Sample size for detecting differentially expressed genes in microarray experiments. BMC Genomics. 2004;5:87. [PMID: 15533245 DOI: 10.1186/1471-2164-5-87]

Potential molecular patterns for tuberculosis susceptibility in diabetic patients with poor glycaemic control: a pilot study

Type 2 diabetes (DM2) is an increasingly prevalent disease that challenges tuberculosis (TB) control strategies worldwide. It is significant that DM2 patients with poor glycemic control (PDM2) are prone to developing tuberculosis. Furthermore, elucidating the molecular mechanisms that govern this susceptibility is imperative to address this problem. Therefore, a pilot transcriptomic study was performed. Human blood samples from healthy controls (CTRL, HbA1c < 6.5%), tuberculosis (TB), comorbidity TB-DM2, DM2 (HbA1c 6.5-8.9%), and PDM2 (HbA1c > 10%) groups (n = 4 each) were analyzed by differential expression using microarrays. We use a network strategy to identify potential molecular patterns linking the differentially expressed genes (DEGs) specific for TB-DM2 and PDM2 (p-value < 0.05, fold change > 2). We define OSM, PRKCD, and SOCS3 as key regulatory genes (KRGs) that modulate the immune system and related pathways. RT-qPCR assays confirmed upregulation of OSM, PRKCD, and SOCS3 genes (p < 0.05) in TB-DM2 patients (n = 18) compared to CTRL, DM2, PDM2, or TB groups (n = 17, 19, 15, and 9, respectively). Furthermore, OSM, PRKCD, and SOCS3 were associated with PDM2 susceptibility pathways toward TB-DM2 and formed a putative protein-protein interaction confirmed in STRING. Our results reveal potential molecular patterns where OSM, PRKCD, and SOCS3 are KRGs underlying the compromised immune response and susceptibility of patients with PDM2 to develop tuberculosis. Therefore, this work paved the way for fundamental research of new molecular targets in TB-DM2. Addressing their cellular implications, and the impact on the diagnosis, treatment, and clinical management of TB-DM2 could help improve the strategy to end tuberculosis for this vulnerable population.

A 15-Gene-Based Risk Signature for Predicting Overall Survival in SCLC Patients Who Have Undergone Surgical Resection

Small cell lung cancer (SCLC) is a malignancy with a poor prognosis whose treatment has not progressed for decades. The survival benefit of surgery and the selection of surgical candidates are still controversial in SCLC. This study is the first report to identify transcriptomic alterations associated with prognosis and propose a gene expression-based risk signature that can be used to predict overall survival (OS) in SCLC patients who have undergone potentially curative surgery. An integrative transcriptome analysis of three gene expression datasets (GSE30219, GSE43346, and GSE149507) revealed 1734 up-regulated and 2907 down-regulated genes. Cox-Mantel test, Cox regression, and Lasso regression analyses were used to identify genes to be included in the risk signature. EGAD00001001244 and GSE60052-cohorts were used for internal and external validation, respectively. Overall survival was significantly poorer in patients with high-risk scores compared to the low-risk group. The discriminatory performance of the risk signature was superior to other parameters. Multivariate analysis showed that the risk signature has the potential to be an independent predictor of prognosis. The prognostic genes were enriched in pathways including regulation of transcription, cell cycle, cell metabolism, and angiogenesis. Determining the roles of the identified prognostic genes in the pathogenesis of SCLC may contribute to the development of new treatment strategies. The risk signature needs to be validated in a larger cohort of patients to test its usefulness in clinical decision-making.

Differentially Expressed Candidate miRNAs of Day 16 Bovine Embryos on the Regulation of Pregnancy Establishment in Dairy Cows

Recent advances in high-throughput in silico techniques translate experimental data into meaningful biological networks through which the role of individual proteins, interactions, and their biological functions are comprehended. The study objective was to identify differentially expressed (DE) miRNAs between the day 16 competent, elongated embryo from normal cows and the day 16 noncompetent, tubular embryos from repeat breeder cows, assimilate DE-miRNAs to their target genes, and group target genes based on biological function using in silico methods. The 84 prioritized bovine-specific miRNAs were investigated by RT-PCR, and the results showed that 19 were differentially expressed (11 up- and 8 down-regulated) in the competent embryos compared to noncompetent ones (p ≤ 0.05; fold regulation ≥ 2 magnitudes). Top-ranked integrated genes of DE-miRNAs predicted various biological and molecular functions, cellular processes, and signaling pathways. Further, analysis of the categorized groups of genes showed association with signaling pathways, turning on or off key genes and transcription factors regulating the development of embryo, placenta, and various organs. In conclusion, highly DE-miRNAs in day 16 bovine conceptus regulated the embryogenesis and pregnancy establishment. The elucidated miRNA-mRNA interactions in this study were mostly based on predictions from public databases. Therefore, the causal regulations of these interactions and mechanisms require further functional characterization.

Tsc2 mutation rather than Tsc1 mutation dominantly causes a social deficit in a mouse model of tuberous sclerosis complex

BACKGROUND

Tuberous sclerosis complex (TSC) is an autosomal dominant disorder that is associated with neurological symptoms, including autism spectrum disorder. Tuberous sclerosis complex is caused by pathogenic germline mutations of either the TSC1 or TSC2 gene, but somatic mutations were identified in both genes, and the combined effects of TSC1 and TSC2 mutations have been unknown.

METHODS

The present study investigated social behaviors by the social interaction test and three-chambered sociability tests, effects of rapamycin treatment, and gene expression profiles with a gene expression microarray in Tsc1 and Tsc2 double heterozygous mutant (TscD^+/-) mice.

RESULTS

TscD^+/- mice exhibited impairments in social behaviors, and the severity of impairments was similar to Tsc2^+/- mice rather than Tsc1^+/- mice. Impairments in social behaviors were rescued by rapamycin treatment in all mutant mice. Gene expression profiles in the brain were greatly altered in TscD^+/- mice more than in Tsc1^+/- and Tsc2^+/- mice. The gene expression changes compared with wild type (WT) mice were similar between TscD^+/- and Tsc2^+/- mice, and the overlapping genes whose expression was altered in mutant mice compared with WT mice were enriched in the neoplasm- and inflammation-related canonical pathways. The "signal transducer and activator of transcription 3, interferon regulatory factor 1, interferon regulatory factor 4, interleukin-2R α chain, and interferon-γ" signaling pathway, which is initiated from signal transducer and activator of transcription 4 and PDZ and LIM domain protein 2, was associated with impairments in social behaviors in all mutant mice.

LIMITATIONS

It is unclear whether the signaling pathway also plays a critical role in autism spectrum disorders not caused by Tsc1 and Tsc2 mutations.

CONCLUSIONS

These findings suggest that TSC1 and TSC2 double mutations cause autistic behaviors similarly to TSC2 mutations, although significant changes in gene expression were attributable to the double mutations. These findings contribute to the knowledge of genotype-phenotype correlations in TSC and suggest that mutations in both the TSC1 and TSC2 genes act in concert to cause neurological symptoms, including autism spectrum disorder.

BluePrint breast cancer molecular subtyping recognizes single and dual subtype tumors with implications for therapeutic guidance

PURPOSE

BluePrint (BP) is an 80-gene molecular subtyping test that classifies early-stage breast cancer (EBC) into Basal, Luminal, and HER2 subtypes. In most cases, breast tumors have one dominant subtype, representative of a single activated pathway. However, some tumors show a statistically equal representation of more than one subtype, referred to as dual subtype. This study aims to identify and examine dual subtype tumors by BP to understand their biology and possible implications for treatment guidance.

METHODS

The BP scores of over 15,000 tumor samples from EBC patients were analyzed, and the differences between the highest and the lowest scoring subtypes were calculated. Based upon the distribution of the differences between BP scores, a threshold was determined for each subtype to identify dual versus single subtypes.

