Biology subjects

Cao, Z.

Publications and source records attributed to Cao, Z..

At least 109 records · Page 6Linked to original sources

bioRxiv2022

Multiomics reveal that silk fibroin and sericin differentially potentiate the paracrine functions of mesenchymal stem cells and enhance tissue regeneration

Silk fibroin (SF) and sericin (SS), the two major proteins of silk, are attractive biomaterials that show great potential in regenerative medicine. However, their biochemical interactions with stem cells were not fully understood. Here, we employed multiomics to obtain a global view of the triggered cellular processes and pathways of MSCs by SF and SS. Integrated RNA-seq and proteomics revealed that SF and SS strongly enhanced the paracrine activity of MSCs through differentially activating integrin and glycolytic pathways, rather than directly regulating stem cell fate to initiate multiple but distinct biological processes in MSCs. Those specific paracrine signals of MSCs stimulated by SF and SS effectively promoted skin wound healing by influencing the behaviors of multiple resident cells in skin wound microenvironments. This study provides comprehensive and reliable insights into the cellular interactions with SF and SS, enabling future development of silk-based therapeutics for tissue engineering and stem cell therapy.

bioengineering↗

bioRxiv2022

Quartet RNA reference materials and ratio-based reference datasets for reliable transcriptomic profiling

As an indispensable tool for transcriptome-wide analysis of differential gene expression, RNA sequencing (RNAseq) has demonstrated great potential in clinical applications. However, the lack of multi-group RNA reference materials of biological relevance and the corresponding reference datasets for assessing the reliability of RNAseq hampers its wide clinical applications wherein the underlying biological differences among study groups are often small. As part of the Quartet Project for quality control and data integration of multiomic profiling, we established four RNA reference materials derived from immortalized B-lymphoblastoid cell lines from four members of a monozygotic twin family. Additionally, we constructed ratio-based transcriptome-wide reference datasets using multi-batch RNAseq datasets, providing "ground truth" for benchmarking. Moreover, Quartet-sample-based quality metrics were developed for assessing reliability of RNAseq technology in terms of intra-batch proficiency and cross-batch reproducibility. The small intrinsic biological differences among the Quartet samples enable sensitive assessment of performance of transcriptomic measurements. The Quartet RNA reference materials combined with the reference datasets can be served as unique resources for assessing data quality and improving reliability of transcriptomic profiling.

genetics↗

bioRxiv2022

Sequential functional differentiation of luminal epithelial cells regulated by maternal and embryonic signaling in the mouse endometrium

Embryo implantation requires temporospatial maternal-embryo dialog to regulate the interactions between an activated blastocyst and a receptive endometrium; however, the details of the related cell-specific coordination is largely unknown because of the cellular complexity and dynamic developmental processes of both the embryo and endometrium during peri-implantation. By comparing single-cell RNA (scRNA) data obtained from entire uteri of pregnant and pseudo-pregnant mice with embryos that were in the oviduct (dpc2.5 days post-coitum [dpc]) or had entered the uterus (after 3.5 dpc), we found a maternal estrogen and progesterone signaling-dependent functional differentiation process in which progesterone induced estrogen-responsive luminal epithelial cells to differentiate into two new types of epithelial cells: adhesion epithelial cells (AECs) and supporting epithelial cells (SECs) on 2.5 dpc. In addition to maternal signaling data, the uterine scRNA and corresponding embryo bulk sequencing data were obtained, and analyses revealed that embryonic Pdgfa and Efna3/4 signaling activated AECs and SECs, enhancing the attachment of embryos to the endometrium after implantation. Specifically, embryonic signaling induced additional transformation of AECs by preventing adjacent AECs, but not AECs away from the embryo in the implantation chamber, from undergoing apoptosis. Furthermore, we demonstrated that epithelial cell differentiation and related regulatory signaling were largely conserved in humans and mice. In the human endometrium, developmental defects of SOX9-positive epithelial cells similar to luminal epithelial cells were related to thin endometrium. Our work provides comprehensive and systematic information on endometrial luminal epithelial cell development directed by maternal and embryonic signaling, which is important for endometrial receptivity and embryo implantation.

