Mapping cell-to-tissue graphs across human placenta histology whole slide images using deep learning with HAPPY.

Accurate placenta pathology assessment is essential for managing maternal and newborn health, but the placenta's heterogeneity and temporal variability pose challenges for histology analysis. To address this issue, we developed the 'Histology Analysis Pipeline.PY' (HAPPY), a deep learning hierarchical method for quantifying the variability of cells and micro-anatomical tissue structures across placenta histology whole slide images. HAPPY differs from patch-based features or segmentation approaches by following an interpretable biological hierarchy, representing cells and cellular communities within tissues at a single-cell resolution across whole slide images. We present a set of quantitative metrics from healthy term placentas as a baseline for future assessments of placenta health and we show how these metrics deviate in placentas with clinically significant placental infarction. HAPPY's cell and tissue predictions closely replicate those from independent clinical experts and placental biology literature.

Deep learning and single-cell phenotyping for rapid antimicrobial susceptibility detection in Escherichia coli.

The rise of antimicrobial resistance (AMR) is one of the greatest public health challenges, already causing up to 1.2 million deaths annually and rising. Current culture-based turnaround times for bacterial identification in clinical samples and antimicrobial susceptibility testing (AST) are typically 18-24 h. We present a novel proof-of-concept methodological advance in susceptibility testing based on the deep-learning of single-cell specific morphological phenotypes directly associated with antimicrobial susceptibility in Escherichia coli. Our models can reliably (80% single-cell accuracy) classify untreated and treated susceptible cells for a lab-reference fully susceptible E. coli strain, across four antibiotics (ciprofloxacin, gentamicin, rifampicin and co-amoxiclav). For ciprofloxacin, we demonstrate our models reveal significant (p < 0.001) differences between bacterial cell populations affected and unaffected by antibiotic treatment, and show that given treatment with a fixed concentration of 10 mg/L over 30 min these phenotypic effects correlate with clinical susceptibility defined by established clinical breakpoints. Deploying our approach on cell populations from six E. coli strains obtained from human bloodstream infections with varying degrees of ciprofloxacin resistance and treated with a range of ciprofloxacin concentrations, we show single-cell phenotyping has the potential to provide equivalent information to growth-based AST assays, but in as little as 30 min.

Democratising or disrupting diagnosis? Ethical issues raised by the use of AI tools for rare disease diagnosis.

Computational phenotyping (CP) technology uses facial recognition algorithms to classify and potentially diagnose rare genetic disorders on the basis of digitised facial images. This AI technology has a number of research as well as clinical applications, such as supporting diagnostic decision-making. Using the example of CP, we examine stakeholders' views of the benefits and costs of using AI as a diagnostic tool within the clinic. Through a series of in-depth interviews (n â€‹= â€‹20) with: clinicians, clinical researchers, data scientists, industry and support group representatives, we report stakeholder views regarding the adoption of this technology in a clinical setting. While most interviewees were supportive of employing CP as a diagnostic tool in some capacity we observed ambivalence around the potential for artificial intelligence to overcome diagnostic uncertainty in a clinical context. Thus, while there was widespread agreement amongst interviewees concerning the public benefits of AI assisted diagnosis, namely, its potential to increase diagnostic yield and enable faster more objective and accurate diagnoses by up skilling non specialists and thereby enabling access to diagnosis that is potentially lacking, interviewees also raised concerns about ensuring algorithmic reliability, expunging algorithmic bias and that the use of AI could result in deskilling the specialist clinical workforce. We conclude that, prior to widespread clinical implementation, on-going reflection is needed regarding the trade-offs required to determine acceptable levels of bias and conclude that diagnostic AI tools should only be employed as an assistive technology within the dysmorphology clinic.

The genetic architecture of changes in adiposity during adulthood.

