Biology subjects

Yu, F.

Publications and source records attributed to Yu, F..

At least 163 records · Page 9Linked to original sources

bioRxiv2020

Keratin5-BMP4 mechanosignaling regulates reciprocal acetylation and methylation at H3K9 to define blastema cell remodeling during zebrafish heart regeneration

Heart regeneration after myocardial infarction remains challenging due to scar and ischemia-reperfusion injury. Here, we show that zebrafish blastema regeneration can effectively resalvage the wound myocardium and blood clot through cytoplasmic exocytosis and nuclear reorganization. The cell remodeling process are also visualized by spatiotemporal expression of three core blastema genes: alpha-SMA- which marks for fibrogenesis, Flk1for angiogenesis/hematopoiesis, Pax3a for remusculogensis, and by characteristic chromatin depositions of H3K9Ac/H3K9Me3. Genome-wide enhancer identification links the depositions of the two histone marks to the chromatin state and these three core blastema cell phenotypes. When the blastema subcellular fractions are introduced into the cultured zebrafish embryonic fibroblasts the altered transcription profile is comparable to the blastema transcription in terms of extracellular matrix structural constituent, vasculature development/angiogenesis, and cardiac muscle regeneration. From the subcellular fractions we identify 15 extracellular components and intermediate filaments, and show that introduction of human Krt5 and noggin peptides conversely regulate PAC2 cells F-actin reorganization, chromatin depositions of H3K9Ac/ H3K9Me3 and phosphorylation of Smad, which are accompanied by characteristic transcriptions of bmp, bmp4, three core blastema genes as well as specific histone acetylation/methylation-related genes. Collectively, this study establishes a new Krt5-BMP4 mechanosignaling cascade that links extracellular molecules to chromatin modifications and regulates blastema cell remodeling, thus providing mechanistic insights into the mesoderm-derived blastema regeneration and underlining a therapy strategy for myocardial infarction.

cell biology↗

bioRxiv2020

Probing the Hidden Sensitivity of Intrinsically Disordered Proteins to their Chemical Environment

Intrinsically disordered proteins and protein-regions (IDRs) make up roughly 30% of the human proteome and play vital roles in a wide variety of biological processes. Given a lack of persistent tertiary structure, all of the residues in an IDR are, to some extent, solvent exposed. This extensive surface area, coupled with the absence of strong intramolecular contacts, makes IDRs inherently sensitive to their chemical environment. Despite this sensitivity, our understanding of how IDR structural ensembles are influenced by changes in their chemical environment is limited. This is particularly relevant given a growing body of evidence showing that IDR function is linked to the underlying structural ensemble. We develop and use a combined experimental, computational, and analytical framework for high-throughput characterization of IDR sensitivity we call solution space scanning. Our framework reveals that IDRs show sequence-dependent sensitivity to solution chemistry, with complex behavior that can be interpreted through relatively simple polymer models. Our results imply that solution-responsive IDRs are ubiquitous and can provide an additional layer of biological regulation.

biophysics↗

bioRxiv2020

Numerical simulation of the two-locus Wright-Fisher stochastic differential equation with application to approximating transition probability densities

Over the past decade there has been an increasing focus on the application of the Wright-Fisher diffusion to the inference of natural selection from genetic time series. A key ingredient for modelling the trajectory of gene frequencies through the Wright-Fisher diffusion is its transition probability density function. Recent advances in DNA sequencing techniques have made it possible to monitor genomes in great detail over time, which presents opportunities for investigating natural selection while accounting for genetic recombination and local linkage. However, most existing methods for computing the transition probability density function of the Wright-Fisher diffusion are only applicable to one-locus problems. To address two-locus problems, in this work we propose a novel numerical scheme for the Wright-Fisher stochastic differential equation of population dynamics under natural selection at two linked loci. Our key innovation is that we reformulate the stochastic differential equation in a closed form that is amenable to simulation, which enables us to avoid boundary issues and reduce computational costs. We also propose an adaptive importance sampling approach based on the proposal introduced by Fearnhead (2008) for computing the transition probability density of the Wright-Fisher diffusion between any two observed states. We show through extensive simulation studies that our approach can achieve comparable performance to the method of Fearnhead (2008) but can avoid manually tuning the parameter{rho} to deliver superior performance for different observed states.