RESULTS

Approximately 97% of samples had one single activated BluePrint molecular subtype, whereas ~ 3% of samples were classified as BP dual subtype. The most frequently occurring dual subtypes were the Luminal-Basal-type and Luminal-HER2-type. Luminal-Basal-type displays a distinct biology from the Luminal single type and Basal single type. Burstein's classification of the single and dual Basal samples showed that the Luminal-Basal-type is mostly classified as 'luminal androgen receptor' and 'mesenchymal' subtypes, supporting molecular evidence of AR activation in the Luminal-Basal-type tumors. Tumors classified as Luminal-HER2-type resemble features of both Luminal-single-type and HER2-single-type. However, patients with dual Luminal-HER2-type have a lower pathological complete response after receiving HER2-targeted therapies in addition to chemotherapy in comparison with patients with a HER2-single-type.

CONCLUSION

This study demonstrates that BP identifies tumors with two active functional pathways (dual subtype) with specific transcriptional characteristics and highlights the added value of distinguishing BP dual from single subtypes as evidenced by distinct treatment response rates.

A review of bioinformatics tools and web servers in different microarray platforms used in cancer research

Over the past decade, conventional lab work strategies have gradually shifted from being limited to a laboratory setting towards a bioinformatics era to help manage and process the vast amounts of data generated by omics technologies. The present work outlines the latest contributions of bioinformatics in analyzing microarray data and their application to cancer. We dissect different microarray platforms and their use in gene expression in cancer models. We highlight how computational advances empowered the microarray technology in gene expression analysis. The study on protein-protein interaction databases classified into primary, derived, meta-database, and prediction databases describes the strategies to curate and predict novel interaction networks in silico. In addition, we summarize the areas of bioinformatics where neural graph networks are currently being used, such as protein functions, protein interaction prediction, and in silico drug discovery and development. We also discuss the role of deep learning as a potential tool in the prognosis, diagnosis, and treatment of cancer. Integrating these resources efficiently, practically, and ethically is likely to be the most challenging task for the healthcare industry over the next decade; however, we believe that it is achievable in the long term.

Bioinformatics-Led Discovery of Osteoarthritis Biomarkers and Inflammatory Infiltrates

The molecular mechanisms of osteoarthritis, the most common chronic disease, remain unexplained. This study aimed to use bioinformatic methods to identify the key biomarkers and immune infiltration in osteoarthritis. Gene expression profiles (GSE55235, GSE55457, GSE77298, and GSE82107) were selected from the Gene Expression Omnibus database. A protein-protein interaction network was created, and functional enrichment analysis and genomic enrichment analysis were performed using the Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genome (KEGG) databases. Immune cell infiltration between osteoarthritic tissues and control tissues was analyzed using the CIBERSORT method. Identify immune patterns using the ConsensusClusterPlus package in R software using a consistent clustering approach. Molecular biological investigations were performed to discover the important genes in cartilage cells. A total of 105 differentially expressed genes were identified. Differentially expressed genes were enriched in immunological response, chemokine-mediated signaling pathway, and inflammatory response revealed by the analysis of GO and KEGG databases. Two distinct immune patterns (ClusterA and ClusterB) were identified using the ConsensusClusterPlus. Cluster A patients had significantly lower resting dendritic cells, M2 macrophages, resting mast cells, activated natural killer cells and regulatory T cells than Cluster B patients. The expression levels of TCA1, TLR7, MMP9, CXCL10, CXCL13, HLA-DRA, and ADIPOQSPP1 were significantly higher in the IL-1β-induced group than in the osteoarthritis group in an in vitro qPCR experiment. Explaining the differences in immune infiltration between osteoarthritic tissues and normal tissues will contribute to the understanding of the development of osteoarthritis.

Anti-inflammatory effects of recreational marijuana in virally suppressed youth with HIV-1 are reversed by use of tobacco products in combination with marijuana

BACKGROUND

Marijuana's putative anti-inflammatory properties may benefit HIV-associated comorbidities. How recreational marijuana use affects gene expression in peripheral blood cells (PBC) among youth with HIV-1 (YWH) is unknown.

APPROACH

YWH with defined substance use (n = 54) receiving similar antiretroviral therapy (ART) were assigned to one of four analysis groups: YWH with detectable plasma HIV-1 (> 50 RNA copies/ml) who did not use substances (H+V+S-), and YWH with undetectable plasma HIV-1 who did not use substances (H+V-S-), or used marijuana alone (H+V-S+[M]), or marijuana in combination with tobacco (H+V-S+[M/T]). Non-substance using youth without HIV infection (H-S-, n = 25) provided a reference group. PBC mRNA was profiled by Affymetrix GeneChip Human Genome U133 Plus 2.0 Array. Differentially expressed genes (DEG) within outcome groups were identified by Significance Analysis of Microarrays and used for Hierarchical Clustering, Principal Component Analysis, and Ingenuity Pathways Analysis.

RESULTS

HIV-1 replication resulted in > 3000 DEG involving 27 perturbed pathways. Viral suppression reduced DEG to 313, normalized all 27 pathways, and down-regulated two additional pathways, while marijuana use among virally suppressed YWH resulted in 434 DEG and no perturbed pathways. Relative to H+V-S-, multiple DEG normalized in H+V-S+[M]. In contrast, H+V-S+[M/T] had 1140 DEG and 10 dysregulated pathways, including multiple proinflammatory genes and six pathways shared by H+V+S-.

CONCLUSIONS

YWH receiving ART display unique transcriptome bioprofiles based on viral replication and substance use. In the context of HIV suppression, marijuana use, alone or combined with tobacco, has opposing effects on inflammatory gene expression.

Identifying Immunological and Clinical Predictors of COVID-19 Severity and Sequelae by Mathematical Modeling

Since its emergence as a pandemic in March 2020, coronavirus disease (COVID-19) outcome has been explored via several predictive models, using specific clinical or biochemical parameters. In the current study, we developed an integrative non-linear predictive model of COVID-19 outcome, using clinical, biochemical, immunological, and radiological data of patients with different disease severities. Initially, the immunological signature of the disease was investigated through transcriptomics analysis of nasopharyngeal swab samples of patients with different COVID-19 severity versus control subjects (exploratory cohort, n=61), identifying significant differential expression of several cytokines. Accordingly, 24 cytokines were validated using a multiplex assay in the serum of COVID-19 patients and control subjects (validation cohort, n=77). Predictors of severity were Interleukin (IL)-10, Programmed Death-Ligand-1 (PDL-1), Tumor necrosis factors-α, absolute neutrophil count, C-reactive protein, lactate dehydrogenase, blood urea nitrogen, and ferritin; with high predictive efficacy (AUC=0.93 and 0.98 using ROC analysis of the predictive capacity of cytokines and biochemical markers, respectively). Increased IL-6 and granzyme B were found to predict liver injury in COVID-19 patients, whereas interferon-gamma (IFN-γ), IL-1 receptor-a (IL-1Ra) and PD-L1 were predictors of remarkable radiological findings. The model revealed consistent elevation of IL-15 and IL-10 in severe cases. Combining basic biochemical and radiological investigations with a limited number of curated cytokines will likely attain accurate predictive value in COVID-19. The model-derived cytokines highlight critical pathways in the pathophysiology of the COVID-19 with insight towards potential therapeutic targets. Our modeling methodology can be implemented using new datasets to identify key players and predict outcomes in new variants of COVID-19.

