Spontaneous persistent activity and inactivity in vivo reveals differential cortico-entorhinal functional connectivity.

Understanding the functional connectivity between brain regions and its emergent dynamics is a central challenge. Here we present a theory-experiment hybrid approach involving iteration between a minimal computational model and in vivo electrophysiological measurements. Our model not only predicted spontaneous persistent activity (SPA) during Up-Down-State oscillations, but also inactivity (SPI), which has never been reported. These were confirmed in vivo in the membrane potential of neurons, especially from layer 3 of the medial and lateral entorhinal cortices. The data was then used to constrain two free parameters, yielding a unique, experimentally determined model for each neuron. Analytic and computational analysis of the model generated a dozen quantitative predictions about network dynamics, which were all confirmed in vivo to high accuracy. Our technique predicted functional connectivity; e. g. the recurrent excitation is stronger in the medial than lateral entorhinal cortex. This too was confirmed with connectomics data. This technique uncovers how differential cortico-entorhinal dialogue generates SPA and SPI, which could form an energetically efficient working-memory substrate and influence the consolidation of memories during sleep. More broadly, our procedure can reveal the functional connectivity of large networks and a theory of their emergent dynamics.

Effectiveness of bio-effectors on maize, wheat and tomato performance and phosphorus acquisition from greenhouse to field scales in Europe and Israel: a meta-analysis.

Biostimulants (Bio-effectors, BEs) comprise plant growth-promoting microorganisms and active natural substances that promote plant nutrient-acquisition, stress resilience, growth, crop quality and yield. Unfortunately, the effectiveness of BEs, particularly under field conditions, appears highly variable and poorly quantified. Using random model meta-analyses tools, we summarize the effects of 107 BE treatments on the performance of major crops, mainly conducted within the EU-funded project BIOFECTOR with a focus on phosphorus (P) nutrition, over five years. Our analyses comprised 94 controlled pot and 47 field experiments under different geoclimatic conditions, with variable stress levels across European countries and Israel. The results show an average growth/yield increase by 9.3% (n=945), with substantial differences between crops (tomato > maize > wheat) and growth conditions (controlled nursery + field (Seed germination and nursery under controlled conditions and young plants transplanted to the field) > controlled > field). Average crop growth responses were independent of BE type, P fertilizer type, soil pH and plant-available soil P (water-P, Olsen-P or Calcium acetate lactate-P). BE effectiveness profited from manure and other organic fertilizers, increasing soil pH and presence of abiotic stresses (cold, drought/heat or salinity). Systematic meta-studies based on published literature commonly face the inherent problem of publication bias where the most suspected form is the selective publication of statistically significant results. In this meta-analysis, however, the results obtained from all experiments within the project are included. Therefore, it is free of publication bias. In contrast to reviews of published literature, our unique study design is based on a common standardized protocol which applies to all experiments conducted within the project to reduce sources of variability. Based on data of crop growth, yield and P acquisition, we conclude that application of BEs can save fertilizer resources in the future, but the efficiency of BE application depends on cropping systems and environments.

A Mesp1-dependent developmental breakpoint in transcriptional and epigenomic specification of early cardiac precursors.

Transcriptional networks governing cardiac precursor cell (CPC) specification are incompletely understood owing, in part, to limitations in distinguishing CPCs from non-cardiac mesoderm in early gastrulation. We leveraged detection of early cardiac lineage transgenes within a granular single-cell transcriptomic time course of mouse embryos to identify emerging CPCs and describe their transcriptional profiles. Mesp1, a transiently expressed mesodermal transcription factor, is canonically described as an early regulator of cardiac specification. However, we observed perdurance of CPC transgene-expressing cells in Mesp1 mutants, albeit mislocalized, prompting us to investigate the scope of the role of Mesp1 in CPC emergence and differentiation. Mesp1 mutant CPCs failed to robustly activate markers of cardiomyocyte maturity and crucial cardiac transcription factors, yet they exhibited transcriptional profiles resembling cardiac mesoderm progressing towards cardiomyocyte fates. Single-cell chromatin accessibility analysis defined a Mesp1-dependent developmental breakpoint in cardiac lineage progression at a shift from mesendoderm transcriptional networks to those necessary for cardiac patterning and morphogenesis. These results reveal Mesp1-independent aspects of early CPC specification and underscore a Mesp1-dependent regulatory landscape required for progression through cardiogenesis.

