Biology subjects

Gu, S.

Publications and source records attributed to Gu, S..

At least 73 records · Page 4Linked to original sources

bioRxiv2023

NMR measurements of transient low-populated tautomeric and anionic Watson-Crick-like G·T/U in RNA:DNA hybrids: Implications for the fidelity of transcription and CRISPR/Cas9 gene editing

Many biochemical processes use the Watson-Crick geometry to distinguish correct from incorrect base pairing. However, on rare occasions, mismatches such as G*T/U can transiently adopt Watson-Crick-like conformations through tautomerization or ionization of the bases, giving rise to replicative and translational errors. The propensities to form Watson-Crick-like mismatches in RNA:DNA hybrids remain unknown, making it unclear whether they can also contribute to errors during processes such as transcription and CRISPR/Cas editing. Here, using NMR R1{rho} experiments, we show that dG*rU and dT*rG mismatches in two RNA:DNA hybrids transiently form tautomeric (Genol*T/U {rightleftarrows}G*Tenol/Uenol) and anionic (G*T-/U-) Watson-Crick-like conformations. The tautomerization dynamics were like those measured in A-RNA and B-DNA duplexes. However, anionic dG*rU- formed with a ten-fold higher propensity relative to dT-*rG and dG*dT- and this could be attributed to the lower pKa ({Delta}pKa [~]0.4-0.9) of U versus T. Our findings suggest plausible roles for Watson-Crick-like G*T/U mismatches in transcriptional errors and CRISPR/Cas9 off-target gene editing, uncover a crucial difference between the chemical dynamics of G*U versus G*T, and indicate that anionic Watson-Crick-like G*U- could play a significant role evading Watson-Crick fidelity checkpoints in RNA:DNA hybrids and RNA duplexes.

biophysics↗

bioRxiv2023

Recombinant SARS-CoV-2 lacking initiating and internal methionine codons within ORF10 is attenuated in vivo

SARS-CoV-2 has been proposed to encode ORF10 as the 3 terminal gene in the viral genome. However, the potential role and even existence of a functional ORF10 product has been the subject of debate. There are significant structural features in the viral genomic RNA that could, by themselves, explain the retention of the ORF10 nucleotide sequences without the need for a functional protein product. To explore this question further we made two recombinant viruses, firstly a control virus (WT) based on the genome sequence of the original Wuhan isolate and with the inclusion of the early D614G mutation in the Spike protein. We also made a second virus, identical to WT except for two additional changes that replaced the initiating ORF10 start codon and an internal methionine codon for stop codons (ORF10KO). Here we show that the two viruses have apparently identical growth kinetics in a VeroE6 cell line that over expresses TMPRSS2 (VTN cells). However, in A549 cells over expressing ACE2 and TMPRSS2 (A549-AT cells) the ORF10KO virus appears to have a small growth rate advantage. Growth competition experiments were used whereby the two viruses were mixed, passaged in either VTN or A549-AT cells and the resulting output virus was sequenced. We found that in VTN cells the WT virus quickly dominated whereas in the A549-AT cells the ORF10KO virus dominated. We then used a hamster model of SARS-CoV-2 infection and determined that the ORF10KO virus has attenuated pathogenicity (as measured by weight loss). We found an almost 10-fold reduction in viral titre in the lower respiratory tract for ORF10KO vs WT. In contrast, the WT and ORF10KO viruses had similar titres in the upper respiratory tract. Sequencing of viral RNA in the lungs of hamsters infected with ORF10KO virus revealed that this virus frequently reverts to WT. Our data suggests that the retention of a functional ORF10 sequence is highly desirable for SARS-CoV-2 infection of hamsters and affects the viruss ability to propagate in the lower respiratory tract.

microbiology↗

bioRxiv2023

Rat microbial biogeography and age-dependent lactic acid bacteria in healthy lungs

