ASSESSMENT OF PROCEDURAL ASPECTS AND QUALITY CONTROL IN HUMAN PLACENTAL RNA ISOLATION PROTOCOLS

poster abstractHigh quality RNA is of paramount importance in accurately interpreting gene expression changes in the placenta throughout pregnancy, as well as in common placental pathologies. The purpose of this study was to develop a standard operating procedure for the collection of human placental tissue and isolation of high quality RNA for pregnancy-related molecular studies. To accomplish this task, we compared several different parameters to minimize RNA degradation, including preservation (liquid nitrogen vs. RNAlater), dis-ruption (mortar/pestle vs. homogenization), and isolation (Trizol vs. RNeasy). We performed 150 RNA isolations from 30 term placentas. The overall yield was 365 ± 197 ng RNA per mg of tissue. The A260/280 ratio for all samples was 2.11 ± 0.1 (mean ± s.d.) and the RQI was 7.1 ± 1.4. No significant differences in RNA purity, yield, or quality were observed between different placental collections or RNA isolation techniques. However, poor RQI values of 2.7 to 3.3 were obtained after brief thawing of frozen placental samples. We also compared storage of RNAlater stabilized tissue at 4 de-grees or room temperature for 1 day, 7 days, and 30 days. The integrity of RNA stored at room temperature for 1 day was significantly better (P‹0.05 RQI 7.3 ± 0.58, mean ± s.d) than RNA stored at room temperature for 30 days (RQI 5.0 ±1.2, mean ± s.d). The results of these studies will be useful for establishing standard procedures for placenta collection for pregnancy biobanks

Cytogenetic features of human trophoblast cell lines SWAN-71 and 3A-subE

Immortalization of primary cells with telomerase is thought to maintain normal phenotypic properties and avoid chromosomal abnormalities and other cancer-associated changes that occur following simian virus 40 tumor antigen (SV40 Tag) induced immortalization. However, we report that the human telomerase reverse transcriptase (hTERT)-immortalized SWAN-71 trophoblast cell line has a near pentaploid 103∼119,XXXX[cp20] karyotype. Additionally, DNA typing analysis indicated that SWAN-71 cells have acquired microsatellite instability. In comparison, the post-crisis SV40-transformed trophoblast cell line 3A-subE was hypertriploid 69∼81,XX[cp20]. Both cell lines contained multiple specific clonal rearrangements. These findings emphasize the need to monitor for genetic instability in hTERT-immortalized cells

Differential Splicing of Skipped Exons Predicts Drug Response in Cancer Cell Lines

Alternative splicing of pre-mRNA transcripts is an important regulatory mechanism that increases the diversity of gene products in eukaryotes. Various studies have linked specific transcript isoforms to altered drug response in cancer; however, few algorithms have incorporated splicing information into drug response prediction. In this study, we evaluated whether basal-level splicing information could be used to predict drug sensitivity by constructing doxorubicin-sensitivity classification models with splicing and expression data. We detailed splicing differences between sensitive and resistant cell lines by implementing quasi-binomial generalized linear modeling (QBGLM) and found altered inclusion of 277 skipped exons. We additionally conducted RNA-binding protein (RBP) binding motif enrichment and differential expression analysis to characterize cis- and trans-acting elements that potentially influence doxorubicin response-mediating splicing alterations. Our results showed that a classification model built with skipped exon data exhibited strong predictive power. We discovered an association between differentially spliced events and epithelial-mesenchymal transition (EMT) and observed motif enrichment, as well as differential expression of RBFOX and ELAVL RBP family members. Our work demonstrates the potential of incorporating splicing data into drug response algorithms and the utility of a QBGLM approach for fast, scalable identification of relevant splicing differences between large groups of samples

Lipopolysaccharides Improve Mesenchymal Stem Cell-Mediated Cardioprotection by MyD88 and stat

Bone marrow-derived mesenchymal stem cells (MSCs) improve cardiac function after ischemia/reperfusion injury, in part, due to the release of cytoprotective paracrine factors. Toll-like receptor 4 (TLR4) is expressed in MSCs and regulates the expression of cytoprotective factors, cytokines, and chemokines. Lipopolysaccharide (LPS) stimulation of TLR4 activates two distinct signaling pathways that are either MyD88 dependent or MyD88 independent/TIR-domain-containing adapter-inducing interferon-β (TRIF) dependent. While it was reported previously that LPS treatment improved MSC-mediated cardioprotection, the mechanism underlying such improved effect remains unknown. To study the role of MyD88 signaling in MSC cardioprotective activity, wild type (WT) and MyD88-/- MSCs were treated with LPS (200 ng/mL) for 24 h. WT and MyD88-/- MSCs with or without LPS pretreatment were infused into the coronary circulation of isolated mouse hearts (Langendorff model) and then subjected to ischemia (25 min) and reperfusion (50 min). Saline served as a negative control. Both untreated and LPS-pretreated WT MSCs significantly improved postischemic recovery of myocardial function of isolated mouse hearts, as evidenced by improved left ventricular developed pressure and ventricular contractility assessment (ie, the rate of left ventricle pressure change over time, ± dp/dt). LPS-pretreated WT MSCs conferred better cardiac function recovery than untreated MSCs; however, such effect of LPS was abolished when using MyD88-/- MSCs. In addition, LPS stimulated stat3 activity in WT MSCs, but not MyD88-/- MSCs. stat3 small interfering RNA abolished the effect of LPS in improving the cardioprotection of WT MSCs. In conclusion, this study demonstrates that LPS improves MSC-mediated cardioprotection by MyD88-dependent activation of stat3

