Motifs, binding, and expression : computational studies of transcriptional regulation

Thesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2009.This electronic version was submitted by the student author. The certified thesis is available in the Institute Archives and Special Collections.Cataloged from student-submitted PDF version of thesis.Includes bibliographical references (p. 141-150).Organisms must control gene expression in response to developmental, nutritional, or other environmental cues. This process is known as transcriptional regulation and occurs through complex networks of proteins interacting with specific regulatory sites in the genome. Recently, high throughput variations of experimental techniques like transcriptional profiling and chromatin immunoprecipitation have emerged and taken on increasing importance in the study of regulatory processes. Mining these experiments for useful biological information requires methods that can handle large quantities of noisy data and integrate information from disparate experimental sources in a principled manner. Not coincidentally, computational and statistical methods for analyzing these data have increasingly become a focal point of research efforts. In this thesis we address three key challenges in the analysis of genomic sequence, protein localization, and expression data: (1) learning representations of the specific binding interactions that determine connectivity in regulatory networks, (2) developing physically grounded models describing these interactions, and (3) relating binding to its ultimate effect on the expression of regulated genes. To this end, we present several different algorithms and modeling techniques and apply them to real biological data in yeast, mouse, and human. Our results demonstrate the utility of leveraging multiple sources of information for improving motif analyses of chromatin immunoprecipitation data. Phylogenetic conservation information and knowledge of an immunoprecipitated protein's DNA binding domain are both shown to have great value in this context.(cont.) We next present a biophysically motivated framework for modeling protein-DNA interactions and show how it leads to very natural algorithms for analyzing the binding specificity of an immunoprecipitated protein, and jointly analyzing protein localization data for multiple regulators or multiple conditions. Finally, we present an analysis of transcriptional coregulator binding in a variety of mouse tissues and a method for predicting which proteins form complexes with the coregulator based purely on the sequence of the regions it binds. We detail a simple but powerful model relating regulator binding to gene expression, and show how the position of regulatory regions is of crucial importance for predicting the expression level of nearby genes.by Kenzie Daniel MacIsaac.Ph.D

On the context-dependent scaling of consumer feeding rates

The stability of consumer-resource systems can depend on the form of feeding interactions (i.e. functional responses). Size-based models predict interactions - and thus stability - based on consumer-resource size ratios. However, little is known about how interaction contexts (e.g. simple or complex habitats) might alter scaling relationships. Addressing this, we experimentally measured interactions between a large size range of aquatic predators (4-6400 mg over 1347 feeding trials) and an invasive prey that transitions among habitats: from the water column (3D interactions) to simple and complex benthic substrates (2D interactions). Simple and complex substrates mediated successive reductions in capture rates - particularly around the unimodal optimum - and promoted prey population stability in model simulations. Many real consumer-resource systems transition between 2D and 3D interactions, and along complexity gradients. Thus, Context-Dependent Scaling (CDS) of feeding interactions could represent an unrecognised aspect of food webs, and quantifying the extent of CDS might enhance predictive ecology. © 2016 John Wiley & Sons Ltd/CNRS

Phytochemistry, toxicology, and food potential of velvetbean (Mucuna Adans. spp., Fabaceae)

Abstract in English and FrenchIn IDL-1387

Attenuation and modification of the ballast water microbial community during voyages into the Canadian Arctic

Aim: Ballast water is a major vector of non-indigenous species introductions world-wide. Our understanding of population dynamics of organisms entrained in ballast is largely limited to studies of zooplankton and phytoplankton. Bacteria are more numerous and diverse than zooplankton or phytoplankton, yet remain comparatively understudied. We apply a metagenomics approach to characterize changes in the microbial ballast water community over the course of three voyages on one ship, and assess the effects of ballast water exchange (BWE), spring/summer sampling month and time since voyage start. Location: Quebec City and Deception Bay, Quebec, and the coastal marine region offshore of eastern Canada. Methods: We used universal primers to Ion Torrent sequence a fragment of the bacterial 16S ribosomal DNA for samples collected over three voyages of one ship between Quebec City and Deception Bay in June, July and August 2015. We compared richness (total number of species in the community) and diversity (accounts for both species abundance and evenness) using linear mixed-effects analysis and compared community composition using non-metric multidimensional scaling and permutational multivariate analysis of variance. Initial comparisons were between months. Subsequent analyses focused on each month separately. Results: Ion Torrent sequencing returned c. 2.9 million reads and revealed monthly differences in diversity and richness, and in community structure in ballast water. June had higher richness and diversity than either July or August, and showed most clearly the effect of BWE on the microbial community. Main conclusions: Our results suggest that environmental conditions associated with different spring/summer sampling months drive differences in microbial diversity in ballast water. This study showed that BWE removes some components of the freshwater starting microbial community and replaces them with other taxa. BWE also changed proportional representation of some microbes without removing them completely. It appears that some taxa are resident in ballast tanks and are not removed by BWE. © 2017 John Wiley & Sons Lt

