Expansion of extrafollicular B and T cell subsets in childhood-onset systemic lupus erythematosus.

Introduction: Most childhood-onset SLE patients (cSLE) develop lupus nephritis (cLN), but only a small proportion achieve complete response to current therapies. The prognosis of children with LN and end-stage renal disease is particularly dire. Mortality rates within the first five years of renal replacement therapy may reach 22%. Thus, there is urgent need to decipher and target immune mechanisms that drive cLN. Despite the clear role of autoantibody production in SLE, targeted B cell therapies such as rituximab (anti-CD20) and belimumab (anti-BAFF) have shown only modest efficacy in cLN. While many studies have linked dysregulation of germinal center formation to SLE pathogenesis, other work supports a role for extrafollicular B cell activation in generation of pathogenic antibody secreting cells. However, whether extrafollicular B cell subsets and their T cell collaborators play a role in specific organ involvement in cLN and/or track with disease activity remains unknown. Methods: We analyzed high-dimensional mass cytometry and gene expression data from 24 treatment naïve cSLE patients at the time of diagnosis and longitudinally, applying novel computational tools to identify abnormalities associated with clinical manifestations (cLN) and disease activity (SLEDAI). Results: cSLE patients have an extrafollicular B cell expansion signature, with increased frequency of i) DN2, ii) Bnd2, iii) plasmablasts, and iv) peripheral T helper cells. Most importantly, we discovered that this extrafollicular signature correlates with disease activity in cLN, supporting extrafollicular T/B interactions as a mechanism underlying pediatric renal pathogenesis. Discussion: This study integrates established and emerging themes of extrafollicular B cell involvement in SLE by providing evidence for extrafollicular B and peripheral T helper cell expansion, along with elevated type 1 IFN activation, in a homogeneous cohort of treatment-naïve cSLE patients, a point at which they should display the most extreme state of their immune dysregulation.

Multi-omics subtyping pipeline for chronic obstructive pulmonary disease.

Chronic Obstructive Pulmonary Disease (COPD) is the third leading cause of mortality in the United States; however, COPD has heterogeneous clinical phenotypes. This is the first large scale attempt which uses transcriptomics, proteomics, and metabolomics (multi-omics) to determine whether there are molecularly defined clusters with distinct clinical phenotypes that may underlie the clinical heterogeneity. Subjects included 3,278 subjects from the COPDGene cohort with at least one of the following profiles: whole blood transcriptomes (2,650 subjects); plasma proteomes (1,013 subjects); and plasma metabolomes (1,136 subjects). 489 subjects had all three contemporaneous -omics profiles. Autoencoder embeddings were performed individually for each -omics dataset. Embeddings underwent subspace clustering using MineClus, either individually by -omics or combined, followed by recursive feature selection based on Support Vector Machines. Clusters were tested for associations with clinical variables. Optimal single -omics clustering typically resulted in two clusters. Although there was overlap for individual -omics cluster membership, each -omics cluster tended to be defined by unique molecular pathways. For example, prominent molecular features of the metabolome-based clustering included sphingomyelin, while key molecular features of the transcriptome-based clusters were related to immune and bacterial responses. We also found that when we integrated the -omics data at a later stage, we identified subtypes that varied based on age, severity of disease, in addition to diffusing capacity of the lungs for carbon monoxide, and precent on atrial fibrillation. In contrast, when we integrated the -omics data at an earlier stage by treating all data sets equally, there were no clinical differences between subtypes. Similar to clinical clustering, which has revealed multiple heterogenous clinical phenotypes, we show that transcriptomics, proteomics, and metabolomics tend to define clusters of COPD patients with different clinical characteristics. Thus, integrating these different -omics data sets affords additional insight into the molecular nature of COPD and its heterogeneity.

Subjects at-risk for future development of rheumatoid arthritis demonstrate a PAD4-and TLR-dependent enhanced histone H3 citrullination and proinflammatory cytokine production in CD14hi monocytes.

The presence of anti-citrullinated protein/peptide antibodies (ACPA) and epitope spreading across the target autoantigens is a unique feature of rheumatoid arthritis (RA). ACPA are present in the peripheral blood for several years prior to the onset of arthritis and clinical classification of RA. ACPA recognize multiple citrullinated proteins, including histone H3 (H3). Intracellular citrullination of H3 in neutrophils and T cells is known to regulate immune cell function by promoting neutrophil extracellular trap formation and citrullinated autoantigen release as well as regulating the Th2/Th17 T cell phenotypic balance. However, the roles of H3 citrullination in other immune cells are not fully elucidated. We aimed to explore H3 citrullination and cytokine/metabolomic signatures in peripheral blood immune cells from subjects prior to and after the onset of RA, at baseline and in response to ex vivo toll-like receptor (TLR) stimulation. Here, we analyzed 13 ACPA (+) subjects without arthritis but at-risk for future development of RA, 14 early RA patients, and 13 healthy controls. We found significantly elevated H3 citrullination in CD14hi monocytes, as well as CD1c+ dendritic cells and CD66+ granulocytes. Unsupervised analysis identified two distinct subsets in CD14hi monocytes characterized by H3 modification and unique cytokine/metabolomic signatures. CD14hi monocytes with elevated TLR-stimulated H3 citrullination were significantly increased in ACPA (+) at-risk subjects. These cells were skewed to produce TNFα, MIP1ß, IFNα, and partially IL-12. Additionally, they demonstrate peptidyl arginine deiminase 4 (PAD4) mediated upregulation of the glycolytic enzyme PFKFB3. These CD14hi monocytes with elevated H3 citrullination morphologically formed monocyte extracellular traps (METs). Taken together, dysregulated PAD4-driven cytokine production as well as MET formation in CD14hi monocytes in ACPA (+) at-risk subjects likely plays an important role in the development of RA via promoting and perpetuating inflammation and generation of citrullinated autoantigens.

