Biology subjects

Cheng, L.

Publications and source records attributed to Cheng, L..

At least 163 records · Page 9Linked to original sources

bioRxiv2021

Increased neutralization of SARS-CoV-2 Delta variant by nanobody (Nb22) and the structural basis

Current COVID-19 vaccines need to take at least one month to complete inoculation and then become effective. Around 51% global population are still not fully vaccinated. Instantaneous protection is an unmet need among those who are not fully vaccinated. In addition, breakthrough infections caused by SARS-CoV-2 are widely reported. All these highlight the unmet needing for short-term instantaneous prophylaxis (STIP) in the communities where SARS-CoV-2 is circulating. Previously, we reported nanobodies isolated from an alpaca immunized with the spike protein, exhibiting ultrahigh potency against SARS-CoV-2 and its variants. Herein, we found that Nb22, among our previously reported nanobodies, exhibited ultrapotent neutralization against Delta variant with an IC50 value of 0.41 ng/ml (5.13 pM). Furthermore, the crystal structural analysis revealed that the binding of Nb22 to WH01 and Delta RBDs both effectively blocked the binding of RBD to hACE2. Additionally, intranasal Nb22 exhibited protection against SARS-CoV-2 Delta variant in the post-exposure prophylaxis (PEP) and pre-exposure prophylaxis (PrEP). Of note, intranasal Nb22 also demonstrated high efficacy against SARS-CoV-2 Delta variant in STIP for seven days administered by single dose and exhibited long-lasting retention in the respiratory system for at least one month administered by four doses, providing a means of instantaneous short-term prophylaxis against SARS-CoV-2. Thus, ultrahigh potency, long-lasting retention in the respiratory system as well as stability at room-temperature make the intranasal or inhaled Nb22 to be a potential therapeutic or STIP agent against SARS-CoV-2. Brief summaryNb22 exhibits ultrahigh potency against Delta variant in vitro and is exploited by crystal structural analysis; furthermore, animal study demonstrates high effectiveness in the treatment and short-term instantaneous prophylaxis in hACE2 mice via intranasal administration. HighlightsO_LINb22 exhibits ultrapotent neutralization against Delta variant with an IC50 value of 0.41 ng/ml (5.13 pM). C_LIO_LIStructural analysis elucidates the ultrapotent neutralization of Nb22 against Delta variant. C_LIO_LINb22 demonstrates complete protection in the treatment of Delta variant infection in hACE2 transgenic mice. C_LIO_LIWe complete the proof of concept of STIP against SARS-CoV-2 using intranasal Nb22 with ultrahigh potency and long-lasting retention in respiratory system. C_LI Graphic Abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=199 SRC="FIGDIR/small/459055v2_ufig1.gif" ALT="Figure 1"> View larger version (44K): org.highwire.dtl.DTLVardef@144516corg.highwire.dtl.DTLVardef@3dc17forg.highwire.dtl.DTLVardef@6a8962org.highwire.dtl.DTLVardef@619cd7_HPS_FORMAT_FIGEXP M_FIG C_FIG

