Biology subjects

He, Y.

Publications and source records attributed to He, Y..

At least 451 records · Page 25Linked to original sources

bioRxiv2022

A GPU-based computational framework that bridges Neuron simulation and Artificial Intelligence

Biophysically detailed multi-compartment models are powerful tools to explore computational principles of the brain and also serve as a theoretical framework to generate algorithms for artificial intelligence (AI) systems. However, the expensive computational cost severely limits the applications in both the neuroscience and AI fields. The major bottleneck during simulating detailed compartment models is the ability of a simulator to solve large systems of linear equations. Here, we present a novel Dendritic Hierarchical Scheduling (DHS) method to markedly accelerate such process. We theoretically prove that the DHS implementation is computationally optimal and accurate. This GPU-based method performs at 2-3 orders of magnitude higher speed than that of the classic serial Hines method in the conventional CPU platform. We build a DeepDendrite framework, which integrates the DHS method and the GPU computing engine of the NEURON simulator and demonstrate applications of DeepDendrite in neuroscience and AI tasks. We investigated how spatial patterns of spine inputs affect neuronal excitability in a detailed human pyramidal neuron model with 25,000 spines; and examined how dendrites protect morphologically detailed neural networks against adversarial attacks in typical image classification tasks.

neuroscience↗

bioRxiv2022

Multi-task learning for single-cell multi-modality biology

Current biotechnologies can simultaneously measure multi-modality high-dimensional information from the same cell and tissue samples. To analyze the multi-modality data, common tasks such as joint data analysis and cross-modal prediction have been developed. However, current analytical methods are generally designed to process multi-modality data for one specific task without considering the underlying connections between tasks. Here, we present UnitedNet, a multi-task deep neural network that integrates the tasks of joint group identification and cross-modal prediction to analyze multi-modality data. We have found that multi-task learning for joint group identification and cross-modal prediction significantly improves the performance of each task. When applied to various single-cell multi-modality datasets, UnitedNet shows superior performance in each task, achieving better unsupervised and supervised joint group identification and cross-modal prediction performances compared with state-of-the-art methods. Furthermore, by considering the spatial information of cells as one modality, UnitedNet substantially improves the accuracy of tissue region identification and enables spatially resolved cross-modal prediction.

genomics↗

bioRxiv2022

Perception of self- and externally-generated visual stimuli: Evidence from EEG and behaviour

Efference copy-based forward model mechanisms may help us to distinguish between self- and externally-generated sensory consequences. Previous studies have shown that neural activity and perception associated with self-initiated sensory consequences are reduced (sensory suppression). For example, event-related potentials (ERPs) elicited by tones that follow a button press are reduced in amplitude relative to ERPs elicited by passively attended tones. However, previous EEG studies investigating visual stimuli in this context are rare, provide inconclusive results, and lack adequate control conditions with passive movements. Furthermore, although self-initiation is known to modulate behavioural responses, it is not known whether differences in the amplitude of ERPs also reflect differences in perception of sensory outcomes. In this study, we presented to participants visual stimuli consisting of grey discs following either active button presses, or passive button presses, in which an electromagnet moved the participants finger. Two discs presented visually 500-1250 ms apart followed each button press, and participants judged which of the two was more intense. Early components of the primary visual response (N1 and P2) over the occipital electrodes were suppressed in the active condition. Interestingly, suppression in the intensity judgement task was only correlated with suppression of the visual P2 component. These data support the notion of efference copy-based forward model predictions in the visual sensory modality, but especially later processes (P2) seem to be perceptually relevant. Taken together, the results challenge the assumption that N1 differences reflect perceptual suppression and emphasise the relevance of the P2 ERP component.

neuroscience↗

bioRxiv2022

The leucine-rich repeat (LRR) domain of NLRP3 is required for NLRP3 inflammasome activation in macrophages

The NLRP3 inflammasome is a critical component of innate immunity that defends the host from microbial infections. However, its aberrant activation contributes to the pathogenesis of several inflammatory diseases. Activation of the NLRP3 inflammasome induces the secretion of proinflammatory cytokines IL-1{beta} and IL-18, and pyroptotic cell death. NLRP3 contains a leucine-rich repeat (LRR) domain at its C-terminus. Although posttranslational modifications in this LRR domain have been shown to regulate NLRP3 inflammasome activation, the role of the entire LRR domain in NLRP3 inflammasome activation remains controversial. Here, we generated mouse macrophages that express an endogenous NLRP3 mutant lacking the LRR domain. Deletion of the LRR domain destabilized endogenous NLRP3 protein and abolished NLRP3 inflammasome activation in macrophages. Furthermore, using NLRP3-deficient macrophages that are reconstituted with NLRP3 mutants lacking the LRR domain, we found that deletion of the LRR domain inhibited NLRP3 inflammasome activation. Mechanistically, deletion of the LRR domain abolished NLRP3 self-association, oligomerization, and interaction with the essential regulator NEK7. Our results demonstrate a critical role for the LRR domain in NLRP3 inflammasome activation.