Minimizing Batch Effects in Mass Cytometry Data.

Cytometry by Time-Of-Flight (CyTOF) uses antibodies conjugated to isotopically pure metals to identify and quantify a large number of cellular features with single-cell resolution. A barcoding approach allows for 20 unique samples to be pooled and processed together in one tube, reducing the intra-barcode technical variability. However, with only 20 samples per barcode, multiple barcode sets (batches) are required to address questions in robustly powered study designs. A batch adjustment procedure is required to reduce variability across batches and to facilitate direct comparison of runs performed across multiple barcodes run over weeks, months, or years. We describe a method using technical replicates that are included in each run to determine and apply an appropriate adjustment per batch without manual intervention. The use of technical replicate samples (i.e., anchors or reference samples) avoids assumptions of sample homogeneity among batches, and allows direct estimation of batch effects and appropriate adjustment parameters applicable to all samples within a batch. Quantification of cell subpopulations and mean signal intensity pre- and post-adjustment using both manual gating and unsupervised clustering demonstrate substantial mitigation of batch effects in the anchor samples used for this adjustment calculation, and in a second validation set of technical replicates.

Chronic Liver Disease in Humans Causes Expansion and Differentiation of Liver Lymphatic Endothelial Cells.

Liver lymphatic vessels support liver function by draining interstitial fluid, cholesterol, fat, and immune cells for surveillance in the liver draining lymph node. Chronic liver disease is associated with increased inflammation and immune cell infiltrate. However, it is currently unknown if or how lymphatic vessels respond to increased inflammation and immune cell infiltrate in the liver during chronic disease. Here we demonstrate that lymphatic vessel abundance increases in patients with chronic liver disease and is associated with areas of fibrosis and immune cell infiltration. Using single-cell mRNA sequencing and multi-spectral immunofluorescence analysis we identified liver lymphatic endothelial cells and found that chronic liver disease results in lymphatic endothelial cells (LECs) that are in active cell cycle with increased expression of CCL21. Additionally, we found that LECs from patients with NASH adopt a transcriptional program associated with increased IL13 signaling. Moreover, we found that oxidized low density lipoprotein, associated with NASH pathogenesis, induced the transcription and protein production of IL13 in LECs both in vitro and in a mouse model. Finally, we show that oxidized low density lipoprotein reduced the transcription of PROX1 and decreased lymphatic stability. Together these data indicate that LECs are active participants in the liver, expanding in an attempt to maintain tissue homeostasis. However, when inflammatory signals, such as oxidized low density lipoprotein are increased, as in NASH, lymphatic function declines and liver homeostasis is impeded.

Distinct Trends of DNA Methylation Patterning in the Innate and Adaptive Immune Systems.

DNA methylation and the localization and post-translational modification of nucleosomes are interdependent factors that contribute to the generation of distinct phenotypes from genetically identical cells. With 112 whole-genome bisulfite sequencing datasets from the BLUEPRINT Epigenome Project, we analyzed the global development of DNA methylation patterns during lineage commitment and maturation of a range of immune system effector cells and the cancers that arise from them. We show clear trends in methylation patterns that are distinct in the innate and adaptive arms of the human immune system, both globally and in relation to consistently positioned nucleosomes. Most notable are a progressive loss of methylation in developing lymphocytes and the consistent occurrence of non-CG methylation in specific cell types. Cancer samples from the two lineages are further polarized, suggesting the involvement of distinct lineage-specific epigenetic mechanisms. We anticipate broad utility for this resource as a basis for further comparative epigenetic analyses.

Whole-genome fingerprint of the DNA methylome during human B cell differentiation.

We analyzed the DNA methylome of ten subpopulations spanning the entire B cell differentiation program by whole-genome bisulfite sequencing and high-density microarrays. We observed that non-CpG methylation disappeared upon B cell commitment, whereas CpG methylation changed extensively during B cell maturation, showing an accumulative pattern and affecting around 30% of all measured CpG sites. Early differentiation stages mainly displayed enhancer demethylation, which was associated with upregulation of key B cell transcription factors and affected multiple genes involved in B cell biology. Late differentiation stages, in contrast, showed extensive demethylation of heterochromatin and methylation gain at Polycomb-repressed areas, and genes with apparent functional impact in B cells were not affected. This signature, which has previously been linked to aging and cancer, was particularly widespread in mature cells with an extended lifespan. Comparing B cell neoplasms with their normal counterparts, we determined that they frequently acquire methylation changes in regions already undergoing dynamic methylation during normal B cell differentiation.

