Rat toxicogenomic study reveals analytical consistency across microarray platforms

Quartet RNA reference materials improve the quality of transcriptomic data through ratio-based profiling

Certified RNA reference materials are indispensable for assessing the reliability of RNA sequencing to detect intrinsically small biological differences in clinical settings, such as molecular subtyping of diseases. As part of the Quartet Project for quality control and data integration of multi-omics profiling, we established four RNA reference materials derived from immortalized B-lymphoblastoid cell lines from four members of a monozygotic twin family. Additionally, we constructed ratio-based transcriptome-wide reference datasets between two samples, providing cross-platform and cross-laboratory 'ground truth'. Investigation of the intrinsically subtle biological differences among the Quartet samples enables sensitive assessment of cross-batch integration of transcriptomic measurements at the ratio level. The Quartet RNA reference materials, combined with the ratio-based reference datasets, can serve as unique resources for assessing and improving the quality of transcriptomic data in clinical and biological settings.

Multi-omics data integration using ratio-based quantitative profiling with Quartet reference materials

Characterization and integration of the genome, epigenome, transcriptome, proteome and metabolome of different datasets is difficult owing to a lack of ground truth. Here we develop and characterize suites of publicly available multi-omics reference materials of matched DNA, RNA, protein and metabolites derived from immortalized cell lines from a family quartet of parents and monozygotic twin daughters. These references provide built-in truth defined by relationships among the family members and the information flow from DNA to RNA to protein. We demonstrate how using a ratio-based profiling approach that scales the absolute feature values of a study sample relative to those of a concurrently measured common reference sample produces reproducible and comparable data suitable for integration across batches, labs, platforms and omics types. Our study identifies reference-free 'absolute' feature quantification as the root cause of irreproducibility in multi-omics measurement and data integration and establishes the advantages of ratio-based multi-omics profiling with common reference materials.

Correcting batch effects in large-scale multiomics studies using a reference-material-based ratio method

BACKGROUND

Batch effects are notoriously common technical variations in multiomics data and may result in misleading outcomes if uncorrected or over-corrected. A plethora of batch-effect correction algorithms are proposed to facilitate data integration. However, their respective advantages and limitations are not adequately assessed in terms of omics types, the performance metrics, and the application scenarios.

RESULTS

As part of the Quartet Project for quality control and data integration of multiomics profiling, we comprehensively assess the performance of seven batch effect correction algorithms based on different performance metrics of clinical relevance, i.e., the accuracy of identifying differentially expressed features, the robustness of predictive models, and the ability of accurately clustering cross-batch samples into their own donors. The ratio-based method, i.e., by scaling absolute feature values of study samples relative to those of concurrently profiled reference material(s), is found to be much more effective and broadly applicable than others, especially when batch effects are completely confounded with biological factors of study interests. We further provide practical guidelines for implementing the ratio based approach in increasingly large-scale multiomics studies.

CONCLUSIONS

Multiomics measurements are prone to batch effects, which can be effectively corrected using ratio-based scaling of the multiomics data. Our study lays the foundation for eliminating batch effects at a ratio scale.

Comparison of Rat Hepatocyte 2D-Monocultures and Hepatocytes Non-Parenchymal Cell Co-Cultures for Assessing Chemical Toxicity

Liver responses are the most common endpoints used as the basis for setting exposure standards. Liver hepatocytes play a vital role in biotransformation of xenobiotics, but non-parenchymal cells (NPCs) in the liver are also involved in certain liver responses. Development of in vitro systems that more faithfully capture liver responses to reduce reliance on animals is a major focus of New Approach Methodology (NAMs). Since rodent regulatory studies are frequently the sole source safety assessment data, mode-of-action data, and used for risk assessments, in vitro rodent models that reflect in vivo responses need to be developed to reduce reliance on animal models. In the work presented in this paper, we developed a 2-D hepatocyte monoculture and 2-D liver cell co-culture system using rat liver cells. These models were assessed for conditions for short-term stability of the cultures and phenotypic and transcriptomic responses of 2 prototypic hepatotoxicants compounds - acetaminophen and phenobarbital. The optimized multi-cellular 2-D culture required use of freshly prepared hepatocytes and NPCs from a single rat, a 3:1 ratio of hepatocytes to NPCs and growth medium using 50% Complete Williams E medium (WEM) and 50% Endothelial Cell Medium (ECM). The transcriptomic responses of the 2 model systems to PB were compared to previous studies from TG-Gates on the gene expression changes in intact rats and the co-culture model responses were more representative of the in vivo responses. Transcriptomic read-outs promise to move beyond conventional phenotypic evaluations with these in vitro NAMs and provide insights about modes of action.

Protocol for using Ciclops to build models trained on cross-platform transcriptome data for clinical outcome prediction

Designing robust, generalizable models based on cross-platform data to predict clinical outcomes remains challenging. Building explainable models is important because models may perform differently depending on the conditions of the samples. Here, we describe the use of Ciclops (cross-platform training in clinical outcome predictions), freely available software that can build explainable models to deliver across cross-platform datasets for predicting clinical outcomes. This protocol also utilizes SHAP, a post-training analysis allowing for assessing potential biomarkers of the clinical outcome under study.

For complete details on the use and execution of this protocol, please refer to Zhang et al. (2022).

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Build robust clinical outcome prediction models using cross-platform transcriptome data

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Applicable to datasets from different studies measuring different clinical outcomes

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Perform key preprocessing steps of imputation and cross-platform quantile normalization

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Analyze feature importance in LightGBM, XGBoost, and Random Forest models with SHAP

Publisher’s note: Undertaking any experimental protocol requires adherence to local institutional guidelines for laboratory safety and ethics.

Analyzing biomarker discovery: Estimating the reproducibility of biomarker sets

Many researchers try to understand a biological condition by identifying biomarkers. This is typically done using univariate hypothesis testing over a labeled dataset, declaring a feature to be a biomarker if there is a significant statistical difference between its values for the subjects with different outcomes. However, such sets of proposed biomarkers are often not reproducible – subsequent studies often fail to identify the same sets. Indeed, there is often only a very small overlap between the biomarkers proposed in pairs of related studies that explore the same phenotypes over the same distribution of subjects. This paper first defines the Reproducibility Score for a labeled dataset as a measure (taking values between 0 and 1) of the reproducibility of the results produced by a specified fixed biomarker discovery process for a given distribution of subjects. We then provide ways to reliably estimate this score by defining algorithms that produce an over-bound and an under-bound for this score for a given dataset and biomarker discovery process, for the case of univariate hypothesis testing on dichotomous groups. We confirm that these approximations are meaningful by providing empirical results on a large number of datasets and show that these predictions match known reproducibility results. To encourage others to apply this technique to analyze their biomarker sets, we have also created a publicly available website, https://biomarker.shinyapps.io/BiomarkerReprod/, that produces these Reproducibility Score approximations for any given dataset (with continuous or discrete features and binary class labels).

ConSIG: consistent discovery of molecular signature from OMIC data

The discovery of proper molecular signature from OMIC data is indispensable for determining biological state, physiological condition, disease etiology, and therapeutic response. However, the identified signature is reported to be highly inconsistent, and there is little overlap among the signatures identified from different biological datasets. Such inconsistency raises doubts about the reliability of reported signatures and significantly hampers its biological and clinical applications. Herein, an online tool, ConSIG, was constructed to realize consistent discovery of gene/protein signature from any uploaded transcriptomic/proteomic data. This tool is unique in a) integrating a novel strategy capable of significantly enhancing the consistency of signature discovery, b) determining the optimal signature by collective assessment, and c) confirming the biological relevance by enriching the disease/gene ontology. With the increasingly accumulated concerns about signature consistency and biological relevance, this online tool is expected to be used as an essential complement to other existing tools for OMIC-based signature discovery. ConSIG is freely accessible to all users without login requirement at https://idrblab.org/consig/.

