Unsupervised discovery of ancestry-informative markers and genetic admixture proportions in biobank-scale datasets.

Admixture estimation plays a crucial role in ancestry inference and genome-wide association studies (GWASs). Computer programs such as ADMIXTURE and STRUCTURE are commonly employed to estimate the admixture proportions of sample individuals. However, these programs can be overwhelmed by the computational burdens imposed by the 105 to 106 samples and millions of markers commonly found in modern biobanks. An attractive strategy is to run these programs on a set of ancestry-informative SNP markers (AIMs) that exhibit substantially different frequencies across populations. Unfortunately, existing methods for identifying AIMs require knowing ancestry labels for a subset of the sample. This supervised learning approach creates a chicken and the egg scenario. In this paper, we present an unsupervised, scalable framework that seamlessly carries out AIM selection and likelihood-based estimation of admixture proportions. Our simulated and real data examples show that this approach is scalable to modern biobank datasets. OpenADMIXTURE, our Julia implementation of the method, is open source and available for free.

A fast data-driven method for genotype imputation, phasing and local ancestry inference: MendelImpute.jl.

MOTIVATION: Current methods for genotype imputation and phasing exploit the volume of data in haplotype reference panels and rely on hidden Markov models (HMMs). Existing programs all have essentially the same imputation accuracy, are computationally intensive and generally require prephasing the typed markers. RESULTS: We introduce a novel data-mining method for genotype imputation and phasing that substitutes highly efficient linear algebra routines for HMM calculations. This strategy, embodied in our Julia program MendelImpute.jl, avoids explicit assumptions about recombination and population structure while delivering similar prediction accuracy, better memory usage and an order of magnitude or better run-times compared to the fastest competing method. MendelImpute operates on both dosage data and unphased genotype data and simultaneously imputes missing genotypes and phase at both the typed and untyped SNPs (single nucleotide polymorphisms). Finally, MendelImpute naturally extends to global and local ancestry estimation and lends itself to new strategies for data compression and hence faster data transport and sharing. AVAILABILITY AND IMPLEMENTATION: Software, documentation and scripts to reproduce our results are available from https://github.com/OpenMendel/MendelImpute.jl. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Modern simulation utilities for genetic analysis.

BACKGROUND: Statistical geneticists employ simulation to estimate the power of proposed studies, test new analysis tools, and evaluate properties of causal models. Although there are existing trait simulators, there is ample room for modernization. For example, most phenotype simulators are limited to Gaussian traits or traits transformable to normality, while ignoring qualitative traits and realistic, non-normal trait distributions. Also, modern computer languages, such as Julia, that accommodate parallelization and cloud-based computing are now mainstream but rarely used in older applications. To meet the challenges of contemporary big studies, it is important for geneticists to adopt new computational tools. RESULTS: We present TraitSimulation, an open-source Julia package that makes it trivial to quickly simulate phenotypes under a variety of genetic architectures. This package is integrated into our OpenMendel suite for easy downstream analyses. Julia was purpose-built for scientific programming and provides tremendous speed and memory efficiency, easy access to multi-CPU and GPU hardware, and to distributed and cloud-based parallelization. TraitSimulation is designed to encourage flexible trait simulation, including via the standard devices of applied statistics, generalized linear models (GLMs) and generalized linear mixed models (GLMMs). TraitSimulation also accommodates many study designs: unrelateds, sibships, pedigrees, or a mixture of all three. (Of course, for data with pedigrees or cryptic relationships, the simulation process must include the genetic dependencies among the individuals.) We consider an assortment of trait models and study designs to illustrate integrated simulation and analysis pipelines. Step-by-step instructions for these analyses are available in our electronic Jupyter notebooks on Github. These interactive notebooks are ideal for reproducible research. CONCLUSION: The TraitSimulation package has three main advantages. (1) It leverages the computational efficiency and ease of use of Julia to provide extremely fast, straightforward simulation of even the most complex genetic models, including GLMs and GLMMs. (2) It can be operated entirely within, but is not limited to, the integrated analysis pipeline of OpenMendel. And finally (3), by allowing a wider range of more realistic phenotype models, TraitSimulation brings power calculations and diagnostic tools closer to what investigators might see in real-world analyses.

