Development of predicitve models to distinguish metals from non-metal toxicants, and individual metal from one another.

BACKGROUND: Evaluating the toxicity of chemical mixture and their possible mechanism of action is still a challenge for humans and other organisms. Microarray classifier analysis has shown promise in the toxicogenomic area by identifying biomarkers to predict unknown samples. Our study focuses on identifying gene markers with better sensitivity and specificity, building predictive models to distinguish metals from non-metal toxicants, and individual metal from one another, and furthermore helping understand underlying toxic mechanisms. RESULTS: Based on an independent dataset test, using only 15 gene markers, we were able to distinguish metals from non-metal toxicants with 100% accuracy. Of these, 6 and 9 genes were commonly down- and up-regulated respectively by most of the metals. 8 out of 15 genes belong to membrane protein coding genes. Function well annotated genes in the list include ADORA2B, ARNT, S100G, and DIO3. Also, a 10-gene marker list was identified that can discriminate an individual metal from one another with 100% accuracy. We could find a specific gene marker for each metal in the 10-gene marker list. Function well annotated genes in this list include GSTM2, HSD11B, AREG, and C8B. CONCLUSIONS: Our findings suggest that using a microarray classifier analysis, not only can we create diagnostic classifiers for predicting an exact metal contaminant from a large scale of contaminant pool with high prediction accuracy, but we can also identify valuable biomarkers to help understand the common and underlying toxic mechanisms induced by metals.

Identification of plasma lipid species as promising diagnostic markers for prostate cancer.

BACKGROUND: Prostate cancer is a very common and highly fatal in men. Current non-invasive detection methods like serum biomarker are unsatisfactory. Biomarkers with high accuracy for diagnostic of prostate cancer are urgently needed. Many lipid species have been found related to various cancers. The purpose of our study is to explore the diagnostic value of lipids for prostate cancer. RESULTS: Using triple quadruple liquid chromatography electrospray ionization tandem mass spectrometry, we performed lipidomics profiling of 367 lipids on a total 114 plasma samples from 30 patients with prostate cancer, 38 patients with benign prostatic hyperplasia (BPH), and 46 male healthy controls to evaluate the lipids as potential biomarkers in the diagnosis of prostate cancer. Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway database was used to construct the potential mechanism pathway. After statistical analysis, five lipids were identified as a panel of potential biomarkers for the detection of prostate cancer between prostate cancer group and the BPH group; the sensitivity, specificity, and area under curve (AUC) of the combination of these five lipids were 73.3, 81.6%, and 0.800, respectively. We also identified another panel of five lipids in distinguishing between prostate cancer group and the control group with predictive values of sensitivity at 76.7%, specificity at 80.4%, and AUC at 0.836, respectively. The glycerophospholipid metabolism pathway of the selected lipids was considered as the target pathway. CONCLUSIONS: Our study indicated that the identified plasma lipid biomarkers have potential in the diagnosis of prostate cancer.

Ratio-Based Method To Identify True Biomarkers by Normalizing Circulating ncRNA Sequencing and Quantitative PCR Data.

Recent studies have indicated that circulating noncoding RNAs (ncRNAs) such as miRNAs are stable biomarkers for the diagnosis and prognosis of human diseases. However, due to low concentrations of circulating ncRNAs in blood, data normalization in plasma/serum ncRNA experiments using next-generation sequencing and quantitative real time RT-qPCR is a challenge. We found that the current normalization methods based on synthetic external spiked-in controls or published endogenous miRNA controls are inappropriate as they are not stably expressed and therefore fail to reliably detect differentially expressed ncRNAs. Using the alternative of individual ncRNAs as biomarkers, we considered a ratio-based normalization method calculated taking the ratio of any two ncRNAs in the same sample and used the resulting ratios as biomarkers. We mathematically verified the method to be independent of spiked-in and internal controls, and more robust than existing reference control based normalization methods to identify differentially expressed ncRNAs as potential biomarkers for human diseases. Thus, the ratio-based method can solve the difficult normalization problem for circuiting ncRNA data to identify reliable biomarkers to meet real clinical practice.

