The AMP-CMD® program is a collaborative partnership between the NIDDK, FNIH, pharmaceutical companies, and nonprofit organizations to develop new models for identifying and validating promising biological targets to serve as biomarkers and/or for drug discovery. AMP-CMD aims to provide data and evidence to support:
- Identifying conditions and factors central to robust characterization of patients in disease- and treatment-relevant sub-types;
- Understanding type 2 diabetes risk, initiation, progression, and complications development;
- Associating efficacy and safety of existing therapies with patient sub-types;
- Discovering biomarkers for type 2 diabetes and its complications;
- Improving clinical decision making at the individual patient level;
- Identifying key modulation nodes in disease-driving molecular networks to enhance novel biomarker therapeutic discovery.
There are four awards funded under AMP-CMD:
The next iteration of the AMP-T2D Knowledge Portal
Abstract: Despite much progress in mapping genetic associations for T2D, its complications, and related traits, the underlying causal variants, effector genes, and gene networks are not well understood. The AMP-T2D consortium aims to accelerate the pace by which these disease mechanisms are elucidated, through generation and integration of novel genomic data interrogating genetic associations. This project addresses RFA-DK-19-505 “Limited Competition for the Accelerating Medicines Partnership (AMP) in Type 2 Diabetes Knowledge Portal (UM1)” and will be conducted by the leaders of the current AMP T2D Knowledge Portal (T2DKP). Its goal is to continue development of the T2DKP by enhancing its infrastructure to aggregate, analyze, and visualize genetic and genomic datasets and results, thereby helping to identify causal risk variants and effector genes for T2D and its complications. The goal of specific aim 1 is to enhance the data and knowledge base for the T2DKP. It will enhance the T2DKP software platform to represent additional classes of ‘omic and phenotypic data, integrate datasets relevant to AMP-T2D within a federated data warehouse, develop new methods and analytical pipelines to analyze these data, and enable them to be accessed by download or application programming interfaces (APIs). The goal of specific aim 2 is to enhance the publicly accessible T2DKP web interface. It will develop new web modules to visualize datasets within the T2DKP, extend the T2DKP with tools from external public genomic databases, maintain curated knowledge lists for T2D and its complications, allow users to customize data and tools shown within the T2DKP, and update the T2DKP in response to stakeholder feedback. The goal of specific aim 3 is to coordinate the AMP-T2D consortium. It will provide operational support for AMP-T2D investigators, foster collaborations with external partners, track and ensure the timely release of AMP-T2D datasets, administer an opportunity pool to fund additional AMP-T2D projects, and conduct outreach and training on the T2DKP. Significance: The project would extend the T2DKP with new capabilities to integrate and display datasets with information regarding causal variants, effector transcripts, and gene networks for T2D and its complications. Public access to these data and results will significantly accelerate efforts to identify new therapeutic strategies to prevent, treat, or reverse T2D and its complications.
Functional interrogation of T2D-associated genes in human stem cell-derived models and mice
Abstract: Functional Interrogation of T2D-associated genes in human stem cell-derived models and mice Type 2 Diabetes (T2D) is one of the fastest-growing diseases and a leading cause of death throughout the world. A better understanding of the disease process, including characterization of both the genetic etiology and the contribution of different cell types to disease initiation, progression and heterogeneity promises to reveal new therapeutic targets. Large-scale genome-wide association studies (GWAS) of this common complex trait have driven the rapid identification of hundreds of T2D-associated loci. However, the mechanism(s) through which most of these loci influence disease susceptibility remain poorly understood. Our interdisciplinary team at Penn brings together experts in population genetics, T2D GWAS, biostatistics, metabolic tissue biology, human cellular disease modeling and T2D pathophysiology to tackle this critical knowledge gap. In collaboration with other Consortium groups, we aim to accomplish the following goals. (1) Provide the diabetes research community with a robust pipeline for mapping T2D GWAS variants to effector genes and target tissues. (2) Identify new genes and biological pathways that modulate susceptibility to T2D. (3) Define gene regulatory networks relevant to T2D with the goal of uncovering therapeutic ‘entry points’ for developing new treatments. For (1), we will prioritize “candidate effector transcripts” for downstream functional analyses by integrating multiple sources of data to gain a ‘confluence of evidence’ as to their disease relevance and tissue of action. These sources include publically available datasets, a unique collection of internal resources from the Million Veteran Program, and our own functional genomics (RNA-seq, ATAC-seq, chromatin conformation capture etc.) data generated from stem cell-derived T2D relevant cell types. For (2), we will examine the biological function of prioritized T2D-effector transcripts in human cell models of T2D-relevant tissue types using gain- and loss-of- function methods combined with a battery of physiological, metabolic, molecular phenotyping and genomic approaches. These studies include the use of induced pluripotent stem cell (iPSC) models for pancreatic b cells, hepatocytes, adipocytes and skeletal muscle cells, enabling precise genetic engineering and establishment of multiple cell types in the same genetic background. Through this process, we will identify 10 high priority candidate effector genes, which we will advance for comprehensive in vivo analyses in conditional mutant mouse models. For (3), we will perform network analyses through the integration of our multiple data sources to identify molecular memberships in broader pathways and search for pathway components that are potentially amenable for therapeutic targeting.
