Sex-Specific Whole-Transcriptome Analysis in the Cerebral Cortex of FAE Offspring.

Fetal alcohol spectrum disorders (FASDs) are associated with systemic inflammation and neurodevelopmental abnormalities. Several candidate genes were found to be associated with fetal alcohol exposure (FAE)-associated behaviors, but a sex-specific complete transcriptomic analysis was not performed at the adult stage. Recent studies have shown that they are regulated at the developmental stage. However, the sex-specific role of RNA in FAE offspring brain development and function has not been studied yet. Here, we carried out the first systematic RNA profiling by utilizing a high-throughput transcriptomic (RNA-seq) approach in response to FAE in the brain cortex of male and female offspring at adulthood (P60). Our RNA-seq data analysis suggests that the changes in RNA expression in response to FAE are marked sex-specific. We show that the genes Muc3a, Pttg1, Rec8, Clcnka, Capn11, and pnp2 exhibit significantly higher expression in the male offspring than in the female offspring at P60. FAE female mouse brain sequencing data also show an increased expression of Eno1, Tpm3, and Pcdhb2 compared to male offspring. We performed a pathway analysis using a commercial software package (Ingenuity Pathway Analysis). We found that the sex-specific top regulator genes (Rictor, Gaba, Fmri, Mlxipl) are highly associated with eIF2 (translation initiation), synaptogenesis (the formation of synapses between neurons in the nervous system), sirtuin (metabolic regulation), and estrogen receptor (involved in obesity, aging, and cancer) signaling. Taken together, our transcriptomic results demonstrate that FAE differentially alters RNA expression in the adult brain in a sex-specific manner.

Cell-free DNA in maternal blood and artificial intelligence: accurate prenatal detection of fetal congenital heart defects.

BACKGROUND: DNA cytosine nucleotide methylation (epigenomics and epigenetics) is an important mechanism for controlling gene expression in cardiac development. Combined artificial intelligence and whole-genome epigenomic analysis of circulating cell-free DNA in maternal blood has the potential for the detection of fetal congenital heart defects. OBJECTIVE: This study aimed to use genome-wide DNA cytosine methylation and artificial intelligence analyses of circulating cell-free DNA for the minimally invasive detection of fetal congenital heart defects. STUDY DESIGN: In this prospective study, whole-genome cytosine nucleotide methylation analysis was performed on circulating cell-free DNA using the Illumina Infinium MethylationEPIC BeadChip array. Multiple artificial intelligence approaches were evaluated for the detection of congenital hearts. The Ingenuity Pathway Analysis program was used to identify gene pathways that were epigenetically altered and important in congenital heart defect pathogenesis to further elucidate the pathogenesis of isolated congenital heart defects. RESULTS: There were 12 cases of isolated nonsyndromic congenital heart defects and 26 matched controls. A total of 5918 cytosine nucleotides involving 4976 genes had significantly altered methylation, that is, a P value of <.05 along with ≥5% whole-genome cytosine nucleotide methylation difference, in congenital heart defect cases vs controls. Artificial intelligence analysis of the methylation data achieved excellent congenital heart defect predictive accuracy (areas under the receiver operating characteristic curve, ≥0.92). For example, an artificial intelligence model using a combination of 5 whole-genome cytosine nucleotide markers achieved an area under the receiver operating characteristic curve of 0.97 (95% confidence interval, 0.87-1.0) with 98% sensitivity and 94% specificity. We found epigenetic changes in genes and gene pathways involved in the following important cardiac developmental processes: "cardiovascular system development and function," "cardiac hypertrophy," "congenital heart anomaly," and "cardiovascular disease." This lends biologic plausibility to our findings. CONCLUSION: This study reported the feasibility of minimally invasive detection of fetal congenital heart defect using artificial intelligence and DNA methylation analysis of circulating cell-free DNA for the prediction of fetal congenital heart defect. Furthermore, the findings supported an important role of epigenetic changes in congenital heart defect development.

A Review of Radiation-Induced Alterations of Multi-Omic Profiles, Radiation Injury Biomarkers, and Countermeasures.

