Carbon Nanocage with Maximum Utilization of Atomically Dispersed Iron as Efficient Oxygen Electroreduction Nanoreactor.

As key parameters of electrocatalysts, the density and utilization of active sites determine the electrocatalytic performance toward oxygen reduction reaction. Unfortunately, prevalent oxygen electrocatalysts fail to maximize the utilization of active sites due to inappropriate nanostructural design. Herein, a nano-emulsion induced polymerization self-assembly strategy is employed to prepare hierarchical meso-/microporous N/S co-doped carbon nanocage with atomically dispersed FeN4 (denoted as Meso/Micro-FeNSC). In situ scanning electrochemical microscopy technology reveals the density of available active sites for Meso/Micro-FeNSC reach to 3.57 × 1014 sites cm-2 , representing more than threefold improvement compared to micropore-dominant Micro-FeNSC counterpart (1.07 × 1014 sites cm-2 ). Additionally, the turnover frequency of Meso/Micro-FeNSC is also improved to 0.69 from 0.50 e- site-1 s-1 for Micro-FeNSC. These properties motivate Meso/Micro-FeNSC as efficient oxygen electroreduction electrocatalyst, in terms of outstanding half-wave potential (0.91 V), remarkable kinetic mass specific activity (68.65 A g-1 ), and excellent robustness. The assembled Zn-air batteries with Meso/Micro-FeNSC deliver high peak power density (264.34 mW cm-2 ), large specific capacity (814.09 mA h g-1 ), and long cycle life (>200 h). This work sheds lights on quantifying active site density and the significance of maximum utilization of active sites for rational design of advanced catalysts.

Multi-angle meta-analysis of the gut microbiome in Autism Spectrum Disorder: a step toward understanding patient subgroups.

Observational studies have shown that the composition of the human gut microbiome in children diagnosed with Autism Spectrum Disorder (ASD) differs significantly from that of their neurotypical (NT) counterparts. Thus far, reported ASD-specific microbiome signatures have been inconsistent. To uncover reproducible signatures, we compiled 10 publicly available raw amplicon and metagenomic sequencing datasets alongside new data generated from an internal cohort (the largest ASD cohort to date), unified them with standardized pre-processing methods, and conducted a comprehensive meta-analysis of all taxa and variables detected across multiple studies. By screening metadata to test associations between the microbiome and 52 variables in multiple patient subsets and across multiple datasets, we determined that differentially abundant taxa in ASD versus NT children were dependent upon age, sex, and bowel function, thus marking these variables as potential confounders in case-control ASD studies. Several taxa, including the strains Bacteroides stercoris t__190463 and Clostridium M bolteae t__180407, and the species Granulicatella elegans and Massilioclostridium coli, exhibited differential abundance in ASD compared to NT children only after subjects with bowel dysfunction were removed. Adjusting for age, sex and bowel function resulted in adding or removing significantly differentially abundant taxa in ASD-diagnosed individuals, emphasizing the importance of collecting and controlling for these metadata. We have performed the largest (n = 690) and most comprehensive systematic analysis of ASD gut microbiome data to date. Our study demonstrated the importance of accounting for confounding variables when designing statistical comparative analyses of ASD- and NT-associated gut bacterial profiles. Mitigating these confounders identified robust microbial signatures across cohorts, signifying the importance of accounting for these factors in comparative analyses of ASD and NT-associated gut profiles. Such studies will advance the understanding of different patient groups to deliver appropriate therapeutics by identifying microbiome traits germane to the specific ASD phenotype.

Longitudinal study of stool-associated microbial taxa in sibling pairs with and without autism spectrum disorder.

