Repression of RNA Polymerase II Transcription by a Drosophila Oligopeptide

Background: Germline progenitors resist signals that promote differentiation into somatic cells. This occurs through the transient repression in primordial germ cells of RNA polymerase II, specifically by disrupting Ser2 phosphorylation on its C-terminal domain. Methodology/Principal Findings: Here we show that contrary to expectation the Drosophila polar granule component (pgc) gene functions as a protein rather than a non-coding RNA. Surprisingly, pgc encodes a 71-residue, dimeric, alphahelical oligopeptide repressor. In vivo data show that Pgc ablates Ser2 phosphorylation of the RNA polymerase II C-terminal domain and completely suppresses early zygotic transcription in the soma. Conclusions/Significance: We thus identify pgc as a novel oligopeptide that readily inhibits gene expression. Germ cell repression of transcription in Drosophila is thus catalyzed by a small inhibitor protein

Examining the Connections within the Startup Ecosystem: A Case Study of St. Louis

This paper documents the resurgence of entrepreneurial activity in St. Louis by reporting on the collaboration and local learning within the startup community. This activity is happening both between entrepreneurs and between organizations that provide support, such as mentoring and funding, to entrepreneurs. As these connections deepen, the strength of the entrepreneurial ecosystem grows. Another finding from the research is that activity-based events, where entrepreneurs have the chance to use and practice the skills needed to grow their businesses, are most useful. St. Louis provides a multitude of these activities, such as Startup Weekend, 1 Million Cups, Code Until Dawn, StartLouis, and GlobalHack. Some of these are St. Louis specific, but others have nationwide or global operations, providing important implications for other cities

Intensive care of the cancer patient: recent achievements and remaining challenges

A few decades have passed since intensive care unit (ICU) beds have been available for critically ill patients with cancer. Although the initial reports showed dismal prognosis, recent data suggest that an increased number of patients with solid and hematological malignancies benefit from intensive care support, with dramatically decreased mortality rates. Advances in the management of the underlying malignancies and support of organ dysfunctions have led to survival gains in patients with life-threatening complications from the malignancy itself, as well as infectious and toxic adverse effects related to the oncological treatments. In this review, we will appraise the prognostic factors and discuss the overall perspective related to the management of critically ill patients with cancer. The prognostic significance of certain factors has changed over time. For example, neutropenia or autologous bone marrow transplantation (BMT) have less adverse prognostic implications than two decades ago. Similarly, because hematologists and oncologists select patients for ICU admission based on the characteristics of the malignancy, the underlying malignancy rarely influences short-term survival after ICU admission. Since the recent data do not clearly support the benefit of ICU support to unselected critically ill allogeneic BMT recipients, more outcome research is needed in this subgroup. Because of the overall increased survival that has been reported in critically ill patients with cancer, we outline an easy-to-use and evidence-based ICU admission triage criteria that may help avoid depriving life support to patients with cancer who can benefit. Lastly, we propose a research agenda to address unanswered questions

Noncanonical Compensation of Zygotic X Transcription in Early Drosophila melanogaster Development Revealed through Single-Embryo RNA-Seq

Mmany genes from the X chromosome are expressed at the same level in female and male embryos during early Drosophila development, prior to the establishment of MSL-mediated dosage compensation, suggesting the existence of a novel mechanism

CCC meets ICU: Redefining the role of critical care of cancer patients

<p>Abstract</p> <p>Background</p> <p>Currently the majority of cancer patients are considered ineligible for intensive care treatment and oncologists are struggling to get their patients admitted to intensive care units. Critical care and oncology are frequently two separate worlds that communicate rarely and thus do not share novel developments in their fields. However, cancer medicine is rapidly improving and cancer is eventually becoming a chronic disease. Oncology is therefore characterized by a growing number of older and medically unfit patients that receive numerous novel drug classes with unexpected side effects.</p> <p>Discussion</p> <p>All of these changes will generate more medically challenging patients in acute distress that need to be considered for intensive care. An intense exchange between intensivists, oncologists, psychologists and palliative care specialists is warranted to communicate the developments in each field in order to improve triage and patient treatment. Here, we argue that "critical care of cancer patients" needs to be recognized as a medical subspecialty and that there is an urgent need to develop it systematically.</p> <p>Conclusion</p> <p>As prognosis of cancer improves, novel therapeutic concepts are being introduced and more and more older cancer patients receive full treatment the number of acutely ill patients is growing significantly. This development a major challenge to current concepts of intensive care and it needs to be redefined who of these patients should be treated, for how long and how intensively.</p

