Exploring the effects of experimental parameters and data modeling approaches on in vitro transcriptomic point-of-departure estimates.

Multiple new approach methods (NAMs) are being developed to rapidly screen large numbers of chemicals to aid in hazard evaluation and risk assessments. High-throughput transcriptomics (HTTr) in human cell lines has been proposed as a first-tier screening approach for determining the types of bioactivity a chemical can cause (activation of specific targets vs. generalized cell stress) and for calculating transcriptional points of departure (tPODs) based on changes in gene expression. In the present study, we examine a range of computational methods to calculate tPODs from HTTr data, using six data sets in which MCF7 cells cultured in two different media formulations were treated with a panel of 44 chemicals for 3 different exposure durations (6, 12, 24 hr). The tPOD calculation methods use data at the level of individual genes and gene set signatures, and compare data processed using the ToxCast Pipeline 2 (tcplfit2), BMDExpress and PLIER (Pathway Level Information ExtractoR). Methods were evaluated by comparing to in vitro PODs from a validated set of high-throughput screening (HTS) assays for a set of estrogenic compounds. Key findings include: (1) for a given chemical and set of experimental conditions, tPODs calculated by different methods can vary by several orders of magnitude; (2) tPODs are at least as sensitive to computational methods as to experimental conditions; (3) in comparison to an external reference set of PODs, some methods give generally higher values, principally PLIER and BMDExpress; and (4) the tPODs from HTTr in this one cell type are mostly higher than the overall PODs from a broad battery of targeted in vitro ToxCast assays, reflecting the need to test chemicals in multiple cell types and readout technologies for in vitro hazard screening.

Benchmark Dose Modeling Approaches for Volatile Organic Chemicals Using a Novel Air-Liquid Interface In Vitro Exposure System.

Inhalation is the most relevant route of volatile organic chemical (VOC) exposure; however, due to unique challenges posed by their chemical properties and poor solubility in aqueous solutions, in vitro chemical safety testing is predominantly performed using direct application dosing/submerged exposures. To address the difficulties in screening toxic effects of VOCs, our cell culture exposure system permits cells to be exposed to multiple concentrations at air-liquid interface (ALI) in a 24-well format. ALI exposure methods permit direct chemical-to-cell interaction with the test article at physiological conditions. In the present study, BEAS-2B and primary normal human bronchial epithelial cells (pHBEC) are used to assess gene expression, cytotoxicity, and cell viability responses to a variety of volatile chemicals including acrolein, formaldehyde, 1,3-butadiene, acetaldehyde, 1-bromopropane, carbon tetrachloride, dichloromethane, and trichloroethylene. BEAS-2B cells were exposed to all the test agents, whereas pHBECs were only exposed to the latter 4 listed above. The VOC concentrations tested elicited only slight cell viability changes in both cell types. Gene expression changes were analyzed using benchmark dose (BMD) modeling. The BMD for the most sensitive gene set was within one order of magnitude of the threshold-limit value reported by the American Conference of Governmental Industrial Hygienists, and the most sensitive gene sets impacted by exposure correlate to known adverse health effects recorded in epidemiologic and in vivo exposure studies. Overall, our study outlines a novel in vitro approach for evaluating molecular-based points-of-departure in human airway epithelial cell exposure to volatile chemicals.

Predicting molecular initiating events using chemical target annotations and gene expression.

BACKGROUND: The advent of high-throughput transcriptomic screening technologies has resulted in a wealth of publicly available gene expression data associated with chemical treatments. From a regulatory perspective, data sets that cover a large chemical space and contain reference chemicals offer utility for the prediction of molecular initiating events associated with chemical exposure. Here, we integrate data from a large compendium of transcriptomic responses to chemical exposure with a comprehensive database of chemical-protein associations to train binary classifiers that predict mechanism(s) of action from transcriptomic responses. First, we linked reference chemicals present in the LINCS L1000 gene expression data collection to chemical identifiers in RefChemDB, a database of chemical-protein interactions. Next, we trained binary classifiers on MCF7 human breast cancer cell line derived gene expression profiles and chemical-protein labels using six classification algorithms to identify optimal analysis parameters. To validate classifier accuracy, we used holdout data sets, training-excluded reference chemicals, and empirical significance testing of null models derived from permuted chemical-protein associations. To identify classifiers that have variable predicting performance across training data derived from different cellular contexts, we trained a separate set of binary classifiers on the PC3 human prostate cancer cell line. RESULTS: We trained classifiers using expression data associated with chemical treatments linked to 51 molecular initiating events. This analysis identified and validated 9 high-performing classifiers with empirical p-values lower than 0.05 and internal accuracies ranging from 0.73 to 0.94 and holdout accuracies of 0.68 to 0.92. High-ranking predictions for training-excluded reference chemicals demonstrating that predictive accuracy extends beyond the set of chemicals used in classifier training. To explore differences in classifier performance as a function of training data cellular context, MCF7-trained classifier accuracies were compared to classifiers trained on the PC3 gene expression data for the same molecular initiating events. CONCLUSIONS: This methodology can offer insight in prioritizing candidate perturbagens of interest for targeted screens. This approach can also help guide the selection of relevant cellular contexts for screening classes of candidate perturbagens using cell line specific model performance.