Autism Spectrum Disorder (ASD) is a complex neurodevelopmental disorder influenced by both genetic and environmental factors. Recently, gut dysbiosis has emerged as a powerful contributor to ASD symptoms. In this study, we recruited over 100 age-matched sibling pairs (between 2 and 8 years old) where one had an Autism ASD diagnosis and the other was developing typically (TD) (432 samples total). We collected stool samples over four weeks, tracked over 100 lifestyle and dietary variables, and surveyed behavior measures related to ASD symptoms. We identified 117 amplicon sequencing variants (ASVs) that were significantly different in abundance between sibling pairs across all three timepoints, 11 of which were supported by at least two contrast methods. We additionally identified dietary and lifestyle variables that differ significantly between cohorts, and further linked those variables to the ASVs they statistically relate to. Overall, dietary and lifestyle features were explanatory of ASD phenotype using logistic regression, however, global compositional microbiome features were not. Leveraging our longitudinal behavior questionnaires, we additionally identified 11 ASVs associated with changes in reported anxiety over time within and across all individuals. Lastly, we find that overall microbiome composition (beta-diversity) is associated with specific ASD-related behavioral characteristics.

A Comparative Evaluation of Tools to Predict Metabolite Profiles From Microbiome Sequencing Data.

Metabolomic analyses of human gut microbiome samples can unveil the metabolic potential of host tissues and the numerous microorganisms they support, concurrently. As such, metabolomic information bears immense potential to improve disease diagnosis and therapeutic drug discovery. Unfortunately, as cohort sizes increase, comprehensive metabolomic profiling becomes costly and logistically difficult to perform at a large scale. To address these difficulties, we tested the feasibility of predicting the metabolites of a microbial community based solely on microbiome sequencing data. Paired microbiome sequencing (16S rRNA gene amplicons, shotgun metagenomics, and metatranscriptomics) and metabolome (mass spectrometry and nuclear magnetic resonance spectroscopy) datasets were collected from six independent studies spanning multiple diseases. We used these datasets to evaluate two reference-based gene-to-metabolite prediction pipelines and a machine-learning (ML) based metabolic profile prediction approach. With the pre-trained model on over 900 microbiome-metabolome paired samples, the ML approach yielded the most accurate predictions (i.e., highest F1 scores) of metabolite occurrences in the human gut and outperformed reference-based pipelines in predicting differential metabolites between case and control subjects. Our findings demonstrate the possibility of predicting metabolites from microbiome sequencing data, while highlighting certain limitations in detecting differential metabolites, and provide a framework to evaluate metabolite prediction pipelines, which will ultimately facilitate future investigations on microbial metabolites and human health.

A sliding-bulge structure at the Dicer processing site of pre-miRNAs regulates alternative Dicer processing to generate 5'-isomiRs.

5'-isomiRs expand the repertoire of miRNA targets. However, how they are generated is not well understood. Previously, we showed that for some miRNAs in mammalian cells, Drosha cleaves at multiple sites to generate multiple pre-miRNAs that give rise to multiple 5'-isomiRs. Here, we showed that for some other miRNAs, 5'-isomiRs are generated by alternative Dicer processing. In addition, we showed that in miR-203, alternative Dicer processing is regulated by a conserved sliding-bulge structure at the Dicer processing site, which allows the pre-miRNA molecule to fold into two different structures that are processed differently by Dicer. So far no RNA motif that slides to change conformation and alter a protein-RNA interaction has been reported. Thus, our study revealed a novel RNA motif that regulates 5'-isomiR generation in some miRNAs. It might also contribute to regulating protein-RNA interactions in other biological processes, since it takes only one point mutation to generate the sliding bulge, and there are a large number of different RNAs in the cell.

A CRISPR-Based Screen Identifies Genes Essential for West-Nile-Virus-Induced Cell Death.

West Nile virus (WNV) causes an acute neurological infection attended by massive neuronal cell death. However, the mechanism(s) behind the virus-induced cell death is poorly understood. Using a library containing 77,406 sgRNAs targeting 20,121 genes, we performed a genome-wide screen followed by a second screen with a sub-library. Among the genes identified, seven genes, EMC2, EMC3, SEL1L, DERL2, UBE2G2, UBE2J1, and HRD1, stood out as having the strongest phenotype, whose knockout conferred strong protection against WNV-induced cell death with two different WNV strains and in three cell lines. Interestingly, knockout of these genes did not block WNV replication. Thus, these appear to be essential genes that link WNV replication to downstream cell death pathway(s). In addition, the fact that all of these genes belong to the ER-associated protein degradation (ERAD) pathway suggests that this might be the primary driver of WNV-induced cell death.

