Biology subjects

Huang, B.

Publications and source records attributed to Huang, B..

At least 109 records · Page 6Linked to original sources

bioRxiv2023

Long COVID manifests with T cell dysregulation, inflammation, and an uncoordinated adaptive immune response to SARS-CoV-2

Long COVID (LC), a type of post-acute sequelae of SARS-CoV-2 infection (PASC), occurs after at least 10% of SARS-CoV-2 infections, yet its etiology remains poorly understood. Here, we used multiple "omics" assays (CyTOF, RNAseq/scRNAseq, Olink) and serology to deeply characterize both global and SARS-CoV-2-specific immunity from blood of individuals with clear LC and non-LC clinical trajectories, 8 months following infection and prior to receipt of any SARS-CoV-2 vaccine. Our analysis focused on deep phenotyping of T cells, which play important roles in immunity against SARS-CoV-2 yet may also contribute to COVID-19 pathogenesis. Our findings demonstrate that individuals with LC exhibit systemic inflammation and immune dysregulation. This is evidenced by global differences in T cell subset distribution in ways that imply ongoing immune responses, as well as by sex-specific perturbations in cytolytic subsets. Individuals with LC harbored increased frequencies of CD4+ T cells poised to migrate to inflamed tissues, and exhausted SARS-CoV-2-specific CD8+ T cells. They also harbored significantly higher levels of SARS-CoV-2 antibodies, and in contrast to non-LC individuals, exhibited a mis-coordination between their SARS-CoV-2-specific T and B cell responses. RNAseq/scRNAseq and Olink analyses similarly revealed immune dysregulatory mechanisms, along with non-immune associated perturbations, in individuals with LC. Collectively, our data suggest that proper crosstalk between the humoral and cellular arms of adaptive immunity has broken down in LC, and that this, perhaps in the context of persistent virus, leads to the immune dysregulation, inflammation, and clinical symptoms associated with this debilitating condition.

immunology↗

bioRxiv2023

Unraveling codon usage of Escherichia coli using machine learning

Machine learning is used to investigate the codon usage of protein-encoding genes, which is one of the fundamental questions of molecular biology. The presentation, parameter learning, and decoding of the conditional random field (CRF) model are implemented and then utilized to analyze the codon usage of the genes of Escherichia coli and its phages. Most genes of E. coli use codons conforming to the weights of the model determined by all E. coli genes. Phages use the codons like their host E. coli. Finally, the study evaluates the codon usage of several example genes in the context of the model. These results help to understand the codon usage in E. coli.

systems biology↗

bioRxiv2023

Structure of human NaV1.6 channel reveals Na+ selectivity and pore blockade by 4,9-anhydro-tetrodotoxin

The sodium channel NaV1.6 is widely expressed in neurons of the central and peripheral nervous systems, which plays a critical role in regulating neuronal excitability. Dysfunction of NaV1.6 has been linked to epileptic encephalopathy, intellectual disability and movement disorders. Here we present cryo-EM structures of human NaV1.6/{beta}1/{beta}2 alone and complexed with a guanidinium neurotoxin 4,9-anhydro-tetrodotoxin (4,9-ah-TTX), revealing molecular mechanism of NaV1.6 inhibition by the blocker. In the apo-form structure, two potential Na+ binding sites were revealed in the selectivity filter, suggesting a possible mechanism for Na+ selectivity and conductance. In the 4,9-ah-TTX-bound structure, 4,9-ah-TTX binds to a pocket similar to the tetrodotoxin (TTX) binding site, which occupies the Na+ binding sites and completely blocks the channel. Molecular dynamics simulation results show that subtle conformational differences in the selectivity filter affect the affinity of TTX analogues. Taken together, our results provide important insights into NaV1.6 structure, ion conductance, and inhibition.

biophysics↗

bioRxiv2023

Deep Learning Assisted Smartphone-based Quantitative Microscopy for Label-free Hematological Analysis