immunology↗

bioRxiv2022

Assessment of Human Renal Transporter Based Drug-Drug Interactions Using Proximal Tubule Kidney-Chip

Study of renal transporters is crucial for understanding drug disposition and toxicity, and more importantly, predicting potential drug-drug interactions (DDIs). However, conventional in vitro models often fail to predict renal transporter activity and are not scalable to a predictive clinical outcome due to in vitro-in vivo discrepancy. Here, we successfully developed a human Proximal Tubule Kidney-Chip model that emulated in vivo renal physiology and function to assess renal transporter-based DDIs. Active and improved functionality of key renal transporters including p-glycoprotein (P-gp), multidrug and toxin extrusion (MATE) 1 and 2-K, organic anion transporter (OAT) 1 and 3, and organic cation transporter (OCT) 2 were demonstrated using appropriate probe substrates in Kidney-Chips compared to transwell controls. Moreover, comparative transcriptomic analysis revealed that key efflux and uptake transporters were expressed significantly higher in the Kidney-Chip compared to the transwell. Additionally, key parameters obtained from substrate-inhibitor interactions in the model were used to predict clinical DDIs as well as clearance values, which were closer to in vivo clearances. Overall, these results support that the human Proximal Tubule Kidney-Chip can reliably assess the role of human renal transporters in drug disposition and drug interactions, providing a critical tool to assess renal transport in vitro.

cell biology↗

bioRxiv2022

Machine learning directed organoid morphogenesis uncovers an excitable system driving human axial elongation

The human embryo breaks symmetry to form the anterior-posterior axis of the body. As the embryo elongates along this axis, progenitors in the tailbud give rise to axial tissues that generate the spinal cord, skeleton, and musculature. The mechanisms underlying human axial elongation are unknown. While ethics necessitate in vitro studies, the variability of human organoid systems has hindered mechanistic insights. Here we developed a bioengineering and machine learning framework that optimizes symmetry breaking by tuning the spatial coupling between human pluripotent stem cell-derived organoids. This framework enabled the reproducible generation of hundreds of axially elongating organoids, each possessing a tailbud and an epithelial neural tube with a single lumen. We discovered that an excitable system composed of WNT and FGF signaling drives axial elongation through the induction of a signaling center in the form of neuromesodermal progenitor (NMP)-like cells. The ability of NMP-like cells to function as a signaling center and drive elongation is independent of their potency to generate mesodermal cell types. We further discovered that the instability of the underlying excitable system is suppressed by secreted WNT inhibitors of the secreted frizzled-related protein (SFRP) family. Absence of these inhibitors led to the formation of ectopic tailbuds and branches. Our results identify mechanisms governing stable human axial elongation to achieve robust morphogenesis.

developmental biology↗

bioRxiv2022

Deformable Mirror based Optimal PSF Engineering for 3D Super-resolution Imaging

Point spread function (PSF) engineering is an important technique to encode the properties (e.g., 3D positions, color, and orientation) of single molecule in the shape of the PSF, often with the help of a programmable phase modulator. Deformable mirror (DM) is currently the most widely used phase modulator for fluorescence detection as it shows negligible photon loss. However, it relies on careful calibration for precise wavefront control. Therefore, design of an optimal PSF not only relies on the theoretical calculation of maximum information content, but also the physical behavior of the phase modulator, which is often ignored during the optimization process. Here, we developed a framework of PSF engineering which could generate a device specific optimal PSF for 3D super-resolution imaging using DM. We used our method to generate two types of PSFs with depths of field comparable to the widely used astigmatism and Tetrapod PSFs, respectively. We demonstrated the superior performance of the DM specific optimal PSF over the conventional astigmatism and Tetrapod PSF both theoretically and experimentally.

biophysics↗

bioRxiv2022

Chemotherapy drugs induce different gut microbiota disorder pattern and NODs/RIP2/NF-κB signaling pathway activation that lead to different degrees of intestinal injury

