Table 9 - uploaded by Andrew N Coogan
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Psychiatric disorders such as Alzheimer's disease, schizophrenia and mood disorders are severe and disabling conditions of largely unknown origin and poorly understood pathophysiology. An accurate diagnosis and treatment of these disorders is often complicated by their aetiological and clinical heterogeneity. In recent years proteomic technologies...
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... markers: An important objective of biomarker research, in the field of drug addiction, is to find objective markers for alcohol consumption. Nomura et al. (2004) ana- lysed the serum proteome of alcohol-dependent patients, who were hospitalized for a rehabilitation program (see Table 9). Two proteins (a 5.9 kDA fragment of fibrinogen alpha E-chain and a 7.8 kDA fragment of apoA2) were found to be down-regulated on admission and increased signifi- cantly after 1 week of alcohol abstinence. ...
Citations
... Furthermore, it plays a key role in lipid homeostasis, regulating its downstream target genes, such as FAS, and an accumulation of fatty acids and lipid droplets [88]. B2M reduces the secretion of lipid binding protein-2 [89] and it also participates in protein turnover [90]. This gene regulates processes such as the inhibition of cell death [91]. ...
The identification and selection of genetically superior animals for residual feed intake (RFI) could enhance productivity and minimize environmental impacts. The aim of this study was to use RNA-seq data to identify the differentially expressed genes (DEGs), known non-coding RNAs (ncRNAs), specific biomarkers and enriched biological processes associated with RFI of the liver in Nellore cattle in two genetic groups. In genetic group 1 (G1), 24 extreme RFI animals (12 low RFI (LRFI) versus 12 high RFI (HRFI)) were selected from a population of 60 Nellore bulls. The RNA-seq of the samples from their liver tissues was performed using an Illumina HiSeq 2000. In genetic group 2 (G2), 20 samples of liver tissue of Nellore bulls divergent for RFI (LRFI, n = 10 versus HRFI, n = 10) were selected from 83 animals. The raw data of the G2 were chosen from the ENA repository. A total of 1811 DEGs were found for the G1 and 2054 for the G2 (p-value ≤ 0.05). We detected 88 common genes in both genetic groups, of which 33 were involved in the immune response and in blocking oxidative stress. In addition, seven (B2M, ADSS, SNX2, TUBA4A, ARHGAP18, MECR, and ABCF3) possible gene biomarkers were identified through a receiver operating characteristic analysis (ROC) considering an AUC > 0.70. The B2M gene was overexpressed in the LRFI group. This gene regulates the lipid metabolism protein turnover and inhibits cell death. We also found non-coding RNAs in both groups. MIR25 was up-regulated and SNORD16 was down-regulated in the LRFI for G1. For G2, up-regulated RNase_MRP and SCARNA10 were found. We highlight MIR25 as being able to act by blocking cytotoxicity and oxidative stress and RMRP as a blocker of mitochondrial damage. The biological pathways associated with RFI of the liver in Nellore cattle in the two genetic groups were for energy metabolism, protein turnover, redox homeostasis and the immune response. The common transcripts, biomarkers and metabolic pathways found in the two genetic groups make this unprecedented work even more relevant, since the results are valid for different herds raised in different ways. The results reinforce the biological importance of these known processes but also reveal new insights into the complexity of the liver tissue transcriptome of Nellore cattle.
... The discovery of clinically applicable and reliable biomarkers for BD-II would therefore assist prompt diagnosis and lead to more effective treatment. Proteomics, the study of the identification and quantitative analysis of protein expression in an organism or system, has been proposed as a promising way to identify novel protein biomarkers for certain diseases [3]. We have previously identified candidate protein biomarkers for BD-II including PRDX2, CA-1, FARSB, MMP9, and PCSK9 [4]. ...