Global lipidomics reveals two plasma lipids as novel biomarkers for the detection of squamous cell lung cancer: A pilot study.

Lipids are known to serve important roles in energy storage, membrane structure and signal transduction as well as in human cancers. In the present study, lipidomics was employed in order to identify plasma lipid markers for the early detection of lung cancer. Mass spectrometry was performed to profile 390 individual lipids in 44 plasma samples obtained from a training discovery cohort, which included 22 patients with squamous cell lung carcinoma (SqCC) and 22 high-risk individuals. An additional cohort that included 22 high-risk individuals and 22 patients with SqCC was further used for validation. During the training stage, a total of 20 distinct lipids that were significantly distributed between the high-risk and SqCC cases, were identified. A panel of 2 lipid markers (C18:2 cholesterol esters and sphingomyelin 22:0) were then further defined using the training accuracy values of 95.5% sensitivity, 90.9% specificity and 95.2% area under the receiver operating characteristic curve (AUC). The validation accuracy values applied for the additional cohort were 93.9% sensitivity, 92.9% specificity and 98.7% AUC. Thus, in the present study, 2 lipid markers that were able to discern SqCC patients from high-risk individuals with a high sensitivity, specificity and accuracy, were identified. These results may provide vital information for the development of a quick and safe blood test for the early diagnosis of SqCC.

Plasma small ncRNA pair panels as novel biomarkers for early-stage lung adenocarcinoma screening.

BACKGROUND: Lung cancer is a major cause of cancer-related mortality worldwide, and around two-thirds of patients have metastasis at diagnosis. Thus, detecting lung cancer at an early stage could reduce mortality. Aberrant levels of circulating small non-coding RNAs (small ncRNAs) are potential diagnostic or prognostic markers for lung cancer. We aimed to identify plasma small ncRNA pairs that could be used for early screening and detection of lung adenocarcinoma (LAC). RESULTS: A panel of seven small ncRNA pair ratios could differentiate patients with LAC or benign lung disease from high-risk controls with an area under the curve (AUC) of 100.0%, a sensitivity of 100.0% and a specificity of 100.0% at the training stage (which included 50 patients with early-stage LAC, 35 patients with benign diseases and 29 high-risk controls) and an AUC of 90.2%, a sensitivity of 91.5% and a specificity of 80.4% at the validation stage (which included 44 patients with early-stage LAC, 32 patients with benign diseases and 51 high-risk controls). The same panel could distinguish LAC from high-risk controls with an AUC of 100.0%, a sensitivity of 100.0% and a specificity of 100.0% at the training stage and an AUC of 89.5%, a sensitivity of 85.4% and a specificity of 83.3% at the validation stage. Another panel of five small ncRNA pair ratios (different from the first) was able to differentiate LAC from benign disease with an AUC of 82.0%, a sensitivity of 81.1% and a specificity of 78.1% in the training cohort and an AUC of 74.2%, a sensitivity of 70.4% and a specificity of 72.7% in the validation cohort. CONCLUSIONS: Several small ncRNA pair ratios were identified as markers capable of discerning patients with LAC from those with benign lesions or high-risk control individuals.

Plasma MicroRNA Pair Panels as Novel Biomarkers for Detection of Early Stage Breast Cancer.

