COVID-19 vaccination affects short-term anti-coagulation levels in warfarin treatment.

Vaccines against SARS-CoV-2 have been recommended across the world, yet no study has investigated whether COVID-19 vaccination influences short-term warfarin anti-coagulation levels. Patients on stable warfarin treatment who received anti-SARS-CoV-2 vaccination were prospectively enrolled and followed up for three months. INR values less than 10 days before vaccination (baseline), 3-5 days (short-term) and 6-14 days (medium-term) after vaccination were recorded as INR0, INR1, and INR2, respectively. The variations of INR values within individuals were compared, and the linear mixed effect model was used to evaluate the variations of INR values at different time points. Logistic regression analysis was performed to determine covariates related to INR variations after COVID-19 vaccination. Vaccination safety was also monitored. There was a significant difference in INR values between INR0 and INR1 (2.15 vs. 2.26, p = 0.003), yet no marked difference was found between INR0 and INR2. The linear mixed effect model also demonstrated that INR variation was significant in short-term but not in medium-term or long-term period after vaccination. Logistic regression analysis showed that no investigated covariates, including age, vaccine dose, genetic polymorphisms of VKORC1 and CYP2C9 etc., were associated with short-term INR variations. Two patients (2.11%) reported gingival hemorrhage in the short-term due to increased INR values. The overall safety of COVID-19 vaccines for patients on warfarin was satisfying. COVID-19 vaccines may significantly influence warfarin anticoagulation levels 3-5 days after vaccination. We recommend patients on warfarin to perform at least one INR monitoring within the first week after COVID-19 vaccination.

MaveRegistry: a collaboration platform for multiplexed assays of variant effect.

SUMMARY: Multiplexed assays of variant effect (MAVEs) are capable of experimentally testing all possible single nucleotide or amino acid variants in selected genomic regions, generating 'variant effect maps', which provide biochemical insight and functional evidence to enable more rapid and accurate clinical interpretation of human variation. Because the international community applying MAVE approaches is growing rapidly, we developed the online MaveRegistry platform to catalyze collaboration, reduce redundant efforts, allow stakeholders to nominate targets and enable tracking and sharing of progress on ongoing MAVE projects. AVAILABILITY AND IMPLEMENTATION: MaveRegistry service: https://registry.varianteffect.org. MaveRegistry source code: https://github.com/kvnkuang/maveregistry-front-end.

Prioritizing genes for systematic variant effect mapping.

MOTIVATION: When rare missense variants are clinically interpreted as to their pathogenicity, most are classified as variants of uncertain significance (VUS). Although functional assays can provide strong evidence for variant classification, such results are generally unavailable. Multiplexed assays of variant effect can generate experimental 'variant effect maps' that score nearly all possible missense variants in selected protein targets for their impact on protein function. However, these efforts have not always prioritized proteins for which variant effect maps would have the greatest impact on clinical variant interpretation. RESULTS: Here, we mined databases of clinically interpreted variants and applied three strategies, each building on the previous, to prioritize genes for systematic functional testing of missense variation. The strategies ranked genes (i) by the number of unique missense VUS that had been reported to ClinVar; (ii) by movability- and reappearance-weighted impact scores, to give extra weight to reappearing, movable VUS and (iii) by difficulty-adjusted impact scores, to account for the more resource-intensive nature of generating variant effect maps for longer genes. Our results could be used to guide systematic functional testing of missense variation toward greater impact on clinical variant interpretation. AVAILABILITY AND IMPLEMENTATION: Source code available at: https://github.com/rothlab/mave-gene-prioritization. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

MaveQuest: a web resource for planning experimental tests of human variant effects.

SUMMARY: Fully realizing the promise of personalized medicine will require rapid and accurate classification of pathogenic human variation. Multiplexed assays of variant effect (MAVEs) can experimentally test nearly all possible variants in selected gene targets. Planning a MAVE study involves identifying target genes with clinical impact, and identifying scalable functional assays for that target. Here, we describe MaveQuest, a web-based resource enabling systematic variant effect mapping studies by identifying potential functional assays, disease phenotypes and clinical relevance for nearly all human protein-coding genes. AVAILABILITY AND IMPLEMENTATION: MaveQuest service: https://mavequest.varianteffect.org/. MaveQuest source code: https://github.com/kvnkuang/mavequest-front-end/. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Learning from Floods: Linking flood experience and flood resilience.

