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The increasing availability of single-cell data revolutionizes the understanding of biological mechanisms at cellular resolution. For differential expression analysis in multi-subject single-cell data, negative binomial mixed models account for both subject-level and cell-level overdispersions, but are computationally demanding. Here, we propose an efficient NEgative Binomial mixed model Using a Large-sample Approximation (NEBULA). The speed gain is achieved by analytically solving high-dimensional integrals instead of using the Laplace approximation. We demonstrate that NEBULA is orders of magnitude faster than existing tools and controls false-positive errors in marker gene identification and co-expression analysis. Using NEBULA in Alzheimer’s disease cohort data sets, we found that the cell-level expression of APOE correlated with that of other genetic risk factors (including CLU, CST3, TREM2, C1q, and ITM2B) in a cell-type-specific pattern and an isoform-dependent manner in microglia. NEBULA opens up a new avenue for the broad application of mixed models to large-scale multi-subject single-cell data., The application of negative binomial mixed models (NBMMs) to single-cell data is computationally demanding. To address this issue, Liang He et al. have developed NEBULA, an efficient algorithm that can analyze differential gene expression or co-expression networks in multi-subject single-cell data sets, and validate it on snRNA-seq and scRNA-seq data sets comprising ~200k cells from cohorts of Alzheimer’s disease and multiple sclerosis patients.

Background and ObjectiveTo determine whether rituximab is safe and potentially beneficial, warranting further investigation in an efficacy trial for acetylcholine receptor antibody-positive generalized myasthenia gravis (AChR-Ab+ gMG).MethodsThe B-Cell Targeted Treatment in MG (BeatMG) study was a randomized, double-blind, placebo-controlled, multicenter phase 2 trial that utilized a futility design. Individuals 21–90 years of age, with AChR-Ab+ gMG (MG Foundation of America Class II–IV) and receiving prednisone ≥15 mg/d were eligible. The primary outcome was a measure of steroid-sparing effect, defined as the proportion achieving ≥75% reduction in mean daily prednisone dose in the 4-weeks prior to week 52 and with clinical improvement or no significant worsening as compared to the 4-week period prior to randomization. The coprimary outcome was safety. Secondary outcomes included MG-specific clinical assessments. Fifty-two individuals were randomized (1:1) to a 2-cycle rituximab/placebo regimen, with follow-up through 52 weeks.ResultsOf the 52 participants included, mean ± SD age at enrollment was 55.1 ± 17.1 years; 23 (44.2%) were women and 31 (59.6%) were Myasthenia Gravis Foundation of America Class II. The mean ± SD baseline prednisone dose was 22.1 ± 9.7 mg/d. The primary steroid-sparing outcome was achieved in 60% of those on rituximab vs 56% on placebo. The study reached its futility endpoint (p = 0.03), suggesting that the predefined clinically meaningful improvement of 30% due to rituximab over placebo was unlikely to be achieved in a subsequent, larger trial. No safety issues were identified.DiscussionAlthough rituximab was safe and well-tolerated, these results suggest that there is a low probability of observing the defined clinically meaningful steroid-sparing effect over a 12-month period in a phase 3 trial of mild to moderately symptomatic AChR-Ab+ gMG.Classification of EvidenceThis study provides Class I evidence that for mild to moderate AChR-Ab+ gMG, compared with placebo, rituximab is safe but unlikely to reduce steroid use by an absolute difference of at least 30% at 1 year.Trial Registration InformationClinicalTrials.gov identifier: NCT02110706.

Multisystem inflammatory syndrome in children (MIS-C) is a life-threatening post-infectious complication occurring unpredictably weeks after mild or asymptomatic SARS-CoV-2 infection. We profiled MIS-C, adult COVID-19, and healthy pediatric and adult individuals using single-cell RNA sequencing, flow cytometry, antigen receptor repertoire analysis, and unbiased serum proteomics, which collectively identified a signature in MIS-C patients that correlated with disease severity. Despite having no evidence of active infection, MIS-C patients had elevated S100A-family alarmins and decreased antigen presentation signatures, indicative of myeloid dysfunction. MIS-C patients showed elevated expression of cytotoxicity genes in NK and CD8+ T cells and expansion of specific IgG-expressing plasmablasts. Clinically severe MIS-C patients displayed skewed memory T cell TCR repertoires and autoimmunity characterized by endothelium-reactive IgG. The alarmin, cytotoxicity, TCR repertoire, and plasmablast signatures we defined have potential for application in the clinic to better diagnose and potentially predict disease severity early in the course of MIS-C., Graphical abstract, Multisystem inflammatory syndrome in children (MIS-C) is a life-threatening and unpredictable condition of unknown etiology. Ramaswamy et al. use peripheral blood single-cell transcriptomic profiling along with other techniques to define key innate and adaptive signatures that characterize MIS-C.

