Candidate gene networks and blood biomarkers of methamphetamine-associated psychosis: an integrative RNA-sequencing report

A primer on the use of machine learning to distil knowledge from data in biological psychiatry

Applications of machine learning in the biomedical sciences are growing rapidly. This growth has been spurred by diverse cross-institutional and interdisciplinary collaborations, public availability of large datasets, an increase in the accessibility of analytic routines, and the availability of powerful computing resources. With this increased access and exposure to machine learning comes a responsibility for education and a deeper understanding of its bases and bounds, borne equally by data scientists seeking to ply their analytic wares in medical research and by biomedical scientists seeking to harness such methods to glean knowledge from data. This article provides an accessible and critical review of machine learning for a biomedically informed audience, as well as its applications in psychiatry. The review covers definitions and expositions of commonly used machine learning methods, and historical trends of their use in psychiatry. We also provide a set of standards, namely Guidelines for REporting Machine Learning Investigations in Neuropsychiatry (GREMLIN), for designing and reporting studies that use machine learning as a primary data-analysis approach. Lastly, we propose the establishment of the Machine Learning in Psychiatry (MLPsych) Consortium, enumerate its objectives, and identify areas of opportunity for future applications of machine learning in biological psychiatry. This review serves as a cautiously optimistic primer on machine learning for those on the precipice as they prepare to dive into the field, either as methodological practitioners or well-informed consumers.

Searching for diagnoses and subgroups: a suggestion for criteria

New subgroups of psychiatric disorders are often claimed. In contrast, classification systems have repeatedly had to abandon established subgroups such as paranoid vs. disorganised and catatonic schizophrenia due to lack of empirical evidence. Four criteria are proposed that should be met to claim valid subgroups: 1. distinct distribution of the defining characteristic between groups; 2. significant differences in variables other than those defining the subgroups cross-sectionally and longitudinally; 3. long-term stability; 4. significant differences between groups in aetiology, pathophysiology, and evidence-based therapy. In contrast to examples from somatic medicine, such as type 1 and type 2 diabetes, few psychiatric disorders meet these requirements.

Uncovering transcriptomic biomarkers for enhanced diagnosis of methamphetamine use disorder: a comprehensive review

Introduction

Methamphetamine use disorder (MUD) is a chronic relapsing disorder characterized by compulsive Methamphetamine (MA) use despite its detrimental effects on physical, psychological, and social well-being. The development of MUD is a complex process that involves the interplay of genetic, epigenetic, and environmental factors. The treatment of MUD remains a significant challenge, with no FDA-approved pharmacotherapies currently available. Current diagnostic criteria for MUD rely primarily on self-reporting and behavioral assessments, which have inherent limitations owing to their subjective nature. This lack of objective biomarkers and unidimensional approaches may not fully capture the unique features and consequences of MA addiction.

Methods

We performed a literature search for this review using the Boolean search in the PubMed database.

Results

This review explores existing technologies for identifying transcriptomic biomarkers for MUD diagnosis. We examined non-invasive tissues and scrutinized transcriptomic biomarkers relevant to MUD. Additionally, we investigated transcriptomic biomarkers identified for diagnosing, predicting, and monitoring MUD in non-invasive tissues.

Discussion

Developing and validating non-invasive MUD biomarkers could address these limitations, foster more precise and reliable diagnostic approaches, and ultimately enhance the quality of care for individuals with MA addiction.

A brainnetome atlas-based methamphetamine dependence identification using neighborhood component analysis and machine learning on functional MRI data

Addiction to methamphetamine (MA) is a major public health concern. Developing a predictive model that can classify and characterize the brain-based biomarkers predicting MA addicts may directly lead to improved treatment outcomes. In the current study, we applied the support vector machine (SVM)-based classification method to resting-state functional magnetic resonance imaging (rs-fMRI) data obtained from individuals with methamphetamine use disorder (MUD) and healthy controls (HCs) to identify brain-based features predictive of MUD. Brain connectivity analyses were conducted for 36 individuals with MUD as well as 37 HCs based on the brainnetome atlas, and the neighborhood component analysis was applied for feature selection. Eighteen most relevant features were screened out and fed into the SVM to classify the data. The classifier was able to differentiate individuals with MUD from HCs with a high prediction accuracy, sensitivity, specificity, and AUC of 88.00, 86.84, 89.19, and 0.94, respectively. The top six discriminative features associated with changes in the functional activity of key nodes in the default mode network (DMN), all the remaining discriminative features are related to the thalamic connections within the cortico-striato-thalamo-cortical (CSTC) loop. In addition, the functional connectivity (FC) between the bilateral inferior parietal lobule (IPL) and right cingulate gyrus (CG) was significantly correlated with the duration of methamphetamine use. The results of this study not only indicated that MUD-related FC alterations were predictive of group membership, but also suggested that machine learning techniques could be used for the identification of MUD-related imaging biomarkers.

