Development of the high throughput mammalian PIG-A gene mutation assay in vitro.

The field of genetic toxicology has recently undergone reform which has limited or banned the use of animal models within a number of different industries (cosmetics). Consequently, greater emphasis has been placed on developing novel, highly sensitive, in vitro test systems which can generate robust data to aid regulatory hazard and risk assessment.The main aims of this project were i) to develop a highly sensitive and specific, high throughput mammalian in vitro PIG-A gene mutation assay to enable quantitative dose response modelling and further investigate the potential use of in vitro data within human health assessment, ii) Investigate the genotype to phenotype relationship, a potentially delaying step within future OECD guideline drafting for the current in-vivo Pig-a mutation assay and iii) help develop and optimise a preliminary comprehensive human PIG-A bio­monitoring platform.During in-vitro and ex-vivo PIG-A assay development, flow cytometry was the fundamental technique utilised. Multiple additional laser excitation platforms were evaluated for use, including Amnis ImageStream ™ and laser scanning confocal. Proteomic as well as genomic techniques were used during the supplementary investigations surrounding assay development, with microbiological groundings throughout.The finalised in-vitro assay protocol was established within human, metabolically active, MCL-5 cells. Using the refined assay design, proof of principle experimentations were able to show the potential for future quantitative work and the general promise with this novel approach. The genotype to phenotype relationship validation is currently still on-going following the preliminary work described herein and recent publications. The ex-vivo human PIG-A assay platforms were shown to require further optimisation in terms of sensitivity, excluding red blood cells, but showed good aptitude for future use.Currently it looks promising that further refinement could lead to a comprehensive high content, high-throughput assay system with the potential to be used within future hazard and risk assessmen

Canagliflozin impairs T cell effector function via metabolic suppression in autoimmunity

Augmented T cell function leading to host damage in autoimmunity is supported by metabolic dysregulation, making targeting immunometabolism an attractive therapeutic avenue. Canagliflozin, a type 2 diabetes drug, is a sodium glucose co-transporter 2 (SGLT2) inhibitor with known off-target effects on glutamate dehydrogenase and complex I. However, the effects of SGLT2 inhibitors on human T cell function have not been extensively explored. Here, we show that canagliflozin-treated T cells are compromised in their ability to activate, proliferate, and initiate effector functions. Canagliflozin inhibits T cell receptor signaling, impacting on ERK and mTORC1 activity, concomitantly associated with reduced c-Myc. Compromised c-Myc levels were encapsulated by a failure to engage translational machinery resulting in impaired metabolic protein and solute carrier production among others. Importantly, canagliflozin-treated T cells derived from patients with autoimmune disorders impaired their effector function. Taken together, our work highlights a potential therapeutic avenue for repurposing canagliflozin as an intervention for T cell-mediated autoimmunity

Inter-laboratory automation of the in vitro micronucleus assay using imaging flow cytometry and deep learning

The in vitro micronucleus assay is a globally significant method for DNA damage quantification used for regulatory compound safety testing in addition to inter-individual monitoring of environmental, lifestyle and occupational factors. However, it relies on time-consuming and user-subjective manual scoring. Here we show that imaging flow cytometry and deep learning image classification represents a capable platform for automated, inter-laboratory operation. Images were captured for the cytokinesis-block micronucleus (CBMN) assay across three laboratories using methyl methanesulphonate (1.25–5.0 μg/mL) and/or carbendazim (0.8–1.6 μg/mL) exposures to TK6 cells. Human-scored image sets were assembled and used to train and test the classification abilities of the “DeepFlow” neural network in both intra- and inter-laboratory contexts. Harnessing image diversity across laboratories yielded a network able to score unseen data from an entirely new laboratory without any user configuration. Image classification accuracies of 98%, 95%, 82% and 85% were achieved for ‘mononucleates’, ‘binucleates’, ‘mononucleates with MN’ and ‘binucleates with MN’, respectively. Successful classifications of ‘trinucleates’ (90%) and ‘tetranucleates’ (88%) in addition to ‘other or unscorable’ phenotypes (96%) were also achieved. Attempts to classify extremely rare, tri- and tetranucleated cells with micronuclei into their own categories were less successful (≤ 57%). Benchmark dose analyses of human or automatically scored micronucleus frequency data yielded quantitation of the same equipotent concentration regardless of scoring method. We conclude that this automated approach offers significant potential to broaden the practical utility of the CBMN method across industry, research and clinical domains. We share our strategy using openly-accessible frameworks

The contribution of the voluntary sector to mental health crisis care: a mixed-methods study

