Canakinumab reverses overexpression of inflammatory response genes in tumour necrosis factor receptor-associated periodic syndrome

OBJECTIVE: To explore whether gene expression profiling can identify a molecular mechanism for the clinical benefit of canakinumab treatment in patents with tumour necrosis factor receptor-associated periodic syndrome (TRAPS). METHODS: Blood samples were collected from 20 patients with active TRAPS who received canakinumab 150 mg every 4 weeks for 4 months in an open-label proof-of-concept phase II study, and from 20 aged-matched healthy volunteers. Gene expression levels were evaluated in whole blood samples by microarray analysis for arrays passing quality control checks. RESULTS: Patients with TRAPS exhibited a gene expression signature in blood that differed from that in healthy volunteers. Upon treatment with canakinumab, many genes relevant to disease pathogenesis moved towards levels seen in the healthy volunteers. Canakinumab downregulated the TRAPS-causing gene (TNF super family receptor 1A (TNFRSF1A)), the drug-target gene (interleukin (IL)-1B) and other inflammation-related genes (eg, MAPK14). In addition, several inflammation-related pathways were evident among the differentially expressed genes. Canakinumab treatment reduced neutrophil counts, but the observed expression differences remained after correction for this. CONCLUSIONS: These gene expression data support a model in which canakinumab produces clinical benefit in TRAPS by increasing neutrophil apoptosis and reducing pro-inflammatory signals resulting from the inhibition of IL-1β. Notably, treatment normalised the overexpression of TNFRSF1A, suggesting that canakinumab has a direct impact on the main pathogenic mechanism in TRAPS. TRIAL REGISTRATION NUMBER: NCT01242813

Multiomic analyses implicate a neurodevelopmental program in the pathogenesis of cerebral arachnoid cysts

Cerebral arachnoid cysts (ACs) are one of the most common and poorly understood types of developmental brain lesion. To begin to elucidate AC pathogenesis, we performed an integrated analysis of 617 patient-parent (trio) exomes, 152,898 human brain and mouse meningeal single-cell RNA sequencing transcriptomes and natural language processing data of patient medical records. We found that damaging de novo variants (DNVs) were highly enriched in patients with ACs compared with healthy individuals (P = 1.57 × 10-33). Seven genes harbored an exome-wide significant DNV burden. AC-associated genes were enriched for chromatin modifiers and converged in midgestational transcription networks essential for neural and meningeal development. Unsupervised clustering of patient phenotypes identified four AC subtypes and clinical severity correlated with the presence of a damaging DNV. These data provide insights into the coordinated regulation of brain and meningeal development and implicate epigenomic dysregulation due to DNVs in AC pathogenesis. Our results provide a preliminary indication that, in the appropriate clinical context, ACs may be considered radiographic harbingers of neurodevelopmental pathology warranting genetic testing and neurobehavioral follow-up. These data highlight the utility of a systems-level, multiomics approach to elucidate sporadic structural brain disease

Evidence for 28 genetic disorders discovered by combining healthcare and research data

De novo mutations in protein-coding genes are a well-established cause of developmental disorders. However, genes known to be associated with developmental disorders account for only a minority of the observed excess of such de novo mutations. Here, to identify previously undescribed genes associated with developmental disorders, we integrate healthcare and research exome-sequence data from 31,058 parent–offspring trios of individuals with developmental disorders, and develop a simulation-based statistical test to identify gene-specific enrichment of de novo mutations. We identified 285 genes that were significantly associated with developmental disorders, including 28 that had not previously been robustly associated with developmental disorders. Although we detected more genes associated with developmental disorders, much of the excess of de novo mutations in protein-coding genes remains unaccounted for. Modelling suggests that more than 1,000 genes associated with developmental disorders have not yet been described, many of which are likely to be less penetrant than the currently known genes. Research access to clinical diagnostic datasets will be critical for completing the map of genes associated with developmental disorders

Loss of UGP2 in brain leads to a severe epileptic encephalopathy, emphasizing that bi-allelic isoform-specific start-loss mutations of essential genes can cause genetic diseases.

Developmental and/or epileptic encephalopathies (DEEs) are a group of devastating genetic disorders, resulting in early-onset, therapy-resistant seizures and developmental delay. Here we report on 22 individuals from 15 families presenting with a severe form of intractable epilepsy, severe developmental delay, progressive microcephaly, visual disturbance and similar minor dysmorphisms. Whole exome sequencing identified a recurrent, homozygous variant (chr2:64083454A > G) in the essential UDP-glucose pyrophosphorylase (UGP2) gene in all probands. This rare variant results in a tolerable Met12Val missense change of the longer UGP2 protein isoform but causes a disruption of the start codon of the shorter isoform, which is predominant in brain. We show that the absence of the shorter isoform leads to a reduction of functional UGP2 enzyme in neural stem cells, leading to altered glycogen metabolism, upregulated unfolded protein response and premature neuronal differentiation, as modeled during pluripotent stem cell differentiation in vitro. In contrast, the complete lack of all UGP2 isoforms leads to differentiation defects in multiple lineages in human cells. Reduced expression of Ugp2a/Ugp2b in vivo in zebrafish mimics visual disturbance and mutant animals show a behavioral phenotype. Our study identifies a recurrent start codon mutation in UGP2 as a cause of a novel autosomal recessive DEE syndrome. Importantly, it also shows that isoform-specific start-loss mutations causing expression loss of a tissue-relevant isoform of an essential protein can cause a genetic disease, even when an organism-wide protein absence is incompatible with life. We provide additional examples where a similar disease mechanism applies

Beyond the imitation game: Quantifying and extrapolating the capabilities of language models

Language models demonstrate both quantitative improvement and new qualitative capabilities with increasing scale. Despite their potentially transformative impact, these new capabilities are as yet poorly characterized. In order to inform future research, prepare for disruptive new model capabilities, and ameliorate socially harmful effects, it is vital that we understand the present and near-future capabilities and limitations of language models. To address this challenge, we introduce the Beyond the Imitation Game benchmark (BIG-bench). BIG-bench currently consists of 204 tasks, contributed by 442 authors across 132 institutions. Task topics are diverse, drawing problems from linguistics, childhood development, math, common-sense reasoning, biology, physics, social bias, software development, and beyond. BIG-bench focuses on tasks that are believed to be beyond the capabilities of current language models. We evaluate the behavior of OpenAI's GPT models, Google-internal dense transformer architectures, and Switch-style sparse transformers on BIG-bench, across model sizes spanning millions to hundreds of billions of parameters. In addition, a team of human expert raters performed all tasks in order to provide a strong baseline. Findings include: model performance and calibration both improve with scale, but are poor in absolute terms (and when compared with rater performance); performance is remarkably similar across model classes, though with benefits from sparsity; tasks that improve gradually and predictably commonly involve a large knowledge or memorization component, whereas tasks that exhibit "breakthrough" behavior at a critical scale often involve multiple steps or components, or brittle metrics; social bias typically increases with scale in settings with ambiguous context, but this can be improved with prompting