New insight into methamphetamine-associated heart failure revealed by transcriptomic analyses: Circadian rhythm disorder

Methamphetamine (METH) abuse is a significant public health concern globally. Cardiac toxicity is one of the important characteristics of METH, in addition to its effects on the nervous system. However, to date, research on the cardiotoxic injury induced by METH consumption has been insufficient. To systematically analyze the potential molecular mechanism of cardiac toxicity in METH-associated heart failure (HF), a rat model was constructed with a dose of 10 mg/kg of METH consumption. Cardiac function was evaluated by echocardiography, and HE staining was used to clarify the myocardial histopathological changes. Integrated analyses, including mRNA, miRNA and lncRNA, was performed to analyze the RNA expression profile and the potential molecular mechanisms involved in METH-associated HF. The results showed that METH caused decreased myocardial contractility, with a decreased percent ejection fraction (%EF). Kyoto Encyclopedia of Genes and Genomes (KEGG) and Gene Ontology (GO) analyses of the RNAs with expression changes revealed abnormal circadian rhythm regulation in the METH groups, with circadian rhythm-related genes and their downstream effectors expressed differentially, especially the aryl hydrocarbon receptor nuclear translocator-like (Arntl). Competing endogenous RNA (ceRNA) networks associated with circadian rhythm, including Arntl, was also observed. Therefore, this study revealed that long-term METH consumption was associated with the HF in a rat model by decreasing the %EF, and that the abnormal circadian rhythm could provide new directions for investigating the METH-associated HF, and that the differentially expressed genes in this model could provide candidate genes for the identification and assessment of cardiac toxicity in METH-associated HF, which is fundamental for further understanding of the disease.

Methamphetamine, Neurotransmitters and Neurodevelopment

Methamphetamine (MA), as massively abused psychoactive stimulant, has been associated with many neurological diseases. It has various potent and neurotoxic properties. There are many mechanisms of action that contribute to its neurotoxic and degenerative effects, including excessive neurotransmitter (NEU) release, blockage of NEU uptake transporters, degeneration of NEU receptors, process of oxidative stress etc. MA intoxication is caused by blood-brain barrier disruption resulted from MA-induced oxidation stress. In our laboratory we constantly work on animal research of MA. Our current interest is to investigate processes of MA-induced alteration in neurotransmission, especially during development of laboratory rat. This review will describe current understanding in role of NEUs, which are affected by MA-induced neurotoxicity caused by altering the action of NEUs in the central nervous system (CNS). It also briefly brings information about NEUs development in critical periods of development.

Effect of PPM1F in dorsal raphe 5-HT neurons in regulating methamphetamine-induced conditioned place preference performance in mice

Methamphetamine (METH), a synthetically produced central nervous system stimulant, is one of the most illicit and addictive drugs worldwide. Protein phosphatase Mg2 + /Mn2 + -dependent 1F F (PPM1F) has been reported to exert multiple biological and cellular functions. Nevertheless, the effects of PPM1F and its neuronal substrates on METH addiction remain unclear. Herein, we first established a METH-induced conditioned place preference (CPP) mouse model. We showed that PPM1F is widely distributed in 5-HT neurons of the dorsal raphe nucleus (DRN), and METH treatment decreased the expression of PPM1F in DRN, which was negatively correlated with METH-induced CPP behaviors. Knockout of PPM1F mediated by adeno-associated virus (AAV) in DRN produced enhanced susceptibility to METH-induced CPP, whereas the overexpression of PPM1F in DRN attenuated METH-induced CPP phenotypes. The expression levels of Tryptophan hydroxylase2 (TPH2) and serotonin transporter (SERT) were down-regulated with a concurrent reduction in 5-hydroxytryptamine (5-HT), tryptophan hydroxylase2 (TPH2)-immunoreactivity neurons and 5-HT levels in DRN of PPM1F knockout mice. In the end, decreased expression levels of PPM1F were found in the blood of METH abusers and METH-taking mice. These results suggest that PPM1F in DRN 5-HT neurons regulates METH-induced CPP behaviors by modulating the key components of the 5-HT neurotransmitter system, which might be an important pathological gene and diagnostic marker for METH-induced addiction.

