Biology subjects

Fang, Z.

Publications and source records attributed to Fang, Z..

At least 109 records · Page 6Linked to original sources

bioRxiv2021

RNA-guided cell targeting with CRISPR/RfxCas13d collateral activity in human cells

While single-cell sequencing has allowed rapid identification of novel cell types or states and associated RNA markers, functional studies remain challenging due to the lack of tools that are able to target specific cells based on these markers. Here we show that targeting a single marker RNA with CRISPR/RfxCas13d led to collateral transcriptome destruction in human cells, which can be harnessed to inhibit cell proliferation or to suppress cell state transition.

molecular biology↗

bioRxiv2021

Normalization by orientation anisotropy in human V1-V3

An influential account of neuronal responses in primary visual cortex is the normalized energy model. This model is often implemented as a two-stage computation. The first stage is the extraction of contrast energy, whereby a complex cell computes the squared and summed outputs of a pair of linear filters in quadrature phase. The second stage is normalization, in which a local population of complex cells mutually inhibit one another. Because the population includes cells tuned to a range of orientations and spatial frequencies, the result is that the responses are effectively normalized by the local stimulus contrast. Here, using evidence from human functional MRI, we show that the classical model fails to account for the relative responses to two classes of stimuli: straight, parallel, band-passed contours (gratings), and curved, band-passed contours (snakes). The snakes elicit fMRI responses that are about twice as large as the gratings, yet a traditional divisive normalization model predicts responses that are about the same. Here, motivated by these observations and others from the literature, we implement a divisive normalization model, in which there is a preferential inhibition between cells matched in orientation tuning ("tuned normalization"). We first show that this model accounts for differential responses to these two classes of stimuli. We then show that the model successfully generalizes to other band-pass textures, both in V1 and in extrastriate cortex (V2 and V3). We conclude that even in primary visual cortex, complex features of images such as the degree of heterogeneity, can have large effects on neural responses.

neuroscience↗

bioRxiv2021

DeePVP: Identification and classification of phage virion protein using deep learning

The poor annotation of phage virion protein (PVP) is the bottleneck of many areas of viral research, such as viral phylogenetic analysis, viral host identification and antibacterial drug design. Because of the high diversity of the PVP sequences, the PVP annotation remains a great challenging bioinformatic task. Based on deep learning, we present DeePVP that contains a main module and an extended module. The main module aims to identify the PVPs from non-PVP over a phage genome, while the extended module can further classify the predicted PVP into one of the ten major classes of PVP. Compared with the state-of-the-art tools that can distinguish PVP from non-PVP, DeePVPs main module performs much better, with an F1-score 9.05% higher in the PVP identification task. Compared with PhANNs, a tool that can further classify the predicted PVP into a specific class, the overall accuracy of DeePVPs extended module is approximately 3.72% higher in the PVP classification task. Two application cases on the genome of mycobacteriophage PDRPxv and Escherichia phage HP3 show that the predictions of DeePVP are much more reliable and can better reveal the compact PVP-enriched region, which may be conserved during the viral evolution process, over the phage genome.

bioinformatics↗

bioRxiv2021

Performance Comparison of Computational Prediction Methods for the Function and Pathogenicity of Non-coding Variants

Non-coding variants in the human genome greatly influence some traits and complex diseases by their own regulation and modification effects. Hence, an increasing number of computational methods are developed to predict the effects of variants in the human non-coding sequences. However, it is difficult for users with insufficient knowledge about the performances of computational methods to select appropriate computational methods from dozens of methods. In order to solve this problem, we assessed 12 performance measures of 24 methods on four independent non-coding variant benchmark datasets: (I) rare germline variant from ClinVar, (II) rare somatic variant from COSMIC, (III) common regulatory variant dataset, and (IV) disease associated common variant dataset. All 24 tested methods performed differently under various conditions, indicating that these methods have varying strengths and weaknesses under different scenarios. Importantly, the performance of existing methods was acceptable in the rare germline variant from ClinVar with area under curves (AUCs) of 0.4481 - 0.8033 and poor in the rare somatic variant from COSMIC (AUCs: 0.4984 - 0.7131), common regulatory variant dataset (AUCs: 0.4837 - 0.6472), and disease associated common variant dataset (AUCs: 0.4766 -0.5188). We also compared the prediction performance among 24 methods for non-coding de novo mutations in autism spectrum disorder and found that the CADD and CDTS methods showed better performance. Summarily, we assessed the performances of 24 computational methods under diverse scenarios, providing preliminary advice for proper tool selection and new method development in interpreting non-coding variants.

