Biology subjects

Su, L.

Publications and source records attributed to Su, L..

At least 55 records · Page 3Linked to original sources

bioRxiv2023

Path integration selectively predicts midlife risk of Alzheimer's disease

The entorhinal cortex (EC) is the first cortical region to exhibit neurodegeneration in Alzheimers disease (AD), associated with EC grid cell dysfunction. Given the role of grid cells in path integration, we predicted that path integration impairment would represent the first behavioural change in adults at-risk of AD. Using immersive virtual reality, we found that midlife path integration impairments predicted both hereditary and physiological AD risk, with no corresponding impairment on tests of episodic memory or other spatial behaviours. Impairments related to poorer angular estimation and were associated with hexadirectional grid-like fMRI signal in the posterior-medial EC. These results indicate that altered path integration may represent the transition point from at-risk state to disease onset in AD, prior to impairment in other cognitive domains.

neuroscience↗

bioRxiv2023

Individual Variability in Structural Connectivity Architecture of the Human Brain across the Middle-aged and Old Adult

The human brain shows higher variability in functional connectivity in the heteromodal association cortex but lower variability in the unimodal cortex. As the anatomical substrate of functional connectivity, the temporal-spatial pattern of individual variability in structural connectivity remains largely unknown. In the present study, we depicted the temporal-spatial pattern of individual variability in structural connectivity, which is highest in the limbic regions and lowest in the unimodal sensorimotor regions. With increasing age, the variability in structural connectivity increased. Our results demonstrated that this specific spatial distribution relates to the plasticity of synapses and white matter. We proposed a modified ridge regression model to predict cognition individually and generate idiographic brain mapping. Individual variability in structural connectivity is significantly correlated with idiographic brain mapping. Overall, our study has potential implications for understanding biological and genetic mechanisms of variability in structural connectivity, guiding interventions to promote successful cognitive ageing and interpreting statistical maps in the human connectome.

neuroscience↗

bioRxiv2022

Enhanced STAT5a activation rewires exhausted CD8 T cells during chronic stimulation to acquire a hybrid durable effector like state

Rewiring exhausted CD8 T cells (TEX) towards more functional states is a major goal of cancer immunotherapy but has proven challenging due to the epigenetic stability of TEX. Indeed, TEX are epigenetically programmed by the transcription factor Tox. However, epigenetic changes continue to occur as TEX transition from progenitor (TEXprog), to intermediate (TEXint) and terminal (TEXterm) subsets, suggesting potential developmental flexibility in mature TEX subsets. By examining the transition of TEXprog into TEXint cells, we discovered a reciprocally antagonistic circuit between Stat5a and Tox in TEX cells. Stat5-activity controlled TEXint development, antagonized Tox, and instigated partial effector biology. Stat5 was also essential for TEX reinvigoration by PD-1 blockade. Indeed, temporal induction of Stat5-activity in TEX using an orthogonal IL-2/IL2R{beta}-pair fostered TEXint cell accumulation and synergized with PD-L1 blockade. Constitutive Stat5a activity (STAT5CA) antagonized Tox-dependent TEX epigenetic programming to generate a durable hybrid effector/NK-like population with enhanced tumor control. Finally, enforcing Stat5-signals in established TEXprog partially rewired the TEX epigenetic landscape towards the effector/memory lineage. Together, these data highlight therapeutic opportunities of manipulating Stat5 to rewire TEX towards a durably protective hybrid program.

immunology↗

bioRxiv2022

Anticancer drug doxorubicin spontaneously reacts with GTP and dGTP

Here we reported a spontaneous reaction between anticancer drug doxorubicin and GTP or dGTP. Incubation of doxorubicin with GTP or dGTP at 37 {degrees}C or above yields a covalent product: the doxorubicin-GTP or -dGTP conjugate where a covalent bond is formed between the C14 position of doxorubicin and the 2-amino group of guanine. Density functional theory calculations show the feasibility of this spontaneous reaction. Fluorescence imaging studies demonstrate that the doxorubicin-GTP and -dGTP conjugates cannot enter nuclei although they rapidly accumulate in human SK-OV-3 and NCI/ADR-RES cells. Consequently, the doxorubicin-GTP and -dGTP conjugates are less cytotoxic than doxorubicin. We also demonstrate that doxorubicin binds to ATP, GTP, and other nucleotides with a dissociation constant (Kd) in the sub-millimolar range. Since human cells contain millimolar levels of ATP and GTP, these results suggest that doxorubicin may target ATP and GTP, energy molecules that support essential processes in living organisms.