Insulin receptor-like ectodomain genes and splice variants are found in both arthropods and human brain cDNA

Truncated receptor ectodomains have been described for several classes of cell surface receptors, including those that bind to growth factors, cytokines, immunoglobulins, and adhesion molecules. Soluble receptor isoforms are typically generated by proteolytic cleavage of the cell surface receptor or by alternative splicing of RNA transcripts arising from the same gene encoding the full-length receptor. Both the epidermal growth factor receptor (EGFR) and the insulin receptor (INSR) families produce soluble receptor splice variants in vertebrates and truncated forms of insulin receptor-like sequences have previously been described in Drosophila. The EGFR and INSR ectodomains share significant sequence homology with each other suggestive of a common evolutionary origin. We discovered novel truncated insulin receptor-like variants in several arthropod species. We performed a phylogenetic analysis of the conserved extracellular receptor L1 and L2 subdomains in invertebrate species. While the segregation of insulin receptor-like L1 and L2 domains indicated that an internal domain duplication had occurred only once, the generation of truncated insulin receptor-like sequences has occurred multiple times. The significance of this work is the previously unknown and widespread occurrence of truncated isoforms in arthropods, signifying that these isoforms play an important functional role, potentially related to such isoforms in mammals

Highly robust model of transcription regulator activity predicts breast cancer overall survival

Background: While several multigene signatures are available for predicting breast cancer prognosis, particularly in early stage disease, effective molecular indicators are needed, especially for triple-negative carcinomas, to improve treatments and predict diagnostic outcomes. The objective of this study was to identify transcriptional regulatory networks to better understand mechanisms giving rise to breast cancer development and to incorporate this information into a model for predicting clinical outcomes. Methods: Gene expression profiles from 1097 breast cancer patients were retrieved from The Cancer Genome Atlas (TCGA). Breast cancer-specific transcription regulatory information was identified by considering the binding site information from ENCODE and the top co-expressed targets in TCGA using a nonlinear approach. We then used this information to predict breast cancer patient survival outcome. Result: We built a multiple regulator-based prediction model for breast cancer. This model was validated in more than 5000 breast cancer patients from the Gene Expression Omnibus (GEO) databases. We demonstrated our regulator model was significantly associated with clinical stage and that cell cycle and DNA replication related pathways were significantly enriched in high regulator risk patients. Conclusion: Our findings demonstrate that transcriptional regulator activities can predict patient survival. This finding provides additional biological insights into the mechanisms of breast cancer progression

Epigenetic changes on rat chromosome 4 contribute to disparate alcohol drinking behavior in alcohol-preferring and -nonpreferring rats

Background: Paternal alcohol abuse is a well-recognized risk factor for the development of an alcohol use disorder (AUD). In addition to genetic and environmental risk factors, heritable epigenetic factors also have been proposed to play a key role in the development of AUD. However, it is not clear whether epigenetic factors contribute to the genetic inheritance in families affected by AUD. We used reciprocal crosses of the alcohol-preferring (P) and -nonpreferring (NP) rat lines to test whether epigenetic factors also impacted alcohol drinking in up to two generations of offspring. Methods: F1 offspring derived by reciprocal breeding of P and NP rats were tested for differences in alcohol consumption using a free-choice protocol of 10% ethanol, 20% ethanol, and water that were available concurrently. In a separate experiment, an F2 population was tested for alcohol consumption not only due to genetic differences. These rats were generated from inbred P (iP) and iNP rat lines that were reciprocally bred to produce genetically identical F1 offspring that remained alcohol-naïve. Intercrosses of the F1 generation animals produced the F2 generation. Alcohol consumption was then assessed in the F2 generation using a standard two-bottle choice protocol, and was analyzed using genome-wide linkage analysis. Alcohol consumption measures were also analyzed for sex differences. Results: Average alcohol consumption was higher in the F1 offspring of P vs. NP sires and in the F2 offspring of F0 iP vs. iNP grandsires. Linkage analyses showed the maximum LOD scores for alcohol consumption in both male and female offspring were on chromosome 4 (Chr 4). The LOD score for both sexes considered together was higher when the grandsire was iP vs. iNP (5.0 vs. 3.35, respectively). Furthermore, the F2 population displayed enhanced alcohol consumption when the P alleles from the F0 sire were present. Conclusions: These results demonstrate that epigenetic and/or non-genetic factors mapping to rat chromosome 4 contribute to a transgenerational paternal effect on alcohol consumption in the P and NP rat model of AUD