It has been argued that learning from flood experience contributes to flood resilience. However, it is unclear what such a learning process involves, and it is debatable whether flood experience always leads to flood resilience. To bridge this research gap, we develop the Learning from Floods (LFF) model to articulate the process of learning from flood experience and how it affects flood resilience. The LFF model suggests that flood experience prompts individual and social learning to give rise to flood-related knowledge, which is subject to learning opportunity, learning motivation, and prior knowledge. Flood-related knowledge could inform flood management and/or other action, which however can be limited by barriers, including information and resource availability, attitude, social capital, and policy barriers. Together, flood-related knowledge and its resulting action are considered the lesson learned, which then affects flood resilience through changing floodability, recoverability, adaptability, and/or transformability. We apply the LFF model to discuss the different learning processes and their respective effects on flood resilience in two environments. It suggests that an environment that is well-protected by flood control infrastructure is not conducive to learning about flood mitigation. Subsequently, we call for learning-based flood mitigation to nurture flood resilience in the face of climate change.

DDAH1-V3 transcript might act as miR-21 sponge to maintain balance of DDAH1-V1 in cultured HUVECs.

OBJECTIVE: To investigate whether microRNA (miRNA) miR-21 regulates dimethylarginine dimethylaminohydrolase 1 (DDAH1) expression through binding 3'-UTR region directly in human umbilical venous endothelial cells (HUVECs) and to explore whether DDAH1-V2/V3 transcripts can function as microRNA sponge, thereby modulating DDAH1-V1 expression. METHODS: The DDAH1 3'-UTR containing miR-21 recognizing sequence was cloned into PmirGLO dual-luciferase miRNA target expression plasmid to construct PmirGLO-miR-21. The plasmid and miR-21 (at concentrations of 25, 50, 100 nM, respectively) or negative control (100 nM) were co-transfected into HUVECs, luciferase activity was detected at 24 h. HUVECs were incubated with 2 µg/ml Actinomycin D for the indicated time after miR-21 (25 nM) transfection, half-lives of DDAH1 mRNA were determined. HUVECs were transfected with PmirGLO-miR-21 alone or co-transfected with miR-21 for 24 h, DDAH1 transcripts mRNA, eNOS activity and DDAH1 protein expression were determined. RESULTS: MiR-21 decreased luciferase activity of PmirGLO-miR-21 in a dose-dependent manner (P < 0.05 for 25 nM miR-21, P < 0.01 for 50 nM and 100 nM miR-21), and miR-21 inhibitor increased reporter activity of PmirGLO-miR-21 and mRNA expression of all three DDAH1 transcript variants significantly (P < 0.05, respectively). The degree of increase in endogenous DDAH1 mRNA expression by miR-21 inhibitor was more obvious for DDAH1-V3. Overexpression of miR-21 decreased mRNA expression and mRNA half-life time of all DDAH1 transcripts significantly (P < 0.05), and DDAH1-V2 displayed significantly decreased half-life time than DDAH1-V1 and -V3 with or without miR-21 transfection (P < 0.05, respectively). MiR-21 (100 nM) decreased DDAH1 protein expression and eNOS activity significantly (P < 0.05), which was reversed by PmirGLO-miR-21 transfection (P < 0.05). Transfection of PmirGLO-miR-21 alone increased intracellular miR-21 expression by approximately 5.6-fold, but only showed a trend of increase in DDAH1 protein expression. CONCLUSION: Our results confirmed DDAH1 3'-UTR as a target for miR-21, and endogenous miR-21 showed increased inhibitory effect on DDAH1-V3 transcript. DDAH1 3'-UTR, especially for DDAH1-V3, may function as miR-21 sponge to regulate DDAH1 protein expression. Modulation of miR-21-DDAH1 interaction may provide a new approach for tackling cardiovascular diseases.

