Metabolome-associated psychological comorbidities improvement in irritable bowel syndrome patients receiving a probiotic.

Our recent randomized, placebo-controlled study in Irritable Bowel Syndrome (IBS) patients with diarrhea or alternating bowel habits showed that the probiotic Bifidobacterium longum (BL) NCC3001 improves depression scores and decreases brain emotional reactivity. However, the involved metabolic pathways remain unclear. This analysis aimed to investigate the biochemical pathways underlying the beneficial effects of BL NCC3001 using metabolomic profiling. Patients received probiotic (1x 1010CFU, n=16) or placebo (n=19) daily for 6 weeks. Anxiety and depression were measured using the Hospital Anxiety and Depression Scale. Brain activity in response to negative emotional stimuli was assessed by functional Magnetic Resonance Imaging. Probiotic fecal abundance was quantified by qPCR. Quantitative measurement of specific panels of plasma host-microbial metabolites was performed by mass spectrometry-based metabolomics. Probiotic abundance in feces was associated with improvements in anxiety and depression scores, and a decrease in amygdala activation. The probiotic treatment increased the levels of butyric acid, tryptophan, N-acetyl tryptophan, glycine-conjugated bile acids, and free fatty acids. Butyric acid concentration correlated with lower anxiety and depression scores, and decreased amygdala activation. Furthermore, butyric acid concentration correlated with the probiotic abundance in feces. In patients with non-constipation IBS, improvements in psychological comorbidities and brain emotional reactivity were associated with an increased abundance of BL NCC3001 in feces and specific plasma metabolites, mainly butyric acid. These findings suggest the importance of a probiotic to thrive in the gut and highlight butyric acid as a potential biochemical marker linking microbial metabolism with beneficial effects on the gut-brain axis.

Proteomics reveals unique plasma signatures in constitutional thinness.

PURPOSE: Studying the plasma proteome of control versus constitutionally thin (CT) individuals, exposed to overfeeding, may give insights into weight-gain management, providing relevant information to the clinical entity of weight-gain resistant CT, and discovering new markers for the condition. EXPERIMENTAL DESIGN: Untargeted protein relative quantification of 63 CT and normal-weight individuals was obtained in blood plasma at baseline, during and after an overfeeding challenge using mass spectrometry-based proteomics. RESULTS: The plasma proteome of CT subjects presented limited specificity with respect to controls at baseline. Yet, CT showed lower levels of inflammatory C-reactive protein and larger levels of protective insulin-like growth factor-binding protein 2. Differences were more marked during and after overfeeding. CT plasma proteome showed larger magnitude and significance in response, suggesting enhanced "resilience" and more rapid adaptation to changes. Four proteins behaved similarly between CT and controls, while five were regulated in opposite fashion. Ten proteins were differential during overfeeding in CT only (including increased fatty acid-binding protein and glyceraldehyde-3-phosphate dehydrogenase, and decreased apolipoprotein C-II and transferrin receptor protein 1). CONCLUSIONS AND CLINICAL RELEVANCE: This first proteomic profiling of a CT cohort reveals different plasma proteomes between CT subjects and controls in a longitudinal clinical trial. Our molecular observations further support that the resistance to weight gain in CT subjects appears predominantly biological. CLINICALTRIALS: gov Identifier: NCT02004821.

Proteomics of human biological fluids for biomarker discoveries: technical advances and recent applications.

INTRODUCTION: Biological fluids are routine samples for diagnostic testing and monitoring. Blood samples are typically measured because of their moderate invasive collection and high information content on health and disease. Several body fluids, such as cerebrospinal fluid (CSF), are also studied and suited to specific pathologies. Over the last two decades, proteomics has quested to identify protein biomarkers but with limited success. Recent technologies and refined pipelines have accelerated the profiling of human biological fluids. AREAS COVERED: We review proteomic technologies for the identification of biomarkers. These are based on antibodies/aptamers arrays or mass spectrometry (MS), but new ones are emerging. Advances in scalability and throughput have allowed to better design studies and cope with the limited sample size that has until now prevailed due to technological constraints. With these enablers, plasma/serum, CSF, saliva, tears, urine, and milk proteomes have been further profiled; we provide a non-exhaustive picture of some recent highlights (mainly covering literature from the last 5 years in the Scopus database) using MS-based proteomics. EXPERT OPINION: While proteomics has been in the shadow of genomics for years, proteomic tools and methodologies have reached certain maturity. They are now better suited to discover innovative and robust biofluid biomarkers.

