MagicalRsq-X: A cross-cohort transferable genotype imputation quality metric.

Since genotype imputation was introduced, researchers have been relying on the estimated imputation quality from imputation software to perform post-imputation quality control (QC). However, this quality estimate (denoted as Rsq) performs less well for lower-frequency variants. We recently published MagicalRsq, a machine-learning-based imputation quality calibration, which leverages additional typed markers from the same cohort and outperforms Rsq as a QC metric. In this work, we extended the original MagicalRsq to allow cross-cohort model training and named the new model MagicalRsq-X. We removed the cohort-specific estimated minor allele frequency and included linkage disequilibrium scores and recombination rates as additional features. Leveraging whole-genome sequencing data from TOPMed, specifically participants in the BioMe, JHS, WHI, and MESA studies, we performed comprehensive cross-cohort evaluations for predominantly European and African ancestral individuals based on their inferred global ancestry with the 1000 Genomes and Human Genome Diversity Project data as reference. Our results suggest MagicalRsq-X outperforms Rsq in almost every setting, with 7.3%-14.4% improvement in squared Pearson correlation with true R2, corresponding to 85-218 K variant gains. We further developed a metric to quantify the genetic distances of a target cohort relative to a reference cohort and showed that such metric largely explained the performance of MagicalRsq-X models. Finally, we found MagicalRsq-X saved up to 53 known genome-wide significant variants in one of the largest blood cell trait GWASs that would be missed using the original Rsq for QC. In conclusion, MagicalRsq-X shows superiority for post-imputation QC and benefits genetic studies by distinguishing well and poorly imputed lower-frequency variants.

E93 is indispensable for reproduction in ametabolous and hemimetabolous insects.

Ecdysone-induced protein 93 (E93), known as the 'adult-specifier' transcription factor in insects, triggers metamorphosis in both hemimetabolous and holometabolous insects. Although E93 is conserved in ametabolous insects, its spatiotemporal expression and physiological function remain poorly understood. In this study, we first discover that, in the ametabolous firebrat Thermobia domestica, the previtellogenic ovary exhibits cyclically high E93 expression, and E93 mRNA is broadly distributed in previtellogenic ovarioles. E93 homozygous mutant females of T. domestica exhibit severe fecundity deficiency due to impaired previtellogenic development of the ovarian follicles, likely because E93 induces the expression of genes involved in ECM (extracellular matrix)-receptor interactions during previtellogenesis. Moreover, we reveal that in the hemimetabolous cockroach Blattella germanica, E93 similarly promotes previtellogenic ovarian development. In addition, E93 is also essential for vitellogenesis that is necessary to guarantee ovarian maturation and promotes the vitellogenesis-previtellogenesis switch in the fat body of adult female cockroaches. Our findings deepen the understanding of the roles of E93 in controlling reproduction in insects, and of E93 expression and functional evolution, which are proposed to have made crucial contributions to the origin of insect metamorphosis.

Cell-type-specific 3D epigenomes in the developing human cortex.

Lineage-specific epigenomic changes during human corticogenesis have been difficult to study owing to challenges with sample availability and tissue heterogeneity. For example, previous studies using single-cell RNA sequencing identified at least 9 major cell types and up to 26 distinct subtypes in the dorsal cortex alone1,2. Here we characterize cell-type-specific cis-regulatory chromatin interactions, open chromatin peaks, and transcriptomes for radial glia, intermediate progenitor cells, excitatory neurons, and interneurons isolated from mid-gestational samples of the human cortex. We show that chromatin interactions underlie several aspects of gene regulation, with transposable elements and disease-associated variants enriched at distal interacting regions in a cell-type-specific manner. In addition, promoters with increased levels of chromatin interactivity-termed super-interactive promoters-are enriched for lineage-specific genes, suggesting that interactions at these loci contribute to the fine-tuning of transcription. Finally, we develop CRISPRview, a technique that integrates immunostaining, CRISPR interference, RNAscope, and image analysis to validate cell-type-specific cis-regulatory elements in heterogeneous populations of primary cells. Our findings provide insights into cell-type-specific gene expression patterns in the developing human cortex and advance our understanding of gene regulation and lineage specification during this crucial developmental window.

