Cory H. White

A combined analysis of microarray gene expression studies of the human prefrontal cortex identifies genes implicated in schizophrenia

Small cohort sizes and modest levels of gene expression changes in brain tissue have plagued the statistical approaches employed in microarray studies investigating the mechanism of schizophrenia. To combat these problems a combined analysis of six prior microarray studies was performed to facilitate the robust statistical analysis of gene expression data from the dorsolateral prefrontal cortex of 107 patients with schizophrenia and 118 healthy subjects. Multivariate permutation tests identified 144 genes that were differentially expressed between schizophrenia and control groups. Seventy of these genes were identified as differentially expressed in at least one component microarray study but none of these individual studies had the power to identify the remaining 74 genes, demonstrating the utility of a combined approach. Gene ontology terms and biological pathways that were significantly enriched for differentially expressed genes were related to neuronal cell-cell signaling, mesenchymal induction, and mitogen-activated protein kinase signaling, which have all previously been associated with the etiopathogenesis of schizophrenia. The differential expression of BAG3, C4B, EGR1, MT1X, NEUROD6, SST and S100A8 was confirmed by real-time quantitative PCR in an independent cohort using postmortem human prefrontal cortex samples. Comparison of gene expression between schizophrenic subjects with and without detectable levels of antipsychotics in their blood suggests that the modulation of MT1X and S100A8 may be the result of drug exposure. In conclusion, this combined analysis has resulted in a statistically robust identification of genes whose dysregulation may contribute to the mechanism of schizophrenia.

The effect of cell subset isolation method on gene expression in leukocytes

Multiple scientific disciplines require the isolation of specific subsets of blood cells from patient samples for gene expression analysis by microarray or RNA‐sequencing, preserving disease‐ or treatment‐related signatures. However, little is known with respect to the impact of different cell isolation methods on gene expression and the effects of positive selection, negative selection, and fluorescence activated cell sorting (FACS) have not previously been assessed in parallel. To address this knowledge gap, CD4+ T cells, CD8+ T cells, B cells, and monocytes were isolated from blood samples from five independent donors using positive immunomagnetic selection, negative immunomagnetic selection, and FACS. We hypothesized that positive selection and FACS would yield higher purity but may have an impact on gene expression since both methods utilize antibodies that bind surface receptors of the cell type of interest. Moreover, FACS might upregulate stress response genes due to passage of the cells through the sorter. Microarray gene expression data were generated and subjected to unsupervised clustering and differential gene expression analysis. Surprisingly, these analyses revealed that gene expression signatures were more similar between cells isolated by negative selection and FACS compared to cells isolated by positive selection. Moreover, genes that are involved in the response to stress generally had the highest expression in cells isolated by negative or positive selection and not FACS. Thus, FACS is the recommended method for isolation of leukocyte subsets for gene expression studies since this method results in the purest subset populations and does not appear to induce a stress response.

Differential gene expression in HIV-infected individuals following ART

Previous studies of the effect of ART on gene expression in HIV-infected individuals have identified small numbers of modulated genes. Since these studies were underpowered or cross-sectional in design, a paired analysis of peripheral blood mononuclear cells (PBMCs), isolated before and after ART, from a robust number of HIV-infected patients (N=32) was performed. Gene expression was assayed by microarray and 4157 differentially expressed genes (DEGs) were identified following ART using multivariate permutation tests. Pathways and gene ontology (GO) terms over-represented for DEGs reflected the transition from a period of active virus replication before ART to one of viral suppression (e.g., repression of JAK-STAT signaling) and possible prolonged drug exposure (e.g., oxidative phosphorylation pathway) following ART. CMYC was the DEG whose product made the greatest number of interactions at the protein level in protein interaction networks (PINs), which has implications for the increased incidence of Hodgkins lymphoma (HL) in HIV-infected patients. The differential expression of multiple genes was confirmed by RT-qPCR including well-known drug metabolism genes (e.g., ALOX12 and CYP2S1). Targets not confirmed by RT-qPCR (i.e., GSTM2 and RPL5) were significantly confirmed by droplet digital (ddPCR), which may represent a superior method when confirming DEGs with low fold changes. In conclusion, a paired design revealed that the number of genes modulated following ART was an order of magnitude higher than previously recognized.

Lithium-responsive genes and gene networks in bipolar disorder patient-derived lymphoblastoid cell lines

Lithium (Li) is the mainstay mood stabilizer for the treatment of bipolar disorder (BD), although its mode of action is not yet fully understood nor is it effective in every patient. We sought to elucidate the mechanism of action of Li and to identify surrogate outcome markers that can be used to better understand its therapeutic effects in BD patients classified as good (responders) and poor responders (nonresponders) to Li treatment. To accomplish these goals, RNA-sequencing gene expression profiles of lymphoblastoid cell lines (LCLs) were compared between BD Li responders and nonresponders with healthy controls before and after treatment. Several Li-responsive gene coexpression networks were discovered indicating widespread effects of Li on diverse cellular signaling systems including apoptosis and defense response pathways, protein processing and response to endoplasmic reticulum stress. Individual gene markers were also identified, differing in response to Li between BD responders and nonresponders, involved in processes of cell cycle and nucleotide excision repair that may explain part of the heterogeneity in clinical response to treatment. Results further indicated a Li gene expression signature similar to that observed with clonidine treatment, an α2-adrenoceptor agonist. These findings provide a detailed mechanism of Li in LCLs and highlight putative surrogate outcome markers that may permit for advanced treatment decisions to be made and for facilitating recovery in BD patients.

