Biology subjects

Lindsey, M. L.

Publications and source records attributed to Lindsey, M. L..

4 recordsLinked to original sources

bioRxiv2026

Whole-Blood Transcriptomics Highlights Galectin-1 in Systolic Blood Pressure

Background Galectins regulate inflammation, fibrosis, immunity, and vascular remodeling. Although galectin-3 has been widely studied in cardiovascular disease, the relative contributions of other galectins to blood pressure regulation remain unclear. We systematically evaluated associations between galectin gene expression and systolic blood pressure (SBP) in African Americans. Methods Whole-blood mRNA sequencing data were analyzed from 419 African American participants in the GENE-FORECAST cohort who were free of type 2 diabetes and not receiving antihypertensive medications. LGALS1, LGALS3, LGALS8, and LGALS9 were evaluated using generalized linear models adjusted for age, sex, and body mass index. Associations with cardiac magnetic resonance imaging traits and circulating inflammatory cytokines were also examined. Results LGALS1 showed the strongest association with SBP ({beta}=13.25 mm Hg per standard deviation increase; P=7.57e-15). LGALS9 was positively associated with SBP ({beta}=9.73; P=7.59e-9), whereas LGALS3 showed a weaker positive association ({beta}=4.06; P=0.019). LGALS8 was inversely associated with SBP ({beta}=-6.45; P=1.66e-4), as was the LGALS3/LGALS1 ratio ({beta}=-10.2; P=2.34e-9). Galectin genes also showed distinct associations with cardiac imaging traits. Cytokine associations were modest and inconsistent, although LGALS1 was positively associated with interleukin-1{beta}. Conclusions Whole-blood transcriptomics identified LGALS1 as the strongest galectin correlate of SBP in African Americans. Divergent associations across galectin genes suggest heterogeneous, potentially nonredundant cardiovascular roles. These findings broaden the focus beyond LGALS3 and support further investigation of LGALS1 as a biomarker and possible mediator of hypertension-related vascular stress.

genomics↗

bioRxiv2026

Extracellular Vesicles From Metabolically Healthy Obesity Convey Distinct Molecular Signals That Initiate Endothelial Dysfunction: A Multi-Omics Study in Adults of African Ancestry

BackgroundMetabolically healthy obesity (MHO) is unstable, with up to 80% of individuals progressing to metabolically abnormal obesity (MAO), yet mechanisms underlying this transition remain unclear. African Americans bear a disproportionate burden of obesity-related cardiovascular disease. Circulating extracellular vesicles (EVs) mediate inter-organ communication and may drive MAO-related vascular dysfunction. MethodsAdults of African ancestry were classified as metabolically healthy lean (MHL, n=14), MHO (n=9), or MAO (n=16). Plasma-derived EVs were characterized and their microRNA cargo profiled. Human coronary artery endothelial cells were treated with EVs from each group to assess nitric oxide signaling, oxidative stress, inflammatory activation, and mitochondrial dynamics. ResultsMHO participants exhibited preserved insulin sensitivity and lower inflammation compared with MAO despite comparable adiposity. EVs from MHO carried a distinct microRNA signature enriched in miR-148a-5p, miR-181c-5p, and miR-1255a, linked to antioxidant and matrix regulatory pathways. MAO EVs were enriched in miR-3613-3p, miR-6842-3p, and miR-326, targeting inflammation and insulin resistance pathways. Compared with both MHL and MHO EVs, MAO EVs suppressed endothelial nitric oxide synthase phosphorylation and reduced nitric oxide bioavailability, with increased reactive oxygen species and ICAM-1 expression. MHO EVs induced an intermediate phenotype with disrupted mitochondrial morphology, supporting a graded continuum of endothelial stress. ConclusionsMHO represents a biologically active intermediate state. Circulating EVs from MHO individuals convey molecular signals that impair endothelial and mitochondrial function, predisposing to vascular injury and progression toward MAO. EV-associated microRNAs are mechanistic mediators and candidate biomarkers of metabolic and vascular deterioration in obesity. CLINICAL PERSPECTIVEO_ST_ABSWhat Is New?C_ST_ABSO_LIThis study systematically investigated extracellular vesicles derived from metabolically healthy obese individuals to define direct vesicle effects on endothelial function using integrated omics coupled to functional outputs. C_LIO_LIExtracellular vesicles from metabolically healthy obesity convey a distinct molecular and biological signature that distinguishes lean and metabolically abnormal obesity. C_LIO_LIMetabolic health status, rather than obesity alone, drives extracellular vesicle-mediated endothelial nitric oxide signaling, oxidative stress, inflammation, and mitochondrial dynamics. C_LI What Are the Clinical Implications?O_LIThese findings explain why some individuals with obesity exhibit preserved vascular function while others develop early endothelial dysfunction. C_LIO_LIStratifying obesity by metabolic health status improves cardiovascular risk assessment beyond body mass index alone. C_LIO_LITargeting extracellular vesicle signaling pathways represents a novel strategy to prevent metabolically healthy individuals from progressing to metabolically abnormal obesity. C_LI

molecular biology↗

bioRxiv2025

Multi-omics Integration with GWAS Unveils Molecular Mechanistic Insights for Type 2 Diabetes

