Biology subjects

Zhang, F.

Publications and source records attributed to Zhang, F..

At least 343 records · Page 19Linked to original sources

bioRxiv2021

Drug-driven reclassification of multiple tumour subtypes reveals intrinsic molecular concordance of therapy across histologically disparate cancers

Cancers that are histologically defined as the same type of cancer often need a distinct therapy based on underlying heterogeneity; likewise, histologically disparate cancers can require similar treatment approaches due to intrinsic similarities. A comprehensive analysis integrated with drug response data and genomic alterations, particularly to reveal therapeutic concordance mechanisms across histologically disparate tumour subtypes, has not yet been fully exploited. In this study, we used pharmacogenomic profiling data provided from the Cancer Genome Project (CGP) in a systematic in silico investigation of the pharmacological subtypes of cancers and the intrinsic concordance of molecular mechanisms leading to similar therapeutic responses across histologically disparate tumour subtypes. We further developed a novel approach to redefine cell-to-cell similarity and drug-to-drug similarity from the therapeutic concordance, providing a new point of view to study cancer heterogeneity. Our study identified that histologically different tumours, such as malignant melanoma and colorectal adenocarcinoma, could belong to the same pharmacological subtype regarding drug sensitivity to MEK inhibitors, which was determined by their genomic alterations, high occurrence of BRAF or KRAS mutations. Therapeutic concordance for chemotherapy drugs was identified across histologically disparate hematological tumors mainly due to the extraordinary activation of the cell cycle in blood cancers. A subcluster of SCLC had a more similar profile with hematological tumors, and was associated with the malignant phenotype, with a higher level of MYC expression. We developed a website to store and visualize the pharmacological subtypes of drugs, as well as their connected genomic and expression alterations.

bioinformatics↗

bioRxiv2021

Insulin-like peptide receptor mediated signaling pathways orchestrate regulation of energy homeostasis in the Pacific oyster, Crassostrea gigas

The involvement of insulin/insulin-like growth factor (IIS) signaling pathway in growth regulation of marine invertebrates remains largely unexplored. In this study, we used a fast-growing Pacific oyster (Crassostrea gigas) variety "Haida No.1" as material to unravel the role of IIS system in growth regulation in oysters. Systematic bioinformatics analyses allowed to identify major components of IIS signaling pathway and insulin-like peptide receptor (ILPR) mediated signaling pathways, including PI3K-AKT, RAS-MAPK, and TOR, in C. gigas. Expression levels of the major genes in IIS and its downstream signaling pathways were significantly higher in "Haida No.1" than wild oysters, suggesting their involvement in growth regulation of C. gigas. Expression profiles of IIS and its downstream signaling pathway genes were significantly altered by nutrient abundance and culture temperature. These results suggested that IIS signaling pathway coupled with the ILPR mediated signaling pathways orchestrated energy homeostasis to regulate growth in the Pacific oyster. Research HighlightsO_LIILPR, IRS, IGFBPRP, and IGFALS genes were characterized in the C. gigas. C_LIO_LIMajor genes of IIS signaling pathway were highly expressed in fast-growing C. gigas. C_LIO_LIIIS and downstream pathways participates in energy homeostasis of oysters. C_LIO_LIILPR mediated signaling pathways orchestrate growth regulation in oysters. C_LI

genomics↗

bioRxiv2021

Assembly of chromosome-scale and allele-aware autotetraploid genome of the Chinese alfalfa cultivar Zhongmu-4 and identification of SNP loci associated with 27 agronomic traits

