Biology subjects

Xu, C.

Publications and source records attributed to Xu, C..

At least 343 records · Page 19Linked to original sources

bioRxiv2020

Cross-talk between the methylerythritol phosphate and mevalonic acid pathways of isoprenoid biosynthesis in poplar

The isoprenoids found in plants are extremely important to survive with various human applications, such as flavoring, fragrance, dye, pharmaceuticals, and biomass used for biofuels. Methylerythritol phosphate (MEP) and mevalonic acid (MVA) pathways are critical in plants, responsible for isoprenoid biosynthesis. 1-deoxy-D-xylulose5-phosphate synthase (DXS) and 1-deoxy-D-xylulose5-phosphate reductoisomerase (DXR) catalyze the rate-limiting steps in the MEP pathway, while 3-hydroxy-3-methylglutaryl-CoA reductase (HMGR) catalyzes the rate-limiting step in the MVA pathway. Here, we showed while PtHMGR overexpressors (OEs) exhibited different MEP- and MVA-related gene expressions compared with non-transgenic poplars (NT), the PtDXR-OEs revealed upregulated MEP-related and downregulated MVA-related gene expressions. PtDXR and PtHMGR overexpressions caused changes in MVA-derived trans-zeatin-riboside, isopentenyl adenosine, castasterone, and 6-deoxocastasterone well as MEP-derived carotenoids and gibberellins. In PtHMGR-OEs, the accumulated geranyl diphosphate synthase (GPS) and geranyl pyrophosphate synthase (GPPS) transcript levels in the MEP pathway led to an accumulation of MEP-derived isoprenoids. In contrast, upregulation of farnesyl diphosphate synthase (FPS) expression in the MVA pathway contributed to increased levels of MVA-derived isoprenoids. In addition, PtHMGR-OEs increased MEP-related GPS and GPPS transcript levels, expanded MEP-derived isoprenoid levels, changed FPS transcript levels, and affected MVA-derived isoprenoid yields. These results demonstrate the contribution of MVA and MEP pathways regulating isoprenoid biosynthesis in poplars.

molecular biology↗

bioRxiv2020

SEED-MEDIATED RNA INTERFERENCE OF ANDROGEN SIGNALING AND SURVIVAL NETWORKS INDUCES CELL DEATH IN PROSTATE CANCER CELLS

Resistance to anti-androgen therapy in prostate cancer (PCa) is often driven by genetic and epigenetic aberrations in the androgen receptor (AR) and coregulators that maintain androgen signaling activity. We show that specific small RNAs downregulate expression of multiple essential and AR-coregulatory genes, leading to potent androgen signaling inhibition and PCa cell death. Expression of different sh-/siRNAs designed to target TMEFF2, preferentially reduce viability of PCa, but not benign, cells and growth of murine xenografts. Surprisingly this effect is independent of TMEFF2 expression. Transcriptomic and sh/siRNA seed sequence studies indicate that expression of these toxic shRNAs lead to downregulation of AR-coregulatory and essential genes thru mRNA 3-UTR sequence complementarity to the seed sequence of the toxic shRNAs. These findings reveal a specialized form of the Death Induced by Survival gene Elimination (DISE) mechanism in PCa cells, that we have termed Androgen Network (AN)-DISE, and suggest a novel therapeutic strategy for PCa.

cancer biology↗

bioRxiv2020

Predicting primer and panel off-target rate in QIAseq targeted DNA panels using convolutional neural networks

In QIAseq targeted DNA panels, synthetic primers (short single-strand DNA sequences) are used for target enrichment via complementary DNA binding. Off-target priming could occur in this process when a primer binds to some loci where the DNA sequences are identical or very similar to the target template. These off-target DNA segments go through the rest of the workflow, wasting sequencing resources in unwanted regions. Off-target cannot be avoided if some segments of the target region are repetitive throughout the genome, nor can it be quantified until after sequencing. But if off-target rates can be prospectively predicted, scientists can make informed decisions about investment on high off-target panels. We developed pordle (predicting off-target rate with deep learning and epcr07), a convolutional neural network (CNN) model to predict off-target binding events of a given primer. The neural network was trained using 10 QIAseq DNA panels with 29,274 unique primers and then tested on an independent QIAseq panel with 7,576 primers. The model predicted a 10.5% off-target rate for the test panel, a -0.1% bias from the true value of 10.6%. The model successfully selected the better primer (in terms of off-target rate) for 89.2% of 3,835 pairs of close-by primers in the test panel whose off-target rates differ by at least 10%. The order-preserving property may help panel developers select the optimal primer from a group of candidates, which is a common task in panel design.

