Evaluation of DNA microarray results with quantitative gene expression platforms

Sparassis latifolia and exercise training as complementary medicine mitigated the 5-fluorouracil potent side effects in mice with colorectal cancer: bioinformatics approaches, novel monitoring pathological metrics, screening signatures, and innovative management tactic

BACKGROUND

Prompt identification and assessment of the disease are essential for reducing the death rate associated with colorectal cancer (COL). Identifying specific causal or sensitive components, such as coding RNA (cRNA) and non-coding RNAs (ncRNAs), may greatly aid in the early detection of colorectal cancer.

METHODS

For this purpose, we gave natural chemicals obtained from Sparassis latifolia (SLPs) either alone or in conjunction with chemotherapy (5-Fluorouracil to a mouse colorectal tumor model induced by AOM-DSS. The transcription profile of non-coding RNAs (ncRNAs) and their target hub genes was evaluated using qPCR Real-Time, and ELISA techniques.

RESULTS

MSX2, MMP7, ITIH4, and COL1A2 were identified as factors in inflammation and oxidative stress, leading to the development of COL. The hub genes listed, upstream regulatory factors such as lncRNA PVT1, NEAT1, KCNQ1OT1, SNHG16, and miR-132-3p have been discovered as biomarkers for prognosis and diagnosis of COL. The SLPs and exercise, effectively decreased the size and quantity of tumors.

CONCLUSIONS

This effect may be attributed to the modulation of gene expression levels, including MSX2, MMP7, ITIH4, COL1A2, PVT1, NEAT1, KCNQ1OT1, SNHG16, and miR-132-3p. Ultimately, SLPs and exercise have the capacity to be regarded as complementing and enhancing chemotherapy treatments, owing to their efficacious components.

Transcriptome analysis creates a new era of precision medicine for managing recurrent hepatocellular carcinoma

The high incidence of hepatocellular carcinoma (HCC) recurrence negatively impacts outcomes of patients treated with curative intent despite advances in surgical techniques and other locoregional liver-targeting therapies. Over the past few decades, the emergence of transcriptome analysis tools, including real-time quantitative reverse transcription PCR, microarrays, and RNA sequencing, has not only largely contributed to our knowledge about the pathogenesis of recurrent HCC but also led to the development of outcome prediction models based on differentially expressed gene signatures. In recent years, the single-cell RNA sequencing technique has revolutionized our ability to study the complicated crosstalk between cancer cells and the immune environment, which may benefit further investigations on the role of different immune cells in HCC recurrence and the identification of potential therapeutic targets. In the present article, we summarized the major findings yielded with these transcriptome methods within the framework of a causal model consisting of three domains: primary cancer cells; carcinogenic stimuli; and tumor microenvironment. We provided a comprehensive review of the insights that transcriptome analyses have provided into diagnostics, surveillance, and treatment of HCC recurrence.

Molecular Responses and Degradation Mechanisms of the Herbicide Diuron in Rice Crops

Diuron [DU; 3-(3,4-dichlorophenyl)-1,1-dimethylurea], a widely used herbicide for weed control, arouses ecological and health risks due to its environment persistence. Our findings revealed that DU at 0.125-2.0 mg L^-1 caused oxidative damage to rice. RNA-sequencing profiles disclosed a globally genetic expression landscape of rice under DU treatment. DU mediated downregulated gene encoding photosynthesis and biosynthesis of protein, fatty acid, and carbohydrate. Conversely, it induced the upregulation of numerous genes involved in xenobiotic metabolism, detoxification, and anti-oxidation. Furthermore, 15 DU metabolites produced by metabolic genes were identified, 7 of which include two Phase I-based and 5 Phase II-based derivatives, were reported for the first time. The changes of resistance-related phytohormones, like JA, ABA, and SA, in terms of their contents and molecular-regulated signaling pathways positively responded to DU stress. Our work provides a molecular-scale perspective on the response of rice to DU toxicity and clarifies the biotransformation and degradation fate of DU in rice crops.

bestDEG: a web-based application automatically combines various tools to precisely predict differentially expressed genes (DEGs) from RNA-Seq data

Background

Differential gene expression analysis using RNA sequencing technology (RNA-Seq) has become the most popular technique in transcriptome research. Although many R packages have been developed to analyze differentially expressed genes (DEGs), several evaluations have shown that no single DEG analysis method outperforms all others. The validity of DEG identification could be increased by using multiple methods and producing the consensus results. However, DEG analysis methods are complex and most of them require prior knowledge of a programming language or command-line shell. Users who do not have this knowledge need to invest time and effort to acquire it.

Methods

We developed a novel web application called "bestDEG" to automatically analyze DEGs with different tools and compare the results. A differential expression (DE) analysis pipeline was created combining the edgeR, DESeq2, NOISeq, and EBSeq packages; selected because they use different statistical methods to identify DEGs. bestDEG was evaluated on human datasets from the MicroArray Quality Control (MAQC) project.

Results

The performance of the bestDEG web application with the human datasets showed excellent results, and the consensus method outperformed the other DE analysis methods in terms of precision (94.71%) and specificity (97.01%). bestDEG is a rapid and efficient tool to analyze DEGs. With bestDEG, users can select DE analysis methods and parameters in the user-friendly web interface. bestDEG also provides a Venn diagram and a table of results. Moreover, the consensus method of this tool can maximize the precision or minimize the false discovery rate (FDR), which reduces the cost of gene expression validation by minimizing wet-lab experiments.

Investigating on the influence mechanism of sausage of sea bass on calcium absorption and transport based on Caco-2 cell monolayer model

A monolayer Caco-2 cell model was established to explore the effects of sea bass sausage digestive juice containing phosphate on calcium ion transport. Differential proteins of Caco-2 cells treated with fish sausage juice were detected and analyzed by gene ontology (GO) functional annotation and kyoto encyclopedia of genes and genomes (KEGG) pathway analyses. Results revealed that after treatment with 0.23 mg/mL digestive juice of perch sausage in vitro, Caco-2 cell viability was the highest at 72 h (99.84%). Additionally, 0.23 mg/mL digestive juice of perch sausage in vitro significantly increased calcium ion transport. The transfer volume was 1.396 μg/well. Fish sausages containing phosphate significantly affected the protein expression levels of Caco-2 cells. Two hundred one differential proteins were detected, including 114 up-regulated and 87 down-regulated proteins. The main differential proteins included P02795, Q9P0W0, Q96PU5, Q9GZT9 and Q5EBL8. The adjustment ratios of the fish sausage group were 0.7485, 1.373, 1.2535, 0.6775, and 0.809, respectively. The pathway analysis showed that phosphate affected calcium ion absorption and transport through the P02795 enrichment pathway. The fish sausage group showed that the immune-related functions of cells were affected. This study expounds the effects of water-retaining agents on the nutritional quality of aquatic products and provides theoretical support for the research and application of surimi products.

Identification of novel biomarkers in septic cardiomyopathy via integrated bioinformatics analysis and experimental validation

Purpose: Septic cardiomyopathy (SCM) is an important world public health problem with high morbidity and mortality. It is necessary to identify SCM biomarkers at the genetic level to identify new therapeutic targets and strategies.

Method: DEGs in SCM were identified by comprehensive bioinformatics analysis of microarray datasets (GSE53007 and GSE79962) downloaded from the GEO database. Subsequently, bioinformatics analysis was used to conduct an in-depth exploration of DEGs, including GO and KEGG pathway enrichment analysis, PPI network construction, and key gene identification. The top ten Hub genes were identified, and then the SCM model was constructed by treating HL-1 cells and AC16 cells with LPS, and these top ten Hub genes were examined using qPCR.

Result: STAT3, SOCS3, CCL2, IL1R2, JUNB, S100A9, OSMR, ZFP36, and HAMP were significantly elevated in the established SCM cells model.

Conclusion: After bioinformatics analysis and experimental verification, it was demonstrated that STAT3, SOCS3, CCL2, IL1R2, JUNB, S100A9, OSMR, ZFP36, and HAMP might play important roles in SCM.