Development and validation of an RNA-seq-based transcriptomic risk score for asthma

Recent progress in RNA sequencing (RNA-seq) allows us to explore whole-genome gene expression profiles and to develop predictive model for disease risk. The objective of this study was to develop and validate an RNA-seq-based transcriptomic risk score (RSRS) for disease risk prediction that can simultaneously accommodate demographic information. We analyzed RNA-seq gene expression data from 441 asthmatic and 254 non-asthmatic samples. Logistic least absolute shrinkage and selection operator (Lasso) regression analysis in the training set identified 73 differentially expressed genes (DEG) to form a weighted RSRS that discriminated asthmatics from healthy subjects with area under the curve (AUC) of 0.80 in the testing set after adjustment for age and gender. The 73-gene RSRS was validated in three independent RNA-seq datasets and achieved AUCs of 0.70, 0.77 and 0.60, respectively. To explore their biological and molecular functions in asthma phenotype, we examined the 73 genes by enrichment pathway analysis and found that these genes were significantly (p < 0.0001) enriched for DNA replication, recombination, and repair, cell-to-cell signaling and interaction, and eumelanin biosynthesis and developmental disorder. Further in-silico analyses of the 73 genes using Connectivity map shows that drugs (mepacrine, dactolisib) and genetic perturbagens (PAK1, GSR, RBM15 and TNFRSF12A) were identified and could potentially be repurposed for treating asthma. These findings show the promise for RNA-seq risk scores to stratify and predict disease risk.

POSREG: proteomic signature discovered by simultaneously optimizing its reproducibility and generalizability

Mass spectrometry-based proteomic technique has become indispensable in current exploration of complex and dynamic biological processes. Instrument development has largely ensured the effective production of proteomic data, which necessitates commensurate advances in statistical framework to discover the optimal proteomic signature. Current framework mainly emphasizes the generalizability of the identified signature in predicting the independent data but neglects the reproducibility among signatures identified from independently repeated trials on different sub-dataset. These problems seriously restricted the wide application of the proteomic technique in molecular biology and other related directions. Thus, it is crucial to enable the generalizable and reproducible discovery of the proteomic signature with the subsequent indication of phenotype association. However, no such tool has been developed and available yet. Herein, an online tool, POSREG, was therefore constructed to identify the optimal signature for a set of proteomic data. It works by (i) identifying the proteomic signature of good reproducibility and aggregating them to ensemble feature ranking by ensemble learning, (ii) assessing the generalizability of ensemble feature ranking to acquire the optimal signature and (iii) indicating the phenotype association of discovered signature. POSREG is unique in its capacity of discovering the proteomic signature by simultaneously optimizing its reproducibility and generalizability. It is now accessible free of charge without any registration or login requirement at https://idrblab.org/posreg/.

Biological system considerations for application of toxicogenomics in next-generation risk assessment and predictive toxicology

There is increasing interest in using modern 'omics technologies, such as whole transcriptome sequencing, to inform decisions about human health safety and chemical toxicity hazard. High throughput methodologies using in vitro assays offer a path forward in reducing or eliminating animal testing. However, many aspects of these technologies need assessment before they will gain the trust of regulators and the public as viable alternative test methods for human health and safety. We used a high throughput whole transcriptome sequence assay (TempO-Seq) to assess the use of three widely used cancer cell lines (HepG2, MCF7, and Ishikawa cells) as in vitro systems for determination of cellular modes of action for two well studied compounds with canonical liver responses: ketoconazole and phenobarbital. We evaluated transcriptomic data to infer points of departure for use in risk analyses of compounds. Both compounds displayed shortcomings in evidence for canonical liver-related responses in any cell line, despite a strong dose response in all three. This raises questions about the competence of simple, mono-cultured cancer cell lines as appropriate surrogates for some adverse effects or toxic endpoints. Points of departure derived from benchmark doses were highly consistent across all three cell lines however, indicating the use of transcriptomic BMD analyses for such purposes would be a reliable and consistent approach.

Treatment With the CSF1R Antagonist GW2580, Sensitizes Microglia to Reactive Oxygen Species

Microglia activation and proliferation are hallmarks of many neurodegenerative disorders and may contribute to disease pathogenesis. Neurons actively regulate microglia survival and function, in part by secreting the microglia mitogen interleukin (IL)-34. Both IL-34 and colony stimulating factor (CSF)-1 bind colony stimulating factor receptor (CSFR)1 expressed on microglia. Systemic treatment with central nervous system (CNS) penetrant, CSFR1 antagonists, results in microglia death in a dose dependent matter, while others, such as GW2580, suppress activation during disease states without altering viability. However, it is not known how treatment with non-penetrant CSF1R antagonists, such as GW2580, affect the normal physiology of microglia. To determine how GW2580 affects microglia function, C57BL/6J mice were orally gavaged with vehicle or GW2580 (80mg/kg/d) for 8 days. Body weights and burrowing behavior were measured throughout the experiment. The effects of GW2580 on circulating leukocyte populations, brain microglia morphology, and the transcriptome of magnetically isolated adult brain microglia were determined. Body weights, burrowing behavior, and circulating leukocytes were not affected by treatment. Analysis of Iba-1 stained brain microglia indicated that GW2580 treatment altered morphology, but not cell number. Analysis of RNA-sequencing data indicated that genes related to reactive oxygen species (ROS) regulation and survival were suppressed by treatment. Treatment of primary microglia cultures with GW2580 resulted in a dose-dependent reduction in viability only when the cells were concurrently treated with LPS, an inducer of ROS. Pre-treatment with the ROS inhibitor, YCG063, blocked treatment induced reductions in viability. Finally, GW2580 sensitized microglia to hydrogen peroxide induced cell death. Together, these data suggest that partial CSF1R antagonism may render microglia more susceptible to reactive oxygen and nitrogen species.

Transcriptomic signatures of psychomotor slowing in peripheral blood of depressed patients: evidence for immunometabolic reprogramming

Inflammation impacts basal ganglia motor circuitry in association with psychomotor retardation, a key symptom of major depression (MD). We previously reported associations between circulating protein inflammatory biomarkers and psychomotor slowing as measured by neuropsychological tests probing psychomotor speed in patients with MD. To discover novel transcriptional signatures in peripheral blood immune cells related to psychomotor slowing, microarray data were analyzed in a primary cohort of 88 medically-stable, unmedicated, ambulatory MD patients. Results were confirmed and extended in a second cohort of 57 patients with treatment resistant depression (TRD) before and after anti-inflammatory challenge with the tumor necrosis factor antagonist infliximab versus placebo. Composite scores reflecting pure motor and cognitive-motor processing speed were linearly associated with 403 and 266 gene transcripts in each cohort, respectively (|R| > 0.30, p < 0.01), that were enriched for cytokine signaling and glycolysis-related pathways (p < 0.05). Unsupervised clustering in the primary cohort revealed two psychomotor slowing-associated gene co-expression modules that were enriched for interferon, interleukin-6, aerobic glycolysis, and oxidative phosphorylation pathways (p < 0.05, q < 0.1). Transcripts were predominantly derived from monocytes, plasmacytoid dendritic cells, and natural killer cells (p's < 0.05). In infliximab-treated TRD patients with high plasma C-reactive protein concentrations (>5 mg/L), two differential co-expression modules enriched for oxidative stress and mitochondrial degradation were associated with improvements in psychomotor reaction time (p < 0.05). These results indicate that inflammatory signaling and associated metabolic reprogramming in peripheral blood immune cells are associated with systemic inflammation in depression and may affect relevant brain circuits to promote psychomotor slowing.

Time-dependent genomic response in primary human uroepithelial cells exposed to arsenite for up to 60 days

Evidence from both in vivo and in vitro studies suggests that gene expression changes from long-term exposure to arsenite evolve markedly over time, including reversals in the direction of expression change in key regulatory genes. In this study, human uroepithelial cells from the ureter segments of 4 kidney-donors were continuously treated in culture with arsenite at concentrations of 0.1 or 1 μM for 60 days. Gene expression at 10, 20, 30, 40, and 60 days was determined using Affymetrix human genome microarrays and signal pathway analysis was performed using GeneGo Metacore. Arsenic treated cells continued to proliferate for the full 60-day period, whereas untreated cells ceased proliferating after approximately 30 days. A peak in the number of gene changes in the treated cells compared to untreated controls was observed between 30 and 40 days of exposure, with substantially fewer changes at 10 and 60 days, suggesting remodeling of the cells over time. Consistent with this possibility, the direction of expression change for a number of key genes was reversed between 20 and 30 days, including CFOS and MDM2. While the progression of gene changes was different for each subject, a common pattern was observed in arsenic treated cells over time, with early upregulation of oxidative stress responses (HMOX1, NQ01, TXN, TXNRD1) and down-regulation of immune/inflammatory responses (IKKα). At around 30 days, there was a transition to increased inflammatory and proliferative signaling (AKT, CFOS), evidence of epithelial-to-mesenchymal transition (EMT), and alterations in DNA damage responses (MDM2, ATM). A common element in the changing response of cells to arsenite over time appears to involve up-regulation of MDM2 by inflammatory signaling (through AP-1 and NF-κB), leading to inhibition of P53 function.

