The use and analysis of microarray data

Identification of immune infiltration and PANoptosis-related molecular clusters and predictive model in Alzheimer's disease based on transcriptome analysis

This study aims to explore the expression profile of PANoptosis-related genes (PRGs) and immune infiltration in Alzheimer's disease (AD). Based on the Gene Expression Omnibus database, this study investigated the differentially expressed PRGs and immune cell infiltration in AD and explored related molecular clusters. Gene set variation analysis (GSVA) was used to analyze the expression of Gene Ontology and Kyoto Encyclopedia of Genes and Genomes in different clusters. Weighted gene co-expression network analysis was utilized to find co-expressed gene modules and core genes in the network. By analyzing the intersection genes in random forest, support vector machine, generalized linear model, and extreme gradient boosting (XGB), the XGB model was determined. Eventually, the first five genes (Signal Transducer and Activator of Transcription 3, Tumor Necrosis Factor (TNF) Receptor Superfamily Member 1B, Interleukin 4 Receptor, Chloride Intracellular Channel 1, TNF Receptor Superfamily Member 10B) in XGB model were selected as predictive genes. This research explored the relationship between PANoptosis and AD and established an XGB learning model to evaluate and screen key genes. At the same time, immune infiltration analysis showed that there were different immune infiltration expression profiles in AD.

C. difficile biomarkers, pathogenicity and detection

BACKGROUND

Clostridioides difficile infection (CDI) is the main etiologic agent of antibiotic-associated diarrhea. CDI contributes to gut inflammation and can lead to disruption of the intestinal epithelial barrier. Recently, the rate of CDI cases has been increased. Thus, early diagnosis of C. difficile is critical for controlling the infection and guiding efficacious therapy.

APPROACH

A search strategy was set up using the terms C. difficile biomarkers and diagnosis. The found references were classified into two general categories; conventional and advanced methods.

RESULTS

The pathogenicity and biomarkers of C. difficile, and the collection manners for CDI-suspected specimens were briefly explained. Then, the conventional CDI diagnostic methods were subtly compared in terms of duration, level of difficulty, sensitivity, advantages, and disadvantages. Thereafter, an extensive review of the various newly proposed techniques available for CDI detection was conducted including nucleic acid isothermal amplification-based methods, biosensors, and gene/single-molecule microarrays. Also, the detection mechanisms, pros and cons of these methods were highlighted and compared with each other. In addition, approximately complete information on FDA-approved platforms for CDI diagnosis was collected.

CONCLUSION

To overcome the deficiencies of conventional methods, the potential of advanced methods for C. difficile diagnosis, their direction, perspective, and challenges ahead were discussed.

CyanoStrainChip: A Novel DNA Microarray Tool for High-Throughput Detection of Environmental Cyanobacteria at the Strain Level

Detecting cyanobacteria in environments is an important concern due to their crucial roles in ecosystems, and they can form blooms with the potential to harm humans and nonhuman entities. However, the most widely used methods for high-throughput detection of environmental cyanobacteria, such as 16S rRNA sequencing, typically provide above-species-level resolution, thereby disregarding intraspecific variation. To address this, we developed a novel DNA microarray tool, termed the CyanoStrainChip, that enables strain-level comprehensive profiling of environmental cyanobacteria. The CyanoStrainChip was designed to target 1277 strains; nearly all major groups of cyanobacteria are included by implementing 43,666 genome-wide, strain-specific probes. It demonstrated strong specificity by in vitro mock community experiments. The high correlation (Pearson's R > 0.97) between probe fluorescence intensities and the corresponding DNA amounts (ranging from 1-100 ng) indicated excellent quantitative capability. Consistent cyanobacterial profiles of field samples were observed by both the CyanoStrainChip and next-generation sequencing methods. Furthermore, CyanoStrainChip analysis of surface water samples in Lake Chaohu uncovered a high intraspecific variation of abundance change within the genus Microcystis between different severity levels of cyanobacterial blooms, highlighting two toxic Microcystis strains that are of critical concern for Lake Chaohu harmful blooms suppression. Overall, these results suggest a potential for CyanoStrainChip as a valuable tool for cyanobacterial ecological research and harmful bloom monitoring to supplement existing techniques.

A user-driven machine learning approach for RNA-based sample discrimination and hierarchical classification

RNA-based sample discrimination and classification can be used to provide biological insights and/or distinguish between clinical groups. However, finding informative differences between sample groups can be challenging due to the multidimensional and noisy nature of sequencing data. Here, we apply a machine learning approach for hierarchical discrimination and classification of samples with high-dimensional miRNA expression data. Our protocol comprises data preprocessing, unsupervised learning, feature selection, and machine-learning-based hierarchical classification, alongside open-source MATLAB code.

Characterisation of changes in global genes expression in the lung of ICR mice in response to the inflammation and fibrosis induced by polystyrene nanoplastics inhalation

This study characterised the changes in global gene expression in the lung of ICR mice in response to the inflammation and fibrosis induced by the inhalation of 0.5 μm polystyrene (PS)-nanoplastics (NPs) at various concentrations (4, 8, and 16 μg/mL) for 2 weeks. The total RNA extracted from the lung tissue of NPs-inhaled mice was hybridised into oligonucleotide microarrays. Significant upregulation was detected in several inflammatory responses, including the number of immune cells in bronchoalveolar lavage fluid (BALF), the expression level of inflammatory cytokines, mucin secretion, and histopathological changes, while they accumulated average of 13.38 ± 1.0 μg/g in the lungs of the inhaled ICR mice. Similar responses were observed regarding the levels of fibrosis-related factors in the NPs-inhaled lung of ICR mice, such as pulmonary parenchymal area, expression of pro-fibrotic marker genes, and TGF-β1 downstream signalling without any significant hepatotoxicity and nephrotoxicity. In microarray analyses, 60 genes were upregulated, and 55 genes were downregulated in the lung of ICR mice during inflammation and fibrosis induced by NPs inhalation compared to the Vehicle-inhaled mice. Among these genes, many were categorised into several ontology categories, including the anatomical structure, binding, membrane, and metabolic process. Furthermore, the major genes in the upregulated categories included Igkv14-126000, Egr1, Scel, Lamb3, and Upk3b. In contrast, the major genes in the down-regulated categories were Olfr417, Olfr519, Rps16, Rap2b, and Vmn1r193. These results suggest several gene functional groups and individual genes as specific biomarkers respond to inflammation and fibrosis induced by PS-NPs inhalation in ICR mice.

