Single-Cell RNA Sequencing Technology Landscape in 2023.

Single-cell RNA sequencing (scRNA-seq) has revolutionized our understanding of cellular heterogeneity and the dynamics of gene expression, bearing profound significance in stem cell research. Depending on the starting materials used for analysis, scRNA-seq encompasses scRNA-seq and single-nucleus RNA sequencing (snRNA-seq). scRNA-seq excels in capturing cellular heterogeneity and characterizing rare cell populations within complex tissues, while snRNA-seq is advantageous in situations where intact cell dissociation is challenging or undesirable (eg, epigenomic studies). A number of scRNA-seq technologies have been developed as of late, including but not limited to droplet-based, plate-based, hydrogel-based, and spatial transcriptomics. The number of cells, sequencing depth, and sequencing length in scRNA-seq can vary across different studies. Addressing current technical challenges will drive the future of scRNA-seq, leading to more comprehensive and precise insights into cellular biology and disease mechanisms informing therapeutic interventions.

NASP gene contributes to autism by epigenetic dysregulation of neural and immune pathways.

BACKGROUND: Epigenetics makes substantial contribution to the aetiology of autism spectrum disorder (ASD) and may harbour a unique opportunity to prevent the development of ASD. We aimed to identify novel epigenetic genes involved in ASD aetiology. METHODS: Trio-based whole exome sequencing was conducted on ASD families. Genome editing technique was used to knock out the candidate causal gene in a relevant cell line. ATAC-seq, ChIP-seq and RNA-seq were performed to investigate the functional impact of knockout (KO) or mutation in the candidate gene. RESULTS: We identified a novel candidate gene NASP (nuclear autoantigenic sperm protein) for epigenetic dysregulation in ASD in a Chinese nuclear family including one proband with autism and comorbid atopic disease. The de novo likely gene disruptive variant tNASP(Q289X) subjects the expression of tNASP to nonsense-mediated decay. tNASP KO increases chromatin accessibility, promotes the active promoter state of genes enriched in synaptic signalling and leads to upregulated expression of genes in the neural signalling and immune signalling pathways. Compared with wild-type tNASP, tNASP(Q289X) enhances chromatin accessibility of the genes with enriched expression in the brain. RNA-seq revealed that genes involved in neural and immune signalling are affected by the tNASP mutation, consistent with the phenotypic impact and molecular effects of nasp-1 mutations in Caenorhabditis elegans. Two additional patients with ASD were found carrying deletion or deleterious mutation in the NASP gene. CONCLUSION: We identified novel epigenetic mechanisms mediated by tNASP which may contribute to the pathogenesis of ASD and its immune comorbidity.

Target genes regulated by CLEC16A intronic region associated with common variable immunodeficiency.

BACKGROUND: CLEC16A intron 19 has been identified as a candidate locus for common variable immunodeficiency (CVID). OBJECTIVES: This study sought to elucidate the molecular mechanism by which variants at the CLEC16A intronic locus may contribute to the pathogenesis of CVID. METHODS: The investigators performed fine-mapping of the CLEC16A locus in a CVID cohort, then deleted the candidate functional SNP in T-cell lines by the CRISPR-Cas9 technique and conducted RNA-sequencing to identify target gene(s). The interactions between the CLEC16A locus and its target genes were identified using circular chromosome conformation capture. The transcription factor complexes mediating the chromatin interactions were determined by proteomic approach. The molecular pathways regulated by the CLEC16A locus were examined by RNA-sequencing and reverse phase protein array. RESULTS: This study showed that the CLEC16A locus is an enhancer regulating expression of multiple target genes including a distant gene ATF7IP2 through chromatin interactions. Distinct transcription factor complexes mediate the chromatin interactions in an allele-specific manner. Disruption of the CLEC16A locus affects the AKT signaling pathway, as well as the molecular response of CD4+ T cells to immune stimulation. CONCLUSIONS: Through multiomics and targeted experimental approaches, this study elucidated the underlying target genes and signaling pathways involved in the genetic association of CLEC16A with CVID, and highlighted plausible molecular targets for developing novel therapeutics.