bioinformatics↗

bioRxiv2020

Accurate recognition of colorectal cancer with semi-supervised deep learning on pathological images

BackgroundMachine-assisted recognition of colorectal cancer (CRC) has been mainly focused on supervised deep learning that suffers from a significant bottleneck of requiring massive labeled data. We hypothesize that semi-supervised deep learning leveraging a small amount of labeled data with abundant available unlabeled data can provide a powerful alternative strategy. MethodWe proposed a semi-supervised model based on the mean teacher architecture that provides pathological predictions at both patch- and patient-levels. We demonstrated the general utility of the model utilizing 13,111 CRC whole slide images from 8,803 subjects gathered from 13 independent centers. We compared our proposed method with the prevailing supervised learning and six pathologists. Two extended evaluations on 15,000 lung and 294,912 lymph node images were also performed to confirm the generality of utility of semi-supervised learning for different cancers. ResultsWith a small amount of labeled training patches ([~]3,150 labeled, [~]40,950 unlabeled or [~]6,300 labeled, [~]37,800 unlabeled), the semi-supervised learning (SSL) performed significantly better than the supervised learning (SL, which only used the labeled data) (area under the curve, AUC: 0.90 {+/-} 0.06 vs. 0.84 {+/-} 0.07, P value = 0.02 or AUC: 0.98 {+/-} 0.01 vs. 0.92 {+/-} 0.04, P value = 0.0004). Moreover, we found no significant difference between SL using massive [~]44,100 labeled patches and SSL ([~]6,300 labeled, [~]37,800 unlabeled) at patch-level diagnoses (AUC:0.98 {+/-} 0.01 vs. 0.987 {+/-} 0.01, P value = 0.134) and patient-level diagnoses (average AUC: 0.974 vs. 0.980, P value = 0.117). SSL was close to human pathologists in diagnosis performance (average AUC: 0.972 vs. 0.969). This extended evaluation on lung and lymph node also confirmed when a small amount of labeled data were used, SSL was better than SL, and achieved similar performance as that of SL with massive labeling. ConclusionsWe reported that SSL can achieve excellent performance through a multi-center study. Because SSL dramatically reduces the need and cost of pathological image annotation, it has great potential to effectively build pathological artificial intelligence (AI) platforms in practice.

pathology↗

bioRxiv2020

Conformational dynamics of SARS-CoV-2 trimeric spike glycoprotein in complex with receptor ACE2 revealed by cryo-EM

The recent outbreaks of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) and its rapid international spread pose a global health emergency. The trimeric spike (S) glycoprotein interacts with its receptor human ACE2 to mediate viral entry into host-cells. Here we present cryo-EM structures of an uncharacterized tightly closed SARS-CoV-2 S-trimer and the ACE2-bound-S-trimer at 2.7-Å and 3.8-Å-resolution, respectively. The tightly closed S-trimer with inactivated fusion peptide may represent the ground prefusion state. ACE2 binding to the up receptor-binding domain (RBD) within S-trimer triggers continuous swing-motions of ACE2-RBD, resulting in conformational dynamics of S1 subunits. Noteworthy, SARS-CoV-2 S-trimer appears much more sensitive to ACE2-receptor than SARS-CoV S-trimer in terms of receptor-triggered transformation from the closed prefusion state to the fusion-prone open state, potentially contributing to the superior infectivity of SARS-CoV-2. We defined the RBD T470-T478 loop and residue Y505 as viral determinants for specific recognition of SARS-CoV-2 RBD by ACE2, and provided structural basis of the spike D614G-mutation induced enhanced infectivity. Our findings offer a thorough picture on the mechanism of ACE2-induced conformational transitions of S-trimer from ground prefusion state towards postfusion state, thereby providing important information for development of vaccines and therapeutics aimed to block receptor binding.Competing Interest StatementThe authors have declared no competing interest.View Full Text

molecular biology↗

bioRxiv2020

Structural basis for distinct inflammasome complex assembly by human NLRP1 and CARD8

