Biology subjects

Zhang, A.

Publications and source records attributed to Zhang, A..

12 recordsLinked to original sources

bioRxiv2026

Neogenin-1 marks myeloid-primed fetal hematopoietic stem cells that undergo progressive lineage-restriction with age

During aging, hematopoietic stem cells (HSCs) increasingly shift from balanced to myeloid-biased differentiation, resulting in reduced lymphoid output and impaired adaptive immunity. The question of whether this lineage bias is established in a subset of HSCs during early development or primarily emerges with aging warrants further investigation. Here, we investigate whether myeloid-biased HSCs (my-HSCs) are established at the fetal liver stage by specifically examining Neogenin-1 (NEO1), a previously defined marker of my-HSCs. We identify two distinct populations of Hoxb5+ HSCs in the fetal liver: NEO1+ and NEO1-, with NEO1+ HSCs exhibiting transcriptional and functional characteristics consistent with my-HSCs. With age, my-HSC-associated transcriptional programs become increasingly reinforced across the Hoxb5+ pHSC compartment, with NEO1+ cells showing early enrichment of this program and both NEO1+ and NEO1- cells acquiring broader myeloid-biased features in aging. These findings suggest that lineage programming can begin early in development and is further shaped by age-related changes, potentially contributing to the functional decline observed in the aging hematopoietic system.

developmental biology↗

bioRxiv2018

Study on the Difference of Prokaryotic Flora Structure on the Surface of Micro-nano Coating with Different Antifouling Property

Low surface energy composite antifouling coatings prepared from carbon nanotubes (CNTs) and polydimethylsiloxane (PDMS) have good values for investigation of biofouling-related biological questions on marine biofilm. In order to deeply study the mechanism of antifouling on the surface of CNTs-PDMS coatings, it is necessary to investigate the structure of the microbial flora in the early biofilm on the coating surface. In the present study, the specific aim of this study was to investigate the structure differences of prokaryotic flora in biofilm samples at the early stage of biofouling through 16S rDNA based high-throughput DNA sequencing. By annotating high-throughput DNA sequencing results, this study identified dominant prokaryotic phyla and genera in biofilms of CNTs-PDMS coatings and identified significant differences in microbial composition and its dynamics among different coatings. Though the analysis of the Shannon index, Simpson index, Chao1 index and ACE index, coatings with better antifouling properties and antifouling properties have significant differences in community diversity and abundance, indicating different antifouling properties can affect the type and content of biofilm communities. According to the canonical correspondence analysis (CCA), time and temperature are more related to microbial community distribution, while the diameter and length of nanomaterials are less correlated. Through this study, the differences in microbial composition and content of prokaryotic communities, differences in diversity and abundance of sample communities, the differences between multiple samples and the correlation with important environmental factors were preliminarily analyzed, which laid a decent foundation for further research on the mechanism of anti-fouling on the surface of CNTs and PDMS coatings.

molecular biology↗

bioRxiv2018

ICTD: Inference of cell types and deconvolution -- a next-generation deconvolution method for accurate assess cell population and activities in tumor microenvironment.

We developed a novel deconvolution method, namely Inference of Cell Types and Deconvolution (ICTD) that addresses the fundamental issue of identifiability and robustness in current tissue data deconvolution problem. ICTD provides substantially new capabilities for omics data based characterization of a tissue microenvironment, including (1) maximizing the resolution in identifying resident cell and sub types that truly exists in a tissue, (2) identifying the most reliable marker genes for each cell type, which are tissue and data set specific, (3) handling the stability problem with co-linear cell types, (4) co-deconvoluting with available matched multi-omics data, and (5) inferring functional variations specific to one or several cell types. ICTD is empowered by (i) rigorously derived mathematical conditions of identifiable cell type and cell type specific functions in tissue transcriptomics data and (ii) a semi supervised approach to maximize the knowledge transfer of cell type and functional marker genes identified in single cell or bulk cell data in the analysis of tissue data, and (iii) a novel unsupervised approach to minimize the bias brought by training data. Application of ICTD on real and single cell simulated tissue data validated that the method has consistently good performance for tissue data coming from different species, tissue microenvironments, and experimental platforms. Other than the new capabilities, ICTD outperformed other state-of-the-art devolution methods on prediction accuracy, the resolution of identifiable cell, detection of unknown sub cell types, and assessment of cell type specific functions. The premise of ICTD also lies in characterizing cell-cell interactions and discovering cell types and prognostic markers that are predictive of clinical outcomes.