Assessing normalization methods in mass spectrometry-based proteome profiling of clinical samples.

BACKGROUND: Large-scale proteomic studies have to deal with unwanted variability, especially when samples originate from different centers and multiple analytical batches are needed. Such variability is typically added throughout all the steps of a clinical research study, from human biological sample collection and storage, sample preparation, spectral data acquisition, to peptide and protein quantification. In order to remove such diverse and unwanted variability, normalization of the protein data is performed. There have been already several published reviews comparing normalization methods in the -omics field, but reports focusing on proteomic data generated with mass spectrometry (MS) are much fewer. Additionally, most of these reports have only dealt with small datasets. RESULTS: As a case study, here we focused on the normalization of a large MS-based proteomic dataset obtained from an overweight and obese pan-European cohort, where different normalization methods were evaluated, namely: center standardize, quantile protein, quantile sample, global standardization, ComBat, median centering, mean centering, single standard and removal of unwanted variation (RUV); some of these are generic normalization methods while others have been specifically created to deal with genomic or metabolomic data. We checked how relationships between proteins and clinical variables (e.g., gender, levels of triglycerides or cholesterol) were improved after normalizing the data with the different methods. CONCLUSIONS: Some normalization methods were better adapted for this particular large-scale shotgun proteomic dataset of human plasma samples labeled with isobaric tags and analyzed with liquid chromatography-tandem MS. In particular, quantile sample normalization, RUV, mean and median centering showed very good performances, while quantile protein normalization provided worse results than those obtained with unnormalized data.

DNA Damage, n-3 Long-Chain PUFA Levels and Proteomic Profile in Brazilian Children and Adolescents.

Fatty acids play a significant role in maintaining cellular and DNA protection and we previously found an inverse relationship between blood levels of eicosapentaenoic acid (EPA) and docosahexaenoic acid (DHA) and DNA damage. The aim of this study was to explore differences in proteomic profiles, for 117 pro-inflammatory proteins, in two previously defined groups of individuals with different DNA damage and EPA and DHA levels. Healthy children and adolescents (n = 140) aged 9 to 13 years old in an urban area of Brazil were divided by k-means cluster test into two clusters of DNA damage (tail intensity) using the comet assay (cluster 1 = 5.9% ± 1.2 and cluster 2 = 13.8% ± 3.1) in our previous study. The cluster with higher DNA damage and lower levels of DHA (6.2 ± 1.6 mg/dL; 5.4 ± 1.3 mg/dL, p = 0.003) and EPA (0.6 ± 0.2 mg/dL; 0.5 ± 0.1 mg/dL, p < 0.001) presented increased expression of the proteins CDK8-CCNC, PIK3CA-PIK3R1, KYNU, and PRKCB, which are involved in pro-inflammatory pathways. Our findings support the hypothesis that low levels of n-3 long-chain PUFA may have a less protective role against DNA damage through expression of pro-inflammatory proteins, such as CDK8-CCNC, PIK3CA-PIK3R1, KYNU, and PRKCB.

Contribution of genetic ancestry and polygenic risk score in meeting vitamin B12 needs in healthy Brazilian children and adolescents.

Polymorphisms in genes related to the metabolism of vitamin B12 haven't been examined in a Brazilian population. To (a) determine the correlation between the local genetic ancestry components and vitamin B12 levels using ninety B12-related genes; (b) determine associations between these genes and their SNPs with vitamin B12 levels; (c) determine a polygenic risk score (PRS) using significant variants. This cross-sectional study included 168 children and adolescents, aged 9-13 years old. Total cobalamin was measured in plasma. Genotyping arrays and whole exome data were combined to yield ~ 7000 SNPs in 90 genes related to vitamin B12. The Efficient Local Ancestry Inference was used to estimate local ancestry for African (AFR), Native American, and European (EUR). The association between the genotypes and vitamin B12 levels were determined with generalized estimating equation. Vitamin B12 levels were driven by positive (EUR) and negative (AFR, AMR) correlations with genetic ancestry. A set of 36 variants were used to create a PRS that explained 42% of vitamin level variation. Vitamin B12 levels are influenced by genetic ancestry and a PRS explained almost 50% of the variation in plasma cobalamin in Brazilian children and adolescents.

