Sharmistha Pal

Alternative transcription exceeds alternative splicing in generating the transcriptome diversity of cerebellar development

Despite our growing knowledge that many mammalian genes generate multiple transcript variants that may encode functionally distinct protein isoforms, the transcriptomes of various tissues and their developmental stages are poorly defined. Identifying the transcriptome and its regulation in a cell/tissue is the key to deciphering the cell/tissue-specific functions of a gene. We built a genome-wide inventory of noncoding and protein-coding transcripts (transcriptomes), their promoters (promoteromes) and histone modification states (epigenomes) for developing, and adult cerebella using integrative massive-parallel sequencing and bioinformatics approach. The data consists of 61,525 (12,796 novel) distinct mRNAs transcribed by 29,589 (4792 novel) promoters corresponding to 15,669 protein-coding and 7624 noncoding genes. Importantly, our results show that the transcript variants from a gene are predominantly generated using alternative transcriptional rather than splicing mechanisms, highlighting alternative promoters and transcriptional terminations as major sources of transcriptome diversity. Moreover, H3K4me3, and not H3K27me3, defined the use of alternative promoters, and we identified a combinatorial role of H3K4me3 and H3K27me3 in regulating the expression of transcripts, including transcript variants of a gene during development. We observed a strong bias of both H3K4me3 and H3K27me3 for CpG-rich promoters and an exponential relationship between their enrichment and corresponding transcript expression. Furthermore, the majority of genes associated with neurological diseases expressed multiple transcripts through alternative promoters, and we demonstrated aberrant use of alternative promoters in medulloblastoma, cancer arising in the cerebellum. The transcriptomes of developing and adult cerebella presented in this study emphasize the importance of analyzing gene regulation and function at the isoform level.

Imaging patterns predict patient survival and molecular subtype in glioblastoma via machine learning techniques.

BACKGROUND MRI characteristics of brain gliomas have been used to predict clinical outcome and molecular tumor characteristics. However, previously reported imaging biomarkers have not been sufficiently accurate or reproducible to enter routine clinical practice and often rely on relatively simple MRI measures. The current study leverages advanced image analysis and machine learning algorithms to identify complex and reproducible imaging patterns predictive of overall survival and molecular subtype in glioblastoma (GB). METHODS One hundred five patients with GB were first used to extract approximately 60 diverse features from preoperative multiparametric MRIs. These imaging features were used by a machine learning algorithm to derive imaging predictors of patient survival and molecular subtype. Cross-validation ensured generalizability of these predictors to new patients. Subsequently, the predictors were evaluated in a prospective cohort of 29 new patients. RESULTS Survival curves yielded a hazard ratio of 10.64 for predicted long versus short survivors. The overall, 3-way (long/medium/short survival) accuracy in the prospective cohort approached 80%. Classification of patients into the 4 molecular subtypes of GB achieved 76% accuracy. CONCLUSIONS By employing machine learning techniques, we were able to demonstrate that imaging patterns are highly predictive of patient survival. Additionally, we found that GB subtypes have distinctive imaging phenotypes. These results reveal that when imaging markers related to infiltration, cell density, microvascularity, and blood-brain barrier compromise are integrated via advanced pattern analysis methods, they form very accurate predictive biomarkers. These predictive markers used solely preoperative images, hence they can significantly augment diagnosis and treatment of GB patients.

Genome-wide mapping of RNA Pol-II promoter usage in mouse tissues by ChIP-seq

Alternative promoters that are differentially used in various cellular contexts and tissue types add to the transcriptional complexity in mammalian genome. Identification of alternative promoters and the annotation of their activity in different tissues is one of the major challenges in understanding the transcriptional regulation of the mammalian genes and their isoforms. To determine the use of alternative promoters in different tissues, we performed ChIP-seq experiments using antibody against RNA Pol-II, in five adult mouse tissues (brain, liver, lung, spleen and kidney). Our analysis identified 38 639 Pol-II promoters, including 12 270 novel promoters, for both protein coding and non-coding mouse genes. Of these, 6384 promoters are tissue specific which are CpG poor and we find that only 34% of the novel promoters are located in CpG-rich regions, suggesting that novel promoters are mostly tissue specific. By identifying the Pol-II bound promoter(s) of each annotated gene in a given tissue, we found that 37% of the protein coding genes use alternative promoters in the five mouse tissues. The promoter annotations and ChIP-seq data presented here will aid ongoing efforts of characterizing gene regulatory regions in mammalian genomes.

RP58/ZNF238 directly modulates proneurogenic gene levels and is required for neuronal differentiation and brain expansion

Although neurogenic pathways have been described in the developing neocortex, less is known about mechanisms ensuring correct neuronal differentiation thus also preventing tumor growth. We have shown that RP58 (aka zfp238 or znf238) is highly expressed in differentiating neurons, that its expression is lost or diminished in brain tumors, and that its reintroduction blocks their proliferation. Mice with loss of RP58 die at birth with neocortical defects. Using a novel conditional RP58 allele here we show that its CNS-specific loss yields a novel postnatal phenotype: microencephaly, agenesis of the corpus callosum and cerebellar hypoplasia that resembles the chr1qter deletion microcephaly syndrome in human. RP58 mutant brains maintain precursor pools but have reduced neuronal and increased glial differentiation. Well-timed downregulation of pax6, ngn2 and neuroD1 depends on RP58 mediated transcriptional repression, ngn2 and neuroD1 being direct targets. Thus, RP58 may act to favor neuronal differentiation and brain growth by coherently repressing multiple proneurogenic genes in a timely manner.