genetics↗

bioRxiv2020

The ORF8 Protein of SARS-CoV-2 Mediates Immune Evasion through Potently Downregulating MHC-I

SARS-CoV-2 infection have caused global pandemic and claimed over 5,000,000 tolls1-4. Although the genetic sequences of their etiologic viruses are of high homology, the clinical and pathological characteristics of COVID-19 significantly differ from SARS5,6. Especially, it seems that SARS-CoV-2 undergoes vast replication in vivo without being effectively monitored by anti-viral immunity7. Here, we show that the viral protein encoded from open reading frame 8 (ORF8) of SARS-CoV-2, which shares the least homology with SARS-CoV among all the viral proteins, can directly interact with MHC-I molecules and significantly down-regulates their surface expression on various cell types. In contrast, ORF8a and ORF8b of SARS-CoV do not exert this function. In the ORF8-expressing cells, MHC-I molecules are selectively target for lysosomal degradation by an autophagy-dependent mechanism. As a result, CTLs inefficiently eliminate the ORF8-expressing cells. Our results demonstrate that ORF8 protein disrupts antigen presentation and reduces the recognition and the elimination of virus-infected cells by CTLs8. Therefore, we suggest that the inhibition of ORF8 function could be a strategy to improve the special immune surveillance and accelerate the eradication of SARS-CoV-2 in vivo.

microbiology↗

bioRxiv2020

Fast and Comprehensive N- and O-glycoproteomics analysis with MSFragger-Glyco

Glycosylation is a ubiquitous and heterogeneous post-translational modification (PTM) used to accomplish a wide variety of critical cellular tasks. Recent advances in methods for enrichment and mass spectrometric analysis of intact glycopeptides have produced large-scale, high-quality glycoproteomics datasets, but interpreting this data remains challenging. In addition to being large, complex, and heterogeneous, glycans undergo fragmentation during vibrational activation, making common PTM search strategies ineffective for their identification. We present a computational tool called MSFragger-Glyco for fast and highly sensitive identification of N- and O-linked glycopeptides using open and glycan mass offset search strategies. Reanalysis of recently published N-glycoproteomics data resulted in annotation of 83% more glycopeptide-spectrum matches (glycoPSMs) than in previous results, which translated to substantial increases in the numbers of glycoproteins and glycosites that could be identified. In published O-glycoproteomics data, our method more than doubled the number of glycoPSMs annotated when searching the same peptides as the original search and resulted in up to a 6-fold increase when expanding searches to include large numbers of possible glycan compositions and other modifications. Expanded searches revealed trends in glycan composition and crosstalk with phosphorylation that remained hidden to the original search. With greatly improved spectral annotation, coupled with the fast speed of fragment ion index-based scoring, MSFragger-Glyco makes it possible to comprehensively interrogate glycoproteomics data and illuminate the many roles of glycosylation.

bioinformatics↗

bioRxiv2020

Predicting Longitudinal Disease Severity for Individuals with Parkinson's Disease using Functional MRI and Machine Learning Prognostic Models

Parkinsons disease is the second most common neurodegenerative disorder and is characterized by the loss of ability to control voluntary movements. Predictive biomarkers of progression in Parkinsons Disease are urgently needed to expedite the development of neuroprotective treatments and facilitate discussions about disease prognosis between clinicians and patients. Resting-state functional magnetic resonance imaging (rs-fMRI) shows promise in predicting progression, with derived measures, including regional homogeneity (ReHo) and fractional amplitude of low frequency fluctuations (fALFF), having been previously been associated with current disease severity. In this work, ReHo and fALFF features from 82 Parkinsons Disease subjects are used to train machine learning predictors of baseline clinical severity and progression at 1 year, 2 years, and 4 years follow-up as measured by the Movement Disorder Society Unified Depression Rating Scale (MDS-UPDRS) score. This is the first time that rs-fMRI and machine learning have been combined to predict future disease progression. The machine learning models explain up to 30.4% (R2 = 0.304) of the variance in baseline MDS-UPDRS scores, 55.8% (R2 = 0.558) of the variance in year 1 scores, and 47.1% (R2 = 0.471) of the variance in year 2 scores with high statistical significance (p < 0.0001). For distinguishing high- and low-progression individuals (MDS-UPDRS score above or below the median), the models achieve positive predictive values of up to 71% and negative predictive values of up to 84%. The models learn patterns of ReHo and fALFF measures that predict better and worse prognoses. Higher ReHo and fALFF in regions of the default motor network predicted lower current severity and lower future progression. The rs-fMRI features in the temporal lobe, limbic system, and motor cortex were also identified as predictors. These results present a potential neuroimaging biomarker that accurately predicts progression, which may be useful as a clinical decision-making tool and in future trials of neuroprotective treatments.

