Larry Lam

Epigenetic changes in T-cell and monocyte signatures and production of neurotoxic cytokines in ALS patients

We have investigated transcriptional and epigenetic differences in peripheral blood mononuclear cells (PBMCs) of monozygotic female twins discordant in the diagnosis of amyotrophic lateral sclerosis (ALS). Exploring DNA methylation differences by reduced representation bisulfite sequencing (RRBS), we determined that, over time, the ALS twin developed higher abundances of the CD14 macrophages and lower abundances of T cells compared to the non‐ALS twin. Higher macrophage signature in the ALStwinwas also shownby RNA sequencing (RNA‐seq). Moreover, the twins differed in the methylome at loci near several genes, including EGFR and TNFRSF11A, and in the pathways related to the tretinoin and H3K27me3 markers. We also tested cytokine production by PBMCs. The ALS twins PBMCs spontaneously produced IL‐6 and TNF‐α, whereas PBMCs of the healthy twin produced these cytokines only when stimulated by superoxide dismutase (SOD)‐1. These results and flow cytometric detection of CD45 and CD127 suggest the presence of memory T cells in both twins, but effector T cells only in the ALS twin. The ALS twins PBMC supernatants, but not the healthy twins, were toxic to rat cortical neurons, and this toxicity was strongly inhibited by an IL‐6 receptor antibody (tocilizumab) and less well by TNF‐α and IL‐1β antibodies. The putative neurotoxicity of IL‐6 and TNF‐α is in agreement with a high expression of these cytokines on infiltrating macrophages in the ALS spinal cord. We hypothesize that higher macrophage abundance and increased neurotoxic cytokines have a fundamental role in the phenotype and treatment of certain individuals with ALS.—Lam, L., Chin, L., Halder, R.C., Sagong, B., Famenini, S., Sayre, J., Montoya, D., Rubbi L., Pellegrini, M., Fiala, M. Epigenetic changes in T‐cell and monocyte signatures and production of neurotoxic cytokines in ALS patients. FASEB J. 30, 3461–3473 (2016). www.fasebj.org

MethGo: a comprehensive tool for analyzing whole-genome bisulfite sequencing data

BackgroundDNA methylation is a major epigenetic modification regulating several biological processes. A standard approach to measure DNA methylation is bisulfite sequencing (BS-Seq). BS-Seq couples bisulfite conversion of DNA with next-generation sequencing to profile genome-wide DNA methylation at single base resolution. The analysis of BS-Seq data involves the use of customized aligners for mapping bisulfite converted reads and the bioinformatic pipelines for downstream data analysis.ResultsHere we developed MethGo, a software tool designed for the analysis of data from whole-genome bisulfite sequencing (WGBS) and reduced representation bisulfite sequencing (RRBS). MethGo provides both genomic and epigenomic analyses including: 1) coverage distribution of each cytosine; 2) global cytosine methylation level; 3) cytosine methylation level distribution; 4) cytosine methylation level of genomic elements; 5) chromosome-wide cytosine methylation level distribution; 6) Gene-centric cytosine methylation level; 7) cytosine methylation levels at transcription factor binding sites (TFBSs); 8) single nucleotide polymorphism (SNP) calling, and 9) copy number variation (CNV) calling.ConclusionsMethGo is a simple and effective tool for the analysis of BS-Seq data including both WGBS and RRBS. It contains 9 analyses in 5 major modules to profile (epi)genome. It profiles genome-wide DNA methylation in global and in gene level scale. It can also analyze the methylation pattern around the transcription factor binding sites, and assess genetic variations such as SNPs and CNVs. MethGo is coded in Python and is publically available at http://paoyangchen-laboratory.github.io/methgo/.

Prenatal Growth Patterns and Birthweight Are Associated With Differential DNA Methylation and Gene Expression of Cardiometabolic Risk Genes in Human Placentas: A Discovery-Based Approach

Inherent genetic programming and environmental factors affect fetal growth in utero. Epidemiologic data in growth-altered fetuses, either intrauterine growth restricted (IUGR) or large for gestational age (LGA), demonstrate that these newborns are at increased risk of cardiometabolic disease in adulthood. There is growing evidence that the in utero environment leads to epigenetic modification, contributing to eventual risk of developing heart disease or diabetes. In this study, we used reduced representation bisulfite sequencing to examine genome-wide DNA methylation variation in placental samples from offspring born IUGR, LGA, and appropriate for gestational age (AGA) and to identify differential methylation of genes important for conferring risk of cardiometabolic disease. We found that there were distinct methylation signatures for IUGR, LGA, and AGA groups and identified over 500 differentially methylated genes (DMGs) among these group comparisons. Functional and gene network analyses revealed expected relationships of DMGs to placental physiology and transport, but also identified novel pathways with biologic plausibility and potential clinical importance to cardiometabolic disease. Specific loci for DMGs of interest had methylation patterns that were strongly associated with anthropometric presentations. We further validated altered gene expression of these specific DMGs contributing to vascular and metabolic diseases (SLC36A1, PTPRN2, CASZ1, IL10), thereby establishing transcriptional effects toward assigning functional significance. Our results suggest that the gene expression and methylation state of the human placenta are related and sensitive to the intrauterine environment, as it affects fetal growth patterns. We speculate that these observed changes may affect risk for offspring in developing adult cardiometabolic disease.

