Genome-wide discovery of small RNAs in Mycobacterium tuberculosis

Only few small RNAs (sRNAs) have been characterized in Mycobacterium tuberculosis and their role in regulatory networks is still poorly understood. Here we report a genome-wide characterization of sRNAs in M. tuberculosis integrating experimental and computational analyses. Global RNA-seq analysis of exponentially growing cultures of M. tuberculosis H37Rv had previously identified 1373 sRNA species. In the present report we show that 258 (19%) of these were also identified by microarray expression. This set included 22 intergenic sRNAs, 84 sRNAs mapping within 59/39 UTRs, and 152 antisense sRNAs. Analysis of promoter and terminator consensus sequences identified sigma A promoter consensus sequences for 121 sRNAs (47%), terminator consensus motifs for 22 sRNAs (8.5%), and both motifs for 35 sRNAs (14%). Additionally, 20/23 candidates were visualized by Northern blot analysis and 59 end mapping by primer extension confirmed the RNA-seq data. We also used a computational approach utilizing functional enrichment to identify the pathways targeted by sRNA regulation. We found that antisense sRNAs preferentially regulated transcription of membrane-bound proteins. Genes putatively regulated by novel cis-encoded sRNAs were enriched for two-component systems and for functional pathways involved in hydrogen transport on the membrane

Complex and unexpected dynamics in simple genetic regulatory networks

Peer reviewedPublisher PD

Effects of Age on Optical Coherence Tomography Measurements of Healthy Retinal Nerve Fiber Layer, Macula, and Optic Nerve Head

Purpose—To determine the effects of age on global and sectoral peripapillary retinal nerve fiber layer (RNFL), macular thicknesses and optic nerve head (ONH) parameters in healthy subjects using optical coherence tomography (OCT). Design—Retrospective, cross-sectional observational study. Participants—226 eyes from 124 healthy subjects were included. Methods—Healthy subjects were scanned using the Fast RNFL, Fast Macula, and Fast ONH scan patterns on a Stratus OCT. All global and sectoral RNFL and macular parameters and global ONH parameters were modeled in terms of age using linear mixed effects models. Normalized slopes were also calculated by dividing the slopes by the mean value of the OCT parameter for inter-parameter comparison. Main Outcome Measures—Slope of each OCT parameter across age. Results—All global and sectoral RNFL thickness parameters statistically significantly decreased with increasing age, except for the temporal quadrant and clock hours 8-10, which were not statistically different from a slope of zero. Highest absolute slopes were in the inferior and superior quadrant RNFL and clock hour 1 (superior nasal). Normalized slopes showed similar rate in all sectors except for the temporal clock hours (8-10). All macular thickness parameters statistically significantly decreased with increasing age, except for the central fovea sector, which had a slight positive slope that was not statistically significant. The nasal outer sector had the greatest absolute slope. Normalized macular slope in the outer ring was similar to the normalized slopes in the RNFL. Normalized inner ring had shallower slope than the outer ring with similar rate in all quadrants. Disc area remained nearly constant across the ages, but cup area increased and rim area decreased with age, both of which were statistically significant. Conclusions—Global and regional changes due to the effects of age on RNFL, macula and ONH OCT measurements should be considered when assessing eyes over time.National Institutes of Health (U.S.) (R01-EY13178-09)National Institutes of Health (U.S.) (R01-EY11289-23)National Institutes of Health (U.S.) (P30-EY008098

Intermittent control models of human standing: similarities and differences

Two architectures of intermittent control are compared and contrasted in the context of the single inverted pendulum model often used for describing standing in humans. The architectures are similar insofar as they use periods of open-loop control punctuated by switching events when crossing a switching surface to keep the system state trajectories close to trajectories leading to equilibrium. The architectures differ in two significant ways. Firstly, in one case, the open-loop control trajectory is generated by a system-matched hold, and in the other case, the open-loop control signal is zero. Secondly, prediction is used in one case but not the other. The former difference is examined in this paper. The zero control alternative leads to periodic oscillations associated with limit cycles; whereas the system-matched control alternative gives trajectories (including homoclinic orbits) which contain the equilibrium point and do not have oscillatory behaviour. Despite this difference in behaviour, it is further shown that behaviour can appear similar when either the system is perturbed by additive noise or the system-matched trajectory generation is perturbed. The purpose of the research is to come to a common approach for understanding the theoretical properties of the two alternatives with the twin aims of choosing which provides the best explanation of current experimental data (which may not, by itself, distinguish beween the two alternatives) and suggesting future experiments to distinguish between the two alternatives

