Microarray bioinformatics

Overview of gene expression techniques with an emphasis on vitamin D related studies

Each cell controls when and how its genes must be expressed for proper function. Every function in a cell is driven by signaling molecules through various regulatory cascades. Different cells in a multicellular organism may express very different sets of genes, even though they contain the same DNA. The set of genes expressed in a cell determines the set of proteins and functional RNAs it contains, giving it its unique properties. Malfunction in gene expression harms the cell and can lead to the development of various disease conditions. The use of rapid high-throughput gene expression profiling unravels the complexity of human disease at various levels. Peripheral blood mononuclear cells (PBMC) have been used frequently to understand gene expression homeostasis in various disease conditions. However, more studies are required to validate whether PBMC gene expression patterns accurately reflect the expression of other cells or tissues. Vitamin D, which is responsible for a multitude of health consequences, is also an immune modulatory hormone with major biological activities in the innate and adaptive immune systems. Vitamin D exerts its diverse biological effects in target tissues by regulating gene expression and its deficiency, is recognized as a public health problem worldwide. Understanding the genetic factors that affect vitamin D has the potential benefit that it will make it easier to identify individuals who require supplementation. Different technological advances in gene expression can be used to identify and assess the severity of disease and aid in the development of novel therapeutic interventions. This review focuses on different gene expression approaches and various clinical studies of vitamin D to investigate the role of gene expression in identifying the molecular signature of the disease.

Bioinformatics Identification of Therapeutic Gene Targets for Gastric Cancer

INTRODUCTION

The global prevalence of gastric cancer (GC) is increasing, and novel chemotherapeutic targets are needed.

METHODS

We searched for potential biomarkers for GC in three microarray data sets within the Gene Expression Omnibus (GEO) database. FunRich (v3.1.3) was used to perform Gene Ontology (GO) analyses and STRUN and Cytoscape (v3.6.0) were employed to construct a protein-protein interaction (PPI) network. To explore hub gene expression and survival, we used Gene Expression Profiling Interactive Analysis (GEPIA) and Kaplan-Meier (KM) plotter. Drugs that were closely related to key genes were screened by the Gene Set Cancer Analysis (GSCA), and relevant correlations were verified experimentally. We validated that the sensitivity of a GC cell line to these drugs was correlated with fibrillin 1 (FBN1) mRNA expression levels.

RESULTS

We identified 83 upregulated and 133 downregulated differentially expressed genes (DEGs) and these were enriched with regards to their cellular component (extracellular and exosomes), molecular function (extracellular matrix structural constituent and catalytic activity), and biological process (cell growth and/or maintenance and metabolism). The biological pathways most prominently involved were epithelial-to-mesenchymal transition (EMT) and β3 integrin cell surface interactions. For the PPI network, we selected 10 hub genes, and 70% of these were significantly connected to poor overall survival (OS) in patients with GC. We found a significant link between the expression of FBN1 and two small molecule drugs (PAC-1 and PHA-793887).

CONCLUSIONS

Overall, we suggest that these hub genes can be used as biomarkers and novel targets for GC. FBN1 may be associated with drug resistance in gastric cancer.

