Biology subjects

Zhang, Q.

Publications and source records attributed to Zhang, Q..

At least 775 records · Page 43Linked to original sources

bioRxiv2018

Decreased green autofluorescence intensity of lung parenchyma is a potential non-invasive diagnostic biomarker for lung cancer

Early and non-invasive diagnosis is critical for enhancing the survival rates of lung cancer. In current study we determined the green autoflorescence (AF) of the pulmonary parenchyma of lung cancer patients, using 488 nm and 500 - 550 nm as excitation wavelength and emission wavelength, respectively. Our study has suggested that decreased green AF intensity of pulmonary parenchyma may be a potential diagnostic biomarker for lung cancer: First, the green AF intensity of the cancerous tissues is less than 40% of those of distant non-neoplastic tissues and peri-neoplastic tissues; second, the green AF intensity of both the distant non-neoplastic tissues and cancerous tissues of squamous carcinoma is significantly lower than that of adenocarcinoma; third, the AF intensity of the peri-neoplastic tissues of the lung cancer patients is negatively correlated with the stages of lung cancer; and fourth, our study on the AF spectrum of pulmonary parenchyma has suggested that the AF may result from the AF of the keratins or FAD of the pulmonary parenchyma. Collectively, our study has suggested that decreased green AF intensity of pulmonary parenchyma may become a novel biomarker for non-invasive diagnosis of lung cancer. The green AF intensity may also be used to non-invasively differentiate squamous carcinoma and adenocarcinoma, as well as the stages of lung cancer. Moreover, our diagnostic approach for lung cancer may be used for image-guided surgery of lung cancer for lesions that have not been identified by CT.

biophysics↗

bioRxiv2018

Identification of pathogens in culture-negative infective endocarditis with metagenomic analysis

Pathogens identification is critical for the proper diagnosis and precise treatment of infective endocarditis. Although blood and valve cultures are the gold standard for IE pathogens detection, many cases are culture-negative, especially in patients who had received long-term antibiotic treatment, and precise diagnosis has therefore become a major challenge in the clinic. Metagenomic sequencing can provide both information on the pathogenic strain and the antibiotic susceptibility profile of patient samples without culturing, offering a powerful method to deal with culture-negative cases. In this work, we assessed the feasibility of a metagenomic approach to detect the causative pathogens in resected valves from IE patients.\n\nUsing our in-house developed bioinformatics pipeline, we analyzed the sequencing results generated from both next-generation sequencing and Oxford Nanopore Technologies MinION nanopore sequencing for the direct identification of pathogens from the resected valves of seven clinically culture-negative IE patients according to the modified Duke criteria. Moreover, we were able to simultaneously characterize respective antimicrobial resistance features. This provides clinicians with valuable information to diagnose and treat IE patients after valve replacement surgery.

microbiology↗

bioRxiv2018

Improved prediction of chronological age from DNA methylation limits it as a biomarker of ageing

DNA methylation is associated with age. The deviation of age predicted from DNA methylation from actual age has been proposed as a biomarker for ageing. However, a better prediction of chronological age implies less opportunity for biological age. Here we used 13,661 samples (from blood and saliva) in the age range of 2 to 104 years from 14 cohorts measured on Illumina HumanMethylation450/EPIC arrays to perform prediction analyses. We show that increasing the sample size achieves a smaller prediction error and higher correlations in test datasets. We demonstrate that smaller prediction errors provide a limit to how much variation in biological ageing can be captured by methylation and provide evidence that age predictors from small samples are prone to confounding by cell composition. Our predictor shows a similar or better performance in non-blood tissues including saliva, endometrium, breast, liver, adipose and muscle, compared with Horvaths across-tissue age predictor.

bioinformatics↗

bioRxiv2018

Visualizing APP trafficking and processing reveals its association with neuronal membrane cholesterol

