Modeling And Identification Of Differentially Regulated Genes Using Transcriptomics And Proteomics Data

Photosynthetic organisms are complex dynamical systems, showing a remarkable ability to adapt to different environmental conditions for their survival. Mechanisms underlying the coordination between different cellular processes in these organisms are still poorly understood. In this dissertation we utilize various computational and modeling techniques to analyze transcriptomics and proteomics data sets from several photosynthetic organisms. We try to use changes in expression levels of genes to study responses of these organisms to various environmental conditions such as availability of nutrients, concentrations of chemicals in growth media, and temperature. Three specific problems studied here are transcriptomics modifications in photosynthetic organisms under reduction-oxidation: redox) stress conditions, circadian and diurnal rhythms of cyanobacteria and the effect of incident light patterns on these rhythms, and the coordination between biological processes in cyanobacteria under various growth conditions. Under redox stresses caused by high light treatments, a strong transcriptomic level response, spread across many biological processes, is discovered in the cyanobacterium Synechocystis sp. PCC 6803. Based on statistical tests, expression levels of about 20% of genes in Synechocystis 6803 are identified as significantly affected due to influence of high light. Gene clustering methods reveal that these responses can mainly be classified as transient and consistent responses, depending on the duration of modified behaviors. Many genes related to energy production as well as energy utilization are shown to be strongly affected. Analysis of microarray data under two stress conditions, high light and DCMU treatment, combined with data mining and motif finding algorithms led to a discovery of novel transcription factor, RRTF1 that responds to redox stresses in Arabidopsis thaliana. Time course transcriptomics data from Cyanothece sp. ATCC 51142 have shown strong diurnal rhythms. By combining multiple experimental conditions and using gene classification algorithms based on Fourier scores and angular distances, it is shown that majority of the diurnal genes are in fact light responding. Only about 10% of genes in the genome are categorized as being circadian controlled. A transcription control model based on dynamical systems is employed to identify the interactions between diurnal genes. A phase oscillator network is proposed to model the behavior of different biological processes. Both these models are shown to carry biologically meaningful features. To study the coordination between different biological processes to various environment and genetic modifications, an interaction model is derived using Bayesian network approach, combining all publicly available microarray data sets for Synechocystis sp. PCC 6803. Several novel relationships between biological processes are discovered from the model. Model is used to simulate several experimental conditions, and the response of the model is shown to agree with the experimentally observed behaviors

Robustness of circadian clocks to daylight fluctuations: hints from the picoeucaryote Ostreococcus tauri

The development of systemic approaches in biology has put emphasis on identifying genetic modules whose behavior can be modeled accurately so as to gain insight into their structure and function. However most gene circuits in a cell are under control of external signals and thus quantitative agreement between experimental data and a mathematical model is difficult. Circadian biology has been one notable exception: quantitative models of the internal clock that orchestrates biological processes over the 24-hour diurnal cycle have been constructed for a few organisms, from cyanobacteria to plants and mammals. In most cases, a complex architecture with interlocked feedback loops has been evidenced. Here we present first modeling results for the circadian clock of the green unicellular alga Ostreococcus tauri. Two plant-like clock genes have been shown to play a central role in Ostreococcus clock. We find that their expression time profiles can be accurately reproduced by a minimal model of a two-gene transcriptional feedback loop. Remarkably, best adjustment of data recorded under light/dark alternation is obtained when assuming that the oscillator is not coupled to the diurnal cycle. This suggests that coupling to light is confined to specific time intervals and has no dynamical effect when the oscillator is entrained by the diurnal cycle. This intringuing property may reflect a strategy to minimize the impact of fluctuations in daylight intensity on the core circadian oscillator, a type of perturbation that has been rarely considered when assessing the robustness of circadian clocks

An Optimal Time for Treatment-Predicting Circadian Time by Machine Learning and Mathematical Modelling

Tailoring medical interventions to a particular patient and pathology has been termed personalized medicine. The outcome of cancer treatments is improved when the intervention is timed in accordance with the patient's internal time. Yet, one challenge of personalized medicine is how to consider the biological time of the patient. Prerequisite for this so-called chronotherapy is an accurate characterization of the internal circadian time of the patient. As an alternative to time-consuming measurements in a sleep-laboratory, recent studies in chronobiology predict circadian time by applying machine learning approaches and mathematical modelling to easier accessible observables such as gene expression. Embedding these results into the mathematical dynamics between clock and cancer in mammals, we review the precision of predictions and the potential usage with respect to cancer treatment and discuss whether the patient's internal time and circadian observables, may provide an additional indication for individualized treatment timing. Besides the health improvement, timing treatment may imply financial advantages, by ameliorating side effects of treatments, thus reducing costs. Summarizing the advances of recent years, this review brings together the current clinical standard for measuring biological time, the general assessment of circadian rhythmicity, the usage of rhythmic variables to predict biological time and models of circadian rhythmicity

Transcriptional regulatory logic of the diurnal cycle in the mouse liver.

