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Abstract Damage from ice and potential toxicity of ice-inhibiting cryoprotective agents (CPAs) are key issues in assisted reproduction of humans, domestic and research animals, and endangered species using cryopreserved oocytes and embryos. The nature of ice formed in bovine oocytes (similar in size to oocytes of humans and most other mammals) after rapid cooling and during rapid warming was examined using synchrotron-based time-resolved x-ray diffraction. Using cooling rates, warming rates and CPA concentrations of current practice, oocytes show no ice after cooling but always develop large ice fractions—consistent with crystallization of most free water—during warming, so most ice-related damage must occur during warming. The detailed behavior of ice at warming depended on the nature of ice formed during cooling. Increasing cooling rates allows oocytes soaked as in current practice to remain essentially ice free during both cooling and warming. Much larger convective warming rates are demonstrated and will allow routine ice-free cryopreservation with smaller CPA concentrations. These results clarify the roles of cooling, warming, and CPA concentration in generating ice in oocytes and establish the structure and grain size of ice formed. Ice formation can be eliminated as a factor affecting post-warming oocyte viability and development in many species, improving outcomes and allowing other deleterious effects of the cryopreservation cycle to be independently studied.

Abstract The rapid rise in the availability and scale of scRNA-seq data needs scalable methods for integrative analysis. Though many methods for data integration have been developed, few focus on understanding the heterogeneous effects of biological conditions across different cell populations in integrative analysis. Our proposed scalable approach, scParser, models the heterogeneous effects from biological conditions, which unveils the key mechanisms by which gene expression contributes to phenotypes. Notably, the extended scParser pinpoints biological processes in cell subpopulations that contribute to disease pathogenesis. scParser achieves favorable performance in cell clustering compared to state-of-the-art methods and has a broad and diverse applicability.

Dyslexia and developmental language disorders are important learning difficulties. However, their genetic basis remains poorly understood, and most genetic studies were performed on Europeans. There is a lack of genome-wide association studies (GWAS) on literacy phenotypes of Chinese as a native language and English as a second language (ESL) in a Chinese population. In this study, we conducted GWAS on 34 reading/language-related phenotypes in Hong Kong Chinese bilingual children (including both twins and singletons; total N = 1046). We performed association tests at the single-variant, gene, and pathway levels. In addition, we tested genetic overlap of these phenotypes with other neuropsychiatric disorders, as well as cognitive performance (CP) and educational attainment (EA) using polygenic risk score (PRS) analysis. Totally 5 independent loci (LD-clumped at r[superscript 2] = 0.01; MAF > 0.05) reached genome-wide significance (p < 5e-08; filtered by imputation quality metric Rsq>0.3 and having at least 2 correlated SNPs (r[superscript 2] > 0.5) with p < 1e-3). The loci were associated with a range of language/literacy traits such as Chinese vocabulary, character and word reading, and rapid digit naming, as well as English lexical decision. Several SNPs from these loci mapped to genes that were reported to be associated with EA and other neuropsychiatric phenotypes, such as "MANEA" and "PLXNC1." In PRS analysis, EA and CP showed the most consistent and significant polygenic overlap with a variety of language traits, especially English literacy skills. To summarize, this study revealed the genetic basis of Chinese and English abilities in a group of Chinese bilingual children. Further studies are warranted to replicate the findings.

