HutZ is required for biofilm formation and contributes to the pathogenicity of Edwardsiella piscicida

International audienceAbstractEdwardsiella piscicida is a severe fish pathogen. Haem utilization systems play an important role in bacterial adversity adaptation and pathogenicity. In this study, a speculative haem utilization protein, HutZEp, was characterized in E. piscicida. hutZEp is encoded with two other genes, hutW and hutX, in an operon that is similar to the haem utilization operon hutWXZ identified in V. cholerae. However, protein activity analysis showed that HutZEp is probably not related to hemin utilization. To explore the biological role of HutZEp, a markerless hutZEp in-frame mutant strain, TX01ΔhutZ, was constructed. Deletion of hutZEp did not significantly affect bacterial growth in normal medium, in iron-deficient conditions, or in the presence of haem but significantly retarded bacterial biofilm growth. The expression of known genes related to biofilm growth was not affected by hutZEp deletion, which indicated that HutZEp was probably a novel factor promoting biofilm formation in E. piscicida. Compared to the wild-type TX01, TX01ΔhutZ exhibited markedly compromised tolerance to acid stress and host serum stress. Pathogenicity analysis showed that inactivation of hutZEp significantly impaired the ability of E. piscicida to invade and reproduce in host cells and to infect host tissue. In contrast to TX01, TX01ΔhutZ was defective in blocking host macrophage activation. The expression of hutZEp was directly regulated by the ferric uptake regulator Fur. This study is the first functional characterization of HutZ in a fish pathogen, and these findings suggested that HutZEp is essential for E. piscicida biofilm formation and contributes to host infection

Identification of four hub genes associated with adrenocortical carcinoma progression by WGCNA

Background Adrenocortical carcinoma (ACC) is a rare and aggressive malignant cancer in the adrenal cortex with poor prognosis. Though previous research has attempted to elucidate the progression of ACC, its molecular mechanism remains poorly understood. Methods Gene transcripts per million (TPM) data were downloaded from the UCSC Xena database, which included ACC (The Cancer Genome Atlas, n = 77) and normal samples (Genotype Tissue Expression, n = 128). We used weighted gene co-expression network analysis to identify gene connections. Overall survival (OS) was determined using the univariate Cox model. A protein–protein interaction (PPI) network was constructed by the search tool for the retrieval of interacting genes. Results To determine the critical genes involved in ACC progression, we obtained 2,953 significantly differentially expressed genes and nine modules. Among them, the blue module demonstrated significant correlation with the “Stage” of ACC. Enrichment analysis revealed that genes in the blue module were mainly enriched in cell division, cell cycle, and DNA replication. Combined with the PPI and co-expression networks, we identified four hub genes (i.e., TOP2A, TTK, CHEK1, and CENPA) that were highly expressed in ACC and negatively correlated with OS. Thus, these identified genes may play important roles in the progression of ACC and serve as potential biomarkers for future diagnosis

Service utilization in community health centers in China: a comparison analysis with local hospitals

BACKGROUND: Being an important part of China's Urban Health Care Reform System, Community Health Centers (CHCs) have been established throughout the entire country and are presently undergoing substantial reconstruction. However, the services being delivered by the CHCs are far from reaching their performance targets. In order to assess the role of the CHCs, we examined their performance in six cities located in regions of South-East China. The purpose of this investigation was to identify the utilization and the efficiency of community health resources that are able to provide basic medical and public health services. METHODS: The study was approved by Peking University Health Science Center Institutional Reviewing Board (NO: IRB00001052-T1). Data were collected from all the local health bureaux and processed using SPSS software. Methods of analysis mainly included: descriptive analysis, paired T-test and one-way ANOVA. RESULTS: The six main functions of the CHCs were not fully exploited and the surveys that were collected on their efficiency and utilization of resources indicate that they have a low level of performance and lack the trust of local communities. Furthermore, the CHCs seriously lack funding support and operate under difficult circumstances, and residents have less positive attitudes towards them. CONCLUSION: The community health service must be adjusted according to the requirements of urban medical and health reform, taking into account communities' health needs. More research is required on the living standards and health needs of residents living within the CHC's range, taking into consideration the users' needs in expanding the newly implemented service, and at the same time revising the old service system so as to make the development of CHCs realistic and capable of providing a better service to patients. Several suggestions are put forward for an attainable scheme for developing a community health service

Combination of G-CSF and AMD3100 Improves the Anti-inflammatory Effect of Mesenchymal Stem Cells on Inducing M2 Polarization of Macrophages Through NF-κB-IL1RA Signaling Pathway