bioinformatics↗

bioRxiv2018

A novel interaction between Gβγ and RNA polymerase II regulates cardiac fibrosis.

G{beta}{gamma} subunits are involved in many different signalling processes in various compartments of the cell, including the nucleus. To gain insight into the functions of nuclear G{beta}{gamma}, we investigated the functional role of G{beta}{gamma} signalling in regulation of GPCR-mediated gene expression in primary rat neonatal cardiac fibroblasts. Following activation of the angiotensin II type I receptor in these cells, G{beta}{gamma} dimers interact with RNA polymerase II (RNAPII). Our findings suggest that G{beta}1 recruitment to RNAPII negatively regulates the fibrotic transcriptional response, which can be overcome by strong fibrotic stimuli. The interaction between G{beta}{gamma} subunits and RNAPII expands the role for G{beta}{gamma} signalling in cardiac fibrosis. The G{beta}{gamma}-RNAPII interaction was regulated by signaling pathways in HEK 293 cells that diverged from those operating in cardiac fibroblasts. Thus, the interaction may be a conserved feature of transcriptional regulation although such regulation may be cell-specific.

biochemistry↗

bioRxiv2018

DeepSeqPan, a novel deep convolutional neural network model for pan-specific class I HLA-peptide binding affinity prediction

Interactions between human leukocyte antigens (HLAs) and peptides play a critical role in the human immune system. Accurate computational prediction of HLA-binding peptides can be used for peptide drug discovery. Currently, the best prediction algorithms are neural network based pan-specific models, which take advantage of the large amount of data across HLA alleles. However, current pan-specific models are all based on the pseudo sequence encoding for modeling the binding context and depend on the available HLA protein-peptide bound structures. In this work, we proposed a novel deep convolutional neural network model (DCNN) for HLA-peptide binding prediction, in which the encoding of the HLA sequence and the binding context are both learned by the network itself without requiring the HLA-peptide bound structure information. Our DCNN model is also characterized by its binding context extraction layer and dual outputs with both binding affinity output and binding probability outputs. Evaluation on public benchmark datasets shows that our DeepSeqPan model without HLA structural information in training achieves state-of-the-art performance on a large number of HLA alleles with good generalization capability. Since our model only needs raw sequences from the HLA-peptide binding pairs, it can be applied to binding predictions of HLAs without structure information and can also be applied to other protein binding problems such as protein-DNA and protein-RNA bindings. The implementation code and trained models are freely available at https://github.com/pcpLiu/DeepSeqPan.

bioinformatics↗

bioRxiv2018

Unraveling the genetic architecture of grain size in einkorn wheat through linkage and homology mapping, and transcriptomic profiling

