CD4(+) type 1 T regulatory (Tr1) cells are induced in the periphery and have a pivotal role in promoting and maintaining tolerance. The absence of surface markers that uniquely identify Tr1 cells has limited their study and clinical applications. By gene expression profiling of human Tr1 cell clones, we identified the surface markers CD49b and lymphocyte activation gene 3 (LAG-3) as being stably and selectively coexpressed on mouse and human Tr1 cells. We showed the specificity of these markers in mouse models of intestinal inflammation and helminth infection and in the peripheral blood of healthy volunteers. The coexpression of CD49b and LAG-3 enables the isolation of highly suppressive human Tr1 cells from in vitro anergized cultures and allows the tracking of Tr1 cells in the peripheral blood of subjects who developed tolerance after allogeneic hematopoietic stem cell transplantation. The use of these markers makes it feasible to track Tr1 cells in vivo and purify Tr1 cells for cell therapy to induce or restore tolerance in subjects with immune-mediated diseases.

Background Genomewide association studies of autoimmune diseases have mapped hundreds of susceptibility regions in the genome. However, only for a few association signals has the causal gene been identified, and for even fewer have the causal variant and underlying mechanism been defined. Coincident associations of DNA variants affecting both the risk of autoimmune disease and quantitative immune variables provide an informative route to explore disease mechanisms and drug-targetable pathways. Methods Using case–control samples from Sardinia, Italy, we performed a genomewide association study in multiple sclerosis followed by TNFSF13B locus–specific association testing in systemic lupus erythematosus (SLE). Extensive phenotyping of quantitative immune variables, sequence-based fine mapping, cross-population and cross-phenotype analyses, and gene-expression studies were used to identify the causal variant and elucidate its mechanism of action. Signatures of positive selection were also investigated. Results A variant in TNFSF13B, encoding the cytokine and drug target B-cell activating factor (BAFF), was associated with multiple sclerosis as well as SLE. The disease-risk allele was also associated with up-regulated humoral immunity through increased levels of soluble BAFF, B lymphocytes, and immunoglobulins. The causal variant was identified: an insertion–deletion variant, GCTGT→A (in which A is the risk allele), yielded a shorter transcript that escaped microRNA inhibition and increased production of soluble BAFF, which in turn up-regulated humoral immunity. Population genetic signatures indicated that this autoimmunity variant has been evolutionarily advantageous, most likely by augmenting resistance to malaria. Conclusions A TNFSF13B variant was associated with multiple sclerosis and SLE, and its effects were clarified at the population, cellular, and molecular levels. (Funded by the Italian Foundation for Multiple Sclerosis and others.)

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Tuberous Sclerosis Complex (TSC) is a multisystem genetic disorder characterized by hamartomatous neurological lesions that exhibit abnormal cell proliferation and differentiation. Hyperactivation of mTOR pathway by mutations in either the Tsc1 or Tsc2 gene underlies TSC pathogenesis, but involvement of specific neural cell populations in the formation of TSC-associated neurological lesions remains unclear. We deleted Tsc1 in Emx1-expressing embryonic telencephalic neural stem cells (NSCs) and found that mutant mice faithfully recapitulated TSC neuropathological lesions, such as cortical lamination defects and subependymal nodules (SENs). These alterations were caused by enhanced generation of SVZ neural progeny, followed by their premature differentiation and impaired maturation during both embryonic and postnatal development. Notably, mTORC1-dependent Akt inhibition and STAT3 activation were involved in the reduced self-renewal and earlier neuronal and astroglial differentiation of mutant NSCs. Thus, finely tuned mTOR activation in embryonic NSCs may be critical to prevent development of TSC-associated brain lesions.

Thyroid dysfunction is an important public health problem, which affects 10% of the general population and increases the risk of cardiovascular morbidity and mortality. Many aspects of thyroid hormone regulation have only partly been elucidated, including its transport, metabolism, and genetic determinants. Here we report a large meta-analysis of genome-wide association studies for thyroid function and dysfunction, testing 8 million genetic variants in up to 72,167 individuals. One-hundred-and-nine independent genetic variants are associated with these traits. A genetic risk score, calculated to assess their combined effects on clinical end points, shows significant associations with increased risk of both overt (Graves’ disease) and subclinical thyroid disease, as well as clinical complications. By functional follow-up on selected signals, we identify a novel thyroid hormone transporter (SLC17A4) and a metabolizing enzyme (AADAT). Together, these results provide new knowledge about thyroid hormone physiology and disease, opening new possibilities for therapeutic targets.

Epidermal growth factor receptor (EGFR) is a known diagnostic and, although controversial, prognostic marker of human glioblastoma multiforme (GBM). However, its functional role and biological significance in GBM remain elusive. Here, we show that multiple GBM cell subpopulations could be purified from the specimens of patients with GBM and from cancer stem cell (CSC) lines based on the expression of EGFR and of other putative CSC markers. All these subpopulations are molecularly and functionally distinct, are tumorigenic, and need to express EGFR to promote experimental tumorigenesis. Among them, EGFRexpressing tumor-initiating cells (TIC) display the most malignant functional and molecular phenotype. Accordingly, modulation of EGFR expression by gain-of-function and loss-of-function strategies in GBM CSC lines enhances and reduces their tumorigenic ability, respectively, suggesting that EGFR plays a fundamental role in gliomagenesis. These findings open up the possibility of new therapeutically relevant scenarios, as the presence of functionally heterogeneous EGFR pos and EGFR neg TIC subpopulations within the same tumor might affect clinical response to treatment. Cancer Res; 70(19); 7500-13. ©2010 AACR.

Transposons and γ-retroviruses have been efficiently used as insertional mutagens in different tissues to identify molecular culprits of cancer. However, these systems are characterized by recurring integrations that accumulate in tumor cells, hampering the identification of early cancer-driving events amongst bystander and progression-related events. We developed an insertional mutagenesis platform based on lentiviral vectors (LVV) by which we could efficiently induce hepatocellular carcinoma (HCC) in 3 different mouse models. By virtue of LVV’s replication-deficient nature and broad genome-wide integration pattern, LVV-based insertional mutagenesis allowed identification of 4 new liver cancer genes from a limited number of integrations. We validated the oncogenic potential of all the identified genes in vivo, with different levels of penetrance. Our newly identified cancer genes are likely to play a role in human disease, since they are upregulated and/or amplified/deleted in human HCCs and can predict clinical outcome of patients.

Methods for multiple-testing correction in local expression quantitative trait locus (cis-eQTL) studies are a trade-off between statistical power and computational efficiency. Bonferroni correction, though computationally trivial, is overly conservative and fails to account for linkage disequilibrium between variants. Permutation-based methods are more powerful, though computationally far more intensive. We present an alternative correction method called eigenMT, which runs over 500 times faster than permutations and has adjusted p values that closely approximate empirical ones. To achieve this speed while also maintaining the accuracy of permutation-based methods, we estimate the effective number of independent variants tested for association with a particular gene, termed Meff, by using the eigenvalue decomposition of the genotype correlation matrix. We employ a regularized estimator of the correlation matrix to ensure Meff is robust and yields adjusted p values that closely approximate p values from permutations. Finally, using a common genotype matrix, we show that eigenMT can be applied with even greater efficiency to studies across tissues or conditions. Our method provides a simpler, more efficient approach to multiple-testing correction than existing methods and fits within existing pipelines for eQTL discovery.