A novel 4-gene signature model simultaneously predicting malignant risk of oral potentially malignant disorders and oral squamous cell carcinoma prognosis

OBJECTIVE

Oral squamous cell carcinoma (OSCC) is often diagnosed at late stage with a poor prognosis. The study hereunder aimed to construct a multi-gene model to simultaneously promote early diagnosis of OSCC by evaluating malignant risk of oral potentially malignant disorders (OPMDs) and predict prognosis.

MATERIALS AND METHODS

3 GEO datasets including OPMDs and OSCC samples were obtained for overlapping differentially expressed genes (DEGs) being screened. The predictive model was built with optimal DEGs by SVM algorithm, estimated by receiver operator characteristic curves and validated for double prediction via oral cancer-free survival (for malignant risk of OPMDs) and overall survival time (for OSCC) analysis respectively compared to other models. The protein expression of biomarkers in the model was validated in human samples by immunohistochemistry.

RESULTS

A novel predictive model of 4-gene signature was built based on 12 common DEGs revealed from 3 GEO datasets. It could well distinguish OSCC from OPMDs and normal tissues. Both oral cancer-free survival and overall survival time analysis were significantly poorer in high-risk patients than in low-risk ones in Kaplan Meier survival curve respectively. The protein expression of biomarkers in OSCC was with significant difference compared to normal and OPMDs.

CONCLUSIONS

The novel 4-gene signature model presents strong ability in simultaneous prediction of the malignant risk of OPMDs and OSCC progression, potentially benefiting both the early diagnosis and therapeutic outcomes of OSCC.

Feeding a Saccharomyces cerevisiae fermentation product improves udder health and immune response to a Streptococcus uberis mastitis challenge in mid-lactation dairy cows

Background

We aimed to characterize the protective effects and the molecular mechanisms of action of a Saccharomyces cerevisiae fermentation product (NTK) in response to a mastitis challenge. Eighteen mid-lactation multiparous Holstein cows (n = 9/group) were fed the control diet (CON) or CON supplemented with 19 g/d NTK for 45 d (phase 1, P1) and then infected in the right rear quarter with 2500 CFU of Streptococcus uberis (phase 2, P2). After 36-h, mammary gland and liver biopsies were collected and antibiotic treatment started until the end of P2 (9 d post challenge). Cows were then followed until day 75 (phase 3, P3). Milk yield (MY) and dry matter intake (DMI) were recorded daily. Milk samples for somatic cell score were collected, and rectal and udder temperature, heart and respiration rate were recorded during the challenge period (P2) together with blood samples for metabolite and immune function analyses. Data were analyzed by phase using the PROC MIXED procedure in SAS. Biopsies were used for transcriptomic analysis via RNA-sequencing, followed by pathway analysis.

Results

DMI and MY were not affected by diet in P1, but an interaction with time was recorded in P2 indicating a better recovery from the challenge in NTK compared with CON. NTK reduced rectal temperature, somatic cell score, and temperature of the infected quarter during the challenge. Transcriptome data supported these findings, as NTK supplementation upregulated mammary genes related to immune cell antibacterial function (e.g., CATHL4, NOS2), epithelial tissue protection (e.g. IL17C), and anti-inflammatory activity (e.g., ATF3, BAG3, IER3, G-CSF, GRO1, ZFAND2A). Pathway analysis indicated upregulation of tumor necrosis factor α, heat shock protein response, and p21 related pathways in the response to mastitis in NTK cows. Other pathways for detoxification and cytoprotection functions along with the tight junction pathway were also upregulated in NTK-fed cows.

Conclusions

Overall, results highlighted molecular networks involved in the protective effect of NTK prophylactic supplementation on udder health during a subclinical mastitic event.

Supplementary Information

The online version contains supplementary material available at 10.1186/s40104-021-00560-8.

Identification of candidate repurposable drugs to combat COVID-19 using a signature-based approach

The COVID-19 pandemic caused by the novel SARS-CoV-2 is more contagious than other coronaviruses and has higher rates of mortality than influenza. Identification of effective therapeutics is a crucial tool to treat those infected with SARS-CoV-2 and limit the spread of this novel disease globally. We deployed a bioinformatics workflow to identify candidate drugs for the treatment of COVID-19. Using an "omics" repository, the Library of Integrated Network-Based Cellular Signatures (LINCS), we simultaneously probed transcriptomic signatures of putative COVID-19 drugs and publicly available SARS-CoV-2 infected cell lines to identify novel therapeutics. We identified a shortlist of 20 candidate drugs: 8 are already under trial for the treatment of COVID-19, the remaining 12 have antiviral properties and 6 have antiviral efficacy against coronaviruses specifically, in vitro. All candidate drugs are either FDA approved or are under investigation. Our candidate drug findings are discordant with (i.e., reverse) SARS-CoV-2 transcriptome signatures generated in vitro, and a subset are also identified in transcriptome signatures generated from COVID-19 patient samples, like the MEK inhibitor selumetinib. Overall, our findings provide additional support for drugs that are already being explored as therapeutic agents for the treatment of COVID-19 and identify promising novel targets that are worthy of further investigation.

Oxytocin's anti-inflammatory and proimmune functions in COVID-19: a transcriptomic signature-based approach

The severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has caused a worldwide pandemic, infecting over 16 million people worldwide with a significant mortality rate. However, there is no current Food and Drug Administration-approved drug that treats coronavirus disease 2019 (COVID-19). Damage to T lymphocytes along with the cytokine storm are important factors that lead to exacerbation of clinical cases. Here, we are proposing intravenous oxytocin (OXT) as a candidate for adjunctive therapy for COVID-19. OXT has anti-inflammatory and proimmune adaptive functions. Using the Library of Integrated Network-Based Cellular Signatures (LINCS), we used the transcriptomic signature for carbetocin, an OXT agonist, and compared it to gene knockdown signatures of inflammatory (such as interleukin IL-1β and IL-6) and proimmune markers (including T cell and macrophage cell markers like CD40 and ARG1). We found that carbetocin’s transcriptomic signature has a pattern of concordance with inflammation and immune marker knockdown signatures that are consistent with reduction of inflammation and promotion and sustaining of immune response. This suggests that carbetocin may have potent effects in modulating inflammation, attenuating T cell inhibition, and enhancing T cell activation. Our results also suggest that carbetocin is more effective at inducing immune cell responses than either lopinavir or hydroxychloroquine, both of which have been explored for the treatment of COVID-19.

Protein and gene markers of metabolic dysfunction and inflammation together associate with functional connectivity in reward and motor circuits in depression

Bidirectional relationships between inflammation and metabolic dysfunction may contribute to the pathophysiology of psychiatric illnesses like depression. Metabolic disturbances drive inflammation, which in turn exacerbate metabolic outcomes including insulin resistance. Both inflammatory (e.g. endotoxin, vaccination) and metabolic challenges (e.g. glucose ingestion) have been shown to affect activity and functional connectivity (FC) in brain regions that subserve reward and motor processing. We previously reported relationships between elevated concentrations of endogenous inflammatory markers including C-reactive protein (CRP) and low corticostriatal FC, which correlated with symptoms of anhedonia and motor slowing in major depression (MD). Herein, we examined whether similar relationships were observed between plasma markers related to glucose metabolism (non-fasting concentrations of glucose, insulin, leptin, adiponectin and resistin) in 42 medically-stable, unmedicated MD outpatients who underwent fMRI. A targeted, hypothesis-driven approach was used to assess FC between seeds in subdivisions of the ventral and dorsal striatum and a region in ventromedial prefrontal cortex (VS-vmPFC), which was previously found to correlate with both inflammation and symptoms of anhedonia and motor slowing. Associations between FC and gene expression signatures were also explored. A composite score of all 5 glucose-related markers (with increasing values reflecting higher concentrations) was negatively correlated with both ventral striatum (VS)-vmPFC (r = -0.33, p < 0.05) and dorsal caudal putamen (dcP)-vmPFC (r = -0.51, p < 0.01) FC, and remained significant after adjusting for covariates including body mass index (p < 0.05). Moreover, an interaction between the glucose-related composite score and CRP was observed for these relationships (F[2,33] = 4.3, p < 0.05) whereby significant correlations between the glucose-related metabolic markers and FC was found only in patients with high plasma CRP (>3 mg/L; r = -0.61 to -0.81, p < 0.05). Insulin and resistin were the individual markers most predictive of VS-vmPFC and dcP-mPFC FC, respectively, and insulin, resistin and CRP clustered together and in association with both LV-vmPFC and dcP-vmPFC in principal component analyses. Exploratory whole blood gene expression analyses also confirmed that gene probes negatively associated with FC were enriched for both inflammatory and metabolic pathways (FDR p < 0.05). These results provide preliminary evidence that inflammation and metabolic dysfunction contribute jointly to deficits in reward and motor circuits in MD. Future studies using fasting samples and longitudinal and interventional approaches are required to further elucidate the respective contributions of inflammation and metabolic dysfunction to circuits and symptoms relevant to motivation and motor activity, which may have treatment implications for patients with psychiatric illnesses like depression.