Hematologists evaluate alterations in blood cell enumeration and morphology to confirm the peripheral blood smear findings through manual microscopic examination. However, routine peripheral blood smear analysis is both time-consuming and labor-intensive. Here, we propose a smartphone-based autofluorescence microscopy (Smart-AM) system for imaging label-free blood smears at sub-cellular resolution and performing hematological analysis. Smart-AM enables rapid, high-quality, and label-free visualization of morphological features of different blood cells (leukocytes, erythrocytes, and thrombocytes) and abnormal variations in blood cells. Moreover, assisted with deep learning algorithms, this technique can automatically detect and classify different leukocytes with high accuracy, and transform the autofluorescence images into virtual Giemsa-stained images maintaining significant cellular features. The proposed technique is portable, cost-effective, and user-friendly, making it significant for broad point-of-care applications.

bioengineering↗

bioRxiv2023

Label-free and non-destructive pathology of human lung adenocarcinomas with ultraviolet single-plane illumination microscopy

Lung cancer is one of the leading causes of cancer death worldwide. The diagnosis of lung cancer based on the analysis of formalin-fixed and paraffin-embedded (FFPE) tissues is laborious and time-consuming, failing to guide surgeons intraoperatively. Here we proposed a rapid histological imaging method, termed microscopy with ultraviolet single-plane illumination (MUSI), to enable rapid ex-or in-vivo imaging of fresh and unprocessed tissues in a label-free and non-destructive manner. The MUSI system allows surgical specimens with large irregular surfaces to be screened at a speed of 0.5 mm2/s with a subcellular resolution, which is sufficient to provide immediate feedback to surgeons and pathologists for intraoperative decision-making. We demonstrate that MUSI can differentiate between different subtypes of human lung adenocarcinomas, revealing diagnostically important features that are comparable to the gold standard FFPE histology. As an assistive imaging platform, MUSI could facilitate the development of precise image-guided surgery and revolutionize the current practice in surgical pathology.

pathology↗

bioRxiv2022

Differences in gene expression in field populations of Wolbachia-infected Aedes aegypti mosquitoes with varying release histories in northern Australia

Aedes aegypti is the principal mosquito vector of dengue, yellow fever, Zika and chikungunya viruses. The wMel endosymbiotic bacteria Wolbachia pipientis has been introduced into this vector as a novel biocontrol strategy to stop transmission of these viruses. Mosquitoes with Wolbachia have been released in the field in North Queensland, Australia since 2011, at various locations and over several years, with populations remaining stably infected. Wolbachia infection is known to alter gene expression in its mosquito host, but whether (and how) this changes over the long-term in the context of field releases remains unknown. We sampled mosquitoes from Wolbachia-infected populations with different release histories along a time gradient and performed RNAseq to investigate gene expression changes in the insect host. We observed a significant impact on gene expression in Wolbachia-infected mosquitoes versus uninfected controls, but fewer genes had altered expression in the older releases (e.g. the year 2011) versus the more recent releases (e.g. 2017). Nonetheless, a fundamental signature of Wolbachia infection on host gene expression was observed across all releases, comprising upregulation of immunity and metabolism genes. There was limited downregulation of gene expression in the older releases, but significantly more in the most recent release. Our findings indicate that at > 8 years post-introgression into field populations, Wolbachia continues to profoundly impact host gene expression, particularly genes involved in insect immune response. We suggest that if gene expression changes underlie blocking of virus replication in Wolbachia-infected Ae. aegypti, then refractoriness of these mosquitoes to arboviruses may remain stable over the long-term. Author summaryThe Aedes aegypti mosquito is the main species responsible for urban transmission of dengue, Zika and chikungunya viruses. Control measures, including source reduction and insecticide treatment, have historically struggled to provide sustained control of this species to limit disease. An alternative approach involves releasing mosquitoes harbouring Wolbachia bacteria. Wolbachia inhibits virus transmission by Ae. aegypti and preliminary evidence indicates that dengue incidence is reduced in locations where it has been deployed. In this study, we found that Wolbachia significantly upregulates gene expression in Ae. aegypti at least 8 years after field deployment compared with uninfected controls, although some gene downregulation was also observed. We observed a more muted response in mosquitoes from populations with older release histories, with far fewer genes being differentially regulated versus those from the most recent release. Irrespective of release history, immune response and metabolism genes were significantly upregulated, and to a lesser extent genes related to behaviour. Our results, combined with previous studies that have revealed few changes in the Wolbachia genome post release, provide further evidence of the long-term stability of the Wolbachia-mosquito relationship in the field.

