Transcriptional biomarkers--high throughput screening, quantitative verification, and bioinformatical validation methods

Oncogenic microRNA-1290 and SCAI Gene as Potential Biomarker for Colorectal Carcinoma

INTRODUCTION

Colorectal cancer (CRC) is the world's third most frequent cancer, with a significant mortality rate due to late detection. There is a need to search for biomarkers that can detect colorectal cancer at an early stage. MicroRNAs (miRNAs) regulate several targets that function as oncogenes and/or tumor suppressor genes, so any change in microRNA expression level can predict abnormality.

OBJECTIVE

The objective of the study was to evaluate the expression of miR-1290, and Suppressor of cancer cell invasion (SCAI) gene that may be used as biomarkers for early diagnosis of colorectal carcinoma.

METHODOLOGY

This study included 50 subjects consisting of newly diagnosed colorectal carcinoma patients (n = 25), and healthy controls (n = 25). After RNA isolation and reverse transcription, the expression level of miR-1290 and SCAI gene in the tissues and plasma samples of CRC patients were analyzed using real time PCR and compared with healthy individuals as normal controls. The 2-ΔΔCt formula was used to compute the fold-change, while using miR-16 and GAPDH as reference genes for normalization.

RESULTS

We found that miR-1290 is upregulated, whereas SCAI gene is downregulated in both plasma and tissue samples of CRC patients. For miR-1290, the sensitivity was 96% and specificity was 100%, and for SCAI, 100% sensitivity and 88% specificity was calculated by ROC analysis.

CONCLUSION

The expression of miR-1290 and SCAI gene may be utilized as biomarkers for diagnosis of colorectal carcinoma.

Multidimensional biomarkers for multiple system atrophy: an update and future directions

Multiple system atrophy (MSA) is a fatal progressive neurodegenerative disease. Biomarkers are urgently required for MSA to improve the diagnostic and prognostic accuracy in clinic and facilitate the development and monitoring of disease-modifying therapies. In recent years, significant research efforts have been made in exploring multidimensional biomarkers for MSA. However, currently few biomarkers are available in clinic. In this review, we systematically summarize the latest advances in multidimensional biomarkers for MSA, including biomarkers in fluids, tissues and gut microbiota as well as imaging biomarkers. Future directions for exploration of novel biomarkers and promotion of implementation in clinic are also discussed.

The successful use of -omic technologies to achieve the 'One Health' concept in meat producing animals

Human health and wellbeing are closely linked to healthy domestic animals, a vital wildlife, and an intact ecosystem. This holistic concept is referred to as 'One Health'. In this review, we provide an overview of the potential and the challenges for the use of modern -omics technologies, especially transcriptomics and proteomics, to implement the 'One Health' idea for food-producing animals. These high-throughput studies offer opportunities to find new potential molecular biomarkers to monitor animal health, detect pharmacological interventions and evaluate the wellbeing of farm animals in modern intensive livestock systems.

Obtaining Reliable RT-qPCR Results in Molecular Diagnostics—MIQE Goals and Pitfalls for Transcriptional Biomarker Discovery

In this review, we discuss the development pipeline for transcriptional biomarkers in molecular diagnostics and stress the importance of a reliable gene transcript quantification strategy. Hence, a further focus is put on the MIQE guidelines and how to adapt them for biomarker discovery, from signature validation up to routine diagnostic applications. First, the advantages and pitfalls of the holistic RNA sequencing for biomarker development will be described to establish a candidate biomarker signature. Sequentially, the RT-qPCR confirmation process will be discussed to validate the discovered biomarker signature. Examples for the successful application of RT-qPCR as a fast and reproducible quantification method in routinemolecular diagnostics are provided. Based on the MIQE guidelines, the importance of “key steps” in RT-qPCR is accurately described, e.g., reverse transcription, proper reference gene selection and, finally, the application of automated RT-qPCR data analysis software. In conclusion, RT-qPCR proves to be a valuable tool in the establishment of a disease-specific transcriptional biomarker signature and will have a great future in molecular diagnostics or personalized medicine.

Transcriptomic Biomarkers for Tuberculosis: Validation of NPC2 as a Single mRNA Biomarker to Diagnose TB, Predict Disease Progression, and Monitor Treatment Response

External validation in different cohorts is a key step in the translational development of new biomarkers. We previously described three host mRNA whose expression in peripheral blood is significantly higher (NPC2) or lower (DOCK9 and EPHA4) in individuals with TB compared to latent TB infection (LTBI) and controls. We have now conducted an independent validation of these genes by re-analyzing publicly available transcriptomic datasets from Brazil, China, Haiti, India, South Africa, and the United Kingdom. Comparisons between TB and control/LTBI showed significant differential expression of all three genes (NPC2^high p < 0.01, DOCK9^low p < 0.01, and EPHA4^low p < 0.05). NPC2^high had the highest mean area under the ROC curve (AUROC) for the differentiation of TB vs. controls (0.95) and LTBI (0.94). In addition, NPC2 accurately distinguished TB from the clinically similar conditions pneumonia (AUROC, 0.88), non-active sarcoidosis (0.87), and lung cancer (0.86), but not from active sarcoidosis (0.66). Interestingly, individuals progressing from LTBI to TB showed a constant increase in NPC2 expression with time when compared to non-progressors (p < 0.05), with a significant change closer to manifestation of active disease (≤3 months, p = 0.003). Moreover, NPC2 expression normalized with completion of anti-TB treatment. Taken together, these results validate NPC2 mRNA as a diagnostic host biomarker for active TB independent of host genetic background. Moreover, they reveal its potential to predict progression from latent to active infection and to indicate a response to anti-TB treatment.