MagicalRsq: Machine-learning-based genotype imputation quality calibration.

Whole-genome sequencing (WGS) is the gold standard for fully characterizing genetic variation but is still prohibitively expensive for large samples. To reduce costs, many studies sequence only a subset of individuals or genomic regions, and genotype imputation is used to infer genotypes for the remaining individuals or regions without sequencing data. However, not all variants can be well imputed, and the current state-of-the-art imputation quality metric, denoted as standard Rsq, is poorly calibrated for lower-frequency variants. Here, we propose MagicalRsq, a machine-learning-based method that integrates variant-level imputation and population genetics statistics, to provide a better calibrated imputation quality metric. Leveraging WGS data from the Cystic Fibrosis Genome Project (CFGP), and whole-exome sequence data from UK BioBank (UKB), we performed comprehensive experiments to evaluate the performance of MagicalRsq compared to standard Rsq for partially sequenced studies. We found that MagicalRsq aligns better with true R2 than standard Rsq in almost every situation evaluated, for both European and African ancestry samples. For example, when applying models trained from 1,992 CFGP sequenced samples to an independent 3,103 samples with no sequencing but TOPMed imputation from array genotypes, MagicalRsq, compared to standard Rsq, achieved net gains of 1.4 million rare, 117k low-frequency, and 18k common variants, where net gains were gained numbers of correctly distinguished variants by MagicalRsq over standard Rsq. MagicalRsq can serve as an improved post-imputation quality metric and will benefit downstream analysis by better distinguishing well-imputed variants from those poorly imputed. MagicalRsq is freely available on GitHub.

THUNDER: A reference-free deconvolution method to infer cell type proportions from bulk Hi-C data.

Hi-C data provide population averaged estimates of three-dimensional chromatin contacts across cell types and states in bulk samples. Effective analysis of Hi-C data entails controlling for the potential confounding factor of differential cell type proportions across heterogeneous bulk samples. We propose a novel unsupervised deconvolution method for inferring cell type composition from bulk Hi-C data, the Two-step Hi-c UNsupervised DEconvolution appRoach (THUNDER). We conducted extensive simulations to test THUNDER based on combining two published single-cell Hi-C (scHi-C) datasets. THUNDER more accurately estimates the underlying cell type proportions compared to reference-free methods (e.g., TOAST, and NMF) and is more robust than reference-dependent methods (e.g. MuSiC). We further demonstrate the practical utility of THUNDER to estimate cell type proportions and identify cell-type-specific interactions in Hi-C data from adult human cortex tissue samples. THUNDER will be a useful tool in adjusting for varying cell type composition in population samples, facilitating valid and more powerful downstream analysis such as differential chromatin organization studies. Additionally, THUNDER estimated contact profiles provide a useful exploratory framework to investigate cell-type-specificity of the chromatin interactome while experimental data is still rare.

Super interactive promoters provide insight into cell type-specific regulatory networks in blood lineage cell types.

Existing studies of chromatin conformation have primarily focused on potential enhancers interacting with gene promoters. By contrast, the interactivity of promoters per se, while equally critical to understanding transcriptional control, has been largely unexplored, particularly in a cell type-specific manner for blood lineage cell types. In this study, we leverage promoter capture Hi-C data across a compendium of blood lineage cell types to identify and characterize cell type-specific super-interactive promoters (SIPs). Notably, promoter-interacting regions (PIRs) of SIPs are more likely to overlap with cell type-specific ATAC-seq peaks and GWAS variants for relevant blood cell traits than PIRs of non-SIPs. Moreover, PIRs of cell-type-specific SIPs show enriched heritability of relevant blood cell trait (s), and are more enriched with GWAS variants associated with blood cell traits compared to PIRs of non-SIPs. Further, SIP genes tend to express at a higher level in the corresponding cell type. Importantly, SIP subnetworks incorporating cell-type-specific SIPs and ATAC-seq peaks help interpret GWAS variants. Examples include GWAS variants associated with platelet count near the megakaryocyte SIP gene EPHB3 and variants associated lymphocyte count near the native CD4 T-Cell SIP gene ETS1. Interestingly, around 25.7% ~ 39.6% blood cell traits GWAS variants residing in SIP PIR regions disrupt transcription factor binding motifs. Importantly, our analysis shows the potential of using promoter-centric analyses of chromatin spatial organization data to identify biologically important genes and their regulatory regions.

