The use of transcriptomic biomarkers for personalized medicine

Multi Omics Applications in Biological Systems

Traditional methodologies often fall short in addressing the complexity of biological systems. In this regard, system biology omics have brought invaluable tools for conducting comprehensive analysis. Current sequencing capabilities have revolutionized genetics and genomics studies, as well as the characterization of transcriptional profiling and dynamics of several species and sample types. Biological systems experience complex biochemical processes involving thousands of molecules. These processes occur at different levels that can be studied using mass spectrometry-based (MS-based) analysis, enabling high-throughput proteomics, glycoproteomics, glycomics, metabolomics, and lipidomics analysis. Here, we present the most up-to-date techniques utilized in the completion of omics analysis. Additionally, we include some interesting examples of the applicability of multi omics to a variety of biological systems.

Mesenchymal STRO-1/STRO-3+ precursor cells for the treatment of chronic heart failure with reduced ejection fraction

The heart is susceptible to proinflammatory and profibrotic responses after myocardial injury, leading to further worsening of cardiac dysfunction. Important developments in the management of heart failure with reduced ejection fraction have reduced morbidity and mortality; however, these therapies focus on optimizing cardiac function through hemodynamic and neurohormonal pathways and not by repairing the underlying cardiac injury. The potential of cell-based therapy to reverse cardiac injury has received substantial attention. Herein are examined the phase II and III studies of bone marrow-derived mesenchymal STRO-1+ or STRO-1/STRO-3+ precursor cells in patients with ischemic and nonischemic heart failure with reduced ejection fraction, addressing the safety and efficacy of cell-based therapy throughout multiple clinical trials, the optimal dose and the steps toward revolutionizing the treatment of heart failure.

Transcriptomics, metabolomics, and in-silico drug predictions for liver damage in young and aged burn victims

Burn induces a systemic response affecting multiple organs, including the liver. Since the liver plays a critical role in metabolic, inflammatory, and immune events, a patient with impaired liver often exhibits poor outcomes. The mortality rate after burns in the elderly population is higher than in any other age group, and studies show that the liver of aged animals is more susceptible to injury after burns. Understanding the aged-specific liver response to burns is fundamental to improving health care. Furthermore, no liver-specific therapy exists to treat burn-induced liver damage highlighting a critical gap in burn injury therapeutics. In this study, we analyzed transcriptomics and metabolomics data from the liver of young and aged mice to identify mechanistic pathways and in-silico predict therapeutic targets to prevent or reverse burn-induced liver damage. Our study highlights pathway interactions and master regulators that underlie the differential liver response to burn injury in young and aged animals.

Structural Analysis of Genomic and Proteomic Signatures Reveal Dynamic Expression of Intrinsically Disordered Regions in Breast Cancer and Tissue

Structural features of proteins capture underlying information about protein evolution and function, which enhances the analysis of proteomic and transcriptomic data. Here we develop Structural Analysis of Gene and protein Expression Signatures (SAGES), a method that describes expression data using features calculated from sequence-based prediction methods and 3D structural models. We used SAGES, along with machine learning, to characterize tissues from healthy individuals and those with breast cancer. We analyzed gene expression data from 23 breast cancer patients and genetic mutation data from the COSMIC database as well as 17 breast tumor protein expression profiles. We identified prominent expression of intrinsically disordered regions in breast cancer proteins as well as relationships between drug perturbation signatures and breast cancer disease signatures. Our results suggest that SAGES is generally applicable to describe diverse biological phenomena including disease states and drug effects.

Multiomics Analysis-Based Biomarkers in Diagnosis of Polycystic Ovary Syndrome

Polycystic ovarian syndrome is an utmost communal endocrine, psychological, reproductive, and metabolic disorder that occurs in women of reproductive age with extensive range of clinical manifestations. This may even lead to long-term multiple morbidities including obesity, diabetes mellitus, insulin resistance, cardiovascular disease, infertility, cerebrovascular diseases, and ovarian and endometrial cancer. Women affliction from PCOS in midst assemblage of manifestations allied with menstrual dysfunction and androgen exorbitance, which considerably affects eminence of life. PCOS is recognized as a multifactorial disorder and systemic syndrome in first-degree family members; therefore, the etiology of PCOS syndrome has not been copiously interpreted. The disorder of PCOS comprehends numerous allied health conditions and has influenced various metabolic processes. Due to multifaceted pathophysiology engaging several pathways and proteins, single genetic diagnostic tests cannot be supportive to determine in straight way. Clarification of cellular and biochemical pathways and various genetic players underlying PCOS could upsurge our consideration of pathophysiology of this syndrome. It is requisite to know pathophysiological relationship between biomarker and their reflection towards PCOS disease. Biomarkers deliver vibrantly and potent ways to apprehend the spectrum of PCOS with applications in screening, diagnosis, characterization, and monitoring. This paper relies on the endeavor to point out many candidates as potential biomarkers based on omics technologies, thus highlighting correlation between PCOS disease with innovative technologies. Therefore, the objective of existing review is to encapsulate more findings towards cutting-edge advances in prospective use of biomarkers for PCOS disease. Discussed biomarkers may be fruitful in guiding therapies, addressing disease risk, and predicting clinical outcomes in future.