Effects of the co-administration of MK-801 and clozapine on MiRNA expression profiles in rats

MiRNAs are small, non-coding RNAs that can silence the expression of various target genes by binding their mRNAs and thus regulate a wide range of crucial bodily functions. However, the miRNA expression profile of schizophrenia after antipsychotic mediation is largely unknown. Non-competitive N-methyl-D-aspartic acid (NMDA) receptor antagonists such as MK-801 have provided useful animal models to investigate the effects of schizophrenia-like symptoms in rodent animals. Herein, the hippocampal miRNA expression profiles of Sprague-Dawley rats pretreated with MK-801 were examined after antipsychotic clozapine (CLO) treatment. Total hippocampal RNAs from three groups were subjected to next-generation sequencing (NGS), and bioinformatics analyses, including differential expression and enrichment analyses, were performed. Eight miRNAs were differentially expressed between the MK-801 and vehicle (VEH) control groups. Interestingly, 14 miRNAs were significantly differentially expressed between the CLO + MK-801 and MK-801 groups, among which rno-miR-184 was the most upregulated. Further analyses suggested that these miRNAs modulate target genes that are involved in endocytosis regulation, ubiquitin-mediated proteolysis, and actin cytoskeleton regulation and thus might play important roles in the pathogenesis of schizophrenia. Our results suggest that differentially expressed miRNAs play important roles in the complex pathophysiology of schizophrenia and subsequently impact brain functions.

Treatment Response Prediction and Individualized Identification of Short-Term Abstinence Methamphetamine Dependence Using Brain Graph Metrics

Background: The abuse of methamphetamine (MA) worldwide has gained international attention as the most rapidly growing illicit drug problem. The classification and treatment response prediction of MA addicts are thereby paramount, in order for effective treatments to be more targeted to individuals. However, there has been limited progress. Methods: In the present study, 43 MA-dependent participants and 38 age- and gender-matched healthy controls were enrolled, and their resting-state functional magnetic resonance imaging data were collected. MA-dependent participants who showed 50% reduction in craving were defined as responders to treatment. The present study used the machine learning method, which is a support vector machine (SVM), to detect the most relevant features for discriminating and predicting the treatment response for MA-dependent participants based on the features extracted from the functional graph metrics. Results: A classifier was able to differentiate MA-dependent subjects from normal controls, with a cross-validated prediction accuracy, sensitivity, and specificity of 73.2% [95% confidence interval (CI) = 71.23-74.17%), 66.05% (95% CI = 63.06-69.04%), and 80.35% (95% CI = 77.77-82.93%), respectively, at the individual level. The most accurate combination of classifier features included the nodal efficiency in the right middle temporal gyrus and the community index in the left precentral gyrus and cuneus. Between these two, the community index in the left precentral gyrus had the highest importance. In addition, the classification performance of the other classifier used to predict the treatment response of MA-dependent subjects had an accuracy, sensitivity, and specificity of 71.2% (95% CI = 69.28-73.12%), 86.75% (95% CI = 84.48-88.92%), and 55.65% (95% CI = 52.61-58.79%), respectively, at the individual level. Furthermore, the most accurate combination of classifier features included the nodal clustering coefficient in the right orbital part of the superior frontal gyrus, the nodal local efficiency in the right orbital part of the superior frontal gyrus, and the right triangular part of the inferior frontal gyrus and right temporal pole of middle temporal gyrus. Among these, the nodal local efficiency in the right temporal pole of the middle temporal gyrus had the highest feature importance. Conclusion: The present study identified the most relevant features of MA addiction and treatment based on SVMs and the features extracted from the graph metrics and provided possible biomarkers to differentiate and predict the treatment response for MA-dependent patients. The brain regions involved in the best combinations should be given close attention during the treatment of MA.

