Proteomic Measurements Research Articles

Microbial reaction rate estimation using proteins and proteomes.

Microbes transform their environments using diverse enzymatic reactions. However, it remains challenging to measure microbial reaction rates in natural environments. Despite advances in global quantification of enzyme abundances, the individual relationships between enzyme abundances and their reaction rates have not been systematically examined. Using matched proteomic and reaction rate data from microbial cultures, we show that enzyme abundance is often insufficient to predict its corresponding reaction rate. However, we discovered that global proteomic measurements can be used to make accurate rate predictions of individual reaction rates (median R2 = 0.78). Accurate rate predictions required only a small number of proteins and they did not need explicit prior mechanistic knowledge or environmental context. These results indicate that proteomes are encoders of cellular reaction rates, potentially enabling proteomic measurements in situ to estimate the rates of microbially mediated reactions in natural systems.

Machine learning analysis of population-wide plasma proteins identifies hormonal biomarkers of Parkinson's Disease.

As the number of Parkinson's patients is expected to increase with the growth of the aging population there is a growing need to identify new diagnostic markers that can be used cheaply and routinely to monitor the population, stratify patients towards treatment paths and provide new therapeutic leads. Genetic predisposition and familial forms account for only around 10% of PD cases [1] leaving a large fraction of the population with minimal effective markers for identifying high risk individuals. The establishment of population-wide omics and longitudinal health monitoring studies provides an opportunity to apply machine learning approaches on these unbiased cohorts to identify novel PD markers. Here we present the application of three machine learning models to identify protein plasma biomarkers of PD using plasma proteomics measurements from 43,408 UK Biobank subjects as the training and test set and an additional 103 samples from Parkinson's Progression Markers Initiative (PPMI) as external validation. We identified a group of highly predictive plasma protein markers including known markers such as DDC and CALB2 as well as new markers involved in the JAK-STAT, PI3K-AKT pathways and hormonal signaling. We further demonstrate that these features are well correlated with UPDRS severity scores and stratify these to protective and adversarial features that potentially contribute to the pathogenesis of PD.

Discovery plasma proteomics analyses identify proteins that interact with plasma ptau231 levels to predict longitudinal episodic memory trajectory over a 9‐year follow‐up period

AbstractBackgroundThere has been great progress towards identifying plasma biomarkers for Alzheimer’s disease (AD), though few studies have investigated interactions between proteomic measures and AD pathology. We aimed to identify plasma proteins predictive of episodic memory decline, an early clinical sign of AD, and assess whether such associations were altered by the presence of AD pathology.MethodVanderbilt Memory and Aging Project participants (n=350, 73±7 years, 41% female) without dementia at baseline underwent blood draw and serial neuropsychological assessments over 9‐year follow‐up (mean=6.1 years). Proteins were quantified using Olink® Explore 3072. Linear mixed‐effects regressions related protein levels to longitudinal episodic memory composite scores, including interactions with follow‐up time as the terms of interest. Models adjusted for age, sex, race/ethnicity, education, baseline cognitive status, and apolipoprotein E‐ε4 status. Follow‐up analyses included plasma phosphorylated tau 231 (ptau231) as a covariate, tested proteins x ptau231 interactions, and stratified by ptau231 tertile.ResultHigher levels of 15 proteins (GFAP, NEFL, KLK4, SPON1, GH1, CXCL1, OGN, EPHA2, EDA2R, SHBG, CKAP4, DSG2, CD300LG, CLEC5A, NOTCH3) predicted faster decline in episodic memory (pFDR<0.05). With ptau231 included as a covariate, associations between 5 of the proteins (SHBG, CKAP4, DSG2, CD300LG, NOTCH3) and memory attenuated (pFDR>0.05). 38 proteins interacted with ptau231 to predict memory trajectory (pFDR<0.05). Among participants with high ptau23, 24 proteins (LGALS3, FSTL1, SORT1, CHAD, ADAM23, SERPINA3, COL5A1, SYT1, PTPRZ1, CD34, DPP6, POLR2F, PLAUR, SPAG1, PDE4D, PTK7, CSPG4, LRTM2, MEGF10, MYOC, REG4, SPON1, WIF1, EDDM3B) were associated with memory trajectory. Higher protein levels predicted faster decline, except for EDDM3B, for which lower levels predicted faster decline (p<0.05, pFDR>0.05). Among participants with low ptau231, lower levels of 8 proteins (FSTL1, SORT1, IL6R, PTPRM, AHNAK, AMIGO2, IL1RAP, ROBO1) predicted faster decline in memory. Two proteins (FSTL1, SORT1) were associated with memory trajectory in both low and high ptau231 groups, exhibiting opposite directions of effects in the setting of low versus high ptau231 (see Figures).ConclusionResults demonstrate associations between plasma proteomic measures and longitudinal memory trajectory differ by level of AD pathology. To maximize clinical utility of potential biomarkers discovered in plasma proteomic analyses, future studies should assess interactions with AD core biomarkers.