High-Throughput Transcriptomics Platform for Screening Environmental Chemicals.

New approach methodologies (NAMs) that efficiently provide information about chemical hazard without using whole animals are needed to accelerate the pace of chemical risk assessments. Technological advancements in gene expression assays have made in vitro high-throughput transcriptomics (HTTr) a feasible option for NAMs-based hazard characterization of environmental chemicals. In this study, we evaluated the Templated Oligo with Sequencing Readout (TempO-Seq) assay for HTTr concentration-response screening of a small set of chemicals in the human-derived MCF7 cell model. Our experimental design included a variety of reference samples and reference chemical treatments in order to objectively evaluate TempO-Seq assay performance. To facilitate analysis of these data, we developed a robust and scalable bioinformatics pipeline using open-source tools. We also developed a novel gene expression signature-based concentration-response modeling approach and compared the results to a previously implemented workflow for concentration-response analysis of transcriptomics data using BMDExpress. Analysis of reference samples and reference chemical treatments demonstrated highly reproducible differential gene expression signatures. In addition, we found that aggregating signals from individual genes into gene signatures prior to concentration-response modeling yielded in vitro transcriptional biological pathway altering concentrations (BPACs) that were closely aligned with previous ToxCast high-throughput screening assays. Often these identified signatures were associated with the known molecular target of the chemicals in our test set as the most sensitive components of the overall transcriptional response. This work has resulted in a novel and scalable in vitro HTTr workflow that is suitable for high-throughput hazard evaluation of environmental chemicals.

RNA sequencing of isolated cell populations expressing human APOL1 G2 risk variant reveals molecular correlates of sickle cell nephropathy in zebrafish podocytes.

Kidney failure occurs in 5-13% of individuals with sickle cell disease and is associated with early mortality. Two APOL1 alleles (G1 and G2) have been identified as risk factors for sickle cell disease nephropathy. Both risk alleles are prevalent in individuals with recent African ancestry and have been associated with nephropathic complications in other diseases. Despite the association of G1 and G2 with kidney dysfunction, the mechanisms by which these variants contribute to increased risk remain poorly understood. Previous work in zebrafish models suggest that the G2 risk allele functions as a dominant negative, whereas the G1 allele is a functional null. To understand better the cellular pathology attributed to APOL1 G2, we investigated the in vivo effects of the G2 risk variant on distinct cell types using RNA sequencing. We surveyed APOL1 G2 associated transcriptomic alterations in podocytes and vascular endothelial cells isolated from zebrafish larvae expressing cell-type specific reporters. Our analysis identified many transcripts (n = 7,523) showing differential expression between APOL1 G0 (human wild-type) and APOL1 G2 exposed podocytes. Conversely, relatively few transcripts (n = 107) were differentially expressed when comparing APOL1 G0 and APOL1 G2 exposed endothelial cells. Pathway analysis of differentially expressed transcripts in podocytes showed enrichment for autophagy associated terms such as "Lysosome" and "Phagosome", implicating these pathways in APOL1 G2 associated kidney dysfunction. This work provides insight into the molecular pathology of APOL1 G2 nephropathy which may offer new therapeutic strategies for multiple disease contexts such as sickle cell nephropathy.

Sex-biased hippocampal pathology in the 5XFAD mouse model of Alzheimer's disease: A multi-omic analysis.