Contributions of Fat and Carbohydrate Metabolism to Glucose Homeostasis in Childhood Change With Age and Puberty: A 12-Years Cohort Study (EARLYBIRD 77).

Puberty-a period when susceptibility to the onset of Type 2 diabetes (T2D) increases-is marked with profound physiological and metabolic changes. In the EarlyBird cohort, children who developed impaired fasting glycemia in adolescence already exhibited higher fasting blood glucose at 5 years of age, independent of their body mass index (BMI), suggesting that pubertal factors may modify existing predisposition. Understanding how the physiological changes during childhood influence glucose homeostasis and how the central energy metabolism may help deciphering the mechanisms that underlie the risk of developing T2D in children and adults. We investigated these associations by analyzing glycemic variations with molecular markers of central energy metabolism, substrate oxidation status and pubertal stages in the EarlyBird cohort. The EarlyBird study is a non-interventional, prospective cohort study, that recruited 307 healthy UK children at age 5, and followed them annually throughout childhood for 12 years. Longitudinal data on blood biochemistry, respiratory exchange ratio, and anthropometry, available from 150 children were integrated with fasting glycemia. The gradual rise in blood glucose during childhood associates with age-dependent changes in molecular processes and substrate oxidation status, namely (i) greater pre-pubertal fat utilization, ketogenesis, and fatty acid oxidation, and (ii) greater pubertal carbohydrate oxidation and glycolytic metabolism (Cori and Cahill Cycles) associated with different amino acid exchanges between muscle and other tissues (proline, glutamine, alanine). Since children's metabolic and nutritional requirements evolve during childhood, this study has potential clinical implications for the development of nutritional strategies for disease prevention in children.

Persistent low body weight in humans is associated with higher mitochondrial activity in white adipose tissue.

BACKGROUND: Constitutional thinness (CT) is a state of low but stable body weight (BMI ≤18 kg/m2). CT subjects have normal-range hormonal profiles and food intake but exhibit resistance to weight gain despite living in the modern world's obesogenic environment. OBJECTIVE: The goal of this study is to identify molecular mechanisms underlying this protective phenotype against weight gain. METHODS: We conducted a clinical overfeeding study on 30 CT subjects and 30 controls (BMI 20-25 kg/m2) matched for age and sex. We performed clinical and integrative molecular and transcriptomic analyses on white adipose and muscle tissues. RESULTS: Our results demonstrate that adipocytes were markedly smaller in CT individuals (mean ± SEM: 2174 ± 142 µm 2) compared with controls (3586 ± 216 µm2) (P < 0.01). The mitochondrial respiratory capacity was higher in CT adipose tissue, particularly at the level of complex II of the electron transport chain (2.2-fold increase; P < 0.01). This higher activity was paralleled by an increase in mitochondrial number (CT compared with control: 784 ± 27 compared with 675 ± 30 mitochondrial DNA molecules per cell; P < 0.05). No evidence for uncoupled respiration or "browning" of the white adipose tissue was found. In accordance with the mitochondrial differences, CT subjects had a distinct adipose transcriptomic profile [62 differentially expressed genes (false discovery rate of 0.1 and log fold change >0.75)], with many differentially expressed genes associating with positive metabolic outcomes. Pathway analyses revealed an increase in fatty acid oxidation ( P = 3 × 10-04) but also triglyceride biosynthesis (P = 3.6 × 10-04). No differential response to the overfeeding was observed in the 2 groups. CONCLUSIONS: The distinct molecular signature of the adipose tissue in CT individuals suggests the presence of augm ented futile lipid cycling, rather than mitochondrial uncoupling, as a way to increase energy expenditure in CT individuals. We propose that increased mitochondrial function in adipose tissue is an important mediator in sustaining the low body weight in CT individuals. This knowledge could ultimately allow more targeted approaches for weight management treatment strategies. This trial was registered at clinicaltrials.gov as NCT02004821.

