A Functional Selection Model Explains Evolutionary Robustness Despite Plasticity in Regulatory Networks

Evolutionary rewiring of regulatory networks is an important source of diversity among species. Previous evidence suggested substantial divergence of regulatory networks across species. However, systematically assessing the extent of this plasticity and its functional implications has been challenging due to limited experimental data and the noisy nature of computational predictions. Here, we introduce a novel approach to study cis-regulatory evolution, and use it to trace the regulatory history of 88 DNA motifs of transcription factors across 23 Ascomycota fungi. While motifs are conserved, we find a pervasive gain and loss in the regulation of their target genes. Despite this turnover, the biological processes associated with a motif are generally conserved. We explain these trends using a model with a strong selection to conserve the overall function of a transcription factor, and a much weaker selection over the specific genes it targets. The model also accounts for the turnover of bound targets measured experimentally across species in yeasts and mammals. Thus, selective pressures on regulatory networks mostly tolerate local rewiring, and may allow for subtle fine-tuning of gene regulation during evolution

Polymer mimics of biomacromolecular antifreezes

Antifreeze proteins from polar fish species are remarkable biomacromolecules which prevent the growth of ice crystals. Ice crystal growth is a major problem in cell/tissue cryopreservation for transplantation, transfusion and basic biomedical research, as well as technological applications such as icing of aircraft wings. This review will introduce the rapidly emerging field of synthetic macromolecular (polymer) mimics of antifreeze proteins. Particular focus is placed on designing polymers which have no structural similarities to antifreeze proteins but reproduce the same macroscopic properties, potentially by different molecular-level mechanisms. The application of these polymers to the cryopreservation of donor cells is also introduced

The value of position-specific priors in motif discovery using MEME

<p>Abstract</p> <p>Background</p> <p>Position-specific priors have been shown to be a flexible and elegant way to extend the power of Gibbs sampler-based motif discovery algorithms. Information of many types–including sequence conservation, nucleosome positioning, and negative examples–can be converted into a prior over the location of motif sites, which then guides the sequence motif discovery algorithm. This approach has been shown to confer many of the benefits of conservation-based and discriminative motif discovery approaches on Gibbs sampler-based motif discovery methods, but has not previously been studied with methods based on expectation maximization (EM).</p> <p>Results</p> <p>We extend the popular EM-based MEME algorithm to utilize position-specific priors and demonstrate their effectiveness for discovering transcription factor (TF) motifs in yeast and mouse DNA sequences. Utilizing a discriminative, conservation-based prior dramatically improves MEME's ability to discover motifs in 156 yeast TF ChIP-chip datasets, more than doubling the number of datasets where it finds the correct motif. On these datasets, MEME using the prior has a higher success rate than eight other conservation-based motif discovery approaches. We also show that the same type of prior improves the accuracy of motifs discovered by MEME in mouse TF ChIP-seq data, and that the motifs tend to be of slightly higher quality those found by a Gibbs sampling algorithm using the same prior.</p> <p>Conclusions</p> <p>We conclude that using position-specific priors can substantially increase the power of EM-based motif discovery algorithms such as MEME algorithm.</p

ChIP-PaM: an algorithm to identify protein-DNA interaction using ChIP-Seq data

<p>Abstract</p> <p>Background</p> <p>ChIP-Seq is a powerful tool for identifying the interaction between genomic regulators and their bound DNAs, especially for locating transcription factor binding sites. However, high cost and high rate of false discovery of transcription factor binding sites identified from ChIP-Seq data significantly limit its application.</p> <p>Results</p> <p>Here we report a new algorithm, ChIP-PaM, for identifying transcription factor target regions in ChIP-Seq datasets. This algorithm makes full use of a protein-DNA binding pattern by capitalizing on three lines of evidence: 1) the tag count modelling at the peak position, 2) pattern matching of a specific tag count distribution, and 3) motif searching along the genome. A novel data-based two-step eFDR procedure is proposed to integrate the three lines of evidence to determine significantly enriched regions. Our algorithm requires no technical controls and efficiently discriminates falsely enriched regions from regions enriched by true transcription factor (TF) binding on the basis of ChIP-Seq data only. An analysis of real genomic data is presented to demonstrate our method.</p> <p>Conclusions</p> <p>In a comparison with other existing methods, we found that our algorithm provides more accurate binding site discovery while maintaining comparable statistical power.</p

Vitamin D receptor ChIP-seq in primary CD4+ cells: relationship to serum 25-hydroxyvitamin D levels and autoimmune disease

PMCID: PMC3710212This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited

Scallop swimming kinematics and muscle performance: modelling the effects of "within-animal" variation in temperature sensitivity

