Biology subjects

Ye, Z.

Publications and source records attributed to Ye, Z..

At least 127 records · Page 7Linked to original sources

bioRxiv2021

Comparison of the neuraminidase antigenicity in recently circulating influenza A and vaccine viruses

Neuraminidase (NA or N) antigens in circulating influenza viruses are not extensively evaluated for vaccine strain selection like hemagglutinin (HA or H) even though viral-based influenza vaccines include the recommended strain NA in varying amounts. As NA can also elicit a protective response, we assessed the antigenic similarity of the NAs from human H1N1 and H3N2 viruses that were prevalent between September 2019 to December 2020 to NAs from several recently recommended vaccine strains. To eliminate the dependence on isolates, the enzyme-linked lectin assay for analyzing NA antigenicity was performed with reverse genetic viruses carrying the same HA. Our results show that ferret antisera against NAs from the recommended H1N1 and H3N2 vaccine strains for the 2020-21 northern hemisphere influenza season recognize and inhibit the most prevalent circulating N1s and N2s, suggesting the NAs from the influenza A vaccine and circulating strains are antigenically similar. Comparisons of the recent N2s also revealed a bias in the reactivity of NA antisera from the egg and cell-based H3N2 vaccine strains due to a C-terminal substitution, indicating the C-terminus can influence N2 antigenicity and should receive consideration during the H3N2 strain selection.

microbiology↗

bioRxiv2021

Imaging the ultrastructures and dynamics of live erythrocyte membranes at the single-molecule level with a far-red probe on a microfluidic platform

Both the ultrastructures and dynamics of living erythrocyte membranes provide critical criteria for clinical diagnostics. However, it is challenging to simultaneously visualize these features at the single-molecule level due to the rigid photophysical requirements of different single-molecule imaging techniques. Herein, we rationally developed a far-red boron dipyrromethene membrane (BDP-Mem) probe that not only retained consistent and intensive single-molecule emission but also possessed the capability to photoswitch on cellular membranes. We also constructed a microfluidic platform for the noninvasive trapping and long-term imaging of nonadherent erythrocytes. By combining these advantageous technologies, super-resolution reconstruction and single-molecule tracking of living human RBC membranes were achieved at the molecular scale in a high-throughput fashion. Our integrated paradigm defines a quantitative approach for analyzing living RBC membranes under physiological and pathological conditions, improving imaging precisions and revealing new perspectives for future disease diagnostic approaches.

biophysics↗

bioRxiv2021

Electrophysiological measures from human iPSC-derived neurons are associated with schizophrenia clinical status and predict individual cognitive performance

Neurons derived from human induced pluripotent stem cells (hiPSCs) have been used to model basic cellular aspects of neuropsychiatric disorders, but the relationship between the emergent phenotypes and the clinical characteristics of donor individuals has been unclear. We analyzed RNA expression and indices of cellular function in hiPSC-derived neural progenitors and cortical neurons generated from 13 individuals with high polygenic risk scores (PRS) for schizophrenia and a clinical diagnosis of schizophrenia, along with 15 neurotypical individuals with low PRS. We identified electrophysiological measures associated with diagnosis that implicated altered Na+ channel function and GABA-ergic neurotransmission. Importantly, electrophysiological measures predicted cardinal clinical and cognitive features found in these schizophrenia patients. The identification of basic neuronal physiological properties related to core clinical characteristics of illness is a potentially critical step in generating leads for novel therapeutics.