microbiology↗

bioRxiv2022

Comprehensive structural analysis reveals broad-spectrum neutralizing antibodies against Omicron

The pandemic of COVID-19 caused by SARS-CoV-2 continues to spread around the world. Mutant strains of SARS-CoV-2 are constantly emerging. At present, Omicron variants have become mainstream. In this work, we carried out a systematic and comprehensive analysis of the reported spike protein antibodies, counting the antibodies epitopes and genotypes. We further comprehensively analyzed the impact of Omicron mutations on antibody epitopes and classified these antibodies according to their binding patterns. We found that the epitopes of one class of antibodies were significantly less affected by Omicron mutations than other classes. Binding and virus neutralization experiments show that such antibodies can effectively inhibit the immune escape of Omicron. Cryo-EM results show that this class of antibodies utilizes a conserved mechanism to neutralize SARS-CoV-2. Our results greatly help us deeply understand the impact of Omicron mutations. At the same time, it also provides guidance and insights for developing Omicron antibodies and vaccines.

microbiology↗

bioRxiv2022

Sequence-based Optimized Chaos Game Representation and Deep Learning for Peptide/Protein Classification

As an effective graphical representation method for 1D sequence (e.g., text), Chaos Game Representation (CGR) has been frequently combined with deep learning (DL) for biological analysis. In this study, we developed a unique approach to encode peptide/protein sequences into CGR images for classification. To this end, we designed a novel energy function and enhanced the encoder quality by constructing a Supervised Autoencoders (SAE) neural network. CGR was used to represent the amino acid sequences and such representation was optimized based on the latent variables with SAE. To assess the effectiveness of our new representation scheme, we further employed convolutional neural network (CNN) to build models to study hemolytic/non-hemolytic peptides and the susceptibility/resistance of HIV protease mutants to approved drugs. Comparisons were also conducted with other published methods, and our approach demonstrated superior performance. Supplementary informationavailable online

bioinformatics↗

bioRxiv2022

A benchmark study of protein folding algorithms on nanobodies

Nanobodies, also known as single domain or VHH antibodies, are the artificial recombinant variable domains of heavy-chain-only antibodies. Nanobodies have many unique properties, including small size, good solubility, superior stability, rapid clearance from blood, and deep tissue penetration. Therefore, nanobodies have emerged as promising tools for diagnosing and treating diseases. In recent years, many deep-learning-based protein structure prediction methods have emerged that require only protein sequences as input to obtain highly-credible 3D protein structures. Among them, AlphaFold2, RoseTTAFold, DeepAb, NanoNet, and tFold performed excellently in protein prediction or antibody/nanobody prediction. In this study, we selected 60 nanobody samples with known experimental 3D structures in the Protein Data Bank (PDB). Next, we predicted their 3D structures using these five prediction algorithms from only their 2D amino acid sequences. Then, we individually compared the predicted and experimental structures. Finally, the results are analyzed and discussed.

bioinformatics↗

bioRxiv2022

Accurate and efficient protein sequence designthrough learning concise local environment of residues

