Deletion of the tetA(L) chromosomal region of Bacillus subtilis in a strain designated JC112 increased the strain's sensitivity to low tetracycline concentrations. It also resulted in phenotypic changes that correlate with the previously found role of TetA(L) in mediating electrogenic Na ؉ /H ؉ antiport. Growth of JC112 was impaired relative to that of the wild type at both pH 7.0 and 8.3; Na ؉ -and K ؉ -dependent pH homeostases were impaired at alkaline pH. The phenotype of JC112 was complemented by plasmid-borne tetA(L) and related tet(K) genes; the antiport activity conferred by the tet(K) gene had an apparently higher preference for K ؉ over Na ؉ than that conferred by tetA(L). The data were consistent with TetA(L) being the major Naantiporter involved in pH homeostasis in B. subtilis as well as a significant Na ؉ extrusion system. The phenotype of JC112 was much more pronounced than that of an earlier transposition mutant, JC111, with a disruption in the putative tetA(L) promoter region. Northern (RNA) blot analysis of tetA(L) RNA from wild-type and JC111 strains revealed the same patterns. That JC111 nevertheless exhibited some Na ؉ and alkali sensitivity may be accounted for by disruption of regulatory features that, in the wild type, allow increased tetA(L) expression under specific conditions of pH and monovalent cation concentration. Evidence for several different regulatory effects emerged from studies of lacZ expression from the transposon of JC111 and from a tetA(L)-lacZ translational fusion introduced into the amyE locus of wild-type and JC112 strains.The chromosomal tetA(L) locus of Bacillus subtilis is very similar in sequence and apparent organization to tet loci of certain plasmids of gram-positive bacteria (20,24,25,34,37). It is composed of a short open reading frame (ORF) encoding the tetA(L) leader peptide, which is thought to be involved in translational regulation, and a second ORF, tetA(L), which encodes a porter that catalyzes efflux of a divalent cationtetracycline complex in coupled exchange for protons (i.e., metal-tetracycline/H ϩ antiport) (17,40). Each of these ORFs is preceded by a putative Shine-Dalgarno sequence. On the basis of the nucleotide sequence upstream of the leader region, a single promoter in a position analogous to that of the promoter of plasmid-borne tet genes was identified (20,24,34). The presence of the tetA(L) gene in a B. subtilis strain does not confer resistance to the usual challenge concentrations of the antibiotic (2), and yet attempts to delete the porter-encoding gene were unsuccessful (22). This result suggested that an important physiological role might exist for this gene. Recently, it was found that the tetA(L)-encoded porter does catalyze Co 2ϩ -tetracycline/H ϩ antiport, but the porter also enhances Na ϩ /H ϩ antiport that is physiologically important during the growth of B. subtilis at elevated Na ϩ and pH (8, 17). A Na ϩ -and alkaline pH-sensitive transpositional mutant of B. subtilis, JC111, was disrupted between the putative Ϫ35 and Ϫ10 regio...

Signaling through the innate immune system can promote or suppress allergic sensitization. TLR9 has modulatory effects on the mucosal immune system, and we hypothesized that TLR9 would influence susceptibility to allergic sensitization to foods. We observed that TLR9−/− mice were resistant to peanut-induced anaphylaxis. This was associated with a significant impairment in total IgE and peanut-specific IgE and IgA, but not IgG1 or Th2 cytokine production. TLR9−/− mice had reduced development of Peyer’s patches, but resistance to sensitization was not restricted to oral routes. Rag1-deficient mice were reconstituted with TLR9+/+ or −/− B cells plus CD4+ T cells. TLR9−/− B cells regained the ability to produce IgE in the presence of a wild-type environment. Our results demonstrate that TLR9 on an unknown cell type is required for the development of IgE-producing B cells, and we conclude that TLR9 signaling indirectly shapes the immune response for optimal IgE production.