Simple Summary While targeting programmed cell death (PDCD) 1 is a central treatment against melanoma, little is known about the related protein PDCD4. We defined differences in melanoma PDCD4 subcellular localization (either total cellular or nuclear-only) during oncogenesis, evaluated its presence on tumor-infiltrating immune cells, and determined its impact on survival. High PDCD4 expression resulted in improved survival in patients with primary and intracranial but not extracranial metastatic melanoma. High PDCD4 levels in surrounding tumor tissue were also associated with increased infiltrating immune cells. PDCD4 may be a potentially useful biomarker in melanoma to help guide our understanding of patient prognosis. Methods to increase PDCD4 in those with melanoma brain metastases may also help improve disease response. Abstract Little is known about the subcellular localization and function of programmed cell death 4 (PDCD4) in melanoma. Our past studies suggest PDCD4 interacts with Pleckstrin Homology Domain Containing A5 (PLEKHA5) to influence melanoma brain metastasis outcomes, as high intracranial PDCD4 expression leads to improved survival. We aimed to define the subcellular distribution of PDCD4 in melanoma and in the tumor microenvironment during neoplastic progression and its impact on clinical outcomes. We analyzed multiple tissue microarrays with well-annotated clinicopathological variables using quantitative immunofluorescence and evaluated single-cell RNA-sequencing on a brain metastasis sample to characterize PDCD4+ immune cell subsets. We demonstrate differences in PDCD4 expression during neoplastic progression, with high tumor and stromal PDCD4 levels associated with improved survival in primary melanomas and in intracranial metastases, but not in extracranial metastatic disease. While the expression of PDCD4 is well-documented on CD8+ T cells and natural killer cells, we show that it is also found on B cells and mast cells. PDCD4 expression in the tumor microenvironment is associated with increased immune cell infiltration. Further studies are needed to define the interaction of PDCD4 and PLEKHA5 and to evaluate the utility of this pathway as a therapeutic target in melanoma brain metastasis.

FOXP3(+) Tregs rely on fatty acid β-oxidation–driven (FAO-driven) oxidative phosphorylation (OXPHOS) for differentiation and function. Recent data demonstrate a role for Tregs in the maintenance of tissue homeostasis, with tissue-resident Tregs possessing tissue-specific transcriptomes. However, specific signals that establish tissue-resident Treg programs remain largely unknown. Tregs metabolically rely on FAO, and considering the lipid-rich environments of tissues, we hypothesized that environmental lipids drive Treg homeostasis. First, using human adipose tissue to model tissue residency, we identified oleic acid as the most prevalent free fatty acid. Mechanistically, oleic acid amplified Treg FAO–driven OXPHOS metabolism, creating a positive feedback mechanism that increased the expression of FOXP3 and phosphorylation of STAT5, which enhanced Treg-suppressive function. Comparing the transcriptomic program induced by oleic acid with proinflammatory arachidonic acid, we found that Tregs sorted from peripheral blood and adipose tissue of healthy donors transcriptomically resembled the Tregs treated in vitro with oleic acid, whereas Tregs from patients with multiple sclerosis (MS) more closely resembled an arachidonic acid transcriptomic profile. Finally, we found that oleic acid concentrations were reduced in patients with MS and that exposure of MS Tregs to oleic acid restored defects in their suppressive function. These data demonstrate the importance of fatty acids in regulating tissue inflammatory signals.

A dysregulated immune response against the SARS-CoV-2 virus plays a critical role in severe COVID-19. However, the molecular and cellular mechanisms by which the virus causes lethal immunopathology are poorly understood. Here, we utilize multiomics single-cell analysis to probe dynamic immune responses in patients with stable or progressive manifestations of COVID-19, and assess the effects of tocilizumab, an anti-IL-6 receptor monoclonal antibody. Coordinated profiling of gene expression and cell lineage protein markers reveals a prominent type-1 interferon response across all immune cells, especially in progressive patients. An anti-inflammatory innate immune response and a pre-exhaustion phenotype in activated T cells are hallmarks of progressive disease. Skewed T cell receptor repertoires in CD8+ T cells and uniquely enriched V(D)J sequences are also identified in COVID-19 patients. B cell repertoire and somatic hypermutation analysis are consistent with a primary immune response, with possible contribution from memory B cells. Our in-depth immune profiling reveals dyssynchrony of the innate and adaptive immune interaction in progressive COVID-19, which may contribute to delayed virus clearance and has implications for therapeutic intervention.

Single-cell RNA sequencing (scRNA-seq) has revolutionized biological discovery, providing an unbiased picture of cellular heterogeneity in tissues. While scRNA-seq has been used extensively to provide insight into both healthy systems and diseases, it has not been used for disease prediction or diagnostics. Graph Attention Networks (GAT) have proven to be versatile for a wide range of tasks by learning from both original features and graph structures. Here we present a graph attention model for predicting disease state from single-cell data on a large dataset of Multiple Sclerosis (MS) patients. MS is a disease of the central nervous system that can be difficult to diagnose. We train our model on single-cell data obtained from blood and cerebrospinal fluid (CSF) for a cohort of seven MS patients and six healthy adults (HA), resulting in 66,667 individual cells. We achieve 92 % accuracy in predicting MS, outperforming other state-of-the-art methods such as a graph convolutional network and a random forest classifier. Further, we use the learned graph attention model to get insight into the features (cell types and genes) that are important for this prediction. The graph attention model also allow us to infer a new feature space for the cells that emphasizes the differences between the two conditions. Finally we use the attention weights to learn a new low-dimensional embedding that can be visualized. To the best of our knowledge, this is the first effort to use graph attention, and deep learning in general, to predict disease state from single-cell data. We envision applying this method to single-cell data for other diseases., Incorporated suggestions from anonymous reviewers, Accepted at ACM CHIL 2020, comments welcome

Genome-wide association studies (GWAS) have identified more than 50,000 unique associations with common human traits. While this represents a substantial step forward, establishing the biology underlying these associations has proven extremely difficult. Even determining which cell types and which particular gene(s) are relevant continues to be a challenge. Here, we conduct a cell-specific pathway analysis of the latest GWAS in multiple sclerosis (MS), which had analyzed a total of 47,351 cases and 68,284 healthy controls and found more than 200 non-MHC genome-wide associations. Our analysis identifies pan immune cell as well as cell-specific susceptibility genes in T cells, B cells and monocytes. Finally, genotype-level data from 2,370 patients and 412 controls is used to compute intra-individual and cell-specific susceptibility pathways that offer a biological interpretation of the individual genetic risk to MS. This approach could be adopted in any other complex trait for which genome-wide data is available., Genome-wide association studies (GWAS) have so far uncovered more than 200 loci for multiple sclerosis (MS). Here, the authors integrate data from various sources for a cell type-specific pathway analysis of MS GWAS results that specifically highlights the involvement of the immune system in disease pathogenesis.

