Evaluation of gene expression measurements from commercial microarray platforms

Dual Transcriptomics Reveals Interspecific Interactions between the Mycoparasite Calcarisporium cordycipiticola and Its Host Cordyceps militaris

Calcarisporium cordycipiticola is a mycoparasite of the edible fungus Cordyceps militaris, and mycoparasitism causes devastating diseases of mushrooms. In this study, dual-transcriptomic analysis was performed to reveal interspecific interactions between the mycoparasite C. cordycipiticola and its host C. militaris. At 4 and 8 days postinfection (dpi), 2,959 and 2,077 differentially expressed genes (DEGs) of C. cordycipiticola and 914 and 1,548 DEGs of C. militaris were identified compared with the mycelial stage, respectively, indicating that C. cordycipiticola responded more quickly than C. militaris. Lectins of the pathogen may play a role in the recognition of fungal prey. Both Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analyses showed that primary metabolism was vigorous for the pathogen to colonize the host and that the pathogen's attack substantially altered C. militaris' primary metabolism. C. cordycipiticola upregulated some carbohydrate-active enzyme (CAZyme) genes, including CBM18, GH18, GH16, and GH76, for degrading the host cell wall and defending against host immunity. C. militaris produced excessive reactive oxygen species (ROS) to respond to the infection. The GO term "heme binding" was the only shared term enriched at both stages at 4 and 8 dpi, indicating that iron was important for both the pathogen and the host. The uptake of iron by pathogens through multiple pathways promoted colonization and removed high ROS levels produced by the host. The transcription levels of Cmhsp78, Cmhsp70, and Cmhyd1 in C. militaris responded quickly, and these genes have potential as candidates for the breeding of resistant varieties. This study provides clues for understanding the interactions between a mycoparasite and its mushroom host and will be helpful for the breeding of resistant varieties and disease prevention and control for this edible fungus. IMPORTANCE White mildew disease caused by Calcarisporium cordycipiticola is devastating for the fruiting body cultivation of Cordyceps militaris, a popular and highly valued edible fungus. Here, the pathogenic mechanisms of C. cordycipiticola, the responses of C. militaris to the infection, and the interaction of these two phylogenetically close species were revealed by time course dual-transcriptome profiles. In general, the host C. militaris responds more slowly than the pathogen C. cordycipiticola. For the first time, we found that iron was important for both the mycoparasite and the host. C. cordycipiticola takes up iron by multiple pathways to promote colonization and remove high ROS levels produced by the host. The rapidly responding genes Cmhsp70, Cmhsp78, and Cmhyd1 in C. militaris may have the potential as candidate genes for the breeding of resistant varieties. This study expands our understanding of the mycoparasitic interactions of two species from sister families and will be helpful for the breeding of and disease prevention and control in mushrooms.

Transcriptome Analysis to Understand Salt Stress Regulation Mechanism of Chromohalobacter salexigens ANJ207

Soil salinity is one of the major global issues affecting soil quality and agricultural productivity. The plant growth-promoting halophilic bacteria that can thrive in regions of high salt (NaCl) concentration have the ability to promote the growth of plants in salty environments. In this study, attempts have been made to understand the salinity adaptation of plant growth-promoting moderately halophilic bacteria Chromohalobacter salexigens ANJ207 at the genetic level through transcriptome analysis. In order to identify the stress-responsive genes, the transcriptome sequencing of C. salexigens ANJ207 under different salt concentrations was carried out. Among the 8,936 transcripts obtained, 93 were upregulated while 1,149 were downregulated when the NaCl concentration was increased from 5 to 10%. At 10% NaCl concentration, genes coding for lactate dehydrogenase, catalase, and OsmC-like protein were upregulated. On the other hand, when salinity was increased from 10 to 25%, 1,954 genes were upregulated, while 1,287 were downregulated. At 25% NaCl, genes coding for PNPase, potassium transporter, aconitase, excinuclease subunit ABC, and transposase were found to be upregulated. The quantitative real-time PCR analysis showed an increase in the transcript of genes related to the biosynthesis of glycine betaine coline genes (gbcA, gbcB, and L-pro) and in the transcript of genes related to the uptake of glycine betaine (OpuAC, OpuAA, and OpuAB). The transcription of the genes involved in the biosynthesis of L-hydroxyproline (proD and proS) and one stress response proteolysis gene for periplasmic membrane stress sensing (serP) were also found to be increased. The presence of genes for various compatible solutes and their increase in expression at the high salt concentration indicated that a coordinated contribution by various compatible solutes might be responsible for salinity adaptation in ANJ207. The investigation provides new insights into the functional roles of various genes involved in salt stress tolerance and oxidative stress tolerance produced by high salt concentration in ANJ207 and further support the notion regarding the utilization of bacterium and their gene(s) in ameliorating salinity problem in agriculture.

Ensemble of nucleic acid absolute quantitation modules for copy number variation detection and RNA profiling

Current gold standard for absolute quantitation of a specific DNA sequence is droplet digital PCR (ddPCR), which has been applied to copy number variation (CNV) detection. However, the number of quantitation modules in ddPCR is limited by fluorescence channels, which thus limits the CNV sensitivity due to sampling error following Poisson distribution. Here we develop a PCR-based molecular barcoding NGS approach, quantitative amplicon sequencing (QASeq), for accurate absolute quantitation scalable to over 200 quantitation modules. By attaching barcodes to individual target molecules with high efficiency, 2-plex QASeq exhibits higher and more consistent conversion yield than ddPCR in absolute molecule count quantitation. Multiplexed QASeq improves CNV sensitivity allowing confident distinguishment of 2.05 ploidy from normal 2.00 ploidy. We apply multiplexed QASeq to serial longitudinal plasma cfDNA samples from patients with metastatic ERBB2+ (HER2+ ) breast cancer seeking association with tumor progression. We further show an RNA QASeq panel for targeted expression profiling.

Gene expression analysis of human induced pluripotent stem cells cryopreserved by vitrification using StemCell Keep

In recent years, regenerative medicine research using human somatic and induced pluripotent stem cells has advanced considerably, promoting clinical applications. However, it is essential that these cells are cryopreserved safely and effectively. Most cryopreservation solution agents contain dimethyl sulfoxide (DMSO), which exhibits strong toxicity and can potentially promote cell differentiation. Hence, it is important to explore substitutes for DMSO in cryoprotectant solutions. One such alternative is StemCell Keep (SCK), a DMSO-free solution that has been reported to effectively cryopreserve human induced pluripotent stem cells (hiPS cells). To clarify the effect of cryopreservation agents on cells, DNA microarray analysis is useful, as it can identify a large number of gene expression differences in cryopreserved cells, as well as functional increases in gene groups. In this study, we performed gene expression analysis of SCK-cryopreserved hiPS cells using a DNA microarray gene chip. The hiPS cells vitrified with SCK or DMSO-based vitrification solutions were thawed and cultured on Matrigel under feeder-free conditions, and RNA was extracted for DNA microarray analysis. Genes obtained from DNA microarray data were classified by the keywords of Gene Ontology Biological Process Term, and their relationships were analyzed using DAVID or the GeneMANIA database. SCK-cryopreserved hiPS cells expressed several anti-apoptotic genes, as well as genes related to cell adhesion or proliferation at levels that were nearly equivalent to those of non-frozen hiPS cells. Gene enrichment analysis with selected genes of SCK-cryopreserved hiPS cells whose expression differences were superior to those of DAP-cryopreserved showed strong interactions of negative regulation of apoptotic process, cell adhesion and positive regulation of cell proliferation in DAVID analysis. We demonstrated that SCK successfully maintained the key functions of hiPS cells, including anti-apoptosis, cell adhesion, and cell proliferation, during cryopreservation.