cancer biology↗

bioRxiv2022

Induced CD45 Proximity Potentiates Natural Killer Cell Receptor Antagonism

Natural Killer (NK) cells are a major subset of innate immune cells that are essential for host defense against pathogens and cancer. Two main classes of inhibitory NK receptors (NKR), KIR and CD94/NKG2A, play a key role in suppressing NK activity upon engagement with tumor cells or virus infected cells, limiting their antitumor and antiviral activity. Here, we find that single-chain mouse NKR antagonists linked to a VHH that binds the cell surface phosphatase CD45 potentiate NK and T activity to a greater extent than NKR blocking antibodies alone in vitro. We also uncovered crosstalk between mouse NKG2A and Ly49 that collectively inhibit NK cell activation, such that CD45-NKG2A and CD45-Ly49 bispecific molecules show synergistic effects in their ability to enhance NK cell activation. The basis of the activity enhancement by CD45 ligation may reflect greater antagonism of inhibitory signaling from engagement of MHC I on target cells, combined with other mechanisms, including avidity effects, tonic signaling, antagonism of weak inhibition from engagement of MHC I on non-target cells and possibly CD45 segregation within the NK cell-target cell synapse. These engineered ligands uncover a mechanism for enhancing the activity of mouse NK and T cells that merits evaluation in the context of human NKR antagonist cancer immunotherapies.

bioengineering↗

bioRxiv2022

On the similarities of representations in artificial and brain neural networks for speech recognition

How the human brain supports speech comprehension is an important question in neuroscience. Studying the neurocomputational mechanisms underlying human language is not only critical to understand and develop treatments for many human conditions that impair language and communication but also to inform artificial systems that aim to automatically process and identify natural speech. In recent years, intelligent machines powered by deep learning have achieved near human level of performance in speech recognition. The fields of artificial intelligence and cognitive neuroscience have finally reached a similar phenotypical level despite of their huge differences in implementation, and so deep learning models can--in principle--serve as candidates for mechanistic models of the human auditory system. Utilizing high-performance automatic speech recognition systems, and advanced noninvasive human neuroimaging technology such as magnetoencephalography and multivariate pattern-information analysis, the current study aimed to relate machine-learned representations of speech to recorded human brain representations of the same speech. In one direction, we found a quasi-hierarchical functional organisation in human auditory cortex qualitatively matched with the hidden layers of deep neural networks trained in an automatic speech recognizer. In the reverse direction, we modified the hidden layer organization of the artificial neural network based on neural activation patterns in human brains. The result was a substantial improvement in word recognition accuracy and learned speech representations. We have demonstrated that artificial and brain neural networks can be mutually informative in the domain of speech recognition. Author summaryThe human capacity to recognize individual words from the sound of speech is a cornerstone of our ability to communicate with one another, yet the processes and representations underlying it remain largely unknown. Software systems for automatic speech-to-text provide a plausible model for how speech recognition can be performed. In this study, we used an automatic speech recogniser model to probe recordings from the brains of participants who listened to speech. We found that the parts of the dynamic, evolving representations inside the machine system were a good fit for representations found in the brain recordings, both showing similar hierarchical organisations. Then, we observed where the machines representations diverged from the brains, and made experimental adjustments to the automatic recognizers design so that its representations might better fit the brains. In so doing, we substantially improved the recognizers ability to accurately identify words.

neuroscience↗

bioRxiv2022

CAR-T Engager Proteins Optimize Anti-CD19 CAR-T Cell Therapies for Lymphoma

B cell lymphoma therapy has been transformed by CD19-targeting cellular therapeutics that induce high clinical response rates and impressive remissions in relapsed and refractory patients. However, approximately half of all patients who respond to CD19-directed cell therapy relapse, the majority within six months. One characteristic of relapse is loss or reduction of CD19 expression on malignant B cells. We designed a unique therapeutic to prevent and reverse relapses due to lost or reduced CD19 expression. This novel biologic, a CAR T Engager, binds CD20 and displays the CD19 extracellular domain. This approach increases the apparent CD19 antigen density on CD19-positive/CD20-positive lymphoma cells, and prevents CD19 antigen-oss induced relapse, as CD19 bound to CD20 remains present on the cell surface. We demonstrate that this novel therapeutic prevents and reverses lymphoma relapse in vitro and prevents CD19-negative lymphoma growth and relapse in vivo.

cancer biology↗

bioRxiv2022

Determinants of multiheme cytochrome extracellular electron transfer uncovered by systematic peptide insertion

