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Ren, Y.

Publications and source records attributed to Ren, Y..

At least 181 records · Page 10Linked to original sources

Ensemble perception in the time domain: evidence in favor of logarithmic encoding of time intervals

Although time perception is based on the internal representation of time, whether the subjective timeline is scaled linearly or logarithmically remains an open issue. Evidence from previous research is mixed: while the classical internal-clock model assumes a linear scale with scalar variability, there is evidence that logarithmic timing provides a better fit to behavioral data. A major challenge for investigating the nature of the internal scale is that the retrieval process required for time judgments may involve a remapping of the subjective time back to the objective scale, complicating any direct interpretation of behavioral findings. Here, we used a novel approach, requiring rapid intuitive ensemble averaging of a whole set of time intervals, to probe the subjective timeline. Specifically, observers task was to average a series of successively presented, auditory or visual, intervals in the time range 300-1300 ms. Importantly, the intervals were taken from three sets of durations, which were distributed such that the arithmetic mean (from the linear scale) and the geometric mean (from the logarithmic scale) were clearly distinguishable. Consistently across the three sets and the two presentation modalities, our results revealed subjective averaging to be close to the geometric mean, indicative of a logarithmic timeline underlying time perception.

neuroscience↗

Attenuation of epigenetic regulator SMARCA4 and ERK-ETS signaling suppresses aging-related dopaminergic degeneration

Parkinsons disease (PD) is a complex disease with high heterogeneity. How complex interactions of genetic, environmental factors and aging jointly contribute to dopaminergic degeneration in PD is largely unclear. Here, we applied frequent gene co-expression analysis on human patient substantia nigra-specific microarray datasets to identify potential novel disease-related genes. In vivo Drosophila studies validated two of 32 candidate genes, a chromatin remodeling factor SMARCA4 and a biliverdin reductase BLVRA. Inhibition of SMARCA4 was able to prevent dopaminergic degeneration not only caused by overexpression of BLVRA but also in four most common Drosophila PD models. Mechanistically, aberrant SMARCA4 and BLVRA converged on elevated ERK-ETS activity, attenuation of which by either genetic or pharmacological manipulation effectively suppressed dopaminergic degeneration in vivo. Drug inhibition of MEK/ERK also mitigated mitochondrial defects in PD gene-deficient human cells. Our findings underscore the important role of epigenetic regulators and implicate a common signaling axis for therapeutic intervention in a broad range of aging-related disorders including PD.

neuroscience↗

Connecting omics signatures of diseases, drugs, and mechanisms of actions with iLINCS

There are only a few platforms that integrate multiple omics data types, bioinformatics tools, and interfaces for integrative analyses and visualization that do not require programming skills. Among these, iLINCS is unique in scope and versatility of the data provided and the analytics facilitated. iLINCS (http://ilincs.org) is an integrative web-based platform for analysis of omics data and signatures of cellular perturbations. The platform facilitates analysis of user-submitted omics signatures of diseases and cellular perturbations in the context of a large compendium of pre-computed signatures (>200,000), as well as mining and re-analysis of the large collection of omics datasets (>12,000), pre-computed signatures, and their connections. Analytics workflows driven by user-friendly interfaces enable users with only conceptual understanding of the analysis strategy to execute sophisticated analyses of omics signatures, such as systems biology analyses and interpretation of signatures, mechanism of action analysis, and signature-driven drug repositioning. In summary, iLINCS workflows integrate vast omics data resources and a range of analytics and interactive visualization tools into a comprehensive platform for analysis of omics signatures.

bioinformatics↗

Protein profiling reveals the characteristic changes of complement cascade pathway in the tissues of gastric signet ring cell carcinoma