Omics technologies in allergy and asthma research: An EAACI position paper

Allergic diseases and asthma are heterogenous chronic inflammatory conditions with several distinct complex endotypes. Both environmental and genetic factors can influence the development and progression of allergy. Complex pathogenetic pathways observed in allergic disorders present a challenge in patient management and successful targeted treatment strategies. The increasing availability of high-throughput omics technologies, such as genomics, epigenomics, transcriptomics, proteomics, and metabolomics allows studying biochemical systems and pathophysiological processes underlying allergic responses. Additionally, omics techniques present clinical applicability by functional identification and validation of biomarkers. Therefore, finding molecules or patterns characteristic for distinct immune-inflammatory endotypes, can subsequently influence its development, progression, and treatment. There is a great potential to further increase the effectiveness of single omics approaches by integrating them with other omics, and nonomics data. Systems biology aims to simultaneously and longitudinally understand multiple layers of a complex and multifactorial disease, such as allergy, or asthma by integrating several, separated data sets and generating a complete molecular profile of the condition. With the use of sophisticated biostatistics and machine learning techniques, these approaches provide in-depth insight into individual biological systems and will allow efficient and customized healthcare approaches, called precision medicine. In this EAACI Position Paper, the Task Force "Omics technologies in allergic research" broadly reviewed current advances and applicability of omics techniques in allergic diseases and asthma research, with a focus on methodology and data analysis, aiming to provide researchers (basic and clinical) with a desk reference in the field. The potential of omics strategies in understanding disease pathophysiology and key tools to reach unmet needs in allergy precision medicine, such as successful patients' stratification, accurate disease prognosis, and prediction of treatment efficacy and successful prevention measures are highlighted.

Microarray-based Analysis of Genes, Transcription Factors, and Epigenetic Modifications in Lung Cancer Exposed to Nitric Oxide

BACKGROUND/AIM

Nitric oxide (NO) is recognized as an important biological mediator that exerts several human physiological functions. As its nature is an aqueous soluble gas that can diffuse through cells and tissues, NO can affect cell signaling, the phenotype of cancer and modify surrounding cells. The variety of effects of NO on cancer cell biology has convinced researchers to determine the defined mechanisms of these effects and how to control this mediator for a better understanding as well as for therapeutic gain.

MATERIALS AND METHODS

We used bioinformatics and pharmacological experiments to elucidate the potential regulation and underlying mechanisms of NO in non-small a lung cancer cell model.

RESULTS

Using microarrays, we identified a total of 151 NO-regulated genes (80 up-regulated genes, 71 down-regulated genes) with a strong statistically significant difference compared to untreated controls. Among these, the genes activated by a factor of more than five times were: DCBLD2, MGC24975, RAB40AL, PER3, RCN1, MRPL51, PTTG1, KLF5, NFIX. On the other hand, the expression of RBMS2, PDP2, RBAK, ORMDL2, GRPEL2, ZNF514, MTHFD2, POLR2D, RCBTB1, JOSD1, RPS27, GPR4 genes were significantly decreased by a factor of more than five times. Bioinformatics further revealed that NO exposure of lung cancer cells resulted in a change in transcription factors (TFs) and epigenetic modifications (histone modification and miRNA). Interestingly, NO treatment was shown to potentiate cancer stem cell-related genes and transcription factors Oct4, Klf4, and Myc.

CONCLUSION

Through this comprehensive approach, the present study illustrated the scheme of how NO affects molecular events in lung cancer cells.

Evaluation of frozen tissue-derived prognostic gene expression signatures in FFPE colorectal cancer samples

Defining molecular features that can predict the recurrence of colorectal cancer (CRC) for stage II-III patients remains challenging in cancer research. Most available clinical samples are Formalin-Fixed, Paraffin-Embedded (FFPE). NanoString nCounter® and Affymetrix GeneChip® Human Transcriptome Array 2.0 (HTA) are the two platforms marketed for high-throughput gene expression profiling for FFPE samples. In this study, to evaluate the gene expression of frozen tissue-derived prognostic signatures in FFPE CRC samples, we evaluated the expression of 516 genes from published frozen tissue-derived prognostic signatures in 42 FFPE CRC samples measured by both platforms. Based on HTA platform-derived data, we identified both gene (99 individual genes, FDR < 0.05) and gene set (four of the six reported multi-gene signatures with sufficient information for evaluation, P < 0.05) expression differences associated with survival outcomes. Using nCounter platform-derived data, one of the six multi-gene signatures (P < 0.05) but no individual gene was associated with survival outcomes. Our study indicated that sufficiently high quality RNA could be obtained from FFPE tumor tissues to detect frozen tissue-derived prognostic gene expression signatures for CRC patients.