The multiheme cytochrome MtrA enables microbial respiration by transferring electrons across the outer membrane to extracellular electron acceptors. While structural studies have identified residues that mediate MtrA binding to hemes and to other cytochromes that facilitate extracellular electron transfer (EET), the relative importance of these interactions for EET is not known. To better understand EET, we evaluated how insertion of an octapeptide across all MtrA backbone locations affects Shewanella oneidensis MR-1 respiration on Fe(III). EET efficiency was found to be inversely correlated with insertion proximity to the heme prosthetic groups. Mutants with decreased EET also arose from insertions in a subset of the regions that make residue-residue contacts with the porin MtrB, while all sites contacting the extracellular MtrC presented high peptide insertion tolerance. MtrA variants having peptide insertions within the CXXCH motifs that coordinate heme cofactors retained some ability to support respiration on Fe(III), although these variants presented significantly decreased EET. Furthermore, the fitness of cells expressing different MtrA variants under Fe(III)-respiring conditions correlated with anode reduction. The peptide-insertion profile, which represents the first comprehensive sequence-structure-function map for a multiheme cytochrome, implicates MtrA as a strategic protein engineering target for regulating EET.

synthetic biology↗

bioRxiv2022

The influence of leaf anatomical traits on photosynthetic in coffee leaves in Yunnan of China

Leaf photosynthesis is largely determined by anatomical features. This study aimed to reveal the quantitative effects of the anatomical structure of Coffea arabica leaves on photosynthesis. Pearsons correlation and path analysis were used to explore the correlation between leaf structure and photosynthesis. To calculate the comprehensive evaluation value of the correlation between leaf anatomical traits and photosynthetic parameters, the Criteria Importance Through Intercriteria Correlation (CRITIC) method was used to obtain the objective weight of each photosynthetic parameter. Most of the anatomical traits of the leaf significantly (P<0.05) to highly significantly (P<0.01) correlated with photosynthetic parameters, suggesting that anatomical traits greatly affected the photosynthetic capacity in Coffea arabica leaf. Likewise, path coefficient analysis revealed that the photosynthetic capacity of Arabic coffee leaves is strongly correlated, both directly and indirectly, with the stomatal characteristics of the leaves. The results of the comprehensive evaluation also indicated that leaf thickness (LT) and stomatal density (d) were the anatomical characteristics most closely related to photosynthesis. In these results, understanding the effects of the anatomical structure of coffee leaves on photosynthesis, may provide useful information for coffee breeding programs and the management of coffee plantations to increase photosynthesis.

biophysics↗

bioRxiv2021

SARS-CoV-2-specific T cells in unexposed adults display broad trafficking potential and cross-react with commensal antigens

The baseline composition of T cells directly impacts later response to a pathogen, but the complexity of precursor states remains poorly defined. Here we examined the baseline state of SARS-CoV-2 specific T cells in unexposed individuals. SARS-CoV-2 specific CD4+ T cells were identified in pre-pandemic blood samples by class II peptide-MHC tetramer staining and enrichment. Our data revealed a substantial number of SARS-CoV-2 specific T cells that expressed memory phenotype markers, including memory cells with gut homing receptors. T cell clones generated from tetramer-labeled cells cross-reacted with bacterial peptides and responded to stool lysates in a MHC-dependent manner. Integrated phenotypic analyses revealed additional precursor diversity that included T cells with distinct polarized states and trafficking potential to other barrier tissues. Our findings illustrate a complex pre-existing memory pool poised for immunologic challenges and implicate non-infectious stimuli from commensal colonization as a factor that shapes pre-existing immunity. One Sentence SummaryPre-existing immunity to SARS-CoV-2 contains a complex pool of precursor lymphocytes that include differentiated cells with broad tissue tropism and the potential to cross-react with commensal antigens.

immunology↗

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

Large-scale miRNA-Target Data Analysis to Discover miRNA Co-regulation Network of Abiotic Stress Tolerance in Soybeans