Knowledge extraction from pointer movements and its application to detect uncertainty

Pointer-tracking methods can capture a real-time trace at high spatio-temporal resolution of users' pointer interactions with a graphical user interface. This trace is potentially valuable for research on human-computer interaction (HCI) and for investigating perceptual, cognitive and affective processes during HCI. However, little research has reported spatio-temporal pointer features for the purpose of tracking pointer movements in on-line surveys. In two studies, we identified a set of pointer features and movement patterns and showed that these can be easily distinguished. In a third study, we explored the feasibility of using patterns of interactive pointer movements, or micro-behaviours, to detect response uncertainty. Using logistic regression and k-fold cross-validation in model training and testing, the uncertainty model achieved an estimated performance accuracy of 81%. These findings suggest that micro-behaviours provide a promising approach toward developing a better understanding of the relationship between the dynamics of pointer movements and underlying perceptual, cognitive and affective psychological mechanisms.

Transcriptomic biomarkers for predicting response to neoadjuvant treatment in oesophageal cancer

Oesophageal cancer is a devastating disease with poor outcomes and is the sixth leading cause of cancer death worldwide. In the setting of resectable disease, there is clear evidence that neoadjuvant chemotherapy and chemoradiotherapy result in improved survival. Disappointingly, only 15%-30% of patients obtain a histopathological response to neoadjuvant therapy, often at the expense of significant toxicity. There are no predictive biomarkers in routine clinical use in this setting and the ability to stratify patients for treatment could dramatically improve outcomes. In this review, we aim to outline current progress in evaluating predictive transcriptomic biomarkers for neoadjuvant therapy in oesophageal cancer and discuss the challenges facing biomarker development in this setting. We place these issues in the wider context of recommendations for biomarker development and reporting. The majority of studies focus on messenger RNA (mRNA) and microRNA (miRNA) biomarkers. These studies report a range of different genes involved in a wide variety of pathways and biological processes, and this is explained to a large extent by the different platforms and analysis methods used. Many studies are also vastly underpowered so are not suitable for identifying a candidate biomarker. Multiple molecular subtypes of oesophageal cancer have been proposed, although little is known about how these relate to clinical outcomes. We anticipate that the accumulating wealth of genomic and transcriptomic data and clinical trial collaborations in the coming years will provide unique opportunities to stratify patients in this poor-prognosis disease and recommend that future biomarker development incorporates well-designed retrospective and prospective analyses.

NBIA: a network-based integrative analysis framework - applied to pathway analysis

With the explosion of high-throughput data, effective integrative analyses are needed to decipher the knowledge accumulated in biological databases. Existing meta-analysis approaches in systems biology often focus on hypothesis testing and neglect real expression changes, i.e. effect sizes, across independent studies. In addition, most integrative tools completely ignore the topological order of gene regulatory networks that hold key characteristics in understanding biological processes. Here we introduce a novel meta-analysis framework, Network-Based Integrative Analysis (NBIA), that transforms the challenging meta-analysis problem into a set of standard pathway analysis problems that have been solved efficiently. NBIA utilizes techniques from classical and modern meta-analysis, as well as a network-based analysis, in order to identify patterns of genes and networks that are consistently impacted across multiple studies. We assess the performance of NBIA by comparing it with nine meta-analysis approaches: Impact Analysis, GSA, and GSEA combined with classical meta-analysis methods (Fisher’s and the additive method), plus the three MetaPath approaches that employ multiple datasets. The 10 approaches have been tested on 1,737 samples from 27 expression datasets related to Alzheimer’s disease, acute myeloid leukemia (AML), and influenza. For all of the three diseases, NBIA consistently identifies biological pathways relevant to the underlying diseases while the other 9 methods fail to capture the key phenomena. The identified AML signature is also validated on a completely independent cohort of 167 AML patients. In this independent cohort, the proposed signature identifies two groups of patients that have significantly different survival profiles (Cox p-value 2 × 10⁻⁶). The NBIA framework will be included in the next release of BLMA Bioconductor package (http://bioconductor.org/packages/release/bioc/html/BLMA.html).

Enhancing reproducibility of gene expression analysis with known protein functional relationships: The concept of well-associated protein

Identification of differentially expressed genes (DEGs) is well recognized to be variable across independent replications of genome-wide transcriptional studies. These are often employed to characterize disease state early in the process of discovery and prioritize novel targets aimed at addressing unmet medical need. Increasing reproducibility of biological findings from these studies could potentially positively impact the success rate of new clinical interventions. This work demonstrates that statistically sound combination of gene expression data with prior knowledge about biology in the form of large protein interaction networks can yield quantitatively more reproducible observations from studies characterizing human disease. The novel concept of Well-Associated Proteins (WAPs) introduced herein-gene products significantly associated on protein interaction networks with the differences in transcript levels between control and disease-does not require choosing a differential expression threshold and can be computed efficiently enough to enable false discovery rate estimation via permutation. Reproducibility of WAPs is shown to be on average superior to that of DEGs under easily-quantifiable conditions suggesting that they can yield a significantly more robust description of disease. Enhanced reproducibility of WAPs versus DEGs is first demonstrated with four independent data sets focused on systemic sclerosis. This finding is then validated over thousands of pairs of data sets obtained by random partitions of large studies in several other diseases. Conditions that individual data sets must satisfy to yield robust WAP scores are examined. Reproducible identification of WAPs can potentially benefit drug target selection and precision medicine studies.

Label-Free Detection of Zeptomol miRNA via Peptide Nucleic Acid Hybridization Using Novel Cyclic Voltammetry Method

To harness the applicability of microribonucleic acid (miRNA) as a cancer biomarker, the detection sensitivity of serum miRNA needs to be improved. This study evaluated the detection sensitivity of miRNA hybridization using cyclic voltammograms (CVs) and microelectrode array chips modified with peptide nucleic acid (PNA) probes and 6-hydroxy-1-hexanethiol. We investigated the PNA probe modification pattern on array chips using fluorescently labeled cDNA. The pattern was not uniformly spread over the working electrode (WE) and had a one-dimensional swirl-like pattern. Accordingly, we established a new ion-channel sensor model wherein the WE is negatively biased through the conductive π–π stacks of the PNA/DNA duplexes. This paper discusses the mechanism underlying the voltage shift in the CV curves based on the electric double-layer capacitance. Additionally, the novel hybridization evaluation parameter ΔE is introduced. Compared to conventional evaluation using oxidation current changes, ΔE was more sensitive. Using ΔE and a new hybridization system for ultrasmall amounts of aqueous solutions (as low as 35 pL), 140 zeptomol label-free miRNA were detected without polymerase chain reaction (PCR) amplification at an adequate sensitivity. Herein, the differences in the target molar amount and molar concentration are elucidated from the viewpoint of hybridization sensitivity.

