Biology subjects

Wu, T.

Publications and source records attributed to Wu, T..

At least 199 records · Page 11Linked to original sources

bioRxiv2020

Label-free lymphocytes reconstitution using side scatter for optimal T cell manufacturing

Lymphocyte biology research commonly involves purification of lymphocyte subpopulations by fluorescence-activated cell sorting (FACS) or immunomagnetic separation (IMS), both of which typically rely on antibody labeling of validated cell markers. Methods enabling label-free segregation of lymphocyte subpopulations would be invaluable with regard to less-perturbation, simplicity and cost-effectiveness. Here, we introduce TRuST, a label-free approach for T cell reconstitution using side-scatter (SSC). TRuST-sorted SSClow cells enrich for CD4+ T and naive T cells, while SSChigh cells enrich for CD8+ T, NK and differentiated T cells. Enrichment purity can be improved by computational gate design. SSClow cells have superior expansion capacity and generate more central memory precursors with naive-resembling cytokine responses. Moreover, we find that both T cell differentiation status and CD4/CD8 T ratio in the starting cellular material are critical attributes predicting T cell product quality and quantity. TRuST presents an effective and reliable technique for label-free lymphocytes selection and reconstitution.

immunology↗

bioRxiv2020

Determination of Bacterial Surface Charge Density Via Saturation of Adsorbed Ions

Bacterial surface charge is a critical characteristic of the cells interfacial physiology that influences how the cell interacts with the local environment. A direct, sensitive, and accurate experimental technique capable of quantifying bacterial surface charge is needed to better understand molecular adaptations in interfacial physiology in response to environmental changes. We introduce here the method of second harmonic light scattering (SHS) which is capable of detecting the number of molecular ions adsorbed as counter charges on the exterior bacterial surface, thereby providing a measure of the surface charge. In this first demonstration, we detect the small molecular cation, malachite green, electrostatically adsorbed on the surface of representative strains of Gram-positive and Gram-negative bacteria. Surprisingly, the SHS deduced molecular transport rates through the different cellular ultra-structures are revealed to be nearly identical. However, the adsorption saturation densities on the exterior surfaces of the two bacteria were shown to be characteristically distinct. The negative charge density of the lipopolysaccharide coated outer surface of Gram-negative E. coli (8.7{+/-}1.7 nm-2) was deduced to be seven times larger than that of the protein surface layer of Gram-positive L. rhamnosus (1.2{+/-}0.2 nm-2). The feasibility of SHS deduced bacterial surface charge density for Gram-type differentiation is presented. STATEMENT of SIGNIFICANCEBacterial surface charge density is an important physiological characteristic which determines how the cell interacts with its local environment. Directly measuring the surface charge density, however, is experimentally non-trivial. In this work, we report an experimental method, second harmonic light scattering, that can directly and accurately quantify the surface charge density of individual living bacteria. This is achieved by measuring the number of molecular ions electrostatically adsorbed on the exterior cellular surface as counter charges. It is found that the negative charge density of a representative Gram-negative bacterium is 7 times larger than a representative Gram-positive bacterium. It is suggested that this disparity of surface charge density can be exploited as a basis for Gram-classification of bacteria.