Construction of gene-classifier and co-expression network analysis of genes in association with major depressive disorder

Because the pathogenesis of major depressive disorder (MDD) is still unclear and the accurate diagnosis remains unavailable, we aimed to analyze its molecular mechanisms and develop a gene classifier to improve diagnostic accuracy. We extracted differentially expressed genes from two datasets, GSE45642 (from brain tissue samples) and GSE98793 (from blood samples), and found three key modules to have a significant correlation with MDD traits by weighted gene coexpression network analysis. Hub genes were identified from the key modules according to the connectivity degree in the network and subjected to least absolute shrinkage and selection operator regression analysis. A total of eighty-five hub genes were selected to construct the gene classifier, which had considerable ability to recognize MDD patients in the training set and test set. In addition, the relationship between the key MDD modules and brain tissues indicated that the anterior cingulate should be a notable region in the study of MDD pathogenesis. The results of Gene Ontology (GO) and pathway enrichment analyses reiterate the relationship between depression and immunity. Therefore we identified MDD hub genes in the InnateDB database, and found 14 genes involved in both MDD and the inflammatory response.

Role of Extracellular Vesicles in Substance Abuse and HIV-Related Neurological Pathologies

Extracellular vesicles (EVs) are a broad, heterogeneous class of membranous lipid-bilayer vesicles that facilitate intercellular communication throughout the body. As important carriers of various types of cargo, including proteins, lipids, DNA fragments, and a variety of small noncoding RNAs, including miRNAs, mRNAs, and siRNAs, EVs may play an important role in the development of addiction and other neurological pathologies, particularly those related to HIV. In this review, we summarize the findings of EV studies in the context of methamphetamine (METH), cocaine, nicotine, opioid, and alcohol use disorders, highlighting important EV cargoes that may contribute to addiction. Additionally, as HIV and substance abuse are often comorbid, we discuss the potential role of EVs in the intersection of substance abuse and HIV. Taken together, the studies presented in this comprehensive review shed light on the potential role of EVs in the exacerbation of substance use and HIV. As a subject of growing interest, EVs may continue to provide information about mechanisms and pathogenesis in substance use disorders and CNS pathologies, perhaps allowing for exploration into potential therapeutic options.

Fas and GIT1 signalling in the prefrontal cortex mediate behavioural sensitization to methamphetamine in mice

PURPOSE

Repeated methamphetamine (METH) administration in mice readily produces behavioural sensitization, but the underlying mechanisms remain elusive. The present research aimed to identify new targets affecting METH-induced behavioural sensitization.

METHODS

We first established a mouse model of METH-induced behavioural sensitization. To characterize the animal model, we performed behavioural tests at different stages of behavioural sensitization and simultaneously detected changes in several neurotransmitters in the prefrontal cortex (PFC). Next, we perfromed RNA sequencing (RNA-seq) to screen new targets, which were subsequently and verified by RT-PCR and western blot. Finally, we confirmed the roles of the new targets in METH-induced behavioural sensitization by injection of overexpressed lentiviruses and further detected related protein levels by western blot and histological changes by haematoxylin and eosin (HE) staining.

RESULTS

We successfully established a mouse model of METH-induced behavioural sensitization. The locomotor activities of the mice changed at different stages of sensitization, accompanied by changes in the levels of DA, 5-HT, GABA and glutamate. For RNA-seq analysis, we chose Fas as target, meanwhile, we chose GIT1 as target through literature. The detection of gene expression by RT-PCR indicated that METH-sensitized mice exhibited decreased levels of Fas, MEK1 and CREB and increased levels of Erk1/2 in the PFC. Western blot analysis revealed decreased Fas, GIT1, MEK1 and phosphorylated CREB levels and increased phosphorylated Erk1/2 levels in METH-sensitized mice. Injection of Fas and GIT1 injection showed that overexpression of Fas and GIT1 inhibited the induction of METH sensitization and reversed the changes in neurotransmitter levels and related protein levels, including MEK1, phosphorylated CREB and phosphorylated Erk1/2, in METH-sensitized mice. Overexpression of Fas and GIT1 also reduced histological lesions induced by METH.

CONCLUSION

The findings indicated that the development of behavioural sensitization to METH may be mediated by Fas and GIT1 through the MEK1-Erk1/2-CREB pathway.