cell biology↗

bioRxiv2022

A flexible network of lipid droplet associated proteins support embryonic integrity of C. elegans

In addition to coordinating the storage and mobilization of neutral fat, lipid droplets (LDs) are conserved organelles that can accommodate additional cargos in order to support animal development. However, it is unclear if each type of cargo is matched with a specific subset of LDs. Is the identity of each subset rigidly maintained? Here, we report that SEIP-1/seipin defines a subset of oocyte LDs that are required for proper eggshell formation in C. elegans. Using a photoconvertible fluorescent protein-based imaging assay, we found that SEIP-1 positive LDs were selectively depleted after fertilization, coincident of the formation of a lipid-rich permeability barrier of the eggshell. Loss of SEIP-1 function caused impenetrant embryonic arrest, which could be suppressed by depleting a LD-structural protein PLIN-1/perilipin. The embryonic development of seip-1; plin-1 mutant in turn depended on another LD-associated protein, RAB-18/Rab18. Our results are consistent with the hypothesis that SEIP-1 dependent and independent mechanisms act redundantly to ensure the packaging and export of lipid-rich permeability barrier constituents via LDs. The identity of these LDs, as defined by their associated proteins, exhibits unexpected plasticity that ultimately ensures the survival of embryos ex utero.

cell biology↗

bioRxiv2022

DelaySSAToolkit.jl: stochastic simulation of reaction systems with time delays in Julia

SummaryDelaySSAToolkit.jl is a Julia package for modelling reaction systems with non-Markovian dynamics, specifically those with time delays. These delays implicitly capture multiple intermediate reaction steps and hence serve as an effective model reduction technique for complex systems in biology, chemistry, ecology and genetics. The package implements a variety of exact formulations of the delay stochastic simulation algorithm. Contactxmfu@ecust.edu.cn, zcao@ecust.edu.cn, or ramon.grima@ed.ac.uk Availability and ImplementationThe source code and documentation of DelaySSAToolkit.jl are available at https://github.com/palmtree2013/DelaySSAToolkit.jl.

systems biology↗

bioRxiv2022

A scalp geometry parameter-space for optimization and implementation of conventional TMS coil placement

Transcranial magnetic stimulation efficacy is largely dependent upon coil position and orientation. A good method for describing coil placement is required for both computational optimization (planning) and actual placement (implementation). In coordinate dependent parameter-spaces (CDPs), three-dimensional coordinates are used to represent coil position and three orthogonal unit vectors are used to represent coil orientation. A CDP can precisely describe arbitrary coil placement; therefore it offers great advantage in computational optimization which checks through all possible placements. However, a neuronavigation system is usually required to accurately implement the optimized CDP parameters on a participants head. Routine clinical practice, on the other hand, often uses the International 10-20 system to describe coil placement. Although the 10-20 system can only perform modeling and placement at limited scalp landmarks, it allows the synthesis of different individuals targeting effects to find group-optimal parameters; it also allows manual placement, which is important for commonly-seen use cases without individual MRI scans and navigation devices. This study proposes a new scalp geometry based parameter-space (SGP), integrating the advantages of CDP and 10-20 methods. Our SGP 1) can quantitatively specify all possible conventional coil positions and orientations on an individuals scalp, which is important for electrical modeling and optimization, 2) maintains inter-individual correspondence, which is important for synthesizing TMS effects from different individuals and studies. 3) enables fast and simple manual implementation. Demonstration experiments were conducted to illustrate the application of an SGP-based framework for both individual and group-based optimization. A measurement experiment was performed to evaluate speed, precision and reliability of SGP-based manual implementation; results show it surpasses previous manual placement methods.

neuroscience↗

bioRxiv2022

Supporting per-locus substitution rates improves the accuracy of species networks and avoids spurious reticulations

