SIX2 and SIX3 coordinately regulate functional maturity and fate of human pancreatic ß cells.

The physiological functions of many vital tissues and organs continue to mature after birth, but the genetic mechanisms governing this postnatal maturation remain an unsolved mystery. Human pancreatic ß cells produce and secrete insulin in response to physiological cues like glucose, and these hallmark functions improve in the years after birth. This coincides with expression of the transcription factors SIX2 and SIX3, whose functions in native human ß cells remain unknown. Here, we show that shRNA-mediated SIX2 or SIX3 suppression in human pancreatic adult islets impairs insulin secretion. However, transcriptome studies revealed that SIX2 and SIX3 regulate distinct targets. Loss of SIX2 markedly impaired expression of genes governing ß-cell insulin processing and output, glucose sensing, and electrophysiology, while SIX3 loss led to inappropriate expression of genes normally expressed in fetal ß cells, adult α cells, and other non-ß cells. Chromatin accessibility studies identified genes directly regulated by SIX2. Moreover, ß cells from diabetic humans with impaired insulin secretion also had reduced SIX2 transcript levels. Revealing how SIX2 and SIX3 govern functional maturation and maintain developmental fate in native human ß cells should advance ß-cell replacement and other therapeutic strategies for diabetes.

Intravenous immunoglobulin resistance in Kawasaki disease patients: prediction using clinical data.

BACKGROUND: About 10-20% of Kawasaki disease (KD) patients are resistant to the initial infusion of intravenous immunoglobin (IVIG). The aim of this study was to assess whether IVIG resistance in KD patients could be predicted using standard clinical and laboratory features. METHODS: Data were from two cohorts: a Korean cohort of 7101 KD patients from 2015 to 2017 and a cohort of 649 KD patients from San Diego enrolled from 1998 to 2021. Features included laboratory values, the worst Z-score from the initial echocardiogram or during hospitalization, and the five clinical KD signs at presentation. RESULTS: Five machine learning models achieved a maximum median AUC of 0.711 [IQR: 0.706-0.72] in the Korean cohort and 0.696 [IQR: 0.609-0.722] in the San Diego cohort during stratified 10-fold cross-validation using significant laboratory features identified from univariate analysis. Adding the Z-score, KD clinical signs, or both did not considerably improve the median AUC in either cohort. CONCLUSIONS: Using commonly measured clinical laboratory data alone or in conjunction with echocardiographic findings and clinical features is not sufficient to predict IVIG resistance. Further attempts to predict IVIG resistance will need to incorporate additional data such as transcriptomics, proteomics, and genetics to achieve meaningful predictive utility. IMPACT: We demonstrated that laboratory, echocardiographic, and clinical findings cannot predict intravenous immunoglobin (IVIG) resistance to a clinically meaningful extent using machine learning in a homogenous Asian or ethnically diverse population of patients with Kawasaki disease (KD). Visualizing these features using uniform manifold approximation and projection (UMAP) is an important step to evaluate predictive utility in a qualitative manner. Further attempts to predict IVIG resistance in KD patients will need to incorporate novel biomarkers or other specialized features such as genetic differences or transcriptomics to be clinically useful.

Molecular and genetic regulation of pig pancreatic islet cell development.

Reliance on rodents for understanding pancreatic genetics, development and islet function could limit progress in developing interventions for human diseases such as diabetes mellitus. Similarities of pancreas morphology and function suggest that porcine and human pancreas developmental biology may have useful homologies. However, little is known about pig pancreas development. To fill this knowledge gap, we investigated fetal and neonatal pig pancreas at multiple, crucial developmental stages using modern experimental approaches. Purification of islet ß-, α- and δ-cells followed by transcriptome analysis (RNA-seq) and immunohistology identified cell- and stage-specific regulation, and revealed that pig and human islet cells share characteristic features that are not observed in mice. Morphometric analysis also revealed endocrine cell allocation and architectural similarities between pig and human islets. Our analysis unveiled scores of signaling pathways linked to native islet ß-cell functional maturation, including evidence of fetal α-cell GLP-1 production and signaling to ß-cells. Thus, the findings and resources detailed here show how pig pancreatic islet studies complement other systems for understanding the developmental programs that generate functional islet cells, and that are relevant to human pancreatic diseases.

A Deep Learning Framework for Image-Based Screening of Kawasaki Disease.

