Plasma Proteome Profiling to Assess Human Health and Disease

SummaryProteins in the circulatory system mirror an individual’s physiology. In daily clinical practice, protein levels are generally determined using single-protein immunoassays. High-throughput, quantitative analysis using mass-spectrometry-based proteomics of blood, plasma, and serum would be advantageous but is challenging because of the high dynamic range of protein abundances. Here, we introduce a rapid and robust “plasma proteome profiling” pipeline. This single-run shotgun proteomic workflow does not require protein depletion and enables quantitative analysis of hundreds of plasma proteomes from 1 μl single finger pricks with 20 min gradients. The apolipoprotein family, inflammatory markers such as C-reactive protein, gender-related proteins, and >40 FDA-approved biomarkers are reproducibly quantified (CV <20% with label-free quantification). Furthermore, we functionally interpret a 1,000-protein, quantitative plasma proteome obtained by simple peptide pre-fractionation. Plasma proteome profiling delivers an informative portrait of a person’s health state, and we envision its large-scale use in biomedicine

Proteomics for blood biomarker exploration of severe mental illness: pitfalls of the past and potential for the future

Recent improvements in high-throughput proteomic approaches are likely to constitute an essential advance in biomarker discovery, holding promise for improved personalized care and drug development. These methodologies have been applied to study multivariate protein patterns and provide valuable data of peripheral tissues. To highlight findings of the last decade for three of the most common psychiatric disorders, namely schizophrenia (SZ), bipolar disorder (BD), and major depressive disorder (MDD), we queried PubMed. Here we delve into the findings from thirty studies, which used proteomics and multiplex immunoassay approaches for peripheral blood biomarker exploration. In an explorative approach, we ran enrichment analyses in peripheral blood according to these results and ascertained the overlap between proteomic findings and genetic loci identified in genome-wide association studies (GWAS). The studies we appraised demonstrate that proteomics for psychiatric research has been heterogeneous in aims and methods and limited by insufficient sample sizes, poorly defined case definitions, methodological inhomogeneity, and confounding results constraining the conclusions that can be extracted from them. Here, we discuss possibilities for overcoming methodological challenges for the implementation of proteomic signatures in psychiatric diagnosis and offer an outlook for future investigations. To fulfill the promise of proteomics in mental disease diagnostics, future research will need large, well-defined cohorts in combination with state-of-the-art technologies

Dynamic human liver proteome atlas reveals functional insights into disease pathways

Deeper understanding of liver pathophysiology would benefit from a comprehensive quantitative proteome resource at cell type resolution to predict outcome and design therapy. Here, we quantify more than 150,000 sequence‐unique peptides aggregated into 10,000 proteins across total liver, the major liver cell types, time course of primary cell cultures, and liver disease states. Bioinformatic analysis reveals that half of hepatocyte protein mass is comprised of enzymes and 23% of mitochondrial proteins, twice the proportion of other liver cell types. Using primary cell cultures, we capture dynamic proteome remodeling from tissue states to cell line states, providing useful information for biological or pharmaceutical research. Our extensive data serve as spectral library to characterize a human cohort of non‐alcoholic steatohepatitis and cirrhosis. Dramatic proteome changes in liver tissue include signatures of hepatic stellate cell activation resembling liver cirrhosis and providing functional insights. We built a web‐based dashboard application for the interactive exploration of our resource (www.liverproteome.org)

Proteome profiling in cerebrospinal fluid reveals novel biomarkers of Alzheimer's disease

Neurodegenerative diseases are a growing burden, and there is an urgent need for better biomarkers for diagnosis, prognosis, and treatment efficacy. Structural and functional brain alterations are reflected in the protein composition of cerebrospinal fluid (CSF). Alzheimer's disease (AD) patients have higher CSF levels of tau, but we lack knowledge of systems-wide changes of CSF protein levels that accompany AD. Here, we present a highly reproducible mass spectrometry (MS)-based proteomics workflow for the in-depth analysis of CSF from minimal sample amounts. From three independent studies (197 individuals), we characterize differences in proteins by AD status (> 1,000 proteins, CV < 20%). Proteins with previous links to neurodegeneration such as tau, SOD1, and PARK7 differed most strongly by AD status, providing strong positive controls for our approach. CSF proteome changes in Alzheimer's disease prove to be widespread and often correlated with tau concentrations. Our unbiased screen also reveals a consistent glycolytic signature across our cohorts and a recent study. Machine learning suggests clinical utility of this proteomic signature

Region and cell-type resolved quantitative proteomic map of the human heart

The heart is a central human organ and its diseases are the leading cause of death worldwide, but an in-depth knowledge of the identity and quantity of its constituent proteins is still lacking. Here, we determine the healthy human heart proteome by measuring 16 anatomical regions and three major cardiac cell types by high-resolution mass spectrometry-based proteomics. From low microgram sample amounts, we quantify over 10,700 proteins in this high dynamic range tissue. We combine copy numbers per cell with protein organellar assignments to build a model of the heart proteome at the subcellular level. Analysis of cardiac fibroblasts identifies cellular receptors as potential cell surface markers. Application of our heart map to atrial fibrillation reveals individually distinct mitochondrial dysfunctions. The heart map is available at maxqb. biochem. mpg. de as a resource for future analyses of normal heart function and disease

Proteomics reveals the effects of sustained weight loss on the human plasma proteome