Supplementary Information

The online version contains supplementary material available at 10.1007/s43188-023-00188-y.

Feature selection for high dimensional microarray gene expression data via weighted signal to noise ratio

Feature selection in high dimensional gene expression datasets not only reduces the dimension of the data, but also the execution time and computational cost of the underlying classifier. The current study introduces a novel feature selection method called weighted signal to noise ratio (WSNR) by exploiting the weights of features based on support vectors and signal to noise ratio, with an objective to identify the most informative genes in high dimensional classification problems. The combination of two state-of-the-art procedures enables the extration of the most informative genes. The corresponding weights of these procedures are then multiplied and arranged in decreasing order. Larger weight of a feature indicates its discriminatory power in classifying the tissue samples to their true classes. The current method is validated on eight gene expression datasets. Moreover, results of the proposed method (WSNR) are also compared with four well known feature selection methods. We found that the (WSNR) outperform the other competing methods on 6 out of 8 datasets. Box-plots and Bar-plots of the results of the proposed method and all the other methods are also constructed. The proposed method is further assessed on simulated data. Simulation analysis reveal that (WSNR) outperforms all the other methods included in the study.

Tool and techniques study to plant microbiome current understanding and future needs: an overview

Microorganisms are present in the universe and they play role in beneficial and harmful to human life, society, and environments. Plant microbiome is a broad term in which microbes are present in the rhizo, phyllo, or endophytic region and play several beneficial and harmful roles with the plant. To know of these microorganisms, it is essential to be able to isolate purification and identify them quickly under laboratory conditions. So, to improve the microbial study, several tools and techniques such as microscopy, rRNA, or rDNA sequencing, fingerprinting, probing, clone libraries, chips, and metagenomics have been developed. The major benefits of these techniques are the identification of microbial community through direct analysis as well as it can apply in situ. Without tools and techniques, we cannot understand the roles of microbiomes. This review explains the tools and their roles in the understanding of microbiomes and their ecological diversity in environments.

Development of a clinical microarray system for genetic analysis screening

Objectives

Research on the relationship between diseases and genes and the advancement of genetic analysis technologies have made genetic testing in medical care possible. There are various methods for genetic testing, including PCR-based methods and next-generation sequencing; however, screening tests in clinical laboratories are becoming more diverse; therefore, novel measurement systems and equipment are required to meet the needs of each situation. In this study, we aimed to develop a novel microarray-based genetic analysis system that uses a Peltier element to overcome the issues of conventional microarrays, such as the long measurement time and high cost.

Methods

We constructed a microarray system to detect the UDP-glucuronosyltransferase gene polymorphisms UGT1A1*6 and UGT1A1*28 in patients eligible for irinotecan hydrochloride treatment for use in clinical laboratories. To evaluate the performance of the system, the hybridization temperature and reaction time were determined, and the results were compared with those obtained using a conventional hybridization oven.

Results

The hybridization temperature reached its target in 1/27th of the time required by the conventional system. We assessed 111 human clinical samples and found that our results agreed with those obtained using existing methods. The total time for the newly developed device was reduced by 85 min compared to that for existing methods, as the automated DNA microarray eliminates the time that existing methods spend on manual operation.

Conclusions

The surface treatment technology used in our system enables high-density and strong DNA fixation, allowing the construction of a measurement system suitable for clinical applications.

Gene Expression Profiles of the Immuno-Transcriptome in Equine Asthma

BACKGROUND

Mild equine asthma (MEA) and severe equine asthma (SEA) are two of the most frequent equine airway inflammatory diseases, but knowledge about their pathogenesis is limited. The goal of this study was to investigate gene expression differences in the respiratory tract of MEA- and SEA-affected horses and their relationship with clinical signs.

METHODS

Clinical examination and endoscopy were performed in 8 SEA- and 10 MEA-affected horses and 7 healthy controls. Cytological and microbiological analyses of bronchoalveolar lavage (BAL) fluid were performed. Gene expression profiling of BAL fluid was performed by means of a custom oligo-DNA microarray.

RESULTS

In both MEA and SEA, genes involved in the genesis, length, and motility of respiratory epithelium cilia were downregulated. In MEA, a significant overexpression for genes encoding inflammatory mediators was observed. In SEA, transcripts involved in bronchoconstriction, apoptosis, and hypoxia pathways were significantly upregulated, while genes involved in the formation of the protective muco-protein film were underexpressed. The SEA group also showed enrichment of gene networks activated during human asthma.

CONCLUSIONS

The present study provides new insight into equine asthma pathogenesis, representing the first step in transcriptomic analysis to improve diagnostic and therapeutic approaches for this respiratory disease.

Subtyping and grading of lower-grade gliomas using integrated feature selection and support vector machine

Classifying lower-grade gliomas (LGGs) is a crucial step for accurate therapeutic intervention. The histopathological classification of various subtypes of LGG, including astrocytoma, oligodendroglioma and oligoastrocytoma, suffers from intraobserver and interobserver variability leading to inaccurate classification and greater risk to patient health. We designed an efficient machine learning-based classification framework to diagnose LGG subtypes and grades using transcriptome data. First, we developed an integrated feature selection method based on correlation and support vector machine (SVM) recursive feature elimination. Then, implementation of the SVM classifier achieved superior accuracy compared with other machine learning frameworks. Most importantly, we found that the accuracy of subtype classification is always high (>90%) in a specific grade rather than in mixed grade (~80%) cancer. Differential co-expression analysis revealed higher heterogeneity in mixed grade cancer, resulting in reduced prediction accuracy. Our findings suggest that it is necessary to identify cancer grades and subtypes to attain a higher classification accuracy. Our six-class classification model efficiently predicts the grades and subtypes with an average accuracy of 91% (±0.02). Furthermore, we identify several predictive biomarkers using co-expression, gene set enrichment and survival analysis, indicating our framework is biologically interpretable and can potentially support the clinician.

Integration of Genetic and Immune Infiltration Insights into Data Mining of Multiple Sclerosis Pathogenesis

Background

Multiple sclerosis (MS) is an immune-mediated demyelinating disease of the central nervous system. MS pathogenesis is closely related to the environment, genetic, and immune system, but the underlying interactions have not been clearly elucidated. This study aims to unveil the genetic basis and immune landscape of MS pathogenesis with bioinformatics.