neuroscience↗

bioRxiv2020

Local and global dichotomic dysfunction in resting and evoked functional connectivity precedes tauopathy

Functional network activity alterations are one of the earliest hallmarks of Alzheimers disease (AD), detected prior to amyloidosis and tauopathy. Better understanding the neuronal underpinnings of such network alterations could offer mechanistic insight into AD progression. Here, we examined a mouse model (early-tauopathy 3xTgAD mice) recapitulating this early AD stage. We found resting functional connectivity loss within ventral networks, including the entorhinal cortex, aligning with the spatial distribution of tauopathy reported in humans. Unexpectedly, in contrast to decreased connectivity at rest, 3xTgAD mice show enhanced fMRI signal within several projection areas following optogenetic activation of the entorhinal cortex. We corroborate this finding by demonstrating neuronal facilitation within ventral networks and synaptic hyperexcitability in projection targets. 3xTgAD mice thus reveal a dichotomic hypo-connected resting/hyper-responsive active phenotype. The strong homotopy between the areas affected supports the translatability of this pathophysiological model to tau-related deficits in humans.

neuroscience↗

bioRxiv2020

Characterization and establishment of a novel EBV strain simultaneously associated with nasopharyngeal carcinoma and B-cell lymphoma

Epstein-Barr virus (EBV) - the prototypical human tumor virus - is responsible for 1-2% of the global cancer burden, but divergent strains seem to exist in different geographical regions with distinct predilections for causing lymphoid or epithelial malignancies. Here we report the establishment and characterization of Yu103, an Asia Pacific EBV strain with a highly remarkable provenance of being derived from nasopharyngeal carcinoma biopsy but subsequently propagated in human B-lymphoma cells and xenograft models. Unlike previously characterized EBV strains which are either predominantly B-lymphotropic or epitheliotropic, Yu103 evinces an uncanny capacity to infect and transform both B-lymphocytes and nasopharyngeal epithelial cells. Genomic and phylogenetic analyses indicated that Yu103 EBV lies midway along the spectrum of EBV strains known to drive lymphomagenesis or carcinogenesis, and harbors molecular features which likely account for its unusual properties. To our knowledge, Yu103 EBV is currently the only EBV isolate shown to drive human nasopharyngeal carcinoma and B-lymphoma, and should therefore provide a powerful novel platform for research on EBV-driven hematological and epithelial malignancies.

cancer biology↗

bioRxiv2020

Fast quantitative analysis of timsTOF PASEF data with MSFragger and IonQuant

Ion mobility brings an additional dimension of separation to liquid chromatography-mass spectrometry, improving identification of peptides and proteins in complex mixtures. A recently introduced timsTOF mass spectrometer (Bruker) couples trapped ion mobility separation to time-of-flight mass analysis. With the parallel accumulation serial fragmentation (PASEF) method, the timsTOF platform achieves promising results, yet analysis of the data generated on this platform represents a major bottleneck. Currently, MaxQuant and PEAKS are most commonly used to analyze these data. However, due to the high complexity of timsTOF PASEF data, both require substantial time to perform even standard tryptic searches. Advanced searches (e.g. with many variable modifications, semi- or non-enzymatic searches, or open searches for post-translational modification discovery) are practically impossible. We have extended our fast peptide identification tool MSFragger to support timsTOF PASEF data, and developed a label-free quantification tool, IonQuant, for fast and accurate 4-D feature extraction and quantification. Using a HeLa data set published by Meier et al. (2018), we demonstrate that MSFragger identifies significantly (∼30%) more unique peptides than MaxQuant (1.6.10.43), and performs comparably or better than PEAKS X+ (∼10% more peptides). IonQuant outperforms both in terms of number of quantified proteins while maintaining good quantification precision and accuracy. Runtime tests show that MSFragger and IonQuant can fully process a typical two-hour PASEF run in under 70 minutes on a typical desktop (6 CPU cores, 32 GB RAM), significantly faster than other tools. Finally, through semi-enzymatic searching, we significantly increase the number of identified peptides. Within these semi-tryptic identifications, we report evidence of gas-phase fragmentation prior to MS/MS analysis.Competing Interest StatementThe authors have declared no competing interest.AbbreviationsLC-MSliquid chromatography-mass spectrometryTIMStrapped ion mobility spectrometryTOFtime-of-flightPASEFparallel accumulation-serial fragmentationDDAdata-dependent acquisitionDIAdata-independent acquisitionMS/MStandem mass spectrometryPSMpeptide-spectrum matchLC-IMS-MSliquid chromatography-ion mobility-mass spectrometryXICextracted ion chromatogramCVcoefficient of variationLFQlabel free quantificationFDRFalse Discovery RateCIDcollision-induced dissociationCPUcentral processing unitPTMpost-translational modificationMBRmatch-between-runsView Full Text

