Biology subjects

Cigarroa, F. G.

Publications and source records attributed to Cigarroa, F. G..

3 recordsLinked to original sources

bioRxiv2026

Distinct and shared synergistic effects of high-fat and high-iron diets on liver tumorigenesis and transcriptomic remodeling in mice

Background and AimsThe incidence of hepatocellular carcinoma (HCC) is increasingly driven by metabolic risk factors, including obesity and iron overload. Although high-fat and high-iron diets independently promote hepatocarcinogenesis, their shared and distinct molecular effects remain unclear. We sought to define how dietary fat and iron differentially shape HCC development. ApproachMale C3HeB/FeJ mice were exposed to long-term high-fat (HFD), high-iron (HID), or combined (HFD+HID) diets for 16.5 months. Tumor burden, hepatic iron distribution, mTOR signaling, oxidized phospholipid (OxPL) accumulation, and transcriptomic alterations across normal, adjacent non-tumor, and tumor liver tissues were analyzed using biochemical, histological, and RNA sequencing approaches. ResultsAll diets induced HCC with comparable tumor burden. HID increased iron levels in non-tumor liver tissue but resulted in relative iron depletion within tumors, indicating tumor-specific iron utilization. Tumors from all diet groups showed robust mTOR activation and increased OxPL accumulation, with stronger oxidative stress signatures in HFD and HFD+HID tumors. Transcriptomic analyses revealed conserved oncogenic programs alongside diet-specific signatures, with HFD exerting a dominant effect on metabolic reprogramming and gene dysregulation, whereas HID preferentially enhanced immune and inflammatory signaling. Progressive, monotonic changes in gene expression were observed across disease stages. Cross-species analyses linked diet-induced mouse tumors to immunologically "hot" human HCC subtypes. ConclusionsDietary fat and iron promote HCC through overlapping yet distinct molecular pathways, highlighting metabolic and immune mechanisms as key targets in diet-associated liver cancer.

cancer biology↗

bioRxiv2025

A human and mouse subpopulation of senescent β-cells induces pathologic dysfunction through targetable paracrine signaling

Cellular senescence is a stress response mechanism marked by irreversible growth arrest, upregulation of antiapoptotic pathways, loss of cellular function, and remodelling of the cellular secretory profile. In both humans and mice, pancreatic {beta}-cells undergo senescence with age and insulin resistance. Targeted removal of senescent cells in mouse models of diabetes improves glucose homeostasis, demonstrating the role {beta}-cell senescence in diabetes progression. In contrast, {beta}-cell senescence also promotes immune surveillance, promoting {beta}-cell survival and function. Thus, a better understanding of senescent cells phenotypic and functional heterogeneity is needed to develop effective therapeutic strategies. Herein, we show that subpopulations of senescent {beta}-cells in mice and humans, which were identified through the expression of Cdkn1a (encoding p21Cip1) and Cdkn2a (encoding p16Ink4a) by single-cell RNA sequencing (scRNA-seq), flow cytometry, spatial transcriptomics, and spatial proteomics, exhibit distinct transcriptional and functional identities. The predominant senescent {beta}-cell subpopulation expressed Cdkn1a and was characterized by a lack of glucose responsiveness, high basal insulin secretion, and transcription of canonical SASP factors. The SASP of Cdkn1a-expressing {beta}-cells had non-cell autonomous effects on neighbouring cells. A subset of four SASP factors from Cdkn1a+ cells was sufficient to induce secondary senescence and {beta}-cell dysfunction in vitro. JAK inhibitors (JAK1/2 and JAK1/3) counteracted secondary senescence induction and restored {beta}-cell function in high-fat diet-fed mice and human islets from donors with or without type 2 diabetes. GRAPHICAL ABSTRACT O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=196 SRC="FIGDIR/small/648438v1_ufig1.gif" ALT="Figure 1"> View larger version (34K): org.highwire.dtl.DTLVardef@9b8addorg.highwire.dtl.DTLVardef@1b9c4eborg.highwire.dtl.DTLVardef@12f2964org.highwire.dtl.DTLVardef@1468297_HPS_FORMAT_FIGEXP M_FIG C_FIG

developmental biology↗

bioRxiv2019

An ancestral informative marker panel design for individual ancestry estimation of Hispanic population using whole exome sequencing data

