Biology subjects

Huang, J.

Publications and source records attributed to Huang, J..

At least 577 records · Page 32Linked to original sources

bioRxiv2020

Identification of sulfenylated cysteines in Arabidopsis thaliana proteins using a disulfide-linked peptide reporter

In proteins, hydrogen peroxide (H2O2) reacts with redox-sensitive cysteines to form cysteine sulfenic acid, also known as S-sulfenylation. These cysteine oxidation events can steer diverse cellular processes by altering protein interactions, trafficking, conformation, and function. Previously, we had identified S-sulfenylated proteins by using a tagged proteinaceous probe based on the yeast AP-1-like (Yap1) transcription factor that specifically reacts with sulfenic acids and traps them through a mixed disulfide bond. However, the identity of the S-sulfenylated amino acid residues remained enigmatic. Here, we present a technological advancement to identify in situ sulfenylated cysteines directly by means of the transgenic Yap1 probe. In Arabidopsis thaliana cells, after an initial affinity purification and a tryptic digestion, we further enriched the mixed disulfide-linked peptides with an antibody targeting the YAP1C-derived peptide (C598SEIWDR) that entails the redox-active cysteine. Subsequent mass spectrometry analysis with pLink 2 identified 1,745 YAP1C cross-linked peptides, indicating sulfenylated cysteines in over 1,000 proteins. Approximately 55% of these YAP1C-linked cysteines had previously been reported as redox-sensitive cysteines (S-sulfenylation, S-nitrosylation, and reversibly oxidized cysteines). The presented methodology provides a noninvasive approach to identify sulfenylated cysteines in any species that can be genetically modified.

plant biology↗

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

Identification of Interleukin1β as an Amplifier of Interferon alpha-induced Antiviral Responses

The induction of an interferon-mediated response is the first line of defense against pathogens such as viruses. Yet, the dynamics and extent of interferon alpha (IFN)-induced antiviral genes vary remarkably and comprise three expression clusters: early, intermediate and late. By mathematical modeling based on time-resolved quantitative data, we identified mRNA stability as well as a negative regulatory loop as key mechanisms endogenously controlling the expression dynamics of IFN-induced antiviral genes in hepatocytes. Guided by the mathematical model, we uncovered that this regulatory loop is mediated by the transcription factor IRF2 and showed that knock-down of IRF2 results in enhanced expression of early, intermediate and late IFN-induced antiviral genes. Co-stimulation experiments with different pro-inflammatory cytokines revealed that this amplified expression dynamics of the early, intermediate and late IFN-induced antiviral genes can be mimicked by co-application of IFN and interleukin1 beta (IL1{beta}). Consistently, we found that IL1{beta} enhances IFN-mediated repression of viral replication. Conversely, we observed that in IL1{beta} receptor knock-out mice replication of viruses sensitive to IFN is increased. Thus, IL1{beta} is capable to potentiate IFN-induced antiviral responses and could be exploited to improve antiviral therapies. Author SummaryInnate immune responses contribute to the control of viral infections and the induction of interferon alpha (IFN)-mediated antiviral responses is an important component. However, IFN induces a multitude of antiviral response genes and the expression dynamics of these genes can be classified as early, intermediate and late. Here we show, based on a mathematical modeling approach, that mRNA stability as well as the negative regulator IRF2 control the expression dynamics of IFN-induced antiviral genes. Knock-down of IRF2 resulted in the amplified IFN-mediated induction of the antiviral genes and this amplified expression of antiviral genes could be mimicked by co-stimulation with IFN and IL1{beta}. We observed that co-stimulation with IFN and IL1{beta} enhanced the repression of virus replication and that knock-out of the IL1 receptor in mice resulted in increased replication of a virus sensitive to IFN. In sum, our studies identified IL1{beta} as an important amplifier of IFN-induced antiviral responses.

systems biology↗

bioRxiv2020

Molecular Graph Enhanced Transformer for Retrosynthesis Prediction

With massive possible synthetic routes in chemistry, retrosynthesis prediction is still a challenge for researchers. Recently, retrosynthesis prediction is formulated as a Machine Translation (MT) task. Namely, since each molecule can be represented as a Simplified Molecular-Input Line-Entry System (SMILES) string, the process of retrosynthesis is analogized to a process of language translation from the product to reactants. However, the MT models that applied on SMILES data usually ignore the information of natural atomic connections and the topology of molecules. To make more chemically plausible constrains on the atom representation learning for better performance, in this paper, we propose a Graph Enhanced Transformer (GET) framework, which adopts both the sequential and graphical information of molecules. Four different GET designs are proposed, which fuse the SMILES representations with atom embeddings learned from our improved Graph Neural Network (GNN). Empirical results show that our model significantly outperforms the vanilla Transformer model in test accuracy.