Nod-like receptor (NLR) proteins activate pyroptotic cell death and IL-1 driven inflammation by assembling and activating the inflammasome complex. Closely related NLR proteins, NLRP1 and CARD8 undergo unique auto-proteolysis-dependent activation and are implicated in auto-inflammatory diseases; however, the molecular mechanisms of activation are not understood. Here we report the structural basis of how the activating domains (FIINDUPA-CARD) of NLRP1 and CARD8 self-oligomerize to trigger the assembly of distinct inflammasome complexes. Recombinant FIINDUPA-CARD of NLRP1 forms a two-layered filament, with an inner core composed of oligomerized CARD domains and the outer layer consisting of FIINDUPA rings. Biochemically, oligomerized NLRP1-CARD is sufficient to drive ASC speck formation in cultured human cells via filament formation-a process that is greatly enhanced by NLRP1-FIINDUPA, which forms ring-like oligomers in vitro. In addition, we report the cryo-EM structures of NLRP1-CARD and CARD8-CARD filaments at 3.7 [A], which uncovers unique structural features that enable NLRP1 and CARD8 to discriminate between ASC and pro-caspase-1. In summary, our findings provide unique structural insight into the mechanisms of activation for human NLRP1 and CARD8, uncovering an unexpected level of specificity in inflammasome signaling mediated by heterotypic CARD domain interactions.

immunology↗

bioRxiv2020

Latrophilin-2 repels Teneurin-3+ hippocampal axons during target selection

Parallel information processing is a salient feature of complex nervous systems. For example, the medial and lateral hippocampal networks (MHN and LHN) preferentially process spatial- and object-related information, respectively. However, the mechanisms underlying parallel network assembly during development remain largely unknown. Here, we show that complementary expression of cell-surface molecules Teneurin-3 (Ten3) and Latrophilin-2 (Lphn2) in the MHN and LHN, respectively, guides the precise assembly of both the MHN and LHN. Viral-genetic perturbations in vivo demonstrate that Ten3+ axons are repelled by target-derived Lphn2, revealing that Lphn2/Ten3-mediated repulsion and Ten3/Ten3-mediated attraction cooperate to control precise target selection of MHN axons. In the LHN, Lphn2+ axons are confined to Lphn2+ targets via repulsion from Ten3+ targets. Our findings demonstrate that assembly of parallel hippocampal networks follows a Ten3[->]Ten3, Lphn2[->]Lphn2 rule instructed by reciprocal repulsions.

neuroscience↗

bioRxiv2020

Cryo-EM Structure of Native Human Uromodulin, a Zona Pellucida Module Polymer

Assembly of extracellular filaments and matrices mediating fundamental biological processes such as morphogenesis, hearing, fertilization and antibacterial defense is driven by a ubiquitous polymerization module known as zona pellucida (ZP) "domain". Despite the conservation of this element from hydra to human, no information is available on the filamentous conformation of any ZP module protein. Here we report the cryo-electron microscopy structure of uromodulin (UMOD)/Tamm-Horsfall protein, the most abundant protein in human urine and an archetypal ZP module-containing molecule, in its mature homopolymeric state. UMOD forms a one-start helix with an unprecedented 180-degree twist between subunits enfolded by interdomain linkers that have completely reorganized as a result of propeptide dissociation. Lateral interaction between filaments in the urine generates sheets exposing a checkerboard of binding sites to capture uropathogenic bacteria, and UMOD-based models of mammalian and avian heteromeric egg coat filaments identify a common sperm-binding region at the interface between subunits.

biophysics↗

bioRxiv2020

Conjugate graph resolves the complex structures at the virus integration loci with NGS data