Plasma Proteomic Markers of Iron and Risk of Diabetes in a Cohort of African American and White American Current and Former Smokers.

Little information is available on iron with diabetes risk among African Americans, a population where both anemia and elevated ferritin are common. We tested whether plasma proteomic measurements of ferritin and transferrin were associated with increased diabetes risk in a cohort of current and former African American (NHB) and Non-Hispanic White (NHW) smokers. NHB and NHW participants from the COPDGene study who were free of diabetes (n = 4693) at baseline were followed for incident diabetes. The SomaScan was used to determine the relative amounts of natural log-transformed ferritin, transferrin, and hepcidin. During an average of 5.6 years of follow-up, diabetes incidence was 7.9%. Ferritin at follow-up was higher in NHB than NHW participants (p = <0.0001). Ferritin at follow-up was associated with increased diabetes risk (OR = 1.36, 95% CI = 1.08-1.70), while transferrin was associated with decreased risk (OR = 0.25, 95% CI = 0.08-0.77) controlling for age, sex, BMI, smoking pack-years, hepcidin, CRP, and Il-6. Race-specifically, increased risk associated with higher ferritin levels among NHB (OR = 1.56, 95% CI = 1.13-2.16) but not NHW (OR = 1.22, 95% CI = 0.89-1.68) participants. Sex-specifically, ferritin's relationship was similar among NHB men and women and NHW women (ORs ranging from 1.41-1.59); but not NHW men (OR = 0.98, 95% CI = 0.64-1.49). Similarly, transferrin ORs non-significantly ranged from 0.19-0.30 for NHB men and women and NHW women, but was significant for NHW men (OR = 0.07, 95% CI = 0.01-0.63). Higher body iron stores is associated with increased diabetes risk among both NHB and NHW people. Unsuspected elevated iron stores may increase diabetes risk in NHB patients and should be monitored.

Pathologic and clinical correlates of region-specific brain GFAP in Alzheimer’s disease

Plasma glial fibrillary acidic protein (GFAP) is an emerging biomarker of Alzheimer’s disease (AD), with higher blood GFAP levels linked to faster cognitive decline, particularly among individuals with high brain amyloid burden. However, few studies have examined brain GFAP expression to clarify if peripheral associations reflect brain changes. This study aimed to correlate region-specific GFAP mRNA expression (n = 917) and protein abundance (n=386) with diverse neuropathological measures at autopsy in the Religious Orders Study and Rush Memory and Aging Project (ROS/MAP) and to characterize the interaction between brain GFAP and brain amyloid burden on downstream outcomes. We assessed GFAP gene expression in the dorsolateral prefrontal cortex, caudate nucleus, and posterior cingulate cortex with respect to core AD pathology (amyloid-β and tau), cerebrovascular (microinfarcts, macroinfarcts, and cerebral amyloid angiopathy [CAA]), proteinopathic (TDP-43, Lewy bodies), and cognitive outcomes. These associations were further examined at the protein level using tandem-mass tag proteomic measurements from the dorsolateral prefrontal cortex. We also assessed GFAP interactions with AD neuropathology on downstream outcomes. Cortical GFAP gene and protein expression were significantly upregulated in participants with a neuropathologically confirmed AD diagnosis at autopsy (all PFDR < 3.5e−4), but not in individuals positive for tau pathology and negative for amyloid pathology (all PFDR > 0.05). Higher cortical GFAP levels were associated with increased amyloid pathology, CAA pathology, and faster cognitive decline (all PFDR < 3.3e−3). GFAP’s associations with phosphorylated tau burden and cognition were influenced by amyloid burden, being most pronounced among amyloid-positive individuals, confirming previous in vivo biomarker observations. No associations were observed between GFAP gene expression and outcomes in the caudate nucleus. Our results support previous biomarker findings and suggest that higher brain GFAP levels are associated with higher brain amyloid burden and faster cognitive decline among amyloid-positive individuals.