Gene expression study in monocytes: evidence of inflammatory dysregulation in early-onset obsessive-compulsive disorder

Obsessive-compulsive disorder (OCD) has a complex etiology that seems to include immune dysfunction and alterations in circulating monocytes. To investigate the immune basis and the functional dysregulation of monocytes in this disease, we analyzed gene expression in the peripheral monocytes of pediatric patients with OCD (N = 102) compared to controls (N = 47). We examined gene expression in primary cultures of peripheral monocytes from participants, under basal conditions and under exposure to lipopolysaccharide (LPS) to stimulate immune response. Whole-genome expression was assessed in 8 patients and 8 controls. Differentially expressed genes were identified followed by protein-protein interaction network construction and functional annotation analysis to identify the genes and biological processes that are altered in the monocytes of OCD patients. We also explored the expression levels of selected genes in monocytes from the other participants using qPCR. Several changes in gene expression were observed in the monocytes of OCD patients, with several immune processes involved under basal conditions (antigen processing and presentation, regulation of immune system and leukocyte cell adhesion) and after LPS stimulation (immune and inflammatory response, cytokine production and leukocyte activation). Despite the qPCR analysis provided no significant differences between patients and controls, high correlations were observed between the expression levels of some of the genes and inflammatory markers (i.e., T helper 17 and regulatory T cell levels, total monocyte and proinflammatory monocyte subset levels, and the cytokine production by resting and stimulated monocytes) of the study participants. Our findings provide more evidence of the involvement of monocyte dysregulation in early-onset OCD, indicating a proinflammatory predisposition and an enhanced immune response to environmental triggers.

Burden of rare coding variants in an Italian cohort of familial multiple sclerosis

BACKGROUND

Multiple Sclerosis (MS) is a chronic inflammatory and neurodegenerative demyelinating disease of the central nervous system. It is a complex and heterogeneous disease caused by a combination of genetic and environmental factors, and it can cluster in families.

OBJECTIVE

to evaluate at gene-level the aggregate contribution of predicted damaging low-frequency and rare variants to MS risk in multiplex families.

METHODS

We performed whole exome sequencing (WES) in 28 multiplex MS families with at least 3 MS cases (81 affected and 42 unaffected relatives) and 38 unrelated healthy controls. A gene-based burden test was then performed, focusing on two sets of candidate genes: i) literature-driven selection and ii) data-driven selection.

RESULTS

We identified 11 genes enriched with predicted damaging low-frequency and rare variants in MS compared to healthy individuals. Among them, UBR2 and DST were the two genes with the strongest enrichment (p = 5 × 10^-4 and 3 × 10^-4, respectively); interestingly enough the association signal in UBR2 is driven by rs62414610, which was present in 25% of analysed families.

CONCLUSION

Despite limitations, this is one of the first studies evaluating the aggregate contribution of predicted damaging low-frequency and rare variants in MS families using WES data. A replication effort in independent cohorts is warranted to validate our findings and to evaluate the role of identified genes in MS pathogenesis.

Polycyclic Aromatic Hydrocarbons Alter the Hepatic Expression of Genes Involved in Sanderling (Calidris alba) Pre-migratory Fueling

Polycyclic aromatic hydrocarbons (PAHs) impaired pre-migratory fueling in 49 orally dosed Sanderling (Calidris alba). In the present study, 8 genes related to fat deposition and PAH exposure were measured in liver subsamples from these same shorebirds. At the highest dose (1260 µg total PAH [tPAH]/kg body wt/day), PAH exposure decreased liver basic fatty acid binding protein 1 (Lbfabp) and hepatic lipase (Lipc) expression. The present study reveals candidate molecular-level pathways for observed avian pre-migratory refueling impairment. Environ Toxicol Chem 2021;40:1983-1991. © 2021 SETAC.

Systems Immunology Analysis Reveals the Contribution of Pulmonary and Extrapulmonary Tissues to the Immunopathogenesis of Severe COVID-19 Patients

As one of the current global health conundrums, COVID-19 pandemic caused a dramatic increase of cases exceeding 79 million and 1.7 million deaths worldwide. Severe presentation of COVID-19 is characterized by cytokine storm and chronic inflammation resulting in multi-organ dysfunction. Currently, it is unclear whether extrapulmonary tissues contribute to the cytokine storm mediated-disease exacerbation. In this study, we applied systems immunology analysis to investigate the immunomodulatory effects of SARS-CoV-2 infection in lung, liver, kidney, and heart tissues and the potential contribution of these tissues to cytokines production. Notably, genes associated with neutrophil-mediated immune response (e.g. CXCL1) were particularly upregulated in lung, whereas genes associated with eosinophil-mediated immune response (e.g. CCL11) were particularly upregulated in heart tissue. In contrast, immune responses mediated by monocytes, dendritic cells, T-cells and B-cells were almost similarly dysregulated in all tissue types. Focused analysis of 14 cytokines classically upregulated in COVID-19 patients revealed that only some of these cytokines are dysregulated in lung tissue, whereas the other cytokines are upregulated in extrapulmonary tissues (e.g. IL6 and IL2RA). Investigations of potential mechanisms by which SARS-CoV-2 modulates the immune response and cytokine production revealed a marked dysregulation of NF-κB signaling particularly CBM complex and the NF-κB inhibitor BCL3. Moreover, overexpression of mucin family genes (e.g. MUC3A, MUC4, MUC5B, MUC16, and MUC17) and HSP90AB1 suggest that the exacerbated inflammation activated pulmonary and extrapulmonary tissues remodeling. In addition, we identified multiple sets of immune response associated genes upregulated in a tissue-specific manner (DCLRE1C, CHI3L1, and PARP14 in lung; APOA4, NFASC, WIPF3, and CD34 in liver; LILRA5, ISG20, S100A12, and HLX in kidney; and ASS1 and PTPN1 in heart). Altogether, these findings suggest that the cytokines storm triggered by SARS-CoV-2 infection is potentially the result of dysregulated cytokine production by inflamed pulmonary and extrapulmonary (e.g. liver, kidney, and heart) tissues.

Simulation, power evaluation and sample size recommendation for single-cell RNA-seq

MOTIVATION

Determining the sample size for adequate power to detect statistical significance is a crucial step at the design stage for high-throughput experiments. Even though a number of methods and tools are available for sample size calculation for microarray and RNA-seq in the context of differential expression (DE), this topic in the field of single-cell RNA sequencing is understudied. Moreover, the unique data characteristics present in scRNA-seq such as sparsity and heterogeneity increase the challenge.

RESULTS

We propose POWSC, a simulation-based method, to provide power evaluation and sample size recommendation for single-cell RNA-sequencing DE analysis. POWSC consists of a data simulator that creates realistic expression data, and a power assessor that provides a comprehensive evaluation and visualization of the power and sample size relationship. The data simulator in POWSC outperforms two other state-of-art simulators in capturing key characteristics of real datasets. The power assessor in POWSC provides a variety of power evaluations including stratified and marginal power analyses for DEs characterized by two forms (phase transition or magnitude tuning), under different comparison scenarios. In addition, POWSC offers information for optimizing the tradeoffs between sample size and sequencing depth with the same total reads.

AVAILABILITY AND IMPLEMENTATION

POWSC is an open-source R package available online at https://github.com/suke18/POWSC.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

MicroRNA Expression Profiles in the Subcutaneous Adipose Tissues of Morbidly Obese Chinese Women

INTRODUCTION

Obesity is a main global health issue and an outstanding cause of morbidity and mortality. Exploring miRNA profiling may help further studies on obesity.

METHODS

Three morbidly obese and 5 normal-weight Chinese women were enrolled in the microarray testing group. Abdominal subcutaneous adipose tissue (SAT) samples were excised. Total RNAs including miRNAs were extracted. Affymetrix GeneChip miRNA 4.0 Array was used to compare the expression profiles of miRNAs between the 2 groups. Two algorithms, miRanda and TargetScan, were used to predict target messenger RNAs (mRNAs). Bioinformatics analysis was then done based on the Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) databases. The sample sizes were further expanded to 8 morbidly obese and 9 normal-weight subjects, and quantitative real-time PCR (qRT-PCR) was utilized to verify the expression of differential miRNAs and target genes.