Signet ring cell carcinoma (SRCC) is a histological subtype of gastric cancer that has distinct features in cellular morphology, epidemiology and clinicopathology compared with adenocarcinomas (ACs). Lacking of systematically molecular overview to this disease made a slow progress in diagnosis and therapy for SRCC. In the present proteomics study, the gastric tissues were collected from tumor and adjacent regions including 14 SRCC and 34 AC cases, and laser capture microdissection (LCM) was employed to eradicate cellular heterogeneity of the tissues. Over 6,000 proteins were quantified through data independent acquisition (DIA) mass spectrometry (MS). The quantitative profiles of proteomes in tumor tissues, either AC or SRCC, were dramatically different from that in the corresponding adjacencies, whereas the SRCC proteomes appeared not distinguishable to the AC proteomes via hierarchical clustering. However, focusing on univariate analysis and pathway enrichment unrevealed that some proteins and pathways bared the differences between SRCC and ACs. Importantly, the abundance changes for a bulk of proteins involved in complement cascade were highly associated with SRCC but not so sensitive to the AC status. A hypothesis, therefore, was proposed that the complement cascade was evoked in the SRCC microenvironment upon infiltration, while the SRCC cells survived from the complement cytotoxicity by secreting negative regulators. Moreover, an attempt was made to seek appropriate cell model for gastric SRCC, through proteomic comparison of the 15 gastric cell lines and the gastric tumors. The prediction upon supervised classifier suggested none of these gastric cell lines qualified in mimic to SRCC.

cell biology↗

Nrf1 is endowed with a dominant tumor-repressing effect onto the Wnt/β-Catenin-dependent and -independent signaling networks in the human liver cancer

Our previous work revealed that Nrf1 exerts a tumor-repressing effect because its genomic loss (to yield Nrf1-/-) results in oncogenic activation of Nrf2 and target genes. Interestingly, {beta}-catenin is concurrently activated by loss of Nrf1 in a way similar to {beta}-catenin-driven liver tumor. However, a presumable relationship between Nrf1 and {beta}-catenin is not as yet established. Here, we demonstrate that Nrf1 enhanced ubiquitination of {beta}-catenin for targeting to proteasomal degradation. Conversely, knockdown of Nrf1 by its short-hairpin RNA (shNrf1) caused accumulation of {beta}-catenin so as to translocate the nucleus, allowing activation of a subset of Wnt-{beta}-catenin signaling responsive genes, which leads to the epithelial-mesenchymal transition (EMT) and related cellular processes. Such silencing of Nrf1 resulted in malgrowth of human hepatocellular carcinoma, along with malignant invasion and metastasis to the lung and liver in xenograft model mice. Further transcriptomic sequencing unraveled significant differences in the expression of both Wnt/{beta}-catenin-dependent and -independent responsive genes implicated in the cell process, shape and behavior of the shNrf1-expressing tumor. Notably, we identified that {beta}-catenin is not a target gene of Nrf1, but this CNC-bZIP factor contributes to differential or opposing expression of other critical genes, such as CDH1, Wnt5A, Wnt11A, FZD10, LEF, TCF4, SMAD4, MMP9, PTEN, PI3K, JUN and p53, each of which depends on the positioning of distinct cis-regulatory sequences (e.g., ARE and/or AP-1 binding sites) in the gene promoter contexts. In addition, altered expression profiles of some Wnt-{beta}-catenin signaling proteins were context-dependent, as accompanied by decreased abundances of Nrf1 in the clinic human hepatomas with distinct differentiation. Together, these results corroborate the rationale that Nrf1 acts as a bona fide dominant tumor-repressor, by its intrinsic inhibition of Wnt-{beta}-catenin signaling and relevant independent networks in cancer development and malignant progression.

cancer biology↗

ASCL1-regulated DARPP-32 and t-DARPP stimulate small cell lung cancer growth and neuroendocrine tumor cell survival