A Proteomics-Based Approach for Prediction of Different Cardiovascular Diseases and Dementia.

Many studies have explored whether individual plasma protein biomarkers improve cardiovascular disease risk prediction. We sought to investigate the use of a plasma proteomics-based approach in predicting different cardiovascular outcomes. Among 51 859 UK Biobank participants (mean age, 56.7 years; 45.5% male) without cardiovascular disease and with proteomics measurements, we examined the primary composite outcome of fatal and nonfatal coronary heart disease, stroke, or heart failure (major adverse cardiovascular events), as well as additional secondary cardiovascular outcomes. An exposome-wide association study was conducted using relative protein concentrations, adjusted for a range of classic, demographic, and lifestyle risk factors. A prediction model using only age, sex, and protein markers (protein model) was developed using a least absolute shrinkage and selection operator-regularized approach (derivation: 80% of cohort) and validated using split-sample testing (20% of cohort). Their performance was assessed by comparing calibration, net reclassification index, and c statistic with the PREVENT (Predicting Risk of CVD Events) risk score. Over a median 13.6 years of follow-up, 4857 participants experienced first major adverse cardiovascular events. After adjustment, the proteins most strongly associated with major adverse cardiovascular events included NT-proBNP (N-terminal pro B-type natriuretic peptide; hazard ratio [HR], 1.68 per SD increase), proADM (pro-adrenomedullin; HR, 1.60), GDF-15 (growth differentiation factor-15; HR, 1.47), WFDC2 (WAP four-disulfide core domain protein 2; HR, 1.46), and IGFBP4 (insulin-like growth factor-binding protein 4; HR, 1.41). In total, 222 separate proteins were predictors of all outcomes of interest in the protein model, and 86 were selected for the primary outcome specifically. In the validation cohort, compared with the PREVENT risk factor model, the protein model improved calibration, net reclassification (net reclassification index +0.09), and c statistic for major adverse cardiovascular events (+0.051). The protein model also improved the prediction of other outcomes, including ASCVD (c statistic +0.035), myocardial infarction (+0.023), stroke (+0.024), aortic stenosis (+0.015), heart failure (+0.060), abdominal aortic aneurysm (+0.024), and dementia (+0.068). Measurement of targeted protein biomarkers produced superior prediction of aggregated and disaggregated cardiovascular events. This study represents an important proof of concept for the application of targeted proteomics in predicting a range of cardiovascular outcomes.

Human Plasma Proteomic Profile of Clonal Hematopoiesis.

Plasma proteomic profiles associated with subclinical somatic mutations in blood cells may offer novel insights into downstream clinical consequences. Here, we explore such patterns in clonal hematopoiesis of indeterminate potential (CHIP), which is linked to several cancer and non-cancer outcomes, including coronary artery disease (CAD). Among 61,833 ancestrally diverse participants (3,881 with CHIP) from NHLBI TOPMed and UK Biobank with blood-based DNA sequencing and proteomic measurements (1,148 proteins by SomaScan in TOPMed and 2,917 proteins by Olink in UK Biobank), we identified 32 and 345 unique proteins from TOPMed and UK Biobank, respectively, associated with the most prevalent driver genes (DNMT3A, TET2, and ASXL1). These associations showed substantial heterogeneity by driver genes, sex, and race, and were enriched for immune response and inflammation pathways. Mendelian randomization in humans, coupled with ELISA in hematopoietic Tet2-/- vs wild-type mice validation, disentangled causal proteomic perturbations from TET2 CHIP. Lastly, we identified plasma proteins shared between CHIP and CAD.