Introduction: Breast cancer is the second leading cause of cancer death among females. We sought to identify microRNA (miRNA) markers in breast cancer, and determine whether miRNA expression is predictive of early stage breast cancer. The paired panel of microRNAs is promising. Methods: Global miRNA expression profiling was performed on three pooling samples of plasma from breast cancer, benign lesion and normal, using next generation sequencing technology. Thirteen microRNAs (hsa-miR-21-3p, hsa-miR-192-5p, hsa-miR-221-3p, hsa-miR-451a, hsa-miR-574-5p, hsa-miR-1273g-3p, hsa-miR-152, hsa-miR-22-3p, hsa-miR-222-3p, hsa-miR-30a-5p, hsa-miR-30e-5p, hsa-miR-324-3p, and hsa -miR-382-5p) were subsequently validated using real-time quantitative reverse transcription-polymerase chain reaction (RT-qPCR) in a cohort of 53 breast cancer, 40 benign lesions and 38 normal cases. The pairwise miRNA ratios were calculated as biomarkers to classify breast cancer. Results: According to the model used to predict breast cancer from benign lesions, a panel of five miRNA pairs had high diagnostic power with an AUC of 0.942. The sensitivity, specificity, positive predictive value (PPV) and negative predictive value (NPV) of this model after 10-fold cross validation were 0.881, 0.775, 0.827, and 0.756, respectively. In addition, the other panels of miRNA pairs distinguishing the breast cancer from normal and non-cancer patients had good performance. Conclusion: Certain MicroRNA pairs were identified and deemed effective in breast cancer screening, especially when distinguishing cancer from benign lesions.

Global lipidomics identified plasma lipids as novel biomarkers for early detection of lung cancer.

PURPOSE: Lipids play roles in membrane structure, energy storage, and signal transduction as well as in human cancers. Here we adopt lipidomics to identify plasma lipid markers for early screening and detection of lung cancer. EXPERIMENTAL DESIGN: Using mass spectrometry, we profiled 390 individual lipids using training and validation strategy in a total of 346 plasma samples from 199 early NSCLC patients, including 113 adenocacinoma and 86 squamous cell cancers (SqCC), and from 147 healthy controls. RESULTS: In the training stage, we found distinct lipid groups that were significantly distributed between NSCLC cases and healthy controls. We further defined a panel of four lipid markers (LPE(18:1), ePE(40:4), C(18:2)CE and SM(22:0)) for prediction of early cancer with a accuracy of 82.3% AUC (Area under ROC curve), sensitivity of 81.9% and specificity of 70.7% at the training stage and yielded the predictive power with accuracy (AUC,80.8%), sensitivity 78.7%, specificity 69.4% and in the validation stage. CONCLUSIONS: Using lipidomics we identified several lipid markers capable of discerning early stage lung carcinoma from healthy individuals, which might be further developed as a quick, safe blood test for early diagnosis of this disease.

Plasma lipidomics profiling identified lipid biomarkers in distinguishing early-stage breast cancer from benign lesions.

BACKGROUND: Breast cancer is very common and highly fatal in women. Current non-invasive detection methods like mammograms are unsatisfactory. Lipidomics, a promising detection method, may serve as a novel prognostic approach for breast cancer in high-risk patients. RESULTS: According the predictive model, the combination of 15 lipid species had high diagnostic value. In the training set, sensitivity, specificity, positive predictive value (PPV) and negative predictive value (NPV) of the combination of these 15 lipid species were 83.3%, 92.7%, 89.7%, and 87.9%, respectively. The AUC in the training set was 0.926 (95% CI 0.869-0.982). Similar results were found in the validation set, with the sensitivity, specificity, PPV and NPV at 81.0%, 94.5%, 91.9%, and 86.7%, respectively. The AUC was 0.938 (95% CI 0.889-0.986) in the validation set. METHODS: Using triple quadrupole liquid chromatography electrospray ionization tandem mass spectrometry, this study was to detect global lipid profiling of a total of 194 plasma samples from 84 patients with early-stage breast cancer (stage 0-II) and 110 patients with benign breast disease included in a training set and a validation set. A binary logistic regression was used to build a predictive model for evaluating the lipid species as potential biomarkers in the diagnosis of breast cancer. CONCLUSIONS: The combination of these 15 lipid species as a panel could be used as plasma biomarkers for the diagnosis of breast cancer.

Evaluation of Plasma miR-21 and miR-152 as Diagnostic Biomarkers for Common Types of Human Cancers.