Metformin Inhibits the Type 1 IFN Response in Human CD4+ T Cells.

In systemic lupus erythematosus, defective clearance of apoptotic debris and activation of innate cells result in a chronically activated type 1 IFN response, which can be measured in PBMCs of most patients. Metformin, a widely used prescription drug for Type 2 diabetes, has a therapeutic effect in several mouse models of lupus through mechanisms involving inhibition of oxidative phosphorylation and a decrease in CD4+ T cell activation. In this study, we report that in CD4+ T cells from human healthy controls and human systemic lupus erythematosus patients, metformin inhibits the transcription of IFN-stimulated genes (ISGs) after IFN-α treatment. Accordingly, metformin inhibited the phosphorylation of pSTAT1 (Y701) and its binding to IFN-stimulated response elements that control ISG expression. These effects were independent of AMPK activation or mTORC1 inhibition but were replicated using inhibitors of the electron transport chain respiratory complexes I, III, and IV. This indicates that mitochondrial respiration is required for ISG expression in CD4+ T cells and provides a novel mechanism by which metformin may exert a therapeutic effect in autoimmune diseases.

Immunophenotyping reveals distinct subgroups of lupus patients based on their activated T cell subsets.

OBJECTIVE: This study performed an integrated analysis of the cellular and transcriptional differences in peripheral immune cells between patients with Systemic Lupus Erythematosus (SLE) and healthy controls (HC). METHODS: Peripheral blood was analyzed using standardized flow cytometry panels. Transcriptional analysis of CD4+ T cells was performed by microarrays and Nanostring assays. RESULTS: SLE CD4+ T cells showed an increased expression of oxidative phosphorylation and immunoregulatory genes. SLE patients presented higher frequencies of activated CD38+HLA-DR+ T cells than HC. Hierarchical clustering identified a group of SLE patients among which African Americans were overrepresented, with highly activated T cells, and higher frequencies of Th1, Tfh, and plasmablast cells. T cell activation was positively correlated with metabolic gene expression in SLE patients but not in HC. CONCLUSIONS: SLE subjects presenting with activated T cells and a hyperactive metabolic signature may represent an opportunity to correct aberrant immune activation through targeted metabolic inhibitors.

OPENMENDEL: a cooperative programming project for statistical genetics.

Statistical methods for genome-wide association studies (GWAS) continue to improve. However, the increasing volume and variety of genetic and genomic data make computational speed and ease of data manipulation mandatory in future software. In our view, a collaborative effort of statistical geneticists is required to develop open source software targeted to genetic epidemiology. Our attempt to meet this need is called the OPENMENDEL project (https://openmendel.github.io). It aims to (1) enable interactive and reproducible analyses with informative intermediate results, (2) scale to big data analytics, (3) embrace parallel and distributed computing, (4) adapt to rapid hardware evolution, (5) allow cloud computing, (6) allow integration of varied genetic data types, and (7) foster easy communication between clinicians, geneticists, statisticians, and computer scientists. This article reviews and makes recommendations to the genetic epidemiology community in the context of the OPENMENDEL project.

The utility of trough mycophenolic acid levels for the management of lupus nephritis.