Transcriptomics in Toxicogenomics, Part I: Experimental Design, Technologies, Publicly Available Data, and Regulatory Aspects

The starting point of successful hazard assessment is the generation of unbiased and trustworthy data. Conventional toxicity testing deals with extensive observations of phenotypic endpoints in vivo and complementing in vitro models. The increasing development of novel materials and chemical compounds dictates the need for a better understanding of the molecular changes occurring in exposed biological systems. Transcriptomics enables the exploration of organisms' responses to environmental, chemical, and physical agents by observing the molecular alterations in more detail. Toxicogenomics integrates classical toxicology with omics assays, thus allowing the characterization of the mechanism of action (MOA) of chemical compounds, novel small molecules, and engineered nanomaterials (ENMs). Lack of standardization in data generation and analysis currently hampers the full exploitation of toxicogenomics-based evidence in risk assessment. To fill this gap, TGx methods need to take into account appropriate experimental design and possible pitfalls in the transcriptomic analyses as well as data generation and sharing that adhere to the FAIR (Findable, Accessible, Interoperable, and Reusable) principles. In this review, we summarize the recent advancements in the design and analysis of DNA microarray, RNA sequencing (RNA-Seq), and single-cell RNA-Seq (scRNA-Seq) data. We provide guidelines on exposure time, dose and complex endpoint selection, sample quality considerations and sample randomization. Furthermore, we summarize publicly available data resources and highlight applications of TGx data to understand and predict chemical toxicity potential. Additionally, we discuss the efforts to implement TGx into regulatory decision making to promote alternative methods for risk assessment and to support the 3R (reduction, refinement, and replacement) concept. This review is the first part of a three-article series on Transcriptomics in Toxicogenomics. These initial considerations on Experimental Design, Technologies, Publicly Available Data, Regulatory Aspects, are the starting point for further rigorous and reliable data preprocessing and modeling, described in the second and third part of the review series.

Molecular Correlates of Socioeconomic Status and Clinical Outcomes Following Hematopoietic Cell Transplantation for Leukemia

Background

Clinical outcomes among allogeneic hematopoietic cell transplant (HCT) recipients are negatively affected by low socioeconomic status (SES), yet the biological mechanisms accounting for this health disparity remain to be elucidated. Among unrelated donor HCT recipients with acute myelogenous leukemia, one recent pilot study linked low SES to increased expression of a stress-related gene expression profile known as the conserved transcriptional response to adversity (CTRA) in peripheral blood mononuclear cells, which involves up-regulation of pro-inflammatory genes and down-regulation of genes involved in type I interferon response and antibody synthesis.

Methods

This study examined these relationships using additional measures in a larger archival sample of 261 adults who received an unrelated donor HCT for acute myelogenous leukemia to 1) identify cellular and molecular mechanisms involved in SES-related differences in pre-transplant leukocyte transcriptome profiles, and 2) evaluate pre-transplant CTRA biology associations with clinical outcomes through multivariable analysis controlling for demographic-, disease-, and transplant-related covariates.

Results

Low SES individuals showed increases in classic monocyte activation and pro-inflammatory transcription control pathways as well as decreases in activation of nonclassic monocytes, all consistent with the CTRA biological pattern. Transplant recipients in the highest or lowest quartiles of the CTRA pro-inflammatory gene component had a more than 2-fold elevated hazard of relapse (hazard ratio [HR] = 2.47, 95% confidence interval [CI] = 1.44 to 4.24), P = .001; HR = 2.52, 95% CI = 1.46 to 4.34, P = .001) and more than 20% reduction in leukemia-free survival (HR = 1.57, 95% CI = 1.08 to 2.28, P = .012; HR = 1.49, 95% CI = 1.04 to 2.15, P = .03) compared with the middle quartiles.

Conclusions

These findings identify SES- and CTRA-associated myeloid- and inflammation-related transcriptome signatures in recipient pre-transplant blood samples as a potential novel predictive biomarker of HCT-related clinical outcomes.

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

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

Screening for Preterm Birth: Potential for a Metabolomics Biomarker Panel

The aim of this preliminary study was to investigate the potential of maternal serum to provide metabolomic biomarker candidates for the prediction of spontaneous preterm birth (SPTB) in asymptomatic pregnant women at 15 and/or 20 weeks’ gestation. Metabolomics LC-MS datasets from serum samples at 15- and 20-weeks’ gestation from a cohort of approximately 50 cases (GA < 37 weeks) and 55 controls (GA > 41weeks) were analysed for candidate biomarkers predictive of SPTB. Lists of the top ranked candidate biomarkers from both multivariate and univariate analyses were produced. At the 20 weeks’ GA time-point these lists had high concordance with each other (85%). A subset of 4 of these features produce a biomarker panel that predicts SPTB with a partial Area Under the Curve (pAUC) of 12.2, a sensitivity of 87.8%, a specificity of 57.7% and a p-value of 0.0013 upon 10-fold cross validation using PanelomiX software. This biomarker panel contained mostly features from groups already associated in the literature with preterm birth and consisted of 4 features from the biological groups of “Bile Acids”, “Prostaglandins”, “Vitamin D and derivatives” and “Fatty Acids and Conjugates”.

Worldwide research trends on aristolochic acids (1957-2017): Suggestions for researchers

Aristolochic acids and their derivatives are components of many traditional medicines that have been used for thousands of years, particularly in Asian countries. To study the trends of research into aristolochic acids and provide suggestions for future study, we performed the following work. In this paper, we performed a bibliometric analysis using CiteSpace and HistCite software. We reviewed the three phases of the development of aristolochic acids by using bibliometrics. In addition, we performed a longitudinal review of published review articles over 60 years: 1,217 articles and 189 review articles on the history of aristolochic acid research published between 1957 and 2017 were analyzed. The performances of relevant countries, institutions, and authors are presented; the evolutionary trends of different categories are revealed; the history of research into aristolochic acids is divided into three phases, each of which has unique characteristics; and a roadmap of the historical overview of aristolochic acid research is finally established. Finally, five pertinent suggestions for future research into aristolochic acid are offered: (1) The study of the antitumor efficacy of aristolochic acids is of value; (2) The immune activity of aristolochic acids should be explored further; (3) Researchers should perform a thorough overview of the discovery of naturally occurring aristolochic acids; (4) More efforts should be directed toward exploring the correlation between aristolochic acid mutational signature and various cancers; (5) Further efforts should be devoted to the research and review work related to analytical chemistry. Our study is expected to benefit researchers in shaping future research directions.