Objectives: To explore whether COVID-19 vaccination protects against hospital admission by preventing infections and severe disease. Methods: We leveraged the UK Biobank and studied associations of COVID-19 vaccination (BioNTech-BNT162b2 or Oxford-AstraZeneca-ChAdOx1) with hospitalizations from cardiovascular and other selected diseases (N = 393,544; median follow-up = 54 days among vaccinated individuals). Multivariable Cox, Poisson regression, propensity score matching, and inverse probability treatment weighting analyses were performed. We also performed adjustment using prescription-time distribution matching, and prior event rate ratio. Results: We observed that COVID-19 vaccination (at least one dose), compared with no vaccination, was associated with reduced short-term risks of hospitalizations from stroke (hazard ratio [HR] = 0.178, 95% confidence interval [CI]: 0.127-0.250, P = 1.50e-23), venous thromboembolism (HR = 0.426, CI: 0.270-0.673, P = 2.51e-4), dementia (HR = 0.114, CI: 0.060-0.216; P = 2.24e-11), non-COVID-19 pneumonia (HR = 0.108, CI: 0.080-0.145; P = 2.20e-49), coronary artery disease (HR = 0.563, CI: 0.416-0.762; P = 2.05e-4), chronic obstructive pulmonary disease (HR = 0.212, CI: 0.126-0.357; P = 4.92e-9), type 2 diabetes (HR = 0.216, CI: 0.096-0.486, P = 2.12e-4), heart failure (HR = 0.174, CI: 0.118-0.256, P = 1.34e-18), and renal failure (HR = 0.415, CI: 0.255-0.677, P = 4.19e-4), based on standard Cox regression models. Among the previously mentioned results, reduced hospitalizations for stroke, heart failure, non-COVID-19 pneumonia, and dementia were consistently observed across regression, propensity score matching/inverse probability treatment weighting, prescription-time distribution matching, and prior event rate ratio. The results for two-dose vaccination were similar. Conclusions: Taken together, this study provides further support to the safety and benefits of COVID-19 vaccination, and such benefits may extend beyond reduction of infection risk or severity per se. However, causal relationship cannot be concluded and further studies are required.

Abstract Centrioles are subcellular organelles found at the cilia base with an evolutionarily conserved structure and a shock absorber-like function. In sperm, centrioles are found at the flagellum base and are essential for embryo development in basal animals. Yet, sperm centrioles have evolved diverse forms, sometimes acting like a transmission system, as in cattle, and sometimes becoming dispensable, as in house mice. How the essential sperm centriole evolved to become dispensable in some organisms is unclear. Here, we test the hypothesis that this transition occurred through a cascade of evolutionary changes to the proteins, structure, and function of sperm centrioles and was possibly driven by sperm competition. We found that the final steps in this cascade are associated with a change in the primary structure of the centriolar inner scaffold protein FAM161A in rodents. This information provides the first insight into the molecular mechanisms and adaptive evolution underlying a major evolutionary transition within the internal structure of the mammalian sperm neck.

RNA sequencing (RNA-Seq) is widely used to capture transcriptome dynamics across tissues, biological entities, and conditions. Currently, few or no methods can handle multiple biological variables (e.g., tissues/ phenotypes) and their interactions simultaneously, while also achieving dimension reduction (DR). We propose INSIDER, a general and flexible statistical framework based on matrix factorization, which is freely available at https://github.com/kai0511/insider. INSIDER decomposes variation from different biological variables and their interactions into a shared low-rank latent space. Particularly, it introduces the elastic net penalty to induce sparsity while considering the grouping effects of genes. It can achieve DR of high-dimensional data (of > = 3 dimensions), as opposed to conventional methods (e.g., PCA/NMF) which generally only handle 2D data (e.g., sample × expression). Besides, it enables computing 'adjusted' expression profiles for specific biological variables while controlling variation from other variables. INSIDER is computationally efficient and accommodates missing data. INSIDER also performed similarly or outperformed a close competing method, SDA, as shown in simulations and can handle complex missing data in RNA-Seq data. Moreover, unlike SDA, it can be used when the data cannot be structured into a tensor. Lastly, we demonstrate its usefulness via real data analysis, including clustering donors for disease subtyping, revealing neuro-development trajectory using the BrainSpan data, and uncovering biological processes contributing to variables of interest (e.g., disease status and tissue) and their interactions.

Japanese native horses, which consists of 8 breeds, are threatened with extinction. Embryo transfer (ET) is used to reproduce endangered animals in various mammalian species. We aimed to perform ET using native ponies from Kiso and Hokkaido as donors and recipients, respectively. ET operation included long-distance transport of non-cryopreserved embryos from Nagano Prefecture to Hokkaido. Embryos were transported 1500 km over 9 h in a container maintained at 22°C. After transferring two embryos to two recipients, one mare delivered a healthy live foal. These results demonstrated that reciprocal ET with long-distance transportation of fresh embryos between the isolated breeds may allow for the proliferation of Japanese native horses.