The amyloid precursor protein (APP) has been extensively studied because of its association with Alzheimers disease (AD). However, APP distribution across different subcellular membrane compartments and its function in neurons remains unclear. We generated an APP fusion protein with a pH-sensitive green fluorescent protein at its ectodomain and a pH-insensitive blue fluorescent protein at its cytosolic domain and used it to measure APPs distribution, subcellular trafficking and cleavage in live neurons. This reporter, closely resembling endogenous APP, revealed only a limited correlation between synaptic activities and APP trafficking. However, the synaptic surface distribution of APP was inversely correlated to membrane cholesterol levels, a phenomenon that involves APPs cholesterol-binding site. Mutations within this site not only altered surface APP and cholesterol levels in a dominant negative manner, but also increased synaptic vulnerability to moderate membrane cholesterol reduction. Our results reveal reciprocal modulation of APP and membrane cholesterol levels at synaptic boutons.

neuroscience↗

bioRxiv2018

A rational account of human memory search

Performing everyday tasks requires the ability to search through and retrieve past memories. A central paradigm to study human memory search is the semantic fluency task, where participants are asked to retrieve as many items as possible from a category (e.g. animals). Observed responses tend to be clustered semantically. To understand when our mind decides to switch from one cluster/patch to the next, recent work has proposed two competing mechanisms. Under the first switching mechanism, people make strategic decision to switch away from a depleted patch based on marginal value theorem, similar to optimal foraging in a spatial environment. The second switching mechanism demonstrates that similar behavior patterns can emerge using a random walk on a semantic network, without necessarily involving strategic switches. In the current work, instead of comparing competing switching mechanisms over observed human data, we propose a rational account of the problem by examining what would be the optimal patch-switching policy under the framework of reinforcement learning. The reinforcement learning agent, a Deep Q-Network (DQN), is built upon the random walk model and allows strategic switches based on features of the local semantic patch. After learning from rewards, the resulted policy of the agent gives rise to a third switching mechanism, which outperforms the previous two switching mechanisms. Our results provide theoretical justification of strategies used in human memory research, and shed light on how an optimal AI agent under realistic human constraints can generate hypothesis about human strategies in the same task.

animal behavior and cognition↗

bioRxiv2018

Long-read sequencing identified a causal structural variant in an exome-negative case and enabled preimplantation genetic diagnosis

For a proportion of individuals judged clinically to have a recessive Mendelian disease, only one pathogenic variant can be found from clinical whole exome sequencing (WES), posing a challenge to genetic diagnosis and genetic counseling. Here we describe a case study, where WES identified only one pathogenic variant for an individual suspected to have glycogen storage disease type Ia (GSD-Ia), which is an autosomal recessive disease caused by bi-allelic mutations in the G6PC gene. Through Nanopore long-read whole-genome sequencing, we identified a 7kb deletion covering two exons on the other allele, suggesting that complex structural variants (SVs) may explain a fraction of cases when the second pathogenic allele is missing from WES on recessive diseases. Both breakpoints of the deletion are within Alu elements, and we designed Sanger sequencing and quantitative PCR assays based on the breakpoints for preimplantation genetic diagnosis (PGD) for the family planning on another child. Four embryos were obtained after in vitro fertilization (IVF), and an embryo without deletion in G6PC was transplanted after PGD and was confirmed by prenatal diagnosis, postnatal diagnosis, and subsequent lack of disease symptoms after birth. In summary, we present one of the first examples of using long-read sequencing to identify causal yet complex SVs in exome-negative patients, which subsequently enabled successful personalized PGD.

genetics↗

bioRxiv2018

Quantification of Enterohemorrhagic Escherichia coli O157:H7 proteome using TMT-Based Analysis

