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Competing signal peptides hold the key When a pollen grain lands on a receptive flower's pistil, a complex dance leading to sexual reproduction begins. Liu et al. show some of the early steps that help to distinguish a compatible pollen grain from a random piece of dust. Normally, a stigmatic gatekeeper, the ANJEA–FERONIA receptor kinase complex, perceives signaling peptides produced by the stigma that drive the production of reactive oxygen species at the stigma papillae. Upon pollination, POLLEN COAT PROTEIN B-class peptides compete with those stigmatic peptides for binding to the stigmatic receptor kinase complex. The subsequent decline of stigmatic reactive oxygen species production allows hydration and opens the gates to pollen germination. Science , this issue p. 171

Blockade of programmed death-ligand 1 (PD-L1) by therapeutic antibodies has shown to be a promising strategy in cancer therapy, yet clinical response in many types of cancer, including prostate cancer (PCa), is limited. Tumor cells secrete PD-L1 through exosomes or splice variants, which has been described as a new mechanism for the resistance to PD-L1 blockade therapy in multiple cancers, including PCa. This suggests that cutting off the secretion or expression of PD-L1 might improve the response rate of PD-L1 blockade therapy in PCa treatment. Here we report that p300/CBP inhibition by a small molecule p300/CBP inhibitor dramatically enhanced the efficacy of PD-L1 blockade treatment in a syngeneic model of PCa by blocking both the intrinsic and interferon gamma (IFN-γ)-induced PD-L1 expression. Mechanistically, p300/CBP could be recruited to the promoter of CD274 (encoding PD-L1) by the transcription factor IRF-1, which induced the acetylation of Histone H3 at CD274 promoter followed by the transcription of CD274. A485, a p300/CBP inhibitor, abrogated this process and cut off the secretion of exosomal PD-L1 by blocking the transcription of CD274, which combined with the anti-PD-L1 antibody to reactivate T cells function for tumor attack. This finding reports a new mechanism of how cancer cells regulate PD-L1 expression through epigenetic factors and provides a novel therapeutic approach to enhance the efficacy of immune checkpoint inhibitors treatment.

Background Because androgen receptor (AR) signaling is essential for prostate cancer (PCa) initiation and progression, castration is the main approach for treatment. Unfortunately, patients tend to enter a stage called castration-resistant prostate cancer (CRPC) despite the initial response to castration. For various reasons, AR signaling is reactivated in CRPC. As such, AR signaling inhibitors, such as enzalutamide, has been approved by the Food and Drug Administration to treat CRPC in the clinic. However, the limited success of these new drugs suggests an immediate unmet need to understand the underlying mechanisms for resistance so novel targets can be identified to enhance their efficacy. Methods An unbiased bioinformatics analysis was performed with the existing human patient dataset and RNA-seq results of in-house PCa cell lines to identify new targets to overcome enzalutamide resistance. Cell viability and growth were detected by 3-(4,5-dimethylthiazol-2-yl)-2, 5-diphenyltetrazolium bromide and colony formation assay. Cell invasion and migration were detected by transwell assay. Protein levels were detected by Western blot or immunofluorescence. Results We found that the noncanonical Wnt signaling was activated in enzalutamide-resistant PCa cells and that the activation of noncanonical Wnt signaling was correlated with AR expression and disease progression. This was validated by the elevated expression of noncanonical Wnt pathway members such as Wnt5a, RhoA, and ROCK in enzalutamide-resistant PCa cells in comparison to their enzalutamide-sensitive counterparts. And, both Y27632, an inhibitor of ROCK, and depletion of ROCK enhanced the efficacy of enzalutamide in enzalutamide-resistant PCa cells. Of significance, a combination of Y27632 and enzalutamide inhibited 22RV1-derived xenograft tumor growth synergistically. Finally, ROCK depletion plus enzalutamide treatment inhibited invasion and migration of enzalutamide-resistant PCa cells via inhibition of epithelial-mesenchymal transition. Conclusions The noncanonical Wnt pathway is activated in enzalutamide-resistant PCa and inhibition of noncanonical Wnt pathway overcomes enzalutamide resistance and enhances its efficacy in CRPC.

Nonalcoholic fatty liver disease (NAFLD) is the most common chronic liver disease, and it has gradually become the main disease burden in the world. However, the pathogenesis of NAFLD is complex, involving such things as dyslipidemia, oxidative stress, inflammation, etc. This brings to the table a significant challenge for drug development, and there is still no drug approved by the FDA on the market to treat the disease. GAS and HBA are active ingredients of the orchidaceae plant gastrodia elata and have exhibit effects in ameliorating nervous system diseases caused by oxidative stress. HBA is a metabolite of GAS that could perform lipid regulation and improve oxidative stress on HCD-induced NAFLD larval zebrafish, as reported by previous studies; we therefore explored the role of HBA in lipid regulation and oxidative stress on HCD-induced NAFLD larval zebrafish in vivo and FFA-induced BRL-3A hepatocyte in vitro. The gene expression of lipogenesis, inflammation, and oxidative stress were measured to investigate the underlying mechanism of HBA, and the potential protein target of HBA was explored by immunofluorescence. Altogether, our data highlight the role of HBA in improving NAFLD by use of its lipid-lowering and anti-oxidative properties via the Nrf2/HO-1 signaling pathway, providing a potential therapeutic compound for NAFLD treatment.

This study aims to create a database for quantifying the fraction of metabolism of cytochrome P450 isozymes for cancer drugs approved by the US Food and Drug Administration. A reproducible data collection protocol was developed to extract essential information, including both substrate‐depletion and metabolite‐formation data from publicly available in vitro selective cytochrome P450 enzyme inhibition studies. We estimated the fraction of metabolism from the curated data. To demonstrate the utility of this database, we conducted an in vitro drug interaction prediction for the 42 cancer drugs. In the drug–drug interaction prediction, we identified 31 drug pairs with at least one cancer drug in each pair that had predicted area under concentration ratios > 2. We further found clinical drug interaction pieces of evidence in the literature to support 20 of these 31 drug–drug interaction pairs.

In the prediction of the synergy of drug combinations, systems pharmacology models expand the scope of experiment screening and overcome the limitations of current computational models posed by their lack of mechanical interpretation and integration of gene essentiality. We therefore investigated the synergy of drug combinations for cancer therapies utilizing records in NCI ALMANAC, and we employed logistic regression to test the statistical significance of gene and pathway features in that interaction. We trained our predictive models using 43 NCI-60 cell lines, 165 KEGG pathways, and 114 drug pairs. Scores of drug-combination synergies showed a stronger correlation with pathway than gene features in overall trend analysis and a significant association with both genes and pathways in genome-wide association analyses. However, we observed little overlap of significant gene expressions and essentialities and no significant evidence that associated target and non-target genes and their pathways. We were able to validate four drug-combination pathways between two drug combinations, Nelarabine-Exemestane and Docetaxel-Vermurafenib, and two signaling pathways, PI3K-AKT and AMPK, in 16 cell lines. In conclusion, pathways significantly outperformed genes in predicting drug-combination synergy, and because they have very different mechanisms, gene expression and essentiality should be considered in combination rather than individually to improve this prediction.

Gastrodin (GAS) and its aglycone, p-hydroxybenzyl alcohol (HBA), are both bioactive compounds extracted from Gastrodia elata Blume (GEB). In the current Chinese pharmacopoeia, they are regarded as quality control markers for GEB. In this study, we developed a high-performance liquid chromatography method coupled with a diode array detector to quantify GAS and HBA concentrations in plasma following oral ingestion by rats. For the first time, GAS was detected in vivo after HBA administration. GAS and HBA both had similar pharmacological effects, but the influence of the glucose moiety resulted in different pharmacokinetic characteristics. In this study, the effects of GAS and HBA at different administration durations were investigated in zebrafish larvae. These compounds were found to induce a sedative effect but had different onset times. In conclusion, a biotransformation of HBA to GAS could be observed in the rats. This may be a new insight into the pharmacokinetic characteristics of these bioactive compounds and also relates to the different ways in which they take effect.

