Principles of genetic circuit design

Cells navigate environments, communicate and build complex patterns by initiating gene expression in response to specific signals. Engineers seek to harness this capability to program cells to perform tasks or create chemicals and materials that match the complexity seen in nature. This Review describes new tools that aid the construction of genetic circuits. Circuit dynamics can be influenced by the choice of regulators and changed with expression 'tuning knobs'. We collate the failure modes encountered when assembling circuits, quantify their impact on performance and review mitigation efforts. Finally, we discuss the constraints that arise from circuits having to operate within a living cell. Collectively, better tools, well-characterized parts and a comprehensive understanding of how to compose circuits are leading to a breakthrough in the ability to program living cells for advanced applications, from living therapeutics to the atomic manufacturing of functional materials.National Institute of General Medical Sciences (U.S.) (Grant P50 GM098792)National Institute of General Medical Sciences (U.S.) (Grant R01 GM095765)National Science Foundation (U.S.). Synthetic Biology Engineering Research Center (EEC0540879)Life Technologies, Inc. (A114510)National Science Foundation (U.S.). Graduate Research FellowshipUnited States. Office of Naval Research. Multidisciplinary University Research Initiative (Grant 4500000552

MesoGraph: Automatic profiling of mesothelioma subtypes from histological images.

Mesothelioma is classified into three histological subtypes, epithelioid, sarcomatoid, and biphasic, according to the relative proportions of epithelioid and sarcomatoid tumor cells present. Current guidelines recommend that the sarcomatoid component of each mesothelioma is quantified, as a higher percentage of sarcomatoid pattern in biphasic mesothelioma shows poorer prognosis. In this work, we develop a dual-task graph neural network (GNN) architecture with ranking loss to learn a model capable of scoring regions of tissue down to cellular resolution. This allows quantitative profiling of a tumor sample according to the aggregate sarcomatoid association score. Tissue is represented by a cell graph with both cell-level morphological and regional features. We use an external multicentric test set from Mesobank, on which we demonstrate the predictive performance of our model. We additionally validate our model predictions through an analysis of the typical morphological features of cells according to their predicted score

Malignant Mesothelioma subtyping via sampling driven multiple instance prediction on tissue image and cell morphology data

Malignant Mesothelioma is a difficult to diagnose and highly lethal cancer usually associated with asbestos exposure. It can be broadly classified into three subtypes: Epithelioid, Sarcomatoid, and a hybrid Biphasic subtype in which significant components of both of the previous subtypes are present. Early diagnosis and identification of the subtype informs treatment and can help improve patient outcome. However, the subtyping of malignant mesothelioma, and specifically the recognition of transitional features from routine histology slides has a high level of inter-observer variability. In this work, we propose an end-to-end multiple instance learning (MIL) approach for malignant mesothelioma subtyping. This uses an adaptive instance-based sampling scheme for training deep convolutional neural networks on bags of image patches that allows learning on a wider range of relevant instances compared to max or top-N based MIL approaches. We also investigate augmenting the instance representation to include aggregate cellular morphology features from cell segmentation. The proposed MIL approach enables identification of malignant mesothelial subtypes of specific tissue regions. From this a continuous characterisation of a sample according to predominance of sarcomatoid vs epithelioid regions is possible, thus avoiding the arbitrary and highly subjective categorisation by currently used subtypes. Instance scoring also enables studying tumor heterogeneity and identifying patterns associated with different subtypes. We have evaluated the proposed method on a dataset of 234 tissue micro-array cores with an AUROC of 0.89 ± 0.05 for this task. The dataset and developed methodology is available for the community at: https://github.com/measty/PINS

De Novo Truncating Mutations in WASF1 Cause Intellectual Disability with Seizures.

Next-generation sequencing has been invaluable in the elucidation of the genetic etiology of many subtypes of intellectual disability in recent years. Here, using exome sequencing and whole-genome sequencing, we identified three de novo truncating mutations in WAS protein family member 1 (WASF1) in five unrelated individuals with moderate to profound intellectual disability with autistic features and seizures. WASF1, also known as WAVE1, is part of the WAVE complex and acts as a mediator between Rac-GTPase and actin to induce actin polymerization. The three mutations connected by Matchmaker Exchange were c.1516C>T (p.Arg506Ter), which occurs in three unrelated individuals, c.1558C>T (p.Gln520Ter), and c.1482delinsGCCAGG (p.Ile494MetfsTer23). All three variants are predicted to partially or fully disrupt the C-terminal actin-binding WCA domain. Functional studies using fibroblast cells from two affected individuals with the c.1516C>T mutation showed a truncated WASF1 and a defect in actin remodeling. This study provides evidence that de novo heterozygous mutations in WASF1 cause a rare form of intellectual disability