Replacement per- and polyfluoroalkyl substances (PFAS) are potent modulators of lipogenic and drug metabolizing gene expression signatures in primary human hepatocytes

Per- and polyfluoroalkyl substances (PFAS) are a class of environmental toxicants, and some, such as perfluorooctanesulfonic acid (PFOS) and perfluorooctanoic acid (PFOA), have been associated with hepatic steatosis in rodents and monkeys. It was hypothesized that perfluorosulfonic acids (C4, 6, 8), perfluorocarboxylic acids (C4-14), perfluoro(2-methyl-3-oxahexanoic) acid (HFPO-DA), 1H, 1H, 2H, 2H-perfluorooctanesulfonic acid (6:2 FTS) along with 3 PFOS precursors could induce expression of lipid metabolism genes and lipid deposition in human hepatocytes. Five-donor pooled cryopreserved human hepatocytes were cultured and treated with 0.1% DMSO vehicle or various PFAS (0.25 to 25 μM) in media. After a 48-h treatment, mRNA transcripts related to lipid transport, metabolism, and synthesis were measured using a Quantigene Plex assay. After 72-h treatments, hepatocytes were stained with Nile Red dye to quantify intracellular lipids. Overall, PFAS were transcriptionally active at 25 μM. In this model, lipid accumulation was not observed with C8-C12 treatments. Shorter chain PFAS (C4-C5), 6:2 FTS, and PFOS precursor, metFOSA, induced significant liver lipid accumulation, and gene activation at lower concentrations than legacy PFAS. In summary short chain PFAS and other alternative PFAS were more potent gene inducers, and potential health effects of replacement PFAS should be critically evaluated in humans.

Transcriptomic analysis of human skin wound healing and rejuvenation following ablative fractional laser treatment

Ablative fractional laser treatment is considered the gold standard for skin rejuvenation. In order to understand how fractional laser works to rejuvenate skin, we performed microarray profiling on skin biopsies to identify temporal and dose-response changes in gene expression following fractional laser treatment. The backs of 14 women were treated with ablative fractional laser (Fraxel®) and 4 mm punch biopsies were collected from an untreated site and at the treated sites 1, 3, 7, 14, 21 and 28 days after the single treatment. In addition, in order to understand the effect that multiple fractional laser treatments have on skin rejuvenation, several sites were treated sequentially with either 1, 2, 3, or 4 treatments (with 28 days between treatments) followed by the collection of 4 mm punch biopsies. RNA was extracted from the biopsies, analyzed using Affymetrix U219 chips and gene expression was compared between untreated and treated sites. We observed dramatic changes in gene expression as early as 1 day after fractional laser treatment with changes remaining elevated even after 1 month. Analysis of individual genes demonstrated significant and time related changes in inflammatory, epidermal, and dermal genes, with dermal genes linked to extracellular matrix formation changing at later time points following fractional laser treatment. When comparing the age-related changes in skin gene expression to those induced by fractional laser, it was observed that fractional laser treatment reverses many of the changes in the aging gene expression. Finally, multiple fractional laser treatments, which cover different regions of a treatment area, resulted in a sustained or increased dermal remodeling response, with many genes either differentially regulated or continuously upregulated, supporting previous observations that maximal skin rejuvenation requires multiple fractional laser treatments. In conclusion, fractional laser treatment of human skin activates a number of biological processes involved in wound healing and tissue regeneration.

Tutorial: using nanoneedles for intracellular delivery

Intracellular delivery of advanced therapeutics, including biologicals and supramolecular agents, is complex because of the natural biological barriers that have evolved to protect the cell. Efficient delivery of therapeutic nucleic acids, proteins, peptides and nanoparticles is crucial for clinical adoption of emerging technologies that can benefit disease treatment through gene and cell therapy. Nanoneedles are arrays of vertical high-aspect-ratio nanostructures that can precisely manipulate complex processes at the cell interface, enabling effective intracellular delivery. This emerging technology has already enabled the development of efficient and non-destructive routes for direct access to intracellular environments and delivery of cell-impermeant payloads. However, successful implementation of this technology requires knowledge of several scientific fields, making it complex to access and adopt by researchers who are not directly involved in developing nanoneedle platforms. This presents an obstacle to the widespread adoption of nanoneedle technologies for drug delivery. This tutorial aims to equip researchers with the knowledge required to develop a nanoinjection workflow. It discusses the selection of nanoneedle devices, approaches for cargo loading and strategies for interfacing to biological systems and summarises an array of bioassays that can be used to evaluate the efficacy of intracellular delivery.

Molecular tools for the characterization of seizure susceptibility in genetic rodent models of epilepsy

Epilepsy is a chronic neurological disorder characterized by abnormal neuronal activity that arises from imbalances between excitatory and inhibitory synapses, which are highly correlated to functional and structural changes in specific brain regions. The difference between the normal and the epileptic brain may harbor genetic alterations, gene expression changes, and/or protein alterations in the epileptogenic nucleus. It is becoming increasingly clear that such differences contribute to the development of distinct epilepsy phenotypes. The current major challenges in epilepsy research include understanding the disease progression and clarifying epilepsy classifications by searching for novel molecular biomarkers. Thus, the application of molecular techniques to carry out comprehensive studies at deoxyribonucleic acid, messenger ribonucleic acid, and protein levels is of utmost importance to elucidate molecular dysregulations in the epileptic brain. The present review focused on the great diversity of technical approaches available and new research methodology, which are already being used to study molecular alterations underlying epilepsy. We have grouped the different techniques according to each step in the flow of information from DNA to RNA to proteins, and illustrated with specific examples in animal models of epilepsy, some of which are our own. Separately and collectively, the genomic and proteomic techniques, each with its own strengths and limitations, provide valuable information on molecular mechanisms underlying seizure susceptibility and regulation of neuronal excitability. Determining the molecular differences between genetic rodent models of epilepsy and their wild-type counterparts might be a key in determining mechanisms of seizure susceptibility and epileptogenesis as well as the discovery and development of novel antiepileptic agents. This article is part of the Special Issue "NEWroscience 2018".

Combinations of peptides synergistically activate the regenerative capacity of skin cells in vitro

Objective

To explore synergistic effects related to skin regeneration, peptides with distinct biological mechanisms of action were evaluated in combination with different skin cell lines in the presence or absence of niacinamide (Nam). Furthermore, the synergistic responses of peptide combinations on global gene expression were compared with the changes that occur with fractional laser resurfacing treatment, a gold standard approach for skin rejuvenation, to further define optimal peptide combinations.

Methods

Microarray profiling was used to characterize the biological responses of peptide combinations (+/− Nam) relative to the individual components in epidermal keratinocyte and dermal fibroblast cell lines. Cellular functional assays were utilized to confirm the synergistic effects of peptide combinations. Bioinformatics approaches were used to link the synergistic effects of peptide combinations on gene expression to the transcriptomics of the skin rejuvenation response from fractional laser treatment.

Results

Microarray analysis of skin cells treated with peptide combinations revealed synergistic changes in gene expression compared with individual peptide controls. Bioinformatic analysis of synergy genes in keratinocytes revealed the activation of NRF2‐mediated oxidative stress responses by a combination of Ac‐PPYL, Pal‐KTTKS and Nam. Additional analysis revealed direct downstream transcriptional targets of NRF2/ARE exhibiting synergistic regulation by this combination of materials, which was corroborated by a cellular reporter assay. NRF2‐mediated oxidative stress response pathways were also found to be activated in the transcriptomics of the early skin rejuvenation response to fractional laser treatment, suggesting the importance of this biology in the early stages of tissue repair. Additionally, the second combination of peptides (pal‐KT and Ac‐PPYL) was found to synergistically restore cellular ATP levels that had been depleted due to the presence of ROS, indicating an additional mechanism, whereby peptide synergies may accelerate skin repair.

Conclusion

Through combinatorial synergy studies, we have identified additional in vitro skin repair mechanisms beyond the previously described functions of individual peptides and correlated these to the transcriptomics of the skin rejuvenation response of fractional laser treatment. These findings suggest that specific peptides can act together, via complementary and synergistic mechanisms, to holistically enhance the regenerative capacity of in vitro skin cells.