Protein sequence design has been widely applied in rational protein engineering and increasing the design accuracy and efficiency is highly desired. Here we present ProDESIGN-LE, an accurate and efficient design approach, which adopts a concise but informative representation of residues local environment and trains a transformer to select an appropriate residue at a position from its local environment. ProDESIGN-LE iteratively applies the transformer on the positions in the target structure, eventually acquiring a designed sequence with all residues fitting well with their local environments. ProDESIGN-LE designed sequences for 68 naturally occurring and 129 hallucinated proteins within 20 seconds per protein on average, and the predicted structures from the designed sequences perfectly resemble the target structures with state-of-the-art average TM-score exceeding 0.80. We further experimentally validated ProDESIGN-LE by designing five sequences for an enzyme, chloramphenicol O-acetyltransferase type III (CAT III), and recombinantly expressing the proteins in E. coli. Of these proteins, three exhibited excellent solubility, and one yielded monomeric species with circular dichroism spectra consistent with the natural CAT III protein.

bioinformatics↗

bioRxiv2022

Improving de novo Protein Binder Design with Deep Learning

We explore the improvement of energy-based protein binder design using deep learning. We find that using AlphaFold2 or RoseTTAFold to assess the probability that a designed sequence adopts the designed monomer structure, and the probability that this structure binds the target as designed, increases design success rates nearly 10-fold. We find further that sequence design using ProteinMPNN rather than Rosetta considerably increases computational efficiency.

bioengineering↗

bioRxiv2022

Thinking like a structural biologist: A pocket-based 3D molecule generative model fueled by electron density

We report for the first time the use of experimental electron density (ED) as training data for the generation of drug-like three-dimensional molecules based on the structure of a target protein pocket. Similar to a structural biologist building molecules based on their ED, our model functions with two main components: a generative adversarial network (GAN) to generate the ligand ED in the input pocket and an ED interpretation module for molecule generation. The model was tested on three targets including kinase (HPK1), protease (Covid19-3CL), and nuclear receptor (VDR), and evaluated with a reference dataset composed of over 8,000 compounds that have their activities reported in the literature. The evaluation examined the chemical validity, chemical space distribution-based diversity, and similarity with reference active compounds concerning the molecular structure and pocket-binding mode. Our model can reproduce classical active compounds and can also generate novel molecules with similar binding modes as active compounds, making it a promising tool for library generation supporting high-throughput virtual screening. Our model is available as an online service to academic users via https://edmg.stonewise.cn/#/create.

bioinformatics↗

bioRxiv2022

CELL-E: Biological zero-shot text-to-image synthesis for protein localization prediction

Accurately predicting cellular activities of proteins based on their primary amino acid sequences would greatly improve our understanding of the proteome. In this paper, we present CELL-E, a text-to-image transformer architecture that generates a 2D probability density map of protein distribution within cells. Given a amino acid sequence and a reference image for cell or nucleus morphology, CELL-E offers a more direct representation of protein localization, as opposed to previous in silico methods that rely on pre-defined, discrete class annotations of protein localization to subcellular compartments.

bioinformatics↗

bioRxiv2022

Genome-wide selection inference at short tandem repeats

Short tandem repeats (STRs) comprising repeated sequences of 1-6 bp are one of the largest sources of genetic variation in humans. STRs are known to contribute to a variety of disorders, including Mendelian diseases, complex traits, and cancer. Based on their functional importance, mutations at some STRs are likely to introduce negative effects on reproductive fitness over evolutionary time. We previously developed SISTR (Selection Inference at STRs), a population genetics framework to measure negative selection against individual STR alleles. Here, we extend SISTR to enable joint estimation of the distribution of selection coefficients across a set of STRs. This method (SISTR2) allows for more accurate analysis of a broader range of STRs, including loci with low mutation rates. We apply SISTR2 to explore the range of feasible mutation parameters and demonstrate substantial variation in mutation and selection parameters across different classes of STRs. Finally, we show that de novo STR mutations tend to confer a greater selective burden compared to standing STR variation in the population and measure the relative burden of STRs vs. single nucleotide variants in a typical genome. Overall, we anticipate that the evolutionary insights gained from this study will be important for future studies of variation at STRs and their role in evolution and disease.