Epigenetic annotation studies of genetic risk variants for multiple sclerosis (MS) implicate dysfunctional lymphocytes in MS susceptibility; however, the role of central nervous system (CNS) cells remains unclear. We investigated the effect of the risk variant, rs7665090G, located near NFKB1, on astrocytes. We demonstrated that chromatin is accessible at the risk locus, a prerequisite for its impact on astroglial function. The risk variant was associated with increased NF-κB signaling and target gene expression, driving lymphocyte recruitment, in cultured human astrocytes and astrocytes within MS lesions, and with increased lesional lymphocytic infiltrates and lesion sizes. Thus, our study establishes a link between genetic risk for MS (rs7665090G) and dysfunctional astrocyte responses associated with increased CNS access for peripheral immune cells. MS may therefore result from variant-driven dysregulation of the peripheral immune system and of the CNS, where perturbed CNS cell function aids in establishing local autoimmune inflammation., It is unclear if multiple sclerosis (MS) genetic susceptibility can be mediated through perturbations of CNS-intrinsic pathways. Authors show that the rs7665090 risk variant is associated with astrocyte responses that enhance lymphocyte recruitment, and with increased lymphocyte infiltration and lesion sizes in MS lesions.

Foxp3+regulatory T cells (Tregs) are the central component of peripheral immune tolerance. While dysregulation of the Treg cytokine signature has been observed in autoimmune diseases such as multiple sclerosis (MS) and type 1 diabetes, the regulatory mechanisms balancing pro- and anti-inflammatory cytokine production are not known. Here, we identify imbalance between IFNγ and IL-10 as a shared Treg signature, present in patients with MS and under high salt conditions. By performing RNA-seq analysis on human Treg subpopulations, we identify β-catenin as a key regulator that controls the expression of IFNγ and IL-10. The activated β-catenin signature is enriched specifically in IFNγ+Tregs in humans, and this was confirmedin vivowith Treg-specific β-catenin-stabilized mice exhibiting lethal autoimmunity with a dysfunctional, IFNγ-producing, Treg phenotype. Moreover, we identify PTGER2 as a major factor balancing IFNγ and IL-10 production in the context of a high salt environment, with skewed activation of the β-catenin/SGK1/Foxo axis in IFNγ+Tregs. These findings identify a novel molecular mechanism underlying inflammatory Tregs in human autoimmune disease and reveal a new role for a PTGER2-β-catenin loop in Tregs linking environmental high salt conditions to autoimmunity.

Multiple sclerosis is a common, complex neurological disease, where almost 20% of risk heritability can be attributed to common genetic variants, including >230 identified by genome-wide association studies (Patsopoulos et al., 2017). Multiple strands of evidence suggest that the majority of the remaining heritability is also due to the additive effects of individual variants, rather than epistatic interactions between these variants, or mutations exclusive to individual families. Here, we show in 68,379 cases and controls that as much as 5% of this heritability is explained by low-frequency variation in gene coding sequence. We identify four novel genes driving MS risk independently of common variant signals, which highlight a key role for regulatory T cell homeostasis and regulation, IFNγ biology and NFκB signaling in MS pathogenesis. As low-frequency variants do not show substantial linkage disequilibrium with other variants, and as coding variants are more interpretable and experimentally tractable than non-coding variation, our discoveries constitute a rich resource for dissecting the pathobiology of MS.

Epigenetic annotation studies of genetic risk variants for multiple sclerosis (MS) implicate dysfunctional lymphocytes in MS susceptibility; however, the role of central nervous system (CNS) cells remains unclear. We investigated the effect of the risk variant, rs7665090G, located near NFKB1, on astrocytes. We demonstrated that chromatin is accessible at the risk locus, a prerequisite for its impact on astroglial function. The risk variant was associated with increased NF-κB signaling and target gene expression driving lymphocyte recruitment in cultured human astrocytes and astrocytes within MS lesions, and with increased lesional lymphocytic infiltrates. In MS patients, the risk genotype was associated with increased lesion volumes on MRI. Thus, we established that the rs7665090G variant perturbs astrocyte function resulting in increased CNS access for peripheral immune cells. MS may thus result from variant-driven dysregulation of the peripheral immune system and the CNS, where perturbed CNS cell function aids in establishing local autoimmune inflammation.One Sentence SummaryThe NF-κB relevant multiple sclerosis risk variant, rs7665090G, drives astrocyte responses that promote lesion formation.