Background: Monitoring of mycophenolic acid (MPA) levels may be useful for effective mycophenolate mofetil (MMF) dosing. However, whether commonly obtained trough levels are an acceptable method of surveillance remains debatable. We hypothesized that trough levels of MPA would be a poor predictor of area under the curve (AUC) for MPA. Methods: A total of 51 patients with lupus nephritis who were on MMF 1500 mg twice a day and had a 4-h AUC done were included in this study. MPA levels were measured prior to (C0) and at 1 (C1), 2 (C2) and 4 (C4) h, followed by 1500 mg of MMF. The MPA AUC values were calculated using the linear trapezoidal rule. Regression analysis was used to examine the relationship between the MPA trough and AUC. Differences in the MPA trough and AUC between different clinical and demographic categories were compared using t-tests. Results: When grouped by tertiles there was significant overlap in MPA, AUC 0-4 and MPA trough in all tertiles. Although there was a statistically significant correlation between MPA trough levels and AUC, this association was weak and accounted for only 30% of the variability in MPA trough levels. This relationship might be even more unreliable in men than women. The use of angiotensin-converting enzyme inhibitors or angiotensin receptor blockers was associated with increased MPA trough levels and AUC at 0-4 h (AUC0-4). Conclusion: Trough levels of MPA do not show a strong correlation with AUC. In clinical situations where MPA levels are essential to guide therapy, an AUC0-4 would be a better indicator of the adequacy of treatment.

Fast Genome-Wide QTL Association Mapping on Pedigree and Population Data.

Since most analysis software for genome-wide association studies (GWAS) currently exploit only unrelated individuals, there is a need for efficient applications that can handle general pedigree data or mixtures of both population and pedigree data. Even datasets thought to consist of only unrelated individuals may include cryptic relationships that can lead to false positives if not discovered and controlled for. In addition, family designs possess compelling advantages. They are better equipped to detect rare variants, control for population stratification, and facilitate the study of parent-of-origin effects. Pedigrees selected for extreme trait values often segregate a single gene with strong effect. Finally, many pedigrees are available as an important legacy from the era of linkage analysis. Unfortunately, pedigree likelihoods are notoriously hard to compute. In this paper, we reexamine the computational bottlenecks and implement ultra-fast pedigree-based GWAS analysis. Kinship coefficients can either be based on explicitly provided pedigrees or automatically estimated from dense markers. Our strategy (a) works for random sample data, pedigree data, or a mix of both; (b) entails no loss of power; (c) allows for any number of covariate adjustments, including correction for population stratification; (d) allows for testing SNPs under additive, dominant, and recessive models; and (e) accommodates both univariate and multivariate quantitative traits. On a typical personal computer (six CPU cores at 2.67 GHz), analyzing a univariate HDL (high-density lipoprotein) trait from the San Antonio Family Heart Study (935,392 SNPs on 1,388 individuals in 124 pedigrees) takes less than 2 min and 1.5 GB of memory. Complete multivariate QTL analysis of the three time-points of the longitudinal HDL multivariate trait takes less than 5 min and 1.5 GB of memory. The algorithm is implemented as the Ped-GWAS Analysis (Option 29) in the Mendel statistical genetics package, which is freely available for Macintosh, Linux, and Windows platforms from http://genetics.ucla.edu/software/mendel.

Obesity and associated lifestyles modify the effect of glucose metabolism-related genetic variants on impaired glucose homeostasis among postmenopausal women.

PURPOSE: Impaired glucose metabolism-related genetic variants likely interact with obesity-modifiable factors in response to glucose intolerance, yet their interconnected pathways have not been fully characterized. METHODS: With data from 1,027 postmenopausal participants of the Genomics and Randomized Trials Network study and 15 single-nucleotide polymorphisms (SNPs) associated with glucose homeostasis, we assessed whether obesity, physical activity, and high dietary fat intake interact with the SNP-glucose variations. We used regression analysis plus stratification and graphic approaches. RESULTS: Across carriers of the 15 SNPs, fasting levels of glucose, insulin, and homeostatic model assessment-insulin resistance (HOMA-IR) were higher in obese, inactive, and high fat-diet women than in their respective counterparts. Carriers within subgroups differently demonstrated the direction and/or magnitude of the variants' effect on glucose-relevant traits. Variants in GCKR, GCK, DGKB/TMEM195 (P for interactions = 0.02, 0.02, and 0.01), especially, showed interactions with obesity: obese, inactive, and high fat-diet women had greater increases in fasting glucose, insulin, and HOMA-IR levels. Obese carriers at TCF7L2 variant had greater increases in fasting glucose levels than nonobese carriers (P for interaction = 0.04), whereas active women had greater decreases in insulin and HOMA-IR levels than inactive women (P for interaction = 0.02 in both levels). CONCLUSIONS: Our data support the important role of obesity in modifying glucose homeostasis in response to glucose metabolism-relevant variants. These findings may inform research on the role of glucose homeostasis in the etiology of chronic disease and the development of intervention strategies to reduce risk in postmenopausal women.

