GWAS and meta-analysis identifies 49 genetic variants underlying critical COVID-19.

Critical illness in COVID-19 is an extreme and clinically homogeneous disease phenotype that we have previously shown1 to be highly efficient for discovery of genetic associations2. Despite the advanced stage of illness at presentation, we have shown that host genetics in patients who are critically ill with COVID-19 can identify immunomodulatory therapies with strong beneficial effects in this group3. Here we analyse 24,202 cases of COVID-19 with critical illness comprising a combination of microarray genotype and whole-genome sequencing data from cases of critical illness in the international GenOMICC (11,440 cases) study, combined with other studies recruiting hospitalized patients with a strong focus on severe and critical disease: ISARIC4C (676 cases) and the SCOURGE consortium (5,934 cases). To put these results in the context of existing work, we conduct a meta-analysis of the new GenOMICC genome-wide association study (GWAS) results with previously published data. We find 49 genome-wide significant associations, of which 16 have not been reported previously. To investigate the therapeutic implications of these findings, we infer the structural consequences of protein-coding variants, and combine our GWAS results with gene expression data using a monocyte transcriptome-wide association study (TWAS) model, as well as gene and protein expression using Mendelian randomization. We identify potentially druggable targets in multiple systems, including inflammatory signalling (JAK1), monocyte-macrophage activation and endothelial permeability (PDE4A), immunometabolism (SLC2A5 and AK5), and host factors required for viral entry and replication (TMPRSS2 and RAB2A).

Mapping genomic loci implicates genes and synaptic biology in schizophrenia.

Schizophrenia has a heritability of 60-80%1, much of which is attributable to common risk alleles. Here, in a two-stage genome-wide association study of up to 76,755 individuals with schizophrenia and 243,649 control individuals, we report common variant associations at 287 distinct genomic loci. Associations were concentrated in genes that are expressed in excitatory and inhibitory neurons of the central nervous system, but not in other tissues or cell types. Using fine-mapping and functional genomic data, we identify 120 genes (106 protein-coding) that are likely to underpin associations at some of these loci, including 16 genes with credible causal non-synonymous or untranslated region variation. We also implicate fundamental processes related to neuronal function, including synaptic organization, differentiation and transmission. Fine-mapped candidates were enriched for genes associated with rare disruptive coding variants in people with schizophrenia, including the glutamate receptor subunit GRIN2A and transcription factor SP4, and were also enriched for genes implicated by such variants in neurodevelopmental disorders. We identify biological processes relevant to schizophrenia pathophysiology; show convergence of common and rare variant associations in schizophrenia and neurodevelopmental disorders; and provide a resource of prioritized genes and variants to advance mechanistic studies.

From genetic associations to genes: methods, applications, and challenges.

Genome-wide association studies (GWASs) have identified numerous genetic loci associated with human traits and diseases. However, pinpointing the causal genes remains a challenge, which impedes the translation of GWAS findings into biological insights and medical applications. In this review, we provide an in-depth overview of the methods and technologies used for prioritizing genes from GWAS loci, including gene-based association tests, integrative analysis of GWAS and molecular quantitative trait loci (xQTL) data, linking GWAS variants to target genes through enhancer-gene connection maps, and network-based prioritization. We also outline strategies for generating context-dependent xQTL data and their applications in gene prioritization. We further highlight the potential of gene prioritization in drug repurposing. Lastly, we discuss future challenges and opportunities in this field.

The Effects of Age and Reading Experience on the Lifespan Neurodevelopment for Reading Comprehension.