Tutorial: Guidelines for Single-Cell RT-qPCR

Reverse transcription quantitative PCR (RT-qPCR) has delivered significant insights in understanding the gene expression landscape. Thanks to its precision, sensitivity, flexibility, and cost effectiveness, RT-qPCR has also found utility in advanced single-cell analysis. Single-cell RT-qPCR now represents a well-established method, suitable for an efficient screening prior to single-cell RNA sequencing (scRNA-Seq) experiments, or, oppositely, for validation of hypotheses formulated from high-throughput approaches. Here, we aim to provide a comprehensive summary of the scRT-qPCR method by discussing the limitations of single-cell collection methods, describing the importance of reverse transcription, providing recommendations for the preamplification and primer design, and summarizing essential data processing steps. With the detailed protocol attached in the appendix, this tutorial provides a set of guidelines that allow any researcher to perform scRT-qPCR measurements of the highest standard.

Discovery and validation of FBLN1 and ANT3 as potential biomarkers for early detection of cervical cancer

BACKGROUND

To validate markers for cervical carcinoma (CC) and precancerous lesions related with HPV infections.

METHODS

Three different cervical cancer cell lines C-33A, SiHa and Caski were used for secretome profiling by label-free quantitative proteomics. Cervical exfoliated cells and matching serum samples were collected from 284 patients with normal control (n = 75, 26.41 %), precancerous lesions (n = 88, 30.99 %) and early stage cervical squamous carcinoma (n = 121, 42.61 %). HPV subtyping and quantification was performed by PCR and hybridization. 20 candidate proteins identified in previous screening studies (tissue, plasma, cells) were quantified by ELISA. Finally, highly quantitative parallel reaction monitoring mass spectrometry was used to assess the specificities and sensitivities of candidate serum markers.

RESULTS

While CC was found to be associated with high-risk HPV subtypes, serum antibodies for high risk HPV were not significantly related to the progression of cervical cancer. Significant differences between patient groups were detected for the four proteins CLU, APOA4, APOE and MLH3, but none would allow clinical application due to insufficient sensitivity and specificity and large variability. Subsequent proteomic secretome analysis of cervical cancer cell lines identified a set of 729 common proteins. Cross referencing this dataset with ELISA measurements revealed six candidate proteins of which two, FBLN1 and ANT3, showed co-occurrence with HPV infection (75.7 % and 85 %, respectively) and had promising diagnostic ability in terms of sensitivity and specificity. After the loss of E6/E7 by using CRISPR/Cas9 gene editing, the content of ANT3 and FBLN1 in KoE6/E7 SiHa were downregulated, which indicated the expression of ANT3 and FBLN1 in cervical cancer may be affected by HPV infection.

CONCLUSIONS

FBLN1 and ANT3 might be potential tumor- and HPV-associated serum markers.

Overexpression of SNTG2, TRAF3IP2, and ITGA6 transcripts is associated with osteoporotic vertebral fracture in elderly women from community

Background

Vertebral fractures (VFs) are the most common clinical manifestation of osteoporosis associated with high morbimortality. A personal/familiar history of fractures increases the risk of fractures. The purpose of this study is to identify possible molecular markers associated with osteoporotic VFs in elderly women from community.

Methods

Transcriptomic analysis using Affymetrix HTA2 microarray was performed using whole blood samples of 240 subjects from a population‐based survey (Sao Paulo Ageing & Health [SPAH] study). Only elderly women with osteoporosis diagnosis by densitometry were analyzed, and divided in two groups: VF: women with osteoporosis and VFs versus no vertebral fracture (NVF): women with osteoporosis and NVFs. They were matched for age, chronic disease, medication use, and bone mineral density (BMD). The logistic regression model adjusted for age was applied for transcriptome data analysis. SYBR green‐based quantitative polymerase chain reaction (qPCR) was used to validate the most significant expression changes obtained in the microarray experiment.

Results

Microarray analysis identified 142 differentially expressed genes (DEGs, p < .01), 57 upregulated and 85 downregulated, compared VF versus NVF groups. The DEG with the greatest expression difference was the Gamma2‐Syntrophin (SNTG2) (β = 31.88, p = .005). Validation by qPCR confirmed increased expression in VF group of Syntrophin (SNTG2, fold change = 2.79, p = .009), TRAF3 Interacting Protein2 (TRAF3IP2, fold change = 2.79, p = .020), and Integrin Subunit Alpha 6 (ITGA6, fold change = 2.86, p = .038).

Conclusion

Our data identified and validated the association of SNTG2 (608715), TRAF3IP2 (607043), and ITGA6 (147556) with osteoporotic VF in elderly women, independently of BMD. These results suggest that these transcripts have potential clinical significance and may help to explain the molecular mechanisms and biological functions of vertebral fracture.

Prospects for incorporation of epigenetic biomarkers in human health and environmental risk assessment of chemicals

Epigenetic mechanisms have gained relevance in human health and environmental studies, due to their pivotal role in disease, gene × environment interactions and adaptation to environmental change and/or contamination. Epigenetic mechanisms are highly responsive to external stimuli and a wide range of chemicals has been shown to determine specific epigenetic patterns in several organisms. Furthermore, the mitotic/meiotic inheritance of such epigenetic marks as well as the resulting changes in gene expression and cell/organismal phenotypes has now been demonstrated. Therefore, epigenetic signatures are interesting candidates for linking environmental exposures to disease as well as informing on past exposures to stressors. Accordingly, epigenetic biomarkers could be useful tools in both prospective and retrospective risk assessment but epigenetic endpoints are currently not yet incorporated into risk assessments. Achieving a better understanding on this apparent impasse, as well as identifying routes to promote the application of epigenetic biomarkers within environmental risk assessment frameworks are the objectives of this review. We first compile evidence from human health studies supporting the use of epigenetic exposure-associated changes as reliable biomarkers of exposure. Then, specifically focusing on environmental science, we examine the potential and challenges of developing epigenetic biomarkers for environmental fields, and discuss useful organisms and appropriate sequencing techniques to foster their development in this context. Finally, we discuss the practical incorporation of epigenetic biomarkers in the environmental risk assessment of chemicals, highlighting critical data gaps and making key recommendations for future research within a regulatory context.