At-home blood collection and stabilization in high temperature climates using homeRNA

Expanding whole blood sample collection for transcriptome analysis beyond traditional phlebotomy clinics will open new frontiers for remote immune research and telemedicine. Determining the stability of RNA in blood samples exposed to high ambient temperatures (>30°C) is necessary for deploying home-sampling in settings with elevated temperatures (e.g., studying physiological response to natural disasters that occur in warm locations or in the summer). Recently, we have developed homeRNA, a technology that allows for self-blood sampling and RNA stabilization remotely. homeRNA consists of a lancet-based blood collection device, the Tasso-SST™ which collects up to 0.5 ml of blood from the upper arm, and a custom-built stabilization transfer tube containing RNAlater™. In this study, we investigated the robustness of our homeRNA kit in high temperature settings via two small pilot studies in Doha, Qatar (no. participants = 8), and the Western and South Central USA during the summer of 2021, which included a heatwave of unusually high temperatures in some locations (no. participants = 11). Samples collected from participants in Doha were subjected to rapid external temperature fluctuations from being moved to and from air-conditioned areas and extreme heat environments (up to 41°C external temperature during brief temperature spikes). In the USA pilot study, regions varied in outdoor temperature highs (between 25°C and 43.4°C). All samples that returned a RNA integrity number (RIN) value from the Doha, Qatar group had a RIN ≥7.0, a typical integrity threshold for downstream transcriptomics analysis. RIN values for the Western and South Central USA samples (n = 12 samples) ranged from 6.9–8.7 with 9 out of 12 samples reporting RINs ≥7.0. Overall, our pilot data suggest that homeRNA can be used in some regions that experience elevated temperatures, opening up new geographical frontiers in disseminated transcriptome analysis for applications critical to telemedicine, global health, and expanded clinical research. Further studies, including our ongoing work in Qatar, USA, and Thailand, will continue to test the robustness of homeRNA.

Identifying Potential New Gene Expression-Based Biomarkers in the Peripheral Blood Mononuclear Cells of Hepatitis B-Related Hepatocellular Carcinoma

OBJECTIVE

The analysis of the gene expression of peripheral blood mononuclear cells (PBMCs) is important to clarify the pathogenesis of hepatocellular carcinoma (HCC) and the detection of suitable biomarkers. The purpose of this investigation was to use RNA-sequencing to screen the appropriate differentially expressed genes (DEGs) in the PBMCs for the HCC.

METHODS

The comprehensive transcriptome of extracted RNA of PBMC (n = 20) from patients with chronic hepatitis B (CHB), liver cirrhosis, and early stage of HCC (5 samples per group) was carried out using RNA-sequencing. All raw RNA-sequencing data analyses were performed using conventional RNA-sequencing analysis tools. Next, gene ontology (GO) analyses were carried out to elucidate the biological processes of DEGs. Finally, relative transcript abundance of selected DEGs was verified using qRT-PCR on additional validation groups.

RESULTS

Specifically, 13, 1262, and 1450 DEGs were identified for CHB, liver cirrhosis, and HCC, when compared with the healthy controls. GO enrichment analysis indicated that HCC is closely related to the immune response. Seven DEGs (TYMP, TYROBP, CD14, TGFBI, LILRA2, GNLY, and GZMB) were common to HCC, cirrhosis, and CHB when compared to healthy controls. The data revealed that the expressions of these 7 DEGs were consistent with those from the RNA-sequencing results. Also, the expressions of 7 representative genes that had higher sensitivity were obtained by receiver operating characteristic analysis, which indicated their important diagnostic accuracy for HBV-HCC.

CONCLUSION

This study provides us with new horizons into the biological process and potential prospective clinical diagnosis and prognosis of HCC in the near future.

A Pilot Study of Gene Expression Analysis in Peripheral Blood Mononuclear Cells in Response to a Hypocaloric Mediterranean Diet

Background

Few studies have examined gene expression in peripheral blood mononuclear cells (PBMCs) after a dietary intervention.

Objective

Our study is aimed at evaluating in a pilot study the peripheral blood gene expression in obese patients after weight loss secondary to a hypocaloric Mediterranean diet.

Design

A sample of 11 obese subjects without metabolic syndrome was enrolled. Biochemical, anthropometric parameters and microarray analysis were performed at baseline and after 6 months of dietary intervention.

Results

The mean age was 43.1 ± 6.3 years, and the mean body mass index (BMI) was 38.6 ± 8.1 kg/m². All the next improvements were statistically significant: body weight −7.4 ± 1.9 kg, BMI -2.5 ± 0.2 kg, fat mass −5.7 ± 1.2 kg, waist circumference −5.8 ± 1.2 cm, triglycerides −17.4 ± 6.5 mg/dl, C-reactive protein −3.1 ± 1.5 mg/dL, insulin −2.1 ± 1.0 mUI/L, and HOMA-IR −0.7 ± 0.2 units. We identified 634 differentially expressed genes: 262 genes with relative higher expression levels and 372 with lower expression levels. Cluster analysis showed 35 genes in nutritional disease and 17 genes in endocrine system. The most relevant gene was thyroid peroxidase (TPO), and this gene was overexpressed, and the next genes carbonic anhydrase VI (CA6), caveolin protein 1 (CAV1) and solute carrier family type 12 (SLLC12A3), soluble carrier family type 12 (SLLC12A3), beta 3 receptor (ADRB3), and glutamate receptor ionotropic N methyl D aspartate 2 A (GRIN2A) were all underexpressed.

Conclusion

In PBMC from obese patients after a diet with a Mediterranean pattern, the expression of 634 genes, of the endocrine system and of nutritional disease, is modified.