Small cell lung cancer (SCLC) is the most aggressive form of lung cancer, and new molecular insights are necessary for prognostic and therapeutic advances. Here we demonstrate in orthotopic mouse models that dopamine and cAMP-regulated phosphoprotein, Mr 32000 (DARPP-32) and its N-terminally truncated splice variant t-DARPP promote SCLC growth through increased proliferation, Akt/Erk-mediated survival and anti-apoptotic signaling. DARPP-32 and t-DARPP proteins are overexpressed in SCLC patient-derived tumor tissue, but virtually undetectable in physiologically normal lung. RNA sequencing analysis reveals a subset of SCLC patients with high tumoral t-DARPP expression and upregulated Notch signaling genes, including achaete-scute homologue 1 (ASCL1). We show that DARPP-32 isoforms are transcriptionally activated by ASCL1 in human SCLC cells. Taken together, we demonstrate new regulatory mechanisms of SCLC oncogenesis that suggest DARPP-32 isoforms may represent a negative prognostic indicator for SCLC and serve as a potential target for the development of new therapies.

cancer biology↗

Predicting mechanism of action of cellular perturbations with pathway activity signatures

MotivationMisregulation of signaling pathway activity is etiologic for many human diseases, and modulating activity of signaling pathways is often the preferred therapeutic strategy. Understanding the mechanism of action (MOA) of bioactive chemicals in terms of targeted signaling pathways is the essential first step in evaluating their therapeutic potential. Changes in signaling pathway activity are often not reflected in changes in expression of pathway genes which makes MOA inferences from transcriptional signatures a difficult problem. ResultsWe developed a new computational method for implicating pathway targets of bioactive chemicals and other cellular perturbations by integrated analysis of pathway network topology, the LINCS transcriptional signatures of genetic perturbations of pathway genes and the transcriptional signature of the perturbation. Our methodology accurately predicts signaling pathways targeted by the perturbation when current pathway analysis approaches utilizing only a transcriptional signature of the perturbation fail. Availability and ImplementationOpen source R package paslincs is available at https://github.com/uc-bd2k/paslincs.

systems biology↗

Abcg2-Expressing Side Population Cells Contribute to Cardiomyocyte Renewal through Fusion

The adult mammalian heart has a limited regenerative capacity. Therefore, identification of endogenous cells and mechanisms that contribute to cardiac regeneration is essential for the development of targeted regenerative therapies. The side population (SP) phenotype enriches for stem cells throughout the body, and SP cells have been identified in the heart. We generated a novel Abcg2-driven, genetic lineage-tracing mouse model and show efficient labeling of SP cells. Labeled SP cells give rise to terminally differentiated cells in bone marrow and intestines. In the heart, we find that Abcg2-expressing SP cells contribute to cardiomyocyte labeling, which is further enhanced in response to different forms of cardiac injury. We find that cardiac SP cells fuse with preexisting cardiomyocytes, which stimulates cardiomyocyte cell cycle entry and likely proliferation, instead of directly differentiating into cardiomyocytes. Our findings provide evidence that cardiac SP cells contribute to endogenous cardiac regeneration through cardiomyocyte fusion.

cell biology↗

Title: Risk factors for postoperative meningitis after microsurgery for vestibular schwannoma

OBJECTIVEMeningitis after microsurgery for vestibular schwannoma (VS) is a severe complication and result in high morbidity. But few studies have focused on meningitis after VS surgery alone. The purpose of this study was to identify the risk factors for meningitis after VS surgery.\n\nMETHODSWe undertook a retrospective analysis of all VS patients, who underwent microsurgery of VS and at least live for 7 days after surgery, between 1st June 2015 and 30st November 2018 at West China Hospital of Sichuan University. Univariate and multivariate analyses were performed to identify the risk factors for postoperative meningitis (POM).\n\nRESULTSWe collected 410 patients, 27 of whom had POM. Through univariate analysis, hydrocephalus (p=0. 018), Koos grade IV (p=0.04), The operative duration (> 3 hours p=0.03) and intraoperative bleeding volume ([≥] 400ml p=0. 02) were significantly correlated to POM. Multivariate analysis showed that Koos grade IV (p=0.04; OR=3.19; 95% CI 1.032-3.190), operation duration (> 3 hours p=0.03 OR= 7.927; 95% CI 1.043-60.265), and intraoperative bleeding volume ([≥] 400ml p=0.02; OR=2.551; 95% CI 1.112-5.850) are the independent influencing factors of POM.\n\nCONCLUSIONSKoos grade IV, the duration of operation, and the amount of bleeding were identified as independent risk factors for POM after microsurgery of VS. POM caused a prolonged hospital stay.