Global parameter estimation methods for stochastic biochemical systems

<p>Abstract</p> <p>Background</p> <p>The importance of stochasticity in cellular processes having low number of molecules has resulted in the development of stochastic models such as chemical master equation. As in other modelling frameworks, the accompanying rate constants are important for the end-applications like analyzing system properties (e.g. robustness) or predicting the effects of genetic perturbations. Prior knowledge of kinetic constants is usually limited and the model identification routine typically includes parameter estimation from experimental data. Although the subject of parameter estimation is well-established for deterministic models, it is not yet routine for the chemical master equation. In addition, recent advances in measurement technology have made the quantification of genetic substrates possible to single molecular levels. Thus, the purpose of this work is to develop practical and effective methods for estimating kinetic model parameters in the chemical master equation and other stochastic models from single cell and cell population experimental data.</p> <p>Results</p> <p>Three parameter estimation methods are proposed based on the maximum likelihood and density function distance, including probability and cumulative density functions. Since stochastic models such as chemical master equations are typically solved using a Monte Carlo approach in which only a finite number of Monte Carlo realizations are computationally practical, specific considerations are given to account for the effect of finite sampling in the histogram binning of the state density functions. Applications to three practical case studies showed that while maximum likelihood method can effectively handle low replicate measurements, the density function distance methods, particularly the cumulative density function distance estimation, are more robust in estimating the parameters with consistently higher accuracy, even for systems showing multimodality.</p> <p>Conclusions</p> <p>The parameter estimation methodologies described in this work have provided an effective and practical approach in the estimation of kinetic parameters of stochastic systems from either sparse or dense cell population data. Nevertheless, similar to kinetic parameter estimation in other modelling frameworks, not all parameters can be estimated accurately, which is a common problem arising from the lack of complete parameter identifiability from the available data.</p

Noise-Driven Stem Cell and Progenitor Population Dynamics

BACKGROUND: The balance between maintenance of the stem cell state and terminal differentiation is influenced by the cellular environment. The switching between these states has long been understood as a transition between attractor states of a molecular network. Herein, stochastic fluctuations are either suppressed or can trigger the transition, but they do not actually determine the attractor states. METHODOLOGY/PRINCIPAL FINDINGS: We present a novel mathematical concept in which stem cell and progenitor population dynamics are described as a probabilistic process that arises from cell proliferation and small fluctuations in the state of differentiation. These state fluctuations reflect random transitions between different activation patterns of the underlying regulatory network. Importantly, the associated noise amplitudes are state-dependent and set by the environment. Their variability determines the attractor states, and thus actually governs population dynamics. This model quantitatively reproduces the observed dynamics of differentiation and dedifferentiation in promyelocytic precursor cells. CONCLUSIONS/SIGNIFICANCE: Consequently, state-specific noise modulation by external signals can be instrumental in controlling stem cell and progenitor population dynamics. We propose follow-up experiments for quantifying the imprinting influence of the environment on cellular noise regulation.Engineering and Applied SciencesOther Research Uni

A New Pipeline for the Normalization and Pooling of Metabolomics Data

Pooling metabolomics data across studies is often desirable to increase the statistical power of the analysis. However, this can raise methodological challenges as several preanalytical and analytical factors could introduce differences in measured concentrations and variability between datasets. Specifically, different studies may use variable sample types (e.g., serum versus plasma) collected, treated, and stored according to different protocols, and assayed in different laboratories using different instruments. To address these issues, a new pipeline was developed to normalize and pool metabolomics data through a set of sequential steps: (i) exclusions of the least informative observations and metabolites and removal of outliers; imputation of missing data; (ii) identification of the main sources of variability through principal component partial R-square (PC-PR2) analysis; (iii) application of linear mixed models to remove unwanted variability, including samples' originating study and batch, and preserve biological variations while accounting for potential differences in the residual variances across studies. This pipeline was applied to targeted metabolomics data acquired using Biocrates AbsoluteIDQ kits in eight case-control studies nested within the European Prospective Investigation into Cancer and Nutrition (EPIC) cohort. Comprehensive examination of metabolomics measurements indicated that the pipeline improved the comparability of data across the studies. Our pipeline can be adapted to normalize other molecular data, including biomarkers as well as proteomics data, and could be used for pooling molecular datasets, for example in international consortia, to limit biases introduced by inter-study variability. This versatility of the pipeline makes our work of potential interest to molecular epidemiologists

Comparisons between Chemical Mapping and Binding to Isoenergetic Oligonucleotide Microarrays Reveal Unexpected Patterns of Binding to the Bacillus subtilis RNase P RNA Specificity Domain†

ABSTRACT: Microarrays with isoenergetic pentamer and hexamer 20-O-methyl oligonucleotide probes with LNA (locked nucleic acid) and 2,6-diaminopurine substitutions were used to probe the binding sites on theRNase P RNA specificity domain of Bacillus subtilis. Unexpected binding patterns were revealed. Because of their enhanced binding free energies, isoenergetic probes can break short duplexes, merge adjacent loops, and/or induce refolding. This suggests new approaches to the rational design of short oligonucleotide therapeutics but limits the utility of microarrays for providing constraints for RNA structure determination. The microarray results are compared to results from chemical mapping experiments, which do provide constraints. Results from both types of experiments indicate that the RNase P RNA folds similarly in 1MNaþ and 10 mMMg2þ. Binding of RNA to RNA is important for many natural func-tions, includingproteinsynthesis (1,2), translationregulation (3,4), gene silencing (5, 6), metabolic regulation (7), RNAmodification (8, 9), etc. (10-13). Binding of oligonucleotides toRNAs is impor-tant for therapeutic approaches, such as siRNA, ribozymes, and antisense therapy (14, 15).Much remains to bediscovered, however, of the rules for predicting binding sites andpotential therapeutics