5-Fluorouracil (5-FU), irinotecan (CPT-11), oxaliplatin (L-OHP) and calcium folinate (CF) are the widely used chemotherapy drugs to treat colorectal cancer. However, the use of chemotherapy is often accompanied by intestinal inflammation and gut microbiota disorder. Moreover, the change of gut microbiota may lead to destruction of the intestinal barrier, which contributes to the severity of intestinal injury. There was no detailed comparison of intestinal injury and gut microbiota disorder among 5-FU, CPT-11, L-OHP and CF, which is not benefit for the development of targeted detoxification therapy after chemotherapy. In this project, a model of chemotherapy-induced intestinal injury in tumor-bearing mice was established by intraperitoneal injection of chemotherapy drugs at a clinically equivalent dose. 16S rDNA sequencing was used to detect gut microbiota. We found that 5-FU, CPT-11 and L-OHP caused intestinal injury, inflammatory cytokine (IFN-{gamma}, TNF-, IL-1{beta}, and IL-6) secretion, and gut microbiota disorder. Importantly, we established a complex but clear network between the gut microbiota change pattern and intestinal damage degree induced by different chemotherapy drugs. L-OHP caused the most severe damage in intestine and disorder of gut microbiota, and showed considerable overlap of the microbiota change pattern with 5-FU and CPT-11. The phylogenetic investigation of communities by reconstruction of unobserved states, V1.0 (PICRUSt) analysis showed that the microbiota disorder pattern induced by 5-FU, CPT-11 and L-OHP was related to the NOD like signaling pathway. Therefore, we detected the protein expression of the NODs/RIP2/NF-{kappa}B signaling pathway and found that L-OHP activated that pathway highest. Furthermore, by RDA/CCA analysis, we found that Bifidobacterium, Akkermansia, Allobaculum, Catenibacterium, Mucispirillum, Turicibacter, Helicobacter, Proteus, Escherichia Shigella, Alloprevotealla, Vagococcus, Streptococcus and Candidatus Saccharimonas were highly correlated with the NODs/RIP2/NF-{kappa}B signaling pathway, and influenced by chemotherapy drugs. IMPORTANCEThe chemotherapy-induced intestinal injury limit drugs clinical use. Intestinal injury involves multiple signaling pathways and the disruption of microbiota. Our results suggest that the degree of intestinal injury caused by different drugs of the first-line colorectal chemotherapy regimen is related to the change pattern of microbiota. Moreover, the NODs/RIP2/NF-{kappa}B signaling pathway was activated in different degrees is also related to the change pattern of microbiota. We found L-OHP caused the most severe change of gut microbiota, and showed considerable overlap of the microbiota changes pattern with 5-FU and CPT-11. Here, we have established a network of different chemotherapy drugs, gut microbiota and NODs/RIP2/NF-{kappa}B signaling pathway, which may provide a new basis for further elucidating the mechanism and clinical treatment of intestinal injury caused by chemotherapy.

molecular biology↗

bioRxiv2022

Molecular Architecture of the Chikungunya Virus Replication Complex

All positive-strand (+) RNA viruses assemble membrane-associated replication complexes (RCs) for viral RNA synthesis in virus-infected cells. However, how these multi-component RCs assemble and function in synthesizing, processing, and transporting viral RNAs to the cytosol remains poorly defined. Here, we determined both the structure of the core RNA replicase of chikungunya virus (family Togaviridae) at a near-atomic level and the native RC architecture in its cellular context at the subnanometer resolution, using in vitro reconstitution and in situ electron cryotomography, respectively. Within the core RNA replicase (nsP1+2+4), the viral RNA-dependent RNA polymerase nsP4, in complex with nsP2 helicase-protease, was found to co-fold with the membrane-anchored nsP1 RNA-capping dodecameric ring and is located asymmetrically within nsP1 central pore. This complex forms the minimal core RNA replicase, while the addition of a large cytoplasmic ring next to the C-terminus of nsP1 forms the holo-RNA-RC as observed at the neck of spherules formed in virus-infected cells. These results represent a major conceptual advance in elucidating the molecular mechanisms of RNA virus replication and the principles underlying the molecular architecture of RCs, likely to be shared with many pathogenic (+) RNA viruses. At last, our study will direct the needed development of antiviral therapies targeting RCs of pathogenic viruses. SummaryCryoEM structure of the chikungunya virus replication complex reveals a multicomponent RNA synthesis nanomachine embedded in the plasma membrane of the host cell.

microbiology↗

bioRxiv2022

Time-series monitoring of transgenic maize seedlings phenotyping exhibiting glyphosate tolerance