Rank-in: enabling integrative analysis across microarray and RNA-seq for cancer

Though transcriptomics technologies evolve rapidly in the past decades, integrative analysis of mixed data between microarray and RNA-seq remains challenging due to the inherent variability difference between them. Here, Rank-In was proposed to correct the nonbiological effects across the two technologies, enabling freely blended data for consolidated analysis. Rank-In was rigorously validated via the public cell and tissue samples tested by both technologies. On the two reference samples of the SEQC project, Rank-In not only perfectly classified the 44 profiles but also achieved the best accuracy of 0.9 on predicting TaqMan-validated DEGs. More importantly, on 327 Glioblastoma (GBM) profiles and 248, 523 heterogeneous colon cancer profiles respectively, only Rank-In can successfully discriminate every single cancer profile from normal controls, while the others cannot. Further on different sizes of mixed seq-array GBM profiles, Rank-In can robustly reproduce a median range of DEG overlapping from 0.74 to 0.83 among top genes, whereas the others never exceed 0.72. Being the first effective method enabling mixed data of cross-technology analysis, Rank-In welcomes hybrid of array and seq profiles for integrative study on large/small, paired/unpaired and balanced/imbalanced samples, opening possibility to reduce sampling space of clinical cancer patients. Rank-In can be accessed at http://www.badd-cao.net/rank-in/index.html.

Assessing the replicability of spatial gene expression using atlas data from the adult mouse brain

High-throughput, spatially resolved gene expression techniques are poised to be transformative across biology by overcoming a central limitation in single-cell biology: the lack of information on relationships that organize the cells into the functional groupings characteristic of tissues in complex multicellular organisms. Spatial expression is particularly interesting in the mammalian brain, which has a highly defined structure, strong spatial constraint in its organization, and detailed multimodal phenotypes for cells and ensembles of cells that can be linked to mesoscale properties such as projection patterns, and from there, to circuits generating behavior. However, as with any type of expression data, cross-dataset benchmarking of spatial data is a crucial first step. Here, we assess the replicability, with reference to canonical brain subdivisions, between the Allen Institute's in situ hybridization data from the adult mouse brain (Allen Brain Atlas (ABA)) and a similar dataset collected using spatial transcriptomics (ST). With the advent of tractable spatial techniques, for the first time, we are able to benchmark the Allen Institute's whole-brain, whole-transcriptome spatial expression dataset with a second independent dataset that similarly spans the whole brain and transcriptome. We use regularized linear regression (LASSO), linear regression, and correlation-based feature selection in a supervised learning framework to classify expression samples relative to their assayed location. We show that Allen Reference Atlas labels are classifiable using transcription in both data sets, but that performance is higher in the ABA than in ST. Furthermore, models trained in one dataset and tested in the opposite dataset do not reproduce classification performance bidirectionally. While an identifying expression profile can be found for a given brain area, it does not generalize to the opposite dataset. In general, we found that canonical brain area labels are classifiable in gene expression space within dataset and that our observed performance is not merely reflecting physical distance in the brain. However, we also show that cross-platform classification is not robust. Emerging spatial datasets from the mouse brain will allow further characterization of cross-dataset replicability ultimately providing a valuable reference set for understanding the cell biology of the brain.

Transcriptome-based screening of ion channels and transporters in a migratory chondroprogenitor cell line isolated from late-stage osteoarthritic cartilage

Chondrogenic progenitor cells (CPCs) may be used as an alternative source of cells with potentially superior chondrogenic potential compared to mesenchymal stem cells (MSCs), and could be exploited for future regenerative therapies targeting articular cartilage in degenerative diseases such as osteoarthritis (OA). In this study, we hypothesised that CPCs derived from OA cartilage may be characterised by a distinct channelome. First, a global transcriptomic analysis using Affymetrix microarrays was performed. We studied the profiles of those ion channels and transporter families that may be relevant to chondroprogenitor cell physiology. Following validation of the microarray data with quantitative reverse transcription-polymerase chain reaction, we examined the role of calcium-dependent potassium channels in CPCs and observed functional large-conductance calcium-activated potassium (BK) channels involved in the maintenance of the chondroprogenitor phenotype. In line with our very recent results, we found that the KCNMA1 gene was upregulated in CPCs and observed currents that could be attributed to the BK channel. The BK channel inhibitor paxilline significantly inhibited proliferation, increased the expression of the osteogenic transcription factor RUNX2, enhanced the migration parameters, and completely abolished spontaneous Ca²⁺ events in CPCs. Through characterisation of their channelome we demonstrate that CPCs are a distinct cell population but are highly similar to MSCs in many respects. This study adds key mechanistic data to the in-depth characterisation of CPCs and their phenotype in the context of cartilage regeneration.

The heat shock response, determined by QuantiGene multiplex, is impaired in HD mouse models and not caused by HSF1 reduction

Huntington's disease (HD) is a devastating neurodegenerative disorder, caused by a CAG/polyglutamine repeat expansion, that results in the aggregation of the huntingtin protein, culminating in the deposition of inclusion bodies in HD patient brains. We have previously shown that the heat shock response becomes impaired with disease progression in mouse models of HD. The disruption of this inducible arm of the proteostasis network is likely to exacerbate the pathogenesis of this protein-folding disease. To allow a rapid and more comprehensive analysis of the heat shock response, we have developed, and validated, a 16-plex QuantiGene assay that allows the expression of Hsf1 and nine heat shock genes, to be measured directly, and simultaneously, from mouse tissue. We used this QuantiGene assay to show that, following pharmacological activation in vivo, the heat shock response impairment in tibialis anterior, brain hemispheres and striatum was comparable between zQ175 and R6/2 mice. In contrast, although a heat shock impairment could be detected in R6/2 cortex, this was not apparent in the cortex from zQ175 mice. Whilst the mechanism underlying this impairment remains unknown, our data indicated that it is not caused by a reduction in HSF1 levels, as had been reported.

ON-bipolar cell gene expression during retinal degeneration: Implications for optogenetic visual restoration

Purpose

Retinal bipolar cells survive even in the later stages of inherited retinal degenerations (IRDs) and so are attractive targets for optogenetic approaches to vision restoration. However, it is not known to what extent the remodelling that these cells undergo during degeneration affects their function. Specifically, it is unclear if they are free from metabolic stress, receptive to adeno-associated viral vectors, suitable for opsin-based optogenetic tools and able to propagate signals by releasing neurotransmitter.

Methods

Fluorescence activated cell sorting (FACS) was performed to isolate labelled bipolar cells from dissociated retinae of litter-mates with or without the IRD mutation Pde6b^rd1/rd1 selectively expressing an enhanced yellow fluorescent protein (EYFP) as a marker in ON-bipolar cells. Subsequent mRNA extraction allowed Illumina® microarray comparison of gene expression in bipolar cells from degenerate to those of wild type retinae. Changes in four candidate genes were further investigated at the protein level using retinal immunohistochemistry over the course of degeneration.

Results

A total of sixty differentially expressed transcripts reached statistical significance: these did not include any genes directly associated with native primary bipolar cell signalling, nor changes consistent with metabolic stress. Four significantly altered genes (Srm2, Slf2, Anxa7 & Cntn1), implicated in synaptic remodelling, neurotransmitter release and viral vector entry had immunohistochemical staining colocalising with ON-bipolar cell markers and varying over the course of degeneration.

Conclusion

Our findings suggest relatively few gene expression changes in the context of degeneration: that despite remodelling, bipolar cells are likely to remain viable targets for optogenetic vision restoration. In addition, several genes where changes were seen could provide a basis for investigations to enhance the efficacy of optogenetic therapies.

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Bipolar cells are attractive targets for therapeutic optogenetics in IRDs.

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This is the first cell specific transcriptomic analysis of bipolar cells in an IRD model.

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Bipolar cells maintain expression of genes essential to act as targets for optogenetics.

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Protein staining relating to four candidate genes (Anxa7, Cntn1, Srm2, Sulf2) is confirmed using immunohistochemistry.

Optimal reference genes for real-time quantitative PCR and the expression of sigma factors in Acidithiobacillus caldus under various conditions

AIMS

Acidithiobacillus caldus is an important sulphur-oxidizing bacterium that plays crucial roles in the bioleaching industry. This study aims to analyse the optimal reference gene for real-time quantitative PCR (RT-qPCR) under different conditions and investigate the transcription levels of the sigma factor genes in the stress response.