The laboratory rat emerges as a useful tool for studying the interaction between the host and its microbiome. To advance principles relevant to the human microbiome, we systematically investigated and defined a multi-tissue full lifespan microbial biogeography for healthy Fischer 344 rats. Microbial community profiling data was extracted and integrated with host transcriptomic data from the Sequencing Quality Control (SEQC) consortium. Unsupervised machine learning, Spearmans correlation, taxonomic diversity, and abundance analyses were performed to determine and characterize the rat microbial biogeography and the identification of four inter-tissue microbial heterogeneity patterns (P1-P4). The 11 body habitats harbor a greater diversity of microbes than previously suspected. Lactic acid bacteria (LAB) abundances progressively declined in lungs from breastfeed newborn to adolescence/adult and was below detectable levels in elderly rats. LABs presence and levels in lungs were further evaluated by PCR in the two validation datasets. The lung, testes, thymus, kidney, adrenal, and muscle niches were found to have age-dependent alterations in microbial abundance. P1 is dominated by lung samples. P2 contains the largest sample size and is enriched for environmental species. Liver and muscle samples were mostly classified into P3. Archaea species were exclusively enriched in P4. The 357 pattern-specific microbial signatures were positively correlated with host genes in cell migration and proliferation (P1), DNA damage repair and synaptic transmissions (P2), as well as DNA transcription and cell cycle in P3. Our study established a link between metabolic properties of LAB with lung microbiota maturation and development. Breastfeeding and environmental exposure influence microbiome composition and host health and longevity. The inferred rat microbial biogeography and pattern-specific microbial signatures would be useful for microbiome therapeutic approaches to human health and good quality of life. Graphical Abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=113 SRC="FIGDIR/small/541527v1_ufig1.gif" ALT="Figure 1"> View larger version (31K): org.highwire.dtl.DTLVardef@1387224org.highwire.dtl.DTLVardef@cdb7dborg.highwire.dtl.DTLVardef@1e1c7ccorg.highwire.dtl.DTLVardef@440ca_HPS_FORMAT_FIGEXP M_FIG C_FIG

microbiology↗

bioRxiv2023

Spatial Tracking Across Time (ST AT ): Tracking Neurons Across In-Vivo Imaging Sessions through Optimizing Local Neighborhood Motion Consistency

Chronic calcium imaging has become a powerful and indispensable tool for analyzing the long-term stability and plasticity of neuronal activity. One crucial step of the data processing pipeline is to register individual neurons across imaging sessions, which usually extend over a few days or even months, and show various levels of spatial deformation of the imaged field of view (FOV). Previous solutions align FOVs of all sessions first and then register the same neurons according to their shapes and locations [1, 2]. However, the FOV registration is computational intensive, especially in the case of nonrigid case. Here we propose a cell tracking method that does not require FOV image registration. Specifically, the algorithm STAT (short for Stay T ogether, Align Together, and for Spatial Tracking Across Time) represents neurons from two sessions as two sets of neuronal centroids, uses point set registration (PSR) to find a spatially smooth transformation to align them while assigning correspondences. The optimization method iteratively updates between the general motion and individual neuron identity tracking, an idea seen in the computer vision literatures [3, 4]. Our method can be thought of as a specialization and simplification of these more general methods to calcium imaging neuron tracking. We validate STAT on datasets with simulated nonrigid motion that is hard to motion correct without extensive manual intervention. Next, we test STAT on experimental data from singing birds collected on three different days, and observe stable song-locked activity across days. An example use case of this package is reference [5].

neuroscience↗

bioRxiv2023

Curing GFP-itis in Bacteria with Base Editors: Development of a Genome Editing Science Program Implemented with High School Biology Students

The flexibility and precision of CRISPR-Cas9 and related technologies have made these genome editing tools increasingly popular in agriculture, medicine, and basic science research over the past decade. Genome editing will continue to be relevant and utilized across diverse scientific fields in the future. Given this, students should be introduced to genome editing technologies and encouraged to consider their ethical implications early on in pre-college biology curricula. Furthermore, instruction on this topic presents an opportunity to create partnerships between researchers and educators at the K-12 levels that can strengthen student engagement in science, technology, engineering, and mathematics (STEM). To this end, we present a three-day student-centered learning program to introduce high school students to genome editing technologies through a hands-on base editing experiment in E. coli, accompanied by a relevant background lecture and facilitated ethics discussion. This unique partnership aims to educate students and provides a framework for research institutions to implement genome editing outreach programs at local high schools.