Sustained weight loss is a preferred intervention in a wide range of metabolic conditions, but the effects on an individual's health state remain ill‐defined. Here, we investigate the plasma proteomes of a cohort of 43 obese individuals that had undergone 8 weeks of 12% body weight loss followed by a year of weight maintenance. Using mass spectrometry‐based plasma proteome profiling, we measured 1,294 plasma proteomes. Longitudinal monitoring of the cohort revealed individual‐specific protein levels with wide‐ranging effects of losing weight on the plasma proteome reflected in 93 significantly affected proteins. The adipocyte‐secreted SERPINF1 and apolipoprotein APOF1 were most significantly regulated with fold changes of −16% and +37%, respectively (P < 10(−13)), and the entire apolipoprotein family showed characteristic differential regulation. Clinical laboratory parameters are reflected in the plasma proteome, and eight plasma proteins correlated better with insulin resistance than the known marker adiponectin. Nearly all study participants benefited from weight loss regarding a ten‐protein inflammation panel defined from the proteomics data. We conclude that plasma proteome profiling broadly evaluates and monitors intervention in metabolic diseases

Molecular Predictors of Immunophenotypic Measurable Residual Disease Clearance in Acute Myeloid Leukemia

Measurable residual disease (MRD) is a powerful prognostic factor in acute myeloid leukemia (AML). However, pre-treatment molecular predictors of immunophenotypic MRD clearance remain unclear. We analyzed a dataset of 211 patients with pre-treatment next-generation sequencing who received induction chemotherapy and had MRD assessed by serial immunophenotypic monitoring after induction, subsequent therapy, and allogeneic stem cell transplant (allo-SCT). Induction chemotherapy led to MRD- remission, MRD+ remission, and persistent disease in 35%, 27%, and 38% of patients, respectively. With subsequent therapy, 34% of patients with MRD+ and 26% of patients with persistent disease converted to MRD-. Mutations in CEBPA, NRAS, KRAS, and NPM1 predicted high rates of MRD- remission, while mutations in TP53, SF3B1, ASXL1, and RUNX1 and karyotypic abnormalities including inv (3), monosomy 5 or 7 predicted low rates of MRD- remission. Patients with fewer individual clones were more likely to achieve MRD- remission. Among 132 patients who underwent allo-SCT, outcomes were favorable whether patients achieved early MRD- after induction or later MRD- after subsequent therapy prior to allo-SCT. As MRD conversion with chemotherapy prior to allo-SCT is rarely achieved in patients with specific baseline mutational patterns and high clone numbers, upfront inclusion of these patients into clinical trials should be considered

Risk Prediction for Clonal Cytopenia: Multicenter Real-World Evidence.

Clonal cytopenia of undetermined significance (CCUS) represents a distinct disease entity characterized by myeloid-related somatic mutations with a variant allele fraction of ≥2% in individuals with unexplained cytopenia(s) but without a myeloid neoplasm (MN). Notably, CCUS carries a risk of progressing to MN, particularly in cases featuring high-risk mutations. Understanding CCUS requires dedicated studies to elucidate its risk factors and natural history. Our analysis of 357 CCUS patients investigated the interplay between clonality, cytopenia, and prognosis. Multivariate analysis identified 3 key adverse prognostic factors: the presence of splicing mutation(s) (score = 2 points), platelet count &lt;100×109/L (score = 2.5), and ≥2 mutations (score = 3). Variable scores were based on the coefficients from the Cox proportional hazards model. This led to the development of the Clonal Cytopenia Risk Score (CCRS), which stratified patients into low- (score &lt;2.5 points), intermediate- (score 2.5-&lt;5), and high-risk (score ≥5) groups. The CCRS effectively predicted 2-year cumulative incidence of MN for low- (6.4%), intermediate- (14.1%), and high- (37.2%) risk groups, respectively, by Gray's test (P &lt;.0001). We further validated the CCRS by applying it to an independent CCUS cohort of 104 patients, demonstrating a c-index of 0.64 (P =.005) in stratifying the cumulative incidence of MN. Our study underscores the importance of integrating clinical and molecular data to assess the risk of CCUS progression, making the CCRS a valuable tool that is practical and easily calculable. These findings are clinically relevant, shaping the management strategies for CCUS and informing future clinical trial designs

Haloperidol differentially modulates prepulse inhibition and p50 suppression in healthy humans stratified for low and high gating levels

Schizophrenia patients exhibit deficits in sensory gating as indexed by reduced prepulse inhibition (PPI) and P50 suppression, which have been linked to psychotic symptom formation and cognitive deficits. Although recent evidence suggests that atypical antipsychotics might be superior over typical antipsychotics in reversing PPI and P50 suppression deficits not only in schizophrenia patients, but also in healthy volunteers exhibiting low levels of PPI, the impact of typical antipsychotics on these gating measures is less clear. To explore the impact of the dopamine D2-like receptor system on gating and cognition, the acute effects of haloperidol on PPI, P50 suppression, and cognition were assessed in 26 healthy male volunteers split into subgroups having low vs high PPI or P50 suppression levels using a placebo-controlled within-subject design. Haloperidol failed to increase PPI in subjects exhibiting low levels of PPI, but attenuated PPI in those subjects with high sensorimotor gating levels. Furthermore, haloperidol increased P50 suppression in subjects exhibiting low P50 gating and disrupted P50 suppression in individuals expressing high P50 gating levels. Independently of drug condition, high PPI levels were associated with superior strategy formation and execution times in a subset of cognitive tests. Moreover, haloperidol impaired spatial working memory performance and planning ability. These findings suggest that dopamine D2-like receptors are critically involved in the modulation of P50 suppression in healthy volunteers, and to a lesser extent also in PPI among subjects expressing high sensorimotor gating levels. Furthermore, the results suggest a relation between sensorimotor gating and working memory performance