Vertical stratification of the air microbiome in the lower troposphere.

The troposphere constitutes the final frontier of global ecosystem research due to technical challenges arising from its size, low biomass, and gaseous state. Using a vertical testing array comprising a meteorological tower and a research aircraft, we conducted synchronized measurements of meteorological parameters and airborne biomass (n = 480) in the vertical air column up to 3,500 m. The taxonomic analysis of metagenomic data revealed differing patterns of airborne microbial community composition with respect to time of day and height above ground. The temporal and spatial resolution of our study demonstrated that the diel cycle of airborne microorganisms is a ground-based phenomenon that is entirely absent at heights >1,000 m. In an integrated analysis combining meteorological and biological data, we demonstrate that atmospheric turbulence, identified by potential temperature and high-frequency three-component wind measurements, is the key driver of bioaerosol dynamics in the lower troposphere. Multivariate regression analysis shows that at least 50% of identified airborne microbial taxa (n = ∼10,000) are associated with either ground or height, allowing for an understanding of dispersal patterns of microbial taxa in the vertical air column. Due to the interconnectedness of atmospheric turbulence and temperature, the dynamics of microbial dispersal are likely to be impacted by rising global temperatures, thereby also affecting ecosystems on the planetary surface.

Impaired distal renal potassium handling in streptozotocin-induced diabetic mice.

Diabetes is closely associated with K+ disturbances during disease progression and treatment. However, it remains unclear whether K+ imbalance occurs in diabetes with normal kidney function. In this study, we examined the effects of dietary K+ intake on systemic K+ balance and renal K+ handling in streptozotocin (STZ)-induced diabetic mice. The control and STZ mice were fed low or high K+ diet for 7 days to investigate the role of dietary K+ intake in renal K+ excretion and K+ homeostasis and to explore the underlying mechanism by evaluating K+ secretion-related transport proteins in distal nephrons. K+-deficient diet caused excessive urinary K+ loss, decreased daily K+ balance, and led to severe hypokalemia in STZ mice compared with control mice. In contrast, STZ mice showed an increased daily K+ balance and elevated plasma K+ level under K+-loading conditions. Dysregulation of the NaCl cotransporter (NCC), epithelial Na+ channel (ENaC), and renal outer medullary K+ channel (ROMK) was observed in diabetic mice fed either low or high K+ diet. Moreover, amiloride treatment reduced urinary K+ excretion and corrected hypokalemia in K+-restricted STZ mice. On the other hand, inhibition of SGLT2 by dapagliflozin promoted urinary K+ excretion and normalized plasma K+ levels in K+-supplemented STZ mice, at least partly by increasing ENaC activity. We conclude that STZ mice exhibited abnormal K+ balance and impaired renal K+ handling under either low or high K+ diet, which could be primarily attributed to the dysfunction of ENaC-dependent renal K+ excretion pathway, despite the possible role of NCC.NEW & NOTEWORTHY Neither low dietary K+ intake nor high dietary K+ intake effectively modulates renal K+ excretion and K+ homeostasis in STZ mice, which is closely related to the abnormality of ENaC expression and activity. SGLT2 inhibitor increases urinary K+ excretion and reduces plasma K+ level in STZ mice under high dietary K+ intake, an effect that may be partly due to the upregulation of ENaC activity.

