Rhein kills Actinobacillus pleuropneumoniae, reduces biofilm formation, and effectively treats bacterial lung infections in mice.

Background. Actinobacillus pleuropneumoniae, a member of the Pasteurellaceae family, is known for its highly infectious nature and is the primary causative agent of infectious pleuropneumonia in pigs. This disease poses a considerable threat to the global pig industry and leads to substantial economic losses due to reduced productivity, increased mortality rates, and the need for extensive veterinary care and treatment. Due to the emergence of multi-drug-resistant strains, Chinese herbal medicine is considered one of the best alternatives to antibiotics due to its unique mechanism of action and other properties. As a type of Chinese herbal medicine, Rhein has the advantages of a wide antibacterial spectrum and is less likely to develop drug resistance, which can perfectly solve the limitations of current antibacterial treatments.Methods. The killing effect of Rhein on A. pleuropneumoniae was detected by fluorescence quantification of differential expression changes of key genes, and scanning electron microscopy was used to observe the changes in A. pleuropneumoniae status after Rhein treatment. Establishing a mouse model to observe the treatment of Rhein after A. pleuropneumoniae infection.Results. Here, in this study, we found that Rhein had a good killing effect on A. pleuropneumoniae and that the MIC was 25 µg ml-1. After 3 h of action, Rhein (4×MIC) completely kills A. pleuropneumoniae and Rhein has good stability. In addition, the treatment with Rhein (1×MIC) significantly reduced the formation of bacterial biofilms. Therapeutic evaluation in a murine model showed that Rhein protects mice from A. pleuropneumoniae and relieves lung inflammation. Quantitative RT-PCR (Quantitative reverse transcription polymerase chain reaction is a molecular biology technique that combines both reverse transcription and polymerase chain reaction methods to quantitatively detect the amount of a specific RNA molecule) results showed that Rhein treatment significantly downregulated the expression of the IL-18 (Interleukin refers to a class of cytokines produced by white blood cells), TNF-α, p65 and p38 genes. Along with the downregulation of genes such as IL-18, it means that Rhein has an inhibitory effect on the expression of these genes, thereby reducing the activation of inflammatory cells and the production of inflammatory mediators. This helps reduce inflammation and protects tissue from further damage.Conclusions. This study reports the activity of Rhein against A. pleuropneumoniae and its mechanism, and reveals the ability of Rhein to treat A. pleuropneumoniae infection in mice, laying the foundation for the development of new drugs for bacterial infections.

Correction: Phospholipid scramblase 1 interacts with influenza A virus NP, impairing its nuclear import and thereby suppressing virus replication.

[This corrects the article DOI: 10.1371/journal.ppat.1006851.].

Preliminary view of the distribution and spread of the plasmid-mediated resistance genes in Glaesserella parasuis.

Introduction. Various plasmid-mediated resistance genes have been reported in Glaesserella parasuis, but little is known about their global distribution features, evolution pattern and spread.Gap Statement. The potential mobilization mechanisms of resistance plasmids in G. parasuis have been poorly explored.Aim. The aim of the study was to investigate the prevalence and diversity of plasmid-mediated resistance genes among G. parasuis isolates, and focus on the analysis of the features of the resistance plasmids from G. parasuis.Method. The plasmids tested were sequenced using the Illumina HiSeq platform in conjunction with PCR and inverted PCR. The susceptibility of the host strains was determined by broth microdilution. The transfer of plasmids tested was conducted by electroporation. The sequence data were compared using bioinformatics tools and the data from our laboratory and the National Center for Biotechnology Information (NCBI) database.Results. Nineteen plasmids were identified from our laboratory and these resistance plasmids were functional and transferable. Moreover, we clustered five types of genetic backbones of plasmids from G. parasuis and revealed the global distribution features of the plasmid-mediated resistance genes.Conclusions. This is the first report of the coexistence of tet(H)-bearing type I plasmid and lnu(C)-bearing type II plasmid in one G. parasuis clinical isolate. In addition, this study provides the first view of the global distribution of plasmid-mediated resistance genes and classifies the plasmids in G. parasuis according to their backbone regions.

Synthesis of Furan-Based Diamine and Its Application in the Preparation of Bio-Based Polyimide.