neuroscience↗

bioRxiv2021

"Multi-Agent" Screening Improves the Efficiency of Directed Enzyme Evolution

Enzyme evolution has enabled numerous advances in biotechnology. However, directed evolution programs can still require many iterative rounds of screening to identify optimal mutant sequences. This is due to the sparsity of the fitness landscape, which in turn, is due to "hidden" mutations that only offer improvements synergistically in combination with other mutations. These "hidden" mutations are only identified by evaluating mutant combinations, necessitating large combinatorial libraries or iterative rounds of screening. Here, we report a multi-agent directed evolution approach that incorporates diverse substrate analogues in the screening process. With multiple substrates acting like multiple agents navigating the fitness landscape, we are able to identify "hidden" mutant residues that impact substrate specificity without a need for testing numerous combinations. We initially validate this approach in engineering a malonyl-CoA synthetase for improved activity with a wide variety of non-natural substrates. We found that "hidden" mutations are often distant from the active site, making them hard to predict using popular structure-based methods. Interestingly, many of the "hidden" mutations identified in this case are expected to destabilize interactions between elements of tertiary structure, potentially affecting protein flexibility. This approach may be widely applicable to accelerate enzyme engineering. Lastly, multi-agent system inspired approaches may be more broadly useful in tackling other complex combinatorial search problems in biology. Highlights- "Multi-agent" screening improves directed evolution. - The incorporation of multiple substrates leads to the identification of "hidden" mutations, which can be hard to identify through one substrate. - "Hidden" mutations are often remote from the active site and are expected to interrupt stabilizing side-chain interactions, thus increasing enzyme flexibility.

synthetic biology↗

bioRxiv2021

Deep neural network analysis employing diffusion basis spectrum imaging metrics as classifiers improves prostate cancer detection and grading

Structural and cellular complexity of prostatic histopathology limits the accuracy of noninvasive detection and grading of prostate cancer (PCa). We addressed this limitation by employing a novel diffusion basis spectrum imaging (DBSI) to derive structurally-specific diffusion fingerprints reflecting various underlying prostatic structural and cellular components. We further developed diffusion histology imaging (DHI) by combining DBSI-derived structural fingerprints with a deep neural network (DNN) algorithm to more accurately classify different histopathological features and predict tumor grade in PCa. We examined 243 patients suspected with PCa using in vivo DBSI. The in vivo DBSI-derived diffusion metrics detected coexisting prostatic pathologies distinguishing inflammation, PCa, and benign prostatic hyperplasia. DHI distinguished PCa from benign peripheral and transition zone tissues with over 95% sensitivity and specificity. DHI also demonstrated over 90% sensitivity and specificity for Gleason score noninvasively. We present DHI as a novel diagnostic tool capable of noninvasive detection and grading of PCa. One sentence summaryDiffusion histology imaging noninvasively and accurately detects and grades prostate cancer.

bioinformatics↗

bioRxiv2021

MLL3/MLL4 Histone Methyltranferase Activity Dependent Chromatin Organization at Enhancers during Embryonic Stem Cell Differentiation

MLL3 (KMT2C) and MLL4 (KMT2D), the major mono-methyltransferases of histone H3 lysine 4 (H3K4), are required for cellular differentiation and embryonic development in mammals. We previously observed that MLL3/4 promote long-range chromatin interactions at enhancers, however, it is still unclear how their catalytic activities contribute to enhancer-dependent gene activation in mammalian cell differentiation. To address this question, we mapped histone modifications, long-range chromatin contacts as well as gene expression in MLL3/4 catalytically deficient mouse embryonic stem (ES) cells undergoing differentiation toward neural precursor cells. We showed that MLL3/4 activities are responsible for deposition of H3K4me1 modification and formation of long-range enhancer-promoter contacts at a majority of putative enhancers gained during cell differentiation, but are dispensable for most candidate enhancers found in undifferentiated ES cells that persist through differentiation. While transcriptional induction at most genes is unaltered in the MLL3/4 catalytically deficient cells, genes making more contacts with MLL3/4-dependent putative enhancers are disproportionately affected. These results support that MLL3/4 contributes to cellular differentiation through histone-methyltransferase-activity dependent induction of enhancer-promoter contacts and transcriptional activation at a subset of lineage-specific genes.

cell biology↗

bioRxiv2021

Dendritic domain-specific sampling of long-range axons shapes feedforward and feedback connectivity of L5 neurons