scientific communication and education↗

bioRxiv2022

Enzyme encapsulation peptides bind to the groove between tessellating subunits of the bacterial microcompartment shell

Bacterial microcompartments are prokaryotic organelles consisting of encapsulated enzymes within a thin protein shell. They are involved in metabolic processing including propanediol, choline, glycerol and ethanolamine utilization, and carbon fixation. Enzymes targeted to the inside of the microcompartment frequently possess a cargo-encapsulation peptide, but the binding-site has not been revealed. We provide evidence that the encapsulation peptides bind to the hydrophobic groove formed between tessellating tiles of major shell proteins. In silico docking studies provide a compelling model of peptide binding to this hydrophobic groove. This result is consistent with the now widely accepted view that the convex side of the shell proteins faces the lumen of the microcompartment. Binding between shell protein tiles explains why the encapsulation peptide binding site has been elusive, how encapsulation-peptide bearing enzymes can promote shell assembly, and how the presence of cargo affects the size and shape of the bacterial microcompartment.

biochemistry↗

bioRxiv2022

Upgrading Voxel-wise Encoding Model via Integrated Integration over Features and Brain Networks

A central goal of cognitive neuroscience is to build computational models that predict and explain neural responses to sensory inputs in the cortex. Recent studies attempt to borrow the representation power of deep neural networks (DNN) to predict the brain response and suggest a correspondence between artificial and biological neural networks in their feature representations. However, each DNN instance is often specified for certain computer vision tasks which may not lead to optimal brain correspondence. On the other hand, these voxel-wise encoding models focus on predicting single voxels independently, while brain activity often demonstrates rich and dynamic structures at the population and network levels during cognitive tasks. These two important properties suggest that we can improve the prevalent voxel-wise encoding models by integrating features from DNN models and by integrating cortical network information into the models. In this work, we propose a new unified framework that addresses these two aspects through DNN feature-level ensemble learning and brain atlas-level model integration. Our proposed approach leads to superior performance over previous DNN-based encoding models in predicting whole-brain neural activity during naturalistic video perception. Furthermore, our unified framework also facilitates the investigation of the brains neural representation mechanism by accurately predicting the neural response corresponding to complex visual concepts.

neuroscience↗

bioRxiv2022

MetaTiME: Meta-components of the Tumor Immune Microenvironment

Recent advances in single-cell RNA sequencing have revealed heterogeneous cell types and gene expression states in the non-cancerous cells in tumors. The integration of multiple scRNA-seq datasets across tumors can reveal common cell types and states in the tumor microenvironment (TME). We developed a data driven framework, MetaTiME, to overcome the limitations in resolution and consistency that result from manual labelling using known gene markers. Using millions of TME single cells, MetaTiME learns meta-components that encode independent components of gene expression observed across cancer types. The meta-components are biologically interpretable as cell types, cell states, and signaling activities. By projecting onto the MetaTiME space, we provide a tool to annotate cell states and signature continuums for TME scRNA-seq data. Leveraging epigenetics data, MetaTiME reveals critical transcriptional regulators for the cell states. Overall, MetaTiME learns data-driven meta-components that depict cellular states and gene regulators for tumor immunity and cancer immunotherapy.

genomics↗

bioRxiv2022

Connexin 43 Hemichannels Regulate Mitochondrial ATP Generation, Mobilization, and Mitochondrial Homeostasis against Oxidative Stress

