An in Silico, Biomarker-Based Method for the Evaluation of Virtual Neuropsychiatric Drug Effects
2
Citation
128
Reference
10
Related Paper
Citation Trend
Abstract:
The recent explosion in neuroscience research has markedly increased our understanding of the neurobiological correlates of many psychiatric illnesses, but this has unfortunately not translated into more effective pharmacologic treatments for these conditions. At the same time, researchers have increasingly sought out biological markers, or biomarkers, as a way to categorize psychiatric illness, as these are felt to be closer to underlying genetic and neurobiological vulnerabilities. While biomarker-based drug discovery approaches have tended to employ in vivo (e.g., rodent) or in vitro test systems, relatively little attention has been paid to the potential of computational, or in silico, methodologies. Here we describe such a methodology, using as an example a biophysically detailed computational model of hippocampus that is made to generate putative schizophrenia biomarkers by the inclusion of a number of neuropathological changes that have been associated with the illness (NMDA system deficit, decreased neural connectivity, hyperdopaminergia). We use the specific inability to attune to gamma band (40 Hz) auditory stimulus as our illness biomarker. We expose this system to a large number of virtual medications, defined by systematic variation of model parameters corresponding to five cellular-level effects. The potential efficacy of virtual medications is determined by a wellness metric (WM) that we have developed. We identify a number of virtual agents that consist of combinations of mechanisms, which are not simply reversals of the causative lesions. The manner in which this methodology could be extended to other neuropsychiatric conditions, such as Alzheimer's disease, autism, and fragile X syndrome, is discussed.Keywords:
Fragile X Syndrome
The diagnosis of schizophrenia is currently based on the symptoms and bodily signs rather than on the pathological and physiological markers of the patient. In the search for new molecular targeted therapy medicines, and recurrence of early-warning indicators have become the major focus of contemporary research, because they improve diagnostic accuracy. Biomarkers reflect the physiological, physical and biochemical status of the body, and so have extensive applicability and practical significance. The ascertainment of schizophrenia biomarkers will help diagnose, stratify of disease, and treat of schizophrenia patients. The detection of biomarkers from blood has become a promising area of schizophrenia research. Recently, a series of studies revealed that, MiRNAs play an important role in the genesis of schizophrenia, and their abnormal expressions have the potential to be used as biomarkers of schizophrenia. This article presents and summarizes the value of peripheral blood miRNAs with abnormal expression as the biomarker of schizophrenia.
Diagnostic biomarker
Biomarker Discovery
Cite
Citations (43)
Idiopathic short stature
Cite
Citations (9)
Abstract Background Type 1 diabetes (T1D) is a complex disease and harmful to human health, and most of the existing biomarkers are mainly to measure the disease phenotype after the disease onset (or drastic deterioration). Until now, there is no effective biomarker which can predict the upcoming disease (or pre-disease state) before disease onset or disease deterioration. Further, the detail molecular mechanism for such deterioration of the disease, e.g ., driver genes or causal network of the disease, is still unclear. Methods In this study, we detected early-warning signals of T1D and its leading biomolecular networks based on serial gene expression profiles of NOD (non-obese diabetic) mice by identifying a new type of biomarker, i.e ., dynamical network biomarker (DNB) which forms a specific module for marking the time period just before the drastic deterioration of T1D. Results Two dynamical network biomarkers were obtained to signal the emergence of two critical deteriorations for the disease, and could be used to predict the upcoming sudden changes during the disease progression. We found that the two critical transitions led to peri-insulitis and hyperglycemia in NOD mices, which are consistent with other independent experimental results from literature. Conclusions The identified dynamical network biomarkers can be used to detect the early-warning signals of T1D and predict upcoming disease onset before the drastic deterioration. In addition, we also demonstrated that the leading biomolecular networks are causally related to the initiation and progression of T1D, and provided the biological insight into the molecular mechanism of T1D. Experimental data from literature and functional analysis on DNBs validated the computational results.