Interaction of insulin-like growth factor-I and insulin resistance-related genetic variants with lifestyle factors on postmenopausal breast cancer risk.

PURPOSE: Genetic variants and traits in metabolic signaling pathways may interact with obesity, physical activity, and exogenous estrogen (E), influencing postmenopausal breast cancer risk, but these inter-related pathways are incompletely understood. METHODS: We used 75 single-nucleotide polymorphisms (SNPs) in genes related to insulin-like growth factor-I (IGF-I)/insulin resistance (IR) traits and signaling pathways, and data from 1003 postmenopausal women in Women's Health Initiative Observation ancillary studies. Stratifying via obesity and lifestyle modifiers, we assessed the role of IGF-I/IR traits (fasting IGF-I, IGF-binding protein 3, insulin, glucose, and homeostatic model assessment-insulin resistance) in breast cancer risk as a mediator or influencing factor. RESULTS: Seven SNPs in IGF-I and INS genes were associated with breast cancer risk. These associations differed between non-obese/active and obese/inactive women and between exogenous E non-users and users. The mediation effects of IGF-I/IR traits on the relationship between these SNPs and cancer differed between strata, but only roughly 35% of the cancer risk due to the SNPs was mediated by traits. Similarly, carriers of 20 SNPs in PIK3R1, AKT1/2, and MAPK1 genes (signaling pathways-genetic variants) had different associations with breast cancer between strata, and the proportion of the SNP-cancer relationship explained by traits varied 45-50% between the strata. CONCLUSIONS: Our findings suggest that IGF-I/IR genetic variants interact with obesity and lifestyle factors, altering cancer risk partially through pathways other than IGF-I/IR traits. Unraveling gene-phenotype-lifestyle interactions will provide data on potential genetic targets in clinical trials for cancer prevention and intervention strategies to reduce breast cancer risk.

Effect of genetic variants and traits related to glucose metabolism and their interaction with obesity on breast and colorectal cancer risk among postmenopausal women.

BACKGROUND: Impaired glucose metabolism-related genetic variants and traits likely interact with obesity and related lifestyle factors, influencing postmenopausal breast and colorectal cancer (CRC), but their interconnected pathways are not fully understood. By stratifying via obesity and lifestyles, we partitioned the total effect of glucose metabolism genetic variants on cancer risk into two putative mechanisms: 1) indirect (risk-associated glucose metabolism genetic variants mediated by glucose metabolism traits) and 2) direct (risk-associated glucose metabolism genetic variants through pathways other than glucose metabolism traits) effects. METHOD: Using 16 single-nucleotide polymorphisms (SNPs) associated with glucose metabolism and data from 5379 postmenopausal women in the Women's Health Initiative Harmonized and Imputed Genome-Wide Association Studies, we retrospectively assessed the indirect and direct effects of glucose metabolism-traits (fasting glucose, insulin, and homeostatic model assessment-insulin resistance [HOMA-IR]) using two quantitative tests. RESULTS: Several SNPs were associated with breast cancer and CRC risk, and these SNP-cancer associations differed between non-obese and obese women. In both strata, the direct effect of cancer risk associated with the SNP accounted for the majority of the total effect for most SNPs, with roughly 10% of cancer risk due to the SNP that was from an indirect effect mediated by glucose metabolism traits. No apparent differences in the indirect (glucose metabolism-mediated) effects were seen between non-obese and obese women. It is notable that among obese women, 50% of cancer risk was mediated via glucose metabolism trait, owing to two SNPs: in breast cancer, in relation to GCKR through glucose, and in CRC, in relation to DGKB/TMEM195 through HOMA-IR. CONCLUSIONS: Our findings suggest that glucose metabolism genetic variants interact with obesity, resulting in altered cancer risk through pathways other than those mediated by glucose metabolism traits.