METHODS AND RESULTS

We selected six housekeeping genes and analysed them via RT-qPCR using two energy resources, under four stress conditions. Three statistical approaches BestKeeper, geNorm, and NormFinder were utilized to determine transcription stability of these reference genes. The gapdH gene was the best internal control gene using elemental sulphur as an energy resource and under heat stress, map was the best internal control gene under pH and osmotic stress, era was the best internal control gene for the K₂ S₄ O₆ energy resource, and rpoC was the best internal control gene under Cu²⁺ stress. Furthermore, the expressional levels of 11 sigma factors were analysed by RT-qPCR in the stress response.

CONCLUSIONS

Stable internal control genes for RT-qPCR analysis of A. caldus were determined, and the expression patterns of sigma factor genes of A. caldus were investigated.

SIGNIFICANCE AND IMPACT OF THE STUDY

The identification of the optimal reference gene and analysis of transcription levels of sigma factors in A. caldus can provide clues for reference gene selection and the study of sigma factor function.

Fabrication of Ag nanorods on micropost array for a metal-enhanced fluorescence substrate with a high signal-to-background ratio

Selective fabrication of metallic nanostructures at the spotting area is required to increase the signal-to-background noise ratio (SBR) of the metal-enhanced fluorescence (MEF) substrate. As a simple and cost-effective fabrication method for MEF substrate with high SBR, a glancing angle deposition (GLAD) process of Ag material on the UV-imprinted micropost array (50 μm in height, 300 μm in diameter, and 600 μm in pitch) was proposed to selectively fabricate Ag nanorods on the top of micropost structure (spotting area). Ag nanorod formation at the bottom of the micropost decreased as the deposition angle in Ag GLAD increased. A deposition angle of 89° and deposition thickness of 500 nm were selected as the optimum GLAD conditions to maximize the SBR. The optimum Ag nanorods on micropost array (AgNMPA) MEF substrate provided 71-fold fluorescence signal enhancement and 25-times higher SBR than the bare glass substrate. It also provided 7-times higher SBR than the Ag nanorod MEF substrate, which has a similar Ag nanorod structure but is not selectively formed. The detection limit of AgNMPA was 16- and 4-times lower than that of the amine-functionalized glass substrate and commercial epoxy slide, respectively. Although the fluorescence signal of AgNMPA was similar to that of Ag nanorod substrate, the detection limit was 2-times lower because of the low signal standard deviation caused by the low background noise and clear spot shape.

RNAflow: An Effective and Simple RNA-Seq Differential Gene Expression Pipeline Using Nextflow

RNA-Seq enables the identification and quantification of RNA molecules, often with the aim of detecting differentially expressed genes (DEGs). Although RNA-Seq evolved into a standard technique, there is no universal gold standard for these data's computational analysis. On top of that, previous studies proved the irreproducibility of RNA-Seq studies. Here, we present a portable, scalable, and parallelizable Nextflow RNA-Seq pipeline to detect DEGs, which assures a high level of reproducibility. The pipeline automatically takes care of common pitfalls, such as ribosomal RNA removal and low abundance gene filtering. Apart from various visualizations for the DEG results, we incorporated downstream pathway analysis for common species as Homo sapiens and Mus musculus. We evaluated the DEG detection functionality while using qRT-PCR data serving as a reference and observed a very high correlation of the logarithmized gene expression fold changes.

Emerging therapeutic modality enhancing the efficiency of chemotherapeutic agents against head and neck squamous cell carcinoma cell lines

The current work aimed to evaluate bee venom (BV) cytotoxic effect and its synergistic action when combined with cisplatin (CIS) against four types of head and neck squamous cell carcinoma (HNSCC) cell lines. 3-(4,5-dimethylthiazol-2-Yl)-2,5-diphenyltetrazolium bromide (MTT) assay for cell viability, reverse transcription-polymerase chain reaction (RT-PCR) for expression of BCL2 associated X (BAX), B-cell lymphoma 2 (BCL2) and epidermal growth factor receptor (EGFR) genes and, flow cytometry for cell cycle analysis were performed. MTT assay revealed that BV caused an approximately 50% cell death for UMSCC12, UMSCC29, UMSCC38 and, UMSCC47 cell lines after 72 hr with 54.809 µg/ml, 61.287 µg/ml, 71.328 µg/ml and, 61.045 µg/ml, respectively. RT-PCR demonstrated a significant up-regulation of BAX gene and a significant down-regulation of BCL2 and EGFR genes among single or combined treatments with CIS and BV as compared to vehicle-treated. The cell lines treated with both BV and CIS showed marked elevation of BAX and a notable drop of BCL2 and EGFR expressions than single-treated groups. Cell cycle analysis via flow cytometry revealed significantly increased cells in the G2/M phase in single or combined-treated cell lines with CIS and BV when compared with vehicle-treated. Moreover, a significant decrease in cells in S phases among all single and combined treatments when matched with vehicle-treated. Briefly, the findings of the present study suggest that BV can exert an anti-cancer effect on HNSCC and may have the potentiality for potentiation of CIS cytotoxic effects and reduction of its adverse effects.

Systematic comparison and assessment of RNA-seq procedures for gene expression quantitative analysis

RNA-seq is currently considered the most powerful, robust and adaptable technique for measuring gene expression and transcription activation at genome-wide level. As the analysis of RNA-seq data is complex, it has prompted a large amount of research on algorithms and methods. This has resulted in a substantial increase in the number of options available at each step of the analysis. Consequently, there is no clear consensus about the most appropriate algorithms and pipelines that should be used to analyse RNA-seq data. In the present study, 192 pipelines using alternative methods were applied to 18 samples from two human cell lines and the performance of the results was evaluated. Raw gene expression signal was quantified by non-parametric statistics to measure precision and accuracy. Differential gene expression performance was estimated by testing 17 differential expression methods. The procedures were validated by qRT-PCR in the same samples. This study weighs up the advantages and disadvantages of the tested algorithms and pipelines providing a comprehensive guide to the different methods and procedures applied to the analysis of RNA-seq data, both for the quantification of the raw expression signal and for the differential gene expression.

Clinically applicable 53-Gene prognostic assay predicts chemotherapy benefit in gastric cancer: A multicenter study

BACKGROUND

We previously established a 53-gene prognostic signature for overall survival (OS) of gastric cancer patients. This retrospective multi-center study aimed to develop a clinically applicable gene expression detection assay and to investigate the prognostic value of this signature.

METHODS

A TCGA gastric adenocarcinoma cohort (TCGA-STAD) was used for comparing 53-gene signature with other gene signatures. A high-throughput mRNA hybridization gene expression assay was developed to quantify the expression of 53-genes in formalin-fixed paraffin-embedded tissues of 540 patients enrolled from three hospitals. 180 patents were randomly selected from two hospitals to build a prognostic prediction model based on the 53-gene signature using leave-p-out (one-third out) cross-validation method together with Cox regression and Kaplan-Meier analysis, and the model was assessed on three validation cohorts.

FINDINGS

In the evaluation phase, studies based on TCGA-STAD showed that the 53-gene signature was significantly superior to other three prognostic signatures and was independent of TCGA molecular subtypes and clinical factors. For clinical validation and utility, the prognostic scores were generated using the newly developed assay, which was reliable and sensitive, in 100 sampling training sets and were significantly associated with OS in 100 sampling validation sets. The scores were significantly associated with OS in three independent and combined validation cohorts, and in patients with stages II and III/IV. The multivariate Cox regression demonstrated that the prognostic power of the score was independent of clinical factors, consistent with those findings in the TCGA dataset. Finally, patients with good prognostic scores exhibited significantly a better 5-year OS rate from adjuvant FOLFOX chemotherapy after surgery than from other chemotherapies.

INTERPRETATION

The 53-gene prognostic score system is clinically applicable for predicting the OS of patients independent of clinical factors in gastric cancers, which could also be a promising predictive biomarker for FOLFOX regimen.

FUNDING

Chinese National Science and Technology, National Natural Science Foundation and Natural Science Foundation of Jiangsu Province.