Universal finite-size scaling analysis of Ising models with long-range interactions at the upper critical dimensionality: Isotropic case

We investigate a two-dimensional Ising model with long-range interactions that emerge from a generalization of the magnetic dipolar interaction in spin systems with in-plane spin orientation. This interaction is, in general, anisotropic whereby in the present work we focus on the isotropic case for which the model is found to be at its upper critical dimensionality. To investigate the critical behavior the temperature and field dependence of several quantities are studied by means of Monte Carlo simulations. On the basis of the Privman-Fisher hypothesis and results of the renormalization group the numerical data are analyzed in the framework of a finite-size scaling analysis and compared to finite-size scaling functions derived from a Ginzburg-Landau-Wilson model in zero mode (mean-field) approximation. The obtained excellent agreement suggests that at least in the present case the concept of universal finite-size scaling functions can be extended to the upper critical dimensionality.Comment: revtex4, 10 pages, 5 figures, 1 tabl

Positional clustering improves computational binding site detection and identifies novel cis-regulatory sites in mammalian GABA(A) receptor subunit genes

Understanding transcription factor (TF) mediated control of gene expression remains a major challenge at the interface of computational and experimental biology. Computational techniques predicting TF-binding site specificity are frequently unreliable. On the other hand, comprehensive experimental validation is difficult and time consuming. We introduce a simple strategy that dramatically improves robustness and accuracy of computational binding site prediction. First, we evaluate the rate of recurrence of computational TFBS predictions by commonly used sampling procedures. We find that the vast majority of results are biologically meaningless. However clustering results based on nucleotide position improves predictive power. Additionally, we find that positional clustering increases robustness to long or imperfectly selected input sequences. Positional clustering can also be used as a mechanism to integrate results from multiple sampling approaches for improvements in accuracy over each one alone. Finally, we predict and validate regulatory sequences partially responsible for transcriptional control of the mammalian type A γ-aminobutyric acid receptor (GABA(A)R) subunit genes. Positional clustering is useful for improving computational binding site predictions, with potential application to improving our understanding of mammalian gene expression. In particular, predicted regulatory mechanisms in the mammalian GABA(A)R subunit gene family may open new avenues of research towards understanding this pharmacologically important neurotransmitter receptor system

Network deconvolution as a general method to distinguish direct dependencies in networks

Recognizing direct relationships between variables connected in a network is a pervasive problem in biological, social and information sciences as correlation-based networks contain numerous indirect relationships. Here we present a general method for inferring direct effects from an observed correlation matrix containing both direct and indirect effects. We formulate the problem as the inverse of network convolution, and introduce an algorithm that removes the combined effect of all indirect paths of arbitrary length in a closed-form solution by exploiting eigen-decomposition and infinite-series sums. We demonstrate the effectiveness of our approach in several network applications: distinguishing direct targets in gene expression regulatory networks; recognizing directly interacting amino-acid residues for protein structure prediction from sequence alignments; and distinguishing strong collaborations in co-authorship social networks using connectivity information alone. In addition to its theoretical impact as a foundational graph theoretic tool, our results suggest network deconvolution is widely applicable for computing direct dependencies in network science across diverse disciplines.National Institutes of Health (U.S.) (grant R01 HG004037)National Institutes of Health (U.S.) (grant HG005639)Swiss National Science Foundation (Fellowship)National Science Foundation (U.S.) (NSF CAREER Award 0644282

Atrasentan and renal events in patients with type 2 diabetes and chronic kidney disease (SONAR): a double-blind, randomised, placebo-controlled trial