Human Birth Weight and Reproductive Immunology: Testing for Interactions between Maternal and Offspring KIR and HLA-C Genes.

BACKGROUND/AIMS: Maternal and offspring cell contact at the site of placentation presents a plausible setting for maternal-fetal genotype (MFG) interactions affecting fetal growth. We test hypotheses regarding killer cell immunoglobulin-like receptor (KIR) and HLA-C MFG effects on human birth weight by extending the quantitative MFG (QMFG) test. METHODS: Until recently, association testing for MFG interactions had limited applications. To improve the ability to test for these interactions, we developed the extended QMFG test, a linear mixed-effect model that can use multi-locus genotype data from families. RESULTS: We demonstrate the extended QMFG test's statistical properties. We also show that if an offspring-only model is fit when MFG effects exist, associations can be missed or misattributed. Furthermore, imprecisely modeling the effects of both KIR and HLA-C could result in a failure to replicate if these loci's allele frequencies differ among populations. To further illustrate the extended QMFG test's advantages, we apply the extended QMFG test to a UK cohort study and the Norwegian Mother and Child Cohort (MoBa) study. CONCLUSION: We find a significant KIR-HLA-C interaction effect on birth weight. More generally, the QMFG test can detect genetic associations that may be missed by standard genome-wide association studies for quantitative traits.

The Quantitative-MFG Test: A Linear Mixed Effect Model to Detect Maternal-Offspring Gene Interactions.

Maternal-offspring gene interactions, aka maternal-fetal genotype (MFG) incompatibilities, are neglected in complex diseases and quantitative trait studies. They are implicated in birth to adult onset diseases but there are limited ways to investigate their influence on quantitative traits. We present the quantitative-MFG (QMFG) test, a linear mixed model where maternal and offspring genotypes are fixed effects and residual correlations between family members are random effects. The QMFG handles families of any size, common or general scenarios of MFG incompatibility, and additional covariates. We develop likelihood ratio tests (LRTs) and rapid score tests and show they provide correct inference. In addition, the LRT's alternative model provides unbiased parameter estimates. We show that testing the association of SNPs by fitting a standard model, which only considers the offspring genotypes, has very low power or can lead to incorrect conclusions. We also show that offspring genetic effects are missed if the MFG modeling assumptions are too restrictive. With genome-wide association study data from the San Antonio Family Heart Study, we demonstrate that the QMFG score test is an effective and rapid screening tool. The QMFG test therefore has important potential to identify pathways of complex diseases for which the genetic etiology remains to be discovered.

Increased IFN-α-producing plasmacytoid dendritic cells (pDCs) in human Th1-mediated type 1 diabetes: pDCs augment Th1 responses through IFN-α production.

Increasing evidence suggests that type 1 IFN (IFN-αß) is associated with pathogenesis of Th1-mediated type 1 diabetes (T1D). A major source of IFN-αß is plasmacytoid dendritic cells (pDCs). In this study, we analyzed peripheral blood pDC numbers and functions in at-risk, new-onset, and established T1D patients and controls. We found that subjects at risk for T1D and new-onset and established T1D subjects possessed significantly increased pDCs but similar number of myeloid DCs when compared with controls. pDC numbers were not affected by age in T1D subjects but declined with increasing age in control subjects. It was demonstrated that IFN-α production by PBMCs stimulated with influenza viruses was significantly higher in T1D subjects than in controls, and IFN-α production was correlated with pDC numbers in PBMCs. Of interest, only T1D-associated Coxsackievirus serotype B4 but not B3 induced majority of T1D PBMCs to produce IFN-α, which was confirmed to be secreted by pDCs. Finally, in vitro studies demonstrated IFN-α produced by pDCs augmented Th1 responses, with significantly greater IFN-γ-producing CD4(+) T cells from T1D subjects. These findings indicate that increased pDCs and their IFN-αß production may be associated with this Th1-mediated autoimmune disease, especially under certain viral infections linked to T1D pathogenesis.