Current Scenario and Future Prospects of Endophytic Microbes: Promising Candidates for Abiotic and Biotic Stress Management for Agricultural and Environmental Sustainability.

Globally, substantial research into endophytic microbes is being conducted to increase agricultural and environmental sustainability. Endophytic microbes such as bacteria, actinomycetes, and fungi inhabit ubiquitously within the tissues of all plant species without causing any harm or disease. Endophytes form symbiotic relationships with diverse plant species and can regulate numerous host functions, including resistance to abiotic and biotic stresses, growth and development, and stimulating immune systems. Moreover, plant endophytes play a dominant role in nutrient cycling, biodegradation, and bioremediation, and are widely used in many industries. Endophytes have a stronger predisposition for enhancing mineral and metal solubility by cells through the secretion of organic acids with low molecular weight and metal-specific ligands (such as siderophores) that alter soil pH and boost binding activity. Finally, endophytes synthesize various bioactive compounds with high competence that are promising candidates for new drugs, antibiotics, and medicines. Bioprospecting of endophytic novel secondary metabolites has given momentum to sustainable agriculture for combating environmental stresses. Biotechnological interventions with the aid of endophytes played a pivotal role in crop improvement to mitigate biotic and abiotic stress conditions like drought, salinity, xenobiotic compounds, and heavy metals. Identification of putative genes from endophytes conferring resistance and tolerance to crop diseases, apart from those involved in the accumulation and degradation of contaminants, could open new avenues in agricultural research and development. Furthermore, a detailed molecular and biochemical understanding of endophyte entry and colonization strategy in the host would better help in manipulating crop productivity under changing climatic conditions. Therefore, the present review highlights current research trends based on the SCOPUS database, potential biotechnological interventions of endophytic microorganisms in combating environmental stresses influencing crop productivity, future opportunities of endophytes in improving plant stress tolerance, and their contribution to sustainable remediation of hazardous environmental contaminants.

Salt stress resilience in plants mediated through osmolyte accumulation and its crosstalk mechanism with phytohormones.

Salinity stress is one of the significant abiotic stresses that influence critical metabolic processes in the plant. Salinity stress limits plant growth and development by adversely affecting various physiological and biochemical processes. Enhanced generation of reactive oxygen species (ROS) induced via salinity stress subsequently alters macromolecules such as lipids, proteins, and nucleic acids, and thus constrains crop productivity. Due to which, a decreasing trend in cultivable land and a rising world population raises a question of global food security. In response to salt stress signals, plants adapt defensive mechanisms by orchestrating the synthesis, signaling, and regulation of various osmolytes and phytohormones. Under salinity stress, osmolytes have been investigated to stabilize the osmotic differences between the surrounding of cells and cytosol. They also help in the regulation of protein folding to facilitate protein functioning and stress signaling. Phytohormones play critical roles in eliciting a salinity stress adaptation response in plants. These responses enable the plants to acclimatize to adverse soil conditions. Phytohormones and osmolytes are helpful in minimizing salinity stress-related detrimental effects on plants. These phytohormones modulate the level of osmolytes through alteration in the gene expression pattern of key biosynthetic enzymes and antioxidative enzymes along with their role as signaling molecules. Thus, it becomes vital to understand the roles of these phytohormones on osmolyte accumulation and regulation to conclude the adaptive roles played by plants to avoid salinity stress.