Alfalfa (Medicago sativa L.), the most valuable perennial legume crop, referred to as "Queen of the Forages" for its high nutritional value and yield production among forage crops. Comprehensive genomic information of germplasm resources from different ecological regions and modern breeding strategies, such as molecular-marker assisted breeding are of great importance to breed new alfalfa varieties with environmental resilience. Here, we report assembly of the genome sequence of Zhongmu-4 (ZM-4), one of the most planted cultivars in China, and identification of SNPs associated with alfalfa agronomic traits by Genome-wide Association Studies (GWAS). Sequence of 32 allelic chromosomes was assembled successfully by single molecule real time sequencing and Hi-C technique with ALLHiC algorithm. About 2.74 Gbp contigs, accounting for 88.39% of the estimated genome, were assembled with 2.56 Gbp contigs anchored to 32 pseudo-chromosomes. In comparison with M. truncatula A17, distinctive inversion and translocation on chromosome 1, and between chromosome 4 and 8, respectively, were detected. Moreover, we conducted resequencing of 220 alfalfa accessions collected globally and performed GWAS analysis based on our assembled genome. Population structure analysis demonstrated that alfalfa has a complex genetic relationship among germplasm with different geographic origins. GWAS identified 101 SNPs associated with 27 out of 93 agronomic traits. The updated chromosome-scale and allele-aware genome sequence, coupled with the resequencing data of most global alfalfa germplasm, provides valuable information for alfalfa genetic research, and further analysis of major SNP loci will accelerate unravelling the molecular basis of important agronomic traits and facilitate genetic improvement of alfalfa.

plant biology↗

bioRxiv2021

A Safe and Effective Mucosal RSV Vaccine Consisting of RSV Phosphoprotein and Flagellin Variant

Respiratory syncytial virus (RSV) is a major cause of serious acute lower respiratory tract infection in infants and the elderly. No licensed RSV vaccine available thus far calls for the development of vaccines with new target(s) and vaccination strategies. Here, we constructed a recombinant protein, designated P-KFD1, comprised of RSV phosphoprotein (P) and E. coli K12 strain-derived flagellin variant KFD1. Intranasal (i.n.) immunization with P-KFD1 inhibits RSV replication in both upper and lower respiratory tract, and protects mice against lung disease without vaccine-enhanced disease (VED). The P-specific CD4+ T cells provoked by P-KFD1 i.n. immunization, either reside in or migrate to respiratory tract, mediate protection against RSV infection. Sc-RNA seq and carboxyfluorescein succinimidyl ester (CFSE) labeled cell transfer further characterized the Th1 and Th17 responses induced by P-KFD1. Finally, we found the anti-viral protection depends on either IFN-{gamma} or IL-17A. Collectively, P-KFD1 is promising as a safe and effective mucosal vaccine candidate to prevent RSV infection. HIGHLIGHTSO_LIA new subunit RSV vaccine candidate with new target and vaccination strategy, P-KFD1, is designed and generated C_LIO_LIIntranasal immunization with P-KFD1 protects mice against RSV infection and averts vaccine-enhanced disease C_LIO_LISc-RNA seq and CFSE-labelled cell transfer identified characteristics of the protective CD4+ T cells C_LIO_LILocal and peripheral CD4+ T cells provide protection against RSV infection dependent on either IFN-{gamma} or IL-17A C_LI

immunology↗

bioRxiv2021

Global analysis of protein arginine methylation

Quantitative information about the levels and dynamics of post-translational modifications (PTMs) is critical for an understanding of cellular functions. Protein arginine methylation (ArgMet) is an important subclass of PTMs and is involved in a plethora of (patho)physiological processes. However, due to the lack of methods for global analysis of ArgMet, the link between ArgMet levels, dynamics and (patho)physiology remains largely unknown. We utilized the high sensitivity and robustness of Nuclear Magnetic Resonance (NMR) spectroscopy to develop a general method for the quantification of global protein ArgMet. Our NMR-based approach enables the detection of protein ArgMet in purified proteins, cells, organoids, and mouse tissues. We demonstrate that the process of ArgMet is a highly prevalent PTM and can be modulated by small-molecule inhibitors and metabolites and changes in cancer and during ageing. Thus, our approach enables to address a wide range of biological questions related to ArgMet in health and disease. Graphical Abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=200 SRC="FIGDIR/small/428036v1_ufig1.gif" ALT="Figure 1"> View larger version (68K): org.highwire.dtl.DTLVardef@37a0f1org.highwire.dtl.DTLVardef@273ebdorg.highwire.dtl.DTLVardef@87fcd6org.highwire.dtl.DTLVardef@1e84c07_HPS_FORMAT_FIGEXP M_FIG C_FIG