Trio family and case-control studies of next-generation sequencing data have proven integral to understanding the contribution of rare inherited and de novo single-nucleotide variants to the genetic architecture of complex disease. Ideally, such studies should identify individual risk genes of moderate to large effect size to generate novel treatment hypotheses for further follow-up. However, due to insufficient power, gene set enrichment analyses have come to be relied upon for detecting differences between cases and controls, implicating sets of hundreds of genes rather than specific targets for further investigation. Here, we present a Bayesian statistical framework, termed gTADA, that integrates gene-set membership information with gene-level de novo and rare inherited case-control counts, to prioritize risk genes with excess rare variant burden within enriched gene sets. Applying gTADA to available whole-exome sequencing datasets for several neuropsychiatric conditions, we replicated previously reported gene set enrichments and identified novel risk genes. For epilepsy, gTADA prioritized 40 risk genes (posterior probabilities > 0.95), 6 of which replicate in an independent whole-genome sequencing study. In addition, 30/40 genes are novel genes. We found that epilepsy genes had high protein-protein interaction (PPI) network connectivity, and show specific expression during human brain development. Some of the top prioritized EPI genes were connected to a PPI subnetwork of immune genes and show specific expression in prenatal microglia. We also identified multiple enriched drug-target gene sets for EPI which included immunostimulants as well as known antiepileptics. Immune biology was supported specifically by case-control variants from familial epilepsies rather than do novo mutations in generalized encephalitic epilepsy. meta-analyzing DNMs and rare case-control (CC) variants, an approach that has been particularly successful for autism spectrum disorders (ASD) 9,10 . For epilepsy (EPI), multiple associated genes have been identified through DN based studies 4,5,11 , and in recent years, a number of EPI significant genes have also been identified through CC studies 12,13 . We hypothesized that, as for ASD, additional significant EPI genes could be discovered through the integration of DN and CC data. EPI is a serious brain disorder which includes multiple subtypes. Studies of cases/controls and twins have shown that genetic components have played important roles in EPI [14][15][16] . Some of EPI's subtypes can be explained by single genes, but multiple subtypes might be caused by multiple genes 15 . It is still challenging to develop specific drugs for this disorder. There have been multiple antiepileptic drugs used for EPI treatments; however, 20-30% of EPI patients have not been successful in controlling their seizures by using current medications 17 . Identifying additional genes or gene sets might help better understand its etiology as well as better design drug targets for the disorder.Due to the high p...

Anorexia nervosa (AN) is a complex and serious eating disorder, occurring in ~1% of individuals. Despite having the highest mortality rate of any psychiatric disorder, little is known about the aetiology of AN, and few effective treatments exist.Global efforts to collect large sample sizes of individuals with AN have been highly successful, and a recent study consequently identified the first genome-wide significant locus involved in AN. This result, coupled with other recent studies and epidemiological evidence, suggest that previous characterizations of AN as a purely psychiatric disorder are over-simplified. Rather, both neurological and metabolic pathways may also be involved.In order to elucidate more of the system-specific aetiology of AN, we applied transcriptomic imputation methods to 3,495 cases and 10,982 controls, collected by the Eating Disorders Working Group of the Psychiatric Genomics Consortium (PGC-ED). Transcriptomic Imputation (TI) methods approaches use machine-learning methods to impute tissue-specific gene expression from large genotype data using curated eQTL reference panels. These offer an exciting opportunity to compare gene associations across neurological and metabolic tissues. Here, we applied CommonMind Consortium (CMC) and GTEx-derived gene expression prediction models for 13 brain tissues and 12 tissues with potential metabolic involvement (adipose, adrenal gland, 2 colon, 3 esophagus, liver, pancreas, small intestine, spleen, stomach).We identified 35 significant gene-tissue associations within the large chromosome 12 region described in the recent PGC-ED GWAS. We applied forward stepwise conditional analyses and FINEMAP to associations within this locus to identify putatively causal signals. We identified four independently associated genes; RPS26, C12orf49, SUOX, and RDH16. We also identified two further genome-wide significant gene-tissue associations, both in brain tissues; REEP5, in the dorso-lateral pre-frontal cortex (DLPFC; p=8.52×10−07), and CUL3, in the caudate basal ganglia (p=1.8×10−06). These genes are significantly enriched for associations with anthropometric phenotypes in the UK BioBank, as well as multiple psychiatric, addiction, and appetite/satiety pathways. Our results support a model of AN risk influenced by both metabolic and psychiatric factors.