Epistatic Networks Associated with Parent-of-Origin Effects on Metabolic Traits

Macias-Velasco, J. F.; St. Pierre, C. L.; Wayhart, J. P.; Yin, L.; Spears, L.; Funai, K.; Cheverud, J. M.; Semenkovich, C. F.; Lawson, H. A.

doi:10.1101/579748

bioRxiv · 10.1101/579748

Epistatic Networks Associated with Parent-of-Origin Effects on Metabolic Traits

Macias-Velasco, J. F.·St. Pierre, C. L.·Wayhart, J. P.·Yin, L.·Spears, L.·Funai, K.·Cheverud, J. M.·Semenkovich, C. F.·Lawson, H. A.

View original source ↗Export metadata ↓

Abstract

Parent-of-origin effects (POE) are unexpectedly common in complex traits, including metabolic and neurological diseases. POE can also be modified by the environment, but the architecture of these gene-by-environmental effects on phenotypes remains to be unraveled. Previously, quantitative trait loci (QTL) showing context-specific POE on metabolic traits were mapped in the F16 generation of an advanced intercross between LG/J and SM/J inbred mice. However, these QTL were not enriched for known imprinted genes, suggesting another mechanism is needed to explain these POE phenomena. Here, we use a simple yet powerful F1 reciprocal cross model to test the hypothesis that non-imprinted genes can generate complex POE on metabolic traits through genetic interactions with imprinted genes. Male and female mice from a F1 reciprocal cross of LG/J and SM/J strains were fed either high or low fat diets. We generated expression profiles from three metabolically-relevant tissues: hypothalamus, white adipose, and liver. We identified two classes of parent-of-origin expression biases: genes showing parent-of-origin-dependent allele-specific expression and biallelic genes that are differentially expressed by reciprocal cross. POE patterns of both gene classes are highly tissue-and context-specific, sometimes occurring only in one sex and/or diet cohort in a particular tissue. We then constructed tissue-specific interaction networks among genes from these two classes of POE. A key subset of gene pairs show significant epistasis in the F16 LG/J x SM/J advanced intercross data in cases where the biallelic gene fell within a previously-identified metabolic POE QTL interval. We highlight one such interaction in adipose, between Nnat and Mogat1, which associates with POE on multiple adiposity traits. Both genes localize to the endoplasmic reticulum of adipocytes and play a role in adipogenesis. Additionally, expression of both genes is significantly correlated in human visceral adipose tissue. The genes and networks we present here represent a set of actionable interacting candidates that can be probed to further identify the machinery driving POE on complex traits.

Explore related subjects

systems biology Preprint

Keep this discovery

Explore connections, maps & timelines

BibTeX RIS

Macias-Velasco, J. F., St. Pierre, C. L., Wayhart, J. P., Yin, L., Spears, L., Funai, K., Cheverud, J. M., Semenkovich, C. F., Lawson, H. A.. 2019-03-16. Epistatic Networks Associated with Parent-of-Origin Effects on Metabolic Traits. https://doi.org/10.1101/579748

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related preprints

bioRxiv2026

INFORME: coupling information-theoretic experimental design with nonlinear mixed-effects modeling for efficient observation scheduling

Mathematical models of treatment response can inform individualized therapy, but their calibration often requires longitudinal measurements that are costly, burdensome, and collected on fixed schedules. Such schedules may be inefficient, over-sampling patients whose response is already well characterized while delaying informative measurements for those whose model parameters remain uncertain. We present INFORME (INFORmation-theoretic design with Mixed Effects), a framework that combines Bayesian information-theoretic experimental design with nonlinear mixed-effects modeling to adaptively select each patients next measurement time. Population and response-subgroup parameter distributions learned from an existing cohort provide informative priors, allowing candidate measurement times to be ranked by their expected reduction in patient-specific parameter uncertainty. As observations accumulate, priors can be updated to reflect the response subgroup most consistent with the patients data. We evaluate INFORME in two radiotherapy datasets: 150 synthetic tumor volume trajectories from a hybrid cellular automaton model of prostate cancer spheroids (HD1) and longitudinal tumor volumes from 39 patients with head-and-neck cancer (HD2). In HD1, population priors allowed omission of both pretreatment scans, while adaptive scheduling reduced the protocol from nine scans to three or four, with the response group identified from a single post-treatment scan on day 27. In HD2, the adaptive schedule used three scans instead of six and improved prediction by delaying the first on-treatment scan from week 1 to week 2, avoiding transient dynamics that produced false-positive and false-negative response projections. Across both datasets, the adaptive schedules used a mean of 2.7 scans in stead of seven and advanced completion of the patient-specific prediction by a mean of 15.5 days (95% CI, 6.7-24.3) relative to the equidistant protocol, while treatment duration remained unchanged. INFORME therefore reduces measurement burden and accelerates patient-specific prediction by concentrating observations at times that are most informative for model calibration.