Developing herbicide resistant cultivars is one of the effective methods to solve the safety problem caused by the use of herbicide. In this study, hyperspectral image was used to develop more robust leaf chlorophyll content (LCC) prediction model based on different datasets to finally analyze the response of LCC to glyphosate-stress. Chlorophyll a fluorescence (ChlF) was used to dynamically monitor the photosynthetic physiological response of transgenic glyphosate-resistant and wild glyphosate-sensitive maize seedlings, and applying chemometrics methods to extract time-series features to screen resistant cultivars. Both the proposed two transfer strategies achieved the best prediction of LCC with a coefficient of determination value of 0.84, and relative root mean square error of 4.03 for the prediction set. Based on the predicted LCC and ChlF data, we found the antioxidant system of glyphosate-sensitive plants is too fragile to protect themselves from the damage, while glyphosate-resistant plants could overcome it by activating more powerful antioxidant system. {varphi}E0, VJ, {psi}E0, and M0 could be used to indicate damage caused by glyphosate to differentiate resistant cultivars. This study provided a new methodology to monitor LCC to finally analyze glyphosate tolerance in a time-series manner, and verified the feasibility of ChlF in crop breeding. HighlightThis study proposed a new methodology to monitor leaf chlorophyll content to finally analyze glyphosate tolerance in vivo, and verified the feasibility of chlorophyll a fluorescence in crop breeding.

plant biology↗

bioRxiv2022

Efficient Neutralization of SARS-CoV-2 Omicron and Other VOCs by a Broad Spectrum Antibody 8G3

Numerous mutations in the spike protein of SARS-CoV-2 B.1.1.529 Omicron variant pose a crisis for antibody-based immunotherapies. The efficacy of emergency use authorized (EUA) antibodies that developed in early SARS-CoV-2 pandemic seems to be in flounder. We tested the Omicron neutralization efficacy of an early B cell antibody repertoire as well as several EUA antibodies in pseudovirus and authentic virus systems. More than half of the antibodies in the repertoire that showed good activity against WA1/2020 previously had completely lost neutralizing activity against Omicron, while antibody 8G3 displayed non-regressive activity. EUA antibodies Etesevimab, Casirivimab, Imdevimab and Bamlanivimab were entirely desensitized by Omicron. Only Sotrovimab targeting the non-ACE2 overlap epitope showed a dramatic decrease activity. Antibody 8G3 efficiently neutralized Omicron in pseudovirus and authentic virus systems. The in vivo results showed that Omicron virus was less virulent than the WA1/2020 strain, but still caused deterioration of health and even death in mice. Treatment with 8G3 quickly cleared virus load of mice. Antibody 8G3 also showed excellent activity against other variants of concern (VOCs), especially more efficient against authentic Delta plus virus. Collectively, our results suggest that neutralizing antibodies with breadth remains broad neutralizing activity in tackling SARS-CoV-2 infection despite the universal evasion from EUA antibodies by Omicron variant.

cell biology↗

bioRxiv2022

SpheroidAnalyseR - an online platform for analysing data from 3D spheroids or organoids grown in 96-well plates

Spheroids and organoids are increasingly popular three-dimensional (3D) cell culture models. Spheroid models are more physiologically relevant to a tumour compared to two-dimensional (2D) cultures and organoids are a simplified version of an organ with similar composition. Both can be used in cell migration studies, disease modelling and drug discovery. A drawback of these models is, however, the lack of appropriate analytical tools for high throughput imaging and analysis over a time course. To address this, we have developed an R Shiny app called SpheroidAnalyseR: a simple, fast, effective open-source app that allows the analysis of spheroid or organoid size data generated in a 96-well format. SpheroidAnalyseR processes and analyses datasets of image measurements that can be obtained via a bespoke software, described herein, that automates spheroid imaging and quantification using the Nikon A1R Confocal Laser Scanning Microscope. However, templates are provided to enable users to input spheroid image measurements obtained by user-preferred methods. SpheroidAnalyseR facilitates outlier identification and removal followed by graphical visualisation of spheroid measurements across multiple predefined parameters such as time, cell-type and treatment(s). Spheroid imaging and analysis can, thus, be reduced from hours to minutes, removing the requirement for substantial manual data manipulation in a spreadsheet application. The combination of spheroid generation in 96-well ultra-low attachment microplates, imaging using our bespoke software, and analysis using SpheroidAnalyseR toolkit allows high throughput, longitudinal quantification of 3D spheroid growth whilst minimising user input and significantly improving the efficiency and reproducibility of data analysis. Our bespoke imaging software is available from https://github.com/GliomaGenomics. SpheroidAnalyseR is available at https://spheroidanalyser7.azurewebsites.net, and the source code found at https://github.com/GliomaGenomics. O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=96 SRC="FIGDIR/small/481039v1_ufig1.gif" ALT="Figure 1"> View larger version (40K): org.highwire.dtl.DTLVardef@1f779fborg.highwire.dtl.DTLVardef@698103org.highwire.dtl.DTLVardef@94f252org.highwire.dtl.DTLVardef@b1180e_HPS_FORMAT_FIGEXP M_FIG Graphical abstract - SpheroidAnalyseR main steps. A summary of the process followed within the SpheroidAnalyseR app. Dotted boxes are the steps required to produce the input data for SpheroidAnalyseR. Solid boxes show the 5 main steps of SpheroidAnalyseR: Data input, Preview, Outlier removal, Merging and Plotting. C_FIG