Translation of gastric disease progression at gene level expression

Helicobacter pylori is associated with the development of several lesions in the human stomach. This chronic infection produces gastritis, which can progress to intestinal metaplasia and gastric cancer. To date, there is very little information regarding gene-expression in the different phases of progression caused by chronic H. pylori infection. In this study, we performed a genome-wide gene-expression analysis in gastric biopsies of patients chronically infected with H. pylori, using the potential of high-throughput technologies that have not been fully exploited in this area. Here we illustrate the potential correlation of H. pylori infection with the gene expression changes in follicular gastritis, chronic gastritis and intestinal metaplasia. We also suggest its potential as biomarkers of each condition. An exploratory set of 21 biopsies from patients with follicular gastritis, chronic gastritis, and intestinal metaplasia were analyzed by gene-expression microarrays in order to identify the biological processes altered in each lesion. The microarray data was corroborated by real-time PCR, while 79 Formalin-Fixed Paraffin-Embeded samples were analyzed by immunohistochemistry. Follicular gastritis exhibited significant enrichment in genes associated with glutamate signaling, while chronic gastritis showed a down-regulation in metallothionein 1 and 2 and in oxidative phosphorylation-related genes, which could be associated with the chronic infecton of H. pylori. Intestinal metaplasia exhibited an over-expression of gastrointestinal stem cell markers, such as LGR5 and PROM1, as well as messenger RNA and nucleic acid metabolism-related genes. The gene-expression patterns found in this study provide new comparative information about chronic gastritis, follicular gastritis and intestinal metaplasia that may play an important role in the development of gastric cancer.

Discovery of potential transcriptional biomarkers in broiler chicken for detection of amantadine abuse based on RNA sequencing technology

The aim of this study was to identify candidate transcriptional biomarkers so as to provide a new method for monitoring amantadine residues during the feeding of broiler chicken. RNA sequencing (RNA-seq) and bioinformatic analyses were conducted to examine the transcriptomic changes and screen differentially expressed genes (DEGs) in broiler chicken breast muscle and liver tissues treated with amantadine. The results indicated that a total of 170 DEGs were screened from broiler chicken breast muscle tissues after amantadine was fed. Among the genes, 120 were up-regulated and 50 were down-regulated. The gene ontology (GO) terms for these genes mainly existed in the areas of hydrolase activity, immune reaction and chemokine activity. The significantly enriched pathways in the Kyoto Encyclopedia for Genes and Genomes (KEGG) were in phagosomes, cell adhesion molecules (CAMs), lysosomes and extracellular matrix (ECM) receptors. From the broiler chicken liver tissues, 172 DEGs were screened, among which 116 were up-regulated and 56 were down-regulated. The GO terms of these DEGs were related to functions such as catalytic activities, metabolic activities, oxidation-reduction activities, immune reactions and cofactor binding. The significantly enriched KEGG pathways existed in metabolism, CAM, ECM receptor reaction and drug metabolism-cytochrome P450. According to the fold-change (FC), significance levels, functional annotations and possible biological processes of DEGs, 11 and 9 candidate DEGs related to amantadine treatment were further screened from broiler chicken breast muscle and liver tissues, respectively. In addition, the quantitative real-time polymerase chain reaction (qRT-PCR) verification showed exactly concordant results with the RNA-seq data. Principal components analysis (PCA) on the qRT-PCR data resulted in the separation of treated samples from the control samples in both tissues. The results provided a basis for identification of transcriptional biomarkers for detecting amantadine residues in broiler chicken breast muscle and liver tissues.

Changes in the microRNA expression profile during blood storage

Objectives

For several decades, autologous blood doping (ABD) in sports has been a major problem, and even today there is still no reliable method for satisfactorily detecting ABD. For this kind of doping, stored individual erythrocytes are used to increase stamina and endurance caused by a higher erythrocyte level in the athlete's body. Since there is growing evidence that these cells are enriched with microRNAs (miRNAs), this study has been carried out to discover and validate all miRNAs occurring in fresh blood as well as in stored blood.

Methods

Therefore, small RNA Next Generation Sequencing has been performed, which allows untargeted detection of all miRNAs in a blood sample. The focus of this investigation has been to find miRNA alterations in blood bags after erythrocyte processing and during storage, as compared with fresh blood directly withdrawn from subjects. Blood samples were obtained from 12 healthy, recreationally active male subjects three times before blood donation and from blood bags at several time points after blood processing.

Results

189 miRNAs have been considered stable over two consecutive weeks. A further analysis revealed a complex biomarker signature of 28 miRNAs, consisting of 6 miRNAs that altered during 6 weeks of storage and 22 miRNAs that altered due to processing.

Conclusion

These results suggest that the identified miRNA biomarker signature may be used for the detection of ABD. These 28 miRNA candidates are tested and verified currently in a follow-up study, a human transfusion clinical trial in healthy sportsmen.