Gastrodia elata Blume (GE) is an orchid herbal material widely used in China for thousands of years. Recently, the Chinese government has noted that GE can be legally used as functional food, which brings a huge food market for GE. The processing of herbal materials is considered to be the most critical technology to ensure quality and efficacy. Proper processing can transform raw materials into food or drugs with appropriate ingredients and functions. However, due to the unprocessed or the fresh GE can be easily bought in the market, the ingredient and efficiency difference between fresh and processed GE need to further investigate. Based on the understanding of the mechanism of processing in the GE and its possible application in the dietary treatment for anxious people, this paper studied the ingredient changes of the main active constituents of the GE before and after processing. Meanwhile, blood concentrations of three active ingredient including gastrodin (GAS), p-hydroxylbenzyl alcohol (HBA) and Parishin were investigated. Moreover, the different efficacy of the GE in various animal models and the potential mechanisms were studied. Finally, water steamed GE showed distinct better bioactivity than the fresh GE. Further mechanism based on the metabolomics was investigated. The difference of tryptophan related metabolites was observed and discussed to be the possible cause of the increasing bioactive efficiency in GE with steaming process.

Because there is no effective treatment for late‐stage prostate cancer (PCa) at this moment, identifying novel targets for therapy of advanced PCa is urgently needed. A new network‐based systems biology approach, XDeath, is developed to detect crosstalk of signaling pathways associated with PCa progression. This unique integrated network merges gene causal regulation networks and protein‐protein interactions to identify novel co‐targets for PCa treatment. The results show that polo‐like kinase 1 (Plk1) and DNA methyltransferase 3A (DNMT3a)‐related signaling pathways are robustly enhanced during PCa progression and together they regulate autophagy as a common death mode. Mechanistically, it is shown that Plk1 phosphorylation of DNMT3a leads to its degradation in mitosis and that DNMT3a represses Plk1 transcription to inhibit autophagy in interphase, suggesting a negative feedback loop between these two proteins. Finally, a combination of the DNMT inhibitor 5‐Aza‐2’‐deoxycytidine (5‐Aza) with inhibition of Plk1 suppresses PCa synergistically., A unique approach, XDeath is developed to identify novel targets for advanced prostate cancer (PCa). While Polo‐like kinase 1 (Plk1) phosphorylation of DNA methyltransferase 3A (DNMT3a) leads to its degradation in mitosis, DNMT3a represses Plk1 transcription to inhibit autophagy in interphase. This negative feedback loop provides the rationale for a combination therapy to treat advanced PCa.

Composting is widely used to transform waste into valuable agricultural organic fertilizer. Anaerobic ammonium-oxidizing (anammox) bacteria play an important role in the global nitrogen cycle, but their role in composting remains poorly understood. In the present study, the community structure, diversity, and abundance of anammox bacteria were analyzed using cloning and sequencing methods by targeting the 16S rRNA gene and the hydrazine oxidase gene (hzo) in samples isolated from compost produced from cow manure and rice straw. A total of 25 operational taxonomic units were classified based on 16S rRNA gene clone libraries, and 14 operational taxonomic units were classified based on hzo gene clone libraries. The phylogenetic tree analysis of the 16S rRNA gene and deduced HZO protein sequences from the corresponding encoding genes indicated that the majority of the obtained clones were related to the known anammox bacteria Candidatus "Brocadia," Candidatus "Kuenenia," and Candidatus "Scalindua." The abundances of anammox bacteria were determined by quantitative PCR, and between 2.13 × 105 and 1.15 × 106 16S rRNA gene copies per gram of compost were found. This study provides the first demonstration of the existence of anammox bacteria with limited diversity in cow manure composting.

The Library of Integrated Cellular Signatures (LINCS) project provides comprehensive transcriptome profiling of human cell lines before and after chemical and genetic perturbations. Its L1000 platform utilizes 978 landmark genes to infer the transcript levels of 14,292 genes computationally. Here we conducted the L1000 data quality control analysis by using MCF7, PC3, and A375 cell lines as representative examples. Before perturbations, a promising 80% correlation in transcriptome was observed between L1000- and Affymetrix HU133A-platforms. After library-based shRNA perturbations, a moderate 30% of differentially expressed genes overlapped between any two selected controls viral vectors using the L1000 platform. The mitogen-activated protein kinase, vascular endothelial growth factor, and T-cell receptor pathways were identified as the most significantly shared pathways between chemical and genetic perturbations in cancer cells. In conclusion, L1000 platform is reliable in assessing transcriptome before perturbation. Its response to perturbagens needs to be interpreted with caution. A quality control analysis pipeline of L1000 is recommended before addressing biological questions.

Diversity and abundance of the denitrifying genes nirK, nirS and nosZ were investigated in cow manure compost using polymerase chain reaction-denaturing gradient gel electrophoresis (PCR-DGGE) and real-time quantitative PCR (qPCR), respectively. These three genes were detected in all the stages of the composting process. Phylogenetic analysis showed that the nirK gene was closely related to Rhizobiales, Burkholderiales, the nirS gene was closely related to Pseudomonadales and Burkholderiales, and the nosZ gene was closely related to Rhodospirillales, Rhizobiales, Pseudomonadales, and Alteromonadales. qPCR results showed that the abundance of these three genes (nirK, nirS and nosZ) reached the peak value in the late thermophilic stage of composting and abundance of the nirK gene was higher than that of the nosZ gene and the nirS gene. Redundancy analysis (RDA) showed that the diversity of the nirK and nirS genes was significantly correlated with ammonium (p < 0.05), the diversity of the nosZ gene was significantly correlated with pH (p < 0.05) and the abundance of the nirK nirS and nosZ genes was significantly correlated with temperature (p< 0.05). La diversidad y la abundancia de los genes desnitrificadores nirK, nirS, nosZ en el compost de estiércol de vaca se investigaron por medio de la reacción en cadena de la polimerasa seguida de electroforesis en gel con gradiente de desnaturalización (PCR-DGGE) y por PCR cuantitativa (qPCR) en tiempo real, respectivamente. Estos 3 genes fueron detectados durante todas las fases del compostaje. El análisis filogenético mostró estrecha relación del gen nirK con Rhizobiales y Burkholderiales, del gen nirS con Pseudomonadales y Burkholderiales y del gen nosZ con Rhodospirillales, Rhizobiales, Pseudomonadales y Alteromonadales. Los resultados de la qPCR mostraron que la abundancia de los genes nirK, nirSy nosZ alcanzó el valor máximo en la fase termofÃlica tardÃa del compostaje, y que la abundancia del gen nirK era más elevada que los de los genes nosZ y nirS. El análisis de redundancia (RDA) mostró que la diversidad de los genes nirK y nirS estaba significativamente correlacionada con la concentración de amonio (p

Alternatively-activated pathways have been observed in biological experiments in cancer studies, but the concept had not been fully explored in computational cancer system biology. Therefore, an alternatively-activated pathway identification method was proposed and applied to primary breast cancer and breast cancer liver metastasis research using microarray data. Interestingly, the results show that cytokine-cytokine receptor interaction and calcium signaling were significantly enriched under both conditions. TGF beta signaling was found to be the hub in network topology analysis. In total, three types of alternatively-activated pathways were recognized. In the cytokine-cytokine receptor interaction pathway, four active alteration patterns in gene pairs were noticed. Thirteen cytokine-cytokine receptor pairs with inverse activity changes of both genes were verified by the literature. The second type was that some sub-pathways were active under only one condition. For the third type, nodes were significantly active in both conditions, but with different active genes. In the calcium signaling and TGF beta signaling pathways, node E2F5 and E2F4 were significantly active in primary breast cancer and metastasis, respectively. Overall, our study demonstrated the first time using microarray data to identify alternatively-activated pathways in breast cancer liver metastasis. The results showed that the proposed method was valid and effective, which could be helpful for future research for understanding the mechanism of breast cancer metastasis.