neuroscience↗

Synthetic lethality targeting LKB1 mutant and EGFR wild type human non-small cell lung cancer cells by glucose starvation and SGLT2 inhibition

In this study, we aimed to discover novel therapeutic approaches targeting non-small cell lung cancer (NSCLC) patients without EGFR mutation. First, we found that mutations of EGFR and LKB1 are mutually exclusive in NSCLC. EGFR-WT/LKB1-mutant cells are resistant to EGFR inhibitor erlotinib but are highly susceptible to glucose starvation or SGLT2 inhibitor canagliflozin. Mechanistically, in these cells, glucose starvation causes suppression of AMPK and induction of oxidative stress, leading to cell death. Finally, canagliflozin effectively reduces tumor growth of EGFR-WT/LKB1-mutant NSCLC cells in the mice xenograft model. Our data thus demonstrate that synthetic lethality can be achieved by glucose starvation or SGLT2 inhibition in EGFR-WT/LKB1-mutant NSCLC.\n\nSIGNIFICANCEAt present, EGFR inhibitor-based targeted therapy can only benefit those non-small cell lung cancer (NSCLC) patients with EGFR mutation. Therefore, there is an urgent need to develop alternate targeted therapy for NSCLC with WT EGFR. In this study, we found that mutations of EGFR and LKB1 are mutually exclusive in NSCLC, and more importantly, synthetic lethality can be achieved in EGFR-WT/LKB1-mutant NSCLC cells with glucose starvation or SGLT2 inhibition. Since SGLT2 inhibitors such as canagliflozin are FDA-approved drugs for type II diabetes, our study thus points out a possibility of developing SGLT2 inhibitors as a targeted therapy for NSCLC patients with WT EGFR and mutant LKB1, which will benefit about 15-30% of NSCLC patients.\n\nHIGHLIGHTSO_LIEGFR and LKB1 mutations are mutually exclusive in NSCLC\nC_LIO_LIEGFR-WT/LKB1-mutant NSCLC cells are sensitive to cell death induced by glucose starvation and SGLT2 inhibition\nC_LIO_LIGlucose starvation suppresses AMPK activity in LKB1-mutant NSCLC cells\nC_LIO_LISGLT2 inhibitor canagliflozin causes synthetic lethality in LKB1-mutant NSCLC cells\nC_LI

cancer biology↗

Performance of bitumen coating sheet using biomass pyrolysis oil

The \"green\" production of bitumen has raised increasing interest in recent years to reduce the environmental, energy and petro-based concerns. Bio-oil, prepared by biomass pyrolysis, can be used as substitute for petro-based bitumen in bitumen or bitumen-based coatings, for its similar properties of good adhesion and anti-corrosion characteristics. Although biomass is a renewable and widespread chemicals resource, its value-added utilization is still difficult. Several studies have qualitatively demonstrated the use of bio-bitumen in practical applications. The present study investigates the effects and properties and the incorporation of bio-bitumen shown to improve the performance of traditional petro-bitumen to some extent. Bio-bitumen was prepared from biomass pyrolysis oil and applied to self-adhesive and doped hot-melt sheets. Resulting physical properties demonstrate that bio-bitumen is a potential substitute in bitumen coating sheet.\n\nIMPLICATIONSThis paper is intended to verify the effect of pyrolyzed bio-oil from wheat straw on the performance of bitumen, as well as the feasibility of application in the coating sheet. Up to now, the research on bio-bitumen is mainly in pavement bitumen. In the present research, bio-bitumen was applied to the coating sheet in different proportions. Interestingly, the prepared coating sheet exhibited higher adhesion. Other performances, such as temperature stability, mechanical strength and temperature flexibility of coating sheet showed improvement in the presence of bio- oil, which indicated the suitability of bio-oil in coating sheet bitumen.