[Multivariate Calibration Combined with Mass Spectrometry for Rapid Analysis].

A mixture of four substances of benzaldehyde, iso-octane, butyl acetate, acetophenone were quantitatively analyzed by mass spectrometry combined with chemometrics. The mass chromatogram data of mixture were proceeded with two methods for quantitative analysis. One is feature selection--Multiple Linear Regression (MLR) and the other is full spectrum--Partial Least Squares (PLS). The results show that the RMSEP of benzaldehyde were 0.062 and 0.091 after selecting m/z spectrum and full spectrum respectively; RMSEP of isooctane were 0.048 and 0.057 after selecting spectrum and full spectrum respectively; which of butyl acetate were 0.021 and 0.020 and of acetophenone were 0.010 and 0.032. The feature selection results of the mixture were better than that of the full spectrum modeling results expect butyl acetate which got similar results by the two methods.

Sleep-dependent neuroplastic changes during auditory perceptual learning.

Auditory perceptual learning is accompanied by a significant increase in the amplitude of sensory evoked responses on the second day of training. This is thought to reflect memory consolidation after the first practice session. However, it is unclear whether the changes in sensory evoked responses depend on sleep per se or whether a break between training sessions would sufficiently yield similar changes. To assess the relative contributions of sleep and passage of time (wakefulness) on the sensory evoked responses, we recorded auditory evoked fields using magnetoencephalography while participants performed a vowel segregation task in three different sessions separated by 12h over two consecutive days. The first two practice sessions were scheduled in the morning and evening of the same day for one group and the evening and morning of subsequent days for the other group. For each participant, we modeled the auditory evoked magnetic field with single dipoles in bilateral superior temporal planes. We then examined the amplitudes and latencies of the resulting source waveforms as a function of sleep and passage of time. In both groups, performance gradually improved with repeated testing. Auditory learning was paralleled by increased sustained field between 250 and 350ms after sound onset as well as sensory evoked fields around 200ms after sound onset (i.e., P2m amplitude) for sessions taking place on the same and different days, respectively. These neuromagnetic changes suggest that auditory learning involves a consolidation phase that occurs during the wake state, which is followed by a sleep-dependent consolidation stage indexed by the P2m amplitude.

[Determination of calcium and magnesium in tobacco by near-infrared spectroscopy and least squares-support vector machine].

The purpose of the present paper is to determine calcium and magnesium in tobacco using NIR combined with least squares-support vector machine (LS-SVM). Five hundred ground and dried tobacco samples from Qujing city, Yunnan province, China, were surveyed by a MATRIX-I spectrometer (Bruker Optics, Bremen, Germany). At the beginning of data processing, outliers of samples were eliminated for stability of the model. The rest 487 samples were divided into several calibration sets and validation sets according to a hybrid modeling strategy. Monte-Carlo cross validation was used to choose the best spectral preprocess method from multiplicative scatter correction (MSC), standard normal variate transformation (SNV), S-G smoothing, 1st derivative, etc., and their combinations. To optimize parameters of LS-SVM model, the multilayer grid search and 10-fold cross validation were applied. The final LS-SVM models with the optimizing parameters were trained by the calibration set and accessed by 287 validation samples picked by Kennard-Stone method. For the quantitative model of calcium in tobacco, Savitzky-Golay FIR smoothing with frame size 21 showed the best performance. The regularization parameter λ of LS-SVM was e16.11, while the bandwidth of the RBF kernel σ2 was e8.42. The determination coefficient for prediction (Rc(2)) was 0.9755 and the determination coefficient for prediction (Rp(2)) was 0.9422, better than the performance of PLS model (Rc(2)=0.9593, Rp(2)=0.9344). For the quantitative analysis of magnesium, SNV made the regression model more precise than other preprocess. The optimized λ was e15.25 and σ2 was e6.32. Rc(2) and Rp(2) were 0.9961 and 0.9301, respectively, better than PLS model (Rc(2)=0.9716, Rp(2)=0.8924). After modeling, the whole progress of NIR scan and data analysis for one sample was within tens of seconds. The overall results show that NIR spectroscopy combined with LS-SVM can be efficiently utilized for rapid and accurate analysis of calcium and magnesium in tobacco.