Prognostic and Predictive Molecular Biomarkers for Colorectal Cancer: Updates and Challenges

Colorectal cancer (CRC) is a leading cause of death among cancer patients. This heterogeneous disease is characterized by alterations in multiple molecular pathways throughout its development. Mutations in RAS, along with the mismatch repair gene deficiency, are currently routinely tested in clinics. Such biomarkers provide information for patient risk stratification and for the choice of the best treatment options. Nevertheless, reliable and powerful prognostic markers that can identify “high-risk” CRC patients, who might benefit from adjuvant chemotherapy, in early stages, are currently missing. To bridge this gap, genomic information has increasingly gained interest as a potential method for determining the risk of recurrence. However, due to several limitations of gene-based signatures, these have not yet been clinically implemented. In this review, we describe the different molecular markers in clinical use for CRC, highlight new markers that might become indispensable over the next years, discuss recently developed gene expression-based tests and highlight the challenges in biomarker research.

Are BALB/c Mice Relevant Models for Understanding Sex-Related Differences in Gene Expression in the Human Meibomian Gland?

BACKGROUND

A compelling feature of dry eye disease is that it occurs predominantly in women. We hypothesize that this female prevalence is linked to sex-related differences in the meibomian gland (MG). This gland plays a critical role in maintaining the tear film, and its dysfunction is a major cause of dry eye disease. To understand the factors that underlie MG sexual dimorphism and promote dry eye in women, we seek to identify an optimal model for the human MG. Our goal was to determine whether a murine MG is such a model. Toward that end, we examined whether sex differences in MG gene expression are the same in BALB/c mice and humans.

METHODS

Eyelid tissues were collected from humans (n = 5-7/sex) and BALB/c mice (n = 9/sex). MGs were isolated and processed for the evaluation of gene expression by using microarrays and bioinformatics software.

RESULTS

Our analysis of the 500 most highly expressed genes from human and mouse MGs showed that only 24.4% were the same. Our comparison of 100 genes with the greatest sex-associated differences in human and mouse MGs demonstrated that none were the same. Sex also exerted a significant impact on numerous ontologies, Kyoto Encyclopedia of Genes and Genomes pathways, and chromosomes, but these effects were primarily species-specific.

CONCLUSIONS

Our results indicate that BALB/c mice are not optimal models for understanding sex-related differences in gene expression of the human MG.

Increased expression of TET3 predicts unfavorable prognosis in patients with ovarian cancer-a bioinformatics integrative analysis

Ovarian carcinoma is a lethal gynecological malignancy. Women with ovarian cancer (OC) are highly recurrent and typically diagnosed at late stage. Ten-eleven translocation protein 3 (TET3) belongs to the family of ten-eleven translocations (TETs) which induce DNA demethylation and gene regulation in epigenetic level by converting 5-methylcytosine (5mC) to 5-hydroxymethylcytosine (5hmC). Previous studies indicated that TET3 is overexpressed in ovarian cancer tissues. However, the clinic-pathological functions and prognostic values of TET3 remain unclear. Here we performed an integrative study to identify the role of TET3 by bioinformatics analysis. The TET3 expression in ovarian cancer was assessed with Oncomine database, and validated with TCGA and GTEx database. The correlation of TET3 gene alteration and clinic-pathological functions was addressed by integrative analysis of GEO datasets. Then we showed mainly TET3 gain and diploid but less deletion in ovarian cancer by copy number alteration (CNA) or mutation analysis with cBioPortal. Furthermore, by using Kaplan-Meier plotter (K-M plotter), we evaluated that high TET3 level was associated with poor survival in ovarian cancer patients, which was validated with analysis by PrognoScan database and gene differential analyses with TCGA and GTEx. This is the first study demonstrated that elevated expression of TET3 is associated with poor clinic-pathological functions, poor prognosis, wherein TET3, which presents epigenetic changes or methylation changes, might be served as a diagnostic marker or therapeutic target for ovarian cancer.

Testosterone Influence on Gene Expression in Lacrimal Glands of Mouse Models of Sjögren Syndrome

Purpose

Sjögren syndrome is an autoimmune disorder that occurs almost exclusively in women and is associated with extensive inflammation in lacrimal tissue, an immune-mediated destruction and/or dysfunction of glandular epithelial cells, and a significant decrease in aqueous tear secretion. We discovered that androgens suppress the inflammation in, and enhance the function of, lacrimal glands in female mouse models (e.g., MRL/MpJ-Tnfrsf6^lpr [MRL/lpr]) of Sjögren syndrome. In contrast, others have reported that androgens induce an anomalous immunopathology in lacrimal glands of nonobese diabetic/LtJ (NOD) mice. We tested our hypothesis that these hormone actions reflect unique, strain- and tissue-specific effects, which involve significant changes in the expression of immune-related glandular genes.

Methods

Lacrimal glands were obtained from age-matched, adult, female MRL/lpr and NOD mice after treatment with vehicle or testosterone for up to 3 weeks. Tissues were processed for analysis of differentially expressed mRNAs using CodeLink Bioarrays and Affymetrix GeneChips. Data were analyzed with bioinformatics and statistical software.

Results

Testosterone significantly influenced the expression of numerous immune-related genes, ontologies, and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways in lacrimal glands of MRL/lpr and NOD mice. The nature of this hormone-induced immune response was dependent upon the autoimmune strain, and was not duplicated within lacrimal tissues of nonautoimmune BALB/c mice. The majority of immune-response genes regulated by testosterone were of the inflammatory type.

Conclusions

Our findings support our hypothesis and indicate a major role for the lacrimal gland microenvironment in mediating androgen effects on immune gene expression.

Identifying significantly impacted pathways: a comprehensive review and assessment

BACKGROUND

Many high-throughput experiments compare two phenotypes such as disease vs. healthy, with the goal of understanding the underlying biological phenomena characterizing the given phenotype. Because of the importance of this type of analysis, more than 70 pathway analysis methods have been proposed so far. These can be categorized into two main categories: non-topology-based (non-TB) and topology-based (TB). Although some review papers discuss this topic from different aspects, there is no systematic, large-scale assessment of such methods. Furthermore, the majority of the pathway analysis approaches rely on the assumption of uniformity of p values under the null hypothesis, which is often not true.

RESULTS

This article presents the most comprehensive comparative study on pathway analysis methods available to date. We compare the actual performance of 13 widely used pathway analysis methods in over 1085 analyses. These comparisons were performed using 2601 samples from 75 human disease data sets and 121 samples from 11 knockout mouse data sets. In addition, we investigate the extent to which each method is biased under the null hypothesis. Together, these data and results constitute a reliable benchmark against which future pathway analysis methods could and should be tested.

CONCLUSION

Overall, the result shows that no method is perfect. In general, TB methods appear to perform better than non-TB methods. This is somewhat expected since the TB methods take into consideration the structure of the pathway which is meant to describe the underlying phenomena. We also discover that most, if not all, listed approaches are biased and can produce skewed results under the null.

GSMA: an approach to identify robust global and test Gene Signatures using Meta-Analysis

MOTIVATION

Recent advances in biomedical research have made massive amount of transcriptomic data available in public repositories from different sources. Due to the heterogeneity present in the individual experiments, identifying reproducible biomarkers for a given disease from multiple independent studies has become a major challenge. The widely used meta-analysis approaches, such as Fisher's method, Stouffer's method, minP and maxP, have at least two major limitations: (i) they are sensitive to outliers, and (ii) they perform only one statistical test for each individual study, and hence do not fully utilize the potential sample size to gain statistical power.