A Comparative Study of Cluster Detection Algorithms in Protein-Protein Interaction for Drug Target Discovery and Drug Repurposing

The interactions between drugs and their target proteins induce altered expression of genes involved in complex intracellular networks. The properties of these functional network modules are critical for the identification of drug targets, for drug repurposing, and for understanding the underlying mode of action of the drug. The topological modules generated by a computational approach are defined as functional clusters. However, the functions inferred for these topological modules extracted from a large-scale molecular interaction network, such as a protein–protein interaction (PPI) network, could differ depending on different cluster detection algorithms. Moreover, the dynamic gene expression profiles among tissues or cell types causes differential functional interaction patterns between the molecular components. Thus, the connections in the PPI network should be modified by the transcriptomic landscape of specific cell lines before producing topological clusters. Here, we systematically investigated the clusters of a cell-based PPI network by using four cluster detection algorithms. We subsequently compared the performance of these algorithms for target gene prediction, which integrates gene perturbation data with the cell-based PPI network using two drug target prioritization methods, shortest path and diffusion correlation. In addition, we validated the proportion of perturbed genes in clusters by finding candidate anti-breast cancer drugs and confirming our predictions using literature evidence and cases in the ClinicalTrials.gov. Our results indicate that the Walktrap (CW) clustering algorithm achieved the best performance overall in our comparative study.

Cyclin G1 and TASCC regulate kidney epithelial cell G2-M arrest and fibrotic maladaptive repair

Fibrosis contributes to the progression of chronic kidney disease (CKD). Severe acute kidney injury can lead to CKD through proximal tubular cell (PTC) cycle arrest in the G₂-M phase, with secretion of profibrotic factors. Here, we show that epithelial cells in the G₂-M phase form target of rapamycin (TOR)-autophagy spatial coupling compartments (TASCCs), which promote profibrotic secretion similar to the senescence-associated secretory phenotype. Cyclin G1 (CG1), an atypical cyclin, promoted G₂-M arrest in PTCs and up-regulated TASCC formation. PTC TASCC formation was also present in humans with CKD. Prevention of TASCC formation in cultured PTCs blocked secretion of profibrotic factors. PTC-specific knockout of a key TASCC component reduced the rate of kidney fibrosis progression in mice with CKD. CG1 induction and TASCC formation also occur in liver fibrosis. Deletion of CG1 reduced G₂-M phase cells and TASCC formation in vivo. This study provides mechanistic evidence supporting how profibrotic G₂-M arrest is induced in kidney injury and how G₂-M-arrested PTCs promote fibrosis, identifying new therapeutic targets to mitigate kidney fibrosis.

Sensitization of the Angiotensin II AT1 Receptor Contributes to RKIP-Induced Symptoms of Heart Failure

Inhibition of the G-protein-coupled receptor kinase 2 (GRK2) is an emerging treatment approach for heart failure. Therefore, cardio-protective mechanisms induced by GRK2 inhibition are under investigation. We compared two different GRK2 inhibitors, i.e., (i) the dual-specific GRK2 and raf kinase inhibitor protein, RKIP, and (ii) the dominant-negative GRK2-K220R mutant. We found that RKIP induced a strong sensitization of Gq/11-dependent, heart failure-promoting angiotensin II AT1 receptor signaling. The AT1-sensitizing function of RKIP was mediated by the RKIP-GRK2 interaction because the RKIP-S153V mutant, which does not interact with GRK2, had no effect on AT1-stimulated signaling. In contrast, GRK2-K220R significantly inhibited the AT1-stimulated signal. The in vivo relevance of these major differences between two different approaches of GRK2 inhibition was analyzed by generation of transgenic mice with myocardium-specific expression of RKIP and GRK2-K220R. Our results showed that a moderately increased cardiac protein level of RKIP was sufficient to induce major symptoms of heart failure in aged, 8-months-old RKIP-transgenic mice in two different genetic backgrounds. In contrast, GRK2-K220R protected against chronic pressure overload-induced cardiac dysfunction. The AT1 receptor contributed to RKIP-induced heart failure because treatment with the AT1 receptor antagonist, losartan, retarded symptoms of heart failure in RKIP-transgenic mice. Thus, sensitization of the heart failure-promoting AT1 receptor by the RKIP-GRK2 interaction contributes to heart failure whereas dominant-negative GRK2-K220R is cardioprotective. Because RKIP is up-regulated on cardiac biopsy specimens of heart failure patients, the deduced heart failure-promoting mechanism of RKIP could also be relevant for the human disease.

Butyrate-Induced In Vitro Colonocyte Differentiation Network Model Identifies ITGB1, SYK, CDKN2A, CHAF1A, and LRP1 as the Prognostic Markers for Colorectal Cancer Recurrence

Numerous mechanisms are believed to contribute to the role of dietary fiber-derived butyrate in the protection against the development of colorectal cancers (CRCs). To identify the most crucial butyrate-regulated genes, we exploited whole genome microarray of HT-29 cells differentiated in vitro by butyrate treatment. Butyrate differentiates HT-29 cells by relaxing the perturbation, caused by mutations of Adenomatous polyposis coli (APC) and TP53 genes, the most frequent mutations observed in CRC. We constructed protein-protein interaction network (PPIN) with the differentially expressed genes after butyrate treatment and extracted the hub genes from the PPIN, which also participated in the APC-TP53 network. The idea behind this approach was that the expression of these hub genes also regulated cell differentiation, and subsequently CRC prognosis by evading the APC-TP53 mutational effect. We used mRNA expression profile of these critical hub genes from seven large CRC cohorts. Logistic Regression showed strong evidence for association of these common hubs with CRC recurrence. In this study, we exploited PPIN to reduce the dimension of microarray biologically and identified five prognostic markers for the CRC recurrence, which were validated across different datasets. Moreover, these five biomarkers we identified increase the predictive value of the TNM staging for CRC recurrence.

A Comparison of the TempO-Seq S1500+ Platform to RNA-Seq and Microarray Using Rat Liver Mode of Action Samples

The TempO-Seq^TM platform allows for targeted transcriptomic analysis and is currently used by many groups to perform high-throughput gene expression analysis. Herein we performed a comparison of gene expression characteristics measured using 45 purified RNA samples from the livers of rats exposed to chemicals that fall into one of five modes of action (MOAs). These samples have been previously evaluated using Affymetrix^TM rat genome 230 2.0 microarrays and Illumina® whole transcriptome RNA-Seq. Comparison of these data with TempO-Seq analysis using the rat S1500+ beta gene set identified clear differences in the platforms related to signal to noise, root mean squared error, and/or sources of variability. Microarray and TempO-Seq captured the most variability in terms of MOA and chemical treatment whereas RNA-Seq had higher noise and larger differences between samples within a MOA. However, analysis of the data by hierarchical clustering, gene subnetwork connectivity and biological process representation of MOA-varying genes revealed that the samples clearly grouped by treatment as opposed to gene expression platform. Overall these findings demonstrate that the results from the TempO-Seq platform are consistent with findings on other more established approaches for measuring the genome-wide transcriptome.

A Qualitative Modeling Approach for Whole Genome Prediction Using High-Throughput Toxicogenomics Data and Pathway-Based Validation

Efficient high-throughput transcriptomics (HTT) tools promise inexpensive, rapid assessment of possible biological consequences of human and environmental exposures to tens of thousands of chemicals in commerce. HTT systems have used relatively small sets of gene expression measurements coupled with mathematical prediction methods to estimate genome-wide gene expression and are often trained and validated using pharmaceutical compounds. It is unclear whether these training sets are suitable for general toxicity testing applications and the more diverse chemical space represented by commercial chemicals and environmental contaminants. In this work, we built predictive computational models that inferred whole genome transcriptional profiles from a smaller sample of surrogate genes. The model was trained and validated using a large scale toxicogenomics database with gene expression data from exposure to heterogeneous chemicals from a wide range of classes (the Open TG-GATEs data base). The method of predictor selection was designed to allow high fidelity gene prediction from any pre-existing gene expression data set, regardless of animal species or data measurement platform. Predictive qualitative models were developed with this TG-GATES data that contained gene expression data of human primary hepatocytes with over 941 samples covering 158 compounds. A sequential forward search-based greedy algorithm, combining different fitting approaches and machine learning techniques, was used to find an optimal set of surrogate genes that predicted differential expression changes of the remaining genome. We then used pathway enrichment of up-regulated and down-regulated genes to assess the ability of a limited gene set to determine relevant patterns of tissue response. In addition, we compared prediction performance using the surrogate genes found from our greedy algorithm (referred to as the SV2000) with the landmark genes provided by existing technologies such as L1000 (Genometry) and S1500 (Tox21), finding better predictive performance for the SV2000. The ability of these predictive algorithms to predict pathway level responses is a positive step toward incorporating mode of action (MOA) analysis into the high throughput prioritization and testing of the large number of chemicals in need of safety evaluation.