Stable blood based miRNA species have allowed for the differentiation of patients with various types of cancer. Therefore, specific blood-based miRNA might be considered as a methodology which could be informative of the presence of cancer potentially from multiple distinct organ sites. Recently, miR-21 has been identified as an "oncomir" in various tumors while miR-152 as a tumor suppressor. In this study, we investigated whether circulating miR-21 and miR-152 can be used for early detection of lung cancer (LuCa), colorectal carcinoma (CRC), breast cancer (BrCa) and prostate cancer (PCa), with distinguishing cancer from various benign lesions on these organ sites. We measured the two miRNA levels by using real-time RT-PCR in plasma samples from a total of 204 cancer patients, 159 various benign lesions, and 228 normal subjects. We observed significantly elevated expression of miR-21 and miR-152 in LuCa, CRC, and BrCa when compared with normal controls. We also found upregulation of plasma miR-21 and miR-152 levels in patients with benign lesions of lung and breast, as compared to normal controls, respectively. No significant expression variation of the two miRNAs was observed in PCa or prostatic benign lesions as compared to healthy controls. Receiver operating characteristic (ROC) analyses revealed that miR-21 and/or miR-152 can discriminate LuCa, CRC and BrCa from normal controls. Our results suggest that plasma miR-21 and miR-152 may serve as non-specific noninvasive biomarkers for early screening of LuCa, CRC, and BrCa, but not PCa.

Integrative microRNA and gene profiling data analysis reveals novel biomarkers and mechanisms for lung cancer.

BACKGROUND: Studies on the accuracy of microRNAs (miRNAs) in diagnosing non-small cell lung cancer (NSCLC) have still controversial. Therefore, we conduct to systematically identify miRNAs related to NSCLC, and their target genes expression changes using microarray data sets. METHODS: We screened out five miRNAs and six genes microarray data sets that contained miRNAs and genes expression in NSCLC from Gene Expression Omnibus. RESULTS: Our analysis results indicated that fourteen miRNAs were significantly dysregulated in NSCLC. Five of them were up-regulated (miR-9, miR-708, miR-296-3p, miR-892b, miR-140-5P) while nine were down-regulated (miR-584, miR-218, miR-30b, miR-522, miR486-5P, miR-34c-3p, miR-34b, miR-516b, miR-592). The integrating diagnosis sensitivity (SE) and specificity (SP) were 82.6% and 89.9%, respectively. We also found that 4 target genes (p < 0.05, fold change > 2.0) were significant correlation with the 14 discovered miRNAs, and the classifiers we built from one training set predicted the validation set with higher accuracy (SE = 0.987, SP = 0.824). CONCLUSIONS: Our results demonstrate that integrating miRNAs and target genes are valuable for identifying promising biomarkers, and provided a new insight on underlying mechanism of NSCLC. Further, our well-designed validation studies surely warrant the investigation of the role of target genes related to these 14 miRNAs in the prediction and development of NSCLC.

Comparison of RNA-seq and microarray-based models for clinical endpoint prediction.

BACKGROUND: Gene expression profiling is being widely applied in cancer research to identify biomarkers for clinical endpoint prediction. Since RNA-seq provides a powerful tool for transcriptome-based applications beyond the limitations of microarrays, we sought to systematically evaluate the performance of RNA-seq-based and microarray-based classifiers in this MAQC-III/SEQC study for clinical endpoint prediction using neuroblastoma as a model. RESULTS: We generate gene expression profiles from 498 primary neuroblastomas using both RNA-seq and 44 k microarrays. Characterization of the neuroblastoma transcriptome by RNA-seq reveals that more than 48,000 genes and 200,000 transcripts are being expressed in this malignancy. We also find that RNA-seq provides much more detailed information on specific transcript expression patterns in clinico-genetic neuroblastoma subgroups than microarrays. To systematically compare the power of RNA-seq and microarray-based models in predicting clinical endpoints, we divide the cohort randomly into training and validation sets and develop 360 predictive models on six clinical endpoints of varying predictability. Evaluation of factors potentially affecting model performances reveals that prediction accuracies are most strongly influenced by the nature of the clinical endpoint, whereas technological platforms (RNA-seq vs. microarrays), RNA-seq data analysis pipelines, and feature levels (gene vs. transcript vs. exon-junction level) do not significantly affect performances of the models. CONCLUSIONS: We demonstrate that RNA-seq outperforms microarrays in determining the transcriptomic characteristics of cancer, while RNA-seq and microarray-based models perform similarly in clinical endpoint prediction. Our findings may be valuable to guide future studies on the development of gene expression-based predictive models and their implementation in clinical practice.

