Disruption of mitochondrial energy metabolism is a putative pathogenesis of Diamond-Blackfan anemia.

Energy metabolism in the context of erythropoiesis and related diseases remains largely unexplored. Here, we developed a primary cell model by differentiating hematopoietic stem progenitor cells toward the erythroid lineage and suppressing the mitochondrial oxidative phosphorylation (OXPHOS) pathway. OXPHOS suppression led to differentiation failure of erythroid progenitors and defects in ribosome biogenesis. Ran GTPase-activating protein 1 (RanGAP1) was identified as a target of mitochondrial OXPHOS for ribosomal defects during erythropoiesis. Overexpression of RanGAP1 largely alleviated erythroid defects resulting from OXPHOS suppression. Coenzyme Q10, an activator of OXPHOS, largely rescued erythroid defects and increased RanGAP1 expression. Patients with Diamond-Blackfan anemia (DBA) exhibited OXPHOS suppression and a concomitant suppression of ribosome biogenesis. RNA-seq analysis implied that the substantial mutation (approximately 10%) in OXPHOS genes accounts for OXPHOS suppression in these patients. Conclusively, OXPHOS disruption and the associated disruptive mitochondrial energy metabolism are linked to the pathogenesis of DBA.

Transmission of Mycobacterium tuberculosis in schools: a molecular epidemiological study using whole-genome sequencing in Guangzhou, China.

Background: China is a country with a high burden of tuberculosis (TB). TB outbreaks are frequent in schools. Thus, understanding the transmission patterns is crucial for controlling TB. Method: In this genomic epidemiological study, the conventional epidemiological survey data combined with whole-genome sequencing was used to assess the genotypic distribution and transmission characteristics of Mycobacterium tuberculosis strains isolated from patients with TB attending schools during 2015 to 2019 in Guangzhou, China. Result: The TB incidence was mainly concentrated in regular secondary schools and technical and vocational schools. The incidence of drug resistance among the students was 16.30% (22/135). The phylogenetic tree showed that 79.26% (107/135) and 20.74% (28/135) of the strains belonged to lineage 2 (Beijing genotype) and lineage 4 (Euro-American genotype), respectively. Among the 135 isolates, five clusters with genomic distance within 12 single nucleotide polymorphisms were identified; these clusters included 10 strains, accounting for an overall clustering rate of 7.4% (10/135), which showed a much lower transmission index. The distance between the home or school address and the interval time of symptom onset or diagnosis indicated that campus dissemination and community dissemination may be existed both, and community dissemination is the main. Conclusion and recommendation: TB cases in Guangzhou schools were mainly disseminated and predominantly originated from community transmission. Accordingly, surveillance needs to be strengthened to stop the spread of TB in schools.

A machine learning-assisted model for renal urate underexcretion with genetic and clinical variables among Chinese men with gout.

OBJECTIVES: The objective of this study was to develop and validate a prediction model for renal urate underexcretion (RUE) in male gout patients. METHODS: Men with gout enrolled from multicenter cohorts in China were analyzed as the development and validation data sets. The RUE phenotype was defined as fractional excretion of uric acid (FEUA) <5.5%. Candidate genetic and clinical features were screened by the least absolute shrinkage and selection operator (LASSO) with 10-fold cross-validation. Machine learning algorithms (stochastic gradient descent (SGD), logistic regression, support vector machine) were performed to construct a predictive classifier of RUE. Models were assessed by the area under the receiver operating characteristic curve (AUC) and the precision-recall curve (PRC). RESULTS: One thousand two hundred thirty-eight and two thousand twenty-three patients were enrolled as the development and validation cohorts, with 1220 and 754 randomly chosen patients genotyped, respectively. Rs3775948.GG of SLC2A9/GLUT9, rs504915.AA of NRXN2/URAT1, and 7 clinical features (age, hypertension, nephrolithiasis, blood glucose, serum urate, urea nitrogen, and creatinine) were generated by LASSO. Two additional SNP variants (rs2231142.GG of ABCG2 and rs11231463.GG of SLC22A9/OAT7) were selected based on their contributions to gout in the development cohort and their reported effects on renal urate handling. The optimized classifiers yielded AUCs of ~0.914 and PRCs of ~0.980 using these 11 variables. The SGD model was conducted in the validation cohort with an AUC of 0.899 and the PRC of 0.957. CONCLUSIONS: A prediction model for RUE composed of four SNPs and readily accessible clinical features was established with acceptable accuracy for men with gout.

Translational Informatics for Parkinson's Disease: from Big Biomedical Data to Small Actionable Alterations.

Parkinson's disease (PD) is a common neurological disease in elderly people, and its morbidity and mortality are increasing with the advent of global ageing. The traditional paradigm of moving from small data to big data in biomedical research is shifting toward big data-based identification of small actionable alterations. To highlight the use of big data for precision PD medicine, we review PD big data and informatics for the translation of basic PD research to clinical applications. We emphasize some key findings in clinically actionable changes, such as susceptibility genetic variations for PD risk population screening, biomarkers for the diagnosis and stratification of PD patients, risk factors for PD, and lifestyles for the prevention of PD. The challenges associated with the collection, storage, and modelling of diverse big data for PD precision medicine and healthcare are also summarized. Future perspectives on systems modelling and intelligent medicine for PD monitoring, diagnosis, treatment, and healthcare are discussed in the end.