Feedforward and feedback pathways interact in specific dendritic domains to enable cognitive functions such as predictive processing and learning. Based on axonal projections, hierarchically lower areas are thought to form synapses primarily on dendrites in middle cortical layers, while higher-order areas are posited to target dendrites in layer 1 and in deep layers. However, the extent to which functional synapses form in regions of axo-dendritic overlap has not been extensively studied. Here, we use viral tracing in the secondary visual cortex of mice to map brain-wide inputs to thick-tufted layer 5 pyramidal neurons. Furthermore, we provide a comprehensive map of input locations through subcellular optogenetic circuit mapping. We show that input pathways target distinct dendritic domains with far greater specificity than appears from their axonal branching, often deviating substantially from the canonical patterns. Common assumptions regarding the dendrite-level interaction of feedforward and feedback inputs may thus need revisiting.

neuroscience↗

bioRxiv2021

Mutagenesis of the Ammonium Transporter AcAmt Reveals a Reproductive Role and a Novel Ammonia-Sensing Mechanism in the Malaria Vector Mosquito Anopheles coluzzii

Anopheline mosquitoes are the sole vectors of malaria and rely on olfactory cues for host seeking in which ammonia derived from human sweat plays an essential role. To investigate the function of the Anopheles coluzzii ammonium transporter (AcAmt) in the mosquito olfactory system, we generated an AcAmt null mutant line using CRISPR/Cas9. AcAmt-/- mutants displayed a series of novel phenotypes compared with wild-type mosquitoes including significantly lower insemination rates during mating and increased mortality during eclosion. Furthermore, AcAmt-/- males showed significantly lower sugar consumption while AcAmt-/- females and pupae displayed significantly higher ammonia levels than their wild-type counterparts. Surprisingly, in contrast to previous studies in Drosophila that revealed that the mutation of the ammonium transporter (DmAmt) induces a dramatic reduction of ammonia responses in antennal coeloconic sensilla, no significant differences were observed across a range of peripheral sensory neuron responses to ammonia and other odorants between wild-type and AcAmt-/- females. Taken together, these data support the existence of a unique ammonia-sensing mechanism in mosquitoes and that the ammonium transporter may be an important molecular target for vector control. Key MessagesO_LIMutagenesis of An. coluzzii ammonium transporter AcAmt followed by comprehensive electrophysiological investigation suggest a novel ammonia-sensing pathway in Anopheles mosquitoes. C_LIO_LIAcAmt-/- mutants displayed significant deficiencies in reproduction and eclosion, which are likely due to elevated ammonia levels and reduced ability of sugar feeding. C_LIO_LIAn. coluzzii coeloconic sensilla primarily detect amines and acids. C_LI

neuroscience↗

bioRxiv2020

IOBR: Multi-omics Immuno-Oncology Biological Research to decode tumor microenvironment and signatures

MotivationRecent advance in next generation sequencing has triggered the rapid accumulation of publicly available multi-omics datasets. The application of integrated omics to exploring robust signatures for clinical translation is increasingly highlighted, attributed to the clinical success of immune checkpoint blockade in diverse malignancies. However, effective tools to comprehensively interpret multi-omics data is still warranted to provide increased granularity into intrinsic mechanism of oncogenesis and immunotherapeutic sensitivity. ResultsWe developed a computational tool for effective Immuno-Oncology Biological Research (IOBR), providing comprehensive investigation of estimation of reported or user-built signatures, TME deconvolution and signature construction base on multi-omics data. Notably, IOBR offers batch analyses of these signatures and their correlations with clinical phenotypes, lncRNA profiling, genomic characteristics and signatures generated from single-cell RNA sequencing data in different cancer settings. Additionally, IOBR also integrates multiple existing microenvironmental deconvolution methodologies and signature construction tools for convenient comparison and selection. Collectively, IOBR is a user-friendly tool, to leverage multi-omics data to facilitate immuno-oncology exploration and unveiling of tumor-immune interactions and accelerating precision immunotherapy.

bioinformatics↗

bioRxiv2020

White matter integrity and nicotine dependence in smokers: evaluating vertical and horizontal pleiotropy

