Whole genome sequencing identifies putative associations between genomic polymorphisms and clinical response to the antiepileptic drug levetiracetam

In the context of pharmacogenomics, whole genome sequencing provides a powerful approach for identifying correlations between response variability to specific drugs and genomic polymorphisms in a population, in an unbiased manner. In this study, we employed whole genome sequencing of DNA samples from patients showing extreme response (n=72) and non-response (n=27) to the antiepileptic drug levetiracetam, in order to identify genomic variants that underlie response to the drug. Although no common SNP (MAF>5%) crossed the conventional genome-wide significance threshold of 5e-8, we found common polymorphisms in genes SPNS3, HDC, MDGA2, NSG1 and RASGEF1C, which collectively predict clinical response to levetiracetam in our cohort with ~91% predictive accuracy. Among these genes, HDC, NSG1, MDGA2 and RASGEF1C are potentially implicated in synaptic neurotransmission, while SPNS3 is an atypical solute carrier transporter homologous to SV2A, the known molecular target of levetiracetam. Furthermore, we performed gene- and pathway-based statistical analysis on sets of rare and low-frequency variants (MAF<5%) and we identified associations between the following genes or pathways and response to levetiracetam: a) genes PRKCB and DLG2, which are involved in glutamatergic neurotransmission, a known target of anticonvulsants, including levetiracetam; b) genes FILIP1 and SEMA6D, which are involved in axon guidance and modelling of neural connections; and c) pathways with a role in synaptic neurotransmission, such as WNT5A-dependent internalization of FZD4 and disinhibition of SNARE formation. In summary, our approach to utilise whole genome sequencing on subjects with extreme response phenotypes is a feasible route to generate plausible hypotheses for investigating the genetic factors underlying drug response variability in cases of pharmaco-resistant epilepsy

A two-neuron system for adaptive goal-directed decision-making in Lymnaea

During goal-directed decision-making, animals must integrate information from the external environment and their internal state to maximize resource localization while minimizing energy expenditure. How this complex problem is solved by the nervous system remains poorly understood. Here, using a combined behavioural and neurophysiological approach, we demonstrate that the mollusc Lymnaea performs a sophisticated form of decision-making during food-searching behaviour, using a core system consisting of just two neuron types. The first reports the presence of food and the second encodes motivational state acting as a gain controller for adaptive behaviour in the absence of food. Using an in vitro analogue of the decision-making process, we show that the system employs an energy management strategy, switching between a low- and high-use mode depending on the outcome of the decision. Our study reveals a parsimonious mechanism that drives a complex decision-making process via regulation of levels of tonic inhibition and phasic excitation

Whole-genome sequencing of chronic lymphocytic leukemia identifies subgroups with distinct biological and clinical features.

The value of genome-wide over targeted driver analyses for predicting clinical outcomes of cancer patients is debated. Here, we report the whole-genome sequencing of 485 chronic lymphocytic leukemia patients enrolled in clinical trials as part of the United Kingdom's 100,000 Genomes Project. We identify an extended catalog of recurrent coding and noncoding genetic mutations that represents a source for future studies and provide the most complete high-resolution map of structural variants, copy number changes and global genome features including telomere length, mutational signatures and genomic complexity. We demonstrate the relationship of these features with clinical outcome and show that integration of 186 distinct recurrent genomic alterations defines five genomic subgroups that associate with response to therapy, refining conventional outcome prediction. While requiring independent validation, our findings highlight the potential of whole-genome sequencing to inform future risk stratification in chronic lymphocytic leukemia

Distributed network organization underlying feeding behavior in the mollusk Lymnaea