Mobilized peripheral blood-derived mesenchymal stem cells (PB-MSCs) mainly derived from bone marrow-derived MSCs (BM-MSCs) exert a similar anti-inflammatory effect. However, the mechanism of anti-inflammatory effect of mobilized PB-MSCs by a combination of G-CSF and AMD3100 remains unclear. Cultured rat PB-MSCs mobilized by G-CSF/AMD3100 have shown typical surface markers and potential for multiple differentiations, similar to non-mobilized BM-MSCs. In a co-culture system, rat M0-type macrophages co-cultured with PB-MSCs have shown higher expression of M2 markers including CD206, Arg-1, IL-10, and CCL-22 than BM-MSCs, indicating that PB-MSCs induced greater M0 polarization to M2. Furthermore, compared with BM-MSCs, PB-MSCs in a co-culture system with lipopolysaccharide-induced M1-type macrophages more efficiently promoted M1 polarization to M2, accompanied by increasing expression of CD206, Arg-1, IL-10, and CCL-22 while decreasing expression of M1 markers including iNOS, TNF-α, IL-1β and IL-6, indicating that PB-MSCs triggered greater M1 polarization to M2. Subsequently, polymerase chain reaction arrays showed higher expressions of both IL1rn and Tnfrsf11b in PB-MSCs versus BM-MSCs. In response to an inflammatory niche, such as TNF-α, PB-MSCs have shown higher expression and release of IL1RA, causing greater M2 polarization of macrophages, and the special effects may be almost entirely abolished through the neutralization antibody of IL1RA. Mechanistic studies determined that PB-MSCs showed higher levels NF-κBp65 and NF-κBp-p65 than BM-MSCs, which could be obviously enhanced by TNF-α. And the increased IL1RA expression by TNF-α in PB-MSCs could be markedly canceled by an NF-κB inhibitor PDTC. Interestingly, mimicking the mobilized PB-MSCs by a combination of G-CSF and AMD3100 in vivo, BM-MSCs were treated with G-CSF and/or AMD3100 in vitro, showing the increased expressions of NF-κBp65 and IL1RA, which could be prominently abolished by PDTC. Therefore, targeting IL1rn, gene modification or drug intervention for MSCs may provide a novel therapeutic strategy for human diseases, especially inflammatory diseases

Identification of the shared gene signatures between pulmonary fibrosis and pulmonary hypertension using bioinformatics analysis

Pulmonary fibrosis (PF) and pulmonary hypertension (PH) have common pathophysiological features, such as the significant remodeling of pulmonary parenchyma and vascular wall. There is no effective specific drug in clinical treatment for these two diseases, resulting in a worse prognosis and higher mortality. This study aimed to screen the common key genes and immune characteristics of PF and PH by means of bioinformatics to find new common therapeutic targets. Expression profiles are downloaded from the Gene Expression Database. Weighted gene co-expression network analysis is used to identify the co-expression modules related to PF and PH. We used the ClueGO software to enrich and analyze the common genes in PF and PH and obtained the protein–protein interaction (PPI) network. Then, the differential genes were screened out in another cohort of PF and PH, and the shared genes were crossed. Finally, RT-PCR verification and immune infiltration analysis were performed on the intersection genes. In the result, the positive correlation module with the highest correlation between PF and PH was determined, and it was found that lymphocyte activation is a common feature of the pathophysiology of PF and PH. Eight common characteristic genes (ACTR2, COL5A2, COL6A3, CYSLTR1, IGF1, RSPO3, SCARNA17 and SEL1L) were gained. Immune infiltration showed that compared with the control group, resting CD4 memory T cells were upregulated in PF and PH. Combining the results of crossing characteristic genes in ImmPort database and RT-PCR, the important gene IGF1 was obtained. Knocking down IGF1 could significantly reduce the proliferation and apoptosis resistance in pulmonary microvascular endothelial cells, pulmonary smooth muscle cells, and fibroblasts induced by hypoxia, platelet-derived growth factor-BB (PDGF-BB), and transforming growth factor-β1 (TGF-β1), respectively. Our work identified the common biomarkers of PF and PH and provided a new candidate gene for the potential therapeutic targets of PF and PH in the future

Gaussian Boson Sampling with Pseudo-Photon-Number Resolving Detectors and Quantum Computational Advantage

We report new Gaussian boson sampling experiments with pseudo-photon-number-resolving detection, which register up to 255 photon-click events. We consider partial photon distinguishability and develop a more complete model for characterization of the noisy Gaussian boson sampling. In the quantum computational advantage regime, we use Bayesian tests and correlation function analysis to validate the samples against all current classical mockups. Estimating with the best classical algorithms to date, generating a single ideal sample from the same distribution on the supercomputer Frontier would take ~ 600 years using exact methods, whereas our quantum computer, Jiuzhang 3.0, takes only 1.27 us to produce a sample. Generating the hardest sample from the experiment using an exact algorithm would take Frontier ~ 3.1*10^10 years.Comment: submitted on 10 Apri