Exposure of Toluene Diisocyanate Induces DUSP6 and p53 through Activation of TRPA1 Receptor

Toluene diisocyanate (TDI), a major intermediate agent used in the manufacturing industry, causes respiratory symptoms when exposed to the human body. In this study, we aimed to determine the molecular mechanism of TDI toxicity. To investigate the impact of TDI exposure on global gene expression, we performed transcriptomic analysis of human bronchial epithelial cells (BEAS-2B) after TDI treatment. Differentially expressed genes (DEGs) were sorted and used for clustering and network analysis. Among DEGs, dual-specificity phosphatase 6 (DUSP6) was one of the genes significantly changed by TDI exposure. To verify the expression level of DUSP6 and its effect on lung cells, the mRNA and protein levels of DUSP6 were analyzed. Our results showed that DUSP6 was dose-dependently upregulated by TDI treatment. Thereby, the phosphorylation of ERK1/2, one of the direct inhibitory targets of DUSP6, was decreased. TDI exposure also increased the mRNA level of p53 along with its protein and activity which trans-activates DUSP6. Since TRPA1 is known as a signal integrator activated by TDI, we analyzed the relevance of TRPA1 receptor in DUSP6 regulation. Our data revealed that up-regulation of DUSP6 mediated by TDI was blocked by a specific antagonist against TRPA1. TDI exposure attenuated the apoptotic response, which suggests that it promotes the survival of cancerous cells. In conclusion, our results suggest that TDI induces DUSP6 and p53, but attenuates ERK1/2 activity through TRPA1 receptor activation, leading to cytotoxicity.

Integrated Gene Expression Profiling Analysis Reveals Potential Molecular Mechanisms and Candidate Biomarkers for Early Risk Stratification and Prediction of STEMI and Post-STEMI Heart Failure Patients

Objective: To explore the molecular mechanism and search for the candidate differentially expressed genes (DEGs) with the predictive and prognostic potentiality that is detectable in the whole blood of patients with ST-segment elevation (STEMI) and those with post-STEMI HF. Methods: In this study, we downloaded GSE60993, GSE61144, GSE66360, and GSE59867 datasets from the NCBI-GEO database. DEGs of the datasets were investigated using R. Gene ontology (GO) and pathway enrichment were performed via ClueGO, CluePedia, and DAVID database. A protein interaction network was constructed via STRING. Enriched hub genes were analyzed by Cytoscape software. The least absolute shrinkage and selection operator (LASSO) logistic regression algorithm and receiver operating characteristics analyses were performed to build machine learning models for predicting STEMI. Hub genes for further validated in patients with post-STEMI HF from GSE59867. Results: We identified 90 upregulated DEGs and nine downregulated DEGs convergence in the three datasets (|log₂FC| ≥ 0.8 and adjusted p < 0.05). They were mainly enriched in GO terms relating to cytokine secretion, pattern recognition receptors signaling pathway, and immune cells activation. A cluster of eight genes including ITGAM, CLEC4D, SLC2A3, BST1, MCEMP1, PLAUR, GPR97, and MMP25 was found to be significant. A machine learning model built by SLC2A3, CLEC4D, GPR97, PLAUR, and BST1 exerted great value for STEMI prediction. Besides, ITGAM and BST1 might be candidate prognostic DEGs for post-STEMI HF. Conclusions: We reanalyzed the integrated transcriptomic signature of patients with STEMI showing predictive potentiality and revealed new insights and specific prospective DEGs for STEMI risk stratification and HF development.

Integration of Epigenetic Mechanisms into Non-Genotoxic Carcinogenicity Hazard Assessment: Focus on DNA Methylation and Histone Modifications

Epigenetics involves a series of mechanisms that entail histone and DNA covalent modifications and non-coding RNAs, and that collectively contribute to programing cell functions and differentiation. Epigenetic anomalies and DNA mutations are co-drivers of cellular dysfunctions, including carcinogenesis. Alterations of the epigenetic system occur in cancers whether the initial carcinogenic events are from genotoxic (GTxC) or non-genotoxic (NGTxC) carcinogens. NGTxC are not inherently DNA reactive, they do not have a unifying mode of action and as yet there are no regulatory test guidelines addressing mechanisms of NGTxC. To fil this gap, the Test Guideline Programme of the Organisation for Economic Cooperation and Development is developing a framework for an integrated approach for the testing and assessment (IATA) of NGTxC and is considering assays that address key events of cancer hallmarks. Here, with the intent of better understanding the applicability of epigenetic assays in chemical carcinogenicity assessment, we focus on DNA methylation and histone modifications and review: (1) epigenetic mechanisms contributing to carcinogenesis, (2) epigenetic mechanisms altered following exposure to arsenic, nickel, or phenobarbital in order to identify common carcinogen-specific mechanisms, (3) characteristics of a series of epigenetic assay types, and (4) epigenetic assay validation needs in the context of chemical hazard assessment. As a key component of numerous NGTxC mechanisms of action, epigenetic assays included in IATA assay combinations can contribute to improved chemical carcinogen identification for the better protection of public health.