RESULTS

Here, we propose a gene-level meta-analysis framework that overcomes these limitations and identifies a gene signature that is reliable and reproducible across multiple independent studies of a given disease. The approach provides a comprehensive global signature that can be used to understand the underlying biological phenomena, and a smaller test signature that can be used to classify future samples of a given disease. We demonstrate the utility of the framework by constructing disease signatures for influenza and Alzheimer's disease using nine datasets including 1108 individuals. These signatures are then validated on 12 independent datasets including 912 individuals. The results indicate that the proposed approach performs better than the majority of the existing meta-analysis approaches in terms of both sensitivity as well as specificity. The proposed signatures could be further used in diagnosis, prognosis and identification of therapeutic targets.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Sex Effects on Gene Expression in Lacrimal Glands of Mouse Models of Sjögren Syndrome

Purpose

Sjögren syndrome is an autoimmune disease that occurs primarily in women, and is associated with lacrimal gland inflammation and aqueous-deficient dry eye. We hypothesize that sex-associated differences in lacrimal gland gene expression are very important in promoting lymphocyte accumulation in this tissue and contribute to the onset, progression, and/or severity of the inflammatory disease process. To test our hypothesis, we explored the nature and extent of sex-related differences in gene expression in autoimmune lacrimal glands.

Methods

Lacrimal glands were collected from age-matched, adult, male and female MRL/MpJ-Tnfrsf6^lpr (MRL/lpr) and nonobese diabetic/LtJ (NOD) mice. Glands were processed for the analysis of differentially expressed mRNAs by using CodeLink Bioarrays and Affymetrix GeneChips. Data were evaluated with bioinformatics and statistical software.

Results

Our results show that sex significantly influences the expression of thousands of genes in lacrimal glands of MRL/lpr and NOD mice. The immune nature of this glandular response is very dependent on the Sjögren syndrome model. Lacrimal glands of female, as compared with male, MRL/lpr mice contain a significant increase in the expression of genes related to inflammatory responses, antigen processing, and chemokine pathways. In contrast, it is the lacrimal tissue of NOD males, and not females, that presents with a significantly greater expression of immune-related genes.

Conclusions

These data support our hypothesis that sex-related differences in gene expression contribute to lacrimal gland disease in Sjögren syndrome. Our findings also suggest that factors in the lacrimal gland microenvironment are critically important in mediating these sex-associated immune effects.

Network-Based Approaches for Pathway Level Analysis

Identification of impacted pathways is an important problem because it allows us to gain insights into the underlying biology beyond the detection of differentially expressed genes. In the past decade, a plethora of methods have been developed for this purpose. The last generation of pathway analysis methods are designed to take into account various aspects of pathway topology in order to increase the accuracy of the findings. Here, we cover 34 such topology-based pathway analysis methods published in the past 13 years. We compare these methods on categories related to implementation, availability, input format, graph models, and statistical approaches used to compute pathway level statistics and statistical significance. We also discuss a number of critical challenges that need to be addressed, arising both in methodology and pathway representation, including inconsistent terminology, data format, lack of meaningful benchmarks, and, more importantly, a systematic bias that is present in most existing methods. © 2018 by John Wiley & Sons, Inc.

A Comprehensive Survey of Tools and Software for Active Subnetwork Identification

A recent focus of computational biology has been to integrate the complementary information available in molecular profiles as well as in multiple network databases in order to identify connected regions that show significant changes under different conditions. This allows for capturing dynamic and condition-specific mechanisms of the underlying phenomena and disease stages. Here we review 22 such integrative approaches for active module identification published over the last decade. This article only focuses on tools that are currently available for use and are well-maintained. We compare these methods focusing on their primary features, integrative abilities, network structures, mathematical models, and implementations. We also provide real-world scenarios in which these methods have been successfully applied, as well as highlight outstanding challenges in the field that remain to be addressed. The main objective of this review is to help potential users and researchers to choose the best method that is suitable for their data and analysis purpose.

Fourier transform infrared spectroscopy based spectral biomarkers of metastasized breast cancer progression

Breast cancer is a global health issue and the second leading cause of cancer death in women. Breast cancer tends to migrate to bone and causes bone metastases which is ultimately the cause of death. Here, we report the use of FTIR to identify spectral biomarkers of cancer progression on 3D in vitro model of breast cancer bone metastasis. Our results indicate that the following spectral biomarkers can monitor cancer progression, for example, lipids (CH2 asymmetric/CH2 symmetric stretch), Amide I/Amide II, and RNA/DNA. Principal component analysis also confirmed the involvement of protein, lipids and nucleic acids in cancer progression on sequential culture. The collective observations from this study suggest successful application of FTIR as a non-invasive and accurate method to identify biochemical changes in cancer cells during the progression of breast cancer bone metastasis.

Bioinformatics and Microarray-Based Technologies to Viral Genome Sequence Analysis

Identification of microbial pathogen is an important event which lead to diagnosis, treatment, and control of infections produce by them. The high-throughput technology like microarray and new-generation sequencing machine are able to generate huge amount of nucleotide sequences of viral and bacterial genome of both known and unknown pathogens. Few years ago it was the DNA microarrays which had great potential to screen all the known pathogens and yet to be identified pathogen simultaneously. But after the development of a new generation sequencing, technologies and advance computational approach researchers are looking forward for a complete understanding of microbes and host interactions. The powerful sequencing platform is rapidly transforming the landscape of microbial identification and characterization. As bioinformatics analysis tools and databases are easily available to researchers, the enormous amount of data generated can be meaningfully handled for better understanding of the microbial world. Here in this chapter, we present commentary on how the computational method incorporated with sequencing technique made easy for microbial detection and characterization.

A differential k-mer analysis pipeline for comparing RNA-Seq transcriptome and meta-transcriptome datasets without a reference

Next-generation DNA sequencing technologies, such as RNA-Seq, currently dominate genome-wide gene expression studies. A standard approach to analyse this data requires mapping sequence reads to a reference and counting the number of reads which map to each gene. However, for many transcriptome studies, a suitable reference genome is unavailable, especially for meta-transcriptome studies which assay gene expression from mixed populations of organisms. Where a reference is unavailable, it is possible to generate a reference by the de novo assembly of the sequence reads. However, the high cost of generating high-coverage data for de novo assembly hinders this approach and more importantly the accurate assembly of such data is challenging, especially for meta-transcriptome data, and resulting assemblies frequently suffer from collapsed regions or chimeric sequences. As an alternative to the standard reference mapping approach, we have developed a k-mer-based analysis pipeline (DiffKAP) to identify differentially expressed reads between RNA-Seq datasets without the requirement for a reference. We compared the DiffKAP approach with the traditional Tophat/Cuffdiff method using RNA-Seq data from soybean, which has a suitable reference genome. We subsequently examined differential gene expression for a coral meta-transcriptome where no reference is available, and validated the results using qRT-PCR. We conclude that DiffKAP is an accurate method to study differential gene expression in complex meta-transcriptomes without the requirement of a reference genome.