Microarray Normalization Revisited for Reproducible Breast Cancer Biomarkers

Precision medicine for breast cancer relies on biomarkers to select therapies. However, the reliability of biomarkers drawn from gene expression arrays has been questioned and calls for reassessment, in particular for large datasets. We revisit widely used data-normalization procedures and evaluate differences in outcome in order to pinpoint the most reliable reprocessing methods biomarkers can be based upon. We generated a database of 3753 breast cancer patients out of 38 studies by downloading and curating patient samples from NCBI-GEO. As gene-expression biomarkers, we select the assessment of receptor status and breast cancer subtype classification. Each normalization procedure is applied separately, and biomarkers are then evaluated for each patient. Differences between normalization pipelines are quantified as percentages of patients having outcomes different for each pipeline. Some normalization procedures lead to quite consistent biomarkers, differing only in 1-2% of patients. Other normalization procedures—some of them have been used in many clinical studies—end up with distrusting discrepancies (10% and more). A good deal of doubt regarding the reliability of microarrays may root in the haphazard application of inadequate preprocessing pipelines. Several modes of batch corrections are evaluated regarding a possible improvement of receptor prediction from gene expression versus the golden standard of immunohistochemistry. Finally, we nominate those normalization methods yielding consistent and trustable results. Adequate bioinformatics data preprocessing is key and crucial for any subsequent statistics to arrive at trustable results. We conclude with a suggestion for future bioinformatics development to further increase the reliability of cancer biomarkers.

Glycogene Expression Profiling of Hepatic Cells by RNA-Seq Analysis for Glyco-Biomarker Identification

Glycans are primarily generated by “glycogenes,” which consist of more than 200 genes for glycosynthesis, including sugar-nucleotide synthases, sugar-nucleotide transporters, and glycosyltransferases. Measuring the expression level of glycogenes is one of the approaches to analyze the glycomes of particular biological and clinical samples. To develop an effective strategy for identifying the glycosylated biomarkers, we performed transcriptome analyses using quantitative real-time polymerase chain reaction (qRT-PCR) arrays and RNA sequencing (RNA-Seq). First, we measured and analyzed the transcriptome from the primary culture of human liver cells and hepatocarcinoma cells using RNA-Seq. This analysis revealed similar but distinctive expression profiles of glycogenes among hepatic cells as indicated by the qRT-PCR arrays, which determined a copy number of 186 glycogenes. Both data sets indicated that altered expression of glycosyltransferases affect the glycosylation of particular glycoproteins, which is consistent with the mass analysis data. Moreover, RNA-Seq analysis can uncover mutations in glycogenes and search differently expressed genes out of more than 50,000 distinct human gene transcripts including candidate biomarkers that were previously reported for hepatocarcinoma cells. Identification of candidate glyco-biomarkers from the expression profile of the glycogenes and proteins from liver cancer tissues available from public database emphasized the possibility that even though the expression level of biomarkers might not be altered, the expression of the glycogenes modifying biomarkers, generating glyco-biomarkers, might be different. Pathway analysis revealed that ~20% of the glycogenes exhibited different expression levels in normal and cancer cells. Thus, transcriptome analyses using both qRT-PCR array and RNA-Seq in combination with glycome and glycoproteome analyses can be advantageous to identify “glyco-biomarkers” by reinforcing information at the expression levels of both glycogenes and proteins.

Integrative Analysis for Elucidating Transcriptomics Landscapes of Glucocorticoid-Induced Osteoporosis

Osteoporosis is the most common bone metabolic disease, characterized by bone mass loss and bone microstructure changes due to unbalanced bone conversion, which increases bone fragility and fracture risk. Glucocorticoids are clinically used to treat a variety of diseases, including inflammation, cancer and autoimmune diseases. However, excess glucocorticoids can cause osteoporosis. Herein we performed an integrated analysis of two glucocorticoid-related microarray datasets. The WGCNA analysis identified 3 and 4 glucocorticoid-related gene modules, respectively. Differential expression analysis revealed 1047 and 844 differentially expressed genes in the two datasets. After integrating differentially expressed glucocorticoid-related genes, we found that most of the robust differentially expressed genes were up-regulated. Through protein-protein interaction analysis, we obtained 158 glucocorticoid-related candidate genes. Enrichment analysis showed that these genes are significantly enriched in the osteoporosis related pathways. Our results provided new insights into glucocorticoid-induced osteoporosis and potential candidate markers of osteoporosis.

Extensive Expression Analysis of Htt Transcripts in Brain Regions from the zQ175 HD Mouse Model Using a QuantiGene Multiplex Assay

Huntington's disease (HD) is an inherited neurodegenerative disorder caused by a CAG repeat expansion within exon 1 of the huntingtin (HTT) gene. HTT mRNA contains 67 exons and does not always splice between exon 1 and exon 2 leading to the production of a small polyadenylated HTTexon1 transcript, and the full-length HTT mRNA has three 3'UTR isoforms. We have developed a QuantiGene multiplex panel for the simultaneous detection of all of these mouse Htt transcripts directly from tissue lysates and demonstrate that this can replace the more work-intensive Taqman qPCR assays. We have applied this to the analysis of brain regions from the zQ175 HD mouse model and wild type littermates at two months of age. We show that the incomplete splicing of Htt occurs throughout the brain and confirm that this originates from the mutant and not endogenous Htt allele. Given that HTTexon1 encodes the highly pathogenic exon 1 HTT protein, it is essential that the levels of all Htt transcripts can be monitored when evaluating HTT lowering approaches. Our QuantiGene panel will allow the rapid comparative assessment of all Htt transcripts in cell lysates and mouse tissues without the need to first extract RNA.

Measurement of mRNA therapeutics: method development and validation challenges

The progression of chemically modified mRNA therapeutics through development pipelines is accelerating for many disease indications and the need to assess these analytes is becoming more routine for the pharmaceutical industry and contract research organizations. This article describes some of the challenges and strategies for performing regulated bioanalysis of modified mRNA therapeutics by comparing the two main analytical approaches – quantitative reverse transcription PCR and branched DNA.

Practical determination of LODP (limit of detection for microarray platform) for the evaluation of microarray platforms

The performance indicator called limit of detection for microarray platform (LODP) was defined in ISO 16578:2013. The methods to determine practical LODP were explored. In general, + 3 SD of the background is used as the signal strength of limit of detection and criteria for dividing positive and negative results. Since the negative signal had been defined differently for each microarray platform, signals obtained from Non-Probe Spots (NPS) installed on the microarrays were defined as the "background" of microarrays. LODP was determined as the lowest concentration of which the average signal exceeded Avg. + 3 SD of the background (NPS) and the signal was significantly different from those of the lower and higher adjacent concentration points measured with a diluted series of reference materials. For reliable qualitative analysis, the positive results can be defined as signals higher than those corresponding to LODP and negative results as lower signals, without determining limit of detection for all target probes. The use of LODP also enables comparisons of platform performances without checking sequence dependencies, and assists to select reliable and fitting platforms for experimental purposes.

Whole transcriptome analysis of diabetic nephropathy in the db/db mouse model of type 2 diabetes

Whole-transcriptome analysis using RNA sequencing (RNA-seq) affords broader insights about gene expression regulatory networks in diabetic nephropathy (DN). To better explore the molecular basis of DN, kidney tissue from db/db DN model mice and control mice were submitted to RNA-seq analysis. Thousands of long noncoding RNAs (lncRNAs) and messenger RNAs (mRNAs) were found to be significantly differentially expressed in the DN group relative to the control group. To research the regulatory mechanism of these lncRNAs and mRNAs, the integrated co-expression networks were constructed for 322 mRNAs and 27 lncRNAs that revealed significantly correlated expression patterns in DN. The potential roles of these co-expressed mRNAs were classified by Gene Ontology enrichment and Kyoto Encyclopedia of Genes and Genomes pathway analyses. The co-expression networks involved 27 lncRNAs interacting with 38 key mRNAs related to metabolic processes, including ND4/4L, Ndufa2/5, Ndufb4/7, Ndufs3, Uqcrc1, Aco2, Alad, Alas1, Alpl, Atp5j2, Coq5, Coq6, Cth, and CytB, all of which are highly related to encoding subunits of the mitochondrial complexes. Thus, mitochondrial dysfunction could result in renal function decline in DN. Seven dysregulated lncRNAs and nine dysregulated mRNAs in the DN model were confirmed by quantitative real-time polymerase chain reaction. The lncRNA-mRNA co-expression network provides novel evidence to support the contention that metabolic changes are associated with metabolic reprogramming in the kidneys, and that these changes play a critical role during the progression of DN.