We have previously identified five candidate proteins (matrix metallopeptidase 9 (MMP9), phenylalanyl-TRNA synthetase subunit beta (FARSB), peroxiredoxin 2 (PRDX2), carbonic anhydrase 1 (CA-1), and proprotein convertase subtilisin/kexin Type 9 (PCSK9)) as potential biomarkers for bipolar II disorder (BD-II). These candidate proteins have been associated with neuroprotective factors (BDNF) and inflammatory factors (cytokines, C-reactive protein (CRP), and tumor necrosis factor-α (TNF-α)). However, the correlations between these proteins with plasma BDNF and inflammatory factors remain unknown. We recruited a total of 185 patients with BD-II and 186 healthy controls. Plasma levels of candidate proteins, BDNF, cytokines (TNF-α, CRP, and interleukin-8 (IL-8)) were assessed from each participant. The correlations between levels of candidate proteins, BDNF, and cytokines were analyzed. In the BD-II group, we found that the level of FARSB was positively correlated with the BDNF level (r = 0.397, p < 0.001) and IL-8 (r = 0.320, p < 0.001). The CA-1 level positively correlated with IL-8 (r = 0.318, p < 0.001). In the control group, we found that the FARSB level positively correlated with the BDNF level (r = 0.648, p < 0.001). The CA-1 level positively correlated with TNF-α (r = 0.231, p = 0.002), while the MMP-9 level positively correlated with the CRP level (r = 0.227, p = 0.002). Our results may help in clarifying the underlying mechanism of these candidate proteins for BD-II.
... Cannabinoids are in the process of being legalized for medicinal uses in some jurisdictions, and this serves as a justification for studying these drugs further. The cause of many mental health indications is not characterized by one protein, but by several proteins in several different categories [61][62][63][64][65] . Thus, the traditional high throughput screening methodology of testing one compound against one protein is not a suitable approach for mental health drug discovery. ...
We have developed the Computational Analysis of Novel Drug Opportunities (CANDO) platform to infer homology of drug behaviour at a proteomic level by constructing and analysing structural compound-proteome interaction signatures of 3,733 compounds with 48,278 proteins in a shotgun manner. We applied the CANDO platform to predict putative therapeutic properties of 428 psychoactive compounds that belong to the phenylethylamine, tryptamine, and cannabinoid chemical classes for treating mental health indications. Our findings indicate that these 428 psychoactives are among the top-ranked predictions for a significant fraction of mental health indications, demonstrating a significant preference for treating such indications over non-mental health indications, relative to randomized controls. Also, we analysed the use of specific tryptamines for the treatment of sleeping disorders, bupropion for substance abuse disorders, and cannabinoids for epilepsy. Our innovative use of the CANDO platform may guide the identification and development of novel therapies for mental health indications and provide an understanding of their causal basis on a detailed mechanistic level. These predictions can be used to provide new leads for preclinical drug development for mental health and other neurological disorders.
... Cannabinoids are in the process of being legalized for medicinal uses in some jurisdictions, and this serves as a justification for studying these drugs further. The cause of many mental health indications is not characterized by one protein, but by several proteins in several different categories [51][52][53][54][55] . Thus, the traditional high throughput screening methodology of testing one compound against one protein is not a suitable approach for mental health drug discovery. ...
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We have developed the Computational Analysis of Novel Drug Opportunities (CANDO) platform to infer homology of drug behavior at a proteomic level by constructing and analyzing structural compound-proteome interaction signatures of 3,733 compounds with 48,278 proteins in a shotgun manner. We applied the CANDO platform to predict putative therapeutic properties of 428 psychoactive compounds that belong to phenylethylamine, tryptamine, and cannabinoid chemical classes for treating mental health indications. Our findings indicate that these 428 psychoactives are among the top-ranked predictions for a significant fraction of mental health indications, demonstrating a significant preference for treating such indications over non-mental health indications, relative to randomized controls (p-value < 10-12). Also, we analyzed the use of specific tryptamines for the treatment of sleeping disorders, bupropion for substance abuse disorders, and cannabinoids for epilepsy. Our innovative use of the CANDO platform may guide the identification and development of novel therapies for mental health indications and provide an understanding of their causal basis on a detailed mechanistic level. These predictions can be used to provide new leads for preclinical drug development for mental health and other neurological disorders.
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... The proteome is the entire collection of "proteins encoded by the genome of an organism at a specific point in time, incorporating the set of isoforms, posttranslational modifications, covalent structures and complex proteinprotein interactions present therein" [508]. Proteomics provides an insight into the character and interactions of proteins and thus of signalling pathways -understanding a proteome allows for development of effective predictive biomarkers [509]. ...