HighlightGenome-wide linkage and homology mapping revealed 17 genomic regions through a high-density einkorn wheat genetic map constructed using RAD-seq, and transcription levels of 20 candidate genes were explored using RNA-seq.\n\nAbstractUnderstanding the genetic architecture of grain size is a prerequisite to manipulate the grain development and improve the yield potential in crops. In this study, we conducted a whole genome-wide QTL mapping of grain size related traits in einkorn wheat by constructing a high-density genetic map, and explored the candidate genes underlying QTL through homologous analysis and RNA sequencing. The high-density genetic map spanned 1873 cM and contained 9937 SNP markers assigned to 1551 bins in seven chromosomes. Strong collinearity and high genome coverage of this map were revealed with the physical maps of wheat and barley. Six grain size related traits were surveyed in five agro-climatic environments with 80% or more broad-sense heritability. In total, 42 QTL were identified and assigned to 17 genomic regions on six chromosomes and accounted for 52.3-66.7% of the phenotypic variations. Thirty homologous genes involved in grain development were located in 12 regions. RNA sequencing provided 4959 genes differentially expressed between the two parents. Twenty differentially expressed genes involved in grain size development and starch biosynthesis were mapped to nine regions that contained 26 QTL, indicating that the starch biosynthesis pathway played a vital role on grain development in einkorn wheat. This study provides new insights into the genetic architecture of grain size in einkorn wheat, the underlying genes enables the understanding of grain development and wheat genetic improvement, and the map facilitates the mapping of quantitative traits, map-based cloning, genome assembling and comparative genomics in wheat taxa.

genetics↗

bioRxiv2018

RPA resolves conflicting activities of accessory proteins during reconstitution of Dmc1-mediated meiotic recombination

Dmc1 catalyzes homology search and strand exchange during meiotic recombination in budding yeast and many other organisms including humans. Here we reconstitute Dmc1 recombination in vitro using six purified proteins including Dmc1 and its accessory proteins RPA, Rad51, Rdh54/Tid1, Mei5-Sae3, and Hop2-Mnd1 to promote D-loop formation between ssDNA and dsDNA substrates. Each accessory protein contributed to Dmc1s activity, with the combination of all six proteins yielding optimal activity. The ssDNA binding protein RPA plays multiple roles in stimulating Dmc1s activity including by overcoming inhibitory effects of ssDNA secondary structure on D-loop reactions, and by stabilizing and elongating D-loops. In addition, we demonstrate that RPA limits inhibitory interactions of Hop2-Mnd1 and Rdh54/Tid1 that otherwise occur during assembly of Dmc1-ssDNA nucleoprotein filaments. Finally, we report interactions between the proteins employed in the biochemical reconstitution including a direct interaction between Rad51 and Dmc1 that is enhanced by Mei5-Sae3.

biochemistry↗

bioRxiv2018

Kynurenine 3-monooxygenase (KMO) is a critical regulator of renal ischemia-reperfusion injury

Acute kidney injury (AKI) following ischemia-reperfusion injury (IRI) has a high mortality and lacks specific therapies. Here, we report that mice lacking kynurenine 3-monooxygenase (KMO) activity (Kmonull mice) are protected against AKI after renal IRI. This advances our previous work showing that KMO blockade protects against acute lung injury and AKI in experimental multiple organ failure caused by acute pancreatitis. We show that KMO is highly expressed in the kidney and exerts major metabolic control over the biologically-active kynurenine metabolites 3-hydroxykynurenine, kynurenic acid and downstream metabolites. In experimental AKI induced by unilateral kidney IRI, Kmonull mice had preserved renal function, reduced renal tubular cell injury, and fewer infiltrating neutrophils compared to wild-type (Kmowt) control mice. Together, these data confirm that flux through KMO contributes to AKI after IRI, and supports the rationale for KMO inhibition as a therapeutic strategy to protect against AKI during critical illness.

pathology↗

bioRxiv2018

The yeast core spliceosome maintains genome integrity through R-loop prevention and alpha-tubulin expression

To achieve genome stability cells must coordinate the action of various DNA transactions including DNA replication, repair, transcription and chromosome segregation. How transcription and RNA processing enable genome stability is only partly understood. Two predominant models have emerged: one involving changes in gene expression that perturb other genome maintenance factors, and another in which genotoxic DNA:RNA hybrids, called R-loops, impair DNA replication. Here we characterize genome instability phenotypes in a panel yeast splicing factor mutants and find that mitotic defects, and in some cases R-loop accumulation, are causes of genome instability. Genome instability in splicing mutants is exacerbated by loss of the spindle-assembly checkpoint protein Mad1. Moreover, removal of the intron from the -tubulin gene TUB1 restores genome integrity. Thus, while R-loops contribute in some settings, defects in yeast splicing predominantly lead to genome instability through effects on gene expression.