microbiology↗

bioRxiv2021

DSCN: double-target selection guided by CRISPR screening and network

Cancer is a complex disease with usually multiple disease mechanisms. Target combination is a better strategy than a single target in developing cancer therapies. However, target combinations are generally more difficult to be predicted. Current CRISPR-cas9 technology enables genome-wide screening for potential targets, but only a handful of genes have been screend as target combinations. Thus, an effective computational approach for selecting candidate target combinations is highly desirable. Selected target combinations also need to be translational between cell lines and cancer patients. We have therefore developed DSCN (double-target selection guided by CRISPR screening and network), a method that matches expression levels in patients and gene essentialities in cell lines through spectral-clustered protein-protein interaction (PPI) network. In DSCN, a sub-sampling approach is developed to model first-target knockdown and its impact on the PPI network, and it also facilitates the selection of a second target. Our analysis first demonstrated high correlation of the DSCN sub-sampling-based gene knockdown model and its predicted differential gene expressions using observed gene expression in 22 pancreatic cell lines before and after MAP2K1 and MAP2K2 inhibition (R2 = 0.75). In our DSCN algorithm, various scoring schemes were evaluated. The diffusion-path method showed the most significant statistical power of differentialting known synthetic lethal (SL) versus non-SL gene pairs (P = 0.001) in pancreatic cancer. The superior performance of DSCN over existing network-based algorithms, such as OptiCon[1] and VIPER[2], in the selection of target combinations is attributable to its ability to calculate combinations for any gene pairs, whereas other approaches focus on the combinations among optimized regulators in the network. DSCNs computational speed is also at least ten times faster than that of other methods. Finally, in applying DSCN to predict target combinations and drug combinations for individual samples (DSCNi), we showed high correlation of DSCNi predicted target combinations with synergistic drug combinations (P = 1e-5) in pancreatic cell lines. In summary, DSCN is a highly effective computational method for the selection of target combinations. Author SummaryCancer therapies require targets to function. Compared to single target, target combination is a better strategy for developing cancer therapies. However, predicting target combination is much complicated than predicting single target. Current CRISPR technology enables whole genome screening of potential targets. But most of the experiments have been conducted on single target (gene) level. To facilitate the prediction of target combinations, we developed DSCN (double-target selection guided by CRISPR screening and network) that utilize single target-level CRISPR screening data and expression profiles for predicting target combinations by connecting cell-line omics-data with tissue omics-data. DSCN showed great accuracy on different cancer types and superior performance compared to existing network-based prediction tools. We also introduced DSCNi derived from DSCN and designed specific for predicting target combinations for single-paitent. We showed synergistic target combinations predicted by DSCNi accurately reflected synergies on drug combination levels. Thus, DSCN and DSCNi have the potential be further applied in personalized medicine field.

bioinformatics↗

bioRxiv2021

Size-scaling promotes senescence-like changes in proteome and organelle content

Senescent cells typically have an enlarged cell size but the reason for this has not been fully elucidated. As abnormal cell size may alter protein concentrations and cellular functionality, we used proteomic data from 59 unperturbed human cell lines to systematically characterize cell-size dependent changes in intracellular protein concentrations and organelle content. Increase in cell size leads to ubiquitous transcriptionally and post-transcriptionally regulated reorganization and dilution of the proteome. Many known senescence proteins display disproportionate size-scaling consistent with their altered expression in senescent cells, while lysosomes and the endoplasmic reticulum expand in larger cells contributing to the senescence phenotype. Analysis of organelle proteome expression identifies p53 and retinoblastoma pathways as mediators of size-scaling, consistent with their role in senescence. Taken together, cell size can alter cellular fitness and function through cumulative reorganization of the proteome and organelle content. An extreme consequence of this pervasive size-scaling appears to be senescence.

cell biology↗

bioRxiv2021

Cell type-specific biotin labeling in vivo resolves regional neuronal proteomic differences in mouse brain

Isolation and proteomic profiling of brain cell types, particularly neurons, pose several technical challenges which limit our ability to resolve distinct cellular phenotypes in neurological diseases. Therefore, we generated a novel mouse line that enables cell type-specific expression of a biotin ligase, TurboID, via Cre-lox strategy for in vivo proximity-dependent biotinylation of proteins. Using adenoviral-based and transgenic approaches, we show striking protein biotinylation in neuronal cell bodies and axons throughout the mouse brain. We quantified more than 2,000 neuron-derived proteins following enrichment that mapped to numerous subcellular compartments. Synaptic, transmembrane transporters, ion channel subunits, and disease-relevant druggable targets were among the most significantly enriched proteins. Remarkably, we resolved brain region-specific proteomic profiles of Camk2a neurons with distinct functional molecular signatures and disease associations that may underlie regional neuronal vulnerability. Leveraging the neuronal specificity of this in vivo biotinylation strategy, we used an antibody-based approach to uncover regionally unique patterns of neuron-derived signaling phospho-proteins and cytokines, particularly in the cortex and cerebellum. Our work provides a proteomic framework to investigate cell type-specific mechanisms driving physiological and pathological states of the brain as well as complex tissues beyond the brain.

neuroscience↗

bioRxiv2021

BAI-Net: Individualized Human Cerebral Cartography using Graph Convolutional Network

Brain atlas is an important tool in the diagnosis and treatment of neurological disorders. However, due to large variations in the organizational principles of individual brains, many challenges remain in clinical applications. Brain atlas individualization network (BAI-Net) is an algorithm that subdivides individual cerebral cortex into segregated areas using brain morphology and connectomes. BAI-Net integrates topological priors derived from a group atlas, adjusts the areal probability using the connectivity context derived from diffusion tractography, and provides reliable and explainable individualized brain parcels across multiple sessions and scanners. We demonstrate that BAI-Net outperforms the conventional iterative clustering approach by capturing significantly heritable topographic variations in individualized cartographies. The topographic variability of BAI-Net cartographies shows strong associations with individual variability in brain morphology, connectivity fingerprints and cognitive behaviors. This study provides a new framework for individualized brain cartography and paves the way of atlas-based precision medicine in clinical practice.