Biomarker Discovery
Gene regulatory network
Cite
Citations (87)
Abstract Objective A clearer understanding of behavioral and blood biomarker differences in individuals with Fragile X Syndrome (FXS) and idiopathic autism is necessary as advancement in gene therapies and medication targeted to the FXS single gene abnormality are being trialed in autism. Our primary objective is to determine Autism Diagnostic Interview-Revised (ADIR) item/domain differences in children with FXS and autism, controlling for age, IQ, and autism severity. Our secondary objective is to demonstrate blood protein biomarker group differences. Method Participants were drawn from a larger study examining amyloid precursor protein (APP) metabolite levels in children with FXS (n = 18) and autism (n = 21) from Indiana University Riley Hospital Clinics. Complete data were available for children with full FXS mutations (n = 9, and autism (n = 9, one girl), matched for age, MSEL IQ, and CARS severity scores. ADI-R was administered to mothers by staff trained to research reliability. Western blot for APP and fragile X mental retardation protein (FMRP) were performed on peripheral blood mononuclear cells. Results No significant group differences are seen via Mann–Whitney U after Bonferroni multiple comparison, likely due to low power. However, two trends in our data corroborate recent reports: social smiling (item 51) is more intact for FXS [U(18) = 20.5,Z = -1.76,p = 0.085, 95%CI = -0.15–1.93,r = 0.41] and Restricted Behavior Scale [U(18) = 21,Z = -1.72,p = 0.09,95%CI = -0.57–2.79,r = 0.40] was more pronounced in autism. Western blot showed variations in APP and FMRP between FXS, autism and controls (Figure 1). Conclusions Trends in behavioral ADI-R scores and blood biomarker APP differences exist between FXS and idiopathic autism. Further study of these preliminary results should inform appropriate treatment endpoints for autism and FXS.
Fragile X Syndrome
Cite
Citations (0)
Biomarker Discovery
Clinical Significance
Cite
Citations (1)
Diabetes is among the most worldwide occurring metabolic disorders.It has created a substantial socioeconomic burden because of no targeted therapy available to date.Epigenetic modifications are playing a significant role in its cause and cure.The food we eat can lead to epigenetic changes, which further can alter the expression of different genes.In the current research, we postulate that meat consumers might have a high risk of diabetes due to miRNA's ability to instigate epigenetic modifications of genes involved in diabetes.We performed bioinformatics analysis in which first we retrieved chicken miRNAs data from miRbase.Then we checked the interaction\with target genes iIGF2BP2, iHNF1B, and iTCF7L2 of obtained miRNAs utilizing miRanda and TargetScan databases.The interaction score obtained indicated good interaction between them.Based ion the predicted miRNA-mRNA interactions score, we drew a miRNA network using miRNet software.The results obtained from this in-silico analysis suggest that chicken consumption may predispose diabetes through miRNAs associated with epigenetic modifications of the gene involved in diabetes.The outcome of this study can be further explored in cell-based, animal-based, and human-based studies.
Consumption
Cite
Citations (0)
Cloning (programming)
Cite
Citations (11)
Comparative Genomics
Cite
Citations (0)
Composite biomarkers of beta-cell injury (miRNAs, autoantibodies, or cytokines) may lead to better biomarkers of diabetes.
Cite
Citations (41)
SUMMARY Alzheimer's disease (AD) is the most common cause of dementia. Currently, a clinical diagnosis of AD is based on evidence of both cognitive and functional decline. Progression is monitored by detailed clinical evaluations over many months to years. It is increasingly clear that to advance disease-modifying therapies for AD, patients must be identified and treated early, before obvious cognitive and functional changes. In addition, better methods are needed to sensitively monitor progression of disease and therapeutic efficacy. Therefore, considerable research has focused on characterizing biomarkers that can identify the disease early as well as accurately monitor disease progression. miRNA offer a unique opportunity for biomarker development. Here, we review research focused on characterizing miRNA as potential biomarkers and as a treatment for disease.
Cite
Citations (52)