Methodology for the inference of gene function from phenotype data.

BackgroundBiomedical ontologies are increasingly instrumental in the advancement of biological research primarily through their use to efficiently consolidate large amounts of data into structured, accessible sets. However, ontology development and usage can be hampered by the segregation of knowledge by domain that occurs due to independent development and use of the ontologies. The ability to infer data associated with one ontology to data associated with another ontology would prove useful in expanding information content and scope. We here focus on relating two ontologies: the Gene Ontology (GO), which encodes canonical gene function, and the Mammalian Phenotype Ontology (MP), which describes non-canonical phenotypes, using statistical methods to suggest GO functional annotations from existing MP phenotype annotations. This work is in contrast to previous studies that have focused on inferring gene function from phenotype primarily through lexical or semantic similarity measures.ResultsWe have designed and tested a set of algorithms that represents a novel methodology to define rules for predicting gene function by examining the emergent structure and relationships between the gene functions and phenotypes rather than inspecting the terms semantically. The algorithms inspect relationships among multiple phenotype terms to deduce if there are cases where they all arise from a single gene function.We apply this methodology to data about genes in the laboratory mouse that are formally represented in the Mouse Genome Informatics (MGI) resource. From the data, 7444 rule instances were generated from five generalized rules, resulting in 4818 unique GO functional predictions for 1796 genes.ConclusionsWe show that our method is capable of inferring high-quality functional annotations from curated phenotype data. As well as creating inferred annotations, our method has the potential to allow for the elucidation of unforeseen, biologically significant associations between gene function and phenotypes that would be overlooked by a semantics-based approach. Future work will include the implementation of the described algorithms for a variety of other model organism databases, taking full advantage of the abundance of available high quality curated data. BMC Bioinformatics 2014; 15:405

Quantifying the local adaptive landscape of a nascent bacterial community

Fitness landscapes largely shape the dynamics of evolution, but it is unclear how they shift upon ecological diversification. By engineering genome-wide knockout libraries of a nascent bacterial community, Ascensao et al. show how ecological and epistatic patterns combine to shape adaptive landscapes

Non-monotonic Response to Monotonic Stimulus: Regulation of Glyoxylate Shunt Gene-Expression Dynamics in <i>Mycobacterium tuberculosis</i>

<div><p>Understanding how dynamical responses of biological networks are constrained by underlying network topology is one of the fundamental goals of systems biology. Here we employ monotone systems theory to formulate a theorem stating necessary conditions for non-monotonic time-response of a biochemical network to a monotonic stimulus. We apply this theorem to analyze the non-monotonic dynamics of the σ^B-regulated glyoxylate shunt gene expression in <i>Mycobacterium tuberculosis</i> cells exposed to hypoxia. We first demonstrate that the known network structure is inconsistent with observed dynamics. To resolve this inconsistency we employ the formulated theorem, modeling simulations and optimization along with follow-up dynamic experimental measurements. We show a requirement for post-translational modulation of σ^B activity in order to reconcile the network dynamics with its topology. The results of this analysis make testable experimental predictions and demonstrate wider applicability of the developed methodology to a wide class of biological systems.</p></div

Gene expression measurements for <i>ideR</i>.

<p>In a well-characterized interaction, σ^B acts as the sole transcriptional regulator of <i>ideR</i>. mRNA of <i>ideR</i> exhibits a non-monotonic response similar to that of <i>lrpI</i>, suggesting that the non-monotonic dynamics are a result of a post-translational regulation of σ^B activity. (*p≤0.05).</p

Gene expression measurements reveal a negative regulation of <i>icl1</i> through <i>clgR</i>.

<p>(A,B) Expression measurements for <i>icl1</i>, <i>lrpI</i> and the accessory sigma factor <i>sigB</i> mRNA for wild-type (A) and <i>clgR</i> knockout strain (B). (C,D) To simplify the model, the dynamics of <i>sigB</i> and <i>clgR</i> under hypoxia were interpolated for wild-type (C) and for <i>clgR</i> knockout strain (D) with a generalized pulse function and served as inputs into the model (See <a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1004741#sec010" target="_blank">Methods</a> for details). (*p≤0.05).</p

Non-monotonic induction of <i>icl1</i>.

<p>(A) Structure of <i>icl1</i> transcriptional regulation network. The central metabolism gene <i>icl1</i> was previously thought to be solely controlled by a simple feedforward network under hypoxic conditions. Arrows represent positive transcriptional regulation. (B) Expression measurements for <i>icl1</i> and the accessory sigma factor <i>sigE</i> mRNA (data from [<a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1004741#pcbi.1004741.ref012" target="_blank">12</a>]). Three independent experiments were performed, and the means (error bars are ± standard deviations) are reported, normalized to the mean value of the first data point. While <i>sigE</i> copy number is increasing, <i>icl1</i> peaks at day 3 and decreases after that. (*p≤0.05).</p

Sample graphical representation of a dynamical system to illustrate the theorem.

<p>The input node <i>u(t)</i> is a known input function. The dynamics of the dependent variables <i>X</i>_<i>i</i>(<i>t</i>) are given by equations of the form of <a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1004741#pcbi.1004741.e001" target="_blank">Eq 1</a>. Arrows connect the nodes connect nodes with non-zero partial derivatives; pointed and blunt arrows correspond to positive and negative partial derivatives respectively (see text for details).</p