# A strategy for discovery of endocrine interactions

> **NIH NIH R00** · UNIVERSITY OF CALIFORNIA-IRVINE · 2020 · $249,000

## Abstract

Project Summary/Abstract
 We developed a bioinformatics framework which uses multi-tissue expression arrays and publicly available
resources to statistically rank and functionally annotate endocrine axes. By applying this method to expression
profiles within the Hybrid Mouse Diversity Panel (HMDP), we identify many known and several novel inter-tissue
circuits. We further show the utility of this approach by uncovering a new adipose-to-skeletal muscle endocrine
axis which shows promise as a therapeutic target for metabolic syndrome in both mice and humans. Functional
experiments show adipose-derived Lipocalin-5 (LCN5) is sufficient to enhance skeletal muscle mitochondrial
activity and gene expression. When overexpressed in a mouse model, the secreted peptide prevents and
rescues diet-induced metabolic syndrome as measured by insulin- and glucose- tolerance. We also show that
the human orthologue of the protein is sufficient to enhance expression of similar oxidation and biogenesis genes
in human muscle cells. We further expand this method to identify adipose-derived Inter α-trypsin inhibitor 5
(Itih5) as a conserved regulator of cardiomyocyte function. Specifically, correlative data in mice and humans
and mechanistic studies show ITIH5 as a suppressor of cardiomyocyte hypertrophy. The goal of this proposal
is to 1) Mechanistically dissect how Lipocalin-5/6 enhances skeletal muscle mitochondrial activity in mice and
humans and 2) Utilize both global and targeted studies to understand biologic processes by which ITIH5 reduces
cardiac hypertrophy in a physiologic and pathophysiologic state.

## Key facts

- **NIH application ID:** 10055105
- **Project number:** 4R00HL138193-03
- **Recipient organization:** UNIVERSITY OF CALIFORNIA-IRVINE
- **Principal Investigator:** Marcus Michael Seldin
- **Activity code:** R00 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2020
- **Award amount:** $249,000
- **Award type:** 4N
- **Project period:** 2018-04-03 → 2022-11-30

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10055105

## Citation

> US National Institutes of Health, RePORTER application 10055105, A strategy for discovery of endocrine interactions (4R00HL138193-03). Retrieved via AI Analytics 2026-05-23 from https://api.ai-analytics.org/grant/nih/10055105. Licensed CC0.

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