Michelle Hermiston, MD, Ph.D.
Assistant Professor, Department of Pediatrics University of California San Francisco, USA
Interrogating Signaling Networks at the Single Cell Level In Primary Human Patient Samples
Multiparameter phosphoflow cytometry is a highly sensitive proteomic approach that enables monitoring of biochemical perturbations at the single cell level. By combining antisera to cell surface markers and key intracellular proteins, perturbations in signaling networks, cell survival and apoptosis mediators, cell cycle regulators, and/or modulators of other cellular processes can be analyzed in a highly reproducible and sensitive manner in the basal state and in response to stimulation or drug treatment. Advantages of this approach include the ability to identify the biochemical consequences of genetic and/or epigenetic changes in small numbers of cells, to map potential interplay between various signaling networks simultaneously in a single cell, and to interrogate potential mechanisms of drug resistance or response in a primary patient sample. Application of this technology to patients with acute lymphoblastic leukemia or the autoimmune disease systemic lupus erythematosus (SLE) will be discussed.
Andrey Panteleyev, Ph.D.
Vice Chair, Division of Molecular Biology, NBICS Centre-Kurchatov Institute, Moscow, Russia
The system of PAS proteins (HIF and AhR) as an interface between environment and skin homeostasis
Regulation of normal skin functions as well as etiology of many skin diseases are both tightly linked to the environmental impact. Nevertheless, molecular aspects of skin-environment communication and mechanisms coordinating skin response to a plurality of environmental stressors remain poorly understood.
Our studies along with the work of other groups have identified the family of PAS dimeric transcription factors as an essential sensory and regulatory component of communication between skin and the environment. This protein family comprises a number of hypoxia-induced factors (HIF-alpha proteins), aryl hydrocarbon receptor (AhR), AhR nuclear translocator (ARNT), and several proteins implicated in control of rhythmic processes (Clock, Period, and Bmal proteins). Together, various PAS proteins (and first of all ARNT – as the central dimerization partner in the family) control such pivotal aspects of cell physiology as drug/xenobiotic metabolism, hypoxic and UV light response, ROS activity, pathogen defense, overall energy balance and breathing pathways.
In his presentation Dr. Panteleyev will focus on the role of ARNT activity and local hypoxia in control of keratinocyte differentiation and cornification. His recent work revealed that ARNT negatively regulates expression of late differentiation genes through modulation of amphiregulin expression and downstream alterations in activity of EGFR pathway. All these effects are highly dependent on epigenetic mechanisms such as histone deacetylation. Characterisation of hypoxia as a key microenvironmental factor in the skin and the role of HIF pathway in control of dermal vasculature and epidermal functions is another major focus of Dr. Panteleyev’s presentation.
In general, the studies of Dr. Panteleyev’s laboratory provide an insight into the PAS-dependent maintenance of skin homeostasis and point to the potential role of these proteins in pathogenesis of environmentally-modulated skin diseases such as barrier defects, desquamation abnormalities, psoriasis, etc.
Srisairam Achuthan, Ph.D.
Senior Scientific Programmer, Research Informatics Division, Department of Information Sciences, City of Hope, CA, USA
Applying Machine learning for Automated Identification of Patient Cohorts
Srisairam Achuthan, Mike Chang, Ajay Shah, Joyce Niland
Patient cohorts for a clinical study are typically identified based on specific selection criteria. In most cases considerable time and effort are spent in finding the most relevant criteria that could potentially lead to a successful study. For complex diseases, this process can be more difficult and error prone since relevant features may not be easily identifiable. Additionally, the information captured in clinical notes is in non-coded text format. Our goal is to discover patterns within the coded and non-coded fields and thereby reveal complex relationships between clinical characteristics across different patients that would be difficult to accomplish manually. Towards this, we have applied machine learning techniques such as artificial neural networks and decision trees to determine patients sharing similar characteristics from available medical records. For this proof of concept study, we used coded and non-coded (i.e., clinical notes) patient data from a clinical database. Coded clinical information such as diagnoses, labs, medications and demographics recorded within the database were pooled together with non-coded information from clinical notes including, smoking status, life style (active / inactive) status derived from clinical notes. The non-coded textual information was identified and interpreted using a Natural Language Processing (NLP) tool I2E from Linguamatics.