Comparative Genomic and Transcriptomic Analyses Reveal the Impacts of Genetic Admixture in Kazaks, Uyghurs, and Huis.

Population admixture results in the combinations of genetic components derived from distinct ancestral populations, which may impact diversity at the genetic, transcriptomic, and phenotypic levels, as well as postadmixture adaptive evolution. Here, we systematically investigated the genomic and transcriptomic diversity in Kazaks, Uyghurs, and Huis-three admixed populations of various Eurasian ancestries living in Xinjiang, China. All three populations showed elevated genetic diversity and closer genetic distance compared with the reference populations across the Eurasian continent. However, we also observed differentiated genomic diversity and inferred different demographic histories among the three populations. Varying ancestry proportions observed in both the global and local aspects corresponded to the population-differentiated genomic diversity, with the most representative signals observed in the genes EDAR, SULT1C4, and SLC24A5. The varying local ancestry partly resulted from the postadmixture local adaptation, with the most significant signals observed in immunity- and metabolism-related pathways. Admixture-shaped genomic diversity further influenced the transcriptomic diversity in the admixed populations; in particular, population-specific regulatory effects were associated with immunity- and metabolism-involved genes such as MTHFR, FCER1G, SDHC, and BDH2. Furthermore, differentially expressed genes between the populations were identified, many of which could be explained by the population-specific regulatory properties, including genes related to health concerns (e.g., AHI1 between Kazak and Uyghurs [P < 6.92 × 10-5] and CTRC between Huis and Uyghurs [P < 2.32 × 10-4]). Our results demonstrate genetic admixture as a driving force in shaping the genomic and transcriptomic diversity of human populations.

Transcriptome-wide association study in UK Biobank Europeans identifies associations with blood cell traits.

Previous genome-wide association studies (GWAS) of hematological traits have identified over 10 000 distinct trait-specific risk loci. However, at these loci, the underlying causal mechanisms remain incompletely characterized. To elucidate novel biology and better understand causal mechanisms at known loci, we performed a transcriptome-wide association study (TWAS) of 29 hematological traits in 399 835 UK Biobank (UKB) participants of European ancestry using gene expression prediction models trained from whole blood RNA-seq data in 922 individuals. We discovered 557 gene-trait associations for hematological traits distinct from previously reported GWAS variants in European populations. Among the 557 associations, 301 were available for replication in a cohort of 141 286 participants of European ancestry from the Million Veteran Program. Of these 301 associations, 108 replicated at a strict Bonferroni adjusted threshold ($\alpha$= 0.05/301). Using our TWAS results, we systematically assigned 4261 out of 16 900 previously identified hematological trait GWAS variants to putative target genes. Compared to coloc, our TWAS results show reduced specificity and increased sensitivity in external datasets to assign variants to target genes.

A novel inherited CARD9 deficiency in an otherwise healthy woman with CNS candidiasis.

Patients with caspase-associated recruitment domain-9 (CARD9) deficiency are more likely to develop invasive fungal disease that affect CNS. However, the understanding of how Candida invades and persists in CNS is still limited. We here reported a 24-year-old woman who were previously immunocompetent and diagnosed with CNS candidiasis. A novel autosomal recessive homozygous CARD9 mutation (c.184 + 5G > T) from this patient was identified using whole genomic sequencing. Furthermore, we extensively characterized the impact of this CARD9 mutation on the host immune response in monocytes, neutrophils and CD4 + T cells, using single cell sequencing and in vitro experiments. Decreased pro-inflammatory cytokine productions of CD14 + monocyte, impaired Th17 cell differentiation, and defective neutrophil accumulation in CNS were found in this patient. In conclusion, this study proposed a novel mechanism of CNS candidiasis development. Patients with CNS candidiasis in absence of known immunodeficiencies should be analyzed for CARD9 gene mutation as the cause of invasive fungal infection predisposition.

Ependyma-expressed CCN1 restricts the size of the neural stem cell pool in the adult ventricular-subventricular zone.

