Biology subjects

Greene, C.

Publications and source records attributed to Greene, C..

4 recordsLinked to original sources

bioRxiv2018

Specific histone modifications associate with alternative exon selection during mammalian development

Alternative splicing (AS) is frequent during early mouse embryonic development. Specific histone post-translational modifications (hPTMs) have been shown to regulate exon splicing by either directly recruiting splice machinery or indirectly modulating transcriptional elongation. In this study, we hypothesized that hPTMs regulate expression of alternatively spliced genes for specific processes during differentiation. To address this notion, we applied an innovative machine learning approach to relate global hPTM enrichment to AS regulation during mammalian tissue development. We found that specific histone modifications, H3K36me3 and H3K4me1, play a dominant role in skipped exon selection among all the tissues and developmental time points examined. In addition, we used iterative random forest model to identify interactions of several hPTMs that associated with skipped exon selection during tissue development. Collectively, our data demonstrated a link between hPTMs and alternative splicing which will drive further experimental studies on the functional relevance of these modifications to alternative splicing.

bioinformatics↗

bioRxiv2017

A Multimodal Strategy Used By A Large c-di-GMP Network

The Pseudomonas fluorescens genome encodes for 50+ proteins involved in -di-GMP signaling. Here, we demonstrate that when tested across 188 nutrients, these enzymes and effectors appear capable of impacting biofilm formation. Transcriptional analysis of network members across [~]50 nutrient conditions indicates that altered gene expression can explain a subset, but not all, of biofilm-formation responses to the nutrients. Additional organization of the network is likely achieved through physical interaction, as determined via probing [~]2000 interactions by bacterial two-hybrid assays. Our analysis revealed a multimodal regulatory strategy, using combinations of ligand-mediated signals, protein-protein interaction and/or transcriptional regulation to further fine-tune c-di-GMP-mediated responses. These results create a profile of a large c-di-GMP network that is used to make important cellular decisions, opening the door to future model building and the ability to engineer this complex circuitry in other bacteria.\n\nAbstract ImportanceCyclic diguanylate (c-di-GMP) is a key signalling molecule regulating bacterial biofilm formation, and many microbes have up to dozens of proteins that make, break or bind this dinucleotide. Thus, a major open question in the field is how signalling specificity is conferred in this context with a soluble signalling molecule. Here, we take a systems approach, using mutational analysis, transcriptional studies and bacterial two-hybrid analysis to interrogate this network. We find that the network typically combines two or more modes of regulation (i.e., transcriptional control with protein-protein interaction) to generate an observed output.

microbiology↗

bioRxiv2017

Enter the matrix: Interpreting unsupervised feature learning with matrix decomposition to discover hidden knowledge in high-throughput omics data

Omics data contains signal from the molecular, physical, and kinetic inter- and intra-cellular interactions that control biological systems. Matrix factorization techniques can reveal low-dimensional structure from high-dimensional data that reflect these interactions. These techniques can uncover new biological knowledge from diverse high-throughput omics data in topics ranging from pathway discovery to time course analysis. We review exemplary applications of matrix factorization for systems-level analyses. We discuss appropriate application of these methods, their limitations, and focus on analysis of results to facilitate optimal biological interpretation. The inference of biologically relevant features with matrix factorization enables discovery from high-throughput data beyond the limits of current biological knowledge--answering questions from high-dimensional data that we have not yet thought to ask.

systems biology↗

bioRxiv2017

Machine learning analysis identifies Drosophila Grunge/Atrophin as an important learning and memory gene required for memory retention and social learning.

High throughput experiments are becoming increasingly common, and scientists must balance hypothesis driven experiments with genome wide data acquisition. We sought to predict novel genes involved in Drosophila learning and long-term memory from existing public high-throughput data. We performed an analysis using PILGRM, which analyzes public gene expression compendia using machine learning. We evaluated the top prediction alongside genes involved in learning and memory in IMP, an interface for functional relationship networks. We identified Grunge/Atrophin (Gug/Atro), a transcriptional repressor, histone deacetylase, as our top candidate. We find, through multiple, distinct assays, that Gug has an active role as a modulator of memory retention in the fly and its function is required in the adult mushroom body. Depletion of Gug specifically in neurons of the adult mushroom body, after cell division and neuronal development is complete, suggests that Gug function is important for memory retention through regulation of neuronal activity, and not by altering neurodevelopment. Our study provides a previously uncharacterized role for Gug as a possible regulator of neuronal plasticity at the interface of memory retention and memory extinction.

genetics↗