Clinical Presentation and Laboratory Findings in Men Versus Women with Myocarditis

Objectives: Understanding sex differences in myocarditis is crucial to improve clinical care. We sought to investigate sex differences focusing on clinical presentation and laboratory parameters. Methods: From 2011 to 2018, 77 patients were diagnosed with myocarditis according to European Society of Cardiology (ESC) criteria with available clinical, laboratory, and cardiac magnetic resonance imaging data. First, we investigated sex differences of clinical and laboratory parameters in the entire cohort of 77 patients. Second, we focused on patients with acute myocarditis (n = 51) defined as recent symptom onset (≤10 days). Results: Myocarditis was present in 63 men (82%) and 14 women (18%). While men most frequently presented with chest pain (78%), a considerable amount of women presented with dyspnea as the only symptom (40%). Within the entire cohort, only creatinine kinase (CK) was higher in men versus women (364 ± 286 vs. 147 ± 148 U/L, p = 0.007), while in patients with acute myocarditis both CK and myoglobin (Mb) were higher in men versus women (CK: 327 ± 223 vs. 112 ± 65 U/L, p = 0.004 and Mb: 111 ± 126 vs. 25 ± 29 μg/L, p = 0.04). No sex differences were found for high-sensitivity troponin T, C-reactive protein, and NT-probrain natriuretic peptide. Conclusions: This is the first study reporting sex differences in clinical presentation and routine laboratory parameters in myocarditis. While clinical presentation appeared to be subtle in women with dyspnea being the only presenting symptom of myocarditis in a considerable part, men typically complained of chest pain. Similarly to observations in myocardial infarction, atypical symptoms and underdiagnosis may be a cause for under-representation of women in cohorts of myocarditis.

Identification of time‑series differentially expressed genes and pathways associated with heart failure post‑myocardial infarction using integrated bioinformatics analysis

Heart failure (HF) secondary to acute myocardial infarction (AMI) is a public health concern. The current study aimed to investigate differentially expressed genes (DEGs) and their possible function in HF post-myocardial infarction. The GSE59867 dataset included microarray data from peripheral blood samples obtained from HF and non-HF patients following AMI at 4 time points (admission, discharge, and 1 and 6 months post-AMI). Time-series DEGs were analyzed using R Bioconductor. Functional enrichment analysis was performed, followed by analysis of protein-protein interactions (PPIs). A total of 108 DEGs on admission, 32 DEGs on discharge, 41 DEGs at 1 month post-AMI and 19 DEGs at 6 months post-AMI were identified. Among these DEGs, 4 genes were downregulated at all the 4 time points. These included fatty acid desaturase 2, leucine rich repeat neuronal protein 3, G-protein coupled receptor 15 and adenylate kinase 5. Functional enrichment analysis revealed that these DEGs were mainly enriched in ‘inflammatory response’, ‘immune response’, ‘toll-like receptor signaling pathway’ and ‘NF-κβ signaling pathway’. Furthermore, PPI network analysis revealed that C-X-C motif chemokine ligand 8 and interleukin 1β were hub genes. The current study identified candidate DEGs and pathways that may serve important roles in the development of HF following AMI. The results obtained in the current study may guide the development of novel therapeutic agents for HF following AMI.

Potential biomarkers of acute myocardial infarction based on weighted gene co-expression network analysis

BACKGROUND

Acute myocardial infarction (AMI) is the common cause of mortality in developed countries. The feasibility of whole-genome gene expression analysis to identify outcome-related genes and dysregulated pathways remains unknown. Molecular marker such as BNP, CRP and other serum inflammatory markers have got the notice at this point. However, these biomarkers exhibit elevated levels in patients with thyroid disease, renal failure and congestive heart failure. In this study, three groups of microarray data sets (GES66360, GSE48060, GSE29532) were collected from GEO, a total of 99, 52 and 55 samples, respectively. Weighted gene co-expression network analysis (WGCNA) was performed to obtain a classifier which composed of related genes that best characterize the AMI.

RESULTS

Here, this study obtained three groups of microarray data sets (GES66360, GSE48060, GSE29532) on AMI blood samples, a total of 99, 52 and 24 samples, respectively. In all, 4672 genes, 3185 genes, 3660 genes were identified in GSE66360, GSE48060, GSE60993 modules, respectively. We preformed WGCNA, GO and KEGG pathway enrichment analysis on these three data sets, finding function enrichment of the differential expression gene on inflammation and immune response. Transcriptome analysis were performed in AMI patients at four time points compared to CAD patients with no history of MI, to determine gene expression profiles and their possible changes during the recovery from myocardial infarction.

CONCLUSIONS

The results suggested that three overlapping genes (FGFBP2, GFOD1 and MLC1) between two modules could be a potential use of gene biomarkers for the diagnose of AMI.

Comparison of statistical methods and the use of quality control samples for batch effect correction in human transcriptome data

Batch effects are technical sources of variation introduced by the necessity of conducting gene expression analyses on different dates due to the large number of biological samples in population-based studies. The aim of this study is to evaluate the performances of linear mixed models (LMM) and Combat in batch effect removal. We also assessed the utility of adding quality control samples in the study design as technical replicates. In order to do so, we simulated gene expression data by adding “treatment” and batch effects to a real gene expression dataset. The performances of LMM and Combat, with and without quality control samples, are assessed in terms of sensitivity and specificity while correcting for the batch effect using a wide range of effect sizes, statistical noise, sample sizes and level of balanced/unbalanced designs. The simulations showed small differences among LMM and Combat. LMM identifies stronger relationships between big effect sizes and gene expression than Combat, while Combat identifies in general more true and false positives than LMM. However, these small differences can still be relevant depending on the research goal. When any of these methods are applied, quality control samples did not reduce the batch effect, showing no added value for including them in the study design.

