Physicochemical properties of environmental media can affect the adsorption of arsenic (As) by microplastics.

Microplastics are emerging pollutants that can adsorb heavy metals and threaten human health through food chain. Recently, there has been increasing interest in understanding the adsorption behavior of heavy metals by microplastics in farmland soil. In particular, arsenic (As), as a carcinogen, has the potential to be adsorbed by soil microplastics. However, the mechanisms and controlling factors of As adsorption by microplastics in farmland soil under natural conditions are still unknown. Here, microplastics and As were respectively added to farmland soils with different physicochemical properties from twelve provinces of China for adsorption experiment. We performed surface analysis of microplastics, quantified As accumulation through quasi-first-order kinetic equation and developed regression models to screen the factors controlling As adsorption. The results showed that the adsorption of As by soil microplastics was a chemical process accompanied by the loss of electrons from oxygen-containing functional groups. Soil cation exchange capacity (CEC) was the main factor controlling the adsorption rate, while soil organic matter (SOM), total nitrogen (TN) and CEC mainly influenced the equilibrium adsorption capacity. This is the first report on microplastic-As adsorption in natural soil, which allows deeper insights into risk assessment, prediction and control of microplastic-As pollution in agricultural soil.

A statistical framework to identify cell types whose genetically regulated proportions are associated with complex diseases.

Finding disease-relevant tissues and cell types can facilitate the identification and investigation of functional genes and variants. In particular, cell type proportions can serve as potential disease predictive biomarkers. In this manuscript, we introduce a novel statistical framework, cell-type Wide Association Study (cWAS), that integrates genetic data with transcriptomics data to identify cell types whose genetically regulated proportions (GRPs) are disease/trait-associated. On simulated and real GWAS data, cWAS showed good statistical power with newly identified significant GRP associations in disease-associated tissues. More specifically, GRPs of endothelial and myofibroblasts in lung tissue were associated with Idiopathic Pulmonary Fibrosis and Chronic Obstructive Pulmonary Disease, respectively. For breast cancer, the GRP of blood CD8+ T cells was negatively associated with breast cancer (BC) risk as well as survival. Overall, cWAS is a powerful tool to reveal cell types associated with complex diseases mediated by GRPs.

RNA sequencing reveals CircRNA expression profiles in chicken embryo fibroblasts infected with velogenic Newcastle disease virus.

Introduction: Newcastle disease virus (NDV) is an important avian pathogen prevalent worldwide; it has an extensive host range and seriously harms the poultry industry. Velogenic NDV strains exhibit high pathogenicity and mortality in chickens. Circular RNAs (circRNAs) are among the most abundant and conserved eukaryotic transcripts. They are part of the innate immunity and antiviral response. However, the relationship between circRNAs and NDV infection is unclear. Methods: In this study, we used circRNA transcriptome sequencing to analyze the differences in circRNA expression profiles post velogenic NDV infection in chicken embryo fibroblasts (CEFs). Gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses were used to reveal significant enrichment of differentially expressed (DE) circRNAs. The circRNA- miRNA-mRNA interaction networks were further predicted. Moreover, circ-EZH2 was selected to determine its effect on NDV infection in CEFs. Results: NDV infection altered circRNA expression profiles in CEFs, and 86 significantly DE circRNAs were identified. GO and KEGG enrichment analyses revealed significant enrichment of DE circRNAs for metabolism-related pathways, such as lysine degradation, glutaminergic synapse, and alanine, aspartic-acid, and glutamic-acid metabolism. The circRNA- miRNA-mRNA interaction networks further demonstrated that CEFs might combat NDV infection by regulating metabolism through circRNA-targeted mRNAs and miRNAs. Furthermore, we verified that circ-EZH2 overexpression and knockdown inhibited and promoted NDV replication, respectively, indicating that circRNAs are involved in NDV replication. Conclusions: These results demonstrate that CEFs exert antiviral responses by forming circRNAs, offering new insights into the mechanisms underlying NDV-host interactions.

A novel Bayesian framework for harmonizing information across tissues and studies to increase cell type deconvolution accuracy.

Computational cell type deconvolution on bulk transcriptomics data can reveal cell type proportion heterogeneity across samples. One critical factor for accurate deconvolution is the reference signature matrix for different cell types. Compared with inferring reference signature matrices from cell lines, rapidly accumulating single-cell RNA-sequencing (scRNA-seq) data provide a richer and less biased resource. However, deriving cell type signature from scRNA-seq data is challenging due to high biological and technical noises. In this article, we introduce a novel Bayesian framework, tranSig, to improve signature matrix inference from scRNA-seq by leveraging shared cell type-specific expression patterns across different tissues and studies. Our simulations show that tranSig is robust to the number of signature genes and tissues specified in the model. Applications of tranSig to bulk RNA sequencing data from peripheral blood, bronchoalveolar lavage and aorta demonstrate its accuracy and power to characterize biological heterogeneity across groups. In summary, tranSig offers an accurate and robust approach to defining gene expression signatures of different cell types, facilitating improved in silico cell type deconvolutions.

Single-cell multi-omics reveals dyssynchrony of the innate and adaptive immune system in progressive COVID-19.

Dysregulated immune responses against the SARS-CoV-2 virus are instrumental in severe COVID-19. However, the immune signatures associated with immunopathology are poorly understood. Here we use multi-omics single-cell analysis to probe the dynamic immune responses in hospitalized patients with stable or progressive course of COVID-19, explore V(D)J repertoires, and assess the cellular effects of tocilizumab. Coordinated profiling of gene expression and cell lineage protein markers shows that S100Ahi/HLA-DRlo classical monocytes and activated LAG-3hi T cells are hallmarks of progressive disease and highlights the abnormal MHC-II/LAG-3 interaction on myeloid and T cells, respectively. We also find skewed T cell receptor repertories in expanded effector CD8+ clones, unmutated IGHG+ B cell clones, and mutated B cell clones with stable somatic hypermutation frequency over time. In conclusion, our in-depth immune profiling reveals dyssynchrony of the innate and adaptive immune interaction in progressive COVID-19.

Reconstructing spatial organizations of chromosomes through manifold learning.

Decoding the spatial organizations of chromosomes has crucial implications for studying eukaryotic gene regulation. Recently, chromosomal conformation capture based technologies, such as Hi-C, have been widely used to uncover the interaction frequencies of genomic loci in a high-throughput and genome-wide manner and provide new insights into the folding of three-dimensional (3D) genome structure. In this paper, we develop a novel manifold learning based framework, called GEM (Genomic organization reconstructor based on conformational Energy and Manifold learning), to reconstruct the three-dimensional organizations of chromosomes by integrating Hi-C data with biophysical feasibility. Unlike previous methods, which explicitly assume specific relationships between Hi-C interaction frequencies and spatial distances, our model directly embeds the neighboring affinities from Hi-C space into 3D Euclidean space. Extensive validations demonstrated that GEM not only greatly outperformed other state-of-art modeling methods but also provided a physically and physiologically valid 3D representations of the organizations of chromosomes. Furthermore, we for the first time apply the modeled chromatin structures to recover long-range genomic interactions missing from original Hi-C data.