Although growing evidence shows that microRNA (miRNA) regulates plant growth and development, miRNA regulatory networks in plants are not well understood. Current experimental studies cannot characterize miRNA regulatory networks on a large scale. This information gap provides a good opportunity to employ computational methods for global analysis and to generate useful models and hypotheses. To address this opportunity, we collected miRNA-target interactions (MTIs) and used MTIs from Arabidopsis thaliana and Medicago truncatula to predict homologous MTIs in soybeans, resulting in 80,235 soybean MTIs in total. A multi-level iterative bi-clustering method was developed to identify 483 soybean miRNA-target regulatory modules (MTRMs). Furthermore, we collected soybean miRNA expression data and corresponding gene expression data in response to abiotic stresses. By clustering these data, 37 MTRMs related to abiotic stresses were identified including stress-specific MTRMs and shared MTRMs. These MTRMs have gene ontology (GO) enrichment in resistance response, iron transport, positive growth regulation, etc. Our study predicts soybean miRNA-target regulatory modules with high confidence under different stresses, constructs miRNA-GO regulatory networks for MTRMs under different stresses and provides miRNA targeting hypotheses for experimental study. The method can be applied to other biological processes and other plants to elucidate miRNA co-regulation mechanisms.

plant biology↗

bioRxiv2021

DNA replication stress stratifies prognosis and enables exploitable therapeutic vulnerabilities of HBV-associated hepatocellular carcinoma: an in silico strategy towards precision oncology

BackgroundHepatitis B virus (HBV), the main risk factor for hepatocellular carcinoma (HCC) development, integrates into the host genome, causing genetic instability, which may trigger malignancies to exhibit chronic DNA replication stress, providing exploitable therapeutic vulnerabilities. Therefore, customizing prognostication approach and expanding therapeutic options are of great clinical significance to HBV-associated HCCs. MethodsA robust machine-learning framework was designed to develop a DNA replication stress-related prognostic index (PIRS) based on 606 retrospectively collected HBV-associated HCC cases. Molecular profiles and drug response of HCC cell lines were leveraged to predict therapeutic targets and agents for patients with high mortality risk. ResultsCompared with established population-based predictors, PIRS manifested superior performance for prognostic prediction in HBV-associated HCCs. Lower PIRS tightly associated with higher expression of HBV oncoproteins, activated immune/metabolism pathways and higher likelihood of responding to immunotherapy; while higher PIRS showed co-occurrence manner with elevated Ki-67 progression marker and cancer stemness, and significantly enriched in DNA replication stress, cell cycle pathways, chromatin remodeling regulons, and presented an immune-cold phenotype with unfavorable clinical outcome. Through large-scale in silico drug screening, four potential therapeutic targets (TOP2A, PRMT1, CSNK1D, and PPIH) and five agents including three topoisomerase inhibitors (teniposide, doxorubicin, and epirubicin) and two CDK inhibitors (JNJ-7706621 and PHA-793887) were identified for patients with high PIRS. ConclusionsOverall, PIRS holds potential to improve the population-based therapeutic strategies in HCC and sheds new insight to the clinical management for those HBV carriers; current analytic framework provides a roadmap for the rational clinical development of personalized treatment.

bioinformatics↗

bioRxiv2021

Evolutionary dynamics of indels in SARS-CoV-2 spike glycoprotein

SARS-CoV-2, responsible for the current COVID-19 pandemic that claimed over 4.2 million lives, belongs to a class of enveloped viruses that undergo quick evolutionary adjustments under selection pressure. Numerous variants have emerged in SARS-CoV-2 that are currently posing a serious challenge to the global vaccination effort and COVID-19 management. The evolutionary dynamics of this virus are only beginning to be explored. In this work, we have analysed 1.79 million spike glycoprotein sequences of SARS-CoV-2 and found that the virus is fine-tuning the spike with numerous amino acid insertions and deletions (indels). Indels seem to have a selective advantage as the proportions of sequences with indels were steadily increasing over time, currently at over 89%, with similar trends across countries/variants. There were as many as 420 unique indel positions and 447 unique combinations of indels. Despite their high frequency, indels resulted in only minimal alteration, including both gain and loss, of N-glycosylation sites. As indels and point mutations are positively correlated and sequences with indels have significantly more point mutations, they have implications in the context of evolutionary dynamics of the SARS-CoV-2 spike glycoprotein.

bioinformatics↗

bioRxiv2021

Small RNA profiling in Mycobacterium insights into stress adapt ability

Mycobacteria would encounter a number of environment changes during infection, and respond to it using different mechanisms. sRNA is a posttranscriptionally regulatory system for the function of genes and has been investigated in many other bacteria. Here, we used Mycobacterium tuberculosis and Mycobacterium bovis BCG infection models and sequenced the whole bacterial RNAs before and after host cells infection. Comparison of differential expressed sRNAs, by using GO and KEGG, and target predication, was carried out. Six pathogenically relevant stresses, drug resistance test, growth rate and morphology were used for screening and identify sRNAs. From these data, we identified a subset of sRNAs that are differentially expressed in multiple infection groups and stress conditions. We found that many of them were associated with lipid metabolism. Among them, ncBCG427, was significantly down-regulated when BCG entered into macrophages, and was associated with increase of biofilm formation and changed in drug susceptibility. Then, reduction of virulence possibility depends on regulating lipid metabolism.