Multiple HLA epitopes contribute to type 1 diabetes susceptibility.

Disease susceptibility for type 1 diabetes is strongly associated with the inheritance of specific HLA alleles. However, conventional allele frequency analysis can miss HLA associations because many alleles are rare. In addition, disparate alleles that have similar peptide-binding sites, or shared epitopes, can be missed. To identify the HLA shared epitopes associated with diabetes, we analyzed high-resolution genotyping for class I and class II loci. The HLA epitopes most strongly associated with susceptibility for disease were DQB1 A(57), DQA1 V(76), DRB1 H(13), and DRB1 K(71), whereas DPB1 YD(9,57), HLA-B C(67), and HLA-C YY(9,116) were more weakly associated. The HLA epitopes strongly associated with resistance were DQB1 D(57), DQA1 Y(80), DRB1 R(13), and DRB1 A(71). A dominant resistance phenotype was observed for individuals bearing a protective HLA epitope, even in the presence of a susceptibility epitope. In addition, an earlier age of disease onset correlated with significantly greater numbers of susceptibility epitopes and fewer resistance epitopes (P < 0.0001). The prevalence of both DQ and DR susceptibility epitopes was higher in patients than in control subjects and was not exclusively a result of linkage disequilibrium, suggesting that multiple HLA epitopes may work together to increase the risk of developing diabetes.

Association of the HLA-DRB1 epitope LA(67, 74) with rheumatoid arthritis and citrullinated vimentin binding.

OBJECTIVE: Although rheumatoid arthritis (RA) has long been associated with an HLA-DRB1 shared epitope, a systematic search for other epitopes has never been conducted. In addition, the relationship between these epitopes and the binding of citrullinated autoantigens has not been investigated. We developed a program that can analyze HLA data for all possible epitopes of up to 5 amino acids and used this program to assess the shared epitope hypothesis in RA. METHODS: We analyzed high-resolution data from the International Histocompatibility Working Group, which included a group of 488 patients with RA and a group of 448 racially and ethnically balanced control subjects, for all combinations of up to 5 amino acids among polymorphic HLA-DRB1 positions 8-93. Statistical significance was determined by chi-square and Fisher's exact tests, with a false discovery rate correction. RESULTS: Three residues (V(11), H(13), and L(67)) were found to have the highest degree of association with RA susceptibility (P < 10(-11)), and D(70) was found to correlate best with RA resistance (P = 2 × 10(-11)). Of >2 million epitopes examined, LA(67, 74) exhibited the highest correlation with RA susceptibility (P = 2 × 10(-20); odds ratio 4.07 [95% confidence interval 3.07-5.39]). HLA alleles containing the LA(67, 74) epitope exhibited significantly greater binding to citrullinated vimentin(65-77) than did alleles containing D(70). Only 1 allele (DRB1*16:02) contained both LA(67, 74) and D(70); it bound citrullinated vimentin weakly and was not associated with RA. CONCLUSION: The findings of these studies confirm the importance of HLA-DRB1 amino acids in pocket 4 for the binding of citrullinated autoantigens and susceptibility to RA.

Biomedical discovery acceleration, with applications to craniofacial development.

The profusion of high-throughput instruments and the explosion of new results in the scientific literature, particularly in molecular biomedicine, is both a blessing and a curse to the bench researcher. Even knowledgeable and experienced scientists can benefit from computational tools that help navigate this vast and rapidly evolving terrain. In this paper, we describe a novel computational approach to this challenge, a knowledge-based system that combines reading, reasoning, and reporting methods to facilitate analysis of experimental data. Reading methods extract information from external resources, either by parsing structured data or using biomedical language processing to extract information from unstructured data, and track knowledge provenance. Reasoning methods enrich the knowledge that results from reading by, for example, noting two genes that are annotated to the same ontology term or database entry. Reasoning is also used to combine all sources into a knowledge network that represents the integration of all sorts of relationships between a pair of genes, and to calculate a combined reliability score. Reporting methods combine the knowledge network with a congruent network constructed from experimental data and visualize the combined network in a tool that facilitates the knowledge-based analysis of that data. An implementation of this approach, called the Hanalyzer, is demonstrated on a large-scale gene expression array dataset relevant to craniofacial development. The use of the tool was critical in the creation of hypotheses regarding the roles of four genes never previously characterized as involved in craniofacial development; each of these hypotheses was validated by further experimental work.

Consistent visualizations of changing knowledge.

Networks are increasingly used in biology to represent complex data in uncomplicated symbolic form. However, as biological knowledge is continually evolving, so must those networks representing this knowledge. Capturing and presenting this type of knowledge change over time is particularly challenging due to the intimate manner in which researchers customize those networks they come into contact with. The effective visualization of this knowledge is important as it creates insight into complex systems and stimulates hypothesis generation and biological discovery. Here we highlight how the retention of user customizations, and the collection and visualization of knowledge associated provenance supports effective and productive network exploration. We also present an extension of the Hanalyzer system, ReOrient, which supports network exploration and analysis in the presence of knowledge change.