RESUMO
Schizophrenia is a debilitating psychiatric disorder with a substantial socioeconomic and humanistic burden. Currently available treatment strategies mostly rely on antipsychotic drugs, which block dopaminergic effects in the mesolimbic pathway of the brain. Although antipsychotic drugs help relieve psychotic symptoms, a definitive cure for schizophrenia has yet to be achieved. Recent advances in neuroinflammation research suggest that proinflammatory processes in the brain could cause alterations in neurobehavioral development and increase vulnerability to schizophrenia. With a growing need for novel strategies in the treatment of schizophrenia, it would be meaningful to review the current evidence supporting the therapeutic potential of anti-inflammatory strategies. This review details the key findings of clinical trials that investigate the efficacy of anti-inflammatory agents as adjuvants to antipsychotic treatment. We further discuss the possibilities of repurposing anti-inflammatory agents and developing novel strategies for the treatment of schizophrenia.
RESUMO
BACKGROUND: Several recent studies showed that next-generation sequencing (NGS)-based human leukocyte antigen (HLA) typing is a feasible and promising technique for variant calling of highly polymorphic regions. To date, however, no method with sufficient read depth has completely solved the allele phasing issue. In this study, we developed a new method (HLAscan) for HLA genotyping using NGS data. RESULTS: HLAscan performs alignment of reads to HLA sequences from the international ImMunoGeneTics project/human leukocyte antigen (IMGT/HLA) database. The distribution of aligned reads was used to calculate a score function to determine correctly phased alleles by progressively removing false-positive alleles. Comparative HLA typing tests using public datasets from the 1000 Genomes Project and the International HapMap Project demonstrated that HLAscan could perform HLA typing more accurately than previously reported NGS-based methods such as HLAreporter and PHLAT. In addition, the results of HLA-A, -B, and -DRB1 typing by HLAscan using data generated by NextGen were identical to those obtained using a Sanger sequencing-based method. We also applied HLAscan to a family dataset with various coverage depths generated on the Illumina HiSeq X-TEN platform. HLAscan identified allele types of HLA-A, -B, -C, -DQB1, and -DRB1 with 100% accuracy for sequences at ≥ 90× depth, and the overall accuracy was 96.9%. CONCLUSIONS: HLAscan, an alignment-based program that takes read distribution into account to determine true allele types, outperformed previously developed HLA typing tools. Therefore, HLAscan can be reliably applied for determination of HLA type across the whole-genome, exome, and target sequences.