cell biology↗

bioRxiv2022

Structural insight into the individual variability architecture of the functional brain connectome

Human cognition and behaviors depend upon the brains functional connectomes, which vary remarkably across individuals. However, whether and how the functional connectome individual variability architecture is structurally constrained remains largely unknown. Using tractography- and morphometry-based network models, we observed the spatial convergence of structural and functional connectome individual variability, with higher variability in heteromodal association regions and lower variability in primary regions. We demonstrated that functional variability is significantly predicted by a unifying structural variability pattern and that this prediction follows a primary-to-heteromodal hierarchical axis, with higher accuracy in primary regions and lower accuracy in heteromodal regions. We further decomposed group-level connectome variability patterns into individual unique contributions and uncovered the structural-functional correspondence that is associated with individual cognitive traits. These results advance our understanding of the structural basis of individual functional variability and suggest the importance of integrating multimodal connectome signatures for individual differences in cognition and behaviors.

neuroscience↗

bioRxiv2022

Metagenomic and Metabolomic Analyses Reveal Synergistic Effects of Fecal Microbiota Transplantation and Anti-PD-1 Therapy on Treating Colorectal Cancer

Anti-PD-1 immunotherapy has saved numerous lives of cancer patients; however, it only exerts efficacy in 10-15% of patients with colorectal cancer. Fecal microbiota transplantation (FMT) is a potential approach to improving the efficacy of anti-PD-1 therapy, whereas the detailed mechanisms and the applicability of this combination therapy remain unclear. In this study, we evaluated the synergistic effect of FMT with anti-PD-1 in curing colorectal tumor-bearing mice using a multi-omics approach. Mice treated with the combination therapy showed superior survival rate and tumor control, compared to the mice received anti-PD-1 therapy or FMT alone. Metagenomic analysis showed that composition of gut microbiota in tumor-bearing mice treated with anti-PD-1 therapy was remarkably altered through receiving FMT. Particularly, Bacteroides genus, including FMT-increased B. thetaiotaomicron, B. fragilis, and FMT-decreased B. ovatus might contribute to the enhanced efficacy of anti-PD-1 therapy. Furthermore, metabolomic analysis upon mouse plasma revealed several potential metabolites that upregulated after FMT, including punicic acid and aspirin, might promote the response to anti-PD-1 therapy via their immunomodulatory functions. This work broadens our understanding of the mechanism by which FMT improves the efficacy of anti-PD-1 therapy, which may contribute to the development of novel microbiota-based anti-cancer therapies.

microbiology↗

bioRxiv2022

dbDEMC 3.0: Functional Exploration of Differentially Expressed miRNAs in Cancers of Human and Model Organisms

microRNAs (miRNAs) are important regulators in gene expression. The deregulation of miRNA expression is widely reported in the transformation from physiological to pathological state of cells. A large amount of differentially expressed miRNAs (DEMs) have been identified in various human cancers by using high-throughput technologies, such as microarray and miRNA-seq. Through mining of published researches with high-throughput experiment information, the database of differentially expressed miRNAs in human cancers (dbDEMC) was constructed with the aim of providing a systematic resource for the storage and query of the DEMs. Here we report an update of the dbDEMC to version 3.0, containing two-fold more data entries than the previous version, now including also data from mouse and rat. The dbDEMC 3.0 contains 3,268 unique DEMs in 40 different cancer types. The current datasets for differential expression analysis have expanded to 9 generalized categories. Moreover, the current release integrates functional annotations of DEMs obtained from experimentally validated targets. The annotations can greatly benefit integrative analysis of DEMs. In summary, dbDEMC 3.0 provides a valuable resource for characterizing molecular functions and regulatory mechanisms of DEMs in human cancers. The dbDEMC 3.0 is freely accessible at https://www.biosino.org/dbDEMC.