Abstract Background and Aims The evolving mutants of SARS‐CoV‐2 have made the COVID‐19 pandemic sustained for over 3 years. In 2022, BA.4 and BA.5 were the Omicron variants dominating the spread globally. Although COVID‐19 was no longer a Public Health Emergency of International Concern (PHEIC) as announced by WHO, the SARS‐CoV‐2 variants remain a challenge to global healthcare under the circumstances of withdrawal and loosening of personal protective behavior in the post‐quarantine era. This study aims to acknowledge the clinical characteristics caused by Omicron BA.4/BA.5 in COVID‐19 naive people and analyze possible factors affecting disease severities. Methods In this retrospective study, we report and analyze the clinical features of 1820 COVID‐19 patients infected with the BA.4/BA.5 Omicron variants of SARS‐CoV‐2 during a local outbreak that occurred in Macao SAR, China, from June to July 2022. Results A total of 83.5% of patients were symptomatic eventually. The most common symptoms were fever, cough, and sore throat. Hypertension, dyslipidemia, and diabetes mellitus were the leading comorbidities. There were significantly more elderly patients (p

Schizophrenia is a heritable neurocognitive disorder affecting about 1% of the population, and usually has an onset age at around 21–25 in males and 25–30 in females. Recent advances in genetics have helped to identify many common and rare variants for the liability to schizophrenia. Earlier evidence appeared to suggest that younger onset age is associated with higher genetic liability to schizophrenia. Clinical longitudinal research also found that early and very-early onset schizophrenia are associated with poor clinical, neurocognitive, and functional profiles. A recent study reported a heritability of 0.33 for schizophrenia onset age, but the genetic basis of this trait in schizophrenia remains elusive. In the pre-Genome-Wide Association Study (GWAS) era, genetic loci found to be associated with onset age were seldom replicated. In the post-Genome-Wide Association Study era, new conceptual frameworks are needed to clarify the role of onset age in genetic research in schizophrenia, and to identify its genetic basis. In this review, we first discussed the potential of onset age as a characterizing/subtyping feature for psychosis, and as an important phenotypic dimension of schizophrenia. Second, we reviewed the methods, samples, findings and limitations of previous genetic research on onset age in schizophrenia. Third, we discussed a potential conceptual framework for studying the genetic basis of onset age, as well as the concepts of susceptibility, modifier, and “mixed” genes. Fourth, we discussed the limitations of this review. Lastly, we discussed the potential clinical implications for genetic research of onset age of schizophrenia, and how future research can unveil the potential mechanisms for this trait.

Summary: Genetics have nominated many schizophrenia risk genes and identified convergent signals between schizophrenia and neurodevelopmental disorders. However, functional interpretation of the nominated genes in the relevant brain cell types is often lacking. We executed interaction proteomics for six schizophrenia risk genes that have also been implicated in neurodevelopment in human induced cortical neurons. The resulting protein network is enriched for common variant risk of schizophrenia in Europeans and East Asians, is down-regulated in layer 5/6 cortical neurons of individuals affected by schizophrenia, and can complement fine-mapping and eQTL data to prioritize additional genes in GWAS loci. A sub-network centered on HCN1 is enriched for common variant risk and contains proteins (HCN4 and AKAP11) enriched for rare protein-truncating mutations in individuals with schizophrenia and bipolar disorder. Our findings showcase brain cell-type-specific interactomes as an organizing framework to facilitate interpretation of genetic and transcriptomic data in schizophrenia and its related disorders.