The development of statistical methods to infer species phylogenies with reticulation (species networks) has led to many discoveries of gene flow between distinct species. However, because the dimensionality of species networks is not fixed, these methods may compensate for kinds of model misspecification, such as assuming a single substitution rate for all genomic loci, by increasing the number of dimensions beyond the true value. The popular full Bayesian species network method MCMC_SEQ has previously made this assumption, so we have added support for the proven Dirichlet model for per-locus rates to enhance its accuracy and avoid spurious results. We studied the effects of this model using simulation and an empirical dataset from Heliconius butterflies. We found that assuming a single substitution rate applies to all loci leads to the inference of spurious reticulation in simulated and empirical datasets when a full Bayesian method is used, however, the summary method InferNetwork_ML is robust to per-locus variation in substitution rates when set to ignore gene tree branch lengths. Our implementation of the model resolves this misspecification and successfully converges to the true species networks. It also infers far more accurate gene trees than assuming a single rate, or independent inference of gene trees. Our implementation of the Dirichlet per-locus rates model is now available in PhyloNet, a software package for phylogenetic inference, open source on GitHub https://github.com/NakhlehLab/PhyloNet.

evolutionary biology↗

bioRxiv2021

RORA regulates neutrophil migration and activation in zebrafish

Neutrophil migration and activation are essential for defense against pathogens. However, this process may also lead to collateral tissue injury. We used microRNA overexpression as a platform and discovered protein-coding genes that regulate neutrophil migration. Here we show that miR-99 decreased the chemotaxis of zebrafish neutrophils and human neutrophil-like cells. In zebrafish neutrophils, miR-99 directly targets the transcriptional factor RAR-related orphan receptor alpha (roraa). Inhibiting ROR, but not the closely related ROR{gamma}, reduced chemotaxis of zebrafish and primary human neutrophils without causing cell death, and increased susceptibility of zebrafish to bacterial infection. Expressing a dominant-negative form of Ror or disrupting the roraa locus specifically in zebrafish neutrophils reduced cell migration. At the transcriptional level, ROR regulates transmembrane signaling receptor activity and protein phosphorylation pathways. Our results, therefore, reveal previously unknown functions of miR- 99 and ROR in regulating neutrophil migration and anti-microbial defense.

cell biology↗

bioRxiv2021

The different brain areas occupied for integrating information of hierarchical linguistic units: a study based on EEG and TMS

Human linguistic units are hierarchical, and our brain responds differently when processing linguistic units during sentence comprehension, especially when the modality of the received signal is different (auditory, visual, or audio-visual). However, it is unclear how the brain processes and integrates language information at different linguistic units (words, phrases, and sentences) provided simultaneously in audio and visual modalities. To address the issue, we presented participants with sequences of short Chinese sentences through auditory or visual or combined audio- visual modalities, while electroencephalographic responses were recorded. With a frequency tagging approach, we analyzed the neural representations of basic linguistic units (i.e., characters/monosyllabic words) and higher-level linguistic structures (i.e., phrases and sentences) across the three modalities separately. We found that audio-visual integration occurs at all linguistic units, and the brain areas involved in the integration varied across different linguistic levels. In particular, the integration of sentences activated the local left prefrontal area. Therefore, we used continuous theta-burst stimulation (cTBS) to verify that the left prefrontal cortex plays a vital role in the audio-visual integration of sentence information. Our findings suggest the advantage of bimodal language comprehension at hierarchical stages in language-related information processing and provide evidence for the causal role of the left prefrontal regions in processing information of audio-visual sentences.

neuroscience↗

bioRxiv2021

Accurate inference of stochastic gene expression from nascent transcript heterogeneity