Background Cancer has been extensively characterized on the basis of genomics. The integration of genetic information about cancers with data on how the cancers respond to target based therapy to help to optimum cancer treatment.Objective The increasing usage of sequencing technology in cancer research and clinical practice has enormously advanced our understanding of cancer mechanisms. The cancer precision medicine is becoming a reality. Although off-label drug usage is a common practice in treating cancer, it suffers from the lack of knowledge base for proper cancer drug selections. This eminent need has become even more apparent considering the upcoming genomics data.Methods In this paper, a personalized medicine knowledge base is constructed by integrating various cancer drugs, drug-target database, and knowledge sources for the proper cancer drugs and their target selections. Based on the knowledge base, a bioinformatics approach for cancer drugs selection in precision medicine is developed. It integrates personal molecular profile data, including copy number variation, mutation, and gene expression.Results By analyzing the 85 triple negative breast cancer (TNBC) patient data in the Cancer Genome Altar, we have shown that 71.7% of the TNBC patients have FDA approved drug targets, and 51.7% of the patients have more than one drug target. Sixty-five drug targets are identified as TNBC treatment targets and 85 candidate drugs are recommended. Many existing TNBC candidate targets, such as Poly (ADP-Ribose) Polymerase 1 (PARP1), Cell division protein kinase 6 (CDK6), epidermal growth factor receptor, etc., were identified. On the other hand, we found some additional targets that are not yet fully investigated in the TNBC, such as Gamma-Glutamyl Hydrolase (GGH), Thymidylate Synthetase (TYMS), Protein Tyrosine Kinase 6 (PTK6), Topoisomerase (DNA) I, Mitochondrial (TOP1MT), Smoothened, Frizzled Class Receptor (SMO), etc. Our additional analysis of target and drug selection strategy is also fully supported by the drug screening data on TNBC cell lines in the Cancer Cell Line Encyclopedia.Conclusions The proposed bioinformatics approach lays a foundation for cancer precision medicine. It supplies much needed knowledge base for the off-label cancer drug usage in clinics.

Background: Experiment based drug combinatory synergy discovery is limited in a moderate scale. Systems biology models can substantially widen the search space, and discover novel mechanisms. While most of systems pharmacology models were designed to optimize the predictive performance for the drug combination synergy, they were limited by their lack of mechanistic interpretations. The gene essentiality data generated from RNAi screening have not been integrated into the systems pharmacology model yet. Methods: Drug combinatory synergy data from NCI-ALMANAC was used in this research. Cancer cell line gene expression and essential score (RNAi screening from DepMap portal), drug targets and KEGG pathways were used in our systems pharmacology model. Two types of models were constructed. The first model predicts a drug combination's synergy using various pathways among various cell lines. The second model predicts various drug combinatory synergy in a cell line. Machine learning approaches, such as SVR, Ridge regression, Random forest and Logistic regulation were investigated in developing this model. Our predictive models were built upon 43 cell lines with both baseline gene expression and essential scores, 165 KEGG pathways, and 198 drug pairs who have at least synergistic drug combinatory effects in 3 or more cell lines. Results: In predicting drug combinatory synergy from both predictive models, the mean R square was merely 0.006 using individual gene expression or essentiality data. The mean R-square increased to 0.022 when using pathways. In drug combination specific predictive model (model 1), 64 and 62 drug combinations were related to more than 10 pathways which had enriched essential and expression genes, respectively. The correlation between expression enrich pathways and essential enriched pathways is only 0.04. In the cell line specific predictive model (model 2), drug combination synergy score from 14 cell lines can be predicted with more than 150 pathways, and 5 cell lines can be predicted with less than 10 pathways. Conclusion: Pathway-based models are more powerful than gene level-based models in predicting drug combination synergy. Gene expressions and essential genes provide much different information in predicting drug combination synergy. Drug combination synergy prediction and its related pathways are dramatically different among different cancer cell lines. Citation Format: Jin Li, Xue Wu, Yang Huo, Enze Liu, Zhi Zeng, Lijun Cheng, Lang Li. Pathway-based drug combinatory synergy prediction using gene expression and essentiality data [abstract]. In: Proceedings of the Annual Meeting of the American Association for Cancer Research 2020; 2020 Apr 27-28 and Jun 22-24. Philadelphia (PA): AACR; Cancer Res 2020;80(16 Suppl):Abstract nr 4397.

As monotherapy, Poly (ADP-ribose) polymerase inhibitors (PARPi) have achieved remarkable success in treating tumors with germline BRCA1/2 mutation due to the synthetic lethality in DNA damage response. The efficacy of PARPi in combination with other therapeutic agents is still under investigation. However, BRCA mutant tumors constitute only 5-10% of total breast cancer diagnoses. Therefore, to extend the benefit of PARPi beyond BRCA mutant tumors, it is imperative to identify other genetic determinants that also contribute to PARPi sensitivity. Such knowledge also provides valuable insight into the development of combination therapies involving PARPi. To identify genes and pathways that are essential for cell survival under PARPi treatment, we performed genome-wide CRISPR-Cas9 knockout screens in a BRCA-functional, triple-negative breast cancer cell line MDA-MB-231 treated with talazoparib at a low dose (IC20, 20nM). Toronto CRISPR human knockout library TKOV3 was introduced into cells using lentivirus at MOI of 0.3~0.4. After puromycin selection, the surviving cells were treated with talazoparib or DMSO for 20 days before harvesting. The sgRNAs were sequenced at ~30 million reads per sample to achieve a 300x coverage over the TKOV3 library. To increase the accuracy of the CRISPR-Cas9 screen result, and to capture the transcriptomic changes during a relative long-term PARPi treatment as used in the screening process, we also did RNAseq profiling in MDA-MD-231 cells cultured in the conditions matched with the CRISPR-Cas9 screen. Using mRNA level as a filter to remove non-expressed genes, we were able to generate a list of candidate genes that have the potential to form synthetic lethal partnership with talazoparib. Our negative selection screen confirmed that loss of key components of DNA damage repair and DNA replication pathways such as ATM, RNASEH2C, ESCO2, EME1, and several components of Fanconi Anemia (FA) core complex sensitizes cells to talazoparib, as has been reported in similar screens using other PARP inhibitors. Meanwhile, our analysis also revealed several previously unrecognized partner genes involved in subcellular trafficking, RNA splicing, and microRNA biogenesis. In addition, the RNAseq profile depicted a transcriptomic response to long-term talazoparib treatment that has many distinctions from the essential pathways shown in the CRISPR-Cas9 screen, suggesting a complex, multi-layer regulation system in cell response to PARP inhibition. Our study identified a set of genes that have potential synthetic lethal interaction with PARPi. The status of these genes can be used to identify subset of triple-negative breast cancer patients who could potentially benefit from PARPi treatment. Strategies targeting these genes represent new opportunities for PARPi combination therapies. Citation Format: Xue Wu, Yue Zhao, Xiaoyu Xie, Xiaoling Xuei, Yunlong Liu, Lijun Cheng, Lang Li. Genome-wide CRISPR-Cas9 screen and RNAseq analysis identify new candidate synthetic lethality partners to PARP inhibitor in triple-negative breast cancer [abstract]. In: Proceedings of the Annual Meeting of the American Association for Cancer Research 2020; 2020 Apr 27-28 and Jun 22-24. Philadelphia (PA): AACR; Cancer Res 2020;80(16 Suppl):Abstract nr 549.