Mutation burden analysis of six common mental disorders in African Americans by whole genome sequencing.

Mental disorders present a global health concern and have limited treatment options. In today's medical practice, medications such as antidepressants are prescribed not only for depression but also for conditions such as anxiety and attention deficit hyperactivity disorder (ADHD). Therefore, identifying gene targets for specific disorders is important and offers improved precision. In this study, we performed a genetic analysis of six common mental disorders-ADHD, anxiety, depression, delays in mental development, intellectual disabilities (IDs) and speech/language disorder-in the ethnic minority of African Americans (AAs) using whole genome sequencing (WGS). WGS data were generated from blood-derived DNA from 4178 AA individuals, including 1384 patients with the diagnosis of at least one mental disorder. Mutation burden analysis was applied based on rare and deleterious mutations in the AA population between cases and controls, and further analyzed in the context of patients with single mental disorder diagnosis. Certain genes uncovered demonstrated significant P-values in mutation burden analysis. In addition, exclusive recurrences in specific type of disorder were scanned through gene-drug interaction databases to assess for availability of potential medications. We uncovered 15 genes harboring deleterious mutations, including 3-Hydroxy-3-Methylglutaryl-CoA Reductase (HMGCR) and Uronyl 2-Sulfotransferase (UST) for ADHD; Farnesyltransferase, CAAX Box, Beta (FNTB) for anxiety; Xin Actin Binding Repeat Containing 2 (XIRP2), Natriuretic Peptide C (NPPC), Serine/Threonine Kinase 33 (STK33), Pannexin 1 (PANX1) and Neurotensin (NTS) for depression; RUNX Family Transcription Factor 3 (RUNX3), Tachykinin Receptor 1 (TACR1) and NADH:Ubiquinone Oxidoreductase Core Subunit S7 (NDUFS7) for delays in mental development; Hepsin (HPN) for ID and Collagen Type VI Alpha 3 Chain (COL6A3), Damage Specific DNA Binding Protein 1 (DDB1) and NADH:Ubiquinone Oxidoreductase Subunit A11 (NDUFA11) for speech/language disorder. Taken together, we have established critical insights into the development of new precision medicine approaches for mental disorders in AAs.

Shared molecular mechanisms and transdiagnostic potential of neurodevelopmental disorders and immune disorders.

The co-occurrence and familial clustering of neurodevelopmental disorders and immune disorders suggest shared genetic risk factors. Based on genome-wide association summary statistics from five neurodevelopmental disorders and four immune disorders, we conducted genome-wide, local genetic correlation and polygenic overlap analysis. We further performed a cross-trait GWAS meta-analysis. Pleotropic loci shared between the two categories of diseases were mapped to candidate genes using multiple algorithms and approaches. Significant genetic correlations were observed between neurodevelopmental disorders and immune disorders, including both positive and negative correlations. Neurodevelopmental disorders exhibited higher polygenicity compared to immune disorders. Around 50%-90% of genetic variants of the immune disorders were shared with neurodevelopmental disorders. The cross-trait meta-analysis revealed 154 genome-wide significant loci, including 8 novel pleiotropic loci. Significant associations were observed for 30 loci with both types of diseases. Pathway analysis on the candidate genes at these loci revealed common pathways shared by the two types of diseases, including neural signaling, inflammatory response, and PI3K-Akt signaling pathway. In addition, 26 of the 30 lead SNPs were associated with blood cell traits. Neurodevelopmental disorders exhibit complex polygenic architecture, with a subset of individuals being at a heightened genetic risk for both neurodevelopmental and immune disorders. The identification of pleiotropic loci has important implications for exploring opportunities for drug repurposing, enabling more accurate patient stratification, and advancing genomics-informed precision in the medical field of neurodevelopmental disorders.

Genomic variants exclusively identified in children with birth defects and concurrent malignant tumors predispose to cancer development.