biophysics↗

bioRxiv2020

A Bioelectric Neural Interface Towards Intuitive Prosthetic Control For Amputees

ObjectiveWhile prosthetic hands with independently actuated digits have become commercially available, state-of-the-art human-machine interfaces (HMI) only permit control over a limited set of grasp patterns, which does not enable amputees to experience sufficient improvement in their daily activities to make an active prosthesis useful. ApproachHere we present a technology platform combining fully-integrated bioelectronics, implantable intrafascicular microelectrodes and deep learning-based artificial intelligence (AI) to facilitate this missing bridge by tapping into the intricate motor control signals of peripheral nerves. The bioelectric neural interface includes an ultra-low-noise neural recording system to sense electroneurography (ENG) signals from microelectrode arrays implanted in the residual nerves, and AI models employing the recurrent neural network (RNN) architecture to decode the subjects motor intention. Main resultsA pilot human study has been carried out on a transradial amputee. We demonstrate that the information channel established by the proposed neural interface is sufficient to provide high accuracy control of a prosthetic hand up to 15 degrees of freedom (DOF). The interface is intuitive as it directly maps complex prosthesis movements to the patients true intention. SignificanceOur study layouts the foundation towards not only a robust and dexterous control strategy for modern neuroprostheses at a near-natural level approaching that of the able hand, but also an intuitive conduit for connecting human minds and machines through the peripheral neural pathways. Clinical trialDExterous Hand Control Through Fascicular Targeting (DEFT). Identifier: NCT02994160.

bioengineering↗

bioRxiv2020

Deorphanisation of novel biogenic amine-gated ion channels identifies a new serotonin receptor for learning

Pentameric ligand-gated ion channels (LGCs) play conserved, critical roles in fast synaptic transmission, and changes in LGC expression and localisation are thought to underlie many forms of learning and memory. The C. elegans genome encodes a large number of LGCs without a known ligand or function. Here, we deorphanize five members of a family of Cys-loop LGCs by characterizing their diverse functional properties that are activated by biogenic amine neurotransmitters. To analyse the neuronal function of these LGCs, we show that a novel serotonin-gated cation channel, LGC-50, is essential for aversive olfactory learning. lgc-50 mutants show a specific defect in learned olfactory avoidance of pathogenic bacteria, a process known to depend on serotonergic neurotransmission. Remarkably, the expression of LGC-50 in neuronal processes is enhanced by olfactory conditioning; thus, the regulated expression of these receptors at synapses appears to represent a molecular cornerstone of the learning mechanism.

neuroscience↗

bioRxiv2020

Combination of deep neural network with attention mechanism enhances the explainability of protein contact prediction

Deep learning has emerged as a revolutionary technology for protein residue-residue contact prediction since the 2012 CASP10 competition. Considerable advancements in the predictive power of the deep learning-based contact predictions have been achieved since then. However, little effort has been put into interpreting the black-box deep learning methods. Algorithms that can interpret the relationship between predicted contact maps and the internal mechanism of the deep learning architectures are needed to explore the essential components of contact inference and improve their explainability. In this study, we present an attention-based convolutional neural network for protein contact prediction, which consists of two attention mechanism-based modules: sequence attention and regional attention. Our benchmark results on the CASP13 free-modeling (FM) targets demonstrate that the two attention modules added on top of existing typical deep learning models exhibit a complementary effect that contributes to predictive improvements. More importantly, the inclusion of the attention mechanism provides interpretable patterns that contain useful insights into the key fold-determining residues in proteins. We expect the attention-based model can provide a reliable and practically interpretable technique that helps break the current bottlenecks in explaining deep neural networks for contact prediction.

bioinformatics↗

bioRxiv2020

A novel oxidase from Alcaligenes sp. HO-1 oxidizes hydroxylamine to N2

Hydroxylamine is a key intermediate of microbial ammonia oxidation and plays an important role in the biogeochemical cycling of N-compounds. Hydroxylamine is oxidized to NO or N2O by hydroxylamine oxidases or cytochrome P460 from heterotrophic or autotrophic bacteria, but its enzymatic oxidation to N2 has not yet been observed. Here, we report on the discovery of a novel oxidase that converts hydroxylamine to N2 from the newly isolated heterotrophic nitrifier Alcaligenes strain HO-1. Strain HO-1 accumulated hydroxylamine and produced N2 from ammonia oxidation. Using transcriptome analysis and heterologous expression via fosmid library screening, we identified three genes (dnfABC) of strain HO-1 that enabled E. coli cells not only to produce hydroxylamine from 15N-labelled ammonium but also to further convert it to 15N2. The three genes were individually cloned and expressed, and their translational products DnfA, DnfB, and DnfC were purified. In vitro DnfA bound to hydroxylamine and catalyzed the conversion of hydroxylamine to N2 in the presence of FAD, NADH and O2. Thus, DnfA was identified as a novel hydroxylamine oxidase and catalyzed a previously unknown N-N bond forming reaction with a yet-to-be discovered mechanism. DnfA homologs were detected in different bacterial groups, suggesting that hydroxylamine oxidation to nitrogen might occur in additional microbial taxa.

