Biology subjects

Clark, N.

Publications and source records attributed to Clark, N..

6 recordsLinked to original sources

bioRxiv2018

Cheminformatics tools for analyzing and designing optimized small molecule libraries

Libraries of highly annotated small molecules have many uses in chemical genetics, drug discovery and drug repurposing. Many such libraries have become available, but few data-driven approaches exist to compare these libraries and design new ones. In this paper, we describe such an approach that makes use of data on binding selectivity, target coverage and induced cellular phenotypes as well as chemical structure and stage of clinical development. We implement the approach as R software and a Web-accessible tool (http://www.smallmoleculesuite.org) that uses incomplete and often confounded public data in combination with user preferences to score and create libraries. Analysis of six kinase inhibitor libraries using our approach reveals dramatic differences among them, leading us to design a new LSP-OptimalKinase library that outperforms all previous collections in terms of target coverage and compact size. We also assemble a mechanism of action library that optimally covers 1852 targets of the liganded genome. Using our tools, individual research groups and companies can quickly analyze private compound collections and public libraries can be progressively improved using the latest data.

systems biology↗

bioRxiv2018

TuxNet: A simple interface to process RNA sequencing data and infer gene regulatory networks

Predicting gene regulatory networks (GRNs) from gene expression profiles has become a common approach for identifying important biological regulators. Despite the increase in the use of inference methods, existing computational approaches do not integrate RNA-sequencing data analysis, are often not automated, and are restricted to users with bioinformatics and programming backgrounds. To address these limitations, we have developed TuxNet, an integrated user-friendly platform, which, with just a few selections, allows to process raw RNA-sequencing data (using the Tuxedo pipeline) and infer GRNs from these processed data. TuxNet is implemented as a graphical user interface and, using expression data from any organism with an existing reference genome, can mine the regulations among genes either by applying a dynamic Bayesian network inference algorithm, GENIST, or a regression tree-based pipeline that uses spatiotemporal data, RTP-STAR. To illustrate the use of TuxNet while getting insight into the regulatory cascade downstream of the Arabidopsis root stem cell regulator PERIANTHIA (PAN), we obtained time course gene expression data of a PAN inducible line and inferred a GRN using GENIST. Using RTP-STAR, we then inferred the network of a PAN secondary downstream gene, ATHB13, for which we obtained wildtype and mutant expression profiles. Our case studies feature the versatility of TuxNet to infer networks using different types of gene expression data (i.e time course and steady-state data) as well as how inference networks are used to identify important regulators.\n\nSUMMARYTuxNet offers a simple interface for non-computational biologists to infer GRNs from raw RNA-seq data.

plant biology↗

bioRxiv2018

Systematic Discovery of the Functional Impact of Somatic Genome Alterations in Individual Tumors through Tumor-specific Causal Inference

We report a tumor-specific causal inference (TCI) framework, which discovers causative somatic genome alterations (SGAs) through inferring causal relationships between SGAs and molecular phenotypes (e.g., transcriptomic, proteomic, or metabolomic changes) within an individual tumor. We applied the TCI algorithm to tumors from The Cancer Genome Atlas (TCGA) and identified those SGAs that causally regulate the differentially expressed genes (DEGs) within each tumor. Overall, TCI identified 634 SGAs that cause cancer-related DEGs in a significant number of tumors, including most of the previously known drivers and many novel candidate cancer drivers. The inferred causal relationships are statistically robust and biologically sensible, and multiple lines of experimental evidence support the predicted functional impact of both well-known and novel candidate drivers. By identifying major candidate drivers and revealing their functional impact in a tumor, TCI shed light on disease mechanisms of each tumor, providing useful information for advancing cancer biology and precision oncology.\n\nSignificance statementsCancer is mainly caused by SGAs. Precision oncology involves identifying and targeting tumor-specific aberrations resulting from causative SGAs. TCI is a novel computational framework for discovering the causative SGAs and their impact on oncogenic processes, thus revealing tumor-specific disease mechanisms. This information can be used to guide precision oncology.

cancer biology↗

bioRxiv2017

A butterfly chromonome reveals selection dynamics during extensive and cryptic chromosomal reshuffling