Transcriptional rates are often estimated by fitting the distribution of mature mRNA numbers measured using smFISH (single molecule fluorescence in situ hybridization) with the distribution predicted by the telegraph model of gene expression, which defines two promoter states of activity and inactivity. However, fluctuations in mature mRNA numbers are strongly affected by processes downstream of transcription. In addition, the telegraph model assumes one gene copy, but in experiments cells may have two gene copies as cells replicate their genome during the cell cycle. Whilst it is often presumed that post-transcriptional noise and gene copy number variation affect transcriptional parameter estimation, the size of the error introduced remains unclear. To address this issue, here we measure both mature and nascent mRNA distributions of GAL10 in yeast cells using smFISH and classify each cell according to its cell cycle phase. We infer transcriptional parameters from mature and nascent mRNA distributions, with and without accounting for cell cycle phase and compare the results to live-cell transcription measurements of the same gene. We find that: (i) correcting for cell cycle dynamics decreases the promoter switching rates and the initiation rate, and increases the fraction of time spent in the active state, as well as the burst size; (ii) additional correction for post-transcriptional noise leads to further increases in the burst size and to a large reduction in the errors in parameter estimation. Furthermore, we outline how to correctly adjust for measurement noise in smFISH due to uncertainty in transcription site localisation when introns cannot be labelled. Simulations with parameters estimated from nascent smFISH data, which is corrected for cell cycle phases and measurement noise, leads to autocorrelation functions that agree with those obtained from live-cell imaging.

systems biology↗

bioRxiv2021

Prediction of therapy outcomes of CLL using gene expression intensity, clustering, and ANN classification of single cell transcriptomes

BackgroundSingle cell transcriptomics is a new technology that enables us to measure the expression levels of genes from an individual cell. The expression information reflects the activity of that individual cell which could be used to indicate the cell types. Chronic lymphocytic leukemia (CLL) is a malignancy of B cells, one of the peripheral blood mononuclear cells subtypes. We applied five analytical tools for the study of single cell gene expression in CLL course of therapy. These tools included the analysis of gene expression distributions - median, interquartile ranges, and percentage above quality control (QC) threshold; hierarchical clustering applied to all cells within individual single cell data sets; and artificial neural network (ANN) for classification of healthy peripheral blood mononuclear cell (PBMC) subtypes. These tools were applied to the analysis of CLL data representing states before and during the therapy. ResultsWe identified patterns in gene expression that distinguished two patients that had complete remission (complete response), a patient that had a relapse, and a patient that had partial remission within three years of Ibrutinib therapy. Patients with complete remission showed a rapid decline of median gene expression counts, and the total number of gene counts below the QC threshold for healthy cells (670 counts) in 80% of more of the cells. These patients also showed the emergence of healthy-like PBMC cluster maps within 120 days of therapy and distinct changes in predicted proportions of PBMC cell types. ConclusionsThe combination of basic statistical analysis, hierarchical clustering, and supervised machine learning identified patterns from gene expression that distinguish four CLL patients treated with Ibrutinib that experienced complete remission, partial remission, or relapse. These preliminary results suggest that new bioinformatics tools for single cell transcriptomics, including ANN comparison to healthy PBMC, offer promise in prognostics of CLL.