Obesity is a heritable disease, characterised by excess adiposity that is measured by body mass index (BMI). While over 1,000 genetic loci are associated with BMI, less is known about the genetic contribution to adiposity trajectories over adulthood. We derive adiposity-change phenotypes from 1.5 million primary-care health records in over 177,000 individuals in UK Biobank to study the genetic architecture of weight-change. Using multiple BMI measurements over time increases power to identify genetic factors affecting baseline BMI. In the largest reported genome-wide study of adiposity-change in adulthood, we identify novel associations with BMI-change at six independent loci, including rs429358 (a missense variant in APOE). The SNP-based heritability of BMI-change (1.98%) is 9-fold lower than that of BMI, and higher in women than in men. The modest genetic correlation between BMI-change and BMI (45.2%) indicates that genetic studies of longitudinal trajectories could uncover novel biology driving quantitative trait values in adulthood.

"I don't think people are ready to trust these algorithms at face value": trust and the use of machine learning algorithms in the diagnosis of rare disease.

BACKGROUND: As the use of AI becomes more pervasive, and computerised systems are used in clinical decision-making, the role of trust in, and the trustworthiness of, AI tools will need to be addressed. Using the case of computational phenotyping to support the diagnosis of rare disease in dysmorphology, this paper explores under what conditions we could place trust in medical AI tools, which employ machine learning. METHODS: Semi-structured qualitative interviews (n = 20) with stakeholders (clinical geneticists, data scientists, bioinformaticians, industry and patient support group spokespersons) who design and/or work with computational phenotyping (CP) systems. The method of constant comparison was used to analyse the interview data. RESULTS: Interviewees emphasized the importance of establishing trust in the use of CP technology in identifying rare diseases. Trust was formulated in two interrelated ways in these data. First, interviewees talked about the importance of using CP tools within the context of a trust relationship; arguing that patients will need to trust clinicians who use AI tools and that clinicians will need to trust AI developers, if they are to adopt this technology. Second, they described a need to establish trust in the technology itself, or in the knowledge it provides-epistemic trust. Interviewees suggested CP tools used for the diagnosis of rare diseases might be perceived as more trustworthy if the user is able to vouchsafe for the technology's reliability and accuracy and the person using/developing them is trusted. CONCLUSION: This study suggests we need to take deliberate and meticulous steps to design reliable or confidence-worthy AI systems for use in healthcare. In addition, we need to devise reliable or confidence-worthy processes that would give rise to reliable systems; these could take the form of RCTs and/or systems of accountability transparency and responsibility that would signify the epistemic trustworthiness of these tools. words 294.

Transcriptome and fatty-acid signatures of adipocyte hypertrophy and its non-invasive MR-based characterization in human adipose tissue.

BACKGROUND: The adipocyte-hypertrophy associated remodeling of fat cell function is considered causal for the development of metabolic disorders. A better understanding of transcriptome and fatty acid (FA) related alterations with adipocyte hypertrophy combined with less-invasive strategies for the detection of the latter can help to increase the prognostic and diagnostic value of adipocyte size and FA composition as markers for metabolic disease. METHODS: To clarify adipocyte-hypertrophy associated transcriptomic alterations, fat cell size was related to RNA-Seq data from white adipose tissue and size-separated adipocytes. The relationship between adipocyte size and adipose tissue FA composition as measured by GC-MS was investigated. MR spectroscopy (MRS) methods for clinical scanning were developed to characterize adipocyte size and FA composition in a fast and non-invasive manner. FINDINGS: With enlarged adipocyte size, substantial transcriptomic alterations of genes involved in mitochondrial function and FA metabolism were observed. Investigations of these two mechanisms revealed a reciprocal relationship between adipocyte size and estimated thermogenic adipocyte content as well as depot-specific correlations of adipocyte size and FA composition. MRS on a clinical scanner was suitable for the in-parallel assessment of adipose morphology and FA composition. INTERPRETATION: The current study provides a comprehensive overview of the adipocyte-hypertrophy associated transcriptomic and FA landscape in both subcutaneous and visceral adipose tissue. MRS represents a promising technique to translate the observed mechanistic, structural and functional changes in WAT with adipocyte hypertrophy into a clinical context for an improved phenotyping of WAT in the context of metabolic diseases. FUNDING: Competence network for obesity (FKZ 42201GI1128), ERC (No 677661, ProFatMRI; No 875488, FatVirtualBiopsy), Else Kröner-Fresenius-Foundation.