Smoking is a heritable behavior and nicotine dependency is complex mechanism supported by both positive and negative reinforcements. We hypothesized that cerebral white matter (WM) may mediate the individual dependency on nicotine integrity because its integrity is altered in smokers and shows dose-related response to nicotine administration. Two vertical and one horizontal pleiotropy pathways that combined individual genetic variations, measure of WM integrity by fractional anisotropy (FA), and nicotine dependence were evaluated in a large epidemiological sample (N=12,264 and 4,654 participants that have genetic, FA measure and nicotine dependence data available for smoking status and cigarettes per day (CPD), respectively) collected UK Biobank. We started by selecting the candidate genetic regions including genetic risk factors associated with smoking from genome-wide association study (GWAS) for causal pathway analysis. Then we identified pleiotropic loci that influence both nicotine dependence and WM integrity from these regions. We tested a horizontal pleiotropy pathway: (A) genetic risk factors associated with smoking were independently affecting both nicotine dependence and WM integrity. We also evaluated two vertical pleiotropy that assumed that individual genetic factors associated with nicotine dependence impacted B) impacted WM integrity which in turn led to higher nicotine dependence vs. C) led to nicotine dependence and resulting white matter alterations. There were 10 and 23 candidate pleiotropic variants identified for smoking status and CPD traits. All these variants exhibited vertical pleiotropy. For smoking status, the genetic effect on smoking status was mediated by FA measures over multiple brain regions. The variants were located in a gene SARDH, which catalyzes the oxidative demethylation of sarcosine that plays a role in reducing tolerance effect on nicotine. Conversely, CPD was a significant mediator in the vertical pleiotropy pathway to FA. The identified variants were located in gene IREB2, that was reported as a susceptibility gene for both neurodegeneration and smoking-induced diseases.

neuroscience↗

bioRxiv2020

Genetic Fine-mapping with Dense Linkage Disequilibrium Blocks: genetics of nicotine dependence

Fine-mapping is an analytical step to perform causal prioritization of the polymorphic variants on a trait-associated genomic region observed from genome-wide association studies (GWAS). The prioritization of causal variants can be challenging due to the linkage disequilibrium (LD) patterns among hundreds to thousands of polymorphisms associated with a trait. We propose a novel{ell} 0 graph norm shrinkage algorithm to select causal variants from dense LD blocks consisting of highly correlated SNPs that may not be proximal or contiguous. We extract dense LD blocks and perform regression shrinkage to calculate a prioritization score to select a parsimonious set of causal variants. Our approach is computationally efficient and allows performing fine-mapping on thousands of polymorphisms. We demonstrate its application using a large UK Biobank (UKBB) sample related to nicotine addiction. Our results suggest that polymorphic variances in both neighboring and distant variants can be consolidated into dense blocks of highly correlated loci. Simulations were used to evaluate and compare the performance of our method and existing fine-mapping algorithms. The results demonstrated that our method outperformed comparable fine-mapping methods with increased sensitivity and reduced false-positive error rate regarding causal variant selection. The application of this method to smoking severity trait in UKBB sample replicated previously reported loci and suggested the causal prioritization of genetic effects on nicotine dependency. Author summaryDisentangling the complex linkage disequilibrium (LD) pattern and selecting the underlying causal variants have been a long-term challenge for genetic fine-mapping. We find that the LD pattern within GWAS loci is intrinsically organized in delicate graph topological structures, which can be effectively learned by our novel{ell} 0 graph norm shrinkage algorithm. The extracted LD graph structure is critical for causal variant selection. Moreover, our method is less constrained by the width of GWAS loci and thus can fine-map a massive number of correlated SNPs.

genetics↗

bioRxiv2020

Balancing the influenza neuraminidase and hemagglutinin responses by exchanging the vaccine virus backbone

Virions are a common antigen source for many viral vaccines. One limitation to using virions is that the antigen abundance is determined by the content of each protein in the virus. This caveat especially applies to viral-based influenza vaccines where the low abundance of the neuraminidase (NA) surface antigen remains a bottleneck for improving the NA antibody response. Here, we used the WHO recommended H1N1 antigens (2019-2020 season) in a systematic analysis to demonstrate that the NA to hemagglutinin (HA) ratio in virions can be improved by exchanging the viral backbone internal genes, thereby preserving the antigens. The purified inactivated virions with higher NA content show a more spherical morphology, a different balance between the HA receptor binding and NA receptor release functions, and induce a better NA inhibitory antibody response in mice. These results indicate that influenza viruses support a range of ratios for a given NA and HA pair that can be utilized to produce viral-based influenza vaccines with increased NA content which can elicit more balanced inhibitory antibody responses to NA and HA.