Increasing utilization of nuclear power enhances the risks associated with industrial accidents, occupational hazards, and the threat of nuclear terrorism. Exposure to ionizing radiation interferes with genomic stability and gene expression resulting in the disruption of normal metabolic processes in cells and organs by inducing complex biological responses. Exposure to high-dose radiation causes acute radiation syndrome, which leads to hematopoietic, gastrointestinal, cerebrovascular, and many other organ-specific injuries. Altered genomic variations, gene expression, metabolite concentrations, and microbiota profiles in blood plasma or tissue samples reflect the whole-body radiation injuries. Hence, multi-omic profiles obtained from high-resolution omics platforms offer a holistic approach for identifying reliable biomarkers to predict the radiation injury of organs and tissues resulting from radiation exposures. In this review, we performed a literature search to systematically catalog the radiation-induced alterations from multi-omic studies and radiation countermeasures. We covered radiation-induced changes in the genomic, transcriptomic, proteomic, metabolomic, lipidomic, and microbiome profiles. Furthermore, we have covered promising multi-omic biomarkers, FDA-approved countermeasure drugs, and other radiation countermeasures that include radioprotectors and radiomitigators. This review presents an overview of radiation-induced alterations of multi-omics profiles and biomarkers, and associated radiation countermeasures.

A novel dual epigenetic approach targeting BET proteins and HDACs in Group 3 (MYC-driven) Medulloblastoma.

BACKGROUND: Medulloblastoma (MB) patients with MYC oncogene amplification or overexpression exhibit extremely poor clinical outcomes and respond poorly to current therapies. Epigenetic deregulation is very common in MYC-driven MB. The bromodomain extra-terminal (BET) proteins and histone deacetylases (HDACs) are epigenetic regulators of MYC transcription and its associated tumorigenic programs. This study aimed to investigate the therapeutic potential of inhibiting the BET proteins and HDACs together in MB. METHODS: Using clinically relevant BET inhibitors (JQ1 or OTX015) and a pan-HDAC inhibitor (panobinostat), we evaluated the effects of combined inhibition on cell growth/survival in MYC-amplified MB cell lines and xenografts and examined underlying molecular mechanism(s). RESULTS: Co-treatment of JQ1 or OTX015 with panobinostat synergistically suppressed growth/survival of MYC-amplified MB cells by inducing G2 cell cycle arrest and apoptosis. Mechanistic investigation using RNA-seq revealed that co-treatment of JQ1 with panobinostat synergistically modulated global gene expression including MYC/HDAC targets. SYK and MSI1 oncogenes were among the top 50 genes synergistically downregulated by JQ1 and panobinostat. RT-PCR and western blot analyses confirmed that JQ1 and panobinostat synergistically inhibited the mRNA and protein expression of MSI1/SYK along with MYC expression. Reduced SYK/MSI expression after BET (specifically, BRD4) gene-knockdown further confirmed the epigenetic regulation of SYK and MSI1 genes. In addition, the combination of OTX015 and panobinostat significantly inhibited tumor growth in MYC-amplified MB xenografted mice by downregulating expression of MYC, compared to single-agent therapy. CONCLUSIONS: Together, our findings demonstrated that dual-inhibition of BET and HDAC proteins of the epigenetic pathway can be a novel therapeutic approach against MYC-driven MB.

The Mammalian Ecdysoneless Protein Interacts with RNA Helicase DDX39A To Regulate Nuclear mRNA Export.

The mammalian orthologue of ecdysoneless (ECD) protein is required for embryogenesis, cell cycle progression, and mitigation of endoplasmic reticulum stress. Here, we identified key components of the mRNA export complexes as binding partners of ECD and characterized the functional interaction of ECD with key mRNA export-related DEAD BOX protein helicase DDX39A. We find that ECD is involved in RNA export through its interaction with DDX39A. ECD knockdown (KD) blocks mRNA export from the nucleus to the cytoplasm, which is rescued by expression of full-length ECD but not an ECD mutant that is defective in interaction with DDX39A. We have previously shown that ECD protein is overexpressed in ErbB2+ breast cancers (BC). In this study, we extended the analyses to two publicly available BC mRNA The Cancer Genome Atlas (TCGA) and Molecular Taxonomy of Breast Cancer International Consortium (METABRIC) data sets. In both data sets, ECD mRNA overexpression correlated with short patient survival, specifically ErbB2+ BC. In the METABRIC data set, ECD overexpression also correlated with poor patient survival in triple-negative breast cancer (TNBC). Furthermore, ECD KD in ErbB2+ BC cells led to a decrease in ErbB2 mRNA level due to a block in its nuclear export and was associated with impairment of oncogenic traits. These findings provide novel mechanistic insight into the physiological and pathological functions of ECD.