Integrated Study of Globally Expressed microRNAs in IL-1β-stimulated Human Osteoarthritis Chondrocytes and Osteoarthritis Relevant Genes: A Microarray and Bioinformatics Analysis

This study was undertaken to identify and characterize the globally expressed microRNAs (miRNAs) involved in interleukin-1β (IL-1β)-induced joint damage and to predict whether miRNAs can regulate the catabolic effects in osteoarthritis (OA) chondrocytes. Out of 1347 miRNAs analyzed by microarrays in IL-1β-stimulated OA chondrocytes, 35 miRNAs were down-regulated, 1 miRNA was up-regulated, and the expression of 1311 miRNAs remained unchanged. Bioinformatics analysis showed the key inflammatory mediators and key molecular pathways are targeted by differentially expressed miRNAs. Novel miRNAs identified could have important diagnostic and therapeutic potentials in the development of novel therapeutic strategies for pain managements in OA.

Comparison of gene expression microarray data with count-based RNA measurements informs microarray interpretation

BACKGROUND

Although numerous investigations have compared gene expression microarray platforms, preprocessing methods and batch correction algorithms using constructed spike-in or dilution datasets, there remains a paucity of studies examining the properties of microarray data using diverse biological samples. Most microarray experiments seek to identify subtle differences between samples with variable background noise, a scenario poorly represented by constructed datasets. Thus, microarray users lack important information regarding the complexities introduced in real-world experimental settings. The recent development of a multiplexed, digital technology for nucleic acid measurement enables counting of individual RNA molecules without amplification and, for the first time, permits such a study.

RESULTS

Using a set of human leukocyte subset RNA samples, we compared previously acquired microarray expression values with RNA molecule counts determined by the nCounter Analysis System (NanoString Technologies) in selected genes. We found that gene measurements across samples correlated well between the two platforms, particularly for high-variance genes, while genes deemed unexpressed by the nCounter generally had both low expression and low variance on the microarray. Confirming previous findings from spike-in and dilution datasets, this "gold-standard" comparison demonstrated signal compression that varied dramatically by expression level and, to a lesser extent, by dataset. Most importantly, examination of three different cell types revealed that noise levels differed across tissues.

CONCLUSIONS

Microarray measurements generally correlate with relative RNA molecule counts within optimal ranges but suffer from expression-dependent accuracy bias and precision that varies across datasets. We urge microarray users to consider expression-level effects in signal interpretation and to evaluate noise properties in each dataset independently.

Molecular methods for pathogen and microbial community detection and characterization: current and potential application in diagnostic microbiology

Clinical microbiology laboratories worldwide have historically relied on phenotypic methods (i.e., culture and biochemical tests) for detection, identification and characterization of virulence traits (e.g., antibiotic resistance genes, toxins) of human pathogens. However, limitations to implementation of molecular methods for human infectious diseases testing are being rapidly overcome allowing for the clinical evaluation and implementation of diverse technologies with expanding diagnostic capabilities. The advantages and limitation of molecular techniques including real-time polymerase chain reaction, partial or whole genome sequencing, molecular typing, microarrays, broad-range PCR and multiplexing will be discussed. Finally, terminal restriction fragment length polymorphism (T-RFLP) and deep sequencing are introduced as technologies at the clinical interface with the potential to dramatically enhance our ability to diagnose infectious diseases and better define the epidemiology and microbial ecology of a wide range of complex infections.

Progress report on microstructured surfaces based on chemical vapor deposition

This book chapter discusses recent advances in the fabrication of microscale surface patterns using chemical vapor deposition polymerization. Reactive poly(p-xylylene) (PPX) coatings are useful for their ability to immobilize specific biomolecules, as determined by the PPX functional group. PPXs can either be modified postdeposition, or they can be patterned onto a substrate in situ. Specific methods discussed in this progress report include microcontact printing, vapor-assisted micropatterning in replica structures, projection lithography-based patterning, and selective polymer deposition.