Loss of the neural-specific BAF subunit ACTL6B relieves repression of early response genes and causes recessive autism

Synaptic activity in neurons leads to the rapid activation of genes involved in mammalian behavior. ATP-dependent chromatin remodelers such as the BAF complex contribute to these responses and are generally thought to activate transcription. However, the mechanisms keeping such "early activation" genes silent have been a mystery. In the course of investigating Mendelian recessive autism, we identified six families with segregating loss-of-function mutations in the neuronal BAF (nBAF) subunit ACTL6B (originally named BAF53b). Accordingly, ACTL6B was the most significantly mutated gene in the Simons Recessive Autism Cohort. At least 14 subunits of the nBAF complex are mutated in autism, collectively making it a major contributor to autism spectrum disorder (ASD). Patient mutations destabilized ACTL6B protein in neurons and rerouted dendrites to the wrong glomerulus in the fly olfactory system. Humans and mice lacking ACTL6B showed corpus callosum hypoplasia, indicating a conserved role for ACTL6B in facilitating neural connectivity. Actl6b knockout mice on two genetic backgrounds exhibited ASD-related behaviors, including social and memory impairments, repetitive behaviors, and hyperactivity. Surprisingly, mutation of Actl6b relieved repression of early response genes including AP1 transcription factors (Fos, Fosl2, Fosb, and Junb), increased chromatin accessibility at AP1 binding sites, and transcriptional changes in late response genes associated with early response transcription factor activity. ACTL6B loss is thus an important cause of recessive ASD, with impaired neuron-specific chromatin repression indicated as a potential mechanism.

Prediction of Alzheimer's disease using blood gene expression data

Identification of AD (Alzheimer's disease)-related genes obtained from blood samples is crucial for early AD diagnosis. We used three public datasets, ADNI, AddNeuroMed1 (ANM1), and ANM2, for this study. Five feature selection methods and five classifiers were used to curate AD-related genes and discriminate AD patients, respectively. In the internal validation (five-fold cross-validation within each dataset), the best average values of the area under the curve (AUC) were 0.657, 0.874, and 0.804 for ADNI, ANMI, and ANM2, respectively. In the external validation (training and test sets from different datasets), the best AUCs were 0.697 (training: ADNI to testing: ANM1), 0.764 (ADNI to ANM2), 0.619 (ANM1 to ADNI), 0.79 (ANM1 to ANM2), 0.655 (ANM2 to ADNI), and 0.859 (ANM2 to ANM1), respectively. These results suggest that although the classification performance of ADNI is relatively lower than that of ANM1 and ANM2, classifiers trained using blood gene expression can be used to classify AD for other data sets. In addition, pathway analysis showed that AD-related genes were enriched with inflammation, mitochondria, and Wnt signaling pathways. Our study suggests that blood gene expression data are useful in predicting the AD classification.

Boolean function network analysis of time course liver transcriptome data to reveal novel circadian transcriptional regulators in mammals

BACKGROUND

Many biological processes in mammals are subject to circadian control at the molecular level. Disruption of circadian rhythms has been demonstrated to be associated with a wide range of diseases, such as diabetes mellitus, mental disorders, and cancer. Although the core circadian genes are well established, there are multiple reports of novel peripheral circadian regulators. The goal of this study was to provide a comprehensive computational analysis to identify novel potential circadian transcriptional regulators.

METHODS

To fulfill the aforementioned goal, we applied a Boolean function network method to analyze the microarray time course mouse and rat liver datasets available in the literature. The inferred direct pairwise relations were further investigated using the functional annotation tool. This approach generated a list of transcription factors (TFs) and cofactors, which were associated with significantly enriched circadian gene ontology (GO) categories.

RESULTS

As a result, we identified 93 transcriptional circadian regulators in mouse and 95 transcriptional circadian regulators in rat. Of these, 19 regulators in mouse and 21 regulators in rat were known, whereas the rest were novel. Furthermore, we validated novel circadian TFs with bioinformatics databases, previous large-scale circadian studies, and related small-scale studies. Moreover, according to predictions inferred from ChIP-Seq experiments reported in the database, 40 of our candidate circadian regulators were confirmed to have circadian genes as direct regulatory targets. In addition, we annotated candidate circadian regulators with disorders that were often associated with disruptions of circadian rhythm in the literature.