bioinformatics↗

bioRxiv2020

OGP: A Repository of Experimentally Characterized O-Glycoproteins to Facilitate Studies on O-Glycosylation

Numerous studies on cancer, biopharmaceuticals, and clinical trials have necessitated comprehensive and precise analysis of protein O-glycosylation. However, the lack of updated and convenient databases deters the storage and utilization of emerging O-glycoprotein data. To resolve this issue, an O-glycoprotein repository named OGP was established in this work. It was constructed with a collection of O-glycoprotein data from different sources. OGP contains 9354 O-glycosylation sites and 11,633 site-specific O-glycans mapping to 2133 O-glycoproteins, and it is the largest O-glycoprotein repository thus far. Based on the recorded O-glycosites, an O-glycosylation site prediction tool was developed. Moreover, an OGP-backed website is already available (http://www.oglyp.org/). The website comprises four specially designed and user-friendly modules: Statistic Analysis, Database Search, Site Prediction, and Data Submit. The first version of OGP repository and the website allow users to obtain vast O-glycoprotein related information, such as protein accession numbers, glycopeptides, site-specific glycan structures, experimental methods, and potential glycosylation sites. O-glycosylation data mining can be performed efficiently on this website, which can greatly facilitates O-glycosylation studies.

bioinformatics↗

bioRxiv2020

Expanding the Coverage of the Metabolic Landscape in Cultivated Rice with Integrated Computational Approaches

Genome-scale metabolomics analysis is increasingly used for pathway and function discovery in post-genomics era. The great potential offered by developed mass spectrometry (MS)-based technology has been hindered by the obstacle that only a small portion of detected metabolites were identifiable so far. To address the critical issue of low identification coverage in metabolomics, we adopted a deep metabolomics analysis strategy by integrating advanced algorithms and expanded reference databases. The experimental reference spectra, and in silico reference spectra were adopted to facilitate the structural annotation. To further characterize the structure of metabolites, two approaches, structural motif search combined with neutral loss scanning, and metabolite association network were incorporated into our strategy. An untargeted metabolomics analysis was performed on 150 rice cultivars using Ultra Performance Liquid Chromatography (UPLC)-Quadrupole (Q)-Orbitrap mass spectrometer. 1939 of 4491 metabolite features in MS/MS spectral tag (MS2T) library were annotated, representing an extension of annotation coverage by an order of magnitude on rice. The differential accumulation patterns of flavonoids between indica and japonica cultivars were revealed, especially O-sulfated flavonoids. A series of closely-related flavonolignans were characterized, adding further evidence for the crucial role of tricin-oligolignols in lignification. Our study provides a great template in the exploration of phytochemical diversity for more plant species.

bioinformatics↗

bioRxiv2020

A transferrable and interpretable multiple instance learning model for microsatellite instability prediction based on histopathology images

BackgroundMicrosatellite instability (MSI) is a negative prognostic factor for colorectal cancer (CRC) and can be used as a predictor of success for immunotherapy in pan-cancer. However, current MSI identification methods are not available for all patients. We propose an ensemble multiple instance learning (MIL)-based deep learning model to predict MSI status directly from histopathology images. DesignTwo cohorts of patients were collected, including 429 from The Cancer Genome Atlas (TCGA-COAD) and 785 from a self-collected Asian data set (Asian-CRC). The initial model was developed and validated in TCGA-COAD, and then generalized in Asian-CRC through transfer learning. The pathological signatures extracted from the model are associated with genotypes for model interpretation. ResultsA model called Ensembled Patch Likelihood Aggregation (EPLA) was developed in the TCGA-COAD training set based on two consecutive stages: patch-level prediction and WSI-level prediction. The EPLA model achieved an area-under-the -curve (AUC) of 0.8848 in the TCGA-COAD test set, which outperformed the state-of-the-art approach, and an AUC of 0.8504 in the Asian-CRC after transfer learning. Furthermore, the five pathological imaging signatures identified using the model are associated with genomic and transcriptomic profiles, which makes the MIL model interpretable. Results show that our model recognizes pathological signatures related to mutation burden, DNA repair pathways, and immunity. ConclusionOur MIL-based deep learning model can effectively predict MSI from histopathology images and are transferable to a new patient cohort. The interpretability of our model by association with genomic and transcriptomic biomarkers lays the foundation for prospective clinical research.

