We evaluated the Templated Oligo with Sequencing Readout (TempO-Seq) assay for High-throughput transcriptomic screening of a small set of environmental chemicals. This assay yields sequencing reads of exactly 50 base pairs that can be rapidly aligned to generate gene counts, and is compatible with cell lysates prepared in multiwell format. The version of the TempO-Seq assay we evaluated provides nearly whole transcriptome coverage (>20,000 genes). This study encompasses 2 replicates of a 384-well plate design. The majority of wells contain U-2 OS cells exposed to 11 test chemicals at 7 different concentrations (two replicate per test chemical, concentration, and plate), and additional reference samples and controls. Controls include DMSO vehicle treatments (22 per plate). Reference samples include bulk lysate MCF7 samples (2 DMSO treated and 2 TSA treated samples per plate), reference chemical treatments (3 chemicals at single conc each, per plate), and vendor-provided reference RNA mixtures (UHRR and HBRR). This dataset is associated with the following publication: Nyffeler, J., C. Willis, D. Harris, L. Taylor, R. Judson, L. Everett, and J. Harrill. Combining phenotypic profiles and targeted RNA-Seq reveals linkages between transcriptional perturbations and chemical effects on cell morphology: retinoic acid as an example.. TOXICOLOGY AND APPLIED PHARMACOLOGY. Academic Press Incorporated, Orlando, FL, USA, 444: 116032, (2022).

Background: mRNA interactions with each other and other signaling molecules define different biological pathways and functions. Researchers have been investigating various tools to analyze these types of interactions. In particular gene co-expression network methods have proved useful in finding and analyzing these molecular interactions. Many different analytical pipelines to identify these interactions networks have been proposed with the aim of identifying an optimal partition of the network where the individual modules are neither too small to make any general inference or too large to be biologically interpretable. Results: In this study we propose a new pipeline to perform gene co-expression network analysis. The proposed pipeline uses WGCNA a widely used software to perform different aspects of gene co-expression network analysis and modularity maximization algorithm to analyze novel RNA-Seq data to understand the effects of low-dose 56Fe ion irradiation on the formation of hepatocellular carcinoma in mice. The network results along with experimental validation show that using WGCNA combined with Modularity provide a more biologically interpretable network in our dataset. Our pipeline showed better performance than the existing clustering algorithm in WGCNA in finding modules and identified a module with mitochondrial subunits that are supported by mitochondrial complex assay. Conclusions: We present a pipeline that can reduce the problem of parameter selection with the existing algorithm in WGCNA for comparable RNA-Seq datasets which may assist in future research to discover novel mRNA interactions and their downstream molecular effects. C57BL16 males were placed into 2 treatment groups and received the following irradiation treatments at Brookhaven National Laboratories (Long Island NY): 600 MeV/n 56Fe (0.2 Gy) and no irradiation. Left liver lobes were collected at 30 60 120 270 and 360 days post-irradiation flash frozen and stored at -80 xc2 xb0C until they could be processed for RNA-Seq. Livers were sampled by taking two 40-micron thick slices using a cryotome at -20 xc2 xb0C. This allowed multiple sampling of the tissue without the tissue going through multiple freeze/thaw cycles. Total RNA was isolated from the liver slices using RNAqueousTM Total RNA Isolation Kit (ThermoFisher Scientific Waltham MA) and rRNA was removed via Ribo-ZeroTM rRNA Removal Kit (Illumina San Diego CA) prior to library preparation with the Illumina TruSeq RNA Library kit. Samples were sequenced in a paired-end 50 base format on an Illumina HiSeq 1500. Reads were aligned to the mouse GRCm38 reference genome using the STAR alignment program version 2.5.3a with the recommended ENCODE options. The -quantMode GeneCounts option was used to obtain read counts per gene based on the Gencode release M14 annotation file. Total number of reads used in analysis varies between 23-35 millions of reads.

The Rodent Research-3 (RR-3) mission was sponsored by the pharmaceutical company Eli Lilly and Co. and the Center for the Advancement of Science in Space to study the effectiveness of a potential countermeasure for the loss of muscle and bone mass that occurs during spaceflight. Twenty BALB/c 18-weeks old female mice (ten controls and ten treated) were flown to the ISS and housed in the Rodent Habitat for 39-42 days. Twenty mice of similar age sex and strain were used for ground controls housed in identical hardware and matching ISS environmental conditions. Basal controls were housed in standard vivarium cages. Spaceflight ground controls and basal groups had blood collected then were euthanized had one hind limb removed and finally whole carcasses were stored at -80 C until dissection. All mice in this data set received only the control/sham injection. Microdissection of retinae from previously frozen eyes and RNA isolation were performed in the laboratory of Dr. Xiao Mao at Loma Linda University.