RESULTS

As per the microarray assay, 58 miRNAs were differentially expressed in the SAT from the morbidly obese and normal-weight groups (Fold >4, p < 0.01, FDR <0.05); 54 of these were downregulated and 4 were upregulated in morbidly obese subjects. A total of 1,333 target genes were jointly predicted by miRanda and TargetScan. Further bioinformatics analysis showed that the differential miRNAs were involved in 269 significant biological functions and 89 significant signaling pathways. The validation experiment by qRT-PCR showed that the expression levels of miRNA-143-5p, miRNA-143-3p, miRNA-145-5p, and let-7a-5p were downregulated in morbidly obese subjects, consistent with the microarray detection. High-mobility group A2 (HMGA2), a target gene of the downregulated miRNA let-7a-5p, was first found to be upregulated 3.19-fold in the SAT of morbidly obese Chinese women when compared to normal-weight controls.

CONCLUSIONS

MiRNA downregulation is a hallmark of intact SAT in a morbidly obese state. Transcription (DNA-dependent), small-molecule metabolic processes, the MAPK signaling pathway, and cancer-related pathways may play important roles in the occurrence and development of obesity. For the first time, we proved that HMGA2, a target gene of let-7a-5p, is upregulated in the SAT of morbidly obese Chinese women.

GEOlimma: differential expression analysis and feature selection using pre-existing microarray data

Background

Differential expression and feature selection analyses are essential steps for the development of accurate diagnostic/prognostic classifiers of complicated human diseases using transcriptomics data. These steps are particularly challenging due to the curse of dimensionality and the presence of technical and biological noise. A promising strategy for overcoming these challenges is the incorporation of pre-existing transcriptomics data in the identification of differentially expressed (DE) genes. This approach has the potential to improve the quality of selected genes, increase classification performance, and enhance biological interpretability. While a number of methods have been developed that use pre-existing data for differential expression analysis, existing methods do not leverage the identities of experimental conditions to create a robust metric for identifying DE genes.

Results

In this study, we propose a novel differential expression and feature selection method—GEOlimma—which combines pre-existing microarray data from the Gene Expression Omnibus (GEO) with the widely-applied Limma method for differential expression analysis. We first quantify differential gene expression across 2481 pairwise comparisons from 602 curated GEO Datasets, and we convert differential expression frequencies to DE prior probabilities. Genes with high DE prior probabilities show enrichment in cell growth and death, signal transduction, and cancer-related biological pathways, while genes with low prior probabilities were enriched in sensory system pathways. We then applied GEOlimma to four differential expression comparisons within two human disease datasets and performed differential expression, feature selection, and supervised classification analyses. Our results suggest that use of GEOlimma provides greater experimental power to detect DE genes compared to Limma, due to its increased effective sample size. Furthermore, in a supervised classification analysis using GEOlimma as a feature selection method, we observed similar or better classification performance than Limma given small, noisy subsets of an asthma dataset.

Conclusions

Our results demonstrate that GEOlimma is a more effective method for differential gene expression and feature selection analyses compared to the standard Limma method. Due to its focus on gene-level differential expression, GEOlimma also has the potential to be applied to other high-throughput biological datasets.

Investigation of the Mechanism of Complement System in Diabetic Nephropathy via Bioinformatics Analysis

OBJECTIVES

Diabetic nephropathy (DN) is a major cause of end-stage renal disease (ESRD) throughout the world, and the identification of novel biomarkers via bioinformatics analysis could provide research foundation for future experimental verification and large-group cohort in DN models and patients.

METHODS

GSE30528, GSE47183, and GSE104948 were downloaded from Gene Expression Omnibus (GEO) database to find differentially expressed genes (DEGs). The difference of gene expression between normal renal tissues and DN renal tissues was firstly screened by GEO2R. Then, the protein-protein interactions (PPIs) of DEGs were performed by STRING database, the result was integrated and visualized via applying Cytoscape software, and the hub genes in this PPI network were selected by MCODE and topological analysis. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses were carried out to determine the molecular mechanisms of DEGs involved in the progression of DN. Finally, the Nephroseq v5 online platform was used to explore the correlation between hub genes and clinical features of DN.

RESULTS

There were 64 DEGs, and 32 hub genes were identified, enriched pathways of hub genes involved in several functions and expression pathways, such as complement binding, extracellular matrix structural constituent, complement cascade related pathways, and ECM proteoglycans. The correlation analysis and subgroup analysis of 7 complement cascade-related hub genes and the clinical characteristics of DN showed that C1QA, C1QB, C3, CFB, ITGB2, VSIG4, and CLU may participate in the development of DN.

CONCLUSIONS

We confirmed that the complement cascade-related hub genes may be the novel biomarkers for DN early diagnosis and targeted treatment.

Gene expression profile of CD14+ blood monocytes following lifestyle-induced weight loss in individuals with metabolic syndrome

Lifestyle-induced weight loss is regarded as an efficient therapy to reverse metabolic syndrome (MetS) and to prevent disease progression. The objective of this study was to investigate whether lifestyle-induced weight loss modulates gene expression in circulating monocytes. We analyzed and compared gene expression in monocytes (CD14⁺ cells) and subcutaneous adipose tissue biopsies by unbiased mRNA profiling. Samples were obtained before and after diet-induced weight loss in well-defined male individuals in a prospective controlled clinical trial (ICTRP Trial Number: U1111-1158-3672). The BMI declined significantly (− 12.6%) in the treatment arm (N = 39) during the 6-month weight loss intervention. This was associated with a significant reduction in hsCRP (− 45.84%) and circulating CD14⁺ cells (− 21.0%). Four genes were differentially expressed (DEG’s) in CD14⁺ cells following weight loss (ZRANB1, RNF25, RB1CC1 and KMT2C). Comparative analyses of paired CD14⁺ monocytes and subcutaneous adipose tissue samples before and after weight loss did not identify common genes differentially regulated in both sample types. Lifestyle-induced weight loss is associated with specific changes in gene expression in circulating CD14⁺ monocytes, which may affect ubiquitination, histone methylation and autophagy.

The role of stem cell antigen-1/Lymphocyte antigen 6A-2/6E-1 knock out in murine epidermis

Stem Cell Antigen-1 (SCA-1) is a central positive marker for isolating stem cells in several tissues in the mouse. However, for the epidermis, this appears to be the opposite since lack of SCA-1 has been shown to identify keratinocyte populations with progenitor characteristics. This study investigates the effect of SCA-1 knockout in murine keratinocytes. We compared Sca-1^EGFP/EGFP knockout and wildtype mice with respect to the three-dimensional morphology of the epidermis, performed functional assays, and generated gene expression profiles on FACS sorted cells. There were no morphological abnormalities on skin, fur, or hair follicles in transgenic knockout mice compared to wild type mice. SCA-1 knockout keratinocytes showed significantly reduced colony-forming efficiency, colony size and proliferation rate in vitro, however, SCA-1 knockout did not alter wound healing efficiency or keratinocyte proliferation rate in vivo. Moreover, gene expression profiling shows that the effect from knockout of SCA-1 in keratinocytes is dissimilar from what has been observed in other tissues. Additionally, tumor assay indicated that SCA-1 knockout decreases the number of formed papillomas. The results indicate a more complex role for SCA-1, which might differ between epidermal keratinocytes during homeostasis and activated conditions.