systems biology↗

bioRxiv2026

Sobetirome, a thyroid hormone receptor beta agonist, is a potential therapeutic agent for pulmonary fibrosis

Idiopathic pulmonary fibrosis (IPF) is a progressive and fatal disease with limited treatment options. Our group previously identified the antifibrotic potential of thyroid hormone, triiodothyronine (T3); however, clinical translation of thyroid hormone therapy is limited by its systemic adverse effects. In this study, we investigate whether sobetirome, a selective and well tolerated thyroid hormone receptor beta (THRB) agonist, offers antifibrotic benefits of thyroid hormone while minimizing systemic toxicity. Our study reveals that sobetirome, administered via intraperitoneal or inhalational routes, effectively mitigates bleomycin-induced pulmonary fibrosis in mice, with no evidence of toxicity. We identified that sobetirome restores mitochondrial homeostasis via activating the THRB-PPARGC1a axis. This protects alveolar type II epithelial cells from injury-induced apoptosis while selectively inducing apoptosis and metabolic reprogramming in apoptosis resistant IPF fibroblasts. Cell-specific deletion of Ppargc1a in either alveolar epithelial cells or fibroblasts abolishes sobetirome-mediated protection, establishing PPARGC1a as an essential mediator of therapeutic response. Importantly, sobetirome reverses fibrosis-associated transcriptional programs in human IPF lung tissue, reducing expression of key fibrosis-associated genes, including collagen I alpha 1 (COL1A1), collagen III alpha 1 (COL3A1), periostin (POSTN), cathepsin K (CTSK), and Chitinase 3 Like 1 (CHI3L1), while promoting extracellular matrix remodeling, epithelial restoration, and tissue homeostasis. Collectively, our findings identify THRB activation as a novel metabolic strategy for reversing pulmonary fibrosis. Across complementary in vitro, in vivo, and human ex vivo models, sobetirome restores mitochondrial function, modulates apoptotic pathways in pathogenic cells, and promotes fibrosis resolution, highlighting its potential as a lung-targeted therapeutic approach for IPF and other fibrotic lung diseases.

systems biology↗

bioRxiv2026

Mechanistic modeling of bacterial translation initiation across growth conditions

Translation frequency in bacteria depends on how ribosomes, mRNAs, and initiation factors are allocated across growth conditions. Here, we developed a mechanistic ODE-based model of Escherichia coli translation that represents initiation, elongation, termination, and coupled auxiliary processes. Growth-dependent abundances were derived from physiological relationships and reprocessed omics data, and simulated outputs were compared with translation-frequency and active-ribosome references. The model predicts a continuous shift from complex-formation-limited toward ribosome-limited behavior as growth increases. This shift is characterized by a decline in free-ribosome abundance, whereas initiation-factor pools remain largely unbound and do not become depleted in parallel. Together with the implemented IF-dependent kinetic term, this preserved availability provides a model-internal route through which productive initiation can be maintained despite increasing ribosome utilization. Consistently, transcript-wide ribosome loading remains below its theoretical maximum, while COG-level simulations reveal distinct sector-specific translation-frequency trajectories. The study therefore provides a resource-allocation framework for interpreting how mRNA--ribosome interactions shape bacterial translation across growth conditions.

systems biology↗