Quantitative RNA-seq Analysis Unveils Osmotic and Thermal Adaptation Mechanisms Relevant for Ectoine Production in Chromohalobacter salexigens

Quantitative RNA sequencing (RNA-seq) and the complementary phenotypic assays were implemented to investigate the transcriptional responses of Chromohalobacter salexigens to osmotic and heat stress. These conditions trigger the synthesis of ectoine and hydroxyectoine, two compatible solutes of biotechnological interest. Our findings revealed that both stresses make a significant impact on C. salexigens global physiology. Apart from compatible solute metabolism, the most relevant adaptation mechanisms were related to “oxidative- and protein-folding- stress responses,” “modulation of respiratory chain and related components,” and “ion homeostasis.” A general salt-dependent induction of genes related to the metabolism of ectoines, as well as repression of ectoine degradation genes by temperature, was observed. Different oxidative stress response mechanisms, secondary or primary, were induced at low and high salinity, respectively, and repressed by temperature. A higher sensitivity to H₂O₂ was observed at high salinity, regardless of temperature. Low salinity induced genes involved in “protein-folding-stress response,” suggesting disturbance of protein homeostasis. Transcriptional shift of genes encoding three types of respiratory NADH dehydrogenases, ATP synthase, quinone pool, Na⁺/H⁺ antiporters, and sodium-solute symporters, was observed depending on salinity and temperature, suggesting modulation of the components of the respiratory chain and additional systems involved in the generation of H⁺ and/or Na⁺ gradients. Remarkably, the Na⁺ intracellular content remained constant regardless of salinity and temperature. Disturbance of Na⁺- and H⁺-gradients with specific ionophores suggested that both gradients influence ectoine production, but with differences depending on the solute, salinity, and temperature conditions. Flagellum genes were strongly induced by salinity, and further induced by temperature. However, salt-induced cell motility was reduced at high temperature, possibly caused by an alteration of Na⁺ permeability by temperature, as dependence of motility on Na⁺-gradient was observed. The transcriptional induction of genes related to the synthesis and transport of siderophores correlated with a higher siderophore production and intracellular iron content only at low salinity. An excess of iron increased hydroxyectoine accumulation by 20% at high salinity. Conversely, it reduced the intracellular content of ectoines by 50% at high salinity plus high temperature. These findings support the relevance of iron homeostasis for osmoadaptation, thermoadaptation and accumulation of ectoines, in C. salexigens.

Elevated Nicotinamide Phosphoribosyl Transferase in Skeletal Muscle Augments Exercise Performance and Mitochondrial Respiratory Capacity Following Exercise Training

Mice overexpressing NAMPT in skeletal muscle (NamptTg mice) develop higher exercise endurance and maximal aerobic capacity (VO₂max) following voluntary exercise training compared to wild-type (WT) mice. Here, we aimed to investigate the mechanisms underlying by determining skeletal muscle mitochondrial respiratory capacity in NamptTg and WT mice. Body weight and body composition, tissue weight (gastrocnemius, quadriceps, soleus, heart, liver, and epididymal white adipose tissue), skeletal muscle and liver glycogen content, VO₂max, skeletal muscle mitochondrial respiratory capacity (measured by high-resolution respirometry), skeletal muscle gene expression (measured by microarray and qPCR), and skeletal muscle protein content (measured by Western blot) were determined following 6 weeks of voluntary exercise training (access to running wheel) in 13-week-old male NamptTg (exercised NamptTg) mice and WT (exercised WT) mice. Daily running distance and running time during the voluntary exercise training protocol were recorded. Daily running distance (p = 0.51) and running time (p = 0.85) were not significantly different between exercised NamptTg mice and exercised WT mice. VO₂max was higher in exercised NamptTg mice compared to exercised WT mice (p = 0.02). Body weight (p = 0.92), fat mass (p = 0.49), lean mass (p = 0.91), tissue weight (all p > 0.05), and skeletal muscle (p = 0.72) and liver (p = 0.94) glycogen content were not significantly different between exercised NamptTg mice and exercised WT mice. Complex I oxidative phosphorylation (OXPHOS) respiratory capacity supported by fatty acid substrates (p < 0.01), maximal (complex I+II) OXPHOS respiratory capacity supported by glycolytic (p = 0.02) and fatty acid (p < 0.01) substrates, and maximal uncoupled respiratory capacity supported by fatty acid substrates (p < 0.01) was higher in exercised NamptTg mice compared to exercised WT mice. Transcriptomic analyses revealed differential expression for genes involved in oxidative metabolism in exercised NamptTg mice compared to exercised WT mice, specifically, enrichment for the gene set related to the SIRT3-mediated signaling pathway. SIRT3 protein content correlated with NAMPT protein content (r = 0.61, p = 0.04). In conclusion, NamptTg mice develop higher exercise capacity following voluntary exercise training compared to WT mice, which is paralleled by higher mitochondrial respiratory capacity in skeletal muscle. The changes in SIRT3 targets suggest that these effects are due to remodeling of mitochondrial function.

Influence of lipopolysaccharide on proinflammatory gene expression in human corneal, conjunctival and meibomian gland epithelial cells

PURPOSE

Lipopolysaccharide (LPS), a bacterial endotoxin, is known to stimulate leuokotriene B4 (LTB4) secretion by human corneal (HCECs), conjunctival (HConjECs) and meibomian gland (HMGECs) epithelial cells. We hypothesize that this LTB4 effect represents an overall induction of proinflammatory gene expression in these cells. Our objective was to test this hypothesis.

METHODS

Immortalized HCECs, HConjECs and HMGECs were cultured in the presence or absence of LPS (15 μg/ml) and ligand binding protein (LBP; 150 ng/ml). Cells were then processed for RNA isolation and the analysis of gene expression by using Illumina BeadChips, background subtraction, cubic spline normalization and GeneSifter software.

RESULTS

Our findings show that LPS induces a striking increase in proinflammatory gene expression in HCECs and HConjECs. These cellular reactions are associated with a significant up-regulation of genes associated with inflammatory and immune responses (e.g. IL-1β, IL-8, and tumor necrosis factor), including those related to chemokine and Toll-like receptor signaling pathways, cytokine-cytokine receptor interactions, and chemotaxis. In contrast, with the exception of Toll-like signaling and associated innate immunity pathways, almost no proinflammatory ontologies were upregulated by LPS in HMGECs.

CONCLUSIONS

Our results support our hypothesis that LPS stimulates proinflammatory gene expression in HCECs and HConjECs. However, our findings also show that LPS does not elicit such proinflammatory responses in HMGECs.

Credentialing Individual Samples for Proteogenomic Analysis

An integrated analysis of DNA, RNA and protein, so called proteogenomic studies, has the potential to greatly increase our understanding of both normal physiology and disease development. However, such studies are challenged by a lack of a systematic approach to credential individual samples resulting in the introduction of noise into the system that limits the ability to identify important biological signals. Indeed, a recent proteogenomic CPTAC study identified 26% of samples as unsatisfactory, resulting in a marked increase in cost and loss of information content. Based on a large-scale analysis of RNA-seq and proteomic data generated by reverse phase protein arrays (RPPA) and by mass spectrometry, we propose a protein-mRNA correlation-based (PMC) score as a robust metric to credential single samples for integrated proteogenomic studies. Samples with high PMC scores have significantly higher protein-mRNA correlation, total protein content and tumor purity. Our results highlight the importance of credentialing individual samples prior to proteogenomic analysis.

Circulating MicroRNA Biomarkers in Melanoma: Tools and Challenges in Personalised Medicine

Effective management of melanoma depends heavily on early diagnosis. When detected in early non-metastatic stages, melanoma is almost 100% curable by surgical resection, however when detected in late metastatic stages III and IV, 5-year survival rates drop to ~50% and 10–25%, respectively, due to limited efficacy of current treatment options. This presents a pressing need to identify biomarkers that can detect patients at high risk of recurrence and progression to metastatic disease, which will allow for early intervention and survival benefit. Accumulating evidence over the past few decades has highlighted the potential use of circulating molecular biomarkers for melanoma diagnosis and prognosis, including lactate dehydrogenase (LDH), S100 calcium-binding protein B (S100B) and circulating tumor DNA (ctDNA) fragments. Since 2010, circulating microRNAs (miRNAs) have been increasingly recognised as more robust non-invasive biomarkers for melanoma due to their structural stability under the harsh conditions of the blood and different conditions of sample processing and isolation. Several pre-analytical and analytical variables challenge the accurate quantification of relative miRNA levels between serum samples or plasma samples, leading to conflicting findings between studies on circulating miRNA biomarkers for melanoma. In this review, we provide a critical summary of the circulating miRNA biomarkers for melanoma published to date.