Access Impediments to Health Care and Social Services Between Anglophone and Francophone African Immigrants Living in Philadelphia with Respect to HIV/AIDS

Objectives To describe the social and cultural differences between Anglophone and Francophone African immigrants which define the impediments that Francophone African immigrants face trying to access health and human services in Philadelphia, Pennsylvania. Methods Surveys and personal interviews were administered to participants in social events, community meetings, and health centers. A Chi-squared analysis was used to contrast the communities. Results Francophone Africans demonstrated less acculturation, education, English fluency, and more legal documentation problems, and thus face greater challenges accessing health care. Anglophone Africans had a higher level of acculturation, fewer language problems, and perceived fewer barriers in accessing health care than Francophone Africans. Conclusions Educating new immigrants, through a more culturally sensitive infectious disease treatment and prevention program, is integral to achieving a higher access and utilization rates of available services; especially in recent Francophone immigrants. A larger study is needed to extend the findings to other cities where immigrants with similar backgrounds or acculturation issues reside

Prognostic model to predict postoperative acute kidney injury in patients undergoing major gastrointestinal surgery based on a national prospective observational cohort study.

Background: Acute illness, existing co-morbidities and surgical stress response can all contribute to postoperative acute kidney injury (AKI) in patients undergoing major gastrointestinal surgery. The aim of this study was prospectively to develop a pragmatic prognostic model to stratify patients according to risk of developing AKI after major gastrointestinal surgery. Methods: This prospective multicentre cohort study included consecutive adults undergoing elective or emergency gastrointestinal resection, liver resection or stoma reversal in 2-week blocks over a continuous 3-month period. The primary outcome was the rate of AKI within 7 days of surgery. Bootstrap stability was used to select clinically plausible risk factors into the model. Internal model validation was carried out by bootstrap validation. Results: A total of 4544 patients were included across 173 centres in the UK and Ireland. The overall rate of AKI was 14·2 per cent (646 of 4544) and the 30-day mortality rate was 1·8 per cent (84 of 4544). Stage 1 AKI was significantly associated with 30-day mortality (unadjusted odds ratio 7·61, 95 per cent c.i. 4·49 to 12·90; P < 0·001), with increasing odds of death with each AKI stage. Six variables were selected for inclusion in the prognostic model: age, sex, ASA grade, preoperative estimated glomerular filtration rate, planned open surgery and preoperative use of either an angiotensin-converting enzyme inhibitor or an angiotensin receptor blocker. Internal validation demonstrated good model discrimination (c-statistic 0·65). Discussion: Following major gastrointestinal surgery, AKI occurred in one in seven patients. This preoperative prognostic model identified patients at high risk of postoperative AKI. Validation in an independent data set is required to ensure generalizability

MesoGraph: Automatic profiling of mesothelioma subtypes from histological images

Data and code availability: • Tissue Micro-array cores and labels for the primary cohort are linked in the github repository at: https://github.com/measty/MesoGraph The Mesobank data is available from Mesobank (https://www.mesobank.com/) on request. This would require the completion of mesobank’s standard application form. It would then be reviewed to make sure that the proposed use of the data is covered by mesobank’s generic ethical approval, and a suitable Data Sharing Agreement would need to be in place before any data is released. • All original code is publicly available at: https://github.com/measty/MesoGraph. • Any additional data is available from the lead contact on request.Supplemental information is available online at: https://www.sciencedirect.com/science/article/pii/S2666379123004032#appsec2 .Copyright © 2023 The Authors. Mesothelioma is classified into three histological subtypes, epithelioid, sarcomatoid, and biphasic, according to the relative proportions of epithelioid and sarcomatoid tumor cells present. Current guidelines recommend that the sarcomatoid component of each mesothelioma is quantified, as a higher percentage of sarcomatoid pattern in biphasic mesothelioma shows poorer prognosis. In this work, we develop a dual-task graph neural network (GNN) architecture with ranking loss to learn a model capable of scoring regions of tissue down to cellular resolution. This allows quantitative profiling of a tumor sample according to the aggregate sarcomatoid association score. Tissue is represented by a cell graph with both cell-level morphological and regional features. We use an external multicentric test set from Mesobank, on which we demonstrate the predictive performance of our model. We additionally validate our model predictions through an analysis of the typical morphological features of cells according to their predicted score.CRUK-STFC Early Detection Innovation Award. F.M. and M.E. also acknowledge funding support from EPSRC grant EP/W02909X/1

Malignant Mesothelioma subtyping via sampling driven multiple instance prediction on tissue image and cell morphology data