bioengineering↗

bioRxiv2020

Role of RgsA in oxidative stress resistance in Pseudomonas aeruginosa

Pseudomonas aeruginosa is an extremely common opportunistic pathogen in clinical practice. Patients with metabolic disorders, hematologic diseases, malignancies, who have undergone surgery or who have received certain treatments are susceptible to this bacterium. In addition, P. aeruginosa is a multidrug-resistant that tends to form biofilms and is refractory to treatment. Small regulatory RNAs are RNA molecules that are 40-500 nucleotides long, possess regulatory function, are ubiquitous in bacteria, and are also known as small RNA (sRNA). sRNAs play important regulatory roles in various vital life processes in diverse bacteria and their quantity and diversity of regulatory functions exceeds that of proteins. In this study, we showed that deletion of the sRNA RgsA decreases the growth rate and ability to resist different concentrations and durations of peroxide in P. aeruginosa. These decreases occur not only in the planktonic state, but also in the biofilm state. Finally, protein mass spectrometry was employed to understand changes in the entire protein spectrum. The results presented herein provide a description of the role of RgsA in the life activities of P. aeruginosa at the molecular, phenotypic, and protein levels.

microbiology↗

bioRxiv2020

Modulation of metabolic functions through Cas13d-mediated gene knockdown in liver

RNA knockdown in vivo carries significant potential for disease modelings and therapies. Despite the emerging approaches of CRISPR/Cas9-mediated permanent knock out of targeted genes, strategies targeting RNA for disruption are advantageous in the treatment of acquired metabolic disorders when permanent modification of the genome DNA is not appropriate, and RNA virus infection diseases when pathogenic DNA is not available (such as SARS-Cov-2 and MERS infections). Recently, Cas13d, a family of RNA-targeting CRISPR effectors, has been shown to accomplish robust down-regulation of cellular RNAs in mammalian cells in vitro. Among the various Cas13d subtypes, CasRx (RfxCas13d) showed the most potent RNA knockdown efficiency in HEK293T cells. However, the RNA-targeting activity of Cas13d still needs to be verified in vivo. In this study, the CasRx system was demonstrated to efficiently and functionally knock down genes related to metabolism functions, including Pten, Pcsk9 and lncLstr, in mouse hepatocytes. CasRx-mediated simultaneous knockdown of multiple genes was also achieved by sgRNA arrays, providing a useful strategy to modulate complex metabolism networks. Moreover, the AAV (adeno-associated virus)-mediated delivery of CasRx and Pcsk9 sgRNAs into mouse liver successfully decreased serum PCSK9, resulting in significant reduction of serum cholesterol levels. Importantly, CasRx-mediated knockdown of Pcsk9 is reversible and Pcsk9 could be repeatedly down-regulated, providing an effective strategy to reversibly modulate metabolic genes. The present work supplies a successful proof-of-concept trial that suggests efficient and regulatory knockdown of target metabolic genes for a designed metabolism modulation in the liver.

genomics↗

bioRxiv2020

The CEPR2-mediated phosphorylation of NRT1.2 mainly regulates ABA import in Arabidopsis

Abscisic acid (ABA) transport plays important role in systematic plant responses to environmental factors. Here, we showed that C-terminally encoded peptide receptor 2 (CEPR2) directly interacted with the ABA transporter NRT1.2. Using transgenic seedlings, we demonstrated that NRT1.2 positively regulated the ABA response, and that CEPR2 acted on NRT1.2 epistatically and negatively. Under normal conditions, CEPR2 phosphorylated NRT1.2 at least at serine 292 to promote the degradation of NRT1.2. However, ABA and serine 292 nonphospharylation strongly inhibited the degradation of NRT1.2, indicating that ABA-inhibited the phosphorylation of NRT1.2. Transport assays in yeast and Xenopus oocytes showed that nonphosphorylated NRT1.2 had high levels of ABA-import activity, but phosphorylated NRT1.2 did not import ABA. Analyses of complement nrt1.2 mutants by mimicking nonphospharylated and phospharylated NRT1.2 confirmed that nonphospharylated NRT1.2S292A had high stability and ABA import activity in planta. Further experiments indicated that NRT1.2 was degraded via the 26S proteasome and vacuolar degradation pathways. UBC32, UBC33, and UBC34 interacted with, and mediated the ubiquitination of NRT1.2. UBC32, UBC33, and UBC34 acted on NRT1.2 epistatically and negatively in planta. Thus, our results suggested the existence of novel plant mechanisms regulating NRT1.2 stability and ABA import activity in response to environmental conditions.

molecular biology↗

bioRxiv2020

Transcriptomic and Proteomic Analyses of a New Cytoplasmic Male Sterile Line with wild Gossypium bickii. Genetic Background