neuroscience↗

bioRxiv2021

Interferon-armed RBD dimer enhances the immunogenicity of RBD for sterilizing immunity against SARS-CoV-2

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has caused a global crisis, urgently necessitating the development of safe, efficacious, convenient-to-store, and low-cost vaccine options. A major challenge is that the receptor-binding domain (RBD)-only vaccine fails to trigger long-lasting protective immunity if used solely for vaccination. To enhance antigen processing and cross-presentation in draining lymph nodes (DLNs), we developed an interferon (IFN)-armed RBD dimerized by immunoglobulin fragment (I-R-F). I-R-F efficiently directs immunity against RBD to DLN. A low dose of I-R-F induces not only high titer long-lasting neutralizing antibodies but also comprehensive T cell responses than RBD, and even provides comprehensive protection in one dose without adjuvant. This study shows that the I-R-F vaccine provides rapid and complete protection throughout upper and lower respiratory tracts against high dose SARS-CoV-2 challenge in rhesus macaques. Due to its potency and safety, this engineered vaccine may become one of the next-generation vaccine candidates in the global race to defeat COVID-19.

immunology↗

bioRxiv2021

Genetic and phenotypic heterogeneity in PNPT1, MYO15A, PTPRQ and SLC12A2 variants detected among hearing impaired assortative mating families in Southern India

Exome analysis was used to resolve the etiology of hearing loss (HL) in four South Indian assortative mating families. Six variants, including three novel ones, were identified in four genes: PNPT1 p.Ala46Gly and p.Asn540Ser, MYO15A p.Leu1485Pro and p.Tyr1891*, PTPRQ p.Gln1336*, and SLC12A2 p.Pro988Ser. Compound heterozygous PNPT1 variants were associated with prelingual profound sensorineural hearing loss (SNHL), vestibular dysfunction and unilateral progressive vision loss in one family. In the second family, MYO15A variants in the myosin motor domain, including a novel variant, were found to be associated with prelingual profound SNHL. A novel PTPRQ variant was associated with postlingual progressive sensorineural/mixed HL and vestibular dysfunction in the third family, with mastoid bone hypopneumatization observed in one family member. In the fourth family, the SLC12A2 novel variant was found to segregate with severe-to-profound HL causing DFNA78, across three generations. Our results suggest a high level of allelic, genotypic and phenotypic heterogeneity of HL in these families. This study is the first to report the association of PNPT1, PTPRQ and SLC12A2 variants with HL in the Indian population.

genetics↗

bioRxiv2021

DGCyTOF: deep learning with graphic cluster visualization to predict cell types of single cell mass cytometry data