The aim of the work reviewed here is to relate the properties of individual neurons to network organization and behavior using the feeding system of the gastropod mollusk, Lymnaea. Food ingestion in this animal involves sequences of rhythmic biting movements that are initiated by the application of a chemical food stimulus to the lips and esophagus. We investigated how individual neurons contribute to various network functions that are required for the generation of feeding behavior such as rhythm generation, initiation ('decision making'), modulation and hunger and satiety. The data support the view that feeding behavior is generated by a distributed type of network organization with individual neurons often contributing to more than one network function, sharing roles with other neurons. Multitasking in a distributed type of network would be 'economically' sensible in the Lymnaea feeding system where only about 100 neurons are available to carry out a variety of complex tasks performed by millions of neurons in the vertebrate nervous system. Having complementary and potentially alternative mechanisms for network functions would also add robustness to what is a 'noisy' network where variable firing rates and synaptic strengths are commonly encountered in electrophysiological recording experiments

Structural and non-coding variants increase the diagnostic yield of clinical whole genome sequencing for rare diseases

BACKGROUND: Whole genome sequencing is increasingly being used for the diagnosis of patients with rare diseases. However, the diagnostic yields of many studies, particularly those conducted in a healthcare setting, are often disappointingly low, at 25–30%. This is in part because although entire genomes are sequenced, analysis is often confined to in silico gene panels or coding regions of the genome. METHODS: We undertook WGS on a cohort of 122 unrelated rare disease patients and their relatives (300 genomes) who had been pre-screened by gene panels or arrays. Patients were recruited from a broad spectrum of clinical specialties. We applied a bioinformatics pipeline that would allow comprehensive analysis of all variant types. We combined established bioinformatics tools for phenotypic and genomic analysis with our novel algorithms (SVRare, ALTSPLICE and GREEN-DB) to detect and annotate structural, splice site and non-coding variants. RESULTS: Our diagnostic yield was 43/122 cases (35%), although 47/122 cases (39%) were considered solved when considering novel candidate genes with supporting functional data into account. Structural, splice site and deep intronic variants contributed to 20/47 (43%) of our solved cases. Five genes that are novel, or were novel at the time of discovery, were identified, whilst a further three genes are putative novel disease genes with evidence of causality. We identified variants of uncertain significance in a further fourteen candidate genes. The phenotypic spectrum associated with RMND1 was expanded to include polymicrogyria. Two patients with secondary findings in FBN1 and KCNQ1 were confirmed to have previously unidentified Marfan and long QT syndromes, respectively, and were referred for further clinical interventions. Clinical diagnoses were changed in six patients and treatment adjustments made for eight individuals, which for five patients was considered life-saving. CONCLUSIONS: Genome sequencing is increasingly being considered as a first-line genetic test in routine clinical settings and can make a substantial contribution to rapidly identifying a causal aetiology for many patients, shortening their diagnostic odyssey. We have demonstrated that structural, splice site and intronic variants make a significant contribution to diagnostic yield and that comprehensive analysis of the entire genome is essential to maximise the value of clinical genome sequencing

Clinical spectrum of STX1B-related epileptic disorders

OBJECTIVE: The aim of this study was to expand the spectrum of epilepsy syndromes related to STX1B, encoding the presynaptic protein syntaxin-1B, and establish genotype-phenotype correlations by identifying further disease-related variants. METHODS: We used next-generation sequencing in the framework of research projects and diagnostic testing. Clinical data and EEGs were reviewed, including already published cases. To estimate the pathogenicity of the variants, we used established and newly developed in silico prediction tools. RESULTS: We describe 17 new variants in STX1B, which are distributed across the whole gene. We discerned 4 different phenotypic groups across the newly identified and previously published patients (49 patients in 23 families): (1) 6 sporadic patients or families (31 affected individuals) with febrile and afebrile seizures with a benign course, generally good drug response, normal development, and without permanent neurologic deficits; (2) 2 patients with genetic generalized epilepsy without febrile seizures and cognitive deficits; (3) 13 patients or families with intractable seizures, developmental regression after seizure onset and additional neuropsychiatric symptoms; (4) 2 patients with focal epilepsy. More often, we found loss-of-function mutations in benign syndromes, whereas missense variants in the SNARE motif of syntaxin-1B were associated with more severe phenotypes. CONCLUSION: These data expand the genetic and phenotypic spectrum of STX1B-related epilepsies to a diverse range of epilepsies that span the International League Against Epilepsy classification. Variants in STX1B are protean and contribute to many different epilepsy phenotypes, similar to SCN1A, the most important gene associated with fever-associated epilepsies