Introduction: Genome-wide CRISPR-Cas9 based loss-of-function screens can be used to find essential genes for proliferation and survival of cancer cells. While recent studies have focused on establishing reference sets of essential and non-essential genes, correcting copy number effect and characterizing off-target effect, it lacks in-depth studies of the effects of gene abundance and sgRNAs that targeting multi-genomic loci. To fill this gap timely, we here present a bioinformatics workflow to reduce false positives in CRISPR-Cas9 screens. Description: Gastric adenocarcinoma cell line AGS was infected with CRISPR knockout library (TKOv3) at a multiplicity of infection of 0.3~0.4. We used the cells right after puromycin selection as the baseline sample, and the cells cultured for 14 days or 20 days as the negative selection samples. The sgRNA inserts were amplified by PCR and the corresponding libraries were sequenced on NextSeq 500 with a single-end 75 bp run, followed by analysis by MAGeCK. The read counts of sgRNAs were normalized by non-essential genes to reduce false positives. The RNA-seq data and copy number data were obtained by CCLE portal. To characterize sgRNAs targeting multiple-genomic loci, Bowtie was used to align sgRNA to the reference human genome (GRCh38) with no mismatch, and only the alignments followed by NGG PAM site were remained for downstream analysis. Summary: Integration of RNA-seq data with CRISPR negative screen results showed that the selection signal was noisy for the lowly expressed genes. The fraction of selected essential genes (overall FDR1) was as low as 0.11% among the genes with the bottom 10% expression level, while 27% among the genes with the top 10% expression level. After filtering out the lowly expressed genes ( Conclusions: Filtering out lowly-expressed genes prior to CRISPR screen data analysis can reduce false positives. In addition, multiple-target sgRNAs can lead to false positives but the effect needs further analysis in a case by case manner. Citation Format: Yue Zhao, Xue Wu, Yuru Wang, Kin Fai Au, Lijun Cheng, Lang Li. New bioinformatics workflow of genome-wide CRISPR-Cas9 knockout screens [abstract]. In: Proceedings of the Annual Meeting of the American Association for Cancer Research 2020; 2020 Apr 27-28 and Jun 22-24. Philadelphia (PA): AACR; Cancer Res 2020;80(16 Suppl):Abstract nr 830.

Schizophrenia and bipolar disorder are complex psychiatric diseases with risks contributed by multiple genes. Dysregulation of gene expression has been implicated in these disorders, but little is known about such dysregulation in the human brain. We analyzed three transcriptome datasets from 394 postmortem brain tissue samples from patients with schizophrenia or bipolar disorder or from healthy control individuals without a known history of psychiatric disease. We built genome-wide coexpression networks that included microRNAs (miRNAs). We identified a coexpression network module that was differentially expressed in the brain tissue from patients compared to healthy control individuals. This module contained genes that were principally involved in glial and neural cell genesis and glial cell differentiation, and included schizophrenia risk genes carrying rare variants. This module included five miRNAs and 545 mRNAs, with six transcription factors serving as hub genes in this module. We found that the most connected transcription factor gene POU3F2, also identified on a genome-wide association study for bipolar disorder, could regulate the miRNA hsa-miR-320e and other putative target mRNAs. These regulatory relationships were replicated using PsychENCODE/BrainGVEX datasets and validated by knockdown and overexpression experiments in SH-SY5Y cells and human neural progenitor cells in vitro. Thus, we identified a brain gene expression module that was enriched for rare coding variants in genes associated with schizophrenia and that contained the putative bipolar disorder risk gene POU3F2 The transcription factor POU3F2 may be a key regulator of gene expression in this disease-associated gene coexpression module.

Women with endometriosis, a benign growth of endometrial tissue outside the uterine cavity, are at increased risk of specific histotypes of epithelial ovarian cancer, such as ovarian endometrioid adenocarcinoma (OEA). Women with OEA who have endometriosis at time of surgical staging demonstrate improved clinical prognosis compared to women with OEA without evidence of endometriosis. However, the molecular contributions of the endometriotic tumor microenvironment to these ovarian cancers remain poorly understood. As a starting point, we used a platform for genome-wide transcriptomic profiling to compare specimens of OEA from women with and without concurrent endometriosis and benign reproductive tract tissues, including proliferative endometrium and typical and atypical endometrioma samples (n = 20). Principle component analysis revealed distinct clustering between benign and malignant samples as well as malignant samples with and without concurrent endometriosis. Examination of gene signatures revealed that OEA with concurrent endometriosis contained a unique molecular signature compared to OEA without concurrent endometriosis, distinguished by 682 unique genes differentially expressed (fold change 1.5, p < 0.01). Bioinformatic analysis of these differentially expressed gene products using ingenuity pathway analysis revealed activation of NFkB signaling, an inflammatory signaling pathway constitutively active in endometriosis. DAVID functional annotation clustering further revealed enrichment in RAS signaling as both cytoskeleton organization and GTPase regulator activity relied heavily on RAS protein signal transduction. Gene set enrichment analysis highlighted immune and inflammatory nodes involved in OEA with concurrent endometriosis. These observations provide novel resources for understanding molecular subtleties potentially involved in OEA within the context of the endometriotic tumor microenvironment.

Probucol Prevents Atrial Remodeling. Introduction: Diabetes mellitus (DM) is an independent risk factor for atrial fibrillation (AF). However, the underlying mechanisms for the increased propensity for AF in the setting of DM and the potential effects of probucol on atrial remodeling remain unclear. Methods and Results: Eighty Japanese rabbits were randomly assigned to normal/control group (Control, n = 20), alloxan-induced diabetic group (DM, n = 20), probucol-treated group (Control-P, n = 20), and probucol-treated diabetic group (DM-P, n = 20). Rabbits in the DPR and CPR groups were orally administered probucol (1,000 mg/day) for 8 weeks. Serum and left atrial tissue malonaldehyde (MDA), superoxide dismutase (SOD), myeloperoxidase (MPO), and catalase (CAT) levels were assessed. Isolated Langendorff perfused rabbit hearts were prepared to evaluate atrial refractory effective period (AERP) and its dispersion (AERPD), interatrial conduction time (IACT), and vulnerability to AF. Atrial interstitial fibrosis was also evaluated. The mRNA expression levels of TNF-α and TLR4 were analyzed. The protein expressions of NF-κB, HSP70, TGF-β, and ERK in left atrial tissue were analyzed by Western blot. Probucol administration decreased the inducibility of AF in diabetic rabbits and attenuated atrial interstitial fibrosis. The DM-P rabbits exhibited significant alleviation of oxidative stress, evidenced by reduced serum and tissue MDA, compared with diabetic rabbits. Moreover, NF-κB, TGF-β, and HSP70 protein expression and TNF-α mRNA expression were significantly downregulated by probucol treatment in alloxan-induced diabetic rabbits. Conclusions: Probucol prevents atrial remodeling and suppresses AF development in alloxan-induced diabetic rabbits. Its inhibitory effects on oxidative stress, NF-κB, TGF-β, and TNF-α overexpression may contribute to its antiremodeling effects. (J Cardiovasc Electrophysiol, Vol. 26, pp. 211-222, February 2015)