A Versatile Workflow for Cerebrospinal Fluid Proteomic Analysis with Mass Spectrometry: A Matter of Choice between Deep Coverage and Sample Throughput.

Human cerebrospinal fluid (CSF) is a sample of choice in the study of brain disorders. This biological fluid circulates in the brain and the spinal cord and contains tissue-specific proteins, indicative of health and disease conditions. Despite its potential as a valid source of biological markers, CSF remains largely understudied as compared to blood, in particular due to its more invasive way of sampling.Challenges remain when performing proteomic analysis in clinical research studies. State-of-the-art mass spectrometry (MS) enables deep characterization of the human proteome. But some technical limitations are cardinal to be addressed, such as the capacity to routinely analyze large cohorts of samples. Importantly, a trade-off still needs to be made between the proteome coverage depth and the number of measured samples. In this context, we developed a scalable automated proteomic pipeline for the analysis of CSF. Because of its versatility, this workflow can be adapted to accommodate proteome coverage and/or sample throughput. It allows us to prepare and quantitatively analyze hundreds to thousands of CSF samples; it can also allow identification of more than 3000 proteins in a CSF sample when coupled with isoelectric focusing fractionation.In this chapter, we describe an end-to-end pipeline for the proteomic analysis of CSF. The main steps of the sample preparation comprise spiking of a standard, protein digestion, isobaric labeling, and purification; these are performed in a 96-well plate format enabling automation. Depending on the targeted depth of the CSF proteome, optional analytical steps can be included, such as the removal of abundant proteins and sample pre-fractionation. Liquid chromatography tandem MS as well as data processing and analysis complete the pipeline.

Precision identification of high-risk phenotypes and progression pathways in severe malaria without requiring longitudinal data.

More than 400,000 deaths from severe malaria (SM) are reported every year, mainly in African children. The diversity of clinical presentations associated with SM indicates important differences in disease pathogenesis that require specific treatment, and this clinical heterogeneity of SM remains poorly understood. Here, we apply tools from machine learning and model-based inference to harness large-scale data and dissect the heterogeneity in patterns of clinical features associated with SM in 2904 Gambian children admitted to hospital with malaria. This quantitative analysis reveals features predicting the severity of individual patient outcomes, and the dynamic pathways of SM progression, notably inferred without requiring longitudinal observations. Bayesian inference of these pathways allows us assign quantitative mortality risks to individual patients. By independently surveying expert practitioners, we show that this data-driven approach agrees with and expands the current state of knowledge on malaria progression, while simultaneously providing a data-supported framework for predicting clinical risk.

Insulin Resistance during normal child growth and development is associated with a distinct blood metabolic phenotype (Earlybird 72).

BACKGROUND: While insulin resistance (IR) is associated with specific metabolite signatures in adults, there have been few truly longitudinal studies in healthy children, either to confirm which abnormalities are present, or to determine whether they precede or result from IR. Therefore, we investigated the association of serum metabolites with IR in childhood in the Earlybird cohort. METHODS: The Earlybird cohort is a well-characterized cohort of healthy children with annual measurements from age 5 to 16 years. For the first time, longitudinal association analyses between individual serum metabolites and homeostatic model assessment (HOMA) of insulin resistance (HOMA-IR) have been performed taking into account the effects of age, growth, puberty, adiposity, and physical activity. RESULTS: IR was higher in girls than in boys and was associated with increasing body mass index (BMI). In longitudinal analysis IR was associated with reduced concentrations of branched-chain amino acids (BCAA), 2-ketobutyrate, citrate and 3-hydroxybutyrate, and higher concentrations of lactate and alanine. These findings demonstrate the widespread biochemical consequences of IR for intermediary metabolism, ketogenesis, and pyruvate oxidation during normal child growth and development. CONCLUSIONS: Longitudinal analysis can differentiate metabolite signatures that precede or follow the development of greater levels of IR. In healthy normal weight children, higher levels of IR are associated with reduced levels of BCAA, ketogenesis, and fuel oxidation. In contrast, elevated lactate concentrations preceded the rise in IR. These changes reveal the metabolite signature of insulin action during normal growth, and they contrast with previous findings in obese children and adults that represent the consequences of IR and obesity.