Kawasaki disease (KD) is a leading cause of acquired heart disease in children and is characterized by the presence of a combination of five clinical signs assessed during the physical examination. Timely treatment of intravenous immunoglobin is needed to prevent coronary artery aneurysm formation, but KD is usually diagnosed when pediatric patients are evaluated by a clinician in the emergency department days after onset. One or more of the five clinical signs usually manifests in pediatric patients prior to ED admission, presenting an opportunity for earlier intervention if families receive guidance to seek medical care as soon as clinical signs are observed along with a fever for at least five days. We present a deep learning framework for a novel screening tool to calculate the relative risk of KD by analyzing images of the five clinical signs. The framework consists of convolutional neural networks to separately calculate the risk for each clinical sign, and a new algorithm to determine what clinical sign is in an image. We achieved a mean accuracy of 90% during 10-fold cross-validation and 88% during external validation for the new algorithm. These results demonstrate the algorithms in the proposed screening tool can be utilized by families to determine if their child should be evaluated by a clinician based on the number of clinical signs consistent with KD.Clinical Relevance- This screening framework has the potential for earlier clinical evaluation and detection of KD to reduce the risk of coronary artery complications.

Implementation of KIDMATCH: A Clinical Decision Support Tool for Diagnosing Pediatric Patients with Multisystem Inflammatory Syndrome and Kawasaki Disease.

Multisystem inflammatory syndrome in children (MIS-C) is a novel disease identified during the COVID-19 pandemic that may lead to cardiac dysfunction or death in pediatric patients. Early detection of MIS-C remains a challenge given the lack of a diagnostic test and its clinical similarities to Kawasaki disease (KD) and other acute childhood illnesses. We developed and validated the KawasakI Disease vs Multisystem InflAmmaTory syndrome in CHildren (KIDMATCH) clinical decision support tool for screening patients for MIS-C, KD, or other febrile illnesses. Here we describe the implementation and iterative refinement of KIDMATCH with provider feedback as a web calculator in the clinical workflow within Rady Children's Hospital. Our findings demonstrate KIDMATCH and its underlying artificial intelligence model have clinical utility in aiding clinicians at the time of initial evaluation within the hospital setting to distinguish patients who have MIS-C, KD, or other febrile illnesses.

Cryopreservation and post-thaw characterization of dissociated human islet cells.

The objective of this study is to optimize the cryopreservation of dissociated islet cells and obtain functional cells that can be used in single-cell transcriptome studies on the pathology and treatment of diabetes. Using an iterative graded freezing approach we obtained viable cells after cooling in 10% dimethyl sulfoxide and 6% hydroxyethyl starch at 1°C/min to -40°C, storage in liquid nitrogen, rapid thaw, and removal of cryoprotectants by serial dilution. The expression of epithelial cell adhesion molecule declined immediately after thaw, but recovered after overnight incubation, while that of an endocrine cell marker (HPi2) remained high after cryopreservation. Patch-clamp electrophysiology revealed differences in channel activities and exocytosis of various islet cell types; however, exocytotic responses, and the biophysical properties of voltage-gated Na+ and Ca2+ channels, are sustained after cryopreservation. Single-cell RNA sequencing indicates that overall transcriptome and crucial exocytosis genes are comparable between fresh and cryopreserved dispersed human islet cells. Thus, we report an optimized procedure for cryopreserving dispersed islet cells that maintained their membrane integrity, along with their molecular and functional phenotypes. Our findings will not only provide a ready source of cells for investigating cellular mechanisms in diabetes but also for bio-engineering pseudo-islets and islet sheets for modeling studies and potential transplant applications.

Multicenter Validation of a Machine Learning Algorithm for Diagnosing Pediatric Patients with Multisystem Inflammatory Syndrome and Kawasaki Disease.