BackgroundType 2 diabetes (T2D) is a complex metabolic disorder driven by genetic and environmental factors. While genome-wide association studies (GWAS) have identified numerous T2D-associated variants, many remain functionally uncharacterized. Integration of GWAS with molecular phenotyping offers a path to revealing biological relevance. We investigated the influence of GWAS-variants, including sub-threshold T2D-associated variants (GWAS p-value [≤] 0.0001), on gene and protein expression to assign functional relevance. MethodsGenetic variants associated with T2D in the GWAS Catalog and present in our whole-genome sequencing (WGS) data were used to perform expression quantitative trait loci (eQTL) analysis in 242 whole-blood mRNA-sequenced samples. The same variants were used to perform protein quantitative trait loci (pQTL) analysis in a set of 362 plasma samples profiled on the Olink platform. For each analysis, the datasets were randomly split into discovery and validation subsets. Associations between variants and mRNA or protein levels were tested by multiple linear regression, and only QTLs that reached a false discovery rate adjusted p-value [≤] 0.05 in the discovery dataset and replicated in the validation dataset (p [≤] 0.05) with same direction of effect were carried forward. QTL-linked mRNAs and proteins were subsequently evaluated for their relationship with T2D status to connect them with T2D pathophysiology. ResultsWe identified 1,291 eQTLs linked to 97 mRNAs and 1,273 pQTLs linked to 22 proteins. Among these, 10 mRNAs and 5 proteins were differentially expressed between non-diabetic and diabetic individuals. Notably, LPL, APOBR, APOM (lipid metabolism), NOTCH2, TREH ({beta}-cell/endocrine regulation), and HLA-A, OAS3 (immune response) converged on three biological axes central to T2D pathophysiology. The directionality of molecular effects was consistent with known disease mechanisms, including insulin resistance (LPL, APOBR), {beta}-cell stress (TREH, NOTCH2), and chronic inflammation (OAS3). ConclusionsOur findings indicate that variants falling below conventional GWAS significance thresholds can have demonstrable effects on gene expression and protein levels. This underscores the importance of prioritizing biological relevance alongside statistical significance, rather than relying solely on rigid p-value cutoffs.

genomics↗

bioRxiv2019

Network analysis reveals a distinct axis of macrophage activation in response to conflicting inflammatory cues

Macrophages are subject to a wide range of cytokine and pathogen signals in vivo, which contribute to differential activation and modulation of inflammation. Understanding the response to multiple, often conflicting, cues that macrophages experience requires a network perspective. Here, we integrate data from literature curation and mRNA expression profiles to develop a large-scale computational model of the macrophage signaling network. In response to stimulation across all pairs of 9 cytokine inputs, the model predicted activation along the classic M1-M2 polarization axis but also a second axis of macrophage activation that distinguishes unstimulated macrophages from a mixed phenotype induced by conflicting cues. Along this second axis, combinations of conflicting stimuli, interleukin 4 (IL4) with lipopolysaccharide (LPS), interferon-{gamma} (IFN{gamma}), IFN{beta}, or tumor necrosis factor- (TNF), produced mutual inhibition of several signaling pathways, e.g. nuclear factor {kappa}B (NF{kappa}B) and signal transducer and activator of transcription 6 (STAT6), but also mutual activation of the phosphoinositide 3-kinases (PI3K) signaling module. In response to combined IFN{gamma} and IL4, the model predicted genes whose expression was mutually inhibited, e.g. inducible nitric oxide synthase (iNOS) and arginase 1 (Arg1), or mutually enhanced, e.g. IL4 receptor- (IL4R) and suppressor of cytokine signaling 1 (SOCS1), which was validated by independent experimental data. Knockdown simulations further predicted network mechanisms underlying functional crosstalk, such as mutual STAT3/STAT6-mediated enhancement of IL4R expression. In summary, the computational model predicts that network crosstalk mediates a broadened spectrum of macrophage activation in response to mixed pro- and anti-inflammatory cytokine cues, making it useful for modeling in vivo scenarios. Summary sentenceNetwork modeling of macrophage activation predicts responses to combinations of cytokines along both the M1-M2 polarization axis and a second axis associated with a mixed macrophage activation phenotype.

systems biology↗