Summarizing performance for genome scale measurement of miRNA: reference samples and metrics

BACKGROUND

The potential utility of microRNA as biomarkers for early detection of cancer and other diseases is being investigated with genome-scale profiling of differentially expressed microRNA. Processes for measurement assurance are critical components of genome-scale measurements. Here, we evaluated the utility of a set of total RNA samples, designed with between-sample differences in the relative abundance of miRNAs, as process controls.

RESULTS

Three pure total human RNA samples (brain, liver, and placenta) and two different mixtures of these components were evaluated as measurement assurance control samples on multiple measurement systems at multiple sites and over multiple rounds. In silico modeling of mixtures provided benchmark values for comparison with physical mixtures. Biomarker development laboratories using next-generation sequencing (NGS) or genome-scale hybridization assays participated in the study and returned data from the samples using their routine workflows. Multiplexed and single assay reverse-transcription PCR (RT-PCR) was used to confirm in silico predicted sample differences. Data visualizations and summary metrics for genome-scale miRNA profiling assessment were developed using this dataset, and a range of performance was observed. These metrics have been incorporated into an online data analysis pipeline and provide a convenient dashboard view of results from experiments following the described design. The website also serves as a repository for the accumulation of performance values providing new participants in the project an opportunity to learn what may be achievable with similar measurement processes.

CONCLUSIONS

The set of reference samples used in this study provides benchmark values suitable for assessing genome-scale miRNA profiling processes. Incorporation of these metrics into an online resource allows laboratories to periodically evaluate their performance and assess any changes introduced into their measurement process.

Microarray Technologies in Fungal Diagnostics

Microarray technologies have been a major research tool in the last decades. In addition they have been introduced into several fields of diagnostics including diagnostics of infectious diseases. Microarrays are highly parallelized assay systems that initially were developed for multiparametric nucleic acid detection. From there on they rapidly developed towards a tool for the detection of all kind of biological compounds (DNA, RNA, proteins, cells, nucleic acids, carbohydrates, etc.) or their modifications (methylation, phosphorylation, etc.). The combination of closed-tube systems and lab on chip devices with microarrays further enabled a higher automation degree with a reduced contamination risk. Microarray-based diagnostic applications currently complement and may in the future replace classical methods in clinical microbiology like blood cultures, resistance determination, microscopic and metabolic analyses as well as biochemical or immunohistochemical assays. In addition, novel diagnostic markers appear, like noncoding RNAs and miRNAs providing additional room for novel nucleic acid based biomarkers. Here I focus an microarray technologies in diagnostics and as research tools, based on nucleic acid-based arrays.

FlyAtlas 2: a new version of the Drosophila melanogaster expression atlas with RNA-Seq, miRNA-Seq and sex-specific data

FlyAtlas 2 (www.flyatlas2.org) is part successor, part complement to the FlyAtlas database and web application for studying the expression of the genes of Drosophila melanogaster in different tissues of adults and larvae. Although generated in the same lab with the same fly line raised on the same diet as FlyAtlas, the FlyAtlas2 resource employs a completely new set of expression data based on RNA-Seq, rather than microarray analysis, and so it allows the user to obtain information for the expression of different transcripts of a gene. Furthermore, the data for somatic tissues are now available for both male and female adult flies, allowing studies of sexual dimorphism. Gene coverage has been extended by the inclusion of microRNAs and many of the RNA genes included in Release 6 of the Drosophila reference genome. The web interface has been modified to accommodate the extra data, but at the same time has been adapted for viewing on small mobile devices. Users also have access to the RNA-Seq reads displayed alongside the annotated Drosophila genome in the (external) UCSC browser, and are able to link out to the previous FlyAtlas resource to compare the data obtained by RNA-Seq with that obtained using microarrays.

Determining Genome-wide Transcript Decay Rates in Proliferating and Quiescent Human Fibroblasts

Quiescence is a temporary, reversible state in which cells have ceased cell division, but retain the capacity to proliferate. Multiple studies, including ours, have demonstrated that quiescence is associated with widespread changes in gene expression. Some of these changes occur through changes in the level or activity of proliferation-associated transcription factors, such as E2F and MYC. We have demonstrated that mRNA decay can also contribute to changes in gene expression between proliferating and quiescent cells. In this protocol, we describe the procedure for establishing proliferating and quiescent cultures of human dermal foreskin fibroblasts. We then describe the procedures for inhibiting new transcription in proliferating and quiescent cells with Actinomycin D (ActD). ActD treatment represents a straightforward and reproducible approach to dissociating new transcription from transcript decay. A disadvantage of ActD treatment is that the time course must be limited to a short time frame because ActD affects cell viability. Transcript levels are monitored over time to determine transcript decay rates. This procedure allows for the identification of genes and isoforms that exhibit differential decay in proliferating versus quiescent fibroblasts.

Methods of Analysis and Meta-Analysis for Identifying Differentially Expressed Genes

Microarray approaches are widely used high-throughput techniques to assess simultaneously the expression of thousands of genes under certain conditions and study the effects of certain treatments, diseases, and developmental stages. The traditional way to perform such experiments is to design oligonucleotide hybridization probes that correspond to specific genes and then measure the expression of the genes in order to determine which of them are up- or down-regulated compared to a condition that is used as a control. Hitherto, individual experiments cannot capture the bigger picture of how a biological system works and, therefore, data integration from multiple experimental studies and external data repositories is necessary to understand the function of genes and their expression patterns under certain conditions. Therefore, the development of methods for handling, integrating, comparing, interpreting and visualizing microarray data is necessary. The selection of an appropriate method for analysing microarray datasets is not an easy task. In this chapter, we provide an overview of the various methods developed for microarray data analysis, as well as suggestions for choosing the appropriate method for microarray meta-analysis.

A de novo self-assembling peptide hydrogel biosensor with covalently immobilised DNA-recognising motifs

We report here the first experimental evidence of a self-assembling three-dimensional (3D) peptide hydrogel, with recognition motifs immobilized on the surface of fibres capable of sequence-specific oligonucleotide detection. These systems have the potential to be further developed into diagnostic and prognostic tools in human pathophysiology.

Gene modules associated with breast cancer distant metastasis-free survival in the PAM50 molecular subtypes

To identify PAM50 subtype–specific associations between distant metastasis-free survival (DMFS) in breast cancer (BC) patients and gene modules describing potentially targetable oncogenic pathways, a comprehensive analysis evaluating the prognostic efficacy of published gene signatures in 2027 BC patients from 13 studies was conducted. We calculated 21 gene modules and computed hazard ratios (HRs) for DMFS for one-unit increases in module score, with and without adjustment for clinical characteristics. By comparing gene expression to survival outcomes, we derived four subtype-specific prognostic signatures for BC. Univariate and multivariate analyses showed that in the luminal A subgroup, E2F3, PTEN and GGI gene module scores were associated with clinical outcome. In the luminal B tumors, RAS was associated with DMFS and in the basal-like tumors, ER was associated with DMFS. Our defined gene modules predicted high-risk patients in multivariate analyses for the basal-like (HR: 2.19, p=2.5×10⁻⁴), luminal A (HR: 3.03, p=7.2×10⁻⁵), luminal B (HR: 3.00, p=2.4×10⁻¹⁰) and HER2+ (HR: 5.49, p=9.7×10⁻¹⁰) subgroups. We found that different modules are associated with DMFS in different BC subtypes. The results of this study could help to identify new therapeutic strategies for specific molecular subgroups of BC, and could enhance efforts to improve patient-specific therapy options.