Transcription Factor GATA4 Regulates Cell Type-Specific Splicing Through Direct Interaction With RNA in Human Induced Pluripotent Stem Cell-Derived Cardiac Progenitors.

BACKGROUND: GATA4 (GATA-binding protein 4), a zinc finger-containing, DNA-binding transcription factor, is essential for normal cardiac development and homeostasis in mice and humans, and mutations in this gene have been reported in human heart defects. Defects in alternative splicing are associated with many heart diseases, yet relatively little is known about how cell type- or cell state-specific alternative splicing is achieved in the heart. Here, we show that GATA4 regulates cell type-specific splicing through direct interaction with RNA and the spliceosome in human induced pluripotent stem cell-derived cardiac progenitors. METHODS: We leveraged a combination of unbiased approaches including affinity purification of GATA4 and mass spectrometry, enhanced cross-linking with immunoprecipitation, electrophoretic mobility shift assays, in vitro splicing assays, and unbiased transcriptomic analysis to uncover GATA4's novel function as a splicing regulator in human induced pluripotent stem cell-derived cardiac progenitors. RESULTS: We found that GATA4 interacts with many members of the spliceosome complex in human induced pluripotent stem cell-derived cardiac progenitors. Enhanced cross-linking with immunoprecipitation demonstrated that GATA4 also directly binds to a large number of mRNAs through defined RNA motifs in a sequence-specific manner. In vitro splicing assays indicated that GATA4 regulates alternative splicing through direct RNA binding, resulting in functionally distinct protein products. Correspondingly, knockdown of GATA4 in human induced pluripotent stem cell-derived cardiac progenitors resulted in differential alternative splicing of genes involved in cytoskeleton organization and calcium ion import, with functional consequences associated with the protein isoforms. CONCLUSIONS: This study shows that in addition to its well described transcriptional function, GATA4 interacts with members of the spliceosome complex and regulates cell type-specific alternative splicing via sequence-specific interactions with RNA. Several genes that have splicing regulated by GATA4 have functional consequences and many are associated with dilated cardiomyopathy, suggesting a novel role for GATA4 in achieving the necessary cardiac proteome in normal and stress-responsive conditions.

Moving bar of light evokes vectorial spatial selectivity in the immobile rat hippocampus.

Visual cortical neurons encode the position and motion direction of specific stimuli retrospectively, without any locomotion or task demand1. The hippocampus, which is a part of the visual system, is hypothesized to require self-motion or a cognitive task to generate allocentric spatial selectivity that is scalar, abstract2,3 and prospective4-7. Here we measured rodent hippocampal selectivity to a moving bar of light in a body-fixed rat to bridge these seeming disparities. About 70% of dorsal CA1 neurons showed stable activity modulation as a function of the angular position of the bar, independent of behaviour and rewards. One-third of tuned cells also encoded the direction of revolution. In other experiments, neurons encoded the distance of the bar, with preference for approaching motion. Collectively, these demonstrate visually evoked vectorial selectivity (VEVS). Unlike place cells, VEVS was retrospective. Changes in the visual stimulus or its predictability did not cause remapping but only caused gradual changes. Most VEVS-tuned neurons behaved like place cells during spatial exploration and the two selectivities were correlated. Thus, VEVS could form the basic building block of hippocampal activity. When combined with self-motion, reward or multisensory stimuli8, it can generate the complexity of prospective representations including allocentric space9, time10,11 and episodes12.

Transcription factor protein interactomes reveal genetic determinants in heart disease.