Reading comprehension is a vital cognitive skill that individuals use throughout their lives. The neurodevelopment of reading comprehension across the lifespan, however, remains underresearched. Furthermore, factors such as maturation and experience significantly influence functional brain development. Given the complexity of reading comprehension, which incorporates lower-level word reading process and higher-level semantic integration process, our study aims to investigate how age and reading experience influence the neurobiology underpinning these two processes across the lifespan. fMRI data of 158 participants aged from 7 to 77 years were collected during a passive word viewing task and a sentence comprehension task to engage the lower- and higher-level processes, respectively. We found that the neurodevelopment of the lower-level process was primarily influenced by age, showing increased activation and connectivity with age in parieto-occipital and middle/inferior frontal lobes related to morphological-semantic mapping while decreased activation in the temporoparietal regions linked to phonological processing. However, the brain function of the higher-level process was primarily influenced by reading experience, exhibiting a greater reliance on the frontotemporal semantic network with enhanced sentence-level reading performance. Furthermore, reading experience did not significantly affect the brain function of children, but had a positive effect on young adults in the lower-level process and on middle-aged and older adults in the higher-level process. These findings indicate that the brain function for lower- and higher-level processes of reading comprehension is differently affected by maturation and reading experience, and the experience effect is contingent on age regarding the two processes.

Keap1 recognizes EIAV early accessory protein Rev to promote antiviral defense.

The Nrf2/Keap1 axis plays a complex role in viral susceptibility, virus-associated inflammation and immune regulation in host cells. However, whether or how the Nrf2/Keap1 axis is involved in the interactions between equine lentiviruses and their hosts remains unclear. Here, we demonstrate that the Nrf2/Keap1 axis was activated during EIAV infection. Mechanistically, EIAV-Rev competitively binds to Keap1 and releases Nrf2 from Keap1-mediated repression, leading to the accumulation of Nrf2 in the nucleus and promoting Nrf2 responsive genes transcription. Subsequently, we demonstrated that the Nrf2/Keap1 axis represses EIAV replication via two independent molecular mechanisms: directly increasing antioxidant enzymes to promote effective cellular resistance against EIAV infection, and repression of Rev-mediated RNA transport through direct interaction between Keap1 and Rev. Together, these data suggest that activation of the Nrf2/Keap1 axis mediates a passive defensive response to combat EIAV infection. The Nrf2/Keap1 axis could be a potential target for developing strategies for combating EIAV infection.

Deep-Cloud: A Deep Neural Network-Based Approach for Analyzing Differentially Expressed Genes of RNA-seq Data.

Presently, the field of analyzing differentially expressed genes (DEGs) of RNA-seq data is still in its infancy, with new approaches constantly being proposed. Taking advantage of deep neural networks to explore gene expression information on RNA-seq data can provide a novel possibility in the biomedical field. In this study, a novel approach based on a deep learning algorithm and cloud model was developed, named Deep-Cloud. Its main advantage is not only using a convolutional neural network and long short-term memory to extract original data features and estimate gene expression of RNA-seq data but also combining the statistical method of the cloud model to quantify the uncertainty and carry out in-depth analysis of the DEGs between the disease groups and the control groups. Compared with traditional analysis software of DEGs, the Deep-cloud model further improves the sensitivity and accuracy of obtaining DEGs from RNA-seq data. Overall, the proposed new approach Deep-cloud paves a new pathway for mining RNA-seq data in the biomedical field.

Prediction of Transcription Factor Binding Sites on Cell-Free DNA Based on Deep Learning.

Transcription factors (TFs) are important regulatory elements for vital cellular activities, and the identification of transcription factor binding sites (TFBS) can help to explore gene regulatory mechanisms. Research studies have proved that cfDNA (cell-free DNA) shows relatively higher coverage at TFBS due to the protection by TF from degradation by nucleases and short fragments of cfDNA are enriched in TFBS. However, there are still great difficulties in the noninvasive identification of TFBSs from experimental techniques. In this study, we propose a deep learning-based approach that can noninvasively predict TFBSs of cfDNA by learning sequence information from known TFBSs through convolutional neural networks. Under the addition of long short-term memory, our model achieved an area under the curve of 84%. Based on this model to predict cfDNA, we found consistent motifs in cfDNA fragments and lower coverage occurred upstream and downstream of these cfDNA fragments, which is consistent with a previous study. We also found that the binding sites of the same TF differ in different cell lines. TF-specific target genes were detected from cfDNA and were enriched in cancer-related pathways. In summary, our method of locating TFBSs from plasma has the potential to reflect the intrinsic regulatory mechanism from a noninvasive perspective and provide technical guidance for dynamic monitoring of disease in clinical practice.