Transcriptome analysis of an incompatible Persea americana-Phytophthora cinnamomi interaction reveals the involvement of SA- and JA-pathways in a successful defense response

Phytophthora cinnamomi Rands (Pc) is a hemibiotrophic oomycete and the causal agent of Phytophthora root rot (PRR) of the commercially important fruit crop avocado (Persea americana Mill.). Plant defense against pathogens is modulated by phytohormone signaling pathways such as salicylic acid (SA), jasmonic acid (JA), ethylene (ET), auxin and abscisic acid. The role of specific signaling pathways induced and regulated during hemibiotroph-plant interactions has been widely debated. Some studies report SA mediated defense while others hypothesize that JA responses restrict the spread of pathogens. This study aimed to identify the role of SA- and JA- associated genes in the defense strategy of a resistant avocado rootstock, Dusa in response to Pc infection. Transcripts associated with SA-mediated defense pathways and lignin biosynthesis were upregulated at 6 hours post-inoculation (hpi). Results suggest that auxin, reactive oxygen species (ROS) and Ca2+ signaling was also important during this early time point, while JA signaling was absent. Both SA and JA defense responses were shown to play a role during defense at 18 hpi. Induction of genes associated with ROS detoxification and cell wall digestion (β-1-3-glucanase) was also observed. Most genes induced at 24 hpi were linked to JA responses. Other processes at play in avocado at 24 hpi include cell wall strengthening, the formation of phenolics and induction of arabinogalactan, a gene linked to Pc zoospore immobility. This study represents the first transcriptome wide analysis of a resistant avocado rootstock treated with SA and JA compared to Pc infection. The results provide evidence of a biphasic defense response against the hemibiotroph, which initially involves SA-mediated gene expression followed by the enrichment of JA-mediated defense from 18 to 24 hpi. Genes and molecular pathways linked to Pc resistance are highlighted and may serve as future targets for manipulation in the development of PRR resistant avocado rootstocks.

Identification of differentially expressed genes by means of outlier detection

Background

An important issue in microarray data is to select, from thousands of genes, a small number of informative differentially expressed (DE) genes which may be key elements for a disease. If each gene is analyzed individually, there is a big number of hypotheses to test and a multiple comparison correction method must be used. Consequently, the resulting cut-off value may be too small. Moreover, an important issue is the selection’s replicability of the DE genes. We present a new method, called ORdensity, to obtain a reproducible selection of DE genes. It takes into account the relation between all genes and it is not a gene-by-gene approach, unlike the usually applied techniques to DE gene selection.

Results

The proposed method returns three measures, related to the concepts of outlier and density of false positives in a neighbourhood, which allow us to identify the DE genes with high classification accuracy. To assess the performance of ORdensity, we used simulated microarray data and four real microarray cancer data sets. The results indicated that the method correctly detects the DE genes; it is competitive with other well accepted methods; the list of DE genes that it obtains is useful for the correct classification or diagnosis of new future samples and, in general, it is more stable than other procedures.

Conclusions

ORdensity is a new method for identifying DE genes that avoids some of the shortcomings of the individual gene identification and it is stable when the original sample is changed by subsamples.

Electronic supplementary material

The online version of this article (10.1186/s12859-018-2318-8) contains supplementary material, which is available to authorized users.

Developments in toxicogenomics: understanding and predicting compound-induced toxicity from gene expression data

The toxicogenomics field aims to understand and predict toxicity by using 'omics' data in order to study systems-level responses to compound treatments. In recent years there has been a rapid increase in publicly available toxicological and 'omics' data, particularly gene expression data, and a corresponding development of methods for its analysis. In this review, we summarize recent progress relating to the analysis of RNA-Seq and microarray data, review relevant databases, and highlight recent applications of toxicogenomics data for understanding and predicting compound toxicity. These include the analysis of differentially expressed genes and their enrichment, signature matching, methods based on interaction networks, and the analysis of co-expression networks. In the future, these state-of-the-art methods will likely be combined with new technologies, such as whole human body models, to produce a comprehensive systems-level understanding of toxicity that reduces the necessity of in vivo toxicity assessment in animal models.

Distinguishing Human Peripheral Blood NK Cells from CD56dimCD16dimCD69+CD103+ Resident Nasal Mucosal Lavage Fluid Cells

Natural killer (NK) cells are members of the innate lymphoid cells group 1 (ILC1s), which play a critical role in innate host defense against viruses and malignancies. While many studies have examined the role of circulating peripheral blood (PB) CD56⁺ NK cells, little is known about the resident CD56⁺ cell population. Therefore, matched CD56⁺ cells from nasal lavage fluid (NLF) and PB of smokers and non-smokers were compared phenotypically, via flow cytometry, and functionally, via NK-cell specific gene expression. NLF and PB CD56⁺ cells had similar expression of CD56, but differentially expressed tissue residency (CD69 and CD103) and cytotoxicity (CD16) markers. In addition, NLF CD56^dim cells expressed lower levels of cytotoxicity-associated genes, perforin (PRF1) and granzyme B (GZMB), and increased levels of cytokines and cell signaling molecules, TRAIL, IFNGR2, and IL8, as compared to PB CD56^dim cells. In smokers, ITGA2 was downregulated in NLF CD56^dim cells, while markers of cytotoxic function were primarily downregulated in PB CD56^dim NK cells. Overall, NLF CD56^dim cells are a unique cell population that likely play a role in orchestrating innate immune responses in the nasal cavity, which is distinct from their role as a non-antigen-restricted cytotoxic CD56^dim lymphocytes in the PB.

The Diverging Routes of BORIS and CTCF: An Interactomic and Phylogenomic Analysis

The CCCTC-binding factor (CTCF) is multi-functional, ubiquitously expressed, and highly conserved from Drosophila to human. It has important roles in transcriptional insulation and the formation of a high-dimensional chromatin structure. CTCF has a paralog called “Brother of Regulator of Imprinted Sites” (BORIS) or “CTCF-like” (CTCFL). It binds DNA at sites similar to those of CTCF. However, the expression profiles of the two proteins are quite different. We investigated the evolutionary trajectories of the two proteins after the duplication event using a phylogenomic and interactomic approach. We find that CTCF has 52 direct interaction partners while CTCFL only has 19. Almost all interactors already existed before the emergence of CTCF and CTCFL. The unique secondary loss of CTCF from several nematodes is paralleled by a loss of two of its interactors, the polycomb repressive complex subunit SuZ12 and the multifunctional transcription factor TYY1. In contrast to earlier studies reporting the absence of BORIS from birds, we present evidence for a multigene synteny block containing CTCFL that is conserved in mammals, reptiles, and several species of birds, indicating that not the entire lineage of birds experienced a loss of CTCFL. Within this synteny block, BORIS and its genomic neighbors seem to be partitioned into two nested chromatin loops. The high expression of SPO11, RAE1, RBM38, and PMEPA1 in male tissues suggests a possible link between CTCFL, meiotic recombination, and fertility-associated phenotypes. Using the 65,700 exomes and the 1000 genomes data, we observed a higher number of intergenic, non-synonymous, and loss-of-function mutations in CTCFL than in CTCF, suggesting a reduced strength of purifying selection, perhaps due to less functional constraint.

Cigarette smoking is associated with an altered vaginal tract metabolomic profile

Cigarette smoking has been associated with both the diagnosis of bacterial vaginosis (BV) and a vaginal microbiota lacking protective Lactobacillus spp. As the mechanism linking smoking with vaginal microbiota and BV is unclear, we sought to compare the vaginal metabolomes of smokers and non-smokers (17 smokers/19 non-smokers). Metabolomic profiles were determined by gas and liquid chromatography mass spectrometry in a cross-sectional study. Analysis of the 16S rRNA gene populations revealed samples clustered into three community state types (CSTs) ---- CST-I (L. crispatus-dominated), CST-III (L. iners-dominated) or CST-IV (low-Lactobacillus). We identified 607 metabolites, including 12 that differed significantly (q-value < 0.05) between smokers and non-smokers. Nicotine, and the breakdown metabolites cotinine and hydroxycotinine were substantially higher in smokers, as expected. Among women categorized to CST-IV, biogenic amines, including agmatine, cadaverine, putrescine, tryptamine and tyramine were substantially higher in smokers, while dipeptides were lower in smokers. These biogenic amines are known to affect the virulence of infective pathogens and contribute to vaginal malodor. Our data suggest that cigarette smoking is associated with differences in important vaginal metabolites, and women who smoke, and particularly women who are also depauperate for Lactobacillus spp., may have increased susceptibilities to urogenital infections and increased malodor.