bioinformatics↗

bioRxiv2020

Teicoplanin potently blocks the cell entry of 2019-nCoV

Since the outbreak of the coronavirus disease 2019 (COVID-19) caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), the public health worldwide has been greatly threatened. The development of an effective treatment for this infection is crucial and urgent but is hampered by the incomplete understanding of the viral infection mechanism and the lack of specific antiviral agents. We previously reported that teicoplanin, a glycopeptide antibiotic that has been commonly used in the clinic to treat bacterial infection, significantly restrained the cell entry of Ebola virus, SARS-CoV and MERS-CoV by specifically inhibiting the activity of cathepsin L (CTSL). Here, we found that the cleavage sites of CTSL on the Spike of SARS-CoV-2 were highly conserved among all the variants. The treatment with teicoplanin suppressed the proteolytic activity of CTSL on Spike and prevented the cellular infection of different pseudotyped SARS-CoV-2 viruses. Teicoplanin potently prevented the entry of authentic SARS-CoV-2 into the cellular cytoplasm with an IC50 of 2.038 M for the Wuhan-Hu-1 reference strain and an IC50 of 2.116 M for the SARS-CoV-2 (D614G) variant. The pre-treatment of teicoplanin also prevented SARS-CoV-2 infection in hACE2 mice. In summary, our data reveal that CTSL is required for both SARS-CoV-2 and SARS-CoV infection and demonstrate the therapeutic potential of teicoplanin for universal anti-CoVs intervention. ImportanceDisease prevention and treatment are two important countermeasures to end the coronavirus disease 2019 (COVID-19). However, severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), the causative agent of COVID-19, evolves all the time, resulting in the emerging of many epidemic SARS-CoV-2 mutants, which significantly impairs the effectiveness of early strain-based vaccines and antibodies. Developing universal vaccines and broad-spectrum antiviral drugs are essential to confront SARS-CoV-2 mutants including those may emerge in the future. Our study reported here showed that the cleavage sites of cellular cathepsin L (CTSL) are highly conserved among all the SARS-CoV-2 mutants and SARS-CoV. The CTSL inhibitor teicoplanin not only inhibited the cell entry of two live SARS-CoV-2 strains and various pseudotyped viruses but also prevented live virus infection in animal models. Based on our previous finding that teicoplanin also inhibited SARS-CoV and MERS-CoV infection, we believe that teicoplanin possesses the potential to become a universal anti-CoVs drug.

microbiology↗

bioRxiv2020

Kupffer Cell Release of Platelet Activating Factor Drives Dose Limiting Toxicities of Nucleic Acid Nanocarriers

