Biology subjects

Binder, H.

Publications and source records attributed to Binder, H..

6 recordsLinked to original sources

bioRxiv2018

PCF11 connects alternative polyadenylation to formation and spontaneous regression of neuroblastoma

Diversification at the transcriptome 3end is an important and evolutionarily conserved layer of gene regulation associated with differentiation and dedifferentiation processes. However the underlying mechanisms and functional consequences are poorly defined. Here, we identify extensive transcriptome-3end-alterations in neuroblastoma, a tumour entity with a paucity of recurrent somatic mutations and an unusually high frequency of spontaneous regression. Utilising extensive RNAi-screening we reveal the landscape and drivers of transcriptome-3end-diversification, discovering PCF11 as critical regulator, directing alternative polyadenylation (APA) of hundreds of transcripts including a differentiation RNA-operon. PCF11 shapes inputs converging on WNT-signalling, and governs cell cycle, proliferation, apoptosis and neurodifferentiation. Postnatal PCF11 down-regulation induces a neurodifferentiation program, and low-level PCF11 in neuroblastoma associates with favourable outcome and spontaneous tumour regression. Our findings document a critical role for APA in tumourigenesis and describe a novel mechanism for cell fate reprogramming in neuroblastoma with important clinical implications. An interactive data repository of transcriptome-wide APA covering >170 RNAis, and an APA-network map with regulatory hubs is provided.

developmental biology↗

bioRxiv2018

A deep learning approach for uncovering lung cancer immunome patterns

Tumor immune cell infiltration is a well known factor related to survival of cancer patients. This has led to deconvolution approaches that can quantify immune cell proportions for each individual. What is missing, is an approach for modeling joint patterns of different immune cell types. We adapt a deep learning approach, deep Boltzmann machines (DBMs), for modeling immune cell gene expression patterns in lung adenocarcinoma. Specifically, a partially partitioned training approach for dealing with a relatively large number of genes. We also propose a sampling-based approach that smooths the original data according to a trained DBM and can be used for visualization and clustering. The identified clusters can subsequently be judged with respect to association with clinical characteristics, such as tumor stage, providing an external criterion for selecting DBM network architecture and tuning parameters for training. We show that the hidden nodes of the trained networks cannot only be linked to clinical characteristics but also to specific genes, which are the visible nodes of the network. We find that hidden nodes that are linked to tumor stage and survival represent expression of T-cell and mast cell genes among others, probably reflecting specific immune cell infiltration patterns. Thus, DBMs, trained and selected by the proposed approach, might provide a useful tool for extracting immune cell gene expression patterns. In the case of lung adenocarcinomas, these patterns are linked to survival as well as other patient characteristics, which could be useful for uncovering the underlying biology.

bioinformatics↗

bioRxiv2018

The exon junction complex regulates the release and phosphorylation of paused RNA polymerase II

Promoter proximal pausing of RNA polymerase II (Pol II) is a widespread transcriptional regulatory step across metazoans. Here we find that the nuclear exon junction complex (pre-EJC) plays a critical and conserved role in this process. Depletion of pre-EJC subunits leads to a global decrease in Pol II pausing and to premature entry into elongation. This effect occurs, at least in part, via non-canonical recruitment of pre-EJC components at promoters. Failure to recruit the pre-EJC at promoters results in increased binding of the positive transcription elongation complex (P-TEFb) and in enhanced Pol II release. Notably, restoring pausing is sufficient to rescue exon skipping and the photoreceptor differentiation defect associated with depletion of pre-EJC components in vivo. We propose that the pre-EJC serves as an early transcriptional checkpoint to prevent premature entry into elongation, ensuring proper recruitment of RNA processing components that are necessary for exon definition.

molecular biology↗

bioRxiv2018

Systematic meta-analyses identify differentially expressed microRNAs in Parkinson’s disease