Adult neural stem cells (NSCs) reside in specialized niches, which hold a balanced number of NSCs, their progeny, and other cells. How niche capacity is regulated to contain a specific number of NSCs remains unclear. Here, we show that ependyma-derived matricellular protein CCN1 (cellular communication network factor 1) negatively regulates niche capacity and NSC number in the adult ventricular-subventricular zone (V-SVZ). Adult ependyma-specific deletion of Ccn1 transiently enhanced NSC proliferation and reduced neuronal differentiation in mice, increasing the numbers of NSCs and NSC units. Although proliferation of NSCs and neurogenesis seen in Ccn1 knockout mice eventually returned to normal, the expanded NSC pool was maintained in the V-SVZ until old age. Inhibition of EGFR signaling prevented expansion of the NSC population observed in CCN1 deficient mice. Thus, ependyma-derived CCN1 restricts NSC expansion in the adult brain to maintain the proper niche capacity of the V-SVZ.

Mild hypothermia reduces lipopolysaccharide-induced microglial activation via down-regulation of Tent5c.

Microglial activation is a critical factor in the pathogenesis and progression of neuroinflammatory diseases. Mild hypothermia, known for its neuroprotective properties, has been shown to alleviate microglial activation. In this study, we explore the differentially expressed (DE) mRNAs and long non-coding RNAs (lncRNAs) in BV-2 microglial cells under different conditions: normal temperature (CN), mild hypothermia (YT), normal temperature with lipopolysaccharide (LPS), and mild hypothermia with LPS (LPS + YT). Venn analysis revealed 119 DE mRNAs that were down-regulated in the LPS + YT vs LPS comparison but up-regulated in the CN vs LPS comparison, primarily enriched in Gene Ontology terms related to immune and inflammatory responses. Furthermore, through Venn analysis of YT vs CN and LPS + YT vs LPS comparisons, we identified 178 DE mRNAs and 432 DE lncRNAs. Among these transcripts, we validated the expression of Tent5c at the protein and mRNA levels. Additionally, siRNA-knockdown of Tent5c attenuated the expression of pro-inflammatory genes (TNF-α, IL-1ß, Agrn, and Fpr2), cellular morphological changes, NLRP3 and p-P65 protein levels, immunofluorescence staining of p-P65 and number of cells with ASC-speck induced by LPS. Furthermore, Tent5c overexpression further potentiated the aforementioned indicators in the context of mild hypothermia with LPS treatment. Collectively, our findings highlight the significant role of Tent5c down-regulation in mediating the anti-inflammatory effects of mild hypothermia.

High-Throughput Screening of pH-Dependent ß-sheet Self-Assembling Peptide.

pH-dependent peptide biomaterials hold tremendous potential for cell delivery and tissue engineering. However, identification of responsive self-assembling sequences with specified secondary structure remains a challenge. In this work, An experimental procedure based on the one-bead one-compound (OBOC) combinatorial library is developed to rapidly screen self-assembling ß-sheet peptides at neutral aqueous solution (pH 7.5) and disassemble at weak acidic condition (pH 6.5). Using the hydrophobic fluorescent molecule thioflavin T (ThT) as a probe, resin beads displaying self-assembling peptides show fluorescence under pH 7.5 due to the insertion of ThT into the hydrophobic domain, and are further cultured in pH 6.5 solution. The beads with extinguished fluorescence are selected. Three heptapeptides are identified that can self-assemble into nanofibers or nanoparticles at pH 7.5 and disassemble at pH 6.5. P1 (LVEFRHY) shows a rapid acid response and morphology transformation with pH modulation. Changes in the charges of histidine and hydrophobic phenyl motif of phenylalanine may play important roles in the formation of pH-responsive ß-sheet nanofiber. This high-throughput screening method provides an efficient way to identify pH-dependent ß-sheet self-assembling peptide and gain insights into structural design of such nanomaterials.

SnapHiC: a computational pipeline to identify chromatin loops from single-cell Hi-C data.