Methods

Gene matrix was retrieved from the gene expression database NCBI-GEO. Then, bioinformatics was used to standardize the samples and obtain differentially expressed genes (DEGs). The protein-protein interaction network was constructed with DEGs on the STRING website. Cytohubba plug-in and MCODE plug-in were used to mine hub genes. Meanwhile, the CIBERSORTX algorithm was used to explore the characteristics of immune cell infiltration in MS brain tissues. Spearman correlation analysis was performed between genes and immune cells, and the correlation between genes and different types of brain tissues was also analyzed using the WGCNA method.

Results

A total of 90 samples from 2 datasets were included, and 882 DEGs and 10 hub genes closely related to MS were extracted. Functional enrichment analysis suggested the role of immune response in MS. Besides, CIBERSORTX algorithm results showed that MS brain tissues contained a variety of infiltrating immune cells. Correlation analysis suggested that the hub genes were highly relevant to chronic active white matter lesions. Certain hub genes played a role in the activation of immune cells such as macrophages and natural killer cells.

Conclusions

Our study shall provide guidance for the further study of the genetic basis and immune infiltration mechanism of MS.

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.

Identification of Key Prognostic Genes of Triple Negative Breast Cancer by LASSO-Based Machine Learning and Bioinformatics Analysis

Improved insight into the molecular mechanisms of triple negative breast cancer (TNBC) is required to predict prognosis and develop a new therapeutic strategy for targeted genes. The aim of this study is to identify key genes which may affect the prognosis of TNBC patients by bioinformatic analysis. In our study, the RNA sequencing (RNA-seq) expression data of 116 breast cancer lacking ER, PR, and HER2 expression and 113 normal tissues were downloaded from The Cancer Genome Atlas (TCGA). We screened out 147 differentially co-expressed genes in TNBC compared to non-cancerous tissue samples by using weighted gene co-expression network analysis (WGCNA) and differential gene expression analysis. Then, Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses were constructed, revealing that 147 genes were mainly enriched in nuclear division, chromosomal region, ATPase activity, and cell cycle signaling. After using Cytoscape software for protein-protein interaction (PPI) network analysis and LASSO feature selection, a total of fifteen key genes were identified. Among them, BUB1 and CENPF were significantly correlated with the overall survival rate (OS) difference of TNBC patients (p value < 0.05). In addition, BUB1, CCNA2, and PACC1 showed significant poor disease-free survival (DFS) in TNBC patients (p value < 0.05), and may serve as candidate biomarkers in TNBC diagnosis. Thus, our results collectively suggest that BUB1, CCNA2, and PACC1 genes could play important roles in the progression of TNBC and provide attractive therapeutic targets.

The ability to classify patients based on gene-expression data varies by algorithm and performance metric

By classifying patients into subgroups, clinicians can provide more effective care than using a uniform approach for all patients. Such subgroups might include patients with a particular disease subtype, patients with a good (or poor) prognosis, or patients most (or least) likely to respond to a particular therapy. Transcriptomic measurements reflect the downstream effects of genomic and epigenomic variations. However, high-throughput technologies generate thousands of measurements per patient, and complex dependencies exist among genes, so it may be infeasible to classify patients using traditional statistical models. Machine-learning classification algorithms can help with this problem. However, hundreds of classification algorithms exist-and most support diverse hyperparameters-so it is difficult for researchers to know which are optimal for gene-expression biomarkers. We performed a benchmark comparison, applying 52 classification algorithms to 50 gene-expression datasets (143 class variables). We evaluated algorithms that represent diverse machine-learning methodologies and have been implemented in general-purpose, open-source, machine-learning libraries. When available, we combined clinical predictors with gene-expression data. Additionally, we evaluated the effects of performing hyperparameter optimization and feature selection using nested cross validation. Kernel- and ensemble-based algorithms consistently outperformed other types of classification algorithms; however, even the top-performing algorithms performed poorly in some cases. Hyperparameter optimization and feature selection typically improved predictive performance, and univariate feature-selection algorithms typically outperformed more sophisticated methods. Together, our findings illustrate that algorithm performance varies considerably when other factors are held constant and thus that algorithm selection is a critical step in biomarker studies.

A Platform Technology for Monitoring the Unfolded Protein Response

The unfolded protein response (UPR) is a complex signal transduction pathway that remodels gene expression in response to proteotoxic stress in the endoplasmic reticulum (ER) and is linked to the development of a range of diseases, including Alzheimer's disease, diabetes, and several types of cancer. UPR induction is typically monitored by measuring the expression level of UPR marker genes. Most tools for quantifying gene expression, including DNA microarrays and quantitative PCR with reverse transcription (RT-PCR), produce snapshots of the cell transcriptome, but are not ideal for measurements requiring temporal resolution of gene expression dynamics. Reporter assays for indirect detection of the UPR typically rely on extrachromosomal expression of reporters under the control of minimal or synthetic regulatory sequences that do not recapitulate the native chromosomal context of the UPR target genes. To address the need for tools to monitor chromosomal gene expression that recapitulate gene expression dynamics from the native chromosomal context and generate a readily detectable signal output, we developed a gene signal amplifier platform that links transcriptional and post-translational regulation of a fluorescent output to the expression of a chromosomal gene marker of the UPR. The platform is based on a genetic circuit that amplifies the output signal with high sensitivity and dynamic resolution and is implemented through chromosomal integration of the gene encoding the main control element of the genetic circuit to link its expression to that of the target gene, thereby generating a platform that can be easily adapted to monitor any UPR target through integration of the main control element at the appropriate chromosomal locus. By recapitulating the transcriptional and translational control mechanisms underlying the expression of UPR targets with high sensitivity, this platform provides a novel technology for monitoring the UPR with superior sensitivity and dynamic resolution.

Network Pharmacology Combined with Bioinformatics to Investigate the Mechanisms and Molecular Targets of Astragalus Radix-Panax notoginseng Herb Pair on Treating Diabetic Nephropathy

Background

Astragalus Radix (AR)-Panax notoginseng (PN), a classical herb pair, has shown significant effects in treating diabetic nephropathy (DN). However, the intrinsic mechanism of AR-PN treating DN is still unclear. This study aims to illustrate the mechanism and molecular targets of AR-PN treating DN based on network pharmacology combined with bioinformatics.

Materials and Methods

The Traditional Chinese Medicine Systems Pharmacology database was used to screen bioactive ingredients of AR-PN. Subsequently, putative targets of bioactive ingredients were predicted utilizing the DrugBank database and converted into genes on UniProtKB database. DN-related targets were retrieved via analyzing published microarray data (GSE30528) from the Gene Expression Omnibus database. Protein-protein interaction networks of AR-PN putative targets and DN-related targets were established to identify candidate targets using Cytoscape 3.8.0. GO and KEGG enrichment analyses of candidate targets were reflected using a plugin ClueGO of Cytoscape. Molecular docking was performed using AutoDock Vina software, and the results were visualized by Pymol software. The diagnostic capacity of hub genes was verified by receiver operating characteristic (ROC) curves.