PACS allows comprehensive dissection of multiple factors governing chromatin accessibility from snATAC-seq data.

Single nucleus ATAC-seq (snATAC-seq) experimental designs have become increasingly complex with multiple factors that might affect chromatin accessibility, including genotype, cell type, tissue of origin, sample location, batch, etc., whose compound effects are difficult to test by existing methods. In addition, current snATAC-seq data present statistical difficulties due to their sparsity and variations in individual sequence capture. To address these problems, we present a zero-adjusted statistical model, Probability model of Accessible Chromatin of Single cells (PACS), that can allow complex hypothesis testing of factors that affect accessibility while accounting for sparse and incomplete data. For differential accessibility analysis, PACS controls the false positive rate and achieves on average a 17% to 122% higher power than existing tools. We demonstrate the effectiveness of PACS through several analysis tasks including supervised cell type annotation, compound hypothesis testing, batch effect correction, and spatiotemporal modeling. We apply PACS to several datasets from a variety of tissues and show its ability to reveal previously undiscovered insights in snATAC-seq data.

SCO-pH: Microfluidic dynamic phenotyping platform for high-throughput screening of single cell acidification.

Studies on the dynamics of single cell phenotyping have been hampered by the lack of quantitative high-throughput metabolism assays. Extracellular acidification, a prominent phenotype, yields significant insights into cellular metabolism, including tumorigenicity. Here, we develop a versatile microfluidic system for single cell optical pH analysis (SCO-pH), which compartmentalizes single cells in 140-pL droplets and immobilizes approximately 40,000 droplets in a two-dimensional array for temporal extracellular pH analysis. SCO-pH distinguishes cells undergoing hyperglycolysis induced by oligomycin A from untreated cells by monitoring their extracellular acidification. To facilitate pH sensing in each droplet, we encapsulate a cell-impermeable pH probe whose fluorescence intensities are quantified. Using this approach, we can differentiate hyperglycolytic cells and concurrently observe single cell heterogeneity in extracellular acidification dynamics. This high-throughput system will be useful in applications that require dynamic phenotyping of single cells with significant heterogeneity.

Benchmarking computational variant effect predictors by their ability to infer human traits.

BACKGROUND: Computational variant effect predictors offer a scalable and increasingly reliable means of interpreting human genetic variation, but concerns of circularity and bias have limited previous methods for evaluating and comparing predictors. Population-level cohorts of genotyped and phenotyped participants that have not been used in predictor training can facilitate an unbiased benchmarking of available methods. Using a curated set of human gene-trait associations with a reported rare-variant burden association, we evaluate the correlations of 24 computational variant effect predictors with associated human traits in the UK Biobank and All of Us cohorts. RESULTS: AlphaMissense outperformed all other predictors in inferring human traits based on rare missense variants in UK Biobank and All of Us participants. The overall rankings of computational variant effect predictors in these two cohorts showed a significant positive correlation. CONCLUSION: We describe a method to assess computational variant effect predictors that sidesteps the limitations of previous evaluations. This approach is generalizable to future predictors and could continue to inform predictor choice for personal and clinical genetics.

How does flood resistance affect learning from flood experiences? A study of two communities in Central China.

Property-level flood risk adaptation (PLFRA) has received significant attention in recent years, as flood resilience has become increasingly important in flood risk management. Earlier studies have indicated that learning from flood experiences can affect flood risk perception and the adoption of PLFRA measures; however, it remains unclear whether and how this learning process can be affected by flood control infrastructure-specifically, the level of flood resistance it offers. This study attempts to answer the question: Do people living in environments with different levels of flood resistance learn different lessons from flood experience, manifested in flood risk perception and PLFRA? We present a comparative study of the rural village of Xinnongcun and the urban community of Nanhuyayuan in Central China. In-person interviews with a total of 34 local residents were conducted to understand how flood experiences affect flood risk perception and PLFRA. We find that learning from flood experiences in the highly flood-resistant environment (Nanhuyayuan) does not contribute to flood risk perception but further enhances flood resistance, whereas learning in a less flood-resistant environment (Xinnongcun) leads to a better understanding of flood risk and promotes PLFRA. We argue that flood resistance can affect the learning from flood experiences. High flood resistance can suppress PLFRA through a different learning process that involves learning inertia and path dependency. In the search for flood resilience, this begs society to re-examine the widespread assertion that both structural and nonstructural measures are important in flood risk management. Supplementary Information: The online version contains supplementary material available at 10.1007/s10584-022-03401-3.