Oncovirus integrations cause copy number variations (CNVs) and complex structural variations (SVs) on host genomes. However, the understanding of how inserted viral DNA impacts the local genome remains limited. The linear structure of the oncovirus integrated local genomic map (LGM) will lay the foundations to understand how oncovirus integrations emerge and compromise the host genome s functioning. We propose a conjugate graph model to reconstruct the rearranged local genomic map at integrated loci. Simulation tests prove the reliability and credibility of the algorithm. Applications of the algorithm to whole-genome sequencing data of Human papillomavirus (HPV) and hepatitis B virus (HBV)-infected cancer samples gained biological insights on oncovirus integrations. We observed five affection patterns of oncovirus integrations from the HPV and HBV-integrated cancer samples, including the exon loss, promoter gain, hyper-amplification of tumor gene, the viral cis-regulation inserted at the single intron and at the intergenic region. We found that the focal duplicates and host SVs are frequent in the HPV-integrated LGMs, while the focal deletions and complex virus SVs are prevalent in HBV-integrated LGMs. Furthermore, with the results yields from our method, we found the enhanced microhomology-mediated end joining (MMEJ) might lead to both HPV and HBV integrations, and conjectured that the HPV integrations might mainly occur during the DNA replication process. The conjugate graph algorithm code and LGM construction pipeline, available at https://github.com/deepomicslab/FuseSV. Key pointsO_LIConjugate graph model resolves complex local genomic strcuture at oncovirus integration loci. C_LIO_LILocal genome maps reveal five affection patterns of oncovirus integrations. C_LIO_LIMicrohomology bases and small insertions are enriched at the junctions of structural variations and virus integrations. C_LIO_LIHPV and HBV integrations may be induced by the enhanced microhomology-meditated mechanism. C_LI

bioinformatics↗

bioRxiv2020

Permeability of the HIV-1 capsid to metabolites modulates viral DNA synthesis

Reverse transcription, an essential event in the HIV-1 lifecycle, requires deoxynucleotide triphosphates (dNTPs) to fuel DNA synthesis, thus requiring penetration of dNTPs into the viral core. The central cavity of the capsid protein (CA) hexamer reveals itself as a plausible channel that allows the passage of dNTPs into assembled capsids. Nevertheless, the molecular mechanism of nucleotide import into the capsid remains unknown. Employing all-atom molecular dynamics simulations, we established that cooperative binding between nucleotides inside a CA hexamer cavity results in energetically-favorable conditions for passive translocation of dNTPs into the HIV-1 capsid. Furthermore, binding of the host cell metabolite inositol hexakisphosphate (IP6) enhances dNTP import, while binding of synthesized molecules like benzenehexacarboxylic acid (BHC) inhibits it. The enhancing effect on reverse transcription by IP6 and the consequences of interactions between CA and nucleotides were corroborated using atomic force microscopy, transmission electron microscopy, and virological assays. Collectively, our results provide an atomistic description of the permeability of the HIV-1 capsid to small molecules and reveal a novel mechanism for the involvement of metabolites in HIV-1 capsid stabilization, nucleotide import and reverse transcription.

biophysics↗

bioRxiv2020

Cryo-EM of mammalian PA28αβ-iCPimmunoproteasome reveals a distinct mechanism of proteasome activation by PA28αβ

The proteasome activator PA28{beta} affects MHC class-I antigen presentation by associating with immunoproteasome core particles (iCPs). However, due to the lack of a mammalian PA28{beta}-iCP structure, how PA28{beta} regulates proteasome remains elusive. Here we present the complete architectures of the mammalian PA28{beta}-iCP immunoproteasome and free iCP at near atomic-resolution by cryo-EM, and determined the spatial arrangement between PA28{beta} and iCP through XL-MS. Our structures revealed a slight leaning of PA28{beta} towards the 3-4 side of iCP, disturbing the allosteric network of the gate-keeper 2/3/4 subunits, resulting in a partial open iCP gate. We found that the binding and activation mechanism of iCP by PA28{beta} is distinct from those of constitutive CP by the homoheptameric TbPA26 or PfPA28. Our study sheds lights on the mechanism of enzymatic activity stimulation of immunoproteasome and suggests that PA28{beta}-iCP has experienced profound remodeling during evolution to achieve its current level of function in immune response.