Background Several of the thousands of human long noncoding RNAs (lncRNAs) have been functionally characterized, yet their potential involvement in hepatocellular carcinoma (HCC) remains poorly understood. Methods LncRNA-HOXD-AS1 was identified by microarray and validated by real-time PCR. The clinicopathological significance of HOXD-AS1 was analyzed by Kaplan-Meier method. Chromatin immunoprecipitation was conducted to examine the mechanism of HOXD-AS1 upregulation. The role of HOXD-AS1 in HCC cells was assessed both in vitro and in vivo. ceRNA function of HOXD-AS1 was evaluated by RNA immunoprecipitation and biotin-coupled miRNA pull down assays. Results In this study, we found that HOXD-AS1 was significantly upregulated in HCC tissues. Clinical investigation demonstrated high expression level of HOXD-AS1 was associated with poor prognosis and high tumor node metastasis stage of HCC patients, and was an independent risk factor for survival. Moreover, our results revealed that STAT3 could specifically interact with the promoter of HOXD-AS1 and activate HOXD-AS1 transcription. Knockdown of HOXD-AS1 significantly inhibited migration and invasion of HCC cells in vitro and distant lung metastasis in vivo. Additionally, HOXD-AS1 was enriched in the cytoplasm, and shared miRNA response elements with SOX4. Overexpression of HOXD-AS1 competitively bound to miR-130a-3p that prevented SOX4 from miRNA-mediated degradation, thus activated the expression of EZH2 and MMP2 and facilitated HCC metastasis. Conclusions In summary, HOXD-AS1 is a prognostic marker for HCC patients and it may play a pro-metastatic role in hepatocarcinogenesis. Electronic supplementary material The online version of this article (doi:10.1186/s12943-017-0680-1) contains supplementary material, which is available to authorized users.

There is a need to individualize assays for tumor molecular phenotyping, given variations in the differentiation status of tumor and normal tissues in different patients. To address this, we performed single-cell genomics of breast tumors and adjacent normal cells propagated for a short duration under growth conditions that enable epithelial reprogramming. Cells analyzed were either unselected for a specific subpopulation or phenotypically defined as undifferentiated and highly clonogenic ALDH+/CD49f+/EpCAM+ luminal progenitors, which express both basal cell and luminal cell–enriched genes. We analyzed 420 tumor cells and 284 adjacent normal cells for expression of 93 genes that included a PAM50-intrinsic subtype classifier and stemness-related genes. ALDH+/CD49f+/EpCAM+ tumor and normal cells clustered differently compared with unselected tumor and normal cells. PAM50 gene-set analyses of ALDH+/CD49f+/EpCAM+ populations efficiently identified major and minor clones of tumor cells, with the major clone resembling clinical parameters of the tumor. Similarly, a stemness-associated gene set identified clones with divergent stemness pathway activation within the same tumor. This refined expression profiling technique distinguished genes truly deregulated in cancer from genes that identify cellular precursors of tumors. Collectively, the assays presented here enable more precise identification of cancer-deregulated genes, allow for early identification of therapeutically targetable tumor cell subpopulations, and ultimately provide a refinement of precision therapeutics for cancer treatment. Cancer Res; 77(10); 2759–69. ©2017 AACR.

Background: Pancreatic ductal adenocarcinoma (PDAC) is one of the most lethal human cancers and shows insensitivity towards many chemotherapeutic drugs and target-based drugs. There is an urgently need to identify PDAC disease mechanism and detect novelty drug targets. With the large availability of protein interaction networks and microarray data supported, to identify the disease essential genes that have biological significance for potential drug targets is a challenge issue still. Methods: In this study, protein interaction network PathPPI, DrugBank targets and 374 transcriptome profiles from Gene Expression Omnibus (GEO) by Affymetrix HU 133 Plus 2.0 array test are collected. These microarray data includes 140 PDAC, 92 pancreatic cancer cell-line, 58 human normal pancreas tissues, and 72 patient original xenograft mouse. Based on these datasets, a novelty-integrated algorithm was developed for drug target prioritization by perturbed gene expression and network information. The integration network included data driven reconstruction network of gene regulatory (GRN) by Bayesian Network and protein interaction PathPPI network. The perturbed gene expression was refer to the differential expression genes of tumor verse adjacent normal. A novel hybrid method based on genetic algorithm (GA) is proposed to search the attractor with the maximum network perturbation for candidate drug targets in PDAC.These targets’ molecular mechanism was revealed further by network structure comparison of PDAC that is consistent with xerograph mouse model and cancer cells. Results: Highly accordance 56 genes with high perturbation score both in mRNAs and protein-protein network were identified and recommended as candidate drug targets for PDAC, including 16 novel targets, such as PSMD2, EPOR and PSMB9. Fifty-four essential genes shown strong concordance among tumor-model, patient drive xenograft mouse and pancreatic cancer cell-line by systematically comparison. We identified 1375 dysregulated genes that enriched in 25 pathways in PDAC by Gene Set Enrichment Analysis, among which 244 genes played as hubs in reconstructed PDAC regulatory network. Conclusion: In the study, we developed a global optimization-based inference of network perturbation to detect attractor for drug-target identification in PDAC tumors. The assembling Bayesian Network-based approach with protein-protein interaction provides a comprehensive information to observe energy transfer of gene perturbation in network to detect global optimum attractor for drug target selection. Citation Format: Lijun Cheng, Enze Liu, Li Lang, Xiaolin Cheng, Xiaotian Kong, Korc Murray. Integrated network analysis reveals potentially novel molecular pathways mechanism and therapeutic targets of pancreatic ductal adenocarcinoma [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2019; 2019 Mar 29-Apr 3; Atlanta, GA. Philadelphia (PA): AACR; Cancer Res 2019;79(13 Suppl):Abstract nr 4257.

The G72/G30 gene complex is a candidate gene for schizophrenia and bipolar disorder. However, G72 and G30 mRNAs are expressed at very low levels in human brain, with only rare splicing forms observed. We report here G72/G30 expression profiles and behavioral changes in a G72/G30 transgenic mouse model. A human BAC clone containing the G72/G30 genomic region was used to establish the transgenic mouse model, on which gene expression studies, western blot and behavioral tests were performed. Relative to their minimal expression in humans, G72 and G30 mRNAs were highly expressed in the transgenic mice, and had a more complex splicing pattern. The highest G72 transcript levels were found in testis, followed by cerebral cortex, with very low or undetectable levels in other tissues. No LG72 (the long putative isoform of G72) protein was detected in the transgenic mice. Whole-genome expression profiling identified 361 genes differentially expressed in transgenic mice compared with wild-type, including genes previously implicated in neurological and psychological disorders. Relative to wild-type mice, the transgenic mice exhibited fewer stereotypic movements in the open field test, higher baseline startle responses in the course of the prepulse inhibition test, and lower hedonic responses in the sucrose preference test. The transcriptome profile changes and multiple mouse behavioral effects suggest that the G72 gene may play a role in modulating behaviors relevant to psychiatric disorders.

Background Proper cell models for breast cancer primary tumors have long been the focal point in the cancer’s research. The genomic comparison between cell lines and tumors can investigate the similarity and dissimilarity and help to select right cell model to mimic tumor tissues to properly evaluate the drug reaction in vitro. In this paper, a comprehensive comparison in copy number variation (CNV), mutation, mRNA expression and protein expression between 68 breast cancer cell lines and 1375 primary breast tumors is conducted and presented. Results Using whole genome expression arrays, strong correlations were observed between cells and tumors. PAM50 gene expression differentiated them into four major breast cancer subtypes: Luminal A and B, HER2amp, and Basal-like in both cells and tumors partially. Genomic CNVs patterns were observed between tumors and cells across chromosomes in general. High C > T and C > G trans-version rates were observed in both cells and tumors, while the cells had slightly higher somatic mutation rates than tumors. Clustering analysis on protein expression data can reasonably recover the breast cancer subtypes in cell lines and tumors. Although the drug-targeted proteins ER/PR and interesting mTOR/GSK3/TS2/PDK1/ER_P118 cluster had shown the consistent patterns between cells and tumor, low protein-based correlations were observed between cells and tumors. The expression consistency of mRNA verse protein between cell line and tumors reaches 0.7076. These important drug targets in breast cancer, ESR1, PGR, HER2, EGFR and AR have a high similarity in mRNA and protein variation in both tumors and cell lines. GATA3 and RP56KB1 are two promising drug targets for breast cancer. A total score developed from the four correlations among four molecular profiles suggests that cell lines, BT483, T47D and MDAMB453 have the highest similarity with tumors. Conclusions The integrated data from across these multiple platforms demonstrates the existence of the similarity and dissimilarity of molecular features between breast cancer tumors and cell lines. The cell lines only mirror some but not all of the molecular properties of primary tumors. The study results add more evidence in selecting cell line models for breast cancer research. Electronic supplementary material The online version of this article (doi:10.1186/s12864-016-2911-z) contains supplementary material, which is available to authorized users.