cell biology↗

bioRxiv2018

Integrated single-nucleotide and structural variation signatures of DNA-repair deficient human cancers

Mutation signatures in cancer genomes reflect endogenous and exogenous mutational processes, offering insights into tumour etiology, features for prognostic and biologic stratification and vulnerabilities to be exploited therapeutically. We present a novel machine learning formalism for improved signature inference, based on multi-modal correlated topic models (MMCTM) which can at once infer signatures from both single nucleotide and structural variation counts derived from cancer genome sequencing data. We exemplify the utility of our approach on two hormone driven, DNA repair deficient cancers: breast and ovary (n=755 cases total). Our results illuminate a new age-associated structural variation signature in breast cancer, and an independently identified substructure within homologous recombination deficient (HRD) tumours in breast and ovarian cancer. Together, our study emphasizes the importance of integrating multiple mutation modes for signature discovery and patient stratification, with biological and clinical implications for DNA repair deficient cancers.

cancer biology↗

bioRxiv2017

Whole Genomes Define Concordance of Matched Primary, Xenograft, and Organoid Models of Pancreas Cancer

Pancreatic ductal adenocarcinoma (PDAC) has the worst prognosis among solid malignancies and improved therapeutic strategies are needed to improve outcomes. Patient-derived xenografts (PDX) and patient-derived organoids (PDO) serve as promising tools to identify new drugs with therapeutic potential in PDAC. For these preclinical disease models to be effective, they should both recapitulate the molecular heterogeneity of PDAC and validate patient-specific therapeutic sensitivities. To date however, deep characterization of PDAC PDX and PDO models and comparison with matched human tumour remains largely unaddressed at the whole genome level. We conducted a comprehensive assessment of the genetic landscape of 16 whole-genome pairs of tumours and matched PDX, from primary PDAC and liver metastasis, including a unique cohort of 5 trios of matched primary tumour, PDX, and PDO. We developed a new pipeline to score concordance between PDAC models and their paired human tumours for genomic events, including mutations, structural variations, and copy number variations. Comparison of genomic events in the tumours and matched disease models displayed single-gene concordance across major PDAC driver genes, and genome-wide similarities of copy number changes. Genome-wide and chromosome-centric analysis of structural variation (SV) events revealed high variability across tumours and disease models, but also highlighted previously unrecognized concordance across chromosomes that demonstrate clustered SV events. Our approach and results demonstrate that PDX and PDO recapitulate PDAC tumourigenesis with respect to simple somatic mutations and copy number changes, and capture major SV events that are found in both resected and metastatic tumours.

bioinformatics↗

bioRxiv2017

Predicting DNA Hybridization Kinetics from Sequence

Hybridization is a key molecular process in biology and biotechnology, but to date there is no predictive model for accurately determining hybridization rate constants based on sequence information. To approach this problem systematically, we first performed 210 fluorescence kinetics experiments to observe the hybridization kinetics of 100 different DNA target and probe pairs (subsequences of the CYCS and VEGF genes) at temperatures ranging from 28 {degrees}C to 55 {degrees}C. Next, we rationally designed 38 features computable based on sequence, each feature individually correlated with hybridization kinetics. These features are used in our implementation of a weighted neighbor voting (WNV) algorithm, in which the hybridization rate constant of an unknown sequence is predicted based on similarity reactions with known rate constants (a.k.a. labeled instances). Automated feature selection and weighting optimization resulted in a final 6-feature WNV model, which can predict hybridization rate constants of new sequences to within a factor of 2 with {approx}74% accuracy and within a factor of 3 with {approx}92% accuracy, based on leave-one-out cross-validation. Predictive understanding of hybridization kinetics allows more efficient design of nucleic acid probes, for example in allowing sparse hybrid-capture panels to more quickly and economically enrich desired regions from genomic DNA.

biophysics↗