mmune checkpoint inhibitors targeting programmed cell death protein 1 (PD-1) have been highly successful in the treatment of cancer. While PD-1 expression has been widely investigated, its role in CD4⁺ effector T cells in the setting of health and cancer remains unclear, particularly in the setting of glioblastoma multiforme (GBM), the most aggressive and common form of brain cancer. We examined the functional and molecular features of PD-1⁺ CD4⁺ CD25—CD127⁺ Foxp3—effector cells in healthy subjects and in patients with GBM. In healthy subjects, we found that PD-1⁺ CD4⁺ effector cells are dysfunctional: they do not proliferate but can secrete large quantities of IFNγ. Strikingly, blocking antibodies against PD-1 did not rescue proliferation. RNA-sequencing revealed features of exhaustion in PD-1⁺ CD4 effectors. In the context of GBM, tumors were enriched in PD-1⁺ CD4⁺ effectors that were similarly dysfunctional and unable to proliferate. Furthermore, we found enrichment of PD-1⁺ TIM-3⁺ CD4⁺ effectors in tumors, suggesting that co-blockade of PD-1 and TIM-3 in GBM may be therapeutically beneficial. RNA-sequencing of blood and tumors from GBM patients revealed distinct differences between CD4⁺ effectors from both compartments with enrichment in multiple gene sets from tumor infiltrating PD-1[superscript —]CD4⁺ effectors cells. Enrichment of these gene sets in tumor suggests a more metabolically active cell state with signaling through other co-receptors. PD-1 expression on CD4 cells identifies a dysfunctional subset refractory to rescue with PD-1 blocking antibodies, suggesting that the influence of immune checkpoint inhibitors may involve recovery of function in the PD-1[superscript —]CD4⁺ T cell compartment. Additionally, co-blockade of PD-1 and TIM-3 in GBM may be therapeutically beneficial., Daniel Lowther

We assembled and analyzed genetic data of 47,351 multiple sclerosis (MS) subjects and 68,284 control subjects and establish a reference map of the genetic architecture of MS that includes 200 autosomal susceptibility variants outside the major histocompatibility complex (MHC), one chromosome X variant, and 32 independent associations within the extended MHC. We used an ensemble of methods to prioritize up to 551 potentially associated MS susceptibility genes, that implicate multiple innate and adaptive pathways distributed across the cellular components of the immune system. Using expression profiles from purified human microglia, we do find enrichment for MS genes in these brain - resident immune cells. Thus, while MS is most likely initially triggered by perturbation of peripheral immune responses the functional responses of microglia and other brain cells are also altered and may have a role in targeting an autoimmune process to the central nervous system.One Sentence Summary:We report a detailed genetic and genomic map of multiple sclerosis, and describe the role of putatively affected genes in the peripheral immune system and brain resident microglia.

International audience; Using the ImmunoChip custom genotyping array, we analyzed 14,498 subjects with multiple sclerosis and 24,091 healthy controls for 161,311 autosomal variants and identified 135 potentially associated regions (P < 1.0 × 10(-4)). In a replication phase, we combined these data with previous genome-wide association study (GWAS) data from an independent 14,802 subjects with multiple sclerosis and 26,703 healthy controls. In these 80,094 individuals of European ancestry, we identified 48 new susceptibility variants (P < 5.0 × 10(-8)), 3 of which we found after conditioning on previously identified variants. Thus, there are now 110 established multiple sclerosis risk variants at 103 discrete loci outside of the major histocompatibility complex. With high-resolution Bayesian fine mapping, we identified five regions where one variant accounted for more than 50% of the posterior probability of association. This study enhances the catalog of multiple sclerosis risk variants and illustrates the value of fine mapping in the resolution of GWAS signals.

Multiple sclerosis is a common disease of the central nervous system in which the interplay between inflammatory and neurodegenerative processes typically results in intermittent neurological disturbance followed by progressive accumulation of disability. Epidemiological studies have shown that genetic factors are primarily responsible for the substantially increased frequency of the disease seen in the relatives of affected individuals, and systematic attempts to identify linkage in multiplex families have confirmed that variation within the major histocompatibility complex (MHC) exerts the greatest individual effect on risk. Modestly powered genome-wide association studies (GWAS) have enabled more than 20 additional risk loci to be identified and have shown that multiple variants exerting modest individual effects have a key role in disease susceptibility. Most of the genetic architecture underlying susceptibility to the disease remains to be defined and is anticipated to require the analysis of sample sizes that are beyond the numbers currently available to individual research groups. In a collaborative GWAS involving 9,772 cases of European descent collected by 23 research groups working in 15 different countries, we have replicated almost all of the previously suggested associations and identified at least a further 29 novel susceptibility loci. Within the MHC we have refined the identity of the HLA-DRB1 risk alleles and confirmed that variation in the HLA-A gene underlies the independent protective effect attributable to the class I region. Immunologically relevant genes are significantly overrepresented among those mapping close to the identified loci and particularly implicate T-helper-cell differentiation in the pathogenesis of multiple sclerosis.

A concern for researchers planning multisite studies is that scanner and T1-weighted sequence-related biases on regional volumes could overshadow true effects, especially for studies with a heterogeneous set of scanners and sequences. Current approaches attempt to harmonize data by standardizing hardware, pulse sequences, and protocols, or by calibrating across sites using phantom-based corrections to ensure the same raw image intensities. We propose to avoid harmonization and phantom-based correction entirely. We hypothesized that the bias of estimated regional volumes is scaled between sites due to the contrast and gradient distortion differences between scanners and sequences. Given this assumption, we provide a new statistical framework and derive a power equation to define inclusion criteria for a set of sites based on the variability of their scaling factors. We estimated the scaling factors of 20 scanners with heterogeneous hardware and sequence parameters by scanning a single set of 12 subjects at sites across the United States and Europe. Regional volumes and their scaling factors were estimated for each site using Freesurfer's segmentation algorithm and ordinary least squares, respectively. The scaling factors were validated by comparing the theoretical and simulated power curves, performing a leave-one-out calibration of regional volumes, and evaluating the absolute agreement of all regional volumes between sites before and after calibration. Using our derived power equation, we were able to define the conditions under which harmonization is not necessary to achieve 80% power. This approach can inform choice of processing pipelines and outcome metrics for multisite studies based on scaling factor variability across sites, enabling collaboration between clinical and research institutions. publisher: Elsevier articletitle: Power estimation for non-standardized multisite studies journaltitle: NeuroImage articlelink: http://dx.doi.org/10.1016/j.neuroimage.2016.03.051 content_type: article copyright: © 2016 The Authors. Published by Elsevier Inc. ispartof: NeuroImage vol:134 pages:281-294 ispartof: location:United States status: published