Low levels of apolipoprotein M are associated with increased risk of ventricular arrhythmias

Abstract Background Ventricular arrhythmia (VA) is a frequent cause of sudden cardiac death (SCD). Treatment to prevent SCD includes device therapy with implantable cardioverter defibrillator (ICD). Although the electrophysiology underlying VA have been described, the molecular understanding of VA risk remains unclear. Purpose To identify proteins that are associated with higher and lower risk of VA. Methods SMASH 1 Study was a prospective observational study of 490 patients treated with ICD. The plasma proteome was analyzed with the Olink Explore 384 Cardiometabolic platform at study inclusion, and patients were followed for mean 3.1± 0.7 years. VA events were defined as adjudicated ventricular fibrillation or sustained ventricular tachycardia registered from ICD recordings and clinical events were recorded from electronic health records. Protein concentrations were quantified relatively as normalized protein expression on a log2-transformed concentrations where a large number represents higher protein level in the sample. Results The mean age was 66±12 years, 83% were male and 51% had a primary prevention ICD-indication. Episode(s) of VA occurred in 137 patients (28%) during follow-up. In total, 353 distinct proteins were successfully analyzed in all patients. Among these, high B-type natriuretic peptide (BNP) and N-terminal pro-BNP (NT-proBNP) levels were associated with a higher risk of VA, while Apolipoprotein M (APOM) was the only protein significantly associated with a lower risk of VA after multiple testing using Benjamini-Hochberg correction (Figure, Panel A). After adjusting for age, sex, body mass index, coronary artery disease, heart failure, renal function, left ventricular ejection fraction and antiarrhythmic medication, only low APOM levels remained significantly associated with VA: HR 0.51 (95%CI 0.32-0.79] per log unit increase, p=0.003. Patients in the lowest quartile of APOM had a particularly high risk of incident VA: HR 2.26 (95%CI 1.60-3.19), p=3.61x10-6, compared to quartile 2-4 (Figure, Panel B). High APOM levels were also associated with a lower risk of HF hospitalizations (HR 0.25 [95%CI 0.16-0.40], p&amp;lt;0.001) and all-cause mortality (HR 0.36 [95%CI 0.22-0.60], p&amp;lt;0.001), and these associations remained significant in adjusted models. Conclusions Low levels of APOM are associated with a greater risk of incident VA, independently of established risk factors. Low APOM also predicted risk of HF hospitalizations and mortality in our cohort, which suggest protective cardiovascular effects of APOM. Figure: Association of individual proteomic measures with risk of ventricular arrhythmias (VA).A:Volcano plots of the associations of individual plasma proteins with incident VA. The dotted line represents the significance level at false discovery rate &amp;lt;0.05 and the red dots are significant proteins after multiple testing using Benjamini-Hochberg correction.B: Risk of VA by quartile of Apolipoprotein M.

Cerebral small vessel injury in mice with damage to ACE2-expressing cerebral vascular endothelial cells and post COVID-19 patients.

The angiotensin-converting enzyme 2 (ACE2), which is expressed in cerebral vascular endothelial cells (CVECs), has been currently identified as a functional receptor for SARS-CoV-2. We specifically induced injury to ACE2-expressing CVECs in mice and evaluated the effects of such targeted damage through magnetic resonance imaging (MRI) and cognitive behavioral tests. In parallel, we recruited a single-center cohort of COVID-19 survivors and further assessed their brain microvascular injury based on cognition and emotional scales, cranial MRI scans, and blood proteomic measurements. Here, we show an array of pathological and behavioral alterations characteristic of cerebral small vessel disease (CSVD) in mice that targeted damage to ACE2-expressing CVECs, and COVID-19 survivors. These CSVD-like manifestations persist for at least 7 months post-recovery from COVID-19. Our findings suggest that SARS-CoV-2 may induce cerebral small vessel damage with persistent sequelae, underscoring the imperative for heightened clinical vigilance in mitigating or treating SARS-CoV-2-mediated cerebral endothelial injury throughout infection and convalescence. Cerebral small vessel disease-associated changes were observed after targeted damage to angiotensin-converting enzyme 2-expressing cerebral vascular endothelial cells. SARS-CoV-2 may induce cerebral small vessel damage with persistent sequelae. Clinical vigilance is needed in preventing SARS-CoV-2-induced cerebral endothelial damage during infection and recovery.

Development, characterization, and replication of proteomic aging clocks: Analysis of 2 population-based cohorts.