bioinformatics↗

bioRxiv2021

Predicting hosts based on early SARS-CoV-2 samples and analyzing later world-wide pandemic in 2020

The SARS-CoV-2 pandemic has raised the concern for identifying hosts of the virus since the early-stage outbreak. To address this problem, we proposed a deep learning method, DeepHoF, based on extracting the viral genomic features automatically, to predict host likelihood scores on five host types, including plant, germ, invertebrate, non-human vertebrate and human, for novel viruses. DeepHoF made up for the lack of an accurate tool applicable to any novel virus and overcame the limitation of the sequence similarity-based methods, reaching a satisfactory AUC of 0.987 on the five-classification. Additionally, to fill the gap in the efficient inference of host species for SARS-CoV-2 using existed tools, we conducted a deep analysis on the host likelihood profile calculated by DeepHoF. Using the isolates sequenced in the earliest stage of COVID-19, we inferred minks, bats, dogs and cats were potential hosts of SARS-CoV-2, while minks might be one of the most noteworthy hosts. Several genes of SARS-CoV-2 demonstrated their significance in determining the host range. Furthermore, the large-scale genome analysis, based on DeepHoFs computation for the later world-wide pandemic in 2020, disclosed the uniformity of host range among SARS-CoV-2 samples and the strong association of SARS-CoV-2 between humans and minks.

bioinformatics↗

bioRxiv2021

Multiple nucleotide polymorphism DNA markers for the accurate evaluation of genetic variations

DNA markers are an essential tool for the detection and evaluation of genetic variations, a central theme in genetics and biology. Effective markers must be highly reproducible, polymorphic, accurate and efficient to profile. We developed multiple dispersed nucleotide polymorphism (MNP) DNA marker and an efficient MNP genotyping method called MNP-Seq. The MNP marker was 17.48% more polymorphic than the highly polymorphic marker of microsatellites on a collection of hybrid rice plants. When applied to genotype more than 80,000 individual MNP markers of diploid rice and polyploidy hybrid cotton varieties which were notoriously difficult to genotype accurately, MNP-Seq finished in two days and achieved accuracies of 99.999% and 99.988%, respectively. We adopted MNP-Seq to reveal the ubiquitous, albeit subtle and neglected, genetic heterogeneities in homonyms of Nipponbare rice, a popular model organism for plant biology. This result raised a question on the consistency of the published results using the model plant. We also used MNP-Seq to accurately and efficiently determine the identities of plant varieties, a key but difficult problem for the protection of plant intellectual property rights. While being applied to plants in the current study, the MNP marker and MNP-Seq are general and readily applicable to similar problems in animals and micro-organisms.

molecular biology↗

bioRxiv2021

Identify phage hosts from metaviromic short reads based on deep learning and Markov chain model

Phages - viruses that infect bacteria and archaea - are dominant in the virosphere and play an important role in the microbial community. It is very important to identify the host of a given phage fragment from metavriome data for understanding the ecological impact of phage in a microbial community. State-of-the-art tools for host identification only present reliable results on long sequences within a narrow candidate host range, while there are a large number of short fragments in real metagenomic data and the taxonomic composition of a microbial community is often complicated. Here, we present a method, named HoPhage, to identify the host of a given phage fragment from metavirome data at the genus level. HoPhage integrates two modules using the deep learning algorithms and the Markov chain model, respectively. By testing on both the artificial benchmark dataset of phage contigs and the real virome data, HoPhage demonstrates a satisfactory performance on short fragments within a wide candidate host range at every taxonomic level. HoPhage is freely available at http://cqb.pku.edu.cn/ZhuLab/HoPhage/.

bioinformatics↗

bioRxiv2021

Analysis of Structural Variation Among Inbred Mouse Strains Identifies Genetic Factors for Autism-Related Traits