bioinformatics↗

bioRxiv2022

AI-assisted Discovery of an Ethnicity-influenced Driver of Cell Transformation in Esophageal and Gastroesophageal Junction Adenocarcinomas

Although Barretts metaplasia of the esophagus (BE) is the only known precursor lesion to esophageal adenocarcinomas (EACs), drivers of the metaplasia[->]dysplasia[->]neoplasia cascade in the esophagus remains incompletely understood. Using an AI-guided network transcriptomics approach, in which EAC initiation and progression is modeled as networks to simplify complex multi-cellular processes, we first predict cellular continuum states and disease driving processes with an unprecedented degree of precision. Key AI-guided predictions are subsequently validated in a human organoid model and patient-derived biopsies of BE, a case-control study of genomics of BE progression, and in a cross-sectional study of 113 patients with BE and EACs. We find that all EACs must originate from BE, pinpoint a CXCL8/IL8{leftrightarrow}neutrophil immune microenvironment as a driver of cellular transformation in both EACs and gastroesophageal junction-ACs. This driver is prominent in Caucasians (Cau), but notably absent in African Americans (AAs). Network-derived gene signatures, independent signatures of neutrophil processes, CXCL8/IL8, and an absolute neutrophil count (ANC) are associated with risk of progression. SNPs associated with ethnic changes in ANC modify that risk. Thus, findings define a racially influenced immunological basis for cell transformation and suggest that benign ethnic neutropenia in AAs may serve as a deterrent to BE[->]EAC progression. BRIEF SUMMARYEsophageal adenocarcinoma (EAC) is a highly lethal cancer among Caucasians, while African Americans are somewhat protected; what factors drive transformation with racial disparity remain unknown. AI-enabled creation of the first computational map of neoplastic progression in the esophagus built and validated using transcriptomic datasets from diverse cohorts of human samples pinpointed CXCL8{leftrightarrow}neutrophil tumor immune-microenvironment as a racially influenced driver of EACs and GEJ-ACs. Computational tools pinpoint a racially influenced driver of cell transformation during BE[->]EAC progression; in doing so, it reveals new novel biology, informs disease modeling, therapeutic strategies, and biomarkers. LAY SUMMARYBy modeling diseases as networks, this work unravels a fundamental race-influenced immunologic driver of cell transformation in adenocarcinomas of the esophagus and the gastroesophageal junction.

cancer biology↗

bioRxiv2022

Single-nuclei transcriptomics of dog hippocampus reveals the distinct cellular mechanism of domestication

The process of dog domestication leads to dramatic differences in behavioral traits compared to grey wolves. A class of putative positively selected genes is related to learning and memory, for instance, long-term potentiation and long-term depression. In this study, we constructed a single-nuclei transcriptomic atlas of the dog hippocampus to illustrate its cell types, cell lineage, and molecular features. Using the transcriptomes of 105,057 single-nuclei from the hippocampus of a Beagle dog, we identified 26 cell clusters and a putative trajectory of oligodendrocyte development. Comparative analysis revealed a significant convergence between dog differentially expressed genes (DEGs) and putative positively selected genes (PSGs). 40 putative PSGs were DEGs in the glutamatergic neurons, especially in the cluster 14, which is related to the regulation of nervous system development. In summary, this study provided a blueprint to understand the cellular mechanism of dog domestication.

evolutionary biology↗

bioRxiv2022

CavitySpace: A database of potential ligand binding sites in the human proteome

The ligand binding sites of a protein provide useful information to uncover its functions and to direct the structure-based drug design. However, as binding site detection relies on the three-dimensional (3D) structural data of proteins, functional analysis based on protein ligand binding sites is formidable for proteins without structural information. Recent developments in protein structure prediction and the 3D structures built by AlphaFold provide an unprecedented opportunity for analyzing ligand binding sites in human proteins. We have used the reliable ligand binding site detection program CAVITY to analyse all the proteins in the human proteome and constructed the CavitySpace database, which is the first pocket library for predicted protein structures. CavitySpace can be used to predict protein function based on pocket information, to identify new druggable protein targets for drug design, and to search for new binding sites for known drugs for drug repurposing. CavitySpace is freely available at http://www.pkumdl.cn:8000/cavityspace/.

bioinformatics↗