bioengineering↗

Fertility intentions and influential factors in Dalian urban city ---A cross-sectional study based on Universal Two-child policy in China

BackgroundIn October, 2015, Chinese government announced that one-child policy had finally been replaced by a universal two-child policy. However, the effects of new policy may be far less than expected. So we conducted this research to explore potential influential factors of fertility intention.\n\nMethodsThis cross-sectional study was conducted and a self-administered questionnaire was designed for collecting socio-demographic information, future fertility intention and influential factors of individual reproductive behavior. The analyses were performed using the SPSS 19.0 statistical software package.\n\nResultsA total of 1370 respondents were interviewed. Our research indicated that the mean ideal number of children was only 1.73 and urban respondents sex preference was symmetrical preference. 79.1% (884) married people had the first child already, only 7.6% (71) respondents had two children. Among 1370 participants, 30.4% respondents stated that they would have a second child; while 69.6% respondents refused to have two children in future (just wanted only a child). Binary logistic regression analysis (model 1) showed that female, older age, lower education lever, birth place was Dalian, lower family income, the ideal number of children were associated with having 1 child in the future. Model 2 (only respondents with childbearing experience) showed that female, lower family income, couldnt get additional financial support from parents were more likely refused to have two children; in additional, the ideal number of children and childbearing experience were significantly influences on future fertility intention.\n\nConclusionFertility intention and reproductive behavior still below replacement in Dalian city. Our results suggest that several factors (including socioeconomic characteristics, economic factors, desired number of child, childbearing experience) have distinctive effects on fertility intention.

epidemiology↗

A novel thermostable aspartic protease from Talaromyces leycettanus and its specific autocatalytic activation through an intermediate transition state

Aspartic proteases exhibit optimum enzyme activity under acidic condition and have been extensively used in food, fermentation and leather industries. In this study, a novel aspartic protease precursor (proTlAPA1) from Talaromyces leycettanus was identified and successfully expressed in Pichia pastoris. Subsequently, the auto-activation processing of the zymogen proTlAPA1 was studied by SDS-PAGE and N-terminal sequencing, under different processing conditions. TlAPA1 shared the highest identity of 70.3 % with the aspartic endopeptidase from Byssochlamys spectabilis (GAD91729) and was classified into a new subgroup of the aspartic protease A1 family, based on evolutionary analysis. Mature TlAPA1 protein displayed an optimal activity at 60 {degrees}C and remained stable at temperatures of 55 {degrees}C and below, indicating the thermostable nature of TlAPA1 aspartic protease. During the auto-activation processing of proTlAPA1, a 45 kDa intermediate was identified that divided the processing mechanism into two steps: formation of intermediates, and activation of the mature protein (TlAPA1). The former step was completely induced by pH of the buffer, while the latter process depended on protease activity. The discovery of the novel aspartic protease TlAPA1 and study of its activation process will contribute to a better understanding of the mechanism of aspartic proteases auto-activation. IMPORTANCEThe novel aspartic protease TlAPA1 was identified from T. leycettanus and expressed as a zymogen (proTlAPA1) in P. pastoris. Enzymatic characteristics of the mature protein were studied and the specific pattern of zymogen conversion was described. The auto-activation processing of proTlAPA1 proceeded in two stages and an intermediate was identified in this process. These results describe a new subgroup of aspartic protease A1 family and provide insights into a novel mode of activation processing in aspartic proteases.