zoology↗

bioRxiv2021

RESEPT: tissue architecture inference and visualization from spatially resolved transcriptomics

Spatially resolved transcriptomics provides a new way to define spatial contexts and understand biological functions in complex diseases. Although some computational frameworks can characterize spatial context via various clustering methods, the detailed spatial architectures and functional zonation often cannot be revealed and localized due to the limited capacities of associating spatial information. We present RESEPT, a deep-learning framework for characterizing and visualizing tissue architecture from spatially resolved transcriptomics. Given inputs as gene expression or RNA velocity, RESEPT learns a three-dimensional embedding with a spatial retained graph neural network from the spatial transcriptomics. The embedding is then visualized by mapping as color channels in an RGB image and segmented with a supervised convolutional neural network model. Based on a benchmark of sixteen 10x Genomics Visium spatial transcriptomics datasets on the human cortex, RESEPT infers and visualizes the tissue architecture accurately. It is noteworthy that, for the in-house AD samples, RESEPT can localize cortex layers and cell types based on a pre-defined region-or cell-type-specific genes and furthermore provide critical insights into the identification of amyloid-beta plaques in Alzheimers disease. Interestingly, in a glioblastoma sample analysis, RESEPT distinguishes tumor-enriched, non-tumor, and regions of neuropil with infiltrating tumor cells in support of clinical and prognostic cancer applications.

bioinformatics↗

bioRxiv2021

Real-time environmental monitoring of contaminants using living electronic sensors

Real-time chemical sensing is needed to counter the global threats posed by pollution. We combine synthetic biology and materials engineering to develop a living bioelectronic sensor platform with minute detection times. Escherichia coli was programmed to reduce an electrode in a chemical-dependent manner using a modular, eight-component, synthetic electron transport chain. This strain produced significantly more current upon exposure to thiosulfate, an anion that causes microbial blooms. Incorporating a protein switch into the synthetic pathway and encapsulation of microbes with electrodes and conductive nanomaterials yielded a living bioelectronic sensor that could detect an endocrine disruptor within two minutes in riverine water, implicating the signal as mass transfer limited. These findings provide a new platform for miniature, low-power sensors that safeguard ecological and human health. One Sentence SummaryChemicals are detected electrically using an allosterically-regulated electron transfer pathway in designer microbes.

synthetic biology↗

bioRxiv2021

Multi-omics consensus ensemble refines the classification of muscle-invasive bladder cancer with stratified prognosis, tumour microenvironment and distinct sensitivity to frontline therapies

The molecular classification of muscle-invasive bladder cancer (MIBC) based on transcriptomic signatures has been extensively studied. The complementary nature of information provided by different molecular profiles motivated us to refine MIBC classification by aggregating multi-omics data. We generated a consensus ensemble through ten multi-omics integrative clustering approaches on 396 MIBCs from TCGA. A total of 701 MIBCs from different sequencing technologies were used for external validation. Associations between subtypes and prognosis, molecular profiles, the tumour microenvironment, and potential response to frontline therapies were further analyzed. Nearest template prediction and random forest classification were used to develop a predictive signature/classifier for MIBC refinement. We identified four integrative consensus subtypes of MIBC, which were further designated basal-inflamed, basal-noninflamed, luminal-excluded and luminal-desert by immune profiling. Of note, the refinement of basal-like MIBC classification adds to the literature by identifying a basal-noninflamed MIBC subtype presenting with a significantly poor outcome and a global immune-cold phenotype, which might be triggered by Chr4 deletion and high activation of the oncogenic NRF2 pathway. In contrast, basal-inflamed MIBC showed high immunocyte infiltration and high expression of potential targets for immunotherapy. Using an external metastatic MIBC cohort in which patients received anti-PD-L1 treatment, we suggested that basal-inflamed MIBC had a higher likelihood of responding to immunotherapy than other MIBCs. The R package "refineMIBC" was offered as a research tool to refine MIBC from a single-sample perspective (https://github.com/xlucpu/refineMIBC). This consensus ensemble refines the intrinsic MIBC subtypes, which provides a blueprint for the clinical development of rational targeted and immunotherapeutic strategies.

cancer biology↗