NRL-Regulated Transcriptome Dynamics of Developing Rod Photoreceptors

SummaryGene regulatory networks (GRNs) guiding differentiation of cell types and cell assemblies in the nervous system are poorly understood because of inherent complexities and interdependence of signaling pathways. Here, we report transcriptome dynamics of differentiating rod photoreceptors in the mammalian retina. Given that the transcription factor NRL determines rod cell fate, we performed expression profiling of developing NRL-positive (rods) and NRL-negative (S-cone-like) mouse photoreceptors. We identified a large-scale, sharp transition in the transcriptome landscape between postnatal days 6 and 10 concordant with rod morphogenesis. Rod-specific temporal DNA methylation corroborated gene expression patterns. De novo assembly and alternative splicing analyses revealed previously unannotated rod-enriched transcripts and the role of NRL in transcript maturation. Furthermore, we defined the relationship of NRL with other transcriptional regulators and downstream cognate effectors. Our studies provide the framework for comprehensive system-level analysis of the GRN underlying the development of a single sensory neuron, the rod photoreceptor

Elucidating the Role of KLF7 in Hematopoiesis

Mentor: Daniel Link From the Washington University Undergraduate Research Digest: WUURD, Volume 8, Issue 1, Fall 2012. Published by the Office of Undergraduate Research, Joy Zalis Kiefer Director of Undergraduate Research and Assistant Dean in the College of Arts & Sciences

Identification of a Hypothesized NMUR/EPOR Complex

Mentor: Martin Carroll, University of Pennsylvania From the Washington University Undergraduate Research Digest: WUURD, Volume 9, Issue 1, Fall 2013. Published by the Office of Undergraduate Research. Joy Zalis Kiefer Director of Undergraduate Research and Assistant Dean in the College of Arts & Sciences

Next generation sequencing technology and genomewide data analysis: Perspectives for retinal research

AbstractThe advent of high throughput next generation sequencing (NGS) has accelerated the pace of discovery of disease-associated genetic variants and genomewide profiling of expressed sequences and epigenetic marks, thereby permitting systems-based analyses of ocular development and disease. Rapid evolution of NGS and associated methodologies presents significant challenges in acquisition, management, and analysis of large data sets and for extracting biologically or clinically relevant information. Here we illustrate the basic design of commonly used NGS-based methods, specifically whole exome sequencing, transcriptome, and epigenome profiling, and provide recommendations for data analyses. We briefly discuss systems biology approaches for integrating multiple data sets to elucidate gene regulatory or disease networks. While we provide examples from the retina, the NGS guidelines reviewed here are applicable to other tissues/cell types as well

Kruppel-like factor 7 overexpression suppresses hematopoietic stem and progenitor cell function

Increased expression of Kruppel-like factor 7 (KLF7) is an independent predictor of poor outcome in pediatric acute lymphoblastic leukemia. The contribution of KLF7 to hematopoiesis has not been previously described. Herein, we characterized the effect on murine hematopoiesis of the loss of KLF7 and enforced expression of KLF7. Long-term multilineage engraftment of Klf7(-/-) cells was comparable with control cells, and self-renewal, as assessed by serial transplantation, was not affected. Enforced expression of KLF7 results in a marked suppression of myeloid progenitor cell growth and a loss of short- and long-term repopulating activity. Interestingly, enforced expression of KLF7, although resulting in multilineage growth suppression that extended to hematopoietic stem cells and common lymphoid progenitors, spared T cells and enhanced the survival of early thymocytes. RNA expression profiling of KLF7-overexpressing hematopoietic progenitors identified several potential target genes mediating these effects. Notably, the known KLF7 target Cdkn1a (p21(Cip1/Waf1)) was not induced by KLF7, and loss of CDKN1A does not rescue the repopulating defect. These results suggest that KLF7 is not required for normal hematopoietic stem and progenitor function, but increased expression, as seen in a subset of lymphoid leukemia, inhibits myeloid cell proliferation and promotes early thymocyte survival. (Blood. 2012; 120(15): 2981-2989