Copyright © 2023 The Authors. Malignant Mesothelioma is a difficult to diagnose and highly lethal cancer usually associated with asbestos exposure. It can be broadly classified into three subtypes: Epithelioid, Sarcomatoid, and a hybrid Biphasic subtype in which significant components of both of the previous subtypes are present. Early diagnosis and identification of the subtype informs treatment and can help improve patient outcome. However, the subtyping of malignant mesothelioma, and specifically the recognition of transitional features from routine histology slides has a high level of inter-observer variability. In this work, we propose an end-to-end multiple instance learning (MIL) approach for malignant mesothelioma subtyping. This uses an adaptive instance-based sampling scheme for training deep convolutional neural networks on bags of image patches that allows learning on a wider range of relevant instances compared to max or top-N based MIL approaches. We also investigate augmenting the instance representation to include aggregate cellular morphology features from cell segmentation. The proposed MIL approach enables identification of malignant mesothelial subtypes of specific tissue regions. From this a continuous characterisation of a sample according to predominance of sarcomatoid vs epithelioid regions is possible, thus avoiding the arbitrary and highly subjective categorisation by currently used subtypes. Instance scoring also enables studying tumor heterogeneity and identifying patterns associated with different subtypes. We have evaluated the proposed method on a dataset of 234 tissue micro-array cores with an AUROC of for this task. The dataset and developed methodology is available for the community at: https://github.com/measty/PINSPRISM project, kindly funded by Cancer Research UK through the CRUK-STFC Early Detection Innovation Award

A functional polymorphism in the SPINK5 gene is associated with asthma in a Chinese Han Population

<p>Abstract</p> <p>Background</p> <p>Mutation in <it>SPINK5 </it>causes Netherton syndrome, a rare recessive skin disease that is accompanied by severe atopic manifestations including atopic dermatitis, allergic rhinitis, asthma, high serum IgE and hypereosinophilia. Recently, single nucleotide polymorphism (SNP) of the <it>SPINK5 </it>was shown to be significantly associated with atopy, atopic dermatitis, asthma, and total serum IgE. In order to determine the role of the <it>SPINK5 </it>in the development of asthma, a case-control study including 669 asthma patients and 711 healthy controls in Han Chinese was conducted.</p> <p>Methods</p> <p>Using PCR-RFLP assay, we genotyped one promoter SNP, -206G>A, and four nonsynonymous SNPs, 1103A>G (Asn368Ser), 1156G>A (Asp386Asn), 1258G>A (Glu420Lys), and 2475G>T (Glu825Asp). Also, we analyzed the functional significance of -206G>A using the luciferase reporter assay and electrophoresis mobility shift assay.</p> <p>Results</p> <p>we found that the G allele at SNP -206G>A was associated with increased asthma susceptibility in our study population (p = 0.002, odds ratio 1.34, 95% confidence interval 1.11–1.60). There was no significant association between any of four nonsynonymous SNPs and asthma. The A allele at -206G>A has a significantly higher transcriptional activity than the G allele. Electrophoresis mobility shift assay also showed a significantly higher binding efficiency of nuclear protein to the A allele compared with the G allele.</p> <p>Conclusion</p> <p>Our findings indicate that the -206G>A polymorphism in the <it>SPINK5 </it>is associated with asthma susceptibility in a Chinese Han population.</p

Long-term effects of flooding on mortality in England and Wales, 1994-2005: controlled interrupted time-series analysis

BACKGROUND: Limited evidence suggests that being flooded may increase mortality and morbidity among affected householders not just at the time of the flood but for months afterwards. The objective of this study is to explore the methods for quantifying such long-term health effects of flooding by analysis of routine mortality registrations in England and Wales. METHODS: Mortality data, geo-referenced by postcode of residence, were linked to a national database of flood events for 1994 to 2005. The ratio of mortality in the post-flood year to that in the pre-flood year within flooded postcodes was compared with that in non-flooded boundary areas (within 5 km of a flood). Further analyses compared the observed number of flood-area deaths in the year after flooding with the number expected from analysis of mortality trends stratified by region, age-group, sex, deprivation group and urban-rural status. RESULTS: Among the 319 recorded floods, there were 771 deaths in the year before flooding and 693 deaths in the year after (post-/pre-flood ratio of 0.90, 95% CI 0.82, 1.00). This ratio did not vary substantially by age, sex, population density or deprivation. A similar post-flood 'deficit' of deaths was suggested by the analyses based on observed/expected deaths. CONCLUSIONS: The observed post-flood 'deficit' of deaths is counter-intuitive and difficult to interpret because of the possible influence of population displacement caused by flooding. The bias that might arise from such displacement remains unquantified but has important implications for future studies that use place of residence as a marker of exposure