BackgroundSingle-cell mass cytometry, also known as cytometry by time of flight (CyTOF) is a powerful high-throughput technology that allows analysis of up to 50 protein markers per cell for the quantification and classification of single cells. Traditional manual gating utilized to identify new cell populations has been inadequate, inefficient, unreliable, and difficult to use, and no algorithms to identify both calibration and new cell populations has been well established. MethodsA deep learning with graphic cluster (DGCyTOF) visualization is developed as a new integrated embedding visualization approach in identifying canonical and new cell types. The DGCyTOF combines deep-learning classification and hierarchical stable-clustering methods to sequentially build a tri-layer construct for known cell types and the identification of new cell types. First, deep classification learning is constructed to distinguish calibration cell populations from all cells by softmax classification assignment under a probability threshold, and graph embedding clustering is then used to identify new cell populations sequentially. In the middle of two-layer, cell labels are automatically adjusted between new and unknown cell populations via a feedback loop using an iteration calibration system to reduce the rate of error in the identification of cell types, and a 3-dimensional (3D) visualization platform is finally developed to display the cell clusters with all cell-population types annotated. ResultsUtilizing two benchmark CyTOF databases comprising up to 43 million cells, we compared accuracy and speed in the identification of cell types among DGCyTOF, DeepCyTOF, and other technologies including dimension reduction with clustering, including Principal Component Analysis (PCA), Factor Analysis (FA), Independent Component Analysis (ICA), Isometric Feature Mapping (Isomap), t-distributed Stochastic Neighbor Embedding (t-SNE), and Uniform Manifold Approximation and Projection (UMAP) with k-means clustering and Gaussian mixture clustering. We observed the DGCyTOF represents a robust complete learning system with high accuracy, speed and visualization by eight measurement criteria. The DGCyTOF displayed F-scores of 0.9921 for CyTOF1 and 0.9992 for CyTOF2 datasets, whereas those scores were only 0.507 and 0.529 for the t-SNE+k-means; 0.565 and 0.59, for UMAP+ k-means. Comparison of DGCyTOF with t-SNE and UMAP visualization in accuracy demonstrated its approximately 35% superiority in predicting cell types. In addition, observation of cell-population distribution was more intuitive in the 3D visualization in DGCyTOF than t-SNE and UMAP visualization. ConclusionsThe DGCyTOF model can automatically assign known labels to single cells with high accuracy using deep-learning classification assembling with traditional graph-clustering and dimension-reduction strategies. Guided by a calibration system, the model seeks optimal accuracy balance among calibration cell populations and unknown cell types, yielding a complete and robust learning system that is highly accurate in the identification of cell populations compared to results using other methods in the analysis of single-cell CyTOF data. Application of the DGCyTOF method to identify cell populations could be extended to the analysis of single-cell RNASeq data and other omics data. Availability and implementationA Python package (Python 3) and analysis scripts for reproducing the results are available at https://lijcheng12.github.io/DGCyTOF/. DGCyTOF python package contactLijun.cheng@osu.edu. Supplementary informationSupplementary data are available at journal **** online. Key pointsO_LIDGCyTOF has been developed for the automatic identification of known and new cell populations. C_LIO_LIOptimal dimension reduction that integrates graphic-clustering technology facilitates highly accurate identification of new cell populations. C_LIO_LIUse of a feedback-calibration system can enhance the homology of cell types for the identification of cell populations. C_LIO_LIThree-dimensional visualization technology allows vivid depiction of cell types to improve their classification. C_LI

bioinformatics↗

bioRxiv2021

NF-kB-repressed Sirt3 mediates testicular cholesterol metabolism and cytoskeleton assembly by P450scc/SOD2 deacetylation

Testicular homeostasis requires the balanced interplay between specific molecules in Sertoli cells, Leydig cells, germ cells. Loss of this coordination can lead to the disruption of spermatogenesis, even male infertility. By operating the upregulation and downregulation of Sirt3 in our male subfertility rats model and two testicular cells models, we indicated that Sirt3 overexpression and activator ameliorated cholesterol metabolism via P450scc deacetylation in Leydig cells, and cytoskeleton assembly via PDLIM1 with SOD2 deacetylation in Sertoli cells and elongating spermatids. In terms of the upstream regulator of Sirt3, the phosphorylation of NF-{kappa}B p65Ser536 stimulated the nuclear translocation of NF-{kappa}B subunits (p50, p65, RelB), which bound to TFBS1 and TFBS2 synchronously in the promoter of Sirt3, repressing Sirt3 transcription. This study demonstrates that NF-{kappa}B-repressed SIRT3 acts directly on cholesterol metabolism of Leydig cells and cytoskeleton assembly of Sertoli cells via P450scc/SOD2 deacetylation to regulate sperm differentiation, influencing spermatogenesis, even male fertility. Research organism: Rat, mouse

developmental biology↗

bioRxiv2021

A potent bispecific nanobody protects hACE2 mice against SARS-CoV-2 infection via intranasal administration