Children with birth defects (BD) express distinct clinical features that often have various medical consequences, one of which is predisposition to the development of cancers. Identification of the underlying genetic mechanisms related to the development of cancer in BD patients would allow for preventive measures. We performed a whole genome sequencing (WGS) study on blood-derived DNA samples from 1566 individuals without chromosomal anomalies, including 454 BD probands with at least one type of malignant tumors, 767 cancer-free BD probands, and 345 healthy individuals. Exclusive recurrent variants were identified in BD-cancer and BD-only patients and mapped to their corresponding genomic regions. We observed statistically significant overlaps for protein-coding/ncRNA with exclusive variants in exons, introns, ncRNAs, and 3'UTR regions. Exclusive exonic variants, especially synonymous variants, tend to occur in prior exons locus in BD-cancer children. Intronic variants close to splicing site (< 500 bp from exon) have little overlaps in BD-cancer and BD-only patients. Exonic variants in non-coding RNA (ncRNA) tend to occur in different ncRNAs exons regardless of the overlaps. Notably, genes with 5' UTR variants are almost mutually exclusive between the two phenotypes. In conclusion, we conducted the first genomic study to explore the impact of recurrent variants exclusive to the two distinguished clinical phenotypes under study, BD with or without cancer, demonstrating enrichment of selective protein-coding/ncRNAs differentially expressed between these two phenotypes, suggesting that selective genetic factors may underlie the molecular processes of pediatric cancer development in BD children.

Application of deep learning algorithm on whole genome sequencing data uncovers structural variants associated with multiple mental disorders in African American patients.

Mental disorders present a global health concern, while the diagnosis of mental disorders can be challenging. The diagnosis is even harder for patients who have more than one type of mental disorder, especially for young toddlers who are not able to complete questionnaires or standardized rating scales for diagnosis. In the past decade, multiple genomic association signals have been reported for mental disorders, some of which present attractive drug targets. Concurrently, machine learning algorithms, especially deep learning algorithms, have been successful in the diagnosis and/or labeling of complex diseases, such as attention deficit hyperactivity disorder (ADHD) or cancer. In this study, we focused on eight common mental disorders, including ADHD, depression, anxiety, autism, intellectual disabilities, speech/language disorder, delays in developments, and oppositional defiant disorder in the ethnic minority of African Americans. Blood-derived whole genome sequencing data from 4179 individuals were generated, including 1384 patients with the diagnosis of at least one mental disorder. The burden of genomic variants in coding/non-coding regions was applied as feature vectors in the deep learning algorithm. Our model showed ~65% accuracy in differentiating patients from controls. Ability to label patients with multiple disorders was similarly successful, with a hamming loss score less than 0.3, while exact diagnostic matches are around 10%. Genes in genomic regions with the highest weights showed enrichment of biological pathways involved in immune responses, antigen/nucleic acid binding, chemokine signaling pathway, and G-protein receptor activities. A noticeable fact is that variants in non-coding regions (e.g., ncRNA, intronic, and intergenic) performed equally well as variants in coding regions; however, unlike coding region variants, variants in non-coding regions do not express genomic hotspots whereas they carry much more narrow standard deviations, indicating they probably serve as alternative markers.

TOPORS as a novel causal gene for Joubert syndrome.

Joubert syndrome (JBTS) is a Mendelian disorder of the primary cilium defined by the clinical triad of hypotonia, developmental delay, and a distinct cerebellar malformation called the molar tooth sign. JBTS is inherited in an autosomal recessive, autosomal dominant, or X-linked recessive manner. Though over 40 genes have been identified as causal for JBTS, molecular diagnosis is not made in 30%-40% of individuals who meet clinical criteria. TOPORS encodes topoisomerase I-binding arginine/serine-rich protein, and homozygosity for a TOPORS missense variant (c.29C > A; p.(Pro10Gln)) was identified in individuals with the ciliopathy oral-facial-digital syndrome in two families of Dominican descent. Here, we report an additional proband of Dominican ancestry with JBTS found by exome sequencing to be homozygous for the identical p.(Pro10Gln) TOPORS missense variant. Query of the Mount Sinai BioMe biobank, which includes 1880 individuals of Dominican ancestry, supports a high carrier frequency of the TOPORS p.(Pro10Gln) variant in individuals of Dominican descent. Our data nominates TOPORS as a novel causal gene for JBTS and suggests that TOPORS variants should be considered in the differential of ciliopathy-spectrum disease in individuals of Dominican ancestry.