Biological age may be estimated by proteomic aging clocks (PACs). Previous published PACs were constructed either in smaller studies or mainly in white individuals, and they used proteomic measures from only one-time point. In this study, we created de novo PACs and compared their performance to published PACs at 2 different time points in the Atherosclerosis Risk in Communities (ARIC) study of white and black participants (around 75% white and 25% black). A total of 4,712 plasma proteins were measured using SomaScan in blood samples collected in 1990 to 1992 from 11,761 midlife participants (aged 46 to 70 years) and in 2011 to 2013 from 5,183 late-life participants (aged 66 to 90 years). The de novo ARIC PACs were constructed by training them against chronological age using elastic net regression in two-thirds of healthy participants in midlife and late life and validated in the remaining one-third of healthy participants at the corresponding time point. We also computed 3 published PACs. We estimated age acceleration for each PAC as residuals after regressing each PAC on chronological age. We also calculated the change in age acceleration from midlife to late life. We examined the associations of age acceleration and change in age acceleration with mortality through 2019 from all-cause, cardiovascular disease (CVD), cancer, and lower respiratory disease (LRD) using Cox proportional hazards regression in participants (irrespective of health) after excluding the training set. The model was adjusted for chronological age, smoking, body mass index (BMI), and other confounders. We externally validated the midlife PAC using the Multi-Ethnic Study of Atherosclerosis (MESA) Exam 1 data. The ARIC PACs had a slightly stronger correlation with chronological age than published PACs in healthy participants at each time point. Associations with mortality were similar for the ARIC PACs and published PACs. For late-life and midlife age acceleration for the ARIC PACs, respectively, hazard ratios (HRs) per 1 standard deviation were 1.65 and 1.38 (both p < 0.001) for all-cause mortality, 1.37 and 1.20 (both p < 0.001) for CVD mortality, 1.21 (p = 0.028) and 1.04 (p = 0.280) for cancer mortality, and 1.68 and 1.36 (both p < 0.001) for LRD mortality. For the change in age acceleration, HRs for all-cause, CVD, and LRD mortality were comparable to the HRs for late-life age acceleration. The association between the change in age acceleration and cancer mortality was not significant. The external validation of the midlife PAC in MESA showed significant associations with mortality, as observed for midlife participants in ARIC. The main limitation is that our PACs were constructed in midlife and late-life participants. It is unknown whether these PACs could be applied to young individuals. In this longitudinal study, we found that the ARIC PACs and published PACs were similarly associated with an increased risk of mortality. These findings suggested that PACs show promise as biomarkers of biological age. PACs may be serve as tools to predict mortality and evaluate the effect of anti-aging lifestyle and therapeutic interventions.

Collagen VI deposition primes the glioblastoma microenvironment for invasion through mechanostimulation of β-catenin signaling.

While glioblastoma (GBM) progression is associated with extensive extracellular matrix (ECM) secretion, the causal contributions of ECM secretion to invasion remain unclear. Here we investigate these contributions by combining engineered materials, proteomics, analysis of patient data, and a model of bevacizumab-resistant GBM. We find that GBM cells cultured in engineered 3D hyaluronic acid hydrogels secrete ECM prior to invasion, particularly in the absence of exogenous ECM ligands. Proteomic measurements reveal extensive secretion of collagen VI, and collagen VI-associated transcripts are correspondingly enriched in microvascular proliferation regions of human GBMs. We further show that bevacizumab-resistant GBM cells deposit more collagen VI than their responsive counterparts, which is associated with marked cell-ECM stiffening. COL6A3 deletion in GBM cells reduces invasion, β-catenin signaling, and expression of mesenchymal markers, and these effects are amplified in hypoxia. Our studies strongly implicate GBM cell-derived collagen VI in microenvironmental remodeling to facilitate invasion.

Serum protein responses to Dietary Approaches to Stop Hypertension (DASH) and DASH-Sodium trials and associations with blood pressure changes.

The Dietary Approaches to Stop Hypertension (DASH) diet reduces blood pressure, but the mechanisms underlying DASH diet-blood pressure relations are not well understood. Proteomic measures may provide insights into the pathophysiological mechanisms through which the DASH diet reduces blood pressure. The DASH (1994-1996) and DASH-Sodium (1997-1999) trials were multicenter, randomized-controlled feeding trials. Proteomic profiling was conducted in serum collected at the end of the feeding period (DASH, N = 215; DASH-Sodium, N = 390). Multivariable linear regression models were used to identify interactions between 71 DASH diet-related proteins and changes in systolic and diastolic blood pressure. Estimates were meta-analyzed across both trials. Elastic net models were used to identify proteins that predict changes in blood pressure. Ten significant interactions were identified [systolic blood pressure: seven proteins; diastolic blood pressure: three proteins], which represented nine unique proteins. A high level of renin at the end of the feeding period was associated with greater reductions in diastolic blood pressure in individuals consuming the control than DASH diets. A high level of procollagen c-endopeptidase enhancer 1 (PCOLCE) and collagen triple helix repeat-containing protein 1 (CTHRC1) were associated with greater reductions in systolic blood pressure in individuals consuming the DASH than control diets, and with elevations in systolic blood pressure in individuals consuming the control diets (P for interaction for all tests < 0.05). Elastic net models identified six additional proteins that predicted change in blood pressure. Several novel proteins were identified that may provide some insight into the relationship between the DASH diet and blood pressure.