genomics↗

bioRxiv2022

The fidelity of transcription in human cells

Accurate transcription is required for the faithful expression of genetic information. To provide insight into the molecular mechanisms that control the fidelity of transcription, we analyzed the landscape of transcription errors in human embryonic stem cells. These measurements provide the first reasonable estimate of the fidelity of transcription in human cells and identify multiple genetic and epigenetic factors that control its accuracy. In addition, we developed a new reporter mouse to identify cell types and tissues that commit these errors the most. These experiments revealed that CA1 and dentate gyrus neurons are highly sensitive to transcriptional mutagenesis, lending new support to the hypothesis that transcription errors play a role in the progression of Alzheimers disease. Taken together, these experiments provide unprecedented insight into the fidelity of gene expression in human cells and the molecular mechanisms that govern the central dogma of life.

genetics↗

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

A Chitin-Binding Protein Ultra-highly expressed in the Outer Fold of Mantle is Related to Shell Colour in Pacific Oyster Crassostrea gigas

Molluscs constitute the second largest phylum of animals in the world, and shell colour and stripes are one of their most important phenotypic characteristics. Studies on the mechanism of shell pigmentation help understand the evolutionary and ecological significance of shell colour and serve as the basis for shell colour breeding. In this study, a matched-pair design was used in comparing the black- and white-striped mantles of the same oyster. The result showed that the stripes of shell surface are corresponding to the stripes of the mantle edge. Transcriptomic analysis identified an uncharacterized protein gene (we named it as Crassostrea gigas chitin-binding protein, CgCBP) highly expressed in the black mantles. Three folds (inner fold, middle fold and outer fold) were found on the mantle edge of oyster, but only the outer fold has the same colour as the shell. Transcriptomic comparison indicated that the three folds of the mantle edge are functionally differentiated, and the CgCBP gene is ultra-highly expressed only in the outer fold. We obtained new black and white shell periostraca from the shell notching experiment and found their structural differences by scanning electron microscope. Chitin was successfully extracted and identified from the shell periostraca. Proteomic analysis revealed differences in protein composition between the two shell periostraca. Particularly, the black shell periostraca have more proteins related to melanin biosynthesis and chitin binding compared with the white ones, and melanin particles were observed in the black mantle edge using transmission electron microscope. Magnetic bead binding and Western blot experiments showed that the CgCBP protein can specifically bind to chitin and in vivo RNAi screening indicated that CgCBP knockdown can change the structure of the shell periostracum and reduce its pigmentation. All these results suggest that the outer fold of mantle may have more important roles in shell pigmentation, shell periostracum structure is functionally correlated with shell pigmentation, and the CgCBP gene ultra-highly expressed in the outer fold may influence shell pigmentation by affecting its periostracum structure.

physiology↗

bioRxiv2022

Highly efficient generation of blastocyst-like structures from mouse totipotent blastomere-like cells

Mammalian embryogenesis begins with a totipotent zygote. Early embryogenesis can be recapitulated by aggregated extended pluripotent stem cells (EPSCs) in a 3D culture system. However, the efficiency of generating blastoids is low and whether other reported totipotent stem cells retained a similar capacity remains unknown. Here we show that spliceosomal repressed totipotent blastomere-like cells (TBLCs) form blastocyst-like structures when aggregated in 3D microwells with around 80% efficiency. TBLC-blastoids resemble blastocyst in morphology and cell-lineage allocation, show similar transcriptional profile with natural blastocyst and contain more TE cells and fewer undefined intermediate cells compared to blastoids from EPSCs. Moreover, TBLC-blastoids can develop beyond the implantation stage in vitro and induce decidualization after transferred into uterus. In summary, we supply an alternative cell type to generate ameliorated blastoids highly efficiently for studying early mouse embryogenesis.

developmental biology↗