Cotton is an important fiber crop but has serious effects of heterosis, in which cytoplasmic male sterility (CMS) being the major cause of heterosis in plants. However, there are no studies done on CMS Yamian A in cotton with the genetic background of the Australian wild Gossypium bickii. Transcriptomic and proteomic results showed that UDP-glucosyltransferase - in the nucleus, 60S ribosomal protein L13a- in the cytoplasm, ribulose-1,5-bisphosphate carboxylase/oxygenase - in the chloroplast, glutathione S-transferase - in the cytoplasm, and ATP synthase F1 subunit 1 - in the mitochondrion were upregulated; while low molecular weight heat shock protein - in the chloroplast and ATP synthase D chain- in the mitochondrion were down-regulated expression at the microspore abortion stage of Yamian A. We constructed an interaction network and this study provides a comprehensive understanding of the mechanism of CMS in cotton by use of in Yamian A, with wild cotton genetic background.

plant biology↗

bioRxiv2020

Breaking down of the healthcare system: Mathematical modelling for controlling the novel coronavirus (2019-nCoV) outbreak in Wuhan, China

A novel coronavirus pneumonia initially identified in Wuhan, China and provisionally named 2019-nCoV has surged in the public. In anticipation of substantial burdens on healthcare system following this human-to-human spread, we aim to scrutinise the currently available information and evaluate the burden of healthcare systems during this outbreak in Wuhan. We applied a modified SIR model to project the actual number of infected cases and the specific burdens on isolation wards and intensive care units, given the scenarios of different diagnosis rates as well as different public health intervention efficacy. Our estimates suggest the actual number of infected cases could be much higher than the reported, with estimated 26,701 cases (as of 28th January 2020) assuming 50% diagnosis rate if no public health interventions were implemented. The estimated burdens on healthcare system could be largely reduced if at least 70% efficacy of public health intervention is achieved.

microbiology↗

bioRxiv2020

Evolutionary forces on A-to-I RNA editing revealed by sequencing individual honeybee drones

Social insects exhibit extensive phenotypic diversities among the genetically similar individuals, suggesting a role for the epigenetic regulations beyond the genome level. The ADAR-mediated adenosine-to-inosine (A-to-I) RNA editing, an evolutionarily conserved mechanism, facilitates adaptive evolution by expanding proteomic diversities. Here, we characterize the A-to-I RNA editome of honeybees (Apis mellifera), identifying 407 high-confidence A-to-I editing sites. Editing is most abundant in the heads, and shows signatures for positive selection. Editing behavior differs between foragers and nurses, suggesting a role for editing in caste differentiation. Although only five sites are conserved between bees and flies, an unexpectedly large number of genes exhibit editing in both species, albeit at different locations, including the nonsynonymous auto-editing of Adar. This convergent evolution, where the same target genes independently acquire recoding events in distant diverged clades, together with the signals of adaptation observed in honeybees alone, further supports the notion of recoding being adaptive.

evolutionary biology↗

bioRxiv2020

The draft genome of the endangered, relictual plant Kingdonia uniflora (Circaeasteraceae, Ranunculales) reveals potential mechanisms and perils of evolutionary specialization

Kingdonia uniflora, an alpine herb, has an extremely narrow distribution and represents a model for studying evolutionary mechanisms of species that have adapted to undisturbed environments for evolutionary long periods of time. We assembled a 1,004.7-Mb draft genome (encoding 43,301 genes) and investigated the evolutionary history of K. uniflora, along with mechanisms related to its endangered status. Phylogenomic analyses based on 497 single copy genes confirmed the sister relationship between K. uniflora and Circaeaster agrestis, which were estimated to have diverged around 52 Mya. Proliferation of LTR retrotransposons in K. uniflora is estimated to occur around 2.7 Mya, coinciding with one recent uplift of the Hengduan Mountains between the late Miocene and late Pliocene. Across 12 species of monocots, early-diverging eudicots and core eudicots, K. uniflora showed significant overrepresentation in gene families associated with DNA repair and underrepresentation in gene families associated with stress response. Most of the plastid ndh genes were found to be lost not only in the plastome but also in the nuclear genome of K. uniflora. During the evolutionary process, the overrepresentation of gene families involved in DNA repair could help asexual K. uniflora reduce the accumulation of deleterious mutations, while at the same time, reducing genetic diversity which is important in responding to environment fluctuations. The underrepresentation of gene families related to stress response and functional loss of ndh genes could be due to lack or loss of ability to respond to environmental changes caused by long-term adaptation to a relatively stable ecological environment.