It is well established that the major histocompatibility complex (MHC) on chromosome 6p21 contains at least one gene that influences susceptibility to multiple sclerosis.1–6 Although this association was first identified more than 30 years ago1 through the study of class I human leukocyte antigens (HLAs), it was quickly realized that this signal was predominantly, if not exclusively, the result of linkage disequilibrium (LD) with class II HLA genes, and that these exert the primary effect on susceptibility.7, 8 The complex nature of the MHC, especially its high gene content, extreme polymorphism, and extensive LD,9 has confounded efforts to resolve the nature of the MHC association in multiple sclerosis, although progress and useful clarifications have been made, especially in recent years. In virtually every population studied, multiple sclerosis is found to be associated with the DRB1*1501 allele.10 The only exceptions are those populations where this allele has a low frequency, and analysis is therefore underpowered; but even in these situations, DRB1*1501 is generally overrepresented in cases.11 The DRB1*1501 allele is carried on a particularly extensive haplotype,12 the most common DR15 haplotype found in white Europeans. As a result, many variants from flanking genes, even some located quite a distance from DRB1, have sufficient LD with DRB1*1501 that they invariably also show evidence for association with the disease in any population where association with DRB1*1501 can be demonstrated.11, 13–17 This extensive LD has made it difficult to establish which of the variants making up this haplotype is primarily responsible for the association. The distinction between DRB1*1501 and DQB1*0602 has been particularly taxing because the LD between these closely mapped genes is especially tight in those populations where the disease is frequent, that is, white Europeans and their migrant descendants. However, recent studies in the admixed African American population indicate the supremacy of the DRB1*1501 allele.18 In the presence of one susceptibility allele it is difficult to identify effects attributable to a second allele,19 especially if the second allele exerts a more modest effect or has a low frequency, or both. However, by analyzing populations where DR15 haplotypes are less common, and by using large cohorts, it has been possible to demonstrate that the DRB1*0301 allele also confers susceptibility to multiple sclerosis, thereby confirming allelic heterogeneity at the DRB1 locus.4, 6, 18, 20 Furthermore available evidence suggests that the susceptibility effects of the DRB1*1501 allele may be modulated by other DRB1 alleles.6, 20 The relation between the MHC and multiple sclerosis is further complicated by the accumulating evidence suggesting that MHC loci mapping outside DRB1 also influence susceptibility to the disease.11, 13–16 Work in animal models suggests that clustering of susceptibility loci is a common phenomenon in complex disease,21 and it therefore appears reasonable to expect that other genes from the MHC region may influence susceptibility to multiple sclerosis. The observation of positive logarithm of odds scores in the MHC region in linkage studies stratified for the effects of DRB1 supports the existence of secondary loci,22–24 although none of these data reaches a level providing statistical confidence. In considering these linkage data, it is important to remember that early linkage studies in multiple sclerosis25–27 were significantly underpowered,28 to the point that they could not even convincingly demonstrate evidence for linkage resulting from the effects of DRB1. Confirmation of linkage in this region has been established only in more recent studies involving many hundreds of families.23, 24, 29 Given the inherently limited resolution of linkage-based studies,28 the absence of statistically significant linkage in the MHC region after exclusion of primary effects attributable to DRB1 does not exclude the presence of secondary loci. Several authors have attempted to identify secondary loci using more powerful association-based methods. Two groups have typed dense microsatellite maps of the region and both found evidence for a secondary locus maximal in a region close to HLA-A: one group identifying the marker D6S1683 just telomeric of HLA-A,11 and the second group implicating a region including HLA-A extending from MOGCA to D6S265 marker.14 Follow-up studies in Norway also found evidence implicating the D6S265 marker.16 In another smaller study, a microsatellite marker close to HLA-C (marker C1_3_2) also showed evidence for an independent effect.15 In contrast, a systematic effort to screen the MHC and flanking regions using single nucleotide polymorphisms (SNPs) found no evidence for association beyond that attributable to DRB1*1501, although this study was limited by a high genotyping failure rate (40%) and, more importantly, a distribution of markers leaving regions close to the classical loci essentially unexplored.17 In all of these studies, statistical power has inevitably been reduced by the processes required to filter out the primary effect attributable to DRB1 and the large correction required for multiple testing. Unfortunately, none of the published studies has used sufficient samples to compensate for these statistical penalties, and thus none is able to provide unequivocal evidence supporting any particular secondary locus.