Transcriptome analysis of peripheral blood mononuclear cells from chronic hepatitis B and hepatocellular carcinoma patients: a network-based attitude

Aim: The aim of the study was constructing a protein–protein interaction network for chronic hepatitis B (CHB) and hepatocellular carcinoma (HCC) patients. Materials & methods: Comprehensive gene expression profile of peripheral blood mononuclear cells of CHB and HCC were obtained from Gene Expression Omnibus/NCBI database. Differentially expressed genes (DEGs) of samples were analyzed using GEO2R web application. Results: The majority of DEGs in both CHB and HCC has been enriched in immune system responses. However, there was a significant disparity between the enrichment of these genes (especially genes associated with Toll-like receptor-and-TNF) in CHB-HCC compared with normal-CHB. Conclusion: The transcriptome properties of peripheral blood mononuclear cells are changed in patients with HBV-HCC. The immune response genes are the most deregulated genes in HCC patients.

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Gene expression analysis identify a metabolic and cell function alterations as a hallmark of obesity without metabolic syndrome in peripheral blood, a pilot study

BACKGROUND

Understanding molecular basis involved in overweight is an important first step in developing therapeutic pathways against excess in body weight gain.

OBJECTIVE

The purpose of our pilot study was to evaluate the gene expression profiles in the peripheral blood of obese patients without other metabolic complications.

DESIGN

A sample of 17 obese patients without metabolic syndrome and 15 non obese control subjects was evaluated in a prospective way. Following 'One-Color Microarray-Based Gene Expression Analysis' protocol Version 5.7 (Agilent p/n 4140-90040), cRNA was hybridized with Whole Human Genome Oligo Microarray Kit (Agilent p/n G2519F-014850) containing 41,000+ unique human genes and transcripts.

RESULTS

The average age of the study group was 43.6 ± 19.7 years with a sex distribution of 64.7% females and 35.3% males. No statistical differences were detected with healthy controls 41.9 ± 12.3 years with a sex distribution of 70% females and 30% males. Obese patients showed 1436 genes that were differentially expressed compared to control group. Ingenuity Pathway Analysis showed that these genes participated in 13 different categories related to metabolism and cellular functions. In the gene set of cellular function, the most important genes were C-terminal region of Nel-like molecule 1 protein (NELL1) and Pigment epithelium-derived factor (SPEDF), both genes were over-expressed. In the gene set of metabolism, insulin growth factor type 1 (IGF1), ApoA5 (apolipoprotein subtype 5), Foxo4 (Forkhead transcription factor 4), ADIPOR1 (receptor of adiponectin type 1) and AQP7 (aquaporin channel proteins7) were over expressed. Moreover, PIKFYVE (PtdIns(3) P 5-kinase), and ROCK-2 (rho-kinase II) were under expressed.

CONCLUSION

We showed that PBMCs from obese subjects presented significant changes in gene expression, exhibiting 1436 differentially expressed genes compared to PBMCs from non-obese subjects. Furthermore, our data showed a number of genes involved in relevant processes implicated in metabolism, with genes presenting high fold-change values (up-regulation and down regulation) associated with lipid, carbohydrate and protein metabolism.

Transcriptomic Analysis Identifies the Effect of Beta-Blocking Agents on a Molecular Pathway of Contraction in the Heart and Predicts Response to Therapy

Over the last decades, beta-blockers have been a key component of heart failure therapy. However, currently there is no method to identify patients who will benefit from beta-blocking therapy versus those who will be unresponsive or worsen. Furthermore, there is an unmet need to better understand molecular mechanisms through which heart failure therapies, such as beta-blockers, improve cardiac function, in order to design novel targeted therapies. Solving these issues is an important step towards personalized medicine. Here, we present a comprehensive transcriptomic analysis of molecular pathways that are affected by beta-blocking agents and a transcriptomic biomarker to predict therapy response.

Highly informative marker sets consisting of genes with low individual degree of differential expression

Genes with significant differential expression are traditionally used to reveal the genetic background underlying phenotypic differences between cancer cells. We hypothesized that informative marker sets can be obtained by combining genes with a relatively low degree of individual differential expression. We developed a method for construction of highly informative gene combinations aimed at the maximization of the cumulative informative power and identified sets of 2–5 genes efficiently predicting recurrence for ER-positive breast cancer patients. The gene combinations constructed on the basis of microarray data were successfully applied to data acquired by RNA-seq. The developed method provides the basis for the generation of highly efficient prognostic and predictive gene signatures for cancer and other diseases. The identified gene sets can potentially reveal novel essential segments of gene interaction networks and pathways implied in cancer progression.

"Omics" in pharmaceutical research: overview, applications, challenges, and future perspectives

In the post-genomic era, biological studies are characterized by the rapid development and wide application of a series of "omics" technologies, including genomics, proteomics, metabolomics, transcriptomics, lipidomics, cytomics, metallomics, ionomics, interactomics, and phenomics. These "omics" are often based on global analyses of biological samples using high through-put analytical approaches and bioinformatics and may provide new insights into biological phenomena. In this paper, the development and advances in these omics made in the past decades are reviewed, especially genomics, transcriptomics, proteomics and metabolomics; the applications of omics technologies in pharmaceutical research are then summarized in the fields of drug target discovery, toxicity evaluation, personalized medicine, and traditional Chinese medicine; and finally, the limitations of omics are discussed, along with the future challenges associated with the multi-omics data processing, dynamics omics analysis, and analytical approaches, as well as amenable solutions and future prospects.