Single-cell Hi-C (scHi-C) analysis has been increasingly used to map chromatin architecture in diverse tissue contexts, but computational tools to define chromatin loops at high resolution from scHi-C data are still lacking. Here, we describe Single-Nucleus Analysis Pipeline for Hi-C (SnapHiC), a method that can identify chromatin loops at high resolution and accuracy from scHi-C data. Using scHi-C data from 742 mouse embryonic stem cells, we benchmark SnapHiC against a number of computational tools developed for mapping chromatin loops and interactions from bulk Hi-C. We further demonstrate its use by analyzing single-nucleus methyl-3C-seq data from 2,869 human prefrontal cortical cells, which uncovers cell type-specific chromatin loops and predicts putative target genes for noncoding sequence variants associated with neuropsychiatric disorders. Our results indicate that SnapHiC could facilitate the analysis of cell type-specific chromatin architecture and gene regulatory programs in complex tissues.

A systematic evaluation of Hi-C data enhancement methods for enhancing PLAC-seq and HiChIP data.

The three-dimensional organization of chromatin plays a critical role in gene regulation. Recently developed technologies, such as HiChIP and proximity ligation-assisted ChIP-Seq (PLAC-seq) (hereafter referred to as HP for brevity), can measure chromosome spatial organization by interrogating chromatin interactions mediated by a protein of interest. While offering cost-efficiency over genome-wide unbiased high-throughput chromosome conformation capture (Hi-C) data, HP data remain sparse at kilobase (Kb) resolution with the current sequencing depth in the order of 108 reads per sample. Deep learning models, including HiCPlus, HiCNN, HiCNN2, DeepHiC and Variationally Encoded Hi-C Loss Enhancer (VEHiCLE), have been developed to enhance the sequencing depth of Hi-C data, but their performance on HP data has not been benchmarked. Here, we performed a comprehensive evaluation of HP data sequencing depth enhancement using models developed for Hi-C data. Specifically, we analyzed various HP data, including Smc1a HiChIP data of the human lymphoblastoid cell line GM12878, H3K4me3 PLAC-seq data of four human neural cell types as well as of mouse embryonic stem cells (mESC), and mESC CCCTC-binding factor (CTCF) PLAC-seq data. Our evaluations lead to the following three findings: (i) most models developed for Hi-C data achieve reasonable performance when applied to HP data (e.g. with Pearson correlation ranging 0.76-0.95 for pairs of loci within 300 Kb), and the enhanced datasets lead to improved statistical power for detecting long-range chromatin interactions, (ii) models trained on HP data outperform those trained on Hi-C data and (iii) most models are transferable across cell types. Our results provide a general guideline for HP data enhancement using existing methods designed for Hi-C data.

A comprehensive comparison on cell-type composition inference for spatial transcriptomics data.

Spatial transcriptomics (ST) technologies allow researchers to examine transcriptional profiles along with maintained positional information. Such spatially resolved transcriptional characterization of intact tissue samples provides an integrated view of gene expression in its natural spatial and functional context. However, high-throughput sequencing-based ST technologies cannot yet reach single cell resolution. Thus, similar to bulk RNA-seq data, gene expression data at ST spot-level reflect transcriptional profiles of multiple cells and entail the inference of cell-type composition within each ST spot for valid and powerful subsequent analyses. Realizing the critical importance of cell-type decomposition, multiple groups have developed ST deconvolution methods. The aim of this work is to review state-of-the-art methods for ST deconvolution, comparing their strengths and weaknesses. In particular, we construct ST spots from single-cell level ST data to assess the performance of 10 methods, with either ideal reference or non-ideal reference. Furthermore, we examine the performance of these methods on spot- and bead-level ST data by comparing estimated cell-type proportions to carefully matched single-cell ST data. In comparing the performance on various tissues and technological platforms, we concluded that RCTD and stereoscope achieve more robust and accurate inferences.

Immunogenicity of peptide-based vaccine composed of epitopes from Echinococcus granulosus rEg.P29.