In vivo nanocarrier-associated toxicity is a significant and poorly understood hurdle to clinical translation of siRNA nanomedicines. In this work, we demonstrate that platelet activating factor (PAF), an inflammatory lipid mediator, plays a key role in nanocarrier-associated toxicities, and that prophylactic inhibition of the PAF receptor (PAFR) completely prevents these toxicities. High-dose intravenous injection of siRNA-polymer nano-complexes (si-NPs) elicited acute, shock-like symptoms (vasodilation and vascular leak) in mice and caused a three-fold increase in blood PAF levels. PAFR inhibition completely prevented these toxicities, indicating PAF activity is a primary driver of systemic si-NP toxicity. Pre-treatment with clodronate liposomes fully abrogated si-NP-associated increases in blood PAF and consequent toxicities, suggesting that nanoparticle uptake by Kupffer macrophages is the source of PAF. Assessment of varied si-NP chemistries further confirmed that toxicity level correlated to relative uptake of the carrier by liver Kupffer cells and that this toxicity mechanism is dependent on the endosome disruptive function of the carrier. Finally, the PAF toxicity mechanism was shown to be generalizable to commercial delivery reagent in vivo-jetPEI(R) and an MC3 lipid nanoparticle formulated to match an FDA-approved siRNA nanomedicine. Greater sensitivity to the PAF mechanism occurs in 4T1 tumor-bearing mice, a mammary tumor model known to exhibit increased circulating leukocytes and potential to respond to inflammatory insult. These results establish Kupffer cell release of PAF as a key mediator of in vivo nucleic acid nanocarrier toxicity and identify PAFR inhibition as an effective prophylactic strategy to increase maximum tolerated dose and reduce nanocarrier-associated adverse events. SignificanceNon-viral nucleic acid nanocarriers can enable in vivo gene therapy, but their potential interaction with innate immune cells can cause dose-limiting toxicities. Nanoparticle toxicities are currently poorly understood, making it difficult to identify relevant design criteria for maximizing nanoparticle safety. This work connects nanoparticle-associated toxicities to the release of platelet activating factor (PAF) by liver Kupffer cells. Small molecule inhibition of the PAF receptor (PAFR) completely prevents severe adverse events associated with high doses of multiple polymer-based formulations and a lipid nanoparticle matching the composition of the first clinically-approved siRNA nanomedicine. This study identifies PAF as a toxicity biomarker for future nanomedicine discovery programs. Further, PAFR inhibition should be explored as a strategy to expand the therapeutic index of nanomedicines.

bioengineering↗

bioRxiv2020

Matrix-targeted Nanoparticles for MMP13 RNA Interference Blocks Post-Traumatic Osteoarthritis.

Osteoarthritis (OA) is a debilitating and prevalent chronic disease, but there are no approved disease modifying OA drugs (DMOADs), only pharmaceuticals for pain management. OA progression, particularly for post-traumatic osteoarthritis (PTOA), is associated with inflammation and enzymatic degradation of the extracellular matrix. In particular, Matrix Metalloproteinase 13 (MMP13) breaks down collagen type 2 (CII), a key structural component of cartilage extracellular matrix, and consequently, matrix degradation fragments perpetuate inflammation and a degenerative cycle that leads to progressive joint pathology. Here, we tested targeted delivery of endosome-escaping, MMP13 RNA interference (RNAi) nanoparticles (NPs) as a DMOAD. The new targeting approach pursued here deviates from the convention of targeting specific cell types (e.g., through cell surface receptors) and instead leverages a monoclonal antibody (mAbCII) that targets extracellular CII that becomes uniquely accessible at early OA focal defects. Targeted mAbCII-siNPs create an in situ NP depot for retention and potent activity within OA joints. The mAbCII-siNPs loaded with MMP13 siRNA (mAbCII-siNP/siMMP13) potently suppressed MMP13 expression (95% silencing) in TNF-stimulated chondrocytes in vitro, and the targeted mAbCII-siNPs had higher binding to trypsin-damaged porcine cartilage than untargeted control NPs. In an acute mechanical injury mouse model of PTOA, mAbCII-siNP/siMMP13 achieved 80% reduction in MMP13 expression (p = 0.00231), whereas a non-targeted control achieved only 55% silencing. In a more severe, PTOA model, weekly mAbCII-siNP/siMMP13 long-term treatment provided significant protection of cartilage integrity (0.45+/- .3 vs 1.6+/-.5 on the OARSI scale; p=0.0166), and overall joint structure (1.3+/-.6 vs 2.8+/-.2 on the Degenerative Joint Disease scale; p<0.05). Intra-articular mAbCII-siNPs better protected articular cartilage (OARSI score) relative to either single or weekly treatment with the clinical gold stand steroid treatment methylprednisolone. Finally, multiplexed gene expression analysis of 254 inflammation-related genes showed that MMP13 inhibition suppressed clusters of genes associated with tissue restructuring, angiogenesis (associated with synovial inflammation and thickening), innate immune response, and proteolysis. This work establishes the new concept of targeting unique local extracellular matrix signatures to sustain retention and increase delivery efficacy of biologics with intracellular activity and also validates the promise of MMP13 RNAi as a DMOAD in a clinically-relevant therapeutic context. O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=148 SRC="FIGDIR/small/925321v1_ufig1.gif" ALT="Figure 1"> View larger version (54K): org.highwire.dtl.DTLVardef@1803795org.highwire.dtl.DTLVardef@c1254dorg.highwire.dtl.DTLVardef@78233eorg.highwire.dtl.DTLVardef@151d96f_HPS_FORMAT_FIGEXP M_FIG O_FLOATNOAbstract Figure:C_FLOATNO PTOA targeted delivery of MMP13 siRNA to block disease progressionThe top left schematic illustrates the progression (left to right) from healthy knee joint, through inflammation induction following traumatic injury, to cartilage loss and degenerative joint disease (including synovial response). Degradation of cartilage enhances inflammation, inducing a degenerative cycle (middle right). The bottom of the graphic illustrates the concept of the matrix targeted nanocarriers for enhanced retention and activity of MMP13 siRNA at sites of cartilage injury. C_FIG