Epigenetic changes mediated by polycomb repressive complex 2 and E2a are associated with drug resistance in a mouse model of lymphoma

BackgroundThe genetic origins of chemotherapy resistance are well established; however, the role of epigenetics in drug resistance is less well understood. To investigate mechanisms of drug resistance, we performed systematic genetic, epigenetic, and transcriptomic analyses of an alkylating agent-sensitive murine lymphoma cell line and a series of resistant lines derived by drug dose escalation.MethodsDose escalation of the alkylating agent mafosfamide was used to create a series of increasingly drug-resistant mouse Burkitt’s lymphoma cell lines. Whole genome sequencing, DNA microarrays, reduced representation bisulfite sequencing, and chromatin immunoprecipitation sequencing were used to identify alterations in DNA sequence, mRNA expression, CpG methylation, and H3K27me3 occupancy, respectively, that were associated with increased resistance.ResultsOur data suggest that acquired resistance cannot be explained by genetic alterations. Based on integration of transcriptional profiles with transcription factor binding data, we hypothesize that resistance is driven by epigenetic plasticity. We observed that the resistant cells had H3K27me3 and DNA methylation profiles distinct from those of the parental lines. Moreover, we observed DNA methylation changes in the promoters of genes regulated by E2a and members of the polycomb repressor complex 2 (PRC2) and differentially expressed genes were enriched for targets of E2a. The integrative analysis considering H3K27me3 further supported a role for PRC2 in mediating resistance. By integrating our results with data from the Immunological Genome Project (Immgen.org), we showed that these transcriptional changes track the B-cell maturation axis.ConclusionsOur data suggest a novel mechanism of drug resistance in which E2a and PRC2 drive changes in the B-cell epigenome; these alterations attenuate alkylating agent treatment-induced apoptosis.

Astrocytes Can Adopt Endothelial Cell Fates in a p53-Dependent Manner

Astrocytes respond to a variety of CNS injuries by cellular enlargement, process outgrowth, and upregulation of extracellular matrix proteins that function to prevent expansion of the injured region. This astrocytic response, though critical to the acute injury response, results in the formation of a glial scar that inhibits neural repair. Scar-forming cells (fibroblasts) in the heart can undergo mesenchymal-endothelial transition into endothelial cell fates following cardiac injury in a process dependent on p53 that can be modulated to augment cardiac repair. Here, we sought to determine whether astrocytes, as the primary scar-forming cell of the CNS, are able to undergo a similar cellular phenotypic transition and adopt endothelial cell fates. Serum deprivation of differentiated astrocytes resulted in a change in cellular morphology and upregulation of endothelial cell marker genes. In a tube formation assay, serum-deprived astrocytes showed a substantial increase in vessel-like morphology that was comparable to human umbilical vein endothelial cells and dependent on p53. RNA sequencing of serum-deprived astrocytes demonstrated an expression profile that mimicked an endothelial rather than astrocyte transcriptome and identified p53 and angiogenic pathways as specifically upregulated. Inhibition of p53 with genetic or pharmacologic strategies inhibited astrocyte-endothelial transition. Astrocyte-endothelial cell transition could also be modulated by miR-194, a microRNA downstream of p53 that affects expression of genes regulating angiogenesis. Together, these studies demonstrate that differentiated astrocytes retain a stimulus-dependent mechanism for cellular transition into an endothelial phenotype that may modulate formation of the glial scar and promote injury-induced angiogenesis.

Epigenome-wide association in adipose tissue from the METSIM cohort

Most epigenome-wide association studies to date have been conducted in blood. However, metabolic syndrome is mediated by a dysregulation of adiposity and therefore it is critical to study adipose tissue in order to understand the effects of this syndrome on epigenomes. To determine if natural variation in DNA methylation was associated with metabolic syndrome traits, we profiled global methylation levels in subcutaneous abdominal adipose tissue. We measured association between 32 clinical traits related to diabetes and obesity in 201 people from the Metabolic Syndrome in Men cohort. We performed epigenome-wide association studies between DNA methylation levels and traits, and identified associations for 13 clinical traits in 21 loci. We prioritized candidate genes in these loci using expression quantitative trait loci, and identified 18 high confidence candidate genes, including known and novel genes associated with diabetes and obesity traits. Using methylation deconvolution, we examined which cell types may be mediating the associations, and concluded that most of the loci we identified were specific to adipocytes. We determined whether the abundance of cell types varies with metabolic traits, and found that macrophages increased in abundance with the severity of metabolic syndrome traits. Finally, we developed a DNA methylation-based biomarker to assess type 2 diabetes risk in adipose tissue. In conclusion, our results demonstrate that profiling DNA methylation in adipose tissue is a powerful tool for understanding the molecular effects of metabolic syndrome on adipose tissue, and can be used in conjunction with traditional genetic analyses to further characterize this disorder.