The genomes of six inbred strains were analyzed using long read (LR) sequencing. The results revealed that structural variants (SV) were very abundant within the genome of inbred mouse strains (4.8 per gene), which indicates that they could impact genetic traits. Analysis of the relationship between SNP and SV alleles across 53 inbred strains indicated that we have a very limited ability to infer whether SV are present using short read sequence data, even when nearby SNP alleles are known. The benefit of having a more complete map of the pattern of genetic variation was demonstrated by identifying at least three genetic factors that could underlie the unique neuroanatomic and behavioral features of BTBR mice that resemble human Autism Spectrum Disorder (ASD). Similar to the genetic findings in human ASD cohorts, the identified BTBR-unique alleles are very rare, and they cause high impact changes in genes that play a role in neurodevelopment and brain function.

genomics↗

bioRxiv2020

Distinguish virulent and temperate phage-derived sequences in metavirome data with a deep learning approach

BackgroundProkaryotic viruses referred to as phages can be divided into virulent and temperate phages. Distinguishing virulent and temperate phage-derived sequences in metavirome data is important for their role in interactions with bacterial hosts and regulations of microbial communities. However there is no experimental or computational approach to classify sequences of these two in culture-independent metavirome effectively, we present a new computational method DeePhage, which can directly and rapidly judge each read or contig as a virulent or temperate phage-derived fragment. FindingsDeePhage utilizes a "one-hot" encoding form to have an overall and detailed representation of DNA sequences. Sequence signatures are detected via a deep learning algorithm, namely a convolutional neural network to extract valuable local features. DeePhage makes better performance than the most related method PHACTS. The accuracy of DeePhage on five-fold validation reach as high as 88%, nearly 30% higher than PHACTS. Evaluation on real metavirome shows DeePhage annotated 54.4% of reliable contigs while PHACTS annotated 44.5%. While running on the same machine, DeePhage reduces computational time than PHACTS by 810 times. Besides, we proposed a new strategy to explore phage transformations in the microbial community by direct detection of the temperate viral fragments from metagenome and metavirome. The detectable transformation of temperate phages provided us a new insight into the potential treatment for human disease. ConclusionsDeePhage is the first tool that can rapidly and efficiently identify two kinds of phage fragments especially for metagenomics analysis with satisfactory performance. DeePhage is freely available via http://cqb.pku.edu.cn/ZhuLab/DeePhage or https://github.com/shufangwu/DeePhage.

bioinformatics↗

bioRxiv2020

Stress Assessment of Vestibular Endurance Training for Civil Aviation Flight Students Based on EEG

ObjectiveThe main goal of our study is to clarify the EEG characteristics of the stress response caused by vestibular endurance training under the real conditions. MethodsTen pilot trainees received a series of acute anti-vertigo training stimulations on the rotary ladder while recording electroencephalographic data (64 electrodes). Afterwards, the subjects anti-vertigo ability was tested for the best performance after 1 month of training, and verifying whether it is relating to the EEG signals we collected before. Results(1) The absolute power of waves in the C3 and C4 regions is same as the difference between 1 min before and 2 min after stimulation, and their activity is enhanced by stimulation. Otherwise, the activation of the C3 region after 5min of stimulation is still significant changed. (2) Discover a spearman rank correlation, the waves in the C3 and C4 the greater the power change, the better the performance of the subject in the proficient stage. ConclusionsC3 and C4 areas are specific brain regions of the stress response of anti-vertigo endurance training, and the absolute power of the wave can be used as a parameter for identifying the degree of motion sickness (MS). The absolute power changes of waves in the C3 and C4 areas are positively correlated with their anti-vertigo potential. SignificanceThe increasing of the absolute power of wave in the C3 and C4 is a manifestation of MS stress adaptability.