New surveillance concepts in food safety in meat producing animals: the advantage of high throughput 'omics' technologies - A review

The misuse of anabolic hormones or illegal drugs is a ubiquitous problem in animal husbandry and in food safety. The ban on growth promotants in food producing animals in the European Union is well controlled. However, application regimens that are difficult to detect persist, including newly designed anabolic drugs and complex hormone cocktails. Therefore identification of molecular endogenous biomarkers which are based on the physiological response after the illicit treatment has become a focus of detection methods. The analysis of the ‘transcriptome’ has been shown to have promise to discover the misuse of anabolic drugs, by indirect detection of their pharmacological action in organs or selected tissues. Various studies have measured gene expression changes after illegal drug or hormone application. So-called transcriptomic biomarkers were quantified at the mRNA and/or microRNA level by reverse transcription-quantitative polymerase chain reaction (RT-qPCR) technology or by more modern ‘omics’ and high throughput technologies including RNA-sequencing (RNA-Seq). With the addition of advanced bioinformatical approaches such as hierarchical clustering analysis or dynamic principal components analysis, a valid ‘biomarker signature’ can be established to discriminate between treated and untreated individuals. It has been shown in numerous animal and cell culture studies, that identification of treated animals is possible via our transcriptional biomarker approach. The high throughput sequencing approach is also capable of discovering new biomarker candidates and, in combination with quantitative RT-qPCR, validation and confirmation of biomarkers has been possible. These results from animal production and food safety studies demonstrate that analysis of the transcriptome has high potential as a new screening method using transcriptional ‘biomarker signatures’ based on the physiological response triggered by illegal substances.

A PCR-based quantitative assay for the evaluation of mRNA integrity in rat samples

Reverse Transcription quantitative real-time PCR (RT-qPCR) is applied to quantify gene transcript levels in a wide range of investigations. Proper assessment of RNA integrity is essential for reliable assessment of gene expression levels, as RNA molecules are acutely vulnerable to degradation. However, RNA quality control measures are still infrequently reported in rat toxicological studies, which impede proper evaluation of gene expression data reliability. The high operational cost of microfluidic capillary electrophoresis systems along with paucity of alternative methods for the quantitative assessment of rat RNA integrity constitute potential hurdles to the systematic implementation and reporting of RNA integrity assessment in rat studies. This manuscript describes the adaptation of an alternative RT-qPCR-based 3':5' assay as an additional option for the quantitative assessment of rat RNA integrity. Two PCR primer sets were designed on the 3' and 5' regions of a rat housekeeping gene to evaluate RNA integrity by measuring the relative expression (3':5' ratio) of these amplicons. The 3':5' ratios were then compared to Agilent Bioanalyzer's RNA integrity number (RIN) for a wide range of RNA samples originating from different tissues, cultured cell lines and rat strains that were prepared freshly, stored for years at -80 °C, purchased commercially or intentionally degraded. The 3':5' ratios and RIN values presented similar assessment of RNA integrity status from intact to heavily degraded samples. Based on the LOWESS regression of this large comparison dataset, 3':5' ratio threshold criteria equivalent to RIN cut-off values can be proposed for the selection of RNA samples for RT-qPCR analyses. This qPCR-based assay is easy to implement, cost-effective, and provides a reliable quantification of RNA integrity to assist in the selection of rat RNA samples suitable for downstream RT-qPCR gene expression analyses.

Clinical significance of the long noncoding RNA RP11-19P22.6-001 in gastric cancer

BACKGROUND

Long non-coding RNAs (lncRNAs) play roles in carcinogenesis; however, the significance of most lncRNAs in gastric cancer is unclear.

OBJECTIVE

To explore the diagnostic value of the long noncoding RNA RP11-19P22.6-001 in gastric cancer.

METHODS

RP11-19P22.6-001 levels in gastric cancer tissues and paired non-tumor tissues were analyzed by quantitative reverse transcription-polymerase chain reaction. Since RP11-19P22.6-001 acts in cis to regulate nitric oxide synthase 2 (NOS2), we also analyzed NOS2 expression and its correlation with gastric cancer. Finally, to analyze the potential diagnostic values of RP11-19P22.6-001, a receiver operating characteristic (ROC) curve was constructed.

RESULTS

RP11-19P22.6-001 expression was significantly downregulated in 70.91% (78/110) of gastric cancer tissues compared to that of adjacent normal tissues. However, NOS2 was upregulated in 75.45% (83/110) of gastric cancer tissues. RP11-19P22.6-001 expression levels were associated with invasion, lymph node metastasis, and TNM stage. The areas under the ROC curves (AUC) were 0.662 and 0.671 for RP11-19P22.6-001 and NOS2, respectively. More importantly, the combined use of these parameters increased the AUC to 0.704.

CONCLUSIONS

RP11-19P22.6-001 and NOS2 may be new biomarkers for the diagnosis and prognosis of gastric cancer. The combined use of lncRNAs and their target genes may be a promising method to increase the diagnostic value of lncRNAs in cancer.

MIP-Based Sensors: Promising New Tools for Cancer Biomarker Determination

Detecting cancer disease at an early stage is one of the most important issues for increasing the survival rate of patients. Cancer biomarker detection helps to provide a diagnosis before the disease becomes incurable in later stages. Biomarkers can also be used to evaluate the progression of therapies and surgery treatments. In recent years, molecularly imprinted polymer (MIP) based sensors have been intensely investigated as promising analytical devices in several fields, including clinical analysis, offering desired portability, fast response, specificity, and low cost. The aim of this review is to provide readers with an overview on recent important achievements in MIP-based sensors coupled to various transducers (e.g., electrochemical, optical, and piezoelectric) for the determination of cancer biomarkers by selected publications from 2012 to 2016.