Analysis of 1508 Plasma Samples by Capillary-Flow Data-Independent Acquisition Profiles Proteomics of Weight Loss and Maintenance.

Comprehensive, high throughput analysis of the plasma proteome has the potential to enable holistic analysis of the health state of an individual. Based on our own experience and the evaluation of recent large-scale plasma mass spectrometry (MS) based proteomic studies, we identified two outstanding challenges: slow and delicate nano-flow liquid chromatography (LC) and irreproducibility of identification of data-dependent acquisition (DDA). We determined an optimal solution reducing these limitations with robust capillary-flow data-independent acquisition (DIA) MS. This platform can measure 31 plasma proteomes per day. Using this setup, we acquired a large-scale plasma study of the diet, obesity and genes dietary (DiOGenes) comprising 1508 samples. Proving the robustness, the complete acquisition was achieved on a single analytical column. Totally, 565 proteins (459 identified with two or more peptide sequences) were profiled with 74% data set completeness. On average 408 proteins (5246 peptides) were identified per acquisition (319 proteins in 90% of all acquisitions). The workflow reproducibility was assessed using 34 quality control pools acquired at regular intervals, resulting in 92% data set completeness with CVs for protein measurements of 10.9%.The profiles of 20 apolipoproteins could be profiled revealing distinct changes. The weight loss and weight maintenance resulted in sustained effects on low-grade inflammation, as well as steroid hormone and lipid metabolism, indicating beneficial effects. Comparison to other large-scale plasma weight loss studies demonstrated high robustness and quality of biomarker candidates identified. Tracking of nonenzymatic glycation indicated a delayed, slight reduction of glycation in the weight maintenance phase. Using stable-isotope-references, we could directly and absolutely quantify 60 proteins in the DIA.In conclusion, we present herein the first large-scale plasma DIA study and one of the largest clinical research proteomic studies to date. Application of this fast and robust workflow has great potential to advance biomarker discovery in plasma.

Analyzing Cerebrospinal Fluid Proteomes to Characterize Central Nervous System Disorders: A Highly Automated Mass Spectrometry-Based Pipeline for Biomarker Discovery.

Over the past decade, liquid chromatography tandem mass spectrometry (LC MS/MS)-based workflows become standard for biomarker discovery in proteomics. These medium- to high-throughput (in terms of protein content) profiling approaches have been applied to clinical research. As a result, human proteomes have been characterized to a greater extent than ever before. However, proteomics in clinical research and biomarker discovery studies has generally been performed with small cohorts of subjects (or pooled samples from larger cohorts). This is problematic, as when aiming to identify novel biomarkers, small studies suffer from inherent and important limitations, as a result of the reduced biological diversity and representativity of human populations. Consequently, larger-scale proteomics will be key to delivering robust biomarker candidates and enabling translation to clinical practice.Cerebrospinal fluid (CSF) is a highly clinically relevant body fluid, and an important source of potential biomarkers for brain-associated damage, such as that induced by traumatic brain injury and stroke, and brain diseases, such as Alzheimer's disease and Parkinson's disease. We have developed a scalable automated proteomic pipeline (ASAP2) for biomarker discovery. This workflow is compatible with larger clinical research studies in terms of sample size, while still allowing several hundred proteins to be measured in CSF by MS. In this chapter, we describe the whole proteomic workflow to analyze human CSF. We further illustrate our protocol with some examples from an analysis of hundreds of human CSF samples, in the specific context of biomarker discovery to characterize central nervous system disorders.

Proteomes of Paired Human Cerebrospinal Fluid and Plasma: Relation to Blood-Brain Barrier Permeability in Older Adults.

