Human Immune Cell Epigenomic Signatures in Response to Infectious Diseases and Chemical Exposures.

Variations in DNA methylation patterns in human tissues have been linked to various environmental exposures and infections. Here, we identified the DNA methylation signatures associated with multiple exposures in nine major immune cell types derived from peripheral blood mononuclear cells (PBMCs) at single-cell resolution. We performed methylome sequencing on 111,180 immune cells obtained from 112 individuals who were exposed to different viruses, bacteria, or chemicals. Our analysis revealed 790,662 differentially methylated regions (DMRs) associated with these exposures, which are mostly individual CpG sites. Additionally, we integrated methylation and ATAC-seq data from same samples and found strong correlations between the two modalities. However, the epigenomic remodeling in these two modalities are complementary. Finally, we identified the minimum set of DMRs that can predict exposures. Overall, our study provides the first comprehensive dataset of single immune cell methylation profiles, along with unique methylation biomarkers for various biological and chemical exposures.

[Índices para la evaluación de la resistencia a la insulina en individuos mexicanos sin diabetes].

OBJECTIVE: To evaluate the correlation between subrogate index for the evaluation of insulin resistance with the M value obtained with the euglycemic-hyperinsulinemic clamp as well as their sensitivity, specificity, and positive and negative predictive values. METHOD: The euglycemic-hyperinsulinemic clamp was performed in subjects having both normal fasting glucose and glycated hemoglobin concentrations. HOMA-IR, QUICKI, HOMA2%S, TyG, TyG*body mass index (BMI) and triglyceride/HDL indexes were calculated. Correlations coefficients were estimated between indexes results and the M-value adjusted by fat-free mass. Areas under the ROC curve were constructed to evaluate overall performance, sensitivity, specificity and predictive values of the subrogate indexes. RESULTS: 57 subjects, 68.4% women, with a mean age of 32.9 ± 11 years-old were included. The subrogate index with the best correlation with the M value was HOMA2%S (r = 0.428), HOMA-IR had the greatest area under the ROC curve (0.683; 95 % confidence interval: 0.503-0.864) and TyG*BMI the best sensitivity (98.2 %) and specificity (51.1 %). CONCLUSIONS: The surrogated indexes for the evaluation of insulin resistance show a significant correlation with the M value obtained with the gold standard. Additional studies are required to determine cut-off values in Mexican population.

OBJETIVO: Evaluar la correlación entre índices subrogados de resistencia a la insulina y el valor M obtenido mediante pinza euglucémica-hiperinsulinémica, así como su sensibilidad, especificidad y valores predictivos positivo y negativo, en mexicanos sin diabetes. MÉTODO: Se realizó una pinza euglucémica-hiperinsulinémica en individuos con glucosa en ayuno y hemoglobina glucosilada normales. Se estimaron los índices HOMA-IR, QUICKI, HOMA2%S, TyG, TyG*índice de masa corporal (IMC) y triglicéridos/colesterol ligado a lipoproteínas de alta densidad. Se estimaron coeficientes de correlación de Pearson entre los resultados y el valor M ajustado por masa libre de grasa. Se construyeron curvas ROC para evaluar su desempeño y se estimaron la sensibilidad, la especificidad y los valores predictivos de los índices. RESULTADOS: Se incluyeron 57 individuos, el 68.4 % mujeres, de 32.9 ± 11 años, con IMC de 26.5 ± 3.9 kg/m2. El índice subrogado con mejor correlación con el valor M fue HOMA2%S (r = 0.428), con la mayor área bajo la curva ROC (0.683; intervalo de confianza del 95 %: 0.503-0.864) HOMA-IR, y con mejor sensibilidad (92.8 %) y especificidad (51.1 %) TyG*IMC. CONCLUSIONES: Los índices subrogados para estimar la resistencia a la insulina muestran una correlación significativa con el valor M obtenido con el método de referencia. Se requieren más estudios para determinar puntos de corte en población mexicana.

METS-IR, a novel score to evaluate insulin sensitivity, is predictive of visceral adiposity and incident type 2 diabetes.