Background: Short-term treatment for people with type 2 diabetes using a low dose of the selective endothelin A receptor antagonist atrasentan reduces albuminuria without causing significant sodium retention. We report the long-term effects of treatment with atrasentan on major renal outcomes. Methods: We did this double-blind, randomised, placebo-controlled trial at 689 sites in 41 countries. We enrolled adults aged 18–85 years with type 2 diabetes, estimated glomerular filtration rate (eGFR)25–75 mL/min per 1·73 m 2 of body surface area, and a urine albumin-to-creatinine ratio (UACR)of 300–5000 mg/g who had received maximum labelled or tolerated renin–angiotensin system inhibition for at least 4 weeks. Participants were given atrasentan 0·75 mg orally daily during an enrichment period before random group assignment. Those with a UACR decrease of at least 30% with no substantial fluid retention during the enrichment period (responders)were included in the double-blind treatment period. Responders were randomly assigned to receive either atrasentan 0·75 mg orally daily or placebo. All patients and investigators were masked to treatment assignment. The primary endpoint was a composite of doubling of serum creatinine (sustained for ≥30 days)or end-stage kidney disease (eGFR <15 mL/min per 1·73 m 2 sustained for ≥90 days, chronic dialysis for ≥90 days, kidney transplantation, or death from kidney failure)in the intention-to-treat population of all responders. Safety was assessed in all patients who received at least one dose of their assigned study treatment. The study is registered with ClinicalTrials.gov, number NCT01858532. Findings: Between May 17, 2013, and July 13, 2017, 11 087 patients were screened; 5117 entered the enrichment period, and 4711 completed the enrichment period. Of these, 2648 patients were responders and were randomly assigned to the atrasentan group (n=1325)or placebo group (n=1323). Median follow-up was 2·2 years (IQR 1·4–2·9). 79 (6·0%)of 1325 patients in the atrasentan group and 105 (7·9%)of 1323 in the placebo group had a primary composite renal endpoint event (hazard ratio [HR]0·65 [95% CI 0·49–0·88]; p=0·0047). Fluid retention and anaemia adverse events, which have been previously attributed to endothelin receptor antagonists, were more frequent in the atrasentan group than in the placebo group. Hospital admission for heart failure occurred in 47 (3·5%)of 1325 patients in the atrasentan group and 34 (2·6%)of 1323 patients in the placebo group (HR 1·33 [95% CI 0·85–2·07]; p=0·208). 58 (4·4%)patients in the atrasentan group and 52 (3·9%)in the placebo group died (HR 1·09 [95% CI 0·75–1·59]; p=0·65). Interpretation: Atrasentan reduced the risk of renal events in patients with diabetes and chronic kidney disease who were selected to optimise efficacy and safety. These data support a potential role for selective endothelin receptor antagonists in protecting renal function in patients with type 2 diabetes at high risk of developing end-stage kidney disease. Funding: AbbVie

Design and baseline characteristics of the finerenone in reducing cardiovascular mortality and morbidity in diabetic kidney disease trial

Background: Among people with diabetes, those with kidney disease have exceptionally high rates of cardiovascular (CV) morbidity and mortality and progression of their underlying kidney disease. Finerenone is a novel, nonsteroidal, selective mineralocorticoid receptor antagonist that has shown to reduce albuminuria in type 2 diabetes (T2D) patients with chronic kidney disease (CKD) while revealing only a low risk of hyperkalemia. However, the effect of finerenone on CV and renal outcomes has not yet been investigated in long-term trials. Patients and Methods: The Finerenone in Reducing CV Mortality and Morbidity in Diabetic Kidney Disease (FIGARO-DKD) trial aims to assess the efficacy and safety of finerenone compared to placebo at reducing clinically important CV and renal outcomes in T2D patients with CKD. FIGARO-DKD is a randomized, double-blind, placebo-controlled, parallel-group, event-driven trial running in 47 countries with an expected duration of approximately 6 years. FIGARO-DKD randomized 7,437 patients with an estimated glomerular filtration rate >= 25 mL/min/1.73 m(2) and albuminuria (urinary albumin-to-creatinine ratio >= 30 to <= 5,000 mg/g). The study has at least 90% power to detect a 20% reduction in the risk of the primary outcome (overall two-sided significance level alpha = 0.05), the composite of time to first occurrence of CV death, nonfatal myocardial infarction, nonfatal stroke, or hospitalization for heart failure. Conclusions: FIGARO-DKD will determine whether an optimally treated cohort of T2D patients with CKD at high risk of CV and renal events will experience cardiorenal benefits with the addition of finerenone to their treatment regimen. Trial Registration: EudraCT number: 2015-000950-39; ClinicalTrials.gov identifier: NCT02545049