A YY1-dependent increase in aerobic metabolism is indispensable for intestinal organogenesis

During late gestation, villi extend into the intestinal lumen to dramatically increase the surface area of the intestinal epithelium, preparing the gut for the neonatal diet. Incomplete development of the intestine is the most common gastrointestinal complication in neonates, but the causes are unclear. We provide evidence in mice that Yin Yang 1 (Yy1) is crucial for intestinal villus development. YY1 loss in the developing endoderm had no apparent consequences until late gestation, after which the intestine differentiated poorly and exhibited severely stunted villi. Transcriptome analysis revealed that YY1 is required for mitochondrial gene expression, and ultrastructural analysis confirmed compromised mitochondrial integrity in the mutant intestine. We found increased oxidative phosphorylation gene expression at the onset of villus elongation, suggesting that aerobic respiration might function as a regulator of villus growth. Mitochondrial inhibitors blocked villus growth in a fashion similar to Yy1 loss, thus further linking oxidative phosphorylation with late-gestation intestinal development. Interestingly, we find that necrotizing enterocolitis patients also exhibit decreased expression of oxidative phosphorylation genes. Our study highlights the still unappreciated role of metabolic regulation during organogenesis, and suggests that it might contribute to neonatal gastrointestinal disorders

The monoclonal antibody combination REGEN-COV protects against SARS-CoV-2 mutational escape in preclinical and human studies.

Monoclonal antibodies against SARS-CoV-2 are a clinically validated therapeutic option against COVID-19. Because rapidly emerging virus mutants are becoming the next major concern in the fight against the global pandemic, it is imperative that these therapeutic treatments provide coverage against circulating variants and do not contribute to development of treatment-induced emergent resistance. To this end, we investigated the sequence diversity of the spike protein and monitored emergence of virus variants in SARS-COV-2 isolates found in COVID-19 patients treated with the two-antibody combination REGEN-COV, as well as in preclinical in vitro studies using single, dual, or triple antibody combinations, and in hamster in vivo studies using REGEN-COV or single monoclonal antibody treatments. Our study demonstrates that the combination of non-competing antibodies in REGEN-COV provides protection against all current SARS-CoV-2 variants of concern/interest and also protects against emergence of new variants and their potential seeding into the population in a clinical setting

Potential of Core-Collapse Supernova Neutrino Detection at JUNO

JUNO is an underground neutrino observatory under construction in Jiangmen, China. It uses 20kton liquid scintillator as target, which enables it to detect supernova burst neutrinos of a large statistics for the next galactic core-collapse supernova (CCSN) and also pre-supernova neutrinos from the nearby CCSN progenitors. All flavors of supernova burst neutrinos can be detected by JUNO via several interaction channels, including inverse beta decay, elastic scattering on electron and proton, interactions on C12 nuclei, etc. This retains the possibility for JUNO to reconstruct the energy spectra of supernova burst neutrinos of all flavors. The real time monitoring systems based on FPGA and DAQ are under development in JUNO, which allow prompt alert and trigger-less data acquisition of CCSN events. The alert performances of both monitoring systems have been thoroughly studied using simulations. Moreover, once a CCSN is tagged, the system can give fast characterizations, such as directionality and light curve

Detection of the Diffuse Supernova Neutrino Background with JUNO

As an underground multi-purpose neutrino detector with 20 kton liquid scintillator, Jiangmen Underground Neutrino Observatory (JUNO) is competitive with and complementary to the water-Cherenkov detectors on the search for the diffuse supernova neutrino background (DSNB). Typical supernova models predict 2-4 events per year within the optimal observation window in the JUNO detector. The dominant background is from the neutral-current (NC) interaction of atmospheric neutrinos with 12C nuclei, which surpasses the DSNB by more than one order of magnitude. We evaluated the systematic uncertainty of NC background from the spread of a variety of data-driven models and further developed a method to determine NC background within 15\% with {\it{in}} {\it{situ}} measurements after ten years of running. Besides, the NC-like backgrounds can be effectively suppressed by the intrinsic pulse-shape discrimination (PSD) capabilities of liquid scintillators. In this talk, I will present in detail the improvements on NC background uncertainty evaluation, PSD discriminator development, and finally, the potential of DSNB sensitivity in JUNO

Risk factors for high CAD-RADS scoring in CAD patients revealed by machine learning methods: a retrospective study

Objective This study aimed to investigate a variety of machine learning (ML) methods to predict the association between cardiovascular risk factors and coronary artery disease-reporting and data system (CAD-RADS) scores. Methods This is a retrospective cohort study. Demographical, cardiovascular risk factors and coronary CT angiography (CCTA) characteristics of the patients were obtained. Coronary artery disease (CAD) was evaluated using CAD-RADS score. The stenosis severity component of the CAD-RADS was stratified into two groups: CAD-RADS score 0-2 group and CAD-RADS score 3–5 group. CAD-RADS scores were predicted with random forest (RF), k-nearest neighbors (KNN), support vector machines (SVM), neural network (NN), decision tree classification (DTC) and linear discriminant analysis (LDA). Prediction sensitivity, specificity, accuracy and area under the curve (AUC) were calculated. Feature importance analysis was utilized to find the most important predictors. Results A total of 442 CAD patients with CCTA examinations were included in this study. 234 (52.9%) subjects were CAD-RADS score 0–2 group and 208 (47.1%) were CAD-RADS score 3–5 group. CAD-RADS score 3-5 group had a high prevalence of hypertension (66.8%), hyperlipidemia (50%) and diabetes mellitus (DM) (35.1%). Age, systolic blood pressure (SBP), mean arterial pressure, pulse pressure, pulse pressure index, plasma fibrinogen, uric acid and blood urea nitrogen were significantly higher (p < 0.001), and high-density lipoprotein (HDL-C) lower (p < 0.001) in CAD-RADS score 3–5 group compared to the CAD-RADS score 0–2 group. Nineteen features were chosen to train the models. RF (AUC = 0.832) and LDA (AUC = 0.81) outperformed SVM (AUC = 0.772), NN (AUC = 0.773), DTC (AUC = 0.682), KNN (AUC = 0.707). Feature importance analysis indicated that plasma fibrinogen, age and DM contributed most to CAD-RADS scores. Conclusion ML algorithms are capable of predicting the correlation between cardiovascular risk factors and CAD-RADS scores with high accuracy

