Lily Wang

Changes in BDNF serum levels in patients with major depression disorder (MDD) after 6 months treatment with sertraline, escitalopram, or venlafaxine

Recent studies have implicated brain-derived neurotrophic factor (BDNF) in the pathophysiology of depression and the activity of antidepressant drugs. Serum BDNF levels are lower in depressed patients, and increase in response to antidepressant medication. However, how BDNF responds to different classes of antidepressant drugs is unknown. We assessed serum BDNF levels in 21 patients with major depressive episode treated with sertraline, escitalopram, or venlafaxine and 20 healthy controls. Serum samples were collected between 10 a.m. and 12 p.m. at baseline, 5 weeks, and 6 months of treatment. BDNF levels were measured via immunoassay. The severity of symptoms and response to treatment were assessed by the Hamilton rating scales for depression (HRSD). Baseline serum BDNF levels were significantly lower in depressed patients compared to controls. Sertraline increased BDNF levels after 5 weeks and 6 months of treatment. Venlafaxine increased BDNF levels only after 6 months. Escitalopram did not affect BDNF levels at either time point. A significant negative association was found between percentage increase in BDNF levels and percentage decreased in HRSD scores after 6 months of treatment. In conclusion, these results suggest that different antidepressant drugs have variable effects on serum BDNF levels. This is true even though the three different drugs were equally effective in relieving symptoms of depression and anxiety.

Gene set analysis of genome-wide association studies: Methodological issues and perspectives

Recent studies have demonstrated that gene set analysis, which tests disease association with genetic variants in a group of functionally related genes, is a promising approach for analyzing and interpreting genome-wide association studies (GWAS) data. These approaches aim to increase power by combining association signals from multiple genes in the same gene set. In addition, gene set analysis can also shed more light on the biological processes underlying complex diseases. However, current approaches for gene set analysis are still in an early stage of development in that analysis results are often prone to sources of bias, including gene set size and gene length, linkage disequilibrium patterns and the presence of overlapping genes. In this paper, we provide an in-depth review of the gene set analysis procedures, along with parameter choices and the particular methodology challenges at each stage. In addition to providing a survey of recently developed tools, we also classify the analysis methods into larger categories and discuss their strengths and limitations. In the last section, we outline several important areas for improving the analytical strategies in gene set analysis.

Defining the Sleep Phenotype in Children with Autism

Sleep concerns are common in children with autism spectrum disorders (ASD). We identified objective sleep measures that differentiated ASD children with and without parental sleep concerns, and related parental concerns and objective measures to aspects of daytime behavior. ASD poor sleepers differed from ASD good sleepers on actigraphic (sleep latency, sleep efficiency, fragmentation) and polysomnographic (sleep latency) measures, and were reported to have more inattention, hyperactivity, and restricted/repetitive behaviors. Fragmentation was correlated with more restricted/repetitive behaviors. This work provides the foundation for focused studies of pathophysiology and targeted interventions to improve sleep in this population.

Distinct Transcriptomes Define Rostral and Caudal Serotonin Neurons

The molecular architecture of developing serotonin (5HT) neurons is poorly understood, yet its determination is likely to be essential for elucidating functional heterogeneity of these cells and the contribution of serotonergic dysfunction to disease pathogenesis. Here, we describe the purification of postmitotic embryonic 5HT neurons by flow cytometry for whole-genome microarray expression profiling of this unitary monoaminergic neuron type. Our studies identified significantly enriched expression of hundreds of unique genes in 5HT neurons, thus providing an abundance of new serotonergic markers. Furthermore, we identified several hundred transcripts encoding homeodomain, axon guidance, cell adhesion, intracellular signaling, ion transport, and imprinted genes associated with various neurodevelopmental disorders that were differentially enriched in developing rostral and caudal 5HT neurons. These findings suggested a homeodomain code that distinguishes rostral and caudal 5HT neurons. Indeed, verification studies demonstrated that Hmx homeodomain and Hox gene expression defined an Hmx+ rostral subtype and Hox+ caudal subtype. Expression of engrailed genes in a subset of 5HT neurons in the rostral domain further distinguished two subtypes defined as Hmx+En+ and Hmx+En−. The differential enrichment of gene sets for different canonical pathways and gene ontology categories provided additional evidence for heterogeneity between rostral and caudal 5HT neurons. These findings demonstrate a deep transcriptome and biological pathway duality for neurons that give rise to the ascending and descending serotonergic subsystems. Our databases provide a rich, clinically relevant resource for definition of 5HT neuron subtypes and elucidation of the genetic networks required for serotonergic function.