The dramatically expanding COVID-19 needs multiple effective countermeasures. Neutralizing antibodies are a potential therapeutic strategy for treating COVID-19. A number of neutralizing nanobodies (Nbs) were reported for their in vitro activities. However, in vivo protection of these nanobodies was not reported in animal models. In the current report, we characterized several RBD-specific Nbs isolated from a screen of an Nb library derived from an alpaca immunized with SARS-CoV-2 spike glycoprotein (S); among them, three Nbs exhibited picomolar potency against SARS-CoV-2 live virus, pseudotyped viruses, and 15 circulating SARS-CoV-2 variants. To improve the efficacy, various configurations of Nbs were engineered. Nb15-NbH-Nb15, a novel trimer constituted of three Nbs, was constructed to be bispecific for human serum albumin (HSA) and RBD of SARS-CoV-2. Nb15-NbH-Nb15 exhibited sub-ng/ml neutralization potency against the wild-type and currently circulating variants of SARS-CoV-2 with a long half-life in vivo. In addition, we showed that intranasal administration of Nb15-NbH-Nb15 provided 100% protection for both prophylactic and therapeutic purposes against SARS-CoV-2 infection in transgenic hACE2 mice. Nb15-NbH-Nb15 is a potential candidate for both prevention and treatment of SARS-CoV-2 through respiratory administration. One sentence summaryNb15-NbH-Nb15, with a novel heterotrimeric bispecific configuration, exhibited potent and broad neutralization potency against SARS-CoV-2 in vitro and provided in vivo protection against SARS-CoV-2 infection in hACE2 transgenic mice via intranasal delivery. O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=156 SRC="FIGDIR/small/429275v1_ufig1.gif" ALT="Figure 1"> View larger version (47K): org.highwire.dtl.DTLVardef@a30bc8org.highwire.dtl.DTLVardef@5a872eorg.highwire.dtl.DTLVardef@1610f74org.highwire.dtl.DTLVardef@13d9bd8_HPS_FORMAT_FIGEXP M_FIG Graphical abstract: C_FIG HighlightsO_LIWe described a novel heterotrimeric configuration of Nb-NbH-Nb (Nb15-NbH-Nb15) that exhibited improved viral inhibition and stability. C_LIO_LINb15-NbH-Nb15 provides ultrahigh neutralization potency against SARS-CoV-2 wild type and 18 mutant variants, including the current circulating variants of D614G and N501Y predominantly in the UK and South Africa. C_LIO_LIIt is the first to demonstrate the Nbs efficacy in preventing and treating SARS-CoV-2 infection in hACE2 transgenic mice via intranasal delivery. C_LI

microbiology↗

bioRxiv2021

TWO-SIGMA-G: A New Competitive Gene Set Testing Framework for scRNA-seq Data Accounting for Inter-Gene and Cell-Cell Correlation

We propose TWO-SIGMA-G, a competitive gene set test for scRNA-seq data. TWO-SIGMA-G uses a mixed-effects regression model based on our previously published TWO-SIGMA to test for differential expression at the gene-level. This regression-based model provides flexibility and rigor at the gene-level in (1) handling complex experimental designs, (2) accounting for the correlation between biological replicates, and (3) accommodating the distribution of scRNA-seq data to improve statistical inference. Moreover, TWO-SIGMA-G uses a novel approach to adjust for inter-gene-correlation (IGC) at the set-level to control the set-level false positive rate. Simulations demonstrate that TWO-SIGMA-G preserves type-I error and increases power in the presence of IGC compared to other methods. Application to two datasets identified HIV-associated Interferon pathways in xenograft mice and pathways associated with Alzheimers disease progression in humans.

genomics↗

bioRxiv2021

Divergent Connectional Asymmetries of the Inferior Parietal Lobule Shape Hemispheric Specialization in Humans, Chimpanzees, and Macaque Monkeys

The inferior parietal lobule (IPL) is one of the most expanded cortical regions in humans relative to other primates. It is also among the most structurally and functionally asymmetric regions in the human cerebral cortex. Whether the structural and connectional asymmetries of IPL subdivisions differ across primate species and how this relates to functional asymmetries remain unclear. We identified IPL subregions that exhibited positive allometric in both hemispheres, scaling across rhesus macaque monkeys, chimpanzees, and humans. The patterns of IPL subregions asymmetry were similar in chimpanzees and humans, but no IPL asymmetries were evident in macaques. Among the comparative sample of primates, humans showed the most widespread asymmetric connections in the frontal, parietal, and temporal cortices, constituting leftward asymmetric networks that may provide an anatomical basis for language and tool use. Unique human asymmetric connectivity between the IPL and primary motor cortex might be related to handedness. These findings suggest that structural and connectional asymmetries may underlie hemispheric specialization of the human brain.

neuroscience↗

bioRxiv2021

Incidence and Characteristics of Co-infection and Secondary Infection in Patients with COVID-19