Identification of biomarkers that distinguish chemical contaminants based on gene expression profiles.

BACKGROUND: High throughput transcriptomics profiles such as those generated using microarrays have been useful in identifying biomarkers for different classification and toxicity prediction purposes. Here, we investigated the use of microarrays to predict chemical toxicants and their possible mechanisms of action. RESULTS: In this study, in vitro cultures of primary rat hepatocytes were exposed to 105 chemicals and vehicle controls, representing 14 compound classes. We comprehensively compared various normalization of gene expression profiles, feature selection and classification algorithms for the classification of these 105 chemicals into14 compound classes. We found that normalization had little effect on the averaged classification accuracy. Two support vector machine (SVM) methods, LibSVM and sequential minimal optimization, had better classification performance than other methods. SVM recursive feature selection (SVM-RFE) had the highest overfitting rate when an independent dataset was used for a prediction. Therefore, we developed a new feature selection algorithm called gradient method that had a relatively high training classification as well as prediction accuracy with the lowest overfitting rate of the methods tested. Analysis of biomarkers that distinguished the 14 classes of compounds identified a group of genes principally involved in cell cycle function that were significantly downregulated by metal and inflammatory compounds, but were induced by anti-microbial, cancer related drugs, pesticides, and PXR mediators. CONCLUSIONS: Our results indicate that using microarrays and a supervised machine learning approach to predict chemical toxicants, their potential toxicity and mechanisms of action is practical and efficient. Choosing the right feature and classification algorithms for this multiple category classification and prediction is critical.

MicroRNA and messenger RNA profiling reveals new biomarkers and mechanisms for RDX induced neurotoxicity.

BACKGROUND: RDX is a well-known pollutant to induce neurotoxicity. MicroRNAs (miRNA) and messenger RNA (mRNA) profiles are useful tools for toxicogenomics studies. It is worthy to integrate MiRNA and mRNA expression data to understand RDX-induced neurotoxicity. RESULTS: Rats were treated with or without RDX for 48 h. Both miRNA and mRNA profiles were conducted using brain tissues. Nine miRNAs were significantly regulated by RDX. Of these, 6 and 3 miRNAs were up- and down-regulated respectively. The putative target genes of RDX-regulated miRNAs were highly nervous system function genes and pathways enriched. Fifteen differentially genes altered by RDX from mRNA profiles were the putative targets of regulated miRNAs. The induction of miR-71, miR-27ab, miR-98, and miR-135a expression by RDX, could reduce the expression of the genes POLE4, C5ORF13, SULF1 and ROCK2, and eventually induce neurotoxicity. Over-expression of miR-27ab, or reduction of the expression of unknown miRNAs by RDX, could up-regulate HMGCR expression and contribute to neurotoxicity. RDX regulated immune and inflammation response miRNAs and genes could contribute to RDX- induced neurotoxicity and other toxicities as well as animal defending reaction response to RDX exposure. CONCLUSIONS: Our results demonstrate that integrating miRNA and mRNA profiles is valuable to indentify novel biomarkers and molecular mechanisms for RDX-induced neurological disorder and neurotoxicity.

Commonality and differences of methylation signatures in the plasma of patients with pancreatic cancer and colorectal cancer.

