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Background: Neuroprotection for Parkinson's disease (PD) remains elusive. Biomarkers hold the promise of removing roadblocks to therapy development. The National Institute of Neurological Disorders and Stroke has therefore established the Parkinson's Disease Biomarkers Program to promote discovery of PD biomarkers for use in phase II and III clinical trials., Methods: Using a novel consortium design, the Parkinson's Disease Biomarker Program is focused on the development of clinical and laboratory-based biomarkers for PD diagnosis, progression, and prognosis. Standardized operating procedures and pooled reference samples were created to allow cross-project comparisons and assessment of batch effects. A web-based Data Management Resource facilitates rapid sharing of data and biosamples across the research community for additional biomarker projects., Results: Eleven consortium projects are ongoing, seven of which recruit participants and obtain biosamples. As of October 2014, 1,082 participants have enrolled (620 PD, 101 with other causes of parkinsonism, 23 essential tremor, and 338 controls), 1,040 of whom have at least one biosample. Six thousand eight hundred ninety-eight total biosamples are available from baseline, 6-, 12-, and 18-month visits: 1,006 DNA, 1,661 RNA, 1,419 whole blood, 1,382 plasma, 1,200 serum, and 230 cerebrospinal fluid (CSF). Quality control analysis of plasma, serum, and CSF samples indicates that almost all samples are high quality (24 of 2,812 samples exceed acceptable hemoglobin levels)., Conclusions: By making samples and data widely available, using stringent operating procedures based on existing standards, hypothesis testing for biomarker discovery, and providing a resource that complements existing programs, the Parkinson's Disease Biomarker Program will accelerate the pace of PD biomarker research. © 2015 International Parkinson and Movement Disorder Society., (© 2015 International Parkinson and Movement Disorder Society.)

Autism spectrum disorder (ASD) is a neurodevelopmental disorder characterized by impairments in social interaction and communication, and restricted, repetitive patterns of behavior. In order to identify individuals with ASD and initiate interventions at the earliest possible age, biomarkers for the disorder are desirable. Research findings have identified widespread changes in the immune system in children with autism, at both systemic and cellular levels. In an attempt to find candidate antibody biomarkers for ASD, highly complex libraries of peptoids (oligo-N-substituted glycines) were screened for compounds that preferentially bind IgG from boys with ASD over typically developing (TD) boys. Unexpectedly, many peptoids were identified that preferentially bound IgG from TD boys. One of these peptoids was studied further and found to bind significantly higher levels (>2-fold) of the IgG1 subtype in serum from TD boys (n = 60) compared to ASD boys (n = 74), as well as compared to older adult males (n = 53). Together these data suggest that ASD boys have reduced levels (>50%) of an IgG1 antibody, which resembles the level found normally with advanced age. In this discovery study, the ASD1 peptoid was 66% accurate in predicting ASD.

A high-throughput software pipeline for analyzing high-performance mass spectral data sets has been developed to facilitate rapid and accurate biomarker determination. The software exploits the mass precision and resolution of high-performance instrumentation, bypasses peak-finding steps, and instead uses discrete m/z data points to identify putative biomarkers. The technique is insensitive to peak shape, and works on overlapping and non-Gaussian peaks which can confound peak-finding algorithms. Methods are presented to assess data set quality and the suitability of groups of m/z values that map to peaks as potential biomarkers. The algorithm is demonstrated with serum mass spectra from patients with and without ovarian cancer. Biomarker candidates are identified and ranked by their ability to discriminate between cancer and noncancer conditions. Their discriminating power is tested by classifying unknowns using a simple distance calculation, and a sensitivity of 95.6% and a specificity of 97.1% are obtained. In contrast, the sensitivity of the ovarian cancer blood marker CA125 is approximately 50% for stage I/II and approximately 80% for stage III/IV cancers. While the generalizability of these markers is currently unknown, we have demonstrated the ability of our analytical package to extract biomarker candidates from high-performance mass spectral data.