bioengineering↗

bioRxiv2020

scTPA: A web tool for single-cell transcriptome analysis of pathway activation signatures

The most fundamental challenge in current single-cell RNA-seq data analysis is functional interpretation and annotation of cell clusters. The biological pathways in distinct cell types have different activation patterns, which facilitates understanding cell functions in single-cell transcriptomics. However, no effective web tool has been implemented for single-cell transcriptomic data analysis based on prior biological pathway knowledge. Here, we introduce scTPA (http://sctpa.bio-data.cn/sctpa), which is a web-based platform providing pathway-based analysis of single-cell RNA-seq data in human and mouse. scTPA incorporates four widely-used gene set enrichment methods to estimate the pathway activation scores of single cells based on a collection of available biological pathways with different functional and taxonomic classifications. The clustering analysis and cell-type-specific activation pathway identification were provided for the functional interpretation of cell types from pathway-oriented perspective. An intuitive interface allows users to conveniently visualize and download single-cell pathway signatures. Together, scTPA is a comprehensive tool to identify pathway activation signatures for dissecting single cell heterogeneity.

genomics↗

bioRxiv2019

Maximum likelihood estimation of natural selection and allele age from time series data of allele frequencies

Temporally spaced genetic data allow for more accurate inference of population genetic parameters and hypothesis testing on the recent action of natural selection. In this work, we develop a novel likelihood-based method for jointly estimating selection coefficient and allele age from time series data of allele frequencies. Our approach is based on a hidden Markov model where the underlying process is a Wright-Fisher diffusion conditioned to survive until the time of the most recent sample. This formulation circumvents the assumption required in existing methods that the allele is created by mutation at a certain low frequency. We calculate the likelihood by numerically solving the resulting Kolmogorov backward equation backwards in time while re-weighting the solution with the emission probabilities of the observation at each sampling time point. This procedure reduces the two-dimensional numerical search for the maximum of the likelihood surface for both the selection coefficient and the allele age to a one-dimensional search over the selection coefficient only. We illustrate through extensive simulations that our method can produce accurate estimates of the selection coefficient and the allele age under both constant and non-constant demographic histories. We apply our approach to re-analyse ancient DNA data associated with horse base coat colours. We find that ignoring demographic histories or grouping raw samples can significantly bias the inference results.

genetics↗

bioRxiv2019

Accumulated degeneration of transcriptional regulation contributes to disease development and detrimental clinical outcomes of Alzheimer’s disease

Alzheimers disease (AD) is extremely complex for both causal mechanism and clinical manifestation, requiring efforts to uncover its diversity and the corresponding mechanisms. Here, we applied a modelling analysis to investigate the regulation divergence among a large-scale cohort of AD patients. We found that transcription regulation tended to get degenerated in AD patients, which contributed to disease development and the detrimental clinical outcomes, mainly by disrupting protein degradation, neuroinflammation, mitochondrial and synaptic functions. To measure the accumulated effects, we came up with a new concept, regulation loss burden, which better correlated with AD related clinical manifestations and the ageing process. The epigenetic studies to multiple active regulation marks also supported a tendency of regulation loss in AD patients. Our finding can lead to a unified model as AD causal mechanism, where AD and its diversity are contributed by accumulated degeneration of transcriptional regulation.\n\nThe significance of this study is that: (1) it is the first system biology investigation to transcription regulation divergence among AD patients; (2) we observed an accumulated degeneration of transcription regulation, which well correlates with detrimental clinical outcomes; (3) transcriptional degeneration also contributes to the ageing process, where its correlation with ages is up to 0.78.