Trans-ethnic genomic informed risk assessment for Alzheimer's disease: An International Hundred K+ Cohorts Consortium study.

BACKGROUND: As a collaboration model between the International HundredK+ Cohorts Consortium (IHCC) and the Davos Alzheimer's Collaborative (DAC), our aim was to develop a trans-ethnic genomic informed risk assessment (GIRA) algorithm for Alzheimer's disease (AD). METHODS: The GIRA model was created to include polygenic risk score calculated from the AD genome-wide association study loci, the apolipoprotein E haplotypes, and non-genetic covariates including age, sex, and the first three principal components of population substructure. RESULTS: We validated the performance of the GIRA model in different populations. The proteomic study in the participant sites identified proteins related to female infertility and autoimmune thyroiditis and associated with the risk scores of AD. CONCLUSIONS: As the initial effort by the IHCC to leverage existing large-scale datasets in a collaborative setting with DAC, we developed a trans-ethnic GIRA for AD with the potential of identifying individuals at high risk of developing AD for future clinical applications.

Genetic analysis for type 1 diabetes genes in juvenile dermatomyositis unveils genetic disease overlap.

OBJECTIVES: JDM is a serious autoimmune and complex genetic disease. Another autoimmune genetic disease, type 1 diabetes (T1D), has been observed for significantly increased prevalence in families with JDM, while increased JDM risk has also been observed in T1D cases. This study aimed to study whether these two autoimmune diseases, JDM and T1D, share common genetic susceptibility. METHODS: From 169 JDM families, 121 unrelated cases with European ancestry (EA) were identified by genome-wide genotyping, principal component analysis and identical-by-descent (IBD) analysis. T1D genetic risk score (GRS) were calculated in these cases and were compared with 361 EA T1D cases and 1943 non-diabetes EA controls. A total of 113 cases of the 121 unrelated European cases were sequenced by whole exome sequencing. RESULTS: We observed increased T1D GRS in JDM cases (P = 9.42E-05). Using whole exome sequencing, we uncovered the T1D genes, phospholipase B1, cystic fibrosis transmembrane conductance regulator, tyrosine hydroxylase, CD6 molecule, perforin 1 and dynein axonemal heavy chain 2, potentially associated with JDM by the burden test of rare functional coding variants. CONCLUSION: Novel mechanisms of JDM related to these T1D genes are suggested by this study, which may imply novel therapeutic targets for JDM and warrant further study.

Metabolomic profiling for dyslipidemia in pediatric patients with sickle cell disease, on behalf of the IHCC consortium.

BACKGROUND: Previous study has shown that dyslipidemia is common in patients with Sickle cell disease (SCD) and is associated with more serious SCD complications. METHODS: This study investigated systematically dyslipidemia in SCD using a state-of-art nuclear magnetic resonance (NMR) metabolomics platform, including 147 pediatric cases with SCD and 1234 controls without SCD. We examined 249 metabolomic biomarkers, including 98 biomarkers for lipoprotein subclasses, 70 biomarkers for relative lipoprotein lipid concentrations, plus biomarkers for fatty acids and phospholipids. RESULTS: Specific patterns of hypolipoproteinemia and hypocholesterolemia in pediatric SCD were observed in lipoprotein subclasses other than larger VLDL subclasses. Triglycerides are not significantly changed in SCD, except increased relative concentrations in lipoprotein subclasses. Decreased plasma FFAs (including total-FA, SFA, PUFA, Omega-6, and linoleic acid) and decreased plasma phospholipids were observed in SCD. CONCLUSION: This study scrutinized, for the first time, lipoprotein subclasses in pediatric patients with SCD, and identified SCD-specific dyslipidemia from altered lipoprotein metabolism. The findings of this study depict a broad panorama of lipid metabolism and nutrition in SCD, suggesting the potential of specific dietary supplementation of the deficient nutrients for the management of SCD.