microbiology↗

bioRxiv2020

Sigflow: an automated and comprehensive pipeline for cancer genome mutational signature analysis

SummaryMutational signatures are recurring DNA alteration patterns caused by distinct mutational events during the evolution of cancer. In recent years, several bioinformatics tools are available for mutational signature analysis. However, most of them focus on specific type of mutation, or have limited scope of application. A pipeline tool for comprehensive mutational signature analysis is still lacking. Here we present Sigflow pipeline, which provides an one-stop solution for de novo signature extraction, reference signature fitting, signature stability analysis, sample clustering based on signature exposure in different types of genome DNA alterations including single base substitution (SBS), doublet base substitution (DBS), small insertion and deletion (INDEL), and copy number alteration. A Docker image is constructed to solve the complex and time-consuming installation issues, and this enables reproducible research by version control of all dependent tools along with their environments. The Sigflow pipeline can be applied to both human and mouse genomes. Availability and implementationSigflow is an open source software under academic free license (AFL) v3.0 and it is freely available at https://github.com/ShixiangWang/sigminer.workflow or https://hub.docker.com/r/shixiangwang/sigflow. Contactliuxs@shanghaitech.edu.cn

genomics↗

bioRxiv2020

Morphometrical brain markers of sex difference

Many major neuropsychiatric pathologies, some of which appear in adolescence, show differentiated prevalence, onset, and symptomatology across the biological sexes. Therefore, mapping differences in brain structure between males and females during this critical developmental period may provide information about the neural mechanisms underlying the dimorphism of these pathologies. Utilizing a large dataset collected through the Adolescent Brain Cognitive Development study, we investigated the differences of adolescent (9-10 years old) male and female brains (n = 8325) by using a linear Support-Vector Machine Classifier to predict sex based on morphometry and image intensity values of structural brain imaging data. The classifier correctly classified the sex of 86% individuals with the insula, the precentral and postcentral gyri, and the pericallosal sulcus as the most discernable features. The role of these significant dimorphic features in psychopathology was explored by testing them as mediators between sex and clinical symptomology. The results demonstrate the existence of morphometrical brain markers of sex difference. Significance StatementMany psychiatric pathologies express differently across the sexes. Therefore, an understanding of the differences in brain structure between males and females during the critical developmental period of adolescence may provide the insights about the dimorphism of clinical symptomology and the general functions of the dimorphic brain structures. Using machine learning, we successfully classified males and females with a high accuracy based on morphometry and image intensity data extracted from structural MRI scans. The features which significantly contributed to classification were examined to determine brain regions which are dimorphic during adolescence. The relevance of these brain regions to the expression of psychopathology symptoms was also explored.

neuroscience↗

bioRxiv2020

Integrative Proteomic Analysis of Posttranslational Modification in The Inflammatory Response

The posttranslational modification (PTM) of proteins, particularly acetylation, phosphorylation and ubiquitination, plays a critical role in the host innate immune response. PTMs dynamic changes and the crosstalk among them are complicated. To build a comprehensive dynamic network of inflammation related proteins, we integrated data from the whole cell proteome (WCP), acetylome, phosphoproteome and ubiquitinome of human and mouse macrophages. Our datasets of acetylation, phosphorylation and ubiquitination sites helped identify PTM crosstalk within and across proteins involved in the inflammatory response. Stimulation of macrophages by lipopolysaccharide (LPS) resulted in both degradative and non-degradative ubiquitination. Moreover, this study contributes to the interpretation of the roles of known inflammatory molecules and the discovery of novel inflammatory proteins.