Inherited variants in CHD3 show variable expressivity in Snijders Blok-Campeau syndrome.

PURPOSE: Common diagnostic next-generation sequencing strategies are not optimized to identify inherited variants in genes associated with dominant neurodevelopmental disorders as causal when the transmitting parent is clinically unaffected, leaving a significant number of cases with neurodevelopmental disorders undiagnosed. METHODS: We characterized 21 families with inherited heterozygous missense or protein-truncating variants in CHD3, a gene in which de novo variants cause Snijders Blok-Campeau syndrome. RESULTS: Computational facial and Human Phenotype Ontology-based comparisons showed that the phenotype of probands with inherited CHD3 variants overlaps with the phenotype previously associated with de novo CHD3 variants, whereas heterozygote parents are mildly or not affected, suggesting variable expressivity. In addition, similarly reduced expression levels of CHD3 protein in cells of an affected proband and of healthy family members with a CHD3 protein-truncating variant suggested that compensation of expression from the wild-type allele is unlikely to be an underlying mechanism. Notably, most inherited CHD3 variants were maternally transmitted. CONCLUSION: Our results point to a significant role of inherited variation in Snijders Blok-Campeau syndrome, a finding that is critical for correct variant interpretation and genetic counseling and warrants further investigation toward understanding the broader contributions of such variation to the landscape of human disease.

CHEDDA syndrome is an underrecognized neurodevelopmental disorder with a highly restricted ATN1 mutation spectrum.

We describe the clinical features of nine unrelated individuals with rare de novo missense or in-frame deletions/duplications within the "HX motif" of exon 7 of ATN1. We previously proposed that individuals with such variants should be considered as being affected by the syndromic condition of congenital hypotonia, epilepsy, developmental delay, and digital anomalies (CHEDDA), distinct from dentatorubral-pallidoluysian atrophy (DRPLA) secondary to expansion variants in exon 5 of ATN1. We confirm that the universal phenotypic features of CHEDDA are distinctive facial features and global developmental delay. Infantile hypotonia and minor hand and feet differences are common and can present as arthrogryposis. Common comorbidities include severe feeding difficulties, often requiring gastrostomy support, as well as visual and hearing impairments. Epilepsy and congenital malformations of the brain, heart, and genitourinary systems are frequent but not universal. Our study confirms the clinical entity of CHEDDA secondary to a mutational signature restricted to exon 7 of ATN1. We propose a clinical schedule for assessment upon diagnosis, surveillance, and early intervention including the potential of neuroimaging for prognostication.

A DNA repair disorder caused by de novo monoallelic DDB1 variants is associated with a neurodevelopmental syndrome.

The DNA damage-binding protein 1 (DDB1) is part of the CUL4-DDB1 ubiquitin E3 ligase complex (CRL4), which is essential for DNA repair, chromatin remodeling, DNA replication, and signal transduction. Loss-of-function variants in genes encoding the complex components CUL4 and PHIP have been reported to cause syndromic intellectual disability with hypotonia and obesity, but no phenotype has been reported in association with DDB1 variants. Here, we report eight unrelated individuals, identified through Matchmaker Exchange, with de novo monoallelic variants in DDB1, including one recurrent variant in four individuals. The affected individuals have a consistent phenotype of hypotonia, mild to moderate intellectual disability, and similar facies, including horizontal or slightly bowed eyebrows, deep-set eyes, full cheeks, a short nose, and large, fleshy and forward-facing earlobes, demonstrated in the composite face generated from the cohort. Digital anomalies, including brachydactyly and syndactyly, were common. Three older individuals have obesity. We show that cells derived from affected individuals have altered DDB1 function resulting in abnormal DNA damage signatures and histone methylation following UV-induced DNA damage. Overall, our study adds to the growing family of neurodevelopmental phenotypes mediated by disruption of the CRL4 ubiquitin ligase pathway and begins to delineate the phenotypic and molecular effects of DDB1 misregulation.

Mutation-specific pathophysiological mechanisms define different neurodevelopmental disorders associated with SATB1 dysfunction.