Bridging the gap between type 2 diabetes GWAS and therapeutic targets
Type 2 diabetes (T2D) is a heterogeneous disorder characterized by resistance of hepatic, skeletal muscle and adipose tissues to insulin and a relative deficiency of insulin secretion by pancreatic β cells. T2D has a substantial genetic component, and over the past decade human genetic studies have identified over 400 association signals across diverse populations. However, in most cases the specific variants and genes responsible for these association signals are not known. T2D signals include loci for which functions of the protein products encoded by nearby genes are poorly characterized, the closest known gene is distant, or more than one gene appears to be a plausible biological candidate. Identifying the causal variants, the regulatory gene networks affected by the change in DNA sequence, and the mechanisms by which such variation leads to disease are critical steps toward understanding the genetic architecture of T2D, validating potential drug targets, and developing novel therapeutic strategies. Here, we propose large-scale multi-disciplinary functional genomics projects in islet, liver, adipose and muscle cells to determine the contributions and mechanisms underlying T2D risk-associated variants and their downstream effector transcripts. Throughout the project, we leverage our prior and ongoing generation of genomic data sets and genome-wide and targeted screens for function of variants and genes. To complement these efforts, we will first collect genome-wide array and sequencing-based association study results, identify conditionally distinct association signals and construct credible sets of variants. We propose to link variants to effector transcripts through analyses of genome-wide transcriptomic and epigenomic data, perturbation assays that alter thousands of variant-containing regulatory elements and effector transcripts, perturbations of tens of specific variants, and integrative computational analyses. Next, we propose systematic evaluation of hundreds of potential effector transcripts through use of genome-wide and targeted screens of insulin secretion, lipid accumulation, mitochondrial function, glucose uptake, and differentiation state, with assay selection depending on cell type. Based on these results, we propose focused studies on tens to hundreds of potential effector transcripts to evaluate electrophysiology, gluconeogenesis, lipid metabolism and signaling pathways, and we propose thorough investigation the context-specific mechanism of action of individual genes. Finally, we propose to analyze, integrate, and visualize all data by placing effector transcripts into cell-type and environmental context-specific networks, selecting network nodes as candidate biomarkers and modulation points for drugs, and building a framework to understand the tissue-specific contribution of variants and transcripts to individual disease heterogeneity. Successful completion of these aims will translate T2D association signals into biological insights and therapeutic targets.
TOPMed omics of type 2 diabetes and quantitative traits
Abstract: Type 2 diabetes continues to spread globally due to unhealthy environment interacting with genetics. Recent genetic discoveries of >700 variants at >400 loci associated with type 2 diabetes (T2D) and its related quantitative traits (QTs: fasting glucose (FG), insulin (FI) and hemoglobin A1c (A1c)) give insight into new T2D pathobiology. However, most discoveries have been in whites; studies in minority groups disproportionately affected by T2D are needed. Also, most associations are in the non-coding genome, indicating that whole genome sequence (WGS) analysis is needed for full variant and effector gene characterization. The NHLBI Trans-Omics for Precision Medicine (TOPMed) study includes WGS from 21,493 cases of prevalent T2D and 63,541 controls from five populations (41,557 Euro, 23,203 AA, 16,213 Latino, 2,867 Asian, 1,194 Samoan Adiposity Study) from 28 cohorts and up to 54,407 non-T2D individuals with FG, FI or HbA1c, as well as age of T2D onset, level of glycemic control and longitudinal follow-up for incident T2D events. In this project Aim 1 is to test WGS-wide in five ancestry groups for known and new common and rare variants associated T2D and QTs. We will conduct analyses in the NHLBI BioData Catalyst. Replication of novel variants is available in >1 million individuals of diverse ancestry from six biobanks with T2D (UKBB, BioME, BioVU, Partners BB, REGARDS, MVP) with TOPMed-imputed genomic array data. For health translation, we will group T2D genetic risk variants into polygenic risk scores (PRSs) that predict future T2D or characterize specific physiological axes, and use variants in Mendelian Randomization (MR) tests of disease causality. Next, TOPMed has blood omic measures from five ancestry groups that may also identify novel biological networks relevant to T2D pathobiology, including whole blood DNA methylation (measured by sequencing or microarrays, N=11,131), transcriptomics (RNA-seq) (N=8,334), proteomics (SomaLogic aptamers or Olink proteomics, N=7,897) and metabolomics (liquid chromatography/mass spectroscopy, N=11,631). In Aim 2, we will test omic signatures associated with T2D and QTs individually and in multidimensional omic and genomic network models of the pathobiology of T2D. Finally, in Aim 3 we plan to integrate TOPMed WGS and omic results with bespoke cell or tissue-specific (beta cell, islet, liver, fat and muscle) omic and epigenomic annotation (ATAC-seq, RNA-seq, Hi-C, ChIP-seq) in the Accelerating Medicine Partnership (AMP) T2D Diabetes epiGenome Atlas, and with hundreds of additional genomic trait associations in the AMP T2D Knowledge Portal (T2DKP) for ‘in silico variant-to-function’ and phenomic studies. Complete functional mapping with blood and tissue-specific omic integration of the human T2D and QT genome is on the horizon. Our multidisciplinary, multicenter team has a proven track record in genetics and omic discovery. We are actively working with TOPMed, AMP T2D DGA and T2DKP data. We are well positioned to achieve the Aims of the proposal, with the intention to find new approaches to address the global epidemic of T2D in all populations at risk.