CONCLUSION

In summary, our computational analysis, which was followed by an extensive verification by means of a literature review, can contribute to translational study from endocrinology to cancer research and provide insights for future investigation.

Methamphetamine-associated psychosis: Clinical presentation, biological basis, and treatment options

INTRODUCTION

Methamphetamine associated psychosis (MAP) represents a mental disorder induced by chronic methamphetamine use in a subset of users. The prevalence of the disorder has increased in several countries in Europe and Asia where methamphetamine use has increased. MAP remains difficult to distinguish from primary psychiatric disorders, especially schizophrenia, creating complications in prescribing treatment plans to patients.

DESIGN

This narrative review sought to summarize difficulties related to MAP diagnosis and highlight the need for a better treatment model. Current best practices are described and potential novel therapies and future research suggested.

RESULTS

Results suggest that clear biological and clinical differences appear between patients presenting with MAP and schizophrenia and that there may exist distinct subgroups within MAP itself. MAP-specific treatment studies have been few and have focused on the use of antipsychotic medication. Antipsychotic treatment has been shown to alleviate the psychotic symptoms of MAP but produce debilitating adverse effects and fail to adequately address methamphetamine use in patients.

CONCLUSIONS

Continued identification of subgroups within the heterogenous MAP population may lead to better diagnosis, treatment, and outcomes for patients. Psychosocial therapies should be explored in addressing the cooccurring substance use and psychosis in the treatment of MAP.

Regional Analysis of the Brain Transcriptome in Mice Bred for High and Low Methamphetamine Consumption

Transcriptome profiling can broadly characterize drug effects and risk for addiction in the absence of drug exposure. Modern large-scale molecular methods, including RNA-sequencing (RNA-Seq), have been extensively applied to alcohol-related disease traits, but rarely to risk for methamphetamine (MA) addiction. We used RNA-Seq data from selectively bred mice with high or low risk for voluntary MA intake to construct coexpression and cosplicing networks for differential risk. Three brain reward circuitry regions were explored, the nucleus accumbens (NAc), prefrontal cortex (PFC), and ventral midbrain (VMB). With respect to differential gene expression and wiring, the VMB was more strongly affected than either the PFC or NAc. Coexpression network connectivity was higher in the low MA drinking line than in the high MA drinking line in the VMB, oppositely affected in the NAc, and little impacted in the PFC. Gene modules protected from the effects of selection may help to eliminate certain mechanisms from significant involvement in risk for MA intake. One such module was enriched in genes with dopamine-associated annotations. Overall, the data suggest that mitochondrial function and glutamate-mediated synaptic plasticity have key roles in the outcomes of selective breeding for high versus low levels of MA intake.

Evidence for type-specific DNA methylation patterns in epilepsy: a discordant monozygotic twin approach

Aim: Epilepsy is a common neurological disorder characterized by recurrent seizures. We performed epigenetic analyses between and within 15 monozygotic (MZ) twin pairs discordant for focal or generalized epilepsy. Methods: DNA methylation analysis was performed using Illumina Infinium MethylationEPIC arrays, in blood and buccal samples. Results: Differentially methylated regions between epilepsy types associated with PM20D1 and GFPT2 genes in both tissues. Within MZ discordant twin pairs, differentially methylated regions associated with OTX1 and ARID5B genes for generalized epilepsy and TTC39C and DLX5 genes for focal epilepsy. Conclusion: This is the first epigenome-wide association study, utilizing the discordant MZ co-twin model, to deepen our understanding of the neurobiology of epilepsy.

Machine learning in schizophrenia genomics, a case-control study using 5,090 exomes

Our hypothesis is that machine learning (ML) analysis of whole exome sequencing (WES) data can be used to identify individuals at high risk for schizophrenia (SCZ). This study applies ML to WES data from 2,545 individuals with SCZ and 2,545 unaffected individuals, accessed via the database of genotypes and phenotypes (dbGaP). Single nucleotide variants and small insertions and deletions were annotated by ANNOVAR using the reference genome hg19/GRCh37. Rare (predicted functional) variants with a minor allele frequency ≤1% and genotype quality ≥90 including missense, frameshift, stop gain, stop loss, intronic, and exonic splicing variants were selected. A file containing all cases and controls, the names of genes with variants meeting our criteria, and the number of variants per gene for each individual, was used for ML analysis. The supervised machine-learning algorithm used the patterns of variants observed in the different genes to determine which subset of genes can best predict that an individual is affected. Seventy percent of the data was used to train the algorithm and the remaining 30% of data (n = 1,526) was used to evaluate its efficiency. The supervised ML algorithm, gradient boosted trees with regularization (eXtreme Gradient Boosting implementation) was the best performing algorithm yielding promising results (accuracy: 85.7%, specificity: 86.6%, sensitivity: 84.9%, area under the receiver-operator characteristic curve: 0.95). The top 50 features (genes) of the algorithm were analyzed using bioinformatics resources for new insights about the pathophysiology of SCZ. This manuscript presents a novel predictor which could potentially enable studies exploring disease-modifying intervention in the early stages of the disease.