BACKGROUND: The prognosis for patients with glioblastoma (GBM) remains poor despite the availability of first-line therapies including surgery and chemoradiation. Recent evidence suggests that there are varying degrees of lymphocyte infiltration in GBM, and the composition may have prognostic significance. Expansion of regulatory T cells (CD4+FoxP3+, Treg) at the tumor site has been observed and is implicated in cancer-associated immunity in GBM. Immune checkpoint inhibitors, such as the fully human anti-PD-1 monoclonal antibody nivolumab, have shown promising antitumor activity in both solid tumors and preclinical glioma models. The CHECKMATE-143 study is evaluating nivolumab as monotherapy and in combination with ipilimumab (anti-CTLA-4) in patients with recurrent GBM (Cohorts 1 and 1b). As part of the initial phase of this study, T-cell maturation, activation, exhaustion, and effector function, as well as Treg frequencies and function and myeloid-derived suppressor cell frequencies were examined. METHODS: Analyses were performed on peripheral blood drawn from patients at pre-determined time points and upon tumor tissue samples obtained if resection was performed at the time of progression or suspected progression. Baseline and on-treatment samples were obtained from patients (n = 20) randomized 1:1 to receive either nivolumab 3 mg/kg Q2W or nivolumab 1 mg/kg + ipilimumab 3 mg/kg Q3W followed by nivolumab 3 mg/kg Q2W. Phenotypic analysis was determined by CyTOF and flow cytometry; Treg suppressive function by thymidine incorporation; cytokine release by multiplex immunoassay; and gene expression by RNAseq. RESULTS: Treatment-related changes in lymphocyte populations (including but not limited to T, B, and NK cells, granulocytes, and memory and effector T-cell subsets) in peripheral blood and resected tumor will be reported. CONCLUSIONS: Peripheral and tumor biomarker analyses of GBM subjects treated with checkpoint inhibitors will provide insights into treatment-related changes versus tumor progression.

BACKGROUND: Foxp3+ regulatory T cells (Tregs) are central in ensuring protective immune responses while preventing immunopathology. While Tregs exhibit defective function in autoimmune disorders, they appear to be enriched in tumor immune infiltrates, suggesting a role in failure of immune-mediated tumor rejection. Tumor-infiltrating Tregs express higher levels of the co-inhibitory receptor PD-1 compared to circulating Tregs, especially in patients with glioblastoma. PD-1 is overexpressed by effector T cells in chronic immune responses and plays a pivotal role in driving T cell exhaustion in these conditions. METHODS: We examined the phenotype and function of CD4+CD25+CD127−Foxp3+ Tregs from peripheral blood and tumor infiltrates of patients with different grades of glioma, including glioblastoma (n = 20). Assessments included functional evaluation of Tregs in co-culture suppression assays with blood-derived T effector cells, and transcriptional profiling by RNA sequencing. RESULTS: By comparing the functionality of Tregs derived from blood and tumors of patients with glioblastoma, we established increased Treg expression of PD-1 is associated with impaired suppressive function and production of proinflammatory cytokines. Transcriptional profiling of PD-1+ Tregs revealed tumor-infiltrating PD-1+ Tregs are enriched for several exhaustion signatures, confirming PD-1 is a marker of exhaustion not only for effector T cells but also for Tregs. Finally, we investigated phenotype and function of PD-1+ Tregs in the circulation of healthy individuals (no known history of cancer, autoimmune disease, or other inflammatory conditions), and observed a similar loss of suppressive function and an exhausted transcriptional profile. CONCLUSIONS: Expression of PD-1 marks a population of dysfunctional Tregs that occurs in normal immunity and is enriched in cancer, specifically in the tumor. That Tregs from the tumor fail to suppress effector immune responses suggests that loss of effector T-cell function is more likely to result from chronic stimulation by the tumor than from active regulation by Tregs.

BACKGROUND: The PD-1/PD-L1 immunomodulatory pathway plays a central role in regulating T-cell effector function and has been implicated in immune evasion in glioblastoma. It is also known that populations of tumor-infiltrating lymphocytes may be responsible for disease progression and poor prognosis. A substantial number of these lymphocytes are T effector cells (CD4+CD25−CD127+), however little is known about their function in the tumor microenvironment in glioblastoma or their expression of PD-1. METHODS: We characterized functional differences between T effector cells derived from blood and tumors of patients with glioblastoma (n = 20), with the hypothesis that PD-1 would mark a dysfunctional T effector population. T effector cell proliferative capacity was assessed by stimulation with anti-CD3/anti-CD28/anti-CD2 in thymidine incorporation assays; cytokine production profiles were assessed ex vivo by stimulation with PMA and ionomycin; transcriptional profiling was determined by RNA sequencing. We also performed transcriptional profiling on PD-1+ and PD-1− T effector subsets isolated from the peripheral blood and tumors of patients with glioblastoma and from the peripheral blood of healthy donors. RESULTS: The frequency of PD-1+TIM-3+ T effectors in the peripheral blood of patients with glioblastoma was significantly increased compared to healthy controls. Tumor-derived PD-1+ T effectors displayed significantly less proliferation but retained the ability to produce inflammatory cytokines, including IFNγ. PD-1+ T effectors derived from healthy donors displayed impaired proliferation, which could not be rescued by PD-1/PD-L1 blockade or the addition of IL-2. PD-1+ T effector cells were enriched in several gene sets, indicating that PD-1+ T effector cells may be clonally exhausted. We also identified several genes that differentiated tumor-derived and peripheral blood-derived PD-1+ T effectors in glioblastoma patients. CONCLUSIONS: These data indicate that PD-1 expression marks a dysfunctional population of clonally exhausted T effector cells that may be important in the context of glioblastoma.

Association studies have greatly refined the understanding of how variation within the human leukocyte antigen (HLA) genes influences risk of multiple sclerosis. However, the extent to which major effects are modulated by interactions is poorly characterized. We analyzed high-density SNP data on 17,465 cases and 30,385 controls from 11 cohorts of European ancestry, in combination with imputation of classical HLA alleles, to build a high-resolution map of HLA genetic risk and assess the evidence for interactions involving classical HLA alleles. Among new and previously identified class II risk alleles (HLA-DRB1*15:01, HLA-DRB1*13:03, HLA-DRB1*03:01, HLA-DRB1*08:01 and HLA-DQB1*03:02) and class I protective alleles (HLA-A*02:01, HLA-B*44:02, HLA-B*38:01 and HLA-B*55:01), we find evidence for two interactions involving pairs of class II alleles: HLA-DQA1*01:01-HLA-DRB1*15:01 and HLA-DQB1*03:01-HLA-DQB1*03:02. We find no evidence for interactions between classical HLA alleles and non-HLA risk-associated variants and estimate a minimal effect of polygenic epistasis in modulating major risk alleles.