Recommendations for the analysis of gene expression data to identify intrinsic differences between similar tissues

Identifying genes involved in functional differences between similar tissues from expression profiles is challenging, because the expected differences in expression levels are small. To exemplify this challenge, we studied the expression profiles of two skeletal muscles, deltoid and biceps, in healthy individuals. We provide a series of guides and recommendations for the analysis of this type of studies. These include how to account for batch effects and inter-individual differences to optimize the detection of gene signatures associated with tissue function. We provide guidance on the selection of optimal settings for constructing gene co-expression networks through parameter sweeps of settings and calculation of the overlap with an established knowledge network. Our main recommendation is to use a combination of the data-driven approaches, such as differential gene expression analysis and gene co-expression network analysis, and hypothesis-driven approaches, such as gene set connectivity analysis. Accordingly, we detected differences in metabolic gene expression between deltoid and biceps that were supported by both data- and hypothesis-driven approaches. Finally, we provide a bioinformatic framework that support the biological interpretation of expression profiles from related tissues from this combination of approaches, which is available at github.com/tabbassidaloii/AnalysisFrameworkSimilarTissues.

Transcriptional changes in prostate of men on active surveillance after a 12-mo glucoraphanin-rich broccoli intervention-results from the Effect of Sulforaphane on prostate CAncer PrEvention (ESCAPE) randomized controlled trial

BACKGROUND

Epidemiological evidence suggests that consumption of cruciferous vegetables is associated with reduced risk of prostate cancer progression, largely attributed to the biological activity of glucosinolate degradation products, such as sulforaphane derived from glucoraphanin. Because there are few therapeutic interventions for men on active surveillance for prostate cancer to reduce the risk of cancer progression, dietary approaches are an appealing option for patients.

OBJECTIVE

We evaluated whether consumption of a glucoraphanin-rich broccoli soup for 1 y leads to changes in gene expression in prostate tissue of men with localized prostate cancer.

METHODS

Forty-nine men on active surveillance completed a 3-arm parallel randomized double-blinded intervention study for 12 mo and underwent transperineal template biopsy procedures and dietary assessment at the start and end of the study. Patients received a weekly 300 mL portion of soup made from a standard broccoli (control) or from 1 of 2 experimental broccoli genotypes with enhanced concentrations of glucoraphanin, delivering 3 and 7 times that of the control, respectively. Gene expression in tissues from each patient obtained before and after the dietary intervention was quantified by RNA sequencing followed by gene set enrichment analyses.

RESULTS

In the control arm, there were several hundred changes in gene expression in nonneoplastic tissue during the 12 mo. These were associated with an increase in expression of potentially oncogenic pathways including inflammation processes and epithelial-mesenchymal transition. Changes in gene expression and associated oncogenic pathways were attenuated in men on the glucoraphanin-rich broccoli soup in a dose-dependent manner. Although the study was not powered to assess clinical progression, an inverse association between consumption of cruciferous vegetables and cancer progression was observed.

CONCLUSION

Consuming glucoraphanin-rich broccoli soup affected gene expression in the prostate of men on active surveillance, consistent with a reduction in the risk of cancer progression. This trial was registered at clinicaltrials.gov as NCT01950143.

The peripheral blood transcriptome identifies dysregulation of inflammatory response genes in polycystic ovary syndrome

Polycystic ovary syndrome (PCOS) is a common endocrine disorder in women, resulting in ovulation failure and other metabolic problems. However, the underlying mechanisms of it remain largely uncertain due to the complexity of clinical manifestations. This systemic disorder is involved in endocrine, metabolism, immune system and many organs, and few studies have explored peripheral blood transcriptome in patients with PCOS. We performed gene expression profiling of peripheral blood from 8 PCOS patients and eight healthy women with microarray. The significance analysis of microarray (SAM) software was employed to screen the differentially expressed genes (DEGs) and gene ontology (GO) was used for functional enrichment analysis. In total, 181 DEGs with fold-changes >2.0 and q-values <0.05 were identified between the two groups. Among them, 149 were up-regulated and 32 down-regulated in PCOS. Unsupervised clustering of expressed genes could readily differentiate PCOS from control. More importantly, inflammatory response pathway including 14 dysregulated genes was highly enriched in PCOS. Furthermore, 10 DEGs were validated using quantitative reverse-transcription PCR (qRT-PCR) assays. Our study provides independent evidence for the involvement of systemic inflammatory response in PCOS and it may facilitate a greater understanding of this complex disease.

Construction and Optimization of a Large Gene Coexpression Network in Maize Using RNA-Seq Data

With the emergence of massively parallel sequencing, genomewide expression data production has reached an unprecedented level. This abundance of data has greatly facilitated maize research, but may not be amenable to traditional analysis techniques that were optimized for other data types. Using publicly available data, a gene coexpression network (GCN) can be constructed and used for gene function prediction, candidate gene selection, and improving understanding of regulatory pathways. Several GCN studies have been done in maize (Zea mays), mostly using microarray datasets. To build an optimal GCN from plant materials RNA-Seq data, parameters for expression data normalization and network inference were evaluated. A comprehensive evaluation of these two parameters and a ranked aggregation strategy on network performance, using libraries from 1266 maize samples, were conducted. Three normalization methods and 10 inference methods, including six correlation and four mutual information methods, were tested. The three normalization methods had very similar performance. For network inference, correlation methods performed better than mutual information methods at some genes. Increasing sample size also had a positive effect on GCN. Aggregating single networks together resulted in improved performance compared to single networks.

BACE1-Deficient Mice Exhibit Alterations in Immune System Pathways

BACE1 encodes for the beta-site amyloid precursor protein cleaving enzyme 1 or β-secretase. Genetic deletion of Bace1 leads to behavioral alterations and affects midbrain dopaminergic signaling and memory processes. In order to further understand the role of BACE1 in brain function and behavior, we performed microarray transcriptome profiling and gene pathway analysis in the hippocampus of BACE1-deficient mice compared to wild type. We identified a total of 91 differentially expressed genes (DEGs), mostly enriched in pathways related to the immune and inflammation systems, particularly IL-9 and NF-κB activation pathways. Serum levels of IL-9 were elevated in BACE1-deficient mice. Our network analysis supports an intimate connection between immune response via NF-κB and BACE1 signaling through the NRG1/Akt1 pathway. Our findings warrant future mechanistic studies to determine if BACE1 signaling and the IL-9 pathway interact to alter behavior and brain function. This study opens new avenues in the investigation of hippocampus-related neuroimmunological and neuroinflammation-associated disorders.

Serum bile acids and GLP-1 decrease following telemetric induced weight loss: results of a randomized controlled trial

Bile acids (BAs) are increasingly recognised as metabolic regulators, potentially improving insulin sensitivity following bariatric surgery. However, physiological relevance of such observations remains unknown. Hence, we analysed serum BA composition and associated gut-derived hormone levels following lifestyle-induced weight loss in individuals with metabolic syndrome (MetS). 74 non-smoking men (45–55 yr) with MetS were randomised to a lifestyle-induced weight loss program (supervision via telemonitoring) or to a control arm. Before and after a 6 months intervention period clinical and laboratory parameters, body composition, serum BA profile, FGF-19, and GLP-1 concentrations were determined in fasting blood samples. 30 participants in the control and 33 participants in the treatment arm completed the study and were included in the data analysis. In participants of the treatment arm lifestyle-induced weight loss resulted in markedly improved insulin sensitivity. Serum levels of BA species and total GLP-1 decreased, while FGF-19 remained stable. Serum BA composition changed towards an increased 12α-hydroxylated/non-12α-hydroxylated ratio. None of these parameters changed in participants of the control arm. Our results demonstrate that improved metabolic control by lifestyle modifications lowers serum levels of BAs and GLP-1 and changes serum BA composition towards an increased 12α/non-12α ratio (ICTRP Trial Number: U1111-1158-3672).