Theoretical comparison of fructose with methylglucoside for the production of formate and levulinate catalyzed by Brønsted acids in a methanol solution.

For the conversion of fructose/methylglucoside (MG) into both methyl formate (MF) and methyl levulinate (MLev), the C-source of formate [HCOO]- remains unclear at the molecular level. Herein, reaction mechanisms catalyzed by [CH3OH2]+ in a methanol solution were theoretically investigated at the PBE0/6-311++G(d,p) level. For the conversion of fructose into MF and MLev, the formate [HCOO]- comes from the C1-atom of fructose, in which the rate-determining step lies in the reaction of 5-hydroxymethylfurfural (HMF) with CH3OH to yield MF and MLev. The reaction of fructose with CH3OH kinetically tends to generate HMF intermediates rather than yield (MF + MLev). When MG is dissolved in a methanol solution, its O2, O3, and O4 atoms are closer to the first layer of the solvent than O1, O5, and O6 atoms. For the dehydration of MG with methanol into MF and MLev, the formate [HCOO]- stems from the dominant C1- and secondary C3-atoms of MG. Kinetically, MG is ready to yield (MF + MLev), whereas fructose can induce the reaction to remain at the HMF intermediate, inhibiting the further conversion of HMF with CH3OH into MF and MLev. If MG isomerizes into fructose, the reaction will be more preferable for yielding HMF rather than (MF + MLev).

Anion Modulation of Ag-Imidazole Cuboctahedral Cage Microenvironments for Efficient Electrocatalytic CO2 Reduction.

How to achieve CO2 electroreduction in high efficiency is a current challenge with the mechanism not well understood yet. The metal-organic cages with multiple metal sites, tunable active centers, and well-defined microenvironments may provide a promising catalyst model. Here, we report self-assembly of Ag4L4 type cuboctahedral cages from coordination dynamic Ag+ ion and triangular imidazolyl ligand 1,3,5-tris(1-benzylbenzimidazol-2-yl) benzene (Ag-MOC-X, X=NO3, ClO4, BF4) via anion template effect. Notably, Ag-MOC-NO3 achieves the highest CO faradaic efficiency in pH-universal electrolytes of 86.1 % (acidic), 94.1 % (neutral) and 95.3 % (alkaline), much higher than those of Ag-MOC-ClO4 and Ag-MOC-BF4 with just different counter anions. In situ attenuated total reflection Fourier transform infrared spectroscopy observes formation of vital intermediate *COOH for CO2-to-CO conversion. The density functional theory calculations suggest that the adsorption of CO2 on unsaturated Ag-site is stabilized by C-H⋅⋅⋅O hydrogen-bonding of CO2 in a microenvironment surrounded by three benzimidazole rings, and the activation of CO2 is dependent on the coordination dynamics of Ag-centers modulated by the hosted anions through Ag⋅⋅⋅X interactions. This work offers a supramolecular electrocatalytic strategy based on Ag-coordination geometry and host-guest interaction regulation of MOCs as high-efficient electrocatalysts for CO2 reduction to CO which is a key intermediate in chemical industry process.

A comprehensive review of matcha: production, food application, potential health benefits, and gastrointestinal fate of main phenolics.

Matcha, a powder processed from tea leaves, has a unique green tea flavor and appealing color, in addition to many other sought after functional properties for a wide range of formulated food applications (e.g., dairy products, bakery products, and beverage). The properties of matcha are influenced by cultivation method and processing post-harvest. The transition from drinking tea infusion to eating whole leaves provides a healthy option for the delivery of functional component and tea phenolics in various food matrix. The aim of this review is to describe the physico-chemical properties of matcha, the specific requirements for tea cultivation and industrial processing. The quality of matcha mainly depends on the quality of fresh tea leaves, which is affected by preharvest factors including tea cultivar, shading treatment, and fertilization. Shading is the key measure to increase greenness, reduce bitterness and astringency, and enhance umami taste of matcha. The potential health benefits of matcha and the gastrointestinal fate of main phenolics in matcha are covered. The chemical compositions and bioactivities of fiber-bound phenolics in matcha and other plant materials are discussed. The fiber-bound phenolics are considered promising components which endow matcha with boosted bioavailability of phenolics and health benefits through modulating gut microbiota.