Biopsy Proteome Score Performs Well as an Effect Measure in a Gluten Challenge Trial of Celiac Disease

Background and aimsDevelopment of novel treatments for celiac disease (CeD) is dependent on precise tools to monitor changes in gluten-induced mucosal damage. Current histology measures are subjective and difficult to standardize. Biopsy proteome scoring is an objective alternative to histology which is based on robust changes in biological pathways that directly reflect gluten-induced mucosal damage. In this study we aimed to evaluate biopsy proteome scoring as an effect measure in a clinical trial setting by measuring intestinal remodeling in response to oral gluten challenge. MethodsWe analyzed biopsies from a 14-day gluten challenge trial of treated CeD patients that consumed 3 g (n = 6) or 10 g (n = 7) gluten per day. Sections from individually embedded biopsies collected before and after challenge were processed for proteome scoring (n = 109) and measurement of villous height to crypt depth (Vh:Cd) ratio (n = 58). Proteome scores were compared to histology, intraepithelial lymphocyte (IEL) frequency and plasma interleukin-2 (IL-2). ResultsChange in proteome scores were significant for the group of patients who consumed 10 g gluten, but not for the group who consumed 3 g gluten. Altogether, 8 of 13 patients had changes in delta proteome scores above cut-off. Proteome scores correlated with Vh:Cd ratios both at run-in and at day 15. Proteome scores at day 15 correlated with IEL frequency and with plasma IL-2 levels measured 4 hours post gluten intake. ConclusionBiopsy proteome scoring is a simple and reliable measure of gluten-induced mucosal remodeling in response to 14-day oral gluten challenge (ClinicalTrials.gov, number NCT03409796).

Nomination of a novel plasma protein biomarker panel capable of classifying Alzheimer's disease dementia with high accuracy in an African American cohort.

African Americans (AA) are widely underrepresented in plasma biomarker studies for Alzheimer's disease (AD) and current diagnostic biomarker candidates do not reflect the heterogeneity of AD. Untargeted proteome measurements were obtained using the SomaScan 7k platform to identify novel plasma biomarkers for AD in a cohort of AA clinically diagnosed as AD dementia (n=183) or cognitively unimpaired (CU, n=145). Machine learning approaches were implemented to identify the set of plasma proteins that yields the best classification accuracy. A plasma protein panel achieved an area under the curve (AUC) of 0.91 to classify AD dementia vs CU. The reproducibility of this finding was observed in the ANMerge plasma and AMP-AD Diversity brain datasets (AUC=0.83; AUC=0.94). This study demonstrates the potential of biomarker discovery through untargeted plasma proteomics and machine learning approaches. Our findings also highlight the potential importance of the matrisome and cerebrovascular dysfunction in AD pathophysiology.

Impact of hormonal therapy on HIV-1 immune markers in cis women and gender minorities.

Although sex hormones are recognized to induce immune variations, the effect of hormonal therapy use on immunity is only poorly understood. Here, we quantified how hormonal therapy use affects HIV-1 immune markers in cis women (CW) and trans women and non-binary people (TNBP) with HIV. We considered CD4, CD8 and lymphocyte measurements from cis men (CM), CW and TNBP in the Swiss HIV Cohort Study. We modelled HIV-1 markers using linear mixed-effects models with an interaction between 'gender' (CW, TNBP) and 'hormonal therapy use' (yes/no). Models were adjusted on age, ethnicity, education level, time since start of antiretroviral therapy and use of intravenous drugs. We assessed the inflammatory effect of hormonal therapy use in 31 TNBP using serum proteomics measurements of 92 inflammation markers. We included 54 083 measurements from 3092 CW and 83 TNBP, and 147 230 measurements from 8611 CM. Hormonal therapy use increased CD4 count and CD4:CD8 ratio in TNBP more than in CW (pinteraction = 0.02 and 0.007, respectively). TNBP with hormonal therapy use had significantly higher CD4 counts [median = 772 cells/μL, interquartile range (IQR): 520-1006] than without (617 cells/μL, 426-892). This was similar to the effect of CW versus CM on CD4 T cells. Hormonal therapy use did not affect serum protein concentrations in TNBP. This study highlights the potential role of hormonal therapy use in modulating the immune system among other biological and social factors, especially in TNBP with HIV.