Red panda: a novel method for detecting variants in single-cell RNA sequencing.

BACKGROUND: Single-cell sequencing enables us to better understand genetic diseases, such as cancer or autoimmune disorders, which are often affected by changes in rare cells. Currently, no existing software is aimed at identifying single nucleotide variations or micro (1-50 bp) insertions and deletions in single-cell RNA sequencing (scRNA-seq) data. Generating high-quality variant data is vital to the study of the aforementioned diseases, among others. RESULTS: In this study, we report the design and implementation of Red Panda, a novel method to accurately identify variants in scRNA-seq data. Variants were called on scRNA-seq data from human articular chondrocytes, mouse embryonic fibroblasts (MEFs), and simulated data stemming from the MEF alignments. Red Panda had the highest Positive Predictive Value at 45.0%, while other tools-FreeBayes, GATK HaplotypeCaller, GATK UnifiedGenotyper, Monovar, and Platypus-ranged from 5.8-41.53%. From the simulated data, Red Panda had the highest sensitivity at 72.44%. CONCLUSIONS: We show that our method provides a novel and improved mechanism to identify variants in scRNA-seq as compared to currently existing software. However, methods for identification of genomic variants using scRNA-seq data can be still improved.

Pan-Cancer Analysis Reveals the Diverse Landscape of Novel Sense and Antisense Fusion Transcripts.

Gene fusions that contribute to oncogenicity can be explored for identifying cancer biomarkers and potential drug targets. To investigate the nature and distribution of fusion transcripts in cancer, we examined the transcriptome data of about 9,000 primary tumors from 33 different cancers in TCGA (The Cancer Genome Atlas) along with cell line data from CCLE (Cancer Cell Line Encyclopedia) using ChimeRScope, a novel fusion detection algorithm. We identified several fusions with sense (canonical, 39%) or antisense (non-canonical, 61%) transcripts recurrent across cancers. The majority of the recurrent non-canonical fusions found in our study are novel, unexplored, and exhibited highly variable profiles across cancers, with breast cancer and glioblastoma having the highest and lowest rates, respectively. Overall, 4,344 recurrent fusions were identified from TCGA in this study, of which 70% were novel. Additional analysis of 802 tumor-derived cell line transcriptome data across 20 cancers revealed significant variability in recurrent fusion profiles between primary tumors and corresponding cell lines. A subset of canonical and non-canonical fusions was validated by examining the structural variation evidence in whole-genome sequencing (WGS) data or by Sanger sequencing of fusion junctions. Several recurrent fusion genes identified in our study show promise for drug repurposing in basket trials and present opportunities for mechanistic studies.

Loss of the Nuclear Pool of Ubiquitin Ligase CHIP/STUB1 in Breast Cancer Unleashes the MZF1-Cathepsin Pro-oncogenic Program.