Genome-wide ultraconserved elements exhibit higher phylogenetic informativeness than traditional gene markers in percomorph fishes.

Ultraconserved elements (UCEs) have become popular markers in phylogenomic studies because of their cost effectiveness and their potential to resolve problematic phylogenetic relationships. Although UCE datasets typically contain a much larger number of loci and sites than more traditional datasets of PCR-amplified, single-copy, protein coding genes, a fraction of UCE sites are expected to be part of a nearly invariant core, and the relative performance of UCE datasets versus protein coding gene datasets is poorly understood. Here we use phylogenetic informativeness (PI) to compare the resolving power of multi-locus and UCE datasets in a sample of percomorph fishes with sequenced genomes (genome-enabled). We compare three data sets: UCE core regions, flanking sequence adjacent to the UCE core and a set of ten protein coding genes commonly used in fish systematics. We found the net informativeness of UCE core and flank regions to be roughly ten-fold and 100-fold more informative than that of the protein coding genes. On a per locus basis UCEs and protein coding genes exhibited similar levels of phylogenetic informativeness. Our results suggest that UCEs offer enormous potential for resolving relationships across the percomorph tree of life.

Maintenance of anti-Sm/RNP autoantibody production by plasma cells residing in ectopic lymphoid tissue and bone marrow memory B cells.

Although ectopic lymphoid tissue formation is associated with many autoimmune diseases, it is unclear whether it serves a functional role in autoimmune responses. 2,6,10,14-Tetramethylpentadecane causes chronic peritoneal inflammation and lupus-like disease with autoantibody production and ectopic lymphoid tissue (lipogranuloma) formation. A novel transplantation model was used to show that transplanted lipogranulomas retain their lymphoid structure over a prolonged period in the absence of chronic peritoneal inflammation. Recipients of transplanted lipogranulomas produced anti-U1A autoantibodies derived exclusively from the donor, despite nearly complete repopulation of the transplanted lipogranulomas by host lymphocytes. The presence of ectopic lymphoid tissue alone was insufficient, as an anti-U1A response was not generated by the host in the absence of ongoing peritoneal inflammation. Donor-derived anti-U1A autoantibodies were produced for up to 2 mo by plasma cells/plasmablasts recruited to the ectopic lymphoid tissue by CXCR4. Although CD4(+) T cells were not required for autoantibody production from the transplanted lipogranulomas, de novo generation of anti-U1A plasma cells/plasmablasts was reduced following T cell depletion. Significantly, a population of memory B cells was identified in the bone marrow and spleen that did not produce anti-U1A autoantibodies unless stimulated by LPS to undergo terminal differentiation. We conclude that 2,6,10,14-tetramethylpentadecane promotes the T cell-dependent development of class-switched, autoreactive memory B cells and plasma cells/plasmablasts. The latter home to ectopic lymphoid tissue and continue to produce autoantibodies after transplantation and in the absence of peritoneal inflammation. However, peritoneal inflammation appears necessary to generate autoreactive B cells de novo.

Positive correlation of STAT1 and miR-146a with anemia in patients with systemic lupus erythematosus.