Isoform level expression profiles provide better cancer signatures than gene level expression profiles

BackgroundThe majority of mammalian genes generate multiple transcript variants and protein isoforms through alternative transcription and/or alternative splicing, and the dynamic changes at the transcript/isoform level between non-oncogenic and cancer cells remain largely unexplored. We hypothesized that isoform level expression profiles would be better than gene level expression profiles at discriminating between non-oncogenic and cancer cellsgene level.MethodsWe analyzed 160 Affymetrix exon-array datasets, comprising cell lines of non-oncogenic or oncogenic tissue origins. We obtained the transcript-level and gene level expression estimates, and used unsupervised and supervised clustering algorithms to study the profile similarity between the samples at both gene and isoform levels.ResultsHierarchical clustering, based on isoform level expressions, effectively grouped the non-oncogenic and oncogenic cell lines with a virtually perfect homogeneity-grouping rate (97.5%), regardless of the tissue origin of the cell lines. However, gene levelthis rate was much lower, being 75% at best based on the gene level expressions. Statistical analyses of the difference between cancer and non-oncogenic samples identified the existence of numerous genes with differentially expressed isoforms, which otherwise were not significant at the gene level. We also found that canonical pathways of protein ubiquitination, purine metabolism, and breast-cancer regulation by stathmin1 were significantly enriched among genes thatshow differential expression at isoform level but not at gene level.ConclusionsIn summary, cancer cell lines, regardless of their tissue of origin, can be effectively discriminated from non-cancer cell lines at isoform level, but not at gene level. This study suggests the existence of an isoform signature, rather than a gene signature, which could be used to distinguish cancer cells from normal cells.

Isoform-level gene signature improves prognostic stratification and accurately classifies glioblastoma subtypes

Molecular stratification of tumors is essential for developing personalized therapies. Although patient stratification strategies have been successful; computational methods to accurately translate the gene-signature from high-throughput platform to a clinically adaptable low-dimensional platform are currently lacking. Here, we describe PIGExClass (platform-independent isoform-level gene-expression based classification-system), a novel computational approach to derive and then transfer gene-signatures from one analytical platform to another. We applied PIGExClass to design a reverse transcriptase-quantitative polymerase chain reaction (RT-qPCR) based molecular-subtyping assay for glioblastoma multiforme (GBM), the most aggressive primary brain tumors. Unsupervised clustering of TCGA (the Cancer Genome Altas Consortium) GBM samples, based on isoform-level gene-expression profiles, recaptured the four known molecular subgroups but switched the subtype for 19% of the samples, resulting in significant (P = 0.0103) survival differences among the refined subgroups. PIGExClass derived four-class classifier, which requires only 121 transcript-variants, assigns GBM patients’ molecular subtype with 92% accuracy. This classifier was translated to an RT-qPCR assay and validated in an independent cohort of 206 GBM samples. Our results demonstrate the efficacy of PIGExClass in the design of clinically adaptable molecular subtyping assay and have implications for developing robust diagnostic assays for cancer patient stratification.

Annotation of gene promoters by integrative data-mining of ChIP-seq Pol-II enrichment data.

BackgroundUse of alternative gene promoters that drive widespread cell-type, tissue-type or developmental gene regulation in mammalian genomes is a common phenomenon. Chromatin immunoprecipitation methods coupled with DNA microarray (ChIP-chip) or massive parallel sequencing (ChIP-seq) are enabling genome-wide identification of active promoters in different cellular conditions using antibodies against Pol-II. However, these methods produce enrichment not only near the gene promoters but also inside the genes and other genomic regions due to the non-specificity of the antibodies used in ChIP. Further, the use of these methods is limited by their high cost and strong dependence on cellular type and context.MethodsWe trained and tested different state-of-art ensemble and meta classification methods for identification of Pol-II enriched promoter and Pol-II enriched non-promoter sequences, each of length 500 bp. The classification models were trained and tested on a bench-mark dataset, using a set of 39 different feature variables that are based on chromatin modification signatures and various DNA sequence features. The best performing model was applied on seven published ChIP-seq Pol-II datasets to provide genome wide annotation of mouse gene promoters.ResultsWe present a novel algorithm based on supervised learning methods to discriminate promoter associated Pol-II enrichment from enrichment elsewhere in the genome in ChIP-chip/seq profiles. We accumulated a dataset of 11,773 promoter and 46,167 non-promoter sequences, each of length 500 bp, generated from RNA Pol-II ChIP-seq data of five tissues (Brain, Kidney, Liver, Lung and Spleen). We evaluated the classification models in building the best predictor and found that Bagging and Random Forest based approaches give the best accuracy. We implemented the algorithm on seven different published ChIP-seq datasets to provide a comprehensive set of promoter annotations for both protein-coding and non-coding genes in the mouse genome. The resulting annotations contain 13,413 (4,747) protein-coding (non-coding) genes with single promoters and 9,929 (1,858) protein-coding (non-coding) genes with two or more alternative promoters, and a significant number of unassigned novel promoters.ConclusionOur new algorithm can successfully predict the promoters from the genome wide profile of Pol-II bound regions. In addition, our algorithm performs significantly better than existing promoter prediction methods and can be applied for genome-wide predictions of Pol-II promoters.