Clinical spectrum of STX1B-related epileptic disorders

Objective The aim of this study was to expand the spectrum of epilepsy syndromes related to STX1B, encoding the presynaptic protein syntaxin- 1B, and establish genotype-phenotype correlations by identifying further disease related variants. Methods We used next-generation sequencing in the framework of research projects and diagnostic testing. Clinical data and EEGs were reviewed, including already published cases. To estimate the pathogenicity of the variants, we used established and newly developed in silico prediction tools. Results We describe 17 new variants in STX1B, which are distributed across the whole gene. We discerned 4 different phenotypic groups across the newly identified and previously published patients (49 patients in 23 families): (1) 6 sporadic patients or families (31 affected individuals) with febrile and afebrile seizures with a benign course, generally good drug response, normal development, and without permanent neurologic deficits; (2) 2 patients with genetic generalized epilepsy without febrile seizures and cognitive deficits; (3) 13 patients or families with intractable seizures, developmental regression after seizure onset and additional neuropsychiatric symptoms; (4) 2 patients with focal epilepsy. More often, we found loss-of-function mutations in benign syndromes, whereas missense variants in the SNARE motif of syntaxin-1B were associated with more severe phenotypes. Conclusion These data expand the genetic and phenotypic spectrum of STX1B-related epilepsies to a diverse range of epilepsies that span the International League Against Epilepsy classification. Variants in STX1B are protean and contribute to many different epilepsy phenotypes, similar to SCN1A, the most important gene associated with fever-associated epilepsies

Structural and non-coding variants increase the diagnostic yield of clinical whole genome sequencing for rare diseases.

BACKGROUND: Whole genome sequencing is increasingly being used for the diagnosis of patients with rare diseases. However, the diagnostic yields of many studies, particularly those conducted in a healthcare setting, are often disappointingly low, at 25-30%. This is in part because although entire genomes are sequenced, analysis is often confined to in silico gene panels or coding regions of the genome. METHODS: We undertook WGS on a cohort of 122 unrelated rare disease patients and their relatives (300 genomes) who had been pre-screened by gene panels or arrays. Patients were recruited from a broad spectrum of clinical specialties. We applied a bioinformatics pipeline that would allow comprehensive analysis of all variant types. We combined established bioinformatics tools for phenotypic and genomic analysis with our novel algorithms (SVRare, ALTSPLICE and GREEN-DB) to detect and annotate structural, splice site and non-coding variants. RESULTS: Our diagnostic yield was 43/122 cases (35%), although 47/122 cases (39%) were considered solved when considering novel candidate genes with supporting functional data into account. Structural, splice site and deep intronic variants contributed to 20/47 (43%) of our solved cases. Five genes that are novel, or were novel at the time of discovery, were identified, whilst a further three genes are putative novel disease genes with evidence of causality. We identified variants of uncertain significance in a further fourteen candidate genes. The phenotypic spectrum associated with RMND1 was expanded to include polymicrogyria. Two patients with secondary findings in FBN1 and KCNQ1 were confirmed to have previously unidentified Marfan and long QT syndromes, respectively, and were referred for further clinical interventions. Clinical diagnoses were changed in six patients and treatment adjustments made for eight individuals, which for five patients was considered life-saving. CONCLUSIONS: Genome sequencing is increasingly being considered as a first-line genetic test in routine clinical settings and can make a substantial contribution to rapidly identifying a causal aetiology for many patients, shortening their diagnostic odyssey. We have demonstrated that structural, splice site and intronic variants make a significant contribution to diagnostic yield and that comprehensive analysis of the entire genome is essential to maximise the value of clinical genome sequencing