bioinformatics↗

bioRxiv2021

Artificial Neural Networks for classification of single cell gene expression

BackgroundSingle-cell transcriptome (SCT) sequencing technology has reached the level of high-throughput technology where gene expression can be measured concurrently from large numbers of cells. The results of gene expression studies are highly reproducible when strict protocols and standard operating procedures (SOP) are followed. However, differences in sample processing conditions result in significant changes in gene expression profiles making direct comparison of different studies difficult. Unsupervised machine learning (ML) uses clustering algorithms combined with semi-automated cell labeling and manual annotation of individual cells. They do not scale up well and a workflow used on a specific dataset will not perform well with other studies. Supervised ML classification shows superior classification accuracy and generalization properties as compared to unsupervised ML methods. We describe a supervised ML method that deploys artificial neural networks (ANN), for 5-class classification of healthy peripheral blood mononuclear cells (PBMC) from multiple diverse studies. ResultsWe used 58 data sets to train ANN incrementally - over ten cycles of training and testing. The sample processing involved four protocols: separation of PBMC, separation of PBMC + enrichment (by negative selection), separation of PBMC + FACS, and separation of PBMC + MACS. The training data set included between 85 and 110 thousand cells, and the test set had approximately 13 thousand cells. Training and testing were done with various combinations of data sets from four principal data sources. The overall accuracy of classification on independent data sets reached 5-class classification accuracy of 94%. Classification accuracy for B cells, monocytes, and T cells exceeded 95%. Classification accuracy of natural killer (NK) cells was 75% because of the similarity between NK cells and T cell subsets. The accuracy of dendritic cells (DC) was low due to very low numbers of DC in the training sets. ConclusionsThe incremental learning ANN model can accurately classify the main types of PBMC. With the inclusion of more DC and resolving ambiguities between T cell and NK cell gene expression profiles, we will enable high accuracy supervised ML classification of PBMC. We assembled a reference data set for healthy PBMC and demonstrated a proof-of-concept for supervised ANN method in classification of previously unseen SCT data. The classification shows high accuracy, that is consistent across different studies and sample processing methods.

bioinformatics↗

bioRxiv2021

The first crested duck genome reveals clues to genetic compensation and crest cushion formation

The Chinese crested (CC) duck is a unique indigenous waterfowl breed with a phenotypic crest trait that affects its high survival rate. Therefore, the CC duck is an ideal model to investigate the genetic compensation response to maintain genetic stability. In the present study, we first generated a chromosome-level genome of CC ducks. Comparative genomics revealed genes related to tissue repair, immune function, and tumors were under strong positive selection, which suggested that these adaptive changes might enhance cancer resistance and immune response to maintain the genetic stability of CC ducks. We sub-assembled a Chinese spot-billed duck genome and detected genome-assembled structure variants among three ducks. Functional analysis revealed that a large number of structural variants were related to the immune system, which strongly suggests the occurrence of genetic compensation in the anti-tumor and immune systems to further support the survival of CC ducks. Moreover, we confirmed that the CC duck originated from the mallard ducks. Finally, we revealed the physiological and genetic basis of crest traits and identified a causative mutation in TAS2R40 that leads to crest formation. Overall, the findings of this study provide new insights into the role of genetic compensation in adaptive evolution.

genomics↗

bioRxiv2021

Genomic rearrangements have consequences for introgression breeding as revealed by genome assemblies of wild and cultivated lentil species

Understanding the genomic relationship between wild and cultivated genomes would facilitate access to the untapped variability found in crop wild relatives. We developed genome assemblies of a cultivated lentil (Lens culinaris) as well as a wild relative (L. ervoides). Comparative analyses revealed large-scale structural rearrangements and additional repetitive DNA in the cultivated genome, resulting in regions of reduced recombination, segregation distortion and permanent heterozygosity in the offspring of a cross between the two species. These novel findings provide plant breeders with better insight into how best to approach accessing the novel variability available in wild relatives.

plant biology↗

bioRxiv2021

A Random Forest Classifier for Protein-Protein Docking Models

Herein, we present the results of a machine learning approach we developed to single out correct 3D docking models of protein-protein complexes obtained by popular docking software. To this aim, we generated a set of {approx}7x106 docking models with three different docking programs (HADDOCK, FTDock and ZDOCK) for the 230 complexes in the protein-protein interaction benchmark, version 5 (BM5). Three different machine-learning approaches (Random Forest, Supported Vector Machine and Perceptron) were used to train classifiers with 158 different scoring functions (features). The Random Forest algorithm outperformed the other two algorithms and was selected for further optimization. Using a features selection algorithm, and optimizing the random forest hyperparameters, allowed us to train and validate a random forest classifier, named CoDES (COnservation Driven Expert System). Testing of CoDES on independent datasets, as well as results of its comparative performance with machine-learning methods recently developed in the field for the scoring of docking decoys, confirm its state-of-the-art ability to discriminate correct from incorrect decoys both in terms of global parameters and in terms of decoys ranked at the top positions.