Gene expression profiling reveals potential prognostic biomarkers associated with the progression of heart failure

Background

Heart failure (HF) is the most common cause of morbidity and mortality in developed countries. Here, we identify biologically relevant transcripts that are significantly altered in the early phase of myocardial infarction and are associated with the development of post-myocardial infarction HF.

Methods

We collected peripheral blood samples from patients with ST-segment elevation myocardial infarction (STEMI): n = 111 and n = 41 patients from the study and validation groups, respectively. Control groups comprised patients with a stable coronary artery disease and without a history of myocardial infarction. Based on plasma NT-proBNP level and left ventricular ejection fraction parameters the STEMI patients were divided into HF and non-HF groups. Microarrays were used to analyze mRNA levels in peripheral blood mononuclear cells (PBMCs) isolated from the study group at four time points and control group. Microarray results were validated by RT-qPCR using whole blood RNA from the validation group.

Results

Samples from the first three time points (admission, discharge, and 1 month after AMI) were compared with the samples from the same patients collected 6 months after AMI (stable phase) and with the control group. The greatest differences in transcriptional profiles were observed on admission and they gradually stabilized during the follow-up. We have also identified a set of genes the expression of which on the first day of STEMI differed significantly between patients who developed HF after 6 months of observation and those who did not. RNASE1, FMN1, and JDP2 were selected for further analysis and their early up-regulation was confirmed in HF patients from both the study and validation groups. Significant correlations were found between expression levels of these biomarkers and clinical parameters. The receiver operating characteristic (ROC) curves indicated a good prognostic value of the genes chosen.

Conclusions

This study demonstrates an altered gene expression profile in PBMCs during acute myocardial infarction and through the follow-up. The identified gene expression changes at the early phase of STEMI that differentiated the patients who developed HF from those who did not could serve as a convenient tool contributing to the prognosis of heart failure.

Electronic supplementary material

The online version of this article (doi:10.1186/s13073-015-0149-z) contains supplementary material, which is available to authorized users.

Estimation of treatment policies based on functional predictors

Biosignatures such as brain scans, mass spectrometry, or gene expression profiles might one day be used to guide treatment selection and improve outcomes. This article develops a way of estimating optimal treatment policies based on data from randomized clinical trials by interpreting patient biosignatures as functional predictors. A flexible functional regression model is used to represent the treatment effect and construct the estimated policy. The effectiveness of the estimated policy is assessed by furnishing prediction intervals for the mean outcome when all patients follow the policy. The validity of these prediction intervals is established under mild regularity conditions on the functional regression model. The performance of the proposed approach is evaluated in numerical studies.

Big data: the next frontier for innovation in therapeutics and healthcare

Advancements in genomics and personalized medicine not only effect healthcare delivery from patient and provider standpoints, but also reshape biomedical discovery. We are in the era of the '-omics', wherein an individual's genome, transcriptome, proteome and metabolome can be scrutinized to the finest resolution to paint a personalized biochemical fingerprint that enables tailored treatments, prognoses, risk factors, etc. Digitization of this information parlays into 'big data' informatics-driven evidence-based medical practice. While individualized patient management is a key beneficiary of next-generation medical informatics, this data also harbors a wealth of novel therapeutic discoveries waiting to be uncovered. 'Big data' informatics allows for networks-driven systems pharmacodynamics whereby drug information can be coupled to cellular- and organ-level physiology for determining whole-body outcomes. Patient '-omics' data can be integrated for ontology-based data-mining for the discovery of new biological associations and drug targets. Here we highlight the potential of 'big data' informatics for clinical pharmacology.

Introduction: why analyze single cells?

Powerful methods in molecular biology are abundant; however, in many fields including hematology, stem cell biology, tissue engineering, and cancer biology, data from tools and assays that analyze the average signals from many cells may not yield the desired result because the cells of interest may be in the minority-their behavior masked by the majority-or because the dynamics of the populations of interest are offset in time. Accurate characterization of samples with high cellular heterogeneity may only be achieved by analyzing single cells. In this chapter, we discuss the rationale for performing analyses on individual cells in more depth, cover the fields of study in which single-cell behavior is yielding new insights into biological and clinical questions, and speculate on how single-cell analysis will be critical in the future.

Systems biology and heart failure: concepts, methods, and potential research applications

Dramatic advances in molecular biology dominated twentieth century biomedical science and delineated the function of individual genes and molecules in exquisite detail. However, biological processes cannot be fully understood based on the properties of individual genes and molecules alone, since these elements act in concert to enable the specific functions that make for living cells and organisms. The discipline of systems biology provides a novel conceptual framework for understanding biological phenomenon. Systems biology synthesizes information concerning the interactions of genes and molecules and allows characterization of the supramolecular networks and functional modules that represent the most essential aspects of cell organization and physiology.

Transcriptomic biomarkers for the accurate diagnosis of myocarditis

BACKGROUND

Lymphocytic myocarditis is a clinically important condition that is difficult to diagnose and distinguish. We hypothesized that the transcriptome obtained from an endomyocardial biopsy would yield clinically relevant and accurate molecular signatures.