neuroscience↗

bioRxiv2020

A Human Multi-Lineage Hepatic Organoid Model for Liver Fibrosis

BackgroundTo characterize fibrogenic mechanisms, genome engineering and a human hepatic organoid system was used to produce an in vitro model for human liver fibrosis. Methods and resultsHuman hepatic organoids that were engineered to express the most common causative mutation for Autosomal Recessive Polycystic Kidney Disease (ARPKD) developed the key features of ARPKD liver pathology (abnormal bile ducts and hepatic fibrosis) in only 21 days. Second harmonic generation microscopy confirmed that the ARPKD mutation increased collagen abundance and thick collagen fiber production in hepatic organoids; and we demonstrated that these changes mirrored that occurring in ARPKD liver tissue. Transcriptomic and other analyses indicated that the ARPKD mutation generates cholangiocytes with increased TGF{beta}-associated pathway activation, which are actively involved in collagen fiber generation. The abnormal cholangiocytes promote the expansion of collagen-producing myofibroblasts with markedly increased PDGFR{beta} protein expression and an activated STAT3 signaling pathway. Moreover, the transcriptome of ARPKD organoid myofibroblasts resembled that of myofibroblasts in liver tissue obtained from patients with commonly occurring acquired forms of liver fibrosis. The involvement of the PDGFRB pathway was confirmed by the anti-fibrotic effect observed when ARPKD organoids were treated with PDGFRB inhibitors. ConclusionsBesides providing mechanistic insight into the pathogenesis of congenital (and possibly acquired) forms of liver fibrosis, ARPKD organoids could also be used to test the anti-fibrotic efficacy of potential anti-fibrotic therapies.

cell biology↗

bioRxiv2020

Performance characterization of PCR-free whole genome sequencing for clinical diagnosis

PurposeTo evaluate the performance of PCR-free whole genome sequencing (WGS) for clinical diagnosis, and thereby revealing how experimental parameters affect variant detection. MethodsAll the 5 NA12878 samples were sequenced using MGISEQ-2000. NA12878 samples underwent WGS with differing DNA input and library preparation protocol (PCR-based versus PCR-free protocols for library preparation). The DP (depth of coverage) and GQ (genotype quality) of each sample were compared. We developed a systematic WGS pipeline for the analysis of down-sampling samples of the 5 NA12878 samples. The performance of each sample was measured for sensitivity, coverage of depth and breadth of coverage of disease-related genes and CNVs. ResultsIn general, NA12878-2 (PCR-free WGS) showed better DP and GQ distribution than NA12878-1 (PCR-based WGS). With a mean depth of ~40X, the sensitivity of homozyous and heterozygous SNPs of NA12878-2 showed higher sensitivity (>99.77% and > 99.82%) than NA12878-1, and positive predictive value (PPV) exceeded 99.98% and 99.07%. The sensitivity and PPV of homozygous and heterozygous indels for NA12878-2 (PCR-free WGS) showed great improvement than NA128878-1. The breadths of coverage for disease-related genes and CNVs are slightly better for samples with PCR-free library preparation protocol than the sample with PCR-based library preparation protocol. DNA input also influences the performance of variant detection in samples with PCR-free WGS. ConclusionDifferent experimental parameters may affect variant detection for clinical WGS. Clinical scientists should know the range of sensitivity of variants for different methods of WGS, which would be useful when interpreting and delivering clinical reports.

genetics↗

bioRxiv2020

Impact of N-terminally substituted glucagon family receptor agonists on signal bias, trafficking and downstream responses

Receptors for the peptide hormones glucagon-like peptide-1 (GLP-1), glucose-dependent insulinotropic polypeptide (GIP) and glucagon (GCG) are important regulators of insulin secretion and energy metabolism. Recently described GLP-1 receptor agonists showing signal bias in favour of cyclic AMP over {beta}-arrestin-2 recruitment have delivered promising results in preclinical studies. Here we first sought to establish the role of {beta}-arrestins in the control of intracellular signalling and trafficking responses at the closely related GLP-1, GIP and GCG receptors, through studies performed in cells depleted of both {beta}-arrestin isoforms. We also generated analogues of GLP-1, GCG and GIP which in some cases showed selective reduction in {beta}-arrestin-2 recruitment versus cAMP signalling compared to the parent peptide. Despite reduced acute signalling potency and/or efficacy, some biased GLP-1 and GIP analogues increased maximal sustained insulin secretion from INS-1 832/3 clonal beta cells, although only at high agonist concentrations. Biased GCG analogues did not affect maximal insulin release, or glucose output in hepatocytes.