immunology↗

bioRxiv2020

Cortical microinfarcts potentiate recurrent ischemic injury through NLRP3-dependent trained immunity

Microinfarcts are common among the elderly, and patients with microinfarcts are more vulnerable to another stroke. However, the potential effect of microinfarct on recurrent stroke remains elusive. In this study, we investigated the detrimental effect of microinfarct on recurrent stroke in mice. Microinfarct was induced using two-photon laser and photothrombotic stroke was induced in the cortex contralateral to microinfarct four weeks later. We found that CMI could trigger the formation of innate immune memory, which exacerbated the pro-inflammatory response and ischemic injury in second photothrombotic stroke. Furthermore, we clarified the role of NLRP3 inflammasome in the nuclei of microglia, which interacts with the MLL1 complex and thereby increases H3K4 methylation, suggesting that NLRP3 is critical in microinfarct-induced innate immune memory. Additionally, NLRP3 knockout in microglia attenuated microinfarct-induced detrimental effects on recurrent stroke. Our study highlights the detrimental effect of trained immunity on the recurrent stroke and reveals the important role of NLRP3 in mediating the formation of this memory, which may be a therapeutic target to mitigate recurrent strokes.

immunology↗

bioRxiv2020

Supramodal executive control of attention: evidence from unimodal and crossmodal dual conflict effects

Although we have demonstrated that the executive control of attention acts supramodally as shown by significant correlation between conflict effects measures in visual and auditory tasks, no direct evidence of the equivalence in the computational mechanisms governing the allocation of executive control resources within and across modalities has been found. Here, in two independent groups of 40 participants, we examined the interaction effects of conflict processing in both unimodal (visual) and crossmodal (visual and auditory) dual-conflict paradigms (flanker conflict processing in Task 1 and then in the following Task 2) with a manipulation of the stimulus onset asynchrony (SOA). In both the unimodal and the crossmodal dual-conflict paradigms, the conflict processing of Task 1 interfered with the conflict processing of Task 2 when the SOA was short, reflecting an additive interference effect of Task 1 on Task 2 under the time constraints. These results suggest that there is a unified entity that oversees conflict processing acting supramodally by implementing comparable mechanisms in unimodal and crossmodal scenarios.

neuroscience↗

bioRxiv2020

Revisiting neoantigen depletion signal in the untreated cancer genome

This study is arising from Van den Eynden J. et al. Nature Genetics. Lack of detectable neoantigen depletion signals in the untreated cancer genome. Van den Eynden J. et al. tried to address a very important scientific question: could the immune system eliminate cancer cells with immunogenic mutations in untreated situation? Van den Eynden J. et al. first annotated the human exome into "HLA-binding regions" and "non HLA-binding regions" based on the predicted binding affinity of nonapeptides translated from the un-mutated reference coding genome with type I HLA alleles. They hypothesized that if neoantigen depletion signal exist, the nonsynonymous mutations in "HLA-binding regions" will be negatively selected during cancer evolution, while nonsynonymous mutation in "non HLA-binding regions" will not be negatively selected. This will lead to decreased nonsynonymous vs synonymous mutation ratio (n/s) in "HLA-binding regions" compared with "non HLA-binding regions". They defined HLA-binding mutation ratio (HBMR) as the ratio of n/s in "HLA-binding regions" to "non HLA-binding regions", and reported that HBMRs are close to 1 in different types of cancer after background corrections, meaning neoantigen depletion signals are not detectable in different types of cancer. The fundamental problem of their hypothesis lies in that the actual neoantigens with immunogenicity do not overlap with their defined "HLA-binding regions". Actually, most neoantigens with immunogenicity are not located in "HLA-binding regions", when dissimilarity between mutant and wild type peptide are considered. It is the neoantigen with immunogenicity, but not nonsynonymous mutation in their defined "HLA-binding regions" undergo immunoediting based negative selection. Thus the results reported in that study are fundamentally flawed, and at this current stage we could not draw a solid conclusion as to whether the neoantigen depletion signal exists or not.

