Translational Core

Translational Core

Core Leader: Ron Korstanje
Phone: 207-288-6992
email: ron.korstanje@jax.org

The Translational Core of the Jackson Laboratory Nathan Shock Center (JAX NSC) develops and provides resources for the other NSCs and the aging community to aid in comparative and translational aging research. We are developing a pipeline that allows for efficient screening and prioritization of candidate genes identified in human studies, first in C.elegans then using mouse models. Preliminary studies for the different parts of the pipeline show their effectiveness and we have successfully tested a set of 149 genes in C.elegans, which were identified in human studies as being associated with aging. Knockdown showed a significant difference in lifespan for 65% of the genes. Novel mouse models were developed for two of these genes and are currently in lifespan studies. Preliminary data shows significant differences in these models compared to wild type animals for several age-related traits. To enable this pipeline and make it available as a resource to the aging community, the specific aims of the Translational Core are to:

  • Aim 1. Identify the most promising associations for aging and age-related phenotypes in humans using data from the Framingham Heart Study and the CHARGE consortium to identify candidate genes to test in C.elegans and mouse models.
  • Aim 2. Develop tools and resources to enable candidate gene testing and prioritization in C.elegans.
  • Aim 3. Develop novel mouse models of aging that allow further study of candidate genes in the Animal and Phenotyping Core

Aim 1. Identify the most promising associations for aging and age-related phenotypes in humans

Disease incidences increase with age, but the molecular characteristics of ageing that lead to increased disease susceptibility remain inadequately understood. We perform a whole-blood gene expression meta-analysis in 14,983 individuals of European ancestry (including replication) and identify 1,497 genes that are differentially expressed with chronological age. The age-associated genes do not harbor more age-associated CpG-methylation sites than other genes, but are instead enriched for the presence of potentially functional CpG-methylation sites in enhancer and insulator regions that associate with both chronological age and gene expression levels. We further used the gene expression profiles to calculate the ‘transcriptomic age’ of an individual, and show that differences between transcriptomic age and chronological age are associated with biological features linked to ageing, such as blood pressure, cholesterol levels, fasting glucose, and body mass index. The transcriptomic prediction model adds biological relevance and complements existing epigenetic prediction models, and can be used by others to calculate transcriptomic age in external cohorts.

Published in Nature Communications (2015)

We selected the top 125 genes from this study and developed a novel tool (Aim 2) to identify the C.elegans orthologs of these genes.

Aim 2. Develop tools and resources to enable candidate gene testing and prioritization in C.elegans

Tool development

The rapid advancement of technology in genomics and targeted genetic manipulation has made comparative biology an increasingly prominent strategy to model human disease processes. Predicting orthology relationships between species is a vital component of comparative biology. Dozens of strategies for predicting orthologs have been developed using combinations of gene and protein sequence, phylogenetic history, and functional interaction with progressively increasing accuracy. A relatively new class of orthology prediction strategies combines aspects of multiple methods into meta-tools, resulting in improved prediction performance. Here we present WORMHOLE, a novel ortholog prediction meta-tool that applies machine learning to integrate 17 distinct ortholog prediction algorithms to identify novel least diverged orthologs (LDOs) between 6 eukaryotic species—humans, mice, zebrafish, fruit flies, nematodes, and budding yeast. Machine learning allows WORMHOLE to intelligently incorporate predictions from a wide-spectrum of strategies in order to form aggregate predictions of LDOs with high confidence. In this study we demonstrate the performance of WORMHOLE across each combination of query and target species. We show that WORMHOLE is particularly adept at improving LDO prediction performance between distantly related species, expanding the pool of LDOs while maintaining low evolutionary distance and a high level of functional relatedness between genes in LDO pairs. We present extensive validation, including cross-validated prediction of PANTHER LDOs and evaluation of evolutionary divergence and functional similarity, and discuss future applications of machine learning in ortholog prediction. A WORMHOLE web tool has been developed and is available at http://wormhole.jax.org/.

Published in PLOS Computational Biology (2016)

WORMHOLE was able to reliably identify the orthologs of 82 human genes (from the initial 125 candidate genes) and these were tested in C.elegans for their effect on lifespan.

Candidate gene testing

We report a systematic RNAi longevity screen of 82 Caenorhabditis elegans genes selected based on orthology to human genes differentially expressed with age. We find substantial enrichment in genes for which knockdown increased lifespan. This enrichment is markedly higher than published genome-wide longevity screens in C. elegans and similar to screens that pre-selected candidates based on longevity-correlated metrics (e.g. stress resistance). Of the 50 genes that affected lifespan, 46 were previously unreported. The five genes with the greatest impact on lifespan (>20% extension) encode the enzyme kynureninase (kynu-1), a neuronal leucine-rich repeat protein (iglr-1), a tetraspanin (tsp-3), a regulator of calcineurin (rcan-1), and a voltage-gated calcium channel subunit (unc-36). Knockdown of each gene extended healthspan without impairing reproduction. kynu-1(RNAi) alone delayed pathology in C. elegans models of Alzheimer’s and Huntington’s disease. Each gene displayed a distinct pattern of interaction with known aging pathways. In the context of published work, kynu-1, tsp-3, and rcan-1 are of particular interest for immediate follow up. kynu-1 is an understudied member of the kynurenine metabolic pathway with a mechanistically distinct impact on lifespan. Our data suggest that tsp-3 is a novel modulator of hypoxic signaling and rcan-1 is a context-specific calcineurin regulator. Our results validate C. elegans as a comparative tool for prioritizing human candidate aging genes, confirm age-associated gene expression data as valuable source of novel longevity determinants, and prioritize select genes for mechanistic follow up.

Manuscript for Aging Cell is in revision

The most robust candidate gene was kynu-1, which is in the kynurenine pathway. The Korstanje lab had already an interest in this pathway as mutations in other enzymes in this pathway leads to renal damage (Korstanje et al.,2016). We therefore decided to focus on this pathway and its effect on lifespan and age-related phenotypes.

The kynurenine pathway

Knockdown or mutation of single-gene enzymes in C.elegans extends lifespan

 

3HAA supplementation is sufficient to extend lifespan in C.elegans

Aim 3. Develop novel mouse models of aging that allow further study of candidate genes in the Animal and Phenotyping Core

Afmid knockout

Kmo knockout

Kynu knockout

Haao knockout