A biological function based biomarker panel optimization process

Implementation of multi-gene biomarker panels identified from high throughput data, including microarray or next generation sequencing, need to be adapted to a platform suitable in a clinical setting such as quantitative polymerase chain reaction. However, technical challenges when transitioning from one measurement platform to another, such as inconsistent measurement results can affect panel development. We describe a process to overcome the challenges by replacing poor performing genes during platform transition and reducing the number of features without impacting classification performance. This approach assumes that a diagnostic panel reflects the effect of dysregulated biological processes associated with a disease, and genes involved in the same biological processes and coordinately affected by a disease share a similar discriminatory power. The utility of this optimization process was assessed using a published sepsis diagnostic panel. Substitution of more than half of the genes and/or reducing genes based on biological processes did not negatively affect the performance of the sepsis diagnostic panel. Our results suggest a systematic gene substitution and reduction process based on biological function can be used to alleviate the challenges associated with clinical development of biomarker panels.

aFold - using polynomial uncertainty modelling for differential gene expression estimation from RNA sequencing data

Background

Data normalization and identification of significant differential expression represent crucial steps in RNA-Seq analysis. Many available tools rely on assumptions that are often not met by real data, including the common assumption of symmetrical distribution of up- and down-regulated genes, the presence of only few differentially expressed genes and/or few outliers. Moreover, the cut-off for selecting significantly differentially expressed genes for further downstream analysis often depend on arbitrary choices.

Results

We here introduce a new tool for estimating differential expression in noisy real-life data. It employs a novel normalization procedure (qtotal), which takes account of the overall distribution of read counts for data standardization enhancing reliable identification of differential gene expression, especially in case of asymmetrical distributions of up- and downregulated genes. The tool then introduces a polynomial algorithm (aFold) to model the uncertainty of read counts across treatments and genes. We extensively benchmark aFold on a variety of simulated and validated real-life data sets (e.g. ABRF, SEQC and MAQC-II) and show a higher ability to correctly identify differentially expressed genes under most tested conditions. aFold infers fold change values that are comparable across experiments, thereby facilitating data clustering, visualization, and other downstream applications.

Conclusions

We here present a new transcriptomics analysis tool that includes both a data normalization method and a differential expression analysis approach. The new tool is shown to enhance reliable identification of significant differential expression across distinct data distributions. It outcompetes alternative procedures in case of asymmetrical distributions of up- versus down-regulated genes and also the presence of outliers, all common to real data sets.

Electronic supplementary material

The online version of this article (10.1186/s12864-019-5686-1) contains supplementary material, which is available to authorized users.

Pharmacokinetics and ex vivo anti-inflammatory effects of oral misoprostol in horses

BACKGROUND

Misoprostol is an E prostanoid (EP) 2, 3 and 4 receptor agonist that is anecdotally used to treat and prevent NSAID-induced GI injury in horses. Misoprostol elicits anti-inflammatory effects in vivo in men and rodents, and inhibits TNFα production in equine leucocytes in vitro.

OBJECTIVE

Define the pharmacokinetic parameters of oral misoprostol in horses, and determine the inhibitory effect of oral misoprostol administration on equine leucocyte TNFα production in an ex vivo inflammation model.

STUDY DESIGN

Pharmacokinetic study, ex vivo experimental study.

METHODS

Six healthy adult horses of mixed breeds were used. In phase one, horses were given 5 μg/kg misoprostol orally, and blood was collected at predetermined times for determination of misoprostol free acid (MFA) by UHPLC-MS/MS. Pharmacokinetic parameters were calculated. In phase two, horses were dosed as in phase one, and blood was collected at T0, 0.5, 1 and 4 h following misoprostol administration for leucocyte isolation. Leucocytes were stimulated with 100 ng/mL LPS, and TNFα mRNA concentrations were determined via quantitative real-time PCR.

RESULTS

About 5 μg/kg oral misoprostol produced a rapid time to maximum concentration (Tmax ) of 23.4 ± 2.4 min, with a maximum concentration (Cmax ) of 0.29 ± 0.07 ng/mL and area under the curve (AUC0-∞ ) of 0.4 ± 0.12 h ng/mL. LPS stimulation of equine leucocytes ex vivo significantly increased TNFα mRNA concentrations, and there was no significant effect of misoprostol even at the Tmax .

MAIN LIMITATIONS

Only a single dose was used, and sample size was small.

CONCLUSIONS

Misoprostol is rapidly absorbed following oral administration in horses, and a single 5 μg/kg dose had no significant inhibitory effect on ex vivo LPS-stimulated TNFα mRNA production in leucocytes. Further studies analysing different dosing strategies, including repeat administration or combination with other anti-inflammatory drugs, are warranted.

DegNorm: normalization of generalized transcript degradation improves accuracy in RNA-seq analysis

RNA degradation affects RNA-seq quality when profiling transcriptional activities in cells. Here, we show that transcript degradation is both gene- and sample-specific and is a common and significant factor that may bias the results in RNA-seq analysis. Most existing global normalization approaches are ineffective to correct for degradation bias. We propose a novel pipeline named DegNorm to adjust the read counts for transcript degradation heterogeneity on a gene-by-gene basis while simultaneously controlling for the sequencing depth. The robust and effective performance of this method is demonstrated in an extensive set of simulated and real RNA-seq data.

Changes in expression of p53 and inflammatory factors in patients with ulcerative colitis

Ulcerative colitis (UC) is an important risk factor for the occurrence of colon cancer, and changes in expression of p53 and inflammatory factors are closely related to the pathogenesis of colon cancer. Therefore, changes in expression of p53 and inflammatory factors in UC were investigated to explore its intrinsic pathogenetic laws. The levels of inflammatory factors, such as interleukin-6 (IL-6), tumor necrosis factor-α (TNF-α), IL-10 and IL-4, in peripheral blood of UC patients and healthy adults were detected via enzyme-linked immunosorbent assay, and the changes in p53 expression were analyzed via immunohistochemistry and western blotting, and the correlation of p53 expression with changes in cytokines was also analyzed. Moreover, changes in 45S preribosomal ribonucleic acid (preRNA) transcriptional activity in four kinds of cell lines exposed to IL-6 were analyzed and determined by using reverse transcription-quantitative polymerase chain reaction. Finally, the C-myc protein expression after IL-6 stimulation was analyzed and evaluated via western blot analysis. The levels of IL-6 and TNF-α in peripheral blood in the UC patient group were significantly increased compared with those in the healthy adult group (P<0.01), while the levels of IL-10 and IL-4 in peripheral blood were significantly decreased compared with those in the healthy adult group (P<0.01). The p53 expression had a significant negative correlation with IL-6. The results showed that IL-6 activated C-myc messenger RNA (mRNA) translation, thereby upregulating the ribosomal RNA (rRNA) transcription. Additionally, IL-6 stimulated the mouse double minute 2 homolog (MDM2)-mediated proteasomal degradation of p53 by reducing the availability of ribosomal protein used for MDM2 binding, thus resulting in the downregulation of p53 protein expression. The findings of the study show that, expression level of IL-6 was increased in UC patients, which regulates the downregulation of p53 expression level and plays a role in tumorigenesis through enhancing cell proliferation and inhibiting apoptosis.