Burden of rare coding variants reveals genetic heterogeneity between obese and non-obese asthma patients in the African American population.

BACKGROUND: Asthma is a complex condition largely attributed to the interactions among genes and environments as a heterogeneous phenotype. Obesity is significantly associated with asthma development, and genetic studies on obese vs. non-obese asthma are warranted. METHODS: To investigate asthma in the minority African American (AA) population with or without obesity, we performed a whole genome sequencing (WGS) study on blood-derived DNA of 4289 AA individuals, included 2226 asthma patients (1364 with obesity and 862 without obesity) and 2006 controls without asthma. The burden analysis of functional rare coding variants was performed by comparing asthma vs. controls and by stratified analysis of obese vs. non-obese asthma, respectively. RESULTS: Among the top 66 genes with P < 0.01 in the asthma vs. control analysis, stratified analysis by obesity showed inverse correlation of natural logarithm (LN) of P value between obese and non-obese asthma (r = - 0.757, P = 1.90E-13). Five genes previously reported in a genome-wide association study (GWAS) on asthma, including TSLP, SLC9A4, PSMB8, IGSF5, and IKZF4 were demonstrated association in the asthma vs. control analysis. The associations of IKZF4 and IGSF5 are only associated with obese asthma; and the association of SLC9A4 is only observed in non-obese asthma. In addition, the association of RSPH3 (the gene is related to primary ciliary dyskinesia) is observed in non-obese asthma. CONCLUSIONS: These findings highlight genetic heterogeneity between obese and non-obese asthma in patients of AA ancestry.

Improved genetic risk scoring algorithm for type 1 diabetes prediction.

BACKGROUND: Precise risk prediction of type 1 diabetes (T1D) facilitates early intervention and identification of risk factors prior to irreversible beta-islet cell destruction, and can significantly improve T1D prevention and clinical care. Sharp et al. developed a genetic risk scoring (GRS) system for T1D (T1D-GRS2) capable of predicting T1D risk in children of European ancestry. The T1D-GRS2 was developed on the basis of causal genetic variants, thus may be applicable to minor populations, while a trans-ethnic GRS for T1D may avoid the exacerbation of health disparities due to the lack of genomic information in minorities. METHODS: Here, we describe a T1D-GRS2 calculator validated in two independent cohorts, including African American children and European American children. Participants were recruited by the Center for Applied Genomics at the Children's Hospital of Philadelphia. RESULTS: It demonstrates that GRS2 is applicable to the T1D risk prediction in the AA cohort, while population-specific thresholds are needed for different populations. CONCLUSIONS: The study highlights the potential to further improve T1D-GRS2 performance with the inclusion of additional genetic markers.

Association of novel rare coding variants with juvenile idiopathic arthritis.

OBJECTIVE: Juvenile idiopathic arthritis (JIA) is the most common type of arthritis among children, but a few studies have investigated the contribution of rare variants to JIA. In this study, we aimed to identify rare coding variants associated with JIA for the genome-wide landscape. METHODS: We established a rare variant calling and filtering pipeline and performed rare coding variant and gene-based association analyses on three RNA-seq datasets composed of 228 JIA patients in the Gene Expression Omnibus against different sets of controls, and further conducted replication in our whole-exome sequencing (WES) data of 56 JIA patients. Then we conducted differential gene expression analysis and assessed the impact of recurrent functional coding variants on gene expression and signalling pathway. RESULTS: By the RNA-seq data, we identified variants in two genes reported in literature as JIA causal variants, as well as additional 63 recurrent rare coding variants seen only in JIA patients. Among the 44 recurrent rare variants found in polyarticular patients, 10 were replicated by our WES of patients with the same JIA subtype. Several genes with recurrent functional rare coding variants have also common variants associated with autoimmune diseases. We observed immune pathways enriched for the genes with rare coding variants and differentially expressed genes. CONCLUSION: This study elucidated a novel landscape of recurrent rare coding variants in JIA patients and uncovered significant associations with JIA at the gene pathway level. The convergence of common variants and rare variants for autoimmune diseases is also highlighted in this study.