Results

Twenty-two bioactive ingredients and 189 putative targets of AR-PN were obtained. Eight hundred and fifty differently expressed genes related to DN were screened. The PPI network showed that 115 candidate targets of AR-PN against DN were identified. GO and KEGG analyses revealed that candidate targets of AR-PN against DN were mainly involved in the apoptosis, oxidative stress, cell cycle, and inflammation response, regulating the PI3K-Akt signaling pathway, cell cycle, and MAPK signaling pathway. Moreover, MAPK1, AKT1, GSK3B, CDKN1A, TP53, RELA, MYC, GRB2, JUN, and EGFR were considered as the core potential therapeutic targets. Molecular docking demonstrated that these core targets had a great binding affinity with quercetin, kaempferol, isorhamnetin, and formononetin components. ROC curve analysis showed that AKT1, TP53, RELA, JUN, CDKN1A, and EGFR are effective in discriminating DN from controls.

Conclusions

AR-PN against DN may exert its renoprotective effects via various bioactive chemicals and the related pharmacological pathways, involving multiple molecular targets, which may be a promising herb pair treating DN. Nevertheless, these results should be further validated by experimental evidence.

Identification of Candidate Biomarkers for Idiopathic Thrombocytopenic Purpura by Bioinformatics Analysis of Microarray Data

Idiopathic Thrombocytopenic Purpura (ITP) is a multifactorial disease with decreased count of platelet that can lead to bruising and bleeding manifestations. This study was intended to identify critical genes associated with chronic ITP. The gene expression profile GSE46922 was downloaded from the Gene Expression Omnibus database to recognize Differentially Expressed Genes (DEGs) by R software. Gene ontology and pathway analyses were performed by DAVID. The biological network was constructed using the Cytoscape. Molecular Complex Detection (MCODE) was applied for detecting module analysis. Transcription factors were identified by the PANTHER classification system database and the gene regulatory network was constructed by Cytoscape. One hundred thirty-two DEGs were screened from comparison newly diagnosed ITP than chronic ITP. Biological process analysis revealed that the DEGs were enriched in terms of positive regulation of autophagy and prohibiting apoptosis in the chronic phase. KEGG pathway analysis showed that the DEGs were enriched in the ErbB signaling pathway, mRNA surveillance pathway, Estrogen signaling pathway, and Notch signaling pathway. Additionally, the biological network was established, and five modules were extracted from the network. ARRB1, VIM, SF1, BUB3, GRK5, and RHOG were detected as hub genes that also belonged to the modules. SF1 also was identified as a hub-TF gene. To sum up, microarray data analysis could perform a panel of genes that provides new clues for diagnosing chronic ITP.

Controlling the immobilization process of an optically enhanced protein microarray for highly reproducible immunoassay

By virtue of its high throughput multiplex detection capability, superior read-out sensitivity, and tiny analyte consumption, an optically enhanced protein microarray assay has been developed as a promising diagnostic tool for various applications, ranging from the field of pharmacology to diagnostics. However, so far, the development of an optically enhanced protein microarray (OEPM) toward widespread commercial availability is mainly hampered by insufficient detection reproducibility. Here, we develop an OEPM platform with an order of magnitude optical enhancement induced by the interference effect. High assay reproducibility of the OEPM is achieved by optimizing the protein immobilization schemes, linking to the surface energy of the substrate, surfactant-tuned wetting ability, and the washing and drying dynamics. As a result, smearing-free and uniform spot arrays with a coefficient of variation less than 7% can be achieved. Furthermore, we demonstrate the assay performance of the OEPM by detecting five biomarkers, showing an order of magnitude higher sensitivity, many-fold higher throughput, and 10 times less analyte consumption than those of the commercial enzyme-linked immunosorbent assay kits. Our results provide new insight for improving the reproducibility of OEPMs toward practical and commercial diagnostic assays.

Identifying the Mechanisms and Molecular Targets of Yizhiqingxin Formula on Alzheimer's Disease: Coupling Network Pharmacology with GEO Database

Background

Yizhiqingxin formula (YZQX) is a promising formula for the treatment of Alzheimer’s disease (AD) with significant clinical effects. Here, we coupled a network pharmacology approach with the Gene Expression Omnibus (GEO) database to illustrate comprehensive mechanisms and screen for molecular targets of YZQX for AD treatment.

Methods

First, active ingredients of YZQX were screened for the Traditional Chinese Medicine Systems Pharmacology (TCMSP) database with the absorption, distribution, metabolism, and excretion (ADME) parameters. Subsequently, putative targets of active ingredients were predicted using the DrugBank database. AD-related targets were retrieved by analyzing published microarray data (accession number GSE5281). Protein–protein interaction (PPI) networks of YZQX putative targets and AD-related targets were constructed visually and merged to identify candidate targets for YZQX against AD using Cytoscape 3.7.2 software. We performed gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis to further clarify the biological functions of the candidate targets. The gene-pathway network was established to filter for key target genes.

Results

Forty-three active ingredients were identified, and 193 putative target genes were predicted. Seven hundred and ten targets related to AD were screened with |log2 FC| > 1 and P < 0.05. Based on the PPI network, 110 target genes of YZQX against AD were identified. Moreover, 32 related pathways including the PI3K-Akt signaling pathway, MAPK signaling pathway, ubiquitin-mediated proteolysis, apoptosis and the NF-kappa B signaling pathway were significantly enriched. In the gene-pathway network, MAPK1, AKT1, TP53, MDM2, EGFR, RELA, SRC, GRB2, CUL1, and MYC targets are putative core genes for YZQX in AD treatment.

Conclusion

YZQX against AD may exert its neuroprotective effect via the PI3K-Akt signaling pathway, MAPK signaling pathway, and ubiquitin-mediated proteolysis. YZQX may be a promising drug that can be used in the treatment of AD.

Alterations in Patterns of Gene Expression and Perturbed Pathways in the Gut-Brain Axis Are Associated With Chemotherapy-Induced Nausea

CONTEXT

Despite current advances in antiemetic treatments, approximately 50% of oncology patients experience chemotherapy-induced nausea (CIN).