Background Retinoids are very potent inducers of cellular differentiation and apoptosis, and are efficient anti-tumoral agents. Synthetic retinoids are designed to restrict their toxicity and side effects, mostly by increasing their selectivity toward each isotype of retinoic acids receptors (RARα,β, γ and RXRα, β, γ). We however previously showed that retinoids displayed very different abilities to activate retinoid-inducible reporter genes, and that these differential properties were correlated to the ability of a given ligand to promote SRC-1 recruitment by DNA-bound RXR:RAR heterodimers. This suggested that gene-selective modulation could be achieved by structurally distinct retinoids. Results Using the differential display mRNA technique, we identified several genes on the basis of their differential induction by natural or synthetic retinoids in human cervix adenocarcinoma cells. Furthermore, this differential ability to regulate promoter activities was also observed in murine P19 cells for the RARβ2 and CRABPII gene, showing conclusively that retinoid structure has a dramatic impact on the regulation of endogenous genes. Conclusions Our findings therefore show that some degree of selective induction or repression of gene expression may be achieved when using appropriately designed ligands for retinoic acid receptors, extending the concept of selective modulators from estrogen and peroxisome proliferator activated receptors to the class of retinoid receptors.

We differentiated human induced pluripotent stem cells (hiPSCs) to embryonic endoderm and sought to identify a tipping point at which the developing system did not recover from perturbations caused by exposure to a known teratogen, all-trans retinoic acid (ATRA). Differentiating iPSC-derived endoderm was exposed to five concentrations of ATRA between 0.001 and 10 µM at 6h, 96h, or 192h and assessed for forkhead box A2 (FOXA2) protein expression and global gene transcript expression measured by RNA-sequencing. A tipping point of 17±11 nM was identified where patterns of differentially expressed genes supported a shift in the developmental trajectory away from embryonic endoderm in favor of mesoderm and extraembryonic endoderm. Five concentrations of all-trans retinoic acid (ATRA) between 0.001 and 10 µM were compared to time-matched 0.1% DMSO controls at three timepoints (6h, 96h, and 192h) in differentiating endoderm. Two biological replicates were used. Undifferentiated controls (not in DMSO) were also included in duplicate as internal controls for 6h, 96h, and 144h. This dataset is associated with the following publication: Saili, K., T. Antonijevic, T. Zurlinden, I. Shah, C. Deisenroth, and T. Knudsen. Molecular characterization of a toxicological tipping point during human stem cell differentiation. REPRODUCTIVE TOXICOLOGY. Elsevier Science Ltd, New York, NY, USA, 91(January 2020): 1-13, (2020).

Supplementary data for "Comparison of whole transcriptome and targeted RNA sequencing for ecological high-throughput transcriptomics". This dataset is associated with the following publication: Villeneuve, D., M. Nash, A. Biales, K. Bush, G. Evensen, L. Everett, J. Haselman, M. Hazemi, M. Le, H. Poynton, B. Seligmann , L. Wehmas, J. Yeakley, and K. Flynn. Comparison of whole transcriptome and targeted RNA sequencing for ecological high-throughput transcriptomics. REGULATORY TOXICOLOGY AND PHARMACOLOGY. Elsevier Science Ltd, New York, NY, USA, 162: 105898, (2025).

Dataset for Baker, et al., Identifying candidate reference chemicals for in vitro testing of the retinoid pathway for predictive developmental toxicity, published in journal Altex, https://doi.org/10.14573/altex.2202231 The two zip files are Excel Macro files, but could not be loaded into SciHub unless they were converted to zip files. This tool was built using the EPA's LitDB, a database of MeSH terms from PubMed/Medline records downloaded from NLM. 1. A set of MeSH terms for targets of interest was assembled. 2. The database was searched for occurrences of those terms with antagonist OR agonist subheading in PubMed citations. 3. Non-protein chemicals annotated as major topics in that set of articles was extracted. 4. The chemical MeSH term and the protein target MeSH term were output to Excel in a long format (Detail sheet) and pivot table format (Overview sheet). 5. VBA code was added that allows navigation between the Overview sheet and the Detail sheet. This dataset is associated with the following publication: Baker, N., J. Pierro, L. Taylor, and T. Knudsen. Identifying candidate reference chemicals for in vitro testing of the retinoid pathway for predictive developmental toxicity. ALTEX. Society ALTEX Edition, Kuesnacht, SWITZERLAND, 40(2): 217-236, (2023).