Furan-based compounds are a new class of compounds characteristic of wide abundance, feasible availability, and environmental friendliness. Presently, polyimide (PI) is the best membrane insulation material in the world, which is widely used in the fields of national defense, liquid crystals, lasers, and so on. At present, most polyimides are synthesized using petroleum-based monomers bearing benzene rings, while furan-based compounds bearing furan rings are rarely used as monomers. The production of petroleum-based monomers is always associated with many environmental issues, and their substitution with furan-based compounds seems a solution to addressing these issues. In this paper, t-butoxycarbonylglycine (BOC-glycine) and 2,5-furandimethanol, bearing furan rings, were employed to synthesize BOC-glycine 2,5-furandimethyl ester, which was further applied for the synthesis of furan-based diamine. This diamine is generally used to synthesize bio-based PI. Their structures and properties were thoroughly characterized. The characterization results showed that BOC-glycine could be effectively obtained using different posttreatment methods. And BOC-glycine 2,5-furandimethyl ester could be effectively obtained by optimizing the accelerating agent of 1,3-dicyclohexylcarbodiimide(DCC) with either 1.25 mol/L or 1.875 mol/L as the optimum value. The PIs originated from furan-based compounds were synthesized and their thermal stability and surface morphology were further characterized. Although the obtained membrane was slightly brittle (mostly due to the less rigidity of furan ring as compared with benzene ring), the excellent thermal stability and smooth surface endow it a potential substitution for petroleum-based polymers. And the current research is also expected to shed some insight into the design and the fabrication of eco-friendly polymers.

Peripheral blood lymphocyte subsets are associated with the clinical outcomes of prostate cancer patients.

BACKGROUND: Many studies have sought associations between the total peripheral blood lymphocyte count and prostate cancer (PCa) prognosis, but not peripheral lymphocyte subsets. We explored the associations between the absolute counts of peripheral lymphocyte subsets and PCa patient survival. METHODS: 135 PCa patients were included in this study. The log-rank test and Kaplan-Meier method were employed to compare overall survival (OS) and progression-free survival (PFS) rates. Univariate and multivariate Cox's regression analyses were employed to identify prognostic factors. Flow cytometry analysis was used to determine the numbers of peripheral lymphocyte subsets. RESULTS: PCa patients with lower absolute counts of certain lymphocyte subsets showed poorer PFS and OS than those with higher absolute counts of these cells. The numbers of CD4+ T cells, CD3+ T cells, and natural killer (NK) cells were significantly higher in PCa patients of tumor node metastasis (TNM) Ⅰ-Ⅱ stages than those of TNM ⅠIII-IV stages. Univariate and multivariate Cox's regression analyses of OS and PFS indicated that neutrophil numbers > 4.81*109/L, CD4+ T cells ≤ 254 /µL, and NK cells ≤ 136 /µL were unfavorably prognostic for patients with PCa. CONCLUSIONS: Lower absolute counts of certain peripheral lymphocyte subsets (NK cells ≤ 136/µL and CD4+ T cells ≤ 254/µL) are prognostically unfavorable for PCa patients.

Do racial and ethnic disparities in following stay-at-home orders influence COVID-19 health outcomes? A mediation analysis approach.

Racial/ethnic disparities are among the top-selective underlying determinants associated with the disproportional impact of the COVID-19 pandemic on human mobility and health outcomes. This study jointly examined county-level racial/ethnic differences in compliance with stay-at-home orders and COVID-19 health outcomes during 2020, leveraging two-year geo-tracking data of mobile devices across ~4.4 million point-of-interests (POIs) in the contiguous United States. Through a set of structural equation modeling, this study quantified how racial/ethnic differences in following stay-at-home orders could mediate COVID-19 health outcomes, controlling for state effects, socioeconomics, demographics, occupation, and partisanship. Results showed that counties with higher Asian populations decreased most in their travel, both in terms of reducing their overall POIs' visiting and increasing their staying home percentage. Moreover, counties with higher White populations experienced the lowest infection rate, while counties with higher African American populations presented the highest case-fatality ratio. Additionally, control variables, particularly partisanship, median household income, percentage of elders, and urbanization, significantly accounted for the county differences in human mobility and COVID-19 health outcomes. Mediation analyses further revealed that human mobility only statistically influenced infection rate but not case-fatality ratio, and such mediation effects varied substantially among racial/ethnic compositions. Last, robustness check of racial gradient at census block group level documented consistent associations but greater magnitude. Taken together, these findings suggest that US residents' responses to COVID-19 are subject to an entrenched and consequential racial/ethnic divide.

Flatten the curve: Empirical evidence on how non-pharmaceutical interventions substituted pharmaceutical treatments during COVID-19 pandemic.