Transcriptomic Analysis Implicates the p53 Signaling Pathway in the Establishment of HIV-1 Latency in Central Memory CD4 T Cells in an In Vitro Model

The search for an HIV-1 cure has been greatly hindered by the presence of a viral reservoir that persists despite antiretroviral therapy (ART). Studies of HIV-1 latency in vivo are also complicated by the low proportion of latently infected cells in HIV-1 infected individuals. A number of models of HIV-1 latency have been developed to examine the signaling pathways and viral determinants of latency and reactivation. A primary cell model of HIV-1 latency, which incorporates the generation of primary central memory CD4 T cells (TCM), full-length virus infection (HIVNL4-3) and ART to suppress virus replication, was used to investigate the establishment of HIV latency using RNA-Seq. Initially, an investigation of host and viral gene expression in the resting and activated states of this model indicated that the resting condition was reflective of a latent state. Then, a comparison of the host transcriptome between the uninfected and latently infected conditions of this model identified 826 differentially expressed genes, many of which were related to p53 signaling. Inhibition of the transcriptional activity of p53 by pifithrin-α during HIV-1 infection reduced the ability of HIV-1 to be reactivated from its latent state by an unknown mechanism. In conclusion, this model may be used to screen latency reversing agents utilized in shock and kill approaches to cure HIV, to search for cellular markers of latency, and to understand the mechanisms by which HIV-1 establishes latency.

Replication competent virus as an important source of bias in HIV latency models utilizing single round viral constructs

The central memory T cell (TCM) model forms a unique HIV-1 latency model based on primary cells that closely resemble in vivo TCM. The virus employed in this model is based on an engineered vector incapable of replication after initial infection. We show that despite this strategy, replication competent viral particles are released into the culture medium due to recombination between overlapping sequences of the env deleted HIV genome that is co-transfected with intact env. This finding emphasizes the need for careful data analysis and interpretation if similar constructs are employed and urges for additional caution during laboratory work.

Transcriptional Modulation of Human Endogenous Retroviruses in Primary CD4(+) T Cells Following Vorinostat Treatment

The greatest obstacle to a cure for HIV is the provirus that integrates into the genome of the infected cell and persists despite antiretroviral therapy. A “shock and kill” approach has been proposed as a strategy for an HIV cure whereby drugs and compounds referred to as latency-reversing agents (LRAs) are used to “shock” the silent provirus into active replication to permit “killing” by virus-induced pathology or immune recognition. The LRA most utilized to date in clinical trials has been the histone deacetylase (HDAC) inhibitor—vorinostat. Potentially, pathological off-target effects of vorinostat may result from the activation of human endogenous retroviruses (HERVs), which share common ancestry with exogenous retroviruses including HIV. To explore the effects of HDAC inhibition on HERV transcription, an unbiased pharmacogenomics approach (total RNA-Seq) was used to evaluate HERV expression following the exposure of primary CD4+ T cells to a high dose of vorinostat. Over 2,000 individual HERV elements were found to be significantly modulated by vorinostat, whereby elements belonging to the ERVL family (e.g., LTR16C and LTR33) were predominantly downregulated, in contrast to LTR12 elements of the HERV-9 family, which exhibited the greatest signal, with the upregulation of 140 distinct elements. The modulation of three different LTR12 elements by vorinostat was confirmed by droplet digital PCR along a dose–response curve. The monitoring of LTR12 expression during clinical trials with vorinostat may be indicated to assess the impact of this HERV on the human genome and host immunity.

Increased circulating resistin levels in early-onset breast cancer patients of normal body mass index correlate with lymph node negative involvement and longer disease free survival: a multi-center POSH cohort serum proteomics study

BackgroundEarly-onset breast cancer (EOBC) affects about one in 300 women aged 40 years or younger and is associated with worse outcomes than later onset breast cancer. This study explored novel serum proteins as surrogate markers of prognosis in patients with EOBC.MethodsSerum samples from EOBC patients (stages 1–3) were analysed using agnostic high-precision quantitative proteomics. Patients received anthracycline-based chemotherapy. The discovery cohort (n = 399) either had more than 5-year disease-free survival (DFS) (good outcome group, n = 203) or DFS of less than 2 years (poor outcome group, n = 196). Expressed proteins were assessed for differential expression between the two groups. Bioinformatics pathway and network analysis in combination with literature research were used to determine clinically relevant proteins. ELISA analysis against an independent sample set from the Prospective study of Outcomes in Sporadic versus Hereditary breast cancer (POSH) cohort (n = 181) was used to validate expression levels of the selected target. Linear and generalized linear modelling was applied to determine the effect of target markers, body mass index (BMI), lymph node involvement (LN), oestrogen receptor (ER), progesterone receptor and human epidermal growth factor receptor 2 status on patients’ outcome.ResultsA total of 5346 unique proteins were analysed (peptide FDR p ≤ 0.05). Of these, 812 were differentially expressed in the good vs poor outcome groups and showed significant enrichment for the insulin signalling (p = 0.01) and the glycolysis/gluconeogenesis (p = 0.01) pathways. These proteins further correlated with interaction networks involving glucose and fatty acid metabolism. A consistent nodal protein to these metabolic networks was resistin (upregulated in the good outcome group, p = 0.009). ELISA validation demonstrated resistin to be upregulated in the good outcome group (p = 0.04), irrespective of BMI and ER status. LN involvement was the only covariate with a significant association with resistin measurements (p = 0.004). An ancillary in-silico observation was the induction of the inflammatory response, leucocyte infiltration, lymphocyte migration and recruitment of phagocytes (p < 0.0001, z-score > 2). Survival analysis showed that resistin overexpression was associated with improved DFS.ConclusionsHigher circulating resistin correlated with node-negative patients and longer DFS independent of BMI and ER status in women with EOBC. Overexpression of serum resistin in EOBC may be a surrogate indicator of improved prognosis.