Machine Learning Reduced Gene/Non-Coding RNA Features That Classify Schizophrenia Patients Accurately and Highlight Insightful Gene Clusters.

RNA-seq has been a powerful method to detect the differentially expressed genes/long non-coding RNAs (lncRNAs) in schizophrenia (SCZ) patients; however, due to overfitting problems differentially expressed targets (DETs) cannot be used properly as biomarkers. This study used machine learning to reduce gene/non-coding RNA features. Dorsolateral prefrontal cortex (dlpfc) RNA-seq data from 254 individuals was obtained from the CommonMind consortium. The average predictive accuracy for SCZ patients was 67% based on coding genes, and 96% based on long non-coding RNAs (lncRNAs). Machine learning is a powerful algorithm to reduce functional biomarkers in SCZ patients. The lncRNAs capture the characteristics of SCZ tissue more accurately than mRNA as the former regulate every level of gene expression, not limited to mRNA levels.

FLNC and MYLK2 Gene Mutations in a Chinese Family with Different Phenotypes of Cardiomyopathy.

Mutations in the sarcomeric protein filamin C (FLNC) gene have been linked to hypertrophic cardiomyopathy (HCM), as they have been determined to increase the risk of ventricular arrhythmia and sudden death. Thus, in this study, we identified a novel missense mutation of FLNC in a Chinese family with HCM, and, interestingly, a second novel truncating mutation of MYLK2 was discobered in one family member with different phenotype.We performed whole-exome sequencing in a Chinese family with HCM of unknown cause. To determine and confirm the function of a novel mutation of FLNC, we introduced the mutant and wild-type gene into AC16 cells (human cardiomyocytes): we then used western blotting to analyze the expression of FLNC in subcellular fractions, and confocal microscope to observe the subcellular distribution of the protein.As per our findings, we were able to identify a novel missense single nucleotide variant (FLNC c.G5935A [p.A1979T]) in the family, which segregates with the disease. FLNC expression levels were observed to be equivalent in both wild-type and p.A1979T cardiomyocytes. However, the expression of the mutant protein has resulted in cytoplasmic protein aggregations, in contrast to wild-type FLNC, which was distributed in the cytoplasm and did not form aggregates. Unexpectedly, a second truncating mutation, NM_033118:exon8:c.G1138T:p.E380X of the MYLK2 gene, was identified in the mother of the proband with dilated cardiomyopathy, which was not found in other subjects.We then identified the FLNC A1979T mutation as a novel pathogenic variant associated with HCM in a Chinese family as well as a second causal mutation in a family member with a distinct phenotype. The possibility that there is more than one causal mutation in cardiomyopathy warrants clinical attention, especially for patients with atypical clinical features.

Liraglutide Ameliorates Lipotoxicity-Induced Oxidative Stress by Activating the NRF2 Pathway in HepG2 Cells.

Although glucagon-like peptide-1 (GLP-1) analogue has been reported to suppress oxidative stress in non-alcoholic fatty liver disease (NAFLD), an effective therapeutic agent for NAFLD is currently unavailable. Therefore, in this study, we aimed to investigate the protective effects of the GLP-1 analogue liraglutide against lipotoxicity-induced oxidative stress in HepG2 cells and to elucidate the underlying mechanisms. HepG2 cells were cultured for 48 hours and treated with a free fatty acid (FFA) mixture: FFA mixture and liraglutide or FFA mixture, liraglutide, and exendin (9-39). Lipid accumulation was examined by oil red O staining. Oxidative stress was assessed by measuring the levels of intracellular reactive oxygen species using 2',7'-dichlorofluorescein diacetate and thiobarbituric acid-reactive substances, whereas antioxidant capacity was assessed by measuring the activity of superoxide dismutase and catalase. Expression of the nuclear factor erythroid-2-related factor 2 (NRF2) gene and the genes encoding antioxidant enzymes was analyzed using quantitative RT-PCR. Cellular and nuclear NRF2 expression levels were assessed using immunofluorescence cell staining and western blotting. Liraglutide treatment reduced high fat-induced lipid formation and the levels of oxidative stress markers and increased antioxidant enzyme activity in HepG2 cells. Liraglutide treatment increased the mRNA expression of NRF2 target genes, induced NRF2 nuclear translocation, and increased nuclear NRF2 levels without altering NRF2 mRNA expression. Collectively, these results indicate that liraglutide exhibits a protective effect against lipotoxicity-induced oxidative stress, possibly via modulation of NRF2 and expression of antioxidant enzymes in liver cells.