Chromosome evolution, an important component of mico- and macroevolutionary dynamics 1-5, presents an enigma in the mega-diverse Lepidoptera6. While most species exhibit constrained chromosome evolution, with nearly identical haploid chromosome counts and chromosome-level shared gene content and collinearity among species despite more than 140 Million years of divergence7, a small fraction of species independently exhibit dramatic changes in chromosomal count due to extensive fission and fusion events that are facilitated by their holocentric chromosomes7-9. Here we address this enigma of simultaneous conservation and dynamism in chromosome evolution in our analysis of the chromonome (chromosome level assembly10) of the green-veined white butterfly, Pieris napi (Pieridae, Linnaeus, 1758). We report an unprecedented reorganization of the standard Lepidopteran chromosome structure via more than 90 fission and fusion events that are cryptic at other scales, as the haploid chromosome number is identical to related genera and gene collinearity within the large rearranged segments matches other Lepidoptera. Furthermore, these rearranged segments are significantly enriched for clusters of functionally related genes and the maintenance of ancient telomeric ends. These results suggest an unexpected role for selection in shaping chromosomal evolution when the structural constraints of monocentricq chromosomes are relaxed.

evolutionary biology↗

bioRxiv2017

Paupar LncRNA Promotes KAP1 Dependent Chromatin Changes And Regulates Subventricular Zone Neurogenesis

Many long non-coding RNAs (lncRNAs) are expressed during central nervous system (CNS) development, yet their in vivo roles and molecular mechanisms of action remain poorly understood. Paupar, a CNS expressed lncRNA, controls neuroblastoma cell growth by binding and modulating the activity of genome-wide transcriptional regulatory elements. We show here that Paupar transcript directly binds KAP1, an essential epigenetic regulatory protein, and thereby regulates the expression of shared target genes important for proliferation and neuronal differentiation. Paupar promotes KAP1 chromatin occupancy and H3K9me3 deposition at a subset of distal targets, through formation of a DNA binding ribonucleoprotein complex containing Paupar, KAP1 and the PAX6 transcription factor. Paupar-KAP1 genome-wide co-occupancy reveals a 4-fold enrichment of overlap between Paupar and KAP1 bound sequences. Furthermore, both Paupar and Kap1 loss of function in vivo accelerates lineage progression in the mouse postnatal subventricular zone (SVZ) stem cell niche and disrupts olfactory bulb neurogenesis. These observations provide important conceptual insights into the trans-acting modes of lncRNA-mediated epigenetic regulation, the mechanisms of KAP1 genomic recruitment and identify Paupar and Kap1 as regulators of SVZ neurogenesis.

neuroscience↗

bioRxiv2017

The integrated stress response pathway activates 4E-BP to bias mRNA translation and boost antimicrobial peptide synthesis in response to bacterial infection

Pathogenic bacterial infection imposes considerable cellular stress on the host and often leads to attenuation of mRNA translation. In this translation-suppressive environment, it is unclear how the host synthesizes various antimicrobial peptides (AMPs) to mount innate immune response. Here, we use Drosophila as a model to demonstrate that AMP production during infection relies on a translation bias mechanism mediated by the inhibitor of cap-dependent translation 4E-BP (Drosophila Thor), and the AMP 5UTRs that can undergo cap-independent translation. We found that 4E-BP is induced upon infection with the pathogenic bacteria Ecc15 by the stress-responsive transcription factor ATF4, and its upstream kinase GCN2. Moreover, loss of gcn2, atf4 or 4e-bp compromised immunity against Ecc15. In 4E-BP mutants, the transcriptional induction of AMPs after infection was unaffected, while the protein levels of AMPs were substantially reduced in their hemolymph. Analysis of the 5UTRs of AMPs using cell-based bicistronic reporters and in vitro translation analysis indicated that AMPs are translated in a cap-independent mechanism. Analysis of bicistronic reporters in the presence of 4E-BP indicate that infection enhances cap-independent translational activity associated with AMP 5UTRs, accounting for enhanced AMP translation during infection.\n\nHighlights O_LI4E-BP is transcriptionally induced by GCN2/ATF signaling in response to bacterial infection\nC_LIO_LI4E-BP mutants show unaltered antimicrobial peptide (AMP) transcript levels, but have reduced AMP translation\nC_LIO_LIAMP 5UTRs are translated cap-independently\nC_LIO_LITranslation bias by 4E-BP drives cap-independent AMP translation\nC_LI

immunology↗