Network-Assisted Investigation of Combined Causal Signals from Genome-Wide Association Studies in Schizophrenia

With the recent success of genome-wide association studies (GWAS), a wealth of association data has been accomplished for more than 200 complex diseases/traits, proposing a strong demand for data integration and interpretation. A combinatory analysis of multiple GWAS datasets, or an integrative analysis of GWAS data and other high-throughput data, has been particularly promising. In this study, we proposed an integrative analysis framework of multiple GWAS datasets by overlaying association signals onto the protein-protein interaction network, and demonstrated it using schizophrenia datasets. Building on a dense module search algorithm, we first searched for significantly enriched subnetworks for schizophrenia in each single GWAS dataset and then implemented a discovery-evaluation strategy to identify module genes with consistent association signals. We validated the module genes in an independent dataset, and also examined them through meta-analysis of the related SNPs using multiple GWAS datasets. As a result, we identified 205 module genes with a joint effect significantly associated with schizophrenia; these module genes included a number of well-studied candidate genes such as DISC1, GNA12, GNA13, GNAI1, GPR17, and GRIN2B. Further functional analysis suggested these genes are involved in neuronal related processes. Additionally, meta-analysis found that 18 SNPs in 9 module genes had P meta<1×10−4, including the gene HLA-DQA1 located in the MHC region on chromosome 6, which was reported in previous studies using the largest cohort of schizophrenia patients to date. These results demonstrated our bi-directional network-based strategy is efficient for identifying disease-associated genes with modest signals in GWAS datasets. This approach can be applied to any other complex diseases/traits where multiple GWAS datasets are available.

A bias-reducing pathway enrichment analysis of genome-wide association data confirmed association of the MHC region with schizophrenia

Background After the recent successes of genome-wide association studies (GWAS), one key challenge is to identify genetic variants that might have a significant joint effect on complex diseases but have failed to be identified individually due to weak to moderate marginal effect. One popular and effective approach is gene set based analysis, which investigates the joint effect of multiple functionally related genes (eg, pathways). However, a typical gene set analysis method is biased towards long genes, a problem that is especially severe in psychiatric diseases. Methods A novel approach was proposed, namely generalised additive model (GAM) for GWAS (gamGWAS), for gene set enrichment analysis of GWAS data, specifically adjusting the gene length bias or the number of single-nucleotide polymorphisms per gene. GAM is applied to estimate the probability of a gene to be selected as significant given its gene length, followed by weighted resampling and computation of empirical p values for the rank of pathways. We demonstrated gamGWAS in two schizophrenia GWAS datasets from the International Schizophrenia Consortium and the Genetic Association Information Network. Results The gamGWAS results not only confirmed previous findings, but also highlighted several immune related pathways. Comparison with other methods indicated that gamGWAS could effectively reduce the correlation between pathway p values and its median gene length. Conclusion gamGWAS can effectively relieve the long gene bias and generate reliable results for GWAS data analysis. It does not require genotype data or permutation of sample labels in the original GWAS data; thus, it is computationally efficient.