Alzheimer's disease is a progressive neurodegenerative disorder and the most common form of dementia. Like many neurological disorders, Alzheimer's disease has a sex-biased epidemiological profile, affecting approximately twice as many women as men. The cause of this sex difference has yet to be elucidated. To identify molecular correlates of this sex bias, we investigated molecular pathology in females and males using the 5XFamilial Alzheimer's disease mutations (5XFAD) genetic mouse model of Alzheimer's disease. We profiled the transcriptome and proteome of the mouse hippocampus during early stages of disease development (1, 2, and 4 months of age). Our analysis reveals 42 genes that are differentially expressed between disease and wild-type animals at 2 months of age, prior to observable plaque deposition. In 4-month-old animals, we detect 1,316 differentially expressed transcripts between transgenic and control 5XFAD mice, many of which are associated with immune function. Additionally, we find that some of these transcriptional perturbations are correlated with altered protein levels in 4-month-old transgenic animals. Importantly, our data indicate that female 5XFAD mouse exhibit more profound pathology than their male counterparts as measured by differences in gene expression. We also find that the 5XFAD transgenes are more highly expressed in female 5XFAD mice than their male counterparts, which could partially account for the sex-biased molecular pathology observed in this dataset.

Sex differences in the molecular signature of the developing mouse hippocampus.

BACKGROUND: A variety of neurological disorders, including Alzheimer's disease, Parkinson's disease, major depressive disorder, dyslexia and autism, are differentially prevalent between females and males. To better understand the possible molecular basis for the sex-biased nature of neurological disorders, we used a developmental series of female and male mice at 1, 2, and 4 months of age to assess both mRNA and protein in the hippocampus with RNA-sequencing and mass-spectrometry, respectively. RESULTS: The transcriptomic analysis identifies 2699 genes that are differentially expressed between animals of different ages. The bulk of these differentially expressed genes are changed in both sexes at one or more ages, but a total of 198 transcripts are differentially expressed between females and males at one or more ages. The number of transcripts that are differentially expressed between females and males is greater in adult animals than in younger animals. Additionally, we identify 69 transcripts that show complex and sex-specific patterns of temporal regulation through postnatal development, 8 of which are heat-shock proteins. We also find a modest correlation between levels of mRNA and protein in the mouse hippocampus (Rho = 0.53). CONCLUSION: This study adds to the substantial body of evidence for transcriptomic regulation in the hippocampus during postnatal development. Additionally, this analysis reveals sex differences in the transcriptome of the developing mouse hippocampus, and further clarifies the need to include both female and male mice in longitudinal studies involving molecular changes in the hippocampus.

Proteomic profiling of NCI-60 extracellular vesicles uncovers common protein cargo and cancer type-specific biomarkers.

Packed with biological information, extracellular vesicles (EVs) offer exciting promise for biomarker discovery and applications in therapeutics and non-invasive diagnostics. Currently, our understanding of EV contents is confined by the limited cells from which vesicles have been characterized utilizing the same enrichment method. Using sixty cell lines from the National Cancer Institute (NCI-60), here we provide the largest proteomic profile of EVs in a single study, identifying 6,071 proteins with 213 common to all isolates. Proteins included established EV markers, and vesicular trafficking proteins such as Rab GTPases and tetraspanins. Differentially-expressed proteins offer potential for cancer diagnosis and prognosis. Network analysis of vesicle quantity and proteomes identified EV components associated with vesicle secretion, including CD81, CD63, syntenin-1, VAMP3, Rab GTPases, and integrins. Integration of vesicle proteomes with whole-cell molecular profiles revealed similarities, suggesting EVs provide a reliable reflection of their progenitor cell content, and are therefore excellent indicators of disease.

Fractionation-dependent improvements in proteome resolution in the mouse hippocampus by IEF LC-MS/MS.

An assessment of fractionated mouse hippocampal peptides was conducted. Protein extract from a single mouse hippocampus was enzymatically digested and fractionated by IEF. Aliquots of fractions were pooled into fewer, more complex samples. The unfractionated lysate, fractions, and pooled fractions were subjected to LC-MS/MS analysis. Samples consisting of many individual fractions had more protein identifications, greater protein sequence coverage, and quantified proteins with more spectral counts than protein extract that was unfractionated or pooled into fewer LC-MS/MS samples. Additionally, prefractionation reduced the median CV for spectral counts as much as 33%. However, the relative gain in proteome resolution was found to saturate with increasing fractionation extent. This study demonstrates how prefractionation by offline IEF can improve the resolution of proteomic investigations of the mouse hippocampus, and that a data-driven pooling methodology can reduce excessive and cost-ineffective fractionation.