OBJECTIVES

The purpose of this study was to evaluate for differentially expressed genes and perturbed pathways associated with the gut-brain axis (GBA) across two independent samples of oncology patients who did and did not experience CIN.

METHODS

Oncology patients (n = 735) completed study questionnaires in the week before their second or third cycle of chemotherapy. CIN occurrence was assessed using the Memorial Symptom Assessment Scale. Gene expression analyses were performed in two independent samples using ribonucleic acid sequencing (Sample 1, n = 357) and microarray (Sample 2, n = 352) methodologies. Fisher's combined probability method was used to determine genes that were differentially expressed and pathways that were perturbed between the two nausea groups across both samples.

RESULTS

CIN was reported by 63.6% of the patients in Sample 1 and 48.9% of the patients in Sample 2. Across the two samples, 703 genes were differentially expressed, and 37 pathways were found to be perturbed between the two CIN groups. We identified nine perturbed pathways that are involved in mechanisms associated with alterations in the GBA (i.e., mucosal inflammation, disruption of gut microbiome).

CONCLUSION

Persistent CIN remains a significant clinical problem. Our study is the first to identify novel GBA-related pathways associated with the occurrence of CIN. Our findings warrant confirmation and suggest directions for future clinical studies to decrease CIN occurrence.

sigFeature: Novel Significant Feature Selection Method for Classification of Gene Expression Data Using Support Vector Machine and t Statistic

Biological data are accumulating at a faster rate, but interpreting them still remains a problem. Classifying biological data into distinct groups is the first step in understanding them. Data classification in response to a certain treatment is an extremely important aspect for differentially expressed genes in making present/absent calls. Many feature selection algorithms have been developed including the support vector machine recursive feature elimination procedure (SVM-RFE) and its variants. Support vector machine RFEs are greedy methods that attempt to find superlative possible combinations leading to binary classification, which may not be biologically significant. To overcome this limitation of SVM-RFE, we propose a novel feature selection algorithm, termed as “sigFeature” (https://bioconductor.org/packages/sigFeature/), based on SVM and t statistic to discover the differentially significant features along with good performance in classification. The “sigFeature” R package is centered around a function called “sigFeature,” which provides automatic selection of features for the binary classification. Using six publicly available microarray data sets (downloaded from Gene Expression Omnibus) with different biological attributes, we further compared the performance of “sigFeature” to three other feature selection algorithms. A small number of selected features (by “sigFeature”) also show higher classification accuracy. For further downstream evaluation of its biological signature, we conducted gene set enrichment analysis with the selected features (genes) from “sigFeature” and compared it with the outputs of other algorithms. We observed that “sigFeature” is able to predict the signature of four out of six microarray data sets accurately, whereas the other algorithms predict less data set signatures. Thus, “sigFeature” is considerably better than related algorithms in discovering differentially significant features from microarray data sets.

The Modulatory Properties of Astragalus membranaceus Treatment on Triple-Negative Breast Cancer: An Integrated Pharmacological Method

Background: Studies have shown that the natural products of Astragalus membranaceus (AM) can effectively interfere with a variety of cancers, but their mechanism of action on breast cancer remains unclear. Triple-negative breast cancer (TNBC) is associated with a severely poor prognosis due to its invasive phenotype and lack of biomarker-driven-targeted therapies. In this study, the potential mechanism of the target composition acting on TNBC was explored by integrated pharmacological models and in vitro experiments.

Materials and Methods: Based on the Gene Expression Omnibus (GEO) database and the relational database of Traditional Chinese Medicines (TCMs), the drug and target components were initially screened to construct a common network module, and multiattribute analysis was then used to characterize the network and obtain key drug-target information. Furthermore, network topology analysis was used to characterize the betweenness and closeness of key hubs in the network. Molecular docking was used to evaluate the affinity between compounds and targets and obtain accurate combination models. Finally, in vitro experiments verified the key component targets. The cell counting kit-8 (CCK-8) assay, invasion assay, and flow cytometric analysis were used to assess cell viability, invasiveness, and apoptosis, respectively, after Astragalus polysaccharides (APS) intervention. We also performed western blot analysis of key proteins to probe the mechanisms of correlated signaling pathways.

Results: We constructed “compound-target” (339 nodes and 695 edges) and “compound-disease” (414 nodes and 6458 edges) networks using interaction data. Topology analysis and molecular docking were used as secondary screens to identify key hubs of the network. Finally, the key component APS and biomarkers PIK3CG, AKT, and BCL2 were identified. The in vitro experimental results confirmed that APS can effectively inhibit TNBC cell activity, reduce invasion, promote apoptosis, and then counteract TNBC symptoms in a dose-dependent manner, most likely by inhibiting the PIK3CG/AKT/BCL2 pathway.

Conclusion: This study provides a rational approach to discovering compounds with a polypharmacology-based therapeutic value. Our data established that APS intervenes with TNBC cell invasion, proliferation, and apoptosis via the PIK3CG/AKT/BCL2 pathway and could thus offer a promising therapeutic strategy for TNBC.

The rise of the distributions: why non-normality is important for understanding the transcriptome and beyond

The application of statistics has been instrumental in clarifying our understanding of the genome. While insights have been derived for almost all levels of genome function, most importantly, statistics has had the greatest impact on improving our knowledge of transcriptional regulation. But the drive to extract the most meaningful inferences from big data can often force us to overlook the fundamental role that statistics plays, and specifically, the basic assumptions that we make about big data. Normality is a statistical property that is often swept up into an assumption that we may or may not be consciously aware of making. This review highlights the inherent value of non-normal distributions to big data analysis by discussing use cases of non-normality that focus on gene expression data. Collectively, these examples help to motivate the premise of why at this stage, now more than ever, non-normality is important for learning about gene regulation, transcriptomics, and more.

Curated compendium of human transcriptional biomarker data

One important use of genome-wide transcriptional profiles is to identify relationships between transcription levels and patient outcomes. These translational insights can guide the development of biomarkers for clinical application. Data from thousands of translational-biomarker studies have been deposited in public repositories, enabling reuse. However, data-reuse efforts require considerable time and expertise because transcriptional data are generated using heterogeneous profiling technologies, preprocessed using diverse normalization procedures, and annotated in non-standard ways. To address this problem, we curated 45 publicly available, translational-biomarker datasets from a variety of human diseases. To increase the data's utility, we reprocessed the raw expression data using a uniform computational pipeline, addressed quality-control problems, mapped the clinical annotations to a controlled vocabulary, and prepared consistently structured, analysis-ready data files. These data, along with scripts we used to prepare the data, are available in a public repository. We believe these data will be particularly useful to researchers seeking to perform benchmarking studies—for example, to compare and optimize machine-learning algorithms' ability to predict biomedical outcomes.