molecular biology↗

Managing genomic variant calling workflows with Swift/T

Genomic variant discovery is frequently performed using the GATK Best Practices variant calling pipeline, a complex workflow with multiple steps, fans/merges, and conditionals. This complexity makes management of the workflow difficult on a computer cluster, especially when running in parallel on large batches of data: hundreds or thousands of samples at a time. Here we describe a wrapper for the GATK-based variant calling workflow using the Swift/T parallel scripting language. Standard built-in features include the flexibility to split by chromosome before variant calling, optionally permitting the analysis to continue when faulty samples are detected, and allowing users to analyze multiple samples in parallel within each cluster node. The use of Swift/T conveys two key advantages: (1) Thanks to the embedded ability of Swift/T to transparently operate in multiple cluster scheduling environments (PBS Torque, SLURM, Cray aprun environment, etc.,) a single workflow is trivially portable across numerous clusters; (2) The leaf functions of Swift/T permit developers to easily swap executables in and out of the workflow, conditional on the analysts choice, which makes the workflow easy to maintain. This modular design permits separation of the workflow into multiple stages and the request of resources optimal for each stage of the pipeline. While Swift/Ts implicit data-level parallelism eliminates the need for the developer to code parallel analysis of multiple samples, it does make debugging of the workflow a bit more difficult, as is the case with any implicitly parallel code. With the above features, users have a powerful and portable way to scale up their variant calling analysis to run in many traditional computer cluster architectures.\n\nhttps://github.com/ncsa/Swift-T-Variant-Calling\n\nhttp://swift-t-variant-calling.readthedocs.io/en/latest/

bioinformatics↗

Baseline human gut microbiota profile in healthy people and standard reporting template

A comprehensive knowledge of the types and ratios of microbes that inhabit the healthy human gut is necessary before any kind of pre-clinical or clinical study can be performed that attempts to alter the microbiome to treat a condition or improve therapy outcome. To address this need we present an innovative scalable comprehensive analysis workflow, a healthy human reference microbiome list and abundance profile (GutFeelingKB), and a novel Fecal Biome Population Report (FecalBiome) with clinical applicability. GutFeelingKB provides a list of 157 organisms (8 phyla, 18 classes, 23 orders, 38 families, 59 genera and 109 species) that forms the baseline biome and therefore can be used as healthy controls for studies related to dysbiosis. The incorporation of microbiome science into routine clinical practice necessitates a standard report for comparison of an individuals microbiome to the growing knowledgebase of \"normal\" microbiome data. The FecalBiome and the underlying technology of GutFeelingKB address this need. The knowledgebase can be useful to regulatory agencies for the assessment of fecal transplant and other microbiome products, as it contains a list of organisms from healthy individuals. In addition to the list of organisms and abundances the study also generated a list of contigs of metagenomics dark matter. In this study, metagenomic dark matter represents sequences that cannot be mapped to any known sequence but can be assembled into contigs of 10,000 nucleotides or higher. These sequences can be used to create primers to study potential novel organisms. All data is freely available from https://hive.biochemistry.gwu.edu/gfkb and NCBIs Short Read Archive.

microbiology↗

Oncogenic activation of Nrf2 by specific knockout of Nrf1α that acts as a dominant tumor repressor

Liver-specific knockout of Nrf1 in mice leads to non-alcoholic steatohepatitis with dyslipidemia, and its deterioration results in spontaneous hepatoma, but the underlying mechanism remains elusive. A similar pathological model is herein reconstructed by using human Nrf1-specific knockout cell lines. We demonstrated that a marked increase of the inflammation marker COX2 in Nrf1-/- cells. Loss of Nrf1 leads to hyperactivation of Nrf2, which results from substantial decreases in both Keap1 and PTEN in Nrf1-/- cells. Further investigation of xenograft mice showed that malignant growth of Nrf1-/--derived tumor is almost abolished by silencing Nrf2, while Nrf1+/+-tumor is markedly repressed by inactive Nrf2-/-{Delta}TA, but unaffected by a priori constitutive activator of caNrf2{Delta}N. Mechanistic studies unraveled there exist opposing and unifying inter-regulatory cross-talks between Nrf1 and Nrf2. Collectively, Nrf1 manifests a dominant tumor-suppressive effect by confining Nrf2 oncogenicity, while Nrf2 can directly activate the transcriptional expression of Nrf1 to form a negative feedback loop.\n\nHIGHLIGHTSO_LIOpposing and unifying inter-regulatory cross-talks between Nrf1 and Nrf2\nC_LIO_LIMalignant growth of Nrf1-/--derived tumor is prevented by silencing Nrf2\nC_LIO_LIHyper-activation of Nrf2 by Nrf1-/- results from decreased Keap1 and PTEN\nC_LIO_LINrf1+/+-tumor is repressed by Nrf2-/-{Delta}TA, but unaltered by its active caNrf2{Delta}N\nC_LI