Gain-of-function miRNA signature by mutant p53 associates with poor cancer outcome

Missense mutation of p53 not only impairs its tumor suppression function, but also causes oncogenic gain of function (GOF). The molecular underpinning of mutant p53 (mutp53) GOF is not fully understood, especially for the potential roles of non-coding genes. Here we identify the microRNA expression profile (microRNAome) of mutp53 on Arg282 by controlled microarray experiments, and clarify the prognostic significance of mutp53-regulated miRNAs in cancers. A predominant repression effect on miRNA expression was found for mutant p53, with 183 significantly downregulated and only 12 upregulated miRNAs. Mutp53 and wild-type (wtp53) commonly upregulate let-7i, and other two miRNAs were upregulated by wtp53 but repressed by mutp53 (miR-610 and miR-3065–3p). Based the mutp53-regulated miRNA signature, a non-negative matrix factorization (NMF) model classified gastric cancer (GC) cases into subgroups with significantly different Disease-free survival (Kaplan-Meier test, P = 0.013). In contrast, the NMF model based on all miRNAs did not associate with cancer outcome. The mutp53 miRNA signature associated with the outcomes of breast cancer (P = 0.024) and hepatocellular cancer (P = 0.012). The miRPath analysis revealed that mutp53-suppressed miRNAs associate with Hippo, TGF-β and stem cell signaling pathways. Taken together, our results highlight a miRNA-mediated GOF mechanism of mutant p53 on Arg282, and suggest the prognostic potential of mutp53-associated miRNA signature.

A regression framework for assessing covariate effects on the reproducibility of high-throughput experiments

The outcome of high-throughput biological experiments is affected by many operational factors in the experimental and data-analytical procedures. Understanding how these factors affect the reproducibility of the outcome is critical for establishing workflows that produce replicable discoveries. In this article, we propose a regression framework, based on a novel cumulative link model, to assess the covariate effects of operational factors on the reproducibility of findings from high-throughput experiments. In contrast to existing graphical approaches, our method allows one to succinctly characterize the simultaneous and independent effects of covariates on reproducibility and to compare reproducibility while controlling for potential confounding variables. We also establish a connection between our model and certain Archimedean copula models. This connection not only offers our regression framework an interpretation in copula models, but also provides guidance on choosing the functional forms of the regression. Furthermore, it also opens a new way to interpret and utilize these copulas in the context of reproducibility. Using simulations, we show that our method produces calibrated type I error and is more powerful in detecting difference in reproducibility than existing measures of agreement. We illustrate the usefulness of our method using a ChIP-seq study and a microarray study.

Toxicogenomic responses of human alveolar epithelial cells to tungsten boride nanoparticles

During the recent years, microarray analysis of gene expression has become an inevitable tool for exploring toxicity of drugs and other chemicals on biological systems. Therefore, toxicogenomics is considered as a fruitful area for searching cellular pathways and mechanisms including cancer, immunological diseases, environmental responses, gene-gene interactions and chemical toxicity. In this work, we examined toxic effects of Tungsten Borides NPs on gene expression profiling of the human lung alveolar epithelial cells (HPAEpiC). In line with this purpose, a single crystal of tungsten boride (mixture of WB and W₂B) nanoparticles was synthesized by means of zone melting method, and characterized via using X-ray crystallography (XRD), transmission electron microscope (TEM), scanning electron microscope (SEM) and energy-dispersive X-ray spectroscopy (EDX) techniques. Cell viability and cytotoxicity were determined by 3-(4,5-dimethyl-thiazol-2-yl) 2,5-diphenyltetrazolium bromide (MTT), neutral red (NR) and lactate dehydrogenase (LDH) release tests. The whole genome microarray expression analysis was performed to find out the effects of WB and W₂B NPs mixture on gene expression of the HPAEpiC cell culture. 123 of 40,000 gene probes were assigned to characterize expression profile for WB/W2B NPs exposure. According to results; 70 genes were up-regulated and 53 genes were down-regulated (≥2 fold change). For further investigations, these genes were functionally classified by using DAVID (The Database for Annotation, Visualization and Integrated Discovery) with gene ontology (GO) analysis. In the light of the data gained from this study, it could be concluded that the mixture of WB/W2B NPs can affect cytokine/chemokine metabolism, angiogenesis and prevent migration/invasion by activating various genes.

Concordance analysis of microarray studies identifies representative gene expression changes in Parkinson's disease: a comparison of 33 human and animal studies

BACKGROUND

As the popularity of transcriptomic analysis has grown, the reported lack of concordance between different studies of the same condition has become a growing concern, raising questions as to the representativeness of different study types, such as non-human disease models or studies of surrogate tissues, to gene expression in the human condition.

METHODS

In a comparison of 33 microarray studies of Parkinson's disease, correlation and clustering analyses were used to determine the factors influencing concordance between studies, including agreement between different tissue types, different microarray platforms, and between neurotoxic and genetic disease models and human Parkinson's disease.

RESULTS

Concordance over all studies is low, with correlation of only 0.05 between differential gene expression signatures on average, but increases within human patients and studies of the same tissue type, rising to 0.38 for studies of human substantia nigra. Agreement of animal models, however, is dependent on model type. Studies of brain tissue from Parkinson's disease patients (specifically the substantia nigra) form a distinct group, showing patterns of differential gene expression noticeably different from that in non-brain tissues and animal models of Parkinson's disease; while comparison with other brain diseases (Alzheimer's disease and brain cancer) suggests that the mixed study types display a general signal of neurodegenerative disease. A meta-analysis of these 33 microarray studies demonstrates the greater ability of studies in humans and highly-affected tissues to identify genes previously known to be associated with Parkinson's disease.

CONCLUSIONS

The observed clustering and concordance results suggest the existence of a 'characteristic' signal of Parkinson's disease found in significantly affected human tissues in humans. These results help to account for the consistency (or lack thereof) so far observed in microarray studies of Parkinson's disease, and act as a guide to the selection of transcriptomic studies most representative of the underlying gene expression changes in the human disease.

Sleep influences the immune response and the rejection process alters sleep pattern: Evidence from a skin allograft model in mice

INTRODUCTION

Sleep generally regulates immune functions in a supportive manner and can affect parameters that are directly involved in the rejection process.

STUDY OBJECTIVES

The first objective was to assess whether sleep deprivation (SD) or sleep restriction (SR) affects the allograft rejection process in mice. The second objective was to investigate whether the rejection process itself modulates the sleep pattern of allografted mice.

DESIGN

Adult BALB/c and C57BL/6J male mice were used as the donors and recipients, respectively, except for the syngeneic group (ISOTX), which received skin from mice of the same strain (C57BL/6J). The recipients were randomly assigned to either one of two control groups - TX (allogenic) or ISOTX (syngeneic) - which underwent stereotaxic surgery to enable sleep recording prior to the allograft but were not sleep deprived; one of two paradoxical sleep deprived groups - SDTX and TXSD - which underwent 72h of continuous SD either before or after the allograft respectively, and one of two sleep restricted groups - SRTX and TXSR - which underwent 21h of SD and 3h of sleep for 15days either before or after the allograft respectively.

INTERVENTIONS

The skin allograft was inspected daily to determine the survival time, expected as 8.0±0.4days in this transplant model under no treatment. The sleep pattern was controlled throughout the rejection process in the SD and SR groups. Draining lymph nodes, spleen, blood and skin grafts were harvested on the 5th day after transplantation for evaluation of the immune parameters related to allograft rejection.

MEASUREMENTS AND RESULTS

In the control groups, we observed a reduction in paradoxical sleep throughout the entire allograft rejection process. Acute and chronic experimental sleep loss in the SD and SR groups produced marked alterations in the immune response. Both SD and SR prolonged allograft survival compared to the non-sleep-deprived group. There were reductions in the following parameters involved in the allograft rejection under sleep loss: CD4⁺ and CD8⁺ T cell subpopulations in the peripheral lymph organs and spleen, circulating sIL-2R levels, graft-infiltrating CD4⁺ T cells and skin allograft global gene expression.