CHIP/STUB1 ubiquitin ligase is a negative co-chaperone for HSP90/HSC70, and its expression is reduced or lost in several cancers, including breast cancer. Using an extensive and well-annotated breast cancer tissue collection, we identified the loss of nuclear but not cytoplasmic CHIP to predict more aggressive tumorigenesis and shorter patient survival, with loss of CHIP in two thirds of ErbB2+ and triple-negative breast cancers (TNBC) and in one third of ER+ breast cancers. Reduced CHIP expression was seen in breast cancer patient-derived xenograft tumors and in ErbB2+ and TNBC cell lines. Ectopic CHIP expression in ErbB2+ lines suppressed in vitro oncogenic traits and in vivo xenograft tumor growth. An unbiased screen for CHIP-regulated nuclear transcription factors identified many candidates whose DNA-binding activity was up- or downregulated by CHIP. We characterized myeloid zinc finger 1 (MZF1) as a CHIP target, given its recently identified role as a positive regulator of cathepsin B/L (CTSB/L)-mediated tumor cell invasion downstream of ErbB2. We show that CHIP negatively regulates CTSB/L expression in ErbB2+ and other breast cancer cell lines. CTSB inhibition abrogates invasion and matrix degradation in vitro and halts ErbB2+ breast cancer cell line xenograft growth. We conclude that loss of CHIP remodels the cellular transcriptome to unleash critical pro-oncogenic pathways, such as the matrix-degrading enzymes of the cathepsin family, whose components can provide new therapeutic opportunities in breast and other cancers with loss of CHIP expression.Significance: These findings reveal a novel targetable pathway of breast oncogenesis unleashed by the loss of tumor suppressor ubiquitin ligase CHIP/STUB1. Cancer Res; 78(10); 2524-35. ©2018 AACR.

Exome analysis reveals differentially mutated gene signatures of stage, grade and subtype in breast cancers.

Breast cancers exhibit highly heterogeneous molecular profiles. Although gene expression profiles have been used to predict the risks and prognostic outcomes of breast cancers, the high variability of gene expression limits its clinical application. In contrast, genetic mutation profiles would be more advantageous than gene expression profiles because genetic mutations can be stably detected and the mutational heterogeneity widely exists in breast cancer genomes. We analyzed 98 breast cancer whole exome samples that were sorted into three subtypes, two grades and two stages. The sum deleterious effect of all mutations in each gene was scored to identify differentially mutated genes (DMGs) for this case-control study. DMGs were corroborated using extensive published knowledge. Functional consequences of deleterious SNVs on protein structure and function were also investigated. Genes such as ERBB2, ESP8, PPP2R4, KIAA0922, SP4, CENPJ, PRCP and SELP that have been experimentally or clinically verified to be tightly associated with breast cancer prognosis are among the DMGs identified in this study. We also identified some genes such as ARL6IP5, RAET1E, and ANO7 that could be crucial for breast cancer development and prognosis. Further, SNVs such as rs1058808, rs2480452, rs61751507, rs79167802, rs11540666, and rs2229437 that potentially influence protein functions are observed at significantly different frequencies in different comparison groups. Protein structure modeling revealed that many non-synonymous SNVs have a deleterious effect on protein stability, structure and function. Mutational profiling at gene- and SNV-level revealed differential patterns within each breast cancer comparison group, and the gene signatures correlate with expected prognostic characteristics of breast cancer classes. Some of the genes and SNVs identified in this study show high promise and are worthy of further investigation by experimental studies.

Identification of ATP binding residues of a protein from its primary sequence.

BACKGROUND: One of the major challenges in post-genomic era is to provide functional annotations for large number of proteins arising from genome sequencing projects. The function of many proteins depends on their interaction with small molecules or ligands. ATP is one such important ligand that plays critical role as a coenzyme in the functionality of many proteins. There is a need to develop method for identifying ATP interacting residues in a ATP binding proteins (ABPs), in order to understand mechanism of protein-ligands interaction. RESULTS: We have compared the amino acid composition of ATP interacting and non-interacting regions of proteins and observed that certain residues are preferred for interaction with ATP. This study describes few models that have been developed for identifying ATP interacting residues in a protein. All these models were trained and tested on 168 non-redundant ABPs chains. First we have developed a Support Vector Machine (SVM) based model using primary sequence of proteins and obtained maximum MCC 0.33 with accuracy of 66.25%. Secondly, another SVM based model was developed using position specific scoring matrix (PSSM) generated by PSI-BLAST. The performance of this model was improved significantly (MCC 0.5) from the previous one, where only the primary sequence of the proteins were used. CONCLUSION: This study demonstrates that it is possible to predict 'ATP interacting residues' in a protein with moderate accuracy using its sequence. The evolutionary information is important for the identification of 'ATP interacting residues', as it provides more information compared to the primary sequence. This method will be useful for researchers studying ATP-binding proteins. Based on this study, a web server has been developed for predicting 'ATP interacting residues' in a protein http://www.imtech.res.in/raghava/atpint/.