For Alzheimer's disease (AD), the most common neurodegenerative disease, there is no simple, cost-effective biomarker for disease identification. Using novel mass spectrometry (MS)-based techniques, and analysis of the albumin-enriched low molecular weight proteome, minute amounts of human serum were analyzed for the measurement of thousands of peptides and proteins in parallel. The mass spectrograms were then evaluated with a novel computer algorithm to identify spectral peaks that discriminate between samples from patients with and without AD. There are four peaks that distinguish AD from control subjects and AD subjects from those with Parkinson's disease (PD). Additionally, after analyzing data from a recently published study of AD and control subjects, we found three discriminating peaks in common with the four from our patient serum samples. The identification of these peptides/proteins, and their direct measurement in patient serum, may allow the development of a simple, cost-effective test for AD.

Background: Metabolites are biological compounds reflecting the functional activity of organs and tissues. Understanding metabolic changes in Alzheimer's disease (AD) can provide insight into potential risk factors in this multifactorial disease and suggest new intervention strategies or improve non-invasive diagnosis.Objective: In this study, we searched for changes in AD metabolism in plasma and frontal brain cortex tissue samples and evaluated the performance of plasma measurements as biomarkers.Methods: This is a case-control study with two tissue cohorts: 158 plasma samples (94 AD, 64 controls; Texas Alzheimer's Research and Care Consortium - TARCC) and 71 postmortem cortex samples (35 AD, 36 controls; Banner Sun Health Research Institute brain bank). We performed targeted mass spectrometry analysis of 630 compounds (106 small molecules: UHPLC-MS/MS, 524 lipids: FIA-MS/MS) and 232 calculated metabolic indicators with a metabolomic kit (Biocrates MxP® Quant 500).Results: We discovered disturbances (FDR≤0.05) in multiple metabolic pathways in AD in both cohorts including microbiome-related metabolites with pro-toxic changes, methylhistidine metabolism, polyamines, corticosteroids, omega-3 fatty acids, acylcarnitines, ceramides, and diglycerides. In AD, plasma reveals elevated triglycerides, and cortex shows altered amino acid metabolism. A cross-validated diagnostic prediction model from plasma achieves AUC = 82% (CI95 = 75-88%); for females specifically, AUC = 88% (CI95 = 80-95%). A reduced model using 20 features achieves AUC = 79% (CI95 = 71-85%); for females AUC = 84% (CI95 = 74-92%).Conclusion: Our findings support the involvement of gut environment in AD and encourage targeting multiple metabolic areas in the design of intervention strategies, including microbiome composition, hormonal balance, nutrients, and muscle homeostasis. [ABSTRACT FROM AUTHOR]

Autism spectrum disorder (ASD) is a neurodevelopmental disorder characterized by deficits in social communication and social interaction and restricted, repetitive patterns of behavior, interests, or activities. Given the lack of specific pharmacological therapy for ASD and the clinical heterogeneity of the disorder, current biomarker research efforts are geared mainly toward identifying markers for determining ASD risk or for assisting with a diagnosis. A wide range of putative biological markers for ASD is currently being investigated. Proteomic analyses indicate that the levels of many proteins in plasma/serum are altered in ASD, suggesting that a panel of proteins may provide a blood biomarker for ASD. Serum samples from 76 boys with ASD and 78 typically developing (TD) boys, 18 months-8 years of age, were analyzed to identify possible early biological markers for ASD. Proteomic analysis of serum was performed using SomaLogic's SOMAScanTM assay 1.3K platform. A total of 1,125 proteins were analyzed. There were 86 downregulated proteins and 52 upregulated proteins in ASD (FDR < 0.05). Combining three different algorithms, we found a panel of 9 proteins that identified ASD with an area under the curve (AUC) = 0.8599±0.0640, with specificity and sensitivity of 0.8217±0.1178 and 0.835±0.1176, respectively. All 9 proteins were significantly different in ASD compared with TD boys, and were significantly correlated with ASD severity as measured by ADOS total scores. Using machine learning methods, a panel of serum proteins was identified that may be useful as a blood biomarker for ASD in boys. Further verification of the protein biomarker panel with independent test sets is warranted. [ABSTRACT FROM AUTHOR]