Escape behaviour was investigated in Queen scallops (Aequipecten opercularis) acclimated to 5, 10 or 15 degrees C and tested at their acclimation temperature. Scallops are active molluscs, able to escape from predators by jet-propelled swimming using a striated muscle working in opposition to an elastic hinge ligament. The first cycle of the escape response was recorded using high-speed video ( 250 Hz) and whole-animal velocity and acceleration determined. Muscle shortening velocity, force and power output were calculated using measurements of valve movement and jet area, and a simple biomechanical model. The average shortening speed of the adductor muscle had a Q(10) of 2.04, significantly reducing the duration of the jetting phase of the cycle with increased temperature. Muscle lengthening velocity and the overall duration of the clap cycle were changed little over the range 5 - 15 degrees C, as these parameters were controlled by the relatively temperature-insensitive, hinge ligament. Improvements in the average power output of the adductor muscle over the first clap cycle ( 222 vs. 139 W kg(-1) wet mass at 15 and 5 degrees C respectively) were not translated into proportional increases in overall swimming velocity, which was only 32% higher at 15 degrees C ( 0.37m s(-1)) than 5 degrees C (0.28 m s(-1))

Widespread translational control contributes to the regulation of Arabidopsis photomorphogenesis

Environmental light regulates and optimizes plant growth and development. Genomic profiling of polysome-associated mRNA reveals that light stimulates dramatic changes in translational regulation, which contribute more to light-induced gene expression changes than transcriptional regulation

A Novel Motif Identified in Dependence Receptors

Programmed cell death signaling is a critical feature of development, cellular turnover, oncogenesis, and neurodegeneration, among other processes. Such signaling may be transduced via specific receptors, either following ligand binding—to death receptors—or following the withdrawal of trophic ligands—from dependence receptors. Although dependence receptors display functional similarities, no common structural domains have been identified. Therefore, we employed the Multiple Expectation Maximization for Motif Elicitation and the Motif Alignment and Search Tool software programs to identify a novel transmembrane motif, dubbed dependence-associated receptor transmembrane (DART) motif, that is common to all described dependence receptors. Of 3,465 human transmembrane proteins, 25 (0.7%) display the DART motif. The predicted secondary structure features an alpha helical structure, with an unusually high percentage of valine residues. At least four of the proteins undergo regulated intramembrane proteolysis. To date, we have not identified a function for this putative domain. We speculate that the DART motif may be involved in protein processing, interaction with other proteins or lipids, or homomultimerization

Predicting promoters in phage genomes using machine learning models

The renewed interest in phages as antibacterial agents has led to the exponentially growing number of sequenced phage genomes. Therefore, the development of novel bioinformatics methods to automate and facilitate phage genome annotation is of utmost importance. The most difficult step of phage genome annotation is the identification of promoters. As the existing methods for predicting promoters are not well suited for phages, we used machine learning models for locating promoters in phage genomes. Several models were created, using different algorithms and datasets, which consisted of known phage promoter and non-promoter sequences. All models showed good performance, but the ANN model provided better results for the smaller dataset (92% of accuracy, 89% of precision and 87% of recall) and the SVM model returned better results for the larger dataset (93% of accuracy, 91% of precision and 80% of recall). Both models were applied to the genome of Pseudomonas phage phiPsa17 and were able to identify both types of promoters, host and phage, found in phage genomes.This study was supported by the Portuguese Foundation for Science andTechnology (FCT) under the scope of the strategic funding of UID/BIO/04469/2019 unit and theProject POCI-01-0145-FEDER-029628. This work was also supported by BioTecNorte operation (NORTE-01-0145-FEDER-000004) funded by the European Regional Development Fundunder the scope of Norte2020 - Programa Operacional Regional do Norte.info:eu-repo/semantics/publishedVersio

Discriminative motif discovery in DNA and protein sequences using the DEME algorithm

<p>Abstract</p> <p>Background</p> <p>Motif discovery aims to detect short, highly conserved patterns in a collection of unaligned DNA or protein sequences. Discriminative motif finding algorithms aim to increase the sensitivity and selectivity of motif discovery by utilizing a second set of sequences, and searching only for patterns that can differentiate the two sets of sequences. Potential applications of discriminative motif discovery include discovering transcription factor binding site motifs in ChIP-chip data and finding protein motifs involved in thermal stability using sets of orthologous proteins from thermophilic and mesophilic organisms.</p> <p>Results</p> <p>We describe DEME, a discriminative motif discovery algorithm for use with protein and DNA sequences. Input to DEME is two sets of sequences; a "positive" set and a "negative" set. DEME represents motifs using a probabilistic model, and uses a novel combination of global and local search to find the motif that optimally discriminates between the two sets of sequences. DEME is unique among discriminative motif finders in that it uses an informative Bayesian prior on protein motif columns, allowing it to incorporate prior knowledge of residue characteristics. We also introduce four, synthetic, discriminative motif discovery problems that are designed for evaluating discriminative motif finders in various biologically motivated contexts. We test DEME using these synthetic problems and on two biological problems: finding yeast transcription factor binding motifs in ChIP-chip data, and finding motifs that discriminate between groups of thermophilic and mesophilic orthologous proteins.</p> <p>Conclusion</p> <p>Using artificial data, we show that DEME is more effective than a non-discriminative approach when there are "decoy" motifs or when a variant of the motif is present in the "negative" sequences. With real data, we show that DEME is as good, but not better than non-discriminative algorithms at discovering yeast transcription factor binding motifs. We also show that DEME can find highly informative thermal-stability protein motifs. Binaries for the stand-alone program DEME is free for academic use and is available at <url>http://bioinformatics.org.au/deme/</url></p