During the outbreak of the COVID-19 pandemic, Non-Pharmaceutical and Pharmaceutical treatments were alternative strategies for governments to intervene. Though many of these intervention methods proved to be effective to stop the spread of COVID-19, i.e., lockdown and curfew, they also posed risk to the economy; in such a scenario, an analysis on how to strike a balance becomes urgent. Our research leverages the mobility big data from the University of Maryland COVID-19 Impact Analysis Platform and employs the Generalized Additive Model (GAM), to understand how the social demographic variables, NPTs (Non-Pharmaceutical Treatments) and PTs (Pharmaceutical Treatments) affect the New Death Rate (NDR) at county-level. We also portray the mutual and interactive effects of NPTs and PTs on NDR. Our results show that there exists a specific usage rate of PTs where its marginal effect starts to suppress the NDR growth, and this specific rate can be reduced through implementing the NPTs.

A big-data driven approach to analyzing and modeling human mobility trend under non-pharmaceutical interventions during COVID-19 pandemic.

During the unprecedented coronavirus disease 2019 (COVID-19) challenge, non-pharmaceutical interventions became a widely adopted strategy to limit physical movements and interactions to mitigate virus transmissions. For situational awareness and decision-support, quickly available yet accurate big-data analytics about human mobility and social distancing is invaluable to agencies and decision-makers. This paper presents a big-data-driven analytical framework that ingests terabytes of data on a daily basis and quantitatively assesses the human mobility trend during COVID-19. Using mobile device location data of over 150 million monthly active samples in the United States (U.S.), the study successfully measures human mobility with three main metrics at the county level: daily average number of trips per person; daily average person-miles traveled; and daily percentage of residents staying home. A set of generalized additive mixed models is employed to disentangle the policy effect on human mobility from other confounding effects including virus effect, socio-demographic effect, weather effect, industry effect, and spatiotemporal autocorrelation. Results reveal the policy plays a limited, time-decreasing, and region-specific effect on human movement. The stay-at-home orders only contribute to a 3.5%-7.9% decrease in human mobility, while the reopening guidelines lead to a 1.6%-5.2% mobility increase. Results also indicate a reasonable spatial heterogeneity among the U.S. counties, wherein the number of confirmed COVID-19 cases, income levels, industry structure, age and racial distribution play important roles. The data informatics generated by the framework are made available to the public for a timely understanding of mobility trends and policy effects, as well as for time-sensitive decision support to further contain the spread of the virus.

Mobile device location data reveal human mobility response to state-level stay-at-home orders during the COVID-19 pandemic in the USA.

One approach to delaying the spread of the novel coronavirus (COVID-19) is to reduce human travel by imposing travel restriction policies. Understanding the actual human mobility response to such policies remains a challenge owing to the lack of an observed and large-scale dataset describing human mobility during the pandemic. This study uses an integrated dataset, consisting of anonymized and privacy-protected location data from over 150 million monthly active samples in the USA, COVID-19 case data and census population information, to uncover mobility changes during COVID-19 and under the stay-at-home state orders in the USA. The study successfully quantifies human mobility responses with three important metrics: daily average number of trips per person; daily average person-miles travelled; and daily percentage of residents staying at home. The data analytics reveal a spontaneous mobility reduction that occurred regardless of government actions and a 'floor' phenomenon, where human mobility reached a lower bound and stopped decreasing soon after each state announced the stay-at-home order. A set of longitudinal models is then developed and confirms that the states' stay-at-home policies have only led to about a 5% reduction in average daily human mobility. Lessons learned from the data analytics and longitudinal models offer valuable insights for government actions in preparation for another COVID-19 surge or another virus outbreak in the future.

Quantifying human mobility behaviour changes during the COVID-19 outbreak in the United States.

Since the first case of the novel coronavirus disease (COVID-19) was confirmed in Wuhan, China, social distancing has been promoted worldwide, including in the United States, as a major community mitigation strategy. However, our understanding remains limited in how people would react to such control measures, as well as how people would resume their normal behaviours when those orders were relaxed. We utilize an integrated dataset of real-time mobile device location data involving 100 million devices in the contiguous United States (plus Alaska and Hawaii) from February 2, 2020 to May 30, 2020. Built upon the common human mobility metrics, we construct a Social Distancing Index (SDI) to evaluate people's mobility pattern changes along with the spread of COVID-19 at different geographic levels. We find that both government orders and local outbreak severity significantly contribute to the strength of social distancing. As people tend to practice less social distancing immediately after they observe a sign of local mitigation, we identify several states and counties with higher risks of continuous community transmission and a second outbreak. Our proposed index could help policymakers and researchers monitor people's real-time mobility behaviours, understand the influence of government orders, and evaluate the risk of local outbreaks.