Background: Autism spectrum disorder (ASD) affects approximately 1 in 68 children in the USA. An ASD blood biomarker may enable early diagnosis and/or identification of new therapeutic targets. Serum samples from ASD and typically developing (TD) boys (n = 30/group) were screened for differences in 110 proteins using a multiplex immunoassay.Results: Eleven proteins were found that together could confirm ASD with modest accuracy using multiple training and test sets. Two of the 11 proteins identified here were further tested using a different detection platform and with a larger sample of ASD and TD boys. The two proteins, thyroid-stimulating hormone (TSH) and interleukin-8 (IL-8), have been previously identified as putative biomarkers for ASD. TSH levels were significantly lower in ASD boys, whereas IL-8 levels were significantly elevated. The diagnostic accuracy for ASD based upon TSH or IL-8 levels alone varied from 74 to 76%, but using both proteins together, the diagnostic accuracy increased to 82%. In addition, TSH levels were negatively correlated with the Autism Diagnostic Observation Schedule subdomain scores.Conclusions: These data suggest that a panel of proteins may be useful as a putative blood biomarker for ASD. [ABSTRACT FROM AUTHOR]

Background: There is a significant need for rapid and cost-effective biomarkers of Alzheimer's disease (AD) for advancement of clinical practice and therapeutic trials. Objective: The aim of the current study was to cross-validate our previously published serum-based algorithm on an independent assay platform as well as validate across tissues and species. Preliminary analyses were conducted to examine the utility in distinguishing AD from non-AD neurological disease (Parkinson's disease, PD). Methods: Serum proteins from our previously published algorithm were quantified from 150 AD cases and 150 controls on the Meso Scale Discovery (MSD) platform. Serum samples were analyzed from 49 PD cases and compared to a random sample of 51 AD cases and 62 controls. Support vector machines (SVM) were used to discriminate PD versus AD versus controls. Human and AD mouse model microvessel images were quantified with HAMAMATSU imaging software. Mouse serum biomarkers were assayed via MSD. Results: Analysis of 21 serum proteins from 150 AD cases and 150 controls yielded an algorithm with sensitivity and specificity of 0.90 for correctly classifying AD. This multi-marker approach was then validated across species and tissue. Assay of the top proteins in human and AD mouse model brain microvessels correctly classified 90-100% of the samples. SVM analyses were highly accurate at distinguishing PD versus AD versus controls. Conclusions: This serum-based biomarker panel should be tested in a community-based setting to determine its utility as a first-line screen for AD and non-AD neurological diseases for primary care providers. [ABSTRACT FROM AUTHOR]

Abstract: Antibodies to a wide range of self-antigens, including those directed against nucleic acids or nucleic acid-binding proteins are the essential biomarkers for diseases such as systemic lupus erythematosus (SLE). Highly complex libraries of nonamers consisting of N-substituted glycines (peptoids) were screened for compounds that bound IgG from patients with SLE and earlier, incomplete autoimmune syndromes. Peptoids were identified that could identify subjects with SLE and related syndromes with a high sensitivity (70%) and specificity (97.5%). Immobilized peptoids were used to isolate IgG from both healthy subjects and SLE patients that reacted with known RNA-binding proteins. In the case of SLE patients, the peptoid-purified IgG reacted with several autoantigens, suggesting that the peptoids are capable of interacting with multiple, structurally similar molecules. These results show that the measurement of IgG binding to peptoids can identify subjects with high levels of pathogenic autoantibodies. [Copyright &y& Elsevier]