Shana White

Bio: Shana White is an academic researcher from University of Cincinnati. The author has contributed to research in topics: Medicine & Loss function. The author has an hindex of 4, co-authored 4 publications receiving 330 citations. Previous affiliations of Shana White include University of Cincinnati Academic Health Center.

The Library of Integrated Network-Based Cellular Signatures NIH Program: System-Level Cataloging of Human Cells Response to Perturbations

Abstract: The Library of Integrated Network-Based Cellular Signatures (LINCS) is an NIH Common Fund program that catalogs how human cells globally respond to chemical, genetic, and disease perturbations. Resources generated by LINCS include experimental and computational methods, visualization tools, molecular and imaging data, and signatures. By assembling an integrated picture of the range of responses of human cells exposed to many perturbations, the LINCS program aims to better understand human disease and to advance the development of new therapies. Perturbations under study include drugs, genetic perturbations, tissue micro-environments, antibodies, and disease-causing mutations. Responses to perturbations are measured by transcript profiling, mass spectrometry, cell imaging, and biochemical methods, among other assays. The LINCS program focuses on cellular physiology shared among tissues and cell types relevant to an array of diseases, including cancer, heart disease, and neurodegenerative disorders. This Perspective describes LINCS technologies, datasets, tools, and approaches to data accessibility and reusability.

The Development of Spasmolytic Polypeptide/TFF2-Expressing Metaplasia (SPEM) During Gastric Repair Is Absent in the Aged Stomach

Abstract: Background & Aims During aging, physiological changes in the stomach result in more tenuous gastric tissue that is less capable of repairing injury, leading to increased susceptibility to chronic ulceration. Spasmolytic polypeptide/trefoil factor 2–expressing metaplasia (SPEM) is known to emerge after parietal cell loss and during Helicobacter pylori infection, however, its role in gastric ulcer repair is unknown. Therefore, we sought to investigate if SPEM plays a role in epithelial regeneration. Methods Acetic acid ulcers were induced in young (2–3 mo) and aged (18–24 mo) C57BL/6 mice to determine the quality of ulcer repair with advancing age. Yellow chameleon 3.0 mice were used to generate yellow fluorescent protein–expressing organoids for transplantation. Yellow fluorescent protein–positive gastric organoids were transplanted into the submucosa and lumen of the stomach immediately after ulcer induction. Gastric tissue was collected and analyzed to determine the engraftment of organoid-derived cells within the regenerating epithelium. Results Wound healing in young mice coincided with the emergence of SPEM within the ulcerated region, a response that was absent in the aged stomach. Although aged mice showed less metaplasia surrounding the ulcerated tissue, organoid-transplanted aged mice showed regenerated gastric glands containing organoid-derived cells. Organoid transplantation in the aged mice led to the emergence of SPEM and gastric regeneration. Conclusions These data show the development of SPEM during gastric repair in response to injury that is absent in the aged stomach. In addition, gastric organoids in an injury/transplantation mouse model promoted gastric regeneration.

Connecting omics signatures of diseases, drugs, and mechanisms of actions with iLINCS

Abstract: There are only a few platforms that integrate multiple omics data types, bioinformatics tools, and interfaces for integrative analyses and visualization that do not require programming skills. Among these, iLINCS is unique in scope and versatility of the data provided and the analytics facilitated. iLINCS (http://ilincs.org) is an integrative web-based platform for analysis of omics data and signatures of cellular perturbations. The platform facilitates analysis of user-submitted omics signatures of diseases and cellular perturbations in the context of a large compendium of pre-computed signatures (>200,000), as well as mining and re-analysis of the large collection of omics datasets (>12,000), pre-computed signatures, and their connections. Analytics workflows driven by user-friendly interfaces enable users with only conceptual understanding of the analysis strategy to execute sophisticated analyses of omics signatures, such as systems biology analyses and interpretation of signatures, mechanism of action analysis, and signature-driven drug repositioning. In summary, iLINCS workflows integrate vast omics data resources and a range of analytics and interactive visualization tools into a comprehensive platform for analysis of omics signatures.