genomics↗

bioRxiv2020

Transcriptional elongation machinery controls vulnerability of breast cancer cells to PRC2 inhibitors

CTR9 is the scaffold subunit in Paf1c, a multifunctional complex regulating multiple steps of RNA Pol II-mediated transcription. Using inducible and stable CTR9 knockdown breast cancer cell lines, we discovered that the expression of a subset of KDMs, including KDM6A and Jarid2, is strictly controlled by CTR9. Global analyses of histone modifications revealed a significant increase of H3K27me3 upon loss of CTR9. Loss of CTR9 results in a decrease of H3K4me3 and H3K36me3 in gene bodies, and elevated levels and genome-wide expansion of H3K27me3. Mechanistically, CTR9 depletion triggers a PRC2 subtype switching from PRC2.2 to PRC2.1. As a consequence, CTR9 depletion generates vulnerability that renders breast cancer cells hypersensitive to PRC2 inhibitors. Our findings that CTR9 demarcates PRC2-mediated H3K27me3 levels and genomic distribution, provide a unique mechanism of transition from transcriptionally active to repressive chromatin states and sheds light on the biological functions of CTR9 in development and cancer.

cancer biology↗

bioRxiv2020

Measuring genetic variation in the multi-ethnic Million Veteran Program (MVP)

The Million Veteran Program (MVP), initiated by the Department of Veterans Affairs (VA), aims to collect consented biosamples from at least one million Veterans. Presently, blood samples have been collected from over 800,000 enrolled participants. The size and diversity of the MVP cohort, as well as the availability of extensive VA electronic health records make it a promising resource for precision medicine. MVP is conducting array-based genotyping to provide genome-wide scan of the entire cohort, in parallel with whole genome sequencing, methylation, and other omics assays. Here, we present the design and performance of MVP 1.0 custom Axiom(R) array, which was designed and developed as a single assay to be used across the multi-ethnic MVP cohort. A unified genetic quality control analysis was developed and conducted on an initial tranche of 485,856 individuals leading to a high-quality dataset of 459,777 unique individuals. 668,418 genetic markers passed quality control and showed high quality genotypes not only on common variants but also on rare variants. We confirmed the substantial ancestral diversity of MVP with nearly 30% non-European individuals, surpassing other large biobanks. We also demonstrated the quality of the MVP dataset by replicating established genetic associations with height in European Americans and African Americans ancestries. This current data set has been made available to approved MVP researchers for genome-wide association studies and other downstream analyses. Further data releases will be available for analysis as recruitment at the VA continues and the cohort expands both in size and diversity.

genetics↗

bioRxiv2020

In-depth 15 H7N9 Human Serum Proteomics Profiling Study

BackgroundHuman infection by avian influenza viruses is characterized by rapid development of acute respiratory distress and severe pneumonia. However, the underlying host response leading to this severe outcome is not well studied. MethodsWe conducted mass spectrometry-based serum proteome profiling on 10 healthy controls and 15 H7N9 infected cases with two time points and carried out statistical and biology functional enrichment analysis. ResultsIn total, we identified 647 proteins, 273 proteins were only found in H7N9 infected cases which might generate from cell leakage/death (apoptosis and/or necrosis) and identified 50 proteins with statistically significant difference between healthy control and H7N9 infected cases from 168 qualified proteins. We also found that M1 and PB2 tightly associated with the hosts HSPA8 (P11142, p=0.0042) which plays an important role in the protein quality control system. ConclusionsH7N9 infection may increase cell programmed/unprogrammed cell death, and we suggested that upregulated extracellular HSPA8 may suppress the H7N9 virion replication via activation amyloid-beta binding network.

molecular biology↗

bioRxiv2020

Module analysis using single-patient differential expression signatures improve the power of association study for Alzheimer's disease

The causal mechanism of Alzheimers disease is extremely complex. It usually requires a huge number of samples to achieve a good statistical power in association studies. In this work, we illustrated a different strategy to identify AD risk genes by clustering AD patients into modules based on their single-patient differential expression signatures. Evaluation suggested that our method could enrich AD patients with common clinical manifestations. Applying it to a cohort of only 310 AD patients, we identified 175 AD risk loci at a strict threshold of empirical p < 0.05 while only two loci were identified using all the AD patients. As an evaluation, we collected 23 AD risk genes reported in a recent large-scale meta-analysis and found that 18 of them were re-discovered by association studies using clustered AD patients, while only three of them were re-discovered using all AD patients. Functional annotation suggested that AD associated genetic variants mainly disturbed neuronal/synaptic function. Our results suggested module analysis, even randomly clustering, helped to enrich AD patients affected by the common risk variants.

genetics↗