The mRNA, miRNA and lncRNA networks in hepatocellular carcinoma: An integrative transcriptomic analysis from Gene Expression Omnibus

Research advances and analysis in the non-protein coding part of the human genome have suggested that microRNAs (miRNAs) and long noncoding RNAs (lncRNAs) are associated with tumor initiation, growth and metastasis. Accumulating studies have demonstrated that a class of miRNAs and lncRNAs are dysregulated in hepatocellular carcinoma (HCC) and closely associated with tumorigenesis, diagnosis and prognosis. In the present study, integrative analysis of published data on multi-level Gene Expression Omnibus (GEO) and a bioinformatics computational approach were used to predict regulatory mechanism networks among differentially expressed mRNAs, miRNAs, and lncRNAs. Firstly, nine microarray expression data sets of mRNAs, miRNAs, and lncRNAs associated with HCC were collected from GEO datasets. Secondly, a total of 628 mRNAs, 15 miRNAs, and 49 lncRNAs were differentially expressed in this integrative analysis. Following this, mRNA, miRNA and lncRNA regulatory or co-expression networks were constructed. From the construction of the regulatory networks, five miRNAs and ten lncRNAs were identified as key differentially expressed noncoding RNAs associated with HCC progression. Finally, the regulatory effects of ten lncRNAs and miRNAs were validated. The study provides a novel insight into the understanding of the transcriptional regulation of HCC, and differentially expressed lncRNAs targeted and regulated by miRNAs were identified and validated in HCC specimens and cell lines.

Meta- and cross-species analyses of insulin resistance based on gene expression datasets in human white adipose tissues

Ample evidence indicates that insulin resistance (IR) is closely related to white adipose tissue (WAT), but the underlying mechanisms of IR pathogenesis are still unclear. Using 352 microarray datasets from seven independent studies, we identified a meta-signature which comprised of 1,413 genes. Our meta-signature was also enriched in overall WAT in in vitro and in vivo IR models. Only 12 core enrichment genes were consistently enriched across all IR models. Among the meta-signature, we identified a drug signature made up of 211 genes with expression levels that were co-regulated by thiazolidinediones and metformin using cross-species analysis. To confirm the clinical relevance of our drug signature, we found that the expression levels of 195 genes in the drug signature were significantly correlated with both homeostasis model assessment 2-IR score and body mass index. Finally, 18 genes from the drug signature were identified by protein-protein interaction network cluster. Four core enrichment genes were included in 18 genes and the expression levels of selected 8 genes were validated by quantitative PCR. These findings suggest that our signatures provide a robust set of genetic markers which can be used to provide a starting point for developing potential therapeutic targets in improving IR in WAT.

HNRNPK inhibits gastric cancer cell proliferation through p53/p21/CCND1 pathway

Gastric cancer (GC) is one of the most common human cancers. The molecular mechanisms underlying GC carcinogenesis and progression are still not well understood. In this study, we showed that heterogeneous nuclear ribonucleoprotein K (HNRNPK) was an effective prognostic marker for GC patients especially in early stage. Overexpression of HNRNPK can retard tumor cell proliferation and colony formation in vitro and inhibit tumor growth in vivo through p53/p21/CCND1 axis. Bioinformatics analyses indicated that HNRNPK associated genes were enriched in cell cycle and DNA replication process. Protein-protein interaction network showed that HNRNPK was physically interacted with p53, p21 and other cancer related genes. Besides, GSEA showed that HNRNPK expression was positively correlated with GAMMA radiation response and DNA repair, while negatively correlated with angiogenesis, TGF-β and Hedgehog pathway activation. Finally, several chemicals including Glycine that may repress GC progression through upregulating HNRNPK are suggested. Our study demonstrated that HNRNPK may play as a tumor suppressor in gastric cancer and could be a potential therapeutic target for GC.

Understanding Biology Through Bioinformatics

During the journey from the discovery of DNA to be the source of genetic information and elucidation of double-helical nature of DNA molecule to the assembly of human genome sequence and there after, bioinformatics has become an integral part of modern biology. Bioinformatics relies substantially on significant contributions made by scientists in various fields, including but not limited to, linguistics, biology, mathematics, computer science, and statistics. There is an ever increasing amount of data to elucidate toxic mechanisms and/or adverse effects of xenobiotics in the field of toxicogenomics. Annotation in combination with various bioinformatics analytical tools can play a crucial role in the understanding of genes and proteins, and can potentially help draw meaningful conclusions from various data sources. This article attempts to present a simple overview of bioinformatics, and an effort is made to discuss annotation.

Gene expression profiles of changes underlying different-sized human rotator cuff tendon tears

BACKGROUND

Progressive cellular and extracellular matrix (ECM) changes related to age and disease severity have been demonstrated in rotator cuff tendon tears. Larger rotator cuff tears demonstrate structural abnormalities that potentially adversely influence healing potential. This study aimed to gain greater insight into the relationship of pathologic changes to tear size by analyzing gene expression profiles from normal rotator cuff tendons, small rotator cuff tears, and large rotator cuff tears.

METHODS

We analyzed gene expression profiles of 28 human rotator cuff tendons using microarrays representing the entire genome; 11 large and 5 small torn rotator cuff tendon specimens were obtained intraoperatively from tear edges, which we compared with 12 age-matched normal controls. We performed real-time polymerase chain reaction and immunohistochemistry for validation.

RESULTS

Torn rotator cuff tendons demonstrated upregulation of a number of key genes, such as matrix metalloproteinase 3, 10, 12, 13, 15, 21, and 25; a disintegrin and metalloproteinase (ADAM) 12, 15, and 22; and aggrecan. Amyloid was downregulated in all tears. Small tears displayed upregulation of bone morphogenetic protein 5. Chemokines and cytokines that may play a role in chemotaxis were altered; interleukins 3, 10, 13, and 15 were upregulated in tears, whereas interleukins 1, 8, 11, 18, and 27 were downregulated.

CONCLUSIONS

The gene expression profiles of normal controls and small and large rotator cuff tear groups differ significantly. Extracellular matrix remodeling genes were found to contribute to rotator cuff tear pathogenesis. Rotator cuff tears displayed upregulation of a number of matrix metalloproteinase (3, 10, 12, 13, 15, 21, and 25), a disintegrin and metalloproteinase (ADAM 12, 15, and 22) genes, and downregulation of some interleukins (1, 8, and 27), which play important roles in chemotaxis. These gene products may potentially have a role as biomarkers of failure of healing or therapeutic targets to improve tendon healing.