OBJECTIVE: We developed a novel non-insulin-based fasting score to evaluate insulin sensitivity validated against the euglycemic-hyperinsulinemic clamp (EHC). We also evaluated its correlation with ectopic fact accumulation and its capacity to predict incident type 2 diabetes mellitus (T2D). DESIGN AND METHODS: The discovery sample was composed by 125 subjects (57 without and 68 with T2D) that underwent an EHC. We defined METS-IR as Ln((2*G0)+TG0)*BMI)/(Ln(HDL-c)) (G0: fasting glucose, TG0: fasting triglycerides, BMI: body mass index, HDL-c: high-density lipoprotein cholesterol), and compared its diagnostic performance against the M-value adjusted by fat-free mass (MFFM) obtained by an EHC. METS-IR was validated in a sample with EHC data, a sample with modified frequently sampled intravenous glucose tolerance test (FSIVGTT) data and a large cohort against HOMA-IR. We evaluated the correlation of the score with intrahepatic and intrapancreatic fat measured using magnetic resonance spectroscopy. Subsequently, we evaluated its ability to predict incident T2D cases in a prospective validation cohort of 6144 subjects. RESULTS: METS-IR demonstrated the better correlation with the MFFM (ρ = -0.622, P < 0.001) and diagnostic performance to detect impaired insulin sensitivity compared to both EHC (AUC: 0.84, 95% CI: 0.78-0.90) and the SI index obtained from the FSIVGTT (AUC: 0.67, 95% CI: 0.53-0.81). METS-IR significantly correlated with intravisceral, intrahepatic and intrapancreatic fat and fasting insulin levels (P < 0.001). After a two-year follow-up, subjects with METS-IR in the highest quartile (>50.39) had the highest adjusted risk to develop T2D (HR: 3.91, 95% CI: 2.25-6.81). Furthermore, subjects with incident T2D had higher baseline METS-IR compared to healthy controls (50.2 ± 10.2 vs 44.7 ± 9.2, P < 0.001). CONCLUSION: METS-IR is a novel score to evaluate cardiometabolic risk in healthy and at-risk subjects and a promising tool for screening of insulin sensitivity.

Epigenomic Diversity in a Global Collection of Arabidopsis thaliana Accessions.

The epigenome orchestrates genome accessibility, functionality, and three-dimensional structure. Because epigenetic variation can impact transcription and thus phenotypes, it may contribute to adaptation. Here, we report 1,107 high-quality single-base resolution methylomes and 1,203 transcriptomes from the 1001 Genomes collection of Arabidopsis thaliana. Although the genetic basis of methylation variation is highly complex, geographic origin is a major predictor of genome-wide DNA methylation levels and of altered gene expression caused by epialleles. Comparison to cistrome and epicistrome datasets identifies associations between transcription factor binding sites, methylation, nucleotide variation, and co-expression modules. Physical maps for nine of the most diverse genomes reveal how transposons and other structural variants shape the epigenome, with dramatic effects on immunity genes. The 1001 Epigenomes Project provides a comprehensive resource for understanding how variation in DNA methylation contributes to molecular and non-molecular phenotypes in natural populations of the most studied model plant.

A user's guide to the Arabidopsis T-DNA insertion mutant collections.

The T-DNA sequence-indexed mutant collections contain insertional mutants for most Arabidopsis thaliana genes and have played an important role in plant biology research for almost two decades. By providing a large source of mutant alleles for in vivo characterization of gene function, this resource has been leveraged thousands of times to study a wide range of problems in plant biology. Our primary goal in this chapter is to provide a general guide to strategies for the effective use of the data and materials in these collections. To do this, we provide a general introduction to the T-DNA insertional sequence-indexed mutant collections with a focus on how best to use the available data sources for good line selection. As isolation of a homozygous line is a common next step once a potential disruption line has been identified, the second half of the chapter provides a step-by-step guide for the design and implementation of a T-DNA genotyping pipeline. Finally, we describe interpretation of genotyping results and include a troubleshooting section for common types of segregation distortions that we have observed. In this chapter we introduce both basic concepts and specific applications to both new and more experienced users of the collections for the design and implementation of small- to large-scale genotyping pipelines.