Congenital heart disease (CHD) is present in 1% of live births, yet identification of causal mutations remains challenging. We hypothesized that genetic determinants for CHDs may lie in the protein interactomes of transcription factors whose mutations cause CHDs. Defining the interactomes of two transcription factors haplo-insufficient in CHD, GATA4 and TBX5, within human cardiac progenitors, and integrating the results with nearly 9,000 exomes from proband-parent trios revealed an enrichment of de novo missense variants associated with CHD within the interactomes. Scoring variants of interactome members based on residue, gene, and proband features identified likely CHD-causing genes, including the epigenetic reader GLYR1. GLYR1 and GATA4 widely co-occupied and co-activated cardiac developmental genes, and the identified GLYR1 missense variant disrupted interaction with GATA4, impairing in vitro and in vivo function in mice. This integrative proteomic and genetic approach provides a framework for prioritizing and interrogating genetic variants in heart disease.

Transient Cell Cycle Induction in Cardiomyocytes to Treat Subacute Ischemic Heart Failure.

BACKGROUND: The regenerative capacity of the heart after myocardial infarction is limited. Our previous study showed that ectopic introduction of 4 cell cycle factors (4F; CDK1 [cyclin-dependent kinase 1], CDK4 [cyclin-dependent kinase 4], CCNB [cyclin B1], and CCND [cyclin D1]) promotes cardiomyocyte proliferation in 15% to 20% of infected cardiomyocytes in vitro and in vivo and improves cardiac function after myocardial infarction in mice. METHODS: Using temporal single-cell RNA sequencing, we aimed to identify the necessary reprogramming stages during the forced cardiomyocyte proliferation with 4F on a single cell basis. Using rat and pig models of ischemic heart failure, we aimed to start the first preclinical testing to introduce 4F gene therapy as a candidate for the treatment of ischemia-induced heart failure. RESULTS: Temporal bulk and single-cell RNA sequencing and further biochemical validations of mature human induced pluripotent stem cell-derived cardiomyocytes treated with either LacZ or 4F adenoviruses revealed full cell cycle reprogramming in 15% of the cardiomyocyte population at 48 hours after infection with 4F, which was associated mainly with sarcomere disassembly and metabolic reprogramming (n=3/time point/group). Transient overexpression of 4F, specifically in cardiomyocytes, was achieved using a polycistronic nonintegrating lentivirus (NIL) encoding 4F; each is driven by a TNNT2 (cardiac troponin T isoform 2) promoter (TNNT2-4Fpolycistronic-NIL). TNNT2-4Fpolycistronic-NIL or control virus was injected intramyocardially 1 week after myocardial infarction in rats (n=10/group) or pigs (n=6-7/group). Four weeks after injection, TNNT2-4Fpolycistronic-NIL-treated animals showed significant improvement in left ventricular ejection fraction and scar size compared with the control virus-treated animals. At 4 months after treatment, rats that received TNNT2-4Fpolycistronic-NIL still showed a sustained improvement in cardiac function and no obvious development of cardiac arrhythmias or systemic tumorigenesis (n=10/group). CONCLUSIONS: This study provides mechanistic insights into the process of forced cardiomyocyte proliferation and advances the clinical feasibility of this approach by minimizing the oncogenic potential of the cell cycle factors owing to the use of a novel transient and cardiomyocyte-specific viral construct.

scNetViz: from single cells to networks using Cytoscape.

Single-cell RNA-sequencing (scRNA-seq) has revolutionized molecular biology and medicine by enabling high-throughput studies of cellular heterogeneity in diverse tissues. Applying network biology approaches to scRNA-seq data can provide useful insights into genes driving heterogeneous cell-type compositions of tissues. Here, we present scNetViz-  a Cytoscape app to aid biological interpretation of cell clusters in scRNA-seq data using network analysis. scNetViz calculates the differential expression of each gene across clusters and then creates a cluster-specific gene functional interaction network between the significantly differentially expressed genes for further analysis, such as pathway enrichment analysis. To automate a complete data analysis workflow, scNetViz integrates parts of the Scanpy software, which is a popular Python package for scRNA-seq data analysis, with Cytoscape apps such as stringApp, cyPlot, and enhancedGraphics. We describe our implementation of methods for accessing data from public single cell atlas projects, differential expression analysis, visualization, and automation. scNetViz enables users to analyze data from public atlases or their own experiments, which we illustrate with two use cases. Analysis can be performed via the Cytoscape GUI or CyREST programming interface using R (RCy3) or Python (py4cytoscape).