Perspectives on:In Vitro Evaluation of Biomedical Polymers

Recently polymeric materials have gained tremendous attention in a wide variety of applications spanning from electronics to environmental and biomedical fields. In this paper, current in vitro methods for polymers biocompatibility assessment are reviewed in combination with new concepts and techniques that appear promising for the development and improvement of in vitro methods with the purpose of reducing animal experimentation. The utilization of medical devices, for example, has always been subordinate to the assessment of their biocompatibility. This aspect, as well as the methods for evaluating biocompatibility have changed over the years as a result of new developments in cell biology that have revolutionized in vitro techniques for assaying polymeric materials for bioapplications.

Principal components analysis and the reported low intrinsic dimensionality of gene expression microarray data

Principal components analysis (PCA) is a common unsupervised method for the analysis of gene expression microarray data, providing information on the overall structure of the analyzed dataset. In the recent years, it has been applied to very large datasets involving many different tissues and cell types, in order to create a low dimensional global map of human gene expression. Here, we reevaluate this approach and show that the linear intrinsic dimensionality of this global map is higher than previously reported. Furthermore, we analyze in which cases PCA fails to detect biologically relevant information and point the reader to methods that overcome these limitations. Our results refine the current understanding of the overall structure of gene expression spaces and show that PCA critically depends on the effect size of the biological signal as well as on the fraction of samples containing this signal.

The Influence of the Global Gene Expression Shift on Downstream Analyses

The assumption that total abundance of RNAs in a cell is roughly the same in different cells is underlying most studies based on gene expression analyses. But experiments have shown that changes in the expression of some master regulators such as c-MYC can cause global shift in the expression of almost all genes in some cell types like cancers. Such shift will violate this assumption and can cause wrong or biased conclusions for standard data analysis practices, such as detection of differentially expressed (DE) genes and molecular classification of tumors based on gene expression. Most existing gene expression data were generated without considering this possibility, and are therefore at the risk of having produced unreliable results if such global shift effect exists in the data. To evaluate this risk, we conducted a systematic study on the possible influence of the global gene expression shift effect on differential expression analysis and on molecular classification analysis. We collected data with known global shift effect and also generated data to simulate different situations of the effect based on a wide collection of real gene expression data, and conducted comparative studies on representative existing methods. We observed that some DE analysis methods are more tolerant to the global shift while others are very sensitive to it. Classification accuracy is not sensitive to the shift and actually can benefit from it, but genes selected for the classification can be greatly affected.

Gene Expression Profiling of Evening Fatigue in Women Undergoing Chemotherapy for Breast Cancer

Moderate-to-severe fatigue occurs in up to 94% of oncology patients undergoing active treatment. Current interventions for fatigue are not efficacious. A major impediment to the development of effective treatments is a lack of understanding of the fundamental mechanisms underlying fatigue. In the current study, differences in phenotypic characteristics and gene expression profiles were evaluated in a sample of breast cancer patients undergoing chemotherapy (CTX) who reported low (n = 19) and high (n = 25) levels of evening fatigue. Compared to the low group, patients in the high evening fatigue group reported lower functional status scores, higher comorbidity scores, and fewer prior cancer treatments. One gene was identified as upregulated and 11 as downregulated in the high evening fatigue group. Gene set analysis found 24 downregulated and 94 simultaneously up- and downregulated pathways between the two fatigue groups. Transcript origin analysis found that differential expression (DE) originated primarily from monocytes and dendritic cell types. Query of public data sources found 18 gene expression experiments with similar DE profiles. Our analyses revealed that inflammation, neurotransmitter regulation, and energy metabolism are likely mechanisms associated with evening fatigue severity; that CTX may contribute to fatigue seen in oncology patients; and that the patterns of gene expression may be shared with other models of fatigue (e.g., physical exercise and pathogen-induced sickness behavior). These results suggest that the mechanisms that underlie fatigue in oncology patients are multifactorial.

Irreproducibility in Preclinical Biomedical Research: Perceptions, Uncertainties, and Knowledge Gaps

Concerns regarding the reliability of biomedical research outcomes were precipitated by two independent reports from the pharmaceutical industry that documented a lack of reproducibility in preclinical research in the areas of oncology, endocrinology, and hematology. Given their potential impact on public health, these concerns have been extensively covered in the media. Assessing the magnitude and scope of irreproducibility is limited by the anecdotal nature of the initial reports and a lack of quantitative data on specific failures to reproduce published research. Nevertheless, remediation activities have focused on needed enhancements in transparency and consistency in the reporting of experimental methodologies and results. While such initiatives can effectively bridge knowledge gaps and facilitate best practices across established and emerging research disciplines and therapeutic areas, concerns remain on how these improve on the historical process of independent replication in validating research findings and their potential to inhibit scientific innovation.

Chemoselective Strategies to Peptide and Protein Bioprobes Immobilization on Microarray Surfaces

Ordered and reproducible bioprobe immobilization onto sensor surfaces is a critical step in the development of reliable analytical devices. A growing awareness of the impact of the immobilization scheme on the consistency of the generated data is driving the demand for chemoselective approaches to immobilize biofunctional ligands, such as peptides, in a predetermined and uniform fashion. Herein, the most intriguing strategies to selective and oriented peptide immobilization are described and discussed. The aim of the current work is to provide the reader a general picture on recent advances made in this field, highlighting the potential associated with each chemoselective strategy. Case studies are described to provide illustrative examples, and cross-references to more topic-focused and exhaustive reviews are proposed throughout the text.

HNRNPC as a candidate biomarker for chemoresistance in gastric cancer

Chemoresistance is a major cause of treatment failure and high mortality in advanced gastric cancer (AGC). Currently, the mechanism of chemoresistance remains unclear, and there is no biomarker to accurately predict the efficacy of chemotherapy. In the present study, we established human gastric cancer (GC) cell lines resistant to 5-fluorouracil (5FU), paclitaxel (TA), or cisplatin (DDP) by gradient drug treatment and generated a novel monoclonal antibody 5B2 targeting heterogeneous nuclear ribonucleoproteins C1/C2 (HNRNPC) overexpressed in chemoresistant GC cells. Overexpressing HNRNPC in GC cells promoted chemoresistance, and knockdown of HNRNPC by small interfering RNA (siRNA) reversed chemoresistance. By utilizing available datasets, we demonstrated that high level of HNRNPC transcript indicated poor overall survival (OS) and free of progression (FP). HNRNPC expression was negatively correlated with OS of GC patients treated with 5FU-based drugs and with time to progression (TTP) of GC patients treated with CF regimen. These data suggest the potential usefulness of HNRNPC as a prognostic and therapeutic marker of GC.