ObjectiveMicroRNA-mediated (dys)regulation of gene expression has been implicated in Parkinsons disease (PD), although results of microRNA expression studies remain inconclusive. We aimed to identify microRNAs that show consistent differential expression across all published expression studies in PD.\n\nMethodsWe performed a systematic literature search on microRNA expression studies in PD and extracted data from eligible publications. After stratification for brain, blood, and cerebrospinal fluid (CSF)-derived specimen we performed meta-analyses across microRNAs assessed in three or more independent datasets. Meta-analyses were performed using effect-size and p-value based methods, as applicable.\n\nResultsAfter screening 599 publications we identified 47 datasets eligible for meta-analysis. On these, we performed 160 meta-analyses on microRNAs quantified in brain (n=125), blood (n=31), or CSF samples (n=4). Twenty-one meta-analyses were performed using effect sizes. We identified 13 significantly (Bonferroni-adjusted =3.13x10-4) differentially expressed microRNAs in brain (n=3) and blood (n=10) with consistent effect directions across studies. The most compelling findings were with hsa-miR-132-3p (p=6.37x10-5), hsa-miR-497-5p (p=1.35x10-4), and hsa-miR-133b (p=1.90x10-4) in brain, and with hsa-miR-221-3p (p=4.49x10-35), hsa-miR-214-3p (p=2.00x10-34), and hsa-miR-29c-3p (p=3.00x10-12) in blood. No significant signals were found in CSF. Analyses of GWAS data for target genes of brain microRNAs showed significant association (=9.40x10-5) of genetic variants in nine loci.\n\nInterpretationWe identified several microRNAs that showed highly significant differential expression in PD. Future studies may assess the possible role of the identified brain miRNAs in pathogenesis and disease progression as well as the potential of the top blood microRNAs as biomarkers for diagnosis, progression or prediction of PD.

genetics↗

bioRxiv2017

Cerebral ischemia-induced genes are increased in acute schizophrenia: An opportunity for clinical translation of genomic research findings

Schizophrenia is a brain disorder of unknown etiology. Brain imaging studies have revealed evidence for hypoperfusion of the frontal cortex (hypofrontality) and progressive brain volume reduction in schizophrenic patients. Mild cerebral ischemia (oligemia) has been postulated as a cause of the disorder. If the ischemia hypothesis for the adult brain is correct, genes induced by cerebral ischemia should be increased in the frontal cortex of schizophrenic patients during acute psychosis. Here, we show for the first time through a combined analysis of gene expression data from all the studies of the Stanley Brain Collection covering the Brodmann area 46 of the frontal cortex and employing the well-established Affymetrix HGU133a microarray platform that genes upregulated by cerebral ischemia are significantly overexpressed (4.5-fold) in the frontal cortex of acute schizophrenic patients (representation factor (RF) 4.5, p < 0.0002) and to a lesser degree in chronic patients (RF 3.9, p < 0.008) in comparison to normal controls. Neurodevelopmental-, repair-, inflammation- and synapse-related genes showed no significant change. The difference between acute and chronic schizophrenic patients regarding cerebral ischemia-induced genes was highly significant (RF 2.8, p < 0.00007). The results reported here are in line with evidence from biochemical, cellular, electroencephalographic, brain imaging, cerebral near-infrared spectroscopy, vascular, and genetic association studies. In summary, our genomic analysis revealed a clear ischemic signature in the frontal cortex of schizophrenia patients, confirming the prediction of the adult ischemia hypothesis for this disorder. This finding suggests new possibilities for the treatment and prevention of schizophrenia.

neuroscience↗

bioRxiv2016

Partitioned learning of deep Boltzmann machines for SNP data

Learning the joint distributions of measurements, and in particular identification of an appropriate low-dimensional manifold, has been found to be a powerful ingredient of deep leaning approaches. Yet, such approaches have hardly been applied to single nucleotide polymorphism (SNP) data, probably due to the high number of features typically exceeding the number of studied individuals. After a brief overview of how deep Boltzmann machines (DBMs), a deep learning approach, can be adapted to SNP data in principle, we specifically present a way to alleviate the dimensionality problem by partitioned learning. We propose a sparse regression approach to coarsely screen the joint distribution of SNPs, followed by training several DBMs on SNP partitions that were identified by the screening. Aggregate features representing SNP patterns and the corresponding SNPs are extracted from the DBMs by a combination of statistical tests and sparse regression. In simulated case-control data, we show how this can uncover complex SNP patterns and augment results from univariate approaches, while maintaining type 1 error control. Time-to-event endpoints are considered in an application with acute myeloid lymphoma patients, where SNP patterns are modeled after a pre-screening based on gene expression data. The proposed approach identified three SNPs that seem to jointly influence survival in a validation data set. This indicates the added value of jointly investigating SNPs compared to standard univariate analyses and makes partitioned learning of DBMs an interesting complementary approach when analyzing SNP data.

bioinformatics↗