Genome-wide association studies (GWAS) are commonly employed to study the genetic basis of complex traits/diseases, and a key question is how much heritability could be explained by all single nucleotide polymorphisms (SNPs) in GWAS. One widely used approach that relies on summary statistics only is linkage disequilibrium score regression (LDSC); however, this approach requires certain assumptions about the effects of SNPs (e.g., all SNPs contribute to heritability and each SNP contributes equal variance). More flexible modeling methods may be useful. We previously developed an approach recovering the “true” effect sizes from a set of observed z-statistics with an empirical Bayes approach, using only summary statistics. However, methods for standard error (SE) estimation are not available yet, limiting the interpretation of our results and the applicability of the approach. In this study, we developed several resampling-based approaches to estimate the SE of SNP-based heritability, including two jackknife and three parametric bootstrap methods. The resampling procedures are performed at the SNP level as it is most common to estimate heritability from GWAS summary statistics alone. Simulations showed that the delete-d-jackknife and parametric bootstrap approaches provide good estimates of the SE. In particular, the parametric bootstrap approaches yield the lowest root-mean-squared-error (RMSE) of the true SE. We also explored various methods for constructing confidence intervals (CIs). In addition, we applied our method to estimate the SNP-based heritability of 12 immune-related traits (levels of cytokines and growth factors) to shed light on their genetic architecture. We also implemented the methods to compute the sum of heritability explained and the corresponding SE in an R package SumVg. In conclusion, SumVg may provide a useful alternative tool for calculating SNP heritability and estimating SE/CI, which does not rely on distributional assumptions of SNP effects.

Spermatogenesis depends on an orchestrated series of developing events in germ cells and full maturation of the somatic microenvironment. To date, the majority of efforts to study cellular heterogeneity in testis has been focused on single-cell gene expression rather than the chromatin landscape shaping gene expression. To advance our understanding of the regulatory programs underlying testicular cell types, we analyzed single-cell chromatin accessibility profiles in more than 25,000 cells from mouse developing testis. We showed that single-cell sequencing assay for transposase-accessible chromatin (scATAC-Seq) allowed us to deconvolve distinct cell populations and identify cis-regulatory elements (CREs) underlying cell-type specification. We identified sets of transcription factors associated with cell type-specific accessibility, revealing novel regulators of cell fate specification and maintenance. Pseudotime reconstruction revealed detailed regulatory dynamics coordinating the sequential developmental progressions of germ cells and somatic cells. This high-resolution dataset also unveiled previously unreported subpopulations within both the Sertoli and Leydig cell groups. Further, we defined candidate target cell types and genes of several genome-wide association study (GWAS) signals, including those associated with testosterone levels and coronary artery disease. Collectively, our data provide a blueprint of the ‘regulon’ of the mouse male germline and supporting somatic cells.

Abstract Research over the past two decades has identified a group of common genetic variants explaining a portion of variance in native language ability. The present study investigates whether the same group of genetic variants are associated with different languages and languages learned at different times in life. We recruited 940 young adults who spoke from childhood Chinese and English as their first (native) (L1) and second (L2) language, respectively, who were learners of a new, third (L3) language. For the variants examined, we found a general decrease of contribution of genes to language functions from native to foreign (L2 and L3) languages, with variance in foreign languages explained largely by non-genetic factors such as musical training and motivation. Furthermore, genetic variants that were found to contribute to traits specific to Chinese and English respectively exerted the strongest effects on L1 and L2. These results seem to speak against the hypothesis of a language- and time-universal genetic core of linguistic functions. Instead, they provide preliminary evidence that genetic contribution to language may depend at least partly on the intricate language-specific features. Future research including a larger sample size, more languages and more genetic variants is required to further explore these hypotheses.