Cooperative Catalysis Mechanism of Brønsted and Lewis Acids from Al(OTf)3 with Methanol for ß-Cellobiose-to-Fructose Conversion: An Experimental and Theoretical Study.

Al-containing catalysts, e.g., Al(OTf)3, show good catalytic performance toward the conversion of cellulose to fructose in methanol solution. Here, we report the catalytic isomerization and alcoholysis mechanisms for the conversion of cellobiose to fructose at the PBE0/6-311++G(d,p), aug-cc-pVTZ theoretical level, combining the relevant experimental verifications of electrospray ionization mass spectrometry (ESI-MS), high-performance liquid chromatography (HPLC), and the attenuated total reflection-infrared (ATR-IR) spectra. From the alcoholysis of Al(OTf)3 in methanol solution, the catalytically active species involves both the [CH3OH2]+ Brønsted acid and the [Al(CH3O)(OTf)(CH3OH)4]+ Lewis acid. There are two reaction pathways, i.e., one through glucose (glycosidic bond cleavage followed by isomerization, w-G) and another through cellobiulose (isomerization followed by glycosidic bond cleavage, w-L). The Lewis acid ([Al(CH3O)(OTf)(CH3OH)4]+) is responsible for the aldose-ketose tautomerization, while the Brønsted acid ([CH3OH2]+) is in charge of ring-opening, ring-closure, and glycosidic bond cleavage. For both w-G and w-L, the rate-determining steps are related to the intramolecular [1,2]-H shift between C1-C2 for the aldose-ketose tautomerization catalyzed by the [Al(CH3O)(OTf)(CH3OH)4]+ species. The Lewis acid ([Al(CH3O)(OTf)(CH3OH)4]+) exhibits higher catalytic activity toward the aldose-ketose tautomerization of glycosyl-chain-glucose to glycosyl-chain-fructose than that of chain-glucose to chain-fructose. Besides, the Brønsted acid ([CH3OH2]+) shows higher catalytic activity toward the glycosidic bond cleavage of cellobiulose than that of cellobiose. Kinetically, the w-L pathway is predominant, whereas the w-G pathway is minor. The theoretically proposed mechanism has been experimentally testified. These insights may advance on the novel design of the catalytic system toward the conversion of cellulose to fructose.

Cell-Free DNA Fragmentomics: The Novel Promising Biomarker.

Cell-free DNA molecules are released into the plasma via apoptotic or necrotic events and active release mechanisms, which carry the genetic and epigenetic information of its origin tissues. However, cfDNA is the mixture of various cell fragments, and the efficient enrichment of cfDNA fragments with diagnostic value remains a great challenge for application in the clinical setting. Evidence from recent years shows that cfDNA fragmentomics' characteristics differ in normal and diseased individuals without the need to distinguish the source of the cfDNA fragments, which makes it a promising novel biomarker. Moreover, cfDNA fragmentomics can identify tissue origins by inferring epigenetic information. Thus, further insights into the fragmentomics of plasma cfDNA shed light on the origin and fragmentation mechanisms of cfDNA during physiological and pathological processes in diseases and enhance our ability to take the advantage of plasma cfDNA as a molecular diagnostic tool. In this review, we focus on the cfDNA fragment characteristics and its potential application, such as fragment length, end motifs, jagged ends, preferred end coordinates, as well as nucleosome footprints, open chromatin region, and gene expression inferred by the cfDNA fragmentation pattern across the genome. Furthermore, we summarize the methods for deducing the tissue of origin by cfDNA fragmentomics.