molecular biology↗

Characterizing Building Blocks of Resource Constrained Biological Networks

Identification of motifs-recurrent and statistically significant patterns-in biological networks is the key to understand the design principles, and to infer governing mechanisms of biological systems. This, however, is a computationally challenging task. This task is further complicated as biological interactions depend on limited resources, i.e., a reaction takes place if the reactant molecule concentrations are above a certain threshold level. This biochemical property implies that network edges can participate in a limited number of motifs simultaneously. Existing motif counting methods ignore this problem. This simplification often leads to inaccurate motif counts (over-or under-estimates), and thus, wrong biological interpretations. In this paper, we develop a novel motif counting algorithm, Partially Overlapping MOtif Counting (POMOC), that considers capacity levels for all interactions in counting motifs. Our experiments on real and synthetic networks demonstrate that motif count using the POMOC method significantly differs from the existing motif counting approaches, and our method extends to large-scale biological networks in practical time. Our results also show that our method makes it possible to characterize the impact of different stress factors on cells organization of network. In this regard, analysis of a S. cerevisiae transcriptional regulatory network using our method shows that oxidative stress is more disruptive to organization and abundance of motifs in this network than mutations of individual genes. Our analysis also suggests that by focusing on the edges that lead to variation in motif counts, our method can be used to find important genes, and to reveal subtle topological and functional differences of the biological networks under different cell states.

bioinformatics↗

Recovered and dead outcome patients caused by influenza A (H7N9) virus infection show different pro-inflammatory cytokine dynamics during disease progress and its application in real-time prognosis

The persistent circulation of influenza A(H7N9) virus within poultry markets and human society leads to sporadic epidemics of influenza infections. Severe pneumonia and acute respiratory distress syndrome (ARDS) caused by the virus lead to high morbidity and mortality rates in patients. Hyper induction of pro-inflammatory cytokines, which is known as \"cytokine storm\", is closely related to the process of viral infection. However, systemic analyses of H7N9 induced cytokine storm and its relationship with disease progress need further illuminated. In our study we collected 75 samples from 24 clinically confirmed H7N9-infected patients at different time points after hospitalization. Those samples were divided into three groups, which were mild, severe and fatal groups, according to disease severity and final outcome. Human cytokine antibody array was performed to demonstrate the dynamic profile of 80 cytokines and chemokines. By comparison among different prognosis groups and time series, we provide a more comprehensive insight into the hypercytokinemia caused by H7N9 influenza virus infection. Different dynamic changes of cytokines/chemokines were observed in H7N9 infected patients with different severity. Further, 33 cytokines or chemokines were found to be correlated with disease development and 11 of them were identified as potential therapeutic targets. Immuno-modulate the cytokine levels of IL-8, IL-10, BLC, MIP-3a, MCP-1, HGF, OPG, OPN, ENA-78, MDC and TGF-{beta} 3 are supposed to be beneficial in curing H7N9 infected patients. Apart from the identification of 35 independent predictors for H7N9 prognosis, we further established a real-time prediction model with multi-cytokine factors for the first time based on maximal relevance minimal redundancy method, and this model was proved to be powerful in predicting whether the H7N9 infection was severe or fatal. It exhibited promising application in prognosing the outcome of a H7N9 infected patients and thus help doctors take effective treatment strategies accordingly.

immunology↗