Abstract Although displaying genetic correlations, psychiatric disorders are clinically defined as categorical entities as they each have distinguishing clinical features and may involve different treatments. Identifying differential genetic variations between these disorders may reveal how the disorders differ biologically and help to guide more personalized treatment. Here we presented a statistical framework and comprehensive analysis to identify genetic markers differentially associated with various psychiatric disorders/traits based on GWAS summary statistics, covering 18 psychiatric traits/disorders and 26 comparisons. We also conducted comprehensive analysis to unravel the genes, pathways and SNP functional categories involved, and the cell types and tissues implicated. We also assessed how well one could distinguish between psychiatric disorders by polygenic risk scores (PRS). SNP-based heritabilities (h 2 snp) were significantly larger than zero for most comparisons. Based on current GWAS data, PRS have mostly modest power to distinguish between psychiatric disorders. For example, we estimated that AUC for distinguishing schizophrenia from major depressive disorder (MDD), bipolar disorder (BPD) from MDD and schizophrenia from BPD were 0.694, 0.602 and 0.618, respectively, while the maximum AUC (based on h 2 snp) were 0.763, 0.749 and 0.726, respectively. We also uncovered differences in each pair of studied traits in terms of their differences in genetic correlation with comorbid traits. For example, clinically defined MDD appeared to more strongly genetically correlated with other psychiatric disorders and heart disease, when compared to non-clinically defined depression in UK Biobank. Our findings highlight genetic differences between psychiatric disorders and the mechanisms involved. PRS may help differential diagnosis of selected psychiatric disorders in the future with larger GWAS samples.

Abstract Background Novel coronavirus disease 2019 (COVID-19) has become a pandemic, and over 80 million cases and over 1.8 million deaths were reported in 2020. This highly contagious virus is spread primarily via respiratory droplets from face-to-face contact and contaminated surfaces as well as potential aerosol spread. Over half of transmissions occur from presymptomatic and asymptomatic carriers. Although several vaccines are currently available for emergency use, there are uncertainties regarding the duration of protection and the efficacy of preventing asymptomatic spread. Thus, personal protective health behaviour and measures against COVID-19 are still widely recommended after immunization. This study aimed to clarify the efficacy of these measures, and the results may provide valuable guidance to policymakers to educate the general public about how to reduce the individual-level risk of COVID-19 infection. Methods This case-control study enrolled 24 laboratory-confirmed COVID-19 patients from Centro Hospitalar Conde de São Januário (C.H.C.S.J.), which was the only hospital designated to manage COVID-19 patients in Macao SAR, China, and 1113 control participants who completed a 14-day mandatory quarantine in 12 designated hotels due to returning from high-risk countries between 17 March and 15 April 2020. A questionnaire was developed to extract demographic information, contact history, and personal health behaviour. Results Participants primarily came from the United Kingdom (33.2%), followed by the United States (10.5%) and Portugal (10.2%). Independent factors for COVID-19 infection were having physical contact with confirmed/suspected COVID-19 patients (adjusted OR, 12.108 [95% CI, 3.380–43.376], P

Blood–brain barrier dysfunction occurs in ageing and in neurodegenerative diseases. Here, the authors use scRNA-seq to identify transcriptomic changes in endothelial cell subtypes in the aged mouse brain, some of which may generalize to human and can be reversed by treatment with a GLP-1R agonist.

Until now, there have been no reports of foals born through embryo transfer after artificial insemination using frozen semen in Japan. The aims of this study were to develop a riding crossbred horse and evaluate the prospects of embryo transfer technology in multiplying horse population. In both donor and recipient mares, luteolysis was induced by the administration of 0.1 mg Cloprostenol to synchronize the onset of estrus, and ovulation was induced by administering 2000 IU human chorionic gonadotropin (hCG) or 0.75 mg Deslorelin. Frozen semen from an Irish Connemara pony stallion was used to breed a Hokkaido native pony mare by deep-horn artificial insemination (dose, 400 × 106 sperm). A non-surgical technique was used to collect embryos from the donor mare at day 7 post-ovulation and transfer them transcervically into the uterus of recipient mares (n = 4) immediately after collection. Weekly blood samples were collected from the recipients throughout pregnancy. A total of four embryos were recovered from seven collection attempts (57% recovery) from a donor mare in a single breeding season. Three of the four transferred embryos maintained successful pregnancy and delivered a healthy live foal (75% birth). A normal progesterone profile was observed throughout gestation in recipient mares. In conclusion, for the first time, to the best of our knowledge, this study describes the birth of foals through non-surgical transcervical embryo transfer in Japan after artificial insemination using frozen semen. We expect that this new crossbreed (Connemara pony × Hokkaido native pony) will be a good riding breed.