The U.S. Tox21 Federal collaboration, which currently quantifies the biological effects of nearly 10,000 chemicals via quantitative high-throughput screening(qHTS) in in vitro model systems, is now making an effort to incorporate gene expression profiling into the existing battery of assays. Whole transcriptome analyses performed on large numbers of samples using microarrays or RNA-Seq is currently cost-prohibitive. Accordingly, the Tox21 Program is pursuing a high-throughput transcriptomics (HTT) method that focuses on the targeted detection of gene expression for a carefully selected subset of the transcriptome that potentially can reduce the cost by a factor of 10-fold, allowing for the analysis of larger numbers of samples. To identify the optimal transcriptome subset, genes were sought that are (1) representative of the highly diverse biological space, (2) capable of serving as a proxy for expression changes in unmeasured genes, and (3) sufficient to provide coverage of well described biological pathways. A hybrid method for gene selection is presented herein that combines data-driven and knowledge-driven concepts into one cohesive method. This dataset is associated with the following publication: Mav, D., R.R. Shah, B.E. Howard, S.S. Auerbach, P.R. Bushel, J.B. Collins, D.L. Gerhold, R. Judson, A.L. Karmaus, E.A. Maull, D.L. Mendrick, B.A. Merrick, N.S. Sipes, D. Svoboda, and R.S. Paules. A hybrid gene selection approach to create the S1500+ targeted gene sets for use in high-throughput transcriptomics. PLoS ONE. Public Library of Science, San Francisco, CA, USA, 13(2): 1-17, (2018).

The U.S. Tox21 Federal collaboration, which currently quantifies the biological effects of nearly 10,000 chemicals via quantitative high-throughput screening(qHTS) in in vitro model systems, is now making an effort to incorporate gene expression profiling into the existing battery of assays. Whole transcriptome analyses performed on large numbers of samples using microarrays or RNA-Seq is currently cost-prohibitive. Accordingly, the Tox21 Program is pursuing a high-throughput transcriptomics (HTT) method that focuses on the targeted detection of gene expression for a carefully selected subset of the transcriptome that potentially can reduce the cost by a factor of 10-fold, allowing for the analysis of larger numbers of samples. To identify the optimal transcriptome subset, genes were sought that are (1) representative of the highly diverse biological space, (2) capable of serving as a proxy for expression changes in unmeasured genes, and (3) sufficient to provide coverage of well described biological pathways. A hybrid method for gene selection is presented herein that combines data-driven and knowledge-driven concepts into one cohesive method. This dataset is associated with the following publication: Mav, D., R.R. Shah, B.E. Howard, S.S. Auerbach, P.R. Bushel, J.B. Collins, D.L. Gerhold, R. Judson, A.L. Karmaus, E.A. Maull, D.L. Mendrick, B.A. Merrick, N.S. Sipes, D. Svoboda, and R.S. Paules. A hybrid gene selection approach to create the S1500+ targeted gene sets for use in high-throughput transcriptomics. PLoS ONE. Public Library of Science, San Francisco, CA, USA, 13(2): 1-17, (2018).

Proper interpretation of RNA sequencing data requires an understanding of assay sensitivity and sources of variability. To this end the External RNA Control Consortium (ERCC) developed a standard set of 92 poly-adenylated RNA transcripts that are orthogonal to mammalian RNA that can be added to RNA extracts before library generation and sequencing. The presence of these RNA standards at known ratios improves interpretation of RNA sequencing datasets. To test the utility of the ERCC RNA controls total RNA extracted from mouse livers from the Rodent Research 1 (flight and ground groups) and Rodent Research 3 (flight and ground groups) missions was sequenced with and without the ERCC control RNA. To allow comparison within and between groups ERCC Mix 1 or Mix 2 were added to half of the samples from each group respectively.