Many organisms exhibit temporal rhythms in gene expression that propel diurnal cycles in physiology. In the liver of mammals, these rhythms are controlled by transcription-translation feedback loops of the core circadian clock and by feeding-fasting cycles. To better understand the regulatory interplay between the circadian clock and feeding rhythms, we mapped DNase I hypersensitive sites (DHSs) in the mouse liver during a diurnal cycle. The intensity of DNase I cleavages cycled at a substantial fraction of all DHSs, suggesting that DHSs harbor regulatory elements that control rhythmic transcription. Using chromatin immunoprecipitation followed by DNA sequencing (ChIP-seq), we found that hypersensitivity cycled in phase with RNA polymerase II (Pol II) loading and H3K27ac histone marks. We then combined the DHSs with temporal Pol II profiles in wild-type (WT) and Bmal1-/- livers to computationally identify transcription factors through which the core clock and feeding-fasting cycles control diurnal rhythms in transcription. While a similar number of mRNAs accumulated rhythmically in Bmal1-/- compared to WT livers, the amplitudes in Bmal1-/- were generally lower. The residual rhythms in Bmal1-/- reflected transcriptional regulators mediating feeding-fasting responses as well as responses to rhythmic systemic signals. Finally, the analysis of DNase I cuts at nucleotide resolution showed dynamically changing footprints consistent with dynamic binding of CLOCK:BMAL1 complexes. Structural modeling suggested that these footprints are driven by a transient heterotetramer binding configuration at peak activity. Together, our temporal DNase I mappings allowed us to decipher the global regulation of diurnal transcription rhythms in the mouse liver

Integrative statistical methods for decoding molecular responses to insect herbivory in Nicotiana attenuata

This work focuses on the development of statistical methods to select features (genes and metabolites) exhibiting induced local and systemic defense responses to insect attack in Nicotiana attenuata along with the extraction of additional information regarding their timing of action. To characterize the dynamics of activation in time and space of herbivory-induced responses, I designed a framework by combining methods previously developed for feature selection and extraction to identify activated network motifs. These motifs are the set of features that are differentially perturbed in local and systemic tissues in response to herbivory. The extraction of multifactorial statistical information in terms of time response variable simultaneously captured the dynamic response of a gene/metabolite in more than one tissue and therefore helped in identifying tissue-specific activation of biochemical pathways during herbivory, their transition points and shared patterns of regulation with other physiological processes and gene-metabolite interactions at the level of isolated motifs. I utilized this framework to evaluate the transcriptional and metabolic dynamics in the roots to investigate their role in aboveground stress responses. I discovered an emergent property of an inversion in root-specific semidiurnal (12h) rhythms in response to simulated leaf herbivory. In addition, I illustrated the benefits of our statistical framework, used for generating spatio-temporally resolved transcriptional/metabolic maps, by visualizing the chronology of the activation of pathways central to signaling, tolerance and defense in N. attenuata. The research described in this thesis, in addition to being valuable in deciphering dynamic responses to insect attack in a whole plant context, lays the foundation for future analyses in which statistical modeling of these networks assisted with experimental data could predict the logical rules governing these dynamic interactions