Trends of adult height in India from 1998 to 2015: Evidence from the National Family and Health Survey.

AIM: The aim of the study is to investigate the trends in adult height between two consecutive surveys of NHFS and explore differences across variables such as gender, wealth, social groups etc. METHODS: We used the NFHS-II (1998-99), NFHS-III (2005-2006) and NFHS-IV (2015-16) (all three for women and last two for men) data to examine the trends in average height. Comparison was done between the two age strata of 15-25 and 26-50 years, across both male and female, to assess the trends. RESULTS: Between NFHS-III and NFHS-IV, the average height of women in the age group of 15-25 showed a decline by 0.12 cm [95% CI, -0.24 to 0.00, p-0.051] while in the 26-50 years age strata it demonstrated significant improvement in the mean height by 0.13 cm [95% CI, 0.02 to 0.023, p-0.015]. However, Between NFHS III and IV, the average height of women in the poorest wealth index category registered a significant decline [-0.57cm, 95% CI, -076 to -0.37, p-0.000]. Between NFHS III and IV, the average height of Scheduled Tribe (ST) women in the age group of 15-25 years also exhibited a significant decline by 0.42 cm, [95% CI, -0.73 to -0.12, p-0.007]. Among men, between the two surveys, both the age groups of 15-25 years and 26-50 years showed significant decline in average height: 1.10 cm [95% CI, -1.31 to -.099 cm, p-0.00] and 0.86 cm [95% CI, -1.03 to -0.69, p-0.000], respectively. CONCLUSION: In the context of an overall increase in average heights worldwide the decline in average height of adults in India is alarming and demands an urgent enquiry. The argument for different standards of height for Indian population as different genetic group needs further scrutiny. However, the trends from India clearly underline the need to examine the non-genetic factors also to understand the interplay of genetic, nutritional and other social and environmental determinants on height.

Phenotypic Differences between the Alzheimer's Disease-Related hAPP-J20 Model and Heterozygous Zbtb20 Knock-Out Mice.

Diverse gene products contribute to the pathogenesis of Alzheimer's disease (AD). Experimental models have helped elucidate their mechanisms and impact on brain functions. Human amyloid precursor protein (hAPP) transgenic mice from line J20 (hAPP-J20 mice) are widely used to simulate key aspects of AD. However, they also carry an insertional mutation in noncoding sequence of one Zbtb20 allele, a gene involved in neural development. We demonstrate that heterozygous hAPP-J20 mice have reduced Zbtb20 expression in some AD-relevant brain regions, but not others, and that Zbtb20 levels are higher in hAPP-J20 mice than heterozygous Zbtb20 knock-out (Zbtb20+/-) mice. Whereas hAPP-J20 mice have premature mortality, severe deficits in learning and memory, other behavioral alterations, and prominent nonconvulsive epileptiform activity, Zbtb20+/- mice do not. Thus, the insertional mutation in hAPP-J20 mice does not ablate the affected Zbtb20 allele and is unlikely to account for the AD-like phenotype of this model.

Introducing R as a smart version of calculators enables beginners to explore it on their own.

Rapid technological advances in the past decades have enabled molecular biologists to generate large-scale and complex data with affordable resource investments, or obtain such data from public repositories. Yet, many graduate students, postdoctoral scholars, and senior researchers in the biosciences find themselves ill-equipped to analyze large-scale data. Global surveys have revealed that active researchers prefer short training workshops to fill their skill gaps. In this article, we focus on the challenge of delivering a short data analysis workshop to absolute beginners in computer programming. We propose that introducing R or other programming languages for data analysis as smart versions of calculators can help lower the communication barrier with absolute beginners. We describe this comparison with a few analogies and hope that other instructors will find them useful. We utilized these in our four-hour long training workshops involving participatory live coding, which we delivered in person and via videoconferencing. Anecdotal evidence suggests that our exposition made R programming seem easy and enabled beginners to explore it on their own.

