Nathan Enoch Lewis - US grants

Project Summary / Abstract Unraveling the mammalian secretory pathway through systems biology data analysis and algorithm development. The mammalian secretory system is key to organismal development, cell-cell communication, and all other cellular functions, since the pathway is the biosynthetic route for thousands of secreted hormones, extracellular matrix modifiers, membrane proteins, and glycans. Its central role also makes it a hub for disease. Alzheimer's disease is associated with plaques formed from proteins that are misfolded in the secretory pathway. Cancer cells alter their microenvironment through the secretion of growth factors and modification of cell surface glycans. Many infectious diseases interact with membrane proteins and glycans during the infection process. While the secretory pathway has been studied extensively for more than a century, the complexity of the system has made it difficult to unravel how thousands of chaperonins, enzymes, transporters, glycans, metabolites, lipids, and RNAs function together to influence health and disease. The goal of this proposed research program is to develop a detailed knowledge base of the secretory pathway and to develop algorithms and tools to use the network for data visualization, analysis, and model simulations, thereby enabling researchers to elucidate how each component influences the system. We will further to apply these tools with large scale single and dual sgRNA/CRISPR screens in order to elucidate novel interactions and mechanisms regulating protein secretion. Specifically, (i) the knowledge base will contain detailed information about all macromolecules involved in the translation, folding, modification, glycosylation, and secretion of proteins. This further includes metabolism, which fuels the pathway. The known functions of each pathway member will be detailed, and their interactions will be described. Since the knowledge base will be organized to enable its use for systems biology analyses, (ii) visualization tools and analysis algorithms will be developed and deployed to identify how changes in each component influence the ability to secrete individual proteins or synthesize specific glycans. (iii) We will leverage the model to integrate large omics data sets we are generating with collaborators (e.g., metabolomics, ribosomal profiling, proteomics, and CRISPR-Cas9 activation and loss-of-function screens) to study regulation of tissue-specific protein secretion. (iv) We will leverage the data to elucidate novel interactions and functions for poorly characterized members of the secretory pathway. This research program will provide, for the first time, a well-defined and curated knowledge base for this complex system, and enable the use of diverse computational systems biology tools to identify the molecular mechanisms underlying different cell phenotypes stemming from changes in the secretory pathway.

In autism, early-age biomarkers are scarce. Research is urgently needed to identify markers that precede symptom onset, convey prognostic information, or indicate disorder subtypes. Our proposed functional genomics study of early development in ASD addresses many of these biomarker goals and is an essential early step in this discovery process. Robust biomarkers have been elusive presumably since ASD is a heterogeneous developmental disorder with thousands of speculated risk genes and potential non-genetic immune factors. We hypothesize that pathway-based transcriptomic biomarkers may be informative, as shown by our recent proof- of-concept study in which leukocyte-based gene expression provided an early diagnostic ASD classifier. Our findings are reasonable since many high confidence ASD genes (e.g., transcription factors, signaling genes, etc.) and networks are as strongly expressed in leukocytes as in brain. Furthermore, hypothesized immune disruptions in ASD should also be reflected in leukocytes, especially since microglia are a type of leukocyte that are established as a brain molecular and cellular pathology in ASD. In our proposed study, we will use 1,500 RNA-Seq datasets from 1,000 ASD and typically and atypically developing toddlers to identify biomolecular pathway biomarkers for early detection, prognosis, clinical progression and clinical subtyping. We will further study biomarker relationships to ASD gene defects and expression patterns in early neural development. Aim 1 will analyze RNA-Seq data from 1,000 1-2 year olds using data-driven and knowledge-based network approaches to identify early ASD diagnostic biomarkers that distinguish ASD (n=390) at ages 1-2 years from non-ASD (n=610) groups. Diagnostic biomarkers will include pathways and co-expression networks to address the heterogeneity across ASD subjects. Aim 2 will identify prognostic RNA-Seq expression patterns in the 390 ASD 1-2 year olds by analyzing gene expression levels to reveal pathways that predict good/poor social and language outcome at ages 3-4 years. Aim 2 will also look longitudinally at ASD (n=300) and typically developing (n=200) expression data to identify transcriptomic trajectories that underlie clinical progression from 1-2 years to 3-4 years in these different clinical outcome subgroups. Aim 3 will examine how variation in developmental functional genomic patterns relates to variation in social and language abilities across diagnostic categories (n=1,000) and within ASD (n=390) using dimensionality reduction and feature selecting regression. Multicollinear regressions will be used to combine multivariate trend observations of dimensionality reduction with the predictive power of regressions. Aim 4 will link key transcriptomic effects in Aims 1 to 3 to genetic variants in high-confidence and probable ASD genes that are linked to disrupted cellular pathways in our ASD subjects. Deleterious variants in those genes will be tested in hematopoietic and neural stem cells using CRISPR-Cas9 to introduce loss-of-function mutations in these genes. RNA-Seq will be used to assay the impact on ASD-relevant cellular pathways.

ABSTRACT The immune system comprises a multitude of major cell types, each of which have unique functions. Understanding these diverse functions is fundamental to unraveling the immune system?s role in combating infectious disease, cancer development, autoimmune diseases, and chronic inflammation. The Immunology Database and Analysis Portal (ImmPort) currently shares data from 391 immunology studies, allowing further analysis by the immunology community. Integrating ImmPort data with additional community tools and data can increase the reach and impact of this high-value resource. To facilitate deeper and more mechanistic insights into the molecular basis of immunological functions, systems biology communities have created and validated computational models and algorithms that can accurately elucidate and quantify the functions of a cell based on analyses of omics data. Our computational toolbox, called CellFIE (Cell Function InferencE), does this by using complex systems biology models to first define modules of genes that work together to accomplish specific cell functions, and then overlaying omics data on these gene modules to infer changes in the activities of these functions in any given sample. The initial implementation of CellFIE has successfully quantified the scale of known metabolic functions for many different cell types, and differentiated functions of immune cell types. Consequently, we propose to expand CellFIE beyond metabolism to capture the synthesis and/or secretion of cytokines and membrane receptors involved in immune cell function. We will also adapt CellFIE to capture functions of specific signaling pathways. To make this computational toolbox and other omics analysis tools accessible and intuitive to researchers globally, we will develop ImmCellFIE, a sustainable, extensible, and usable software package and data repository built on Findable, Accessible, Interoperable and Reusable (FAIR) principles. Importantly, ImmCellFIE will include a web-enabled portal, intuitive visualizations, easy-to-use programmable interfaces for integrating omics data from various immunological databases, and the database annotations resulting from the ImmCellFIE secondary analyses. In this way, we will democratize the complicated systems biology analyses. The metabolic, secretory, and signaling phenotype annotations enabled by an intuitive dashboard and cyberinfrastructure that can easily integrate external data sources and systems will broaden the reach of omics data in ImmPort, other databases, and novel experiments. ImmCellFIE will harness these complex data to unravel detailed mechanisms in immunology research and beyond.