Oxidative stress is a major risk factor that causes osteocyte cell death and bone loss. Prior studies primarily focus on the function of cell surface expressed Cx43 channels. Here, we reported a new role of mitochondrial Cx43 (mtCx43) and hemichannels (HCs) in modulating mitochondria homeostasis and function in bone osteocytes under oxidative stress. In osteocyte MLO-Y4 cells, the translocation of Cx43 to mitochondria was increased under H2O2-induced oxidative stress. H2O2 increased the mtCx43 level accompanied by elevated mitochondrial Cx43 HC activity determined by dye uptake assay. Cx43 knockdown (KD) by CRISPR-Cas9 lentivirus system resulted in impairment of mitochondrial function, primarily manifested as decreased ATP production. Cx43 KD had reduced intracellular reactive oxidative species (ROS) levels and mitochondrial membrane potential. Additionally, live-cell imaging results demonstrated that the proton flux was dependent upon mtCx43 HCs because its activity was specifically inhibited by an antibody targeting Cx43 C-terminus. The co-localization and interaction of mtCx43 and ATP synthase subunit F (ATP5J2) were confirmed by fluorescence resonance energy transfer (FRET) and a protein pull-down assay. Together, our study suggests that mtCx43 hemichannels regulate mitochondrial ATP generation by mediating K+, H+, and ATP transfer across the mitochondrial inner membrane and the interaction with mitochondrial ATP synthase, leading to enhancing the protection capacity of osteocytes against oxidative insults.

cell biology↗

bioRxiv2022

Does nonlinearity exist in the functional connectivity?

This report summarizes experiments on exploring non-linear functional connectivity. Using resting-state functional MRI data from the HCP1200 dataset, we define nodes as 17 functional networks and edges as the joint distribution between times series pairs. Linear dependence is removed before taking the joint distribution. We then employ a test for normality on the joint distribution to find non-normal distribution patterns. However, the result from an experimental run of 10 subjects shows that: only less than 1% of edges is non-normal distributed, and the location of such edges is not consistent across subjects. From this point of view, the non-linear part seems to be governed by random noise.

neuroscience↗

bioRxiv2022

Large library docking for novel SARS-CoV-2 main protease non-covalent and covalent inhibitors

Antiviral therapeutics to treat SARS-CoV-2 are much desired for the on-going pandemic. A well-precedented viral enzyme is the main protease (MPro), which is now targeted by an approved drug and by several investigational drugs. With the inevitable liabilities of these new drugs, and facing viral resistance, there remains a call for new chemical scaffolds against MPro. We virtually docked 1.2 billion non-covalent and a new library of 6.5 million electrophilic molecules against the enzyme structure. From these, 29 non-covalent and 11 covalent inhibitors were identified in 37 series, the most potent having an IC50 of 29 M and 20 M, respectively. Several series were optimized, resulting in inhibitors active in the low micromolar range. Subsequent crystallography confirmed the docking predicted binding modes and may template further optimization. Together, these compounds reveal new chemotypes to aid in further discovery of MPro inhibitors for SARS-CoV-2 and other future coronaviruses.

biophysics↗

bioRxiv2022

Non-ribosomal peptide synthetase domain boundary identification and new motifs discovery based on motif-intermotifs standardized architecture

Non-ribosomal peptide synthetase (NRPS) is a diverse family of biosynthetic enzymes for the assembly of bioactive peptides. Despite advances in microbial sequencing, the lack of a consistent standard for annotating NRPS domains and modules has made data-driven discoveries challenging. To address this, we introduced a standardized architecture for NRPS, by using known conserved motifs to partition typical domains. This motif-and-intermotif standardization allowed for systematic evaluations of sequence properties from a large number of NRPS pathways, resulting in the most comprehensive cross-kingdom C domain subtype classifications to date, as well as the discovery and experimental validation of novel conserved motifs with functional significance. Furthermore, our coevolution analysis revealed important barriers associated with reengineering NRPSs and uncovered the entanglement between phylogeny and substrate specificity in NRPS sequences. Our findings provide a comprehensive and statistically insightful analysis of NRPS sequences, opening avenues for future data-driven discoveries. Author SummaryNRPS, a gigantic enzyme that produces diverse microbial secondary metabolites, provides a rich source for important medical products including antibiotics. Despite the extensive knowledge gained about its structure and the large amount of sequencing data available, the frequent failure of reengineering NRPS in synthetic biology highlights the fact that much is still unknown. In this work, we applied existing knowledge to data mining of NRPS sequences, using well-known conserved motifs to partition NRPS sequences into motif-intermotif architectures. This standardization allows for integrating large amounts of sequences from different sources, providing a comprehensive overview of NRPSs across different kingdoms. Our findings included new C domain subtypes, novel conserved motifs with implication in structural flexibility, and insights into why NRPSs are so difficult to reengineer. To facilitate researchers in related fields, we constructed an online platform "NRPS Motif Finder" for parsing the motif-and-intermotif architecture and C domain subtype classification (http://www.bdainformatics.org/page?type=NRPSMotifFinder). We believe that this knowledge-guided approach not only advances our understanding of NRPSs but also provides a useful methodology for data mining in large-scale biological sequences.