Enterohemorrhagic Escherichia coli (EHEC) O157:H7 is a human pathogen responsible for diarrhea, hemorrhagic colitis and hemolytic uremic syndrome (HUS). EHEC infection is distributed worldwide and numerous outbreaks of diseases caused by enterohemorrhagic have been reported. To promote a comprehensive insight into the molecular basis of EHEC O157:H7 physiology and pathogenesis, the combined proteome of EHEC O157:H7 strains, Clade 8 and Clade 6 isolated from cattle in Argentina, and the standard EDL933 (clade 3) strain has been analyzed. TMT (Tandem Mass Tags)-based quantitative proteomic and emPAI analyses were performed to estimate the protein abundance in EHEC proteome. 2,234 non-redundant proteins of EHEC O157:H7 were identified. A comparison of this result with in silico data of EHEC O157:H7 genome showed that approximately 40% of the predicted proteome of this pathogen were covered. According to the emPAI analysis, 85 proteins were among the most abundant (e.g. GAPDH, FliC H-antigen, Enolase, and GroEL). Tellurite resistance proteins were also highly abundant. COG analysis showed that although most of the identified proteins are related to cellular metabolism, the majority of the most abundant proteins are associated with translation processes. A KEGG enrichment analysis revealed that Glycolysis / Gluconeogenesis was the most significant pathway. On the other hand, the less abundant detected proteins are those related to DNA processes, cell respiration and prophage. Among the proteins that composed the Type III Secretion System, the most abundant protein was EspA. Altogether, the results show a subset of important proteins that contribute to physiology and pathogenicity of EHEC O157:H7.\n\nIMPORTANCEThe study of the abundance of proteins present within a complex mixture of proteins in a cell, under different conditions, can provide important information about the activities of individual protein components and protein networks that are cornerstones for the comprehension of physiological adaptations in response to biological demands promoted by environmental changes. We generated a comprehensive and accurate quantitative list of EHEC O157:H7 proteome, which provides a description of the most abundant proteins produced by this pathogen that were related to physiology and pathogenesis of EHEC. This study provides information and extends the understanding on functional genomics and the biology of this pathogen.

microbiology↗

bioRxiv2018

Germline genetics encode the resistance, risk, and lymphatic metastasis of triple-negative breast cancer in the southern Chinese population

Early identification of the risk for triple-negative breast cancer (TNBC) at the asymptomatic phase could lead to better prognosis. Here we developed a machine learning method to quantify systematic impact of all rare germline mutations on each pathway. We collected 106 TNBC patients and 287 elder healthy women controls. The spectra of activity profiles in multiple pathways were mapped and most pathway activities exhibited globally suppressed by the portfolio of individual germline mutations in TNBC patients. Accordingly, all individuals were delineated into two types: A and B. Type A patients could be differentiated from controls (AUC = 0.89) and sensitive to BRCA1/2 damages; Type B patients can be also differentiated from controls (AUC = 0.69) but probably being protected from BRCA1/2 damages. Further we found that Individuals with the lowest activity of selected pathways had extreme high relative risk (up to 21.67 in type A) and increased lymph node metastasis in these patients. Our study showed that genomic DNA contains information of unimaginable pathogenic factors. And this information is in a distributed form that could be applied to risk assessment for more cancer types. SignificanceWe identified individuals who are more susceptible to triple negative breast cancer. Our method performs much better than previous assessments based on BRCA1/2 damages, even polygenic risk scores. We disclosed previously unimaginable pathogens in a distributed form on genome and extended risk prediction to scenarios for other cancers.

cancer biology↗

bioRxiv2018

Identification of protein abundance changes in biopsy-level hepatocellular carcinoma tissues using PCT-SWATH

In this study, we optimized the pressure-cycling technology (PCT) and SWATH mass spectrometry workflow to analyze biopsy-level tissue samples (2 mg wet weight) from 19 hepatocellular carcinoma (HCC) patients. Using OpenSWATH and pan-human spectral library, we quantified 11,787 proteotypic peptides from 2,579 SwissProt proteins in 76 HCC tissue samples within about 9 working days (from receiving tissue to SWATH data). The coefficient of variation (CV) of peptide yield using PCT was 32.9%, and the R2 of peptide quantification was 0.9729. We identified protein changes in malignant tissues compared to matched control samples in HCC patients, and further stratified patient samples into groups with high -fetoprotein (AFP) expression or HBV infection. In aggregate, the data identified 23 upregulated pathways and 13 ones. We observed enhanced biomolecule synthesis and suppressed small molecular metabolism in liver tumor tissues. 16 proteins of high documented relevance to HCC are highlighted in our data. We also identified changes of virus-infection-related proteins including PKM, CTPS1 and ALDOB in the HBV+ HCC subcohort. In conclusion, we demonstrate the practicality of performing proteomic analysis of biopsy-level tissue samples with PCT-SWATH methodology with moderate effort and within a relatively short timeframe.

systems biology↗

bioRxiv2018

Quantifying the risk of local Zika virus transmission in the continental US during the 2015-2016 ZIKV epidemic