pharmacology and toxicology↗

bioRxiv2020

Characterizing sensitivity and coverage of clinical WGS as a diagnostic test for genetic disorders

BackgroundWith the reduce of cost and incomparable advantages, WGS is likely to change the way of clinical diagnosis of rare and undiagnosed diseases. However, the sensitivity and breadth of coverage of clinical WGS as a diagnostic test for genetic disorders has not been fully evaluated, especially for CNV detection. MethodsAll the samples underwent WGS using MGISEQ-2000. The performance of NA12878, YH cell line, and the Chinese trios were measured for sensitivity, PPV, depth and breadth of coverage. We also compared the performance of WES and WGS using NA12878. The sensitivity and PPV were tested using family-based trio design in the Chinese trios. We also developed a systematic WGS pipeline for the analysis of 8 clinical cases with known disease-causing variants. ResultsIn general, the sensitivity and PPV for SNV/indel detection increased with mean depth, and reached a plateau at a ~40X mean depth using down-sampling samples of NA12878. With a mean depth of 40X, the sensitivity of homozyous and heterozygous SNPs of NA12878 was >99.25% and >99.50% respectively, and PPVs were 99.97% and 98.96%. Homozygous and heterozygous indels showed lower sensitivity and PPV. The sensitivity and PPV is still not 100% even with a mean depth of ~150X. We also observed a substantial variation in the sensitivity of CNV detection across different tools, especially in CNVs with a size of less than 1kb. In general, the breadth of coverage for disease-associated genes and CNVs increased with mean depth. The sensitivity and coverage of WGS (~40X) is better than WES (~120X). Among the Chinese trios with ~40X mean depth, the sensitivity in the offspring was >99.48% and >96.36% for SNP and indel detection, and PPVs were 99.86% and 97.93%. All the 12 previously validated variants in the 8 clinical cases were successfully detected by our WGS pipeline. ConclusionsThe current standard of a mean depth of 40X may be sufficient for SNV/indel detection and identification of most CNVs. Clinical scientists should know the range of sensitivity and PPV for different classes of variants for a particular WGS pipeline, which would be useful when interpreting and delivering clinical reports.

genomics↗

bioRxiv2020

The influence of peptide context on signalling and trafficking of glucagon-like peptide-1 receptor biased agonists

Signal bias and membrane trafficking have recently emerged as important considerations in the therapeutic targeting of the glucagon-like peptide-1 receptor (GLP-1R) in type 2 diabetes and obesity. In the present study, we have evaluated a peptide series with varying sequence homology between native GLP-1 and exendin-4, the archetypal ligands on which approved GLP-1R agonists are based. We find notable differences in agonist-mediated signalling, endocytosis and recycling, dependent both on the introduction of a His [->] Phe switch at position 1 and the specific mid-peptide helical regions and C-termini of the two agonists. These observations were linked to insulin secretion in a beta cell model and provide insights into how ligand factors influence GLP-1R function at the cellular level. Graphical abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=84 SRC="FIGDIR/small/961524v1_ufig1.gif" ALT="Figure 1"> View larger version (25K): org.highwire.dtl.DTLVardef@cb425borg.highwire.dtl.DTLVardef@719e37org.highwire.dtl.DTLVardef@14a45b9org.highwire.dtl.DTLVardef@127afd1_HPS_FORMAT_FIGEXP M_FIG C_FIG

pharmacology and toxicology↗

bioRxiv2020

CCSN: Single Cell RNA Sequencing Data Analysis by Conditional Cell-specific Network

The rapid advancement of single cell technologies has shed new light on the complex mechanisms of cellular heterogeneity. However, compared with bulk RNA sequencing (RNA-seq), single-cell RNA-seq (scRNA-seq) suffers from higher noise and lower coverage, which brings new computational difficulties. Based on statistical independence, cell-specific network (CSN) is able to quantify the overall associations between genes for each cell, yet suffering from a problem of overestimation related to indirect effects. To overcome this problem, we propose the "conditional cell-specific network" (CCSN) method, which can measure the direct associations between genes by eliminating the indirect associations. CCSN can be used for cell clustering and dimension reduction on a network basis of single cells. Intuitively, each CCSN can be viewed as the transformation from less "reliable" gene expression to more "reliable" gene-gene associations in a cell. Based on CCSN, we further design network flow entropy (NFE) to estimate the differentiation potency of a single cell. A number of scRNA-seq datasets were used to demonstrate the advantages of our approach: (1) one direct association network for one cell; (2) most existing scRNA-seq methods designed for gene expression matrices are also applicable to CCSN-transformed degree matrices; (3) CCSN-based NFE helps resolving the direction of differentiation trajectories by quantifying the potency of each cell. CCSN is publicly available at http://sysbio.sibcb.ac.cn/cb/chenlab/soft/CCSN.zip.