Spatial Transcriptome Profiling of Mouse Hippocampal Single Cell Microzone in Parkinson's Disease.

The hippocampus is an important part of the limbic system in the human brain that has essential roles in spatial navigation and cognitive functions. It is still unknown how gene expression changes in single-cell in different spatial locations of the hippocampus of Parkinson's disease. The purpose of this study was to analyze the gene expression features of single cells in different spatial locations of mouse hippocampus, and to explore the effects of gene expression regulation on learning and memory mechanisms. Here, we obtained 74 single-cell samples from different spatial locations in a mouse hippocampus through microdissection technology, and used single-cell RNA-sequencing and spatial transcriptome sequencing to visualize and quantify the single-cell transcriptome features of tissue sections. The results of differential expression analysis showed that the expression of Sv2b, Neurod6, Grp and Stk32b genes in a hippocampus single cell at different locations was significantly different, and the marker genes of CA1, CA3 and DG subregions were identified. The results of gene function enrichment analysis showed that the up-regulated differentially expressed genes Tubb2a, Eno1, Atp2b1, Plk2, Map4, Pex5l, Fibcd1 and Pdzd2 were mainly involved in neuron to neuron synapse, vesicle-mediated transport in synapse, calcium signaling pathway and neurodegenerative disease pathways, thus affecting learning and memory function. It revealed the transcriptome profile and heterogeneity of spatially located cells in the hippocampus of PD for the first time, and demonstrated that the impaired learning and memory ability of PD was affected by the synergistic effect of CA1 and CA3 subregions neuron genes. These results are crucial for understanding the pathological mechanism of the Parkinson's disease and making precise treatment plans.

Conditional GWAS analysis to identify disorder-specific SNPs for psychiatric disorders.

Substantial genetic liability is shared across psychiatric disorders but less is known about risk variants that are specific to a given disorder. We used multi-trait conditional and joint analysis (mtCOJO) to adjust GWAS summary statistics of one disorder for the effects of genetically correlated traits to identify putative disorder-specific SNP associations. We applied mtCOJO to summary statistics for five psychiatric disorders from the Psychiatric Genomics Consortium-schizophrenia (SCZ), bipolar disorder (BIP), major depression (MD), attention-deficit hyperactivity disorder (ADHD) and autism (AUT). Most genome-wide significant variants for these disorders had evidence of pleiotropy (i.e., impact on multiple psychiatric disorders) and hence have reduced mtCOJO conditional effect sizes. However, subsets of genome-wide significant variants had larger conditional effect sizes consistent with disorder-specific effects: 15 of 130 genome-wide significant variants for schizophrenia, 5 of 40 for major depression, 3 of 11 for ADHD and 1 of 2 for autism. We show that decreased expression of VPS29 in the brain may increase risk to SCZ only and increased expression of CSE1L is associated with SCZ and MD, but not with BIP. Likewise, decreased expression of PCDHA7 in the brain is linked to increased risk of MD but decreased risk of SCZ and BIP.

Radical Acylalkylation of 1,3-Enynes To Access Allenic Ketones via N-Heterocyclic Carbene Organocatalysis.

An N-heterocyclic carbene organocatalytic 1,4-difunctionalization of 1,3-enynes was developed. This organocatalytic strategy was suitable for a broad spectrum of substrates to efficiently synthesize allenic ketones bearing diverse substituents. Preliminary mechanistic studies suggest a radical reaction pathway for this organocatalytic acylalkylation process.

Identification of Key Aroma Compounds Responsible for the Floral Ascents of Green and Black Teas from Different Tea Cultivars.