Parent-Based Sleep Education Workshops in Autism:

To determine if parents can successfully teach their children with autism spectrum disorders to become better sleepers, we piloted small group parent education workshops focused on behavioral sleep strategies. Workshops consisted of three 2-hour sessions conducted over consecutive weeks by 2 physicians. Curricula included establishing effective daytime and nighttime habits, initiating a bedtime routine, and optimizing parental interactions at bedtime and during night wakings. Baseline and treatment questionnaires and actigraphy were analyzed in 20 children, ages 3 to 10 years. Improvements after treatment were seen in the total scale and several insomnia-related subscales of the Childrens Sleep Habits Questionnaire. Actigraphy documented reduced sleep latency in children presenting with sleep onset delay. Improvements were also noted in measures of sleep habits and daytime behavior. Brief parent-based behavioral sleep workshops in children with autism spectrum disorders appear effective in improving subjective and objective measures of sleep, sleep habits, and daytime behavior.

Supervised principal component analysis for gene set enrichment of microarray data with continuous or survival outcomes

MOTIVATION Gene set analysis allows formal testing of subtle but coordinated changes in a group of genes, such as those defined by Gene Ontology (GO) or KEGG Pathway databases. We propose a new method for gene set analysis that is based on principal component analysis (PCA) of genes expression values in the gene set. PCA is an effective method for reducing high dimensionality and capture variations in gene expression values. However, one limitation with PCA is that the latent variable identified by the first PC may be unrelated to outcome. RESULTS In the proposed supervised PCA (SPCA) model for gene set analysis, the PCs are estimated from a selected subset of genes that are associated with outcome. As outcome information is used in the gene selection step, this method is supervised, thus called the Supervised PCA model. Because of the gene selection step, test statistic in SPCA model can no longer be approximated well using t-distribution. We propose a two-component mixture distribution based on Gumbel exteme value distributions to account for the gene selection step. We show the proposed method compares favorably to currently available gene set analysis methods using simulated and real microarray data. SOFTWARE The R code for the analysis used in this article are available upon request, we are currently working on implementing the proposed method in an R package.

An Integrated Approach for the Analysis of Biological Pathways using Mixed Models

Gene class, ontology, or pathway testing analysis has become increasingly popular in microarray data analysis. Such approaches allow the integration of gene annotation databases, such as Gene Ontology and KEGG Pathway, to formally test for subtle but coordinated changes at a system level. Higher power in gene class testing is gained by combining weak signals from a number of individual genes in each pathway. We propose an alternative approach for gene-class testing based on mixed models, a class of statistical models that: provides the ability to model and borrow strength across genes that are both up and down in a pathway, operates within a well-established statistical framework amenable to direct control of false positive or false discovery rates, exhibits improved power over widely used methods under normal location-based alternative hypotheses, and handles complex experimental designs for which permutation resampling is difficult. We compare the properties of this mixed models approach with nonparametric method GSEA and parametric method PAGE using a simulation study, and illustrate its application with a diabetes data set and a dose-response data set.

Reliability of Quantitative Sudomotor Axon Reflex Testing and Quantitative Sensory Testing in Neuropathy of Impaired Glucose Regulation

Reproducible neurophysiologic testing paradigms are critical for multicenter studies of neuropathy associated with impaired glucose regulation (IGR), yet the best methodologies and endpoints remain to be established. This study evaluates the reproducibility of neurophysiologic tests within a multicenter research setting. Twenty‐three participants with neuropathy and IGR were recruited from two study sites. The reproducibility of quantitative sudomotor axon reflex test (QSART) and quantitative sensory test (QST) (using the CASE IV system) was determined in a subset of patients at two sessions, and it was calculated from intraclass correlation coefficients (ICCs). QST (cold detection threshold: ICC = 0.80; vibration detection threshold: ICC = 0.75) was more reproducible than QSART (ICC foot = 0.52). The performance of multiple tests in one setting did not improve reproducibility of QST. QST reproducibility in our IGR patients was similar to reports of other studies. QSART reproducibility was significantly lower than QST. In this group of patients, the reproducibility of QSART was unacceptable for use as a secondary endpoint measure in clinical research trials. Muscle Nerve, 2008