bioinformatics↗

bioRxiv2022

TENT2, TUT4, and TUT7 selectively regulate miRNA sequence and abundance

TENTs generate miRNA isoforms by 3 tailing. However, little is known about how tailing regulates miRNA function. Here, we generated isogenic HEK293T cell lines in which TENT2, TUT4 and TUT7 were knocked out individually or in combination. Together with rescue experiments, we characterized TENT-specific effects by deep sequencing, Northern blot and in vitro assays. We found that 3 tailing is not random but highly specific. In addition to its known adenylation, TENT2 contributes to guanylation and uridylation on mature miRNAs. TUT4 uridylates most miRNAs whereas TUT7 is dispensable. Removing adenylation has a marginal impact on miRNA levels. By contrast, abolishing uridylation leads to dysregulation of a set of miRNAs. Besides let-7, miR-181b and miR-222 are negatively regulated by TUT4/7 via distinct mechanisms while the miR-888 cluster is upregulated specifically by TUT7. Our results uncover the selective actions of TENTs in generating 3 isomiRs and pave the way to investigate their functions.

molecular biology↗

bioRxiv2022

Self-organization of songbird neural sequences during social isolation

Behaviors emerge via a combination of experience and innate predis-positions. As the brain matures, it undergoes major changes in cellular, network and functional properties that can be due to sensory experience as well as developmental processes. In normal birdsong learning, neural sequences emerge to control song syllables learned from a tutor. Here, we disambiguate the role of experience and development in neural sequence formation by delaying exposure to a tutor. Using functional calcium imaging, we observe neural sequences in the absence of tutoring, demonstrating that experience is not necessary for the formation of sequences. However, after exposure to a tutor, pre-existing sequences can become tightly associated with new song syllables. Since we delayed tutoring, only half our birds learned new syllables following tutor exposure. The birds that failed to learn were the birds in which pre-tutoring neural sequences were most crystallized, that is, already tightly associated with their (untutored) song.

neuroscience↗

bioRxiv2021

Label-free Cell Tracking Enables Collective Motion Phenotyping in Epithelial Monolayers

Collective cell migration is an umbrella term for a rich variety of cell behaviors, whose distinct character is essential for biological function, notably for cancer metastasis. One essential feature of collective behavior is the motion of cells relative to their immediate neighbors. We introduce an AI-based pipeline to segment and track cell nuclei from phase contrast images. Nuclei segmentation is based on a U-Net convolutional neural network trained on images with nucleus staining. Tracking, based on the Crocker-Grier algorithm, quantifies nuclei movement and allows for robust downstream analysis of collective motion. Since the AI algorithm required no new training data, our approach promises to be applicable to and yield new insights for vast libraries of existing collective motion images. In a systematic analysis of a cell line panel with oncogenic mutations, we find that the collective rearrangement metric, D2min, which reflects non-affine motion, shows promise as an indicator of metastatic potential. Graphical Abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=67 SRC="FIGDIR/small/472148v1_ufig1.gif" ALT="Figure 1"> View larger version (20K): org.highwire.dtl.DTLVardef@82a35corg.highwire.dtl.DTLVardef@b31442org.highwire.dtl.DTLVardef@f703aborg.highwire.dtl.DTLVardef@1120d98_HPS_FORMAT_FIGEXP M_FIG C_FIG HighlightsO_LIVersatile AI-based algorithm can robustly identify individual cells and track their motion from phase contrast images. C_LIO_LIAnalysis of motion of cells relative to nearby neighbors distinguishes weakly tumorigenic (KRas) and metastatic (KRas/PTEN-/-) cell lines. C_LI

biophysics↗

bioRxiv2021

Integrated analyses of the single-cell ATAC-seq and RNA-seq reveal the epigenetic landscape of human ovarian aging