High salt is positively associated with the risk of many diseases. However, little is known about the mechanisms. Here we showed that high salt increased proinflammatory molecules, while decreased anti-inflammatory and proendocytic molecules in both human and mouse macrophages. High salt also potentiated lipopolysaccharide-induced macrophage activation and suppressed interleukin 4-induced macrophage activation. High salt induced the proinflammatory aspects by activating p38/cFos and/or Erk1/2/cFos pathways, while inhibited the anti-inflammatory and proendocytic aspects by Erk1/2/signal transducer and activator of transcription 6 pathway. Consistent with the in vitro results, high-salt diet increased proinflammatory gene expression of mouse alveolar macrophages. In mouse models of acute lung injury, high-salt diet aggravated lipopolysaccharide-induced pulmonary macrophage activation and inflammation in lungs. These results identify a novel macrophage activation state, M(Na), and high salt as a potential environmental risk factor for lung inflammation through the induction of M(Na).

Genome-wide association studies have identified loci underlying human diseases, but the causal nucleotide changes and mechanisms remain largely unknown. Here we developed a fine-mapping algorithm to identify candidate causal variants for 21 autoimmune diseases from genotyping data. We integrated these predictions with transcription and cis-regulatory element annotations, derived by mapping RNA and chromatin in primary immune cells, including resting and stimulated CD4(+) T-cell subsets, regulatory T cells, CD8(+) T cells, B cells, and monocytes. We find that ∼90% of causal variants are non-coding, with ∼60% mapping to immune-cell enhancers, many of which gain histone acetylation and transcribe enhancer-associated RNA upon immune stimulation. Causal variants tend to occur near binding sites for master regulators of immune differentiation and stimulus-dependent gene activation, but only 10-20% directly alter recognizable transcription factor binding motifs. Rather, most non-coding risk variants, including those that alter gene expression, affect non-canonical sequence determinants not well-explained by current gene regulatory models.

T cell responses are initiated by ligation of their cognate T cell receptor by MHC loaded with antigenic peptide, but their response is carefully controlled by a myriad of environmental cues, including co-activation receptors, cytokines, nutrients, growth factors, local oxygen levels, salt concentrations, and microbiome. The complexity of the integration of signals received by T cells is only beginning to be fully understood (1). This research topic in T cell biology aims at highlighting some of the latest research on intrinsic and extrinsic signals regulating T cell responses. The ebook contains 10 articles that encompass key pathways that modulate T cell function and discuss how T cells coordinate their response to environmental cues. One of the first components regulating T cell activation is the expression and subsequent activation of surface receptors. Notably, T cell activation is governed by the co-activation of the TCR and of co-stimulatory or co-inhibitory molecules (2, 3). Expression and activation of co-inhibitory molecules, such as CTLA-4 and PD1 play a key part in turning off effector responses and the balance of expression of co-stimulatory and co-inhibitory receptors needs to be tightly regulated to ensure a proper level of T cell activation (4, 5). The review by Schneider and Rudd nicely illustrates how expression of the co-inhibitory molecule CTLA-4 is regulated, and how that affects T cell activation (6). In recent years, the importance of cellular metabolism in the activation of immune cells, in particular T cells, has been reported. T cell activation requires a change in cellular metabolism to face increased energetic demand. This is an exciting area of research showing that not only changes in metabolism are necessary for cell activation but that they are also actively involved in regulating T cell function and differentiation. This is discussed in several reviews. Craig Byersdorfer focuses on the role of fatty acid oxidation for T cell functions, and notably on its role in graft versus host disease (7). Ramsay and Cantrell discuss the importance of glucose metabolism for T cell function and highlight the role of hypoxia-inducible factor alpha and mTOR in coordinating the responses to the environmental cues (8). They also summarize the importance of the microbiome in regulating T cells, and the key role of the aryl hydrocarbon receptor in sensing microbes. Clovis Palmer and collaborators review glucose metabolism in T cells and also discuss how HIV infection modulates T cell metabolism (9). One of the central questions is how do T cells integrate the multitude of signals received? In their comprehensive review, Chapman and Chi discuss the central role of mTOR in the integration of the many signals received by the T cells that ultimately shape their response (10). Another related aspect of the topic is the impact of infection on T cells, whereby pathogens control the host response, mostly to their advantage, and are able to switch T cell responses to favor their own survival. A study by the group of Francisca Mutapi describes a novel mechanism by which helminth infection downregulates T cell activation, by lowering the level of CD3zeta chain in infected individuals (11). A review from Zaunders further illustrates how HIV infection affects the balance of the various T cell subsets and promotes regulatory T cells and T helper follicular cells (12). Diet can also influence immune homeostasis, with recent studies showing, for instance, how salt can affect Th differentiation (13). The effects of vitamins and their metabolites on T cells are also well described. Colleen Hayes’ group summarizes the latest data on the effects of Vitamin D on T cell responses, and how this can be modulated in autoimmune diseases (14). A review by Leving’s group focuses more particularly on the environmental factors that affect regulatory T cells. This includes cytokines, vitamin A and vitamin D, metabolism, and microbiome (15). Finally, can we therapeutically manipulate the environmental milieu to modulate T cell responses in humans? The study by the group of David Klatzmann highlights the effects of IL-2 in modulating T cell responses in type 1 diabetes (16). While the functions of regulatory CD4+ Tregs have been described years ago, the characterization of CD8+ Tregs is more recent. The authors notably report how injection of low doses of IL-2 modulates the levels of CD8+ Tregs in vivo, in both mice and in patients with type 1 diabetes, following their numbers and phenotype. This study illustrates how exogenous cytokines can modulate in vivo T cell responses, which may prove beneficial for autoimmune diseases. We hope that this compilation of reviews and research that provides an overview on environmental regulators of T cells will give the readers a flavor on the latest development in T cell biology. We would also like to thank all the contributors to this topic and the reviewers for their time in making this ebook possible.