Experimental and real-world evidence supporting the computational repurposing of bumetanide for APOE4-related Alzheimer's disease.

The evident genetic, pathological, and clinical heterogeneity of Alzheimer's disease (AD) poses challenges for traditional drug development. We conducted a computational drug repurposing screen for drugs to treat apolipoprotein (apo) E4-related AD. We first established apoE-genotype-dependent transcriptomic signatures of AD by analyzing publicly-available human brain database. We then queried these signatures against the Connectivity Map database containing transcriptomic perturbations of >1300 drugs to identify those that best reverse apoE-genotype-specific AD signatures. Bumetanide was identified as a top drug for apoE4 AD. Bumetanide treatment of apoE4 mice without or with Aß accumulation rescued electrophysiological, pathological, or cognitive deficits. Single-nucleus RNA-sequencing revealed transcriptomic reversal of AD signatures in specific cell types in these mice, a finding confirmed in apoE4-iPSC-derived neurons. In humans, bumetanide exposure was associated with a significantly lower AD prevalence in individuals over the age of 65 in two electronic health record databases, suggesting effectiveness of bumetanide in preventing AD.

Urn models for stochastic gene expression yield intuitive insights into the probability distributions of single-cell mRNA and protein counts.

Fitting the probability mass functions from analytical solutions of stochastic models of gene expression to the single-cell count distributions of mRNA and protein molecules can yield valuable insights into mechanisms underlying gene expression. Solutions of chemical master equations are available for various kinetic schemes but, even for the basic ON-OFF genetic switch, they take complex forms with generating functions given as hypergeometric functions. Interpretation of gene expression dynamics in terms of bursts is not consistent with the complete range of parameters for these functions. Physical insights into the probability mass functions are essential to ensure proper interpretations but are lacking for models considering genetic switches. To fill this gap, we develop urn models for stochastic gene expression. We sample RNA polymerases or ribosomes from a master urn, which represents the cytosol, and assign them to recipient urns of two or more colors, which represent time intervals in which no switching occurs. Colors of the recipient urns represent sub-systems of the promoter states, and the assignments to urns of a specific color represent gene expression. We use elementary principles of discrete probability theory to solve a range of kinetic models without feedback, including the Peccoud-Ycart model, the Shahrezaei-Swain model, and models with an arbitrary number of promoter states. In the last case, we obtain a novel result for the protein distribution. For activated genes, we show that transcriptional lapses, which are events of gene inactivation for short time intervals separated by long active intervals, quantify the transcriptional dynamics better than bursts. We show that the intuition gained from our urn models may also be useful in understanding existing solutions for models with feedback. We contrast our models with urn models for related distributions, discuss a generalization of the Delaporte distribution for single-cell data analysis, and highlight the limitations of our models.

Stability and nuclear localization of yeast telomerase depend on protein components of RNase P/MRP.

RNase P and MRP are highly conserved, multi-protein/RNA complexes with essential roles in processing ribosomal and tRNAs. Three proteins found in both complexes, Pop1, Pop6, and Pop7 are also telomerase-associated. Here, we determine how temperature sensitive POP1 and POP6 alleles affect yeast telomerase. At permissive temperatures, mutant Pop1/6 have little or no effect on cell growth, global protein levels, the abundance of Est1 and Est2 (telomerase proteins), and the processing of TLC1 (telomerase RNA). However, in pop mutants, TLC1 is more abundant, telomeres are short, and TLC1 accumulates in the cytoplasm. Although Est1/2 binding to TLC1 occurs at normal levels, Est1 (and hence Est3) binding is highly unstable. We propose that Pop-mediated stabilization of Est1 binding to TLC1 is a pre-requisite for formation and nuclear localization of the telomerase holoenzyme. Furthermore, Pop proteins affect TLC1 and the RNA subunits of RNase P/MRP in very different ways.