Gene expression profiling reveals different molecular patterns in G-protein coupled receptor signaling pathways between early- and late-onset preeclampsia

Early-onset preeclampsia and late-onset preeclampsia have been regarded as two different phenotypes with heterogeneous manifestations; To gain insights into the pathogenesis of the two traits, we analyzed the gene expression profiles in preeclamptic placentas. A whole genome-wide microarray was used to determine the gene expression profiles in placental tissues from patients with early-onset (n = 7; <34 weeks), and late-onset (n = 8; >36 weeks) preeclampsia and their controls who delivered preterm (n = 5; <34 weeks) or at term (n = 5; >36 weeks). Genes were termed differentially expressed if they showed a fold-change ≥ 2 and q-value < 0.05. Quantitative real-time reverse transcriptase PCR was used to verify the results. Western blotting was performed to verify the expressions of secreted genes at the protein level.

RESULTS

Six hundred twenty-seven genes were differentially expressed in early-compared with late-onset preeclampsia (177 genes were up-regulated and 450 were down-regulated). Gene ontology analysis identified significant alterations in several biological processes; the top two were immune response and cell surface receptor linked signal transduction. Among the cell surface receptor linked signal transduction-related, differentially expressed genes, those involved in the G-protein coupled receptor protein signaling pathway were significantly enriched. G-protein coupled receptor signaling pathway related genes, such as GPR124 and MRGPRF, were both found to be down-regulated in early-onset preeclampsia. The results were consistent with those of western blotting that the abundance of GPR124 was lower in early-onset compared with late-onset preeclampsia. The different gene expression profiles reflect the different levels of transcription regulation between the two conditions and supported the hypothesis that they are separate disease entities. Moreover, the G-protein coupled receptor signaling pathway related genes may contribute to the mechanism underlying early- and late-onset preeclampsia.

Meta-Analysis of Multiple Sclerosis Microarray Data Reveals Dysregulation in RNA Splicing Regulatory Genes

Abnormalities in RNA metabolism and alternative splicing (AS) are emerging as important players in complex disease phenotypes. In particular, accumulating evidence suggests the existence of pathogenic links between multiple sclerosis (MS) and altered AS, including functional studies showing that an imbalance in alternatively-spliced isoforms may contribute to disease etiology. Here, we tested whether the altered expression of AS-related genes represents a MS-specific signature. A comprehensive comparative analysis of gene expression profiles of publicly-available microarray datasets (190 MS cases, 182 controls), followed by gene-ontology enrichment analysis, highlighted a significant enrichment for differentially-expressed genes involved in RNA metabolism/AS. In detail, a total of 17 genes were found to be differentially expressed in MS in multiple datasets, with CELF1 being dysregulated in five out of seven studies. We confirmed CELF1 downregulation in MS (p = 0.0015) by real-time RT-PCRs on RNA extracted from blood cells of 30 cases and 30 controls. As a proof of concept, we experimentally verified the unbalance in alternatively-spliced isoforms in MS of the NFAT5 gene, a putative CELF1 target. In conclusion, for the first time we provide evidence of a consistent dysregulation of splicing-related genes in MS and we discuss its possible implications in modulating specific AS events in MS susceptibility genes.

Network analysis of gene expression in mice provides new evidence of involvement of the mTOR pathway in antipsychotic-induced extrapyramidal symptoms

To identify potential candidate genes for future pharmacogenetic studies of antipsychotic (AP)-induced extrapyramidal symptoms (EPS), we used gene expression arrays to analyze changes induced by risperidone in mice strains with different susceptibility to EPS. We proposed a systems biology analytical approach that combined the identification of gene co-expression modules related to AP treatment, the construction of protein-protein interaction networks with genes included in identified modules and finally, gene set enrichment analysis of constructed networks. In response to risperidone, mice strain with susceptibility to develop EPS showed downregulation of genes involved in the mammalian target of rapamycin (mTOR) pathway and biological processes related to this pathway. Moreover, we also showed differences in the phosphorylation pattern of the ribosomal protein S6 (rpS6), which is a major downstream effector of mTOR. The present study provides new evidence of the involvement of the mTOR pathway in AP-induced EPS and offers new and valuable markers for pharmacogenetic studies.

Determination of variation parameters as a crucial step in designing TMT-based clinical proteomics experiments

In quantitative shotgun proteomic analyses by liquid chromatography and mass spectrometry, a rigid study design is necessary in order to obtain statistically relevant results. Hypothesis testing, sample size calculation and power estimation are fundamental concepts that require consideration upon designing an experiment. For this reason, the reproducibility and variability of the proteomic platform needs to be assessed. In this study, we evaluate the technical (sample preparation), labeling (isobaric labels), and total (biological + technical + labeling + experimental) variability and reproducibility of a workflow that employs a shotgun LC-MS/MS approach in combination with TMT peptide labeling for the quantification of peripheral blood mononuclear cell (PBMC) proteome. We illustrate that the variability induced by TMT labeling is small when compared to the technical variation. The latter is also responsible for a substantial part of the total variation. Prior knowledge about the experimental variability allows for a correct design, a prerequisite for the detection of biologically significant disease-specific differential proteins in clinical proteomics experiments.

Important options available--from start to finish--for translating proteomics results to clinical chemistry

In the realm of clinical chemistry, the field of clinical proteomics, that is, the application of proteomic methods for understanding mechanisms and enabling diagnosis, prediction, measurement of activity, and treatment response in disease, is first and foremost a discovery and research tool that feeds assay development downstream. Putative new assay candidates generated by proteomics discovery projects compete with well-established assays with known indications, well-described performance, and of known value in specific clinical settings. Careful attention to the many options available in the design, execution, and interpretation of clinical proteomics studies is thus necessary for translation into clinical practice. We here review and discuss important options associated with clinical proteomics endeavors stretching from the planning phases to the final use in clinical chemistry.

Herbal adaptogens combined with protein fractions from bovine colostrum and hen egg yolk reduce liver TNF-α expression and protein carbonylation in Western diet feeding in rats

Background

We examined if a purported anti-inflammatory supplement (AF) abrogated Western-diet (WD)-induced liver pathology in rats. AF contained: 1) protein concentrates from bovine colostrum and avian egg yolk; 2) herbal adaptogens and antioxidants; and 3) acetyl-L-carnitine.

Methods

Nine month-old male Brown Norway rats were allowed ad libitum access to WD for 41–43 days and randomly assigned to WD + AF feeding twice daily for the last 31–33 days (n = 8), or WD and water-placebo feeding twice daily for the last 31–33 days (n = 8). Rats fed a low-fat/low-sucrose diet (CTL, n = 6) for 41–43 days and administered a water-placebo twice daily for the last 31–33 days were also studied. Twenty-four hours following the last gavage-feed, liver samples were analyzed for: a) select mRNAs (via RT-PCR) as well as genome-wide mRNA expression patterns (via RNA-seq); b) lipid deposition; and, c) protein carbonyl and total antioxidant capacity (TAC). Serum was also examined for TAC, 8-isoprostane and clinical chemistry markers.

Results

WD + AF rats experienced a reduction in liver Tnf-α mRNA (-2.8-fold, p < 0.01). Serum and liver TAC was lower in WD + AF versus WD and CTL rats (p < 0.05), likely due to exogenous antioxidant ingredients provided through AF as evidenced by a tendency for mitochondrial SOD2 mRNA to increase in WD + AF versus CTL rats (p = 0.07). Liver fat deposition nor liver protein carbonyl content differed between WD + AF versus WD rats, although liver protein carbonyls tended to be lower in WD + AF versus CTL rats (p = 0.08). RNA-seq revealed that 19 liver mRNAs differed between WD + AF versus WD when both groups were compared with CTL rats (+/- 1.5-fold, p < 0.01). Bioinformatics suggest that AF prevented WD-induced alterations in select genes related to the transport and metabolism of carbohydrates in favor of select genes related to lipid transport and metabolism. Finally, serum clinical safety markers and liver pathology (via lesion counting) suggests that chronic consumption of AF was well tolerated.