Profiling of DNA methylation status of specific genes is a way to screen for colorectal cancer (CRC) and pancreatic cancer (PC) in blood. The commonality of methylation status of cancer-related tumor suppressor genes between CRC and PC is largely unknown. Methylation status of 56 cancer-related genes was compared in plasma of patients in the following cohorts: CRC, PC and healthy controls. Cross validation determined the best model by area under ROC curve (AUC) to differentiate cancer methylation profiles from controls. Optimal preferential gene methylation signatures were derived to differentiate either cancer (CRC or PC) from controls. For CRC alone, a three gene signature (CYCD2, HIC and VHL) had an AUC 0.9310, sensitivity (Sens) = 0.826, specificity (Spec) = 0.9383. For PC alone, an optimal signature consisted of five genes (VHL, MYF3, TMS, GPC3 and SRBC), AUC 0.848; Sens = 0.807, Spec = 0.666. Combined PC and CRC signature or "combined cancer signature" was derived to differentiate either CRC and PC from controls (MDR1, SRBC, VHL, MUC2, RB1, SYK and GPC3) AUC = 0.8177, Sens = 0.6316 Spec = 0.840. In a validation cohort, N = 10 CRC patients, the optimal CRC signature (CYCD2, HIC and VHL) had AUC 0.900. In all derived signatures (CRC, PC and combined cancer signature) the optimal panel used preferential VHL methylation. In conclusion, CRC and PC differ in specific genes methylated in plasma other than VHL. Preferential methylation of VHL is shared in the optimal signature for CRC alone, PC alone and combined PC and CRC. Future investigations may identify additional methylation markers informative for the presence of both CRC and PC.

Integrated lipidomics and transcriptomic analysis of peripheral blood reveals significantly enriched pathways in type 2 diabetes mellitus.

BACKGROUND: Insulin resistance is a key element in the pathogenesis of type 2 diabetes mellitus. Plasma free fatty acids were assumed to mediate the insulin resistance, while the relationship between lipid and glucose disposal remains to be demonstrated across liver, skeletal muscle and blood. METHODS: We profiled both lipidomics and gene expression of 144 total peripheral blood samples, 84 from patients with T2D and 60 from healthy controls. Then, factor and partial least squares models were used to perform a combined analysis of lipidomics and gene expression profiles to uncover the bioprocesses that are associated with lipidomic profiles in type 2 diabetes. RESULTS: According to factor analysis of the lipidomic profile, several species of lipids were found to be correlated with different phenotypes, including diabetes-related C23:2CE, C23:3CE, C23:4CE, ePE36:4, ePE36:5, ePE36:6; race-related (African-American) PI36:1; and sex-related PE34:1 and LPC18:2. The major variance of gene expression profile was not caused by known factors and no significant difference can be directly derived from differential gene expression profile. However, the combination of lipidomic and gene expression analyses allows us to reveal the correlation between the altered lipid profile with significantly enriched pathways, such as one carbon pool by folate, arachidonic acid metabolism, insulin signaling pathway, amino sugar and nucleotide sugar metabolism, propanoate metabolism, and starch and sucrose metabolism. The genes in these pathways showed a good capability to classify diabetes samples. CONCLUSION: Combined analysis of gene expression and lipidomic profiling reveals type 2 diabetes-associated lipid species and enriched biological pathways in peripheral blood, while gene expression profile does not show direct correlation. Our findings provide a new clue to better understand the mechanism of disordered lipid metabolism in association with type 2 diabetes.

Identification of plasma lipid biomarkers for prostate cancer by lipidomics and bioinformatics.