A Bayesian approach for inducing sparsity in generalized linear models with multi-category response

BACKGROUND

The dimension and complexity of high-throughput gene expression data create many challenges for downstream analysis. Several approaches exist to reduce the number of variables with respect to small sample sizes. In this study, we utilized the Generalized Double Pareto (GDP) prior to induce sparsity in a Bayesian Generalized Linear Model (GLM) setting. The approach was evaluated using a publicly available microarray dataset containing 99 samples corresponding to four different prostate cancer subtypes.

RESULTS

A hierarchical Sparse Bayesian GLM using GDP prior (SBGG) was developed to take into account the progressive nature of the response variable. We obtained an average overall classification accuracy between 82.5% and 94%, which was higher than Support Vector Machine, Random Forest or a Sparse Bayesian GLM using double exponential priors. Additionally, SBGG outperforms the other 3 methods in correctly identifying pre-metastatic stages of cancer progression, which can prove extremely valuable for therapeutic and diagnostic purposes. Importantly, using Geneset Cohesion Analysis Tool, we found that the top 100 genes produced by SBGG had an average functional cohesion p-value of 2.0E-4 compared to 0.007 to 0.131 produced by the other methods.

CONCLUSIONS

Using GDP in a Bayesian GLM model applied to cancer progression data results in better subclass prediction. In particular, the method identifies pre-metastatic stages of prostate cancer with substantially better accuracy and produces more functionally relevant gene sets.

Silica Microcapsules for Long-Term, Robust, and Reliable Room Temperature RNA Preservation

As a consequence of the latest revolutionary discoveries on its functions, RNA is certainly the hottest topic at the moment, being an exceptional tool in biology as well as in medicine. For the various applications, a proper RNA storage is required to prevent the degradation of this extremely unstable molecule. Here a novel freezing-free RNA storage strategy is presented, based on its encapsulation in silica spheres. The silica microcapsules protect the RNA by providing a water-free environment. In this way RNA can be safely stored for prolonged periods of time at ambient and elevated temperatures, maintaining its original integrity, as proved by gel-electrophoresis, capillary electrophoresis, and real-time reverse transcription-polymerase chain reaction (RT-qPCR). The RNA degradation rate at 65 °C in silica microcapsules is approximately ten times smaller in comparison to dry RNA samples or to samples stored in RNAstable matrix, a commercially available product. Moreover, RNA half-life at 65 °C is nearly identical to that of DNA within the silica microcapsules. Samples intended for use in gene expression are compatible with further analysis (RT-qPCR, Sanger sequencing). The novel storage technology permits to safely handle, store, and transport RNA samples, avoiding the expensive shipments and the problems of space presented by freezing-based strategies.

The integrated disease network

The growing body of transcriptomic, proteomic, metabolomic and genomic data generated from disease states provides a great opportunity to improve our current understanding of the molecular mechanisms driving diseases and shared between diseases. The use of both clinical and molecular phenotypes will lead to better disease understanding and classification. In this study, we set out to gain novel insights into diseases and their relationships by utilising knowledge gained from system-level molecular data. We integrated different types of biological data including genome-wide association studies data, disease-chemical associations, biological pathways and Gene Ontology annotations into an Integrated Disease Network (IDN), a heterogeneous network where nodes are bio-entities and edges between nodes represent their associations. We also introduced a novel disease similarity measure to infer disease-disease associations from the IDN. Our predicted associations were systemically evaluated against the Medical Subject Heading classification and a statistical measure of disease co-occurrence in PubMed. The strong correlation between our predictions and co-occurrence associations indicated the ability of our approach to recover known disease associations. Furthermore, we presented a case study of Crohn's disease. We demonstrated that our approach not only identified well-established connections between Crohn's disease and other diseases, but also revealed new, interesting connections consistent with emerging literature. Our approach also enabled ready access to the knowledge supporting these new connections, making this a powerful approach for exploring connections between diseases.

Technical data of the transcriptomic analysis performed on tsetse fly symbionts, Sodalis glossinidius and Wigglesworthia glossinidia, harbored, respectively by non-infected, Trypanosoma brucei gambiense infected and self-cured Glossina palpalis gambiensis tsetse flies

Microarray is a powerful and cheap method to identify and quantify gene expression in particular in a mix of total RNA extracted from biological samples such as the tsetse fly gut, including several organisms (here, the fly tissue and the intestinal microorganisms). Besides, biostatistics and bioinformatics allow comparing the transcriptomes from samples collected from differently treated flies, and thus to identify and quantify differential expressed genes. Here, we describe in details a whole microarray transcriptome dataset produced from tsetse flies symbionts, Sodalis glossinidius and Wigglesworthia glossinidia. The tsetse fly midguts were sampled at key steps of tsetse fly infection by trypanosomes, 3-day and 10-day sampling times to target differentially expressed genes involved, respectively, in early events associated with trypanosome entry into the midgut and with the establishment of infection; 20 days to target the genes involved in events occurring later in the infection process. We describe in detail the methodology applied for analyzing the microarray data including differential expression as well as functional annotation of the identified symbiont genes. Both the microarray data and design are available at http://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE48360;http://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE48361;http://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE55931.

Correcting positional correlations in Affymetrix® genome chips

We report and model a previously undescribed systematic error causing spurious excess correlations that depend on the distance between probes on Affymetrix® microarrays. The phenomenon affects pairs of features with large chip separations, up to over 100 probes apart. The effect may have a significant impact on analysis of correlations in large collections of expression data, where the systematic experimental errors are repeated in many data sets. Examples of such studies include analysis of functions and interactions in groups of genes, as well as global properties of genomes. We find that the average correlations between probes on Affymetrix microarrays are larger for smaller chip distances, which points out to a previously undescribed positional artifact. The magnitude of the artifact depends on the design of the chip, and we find it to be especially high for the yeast S98 microarray, where spurious excess correlations reach 0.1 at a distance of 50 probes. We have designed an algorithm to correct this bias and provide new data sets with the corrected expression values. This algorithm was successfully implemented to remove the positional artifact from the S98 chip data while preserving the integrity of the data.