Early and fair COVID-19 outcome risk assessment using robust feature selection

Abstract Personalized medicine plays an important role in treatment optimization for COVID-19 patient management. Early treatment in patients at high risk of severe complications is vital to prevent death and ventilator use. Predicting COVID-19 clinical outcomes using machine learning may provide a fast and data-driven solution for optimizing patient care by estimating the need for early treatment. In addition, it is essential to accurately predict risk across demographic groups, particularly those underrepresented in existing models. Unfortunately, there is a lack of studies demonstrating the equitable performance of machine learning models across patient demographics. To overcome this existing limitation, we generate a robust machine learning model to predict patient-specific risk of death or ventilator use in COVID-19 positive patients using features available at the time of diagnosis. We establish the value of our solution across patient demographics, including gender and race. In addition, we improve clinical trust in our automated predictions by generating interpretable patient clustering, patient-level clinical feature importance, and global clinical feature importance within our large real-world COVID-19 positive patient dataset. We achieved 89.38% area under receiver operating curve (AUROC) performance for severe outcomes prediction and our robust feature ranking approach identified the presence of dementia as a key indicator for worse patient outcomes. We also demonstrated that our deep-learning clustering approach outperforms traditional clustering in separating patients by severity of outcome based on mutual information performance. Finally, we developed an application for automated and fair patient risk assessment with minimal manual data entry using existing data exchange standards

MEDICI: Mining Essentiality Data to Identify Critical Interactions for Cancer Drug Target Discovery and Development

<div><p>Protein-protein interactions (PPIs) mediate the transmission and regulation of oncogenic signals that are essential to cellular proliferation and survival, and thus represent potential targets for anti-cancer therapeutic discovery. Despite their significance, there is no method to experimentally disrupt and interrogate the essentiality of individual endogenous PPIs. The ability to computationally predict or infer <i>PPI essentiality</i> would help prioritize PPIs for drug discovery and help advance understanding of cancer biology. Here we introduce a computational method (MEDICI) to predict <i>PPI essentiality</i> by combining gene knockdown studies with network models of protein interaction pathways in an analytic framework. Our method uses network topology to model how gene silencing can disrupt PPIs, relating the unknown essentialities of individual PPIs to experimentally observed protein essentialities. This model is then deconvolved to recover the unknown essentialities of individual PPIs. We demonstrate the validity of our approach via prediction of sensitivities to compounds based on PPI essentiality and differences in essentiality based on genetic mutations. We further show that lung cancer patients have improved overall survival when specific PPIs are no longer present, suggesting that these PPIs may be potentially new targets for therapeutic development. Software is freely available at <a href="https://github.com/cooperlab/MEDICI" target="_blank">https://github.com/cooperlab/MEDICI</a>. Datasets are available at <a href="https://ctd2.nci.nih.gov/dataPortal" target="_blank">https://ctd2.nci.nih.gov/dataPortal</a>.</p></div

Correlating interaction essentialities with drug sensitivity measures provides insights into mechanisms of action.

<p>We correlated drug sensitivity measures from CCLE with interaction essentiality scores to identify critical interactions that predict therapeutic sensitivity. Sensitivity to the MAPK inhibitor AZD6244 is highly correlated with PRKDC-TP53 interaction essentiality, which is consistent with the well established role of p38-MAPK in cell cycle arrest in response to DNA damage [<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0170339#pone.0170339.ref051" target="_blank">51</a>–<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0170339#pone.0170339.ref053" target="_blank">53</a>].</p

Clustering of most essential PPIs in the superpathway.

<p>(A) Unsupervised Hierarchical Clustering of the 360 most essential PPIs across the 165 cell lines identifies 12 major clusters. The 360 PPIs with an average essentiality score > 0.5 were used to cluster 165 cell lines used in the Achilles shRNA screening study using Cluster and Java Treeview software [<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0170339#pone.0170339.ref029" target="_blank">29</a>]. PPI essentiality data was median centered and clustered by average correlation. Red indicates higher essentiality and blue indicates lower essentiality. Major hubs for each cluster are indicated on the right. (B) Clustering of 5798 PPI-MPER values across 165 cell lines. Red indicates PPI essentiality is greater than the max protein essentiality, and blue indicates the PPI essentiality is less than the max protein essentiality.</p