The systems-level relationship between the proteomes of cerebrospinal fluid (CSF) and plasma has not been comprehensively described so far. Recently developed shotgun proteomic workflows allow for deeper characterization of the proteomes from body fluids in much larger sample size. We deployed state-of-the-art mass spectrometry-based proteomics in paired CSF and plasma samples volunteered by 120 elders with and without cognitive impairment to comprehensively characterize and examine compartmental proteome differences and relationships between both body fluids. We further assessed the influence of blood-brain barrier (BBB) integrity and tested the hypothesis that BBB breakdown can be identified from CSF and plasma proteome alterations in nondemented elders. We quantified 790 proteins in CSF and 422 proteins in plasma, and 255 of the proteins were identified in both compartments. Pearson's statistics determined 28 proteins with associated levels between CSF and plasma. BBB integrity as defined with the CSF/serum albumin index influenced 76 CSF/plasma protein ratios. In least absolute shrinkage and selection operator models, CSF and plasma proteins improved identification of BBB impairment. In conclusion, we provide here a first comprehensive draft map of interacting human CSF and plasma proteomes, in view of their complex and dynamic compositions, and influence of the BBB.

Glucose-dependent phosphorylation signaling pathways and crosstalk to mitochondrial respiration in insulin secreting cells.

BACKGROUND: Glucose is the main secretagogue of pancreatic beta-cells. Uptake and metabolism of the nutrient stimulates the beta-cell to release the blood glucose lowering hormone insulin. This metabolic activation is associated with a pronounced increase in mitochondrial respiration. Glucose stimulation also initiates a number of signal transduction pathways for the coordinated regulation of multiple biological processes required for insulin secretion. METHODS: Shotgun proteomics including TiO2 enrichment of phosphorylated peptides followed by liquid chromatography tandem mass spectrometry on lysates from glucose-stimulated INS-1E cells was used to identify glucose regulated phosphorylated proteins and signal transduction pathways. Kinase substrate enrichment analysis (KSEA) was applied to identify key regulated kinases and phosphatases. Glucose-induced oxygen consumption was measured using a XF96 Seahorse instrument to reveal cross talk between glucose-regulated kinases and mitochondrial activation. RESULTS: Our kinetic analysis of substrate phosphorylation reveal the molecular mechanism leading to rapid activation of insulin biogenesis, vesicle trafficking, insulin granule exocytosis and cytoskeleton remodeling. Kinase-substrate enrichment identified upstream kinases and phosphatases and time-dependent activity changes during glucose stimulation. Activity trajectories of well-known glucose-regulated kinases and phosphatases are described. In addition, we predict activity changes in a number of kinases including NUAK1, not or only poorly studied in the context of the pancreatic beta-cell. Furthermore, we pharmacologically tested whether signaling pathways predicted by kinase-substrate enrichment analysis affected glucose-dependent acceleration of mitochondrial respiration. We find that phosphoinositide 3-kinase, Ca2+/calmodulin dependent protein kinase and protein kinase C contribute to short-term regulation of energy metabolism. CONCLUSIONS: Our results provide a global view into the regulation of kinases and phosphatases in insulin secreting cells and suggest cross talk between glucose-induced signal transduction and mitochondrial activation.

Obesity shows preserved plasma proteome in large independent clinical cohorts.

Holistic human proteome maps are expected to complement comprehensive profile assessment of health and disease phenotypes. However, methodologies to analyze proteomes in human tissue or body fluid samples at relevant scale and performance are still limited in clinical research. Their deployment and demonstration in large enough human populations are even sparser. In the present study, we have characterized and compared the plasma proteomes of two large independent cohorts of obese and overweight individuals using shotgun mass spectrometry (MS)-based proteomics. Herein, we showed, in both populations from different continents of about 500 individuals each, the concordance of plasma protein MS measurements in terms of variability, gender-specificity, and age-relationship. Additionally, we replicated several known and new associations between proteins, clinical and molecular variables, such as insulin and glucose concentrations. In conclusion, our MS-based analyses of plasma samples from independent human cohorts proved the practical feasibility and efficiency of a large and unified discovery/replication approach in proteomics, which was also recently coined "rectangular" design.

Identification of a Novel Clinical Phenotype of Severe Malaria using a Network-Based Clustering Approach.