Background Weaknesses in the provision of mental health crisis support are evident and improvements that include voluntary sector provision are promoted. There is a lack of evidence regarding the contribution of the voluntary sector and how this might be used to the best effect in mental health crisis care. Aim To investigate the contribution of voluntary sector organisations to mental health crisis care in England. Design Multimethod sequential design with a comparative case study. Setting England, with four case studies in North England, East England, the Midlands and London. Method The method included a scoping literature review, a national survey of 1612 voluntary sector organisations, interviews with 27 national stakeholders and detailed mapping of the voluntary sector organisation provision in two regions (the north and south of England) to develop a taxonomy of voluntary sector organisations and to select four case studies. The case studies examined voluntary sector organisation crisis care provision as a system through interviews with local stakeholders (n = 73), eight focus groups with service users and carers and, at an individual level, narrative interviews with service users (n = 47) and carers (n = 12) to understand their crisis experience and service journey. There was extensive patient and public involvement in the study, including service users as co-researchers, to ensure validity. This affected the conduct of the study and the interpretation of the findings. The quality and the impact of the involvement was evaluated and commended. Main findings A mental health crisis is considered a biographical disruption. Voluntary sector organisations can make an important contribution, characterised by a socially oriented and relational approach. Five types of relevant voluntary sector organisations were identified: (1) crisis-specific, (2) general mental health, (3) population-focused, (4) life-event-focused and (5) general social and community voluntary sector organisations. These voluntary sector organisations provide a range of support and have specific expertise. The availability and access to voluntary sector organisations varies and inequalities were evident for rural communities; black, Asian and minority ethnic communities; people who use substances; and people who identified as having a personality disorder. There was little evidence of well-developed crisis systems, with an underdeveloped approach to prevention and a lack of ongoing support. Limitations The survey response was low, reflecting the nature of voluntary sector organisations and demands on their time. This was a descriptive study, so evaluating outcomes from voluntary sector organisation support was beyond the scope of the study. Conclusions The current policy discourse frames a mental health crisis as an urgent event. Viewing a mental health crisis as a biographical disruption would better enable a wide range of contributory factors to be considered and addressed. Voluntary sector organisations have a distinctive and important role to play. The breadth of this contribution needs to be acknowledged and its role as an accessible alternative to inpatient provision prioritised. Future work A whole-system approach to mental health crisis provision is needed. The NHS, local authorities and the voluntary sector should establish how to effectively collaborate to meet the local population’s needs and to ensure the sustainability of the voluntary sector. Service users and carers from all communities need to be central to this. Funding This project was funded by the National Institute for Health Research (NIHR) Health Services and Delivery Research programme and will be published in full in Health Services and Delivery Research; Vol. 8, No. 29. See the NIHR Journals Library website for further project information

Inter-laboratory automation of the in vitro micronucleus assay using imaging flow cytometry and deep learning.

The in vitro micronucleus assay is a globally significant method for DNA damage quantification used for regulatory compound safety testing in addition to inter-individual monitoring of environmental, lifestyle and occupational factors. However, it relies on time-consuming and user-subjective manual scoring. Here we show that imaging flow cytometry and deep learning image classification represents a capable platform for automated, inter-laboratory operation. Images were captured for the cytokinesis-block micronucleus (CBMN) assay across three laboratories using methyl methanesulphonate (1.25-5.0 μg/mL) and/or carbendazim (0.8-1.6 μg/mL) exposures to TK6 cells. Human-scored image sets were assembled and used to train and test the classification abilities of the "DeepFlow" neural network in both intra- and inter-laboratory contexts. Harnessing image diversity across laboratories yielded a network able to score unseen data from an entirely new laboratory without any user configuration. Image classification accuracies of 98%, 95%, 82% and 85% were achieved for 'mononucleates', 'binucleates', 'mononucleates with MN' and 'binucleates with MN', respectively. Successful classifications of 'trinucleates' (90%) and 'tetranucleates' (88%) in addition to 'other or unscorable' phenotypes (96%) were also achieved. Attempts to classify extremely rare, tri- and tetranucleated cells with micronuclei into their own categories were less successful (≤ 57%). Benchmark dose analyses of human or automatically scored micronucleus frequency data yielded quantitation of the same equipotent concentration regardless of scoring method. We conclude that this automated approach offers significant potential to broaden the practical utility of the CBMN method across industry, research and clinical domains. We share our strategy using openly-accessible frameworks

Rare Copy Number Variants in \u3cem\u3eNRXN1\u3c/em\u3e and \u3cem\u3eCNTN6\u3c/em\u3e Increase Risk for Tourette Syndrome

Tourette syndrome (TS) is a model neuropsychiatric disorder thought to arise from abnormal development and/or maintenance of cortico-striato-thalamo-cortical circuits. TS is highly heritable, but its underlying genetic causes are still elusive, and no genome-wide significant loci have been discovered to date. We analyzed a European ancestry sample of 2,434 TS cases and 4,093 ancestry-matched controls for rare (\u3c 1% frequency) copy-number variants (CNVs) using SNP microarray data. We observed an enrichment of global CNV burden that was prominent for large (\u3e 1 Mb), singleton events (OR = 2.28, 95% CI [1.39–3.79], p = 1.2 × 10−3) and known, pathogenic CNVs (OR = 3.03 [1.85–5.07], p = 1.5 × 10−5). We also identified two individual, genome-wide significant loci, each conferring a substantial increase in TS risk (NRXN1 deletions, OR = 20.3, 95% CI [2.6–156.2]; CNTN6 duplications, OR = 10.1, 95% CI [2.3–45.4]). Approximately 1% of TS cases carry one of these CNVs, indicating that rare structural variation contributes significantly to the genetic architecture of TS

Canagliflozin impairs T cell effector function via metabolic suppression in autoimmunity

Augmented T cell function leading to host damage in autoimmunity is supported by metabolic dysregulation, making targeting immunometabolism an attractive therapeutic avenue. Canagliflozin, a type 2 diabetes drug, is a sodium glucose co-transporter 2 (SGLT2) inhibitor with known off-target effects on glutamate dehydrogenase and complex I. However, the effects of SGLT2 inhibitors on human T cell function have not been extensively explored. Here, we show that canagliflozin-treated T cells are compromised in their ability to activate, proliferate, and initiate effector functions. Canagliflozin inhibits T cell receptor signaling, impacting on ERK and mTORC1 activity, concomitantly associated with reduced c-Myc. Compromised c-Myc levels were encapsulated by a failure to engage translational machinery resulting in impaired metabolic protein and solute carrier production among others. Importantly, canagliflozin-treated T cells derived from patients with autoimmune disorders impaired their effector function. Taken together, our work highlights a potential therapeutic avenue for repurposing canagliflozin as an intervention for T cell-mediated autoimmunity