A nanoplasmonic label-free surface-enhanced Raman scattering strategy for non-invasive cancer genetic subtyping in patient samples

Simple nucleic acid detection methods could facilitate the progress of disease diagnostics for clinical uses. An attractive strategy is label-free surface-enhanced Raman scattering (SERS) due to its capability of providing structural fingerprinting of analytes that are close to or on nanomaterial surfaces. However, current label-free SERS approaches for DNA/RNA biomarker detection are limited to short and synthetic nucleic acid targets and have not been fully realized in clinical samples due to two possible reasons: (i) low target copies in limited patient samples and (ii) poor capability in identifying specific biomarkers from complex samples. To resolve these limitations and enable label-free SERS for clinical applications, we herein present a novel strategy based on multiplex reverse transcription-recombinase polymerase amplification (RT-RPA) to enrich multiple RNA biomarkers, followed by label-free SERS with multivariate statistical analysis to directly detect, identify and distinguish between these long amplicons (∼200 bp). As a proof-of-concept clinical demonstration, we employed this strategy for non-invasive subtyping of prostate cancer (PCa). In a training cohort of 43 patient urinary samples, we achieved 93.0% specificity, 95.3% sensitivity, and 94.2% accuracy. We believe that our proposed assay could pave the way for simple and direct label-free SERS detection of multiple long nucleic acid sequences in patient samples, and thus facilitate rapid cancer molecular subtyping for personalized therapies.

A Comparative Analysis of Drug-Induced Hepatotoxicity in Clinically Relevant Situations

Drug-induced toxicity is a significant problem in clinical care. A key problem here is a general understanding of the molecular mechanisms accompanying the transition from desired drug effects to adverse events following administration of either therapeutic or toxic doses, in particular within a patient context. Here, a comparative toxicity analysis was performed for fifteen hepatotoxic drugs by evaluating toxic changes reflecting the transition from therapeutic drug responses to toxic reactions at the cellular level. By use of physiologically-based pharmacokinetic modeling, in vitro toxicity data were first contextualized to quantitatively describe time-resolved drug responses within a patient context. Comparatively studying toxic changes across the considered hepatotoxicants allowed the identification of subsets of drugs sharing similar perturbations on key cellular processes, functional classes of genes, and individual genes. The identified subsets of drugs were next analyzed with regard to drug-related characteristics and their physicochemical properties. Toxic changes were finally evaluated to predict both molecular biomarkers and potential drug-drug interactions. The results may facilitate the early diagnosis of adverse drug events in clinical application.

Liver toxicity may occur at drug levels above the therapeutic range and is thus a crucial problem in clinical care. However, the cellular changes induced by drug administration of therapeutic and toxic doses in humans are still not well understood. We here coupled patient-specific drug concentration-time profiles following oral administration of therapeutic and toxic doses with in vitro drug response data to predict toxic changes that quantitatively reflect the transition from desired drug effects to undesired toxic reactions. These toxic changes were comparatively evaluated for fifteen hepatotoxic drugs to identify subsets of drugs, which show similar drug effects on key cellular processes, functional classes of genes, and individual genes, respectively. In addition, analyzing toxic changes for individual genes allowed the prediction of molecular biomarkers and potential drug-drug interactions. Our results may hence support the early diagnosis of liver toxicity in clinical care in the future and may, moreover, help to assess potential risks of drug combination therapies.

Featured Article: Transcriptional landscape analysis identifies differently expressed genes involved in follicle-stimulating hormone induced postmenopausal osteoporosis

Osteoporosis is a disorder associated with bone tissue reorganization, bone mass, and mineral density. Osteoporosis can severely affect postmenopausal women, causing bone fragility and osteoporotic fractures. The aim of the current study was to compare blood mRNA profiles of postmenopausal women with and without osteoporosis, with the aim of finding different gene expressions and thus targets for future osteoporosis biomarker studies. Our study consisted of transcriptome analysis of whole blood serum from 12 elderly female osteoporotic patients and 12 non-osteoporotic elderly female controls. The transcriptome analysis was performed with RNA sequencing technology. For data analysis, the edgeR package of R Bioconductor was used. Two hundred and fourteen genes were expressed differently in osteoporotic compared with non-osteoporotic patients. Statistical analysis revealed 20 differently expressed genes with a false discovery rate of less than 1.47 × 10-4 among osteoporotic patients. The expression of 10 genes were up-regulated and 10 down-regulated. Further statistical analysis identified a potential osteoporosis mRNA biomarker pattern consisting of six genes: CACNA1G, ALG13, SBK1, GGT7, MBNL3, and RIOK3. Functional ingenuity pathway analysis identified the strongest candidate genes with regard to potential involvement in a follicle-stimulating hormone activated network of increased osteoclast activity and hypogonadal bone loss. The differentially expressed genes identified in this study may contribute to future research of postmenopausal osteoporosis blood biomarkers.

Discovery of biomarkers in rare diseases: innovative approaches by predictive and personalized medicine

There are more than 8000 rare diseases (RDs) that affect >5 % of the world’s population. Many of the RDs have no effective treatment and lack of knowledge creates delayed diagnosis making management difficult. The emerging concept of the personalized medicine allows for early screening, diagnosis, and individualized treatment of human diseases. In this context, the discovery of biomarkers in RDs will be of prime importance to enable timely prevention and effective treatment. Since 80 % of RDs are of genetic origin, identification of new genes and causative mutations become valuable biomarkers. Furthermore, dynamic markers such as expressed genes, metabolites, and proteins are also very important to follow prognosis and response the therapy. Recent advances in omics technologies and their use in combination can define pathophysiological pathways that can be drug targets. Biomarker discovery and their use in diagnosis in RDs is a major pillar in RD research.