Parkinson’s disease is the second most common neurodegenerative disorder without effective treatment. It is generally sporadic with unknown etiology. However, genetic studies of rare familial forms have led to the identification of mutations in several genes, which are linked to typical Parkinson’s disease or parkinsonian disorders. The pathogenesis of Parkinson’s disease remain largely elusive. Here, we report a novel genetic locus for an autosomal dominant, clinically typical and Lewy body confirmed Parkinson’s disease on the short arm of chromosome 20 (20pter-p12) and TMEM230 as the disease-causing gene. We show that TMEM230 encodes a transmembrane protein of secretory/recycling vesicles, including synaptic vesicles in neurons. The disease-linked TMEM230 mutants impair synaptic vesicle trafficking. Our data provide the first genetic evidence that a mutant transmembrane protein of synaptic vesicles in neurons is etiologically linked to Parkinson’s disease, with novel implications in understanding the pathogenic mechanism of Parkinson’s disease and for developing rational therapies.

We conducted a systematic study of top susceptibility variants from a genome-wide association (GWA) study of bipolar disorder to gain insight into the functional consequences of genetic variation influencing disease risk. We report here the results of experiments to explore the effects of these susceptibility variants on DNA methylation and mRNA expression in human cerebellum samples. Among the top susceptibility variants, we identified an enrichment of cis regulatory loci on mRNA expression (eQTLs), and a significant excess of quantitative trait loci for DNA CpG methylation, hereafter referred to as methylation quantitative trait loci (mQTLs). Bipolar disorder susceptibility variants that cis regulate both cerebellar expression and methylation of the same gene are a very small proportion of bipolar disorder susceptibility variants. This finding suggests that mQTLs and eQTLs provide orthogonal ways of functionally annotating genetic variation within the context of studies of pathophysiology in brain. No lymphocyte mQTL enrichment was found, suggesting that mQTL enrichment was specific to the cerebellum, in contrast to eQTLs. Separately, we found that using mQTL information to restrict the number of single-nucleotide polymorphisms studied enhances our ability to detect a significant association. With this restriction a priori informed by the observed functional enrichment, we identified a significant association (rs12618769, P(bonferroni)0.05) from two other GWA studies (TGen+GAIN; 2191 cases and 1434 controls) of bipolar disorder, which we replicated in an independent GWA study (WTCCC). Collectively, our findings highlight the importance of integrating functional annotation of genetic variants for gene expression and DNA methylation to advance the biological understanding of bipolar disorder.

Background Schizophrenia and bipolar disorder are complex mental disorders, with risks contributed by multiple genes. Recently, genome-wide systemic approaches have been used to reveal the associations of hundreds of SNPs with those disorders. Dysregulation of gene expression has been implied, but little is known about such regulation systems in the human brain. Several methods have been developed to achieve measurement of large-scale or multi-level data on interactions in biological systems. Additional biological regulators, such as transcriptional factors (TFs), enhancers, and microRNAs (miRNAs) binding information may help us to unveil the underlying regulatory mechanisms in the networks and suggest causal relationships. Methods We analyzed two data sets using brain tissues from 51 patients with schizophrenia or bipolar disorder and 24 healthy controls. We applied whole genome transcriptome profiling using Affymetrix Human Gene 1.0 ST Array (Affymetrix, Santa Clara, CA) and miRNA sequencing on the Illumina 1G Genome Analyzer. Using weighted gene co-expression network analysis, we integrated gene expression data of mRNA and miRNA from the same brain collections, built mRNA-miRNA co-expression networks and detected differentially expressed modules. In silico predicted binding relationships of TFs and miRNAs were used to validate the putative regulations suggested by co-expression patterns. Using SNP genotype data, expression quantitive trait loci (eQTL) and Network Edge Orienting (NEO) analysis, we resolved causal relationships among the regulators and their targets. We further validated the predicted regulations using RNA interference knockdown experimentally. Results We identified a module differentially expressed between cases and controls (p = 7.6e-4, FDR q = 0.01). This module were enriched for neuron differentiation (p = 4.8e-7, FDR q = 8.5e-4) and neuron development (p = 2.3e-5, FDR q = 4.0e-2), and contained five miRNAs and 501 mRNA genes. Six TFs also served as hub genes in these modules (POU2F1, EPAS1, PAX6, ZNF423, SOX5 and SOX9). The co-expression-suggested regulatory relationships were consistent with the binding relationships predicted by databases. Focusing on those regulations containing TFs and miRNAs, we resolved a regulation cascade from SNP variants (rs16853832) to TF (POU2F1) to miRNA (hsa-mir-320e) to target genes (NR2E1) and ultimately, to disease risks. Discussion We revealed POU2F1 as a key regulator in a neuron differentiation/developmental module associated with disease, and revealed a putative cascade regulation effect. This study showed that we can utilize multi-dimensional data to construct co-expression networks. Causal relationships can be resolved among SNPs, regulatory molecules and their downstream target genes through data integration. Novel genes and their corresponding regulations underlying the disease risks could be revealed.

Disease-free survival in relapsed Ewing's Sarcoma Family of Tumors (ESFT) has not improved over the past 25 years. Current standard-of-care (SOC) agents result in 70% survival in patients with localized ESFT; however, for relapsed patients the survival rates remain between 15-20%. Approximately 85% of ESFTs have the chromosomal translocation t(11;22)(q24;q12) which encodes for the oncogenic EWS/FL1 fusion protein. The EWS/FL1 functions as a potent transcription factor leading to the dysregulated expression of genes that promote and maintain tumorigenesis. A major epigenetic regulator that is a downstream target of EWS/FL1 is the enhancer of Zeste Homolog 2 (EZH2). EZH2 is the catalytic component of the polycomb repressor complex 2 (PRC2). Notably, it is overexpressed in ESFT and maintains tumor oncogenicity by tri-methylating histone 3 lysine 27 (H3K27me3) to modulate gene expression. Genome and transcriptome data obtained by the Pediatric Precision Genomics Program at Riley Hospital for Children at Indiana University Health (IUH) indicate that EZH2 is expressed at high levels in ESFT biopsies. Additionally, other groups have reported that high levels of EZH2 protein in ESFT and other cancers correlate with increased chemoresistance to SOC therapy. We are testing tested the hypothesis that EZH2 contributes to chemoresistance in ESFT by regulating critical growth and survival genes. In addition, we are investigating if pharmacological inhibition of EZH2 will enhance sensitivity to the cytotoxic effects of SOC agents. Pediatric primary and relapsed ESFT cell lines and ESFT xenografts were validated for the EWS/FLI, EZH2, and H3K27me3 signatures. In vitro- and in vivo-pharmacodynamic studies of EZH2 inhibition via tazemetostat were conducted to optimize dosing effect. In ESFT cell lines (TC71, A673, CHLA-9, and CHLA-10), tazemetostat dose-response experiments indicated a significant reduction of H3K27me3 by one day post-treatment which was either sustained or completely blocked by 7-days post-treatment compared to vehicle treated (p Citation Format: Pankita H. Pandya, Barbara Bailey, Adily E. Elmi, Heather R. Bates, Courtney N. Hemenway, Anthony L. Sinn, Khadijeh Bijangi-Vishehsaraei, M. Reza Saadatzadeh, Harlan E. Shannon, Jixin Ding, Mark S. Marshall, Michael J. Ferguson, Lijun Cheng, Lang Li, Mary E. Murray, Jamie L. Renbarger, Karen E. Pollok. Preclinical validation of EZH2 as a therapeutic target in pediatric Ewing's sarcoma [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2018; 2018 Apr 14-18; Chicago, IL. Philadelphia (PA): AACR; Cancer Res 2018;78(13 Suppl):Abstract nr 3180.