microbiology↗

bioRxiv2020

Optimization of Spectral Library Size Improves DIA-MS Proteome Coverage

Efficient peptide and protein identification from data-independent acquisition mass spectrometric (DIA-MS) data typically rely on an experiment-specific spectral library with a suitable size. Here, we report a computational strategy for optimizing the spectral library for a specific DIA dataset based on a comprehensive spectral library, which is accomplished by a priori analysis of the DIA dataset. This strategy achieved up to 44.7% increase in peptide identification and 38.1% increase in protein identification in the test dataset of six colorectal tumor samples compared with the comprehensive pan-human library strategy. We further applied this strategy to 389 carcinoma samples from 15 tumor datasets and observed up to 39.2% increase in peptide identification and 19.0% increase in protein identification. In summary, we present a computational strategy for spectral library size optimization to achieve deeper proteome coverage of DIA-MS data.

bioinformatics↗

bioRxiv2020

MarkovHC: Markov hierarchical clustering for the topological structure of high-dimensional single-cell omics data

Distinguishing cell types and cell states is one of the fundamental questions in single-cell studies. Meanwhile, exploring the lineage relations among cells and finding the path and critical points in the cell fate transition are also of great importance. Existing unsupervised clustering methods and lineage trajectory reconstruction methods often face several challenges such as clustering data of arbitrary shapes, tracking precise trajectories and identifying critical points. Certain adaptive landscape approach1-3, which constructs a pseudo-energy landscape of the dynamical system, may be used to explore such problems. Thus, we propose Markov hierarchical clustering algorithm (MarkovHC), which reconstructs multi-scale pseudo-energy landscape by exploiting underlying metastability structure in an exponentially perturbed Markov chain4. A Markov process describes the random walk of a hypothetically traveling cell in the corresponding pseudo-energy landscape over possible gene expression states. Technically, MarkovHC integrates the tasks of cell classification, trajectory reconstruction, and critical point identification in a single theoretical framework consistent with topological data analysis (TDA)5. In addition to the algorithm development and simulation tests, we also applied MarkovHC to diverse types of real biological data: single-cell RNA-Seq data, cytometry data, and single-cell ATAC-Seq data. Remarkably, when applying to single-cell RNA-Seq data of human ESC derived progenitor cells6, MarkovHC not only could successfully identify known cell types, but also discover new cell types and stages. In addition, when using MarkovHC to analyze single-cell RNA-Seq data of human preimplantation embryos in early development7, the hierarchical structure of the lineage trajectories was faithfully reconstituted. Furthermore, the critical points representing important stage transitions had also been identified by MarkovHC from early gastric cancer data8. In summary, these results demonstrate that MarkovHC is a powerful tool based on rigorous metastability theory to explore hierarchical structures of biological data, to identify a cell sub-population (basin) and a critical point (stage transition), and to track a lineage trajectory (differentiation path). HighlightsO_LIMarkovHC explores the topology hierarchy in high-dimensional data. C_LIO_LIMarkovHC can find clusters (basins) and cores (attractors) of clusters in different scales. C_LIO_LIThe trajectory of state transition (transition paths) and critical points in the process of state transition (critical points) among clusters can be tracked. C_LIO_LIMarkovHC can be applied on diverse types of single-cell omics data. C_LI

bioinformatics↗

bioRxiv2020

Neuropixels 2.0: A miniaturized high-density probe for stable, long-term brain recordings

To study the dynamics of neural processing across timescales, we require the ability to follow the spiking of thousands of individually separable neurons over weeks and months, during unrestrained behavior. To address this need, we introduce the Neuropixels 2.0 probe together with novel analysis algorithms. The new probe has over 5,000 sites and is miniaturized such that two probes plus a headstage, recording 768 sites at once, weigh just over 1 g, suitable for implanting chronically in small mammals. Recordings with high quality signals persisting for at least two months were reliably obtained in two species and six different labs. Improved site density and arrangement combined with new data processing methods enable automatic post-hoc stabilization of data despite brain movements during behavior and across days, allowing recording from the same neurons in the mouse visual cortex for over 2 months. Additionally, an optional configuration allows for recording from multiple sites per available channel, with a penalty to signal-to-noise ratio. These probes and algorithms enable stable recordings from >10,000 sites during free behavior in small animals such as mice.