A case-control genome wide association study of substance use disorder (SUD) identifies novel variants on chromosome 7p14.1 in patients from the United Arab Emirates (UAE)

Genome wide association studies (GWASs) have provided insights into the molecular basis of the disorder in different population. This study presents the first GWAS of substance use disorder (SUD) in patients from the United Arab Emirates (UAE). The aim was to identify genetic association(s) that may provide insights into the molecular basis of the disorder. The GWAS discovery cohort consisted of 512 (250 cases and 262 controls) male participants from the UAE. Controls with no prior history of SUD were available from the Emirates family registry. The replication cohort consisted of 520 (415 cases and 105 controls) Australian male Caucasian participants. The GWAS discovery samples were genotyped for 4.6 million single nucleotide polymorphism (SNP). The replication cohort was genotyped using TaqMan assay. The GWAS association analysis identified three potential SNPs rs118129027 (p-value = 6.24 × 10^-8 ), rs74477937 (p-value = 8.56 × 10^-8 ) and rs78707086 (p-value = 8.55 × 10^-8 ) on ch7p14.1, that did not meet the GWAS significance threshold but were highly suggestive. In the replication cohort, the association of the three top SNPs did not reach statistical significance. In a meta-analysis of the discovery and the replication cohorts, there were no strengthen evidence for association of the three SNPs. The top identified rs118129027 overlaps with a regulatory factor (enhancer) region that targets three neighboring genes LOC105375237, LOC105375240, and YAE1D1. The YAE1D1, which represents a potential locus that is involved in regulating translation initiation pathway. Novel associations that require further confirmation were identified, suggesting a new insight to the genetic basis of SUD.

Transcriptome profiling of whisker follicles in methamphetamine self-administered rats

Methamphetamine (MA) is a highly addictive psychostimulant that disturbs the central nervous system; therefore, diagnosis of MA addiction is important in clinical and forensic toxicology. In this study, a MA self-administration rat model was used to illustrate the gene expression profiling of the rewarding effect caused by MA. RNA-sequencing was performed to examine changes in gene expression in rat whisker follicles collected before self-administration, after MA self-administration, and after withdrawal sessions. We identified six distinct groups of genes, with statistically significant expression patterns. By constructing the functional association network of these genes and performing the subsequent topological analysis, we identified 43 genes, which have the potential to regulate MA reward and addiction. The gene pathways were then analysed using the Reactome and Knowledgebase for Addiction-Related Gene database, and it was found that genes and pathways associated with Alzheimer's disease and the heparan sulfate biosynthesis were enriched in MA self-administration rats. The findings suggest that changes of the genes identified in rat whisker follicles may be useful indicators of the rewarding effect of MA. Further studies are needed to provide a comprehensive understanding of MA addiction.

Effects of methamphetamine abuse on spatial cognitive function

Methamphetamine (MA) abuse has been rising rapidly over the past decade, however, its impact in spatial cognitive function remains unknown. To understand its effect on visuospatial ability and spatial orientation ability, 40 MA users and 40 non-MA users conducted the Simple Reaction Task (Task 1), the Spatial Orientation Task (Task 2), and the Mental Rotation Task (Task 3), respectively. There was no significant difference in either accuracy or reaction time (RT) between 2 groups in Task 1. During Task 2, in comparison with non-MA users, MA users performed poorer on RT, but not in accuracy for foot and hand stimuli. In addition, both non-MA and MA users responded much more quickly to upward stimuli than downward stimuli on vertical surface, however, only non-MA users exhibited leftward visual field advantage in horizontal orientation processing. As for Task 3, MA users exhibited poorer performance and more errors than their healthy counterparts. For each group, linear relationship was revealed between RT and orientation angle, whereas MA abuse led to longer intercept for all stimuli involved. Our findings suggested that MA abuse may lead to a general deficit in the visuospatial ability and the spatial orientation ability with more serious impact in the former.