High prevalence of elevated serum liver enzymes in Chinese children suggests metabolic syndrome as a common risk factor.

AIM: This study investigated the pattern of liver enzymes in a large cohort of Chinese children and adolescents, including 16 383 individuals aged 4-18 years old recruited at six medical centres in China. METHODS: Clinical data were collected including weight, height, blood pressure, alanine aminotransferase, aspartate aminotransferase and fasting lipid panel. We used an unsupervised machine learning algorithm, the K-means clustering method, to identify different patterns of increased liver enzymes. RESULTS: Six clusters of elevated enzymes patterns were identified. The most common in 2.18% (325) of youth was elevated transaminases in the absence of features of metabolic syndrome(MetS), and they were thinner, and more likely to be from urban areas. The second cluster, with 1.47% (n = 220) youth had the most notable MetS features. They were older, obese and had central obesity, higher BP, triglycerides cholesterol and lower high-density lipoprotein cholesterol. Cluster 3 (0.6%, N = 90) had mild MetS, and cluster 4 (0.06%, N = 9), 5 (0.03%, N = 5) and 6 (0.007%, N = 1) were not related to MetS. CONCLUSIONS: We identified two distinct groups of children with both increased liver enzymes and MetS features in this population sample of Chinese children. One of the two groups had increased liver enzymes as the predominant clinical features at a younger age, suggesting genetic susceptibility to the condition. Further work to understand the increased MetS risk in cluster 2 is warranted.

The Infection Rate of COVID-19 in Wuhan, China: Combined Analysis of Population Samples.

BACKGROUND: The coronavirus disease (COVID-19) pandemic began in Wuhan, China, in December 2019. Wuhan had a much higher mortality rate than the rest of China. However, a large number of asymptomatic infections in Wuhan may have never been diagnosed, contributing to an overestimated mortality rate. OBJECTIVE: This study aims to obtain an accurate estimate of infections in Wuhan using internet data. METHODS: In this study, we performed a combined analysis of the infection rate among evacuated foreign citizens to estimate the infection rate in Wuhan in late January and early February. RESULTS: Based on our analysis, the combined infection rate of the foreign evacuees was 0.013 (95% CI 0.008-0.022). Therefore, we estimate the number of infected people in Wuhan to be 143,000 (range 88,000-242,000), which is significantly higher than previous estimates. Our study indicates that a large number of infections in Wuhan were not diagnosed, which has resulted in an overestimated case fatality rate. CONCLUSIONS: Increased awareness of the original infection rate of Wuhan is critical for proper public health measures at all levels, as well as to eliminate panic caused by overestimated mortality rates that may bias health policy actions by the authorities.

Application of ACMG criteria to classify variants in the human gene mutation database.

This study aimed to obtain a quantitative assessment of the occurrence of contradictory evidence in functional classification of genetic variation, according to the American College of Medical Genetics and Genomics (ACMG) guidelines. We analyzed 140,883 genetic variation in the Human Gene Mutation Database (HGMD). The 2014 release of the HGMD dataset before the publication of the ACMG guidelines was used for its independence from the ACMG guidelines. Evidence for benign classification, BS2 (0.37%), was identified among variants classified as pathogenic. For likely pathogenic variation, BP1 (2.99%) and BS2 (0.37%) were identified. PM1 is commonly observed among variants classified as benign (28.45%), while PM2 and PM1 are commonly identified among variants classified as likely benign (48.91% and 42.95%, respectively). Taken together, these observations will inform better approaches to apply the ACMG guidelines.