An extracellular matrix-related prognostic and predictive indicator for early-stage non-small cell lung cancer

The prognosis and prediction of adjuvant chemotherapy (ACT) response in early-stage non-small cell lung cancer (NSCLC) patients remain poor in this era of personalized medicine. We hypothesize that extracellular matrix (ECM)-associated components could be potential markers for better diagnosis and prognosis due to their differential expression in 1,943 primary NSCLC tumors as compared to 303 normal lung tissues. Here we develop a 29-gene ECM-related prognostic and predictive indicator (EPPI). We validate a robust performance of the EPPI risk scoring system in multiple independent data sets, comprising a total of 2,071 early-stage NSCLC tumors. Patients are stratified according to the universal cutoff score based on the EPPI when applied in the clinical setting; the low-risk group has significantly better survival outcome. The functional EPPI gene set represents a potential genomic tool to improve patient selection in early-stage NSCLC to further derive the best benefits of ACT and prevent unnecessary treatment or ACT-associated morbidity.

Prognosis and prediction of adjuvant chemotherapy response in non-small cell lung cancer can have significant clinical impact. Here, the authors show that differential expression of a 29 extracellular matrix gene indicator, EPPI, can predict patient outcome.

Hepatotoxic combination effects of three azole fungicides in a broad dose range

Single active substances of pesticides are thoroughly examined for their toxicity before approval. In this context, the liver is frequently found to be the main target organ. Since consumers are generally exposed to multiple residues of different active substances via the diet, it is important to analyse combinations of active substances for potential mixture effects. For the (tri-)azoles, a group of agricultural fungicides and antifungal drugs, combination effects on the liver are likely because of a similar mode of action. Hepatotoxic effects of mixtures of two triazoles (cyproconazole and epoxiconazole) and an imidazole (prochloraz) were investigated in a 28-day feeding study in rats at three dose levels ranging from a typical toxicological reference value to a clear effect dose. Test parameters included organ weights, clinical chemistry, histopathology and morphometry. In addition, molecular parameters were investigated by means of pathway-focused gene expression arrays, quantitative real-time PCR and enzyme activity assays. Effects were compared to those caused by the individual substances as observed at the same dose levels in a previous study. Mixture effects were substantiated by increases in relative and absolute liver weights, histopathological findings and alterations in clinical chemistry parameters at the top dose level. On the molecular level also at lower dose levels, additive effects could be observed for the induction of several cytochrome P 450 enzymes (Cyp1a1, Cyp2b1, Cyp3a2), transporters (Abcb1a, Abcc3) and of genes encoding for enzymes involved in fatty acid or phospholipid metabolism (Ppargc1a, Sc4 mol). In most cases, treatment with mixtures caused a more pronounced effect as compared to the individual substances. However, the assumption of dose additivity was in general sufficiently conservative to cover mixture effects observed under the conditions of the present study.

Differential gene expression in disease: a comparison between high-throughput studies and the literature

BACKGROUND

Differential gene expression is important to understand the biological differences between healthy and diseased states. Two common sources of differential gene expression data are microarray studies and the biomedical literature.

METHODS

With the aid of text mining and gene expression analysis we have examined the comparative properties of these two sources of differential gene expression data.

RESULTS

The literature shows a preference for reporting genes associated to higher fold changes in microarray data, rather than genes that are simply significantly differentially expressed. Thus, the resemblance between the literature and microarray data increases when the fold-change threshold for microarray data is increased. Moreover, the literature has a reporting preference for differentially expressed genes that (1) are overexpressed rather than underexpressed; (2) are overexpressed in multiple diseases; and (3) are popular in the biomedical literature at large. Additionally, the degree to which diseases are similar depends on whether microarray data or the literature is used to compare them. Finally, vaguely-qualified reports of differential expression magnitudes in the literature have only small correlation with microarray fold-change data.

CONCLUSIONS

Reporting biases of differential gene expression in the literature can be affecting our appreciation of disease biology and of the degree of similarity that actually exists between different diseases.

Generating a robust prediction model for stage I lung adenocarcinoma recurrence after surgical resection

Lung cancer mortality remains high even after successful resection. Adjuvant treatment benefits stage II and III patients, but not stage I patients, and most studies fail to predict recurrence in stage I patients. Our study included 211 lung adenocarcinoma patients (stages I-IIIA; 81% stage I) who received curative resections at Taipei Veterans General Hospital between January 2001 and December 2012. We generated a prediction model using 153 samples, with validation using an additional 58 clinical outcome-blinded samples. Gene expression profiles were generated using formalin-fixed, paraffin-embedded tissue samples and microarrays. Data analysis was performed using a supervised clustering method. The prediction model generated from mixed stage samples successfully separated patients at high vs. low risk for recurrence. The validation tests hazard ratio (HR = 4.38) was similar to that of the training tests (HR = 4.53), indicating a robust training process. Our prediction model successfully distinguished high- from low-risk stage IA and IB patients, with a difference in 5-year disease-free survival between high- and low-risk patients of 42% for stage IA and 45% for stage IB (p < 0.05). We present a novel and effective model for identifying lung adenocarcinoma patients at high risk for recurrence who may benefit from adjuvant therapy. Our prediction performance of the difference in disease free survival between high risk and low risk groups demonstrates more than two fold improvement over earlier published results.

Building personalized treatment plans for early-stage colorectal cancer patients

We developed a series of models to predict the likelihood of recurrence and the response to chemotherapy for the personalized treatment of stage I and II colorectal cancer patients. A recurrence prediction model was developed from 235 stage I/II patients. The model successfully distinguished between high-risk and low-risk groups, with a hazard ratio of recurrence of 4.66 (p < 0.0001). More importantly, the model was accurate for both stage I (hazard ratio = 5.87, p = 0.0006) and stage II (hazard ratio = 4.30, p < 0.0001) disease. This model performed much better than the Oncotype and ColoPrint commercial services in identifying patients at high risk for stage II recurrence. And unlike the commercial services, the robust model included recurrence prediction for stage I patients. As stage I/II CRC patients usually do not receive chemotherapy, we generated chemotherapy efficacy prediction models with data from 358 stage III patients. The predictions were highly accurate: the hazard ratio of recurrence for responders vs. non-responders was 4.13 for those treated with FOLFOX (p < 0.0001), and 3.16 (p = 0.0012) for those treated with fluorouracil. We have thus created a prognostic model that accurately identifies patients at high risk for recurrence, and the first accurate chemotherapy efficacy prediction model for individual patients. In the future, complete personalized treatment plans for stage I/II patients may be developed if the drug prediction models generated from stage III patients are verified to be effective for stage I and II patients in prospective studies.