The recent descriptions of widespread random monoallelic expression (RMAE) of genes distributed throughout the autosomal genome indicate that there are more genes subject to RMAE on autosomes than the number of genes on the X chromosome where X-inactivation dictates RMAE of X-linked genes. Several of the autosomal genes that undergo RMAE have independently been implicated in human Mendelian disorders. Thus, parsing the relationship between allele-specific expression of these genes and disease is of interest. Mutations in the human forkhead box P2 gene, FOXP2 , cause developmental verbal dyspraxia with profound speech and language deficits. Here, we show that the human FOXP2 gene undergoes RMAE. Studying an individual with developmental verbal dyspraxia, we identify a deletion 3 Mb away from the FOXP2 gene, which impacts FOXP2 gene expression in cis. Together these data suggest the intriguing possibility that RMAE impacts the haploinsufficiency phenotypes observed for FOXP2 mutations.

IL-10-producing B cells have a regulatory effect in various mouse models for immune-mediated disorders via secretion of IL-10, a potent immunoregulatory cytokine. However, currently, the signaling pathways that regulate IL-10 production in B cells are not well understood. Here, we show that TLR signaling, but not BCR activation or CD40 ligation, induces potent production of IL-10 in human B cells. We demonstrate that the activation of STAT3 and ERK is required for TLR-induced IL-10 production by B cells, since inhibition of STAT3 or ERK activation abrogates TLR-induced IL-10 production. We also uncover a novel function of the TLR-MyD88-STAT3 pathway in B cells, namely controlling IL-10 production, in addition to the known role for this pathway in antibody production. Furthermore, IFN-α, a member of the type I IFN family, differentially modulates TLR7/8- and TLR9-activated STAT3 and ERK in B cells, which provides an explanation for our findings that IFN-α enhances TLR7/8-induced, but not TLR9-induced IL-10 production. These results yield insights into the mechanisms by which TLR signaling regulates IL-10 production in B cells and how type I IFN modulates TLR-mediated IL-10 production by B cells, therefore providing potential targets to modulate the function of IL-10-producing B cells.

We identified three retinoid-related orphan receptor gamma t (RORγt)-specific inhibitors that suppress T helper 17 (Th17) cell responses, including Th17-cell-mediated autoimmune disease. We systemically characterized RORγt binding in the presence and absence of drugs with corresponding whole-genome transcriptome sequencing. RORγt acts as a direct activator of Th17 cell signature genes and a direct repressor of signature genes from other T cell lineages; its strongest transcriptional effects are on cis-regulatory sites containing the RORα binding motif. RORγt is central in a densely interconnected regulatory network that shapes the balance of T cell differentiation. Here, the three inhibitors modulated the RORγt-dependent transcriptional network to varying extents and through distinct mechanisms. Whereas one inhibitor displaced RORγt from its target loci, the other two inhibitors affected transcription predominantly without removing DNA binding. Our work illustrates the power of a system-scale analysis of transcriptional regulation to characterize potential therapeutic compounds that inhibit pathogenic Th17 cells and suppress autoimmunity., United States. National Institutes of Health (DP1OD003958-01)

In biomedical applications, an experimenter encounters different potential sources of variation in data such as individual samples, multiple experimental conditions, and multivariate responses of a panel of markers such as from a signaling network. In multiparametric cytometry, which is often used for analyzing patient samples, such issues are critical. While computational methods can identify cell populations in individual samples, without the ability to automatically match them across samples, it is difficult to compare and characterize the populations in typical experiments, such as those responding to various stimulations or distinctive of particular patients or time-points, especially when there are many samples. Joint Clustering and Matching (JCM) is a multi-level framework for simultaneous modeling and registration of populations across a cohort. JCM models every population with a robust multivariate probability distribution. Simultaneously, JCM fits a random-effects model to construct an overall batch template – used for registering populations across samples, and classifying new samples. By tackling systems-level variation, JCM supports practical biomedical applications involving large cohorts. Software for fitting the JCM models have been implemented in an R package EMMIX-JCM, available from http://www.maths.uq.edu.au/~gjm/mix_soft/EMMIX-JCM/.

CD4(+) T helper cells are a central element of the adaptive immune system. They protect the organism against a wide range of pathogens and are able to initiate and control many immune reactions in combination with other cells of the adaptive and the innate immune system. Starting from a naive cell, CD4(+) T cells can differentiate into various effector cell populations with specialized function. This subset specific differentiation depends on numerous signals and the strength of stimulation. However, recent data have shown that differentiated CD4(+) T cell subpopulations display a high grade of plasticity and that their initial differentiation is not an endpoint of T cell development. In particular, FoxP3(+) regulatory T cells (Treg) and Th17 effector T cells demonstrate a high grade of plasticity, which allow a functional adaptation to various physiological situations during an immune response. However, the plasticity of Treg and Th17 cells might also be a critical factor for autoimmune disease. Here we discuss the recent developments in CD4(+) T cell plasticity with a focus on Treg and Th17 cells and its role in human autoimmune disease, in particular multiple sclerosis (MS).