Direct comparison of mass cytometry and single-cell RNA sequencing of human peripheral blood mononuclear cells

Single-cell methods offer a high-resolution approach for characterizing cell populations. Many studies rely on single-cell transcriptomics to draw conclusions regarding cell state and behavior, with the underlying assumption that transcriptomic readouts largely parallel their protein counterparts and subsequent activity. However, the relationship between transcriptomic and proteomic measurements is imprecise, and thus datasets that probe the extent of their concordance will be useful to refine such conclusions. Additionally, novel single-cell analysis tools often lack appropriate gold standard datasets for the purposes of assessment. Integrative (combining the two data modalities) and predictive (using one modality to improve results from the other) approaches in particular, would benefit from transcriptomic and proteomic data from the same sample of cells. For these reasons, we performed single-cell RNA sequencing, mass cytometry, and flow cytometry on a split-sample of human peripheral blood mononuclear cells. We directly compare the proportions of specific cell types resolved by each technique, and further describe the extent to which protein and mRNA measurements correlate within distinct cell types.

Characterization of clinical data for patient stratification in moderate osteoarthritis with support vector machines, regulatory network models, and verification against osteoarthritis Initiative data

Knee osteoarthritis (OA) diagnosis is based on symptoms, assessed through questionnaires such as the WOMAC. However, the inconsistency of pain recording and the discrepancy between joint phenotype and symptoms highlight the need for objective biomarkers in knee OA diagnosis. To this end, we study relationships among clinical and molecular data in a cohort of women (n = 51) with Kellgren–Lawrence grade 2–3 knee OA through a Support Vector Machine (SVM) and a regulation network model. Clinical descriptors (i.e., pain catastrophism, depression, functionality, joint pain, rigidity, sensitization and synovitis) are used to classify patients. A Youden’s test is performed for each classifier to determine optimal binarization thresholds for the descriptors. Thresholds are tested against patient stratification according to baseline WOMAC data from the Osteoarthritis Initiative, and the mean accuracy is 0.97. For our cohort, the data used as SVM inputs are knee OA descriptors, synovial fluid proteomic measurements (n = 25), and transcription factor activation obtained from regulatory network model stimulated with the synovial fluid measurements. The relative weights after classification reflect input importance. The performance of each classifier is evaluated through ROC-AUC analysis. The best classifier with clinical data is pain catastrophism (AUC = 0.9), highly influenced by funcionality and pain sensetization, suggesting that kinesophobia is involved in pain perception. With synovial fluid proteins used as input, leptin strongly influences every classifier, suggesting the importance of low-grade inflammation. When transcription factors are used, the mean AUC is limited to 0.608, which can be related to the pleomorphic behaviour of osteoarthritic chondrocytes. Nevertheless, funcionality has an AUC of 0.7 with a decisive importance of FOXO downregulation. Though larger and longitudinal cohorts are needed, this unique combination of SVM and regulatory network model shall help to stratify knee OA patients more objectively.

Glial cell injury and atrophied lesion volume as measures of chronic multiple sclerosis inflammation