BackgroundLocal mosquito-borne Zika virus (ZIKV) transmission has been reported in two counties of the continental United State (US), prompting the issuance of travel, prevention, and testing guidance across the continental US. Large uncertainty, however, surrounds the quantification of the actual risk of ZIKV introduction and autochthonous transmission across different areas of the US.\n\nMethodWe present a framework for the projection of ZIKV autochthonous transmission in the continental US during the 2015-2016 epidemic, using a data-driven stochastic and spatial epidemic model accounting for seasonal, environmental and detailed population data. The model generates an ensemble of travel-related case counts and simulate their potential to trigger local transmission at individual level.\n\nResultsWe estimate the risk of ZIKV introduction and local transmission at the county level and at the 0.025{degrees} x 0.025{degrees} cell level across the continental US. We provide a risk measure based on the probability of observing local transmission in a specific location during a ZIKV epidemic modeled after the one observed during the years 2015-2016. The high spatial and temporal resolutions of the model allow us to generate statistical estimates of the number of ZIKV introductions leading to local transmission in each location. We find that the risk is spatially heterogeneously distributed and concentrated in a few specific areas that account for less than 1% of the continental US population. Locations in Texas and Florida that have actually experienced local ZIKV transmission are among the places at highest risk according to our results. We also provide an analysis of the key determinants for local transmission, and identify the key introduction routes and their contributions to ZIKV spread in the continental US.\n\nConclusionsThis framework provides quantitative risk estimates, fully captures the stochas-ticity of ZIKV introduction events, and is not biased by the under-ascertainment of cases due to asymptomatic infections. It provides general information on key risk determinants and data with potential uses in defining public health recommendations and guidance about ZIKV risk in the US.

epidemiology↗

bioRxiv2018

Epigenetic prediction of complex traits and death

BackgroundGenome-wide DNA methylation (DNAm) profiling has allowed for the development of molecular predictors for a multitude of traits and diseases. Such predictors may be more accurate than the self-reported phenotypes, and could have clinical applications. Here, penalised regression models were used to develop DNAm predictors for body mass index (BMI), smoking status, alcohol consumption, and educational attainment in a cohort of 5,100 individuals. Using an independent test cohort comprising 906 individuals, the proportion of phenotypic variance explained in each trait was examined for DNAm-based and genetic predictors. Receiver operator characteristic curves were generated to investigate the predictive performance of DNAm-based predictors, using dichotomised phenotypes. The relationship between DNAm scores and all-cause mortality (n = 214 events) was assessed via Cox proportional-hazards models.\n\nResultsThe DNAm-based predictors explained different proportions of the phenotypic variance for BMI (12%), smoking (60%), alcohol consumption (12%) and education (3%). The combined genetic and DNAm predictors explained 20% of the variance in BMI, 61% in smoking, 13% in alcohol consumption, and 6% in education. DNAm predictors for smoking, alcohol, and education but not BMI predicted mortality in univariate models. The predictors showed moderate discrimination of obesity (AUC=0.67) and alcohol consumption (AUC=0.75), and excellent discrimination of current smoking status (AUC=0.98). There was poorer discrimination of college-educated individuals (AUC=0.59).\n\nConclusionsDNAm predictors correlate with lifestyle factors that are associated with health and mortality. They may supplement DNAm-based predictors of age to identify the lifestyle profiles of individuals and predict disease risk.\n\nList of abbreviations

genomics↗

bioRxiv2018

Cullin1 represses systematic inflammasome activation by binding and catalyzing NLRP3 ubiquitination

Activation of the NLRP3 inflammasome is a key process of host immune response, the first line of defense against cellular stresses and pathogen infections. However, excessive inflammasome activation damages the hosts, and thus it must be precisely controlled. The mechanism underlying the repression of systematic inflammasome activation remains largely unknown. This study reveals that CUL1, a key component of the SCF E3 ligase, plays a critical role in regulation of the inflammasome. CUL1 suppresses the inflammasome activation in HEK293T cells, inhibits endogenous NLRP3 in macrophages, and represses inflammatory responses in C57BL/6 mice. Under normal physiological conditions, CUL1 interacts with NLRP3 to disrupt the inflammasome assembly, and catalyzes NLRP3 ubiquitination to repress the inflammasome activation. In response to inflammatory stimuli, CUL1 disassociates from NLRP3 to release the repression of NLRP3 inflammasome activation. This work reveals a distinct mechanism underlying the repression of inflammasome activation under physiological conditions and the induction of inflammasome activation in response to inflammatory stimuli, and thus provides insights into the prevention and treatment of infectious and inflammatory diseases.