Whereas large-scale statistical analyses can robustly identify disease-gene relationships, they do not accurately capture genotype-phenotype correlations or disease mechanisms. We use multiple lines of independent evidence to show that different variant types in a single gene, SATB1, cause clinically overlapping but distinct neurodevelopmental disorders. Clinical evaluation of 42 individuals carrying SATB1 variants identified overt genotype-phenotype relationships, associated with different pathophysiological mechanisms, established by functional assays. Missense variants in the CUT1 and CUT2 DNA-binding domains result in stronger chromatin binding, increased transcriptional repression, and a severe phenotype. In contrast, variants predicted to result in haploinsufficiency are associated with a milder clinical presentation. A similarly mild phenotype is observed for individuals with premature protein truncating variants that escape nonsense-mediated decay, which are transcriptionally active but mislocalized in the cell. Our results suggest that in-depth mutation-specific genotype-phenotype studies are essential to capture full disease complexity and to explain phenotypic variability.

Machine Learning based histology phenotyping to investigate the epidemiologic and genetic basis of adipocyte morphology and cardiometabolic traits.

Genetic studies have recently highlighted the importance of fat distribution, as well as overall adiposity, in the pathogenesis of obesity-associated diseases. Using a large study (n = 1,288) from 4 independent cohorts, we aimed to investigate the relationship between mean adipocyte area and obesity-related traits, and identify genetic factors associated with adipocyte cell size. To perform the first large-scale study of automatic adipocyte phenotyping using both histological and genetic data, we developed a deep learning-based method, the Adipocyte U-Net, to rapidly derive mean adipocyte area estimates from histology images. We validate our method using three state-of-the-art approaches; CellProfiler, Adiposoft and floating adipocytes fractions, all run blindly on two external cohorts. We observe high concordance between our method and the state-of-the-art approaches (Adipocyte U-net vs. CellProfiler: R2visceral = 0.94, P < 2.2 × 10-16, R2subcutaneous = 0.91, P < 2.2 × 10-16), and faster run times (10,000 images: 6mins vs 3.5hrs). We applied the Adipocyte U-Net to 4 cohorts with histology, genetic, and phenotypic data (total N = 820). After meta-analysis, we found that mean adipocyte area positively correlated with body mass index (BMI) (Psubq = 8.13 × 10-69, ßsubq = 0.45; Pvisc = 2.5 × 10-55, ßvisc = 0.49; average R2 across cohorts = 0.49) and that adipocytes in subcutaneous depots are larger than their visceral counterparts (Pmeta = 9.8 × 10-7). Lastly, we performed the largest GWAS and subsequent meta-analysis of mean adipocyte area and intra-individual adipocyte variation (N = 820). Despite having twice the number of samples than any similar study, we found no genome-wide significant associations, suggesting that larger sample sizes and a homogenous collection of adipose tissue are likely needed to identify robust genetic associations.

The case for open science: rare diseases.

The premise of Open Science is that research and medical management will progress faster if data and knowledge are openly shared. The value of Open Science is nowhere more important and appreciated than in the rare disease (RD) community. Research into RDs has been limited by insufficient patient data and resources, a paucity of trained disease experts, and lack of therapeutics, leading to long delays in diagnosis and treatment. These issues can be ameliorated by following the principles and practices of sharing that are intrinsic to Open Science. Here, we describe how the RD community has adopted the core pillars of Open Science, adding new initiatives to promote care and research for RD patients and, ultimately, for all of medicine. We also present recommendations that can advance Open Science more globally.

Enabling Global Clinical Collaborations on Identifiable Patient Data: The Minerva Initiative.

The clinical utility of computational phenotyping for both genetic and rare diseases is increasingly appreciated; however, its true potential is yet to be fully realized. Alongside the growing clinical and research availability of sequencing technologies, precise deep and scalable phenotyping is required to serve unmet need in genetic and rare diseases. To improve the lives of individuals affected with rare diseases through deep phenotyping, global big data interrogation is necessary to aid our understanding of disease biology, assist diagnosis, and develop targeted treatment strategies. This includes the application of cutting-edge machine learning methods to image data. As with most digital tools employed in health care, there are ethical and data governance challenges associated with using identifiable personal image data. There are also risks with failing to deliver on the patient benefits of these new technologies, the biggest of which is posed by data siloing. The Minerva Initiative has been designed to enable the public good of deep phenotyping while mitigating these ethical risks. Its open structure, enabling collaboration and data sharing between individuals, clinicians, researchers and private enterprise, is key for delivering precision public health.