bioinformatics↗

bioRxiv2021

Spatial profiling of microbial communities by sequential FISH with error-robust encoding

Spatial analysis of microbiomes at single cell resolution with high multiplexity and accuracy has remained challenging. Here we present spatial profiling of a microbiome using sequential error-robust fluorescence in situ hybridization (SEER-FISH), a highly multiplexed and accurate imaging method that allows mapping of microbial communities at micron-scale. We show that multiplexity of RNA profiling in microbiomes can be increased significantly by sequential rounds of probe hybridization and dissociation. Combined with error-correction strategies, we demonstrate that SEER-FISH enables accurate taxonomic identification in complex microbial communities. Using microbial communities composed of diverse bacterial taxa isolated from plant rhizospheres, we show that SEER-FISH can quantify the abundance of each taxon and map microbial biogeography on roots. SEER-FISH should enable accurate spatial profiling of the ecology and function of complex microbial communities.

microbiology↗

bioRxiv2021

Natural variations in the P-type ATPase heavy metal transporter ZmCd1 controlling cadmium accumulation in maize grains

Cadmium (Cd) accumulation in maize grains is detrimental to human health. Developing maize varieties with low-Cd contents via marker-assisted selection is important for ensuring the production of maize grains safe for consumption. However, the key gene controlling maize grain Cd accumulation has not been cloned. In this study, we identified two major loci for maize grain Cd accumulation (qCd1 and qCd2) on chromosome 2 during a genome-wide association study (GWAS). The qCd1 locus was analyzed by bulked segregant RNA-seq and fine mapping with a biparental segregating population of Jing724 (low-Cd line) and Mo17 (high-Cd line). The ZmCd1 candidate gene in the qCd1 locus encodes a vacuolar membrane-localized heavy metal P-type ATPase transporter, ZmHMA3, which is orthologous to the tonoplast Cd transporter OsHMA3. Genomic DNA sequence and transcript analyses suggested that a transposon in intron 1 of ZmCd1 is responsible for the abnormal amino acid sequence in Mo17. An EMS mutant analysis and an allelism test confirmed ZmCd1 influences maize grain Cd accumulation. The natural variations in ZmCd1 were used to develop four PCR-based molecular markers, which revealed five ZmCd1 haplotypes in the GWAS population. The molecular markers were also used to predict the grain Cd contents in commonly cultivated maize germplasms in China. The predicted Cd contents for 36 inbred lines and 13 hybrids were consistent with the measured Cd contents. Furthermore, several low-Cd elite inbred lines and hybrids were identified, including Jing2416, MC01, Jingnonke728, and Jingke968. Therefore, the molecular markers developed in this study are applicable for molecular breeding and developing maize varieties with low grain Cd contents.

plant biology↗

bioRxiv2020

ALC1 links chromatin accessibility to PARP inhibitor response in homologous recombination deficient cells

The response to Poly (ADP-ribose) polymerase inhibitors (PARPi) is dictated by homologous recombination (HR) DNA repair mechanisms and the abundance of lesions that trap PARP enzymes on chromatin. It remains unclear, however, if the established role of PARP in promoting chromatin accessibility impacts viability in these settings. Using a CRISPR based screen, we identify the PAR-binding Snf2-like ATPase, ALC1/CHD1L, as a key determinant of PARPi toxicity in HR-deficient cells. ALC1 loss reduced viability of BRCA mutant cells and enhanced their sensitivity to PARPi by up to 250-fold, while overcoming several known resistance mechanisms. ALC1 loss was not epistatic to other repair pathways that execute the PARPi response. Instead, ALC1 deficiency reduced chromatin accessibility concomitant with a decrease in the association of repair factors. This resulted in an accumulation of replication associated DNA damage and a reliance on HR. These findings establish PAR-dependent chromatin remodeling as a mechanistically distinct aspect of PARPi responses, implicating ALC1 inhibition as a new approach to overcome therapeutic resistance in HR-deficient cancers.

cancer biology↗