immunology↗

bioRxiv2018

Brain-Specific Deletion of GIT1 Impairs Cognition and Alters Phosphorylation of Synaptic Protein Networks Implicated in Schizophrenia Susceptibility

Despite tremendous effort, the molecular and cellular basis of cognitive deficits in schizophrenia remain poorly understood. Recent progress in elucidating the genetic architecture of schizophrenia has highlighted the association of multiple loci and rare variants that may impact susceptibility. One key example, given their potential etiopathogenic and therapeutic relevance, is a set of genes that encode proteins that regulate excitatory glutamatergic synapses in brain. A critical next step is to delineate specifically how such genetic variation impacts synaptic plasticity and to determine if and how the encoded proteins interact biochemically with one another to control cognitive function in a convergent manner. Towards this goal, here we study the roles of GPCR-kinase interacting protein 1 (GIT1), a synaptic scaffolding and signaling protein with damaging coding variants found in schizophrenia patients, as well as copy number variants found in patients with neurodevelopmental disorders. We generated conditional neural-selective GIT1 knockout mice and find that these mice have deficits in fear conditioning learning and spatial memory. Using global quantitative phospho-proteomics, we revealed that GIT1 deletion in brain perturbs specific networks of GIT1-interacting synaptic proteins. Importantly, several schizophrenia and neurodevelopmental disorder risk genes are present within these networks. We propose that GIT1 regulates the phosphorylation of a network of synaptic proteins and other critical regulators of neuroplasticity, and that perturbation of these networks may contribute to cognitive deficits observed in schizophrenia and neurodevelopmental disorders.

neuroscience↗

bioRxiv2018

Arsenite exposure inhibits histone acetyltransferase p300 for attenuating H3K27ac at enhancers in low-dose exposed mouse embryonic fibroblast cells

Epidemiological investigations have validated the positive relationships between arsenic in drinking water and several cancers, including skin, liver and lung cancers. Besides genotoxicity, arsenic exposure-related pathogenesis of disease is widely considered through epigenetic mechanisms; however, the underlying mechanistic insight remains elusive. Herein we explore the initial epigenetic changes via acute low-dose arsenite exposures of mouse embryonic fibroblast (MEF) cells and Dot1L knockout MEF (Dot1L-/- for abbreviation) cells. Our RNA-seq and Western blot data demonstrated that, in both cell lines, acute low-dose arsenite exposure abolished histone acetyltransferase p300 at the RNA level and subsequent protein level. Consequently, p300-specific main target histone H3K27ac, a marker separating active from poised enhancers, decreased dramatically as validated by both Western blot and ChIP-seq analyses. Concomitantly, H3K4me1 as another well-known marker for enhancers also showed significant decreases, suggesting an underappreciated crosstalk between H3K4me1 and H3K27ac involved in arsenite exposure. Significantly, arsenite exposure-reduced H3K27ac and H3K4me1 inhibit the expression of genes including EP300 itself and Kruppel Like Factor 4(Klf4), a tumor suppressor gene. Collectively, our investigations identify p300 as an internal bridging factor within cells to sense external environmental arsenite exposure to alter chromatin, thereby changing gene transcription for disease pathogenesis.

pharmacology and toxicology↗

bioRxiv2018

An epigenetic score for BMI based on DNA methylation correlates with poor physical health and major disease in the Lothian Birth Cohort 1936.