biophysics↗

bioRxiv2020

Ratio of the interferon-γ signature to the immunosuppression signature predicts anti-PD-1 therapy response in melanoma

Immune checkpoint inhibitor (ICI) treatments produce clinical benefit in many patients. However, better pretreatment predictive biomarkers for ICI are still needed to help match individual patients to the treatment most likely to be of benefit. Existing gene expression profiling (GEP)-based biomarkers for ICI are primarily focused on measuring a T cell-inflamed tumour microenvironment that contributes positively to the response to ICI. Here, we identified an immunosuppression signature (IMS) through analysing RNA sequencing data from a combined discovery cohort (n = 120) consisting of three publicly available melanoma datasets. Using the ratio of an established IFN-{gamma} signature and IMS led to consistently better prediction of the ICI therapy outcome compared to a collection of nine published GEP signatures from the literature on a newly generated internal validation cohort (n = 55) and three published datasets of metastatic melanoma treated with anti-PD-1 (n = 48) and anti-CTLA-4 (n = 42) as well as in patients with gastric cancer treated with antiPD-1 (n = 45), demonstrating the potential utility of IMS as a predictive/prognostic biomarker that complements existing GEP signatures for immunotherapy.

immunology↗

bioRxiv2020

Pod indehiscence in common bean is associated to the fine regulation of PvMYB26 and a non-functional abscission layer

In legumes, pod shattering occurs when mature pods dehisce along the sutures, and detachment of the valves promotes seed dispersal. In Phaseolus vulgaris, the major locus qPD5.1-Pv for pod indehiscence was identified recently. We developed a BC4/F4 introgression line population and narrowed the major locus down to a 22.5-kb region. Here, gene expression and a parallel histological analysis of dehiscent and indehiscent pods identified an AtMYB26 orthologue as the best candidate for loss of pod shattering, on a genomic region ~11 kb downstream of the highest associated peak. Based on mapping and expression data, we propose early and fine up-regulation of PvMYB26 in dehiscent pods. Detailed histological analysis establishes that pod indehiscence is associated to the lack of a functional abscission layer in the ventral sheath, and that the key anatomical modifications associated with pod shattering in common bean occur early during pod development. We finally propose that loss of pod shattering in legumes resulted from histological convergent evolution and that this is the result of selection at orthologous loci. One-sentence summaryA non-functional abscission layer determines the loss of pod shattering; mapping data, and parallel gene expression and histological analysis support PvMYB26 as the candidate gene for pod indehiscence.

genetics↗

bioRxiv2020

AutoOmics: An AutoML Tool for Multi-Omics Research

Deep learning is very promising in solving problems in omics research, such as genomics, epigenomics, proteomics, and metabolics. The design of neural network architecture is very important in modeling omics data against different scientific problems. Residual fully-connected neural network (RFCN) was proposed to provide better neural network architectures for modeling omics data. The next challenge for omics research is how to integrate informations from different omics data using deep learning, so that information from different molecular system levels could be combined to predict the target. In this paper, we present a novel multimodal approach that could efficiently integrate information from different omics data and achieve better accuracy than previous approaches. We evaluate our method in four different tasks: drug repositioning, target gene prediction, breast cancer subtyping and cancer type prediction, and all the four tasks achieved state of art performances. The multimodal approach is implemented in AutoGenome V2 and is also powered with all the previous AutoML convenience to facilitate biomedical researchers.

bioinformatics↗

bioRxiv2020

Reusable and Sensitive Detection Method for Exonuclease III Activity by DNB Nanoarrays Based on cPAS Sequencing Technology

AbstractionIn this article, we have designed a sensitive and recycled DNB (DNA nanoball) nanoarrays sequencing complex structures based on BGISEQ-500RS sequencer for the monitoring performance of Exo III activity. In the shortage of Exo III, the effective number ratio of DNB would be captured by an optical system due to one fluorescent. In contrast, in the presence of Exo III, some DNB would disappear or discard from the fields of the optical system by fluorescence extinction and uncleaned fluorescent, respectively. As a result, the effective number of DNB of this strategy was relative to the concentration of Exo III. For Exo III, our strategy showed a highly sensitive linear response in the low detection range of 0.01 U/mL to 0.5 U/mL, with detection limits below 0.01 U/mL. With the comparison between DNB nanoarrays and other fluorescent sensors, this study possessed superior sensitivity, selectivity, and reusability, accompanying with the low cost and simple setup.