[Hydrogeochemical Investigations of Groundwater in the Lingbei Area, Leizhou Peninsula].

Groundwater is an important source of water supply in the Leizhou Peninsula. In August 2018, five surface water samples, 18 shallow pore water samples, 14 middle-deep pore water samples, and 27 pore fissure water samples were collected in the Lingbei area, the northern part of the Leizhou Peninsula. Major ion concentrations, as well as H, O isotope composition (of δ18O and δ2H) were analyzed. The results show that groundwater pH values, total hardness, concentrations of K+, TDS, Cl-, and SO42- are low, while H2SiO3(aq) and NO3- concentrations are relatively high. For pores and fissures water, hydrochemical types are mainly Mg-Ca-HCO3, Mg-Ca-HCO3-Cl, and Cl- loadings are significantly increased along the flow path. Ca-Cl, Na-Ca-HCO3-Cl, and Na-Ca-Mg-HCO3-Cl types predominate in shallow pore water. For middle-deep pore water, the types are primarily Mg-Ca-HCO3, Na-Ca-Mg-HCO3, K-Na-HCO3-SO4, and concentrations of K+, Na+, Cl-, and SO42- are modestly increased along the flow path. We find that the groundwater is of meteoric origin, groundwater Cl- and Na+ originate from marine atmospheric precipitation, Mg+, Ca2+, and HCO3- are mainly derived from silicate weathering, and NO3- principally arises from chemical fertilizer. Shallow pore water and fissure pore water are affected by evaporation concentration, whereas cation exchange is important for middle-deep pore water. The milligram equivalent ratio of nitrate in groundwater reaches 28.3%. After taking into account the nitrate, 50.85% of the sampling water is NO3 type, and displays a pollution trend. Our results contribute to the sustainable utilization of groundwater in the study area and other similar areas.

Mobile device data reveal the dynamics in a positive relationship between human mobility and COVID-19 infections.

Accurately estimating human mobility and gauging its relationship with virus transmission is critical for the control of COVID-19 spreading. Using mobile device location data of over 100 million monthly active samples, we compute origin-destination travel demand and aggregate mobility inflow at each US county from March 1 to June 9, 2020. Then, we quantify the change of mobility inflow across the nation and statistically model the time-varying relationship between inflow and the infections. We find that external travel to other counties decreased by 35% soon after the nation entered the emergency situation, but recovered rapidly during the partial reopening phase. Moreover, our simultaneous equations analysis highlights the dynamics in a positive relationship between mobility inflow and the number of infections during the COVID-19 onset. This relationship is found to be increasingly stronger in partially reopened regions. Our study provides a quick reference and timely data availability for researchers and decision makers to understand the national mobility trends before and during the pandemic. The modeling results can be used to predict mobility and transmissions risks and integrated with epidemics models to further assess the public health outcomes.

Phospholipid scramblase 1 interacts with influenza A virus NP, impairing its nuclear import and thereby suppressing virus replication.

Transcription and replication of the influenza A virus (IAV) genome occur in the nucleus of infected cells and are carried out by the viral ribonucleoprotein complex (vRNP). As a major component of the vRNP complex, the viral nucleoprotein (NP) mediates the nuclear import of the vRNP complex via its nuclear localization signals (NLSs). Clearly, an effective way for the host to antagonize IAV infection would be by targeting vRNP nuclear import. Here, we identified phospholipid scramblase 1 (PLSCR1) as a binding partner of NP by using a yeast two-hybrid (Y2H) screen. The interaction between NP and PLSCR1 in mammalian cells was demonstrated by using co-immunoprecipitation and pull-down assays. We found that the stable overexpression of PLSCR1 suppressed the nuclear import of NP, hindered the virus life cycle, and significantly inhibited the replication of various influenza subtypes. In contrast, siRNA knockdown or CRISPR/Cas9 knockout of PLSCR1 increased virus propagation. Further analysis indicated that the inhibitory effect of PLSCR1 on the nuclear import of NP was not caused by affecting the phosphorylation status of NP or by stimulating the interferon (IFN) pathways. Instead, PLSCR1 was found to form a trimeric complex with NP and members of the importin α family, which inhibited the incorporation of importin ß, a key mediator of the classical nuclear import pathway, into the complex, thus impairing the nuclear import of NP and suppressing virus replication. Our results demonstrate that PLSCR1 negatively regulates virus replication by interacting with NP in the cytoplasm and preventing its nuclear import.