Transcriptomic analysis of the hippocampus from six inbred strains of mice suggests a basis for sex-specific susceptibility and severity of neurological disorders.

Identifying sex differences in gene expression within the brain is critical for determining why multiple neurological and behavioral disorders differentially affect males and females. Several disorders are more common or severe in males (e.g., autism and schizophrenia) or in females (e.g., Alzheimer's disease and depression). We analyzed transcriptomic data from the mouse hippocampus of six inbred strains (129S1/SvImJ, A/J, C57BL/6J, DBA/1J, DBA/2J, and PWD/Ph) to provide a perspective on differences between male and female gene expression. Our data show that 1) gene expression differences in males vs. females varies substantially across the strains, 2) only a few genes are differentially expressed across all of the strains (termed core genes), and 3) >2,600 genes differ in the individual strain comparisons (termed noncore genes). We found that DBA/2J uniquely has a substantial majority (89%) of differentially expressed genes (DEGs) that are more highly expressed in females than in males (female-biased); 129/SvImJ has a majority (69%) of DEGs that are more highly expressed in males. To gain insight into the function of the DEGs, we examined gene ontology and pathway and phenotype enrichment and found significant enrichment in phenotypes related to abnormal nervous system morphology and physiology, among others. In addition, several pathways are enriched significantly, including Alzheimer's disease (AD), with 32 genes implicated in AD, eight of which are male-biased. Three of the male-biased genes have been implicated in a neuroprotective role in AD. Our transcriptomic data provide new insight into the possible genetic bases for sex-specific susceptibility and severity of brain disorders. J. Comp. Neurol. 524:2696-2710, 2016. © 2016 Wiley Periodicals, Inc.

An Examination of Dynamic Gene Expression Changes in the Mouse Brain During Pregnancy and the Postpartum Period.

The developmental transition to motherhood requires gene expression changes that alter the brain to drive the female to perform maternal behaviors. We broadly examined the global transcriptional response in the mouse maternal brain, by examining four brain regions: hypothalamus, hippocampus, neocortex, and cerebellum, in virgin females, two pregnancy time points, and three postpartum time points. We find that overall there are hundreds of differentially expressed genes, but each brain region and time point shows a unique molecular signature, with only 49 genes differentially expressed in all four regions. Interestingly, a set of "early-response genes" is repressed in all brain regions during pregnancy and postpartum stages. Several genes previously implicated in underlying postpartum depression change expression. This study serves as an atlas of gene expression changes in the maternal brain, with the results demonstrating that pregnancy, parturition, and postpartum maternal experience substantially impact diverse brain regions.

Differential effects of acellular embryonic matrices on pluripotent stem cell expansion and neural differentiation.

Extracellular matrices (ECM) derived from pluripotent stem cells (PSCs) provide a unique tissue microenvironment that can direct cellular differentiation and tissue regeneration, and rejuvenate aged progenitor cells. The unlimited growth capacity of PSCs allows for the scalable generation of PSC-secreted ECMs. Therefore, the derivation and characterization of PSC-derived ECMs is of critical importance in drug screening, disease modeling and tissue regeneration. In this study, 3-D ECMs were generated from decellularized undifferentiated embryonic stem cell (ESC) aggregates (AGG), spontaneously differentiated embryoid bodies (EB), and ESC-derived neural progenitor cell (NPC) aggregates. The capacities of different ECMs to direct proliferation and neural differentiation of the reseeded mouse ESCs and human induced pluripotent stem cells (iPSCs) were characterized. Proteomic analysis by liquid chromatography-tandem mass spectrometry (LC-MS/MS) revealed protein expression profiles that reflected distinct niche properties for each tested ECM group. The reseeded mouse ESCs and human iPSCs responded to different types of ECMs with different cellular phenotypes. Cells grown on the AGG-ECM displayed high levels of pluripotent markers Oct-4 and Nanog, while the cells grown on the NPC-ECM showed increased expression of neural marker ß-tubulin III. The expression levels of ß-catenin were high for cells grown on the AGG-ECM and the EB-ECM, but reduced in cells grown on the NPC-ECM, indicating a possible role of Wnt/ß-catenin signaling in the cell-matrix interactions. This study demonstrates that PSC-derived ECMs can influence stem cell fate decisions by providing a spectrum of stem cell niche microenvironments during tissue development.