Annatto () δ-TCT supplementation protected against embryonic DNA damages through alterations in PI3K/ Akt-Cyclin D1 pathway

Protective action by annatto-derived delta-tocotrienol (δ-TCT) and soy-derived alpha-tocopherol (α-TOC) through the regulation of PI3K/Akt-Cyclin D1 pathway against the nicotine-induced DNA damages is the focus of the present study. Nicotine, which has been widely reported to have numerous adverse effects on the reproductive system, was used as reproductive toxicant. 48 female balb/c mice (6-8 weeks) (23-25 g) were randomly divided into 8 groups (G1-G8; n = 6) and treated with either nicotine or/and annatto δ-TCT/soy α-TOC for 7 consecutive days. On Day 8, the females were superovulated and mated before euthanized for embryo collection (46 hours post-coitum). Fifty 2-cell embryos from each group were used in gene expression analysis using Affymetrix QuantiGene Plex2.0 assay. Findings indicated that nicotine (G2) significantly decreased (p < 0.05) the number of produced 2-cell embryos compared to control (G1). Intervention with mixed annatto δ-TCT (G3) and pure annatto δ-TCT (G4) significantly increased the number of produced 2-cell embryos by 127 % and 79 % respectively compared to G2, but these were lower than G1. Concurrent treatment with soy α-TOC (G5) decreased embryo production by 7 %. Supplementations with δ-TCT and α-TOC alone (G6-G8) significantly increased (p < 0.05) the number of produced 2-cell embryos by 50 %, 36 % and 41 % respectively, compared to control (G1). These results were found to be associated with the alterations in the PI3K/Akt-Cyclin D1 gene expressions, indicating the inhibitory effects of annatto δ-TCT and soy α-TOC against the nicotinic embryonic damages. To our knowledge, this is the first attempt on studying the benefits of annatto δ-TCT on murine preimplantation 2-cell embryos.

Novel Data Transformations for RNA-seq Differential Expression Analysis

We propose eight data transformations (r, r2, rv, rv2, l, l2, lv, and lv2) for RNA-seq data analysis aiming to make the transformed sample mean to be representative of the distribution center since it is not always possible to transform count data to satisfy the normality assumption. Simulation studies showed that for data sets with small (e.g., nCases = nControls = 3) or large sample size (e.g., nCases = nControls = 100) limma based on data from the l, l2, and r2 transformations performed better than limma based on data from the voom transformation in term of accuracy, FDR, and FNR. For datasets with moderate sample size (e.g., nCases = nControls = 30 or 50), limma with the rv and rv2 transformations performed similarly to limma with the voom transformation. Real data analysis results are consistent with simulation analysis results: limma with the r, l, r2, and l2 transformation performed better than limma with the voom transformation when sample sizes are small or large; limma with the rv and rv2 transformations performed similarly to limma with the voom transformation when sample sizes are moderate. We also observed from our data analyses that for datasets with large sample size, the gene-selection via the Wilcoxon rank sum test (a non-parametric two sample test method) based on the raw data outperformed limma based on the transformed data.

Cold Ischemia Score: An mRNA Assay for the Detection of Extended Cold Ischemia in Formalin-Fixed, Paraffin-Embedded Tissue

Although there are thousands of formalin-fixed paraffin-embedded (FFPE) tissue blocks potentially available for scientific research, many are of questionable quality, partly due to unknown preanalytical variables. We analyzed FFPE tissue biospecimens as part of the National Cancer Institute (NCI) Biospecimen Preanalytical Variables program to identify mRNA markers denoting cold ischemic time. The mRNA was extracted from colon, kidney, and ovary cancer FFPE blocks (40 patients, 10-12 hr fixation time) with 1, 2, 3, and 12 hr cold ischemic times, then analyzed using qRT-PCR for 23 genes selected following a literature search. No genes tested could determine short ischemic times (1-3 hr). However, a combination of three unstable genes normalized to a more stable gene could generate a "Cold Ischemia Score" that could distinguish 1 to 3 hr cold ischemia from 12 hr cold ischemia with 62% sensitivity and 84% specificity.

Annatto (Bixa orellana) δ-TCT Supplementation Protection against Embryonic Malformations through Alterations in PI3K/Akt-Cyclin D1 Pathway

Protective action by annatto-derived delta-tocotrienol (δ-TCT) and soy-derived alpha-tocopherol (α-TOC) through the regulation of the PI3K/Akt-cyclin D1 pathway against nicotine-induced DNA damage is the focus of the present study. Nicotine, which has been widely reported to have numerous adverse effects on the reproductive system, was used as a reproductive toxicant. 48 female balb/c mice (6⁻8 weeks) (23⁻25 g) were randomly divided into eight groups (Grp.1⁻Grp.8; n = 6) and treated with either nicotine or/and annatto δ-TCT/soy α-TOC for seven consecutive days. On Day 8, the females were superovulated and mated before euthanization for embryo collection (46 h post-coitum). Fifty 2-cell embryos from each group were used in gene expression analysis using Affymetrix QuantiGene Plex2.0 assay. Findings indicated that nicotine (Grp.2) significantly decreased (p < 0.05) the number of produced 2-cell embryos compared to the control (Grp.1). Intervention with mixed annatto δ-TCT (Grp.3) and pure annatto δ-TCT (Grp.4) significantly increased the number of produced 2-cell embryos by 127% and 79%, respectively compared to Grp.2, but these were lower than Grp.1. Concurrent treatment with soy α-TOC (Grp.5) decreased embryo production by 7%. Supplementations with δ-TCT and α-TOC alone (Grp.6-Grp.8) significantly increased (p < 0.05) the number of produced 2-cell embryos by 50%, 36%, and 41%, respectively, compared to control (Grp.1). These results were found to be associated with alterations in the PI3K/Akt-Cyclin D1 genes expressions, indicating the inhibitory effects of annatto δ-TCT and soy α-TOC against nicotinic embryonic damage. To our knowledge, this is the first attempt in studying the benefits of annatto δ-TCT on murine preimplantation 2-cell embryos.

Abnormal response of costal chondrocytes to acidosis in patients with chest wall deformity

Costal cartilage is much understudied compared to the load bearing cartilages. Abnormally grown costal cartilages are associated with the inherited chest wall deformities pectus excavatum and pectus carinatum resulting in sunken or pigeon chest respectively. A lack of understanding of the ultrastructural and molecular biology properties of costal cartilage is a major confounder in predicting causes and outcomes of these disorders. Due to the avascular nature of cartilage, chondrocytes metabolize glycolytically, producing an acidic environment. During physical activity hydrogen ions move within cartilage driven by compressive forces, thus at any one time, chondrocytes experience transient changes in pH. A variety of ion channels on chondrocytes plasma membrane equip them to function in the rapidly changing conditions they experience. In this paper we describe reduced expression of the ASIC2 gene encoding the acid sensing ion channel isoform 2 (previously referred to as ACCN1 or ACCN) in patients with chest wall deformities. We hypothesized that chondrocytes from these patients cannot respond normally to changes in pH that are an integral part of the biology of this tissue. Activation of ASICs indirectly creates a cascade ultimately dependent on intracellular calcium transients. The objective of this paper was to compare internal calcium signaling in response to external pH changes in costal chondrocytes from patients with chest wall deformities and healthy individuals. Although the molecular mechanism through which chondrocytes are regulated by acidosis remains unknown, we observed reduced amplitudes of calcium rise in patient chondrocytes exposed to low pH that become further impaired upon repeat exposure.

Fabrication of Highly Packed Plasmonic Nanolens Array Using Polymer Nanoimprinted Nanodots for an Enhanced Fluorescence Substrate

A simple and cost-effective fabrication method for plasmonic nanolens arrays (PNA) with a narrow gap has been proposed for fabricating enhanced fluorescence substrates, in which the fluorophores interacting with the enhanced electromagnetic field generated by localized surface plasmons provide a higher fluorescence signal. The PNA was fabricated by the sequential depositions of the SiO₂ and Ag layers on a UV-nanoimprinted nanodot array with a pitch of 500 nm, a diameter of 250 nm, and a height of 100 nm. During the deposition processes, the shape of the nanodots changed to that of nanolenses, and the gap between the nanolenses was decreased via sidewall deposition. To examine the feasibility of the fabricated PNA for enhanced fluorescence application, a streptavidin-Cy5 (SA-Cy5) conjugate dissolved in a saline buffer solution was spotted on the PNA, and the fluorescence signals of the SA-Cy5 were measured and compared with those on a bare glass substrate. The enhancement factor was affected by the gap between the nanolenses, and the maximum enhancement factor of ~128 was obtained from the PNA with a SiO₂ layer thickness of 150 nm and an Ag layer thickness of 100 nm. Finally, an electromagnetic field analysis was used to examine the fluorescence signal enhancement, and was conducted using rigorous coupled wave analysis.