Cognitive Performance Scores for the Pediatric Automated Neuropsychological Assessment Metrics in Childhood-Onset Systemic Lupus Erythematosus

Abstract: Background/Purpose: Children with SLE (cSLE) can experience neuropsychiatric SLE (NPSLE), commonly manifesting as neurocognitive dysfunction which can interfere with normal development. Formal neurocognitive testing (FNCT) is the most accepted test for diagnosing neurocognitive deficits (NCD). Access to it is limited and costly, and it is time-consuming. The Pediatric Automated Neurophysiological Assessments Metrics (PedANAM) is a computerized battery of 10 subtests measuring various aspects of cognitive ability. Concurrent validity of PedANAM test scores with FNCT has been demonstrated. However, the PedANAM generates several measures of accuracy (% of correct responses), processing speed and efficiency, and it is unclear how they can be used in a clinical setting. The usefulness of the PedANAM as a screen for NCD would be enhanced by the development of a PedANAM Cognitive Performance Score (PedANAM-CPS) to represent aspects of PedANAM task performance that are sensitive to NCD in cSLE. The purpose of this study was to develop a PedANAM-CPS for use in cSLE, using statistical methods. Methods: cSLE patients (pts) and age plus sex-matched healthy controls enrolled in a study of cognitive functioning and neuroimaging were studied. At the time of enrollment (visit 1—V1) and 18 months later (visit 2—V2), subjects completed the PedANAM and FNCT. Three candidate PedANAM-CPS measurement approaches were explored via 3 statistical methods: 1) Simple mean—of all subtest accuracy scores; 2) Logit score—via a logistic regression model; 3) PCA score—using a Principal Component Analysis (PCA) method. The latter 2 methods assigned in a different way a statistical weight to each subtest accuracy score. Fixed effect models were used to compare performance scores between groups. Receiver operating characteristic (ROC) curves were used to assess the accuracy of the CPS as predictors of NCD as determined by FNCT. Results: 77 children (female = 68%) were evaluated at V1; Nine cSLE pts with NCD, 31 with cSLE and no NCD, and 37 control with no NCD as per FNCT. At V1, age (values are mean ± standard deviation) of children was 13.6 ± 2.4 years. For cSLE pts, disease activity (SLEDAI) was 4.9 ± 4.4, and 77.5% were on oral prednisone (19.8 ± 17.4 mg). Table 1 summarizes the PedANAM-CPS for all methods at V1. The Logit score best discriminated the groups, especially contrasting the NCD group against the other groups. The Logit and PCA scores showed g 82% area under the ROC curve during the validation stage using V2 data. Table 1. Summary of PedANAM Cognitive Performance Score (PedANAMCPS) Visit Score Mean ± SD p-value (1) Control (2) cSLE No NCD (3) cSLE w. NCD (1) vs. 2) (1) vs. (3) (1) vs. (3) 1 Simple mean 88.79 ± 0.97 88.91 ± 1.06 85.18 ± 1.96 0.931 0.103 0.098 Logit score −0.04 ± 0.13 −0.04 ± 0.15 0.84 ± 0.27 0.992 0.005 0.006 PCA score 438.80 ± 4.22 441.30 ± 4.61 421.69 ± 8.55 0.690 0.077 0.057 Conclusion: Candidate PedANAM-CPS derived from the Logit and PCA scores seem to perform better than an index based on the simple means at discriminating cSLE pts with NCD from cSLE and control children with normal cognition. Further analysis in a larger sample is needed to better determine accuracy performance scores with clinical relevance.

Connecting omics signatures and revealing biological mechanisms with iLINCS

Abstract: Abstract There are only a few platforms that integrate multiple omics data types, bioinformatics tools, and interfaces for integrative analyses and visualization that do not require programming skills. Here we present iLINCS ( http://ilincs.org ), an integrative web-based platform for analysis of omics data and signatures of cellular perturbations. The platform facilitates mining and re-analysis of the large collection of omics datasets (>34,000), pre-computed signatures (>200,000), and their connections, as well as the analysis of user-submitted omics signatures of diseases and cellular perturbations. iLINCS analysis workflows integrate vast omics data resources and a range of analytics and interactive visualization tools into a comprehensive platform for analysis of omics signatures. iLINCS user-friendly interfaces enable execution of sophisticated analyses of omics signatures, mechanism of action analysis, and signature-driven drug repositioning. We illustrate the utility of iLINCS with three use cases involving analysis of cancer proteogenomic signatures, COVID 19 transcriptomic signatures and mTOR signaling.

mTOR Signaling in Growth, Metabolism, and Disease

Abstract: The mechanistic target of rapamycin (mTOR) coordinates eukaryotic cell growth and metabolism with environmental inputs, including nutrients and growth factors. Extensive research over the past two decades has established a central role for mTOR in regulating many fundamental cell processes, from protein synthesis to autophagy, and deregulated mTOR signaling is implicated in the progression of cancer and diabetes, as well as the aging process. Here, we review recent advances in our understanding of mTOR function, regulation, and importance in mammalian physiology. We also highlight how the mTOR signaling network contributes to human disease and discuss the current and future prospects for therapeutically targeting mTOR in the clinic.