neuroscience↗

bioRxiv2020

Genome-wide association study reveals the genetic architecture of 27 yield-related traits in tomato

Tomato (Solanum lycopersicum) is a highly valuable vegetable crop and yield is one of the most important traits. Uncovering the genetic architecture of yield-related traits in tomato is critical for the management of vegetative and reproductive development, thereby enhancing yield. Here we perform a comprehensive genome-wide association study for 27 yield-related traits in tomato. A total of 239 significant associations corresponding to 129 loci, harboring many reported and novel genes related to vegetative and reproductive development, were identified, and these loci explained an average of [~]8.8% of the phenotypic variance. A total of 51 loci associated with 25 traits have been under selection, especially during tomato improvement. Furthermore, a candidate gene, SlALMT15 that encodes an aluminum-activated malate transporter, was functionally characterized and shown to act as a pivotal regulator of leaf stomata formation through increasing photosynthesis and drought resistance. This study provides valuable information for tomato genetic research and breeding.

plant biology↗

bioRxiv2020

Mutagenesis of the Orco Odorant Receptor Co-receptor Impairs Olfactory Function in the Malaria Vector Anopheles coluzzii

Mosquitoes rely heavily on their olfactory systems for host seeking, selection of oviposition sites, and avoiding predators and other environmental dangers. Of these behaviors, the preferential selection of a human blood-meal host drives the vectorial capacity of anthropophilic female Anopheles coluzzii mosquitoes. Olfactory receptor neurons (ORNs) are dispersed across several appendages on the head and express an obligate odorant receptor co-receptor (Orco) coupled with a "tuning" odorant receptor (OR) to form heteromeric, odor-gated ion channels in the membrane of these neurons. To examine the mechanistic and functional contributions of Orco/OR complexes to the chemosensory processes of An. coluzzii, we utilized CRISPR/Cas9 gene editing to create a line of homozygous, Orco-knockout, mutant mosquitoes. As expected, orco-/- ORNs across both adult and larval stages of An. coluzzii display significantly lower background activity and lack nearly all odor-evoked responses. In addition, blood-meal-seeking, adult female, orco- /- mutant mosquitoes exhibit severely reduced attraction to human- and non-human-derived odors while gravid females are significantly less responsive to established oviposition attractants. These results reinforce observations in other insects that Orco is crucial in maintaining the activity of ORNs. In that light, it significantly influences a range of olfactory-driven behaviors central to the anthropophilic host preference that is critical to the vectorial capacity of An. coluzzii as a primary vector for human malaria.

neuroscience↗

bioRxiv2020

Two-stage Dynamic Deregulation of Metabolism Improves Process Robustness & Scalability in Engineered E. coli.

We report improved strain and bioprocess robustness as a result of the dynamic deregulation of central metabolism using two-stage dynamic control. Dynamic control is implemented using combinations of CRISPR interference and controlled proteolysis to reduce levels of central metabolic enzymes in the context of a standardized two-stage bioprocesses. Reducing the levels of key enzymes alters metabolite pools resulting in deregulation of the metabolic network. The deregulated network is more robust to environmental conditions improving process robustness, which in turn leads to predictable scalability from high throughput small scale screens to fully instrumented bioreactors as well as to pilot scale production. Additionally, as these two-stage bioprocesses are standardized, a need for traditional process optimization is minimized. Predictive high throughput approaches that translate to larger scales are critical for metabolic engineering programs to truly take advantage of the rapidly increasing throughput and decreasing costs of synthetic biology. In this work we demonstrate that the improved robustness of E. coli strains engineered for the improved scalability of the important industrial chemicals alanine, citramalate and xylitol, from microtiter plates to pilot reactors.

synthetic biology↗