Next-generation phenotyping using computer vision algorithms in rare genomic neurodevelopmental disorders.

PURPOSE: The interpretation of genetic variants after genome-wide analysis is complex in heterogeneous disorders such as intellectual disability (ID). We investigate whether algorithms can be used to detect if a facial gestalt is present for three novel ID syndromes and if these techniques can help interpret variants of uncertain significance. METHODS: Facial features were extracted from photos of ID patients harboring a pathogenic variant in three novel ID genes (PACS1, PPM1D, and PHIP) using algorithms that model human facial dysmorphism, and facial recognition. The resulting features were combined into a hybrid model to compare the three cohorts against a background ID population. RESULTS: We validated our model using images from 71 individuals with Koolen-de Vries syndrome, and then show that facial gestalts are present for individuals with a pathogenic variant in PACS1 (p = 8 × 10-4), PPM1D (p = 4.65 × 10-2), and PHIP (p = 6.3 × 10-3). Moreover, two individuals with a de novo missense variant of uncertain significance in PHIP have significant similarity to the expected facial phenotype of PHIP patients (p < 1.52 × 10-2). CONCLUSION: Our results show that analysis of facial photos can be used to detect previously unknown facial gestalts for novel ID syndromes, which will facilitate both clinical and molecular diagnosis of rare and novel syndromes.

Deep clinical and biological phenotyping of the preterm birth and small for gestational age syndromes: The INTERBIO-21 st Newborn Case-Control Study protocol.

Background: INTERBIO-21 st is Phase II of the INTERGROWTH-21 st Project, the population-based, research initiative involving nearly 70,000 mothers and babies worldwide coordinated by Oxford University and performed by a multidisciplinary network of more than 400 healthcare professionals and scientists from 35 institutions in 21 countries worldwide. Phase I, conducted 2008-2015, consisted of nine complementary studies designed to describe optimal human growth and neurodevelopment, based conceptually on the WHO prescriptive approach. The studies generated a set of international standards for monitoring growth and neurodevelopment, which complement the existing WHO Child Growth Standards. Phase II aims to improve the functional classification of the highly heterogenous preterm birth and fetal growth restriction syndromes through a better understanding of how environmental exposures, clinical conditions and nutrition influence patterns of human growth from conception to childhood, as well as specific neurodevelopmental domains and associated behaviors at 2 years of age. Methods: In the INTERBIO-21 st Newborn Case-Control Study, a major component of Phase II, our objective is to investigate the mechanisms potentially responsible for preterm birth and small for gestational age and their interactions, using deep phenotyping of clinical, growth and epidemiological data and associated nutritional, biochemical, omic and histological profiles. Here we describe the study sites, population characteristics, study design, methodology and standardization procedures for the collection of longitudinal clinical data and biological samples (maternal blood, umbilical cord blood, placental tissue, maternal feces and infant buccal swabs) for the study that was conducted between 2012 and 2018 in Brazil, Kenya, Pakistan, South Africa, Thailand and the UK. Discussion: Our study provides a unique resource for the planned analyses given the range of potentially disadvantageous exposures (including poor nutrition, pregnancy complications and infections) in geographically diverse populations worldwide. The study should enhance current medical knowledge and provide new insights into environmental influences on human growth and neurodevelopment.

PURA syndrome: clinical delineation and genotype-phenotype study in 32 individuals with review of published literature.