METHODS AND RESULTS

Microarray analysis was performed on samples from patients with histologically proven lymphocytic myocarditis (n=16) and idiopathic dilated cardiomyopathy (n=32) to develop accurate diagnostic transcriptome-based biomarkers using multiple classification algorithms. We identified 9878 differentially expressed genes in lymphocytic myocarditis versus idiopathic dilated cardiomyopathy (fold change >1.2; false discovery rate <5%) from which a transcriptome-based biomarker containing 62 genes was identified that distinguished myocarditis with 100% sensitivity (95% confidence interval, 46 to 100) and 100% specificity (95% confidence interval, 66 to 100) and was generalizable to a broad range of secondary cardiomyopathies associated with inflammation (n=27), ischemic cardiomyopathy (n=8), and the normal heart (n=11). Multiple classification algorithms and quantitative real-time reverse-transcription polymerase chain reaction analysis further reduced this subset to a highly robust molecular signature of 13 genes, which still performed with 100% accuracy.

CONCLUSIONS

Together, these findings demonstrate that transcriptomic biomarkers from a single endomyocardial biopsy can improve the clinical detection of patients with inflammatory diseases of the heart. This approach advances the clinical management and treatment of cardiac disorders with highly variable outcome.

Optimizing high dimensional gene expression studies for immune response following smallpox vaccination using Taqman® low density immune arrays

INTRODUCTION

We sought to determine the time and vaccinia virus dose combination that would maximize the number of acute immune response changes in response to vaccinia stimulation in preparation for a large gene expression microarray experiment.

METHODS

PBMCs from ten subjects were exposed to five vaccinia virus doses for three lengths of time. Gene expression was measured for 90 immune response genes via Taqman® Low Density Immune Arrays. Expression data were normalized via model-based non-linear normalization. Linear mixed effects model results were used to standardize changes across genes and determine the time/multiplicity of infection (MOI) combination with the largest number of changes.

RESULTS

The greatest number of changes occurred with a MOI of 5.0 and exposure time of 48 h. Further inspection revealed that most changes had occurred earlier and faded at this combination. The second highest number of changes was found at a MOI of 0.5 PFU/cell and time of 18 h.

CONCLUSIONS

We conclude a time of 18 h with a MOI of 0.5 PFU/cell is the optimal time/MOI combination for the full scale gene expression study. The strategy described herein is a general and resource efficient way to make critical decisions regarding experimental parameters for studies utilizing expensive assays that interrogate a large number of variables.

Genomic and metabolomic advances in the identification of disease and adverse event biomarkers

Incomplete knowledge of tissue pathogenesis is hampering the identification of biomarkers for the appropriate therapeutic targets to prevent or inhibit disease processes, and the prediction and diagnosis of injury due to disease and adverse events of drug therapy. The revolution in genomics and metabolomics, combined with advanced bioinformatics and computational methods for mining such large, complex data sets, are beginning to provide critical insights into tissue injury. Such results will move us closer to the promise of personalized medicine.

Gene expression profiling: classification of mice with left ventricle systolic dysfunction using microarray analysis

OBJECTIVE

We tested the hypothesis that a set of differentially expressed genes could be used to classify mice according to cardiovascular phenotype after prolonged catecholamine stress.

DESIGN

Prospective, randomized study.

SETTING

University-based research laboratory.

SUBJECTS

One hundred seventy-three male mice were studied: wild-type (WT) C57, WT FVB, WT B6129SF2/J, and beta2 adrenergic receptor knockout.

INTERVENTIONS

Mice of each genotype were randomly assigned to 14-day infusions of isoproterenol (120 microg/g/day) or no treatment. Approximately half of the animals underwent left ventricle pressure volume loop analysis. The remaining animals were killed for extraction of messenger RNA from whole heart preparations for microarray analysis.

MEASUREMENTS AND MAIN RESULTS

We observed that WT FVB and beta2 adrenergic receptor knockout mice developed systolic dysfunction in response to continuous catecholamine infusion, whereas WT C57 mice developed diastolic dysfunction. Using these mice as the derivation cohort, we identified a set of 83 genes whose differential expression correlated with left ventricle systolic dysfunction. The gene set was then used to accurately classify mice from a separate group (WT B6129SF2/J) into the cohort that developed left ventricle systolic dysfunction after catecholamine stress.

CONCLUSIONS

The differential expression pattern of 83 genes can be used to accurately classify mice according to physiological phenotype after catecholamine stress.

The gene expression profile of patients with new-onset heart failure reveals important gender-specific differences

AIMS

We sought to test the hypothesis that inherent biological factors contribute to gender differences in disease pathophysiology of new-onset heart failure (HF), which can be detected from the transcriptome of a single endomyocardial biopsy (EMB).

METHODS AND RESULTS

We analysed samples from male (n = 29) and female patients (n = 14) with idiopathic dilated cardiomyopathy (IDCM) and new-onset HF with U133 Plus 2.0 microarrays (Affymetrix) and significance analysis of microarrays (SAM). There were 35 overexpressed and 16 downregulated transcripts in men vs. women [q < 5%, fold change (FC) > 1.2]. In addition to overexpression of Y-chromosome-related transcripts (n = 18), such as USP9Y (FC > 13.1), DDX3Y (FC > 11.3), RPS4Y1 (FC > 9.9), and EIF1AY (FC > 11.8) in males, there was overexpression of CD24 (FC > 5.6) and KCNK1 (FC > 1.5). In females, XIST was highly overexpressed (FC > 28.9), together with X-linked zinc finger proteins (FC > 1.9) and autosomal genes GATAD1 (FC > 1.6), SLC2A12 (FC > 2.9), and PDE6B (FC > 1.5). Analysis of a public data set of end-stage IDCM (n = 15) resulted in approximately 85% overlap with our findings.