Distinct expression profiles of stress defense and DNA repair genes in Daphnia pulex exposed to cadmium, zinc, and quantum dots.

The ever-increasing production and use of nanocrystaline semiconductors (Quantum dots; QDs) will inevitably result in increased appearance of these nanomaterials in the aquatic environment. However, the behavior and potential toxicity of heavy metal constituted nanoparticulates in aquatic invertebrates is largely unknown, especially with regard to molecular responses. The freshwater crustacean Daphnia pulex is a well-suited toxicological and ecological model to study molecular responses to environmental stressors. In this study, D. pulex were exposed for 48 h to sublethal doses of QDs (25% and 50% of LC50) with differing spectral properties (CdTe and CdSe/ZnS QDs) and Cd and Zn salts. Our data suggest that acute exposure to both CdSO4 and Cd-based QDs leads to Cd uptake in vivo, which was biologically supported by the observation of increased expression of metallothionein (MT-1). Furthermore, Cd, Zn, and CdSe/ZnS QDs induced different patterns of gene expression regarding stress defense and DNA repair, which furthers our knowledge regarding which response pathways are affected by nanoparticulate forms of metals versus ionic forms in aquatic crustaceans.

RNA-seq analysis to identify novel roles of scleraxis during embryonic mouse heart valve remodeling.

Heart valve disease affects up to 30% of the population and has been shown to have origins during embryonic development. Valvulogenesis begins with formation of endocardial cushions in the atrioventricular canal and outflow tract regions. Subsequently, endocardial cushions remodel, elongate and progressively form mature valve structures composed of a highly organized connective tissue that provides the necessary biomechanical function throughout life. While endocardial cushion formation has been well studied, the processes required for valve remodeling are less well understood. The transcription factor Scleraxis (Scx) is detected in mouse valves from E15.5 during initial stages of remodeling, and expression remains high until birth when formation of the highly organized mature structure is complete. Heart valves from Scx-/- mice are abnormally thick and develop fibrotic phenotypes similar to human disease by juvenile stages. These phenotypes begin around E15.5 and are associated with defects in connective tissue organization and valve interstitial cell differentiation. In order to understand the etiology of this phenotype, we analyzed the transcriptome of remodeling valves isolated from E15.5 Scx-/- embryos using RNA-seq. From this, we have identified a profile of protein and non-protein mRNAs that are dependent on Scx function and using bioinformatics we can predict the molecular functions and biological processes affected by these genes. These include processes and functions associated with gene regulation (methyltransferase activity, DNA binding, Notch signaling), vitamin A metabolism (retinoic acid biosynthesis) and cellular development (cell morphology, cell assembly and organization). In addition, several mRNAs are affected by alternative splicing events in the absence of Scx, suggesting additional roles in post-transcriptional modification. In summary, our findings have identified transcriptome profiles from abnormal heart valves isolated from E15.5 Scx-/- embryos that could be used in the future to understand mechanisms of heart valve disease in the human population.

Pol III Promoters to Express Small RNAs: Delineation of Transcription Initiation.