12-h clock regulation of genetic information flow by XBP1s

© The Author(s), 2020. This article is distributed under the terms of the Creative Commons Attribution License. The definitive version was published in Pan, Y., Ballance, H., Meng, H., Gonzalez, N., Kim, S., Abdurehman, L., York, B., Chen, X., Schnytzer, Y., Levy, O., Dacso, C. C., McClung, C. A., O'Malley, B. W., Liu, S., & Zhu, B. 12-h clock regulation of genetic information flow by XBP1s. Plos Biology, 18(1), (2020): e3000580, doi:10.1371/journal.pbio.3000580.Our group recently characterized a cell-autonomous mammalian 12-h clock independent from the circadian clock, but its function and mechanism of regulation remain poorly understood. Here, we show that in mouse liver, transcriptional regulation significantly contributes to the establishment of 12-h rhythms of mRNA expression in a manner dependent on Spliced Form of X-box Binding Protein 1 (XBP1s). Mechanistically, the motif stringency of XBP1s promoter binding sites dictates XBP1s’s ability to drive 12-h rhythms of nascent mRNA transcription at dawn and dusk, which are enriched for basal transcription regulation, mRNA processing and export, ribosome biogenesis, translation initiation, and protein processing/sorting in the Endoplasmic Reticulum (ER)-Golgi in a temporal order consistent with the progressive molecular processing sequence described by the central dogma information flow (CEDIF). We further identified GA-binding proteins (GABPs) as putative novel transcriptional regulators driving 12-h rhythms of gene expression with more diverse phases. These 12-h rhythms of gene expression are cell autonomous and evolutionarily conserved in marine animals possessing a circatidal clock. Our results demonstrate an evolutionarily conserved, intricate network of transcriptional control of the mammalian 12-h clock that mediates diverse biological pathways. We speculate that the 12-h clock is coopted to accommodate elevated gene expression and processing in mammals at the two rush hours, with the particular genes processed at each rush hour regulated by the circadian and/or tissue-specific pathways.This study was supported by the American Diabetes Association junior faculty development award 1-18-JDF-025 to B.Z., by funding from National Institute of Health HD07879 and 1P01DK113954 to B.W.O, by funding from National Science Foundation award 1703170 to C.C.D. and B.Z., and by funding from Brockman Foundation to C.C.D and B.W.O. This work was further supported by the UPMC Genome Center with funding from UPMC’s Immunotherapy and Transplant Center. This research was supported in part by the University of Pittsburgh Center for Research Computing through the resources provided. Research reported in this publication was further supported by the National Institute of Diabetes And Digestive And Kidney Diseases of the National Institutes of Health under award number P30DK120531 to Pittsburgh Liver Research Center, in which both S.L. and B.Z. are members. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript

Microarray data can predict diurnal changes of starch content in the picoalga Ostreococcus

<p>Abstract</p> <p>Background</p> <p>The storage of photosynthetic carbohydrate products such as starch is subject to complex regulation, effected at both transcriptional and post-translational levels. The relevant genes in plants show pronounced daily regulation. Their temporal RNA expression profiles, however, do not predict the dynamics of metabolite levels, due to the divergence of enzyme activity from the RNA profiles.</p> <p>Unicellular phytoplankton retains the complexity of plant carbohydrate metabolism, and recent transcriptomic profiling suggests a major input of transcriptional regulation.</p> <p>Results</p> <p>We used a quasi-steady-state, constraint-based modelling approach to infer the dynamics of starch content during the 12 h light/12 h dark cycle in the model alga <it>Ostreococcus tauri</it>. Measured RNA expression datasets from microarray analysis were integrated with a detailed stoichiometric reconstruction of starch metabolism in <it>O. tauri </it>in order to predict the optimal flux distribution and the dynamics of the starch content in the light/dark cycle. The predicted starch profile was validated by experimental data over the 24 h cycle. The main genetic regulatory targets within the pathway were predicted by <it>in silico </it>analysis.</p> <p>Conclusions</p> <p>A single-reaction description of starch production is not able to account for the observed variability of diurnal activity profiles of starch-related enzymes. We developed a detailed reaction model of starch metabolism, which, to our knowledge, is the first attempt to describe this polysaccharide polymerization while preserving the mass balance relationships. Our model and method demonstrate the utility of a quasi-steady-state approach for inferring dynamic metabolic information in <it>O. tauri </it>directly from time-series gene expression data.</p

Guidelines for Genome-Scale Analysis of Biological Rhythms

Genome biology approaches have made enormous contributions to our understanding of biological rhythms, particularly in identifying outputs of the clock, including RNAs, proteins, and metabolites, whose abundance oscillates throughout the day. These methods hold significant promise for future discovery, particularly when combined with computational modeling. However, genome-scale experiments are costly and laborious, yielding “big data” that are conceptually and statistically difficult to analyze. There is no obvious consensus regarding design or analysis. Here we discuss the relevant technical considerations to generate reproducible, statistically sound, and broadly useful genome-scale data. Rather than suggest a set of rigid rules, we aim to codify principles by which investigators, reviewers, and readers of the primary literature can evaluate the suitability of different experimental designs for measuring different aspects of biological rhythms. We introduce CircaInSilico, a web-based application for generating synthetic genome biology data to benchmark statistical methods for studying biological rhythms. Finally, we discuss several unmet analytical needs, including applications to clinical medicine, and suggest productive avenues to address them