systems biology↗

bioRxiv2021

A parsimonious model for mass-univariate vertex-wise analysis

2.Covariance between grey-matter measurements can reflect structural or functional brain networks though it has also been shown to be influenced by confounding factors (e.g. age, head size, scanner), which could lead to lower mapping precision (increased size of associated clusters) and create distal false positives associations in mass-univariate vertex-wise analyses. We evaluated this concern by performing state-of-the-art mass-univariate analyses (general linear model, GLM) on traits simulated from real vertex-wise grey matter data (including cortical and subcortical thickness and surface area). We contrasted the results with those from linear mixed models (LMMs), which have been shown to overcome similar issues in omics association studies. We showed that when performed on a large sample (N=8,662, UK Biobank), GLMs yielded large spatial clusters of significant vertices and greatly inflated false positive rate (Family Wise Error Rate: FWER=1, cluster false discovery rate: FDR>0.6). We showed that LMMs resulted in more parsimonious results: smaller clusters and reduced false positive rate (yet FWER>5% after Bonferroni correction) but at a cost of increased computation. In practice, the parsimony of LMMs results from controlling for the joint effect of all vertices, which prevents local and distal redundant associations from reaching significance. Next, we performed mass-univariate association analyses on five real UKB traits (age, sex, BMI, fluid intelligence and smoking status) and LMM yielded fewer and more localised associations. We identified 19 significant clusters displaying small associations with age, sex and BMI, which suggest a complex architecture of at least dozens of associated areas with those phenotypes.

neuroscience↗

bioRxiv2021

Tandem-Cleavage Linkers Improve the In Vivo Stability and Tolerability of Antibody-Drug Conjugates

Although peptide motifs represent the majority of cleavable linkers used in clinical-stage antibody-drug conjugates (ADCs), the sequences are often sensitive to cleavage by extracellular enzymes, such as elastase, leading to systemic release of the cytotoxic payload. This action reduces the therapeutic index by causing off-target toxicities that can be dose-limiting. For example, a common side-effect of ADCs made using peptide-cleavable linkers is myelosuppression, including neutropenia. Only a few reports describe methods for optimizing peptide linkers to maintain efficient and potent tumor payload delivery while enhancing circulating stability. Herein, we address these critical limitations through the development of a tandem-cleavage linker strategy, where two sequential enzymatic cleavage events mediate payload release. We prepared dipeptides that are protected from degradation in the circulation by a sterically-encumbering glucuronide moiety. Upon ADC internalization and lysosomal degradation, the monosaccharide is removed and the exposed dipeptide is degraded, liberating the attached payload inside the target cell. We used CD79b-targeted monomethyl auristatin E (MMAE) conjugates as our model system, and compared the stability, efficacy, and tolerability of ADCs made with tandem-cleavage linkers to ADCs made using standard technology with the vedotin linker. The results--where rat studies showed dramatically improved tolerability in the hematopoietic compartment--highlight the role that linker stability plays in efficacy and tolerability, and offer a means of improving an ADCs therapeutic index for improved patient outcomes.

pharmacology and toxicology↗

bioRxiv2021

SAINT: automatic taxonomy embedding and categorization by Siamese triplet network