Topological Arrangement of Cardiac Fibroblasts Regulates Cellular Plasticity

Rationale: Cardiac fibroblasts do not form a syncytium but reside in the interstitium between myocytes. This topological relationship between fibroblasts and myocytes is maintained throughout postnatal life until an acute myocardial injury occurs, when fibroblasts are recruited to, proliferate and aggregate in the region of myocyte necrosis. The accumulation or aggregation of fibroblasts in the area of injury thus represents a unique event in the life cycle of the fibroblast, but little is known about how changes in the topological arrangement of fibroblasts after cardiac injury affect fibroblast function. Objective: The objective of the study was to investigate how changes in topological states of cardiac fibroblasts (such as after cardiac injury) affect cellular phenotype. Methods and Results: Using 2 and 3-dimensional (2D versus 3D) culture conditions, we show that simple aggregation of cardiac fibroblasts is sufficient by itself to induce genome-wide changes in gene expression and chromatin remodeling. Remarkably, gene expression changes are reversible after the transition from a 3D back to 2D state demonstrating a topological regulation of cellular plasticity. Genes induced by fibroblast aggregation are strongly associated and predictive of adverse cardiac outcomes and remodeling in mouse models of cardiac hypertrophy and failure. Using solvent-based tissue clearing techniques to create optically transparent cardiac scar tissue, we show that fibroblasts in the region of dense scar tissue express markers that are induced by fibroblasts in the 3D conformation. Finally, using live cell interferometry, a quantitative phase microscopy technique to detect absolute changes in single cell biomass, we demonstrate that conditioned medium collected from fibroblasts in 3D conformation compared with that from a 2D state significantly increases cardiomyocyte cell hypertrophy. Conclusions: Taken together, these findings demonstrate that simple topological changes in cardiac fibroblast organization are sufficient to induce chromatin remodeling and global changes in gene expression with potential functional consequences for the healing heart.

Epigenome-wide association in adipose tissue from the METSIM cohort identifies novel loci and the involvement of adipocytes and macrophages in diabetes traits

Most epigenome-wide association studies to date have been conducted in blood. However, metabolic syndrome is mediated by a dysregulation of adiposity and therefore it is critical to study adipose tissue in order to understand the effects of this syndrome on epigenomes. To determine if natural variation in DNA methylation was associated with metabolic syndrome traits, we profiled global methylation levels in subcutaneous abdominal adipose tissue. We measured association between 32 clinical traits related to diabetes and obesity in 201 people from the Metabolic Syndrome In Men cohort. We performed epigenome-wide association studies between DNA methylation levels and traits, and identified associations for 13 clinical traits in 21 loci. We prioritized candidate genes in these loci using eQTL, and identified 18 high confidence candidate genes, including known and novel genes associated with diabetes and obesity traits. Using methylation deconvolution, we examined which cell types may be mediating the associations, and concluded that most of the loci we identified were specific to adipocytes. We determined whether the abundance of cell types varies with metabolic traits, and found that macrophages increased in abundance with the severity of metabolic syndrome traits. Finally, we developed a DNA methylation based biomarker to assess type II diabetes risk in adipose tissue. In conclusion, our results demonstrate that profiling DNA methylation in adipose tissue is a powerful tool for understanding the molecular effects of metabolic syndrome on adipose tissue, and can be used in conjunction with traditional genetic analyses to further characterize this disorder.

SaVanT: a web-based tool for the sample-level visualization of molecular signatures in gene expression profiles

BackgroundMolecular signatures are collections of genes characteristic of a particular cell type, tissue, disease, or perturbation. Signatures can also be used to interpret expression profiles generated from heterogeneous samples. Large collections of gene signatures have been previously developed and catalogued in the MSigDB database. In addition, several consortia and large-scale projects have systematically profiled broad collections of purified primary cells, molecular perturbations of cell types, and tissues from specific diseases, and the specificity and breadth of these datasets can be leveraged to create additional molecular signatures. However, to date there are few tools that allow the visualization of individual signatures across large numbers of expression profiles. Signature visualization of individual samples allows, for example, the identification of patient subcategories a priori on the basis of well-defined molecular signatures.ResultHere, we generate and compile 10,985 signatures (636 newly-generated and 10,349 previously available from MSigDB) and provide a web-based Signature Visualization Tool (SaVanT; http://newpathways.mcdb.ucla.edu/savant), to visualize these signatures in user-generated expression data. We show that using SaVanT, immune activation signatures can distinguish patients with different types of acute infections (influenza A and bacterial pneumonia). Furthermore, SaVanT is able to identify the prominent signatures within each patient group, and identify the primary cell types underlying different leukemias (acute myeloid and acute lymphoblastic) and skin disorders.ConclusionsThe development of SaVanT facilitates large-scale analysis of gene expression profiles on a patient-level basis to identify patient subphenotypes, or potential therapeutic target pathways.