BACKGROUND: Multisystem inflammatory syndrome in children (MIS-C) is a novel disease identified during the COVID-19 pandemic characterized by systemic inflammation following SARS-CoV-2 infection. Delays in diagnosing MIS-C may lead to more severe disease with cardiac dysfunction or death. Most pediatric patients recover fully with anti-inflammatory treatments, but early detection of MIS-C remains a challenge given its clinical similarities to Kawasaki disease (KD) and other acute childhood illnesses. METHODS: We developed KIDMATCH ( K awasak I D isease vs M ultisystem Infl A mma T ory syndrome in CH ildren), a deep learning algorithm for screening patients for MIS-C, KD, or other febrile illness, using age, the five classical clinical KD signs, and 17 laboratory measurements prospectively collected within 24 hours of admission to the emergency department from 1448 patients diagnosed with KD or other febrile illness between January 1, 2009 and December 31, 2019 at Rady Children's Hospital. For MIS-C patients, the same data was collected from 131 patients between May 14, 2020 to June 18, 2021 at Rady Children's Hospital, Connecticut Children's Hospital, and Children's Hospital Los Angeles. We trained a two-stage model consisting of feedforward neural networks to distinguish between MIS-C and non MIS-C patients and then KD and other febrile illness. After internally validating the algorithm using 10-fold cross validation, we incorporated a conformal prediction framework to tag patients with erroneous data or distribution shifts, enhancing the model generalizability and confidence by flagging unfamiliar cases as indeterminate instead of making spurious predictions. We externally validated KIDMATCH on 175 MIS-C patients from 16 hospitals across the United States. FINDINGS: KIDMATCH achieved a high median area under the curve in the 10-fold cross validation of 0.988 [IQR: 0.98-0.993] in the first stage and 0.96 [IQR: 0.956-0.972] in the second stage using thresholds set at 95% sensitivity to detect positive MIS-C and KD cases respectively during training. External validation of KIDMATCH on MIS-C patients correctly classified 76/83 (2 rejected) patients from the CHARMS consortium, 47/50 (1 rejected) patients from Boston Children's Hospital, and 36/42 (2 rejected) patients from Children's National Hospital. INTERPRETATION: KIDMATCH has the potential to aid frontline clinicians with distinguishing between MIS-C, KD, and similar febrile illnesses in a timely manner to allow prompt treatment and prevent severe complications. FUNDING: Eunice Kennedy Shriver National Institute of Child Health and Human Development, National Heart, Lung, and Blood Institute, Patient-Centered Outcomes Research Institute, National Library of Medicine.

A machine-learning algorithm for diagnosis of multisystem inflammatory syndrome in children and Kawasaki disease in the USA: a retrospective model development and validation study.

BACKGROUND: Multisystem inflammatory syndrome in children (MIS-C) is a novel disease that was identified during the COVID-19 pandemic and is characterised by systemic inflammation following SARS-CoV-2 infection. Early detection of MIS-C is a challenge given its clinical similarities to Kawasaki disease and other acute febrile childhood illnesses. We aimed to develop and validate an artificial intelligence algorithm that can distinguish among MIS-C, Kawasaki disease, and other similar febrile illnesses and aid in the diagnosis of patients in the emergency department and acute care setting. METHODS: In this retrospective model development and validation study, we developed a deep-learning algorithm called KIDMATCH (Kawasaki Disease vs Multisystem Inflammatory Syndrome in Children) using patient age, the five classic clinical Kawasaki disease signs, and 17 laboratory measurements. All features were prospectively collected at the time of initial evaluation from patients diagnosed with Kawasaki disease or other febrile illness between Jan 1, 2009, and Dec 31, 2019, at Rady Children's Hospital in San Diego (CA, USA). For patients with MIS-C, the same data were collected from patients between May 7, 2020, and July 20, 2021, at Rady Children's Hospital, Connecticut Children's Medical Center in Hartford (CT, USA), and Children's Hospital Los Angeles (CA, USA). We trained a two-stage model consisting of feedforward neural networks to distinguish between patients with MIS-C and those without and then those with Kawasaki disease and other febrile illnesses. After internally validating the algorithm using stratified tenfold cross-validation, we incorporated a conformal prediction framework to tag patients with erroneous data or distribution shifts. We finally externally validated KIDMATCH on patients with MIS-C enrolled between April 22, 2020, and July 21, 2021, from Boston Children's Hospital (MA, USA), Children's National Hospital (Washington, DC, USA), and the CHARMS Study Group consortium of 14 US hospitals. FINDINGS: 1517 patients diagnosed at Rady Children's Hospital between Jan 1, 2009, and June 7, 2021, with MIS-C (n=69), Kawasaki disease (n=775), or other febrile illnesses (n=673) were identified for internal validation, with an additional 16 patients with MIS-C included from Connecticut Children's Medical Center and 50 from Children's Hospital Los Angeles between May 7, 2020, and July 20, 2021. KIDMATCH achieved a median area under the receiver operating characteristic curve during internal validation of 98·8% (IQR 98·0-99·3) in the first stage and 96·0% (95·6-97·2) in the second stage. We externally validated KIDMATCH on 175 patients with MIS-C from Boston Children's Hospital (n=50), Children's National Hospital (n=42), and the CHARMS Study Group consortium of 14 US hospitals (n=83). External validation of KIDMATCH on patients with MIS-C correctly classified 76 of 81 patients (94% accuracy, two rejected by conformal prediction) from 14 hospitals in the CHARMS Study Group consortium, 47 of 49 patients (96% accuracy, one rejected by conformal prediction) from Boston Children's Hospital, and 36 of 40 patients (90% accuracy, two rejected by conformal prediction) from Children's National Hospital. INTERPRETATION: KIDMATCH has the potential to aid front-line clinicians to distinguish between MIS-C, Kawasaki disease, and other similar febrile illnesses to allow prompt treatment and prevent severe complications. FUNDING: US Eunice Kennedy Shriver National Institute of Child Health and Human Development, US National Heart, Lung, and Blood Institute, US Patient-Centered Outcomes Research Institute, US National Library of Medicine, the McCance Foundation, and the Gordon and Marilyn Macklin Foundation.