CONCLUSION

This is the first study that identified gender-specific transcriptomic differences in new-onset HF. Our findings may offer novel insights into fundamental biological differences in the pathophysiology of HF between sexes and provide a platform for personalized medicine.

Application of pharmacogenomics to vaccines

The field of pharmacogenomics and pharmacogenetics provides a promising science base for vaccine research and development. A broad range of phenotype/genotype data combined with high-throughput genetic sequencing and bioinformatics are increasingly being integrated into this emerging field of vaccinomics. This paper discusses the hypothesis of the 'immune response gene network' and genetic (and bioinformatic) strategies to study associations between immune response gene polymorphisms and variations in humoral and cellular immune responses to prophylactic viral vaccines, such as measles-mumps-rubella, influenza, HIV, hepatitis B and smallpox. Immunogenetic studies reveal promising new vaccine targets by providing a better understanding of the mechanisms by which gene polymorphisms may influence innate and adaptive immune responses to vaccines, including vaccine failure and vaccine-associated adverse events. Additional benefits from vaccinomic studies include the development of personalized vaccines, the development of novel vaccines and the development of novel vaccine adjuvants.

Cardiovascular genetic medicine: genomic assessment of prognosis and diagnosis in patients with cardiomyopathy and heart failure

In the last half century, epidemiologic studies and basic science investigations revealed that hypertension (Kannel et al., Ann Intern Med 55:33-50, 1961), hyperlipidemia (Dawber et al., Am J Public Health Nations Health 49:1349-1356, 1959), diabetes (Kannel et al., Am J Cardiol 34(1):29-34, 1974), smoking (Dawber et al., Am J Public Health Nations Health 49:1349-1356, 1959), and inflammation (Rossmann et al., Exp Gerontol 43(3):229-237, 2008) posed increased risk for cardiovascular disease. These associations served both as risk factors and offered insight into disease pathophysiology. Currently, it is increasingly appreciated that polygenic factors may also play a role as etiologic or risk factors (Chakravarti and Little, Nature 421(6921):412-414, 2003; Dorn and Molkentin, Circulation 109(2):150-158, 2004). Recent technologic advances in genomic screening make the search for these factors possible, and robust technologies are now available for both entire genome screening for expression or single nucleotide polymorphisms. In this paper, we review the basic principles of gene expression and molecular signature analysis in the context of potential clinical applications of transcriptomics.

Transcriptomic biomarkers for individual risk assessment in new-onset heart failure

BACKGROUND

Prediction of prognosis remains a major unmet need in new-onset heart failure (HF). Although several clinical tests are in use, none accurately distinguish between patients with poor versus excellent survival. We hypothesized that a transcriptomic signature, generated from a single endomyocardial biopsy, could serve as a novel prognostic biomarker in HF.

METHODS AND RESULTS

Endomyocardial biopsy samples and clinical data were collected from all patients presenting with new-onset HF from 1997 to 2006. Among a total of 350 endomyocardial biopsy samples, 180 were identified as idiopathic dilated cardiomyopathy. Patients with phenotypic extremes in survival were selected: good prognosis (event-free survival for at least 5 years; n=25) and poor prognosis (events [death, requirement for left ventricular assist device, or cardiac transplant] within the first 2 years of presentation with HF symptoms; n=18). We used human U133 Plus 2.0 microarrays (Affymetrix) and analyzed the data with significance analysis of microarrays and prediction analysis of microarrays. We identified 46 overexpressed genes in patients with good versus poor prognosis, of which 45 genes were selected by prediction analysis of microarrays for prediction of prognosis in a train set (n=29) with subsequent validation in test sets (n=14 each). The biomarker performed with 74% sensitivity (95% CI 69% to 79%) and 90% specificity (95% CI 87% to 93%) after 50 random partitions.

CONCLUSIONS

These findings suggest the potential of transcriptomic biomarkers to predict prognosis in patients with new-onset HF from a single endomyocardial biopsy sample. In addition, our findings offer potential novel therapeutic targets for HF and cardiomyopathy.

Adipose gene expression prior to weight loss can differentiate and weakly predict dietary responders

BACKGROUND

The ability to identify obese individuals who will successfully lose weight in response to dietary intervention will revolutionize disease management. Therefore, we asked whether it is possible to identify subjects who will lose weight during dietary intervention using only a single gene expression snapshot.

METHODOLOGY/PRINCIPAL FINDINGS

The present study involved 54 female subjects from the Nutrient-Gene Interactions in Human Obesity-Implications for Dietary Guidelines (NUGENOB) trial to determine whether subcutaneous adipose tissue gene expression could be used to predict weight loss prior to the 10-week consumption of a low-fat hypocaloric diet. Using several statistical tests revealed that the gene expression profiles of responders (8-12 kgs weight loss) could always be differentiated from non-responders (<4 kgs weight loss). We also assessed whether this differentiation was sufficient for prediction. Using a bottom-up (i.e. black-box) approach, standard class prediction algorithms were able to predict dietary responders with up to 61.1%+/-8.1% accuracy. Using a top-down approach (i.e. using differentially expressed genes to build a classifier) improved prediction accuracy to 80.9%+/-2.2%.

CONCLUSION

Adipose gene expression profiling prior to the consumption of a low-fat diet is able to differentiate responders from non-responders as well as serve as a weak predictor of subjects destined to lose weight. While the degree of prediction accuracy currently achieved with a gene expression snapshot is perhaps insufficient for clinical use, this work reveals that the comprehensive molecular signature of adipose tissue paves the way for the future of personalized nutrition.