ObjectiveThe etiology and epidemiology of co-infection and secondary infection in COVID-19 patients remain unknown. The study aims to investigate the occurrence and characteristics of co-infection and secondary infection in COVID-19 patients, mainly focusing on Streptococcus pneumoniae co-infections. MethodsThis study was a prospective, observational cohort study of the inpatients diagnosed with COVID-19 in two designated hospitals in south China enrolled between Jan 11 and Feb 22, 2020. The urine specimen was collected on admission and applied for pneumococcal urinary antigen tests (PUATs). Demographic, clinical and microbiological data of patients were recorded simultaneously. ResultA total of 146 patients with a confirm diagnosis of COVID-19 at the median age of 50.0 years (IQR 36.0-61.0) were enrolled, in which, 16 (11.0%) were classified as severe cases and 130 (89.0%) as non-severe cases. Of the enrolled patients, only 3 (2.1%) were considered to present the co-infection, in which 1 was co-infected with S.pneumoniae, 1 with B. Ovatus infection and the other one with Influenza A virus infection. Secondary infection occurred in 16 patients, with S. maltophilia as the most commonly isolated pathogen (43.8%), followed by P. aeruginosa (25.0%), E. aerogenes (25.0%), C. parapsilosis (25.0%) and A. fumigates (18.8%). ConclusionPatients with confirmed COVID-19 were rarely co-infected with Streptococcus pneumoniae or other pathogens, indicating that the application of antibiotics against CAP on admission may not be necessary in the treatment of COVID-19 cases.

microbiology↗

bioRxiv2020

The dynamic landscape of parasitaemia dependent intestinal microbiota shifting at species level and the correlated gut transcriptome during Plasmodium yoelii infection

BackgroundMalaria, caused by Plasmodium, is a global life-threatening infection disease especially during the COVID-19 pandemic. However, it is still unclear about the dynamic change and the interactions between intestinal microbiota and host immunity. Here, we investigated the change of intestinal microbiome and transcriptome during the whole Plasmodium infection process in mice to analyze the dynamic landscape of parasitaemia dependent intestinal microbiota shifting and related to host immunity. ResultsThere were significant parasitaemia dependent changes of intestinal microbiota and transcriptome, and the microbiota was significantly correlated to the intestinal immunity. We found that (i) the diversity and composition of the intestinal microbiota represented a significant correlation along with the Plasmodium infection in family, genus and species level; (ii) the up-regulated genes from the intestinal transcriptome were mainly enriched in immune cell differentiation pathways along with the malaria development, particularly, naive CD4+ T cells differentiation; (iii) the abundance of the parasitaemia phase-specific microbiota represented a high correlation with the phase-specific immune cells development, particularly, Th1 cell with family Bacteroidales BS11 gut group, genera Prevotella 9, Ruminococcaceae UCG 008, Moryella and specie Sutterella*, Th2 cell with specie Sutterella*, Th17 cell with family Peptococcaceae, genus Lachnospiraceae FCS020 group and spices Ruminococcus 1*, Ruminococcus UGG 014* and Eubacterium plexicaudatum ASF492, Tfh and B cell with genera Moryella and species Erysipelotrichaceae bacterium canine oral taxon 255. ConclusionThere was a remarkable dynamic landscape of the parasitaemia dependent shifting of intestinal microbiota and immunity, and a notable correlation between the abundance of intestinal microbiota.

immunology↗

bioRxiv2020

Expanding diversity of Asgard archaea and the elusive ancestry of eukaryotes

Comparative analysis of 162 (nearly) complete genomes of Asgard archaea, including 75 not reported previously, substantially expands the phylogenetic and metabolic diversity of the Asgard superphylum, with six additional phyla proposed. Phylogenetic analysis does not strongly support origin of eukaryotes from within Asgard, leaning instead towards a three-domain topology, with eukaryotes branching outside archaea. Comprehensive protein domain analysis in the 162 Asgard genomes results in a major expansion of the set of eukaryote signature proteins (ESPs). The Asgard ESPs show variable phyletic distributions and domain architectures, suggestive of dynamic evolution via horizontal gene transfer (HGT), gene loss, gene duplication and domain shuffling. The results appear best compatible with the origin of the conserved core of eukaryote genes from an unknown ancestral lineage deep within or outside the extant archaeal diversity. Such hypothetical ancestors would accumulate components of the mobile archaeal eukaryome via extensive HGT, eventually, giving rise to eukaryote-like cells.

genomics↗

bioRxiv2020

Structural basis for bivalent binding and inhibition of SARS-CoV-2 infection by human potent neutralizing antibodies