Echinococcus granulosus is one of the main causes of economic loss in the livestock industry because of its food-borne transmission. Cutting off the transmission route is a valid prevention method, and vaccines are the most effective means of controlling and eliminating infectious diseases. However, no human-related vaccine has been yet marketed. As a genetic engineering vaccine, recombinant protein P29 of E. granulosus (rEg.P29) could provide protection against deadly challenges. In this study, we generated peptide vaccines (rEg.P29T , rEg.P29B , and rEg.P29T+B ) based on rEg.P29 and an immunized model was established by subcutaneous immunization. Further evaluation showed that peptide vaccine immunization in mice induced T helper type 1 (Th1)-mediated cellular immune responses, leading to high levels of rEg.P29 or rEg.P29B -specific antibodies. In addition, rEg.P29T+B immunization can induce a higher antibody and cytokine production level than single-epitope vaccines, and immune memory is also longer. Collectively, these results suggest that rEg.P29T+B has the potential to be developed as an efficient subunit vaccine for use in areas where E. granulosus is endemic.

Rare tandem repeat expansions associate with genes involved in synaptic and neuronal signaling functions in schizophrenia.

Tandem repeat expansions (TREs) are associated with over 60 monogenic disorders and have recently been implicated in complex disorders such as cancer and autism spectrum disorder. The role of TREs in schizophrenia is now emerging. In this study, we have performed a genome-wide investigation of TREs in schizophrenia. Using genome sequence data from 1154 Swedish schizophrenia cases and 934 ancestry-matched population controls, we have detected genome-wide rare (<0.1% population frequency) TREs that have motifs with a length of 2-20 base pairs. We find that the proportion of individuals carrying rare TREs is significantly higher in the schizophrenia group. There is a significantly higher burden of rare TREs in schizophrenia cases than in controls in genic regions, particularly in postsynaptic genes, in genes overlapping brain expression quantitative trait loci, and in brain-expressed genes that are differentially expressed between schizophrenia cases and controls. We demonstrate that TRE-associated genes are more constrained and primarily impact synaptic and neuronal signaling functions. These results have been replicated in an independent Canadian sample that consisted of 252 schizophrenia cases of European ancestry and 222 ancestry-matched controls. Our results support the involvement of rare TREs in schizophrenia etiology.

Fabrication of Hollow and Hierarchical CuO Micro-Nano Cubes Wrapped by Reduced Graphene Oxide as a Prospective Anode for SIBs.

In this study, hollow and hierarchical CuO micro-nano cubes wrapped by reduced graphene oxide (H-CuO MNCs@rGO) were designed and successfully fabricated via a novel three-step wet-chemical method. Benefiting from its unique hollow and hierarchical micro-nano structures, H-CuO MNCs@rGO exhibited significantly enhanced electrochemical Na+ storage performance when utilized as anode material for sodium-ion batteries (SIBs). Specifically, H-CuO MNCs@rGO demonstrated a specific capacity of 380.9 mAh g-1 in the initial reversible cycle and a capacity retention of 218.9 mAh g-1 after 150 cycles at a current density of 300 mA g-1. Furthermore, through the dominant pseudocapacitive behavior, an optimized rate capability of 221.2 mAh g-1 at 800 mA g-1 can be obtained for H-CuO MNCs@rGO. The comprehensive Na+ storage properties of H-CuO MNCs@rGO obviously exceeded those of hollow CuO cubes (H-CuO MNCs) and bulk CuO anodes. Such enhanced Na+ storage performances of H-CuO MNCs@rGO can be attributed to its reasonable hollow and hierarchical micro-nano structures, which provide abundant redox active sites, shorten Na+ migration pathway, buffer volume expansion, and improve electronic/ionic conductivity during sodiation/desodiation process. Our strategy provides a facile and innovative approach for the design of CuO with rational micro-nano structure as a high-performance anode for SIBs, which would also be a guiding way for tailoring transition metal oxides in other scalable and functional applications.