Deciphering the connectivity structure of biological networks using MixNet

<p>Abstract</p> <p>Background</p> <p>As biological networks often show complex topological features, mathematical methods are required to extract meaningful information. Clustering methods are useful in this setting, as they allow the summary of the network's topology into a small number of relevant classes. Different strategies are possible for clustering, and in this article we focus on a model-based strategy that aims at clustering nodes based on their connectivity profiles.</p> <p>Results</p> <p>We present MixNet, the first publicly available computer software that analyzes biological networks using mixture models. We apply this method to various networks such as the <it>E. coli </it>transcriptional regulatory network, the macaque cortex network, a foodweb network and the <it>Buchnera aphidicola </it>metabolic network. This method is also compared with other approaches such as module identification or hierarchical clustering.</p> <p>Conclusion</p> <p>We show how MixNet can be used to extract meaningful biological information, and to give a summary of the networks topology that highlights important biological features. This approach is powerful as MixNet is adaptive to the network under study, and finds structural information without any a priori on the structure that is investigated. This makes MixNet a very powerful tool to summarize and decipher the connectivity structure of biological networks.</p

Stochastic Modeling for the Expression of a Gene Regulated by Competing Transcription Factors

It is widely accepted that gene expression regulation is a stochastic event. The common approach for its computer simulation requires detailed information on the interactions of individual molecules, which is often not available for the analyses of biological experiments. As an alternative approach, we employed a more intuitive model to simulate the experimental result, the Markov-chain model, in which a gene is regulated by activators and repressors, which bind the same site in a mutually exclusive manner. Our stochastic simulation in the presence of both activators and repressors predicted a Hill-coefficient of the dose-response curve closer to the experimentally observed value than the calculated value based on the simple additive effects of activators alone and repressors alone. The simulation also reproduced the heterogeneity of gene expression levels among individual cells observed by Fluorescence Activated Cell Sorting analysis. Therefore, our approach may help to apply stochastic simulations to broader experimental data

Systematic Single-Cell Analysis of Pichia pastoris Reveals Secretory Capacity Limits Productivity

Biopharmaceuticals represent the fastest growing sector of the global pharmaceutical industry. Cost-efficient production of these biologic drugs requires a robust host organism for generating high titers of protein during fermentation. Understanding key cellular processes that limit protein production and secretion is, therefore, essential for rational strain engineering. Here, with single-cell resolution, we systematically analysed the productivity of a series of Pichia pastoris strains that produce different proteins both constitutively and inducibly. We characterized each strain by qPCR, RT-qPCR, microengraving, and imaging cytometry. We then developed a simple mathematical model describing the flux of folded protein through the ER. This combination of single-cell measurements and computational modelling shows that protein trafficking through the secretory machinery is often the rate-limiting step in single-cell production, and strategies to enhance the overall capacity of protein secretion within hosts for the production of heterologous proteins may improve productivity

Analysis and Computational Dissection of Molecular Signature Multiplicity

Molecular signatures are computational or mathematical models created to diagnose disease and other phenotypes and to predict clinical outcomes and response to treatment. It is widely recognized that molecular signatures constitute one of the most important translational and basic science developments enabled by recent high-throughput molecular assays. A perplexing phenomenon that characterizes high-throughput data analysis is the ubiquitous multiplicity of molecular signatures. Multiplicity is a special form of data analysis instability in which different analysis methods used on the same data, or different samples from the same population lead to different but apparently maximally predictive signatures. This phenomenon has far-reaching implications for biological discovery and development of next generation patient diagnostics and personalized treatments. Currently the causes and interpretation of signature multiplicity are unknown, and several, often contradictory, conjectures have been made to explain it. We present a formal characterization of signature multiplicity and a new efficient algorithm that offers theoretical guarantees for extracting the set of maximally predictive and non-redundant signatures independent of distribution. The new algorithm identifies exactly the set of optimal signatures in controlled experiments and yields signatures with significantly better predictivity and reproducibility than previous algorithms in human microarray gene expression datasets. Our results shed light on the causes of signature multiplicity, provide computational tools for studying it empirically and introduce a framework for in silico bioequivalence of this important new class of diagnostic and personalized medicine modalities

Built Shallow to Maintain Homeostasis and Persistent Infection: Insight into the Transcriptional Regulatory Network of the Gastric Human Pathogen Helicobacter pylori

Transcriptional regulatory networks (TRNs) transduce environmental signals into coordinated output expression of the genome. Accordingly, they are central for the adaptation of bacteria to their living environments and in host–pathogen interactions. Few attempts have been made to describe a TRN for a human pathogen, because even in model organisms, such as Escherichia coli, the analysis is hindered by the large number of transcription factors involved. In light of the paucity of regulators, the gastric human pathogen Helicobacter pylori represents a very appealing system for understanding how bacterial TRNs are wired up to support infection in the host. Herein, we review and analyze the available molecular and “-omic” data in a coherent ensemble, including protein–DNA and protein–protein interactions relevant for transcriptional control of pathogenic responses. The analysis covers ∼80% of the annotated H. pylori regulators, and provides to our knowledge the first in-depth description of a TRN for an important pathogen. The emerging picture indicates a shallow TRN, made of four main modules (origons) that process the physiological responses needed to colonize the gastric niche. Specific network motifs confer distinct transcriptional response dynamics to the TRN, while long regulatory cascades are absent. Rather than having a plethora of specialized regulators, the TRN of H. pylori appears to transduce separate environmental inputs by using different combinations of a small set of regulators