genomics↗

bioRxiv2020

The Past, Present and Future of Elephant Landscapes in Asia

Habitat loss drives species declines worldwide, but is seldom quantified over centennial timescales. We constructed ecological niche models for Asian elephants based on land-use change between 850-2015, and predictions under six different climate/socioeconomic scenarios from 2015-2099. We find that over 64% of suitable natural habitat across diverse ecosystems was lost over the past three centuries. Average patch size dropped 83% from approximately 99,000 km2 to 16,000 km2 and the area occupied by the largest patch decreased 83% from ~ 4 million km2 (45% of area) to 54,000 km2 (~7.5% of area). Over half of current elephant range appears unsuitable. Habitat availability is predicted to decline further this century across all scenarios. The most severe losses occur under RCP3.4-SSP4, representing mid-range emissions but high regional inequities. We conclude that climate change mitigation measures must include policies to ensure inter-regional socioeconomic equity to safeguard landscapes for elephants, humans, and other species.

ecology↗

bioRxiv2020

Single-cell atlas of a non-human primate reveals new pathogenic mechanisms of COVID-19

Stopping COVID-19 is a priority worldwide. Understanding which cell types are targeted by SARS-CoV-2 virus, whether interspecies differences exist, and how variations in cell state influence viral entry is fundamental for accelerating therapeutic and preventative approaches. In this endeavor, we profiled the transcriptome of nine tissues from a Macaca fascicularis monkey at single-cell resolution. The distribution of SARS-CoV-2 facilitators, ACE2 and TMRPSS2, in different cell subtypes showed substantial heterogeneity across lung, kidney, and liver. Through co-expression analysis, we identified immunomodulatory proteins such as IDO2 and ANPEP as potential SARS-CoV-2 targets responsible for immune cell exhaustion. Furthermore, single-cell chromatin accessibility analysis of the kidney unveiled a plausible link between IL6-mediated innate immune responses aiming to protect tissue and enhanced ACE2 expression that could promote viral entry. Our work constitutes a unique resource for understanding the physiology and pathophysiology of two phylogenetically close species, which might guide in the development of therapeutic approaches in humans. Bullet pointsO_LIWe generated a single-cell transcriptome atlas of 9 monkey tissues to study COVID-19. C_LIO_LIACE2+TMPRSS2+ epithelial cells of lung, kidney and liver are targets for SARS-CoV-2. C_LIO_LIACE2 correlation analysis shows IDO2 and ANPEP as potential therapeutic opportunities. C_LIO_LIWe unveil a link between IL6, STAT transcription factors and boosted SARS-CoV-2 entry. C_LI

cell biology↗

bioRxiv2020

Three Essential Resources to Improve Differential Scanning Fluorimetry (DSF) Experiments

Differential Scanning Fluorimetry (DSF) is a method that enables rapid determination of a proteins apparent melting temperature (Tma). Owing to its high throughput, DSF has found widespread application in fields ranging from structural biology to chemical screening. Yet DSF has developed two opposing reputations: one as an indispensable laboratory tool to probe protein stability, another as a frustrating platform that often fails. Here, we aim to reconcile these disparate reputations and help users perform more successful DSF experiments with three resources: an updated, interactive theoretical framework, practical tips, and online data analysis. We anticipate that these resources, made available online at DSFworld (https://gestwickilab.shinyapps.io/dsfworld/), will broaden the utility of DSF.

biochemistry↗

bioRxiv2020

DeepDist: real-value inter-residue distance prediction with deep residual network