Pol III promoters such as U6 are commonly used to express small RNAs, including small interfering RNA, short hairpin RNA, and guide RNA, for the clustered regularly interspaced short palindromic repeats genome-editing system. However, whether the small RNAs were precisely expressed as desired has not been studied. Here, using deep sequencing to analyze small RNAs, we show that, for mouse U6 promoter, sequences immediately upstream of the putative initiation site, which is often modified to accommodate the restriction enzyme sites that enable easy cloning of small RNAs, are critical for precise transcription initiation. When the promoter is kept unmodified, transcription starts precisely from the first available A or G within the range of positions -1 to +2. In addition, we show that transcription from another commonly used pol III promoter, H1, starts at multiple sites, which results in variability at the 5' end of the transcripts. Thus, inaccuracy of 5' end of small RNA transcripts might be a common problem when using these promoters to express small RNAs based on currently believed concepts. Our study provides general guidelines for minimizing the variability of initiation, thereby enabling more accurate expression of small RNAs.

Lower and upper stem-single-stranded RNA junctions together determine the Drosha cleavage site.

Microprocessor [Drosha-DGCR8 (DiGeorge syndrome critical region gene 8) complex] processing of primary microRNA (pri-miRNA) is the critical first step in miRNA biogenesis, but how the Drosha cleavage site is determined has been unclear. Previous models proposed that the Drosha-DGCR8 complex measures either ~22 nt from the upper stem-single-stranded RNA (ssRNA, terminal loop) junction or ~11 nt from the lower stem-ssRNA junction to determine the cleavage site. Here, using miRNA-offset RNAs to determine the Drosha cleavage site, we show that the Microprocessor measures the distances from both the lower and upper stem-ssRNA junctions to determine the cleavage site in human cells, and optimal distances from both structures are critical to the precision of Drosha processing. If the distances are not optimal, Drosha tends to cleave at multiple sites, which can, in turn, generate multiple 5' isomiRs. Thus, our results also reveal a mechanism of 5' isomiR generation.

Emergence of genotype I of Japanese encephalitis virus as the dominant genotype in Asia.

Japanese encephalitis virus (JEV), a mosquito-borne zoonotic pathogen, is one of the major causes of viral encephalitis worldwide. Previous phylogenetic studies based on the envelope protein indicated that there are four genotypes, and surveillance data suggest that genotype I is gradually replacing genotype III as the dominant strain. Here we report an evolutionary analysis based on 98 full-length genome sequences of JEV, including 67 new samples isolated from humans, pigs, mosquitoes, midges. and bats in affected areas. To investigate the relationships between the genotypes and the significance of genotype I in recent epidemics, we estimated evolutionary rates, ages of common ancestors, and population demographics. Our results indicate that the genotypes diverged in the order IV, III, II, and I and that the genetic diversity of genotype III has decreased rapidly while that of genotype I has increased gradually, consistent with its emergence as the dominant genotype.

MiRPara: a SVM-based software tool for prediction of most probable microRNA coding regions in genome scale sequences.

BACKGROUND: MicroRNAs are a family of ~22 nt small RNAs that can regulate gene expression at the post-transcriptional level. Identification of these molecules and their targets can aid understanding of regulatory processes. Recently, HTS has become a common identification method but there are two major limitations associated with the technique. Firstly, the method has low efficiency, with typically less than 1 in 10,000 sequences representing miRNA reads and secondly the method preferentially targets highly expressed miRNAs. If sequences are available, computational methods can provide a screening step to investigate the value of an HTS study and aid interpretation of results. However, current methods can only predict miRNAs for short fragments and have usually been trained against small datasets which don't always reflect the diversity of these molecules. RESULTS: We have developed a software tool, miRPara, that predicts most probable mature miRNA coding regions from genome scale sequences in a species specific manner. We classified sequences from miRBase into animal, plant and overall categories and used a support vector machine to train three models based on an initial set of 77 parameters related to the physical properties of the pre-miRNA and its miRNAs. By applying parameter filtering we found a subset of ~25 parameters produced higher prediction ability compared to the full set. Our software achieves an accuracy of up to 80% against experimentally verified mature miRNAs, making it one of the most accurate methods available. CONCLUSIONS: miRPara is an effective tool for locating miRNAs coding regions in genome sequences and can be used as a screening step prior to HTS experiments. It is available at http://www.whiov.ac.cn/bioinformatics/mirpara.