BackgroundEuropeans and American Indians were major genetic ancestry of Hispanics in the U.S. In those ancestral groups, it has markedly different incidence rates and outcomes in many types of cancers. Therefore, the genetic admixture may cause biased genetic association study with cancer susceptibility variants specifically in Hispanics. The incidence rate and genetic mutational pattern of liver cancer have been shown substantial disparity between Hispanic, Asian and non-Hispanic white populations. Currently, ancestry informative marker (AIM) panels have been widely utilized with up to a few hundred ancestry-informative single nucleotide polymorphisms (SNPs) to infer ancestry admixture. Notably, current available AIMs are predominantly located in intron and intergenic regions, while the whole exome sequencing (WES) protocols commonly used in translational research and clinical practice do not contain these markers, thus, the challenge to accurately determine a patients admixture proportion without subject to additional DNA testing.\n\nMethodsHere we designed a bioinformatics pipeline to obtain an AIM panel. The panel infers 3-way genetic admixture from three distinct continental populations (African (AFR), European (EUR), and East Asian (EAS)) constraint within evolutionary-conserved exome regions. Briefly, we extract [~]1 million exonic SNPs from all individuals of three populations in the 1000 Genomes Project. Then, the SNPs were trimmed by their linkage disequilibrium (LD), restricted to biallelic variants only, and assembled as an AIM panel with the top ancestral informativeness statistics based on the In-statistic. The selected AIM panel was applied to training dataset and clinical dataset. Finally, The ancestral proportions of each individual was estimated by STRUCTURE.\n\nResultsIn this study, the optimally selected AIM panel with 250 markers, or the UT-AIM250 panel, was performed with better accuracy as one of the published AIM panels when we tested with 3 ancestral populations (Accuracy: 0.995 {+/-} 0.012 for AFR, 0.997 {+/-} 0.007 for EUR, and 0.994 {+/-} 0.012 for EAS). We demonstrated the utility of UT-AIM250 panel on the admixed American (AMR) of the 1000 Genomes Project and obtained similar results (AFR: 0.085 {+/-} 0.098; EUR: 0.665 {+/-} 0.182; and EAS 0.250 {+/-} 0.205) to previously published AIM panels (Phillips-AIM34: AFR: 0.096 {+/-} 0.127, EUR: 0.575 {+/-} 0.29; and EAS: 0.330 {+/-} 0.315; Wei-AIM278: AFR: 0.070 {+/-} 0.096, EUR: 0.537 {+/-} 0.267, and EAS: 0.393 {+/-} 0.300) with no significant difference (Pearson correlation, P < 10-50, n = 347 samples). Subsequently, we applied UT-AIM250 panel to clinical datasets of self-reported Hispanic patients in South Texas with hepatocellular carcinoma (26 patients). Our estimated admixture proportions from adjacent non-cancer liver tissue data of Hispanics in South Texas is (AFR: 0.065 {+/-} 0.043; EUR: 0.594 {+/-} 0.150; and EAS: 0.341 {+/-} 0.160), with smaller variation due to its unique Texan/Mexican American population in South Texas. Similar admixture proportion from the corresponding tumor tissue we also obtained. In addition, we estimated admixture proportions of entire TCGA-LIHC samples (376 patients) using UT-AIM250 panel. We demonstrated that our AIM panel estimate consistent admixture proportions from DNAs derived from tumor and normal tissues, and 2 possible incorrect reported race/ethnicity, and/or provide race/ethnicity determination if necessary.\n\nConclusionsTaken together, we demonstrated the feasibility of using evolutionary-conserved exome regions to distinguish genetic ancestry descendants based on 3 continental-ancestry proportion, provided a robust and reliable control for sample collection or patient stratification for genetic analysis. R implementation of UT-AIM250 is available at https://github.com/chenlabgccri/UT-AIM250.

bioinformatics↗