MotivationDriven by deep learning techniques, inter-residue contact/distance prediction has been significantly improved and substantially enhanced ab initio protein structure prediction. Currently all the distance prediction methods classify inter-residue distances into multiple distance intervals (i.e. a multi-classification problem) instead of directly predicting real-value distances (i.e. a regression problem). The output of the former has to be converted into real-value distances in order to be used in tertiary structure prediction. ResultsTo explore the potentials of predicting real-value inter-residue distances, we develop a multi-task deep learning distance predictor (DeepDist) based on new residual convolutional network architectures to simultaneously predict real-value inter-residue distances and classify them into multiple distance intervals. We demonstrate that predicting the real-value distance map and multi-class distance map at the same time performs better than predicting real-value distances alone, indicating their complementarity. On 43 CASP13 hard domains, the average mean square error (MSE) of DeepDists real-value distance predictions is 0.896 [A] when filtering out the predicted distance >=16 [A], which is lower than 1.003 [A] of DeepDists multi-class distance predictions. When the predicted real-value distances are converted to binary contact predictions at 8[A] threshold, the precisions of top L/5 and L/2 contact predictions are 78.6% and 64.5%, respectively, higher than the best results reported in the CASP13 experiment. These results demonstrate that the real-value distance prediction can predict inter-residue distances well and improve binary contact prediction over the existing state-of-the-art methods. Moreover, the predicted real-value distances can be directly used to reconstruct protein tertiary structures better than multi-class distance predictions due to the lower MSE.

bioinformatics↗

bioRxiv2020

Integrated analysis of the aging brain transcriptome and proteome in tauopathy

BackgroundTau neurofibrillary tangle pathology characterizes Alzheimers disease and other neurodegenerative tauopathies. Brain gene expression profiles can reveal mechanisms; however, few studies have systematically examined both the transcriptome and proteome or differentiated Tau- versus age-dependent changes. MethodsPaired, longitudinal RNA-sequencing and mass-spectrometry were performed in a Drosophila model of tauopathy, based on pan-neuronal expression of human wildtype Tau (TauWT) or a mutation causing frontotemporal dementia (TauR406W). Tau-induced, differentially expressed transcripts and proteins were examined cross-sectionally or using linear regression and adjusting for age. Hierarchical clustering was performed to highlight network perturbations, and we examined overlaps with human brain gene expression profiles in tauopathy. ResultsTauWT induced 1,514 and 213 differentially expressed transcripts and proteins, respectively. TauR406W had a substantially greater impact, causing changes in 5,494 transcripts and 697 proteins. There was a ~70% overlap between age- and Tau-induced changes and our analyses reveal pervasive bi-directional interactions. Strikingly, 42% of Tau-induced transcripts were discordant in the proteome, showing opposite direction of change. Tau-responsive gene expression networks strongly implicate innate immune activation, despite the absence of microglia in flies. Cross-species analyses pinpoint human brain gene perturbations specifically triggered by Tau pathology and/or aging, and further differentiate between disease amplifying and protective changes. ConclusionsOur results comprise a powerful, cross-species functional genomics resource for tauopathy, revealing Tau-mediated disruption of gene expression, including dynamic, age-dependent interactions between the brain transcriptome and proteome.

genomics↗

bioRxiv2020

Single-molecule orientation localization microscopy for resolving structural heterogeneities within amyloid fibrils

Simultaneous measurements of single-molecule positions and orientations provide critical insight into a variety of biological and chemical processes. Various engineered point spread functions (PSFs) have been introduced for measuring the orientation and rotational diffusion of dipole-like emitters, but the widely used Cramer-Rao bound (CRB) only evaluates performance for one specific orientation at a time. Here, we report a performance metric, termed variance upper bound (VUB), that yields a global maximum CRB for all possible molecular orientations, thereby enabling the measurement performance of any PSF to be computed efficiently (~1000x faster than calculating average CRB). Our VUB reveals that the simple polarized standard PSF provides robust and precise orientation measurements if emitters are near a refractive index interface. Using this PSF, we measure the orientations and positions of Nile red (NR) molecules transiently bound to amyloid aggregates. Our super-resolved images reveal the main binding mode of NR on amyloid fiber surfaces, as well as structural heterogeneities along amyloid fibrillar networks, that cannot be resolved by single-molecule localization alone.

biophysics↗