Despite significant research efforts aimed at understanding the neurobiological underpinnings of mood (depression, bipolar disorder) and psychotic disorders, the diagnosis and evaluation of treatment of these disorders are still based solely on relatively subjective assessment of symptoms as well as psychometric evaluations. Therefore, biological markers aimed at improving the current classification of psychotic and mood-related disorders, and that will enable patients to be stratified on a biological basis into more homogeneous clinically distinct subgroups, are urgently needed. The attainment of this goal can be facilitated by identifying biomarkers that accurately reflect pathophysiologic processes in these disorders. This review postulates that the field of psychotic and mood disorder research has advanced sufficiently to develop biochemical hypotheses of the etiopathology of the particular illness and to target the same for more effective disease modifying therapy. This implies that a “one-size fits all” paradigm in the treatment of psychotic and mood disorders is not a viable approach, but that a customized regime based on individual biological abnormalities would pave the way forward to more effective treatment. In reviewing the clinical and preclinical literature, this paper discusses the most highly regarded pathophysiologic processes in mood and psychotic disorders, thereby providing a scaffold for the selection of suitable biomarkers for future studies in this field, to develope biomarker panels, as well as to improve diagnosis and to customize treatment regimens for better therapeutic outcomes.
... Proteomics is an emerging field that focuses on the study of proteins. The workhorse of proteomic research is largely mass spectrometry (MS), which can be used to provide unbiased assessment of the protein components of a biological sample [1] [2] [3] [4] [5] [6] [7] [8] [9]. ...
Biomarkers are greatly needed in the fields of neurology and psychiatry, to provide objective and earlier diagnoses of CNS conditions. Proteomics and other omic mass-spectrometry-based technologies are tools currently being utilized in much recent CNS research. Saliva is an interesting alternative biomaterial for the proteomic study of CNS disorders, with several advantages. Collection is non-invasive and saliva has many proteins. It is easier to collect than blood and can be collected by professionals without formal medical training. For psychiatric and neurological patients, supplying a saliva sample is less anxiety-provoking than providing a blood sample, and is less embarrassing than producing a urine specimen. The use of saliva as a biomaterial has been researched for the diagnosis of and greater understanding of several CNS conditions, including neurodegenerative diseases, autism and depression. Salivary biomarkers could be used to rule out non-psychiatric conditions that are often mistaken for psychiatric/neurological conditions, such as fibromyalgia, and potentially to assess cognitive ability in individuals with compromised brain function. As mass spectrometry and omics technology advances, the sensitivity and utility of assessing CNS conditions using distal human biomaterials such as saliva is becoming increasingly possible. This article is protected by copyright. All rights reserved.
... To date, experimental and clinical evidence has generated the idea that several serum proteins, considered as biomarkers, are strictly correlated with the pathophysiology of the autistic disorder [17][18][19][20]. In particular, significant changes in inflammation-related proteins suggested that at least some autistic children display a subclinical inflammatory state [21]. ...
Inflammation has been advocated as a possible common central mechanism for developmental cognitive impairment. Rett syndrome (RTT) is a devastating neurodevelopmental disorder, mainly caused by de novo loss-of-function mutations in the gene encoding MeCP2. Here, we investigated plasma acute phase response (APR) in stage II (i.e., "pseudo-autistic") RTT patients by routine haematology/clinical chemistry and proteomic 2-DE/MALDI-TOF analyses as a function of four major MECP2 gene mutation types (R306C, T158M, R168X, and large deletions). Elevated erythrocyte sedimentation rate values (median 33.0 mm/h versus 8.0 mm/h, P < 0.0001) were detectable in RTT, whereas C-reactive protein levels were unchanged (P = 0.63). The 2-DE analysis identified significant changes for a total of 17 proteins, the majority of which were categorized as APR proteins, either positive (n = 6 spots) or negative (n = 9 spots), and to a lesser extent as proteins involved in the immune system (n = 2 spots), with some proteins having overlapping functions on metabolism (n = 7 spots). The number of protein changes was proportional to the severity of the mutation. Our findings reveal for the first time the presence of a subclinical chronic inflammatory status related to the "pseudo-autistic" phase of RTT, which is related to the severity carried by the MECP2 gene mutation.
... Additional characteristics of protein biomarkers, such as expression levels, post-translational modifications, protein interactions, lead to better understanding of nervous system physiology under normal and pathological conditions and greater insight into the pathophysiology of psychiatric disorders (Taurines et al. 2011). ...
The etiology and pathogenesis of many psychiatric disorders are unclear with many signaling pathways and complex interactions still unknown. Primary information provided from gene expression or brain activity imaging experiments is useful, but can have limitations. There is a current effort focusing on the discovery of diagnostic and prognostic proteomic potential biomarkers for psychiatric disorders. Despite this work, there is still no biological diagnostic test available for any mental disorder. Biomarkers may advance the care of psychiatric illnesses and have great potential to knowledge of psychiatric disorders but several drawbacks must be considered. Here, we describe the potential of proteomic biomarkers for better understanding and diagnosis of psychiatric disorders and current putative biomarkers for schizophrenia, depression, autism spectrum disorder and attention deficit/hyperactivity disorder.