BackgroundThe relationship between obesity and adverse health is well established, but little is known about the contribution of DNA methylation to obesity-related health outcomes. Additionally, it is of interest whether such contributions are independent of those attributed by the most widely used clinical measure of body mass - the Body Mass Index (BMI).\n\nMethodWe tested whether an epigenetic BMI score accounts for inter-individual variation in health-related, cognitive, psychosocial and lifestyle outcomes in the Lothian Birth Cohort 1936 (n=903). Weights for the epigenetic BMI score were derived using penalised regression on methylation data from unrelated Generation Scotland participants (n=2566).\n\nResultsThe Epigenetic BMI score was associated with variables related to poor physical health (R2 ranges from 0.02-0.10), metabolic syndrome (R2 ranges from 0.01-0.09), lower crystallised intelligence (R2=0.01), lower health-related quality of life (R2=0.02), physical inactivity (R2=0.02), and social deprivation (R2=0.02). The epigenetic BMI score (per SD) was also associated with self-reported type 2 diabetes (OR 2.25, 95 % CI 1.74, 2.94), cardiovascular disease (OR 1.44, 95 % CI 1.23, 1.69) and high blood pressure (OR 1.21, 95% CI 1.13, 1.48; all at p<0.0011 after Bonferroni correction).\n\nConclusionsOur results show that regression models with epigenetic and phenotypic BMI scores as predictors account for a greater proportion of all outcome variables than either predictor alone, demonstrating independent and additive effects of epigenetic and phenotypic BMI scores.

epidemiology↗

bioRxiv2018

Generating perfusion maps from structural optical coherence tomography with artificial intelligence

Despite advances in artificial intelligence (AI), its application in medical imaging has been burdened and limited by expert-generated labels. We used images from optical coherence tomography angiography (OCTA), a relatively new imaging modality that measures retinal blood flow, to train an AI algorithm to generate flow maps from standard optical coherence tomography (OCT) images, exceeding the ability and bypassing the need for expert labeling. Deep learning was able to infer flow from single structural OCT images with similar fidelity to OCTA and significantly better than expert clinicians (P < 0.00001). Our model allows generating flow maps from large volumes of previously collected OCT data in existing clinical trials and clinical practice. This finding demonstrates a novel application of AI to medical imaging, whereby subtle regularities between different modalities are used to image the same body part and AI is used to generate detailed inferences of tissue function from structure imaging.

neuroscience↗

bioRxiv2018

RAD51 Enhances Zygotic Interhomolog Repair

Recent advances in genome editing have greatly improved knock-in (KI) efficiency1-9. Searching for factors to further improve KI efficiency for therapeutic use and generation of non-human primate (NHP) models, we found that the strand exchange protein RAD51 can significantly increase homozygous KI using CRISPR/Cas9 in mouse embryos through an interhomolog repair (IHR) mechanism. IHR is well-described in the context of meiosis10, but only occurs at low frequencies in mitotic cells11,12 and its existence in zygotes is controversial. Using a variety of approaches, we provide evidence for an endogenous IHR mechanism in zygotes that can be enhanced by RAD51. We show that this process can be harnessed for generating homozygous KI animals from wildtype zygotes based on exogenous donors and for converting heterozygous alleles into homozygous alleles without exogenous templates. Furthermore, we elucidate additional factors that contribute to zygotic IHR and identify a RAD51 mutant capable of insertion-deletion (indel)-free stimulation of IHR. Thus, our study provides conclusive evidence for the existence of zygotic IHR and demonstrates methods to enhance IHR for potential use in gene drives, gene therapy, and biotechnology.

molecular biology↗

bioRxiv2018

Loss of SDHB reprograms energy metabolisms and inhibits high fat diet induced metabolic syndromes

Mitochondrial respiratory complex II utilizes succinate, key substrate of the Krebs cycle, for oxidative phosphorylation, which is essential for glucose metabolism. Mutations of complex II cause cancers and mitochondrial diseases, raising a critical question of the (patho-)physiological functions. To address the fundamental role of complex II in systemic energy metabolism, we specifically knockout SDHB in mice liver, a key complex II subunit that tethers the catalytic SDHA subunit and transfers the electrons to ubiquinone, and found that SHDB deficiency abolishes the assembly of complex II without affecting other respiration complexes while largely retaining SDHA stability. SHDB ablation reprograms energy metabolism and hyperactivates the glycolysis, Krebs cycle and {beta}-oxidation pathways, leading to catastrophic energy deficit and early death. Strikingly, sucrose supplementation or high fat diet resumes both glucose and lipid metabolism and prevent early death. Also, SDHB deficient mice are completely resistant to high fat diet induced obesity. Our findings reveal that the unanticipated role of complex II orchestrating both lipid and glucose metabolisms, and suggest that SDHB is an ideal therapeutic target for combating obesity.

molecular biology↗