Drug development is a very costly and lengthy process, while repositioned or repurposed drugs could be brought into clinical practice within a shorter time-frame and at a much reduced cost. Numerous computational approaches to drug repositioning have been developed, but methods utilizing genome-wide association studies (GWASs) data are less explored.The past decade has observed a massive growth in the amount of data from GWAS; the rich information contained in GWAS has great potential to guide drug repositioning or discovery. While multiple tools are available for finding the most relevant genes from GWAS hits, searching for top susceptibility genes is only one way to guide repositioning, which has its own limitations.Here we provide a comprehensive review of different computational approaches that employ GWAS data to guide drug repositioning. These methods include selecting top candidate genes from GWAS as drug targets, deducing drug candidates based on drug-drug and disease-disease similarities, searching for reversed expression profiles between drugs and diseases, pathway-based methods as well as approaches based on analysis of biological networks. Each method is illustrated with examples, and their respective strengths and limitations are discussed. We also discussed several areas for future research.

BackgroundCOVID-19 is a major public health concern. Given the extent of the pandemic, it is urgent to identify risk factors associated with disease severity. More accurate prediction of those at risk of developing severe infections is of high clinical importance. ObjectiveBased on the UK Biobank (UKBB), we aimed to build machine learning models to predict the risk of developing severe or fatal infections, and uncover major risk factors involved. MethodsWe first restricted the analysis to infected individuals (n=7846), then performed analysis at a population level, considering those with no known infection as controls (ncontrols=465,728). Hospitalization was used as a proxy for severity. A total of 97 clinical variables (collected prior to the COVID-19 outbreak) covering demographic variables, comorbidities, blood measurements (eg, hematological/liver/renal function/metabolic parameters), anthropometric measures, and other risk factors (eg, smoking/drinking) were included as predictors. We also constructed a simplified (lite) prediction model using 27 covariates that can be more easily obtained (demographic and comorbidity data). XGboost (gradient-boosted trees) was used for prediction and predictive performance was assessed by cross-validation. Variable importance was quantified by Shapley values (ShapVal), permutation importance (PermImp), and accuracy gain. Shapley dependency and interaction plots were used to evaluate the pattern of relationships between risk factors and outcomes. ResultsA total of 2386 severe and 477 fatal cases were identified. For analyses within infected individuals (n=7846), our prediction model achieved area under the receiving-operating characteristic curve (AUC–ROC) of 0.723 (95% CI 0.711-0.736) and 0.814 (95% CI 0.791-0.838) for severe and fatal infections, respectively. The top 5 contributing factors (sorted by ShapVal) for severity were age, number of drugs taken (cnt_tx), cystatin C (reflecting renal function), waist-to-hip ratio (WHR), and Townsend deprivation index (TDI). For mortality, the top features were age, testosterone, cnt_tx, waist circumference (WC), and red cell distribution width. For analyses involving the whole UKBB population, AUCs for severity and fatality were 0.696 (95% CI 0.684-0.708) and 0.825 (95% CI 0.802-0.848), respectively. The same top 5 risk factors were identified for both outcomes, namely, age, cnt_tx, WC, WHR, and TDI. Apart from the above, age, cystatin C, TDI, and cnt_tx were among the top 10 across all 4 analyses. Other diseases top ranked by ShapVal or PermImp were type 2 diabetes mellitus (T2DM), coronary artery disease, atrial fibrillation, and dementia, among others. For the “lite” models, predictive performances were broadly similar, with estimated AUCs of 0.716, 0.818, 0.696, and 0.830, respectively. The top ranked variables were similar to above, including age, cnt_tx, WC, sex (male), and T2DM. ConclusionsWe identified numerous baseline clinical risk factors for severe/fatal infection by XGboost. For example, age, central obesity, impaired renal function, multiple comorbidities, and cardiometabolic abnormalities may predispose to poorer outcomes. The prediction models may be useful at a population level to identify those susceptible to developing severe/fatal infections, facilitating targeted prevention strategies. A risk-prediction tool is also available online. Further replications in independent cohorts are required to verify our findings.