Studying the molecular mechanisms of ovarian aging is crucial for understanding the age-related fertility issues in females. Recently, a single-cell transcriptomic roadmap of ovarian aging based on non-human primates revealed the molecular signatures of the oocytes at different developmental stages. Herein, we present the first epigenetic landscape of human ovarian aging, through an integrated analysis of the single-cell assay for transposase-accessible chromatin using sequencing (scATAC-seq) and single-cell RNA-seq. We depicted the transcriptional profiles and chromatin accessibility of the ovarian tissues isolated from old (n=4) and young (n=2) donors. The unsupervised clustering of data revealed seven distinct cell populations in the ovarian tissues and six subtypes of oocytes, which could be distinguished by age difference. Further analysis of the scATAC-seq data from the young and old oocytes revealed that the interaction between the Notch signaling pathway and AP-1 family transcription factors may crucially determine oocyte aging. Finally, a machine-learning algorithm was applied to calculate the optimal model based on the single-cell dataset for predicting oocyte aging, which exhibited excellent accuracy with a cross-validated area under the receiver operating characteristics score of 0.99. In summary, this study provides a comprehensive understanding of human ovarian aging at both the transcriptomic and epigenetic levels, based on an integrated analysis of large-scale single-cell datasets. We believe our results will shed light on the discovery of potential therapeutic targets or diagnostic markers for age-related ovarian disorders.

genetics↗

bioRxiv2021

Biological network inference from single-cell multi-omics data using heterogeneous graph transformer

We present DeepMAPS (Deep learning-based Multi-omics Analysis Platform for Single-cell data) for biological network inference from single-cell multi-omics (scMulti-omics). DeepMAPS includes both cells and genes in a heterogeneous graph to simultaneously infer cell-cell, cell-gene, and gene-gene relations. The multi-head attention mechanism in a graph transformer considers the heterogeneous relation among cells and genes within both local and global context, making DeepMAPS robust to data noise and scale. We benchmarked DeepMAPS on 18 scMulti-omics datasets for cell clustering and biological network inference, and the results showed that our method outperformed various existing tools. We further applied DeepMAPS on lung tumor leukocyte CITE-seq data and matched diffuse small lymphocytic lymphoma scRNA-seq and scATAC-seq data. In both cases, DeepMAPS showed competitive performance in cell clustering and predicted biologically meaningful cell-cell communication pathways based on the inferred gene networks. Note that we deployed a webserver using DeepMAPS implementation equipped with multiple functions and visualizations to improve the feasibility and reproducibility of scMulti-omics data analysis. Overall, DeepMAPS represents a heterogeneous graph transformer for single-cell study and may benefit the use of scMulti-omics data in various biological systems.

bioinformatics↗

bioRxiv2021

Data-driven discovery of targets for bipotent anticancer drugs identifies Estrogen Related Receptor Alpha

Drugs that kill tumors through multiple mechanisms have potential for broad clinical benefits, with a reduced propensity to resistance. We developed BipotentR, a computational approach to find cancer-cell-specific regulators that simultaneously modulate tumor immunity and another oncogenic pathway. Using tumor metabolism as proof-of-principle, BipotentR identified 38 candidate immune-metabolic regulators by combining epigenomes with bulk and single-cell tumor transcriptomes from patients. Inhibition of top candidate ESRRA (Estrogen Related Receptor Alpha) killed tumors by direct effects on energy metabolism and two immune mechanisms: (i) cytokine induction, causing proinflammatory macrophage polarization (ii) antigen-presentation stimulation, recruiting CD8+T cells into tumors. ESRRA is activated in immune-suppressive and immunotherapy-resistant tumors of many types, suggesting broad clinical relevance. We also applied BipotentR to angiogenesis and growth-suppressor pathways, demonstrating a widely applicable approach to identify drug targets that act simultaneously through multiple mechanisms. BipotentR is publicly available at http://bipotentr.dfci.harvard.edu/. One-Sentence SummaryBipotentR identifies targets for bipotent anticancer drugs, as shown by the energy and immune effects of ESRRA inhibition.

cancer biology↗