neuroscience↗

bioRxiv2019

From phages to mammalian viruses: viral receptors play a central role in protein-protein interaction network

MotivationReceptors on host cells play a critical role in viral infection. How phages select receptors is still unknown.\n\nResultsHere, we manually curated a high-quality database named phageReceptor, including 355 pairs of phage-host receptor interactions, 280 unique viral species or sub-species and 64 bacterial species. Sugars and proteins were most widely used by phages as receptors. The receptor usage of phages in Gram-positive bacteria was different from that in Gram-negative bacteria. Most protein receptors were located on the outer membrane. The protein receptors were highly diverse in their structures, and had little homology with mammalian virus receptors. Further functional characterization of phage protein receptors in Escherichia coli showed that they had larger node degrees and betweennesses in the protein-protein interaction (PPI) network, and higher expression levels, than other outer membrane proteins, plasma membrane proteins, or other intracellular proteins. These findings were consistent with what observed for mammalian virus receptors, suggesting that viral protein receptors play a central role in the hosts PPI network. The study deepens our understanding of virus-host interactions.\n\nAvailabilityThe database of phageReceptor is publicly accessible at http://www.computationalbiology.cn/viralRecepetor/index.html.

microbiology↗

bioRxiv2019

DDAP: docking domain affinity and biosynthetic pathway prediction tool for type I polyketide synthases

SummaryDDAP is a tool for predicting the biosynthetic pathways of the products of type I modular polyketide synthase (PKS) with the focus on providing a more accurate prediction of the ordering of proteins and substrates in the pathway. In this study, the module docking domain (DD) affinity prediction performance on a hold-out testing data set reached AUC = 0.88; the MRR of pathway prediction reached 0.67. DDAP has advantages compared to previous informatics tools in several aspects: (i) it does not rely on large databases, making it a high efficiency tool, (ii) the predicted DD affinity is represented by a probability (0 to 1), which is more intuitive than raw scores, (iii) its performance is competitive compared to the current popular rule-based algorithm. To the best of our knowledge, DDAP is so far the first machine learning based algorithm for type I PKS pathway prediction. We also established the first database of type I modular PKSs, featuring a comprehensive annotation of available docking domains information in bacterial biosynthetic pathways.\n\nAvailability and implementationThe DDAP database is available at https://tylii.github.io/ddap. The prediction algorithm DDAP is freely available on GitHub (https://github.com/tylii/ddap) and released under the MIT license.\n\nContactukarvind@umich.edu

bioinformatics↗

bioRxiv2019

Alteration of Gut Microbiome in Lung Cancer Patients

Lung cancer is the leading cause of cancer death. Better understanding of factors and pathways involved in lung cancer is needed to improve diagnose and treatment strategies. Recent studies have provided insights into the possible correlation between intestinal dysbiosis and cancer development. Although the immunological relationship between gut and lung had been suggested by many researches, however, to date, no study had investigated the characterization of gut microbiome in treatment naive lung cancer patients, whether it is distinct from that of health individuals and contribute to the onset and development of lung cancer remain unclear. In this study, we investigated whether gut microbiome of lung cancer patients (LC, n=28) is altered compare with that of matched healthy individuals (HC, n=19) by high throughout sequencing of the V3-V4 regions of 16S rDNA in their fecal samples. We also identified microbiota signatures specific for different histological types of lung cancer, including SSC, ADC, and SCLC. The gut microbiome of lung cancer patients is characterized by decreased relative abundance of Prevotella, and increased bacteria groups such as Actinomyces, and Streptococcus, etc. We also detected a mild structural shift in gut microbiome between ADC and SCLC patients. Our results showed that the gut microbiome of lung cancer patients altered significantly compared with healthy individuals. However, the association between microbial dysbiosis and lung cancer is not clearly understood, future studies involving larger cohorts and metagenomics, or metabolomics, may elucidate the correlations between gut microbiota and lung cancer development.\n\nIMPORTANCEThis is the first report to show the alteration of gut microbiome in lung cancer patients. Our results showed that the gut microbiome of lung cancer patients altered significantly compared with healthy individuals.

microbiology↗