Transcriptomic Biomarkers for Tuberculosis: Evaluation of DOCK9. EPHA4, and NPC2 mRNA Expression in Peripheral Blood

Lately, much effort has been made to find mRNA biomarkers for tuberculosis (TB) disease/infection with microarray-based approaches. In a pilot investigation, through RNA sequencing technology, we observed a prominent modulation of DOCK9, EPHA4, and NPC2 mRNA abundance in the blood of TB patients. To corroborate these findings, independent validations were performed in cohorts from different areas. Gene expression levels in blood were evaluated by quantitative real-time PCR (Brazil, n = 129) or reanalysis of public microarray data (UK: n = 96; South Africa: n = 51; Germany: n = 26; and UK/France: n = 63). In the Brazilian cohort, significant modulation of all target-genes was observed comparing TB vs. healthy recent close TB contacts (rCt). With a 92% specificity, NPC2 mRNA high expression (NPC2^high) showed the highest sensitivity (85%, 95% CI 65%–96%; area under the ROC curve [AUROC] = 0.88), followed by EPHA4 (53%, 95% CI 33%–73%, AUROC = 0.73) and DOCK9 (19%, 95% CI 7%–40%; AUROC = 0.66). All the other reanalyzed cohorts corroborated the potential of NPC2^high as a biomarker for TB (sensitivity: 82–100%; specificity: 94–97%). An NPC2^high profile was also observed in 60% (29/48) of the tuberculin skin test positive rCt, and additional follow-up evaluation revealed changes in the expression levels of NPC2 during the different stages of Mycobacterium tuberculosis infection, suggesting that further studies are needed to evaluate modulation of this gene during latent TB and/or progression to active disease. Considering its high specificity, our data indicate, for the first time, that NPC2^high might serve as an accurate single-gene biomarker for TB.

Identification of Pharmacodynamic Transcript Biomarkers in Response to FGFR Inhibition by AZD4547

The challenge of developing effective pharmacodynamic biomarkers for preclinical and clinical testing of FGFR signaling inhibition is significant. Assays that rely on the measurement of phospho-protein epitopes can be limited by the availability of effective antibody detection reagents. Transcript profiling enables accurate quantification of many biomarkers and provides a broader representation of pathway modulation. To identify dynamic transcript biomarkers of FGFR signaling inhibition by AZD4547, a potent inhibitor of FGF receptors 1, 2, and 3, a gene expression profiling study was performed in FGFR2-amplified, drug-sensitive tumor cell lines. Consistent with known signaling pathways activated by FGFR, we identified transcript biomarkers downstream of the RAS-MAPK and PI3K/AKT pathways. Using different tumor cell lines in vitro and xenografts in vivo, we confirmed that some of these transcript biomarkers (DUSP6, ETV5, YPEL2) were modulated downstream of oncogenic FGFR1, 2, 3, whereas others showed selective modulation only by FGFR2 signaling (EGR1). These transcripts showed consistent time-dependent modulation, corresponding to the plasma exposure of AZD4547 and inhibition of phosphorylation of the downstream signaling molecules FRS2 or ERK. Combination of FGFR and AKT inhibition in an FGFR2-mutated endometrial cancer xenograft model enhanced modulation of transcript biomarkers from the PI3K/AKT pathway and tumor growth inhibition. These biomarkers were detected on the clinically validated nanoString platform. Taken together, these data identified novel dynamic transcript biomarkers of FGFR inhibition that were validated in a number of in vivo models, and which are more robustly modulated by FGFR inhibition than some conventional downstream signaling protein biomarkers. Mol Cancer Ther; 15(11); 2802-13. ©2016 AACR.

Downregulation of long noncoding RNA NONHSAT037832 in papillary thyroid carcinoma and its clinical significance

Long noncoding RNA (lncRNA) is a kind of RNA that is longer than 200 nucleotides with limited or no protein-coding potential. Studies have proved that lncRNAs play important regulatory roles in gene expression and contribute to oncogenesis and cancer metastasis. However, the expression level of lncRNAs and their clinicopathologic significance in papillary thyroid carcinoma (PTC) have not been well studied. In this study, we investigated the expression level of a novel lncRNA NONHSAT037832 in PTC and paired noncancerous thyroid tissues as well as some cell lines by quantitative real-time polymerase chain reaction. The association between the expression level of NONHSAT037832 and clinicopathologic characteristics of patients with PTC was further analyzed. Three receiver operating characteristic curves (ROCs) were established to evaluate the diagnostic value of NONHSAT037832. The results suggested that the expression level of NONHSAT037832 was significantly decreased in PTC compared with paired noncancerous tissues (P < 0.01). And, NONHSAT037832 was also significantly downregulated in two PTC cell lines (K1 and IHH-4) compared to normal thyroid follicular epithelial cell line Nthy-ori 3-1 (P < 0.01). Downregulated NONHSAT037832 was significantly associated with lymph node metastasis (P = 0.015) and tumor size (P = 0.032). The ROCs revealed that NONHSAT037832 had a high diagnostic value for differentiating between PTC and noncancerous diseases as well as identifying PTC with lymph node metastasis and larger tumors (≥3 cm). The area under curve was up to 0.897 (95%CI = 0.852-0.942, P = 0.000), 0.641 (95%CI = 0.519-0.762, P = 0.033), and 0.702 (95%CI = 0.567-0.827, P = 0.008), respectively. This study indicated that NONHSAT037832 might serve as a potential biomarker of PTC.

The potential of circulating extracellular small RNAs (smexRNA) in veterinary diagnostics-Identifying biomarker signatures by multivariate data analysis

Worldwide growth and performance-enhancing substances are used in cattle husbandry to increase productivity. In certain countries however e.g., in the EU, these practices are forbidden to prevent the consumers from potential health risks of substance residues in food. To maximize economic profit, ‘black sheep‘ among farmers might circumvent the detection methods used in routine controls, which highlights the need for an innovative and reliable detection method. Transcriptomics is a promising new approach in the discovery of veterinary medicine biomarkers and also a missing puzzle piece, as up to date, metabolomics and proteomics are paramount. Due to increased stability and easy sampling, circulating extracellular small RNAs (smexRNAs) in bovine plasma were small RNA-sequenced and their potential to serve as biomarker candidates was evaluated using multivariate data analysis tools.