PipelineDog: a simple and flexible graphic pipeline construction and maintenance tool

Analysis pipelines are an essential part of bioinformatics research, and ad hoc pipelines are frequently created by researchers for prototyping and proof-of-concept purposes. However, most existing pipeline management system or workflow engines are too complex for rapid prototyping or learning the pipeline concept. A lightweight, user-friendly and flexible solution is thus desirable. In this study, we developed a new pipeline construction and maintenance tool, PipelineDog. This is a web-based integrated development environment with a modern web graphical user interface. It offers cross-platform compatibility, project management capabilities, code formatting and error checking functions and an online repository. It uses an easy-to-read/write script system that encourages code reuse. With the online repository, it also encourages sharing of pipelines, which enhances analysis reproducibility and accountability. For most users, PipelineDog requires no software installation. Overall, this web application provides a way to rapidly create and easily manage pipelines. Availability and implementation PipelineDog web app is freely available at http://web.pipeline.dog. The command line version is available at http://www.npmjs.com/package/pipelinedog and online repository at http://repo.pipeline.dog. Contact [email protected] or [email protected] or [email protected] Supplementary informationSupplementary dataare available at Bioinformatics online

Extremely low-coverage whole genome sequencing in South Asians captures population genomics information

Abstract Background The cost of Whole Genome Sequencing (WGS) has decreased tremendously in recent years due to advances in next-generation sequencing technologies. Nevertheless, the cost of carrying out large-scale cohort studies using WGS is still daunting. Past simulation studies with coverage at ~2x have shown promise for using low coverage WGS in studies focused on variant discovery, association study replications, and population genomics characterization. However, the performance of low coverage WGS in populations with a complex history and no reference panel remains to be determined. Results South Indian populations are known to have a complex population structure and are an example of a major population group that lacks adequate reference panels. To test the performance of extremely low-coverage WGS (EXL-WGS) in populations with a complex history and to provide a reference resource for South Indian populations, we performed EXL-WGS on 185 South Indian individuals from eight populations to ~1.6x coverage. Using two variant discovery pipelines, SNPTools and GATK, we generated a consensus call set that has ~90% sensitivity for identifying common variants (minor allele frequency ≥ 10%). Imputation further improves the sensitivity of our call set. In addition, we obtained high-coverage for the whole mitochondrial genome to infer the maternal lineage evolutionary history of the Indian samples. Conclusions Overall, we demonstrate that EXL-WGS with imputation can be a valuable study design for variant discovery with a dramatically lower cost than standard WGS, even in populations with a complex history and without available reference data. In addition, the South Indian EXL-WGS data generated in this study will provide a valuable resource for future Indian genomic studies

MicroRNA and MicroRNA-Target Variants Associated with Autism Spectrum Disorder and Related Disorders

Autism spectrum disorder (ASD) is a childhood neurodevelopmental disorder with a complex and heterogeneous genetic etiology. MicroRNA (miRNA), a class of small non-coding RNAs, could regulate ASD risk genes post-transcriptionally and affect broad molecular pathways related to ASD and associated disorders. Using whole-genome sequencing, we analyzed 272 samples in 73 families in the New Jersey Language and Autism Genetics Study (NJLAGS) cohort. Families with at least one ASD patient were recruited and were further assessed for language impairment, reading impairment, and other associated phenotypes. A total of 5104 miRNA variants and 1,181,148 3&prime; untranslated region (3&prime; UTR) variants were identified in the dataset. After applying several filtering criteria, including population allele frequency, brain expression, miRNA functional regions, and inheritance patterns, we identified high-confidence variants in five brain-expressed miRNAs (targeting 326 genes) and 3&prime; UTR miRNA target regions of 152 genes. Some genes, such as SCP2 and UCGC, were identified in multiple families. Using Gene Ontology overrepresentation analysis and protein&ndash;protein interaction network analysis, we identified clusters of genes and pathways that are important for neurodevelopment. The miRNAs and miRNA target genes identified in this study are potentially involved in neurodevelopmental disorders and should be considered for further functional studies

Degree of Tissue Differentiation Dictates Susceptibility to BRAF-Driven Colorectal Cancer

Oncogenic mutations in BRAF are believed to initiate serrated colorectal cancers; however, the mechanisms of BRAF-driven colon cancer are unclear. We find that oncogenic BRAF paradoxically suppresses stem cell renewal and instead promotes differentiation. Correspondingly, tumor formation is inefficient in BRAF-driven mouse models of colon cancer. By reducing levels of differentiation via genetic manipulation of either of two distinct differentiation-promoting factors (Smad4 or Cdx2), stem cell activity is restored in BRAFV600E intestines, and the oncogenic capacity of BRAFV600E is amplified. In human patients, we observe that reduced levels of differentiation in normal tissue is associated with increased susceptibility to serrated colon tumors. Together, these findings help resolve the conditions necessary for BRAF-driven colon cancer initiation. Additionally, our results predict that genetic and/or environmental factors that reduce tissue differentiation will increase susceptibility to serrated colon cancer. These findings offer an opportunity to identify susceptible individuals by assessing their tissue-differentiation status