BACKGROUND: Lipids have critical functions in cellular energy storage, structure and signaling. Many individual lipid molecules have been associated with the evolution of prostate cancer; however, none of them has been approved to be used as a biomarker. The aim of this study is to identify lipid molecules from hundreds plasma apparent lipid species as biomarkers for diagnosis of prostate cancer. METHODOLOGY/PRINCIPAL FINDINGS: Using lipidomics, lipid profiling of 390 individual apparent lipid species was performed on 141 plasma samples from 105 patients with prostate cancer and 36 male controls. High throughput data generated from lipidomics were analyzed using bioinformatic and statistical methods. From 390 apparent lipid species, 35 species were demonstrated to have potential in differentiation of prostate cancer. Within the 35 species, 12 were identified as individual plasma lipid biomarkers for diagnosis of prostate cancer with a sensitivity above 80%, specificity above 50% and accuracy above 80%. Using top 15 of 35 potential biomarkers together increased predictive power dramatically in diagnosis of prostate cancer with a sensitivity of 93.6%, specificity of 90.1% and accuracy of 97.3%. Principal component analysis (PCA) and hierarchical clustering analysis (HCA) demonstrated that patient and control populations were visually separated by identified lipid biomarkers. RandomForest and 10-fold cross validation analyses demonstrated that the identified lipid biomarkers were able to predict unknown populations accurately, and this was not influenced by patient's age and race. Three out of 13 lipid classes, phosphatidylethanolamine (PE), ether-linked phosphatidylethanolamine (ePE) and ether-linked phosphatidylcholine (ePC) could be considered as biomarkers in diagnosis of prostate cancer. CONCLUSIONS/SIGNIFICANCE: Using lipidomics and bioinformatic and statistical methods, we have identified a few out of hundreds plasma apparent lipid molecular species as biomarkers for diagnosis of prostate cancer with a high sensitivity, specificity and accuracy.

Analysis of common and specific mechanisms of liver function affected by nitrotoluene compounds.

BACKGROUND: Nitrotoluenes are widely used chemical manufacturing and munitions applications. This group of chemicals has been shown to cause a range of effects from anemia and hypercholesterolemia to testicular atrophy. We have examined the molecular and functional effects of five different, but structurally related, nitrotoluenes on using an integrative systems biology approach to gain insight into common and disparate mechanisms underlying effects caused by these chemicals. METHODOLOGY/PRINCIPAL FINDINGS: Sprague-Dawley female rats were exposed via gavage to one of five concentrations of one of five nitrotoluenes [2,4,6-trinitrotoluene (TNT), 2-amino-4,6-dinitrotoluene (2ADNT) 4-amino-2,6-dinitrotoulene (4ADNT), 2,4-dinitrotoluene (2,4DNT) and 2,6-dinitrotoluene (2,6DNT)] with necropsy and tissue collection at 24 or 48 h. Gene expression profile results correlated well with clinical data and liver histopathology that lead to the concept that hematotoxicity was followed by hepatotoxicity. Overall, 2,4DNT, 2,6DNT and TNT had stronger effects than 2ADNT and 4ADNT. Common functional terms, gene expression patterns, pathways and networks were regulated across all nitrotoluenes. These pathways included NRF2-mediated oxidative stress response, aryl hydrocarbon receptor signaling, LPS/IL-1 mediated inhibition of RXR function, xenobiotic metabolism signaling and metabolism of xenobiotics by cytochrome P450. One biological process common to all compounds, lipid metabolism, was found to be impacted both at the transcriptional and lipid production level. CONCLUSIONS/SIGNIFICANCE: A systems biology strategy was used to identify biochemical pathways affected by five nitroaromatic compounds and to integrate data that tie biochemical alterations to pathological changes. An integrative graphical network model was constructed by combining genomic, gene pathway, lipidomic, and physiological endpoint results to better understand mechanisms of liver toxicity and physiological endpoints affected by these compounds.

Transcriptomic profiles of peripheral white blood cells in type II diabetes and racial differences in expression profiles.