PURPOSE: Anemia is one of the most common hematological manifestations in SLE patients, occurring in about 50% of active cases. STAT1 is a critical signaling molecule required for the production of type-1 interferon (I-IFN), CCL2, and CXCL10, all of which are upregulated in SLE. Overexpression of STAT1 has been described to be involved in anemia in animal models. The aim of this study is to analyze how these components are involved in SLE-associated anemia. METHODS: Blood samples were collected from 39 healthy donors and 101 SLE patients fulfilling ACR criteria. Samples were collected from a total of 180 visits (58 patients had 2 or more visits) of which 52 visits included a diagnosis of anemia. Healthy donors had only single visit. Total RNA, isolated from leukocytes, was analyzed by Taqman qPCR. Relative expression levels of I-IFN signature genes, chemokines, and miR-146a were determined by the ΔΔCT method. Results were correlated with clinical data and analyzed by the Wilcoxon/Kruskal-Wallis test and Fisher's exact test. RESULTS: Significant increases in IFN score (p < 0.0001), STAT1 (p < 0.0001), miR-146a (p < 0.0005), CCL2 (p = 0.0047), and CXCL10 (p = 0.017), as well as a significant decrease in pri-miR-146a (p = 0.0002), were detected in the anemic SLE patient visits (n = 52) compared to non-anemic SLE visits (n = 128). Regardless of disease activity, lupus nephritis, or race, anemic SLE patients displayed significantly elevated levels of STAT1 and miR-146a compared to non-anemic SLE patients. CONCLUSIONS: STAT1 and miR-146a may be upregulated during inflammation and via proinflammatory cytokines and chemokines in SLE. Prolonged upregulation of STAT1 and miR-146a appears to play an important role in anemia in SLE patients.

Mendel: the Swiss army knife of genetic analysis programs.

UNLABELLED: Mendel is one of the few statistical genetics packages that provide a full spectrum of gene mapping methods, ranging from parametric linkage in large pedigrees to genome-wide association with rare variants. Our latest additions to Mendel anticipate and respond to the needs of the genetics community. Compared with earlier versions, Mendel is faster and easier to use and has a wider range of applications. Supported platforms include Linux, MacOS and Windows. AVAILABILITY: Free from www.genetics.ucla.edu/software/mendel.

Pre-B cell leukemia homeobox 1 is associated with lupus susceptibility in mice and humans.

Sle1a.1 is part of the Sle1 susceptibility locus, which has the strongest association with lupus nephritis in the NZM2410 mouse model. In this study, we show that Sle1a.1 results in the production of activated and autoreactive CD4(+) T cells. Additionally, Sle1a.1 expression reduces the peripheral regulatory T cell pool, as well as induces a defective response of CD4(+) T cells to the retinoic acid expansion of TGF-ß-induced regulatory T cells. At the molecular level, Sle1a.1 corresponds to an increased expression of a novel splice isoform of Pbx1, Pbx1-d. Pbx1-d overexpression is sufficient to induce an activated/inflammatory phenotype in Jurkat T cells and to decrease their apoptotic response to retinoic acid. PBX1-d is expressed more frequently in the CD4(+) T cells from lupus patients than from healthy controls, and its presence correlates with an increased central memory T cell population. These findings indicate that Pbx1 is a novel lupus susceptibility gene that regulates T cell activation and tolerance.

Lupus susceptibility gene Pbx1 controls the development, stability, and function of regulatory T cells via Rtkn2 expression.

The maintenance of regulatory T (Treg) cells critically prevents autoimmunity. Pre-B cell leukemia transcription factor 1 (Pbx1) variants are associated with lupus susceptibility, particularly through the expression of a dominant negative isoform Pbx1-d in CD4+ T cells. Pbx1-d overexpression impaired Treg cell homeostasis and promoted inflammatory CD4+ T cells. Here, we showed a high expression of Pbx1 in human and murine Treg cells, which is decreased in lupus patients and mice. Pbx1 deficiency or Pbx1-d overexpression reduced the number, stability, and suppressive activity of Treg cells, which increased murine responses to immunization and autoimmune induction. Mechanistically, Pbx1 deficiency altered the expression of genes implicated in cell cycle and apoptosis in Treg cells. Intriguingly, Rtkn2, a Rho-GTPase previously associated with Treg homeostasis, was directly transactivated by Pbx1. Our results suggest that the maintenance of Treg cell homeostasis and stability by Pbx1 through cell cycle progression prevent the expansion of inflammatory T cells that otherwise exacerbates lupus progression in the hosts.