MotivationUnderstanding the phylogenetic relationship among organisms is the key in contemporary evolutionary study and sequence analysis is the workhorse towards this goal. Conventional approaches to sequence analysis are based on sequence alignment, which is neither scalable to large-scale datasets due to computational inefficiency nor adaptive to next-generation sequencing (NGS) data. Alignment-free approaches are typically used as computationally effective alternatives yet still suffering the high demand of memory consumption. One desirable sequence comparison method at large-scale requires succinctly-organized sequence data management, as well as prompt sequence retrieval given a never-before-seen sequence as query. ResultsIn this paper, we proposed a novel approach, referred to as SAINT, for efficient and accurate alignment-free sequence comparison. Compared to existing alignment-free sequence comparison methods, SAINT offers advantages in two aspects: (1) SAINT is a weakly-supervised learning method where the embedding function is learned automatically from the easily-acquired data; (2) SAINT utilizes the non-linear deep learning-based model which potentially better captures the complicated relationship among genome sequences. We have applied SAINT to real-world datasets to demonstrate its empirical utility, both qualitatively and quantitatively. Considering the extensive applicability of alignment-free sequence comparison methods, we expect SAINT to motivate a more extensive set of applications in sequence comparison at large scale. AvailabilityThe open source, Apache licensed, python-implemented code will be available upon acceptance. Supplementary informationSupplementary data are available at Bioinformatics online.

bioinformatics↗

bioRxiv2021

High-content single-cell combinatorial indexing

Single-cell genomics assays have emerged as a dominant platform for interrogating complex biological systems. Methods to capture various properties at the single-cell level typically suffer a tradeoff between cell count and information content, which is defined by the number of unique and usable reads acquired per cell. We and others have described workflows that utilize single-cell combinatorial indexing (sci)1, leveraging transposase-based library construction2 to assess a variety of genomic properties in high throughput; however, these techniques often produce sparse coverage for the property of interest. Here, we describe a novel adaptor-switching strategy, s3, capable of producing one-to-two order-of-magnitude improvements in usable reads obtained per cell for chromatin accessibility (s3-ATAC), whole genome sequencing (s3-WGS), and whole genome plus chromatin conformation (s3-GCC), while retaining the same high-throughput capabilities of predecessor sci technologies. We apply s3 to produce high-coverage single-cell ATAC-seq profiles of mouse brain and human cortex tissue; and whole genome and chromatin contact maps for two low-passage patient-derived cell lines from a primary pancreatic tumor.

genetics↗

bioRxiv2021

ERK signaling licenses SKN-1A/NRF1 for proteasome production and proteasomal stress resistance

The ubiquitin-proteasome system is vital for cell growth and homeostasis, but for most cancers proteasomal inhibition has not been effective as a therapy. Normal and cancer cells adapt to proteasomal stress through an evolutionarily conserved recovery response, in which the transcription factor NRF1 upregulates proteasome subunit genes. Starting with a C. elegans screen to identify regulators of the recovery response, here we show that this response depends upon phosphorylation of NRF1 on a single residue by the growth factor-activated kinase ERK1/2. Inhibition of this phosphorylation impairs NRF1 nuclear localization and proteasome gene activation, sensitizes C. elegans and cancer cells to proteasomal stress, and synergizes with proteasome inhibition to retard human melanoma growth in vivo in a mouse model. The evolutionarily conserved ERK1/2-NRF1 axis couples proteasome production to growth signaling, and represents a promising new strategy for expanding the range and efficacy of proteasomal inhibition therapy in cancer.

cell biology↗

bioRxiv2020

Multidimensional brain-age prediction reveals altered brain developmental trajectory in psychiatric disorders