biochemistry↗

bioRxiv2020

Accurate Diagnosis of Colorectal Cancer Based on Histopathology Images Using Artificial Intelligence

BackgroundAccurate and robust pathological image analysis for colorectal cancer (CRC) diagnosis is time-consuming and knowledge-intensive, but is essential for CRC patients treatment. The current heavy workload of pathologists in clinics/hospitals may easily lead to unconscious misdiagnosis of CRC based on their daily image analyses. MethodsBased on a state-of-the-art transfer-learned deep convolutional neural network in artificial intelligence (AI), we proposed a novel patch aggregation strategy for clinic CRC prediction/diagnosis using weakly labeled pathological whole slide image (WSI) patches. This approach was trained and validated using an unprecedented and enormously large number of 170,099 patches, >14,680 WSIs, from >9,631 subjects that covered diverse and representative clinical cases from multi-independent-sources across China, U.S., and Germany. ResultsOur innovative AI tool was consistently nearly perfectly agreed with (average Kappa-statistic 0.896) and even often better than most of the experienced expert pathologists when tested in diagnosing CRC WSIs from multi-centers. The average area under the receiver operating characteristics curve (AUC) of AI was greater than that of the pathologists (0.981 vs 0.970) and achieved the best performance among the application of other AI methods to CRC diagnosis. Our AI-generated heatmap highlights the image regions of cancer tissue/cells. ConclusionsThis first-ever generalizable AI system can handle large amounts of WSIs consistently and robustly without potential bias due to fatigue commonly experienced by clinical pathologists. Hence, it will drastically alleviate the heavy clinical burden of daily pathology diagnosis, and improve the treatment for CRC patients. This tool is generalizable to other cancer diagnosis based on image recognition.

pathology↗

bioRxiv2020

Global Landscape of Native Protein Complexes in Synechocystis sp. PCC 6803

Synechocystis sp. PCC 6803 (hereafter: Synechocystis) is a model organism for studying photosynthesis, energy metabolism, and environmental stress. Though known as the first fully sequenced phototrophic organism, Synechocystis still has almost half of its proteome without functional annotations. In this study, we obtained 291 protein complexes, including 24,092 protein-protein interactions (PPIs) among 2062 proteins by using co-fractionation and LC/MS/MS. The additional level of PPIs information not only revealed the roles of photosynthesis in metabolism, cell motility, DNA repair, cell division, and other physiological processes, but also showed how protein functions vary from bacteria to higher plants due to the changed interaction partner. It also allows us to uncover functions of hypothetical proteins, such as Sll0445, Sll0446, S110447 participating in photosynthesis and cell motility, and Sll1334 regulating the expression of fatty acid. Here we presented the most extensive protein interaction data in Synechocystis so far, which might provide critical insights into the fundamental molecular mechanism in Cyanobacterium.

biochemistry↗

bioRxiv2020

Migration dynamics of an important rice pest: the brown planthopper (Nilaparvata lugens) across Asia - insights from population genomics

Brown planthoppers (Nilaparvata lugens) are the most serious insect pests of rice, one of the worlds most important staple crops. They reproduce year-round in the tropical parts of their distribution, but cannot overwinter in the temperate areas where they occur, and invade seasonally from elsewhere. Decades of research has not revealed their source unambiguously. We therefore sequenced the genomes of brown planthopper populations from across temperate and tropical parts of their distribution and show that the Indochinese peninsula is the major source of migration into temperate China. The Philippines, once considered a key source, is not significant, with little evidence for their migration into China. We find support for immigration from the west of China contributing to these regional dynamics. The lack of connectivity between the Philippines and mainland China explains the different evolution of Imidacloprid resistance in these populations. This study highlights the promise of whole genome sequence data to understand migration when gene flow is high - a situation that has been difficult to resolve using traditional genetic markers.

genomics↗