Chemicals underlying the floral aroma of dry teas needs multi-dimensional investigations. Green, black, and freeze-dried tea samples were produced from five tea cultivars, and only 'Chunyu2' and 'Jinguanyin' dry teas had floral scents. 'Chunyu2' green tea contained the highest content of total volatiles (134.75 µg/g) among green tea samples, while 'Jinguanyin' black tea contained the highest content of total volatiles (1908.05 µg/g) among black tea samples. The principal component analysis study showed that 'Chunyu2' and 'Jinguanyin' green teas and 'Chunyu2' black tea were characterized by the abundant presence of certain alcohols with floral aroma, while 'Jinguanyin' black tea was discriminated due to the high levels of certain alcohols, esters, and aldehydes. A total of 27 shared volatiles were present in different tea samples, and the contents of 7 floral odorants in dry teas had correlations with those in fresh tea leaves (p < 0.05). Thus, the tea cultivar is crucial to the floral scent of dry tea, and these seven volatiles could be promising breeding indices.

Remote C(sp3 )-H Acylation of Amides and Cascade Cyclization via N-Heterocyclic Carbene Organocatalysis.

The direct functionalization of inert C(sp3 )-H bonds under environmentally benign catalytic conditions remains a challenging task in synthetic chemistry. Here, we report an organocatalytic remote C(sp3 )-H acylation of amides and cascade cyclization through a radical-mediated 1,5-hydrogen atom transfer mechanism using N-heterocyclic carbene as the catalyst. Notably, a diversity of nitrogen-containing substrates, including simple linear aliphatic carbamates and ortho-alkyl benzamides, can be successfully applied to this organocatalytic system. With the established protocol, over 120 examples of functionalized δ-amino ketones and isoquinolinones with diverse substituents were easily synthesized in up to 99 % yield under mild conditions. The robustness and generality of the organocatalytic strategy were further highlighted by the successful acylation of unactivated C(sp3 )-H bonds and late-stage modification of pharmaceutical molecules. Then, the asymmetric control of the radical reaction was attempted and proven feasible by using a newly designed chiral thiazolium catalyst, and moderate enantioselectivity was obtained at the current stage. Preliminary mechanistic investigations including several control reactions, KIE experiments, and computational studies shed light on the organocatalytic radical reaction mechanism.

Oxidative Radical NHC Catalysis: Divergent Difunctionalization of Olefins through Intermolecular Hydrogen Atom Transfer.

Oxidative N-heterocyclic carbene (NHC) organocatalysis, typically leading to the formation of acyl azolium reactive intermediates, constitutes one of the most important activation strategies for the NHC-catalyzed chemical transformations. Here, we report an unprecedented oxidative radical NHC catalysis by using peroxyester as the external single-electron oxidant to realize divergent difunctionalization of olefins. The key to success of this chemistry is the catalytic generation of oxygen radicals that could trigger an intermolecular hydrogen atom transfer to activate the inert C-H bonds, thereby enabling the productive radical relay process. With this protocol, commonly used general chemicals could serve as radical precursors to allow efficient synthesis of value-added products in a straightforward and cost-effective manner. Preliminary mechanistic investigations, including control experiments and DFT calculations, shed light on the NHC organocatalytic radical reaction mechanism.

Associated functional network development and language abilities in children.

During childhood, the brain is gradually converging to the efficient functional architecture observed in adults. How the brain's functional architecture evolves with age, particularly in young children, is however, not well understood. We examined the functional connectivity of the core language regions, in association with cortical growth and language abilities, in 175 young children in the age range of 4 to 9 years. We analyzed the brain's developmental changes using resting-state functional and T1-weighted structural magnetic resonance imaging data. The results showed increased functional connectivity strength with age between the pars triangularis of the left inferior frontal gyrus and left temporoparietal regions (cohen's d = 0.54, CI: 0.24 - 0.84), associated with children's language abilities. Stronger functional connectivity between bilateral prefrontal and temporoparietal regions was associated with better language abilities regardless of age. In addition, the stronger functional connectivity between the left inferior frontal and temporoparietal regions was associated with larger surface area and thinner cortical thickness in these regions, which in turn was associated with superior language abilities. Thus, using functional and structural brain indices, coupled with behavioral measures, we elucidate the association of functional language network development, language ability, and cortical growth, thereby adding to our understanding of the neural basis of language acquisition in young children.