The BioGRID interaction database: 2019 update

Abstract: The Biological General Repository for Interaction Datasets (BioGRID: https://thebiogrid.org) is an open access database dedicated to the curation and archival storage of protein, genetic and chemical interactions for all major model organism species and humans. As of September 2018 (build 3.4.164), BioGRID contains records for 1 598 688 biological interactions manually annotated from 55 809 publications for 71 species, as classified by an updated set of controlled vocabularies for experimental detection methods. BioGRID also houses records for >700 000 post-translational modification sites. BioGRID now captures chemical interaction data, including chemical-protein interactions for human drug targets drawn from the DrugBank database and manually curated bioactive compounds reported in the literature. A new dedicated aspect of BioGRID annotates genome-wide CRISPR/Cas9-based screens that report gene-phenotype and gene-gene relationships. An extension of the BioGRID resource called the Open Repository for CRISPR Screens (ORCS) database (https://orcs.thebiogrid.org) currently contains over 500 genome-wide screens carried out in human or mouse cell lines. All data in BioGRID is made freely available without restriction, is directly downloadable in standard formats and can be readily incorporated into existing applications via our web service platforms. BioGRID data are also freely distributed through partner model organism databases and meta-databases.

The intestinal epithelial barrier: a therapeutic target?

Abstract: A fundamental function of the intestinal epithelium is to act as a barrier that limits interactions between luminal contents such as the intestinal microbiota, the underlying immune system and the remainder of the body, while supporting vectorial transport of nutrients, water and waste products. Epithelial barrier function requires a contiguous layer of cells as well as the junctions that seal the paracellular space between epithelial cells. Compromised intestinal barrier function has been associated with a number of disease states, both intestinal and systemic. Unfortunately, most current clinical data are correlative, making it difficult to separate cause from effect in interpreting the importance of barrier loss. Some data from experimental animal models suggest that compromised epithelial integrity might have a pathogenic role in specific gastrointestinal diseases, but no FDA-approved agents that target the epithelial barrier are presently available. To develop such therapies, a deeper understanding of both disease pathogenesis and mechanisms of barrier regulation must be reached. Here, we review and discuss mechanisms of intestinal barrier loss and the role of intestinal epithelial barrier function in pathogenesis of both intestinal and systemic diseases. We conclude with a discussion of potential strategies to restore the epithelial barrier.

QSAR without borders

Abstract: Prediction of chemical bioactivity and physical properties has been one of the most important applications of statistical and more recently, machine learning and artificial intelligence methods in chemical sciences. This field of research, broadly known as quantitative structure–activity relationships (QSAR) modeling, has developed many important algorithms and has found a broad range of applications in physical organic and medicinal chemistry in the past 55+ years. This Perspective summarizes recent technological advances in QSAR modeling but it also highlights the applicability of algorithms, modeling methods, and validation practices developed in QSAR to a wide range of research areas outside of traditional QSAR boundaries including synthesis planning, nanotechnology, materials science, biomaterials, and clinical informatics. As modern research methods generate rapidly increasing amounts of data, the knowledge of robust data-driven modelling methods professed within the QSAR field can become essential for scientists working both within and outside of chemical research. We hope that this contribution highlighting the generalizable components of QSAR modeling will serve to address this challenge.

Mapping the landscape of artificial intelligence applications against COVID-19

Abstract: COVID-19, the disease caused by the SARS-CoV-2 virus, has been declared a pandemic by the World Health Organization, which has reported over 18 million confirmed cases as of August 5, 2020 In this review, we present an overview of recent studies using Machine Learning and, more broadly, Artificial Intelligence, to tackle many aspects of the COVID-19 crisis We have identified applications that address challenges posed by COVID-19 at different scales, including: molecular, by identifying new or existing drugs for treatment;clinical, by supporting diagnosis and evaluating prognosis based on medical imaging and non-invasive measures;and societal, by tracking both the epidemic and the accompanying infodemic using multiple data sources We also review datasets, tools, and resources needed to facilitate Artificial Intelligence research, and discuss strategic considerations related to the operational implementation of multidisciplinary partnerships and open science We highlight the need for international cooperation to maximize the potential of AI in this and future pandemics ©2020 AI Access Foundation All rights reserved