Host Cellular Protein TRAPPC6AΔ Interacts with Influenza A Virus M2 Protein and Regulates Viral Propagation by Modulating M2 Trafficking.

Influenza A virus (IAV) matrix protein 2 (M2) plays multiple roles in the early and late phases of viral infection. Once synthesized, M2 is translocated to the endoplasmic reticulum (ER), travels to the Golgi apparatus, and is sorted at the trans-Golgi network (TGN) for transport to the apical plasma membrane, where it functions in virus budding. We hypothesized that M2 trafficking along with its secretory pathway must be finely regulated, and host factors could be involved in this process. However, no studies examining the role of host factors in M2 posttranslational transport have been reported. Here, we used a yeast two-hybrid (Y2H) system to screen for host proteins that interact with the M2 protein and identified transport protein particle complex 6A (TRAPPC6A) as a potential binding partner. We found that both TRAPPC6A and its N-terminal internal-deletion isoform, TRAPPC6A delta (TRAPPC6AΔ), interact with M2. Truncation and mutation analyses showed that the highly conserved leucine residue at position 96 of M2 is critical for mediating this interaction. The role of TRAPPC6AΔ in the viral life cycle was investigated by the knockdown of endogenous TRAPPC6AΔ with small interfering RNA (siRNA) and by generating a recombinant virus that was unable to interact with TRAPPC6A/TRAPPC6AΔ. The results indicated that TRAPPC6AΔ, through its interaction with M2, slows M2 trafficking to the apical plasma membrane, favors viral replication in vitro, and positively modulates virus virulence in mice. IMPORTANCE: The influenza A virus M2 protein regulates the trafficking of not only other proteins but also itself along the secretory pathway. However, the host factors involved in the regulation of the posttranslational transport of M2 are largely unknown. In this study, we identified TRAPPC6A and its N-terminal internal-deletion isoform, TRAPPC6AΔ, as interacting partners of M2. We found that the leucine (L) residue at position 96 of M2 is critical for mediating this interaction, which leads us to propose that the high level of conservation of 96L is a consequence of M2 adaptation to its interacting host factor TRAPPC6A/TRAPPC6AΔ. Importantly, we discovered that TRAPPC6AΔ can positively regulate viral replication in vitro by modulating M2 trafficking to the plasma membrane.

H6 influenza viruses pose a potential threat to human health.

UNLABELLED: Influenza viruses of the H6 subtype have been isolated from wild and domestic aquatic and terrestrial avian species throughout the world since their first detection in a turkey in Massachusetts in 1965. Since 1997, H6 viruses with different neuraminidase (NA) subtypes have been detected frequently in the live poultry markets of southern China. Although sequence information has been gathered over the last few years, the H6 viruses have not been fully biologically characterized. To investigate the potential risk posed by H6 viruses to humans, here we assessed the receptor-binding preference, replication, and transmissibility in mammals of a series of H6 viruses isolated from live poultry markets in southern China from 2008 to 2011. Among the 257 H6 strains tested, 87 viruses recognized the human type receptor. Genome sequence analysis of 38 representative H6 viruses revealed 30 different genotypes, indicating that these viruses are actively circulating and reassorting in nature. Thirty-seven of 38 viruses tested in mice replicated efficiently in the lungs and some caused mild disease; none, however, were lethal. We also tested the direct contact transmission of 10 H6 viruses in guinea pigs and found that 5 viruses did not transmit to the contact animals, 3 viruses transmitted to one of the three contact animals, and 2 viruses transmitted to all three contact animals. Our study demonstrates that the H6 avian influenza viruses pose a clear threat to human health and emphasizes the need for continued surveillance and evaluation of the H6 influenza viruses circulating in nature. IMPORTANCE: Avian influenza viruses continue to present a challenge to human health. Research and pandemic preparedness have largely focused on the H5 and H7 subtype influenza viruses in recent years. Influenza viruses of the H6 subtype have been isolated from wild and domestic aquatic and terrestrial avian species throughout the world since their first detection in the United States in 1965. Since 1997, H6 viruses have been detected frequently in the live poultry markets of southern China; however, the biological characterization of these viruses is very limited. Here, we assessed the receptor-binding preference, replication, and transmissibility in mammals of a series of H6 viruses isolated from live poultry markets in southern China and found that 34% of the viruses are able to bind human type receptors and that some of them are able to transmit efficiently to contact animals. Our study demonstrates that the H6 viruses pose a clear threat to human health.