Neuroimaging-based brain-age prediction has emerged as an important new approach for studying brain development. However, brain regions change in different ways and at different rates. Unitary brain-age indices used in previous studies represent developmental status averaged across the whole brain and therefore do not capture the divergent developmental trajectories of various brain structures. Importantly, this staggered developmental unfolding, determined by genetics and postnatal experience, is implicated in the progression of psychiatric and neurological disorders. Here we propose an analytic method for computing a multidimensional brain-age index (MBAI) that provides regional age predictions. Using a database of 556 subjects (ages 8-21) that includes psychiatric and neurological patients as well as healthy controls, we conducted robust regression and cluster analyses to identify clusters of imaging features with distinct developmental trajectories. We then built machine-learning models to obtain brain-age predictions from each of the identified clusters to form the MBAI. Our results show that the MBAI provides a flexible analysis of region-specific brain-age changes that are invisible to unidimensional brain-age prediction methods. Importantly, brain ages computed from region-specific feature clusters contain complementary information and demonstrate differential ability to classify disorder groups (e.g., specific phobia, depression, ADHD) from healthy controls. Compared to unidimensional brain-age indices, we show that the MBAI is sensitive to alterations in brain structures and captures distinct regional change patterns which may serve as biomarkers that may contribute to our understanding of healthy and pathological brain development and to the characterization, diagnosis, and, potentially, treatment of various disorders.

neuroscience↗

bioRxiv2020

Multi-cellular communities are perturbed in the aging human brain and with Alzheimer's disease

The role of different cell types and their interactions in Alzheimers disease (AD) is an open question. Here we pursued it by assembling a high-resolution cellular map of the aging frontal cortex by single nucleus RNA-seq of 24 individuals with different clinicopathologic characteristics. We used the map to infer the neocortical cellular architecture of 638 individuals profiled by bulk RNA-seq, providing the sample size necessary for identifying statistically robust associations. We uncovered diverse cell populations associated with AD, including inhibitory neuronal subtypes and oligodendroglial states. We further recovered a network of multicellular communities, each composed of coordinated subpopulations of neuronal, glial and endothelial cells, and found that two of these communities are altered in AD. Finally, we used mediation analyses to prioritize cellular changes that might contribute to cognitive decline. Thus, our deconstruction of the aging neocortex provides a roadmap for evaluating the cellular microenvironments underlying AD and dementia.

neuroscience↗

bioRxiv2020

An expedited approach towards the rationale design of non-covalent SARS-CoV-2 main protease inhibitors with in vitro antiviral activity

The main protease (Mpro) of SARS-CoV-2 is a validated antiviral drug target. Several Mpro inhibitors have been reported with potent enzymatic inhibition and cellular antiviral activity, including GC376, boceprevir, calpain inhibitors II and XII, each containing a reactive warhead that covalently modifies the catalytic Cys145. In this study, we report an expedited drug discovery approach by coupling structure-based design and Ugi four-component (Ugi-4CR) reaction methodology to the design of non-covalent Mpro inhibitors. The most potent compound 23R had cellular antiviral activity similar to covalent inhibitors such as GC376. Our designs were guided by overlaying the structure of SARS-CoV Mpro + ML188 (R), a non-covalent inhibitor derived from Ug-4CR, with the X-ray crystal structures of SARS-CoV-2 Mpro + calpain inhibitor XII/GC376/UAWJ247. Binding site analysis suggests a strategy of extending the P2 and P3 substitutions in ML188 (R) to achieve optimal shape complementary with SARS-CoV-2 Mpro. Lead optimization led to the discovery of 23R, which inhibits SARS-CoV-2 Mpro and SARS-CoV-2 viral replication with an IC50 of 0.31 M and EC50 of 1.27 M, respectively. The binding and specificity of 23R to SARS-CoV-2 Mpro were confirmed in a thermal shift assay and native mass spectrometry assay. The co-crystal structure of SARS-CoV-2 Mpro with 23R revealed the P2 biphenyl fits snuggly into the S2 pocket and the benzyl group in the -methylbenzyl faces towards the core of the enzyme, occupying a previously unexplored binding site located in between the S2 and S4 pockets. Overall, this study revealed the most potent non-covalent SARS-CoV-2 Mpro inhibitors reported to date and a novel binding pocket that can be explored for Mpro inhibitor design.

pharmacology and toxicology↗

bioRxiv2020

A 4-lineage statistical suite to evaluate the support of large-scale retrotransposon insertion data to reconstruct evolutionary trees