Parallel changes in serum proteins and diffusion tensor imaging in methamphetamine-associated psychosis

Methamphetamine-associated psychosis (MAP) involves widespread neurocognitive and molecular deficits, however accurate diagnosis remains challenging. Integrating relationships between biological markers, brain imaging and clinical parameters may provide an improved mechanistic understanding of MAP, that could in turn drive the development of better diagnostics and treatment approaches. We applied selected reaction monitoring (SRM)-based proteomics, profiling 43 proteins in serum previously implicated in the etiology of major psychiatric disorders, and integrated these data with diffusion tensor imaging (DTI) and psychometric measurements from patients diagnosed with MAP (N = 12), methamphetamine dependence without psychosis (MA; N = 14) and healthy controls (N = 16). Protein analysis identified changes in APOC2 and APOH, which differed significantly in MAP compared to MA and controls. DTI analysis indicated widespread increases in mean diffusivity and radial diffusivity delineating extensive loss of white matter integrity and axon demyelination in MAP. Upon integration, several co-linear relationships between serum proteins and DTI measures reported in healthy controls were disrupted in MA and MAP groups; these involved areas of the brain critical for memory and social emotional processing. These findings suggest that serum proteomics and DTI are sensitive measures for detecting pathophysiological changes in MAP and describe a potential diagnostic fingerprint of the disorder.

DisGeNET: a comprehensive platform integrating information on human disease-associated genes and variants

The information about the genetic basis of human diseases lies at the heart of precision medicine and drug discovery. However, to realize its full potential to support these goals, several problems, such as fragmentation, heterogeneity, availability and different conceptualization of the data must be overcome. To provide the community with a resource free of these hurdles, we have developed DisGeNET (http://www.disgenet.org), one of the largest available collections of genes and variants involved in human diseases. DisGeNET integrates data from expert curated repositories, GWAS catalogues, animal models and the scientific literature. DisGeNET data are homogeneously annotated with controlled vocabularies and community-driven ontologies. Additionally, several original metrics are provided to assist the prioritization of genotype-phenotype relationships. The information is accessible through a web interface, a Cytoscape App, an RDF SPARQL endpoint, scripts in several programming languages and an R package. DisGeNET is a versatile platform that can be used for different research purposes including the investigation of the molecular underpinnings of specific human diseases and their comorbidities, the analysis of the properties of disease genes, the generation of hypothesis on drug therapeutic action and drug adverse effects, the validation of computationally predicted disease genes and the evaluation of text-mining methods performance.

Systematic review of blood transcriptome profiling in neuropsychiatric disorders: guidelines for biomarker discovery

INTRODUCTION

The utility of blood for genome-wide gene expression profiling and biomarker discovery has received much attention in patients diagnosed with major neuropsychiatric disorders. While numerous studies have been conducted, statistical rigor and clarity in terms of blood-based biomarker discovery, validation, and testing are needed.

METHODS

We conducted a systematic review of the literature to investigate methodological approaches and to assess the value of blood transcriptome profiling in research on mental disorders. We were particularly interested in statistical considerations related to machine learning, gene network analyses, and convergence across different disorders.

RESULTS

A total of 108 peripheral blood transcriptome studies across 15 disorders were surveyed: 25 studies used a variety of machine learning techniques to assess putative clinical viability of the candidate biomarkers; 11 leveraged a higher-order systems-level perspective to identify gene module-based biomarkers; and nine performed analyses across two or more neuropsychiatric phenotypes. Notably, ~50% of the surveyed studies included fewer than 50 samples (cases and controls), while ~75% included less than 100.

CONCLUSIONS

Detailed consideration of statistical analysis in the early stages of experimental planning is critical to ensure blood-based biomarker discovery and validation. Statistical guidelines are presented to enhance implementation and reproducibility of machine learning and gene network analyses across independent studies. Future studies capitalizing on larger sample sizes and emerging next-generation technologies set the stage for moving the field forwards. Copyright © 2016 John Wiley & Sons, Ltd.