Conclusions

AF supplementation elicits select metabolic, anti-inflammatory, and anti-oxidant properties which was in spite of WD feeding and persisted up to 24 hours after receiving a final dose.

Peripheral blood mononuclear cells microRNA predicts treatment outcome of hepatitis C virus genotype 1 infection

BACKGROUNDS

Chronic hepatitis C virus (HCV) infection has been associated with induction of microRNAs (miRNAs) in peripheral blood mononuclear cells (PBMC). We aimed to evaluate the role of PBMC-miRNAs in the treatment outcome to antiviral therapy for HCV genotype 1 (HCV-1) patients.

METHODS

Treatment-naive chronic HCV-1 patients, including 13 in screening phase and 48 in validation phase, were treated with 48weeks of peginterferon/ribavirin. The primary end-point was the achievement of a sustained virological response (SVR, HCV RNA undetectable during 24weeks post-treatment follow-up). Expression profiling of PBMC-miRNAs was performed by quantitative PCR-based array in typical responders and null-responders. Then candidate PBMC-miRNAs were validated by quantitative PCR in an independent validation set.

RESULTS

PBMC-miR-125b was significantly predictive of an SVR, with expression levels of 5.28-fold lower in sustained responders versus null-responders (p=0.0163). In multivariate analysis, PBMC-miR-125b was significantly associated with the achievement of SVR (per 2-fold decrease, odds ratio/95% confidence interval (OR/CI): 2.07/1.14-6.31) independent of sex, age and interleukin-28B genotype. In patients who did not achieve a rapid virological response (RVR, undetectable HCV RNA at treatment week 4), PBMC-miR-125b was the only predictive factor of an SVR (per 2-fold decrease, OR/CI: 2.07/1.14-6.31). However, the circulating and hepatic miR-125b did not show significant difference between responders and non-responders.

CONCLUSIONS

PBMC-miR-125b expression levels were inversely related to the achievement of an SVR in HCV-1 patients, independent of interleukin-28B genotype, and was the single predictor of SVR in non-RVR patients.

Transcriptome profiling of human hippocampus dentate gyrus granule cells in mental illness

This study is, to the best of our knowledge, the first application of whole transcriptome sequencing (RNA-seq) to cells isolated from postmortem human brain by laser capture microdissection. We investigated the transcriptome of dentate gyrus (DG) granule cells in postmortem human hippocampus in 79 subjects with mental illness (schizophrenia, bipolar disorder, major depression) and nonpsychiatric controls. We show that the choice of normalization approach for analysis of RNA-seq data had a strong effect on results; under our experimental conditions a nonstandard normalization method gave superior results. We found evidence of disrupted signaling by miR-182 in mental illness. This was confirmed using a novel method of leveraging microRNA genetic variant information to indicate active targeting. In healthy subjects and those with bipolar disorder, carriers of a high- vs those with a low-expressing genotype of miR-182 had different levels of miR-182 target gene expression, indicating an active role of miR-182 in shaping the DG transcriptome for those subject groups. By contrast, comparing the transcriptome between carriers of different genotypes among subjects with major depression and schizophrenia suggested a loss of DG miR-182 signaling in these conditions.

Microarray RNA transcriptional profiling: Part I. Platforms, experimental design and standardization

This review summarizes, in a balanced and comprehensive manner, the various components of microarrays and their types, substrate architecture, platforms for microarray probe implementation, standardizations and confounders. The review is intended to familiarize the beginner with the principles of experimental design and the selection of an appropriate microarray platform. This parallel technology has revolutionized transcriptomic approaches to data profiling and has a major role in the identification of expressed genes, classification and diagnosis studies. The technology is still evolving and guidelines for standardization and reporting have been developed and are being improved.

Gene expression analysis of relapsing-remitting, primary progressive and secondary progressive multiple sclerosis

BACKGROUND

Previous studies of multiple sclerosis (MS) have indicated differences in the pathogenesis in relapsing-remitting (RRMS), secondary progressive (SPMS) and primary progressive (PPMS) disease.

OBJECTIVE

We hypothesized that different MS subtypes would show differences in gene expression that could be traced to specific subsets of peripheral blood mononuclear cells (PBMCs).

METHODS

Gene expression in RRMS, SPMS, PPMS and healthy control (HC) PBMCs was analyzed on Affymetrix arrays. In addition, we studied gene expression in pools of purified PBMC subsets.

RESULTS

We found 380 genes that were differentially expressed in RRMS, PPMS, SPMS and HCs (false discovery rate < 5%). There were no major differences between the subtypes of MS. The genes showing most prominent expression changes in RRMS were associated with adaptive immune pathways, while genes in PPMS were associated with innate immune system pathways. SPMS patients shared pathways with RRMS and PPMS patients. Gene expression changes were most prominent in B cells, CD8+ T cells and monocytes.

CONCLUSION

Differences in gene expression, which could be traced to B cells, CD8+ T cells and monocytes, were found between MS patients and HCs but only minor differences were observed between MS subgroups.

Assessing the validity and reproducibility of genome-scale predictions

MOTIVATION

Validation and reproducibility of results is a central and pressing issue in genomics. Several recent embarrassing incidents involving the irreproducibility of high-profile studies have illustrated the importance of this issue and the need for rigorous methods for the assessment of reproducibility.

RESULTS

Here, we describe an existing statistical model that is very well suited to this problem. We explain its utility for assessing the reproducibility of validation experiments, and apply it to a genome-scale study of adenosine deaminase acting on RNA (ADAR)-mediated RNA editing in Drosophila. We also introduce a statistical method for planning validation experiments that will obtain the tightest reproducibility confidence limits, which, for a fixed total number of experiments, returns the optimal number of replicates for the study.

AVAILABILITY

Downloadable software and a web service for both the analysis of data from a reproducibility study and for the optimal design of these studies is provided at http://ccmbweb.ccv.brown.edu/reproducibility.html .

Data Mining and Meta-Analysis on DNA Microarray Data

Microarray technology enables high-throughput parallel gene expression analysis, and use has grown exponentially thanks to the development of a variety of applications for expression, genetics and epigenetic studies. A wealth of data is now available from public repositories, providing unprecedented opportunities for meta-analysis approaches, which could generate new biological information, unrelated to the original scope of individual studies. This study provides a guideline for identification of biological significance of the statistically-selected differentially-expressed genes derived from gene expression arrays as well as to suggest further analysis pathways. The authors review the prerequisites for data-mining and meta-analysis, summarize the conceptual methods to derive biological information from microarray data and suggest software for each category of data mining or meta-analysis.

Chronic psychosocial stressors and salivary biomarkers in emerging adults

We investigated whole saliva as a source of biomarkers to distinguish individuals who have, and who have not, been chronically exposed to severe and threatening life difficulties. We evaluated RNA and DNA metrics, expression of 37 candidate genes, and cortisol release in response to the Trier Social Stress Test, as well as clinical characteristics, from 48 individuals stratified on chronic exposure to psychosocial stressors within the last year as measured by the Life Events and Difficulties Schedule. Candidate genes were selected based on their differential gene expression ratio in circulating monocytes from a published genome-wide analysis of adults experiencing different levels of exposure to a chronic stressor. In univariate analyses, we observed significantly decreased RNA integrity (RIN) score (P = 0.04), and reduced expression of glucocorticoid receptor-regulated genes (Ps < 0.05) in whole saliva RNA from individuals exposed to chronic stressors, as compared to those with no exposure. In those exposed, we observed significantly decreased BMI (P < 0.001), increased ever-smoking and increased lifetime alcohol abuse or dependence (P ≤ 0.03), and a reduction of cortisol release. In post hoc multivariate analyses including clinical and biospecimen-derived variables, we consistently observed significantly decreased expression of IL8 (Ps<0.05) in individuals exposed, with no significant association to RIN score. Alcohol use disorders, tobacco use, a reduced acute stress response and decreased salivary IL8 gene expression characterize emerging adults chronically exposed to severe and threatening psychosocial stressors.