... We will limit most of our discussion to work published over the last five years (reviews of older work in the field of neuroproteomics can be found in a number of articles [38][39][40][41]. A number of other recent reviews also highlight the application of proteomic studies to studies of the nervous system [42][43][44]. ...
The field of proteomics is undergoing rapid development in a number of different areas including improvements in mass spectrometric platforms, peptide identification algorithms and bioinformatics. In particular, new and/or improved approaches have established robust methods that not only allow for in-depth and accurate peptide and protein identification and modification, but also allow for sensitive measurement of relative or absolute quantitation. These methods are beginning to be applied to the area of neuroproteomics, but the central nervous system poses many specific challenges in terms of quantitative proteomics, given the large number of different neuronal cell types that are intermixed and that exhibit distinct patterns of gene and protein expression. This review highlights the recent advances that have been made in quantitative neuroproteomics, with a focus on work published over the last five years that applies emerging methods to normal brain function as well as to various neuropsychiatric disorders including schizophrenia and drug addiction as well as of neurodegenerative diseases including Parkinson's disease and Alzheimer's disease. While older methods such as two-dimensional polyacrylamide electrophoresis continued to be used, a variety of more in-depth MS-based approaches including both label (ICAT, iTRAQ, TMT, SILAC, SILAM), label-free (label-free, MRM, SWATH) and absolute quantification methods, are rapidly being applied to neurobiological investigations of normal and diseased brain tissue as well as of cerebrospinal fluid (CSF). While the biological implications of many of these studies remain to be clearly established, that there is a clear need for standardization of experimental design and data analysis, and that the analysis of protein changes in specific neuronal cell types in the central nervous system remains a serious challenge, it appears that the quality and depth of the more recent quantitative proteomics studies is beginning to shed light on a number of aspects of neuroscience that relates to normal brain function as well as of the changes in protein expression and regulation that occurs in neuropsychiatric and neurodegenerative disorders.
... The long-standing search for accurate biomarkers from many conditions/diseases impacting neuropsychological functioning include, but are not limited to, Alzheimer's disease (AD) [1][2][3][4], traumatic brain injury (TBI) [5,6], schizophrenia [7,8], alcohol use/abuse [9], and mood disorders [9][10][11]. For example, we recently created a serum-based biomarker algorithm that yielded excellent diagnostic accuracy in separating AD cases from controls [1,12,13]. ...
... The long-standing search for accurate biomarkers from many conditions/diseases impacting neuropsychological functioning include, but are not limited to, Alzheimer's disease (AD) [1][2][3][4], traumatic brain injury (TBI) [5,6], schizophrenia [7,8], alcohol use/abuse [9], and mood disorders [9][10][11]. For example, we recently created a serum-based biomarker algorithm that yielded excellent diagnostic accuracy in separating AD cases from controls [1,12,13]. ...
... Blood-based biomarkers are preferable as they are more cost and time efficient and more conveniently acquired than CSF or neuroimaging [19]. An additional advantage of proteomic approaches to biomarker identification is the potential to discover alterations at the protein level that may be closely related to the pathophysiological process(es) underlying complex conditions and disease states [9]. The purpose of the present study was to take a first step towards creating serumbased biomarker algorithms of neuropsychological functioning. ...
Background:
Prior work on the link between blood-based biomarkers and cognitive status has largely been based on dichotomous classifications rather than detailed neuropsychological functioning. The current project was designed to create serum-based biomarker algorithms that predict neuropsychological test performance.
Methods:
A battery of neuropsychological measures was administered. Random forest analyses were utilized to create neuropsychological test-specific biomarker risk scores in a training set that were entered into linear regression models predicting the respective test scores in the test set. Serum multiplex biomarker data were analyzed on 108 proteins from 395 participants (197 Alzheimer patients and 198 controls) from the Texas Alzheimer's Research and Care Consortium.
Results:
The biomarker risk scores were significant predictors (p < 0.05) of scores on all neuropsychological tests. With the exception of premorbid intellectual status (6.6%), the biomarker risk scores alone accounted for a minimum of 12.9% of the variance in neuropsychological scores. Biomarker algorithms (biomarker risk scores and demographics) accounted for substantially more variance in scores. Review of the variable importance plots indicated differential patterns of biomarker significance for each test, suggesting the possibility of domain-specific biomarker algorithms.
Conclusions:
Our findings provide proof of concept for a novel area of scientific discovery, which we term 'molecular neuropsychology'.