Learning Proteome Domain Folding Using LSTMs in an Empirical Kernel Space.

The recognition of protein structural folds is the starting point for protein function inference and for many structural prediction tools. We previously introduced the idea of using empirical comparisons to create a data-augmented feature space called PESS (Protein Empirical Structure Space)1 as a novel approach for protein structure prediction. Here, we extend the previous approach by generating the PESS feature space over fixed-length subsequences of query peptides, and applying a sequential neural network model, with one long short-term memory cell layer followed by a fully connected layer. Using this approach, we show that only a small group of domains as a training set is needed to achieve near state-of-the-art accuracy on fold recognition. Our method improves on the previous approach by reducing the training set required and improving the model's ability to generalize across species, which will help fold prediction for newly discovered proteins.

Noncoding genetic variation in GATA3 increases acute lymphoblastic leukemia risk through local and global changes in chromatin conformation.

Inherited noncoding genetic variants confer significant disease susceptibility to childhood acute lymphoblastic leukemia (ALL) but the molecular processes linking germline polymorphisms with somatic lesions in this cancer are poorly understood. Through targeted sequencing in 5,008 patients, we identified a key regulatory germline variant in GATA3 associated with Philadelphia chromosome-like ALL (Ph-like ALL). Using CRISPR-Cas9 editing and samples from patients with Ph-like ALL, we showed that this variant activated a strong enhancer that upregulated GATA3 transcription. This, in turn, reshaped global chromatin accessibility and three-dimensional genome organization, including regions proximal to the ALL oncogene CRLF2. Finally, we showed that GATA3 directly regulated CRLF2 and potentiated the JAK-STAT oncogenic effects during leukemogenesis. Taken together, we provide evidence for a distinct mechanism by which a germline noncoding variant contributes to oncogene activation, epigenetic regulation and three-dimensional genome reprogramming.

Uncovering the Contribution of Moderate-Penetrance Susceptibility Genes to Breast Cancer by Whole-Exome Sequencing and Targeted Enrichment Sequencing of Candidate Genes in Women of European Ancestry.

Rare variants in at least 10 genes, including BRCA1, BRCA2, PALB2, ATM, and CHEK2, are associated with increased risk of breast cancer; however, these variants, in combination with common variants identified through genome-wide association studies, explain only a fraction of the familial aggregation of the disease. To identify further susceptibility genes, we performed a two-stage whole-exome sequencing study. In the discovery stage, samples from 1528 breast cancer cases enriched for breast cancer susceptibility and 3733 geographically matched unaffected controls were sequenced. Using five different filtering and gene prioritization strategies, 198 genes were selected for further validation. These genes, and a panel of 32 known or suspected breast cancer susceptibility genes, were assessed in a validation set of 6211 cases and 6019 controls for their association with risk of breast cancer overall, and by estrogen receptor (ER) disease subtypes, using gene burden tests applied to loss-of-function and rare missense variants. Twenty genes showed nominal evidence of association (p-value < 0.05) with either overall or subtype-specific breast cancer. Our study had the statistical power to detect susceptibility genes with effect sizes similar to ATM, CHEK2, and PALB2, however, it was underpowered to identify genes in which susceptibility variants are rarer or confer smaller effect sizes. Larger sample sizes would be required in order to identify such genes.

Low expression of miR­532­3p contributes to cerebral ischemia/reperfusion oxidative stress injury by directly targeting NOX2.