Gene expression profiles of alveolar type II cells of chronic obstructive pulmonary disease: a case-control study

OBJECTIVES

The aim of this study was to identify the gene expression pattern specific in alveolar epithelial type II cells (ATII cells) isolated from patients with chronic obstructive pulmonary disease (COPD).

DESIGN

Case control.

SETTING

Two hospitals in Japan.

PARTICIPANTS

Three patients without COPD and three patients with COPD in microarray analyses. Five smokers without COPD and nine smokers with COPD in the following analyses. PRIMARY AND SECONDARY OUTCOME MEASURED: Primary outcome included identification of differentially expressed genes and activated or inhibited pathways in ATII cells of the patients with COPD, compared to those of the patients without COPD, using Affymetrix gene expression arrays. Secondary outcome included validation of the results of microarray analyses by quantitative reverse transcription-PCR.

RESULTS

We isolated ATII cells from COPD and non-COPD lungs using fluorescence-activated cell sorting. We performed Affymetrix gene expression arrays on both types of ATII cells. Gene set enrichment analyses revealed that two major gene sets were enriched in ATII cells from COPD lungs: interferon-responsive gene sets and gene sets associated with cell cycle progression. Gene ontology term enrichment analyses indicated that among the interferon-stimulated genes, ATII cells in COPD expressed genes such as PSMB8, PSMB9, TAP1 and TAP2 associated with the antigen processing and presentation pathway. We validated the results of the microarray analyses using quantitative reverse transcriptase-PCR. In addition, FACS analysis indicated that the percentage of ATII cells to CD45-negative lung cells isolated from COPD lungs were significantly increased more than that from non-COPD lungs.

CONCLUSIONS

Our study demonstrated that interferon-stimulated genes involved in the antigen processing and presentation pathway and genes involved in cell cycle progression were enriched in ATII cells of the patients with COPD. These pathways might alter phenotypes of ATII cells in COPD lungs.

Why is there a lack of consensus on molecular subgroups of glioblastoma? Understanding the nature of biological and statistical variability in glioblastoma expression data

INTRODUCTION

Gene expression patterns characterizing clinically-relevant molecular subgroups of glioblastoma are difficult to reproduce. We suspect a combination of biological and analytic factors confounds interpretation of glioblastoma expression data. We seek to clarify the nature and relative contributions of these factors, to focus additional investigations, and to improve the accuracy and consistency of translational glioblastoma analyses.

METHODS

We analyzed gene expression and clinical data for 340 glioblastomas in The Cancer Genome Atlas (TCGA). We developed a logic model to analyze potential sources of biological, technical, and analytic variability and used standard linear classifiers and linear dimensional reduction algorithms to investigate the nature and relative contributions of each factor.

RESULTS

Commonly-described sources of classification error, including individual sample characteristics, batch effects, and analytic and technical noise make measurable but proportionally minor contributions to inconsistent molecular classification. Our analysis suggests that three, previously underappreciated factors may account for a larger fraction of classification errors: inherent non-linear/non-orthogonal relationships among the genes used in conjunction with classification algorithms that assume linearity; skewed data distributions assumed to be Gaussian; and biologic variability (noise) among tumors, of which we propose three types.

CONCLUSIONS

Our analysis of the TCGA data demonstrates a contributory role for technical factors in molecular classification inconsistencies in glioblastoma but also suggests that biological variability, abnormal data distribution, and non-linear relationships among genes may be responsible for a proportionally larger component of classification error. These findings may have important implications for both glioblastoma research and for translational application of other large-volume biological databases.

Identification of human housekeeping genes and tissue-selective genes by microarray meta-analysis

Background

Categorizing protein-encoding transcriptomes of normal tissues into housekeeping genes and tissue-selective genes is a fundamental step toward studies of genetic functions and genetic associations to tissue-specific diseases. Previous studies have been mainly based on a few data sets with limited samples in each tissue, which restrained the representativeness of their identified genes, and resulted in low consensus among them.

Results

This study compiled 1,431 samples in 43 normal human tissues from 104 microarray data sets. We developed a new method to improve gene expression assessment, and showed that more than ten samples are needed to robustly identify the protein-encoding transcriptome of a tissue. We identified 2,064 housekeeping genes and 2,293 tissue-selective genes, and analyzed gene lists by functional enrichment analysis. The housekeeping genes are mainly involved in fundamental cellular functions, and the tissue-selective genes are strikingly related to functions and diseases corresponding to tissue-origin. We also compared agreements and related functions among our housekeeping genes and those of previous studies, and pointed out some reasons for the low consensuses.

Conclusions

The results indicate that sufficient samples have improved the identification of protein-encoding transcriptome of a tissue. Comprehensive meta-analysis has proved the high quality of our identified HK and TS genes. These results could offer a useful resource for future research on functional and genomic features of HK and TS genes.

Microarray data analysis and mining tools

Microarrays are one of the latest breakthroughs in experimental molecular biology that allow monitoring the expression levels of tens of thousands of genes simultaneously. Arrays have been applied to studies in gene expression, genome mapping, SNP discrimination, transcription factor activity, toxicity, pathogen identification and many other applications. In this paper we concentrate on discussing various bioinformatics tools used for microarray data mining tasks with its underlying algorithms, web resources and relevant reference. We emphasize this paper mainly for digital biologists to get an aware about the plethora of tools and programs available for microarray data analysis. First, we report the common data mining applications such as selecting differentially expressed genes, clustering, and classification. Next, we focused on gene expression based knowledge discovery studies such as transcription factor binding site analysis, pathway analysis, protein- protein interaction network analysis and gene enrichment analysis.

All Our Babies Cohort Study: recruitment of a cohort to predict women at risk of preterm birth through the examination of gene expression profiles and the environment

Background

Preterm birth is the leading cause of perinatal morbidity and mortality. Risk factors for preterm birth include a personal or familial history of preterm delivery, ethnicity and low socioeconomic status yet the ability to predict preterm delivery before the onset of preterm labour evades clinical practice. Evidence suggests that genetics may play a role in the multi-factorial pathophysiology of preterm birth. The All Our Babies Study is an on-going community based longitudinal cohort study that was designed to establish a cohort of women to investigate how a women's genetics and environment contribute to the pathophysiology of preterm birth. Specifically this study will examine the predictive potential of maternal leukocytes for predicting preterm birth in non-labouring women through the examination of gene expression profiles and gene-environment interactions.

Methods/Design

Collaborations have been established between clinical lab services, the provincial health service provider and researchers to create an interdisciplinary study design for the All Our Babies Study. A birth cohort of 2000 women has been established to address this research question. Women provide informed consent for blood sample collection, linkage to medical records and complete questionnaires related to prenatal health, service utilization, social support, emotional and physical health, demographics, and breast and infant feeding. Maternal blood samples are collected in PAXgene™ RNA tubes between 18-22 and 28-32 weeks gestation for transcriptomic analyses.

Discussion

The All Our Babies Study is an example of how investment in clinical-academic-community partnerships can improve research efficiency and accelerate the recruitment and data collection phases of a study. Establishing these partnerships during the study design phase and maintaining these relationships through the duration of the study provides the unique opportunity to investigate the multi-causal factors of preterm birth. The overall All Our Babies Study results can potentially lead to healthier pregnancies, mothers, infants and children.