CRISPR-based genome editing in primary human pancreatic islet cells.

Gene targeting studies in primary human islets could advance our understanding of mechanisms driving diabetes pathogenesis. Here, we demonstrate successful genome editing in primary human islets using clustered regularly interspaced short palindromic repeats (CRISPR) and CRISPR-associated protein 9 (Cas9). CRISPR-based targeting efficiently mutated protein-coding exons, resulting in acute loss of islet ß-cell regulators, like the transcription factor PDX1 and the KATP channel subunit KIR6.2, accompanied by impaired ß-cell regulation and function. CRISPR targeting of non-coding DNA harboring type 2 diabetes (T2D) risk variants revealed changes in ABCC8, SIX2 and SIX3 expression, and impaired ß-cell function, thereby linking regulatory elements in these target genes to T2D genetic susceptibility. Advances here establish a paradigm for genetic studies in human islet cells, and reveal regulatory and genetic mechanisms linking non-coding variants to human diabetes risk.

Discovering human diabetes-risk gene function with genetics and physiological assays.

Developing systems to identify the cell type-specific functions regulated by genes linked to type 2 diabetes (T2D) risk could transform our understanding of the genetic basis of this disease. However, in vivo systems for efficiently discovering T2D risk gene functions relevant to human cells are currently lacking. Here we describe powerful interdisciplinary approaches combining Drosophila genetics and physiology with human islet biology to address this fundamental gap in diabetes research. We identify Drosophila orthologs of T2D-risk genes that regulate insulin output. With human islets, we perform genetic studies and identify cognate human T2D-risk genes that regulate human beta cell function. Loss of BCL11A, a transcriptional regulator, in primary human islet cells leads to enhanced insulin secretion. Gene expression profiling reveals BCL11A-dependent regulation of multiple genes involved in insulin exocytosis. Thus, genetic and physiological systems described here advance the capacity to identify cell-specific T2D risk gene functions.

Loss of glutamate signaling from the thalamus to dorsal striatum impairs motor function and slows the execution of learned behaviors.

Parkinson's disease (PD) is primarily associated with the degeneration of midbrain dopamine neurons, but it is now appreciated that pathological processes like Lewy-body inclusions and cell loss affect several other brain regions, including the central lateral (CL) and centromedian/parafascicular (CM/PF) thalamic regions. These thalamic glutamatergic neurons provide a non-cortical excitatory input to the dorsal striatum, a major projection field of dopamine neurons. To determine how thalamostriatal signaling may contribute to cognitive and motor abnormalities found in PD, we used a viral vector approach to generate mice with loss of thalamostriatal glutamate signaling specifically restricted to the dorsal striatum (CAV2Cre-Slc17a6lox/lox mice). We measured motor function and behaviors corresponding to cognitive domains (visuospatial function, attention, executive function, and working memory) affected in PD. CAV2Cre-Slc17a6lox/lox mice were impaired in motor coordination tasks such as the rotarod and beam-walk tests compared with controls (CAV2Cre-Slc17a6+/+ mice). They did not demonstrate much cognitive impairment in the Morris water maze or a water U-maze, but had slower processing reaction times in those tests and in a two-way active avoidance task. These mice could model an aspect of bradyphrenia, the slowness of thought that is often seen in patients with PD and other neurological disorders.