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Disease-causing variants in key immune homeostasis genes can lead to monogenic autoimmune diabetes. Some individuals carrying disease-causing variants do not develop autoimmune diabetes, even though they develop another autoimmune disease. We aimed to determine whether type 1 diabetes polygenic risk contributes to phenotypic presentation in monogenic autoimmune diabetes. We used a 67 single nucleotide polymorphism type 1 diabetes genetic risk score (T1D-GRS) to determine polygenic risk in 62 individuals with monogenic autoimmune diabetes and 180 control individuals with nonautoimmune neonatal diabetes (NDM). We used population-based control participants (n = 10,405) and individuals with type 1 diabetes (n = 285) as a comparator. Individuals with monogenic autoimmune diabetes had higher T1D-GRSs compared with individuals with nonautoimmune NDM (mean 11.3 vs. 9.8; P = 1.7 × 10−5) and controls (mean 10.3; P = 7.5 × 10−6), but the levels were markedly lower than those for individuals with type 1 diabetes (14.9; P = 3.3 × 10−21). These differences were explained by individuals with monogenic autoimmune diabetes having higher class II HLA genetic risk, specifically from the DRB1* 03:01 -DQA1 *05:01 -DQB1 *02:01 haplotype (DR3-DQ2) (P < 0.01). In the presence of monogenic autoimmunity, the polygenic class II HLA susceptibility contributes to development of autoimmune diabetes. This suggests a role of class II HLA in targeting the dysregulated immune response toward the β-cell. Article Highlights: There is variability in early-onset autoimmune diabetes presentation in individuals with monogenic autoimmunity; the mechanism(s) underlying this is unclear. We examined whether type 1 diabetes (T1D) polygenic risk contributes to clinical phenotype in monogenic autoimmune diabetes. Individuals with monogenic autoimmune diabetes had higher T1D genetic risk scores compared with control cohorts, driven largely by increased presence of T1D-risk DR3-DQ2 haplotype. Established T1D polygenic risk alleles, particularly class II HLA genes, contribute to clinical presentation in monogenic autoimmunity. [ABSTRACT FROM AUTHOR]

Objective: To investigate whether angiogenic biomarker concentrations differ between women who deliver small-for-gestational-age (SGA) infants (<10th centile birth weight for gestational age) compared with controls, because identifying SGA risk early could improve outcomes. Methods: This case-control study compared serum concentrations of angiogenic biomarkers before 24 weeks of pregnancy from 62 women who delivered SGA infants (cases) and 62 control women from an urban Zambian cohort. Odds of delivering an SGA infant were calculated using conditional logistic regression. Results: Placental growth factor (PlGF), soluble fms-like tyrosine kinase (sFLT-1) and soluble endoglin (sEng) in controls were 37.74 pg/mL (interquartile range [IQR] 23.12– 63.15), 2525.18 pg/mL (IQR 1502.21–4265.54) and 2408.18 pg/mL (IQR 1854.87– 3017.94), respectively. SGA cases had higher PlGF (40.50 pg/mL, IQR 22.81–67.94) and sFLT-1 (2613.06 pg/mL, IQR 1720.58–3722.50), and lower sEng (2038.06 pg/mL, IQR 1445.25–3372.26). Participants with sEng concentration below and concomitant sFLT-1 concentration above their respective thresholds (n = 40) had five-fold higher odds of SGA (adjusted odds ratio 4.77, 95% confidence interval 1.61–14.1; P = 0.005). Conclusion: Biomarker concentrations were similar between cases and controls. Participants with concomitant low sEng and high sFLT-1 had the highest odds of SGA, suggesting that a combination of biomarkers may better for predicting SGA than single biomarkers. [ABSTRACT FROM AUTHOR]

Mislabeling samples or data with the wrong participant information can affect study integrity and lead investigators to draw inaccurate conclusions. Quality control to prevent these types of errors is commonly embedded into the analysis of genomic datasets, but a similar identification strategy is not standard for cytometric data. Here, we present a method for detecting sample identification errors in cytometric data using expression of human leukocyte antigen (HLA) class I alleles. We measured HLA‐A*02 and HLA‐B*07 expression in three longitudinal samples from 41 participants using a 33‐marker CyTOF panel designed to identify major immune cell types. 3/123 samples (2.4%) showed HLA allele expression that did not match their longitudinal pairs. Furthermore, these same three samples' cytometric signature did not match qPCR HLA class I allele data, suggesting that they were accurately identified as mismatches. We conclude that this technique is useful for detecting sample‐labeling errors in cytometric analyses of longitudinal data. This technique could also be used in conjunction with another method, like GWAS or PCR, to detect errors in cross‐sectional data. We suggest widespread adoption of this or similar techniques will improve the quality of clinical studies that utilize cytometry. [ABSTRACT FROM AUTHOR]

Wound healing results from complex signaling between cells and their environment in response to injury. Fibroblasts residing within the extracellular matrix (ECM) of various connective tissues are critical for matrix synthesis and repair. Upon injury or chronic insult, these cells activate into wound-healing cells, called myofibroblasts, and repair the damaged tissue through enzyme and protein secretion. However, misregulation and persistence of myofibroblasts can lead to uncontrolled accumulation of matrix proteins, tissue stiffening, and ultimately disease. Extracellular cues are important regulators of fibroblast activation and have been implicated in their persistence. Hydrogel-based culture models have emerged as useful tools to examine fibroblast response to ECM cues presented during these complex processes. In this Mini-Review, we will provide an overview of these model systems, which are built upon naturally-derived or synthetic materials, and mimic relevant biophysical and biochemical properties of the native ECM with different levels of control. Additionally, we will discuss the application of these hydrogel-based systems for the examination of fibroblast function and fate, including adhesion, migration, and activation, as well as approaches for mimicking both static and temporal aspects of extracellular environments. Specifically, we will highlight hydrogels that have been used to investigate the effects of matrix rigidity, protein binding, and cytokine signaling on fibroblast activation. Last, we will describe future directions for the design of hydrogels to develop improved synthetic models that mimic the complex extracellular environment.