CONCLUSIONS

We provide, as far as we are aware, the first evidence in vivo that the immune response can alter the normal sleep pattern, and that sleep loss can conversely affect the immune response related to graft rejection.

A note on statistical repeatability and study design for high-throughput assays

Characterizing the technical precision of measurements is a necessary stage in the planning of experiments and in the formal sample size calculation for optimal design. Instruments that measure multiple analytes simultaneously, such as in high-throughput assays arising in biomedical research, pose particular challenges from a statistical perspective. The current most popular method for assessing precision of high-throughput assays is by scatterplotting data from technical replicates. Here, we question the statistical rationale of this approach from both an empirical and theoretical perspective, illustrating our discussion using four example data sets from different genomic platforms. We demonstrate that such scatterplots convey little statistical information of relevance and are potentially highly misleading. We present an alternative framework for assessing the precision of high-throughput assays and planning biomedical experiments. Our methods are based on repeatability-a long-established statistical quantity also known as the intraclass correlation coefficient. We provide guidance and software for estimation and visualization of repeatability of high-throughput assays, and for its incorporation into study design. © 2016 The Authors. Statistics in Medicine Published by John Wiley & Sons Ltd.

Whole genome expression profiling associates activation of unfolded protein response with impaired production and release of epinephrine after recurrent hypoglycemia

Recurrent hypoglycemia can occur as a major complication of insulin replacement therapy, limiting the long-term health benefits of intense glycemic control in type 1 and advanced type 2 diabetic patients. It impairs the normal counter-regulatory hormonal and behavioral responses to glucose deprivation, a phenomenon known as hypoglycemia associated autonomic failure (HAAF). The molecular mechanisms leading to defective counter-regulation are not completely understood. We hypothesized that both neuronal (excessive cholinergic signaling between the splanchnic nerve fibers and the adrenal medulla) and humoral factors contribute to the impaired epinephrine production and release in HAAF. To gain further insight into the molecular mechanism(s) mediating the blunted epinephrine responses following recurrent hypoglycemia, we utilized a global gene expression profiling approach. We characterized the transcriptomes during recurrent (defective counter-regulation model) and acute hypoglycemia (normal counter-regulation group) in the adrenal medulla of normal Sprague-Dawley rats. Based on comparison analysis of differentially expressed genes, a set of unique genes that are activated only at specific time points after recurrent hypoglycemia were revealed. A complementary bioinformatics analysis of the functional category, pathway, and integrated network indicated activation of the unfolded protein response. Furthermore, at least three additional pathways/interaction networks altered in the adrenal medulla following recurrent hypoglycemia were identified, which may contribute to the impaired epinephrine secretion in HAAF: greatly increased neuropeptide signaling (proenkephalin, neuropeptide Y, galanin); altered ion homeostasis (Na+, K+, Ca2+) and downregulation of genes involved in Ca2+-dependent exocytosis of secretory vesicles. Given the pleiotropic effects of the unfolded protein response in different organs, involved in maintaining glucose homeostasis, these findings uncover broader general mechanisms that arise following recurrent hypoglycemia which may afford clinicians an opportunity to modulate the magnitude of HAAF syndrome.

Prepartum body condition score and plane of nutrition affect the hepatic transcriptome during the transition period in grazing dairy cows

Background

A transcriptomic approach was used to evaluate potential interactions between prepartum body condition score (BCS) and feeding management in the weeks before calving on hepatic metabolism during the periparturient period.

Methods

Thirty-two mid-lactation grazing dairy cows of mixed age and breed were randomly allocated to one of four treatment groups in a 2 × 2 factorial arrangement: two prepartum BCS categories [4.0 (thin, BCS4) and 5.0 (optimal, BCS5); based on a 10-point scale], and two levels of energy intake during the 3 weeks preceding calving (75 and 125 % of estimated requirements). Liver samples were obtained at −7, 7, and 28 d relative to parturition and subsequent RNA was hybridized to the Agilent 44 K Bovine (V2) Microarray chip. The Dynamic Impact Approach was used for pathway analysis, and Ingenuity Pathway Analysis was used for gene network analysis.

Results

The greater number of differentially expressed genes in BCS4 cows in response to prepartum feed allowance (1071 vs 310, over the entire transition period) indicates that these animals were more responsive to prepartum nutrition management than optimally-conditioned cows. However, independent of prepartum BCS, pathway analysis revealed that prepartal feeding level had a marked effect on carbohydrate, amino acid, lipid, and glycan metabolism. Altered carbohydrate and amino acid metabolism suggest a greater and more prolonged negative energy balance postpartum in BCS5 cows overfed prepartum. This is supported by opposite effects of prepartum feeding in BCS4 compared with BCS5 cows in pathways encompassing amino acid, vitamin, and co-factor metabolism. The prepartum feed restriction ameliorates the metabolic adaptation to the onset of lactation in BCS5 cows, while detrimentally affecting BCS4 cows, which seem to better adapt when overfed. Alterations in the glycosaminoglycans synthesis pathway support this idea, indicating better hepatic health status in feed-restricted BCS5 and overfed BCS4 cows. Furthermore, IPA network analysis suggests liver damage in feed-restricted thin cows, likely due to metabolic overload.

Conclusion

Overall, the data support the hypothesis that overfeeding in late-pregnancy should be limited to underconditioned cows, while cows with optimal degree of body condition should be maintained on an energy-restricted diet.

Electronic supplementary material

The online version of this article (doi:10.1186/s12864-016-3191-3) contains supplementary material, which is available to authorized users.

A Systems Biology Comparison of Ovarian Cancers Implicates Putative Somatic Driver Mutations through Protein-Protein Interaction Models

Ovarian carcinomas can be aggressive with a high mortality rate (e.g., high-grade serous ovarian carcinomas, or HGSOCs), or indolent with much better long-term outcomes (e.g., low-malignant-potential, or LMP, serous ovarian carcinomas). By comparing LMP and HGSOC tumors, we can gain insight into the mechanisms underlying malignant progression in ovarian cancer. However, previous studies of the two subtypes have been focused on gene expression analysis. Here, we applied a systems biology approach, integrating gene expression profiles derived from two independent data sets containing both LMP and HGSOC tumors with protein-protein interaction data. Genes and related networks implicated by both data sets involved both known and novel disease mechanisms and highlighted the different roles of BRCA1 and CREBBP in the two tumor types. In addition, the incorporation of somatic mutation data revealed that amplification of PAK4 is associated with poor survival in patients with HGSOC. Thus, perturbations in protein interaction networks demonstrate differential trafficking of network information between malignant and benign ovarian cancers. The novel network-based molecular signatures identified here may be used to identify new targets for intervention and to improve the treatment of invasive ovarian cancer as well as early diagnosis.

Q-GDEMAR: a general method for the identification of differentially expressed genes in microarrays with unbalanced groups

Microarray analysis is a powerful tool to simultaneously determine the pattern of transcription of large amounts of genes. For data post-processing distinct computational methods are currently used that, however, lead to different results regarding the genes expressed differentially. Herein, a new methodology for microarray data analysis named Q-GDEMAR is presented. It combines the quantile characterization of the entire distribution together with the Gaussian deconvolution of the central region of the microarray data distribution. Three discriminant variable variants are proposed that allow us to summarize data and compare groups even when their size is strongly unbalanced. In addition, a simple procedure to compute the false discovery rate (FDR) is also presented. The performance of the method is compared with that observed when using LIMMA (Linear Models Microarray) software as reference. In 58 out of 68 cases, Q-GDEMAR showed a higher sensitivity than LIMMA to detect differentially expressed genes (p = 1 × 10(-10)). The proposed method does not produce biased information, detecting genes with high sensitivity equally well at both tails of the distribution (p = 0.7428). Moreover, all detected genes were associated with very low levels of FDR (median value = 0.67%, interquartile range = 0.87%). Q-GDEMAR can be used as a general method for microarray analysis, but is particularly indicated when the conditions to be compared are unbalanced. The superior performance of Q-GEDEMAR is the consequence of its higher discriminative power and, the fact that it yields a univocal correspondence between the p-values and the values of the discriminating variable. Q-GDEMAR was tested only using Affymetrix microarrays. However, given that it operates after the step of data standardization, it can be used with the same quality features on any of the available mono- or dual-channel microarray platforms.