After running the data evaluation pipeline, the proportion of miRNAs (microRNAs) and piRNAs (PIWI-interacting small non-coding RNAs) on the total sequenced reads was calculated. Additionally, top 10 signatures were compared which revealed that the readcount data sets were highly affected by the most abundant miRNA and piRNA profiles. To evaluate the discriminative power of multivariate data analyses to identify animals after veterinary drug application on the basis of smexRNAs, OPLS-DA was performed. In summary, the quality of miRNA models using all mapped reads for both treatment groups (animals treated with steroid hormones or the β-agonist clenbuterol) is predominant to those generated with combined data sets or piRNAs alone. Using multivariate projection methodologies like OPLS-DA have proven the best potential to generate discriminative miRNA models, supported by small RNA-Seq data. Based on the presented comparative OPLS-DA, miRNAs are the favorable smexRNA biomarker candidates in the research field of veterinary drug abuse.

Optimization of extraction of circulating RNAs from plasma--enabling small RNA sequencing

There are several protocols and kits for the extraction of circulating RNAs from plasma with a following quantification of specific genes via RT-qPCR. Due to the marginal amount of cell-free RNA in plasma samples, the total RNA yield is insufficient to perform Next-Generation Sequencing (NGS), the state-of-the-art technology in massive parallel sequencing that enables a comprehensive characterization of the whole transcriptome. Screening the transcriptome for biomarker signatures accelerates progress in biomarker profiling for molecular diagnostics, early disease detection or food safety. Therefore, the aim was to optimize a method that enables the extraction of sufficient amounts of total RNA from bovine plasma to generate good-quality small RNA Sequencing (small RNA-Seq) data. An increased volume of plasma (9 ml) was processed using the Qiagen miRNeasy Serum/Plasma Kit in combination with the QIAvac24 Plus system, a vacuum manifold that enables handling of high volumes during RNA isolation. 35 ng of total RNA were passed on to cDNA library preparation followed by small RNA high-throughput sequencing analysis on the Illumina HiSeq2000 platform. Raw sequencing reads were processed by a data analysis pipeline using different free software solutions. Seq-data was trimmed, quality checked, gradually selected for miRNAs/piRNAs and aligned to small RNA reference annotation indexes. Mapping to human reference indexes resulted in 4.8±2.8% of mature miRNAs and 1.4±0.8% of piRNAs and of 5.0±2.9% of mature miRNAs for bos taurus.

A survey of tools for the analysis of quantitative PCR (qPCR) data

Real-time quantitative polymerase-chain-reaction (qPCR) is a standard technique in most laboratories used for various applications in basic research. Analysis of qPCR data is a crucial part of the entire experiment, which has led to the development of a plethora of methods. The released tools either cover specific parts of the workflow or provide complete analysis solutions. Here, we surveyed 27 open-access software packages and tools for the analysis of qPCR data. The survey includes 8 Microsoft Windows, 5 web-based, 9 R-based and 5 tools from other platforms. Reviewed packages and tools support the analysis of different qPCR applications, such as RNA quantification, DNA methylation, genotyping, identification of copy number variations, and digital PCR. We report an overview of the functionality, features and specific requirements of the individual software tools, such as data exchange formats, availability of a graphical user interface, included procedures for graphical data presentation, and offered statistical methods. In addition, we provide an overview about quantification strategies, and report various applications of qPCR. Our comprehensive survey showed that most tools use their own file format and only a fraction of the currently existing tools support the standardized data exchange format RDML. To allow a more streamlined and comparable analysis of qPCR data, more vendors and tools need to adapt the standardized format to encourage the exchange of data between instrument software, analysis tools, and researchers.

RNA-Seq transcriptome analysis to identify genes involved in metabolism-based diclofop resistance in Lolium rigidum

Weed control failures due to herbicide resistance are an increasing and worldwide problem that significantly affect crop yields. Metabolism-based herbicide resistance (referred to as metabolic resistance) in weeds is not well characterized at the genetic level. An RNA-Seq transcriptome analysis was used to find candidate genes that conferred metabolic resistance to the herbicide diclofop in a diclofop-resistant population (R) of the major global weed Lolium rigidum. A reference cDNA transcriptome (19 623 contigs) was assembled and assigned putative annotations. Global gene expression was measured using Illumina reads from untreated control, adjuvant-only control, and diclofop treatment of R and susceptible (S). Contigs that showed constitutive expression differences between untreated R and untreated S were selected for further validation analysis, including 11 contigs putatively annotated as cytochrome P450 (CytP450), glutathione transferase (GST), or glucosyltransferase (GT), and 17 additional contigs with annotations related to metabolism or signal transduction. In a forward genetics validation experiment, nine contigs had constitutive up-regulation in R individuals from a segregating F2 population, including three CytP450, one nitronate monooxygenase (NMO), three GST, and one GT. Principal component analysis using these nine contigs differentiated F2 -R from F2 -S individuals. In a physiological validation experiment in which 2,4-D pre-treatment induced diclofop protection in S individuals due to increased metabolism, seven of the nine genetically validated contigs were induced significantly. Four contigs (two CytP450, NMO, and GT) were consistently highly expressed in nine field-evolved metabolic resistant L. rigidum populations. These four contigs were strongly associated with the resistance phenotype and are major candidates for contributing to metabolic diclofop resistance.