Neutralizing monoclonal antibodies (nAbs) to severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) represent promising candidates for clinical intervention against coronavirus virus diseases 2019 (COVID-19). We isolated a large number of nAbs from SARS-CoV-2 infected individuals capable of disrupting proper interaction between the receptor binding domain (RBD) of the viral spike (S) protein and the receptor angiotensin converting enzyme 2 (ACE2). In order to understand the mechanism of these nAbs on neutralizing SARS-CoV-2 virus infections, we have performed cryo-EM analysis and here report cryo-EM structures of the ten most potent nAbs in their native full-length IgG or Fab forms bound to the trimeric S protein of SARS-CoV-2. The bivalent binding of the full-length IgG is found to associate with more RBD in the "up" conformation than the monovalent binding of Fab, perhaps contributing to the enhanced neutralizing activity of IgG and triggering more shedding of the S1 subunit from the S protein. Comparison of large number of nAbs identified common and unique structural features associated with their potent neutralizing activities. This work provides structural basis for further understanding the mechanism of nAbs, especially through revealing the bivalent binding and their correlation with more potent neutralization and the shedding of S1 subunit.

microbiology↗

bioRxiv2020

Molecular signaling pathways underlying schizophrenia

The molecular pathophysiological mechanisms underlying schizophrenia have remained unknown, and no treatment exists for primary prevention. Studies using stem cell-derived neurons have investigated differentially expressed genes (DEGs) and GO and KEGG pathways between patients and controls, but not analyzed data-driven causal molecular pathways involved. We used Ingenuity Pathway Analysis (IPA) to analyze canonical and causal pathways in two different datasets, including patients from Finland and USA. The most significant findings in canonical pathway analysis were observed for glutamate receptor signaling, hepatic fibrosis, and glycoprotein 6 (GP6) pathways in the Finnish dataset, and GP6 and hepatic fibrosis pathways in the US dataset. In data-driven causal pathways, ADCYAP1, ADAMTS, and CACNA genes were involved in the majority of the top 10 pathways differentiating patients and controls in both Finnish and US datasets. In contrast, no dopamine-specific genes were consistently involved. Results from a Finnish nation-wide database showed that the risk of schizophrenia relapse was 41% lower among first-episode patients during the use of losartan, the master regulator of an ADCYAP1, ADAMTS, and CACNA -related pathway, compared to those time periods when the same individual did not use the drug. This association was not attributable to general adherence to drug treatments. The results from the two independent datasets suggest that the GP6 signaling pathway, and the ADCYAP1, ADAMTS, and CACNA -related purine, oxidative stress, and glutamatergic signaling pathways are primary pathophysiological alterations in schizophrenia among patients with European ancestry. While no reproducible dopaminergic alterations were observed, the results imply that agents such as losartan, and ADCYAP1/PACAP -deficit alleviators, such as metabotropic glutamate 2/3 agonist MGS0028 and 5-HT7 antagonists - which have shown beneficial effects in an experimental Adcyap1-/- mouse model for schizophrenia - could be potential treatments before the full manifestation of illness involving dopaminergic abnormalities.

neuroscience↗

bioRxiv2020

Defining the lineage of thermogenic perivascular adipose tissue

SummaryBrown adipose tissue can expend large amounts of energy and thus increasing its amount or activity is a promising therapeutic approach to combat metabolic disease. In humans, major deposits of brown fat cells are found intimately associated with large blood vessels, corresponding to perivascular adipose tissue (PVAT). However, the cellular origins of PVAT are poorly understood. Here, we applied single cell transcriptomic analyses, ex vivo adipogenesis assays, and genetic fate mapping in vivo to determine the identity of perivascular adipocyte progenitors. We found that thoracic PVAT initially develops from a fibroblastic lineage, comprising progenitor cells (Pdgfra+;Ly6a+;Pparg−) and preadipocytes (Pdgfra+;Ly6a−;Pparg+). Progenitors and preadipocytes in PVAT shared transcriptional similarity with analogous cell types in white adipose tissue, pointing towards a conserved adipose cell lineage hierarchy. Interestingly, the aortic adventitia of adult animals contained a novel population of adipogenic smooth muscle cells (Myh11+; Pdgfra−; Pparg+) possessing the capacity to generate adipocytes in vitro and in vivo. Taken together, these studies define distinct populations of fibroblastic and smooth muscle progenitor cells for thermogenic PVAT, providing a crucial foundation for developing strategies to augment brown fat activity.Competing Interest StatementThe authors have declared no competing interest.View Full Text

developmental biology↗