Retrophylogenomics makes use of genome-wide retrotransposon presence/absence insertion patterns to resolve questions in phylogeny and population genetics. In the genomics era, evaluating high-throughput data requires the associated development of appropriately powerful statistical tools. The currently used KKSC 3-lineage statistical test for estimating the significance of retrophylogenomic data is limited by the number of possible tree topologies it can assess in one step. To improve on this, we have extended the analysis to simultaneously compare 4-lineages, enabling us to evaluate ten distinct presence/absence insertion patterns for 26 possible tree topologies plus 129 trees with different incidences of hybridization or introgression. The new tool provides statistics for cases involving multiple ancestral hybridizations/introgressions, ancestral incomplete lineage sorting, bifurcation, and polytomy. The test is embedded in a user-friendly web R-application (http://retrogenomics.uni-muenster.de:3838/hammlet/) and is available for use by the scientific community.

evolutionary biology↗

bioRxiv2020

Pluripotent stem cell derived models of neurological diseases reveal early transcriptional heterogeneity

BackgroundMany neurodegenerative diseases (NDs) develop only later in life, when cells in the nervous system lose their structure or function. In genetic forms of NDs, this late onset phenomenon remains largely unexplained. ResultsAnalyzing single cell RNA sequencing (scRNA-seq) from Alzheimers disease (AD) patients, we find increased transcriptional heterogeneity in AD excitatory neurons. We hypothesized that transcriptional heterogeneity precedes ND pathologies. To test this idea experimentally, we used juvenile forms (72Q; 180Q) of Huntingtons disease (HD) iPSCs, differentiated them into committed neuronal progenitors, and obtained single cell expression profiles. We show a global increase in gene expression variability in HD. Autophagy genes become more stable, while energy and actin-related genes become more variable in the mutant cells. Knocking-down several differentially-variable genes resulted in increased aggregate formation, a pathology associated with HD. We further validated the increased transcriptional heterogeneity in CHD8+/- cells, a model for autism spectrum disorder. ConclusionsOverall, our results suggest that although NDs develop over time, transcriptional regulation imbalance is present already at very early developmental stages. Therefore, an intervention aimed at this early phenotype may be of high diagnostic value.

molecular biology↗

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

The SC-SNc pathway boosts appetitive locomotion in predatory hunting

Appetitive locomotion is essential for organisms to approach rewards, such as food and prey. How the brain controls appetitive locomotion is poorly understood. In a naturalistic goal-directed behavior--predatory hunting, we demonstrate an excitatory brain circuit from the superior colliculus (SC) to the substantia nigra pars compacta (SNc) to boost appetitive locomotion. The SC-SNc pathway transmitted locomotion-speed signals to dopamine neurons and triggered dopamine release in the dorsal striatum. Activation of this pathway increased the speed and frequency of approach during predatory hunting, an effect that depended on the activities of SNc dopamine neurons. Conversely, synaptic inactivation of this pathway impaired appetitive locomotion but not defensive or exploratory locomotion. Together, these data revealed the SC as an important source to provide locomotion-related signals to SNc dopamine neurons to boost appetitive locomotion.

neuroscience↗

bioRxiv2020

High-dimensional association detection in large scale genomic data

Joint analyses of genomic datasets obtained in multiple different conditions are essential for understanding the biological mechanism that drives tissue-specificity and cell differentiation, but they still remain computationally challenging. To address this we introduce CLIMB (Composite LIkelihood eMpirical Bayes), a statistical methodology that learns patterns of condition-specificity present in genomic data. CLIMB provides a generic framework facilitating a host of analyses, such as clustering genomic features sharing similar condition-specific patterns and identifying which of these features are involved in cell fate commitment. We apply CLIMB to three sets of hematopoietic data, which examine CTCF ChIP-seq measured in 17 different cell populations, RNA-seq measured across constituent cell populations in three committed lineages, and DNase-seq in 38 cell populations. Our results show that CLIMB improves upon existing alternatives in statistical precision, while capturing interpretable and biologically relevant clusters in the data.

bioinformatics↗