RegSNPs-intron: a computational framework for predicting pathogenic impact of intronic single nucleotide variants

Single nucleotide variants (SNVs) in intronic regions have yet to be systematically investigated for their disease-causing potential. Using known pathogenic and neutral intronic SNVs (iSNVs) as training data, we develop the RegSNPs-intron algorithm based on a random forest classifier that integrates RNA splicing, protein structure, and evolutionary conservation features. RegSNPs-intron showed excellent performance in evaluating the pathogenic impacts of iSNVs. Using a high-throughput functional reporter assay called ASSET-seq (ASsay for Splicing using ExonTrap and sequencing), we evaluate the impact of RegSNPs-intron predictions on splicing outcome. Together, RegSNPs-intron and ASSET-seq enable effective prioritization of iSNVs for disease pathogenesis

Integrative analysis of histopathological images and chromatin accessibility data for estrogen receptor-positive breast cancer

Background: Existing studies have demonstrated that the integrative analysis of histopathological images and genomic data can be used to better understand the onset and progression of many diseases, as well as identify new diagnostic and prognostic biomarkers. However, since the development of pathological phenotypes are influenced by a variety of complex biological processes, complete understanding of the underlying gene regulatory mechanisms for the cell and tissue morphology is still a challenge. In this study, we explored the relationship between the chromatin accessibility changes and the epithelial tissue proportion in histopathological images of estrogen receptor (ER) positive breast cancer. Methods: An established whole slide image processing pipeline based on deep learning was used to perform global segmentation of epithelial and stromal tissues. We then used canonical correlation analysis to detect the epithelial tissue proportion-associated regulatory regions. By integrating ATAC-seq data with matched RNA-seq data, we found the potential target genes that associated with these regulatory regions. Then we used these genes to perform the following pathway and survival analysis. Results: Using canonical correlation analysis, we detected 436 potential regulatory regions that exhibited significant correlation between quantitative chromatin accessibility changes and the epithelial tissue proportion in tumors from 54 patients (FDR < 0.05). We then found that these 436 regulatory regions were associated with 74 potential target genes. After functional enrichment analysis, we observed that these potential target genes were enriched in cancer-associated pathways. We further demonstrated that using the gene expression signals and the epithelial tissue proportion extracted from this integration framework could stratify patient prognoses more accurately, outperforming predictions based on only omics or image features. Conclusion: This integrative analysis is a useful strategy for identifying potential regulatory regions in the human genome that are associated with tumor tissue quantification. This study will enable efficient prioritization of genomic regulatory regions identified by ATAC-seq data for further studies to validate their causal regulatory function. Ultimately, identifying epithelial tissue proportion-associated regulatory regions will further our understanding of the underlying molecular mechanisms of disease and inform the development of potential therapeutic targets

38766 Massively Parallel Reporter Assay Reveals Functional Impact of 3™-UTR SNPs Associated with Neurological and Psychiatric Disorders

ABSTRACT IMPACT: Screening the effect of thousands of non-coding genetic variants will help identify variants important in the etiology of diseases OBJECTIVES/GOALS: Massively parallel reporter assays (MPRAs) can experimentally evaluate the impact of genetic variants on gene expression. In this study, our objective was to systematically evaluate the functional activity of 3’-UTR SNPs associated with neurological disorders and use those results to help understand their contributions to disease etiology. METHODS/STUDY POPULATION: To choose variants to evaluate with the MPRA, we first gathered SNPs from the GWAS Catalog that were associated with any neurological disorder trait with p-value 0.8) and retrieved all the common 3’-UTR SNPs (allele-frequency > 0.05) within that region. We used an MPRA to measure the impact of these 3’-UTR variants in SH-SY5Y neuroblastoma cells and a microglial cell line. These results were then used to train a deep-learning model to predict the impact of variants and identify features that contribute to the predictions. RESULTS/ANTICIPATED RESULTS: Of the 13,515 3’-UTR SNPs tested, 400 and 657 significantly impacted gene expression in SH-SY5Y and microglia, respectively. Of the 84 SNPs significantly impacted in both cells, the direction of impact was the same in 81. The direction of eQTL in GTEx tissues agreed with the assay SNP effect in SH-SY5Y cells but not microglial cells. The deep-learning model predicted sequence activity level correlated with the experimental activity level (Spearman’s corr = 0.45). The deep-learning model identified several predictive motifs similar to motifs of RNA-binding proteins. DISCUSSION/SIGNIFICANCE OF FINDINGS: This study demonstrates that MPRAs can be used to evaluate the effect of non-coding variants, and the results can be used to train a machine learning model and interpret its predictions. Together, these can help identify causal variants and further understand the etiology of diseases