Background: Although gene transcripts and protein expression have been utilised to classify breast cancer subtypes, it is not clear whether the observed measurement of gene transcript abundance can predict its protein expression. Herein, we attempt to address gene transcript/protein associations using publically-available data on breast cancer tumour tissues and cell lines. Method: Correlation analysis between mRNAs and Reverse-phase protein arrays (RPPA) among 421 primary breast tumours and 33 breast cancer cell lines was conducted. Highly concordant proteins/genes were further analysed in different breast cancer subtypes. Results: The overall accordance of mRNA/RPPA correlation between cell lines and primary tissue is R2 = 0.71. Since most of these genes are well known drug targets, highly concordant gene/RPPA associations not only confirm that these gene transcripts can serve as biomarkers for their protein products in drug target selection, but also imply that breast cancer cell lines can serve as good models for primary breast cancer tumours.

An overall burden of rare structural genomic variants has not been reported in bipolar disorder (BD), although there have been reports of cases with microduplication and microdeletion. Here, we present a genome-wide copy number variant (CNV) survey of 1001 cases and 1034 controls using the Affymetrix single nucleotide polymorphism (SNP) 6.0 SNP and CNV platform. Singleton deletions (deletions that appear only once in the dataset) more than 100 kb in length are present in 16.2% of BD cases in contrast to 12.3% of controls (permutation P=0.007). This effect was more pronounced for age at onset of mania

Using differential display RT-PCR, we identified a gene of 2750 bp from human adult testis, named H-Lse, which encoded a putative protein of 523 amino acids and molecular weight of 58 kd with structural characteristics similar to that of mouse lysosome sialic-acid-specific 9-O-acetylesterase. Northern blot analysis showed a widespread distribution of H-Lse in various human tissues with high expression in the testis, prostate, and colon. In situ hybridization results showed that while H-Lse was not detected in embryonic testis, positive signals were found in spermatocytes but not spermatogonia in adult testis of human. The subcellular localization of H-Lse was visualized by green fluorescent protein (GFP) fused to the amino terminus of H-Lse, showing compartmentalization of H-Lse in large dense-core vesicles, presumably lysosomes, in the cytoplasm. The developmentally regulated and spermatogenic stage-specific expression of H-Lse suggests its possible involvement in the development of the testis and/or differentiation of germ cells.

Discovery of inter-individual functional variations in gene regulatory elements and the observation that tumor and normal tissues of the same organ are in different differentiation states necessitate rethinking of gene expression based subclassification/characterization of tumors. To address this issue, we performed single cell genomics of breast tumors and adjacent-normal cells propagated using epithelial reprogramming growth conditions for a short duration. Epithelial cells analyzed were either unselected for specific subpopulation or phenotypically defined undifferentiated ALDH+/CD49f+/EpCAM+ luminal progenitors present in the normal breast, which express both basal and luminal-enriched genes. Expression of 93 genes that included PAM50 intrinsic subtype classifier and stemness-related genes was analyzed in 420 tumor and 284 adjacent-normal cells. ALDH+/CD49f+/EpCAM+ tumor and normal cells clustered differently compared to unselected tumor and normal cells. PAM50 genes-set analyses of ALDH+/CD49f+/EpCAM+ populations efficiently identified major and minor clones of tumor cells with the major clone resembling clinical parameters of the tumor. Similarly, stemness-associated gene set identified clones with divergent stemness pathway activation within the same patient sample. This refined technique distinguished genes truly deregulated in cancer from genes that identify cellular precursors of tumors. Collectively, assays presented here may enable precise identification of genes deregulated in cancer, early identification of therapeutically-targetable minor population of tumor cells, and eventually to refinement of precision therapeutics. Citation Format: Harikrishna Nakshatri, Manjushree Anjanappa, Angelo Cardoso, Lijun Cheng, Safa Mohamad, Andrea Gunawan, Susan Rice, Yan Dong, Lang Li, Edward Srour. Refining breast cancer characterization through single-cell analysis of ex vivo reprogrammed tumor and adjacent normal cells [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2017; 2017 Apr 1-5; Washington, DC. Philadelphia (PA): AACR; Cancer Res 2017;77(13 Suppl):Abstract nr 1753. doi:10.1158/1538-7445.AM2017-1753

Identification of genomic regions that regulate gene expression can help our understanding of the mechanisms underlying genetic contributions to phenotypic variations. Hence, we consider a mixture model to locate candidate genomic regions that are more frequently associated with gene expression traits. A modified two-sample t-statistic was used, and single-nucleotide polymorphisms (SNPs) with P-values

DNA methylation is an epigenetic modification that regulates gene expression in response to environmental stimuli (1). DNA methylation play a role in the etiology of illnesses such as schizophrenia and bipolar disorder (referred to here as psychosis). Brain studies using methylation-specific polymerase chain reaction (PCR), bisulfite sequencing, or DNA methylation array reported different DNA methylation levels between psychosis and controls (2-5). Abnormal methylation can result in aberrant gene expression and is thought to contribute to the susceptibility to and pathology of psychiatric disorders (1, 6, 7). Over the past decade, abnormal DNA methylation and gene expression have been reported in the brain of patients with psychosis (2, 8, 9). For example, reelin (RELN) plays important roles in brain development and maintenance of synaptic function, and its expression is nearly half down-regulated in psychosis (10, 11). Using postmortem brain samples, two groups have found hypomethylated CpG sites in RELN promoter region in patients with schizophrenia rather than in controls (12, 13). Another study from Tamura et al.'s group (14) observed inverse correlation between DNA methylation and RELN expression (i.e., increased methylation correlated with decreased expression), as well as that the level of DNA methylation is correlated with age in normal controls, but no significant correlation in patients with schizophrenia or bipolar disorder. All of those findings confirm RELN's expression is regulated by DNA methylation, and methylation aberration of RELN may be a risk factor in psychiatric disorders. Correlations between DNA hypermethylation and differential expression of other candidate genes in neuropsychiatric disorders have also been investigated, including dopamine receptor (DRD2, DRD4, DRD5) (15), sex-determining region Y-box containing gene 10 (SOX10) (3), membrane-boundcatechol-O-methyltransferase (MB-COMT) (16), serotonin transporter (17), glutamate metabotropic receptor (18). Those studies imply that DNA methylation may induce lasting and stable changes in gene expression, and consequently, may confer susceptibility to bipolar and schizophrenia. However, to date, there have been no genome-wide studies of the association between DNA methylation and gene expression in the brain to comprehensively identify the functional methylation sites and their corresponding regulatory target genes, and further evaluate their synergic contribution to the development of psychotic disorders. Recent evidence also suggests that the cerebellum plays important roles in psychotic diseases (19, 20). Schmitt et al. (21) observed altered expression of NMDA receptor subunits in the cerebellum of patients with schizophrenia, and Smolin et al. (22) found aberrant expression patterns of genes encoding neuronal ion-channel subunits in the cerebellum in several psychiatric disorders. Our recent whole-genome expression study also detected an expression module in the cerebellum associated with schizophrenia and bipolar disorder (23). Those findings suggest the existence of novel biomarkers for neuropsychiatric conditions in the cerebellum, indicating its potential significant role in the susceptibility to mental disorders, perhaps through robust projections to multiple neocortical regions. It is also possible that gene expression profiles in the cerebellum are shared with other brain regions directly relevant to neuropsychiatric conditions (19, 23). In either case, the cerebellum may serve as a target brain region in the discovery of biomarkers for psychiatric diseases. In this study, using cerebellum brain samples we aimed to identify candidate genes with differential methylation and expression patterns in psychotic patients. We did not observe generalized differences between CpG methylation in the brains of patients versus normal controls, when applying strict statistical criteria. We then tested whether disease-associated changes in CpG methylation were present when methylation levels were correlated with the expression of the corresponding genes. With this in mind, we narrowed our search to correlated changes in DNA methylation and gene expression profiles by considering any CpGs located in cis regions of each gene, that is, the CpG Gene Pairs (CGPs). Next, we analyzed the identified gene with cis-positioned CpGs for differential expression in psychotic patients versus normal controls. Finally, the genes with a correlated CpGs and differential expression were examined for differential methylation in psychotic patients. The correlations between differential expression, methylation, and neuropsychiatric conditions were further tested in independent datasets.