IsoformEx: isoform level gene expression estimation using weighted non-negative least squares from mRNA-Seq data

BackgroundmRNA-Seq technology has revolutionized the field of transcriptomics for identification and quantification of gene transcripts not only at gene level but also at isoform level. Estimating the expression levels of transcript isoforms from mRNA-Seq data is a challenging problem due to the presence of constitutive exons.ResultsWe propose a novel algorithm (IsoformEx) that employs weighted non-negative least squares estimation method to estimate the expression levels of transcript isoforms. Validations based on in silico simulation of mRNA-Seq and qRT-PCR experiments with real mRNA-Seq data showed that IsoformEx could accurately estimate transcript expression levels. In comparisons with published methods, the transcript expression levels estimated by IsoformEx showed higher correlation with known transcript expression levels from simulated mRNA-Seq data, and higher agreement with qRT-PCR measurements of specific transcripts for real mRNA-Seq data.ConclusionsIsoformEx is a fast and accurate algorithm to estimate transcript expression levels and gene expression levels, which takes into account short exons and alternative exons with a weighting scheme. The software is available at http://bioinformatics.wistar.upenn.edu/isoformex.

Shift in Monocyte Apoptosis with Increasing Viral Load and Change in Apoptosis-Related ISG/Bcl2 Family Gene Expression in Chronically HIV-1-Infected Subjects

ABSTRACT Although monocytes and macrophages are targets of HIV-1-mediated immunopathology, the impact of high viremia on activation-induced monocyte apoptosis relative to monocyte and macrophage activation changes remains undetermined. In this study, we determined constitutive and oxidative stress-induced monocyte apoptosis in uninfected and HIV+ individuals across a spectrum of viral loads (n = 35; range, 2,243 to 1,355,998 HIV-1 RNA copies/ml) and CD4 counts (range, 26 to 801 cells/mm3). Both constitutive apoptosis and oxidative stress-induced apoptosis were positively associated with viral load and negatively associated with CD4, with an elevation in apoptosis occurring in patients with more than 40,000 (4.6 log) copies/ml. As expected, expression of Rb1 and interferon-stimulated genes (ISGs), plasma soluble CD163 (sCD163) concentration, and the proportion of CD14++ CD16+ intermediate monocytes were elevated in viremic patients compared to those in uninfected controls. Although CD14++ CD16+ frequencies, sCD14, sCD163, and most ISG expression were not directly associated with a change in apoptosis, sCD14 and ISG expression showed an association with increasing viral load. Multivariable analysis of clinical values and monocyte gene expression identified changes in IFI27, IFITM2, Rb1, and Bcl2 expression as determinants of constitutive apoptosis (P = 3.77 × 10−5; adjusted R 2 = 0.5983), while changes in viral load, IFITM2, Rb1, and Bax expression were determinants of oxidative stress-induced apoptosis (P = 5.59 × 10−5; adjusted R 2 = 0.5996). Our data demonstrate differential activation states in monocytes between levels of viremia in association with differences in apoptosis that may contribute to greater monocyte turnover with high viremia. IMPORTANCE This study characterized differential monocyte activation, apoptosis, and apoptosis-related gene expression in low- versus high-level viremic HIV-1 patients, suggesting a shift in apoptosis regulation that may be associated with disease state. Using single and multivariable analysis of monocyte activation parameters and gene expression, we supported the hypothesis that monocyte apoptosis in HIV disease is a reflection of viremia and activation state with contributions from gene expression changes within the ISG and Bcl2 gene families. Understanding monocyte apoptosis response may inform HIV immunopathogenesis, retention of infected macrophages, and monocyte turnover in low- or high-viral-load states.

Genome-wide mapping of RNA Pol-II promoter usage in mouse tissues by ChIP-seq.

Chromatin immunoprecipitation (ChIP), using antibody against RNA Pol-II, followed by massive parallel sequencing (ChIP-seq) are invaluable techniques for genome-wide identification of alternative promoters and their patterns of use in different tissues, cell types, and/or developmental stages. However, the identification of promoters cannot be performed solely based on the presence of Pol-II enrichment on a genomic location because of its enrichment throughout the transcribed genomic region and lack of highly specific antibodies that can distinguish promoter-bound Pol-II from elongating Pol-II. In order to overcome this limitation, we developed a combined Pol-II ChIP-seq and bioinformatics promoter prediction approach to identify promoter regions and their activity in different mouse tissues. Here, we describe the integrative approach to identify alternative promoters in the mouse genome.