bioinformatics↗

bioRxiv2020

Host and infectivity prediction of Wuhan 2019 novel coronavirus using deep learning algorithm

The recent outbreak of pneumonia in Wuhan, China caused by the 2019 Novel Coronavirus (2019-nCoV) emphasizes the importance of detecting novel viruses and predicting their risks of infecting people. In this report, we introduced the VHP (Virus Host Prediction) to predict the potential hosts of viruses using deep learning algorithm. Our prediction suggests that 2019-nCoV has close infectivity with other human coronaviruses, especially the severe acute respiratory syndrome coronavirus (SARS-CoV), Bat SARS-like Coronaviruses and the Middle East respiratory syndrome coronavirus (MERS-CoV). Based on our prediction, compared to the Coronaviruses infecting other vertebrates, bat coronaviruses are assigned with more similar infectivity patterns with 2019-nCoVs. Furthermore, by comparing the infectivity patterns of all viruses hosted on vertebrates, we found mink viruses show a closer infectivity pattern to 2019-nCov. These consequences of infectivity pattern analysis illustrate that bat and mink may be two candidate reservoirs of 2019-nCov.These results warn us to beware of 2019-nCoV and guide us to further explore the properties and reservoir of it. One Sentence SummaryIt is of great value to identify whether a newly discovered virus has the risk of infecting human. Guo et al. proposed a virus host prediction method based on deep learning to detect what kind of host a virus can infect with DNA sequence as input. Applied to the Wuhan 2019 Novel Coronavirus, our prediction demonstrated that several vertebrate-infectious coronaviruses have strong potential to infect human. This method will be helpful in future viral analysis and early prevention and control of viral pathogens.

systems biology↗

bioRxiv2019

Differences in signalling, trafficking and glucoregulatory properties of glucagon-like peptide-1 receptor agonists exendin-4 and lixisenatide

Background and purposeAmino acid substitutions at the N-termini of glucagon-like peptide-1 receptor agonist (GLP-1RA) peptides result in distinct patterns of intracellular signalling, sub-cellular trafficking and efficacy in vivo. Here we aimed to determine whether sequence differences at the ligand C-termini of clinically approved GLP-1RAs exendin-4 and lixisenatide lead to similar phenomena. We also sought to establish the impact of the C-terminus on signal bias resulting from modifications elsewhere in the molecule. Experimental approachExendin-4, lixisenatide, and N-terminally substituted analogues with biased signalling characteristics were compared across a range of in vitro trafficking and signalling assays in different cell types. Fluorescent ligands and new time-resolved FRET approaches were developed to study agonist behaviours at the cellular and sub-cellular level. Anti-hyperglycaemic and anorectic effects of each parent ligand, and their biased derivatives, were assessed in mice. Key resultsLixisenatide and exendin-4 showed equal binding affinity, but lixisenatide was 5-fold less potent for cAMP signalling. Both peptides were rapidly endocytosed, but the GLP-1R recycled more slowly to the plasma membrane after lixisenatide treatment. These combined deficits resulted in reduced maximal sustained insulin secretion and reduced anti-hyperglycaemic and anorectic effects in mice. N-terminal substitutions to both ligands had favourable effects on their pharmacology, resulting in improved insulin release and lowering of blood glucose. Conclusion and implicationsChanges to the C-terminus of exendin-4 affect signalling potency and GLP-1R trafficking via mechanisms unrelated to GLP-1R occupancy. These differences were associated with changes in their ability to control blood glucose and therefore may be therapeutically relevant.

pharmacology and toxicology↗