Multiplex Quantification Identifies Novel Exercise-regulated Myokines/Cytokines in Plasma and in Glycolytic and Oxidative Skeletal Muscle

Exercise is known to confer major health benefits, but the underlying mechanisms are not well understood. The systemic effects of exercise on multi-organ systems are thought to be partly because of myokines/cytokines secreted by skeletal muscle. The extent to which exercise alters cytokine expression and secretion in different muscle fiber types has not been systematically examined. Here, we assessed changes in 66 mouse cytokines in serum, and in glycolytic (plantaris) and oxidative (soleus) muscles, in response to sprint, endurance, or chronic wheel running. Both acute and short-term exercise significantly altered a large fraction of cytokines in both serum and muscle, twenty-three of which are considered novel exercise-regulated myokines. Most of the secreted cytokine receptors profiled were also altered by physical activity, suggesting an exercise-regulated mechanism that modulates the generation of soluble receptors found in circulation. A greater overlap in cytokine profile was seen between endurance and chronic wheel running. Between fiber types, both acute and chronic exercise induced significantly more cytokine changes in oxidative compared with glycolytic muscle. Further, changes in a subset of circulating cytokines were not matched by their changes in muscle, but instead reflected altered expression in liver and adipose tissues. Last, exercise-induced changes in cytokine mRNA and protein were only minimally correlated in soleus and plantaris. In sum, our results indicate that exercise regulates many cytokines whose pleiotropic actions may be linked to positive health outcomes. These data provide a framework to further understand potential crosstalk between skeletal muscle and other organ compartments.

RNAseq analysis of bronchial epithelial cells to identify COPD-associated genes and SNPs

Background

There is a need for more powerful methods to identify low-effect SNPs that contribute to hereditary COPD pathogenesis. We hypothesized that SNPs contributing to COPD risk through cis-regulatory effects are enriched in genes comprised by bronchial epithelial cell (BEC) expression patterns associated with COPD.

Methods

To test this hypothesis, normal BEC specimens were obtained by bronchoscopy from 60 subjects: 30 subjects with COPD defined by spirometry (FEV1/FVC < 0.7, FEV1% < 80%), and 30 non-COPD controls. Targeted next generation sequencing was used to measure total and allele-specific expression of 35 genes in genome maintenance (GM) genes pathways linked to COPD pathogenesis, including seven TP53 and CEBP transcription factor family members. Shrinkage linear discriminant analysis (SLDA) was used to identify COPD-classification models. COPD GWAS were queried for putative cis-regulatory SNPs in the targeted genes.

Results

On a network basis, TP53 and CEBP transcription factor pathway gene pair network connections, including key DNA repair gene ERCC5, were significantly different in COPD subjects (e.g., Wilcoxon rank sum test for closeness, p-value = 5.0E-11). ERCC5 SNP rs4150275 association with chronic bronchitis was identified in a set of Lung Health Study (LHS) COPD GWAS SNPs restricted to those in putative regulatory regions within the targeted genes, and this association was validated in the COPDgene non-hispanic white (NHW) GWAS. ERCC5 SNP rs4150275 is linked (D’ = 1) to ERCC5 SNP rs17655 which displayed differential allelic expression (DAE) in BEC and is an expression quantitative trait locus (eQTL) in lung tissue (p = 3.2E-7). SNPs in linkage (D’ = 1) with rs17655 were predicted to alter miRNA binding (rs873601). A classifier model that comprised gene features CAT, CEBPG, GPX1, KEAP1, TP73, and XPA had pooled 10-fold cross-validation receiver operator characteristic area under the curve of 75.4% (95% CI: 66.3%–89.3%). The prevalence of DAE was higher than expected (p = 0.0023) in the classifier genes.

Conclusions

GM genes comprised by COPD-associated BEC expression patterns were enriched for SNPs with cis-regulatory function, including a putative cis-rSNP in ERCC5 that was associated with COPD risk. These findings support additional total and allele-specific expression analysis of gene pathways with high prior likelihood for involvement in COPD pathogenesis.

Electronic supplementary material

The online version of this article (10.1186/s12890-018-0603-y) contains supplementary material, which is available to authorized users.

ATTED-II in 2018: A Plant Coexpression Database Based on Investigation of the Statistical Property of the Mutual Rank Index

ATTED-II (http://atted.jp) is a coexpression database for plant species to aid in the discovery of relationships of unknown genes within a species. As an advanced coexpression analysis method, multispecies comparisons have the potential to detect alterations in gene relationships within an evolutionary context. However, determining the validity of comparative coexpression studies is difficult without quantitative assessments of the quality of coexpression data. ATTED-II (version 9) provides 16 coexpression platforms for nine plant species, including seven species supported by both microarray- and RNA sequencing (RNAseq)-based coexpression data. Two independent sources of coexpression data enable the assessment of the reproducibility of coexpression. The latest coexpression data for Arabidopsis (Ath-m.c7-1 and Ath-r.c3-0) showed the highest reproducibility (Jaccard coefficient = 0.13) among previous coexpression data in ATTED-II. We also investigated the statistical basis of the mutual rank (MR) index as a coexpression measure by bootstrap sampling of experimental units. We found that the error distribution of the logit-transformed MR index showed normality with equal variances for each coexpression platform. Because the MR error was strongly correlated with the number of samples for the coexpression data, typical confidence intervals for the MR index can be estimated for any coexpression platform. These new, high-quality coexpression data can be analyzed with any tool in ATTED-II and combined with external resources to obtain insight into plant biology.

Microarray Data Analysis for Transcriptome Profiling

Microarray data have vastly accumulated in the past two decades. Due to the high-throughput characteristic of microarray techniques, it has transformed biological studies from specific genes to transcriptome level, and deeply boosted many fields of biological studies. While microarray offers great advantages for expression profiling, on the other hand it faces a lot challenges for computational analysis. In this chapter, we demonstrate how to perform standard analysis including data preprocessing, quality assessment, differential expression analysis, and general downstream analyses.

Current and Future Methods for mRNA Analysis: A Drive Toward Single Molecule Sequencing

The transcriptome encompasses a range of species including messenger RNA, and other noncoding RNA such as rRNA, tRNA, and short and long noncoding RNAs. Due to the huge role played by mRNA in development and disease, several methods have been developed to sequence and characterize mRNA, with RNA sequencing (RNA-Seq) emerging as the current method of choice particularly for large high-throughput studies. Short-read RNA-Seq which involves sequencing of short cDNA fragments and computationally assembling them to reconstruct the transcriptome, or aligning them to a reference is the most widely used approach. However, due to inherent limitations of this approach in de novo transcriptome assembly and isoform quantification, long-read RNA-Seq approaches, which also happen to be single molecule sequencing approaches, are increasingly becoming the standard for de novo transcriptome assembly and isoform quantification. In this chapter, we review the technical aspects of the current methods of RNA-Seq, both short and long-read approaches, and data analysis methods available. We discuss recent advances in single-cell RNA-Seq and direct RNA-Seq approaches, which perhaps will dominate the future of RNA-Seq.

RNA-Seq differential expression analysis: An extended review and a software tool

The correct identification of differentially expressed genes (DEGs) between specific conditions is a key in the understanding phenotypic variation. High-throughput transcriptome sequencing (RNA-Seq) has become the main option for these studies. Thus, the number of methods and softwares for differential expression analysis from RNA-Seq data also increased rapidly. However, there is no consensus about the most appropriate pipeline or protocol for identifying differentially expressed genes from RNA-Seq data. This work presents an extended review on the topic that includes the evaluation of six methods of mapping reads, including pseudo-alignment and quasi-mapping and nine methods of differential expression analysis from RNA-Seq data. The adopted methods were evaluated based on real RNA-Seq data, using qRT-PCR data as reference (gold-standard). As part of the results, we developed a software that performs all the analysis presented in this work, which is freely available at https://github.com/costasilvati/consexpression. The results indicated that mapping methods have minimal impact on the final DEGs analysis, considering that adopted data have an annotated reference genome. Regarding the adopted experimental model, the DEGs identification methods that have more consistent results were the limma+voom, NOIseq and DESeq2. Additionally, the consensus among five DEGs identification methods guarantees a list of DEGs with great accuracy, indicating that the combination of different methods can produce more suitable results. The consensus option is also included for use in the available software.