The parasite Plasmodium falciparum is the main cause of severe malaria (SM). Despite treatment with antimalarial drugs, more than 400,000 deaths are reported every year, mainly in African children. The diversity of clinical presentations associated with SM highlights important differences in disease pathogenesis that often require specific therapeutic options. The clinical heterogeneity of SM is largely unresolved. Here we report a network-based analysis of clinical phenotypes associated with SM in 2,915 Gambian children admitted to hospital with Plasmodium falciparum malaria. We used a network-based clustering method which revealed a strong correlation between disease heterogeneity and mortality. The analysis identified four distinct clusters of SM and respiratory distress that departed from the WHO definition. Patients in these clusters characteristically presented with liver enlargement and high concentrations of brain natriuretic peptide (BNP), giving support to the potential role of circulatory overload and/or right-sided heart failure as a mechanism of disease. The role of heart failure is controversial in SM and our work suggests that standard clinical management may not be appropriate. We find that our clustering can be a powerful data exploration tool to identify novel disease phenotypes and therapeutic options to reduce malaria-associated mortality.

Alzheimer disease pathology and the cerebrospinal fluid proteome.

BACKGROUND: Altered proteome profiles have been reported in both postmortem brain tissues and body fluids of subjects with Alzheimer disease (AD), but their broad relationships with AD pathology, amyloid pathology, and tau-related neurodegeneration have not yet been fully explored. Using a robust automated MS-based proteomic biomarker discovery workflow, we measured cerebrospinal fluid (CSF) proteomes to explore their association with well-established markers of core AD pathology. METHODS: Cross-sectional analysis was performed on CSF collected from 120 older community-dwelling adults with normal (n = 48) or impaired cognition (n = 72). LC-MS quantified hundreds of proteins in the CSF. CSF concentrations of ß-amyloid 1-42 (Aß1-42), tau, and tau phosphorylated at threonine 181 (P-tau181) were determined with immunoassays. First, we explored proteins relevant to biomarker-defined AD. Then, correlation analysis of CSF proteins with CSF markers of amyloid pathology, neuronal injury, and tau hyperphosphorylation (i.e., Aß1-42, tau, P-tau181) was performed using Pearson's correlation coefficient and Bonferroni correction for multiple comparisons. RESULTS: We quantified 790 proteins in CSF samples with MS. Four CSF proteins showed an association with CSF Aß1-42 levels (p value ≤ 0.05 with correlation coefficient (R) ≥ 0.38). We identified 50 additional CSF proteins associated with CSF tau and 46 proteins associated with CSF P-tau181 (p value ≤ 0.05 with R ≥ 0.37). The majority of those proteins that showed such associations were brain-enriched proteins. Gene Ontology annotation revealed an enrichment for synaptic proteins and proteins originating from reelin-producing cells and the myelin sheath. CONCLUSIONS: We used an MS-based proteomic workflow to profile the CSF proteome in relation to cerebral AD pathology. We report strong evidence of previously reported CSF proteins and several novel CSF proteins specifically associated with amyloid pathology or neuronal injury and tau hyperphosphorylation.

Evaluation of Lymphocyte Response to the Induced Oxidative Stress in a Cohort of Ageing Subjects, including Semisupercentenarians and Their Offspring.

The production of reactive oxygen species (ROS) may promote immunosenescence if not counterbalanced by the antioxidant systems. Cell membranes, proteins, and nucleic acids become the target of ROS and progressively lose their structure and functions. This process could lead to an impairment of the immune response. However, little is known about the capability of the immune cells of elderly individuals to dynamically counteract the oxidative stress. Here, the response of the main lymphocyte subsets to the induced oxidative stress in semisupercentenarians (CENT), their offspring (OFF), elderly controls (CTRL), and young individuals (YO) was analyzed using flow cytometry. The results showed that the ratio of the ROS levels between the induced and noninduced (I/NI) oxidative stress conditions was higher in CTRL and OFF than in CENT and YO, in almost all T, B, and NK subsets. Moreover, the ratio of reduced glutathione levels between I/NI conditions was higher in OFF and CENT compared to the other groups in almost all the subsets. Finally, we observed significant correlations between the response to the induced oxidative stress and the degree of methylation in specific genes on the oxidative stress pathway. Globally, these data suggest that the capability to buffer dynamic changes in the oxidative environment could be a hallmark of longevity in humans.