BackgroundAtrophied lesion volume (aLV), a proposed biomarker of disability progression in multiple sclerosis (MS) and transition into progressive MS (PMS), depicts chronic periventricular white matter (WM) pathology. Meningeal infiltrates, imaged as leptomeningeal contrast enhancement (LMCE), are linked with greater cortical pathology. ObjectivesTo determine the relationship between serum-derived proteomic data with the development of aLV and LMCE in a heterogeneous group of people with MS (pwMS). MethodsProteomic and MRI data for 202 pwMS (148 clinically isolated syndrome /relapsing-remitting MS and 54 progressive MS (PMS)) were acquired at baseline and at 5.4-year follow-up. The concentrations of 21 proteins related to multiple MS pathophysiology pathways were derived using a custom-developed Proximity Extension Assay on the Olink™ platform. The accrual of aLV was determined as the volume of baseline T2-weighted lesions that were replaced by cerebrospinal fluid over the follow-up. Regression models and age-adjusted analysis of covariance (ANCOVA) were used. ResultsOlder age (standardized beta = 0.176, p = 0.022), higher glial fibrillary acidic protein (standardized beta = 0.312, p = 0.001), and lower myelin oligodendrocyte glycoprotein levels (standardized beta = −0.271, p = 0.002) were associated with accrual of aLV over follow-up. This relationship was driven by the pwPMS population. The presence of LMCE at the follow-up visit was not predicted by any baseline proteomic biomarker nor cross-sectionally associated with any protein concentration. ConclusionProteomic markers of glial activation are associated with chronic lesional WM pathology (measured as aLV) and may be specific to the progressive MS phenotype. LMCE presence in MS does not appear to relate to proteomic measures.

Update on Venous Thromboembolism in Orthopaedic Trauma Surgery

Venous thromboembolism (VTE) is a notable contributor to the morbidity and mortality of patients with orthopaedic trauma. Several associations have published guidelines on VTE prophylaxis, with a strong predilection toward low-molecular-weight heparin for chemoprophylaxis. However, previous recommendations may be revisited because recent high-level evidence demonstrated aspirin to be noninferior to low-molecular-weight heparin in preventing serious complications of VTE. Direct oral anticoagulants are emerging as safe alternatives to injectable medication and may offer improved patient compliance. There is no consensus on postinjury duration of chemoprophylaxis as an outpatient. Mechanical prophylaxis remains an important adjunctive VTE preventive measure for inpatients with pelvis and extremity fractures. Proteomics and novel laboratory measurements may be able to predict high-risk patients and detect early thrombus formation. Despite high-quality prospective research over the past decade, we still have much to learn about patient- and injury-specific risk factors.

Novel proteomic signatures may indicate MRI-assessed intrahepatic fat state and changes: The DIRECT PLUS clinical trial.

We demonstrated in the randomized 18-month DIRECT PLUS trial (n = 294) that a Mediterranean (MED) diet, supplemented with polyphenol-rich Mankai duckweed, green tea, and walnuts and restricted in red/processed meat, caused substantial intrahepatic fat (IHF%) loss compared with 2 other healthy diets, reducing NAFLD by half, regardless of similar weight loss. Here, we investigated the baseline proteomic profile associated with IHF% and the changes in proteomics associated with IHF% changes induced by lifestyle intervention. We calculated IHF% by proton magnetic resonance spectroscopy (normal IHF% <5% and abnormal IHF% ≥5%). We assayed baseline and 18-month samples for 95 proteomic biomarkers.Participants (age = 51.3 ± 10.8y; 89% men; and body mass index = 31.3 ± 3.9kg/m 2 ) had an 89.8% 18-month retention rate; 83% had eligible follow-up proteomics measurements, and 78% had follow-up proton magnetic resonance spectroscopy. At baseline, 39 candidate proteins were significantly associated with IHF% (false discovery rate <0.05), mostly related to immune function pathways (eg, hydroxyacid oxidase 1). An IHF% prediction based on the DIRECT PLUS by combined model ( R2 = 0.47, root mean square error = 1.05) successfully predicted IHF% ( R2 = 0.53) during testing and was stronger than separately inputting proteins/traditional markers ( R2 = 0.43/0.44). The 18-month lifestyle intervention induced changes in 18 of the 39 candidate proteins, which were significantly associated with IHF% change, with proteins related to metabolism, extracellular matrix remodeling, and immune function pathways. Thrombospondin-2 protein change was higher in the green-MED compared to the MED group, beyond weight and IHF% loss ( p = 0.01). Protein principal component analysis revealed differences in the third principal component time distinct interactions across abnormal/normal IHF% trajectory combinations; p < 0.05 for all). Our findings suggest novel proteomic signatures that may indicate MRI-assessed IHF state and changes during lifestyle intervention. Specifically, carbonic anhydrase 5A, hydroxyacid oxidase 1, and thrombospondin-2 protein changes are independently associated with IHF% change, and thrombospondin-2 protein change is greater in the green-MED/high polyphenols diet.