BACKGROUND: Along with obesity, physical inactivity, and family history of metabolic disorders, African American ethnicity is a risk factor for type 2 diabetes (T2D) in the United States. However, little is known about the differences in gene expression and transcriptomic profiles of blood in T2D between African Americans (AA) and Caucasians (CAU), and microarray analysis of peripheral white blood cells (WBCs) from these two ethnic groups will facilitate our understanding of the underlying molecular mechanism in T2D and identify genetic biomarkers responsible for the disparities. RESULTS: A whole human genome oligomicroarray of peripheral WBCs was performed on 144 samples obtained from 84 patients with T2D (44 AA and 40 CAU) and 60 healthy controls (28 AA and 32 CAU). The results showed that 30 genes had significant difference in expression between patients and controls (a fold change of <-1.4 or >1.4 with a P value <0.05). These known genes were mainly clustered in three functional categories: immune responses, lipid metabolism, and organismal injury/abnormaly. Transcriptomic analysis also showed that 574 genes were differentially expressed in AA diseased versus AA control, compared to 200 genes in CAU subjects. Pathway study revealed that "Communication between innate and adaptive immune cells"/"Primary immunodeficiency signaling" are significantly down-regulated in AA patients and "Interferon signaling"/"Complement System" are significantly down-regulated in CAU patients. CONCLUSIONS: These newly identified genetic markers in WBCs provide valuable information about the pathophysiology of T2D and can be used for diagnosis and pharmaceutical drug design. Our results also found that AA and CAU patients with T2D express genes and pathways differently.

A new approach to construct pathway connected networks and its application in dose responsive gene expression profiles of rat liver regulated by 2,4DNT.

BACKGROUND: Military and industrial activities have lead to reported release of 2,4-dinitrotoluene (2,4DNT) into soil, groundwater or surface water. It has been reported that 2,4DNT can induce toxic effects on humans and other organisms. However the mechanism of 2,4DNT induced toxicity is still unclear. Although a series of methods for gene network construction have been developed, few instances of applying such technology to generate pathway connected networks have been reported. RESULTS: Microarray analyses were conducted using liver tissue of rats collected 24h after exposure to a single oral gavage with one of five concentrations of 2,4DNT. We observed a strong dose response of differentially expressed genes after 2,4DNT treatment. The most affected pathways included: long term depression, breast cancer regulation by stathmin1, WNT Signaling; and PI3K signaling pathways. In addition, we propose a new approach to construct pathway connected networks regulated by 2,4DNT. We also observed clear dose response pathway networks regulated by 2,4DNT. CONCLUSIONS: We developed a new method for constructing pathway connected networks. This new method was successfully applied to microarray data from liver tissue of 2,4DNT exposed animals and resulted in the identification of unique dose responsive biomarkers in regards to affected pathways.

In vitro gene regulatory networks predict in vivo function of liver.

BACKGROUND: Evolution of toxicity testing is predicated upon using in vitro cell based systems to rapidly screen and predict how a chemical might cause toxicity to an organ in vivo. However, the degree to which we can extend in vitro results to in vivo activity and possible mechanisms of action remains to be fully addressed. RESULTS: Here we use the nitroaromatic 2,4,6-trinitrotoluene (TNT) as a model chemical to compare and determine how we might extrapolate from in vitro data to in vivo effects. We found 341 transcripts differentially expressed in common among in vitro and in vivo assays in response to TNT. The major functional term corresponding to these transcripts was cell cycle. Similarly modulated common pathways were identified between in vitro and in vivo. Furthermore, we uncovered the conserved common transcriptional gene regulatory networks between in vitro and in vivo cellular liver systems that responded to TNT exposure, which mainly contain 2 subnetwork modules: PTTG1 and PIR centered networks. Interestingly, all 7 genes in the PTTG1 module were involved in cell cycle and downregulated by TNT both in vitro and in vivo. CONCLUSIONS: The results of our investigation of TNT effects on gene expression in liver suggest that gene regulatory networks obtained from an in vitro system can predict in vivo function and mechanisms. Inhibiting PTTG1 and its targeted cell cycle related genes could be key mechanism for TNT induced liver toxicity.