Prognostic modeling of oral cancer by gene profiles and clinicopathological co-variables

Accurate staging and outcome prediction is a major problem in clinical management of oral cancer patients, hampering high precision treatment and adjuvant therapy planning. Here, we have built and validated multivariable models that integrate gene signatures with clinical and pathological variables to improve staging and survival prediction of patients with oral squamous cell carcinoma (OSCC). Gene expression profiles from 249 human papillomavirus (HPV)-negative OSCCs were explored to identify a 22-gene lymph node metastasis signature (LNMsig) and a 40-gene overall survival signature (OSsig). To facilitate future clinical implementation and increase performance, these signatures were transferred to quantitative polymerase chain reaction (qPCR) assays and validated in an independent cohort of 125 HPV-negative tumors. When applied in the clinically relevant subgroup of early-stage (cT1-2N0) OSCC, the LNMsig could prevent overtreatment in two-third of the patients. Additionally, the integration of RT-qPCR gene signatures with clinical and pathological variables provided accurate prognostic models for oral cancer, strongly outperforming TNM. Finally, the OSsig gene signature identified a subpopulation of patients, currently considered at low-risk for disease-related survival, who showed an unexpected poor prognosis. These well-validated models will assist in personalizing primary treatment with respect to neck dissection and adjuvant therapies.

Correlation between RNA-Seq and microarrays results using TCGA data

RNA sequencing (RNA-Seq) and microarray are two of the most commonly used high-throughput technologies for transcriptome profiling; however, they both have their own inherent strengths and limitations. This research aims to analyze the correlation between microarrays and RNA-Seq detection of transcripts in the same tissue sample to explore the reproducibility between the techniques. Using data of RNA-Seq v2 and three different microarrays provided by The Cancer Genome Atlas, 11,120 genes of 111 lung squamous cell carcinoma samples were simultaneously detected by the four methods. Then we analyzed the Pearson correlation between microarrays and RNA-Seq. Finally, in the six comparison results, 9984 (89.8%) genes, irrespective of which two methods were used, simultaneously showed the existence of correlation, whereas only 83 (0.1%) genes proved to have no significant correlation in either comparison. In addition, the comparisons between 3266 (29.3%) genes showed high correlation (R≥0.8) in all six comparisons, only for 1643 (14.8%) genes correlation were not as high in either comparison. Meanwhile, transcripts with extreme high or low expression levels were more highly discrepant across the methods. In conclusion, we found that, for most transcripts, the results obtained by RNA-Seq and microarrays were highly reproducible.

Hamming Distance as a Concept in DNA Molecular Recognition

DNA microarrays constitute an in vitro example system of a highly crowded molecular recognition environment. Although they are widely applied in many biological applications, some of the basic mechanisms of the hybridization processes of DNA remain poorly understood. On a microarray, cross-hybridization arises from similarities of sequences that may introduce errors during the transmission of information. Experimentally, we determine an appropriate distance, called minimum Hamming distance, in which the sequences of a set differ. By applying an algorithm based on a graph-theoretical method, we find large orthogonal sets of sequences that are sufficiently different not to exhibit any cross-hybridization. To create such a set, we first derive an analytical solution for the number of sequences that include at least four guanines in a row for a given sequence length and eliminate them from the list of candidate sequences. We experimentally confirm the orthogonality of the largest possible set with a size of 23 for the length of 7. We anticipate our work to be a starting point toward the study of signal propagation in highly competitive environments, besides its obvious application in DNA high throughput experiments.

Identification of potential gene targets in systemic vasculitis using DNA microarray analysis

The present study aimed to identify the involvement of critical genes in systemic vasculitis, to gain an improved understanding of the molecular circuity and to investigate novel potential gene targets for systemic vasculitis treatment. The dual-color cDNA microarray data of GSE16945, consisting of peripheral mononuclear blood cell specimens from 13 patients with systemic vasculitis and 16 healthy controls, was downloaded from the Gene Expression Omnibus database. Differentially expressed genes (DEGs) were screened in systemic vasculitis compared with controls using BRB ArrayTools, followed by the construction of a protein-protein interaction (PPI) network using the clusterProfiler package, and significant functional interaction (FI) module selection. Furthermore, transcriptional factors (TFs) among the identified DEGs were predicted and a transcriptional regulation network was constructed. A total of 173 up- and 93 downregulated genes were identified, which were mainly associated with immune response pathways. FBJ murine osteosarcoma viral oncogene homolog (FOS), ubiquitin B (UBB), signal transducer and activator of transcription 1 (STAT1) and MX dynamin-like GTPase 1 (MX1) were identified as hub proteins in the PPI network. Furthermore, UBB, FOS, and STAT1 were hub proteins in the three identified FI modules, respectively. In total, nine TFs were predicted among the DEGs. Of the DEGs that were predicted to be TFs, STAT1, v-maf avian musculoaponeurotic fibrosarcoma oncogene homolog B (MAFB) and tyrosine 3-monooxygenase/tryptophan 5-monooxygenase activation protein Z (YWHAZ), which interacted with each other, were identified to regulate further DEGs as target genes. Various genes, including FOS, UBB, MX1, STAT1, MAFB, and YWHAZ may be potential targets useful for the treatment of systemic vasculitis.

Profiling transcriptomes of human SH-SY5Y neuroblastoma cells exposed to maleic acid

Background

Maleic acid is a multi-functional chemical widely used in the field of industrial chemistry for producing food additives and food contact materials. As maleic acid may contaminate food by the release from food packages or intentional addition, it raises the concern about the effects of excessive dietary exposure to maleic acid on human health. However, the influence of maleic acid on human health has not been thoroughly studied. In silico toxicogenomics approaches have found the association between maleic acid and nervous system disease in human. The aim of this study is to experimentally explore the effects of maleic acid on human neuronal cells.

Methods

A microarray-based transcriptome profiling was performed to offer a better understanding of the effects of maleic acid on human health. Gene expression profiles of human neuroblastoma SH-SY5Y cells exposed to three concentrations of maleic acid (10, 50, and 100 μM) for 24 h were analyzed. Genes which were differentially expressed in dose-dependent manners were identified and further analyzed with an enrichment analysis. The expression profile of selected genes related to the inferred functional changes was validated using quantitative polymerase chain reaction (qPCR). Specific fluorescence probes were applied to observe the inferred functional changes in maleic acid-treated neuronal cells.

Results

A total of 316 differentially expressed genes (141 upregulated and 175 downregulated) were identified in response to the treatment of maleic acid. The enrichment analysis showed that DNA binding and metal ion binding were the significant molecular functions (MFs) of the neuronal cells affected by maleic acid. Maleic acid exposure decreased the expression of genes associated with calcium and thiol levels of the cells in a dose-dependent manner. The levels of intracellular calcium and thiol levels were also affected by maleic acid dose-dependent.

Discussion

The exposure to maleic acid is found to decrease the cellular calcium and thiol levels in human neuronal cells at both transcriptional and functional levels. This study reported the first transcriptomic profiling of human neuronal cells treated with maleic acid. It is also the first experimental validation of chemical effects predicted by in silico toxicogenomics approaches. The proposed approach may be useful in understanding the potential effects of other poorly characterized chemicals on human health.

Variation-preserving normalization unveils blind spots in gene expression profiling

RNA-Seq and gene expression microarrays provide comprehensive profiles of gene activity, but lack of reproducibility has hindered their application. A key challenge in the data analysis is the normalization of gene expression levels, which is currently performed following the implicit assumption that most genes are not differentially expressed. Here, we present a mathematical approach to normalization that makes no assumption of this sort. We have found that variation in gene expression is much larger than currently believed, and that it can be measured with available assays. Our results also explain, at least partially, the reproducibility problems encountered in transcriptomics studies. We expect that this improvement in detection will help efforts to realize the full potential of gene expression profiling, especially in analyses of cellular processes involving complex modulations of gene expression.