BACKGROUND: De novo mutations in PURA have recently been described to cause PURA syndrome, a neurodevelopmental disorder characterised by severe intellectual disability (ID), epilepsy, feeding difficulties and neonatal hypotonia. OBJECTIVES: To delineate the clinical spectrum of PURA syndrome and study genotype-phenotype correlations. METHODS: Diagnostic or research-based exome or Sanger sequencing was performed in individuals with ID. We systematically collected clinical and mutation data on newly ascertained PURA syndrome individuals, evaluated data of previously reported individuals and performed a computational analysis of photographs. We classified mutations based on predicted effect using 3D in silico models of crystal structures of Drosophila-derived Pur-alpha homologues. Finally, we explored genotype-phenotype correlations by analysis of both recurrent mutations as well as mutation classes. RESULTS: We report mutations in PURA (purine-rich element binding protein A) in 32 individuals, the largest cohort described so far. Evaluation of clinical data, including 22 previously published cases, revealed that all have moderate to severe ID and neonatal-onset symptoms, including hypotonia (96%), respiratory problems (57%), feeding difficulties (77%), exaggerated startle response (44%), hypersomnolence (66%) and hypothermia (35%). Epilepsy (54%) and gastrointestinal (69%), ophthalmological (51%) and endocrine problems (42%) were observed frequently. Computational analysis of facial photographs showed subtle facial dysmorphism. No strong genotype-phenotype correlation was identified by subgrouping mutations into functional classes. CONCLUSION: We delineate the clinical spectrum of PURA syndrome with the identification of 32 additional individuals. The identification of one individual through targeted Sanger sequencing points towards the clinical recognisability of the syndrome. Genotype-phenotype analysis showed no significant correlation between mutation classes and disease severity.

Clinical and molecular consequences of disease-associated de novo mutations in SATB2.

PURPOSE: To characterize features associated with de novo mutations affecting SATB2 function in individuals ascertained on the basis of intellectual disability. METHODS: Twenty previously unreported individuals with 19 different SATB2 mutations (11 loss-of-function and 8 missense variants) were studied. Fibroblasts were used to measure mutant protein production. Subcellular localization and mobility of wild-type and mutant SATB2 were assessed using fluorescently tagged protein. RESULTS: Recurrent clinical features included neurodevelopmental impairment (19/19), absent/near absent speech (16/19), normal somatic growth (17/19), cleft palate (9/19), drooling (12/19), and dental anomalies (8/19). Six of eight missense variants clustered in the first CUT domain. Sibling recurrence due to gonadal mosaicism was seen in one family. A nonsense mutation in the last exon resulted in production of a truncated protein retaining all three DNA-binding domains. SATB2 nuclear mobility was mutation-dependent; p.Arg389Cys in CUT1 increased mobility and both p.Gly515Ser in CUT2 and p.Gln566Lys between CUT2 and HOX reduced mobility. The clinical features in individuals with missense variants were indistinguishable from those with loss of function. CONCLUSION: SATB2 haploinsufficiency is a common cause of syndromic intellectual disability. When mutant SATB2 protein is produced, the protein appears functionally inactive with a disrupted pattern of chromatin or matrix association.Genet Med advance online publication 02 February 2017.

The RNA-editing enzyme ADAR1 controls innate immune responses to RNA.

The ADAR RNA-editing enzymes deaminate adenosine bases to inosines in cellular RNAs. Aberrant interferon expression occurs in patients in whom ADAR1 mutations cause Aicardi-Goutières syndrome (AGS) or dystonia arising from striatal neurodegeneration. Adar1 mutant mouse embryos show aberrant interferon induction and die by embryonic day E12.5. We demonstrate that Adar1 embryonic lethality is rescued to live birth in Adar1; Mavs double mutants in which the antiviral interferon induction response to cytoplasmic double-stranded RNA (dsRNA) is prevented. Aberrant immune responses in Adar1 mutant mouse embryo fibroblasts are dramatically reduced by restoring the expression of editing-active cytoplasmic ADARs. We propose that inosine in cellular RNA inhibits antiviral inflammatory and interferon responses by altering RLR interactions. Transfecting dsRNA oligonucleotides containing inosine-uracil base pairs into Adar1 mutant mouse embryo fibroblasts reduces the aberrant innate immune response. ADAR1 mutations causing AGS affect the activity of the interferon-inducible cytoplasmic isoform more severely than the nuclear isoform.