NADPH oxidase 2 (NOX2) is a major subtype of NOX and is responsible for the generation of reactive oxygen species (ROS) in brain tissues. MicroRNAs (miRNAs/miRs) are important epigenetic regulators of NOX2. The present study aimed to identify the role of NOX2 miRNA­targets in ischemic stroke (IS). A rat cerebral ischemia/reperfusion (CI/R) injury model and a SH­SY5Y cell hypoxia/reoxygenation (H/R) model were used to simulate IS. Gene expression levels, ROS production and apoptosis in tissue or cells were determined, and bioinformatic analysis was conducted for target prediction of miRNA. In vitro experiments, including function­gain and luciferase activity assays, were also performed to assess the roles of miRNAs. The results indicated that NOX2 was significantly increased in brain tissues subjected to I/R and in SH­SY5Y cells subjected to H/R, while the expression of miR­532­3p (putative target of NOX2) was significantly decreased in brain tissues and plasma. Overexpression of miR­532­3p significantly suppressed NOX2 expression and ROS generation in SH­SY5Y cells subjected to H/R, as well as reduced the relative luciferase activity of cells transfected with a reporter gene plasmid. Collectively, these data indicated that miR­532­3p may be a target of NOX2 and a biomarker for CI/R injury. Thus, the present study may provide a novel target for drug development and IS therapy.

A Comprehensive, Flexible Collection of SARS-CoV-2 Coding Regions.

The world is facing a global pandemic of COVID-19 caused by the SARS-CoV-2 coronavirus. Here we describe a collection of codon-optimized coding sequences for SARS-CoV-2 cloned into Gateway-compatible entry vectors, which enable rapid transfer into a variety of expression and tagging vectors. The collection is freely available. We hope that widespread availability of this SARS-CoV-2 resource will enable many subsequent molecular studies to better understand the viral life cycle and how to block it.

How Twitter Can Support the HIV/AIDS Response to Achieve the 2030 Eradication Goal: In-Depth Thematic Analysis of World AIDS Day Tweets.

BACKGROUND: HIV/AIDS is a tremendous public health crisis, with a call for its eradication by 2030. A human rights response through civil society engagement is critical to support and sustain HIV eradication efforts. However, ongoing civil engagement is a challenge. OBJECTIVE: This study aimed to demonstrate the use of Twitter data to assess public sentiment in support of civil society engagement. METHODS: Tweets were collected during World AIDS Days 2014 and 2015. A total of 39,940 unique tweets (>10 billion users) in 2014 and 78,215 unique tweets (>33 billion users) in 2015 were analyzed. Response frequencies were aggregated using natural language processing. Hierarchical rank-2 nonnegative matrix factorization algorithm generated a hierarchy of tweets into binary trees. Tweet hierarchy clusters were thematically organized by the Joint United Nations Programme on HIV/AIDS core action principles and categorized under HIV/AIDS Prevention, Treatment or Care, or Support. RESULTS: Topics tweeted 35 times or more were visualized. Results show a decrease in 2015 in the frequency of tweets associated with the fight to end HIV/AIDS, the recognition of women, and to achieve an AIDS-free generation. Moreover, an increase in tweets was associated with an integrative approach to the HIV/AIDS response. Hierarchical thematic differences in 2015 included no prevention discussion and the recognition of the pandemic's impact and discrimination. In addition, a decrease was observed in motivation to fast track the pandemic's end and combat HIV/AIDS. CONCLUSIONS: The human rights-based response to HIV/AIDS eradication is critical. Findings demonstrate the usefulness of Twitter as a low-cost method to assess public sentiment for enhanced knowledge, increased hope, and revitalized expectations for HIV/AIDS eradication.

The 3D Genome Browser: a web-based browser for visualizing 3D genome organization and long-range chromatin interactions.

Here, we introduce the 3D Genome Browser, http://3dgenome.org , which allows users to conveniently explore both their own and over 300 publicly available chromatin interaction data of different types. We design a new binary data format for Hi-C data that reduces the file size by at least a magnitude and allows users to visualize chromatin interactions over millions of base pairs within seconds. Our browser provides multiple methods linking distal cis-regulatory elements with their potential target genes. Users can seamlessly integrate thousands of other omics data to gain a comprehensive view of both regulatory landscape and 3D genome structure.