Great expectorations: the potential of salivary 'omic' approaches in neonatal intensive care

Among those that require critical care, preterm neonates have the greatest limitations on available blood or body fluids for clinical or research-based assessments. Recent technological advancements have improved our ability to detect genetic, proteomic and microbial material at the nanoscale level, making analyte and biomarker assessment from even the smallest quantities possible. Saliva is a unique body fluid that not only may be noninvasively and repeatedly obtained, but also contains multiple serum components, making it promising for noninvasive assessment of the newborn. The integration of high-throughput or 'omic' approaches on neonatal saliva holds great potential to improve diagnostic and prognostic accuracy for a wide range of developmental and pathological conditions affecting the vulnerable preterm neonatal population. Herein, we review the clinical applications and technical considerations regarding the integration of salivary 'omic' technology into the neonatal intensive care unit.

Increased expression of long intergenic non-coding RNA LINC00152 in gastric cancer and its clinical significance

It has been known that differential expression of long non-coding RNA (lncRNA) plays critical roles in carcinogenesis. However, the significance of lncRNA, especially long intergenic ncRNA (lincRNA, the main type of lncRNA family), in the diagnosis of gastric cancer is largely unknown. The aim of this study was to determine the expression level of LINC00152, a newfound lincRNA, in gastric carcinoma and its clinical association. The expression of LINC00152 in 71 pairs of tumorous and adjacent normal tissues from patients with gastric cancer was detected by quantitative real-time reverse transcription-polymerase chain reaction. And then, the potential associations between its level in gastric cancer tissue and the clinicopathological features were analyzed. Finally, a receiver operating characteristic (ROC) curve was constructed for differentiating patients with gastric cancer from patients with benign gastric diseases. The results showed that the expression level of LINC00152 in gastric carcinoma was significantly increased, compared with matched normal tissue (P=0.045) and normal mucosa from health control (P=0.004), respectively. Levels of LINC00152 in gastric cancer cell lines, BGC-823, MGC-803, and SGC-7901, were significantly higher than those in human normal gastric epithelial cell line GES-1. In addition, high expression of LINC00152 was correlated with invasion (P=0.042). LINC00152 levels in gastric juice from patients with gastric cancer were further found significantly higher than those from normal controls (P=0.002). Moreover, the area under the ROC curve (AUC) was up to 0.645 (95 % CI=0.559-0.740, P=0.003). This study highlights that lincRNA LINC00152 might be a novel biomarker for predicting gastric cancer.

Amniotic fluid metabolomic analysis in spontaneous preterm birth

OBJECTIVE

To identify metabolic changes associated with early spontaneous preterm birth (PTB; <34 weeks) and term births, using high-throughput metabolomics of amniotic fluid (AF) in African American population.

METHOD

In this study, AF samples retrieved from spontaneous PTB (<34 weeks [n = 25]) and normal term birth (n = 25) by transvaginal amniocentesis at the time of labor prior to delivery were subjected to metabolomics analysis. Equal volumes of samples were subjected to a standard solvent extraction method and analyzed using gas chromatography/mass spectrometry (MS) and liquid chromatography/MS/MS. Biochemicals were identified through matching of ion features to a library of biochemical standards. After log transformation and imputation of minimum observed values for each compound, t test, correlation tests, and false discovery rate corrections were used to identify differentially regulated metabolites. Data were controlled for clinical/demographic variables and medication during pregnancy.

RESULTS

Of 348 metabolites measured in AF samples, 121 metabolites had a gestational age effect and 116 differed significantly between PTB and term births. A majority of significantly altered metabolites could be classified into 3 categories, namely, (1) liver function, (2) fatty acid and coenzyme A (CoA) metabolism, and (3) histidine metabolism. The signature of altered liver function was apparent in many cytochrome P450-related pathways including bile acids, steroids, xanthines, heme, and phase II detoxification of xenobiotics with the largest fold change seen with pantothenol, a CoA synthesis inhibitor that was 8-fold more abundant in PTB.

CONCLUSION

Global metabolic profiling of AF revealed alteration in hepatic metabolites involving xenobiotic detoxification and CoA metabolism in PTB. Maternal and/or fetal hepatic function differences may be developmentally related and its contribution PTB as a cause or effect of PTB is still unclear.

Analysis of the transcriptome in molecular epidemiology studies

The human transcriptome is complex, comprising multiple transcript types, mostly in the form of non-coding RNA (ncRNA). The majority of ncRNA is of the long form (lncRNA, ≥ 200 bp), which plays an important role in gene regulation through multiple mechanisms including epigenetics, chromatin modification, control of transcription factor binding, and regulation of alternative splicing. Both mRNA and ncRNA exhibit additional variability in the form of alternative splicing and RNA editing. All aspects of the human transcriptome can potentially be dysregulated by environmental exposures. Next-generation RNA sequencing (RNA-Seq) is the best available methodology to measure this although it has limitations, including experimental bias. The third phase of the MicroArray Quality Control Consortium project (MAQC-III), also called Sequencing Quality Control (SeQC), aims to address these limitations through standardization of experimental and bioinformatic methodologies. A limited number of toxicogenomic studies have been conducted to date using RNA-Seq. This review describes the complexity of the human transcriptome, the application of transcriptomics by RNA-Seq or microarray in molecular epidemiology studies, and limitations of these approaches including the type of cell or tissue analyzed, experimental variation, and confounding. By using good study designs with precise, individual exposure measurements, sufficient power and incorporation of phenotypic anchors, studies in human populations can identify biomarkers of exposure and/or early effect and elucidate mechanisms of action underlying associated diseases, even at low doses. Analysis of datasets at the pathway level can compensate for some of the limitations of RNA-Seq and, as more datasets become available, will increasingly elucidate the exposure-disease continuum.