Anders B. Dohlman

Bio: Anders B. Dohlman is an academic researcher from Icahn School of Medicine at Mount Sinai. The author has contributed to research in topics: Biology & Medicine. The author has an hindex of 5, co-authored 5 publications receiving 286 citations. Previous affiliations of Anders B. Dohlman include Harvard University.

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.

Integration of pan-cancer transcriptomics with RPPA proteomics reveals mechanisms of epithelial-mesenchymal transition.

Abstract: Integrating data from multiple regulatory layers across cancer types could elucidate additional mechanisms of oncogenesis. Using antibody-based protein profiling of 736 cancer cell lines, along with matching transcriptomic data, we show that pan-cancer bimodality in the amounts of mRNA, protein, and protein phosphorylation reveals mechanisms related to the epithelial-mesenchymal transition (EMT). Based on the bimodal expression of E-cadherin, we define an EMT signature consisting of 239 genes, many of which were not previously associated with EMT. By querying gene expression signatures collected from cancer cell lines after small-molecule perturbations, we identify enrichment for histone deacetylase (HDAC) inhibitors as inducers of EMT, and kinase inhibitors as mesenchymal-to-epithelial transition (MET) promoters. Causal modeling of protein-based signaling identifies putative drivers of EMT. In conclusion, integrative analysis of pan-cancer proteomic and transcriptomic data reveals key regulatory mechanisms of oncogenic transformation.

Effect of short-term liver X receptor activation on epidermal barrier features in mild to moderate atopic dermatitis: A randomized controlled trial.

Abstract: Background Liver X receptors (LXRs) are involved in maintaining epidermal barrier and suppressing inflammatory responses in model systems. The LXR agonist VTP-38543 showed promising results in improving barrier function and inflammatory responses in model systems. Objective To assess the safety, tolerability, cellular and molecular changes, and clinical efficacy of the topical VTP-38543 in adults with mild to moderate atopic dermatitis (AD). Methods A total of 104 ambulatory patients with mild to moderate AD were enrolled in this randomized, double-blind, vehicle-controlled trial between December 2015 and September 2016. VTP-38543 cream in 3 concentrations (0.05%, 0.15%, and 1.0%) or placebo was applied twice daily for 28 days. Pretreatment and posttreatment skin biopsy specimens were obtained from a subset of 33 patients. Changes in SCORing of Atopic Dermatitis, Eczema Area and Severity Index, Investigator's Global Assessment, and tissue biomarkers (by real-time polymerase chain reaction and immunostaining) were evaluated. Results Topical VTP-38543 was safe and well tolerated. VTP-38543 significantly increased messenger RNA (mRNA) expression of epidermal barrier differentiation (loricrin and filaggrin, P = .02) and lipid (adenosine triphosphate–binding cassette subfamily G member 1 and sterol regulatory element binding protein 1c, P H 17/T H 22-related (phosphatidylinositol 3, S100 calcium-binding protein A12) and innate immunity (interleukin 6) markers. Conclusion Topical VTP-38543 is safe and well tolerated. Its application led to improvement in barrier differentiation and lipids. Longer-term studies are needed to clarify whether a barrier-based approach can induce meaningful suppression of immune abnormalities. Trial Registration clinicaltrials.gov Identifier: NCT02655679.

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 epigenetics of epithelial-mesenchymal plasticity in cancer

Abstract: Epithelial-mesenchymal transitions (EMTs) are a key requirement for cancer cells to metastasize and colonize in a new environment. Epithelial-mesenchymal plasticity is mediated by master transcription factors and is also subject to complex epigenetic regulation. This Review outlines our current understanding of the interactions between EMT-inducing transcription factors and epigenetic modulators during cancer progression and the therapeutic implications of exploiting this intricate regulatory process. During the course of malignant cancer progression, neoplastic cells undergo dynamic and reversible transitions between multiple phenotypic states, the extremes of which are defined by the expression of epithelial and mesenchymal phenotypes. This plasticity is enabled by underlying shifts in epigenetic regulation. A small cohort of pleiotropically acting transcription factors is widely recognized to effect these shifts by controlling the expression of a constituency of key target genes. These master regulators depend on complex epigenetic regulatory mechanisms, notably the induction of changes in the modifications of chromatin-associated histones, in order to achieve the widespread changes in gene expression observed during epithelial-mesenchymal transitions (EMTs). These associations indicate that an understanding of the functional interactions between such EMT-inducing transcription factors and the modulators of chromatin configuration will provide crucial insights into the fundamental mechanisms underlying cancer progression and may, in the longer term, generate new diagnostic and therapeutic modalities for treating high-grade malignancies.

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

Atopic dermatitis endotypes and implications for targeted therapeutics.

Abstract: Recent research advancements indicate that atopic dermatitis (AD) is a complex disease characterized by different subtypes/phenotypes based on age, disease chronicity, ethnicity, filaggrin and IgE status, and underlying molecular mechanisms/endotypes. This heterogeneity advocates against the traditional "one-size-fits-all" therapeutic approaches still used to manage AD. Precision medicine approaches, striving for targeted, tailored, endotype-driven disease prevention and treatment, rely on detailed definitions of the disease's variability across different phenotypes. Studies have shown that AD harbors different endotypes across different age groups and ethnicities and according to IgE levels and filaggrin mutation status. These include European American versus Asian patients, children versus adults, intrinsic versus extrinsic (IgE status) disease, and patients with and without filaggrin mutations. Therapies targeting different cytokine axes and other mechanisms involved in disease pathogenesis, which are currently being tested for patients with AD across the disease spectrum, will expand our ability to dissect the relative contribution of each of these pathways to disease perpetuation.