracellular matrix (ECM), and carbohydrate metabolism were altered among the study cohort.

Type I Interferon Signaling Disrupts the Hepatic Urea Cycle and Alters Systemic Metabolism to Suppress T Cell Function

Infections induce complex host responses linked to antiviral defense, inflammation, and tissue damage and repair. We hypothesized that the liver, as a central metabolic hub, may orchestrate systemic metabolic changes during infection. We infected mice with chronic lymphocytic choriomeningitis virus (LCMV), performed RNA sequencing and proteomics of liver tissue, and integrated these data with serum metabolomics at different infection phases. Widespread reprogramming of liver metabolism occurred early after infection, correlating with type I interferon (IFN-I) responses. Viral infection induced metabolic alterations of the liver that depended on the interferon alpha/beta receptor (IFNAR1). Hepatocyte-intrinsic IFNAR1 repressed the transcription of metabolic genes, including Otc and Ass1, which encode urea cycle enzymes. This led to decreased arginine and increased ornithine concentrations in the circulation, resulting in suppressed virus-specific CD8+ T cell responses and ameliorated liver pathology. These findings establish IFN-I-induced modulation of hepatic metabolism and the urea cycle as an endogenous mechanism of immunoregulation. VIDEO ABSTRACT.

FASIL-MS: An Integrated Proteomic and Bioinformatic Workflow To Universally Quantitate In Vivo-Acetylated Positional Isomers

Dynamic changes in histone post-translational modifications (PTMs) regulate gene transcription leading to fine-tuning of biological processes such as DNA replication and cell cycle progression. Moreover, specific histone modifications constitute docking sites for recruitment of DNA damage repair proteins and mediation of subsequent cell survival. Therefore, understanding and monitoring changes in histone PTMs that can alter cell proliferation and thus lead to disease progression are of considerable medical interest. In this study, stable isotope labeling with N-acetoxy-D3-succinimide (D3-NAS) was utilized to efficiently derivatize unmodified lysine residues at the protein level. The sample preparation method was streamlined to facilitate buffer exchange between the multiple steps of the protocol by coupling chemical derivatization to filter-aided sample preparation (FASP). Additionally, the mass spectrometry method was adapted to simultaneously coisolate and subsequently cofragment all differentially H3/D3-acetylated histone peptide clusters. Combination of these multiplexed MS(2) spectra with the implementation of a data analysis algorithm enabled the quantitation of each and every in vivo-acetylated DMSO- and SAHA-treated H4(4-17) and H3(18-26) peptide. We have termed our new approach FASIL-MS for filter-aided stable isotopic labeling coupled to mass spectrometry. FASIL-MS enables the universal and site-specific quantitation of peptides with multiple in vivo-acetylated lysine residues. Data are available via ProteomeXchange (PXD003611).

CD4(+) T cell lineage integrity is controlled by the histone deacetylases HDAC1 and HDAC2

Molecular mechanisms that maintain lineage integrity of helper T cells are largely unknown. Here we show histone deacetylases 1 and 2 (HDAC1 and HDAC2) as crucial regulators of this process. Loss of HDAC1 and HDAC2 during late T cell development led to the appearance of major histocompatibility complex (MHC) class II-selected CD4(+) helper T cells that expressed CD8-lineage genes such as Cd8a and Cd8b1. HDAC1 and HDAC2-deficient T helper type 0 (TH0) and TH1 cells further upregulated CD8-lineage genes and acquired a CD8(+) effector T cell program in a manner dependent on Runx-CBFβ complexes, whereas TH2 cells repressed features of the CD8(+) lineage independently of HDAC1 and HDAC2. These results demonstrate that HDAC1 and HDAC2 maintain integrity of the CD4 lineage by repressing Runx-CBFβ complexes that otherwise induce a CD8(+) effector T cell-like program in CD4(+) T cells.