Artificial plant optimization algorithm to detect heart rate & presence of heart disease using machine learning.

In today's world, cardiovascular diseases are prevalent becoming the leading cause of death; more than half of the cardiovascular diseases are due to Coronary Heart Disease (CHD) which generates the demand of predicting them timely so that people can take precautions or treatment before it becomes fatal. For serving this purpose a Modified Artificial Plant Optimization (MAPO) algorithm has been proposed which can be used as an optimal feature selector along with other machine learning algorithms to predict the heart rate using the fingertip video dataset which further predicts the presence or absence of Coronary Heart Disease in an individual at the moment. Initially, the video dataset has been pre-processed, noise is filtered and then MAPO is applied to predict the heart rate with a Pearson correlation and Standard Error Estimate of 0.9541 and 2.418 respectively. The predicted heart rate is used as a feature in other two datasets and MAPO is again applied to optimize the features of both datasets. Different machine learning algorithms are then applied to the optimized dataset to predict values for presence of current heart disease. The result shows that MAPO reduces the dimensionality to the most significant information with comparable accuracies for different machine learning models with maximum dimensionality reduction of 81.25%. MAPO has been compared with other optimizers and outperforms them with better accuracy.

Analytical Expressions and Physics for Single-Cell mRNA Distributions of the lac Operon of E. coli.

Mechanistic models of stochastic gene expression are of considerable interest, but their complexity often precludes tractable analytical expressions for messenger RNA (mRNA) and protein distributions. The lac operon of Escherichia coli is a model system with regulatory elements such as multiple operators and DNA looping that are shared by many operons. Although this system is complex, intuition suggests that fast DNA looping may simplify it by causing the repressor-bound states of the operon to equilibrate rapidly, thus ensuring that the subsequent dynamics are governed by slow transitions between the repressor-free and the equilibrated repressor-bound states. Here, we show that this intuition is correct by applying singular perturbation theory to a mechanistic model of lac transcription with the scaled time constant of DNA looping as the perturbation parameter. We find that at steady state, the repressor-bound states satisfy detailed balance and are dominated by the looped states; moreover, the interaction between the repressor-free and the equilibrated repressor-bound states is described by an extension of the Peccoud-Ycart two-state model in which both (repressor-free and repressor-bound) states support transcription. The solution of this extended two-state model reveals that the steady-state mRNA distribution is a mixture of the Poisson and negative hypergeometric distributions, which reflects mRNAs obtained by transcription from the repressor-bound and repressor-free states. Finally, we show that the physics revealed by perturbation theory makes it easy to derive the extended two-state model equations for complex regulatory architectures.

Genome-Wide Discovery of DEAD-Box RNA Helicase Targets Reveals RNA Structural Remodeling in Transcription Termination.

RNA helicases are a class of enzymes that unwind RNA duplexes in vitro but whose cellular functions are largely enigmatic. Here, we provide evidence that the DEAD-box protein Dbp2 remodels RNA-protein complex (RNP) structure to facilitate efficient termination of transcription in Saccharomyces cerevisiae via the Nrd1-Nab3-Sen1 (NNS) complex. First, we find that loss of DBP2 results in RNA polymerase II accumulation at the 3' ends of small nucleolar RNAs and a subset of mRNAs. In addition, Dbp2 associates with RNA sequence motifs and regions bound by Nrd1 and can promote its recruitment to NNS-targeted regions. Using Structure-seq, we find altered RNA/RNP structures in dbp2∆ cells that correlate with inefficient termination. We also show a positive correlation between the stability of structures in the 3' ends and a requirement for Dbp2 in termination. Taken together, these studies provide a role for RNA remodeling by Dbp2 and further suggests a mechanism whereby RNA structure is exploited for gene regulation.