Schizophrenia and bipolar disorder are highly heritable psychiatric disorders. Associated genetic and gene expression changes have been identified, but many have not been replicated and have unknown functions. We identified groups of genes whose expressions varied together, i.e. co-expression modules, then tested them for association with schizophrenia. Using Weighted Gene Co-expression Network Analysis, we show that two modules were differentially expressed in patients versus controls. One, up-regulated in cerebral cortex, was enriched with neuron differentiation and neuron development genes, as well as disease GWAS genetic signals; the second, altered in cerebral cortex and cerebellum, was enriched with genes involved in neuron protection functions. The findings were preserved in five expression data sets, including sets from three brain regions, from a different microarray platform, and from bipolar disorder patients. From those observations, we propose neuron differentiation and development pathways may be involved in etiologies of both schizophrenia and bipolar disorder, and neuron protection function participates in pathological process of the diseases.

Variations in gene expression among individuals may have multiple downstream implications, including an effect on disease risk. “Genetical genomics” (or expression genetics) uses linkage and association methods to map gene expression phenotypes, connecting genetic variants to expression quantitative trait loci (eQTLs). It represents a promising approach to identifying novel expression regulatory elements in the genome. Studies of human lymphoblastoid cell lines1, liver2 and brain3 have also been reported. Meyers et al.3 studied 193 neuropathologically normal human brain samples from three cortical regions using the Affymetrix 500K Array for genotyping and the Illumina HumanRefseq-8 Expression Array for gene expression measurements. They assessed association between 366,140 SNPs and the expression of 14,078 transcripts, and identified 433 SNP-transcript pairs (99 transcripts) that showed significant cis-association (transcript-specific empirical P value ≤ 0.05); but only 25 of them (involving two genes, KIF1B and IPP) are significant after correcting for all the SNPs and phenotypes (transcripts) tested (Sidak multitranscript-corrected empirical P values ≤ 0.05). We would consider only the two genes truly significant cis- associations as they were the ones surviving correction for all the statistical tests.

We tested whether telomere length is altered in the brains of patients diagnosed with major depression (MD), bipolar disorder (BD) and schizophrenia (SZ) by measuring mean telomere length (mTL) with real-time PCR. The samples are cerebellar gray matter from 46 SZ, 46 BP, and 15 MD patients, and 48 healthy controls. We found no difference in mTL between SZ and controls, BD and controls, MD and controls, or all cases and controls; no correlation between mTL and age was observed, either. This suggests that brain gray matter is unlikely to be related to the telomere length shortening reported in blood of psychiatric patients. White matter deserves further investigation as it has been reported to have a different mTL dynamic from gray matter. Since mTL has been reported to be a heritable quantitative trait, we also carried out genome-wide mapping of genetic factors for mTL, treating mTL as a quantitative trait. No association survived correction of multiple testing for the number of SNPs studied. The previously reported rs2630578 (BICD1) association was not replicated. This suggests that telomere length of cerebellar gray matter is determined by multiple loci with “weak effects.”

At present, about a quarter of all genes in most genomes contain transmembrane (TM) helices, and among the overall cellular interactome, helical membrane protein interaction is a major component. Interactions between membrane proteins play a significant role in a variety of cellular phenomena, including the transduction of signals across membranes, the transfer of membrane proteins between the plasma membrane and internal organelles, and the assembly of oligomeric protein structures. However, current experimental techniques for large-scale detection of protein-protein interactions are biased against membrane proteins. In this paper, we construct membrane protein interaction network based on gene regulatory network model. GRN model is proposed to understand the dynamic and collective control of developmental process and the characters of membrane protein interaction network, including small-world network, scale free distributing and robustness, and its significance for biology. The proposed method is proved to be effective for the study of membrane protein interaction network. The results show that the approach holds a high potential to become a useful tool in prediction of membrane protein interactions.

We have observed extensive interindividual differences in DNA methylation of 8590 CpG sites of 6229 genes in 153 human adult cerebellum samples, enriched in CpG island “shores” and at further distances from CpG islands. To search for genetic factors that regulate this variation, we performed a genome-wide association study (GWAS) mapping of methylation quantitative trait loci (mQTLs) for the 8590 testable CpG sites. cis association refers to correlation of methylation with SNPs within 1 Mb of a CpG site. 736 CpG sites showed phenotype-wide significant cis association with 2878 SNPs (after permutation correction for all tested markers and methylation phenotypes). In trans analysis of methylation, which tests for distant regulation effects, associations of 12 CpG sites and 38 SNPs remained significant after phenotype-wide correction. To examine the functional effects of mQTLs, we analyzed 85 genes that were with genetically regulated methylation we observed and for which we had quality gene expression data. Ten genes showed SNP-methylation-expression three-way associations—the same SNP simultaneously showed significant association with both DNA methylation and gene expression, while DNA methylation was significantly correlated with gene expression. Thus, we demonstrated that DNA methylation is frequently a heritable continuous quantitatively variable trait in human brain. Unlike allele-specific methylation, genetic polymorphisms mark both cis- and trans-regulatory genetic sites at measurable distances from their CpG sites. Some of the genetically regulated DNA methylation is directly connected with genetically regulated gene expression variation.

High mammalian gene expression was obtained for more than twenty different proteins in different cell types by just a few laboratory scale stable gene transfections for each protein. The stable expression vectors were constructed by inserting a naturally-occurring 1.006 kb or a synthetic 0.733 kb DNA fragment (including intron) of extremely GC-rich at the 5′ or/and 3′ flanking regions of these protein genes or their gene promoters. This experiment is the first experimental evidence showing that a non-coding extremely GC-rich DNA fragment is a super “chromatin opening element” and plays an important role in mammalian gene expression. This experiment has further indicated that chromatin-based regulation of mammalian gene expression is at least partially embedded in DNA primary structure, namely DNA GC-content.

A gene coding a novel isoform of DEAD-box protein named testicular DEAD-box protein (tDbp), presumably involved in testicular function, was identified and characterized. Testicular DEAD-box protein was cloned from a human testis cDNA library. The cDNA microarray hybridization showed that it was expressed at a higher level in adult testis than in embryo testis. Reverse transcription-polymerase chain reaction indicated that tDbp was specifically expressed in testis, but not in some other tissues.