Toxin insights into nicotinic acetylcholine receptors

Venomous species have evolved cocktails of bioactive peptides to facilitate prey capture. Given their often exquisite potency and target selectivity, venom peptides provide unique biochemical tools for probing the function of membrane proteins at the molecular level. in the field of the nicotinic acetylcholine receptors (nAChRs), the subtype specific snake alpha-neurotoxins and cone snail alpha-conotoxins have been widely used to probe receptor structure and function in native tissues and recombinant systems. However, only recently has it been possible to generate an accurate molecular view of these nAChR-toxin interactions. Crystal structures of AChBP, a homologue of the nAChR ligand binding domain, have now been solved in complex with alpha-cobratoxin, alpha-conotoxin PnIA and alpha-conotoxin Iml. The orientation of all three toxins in the ACh binding site confirms many of the predictions obtained from mutagenesis and docking simulations on homology models of mammalian nAChR. The precise understanding of the molecular determinants of these complexes is expected to contribute to the development of more selective nAChR modulators. In this commentary, we review the structural data on nAChR-toxin interactions and discuss their implications for the design of novel ligands acting at the nAChR. (c) 2006 Elsevier Inc. All rights reserved.

An ethnopharmacological approach to multidrug -resistant Staphylococcus aureus: Evaluation of Italian plants used in the traditional healing of skin disease

One-third of botanical remedies from southern Italy are used to treat skin and soft tissue infections (SST's). Methicillin-resistant Staphylococcus aureus (MRSA), a common cause of SSTIs, is responsible for increased morbidity and mortality from infections. Therapeutic options are limited by antibiotic resistance. Many plants possess potent antimicrobial compounds for these disorders. Validation of traditional medical practices is important for the people who rely on medicinal plants. Moreover, identification of novel antibiotics and anti-pathogenic agents for MRSA is important to global healthcare.^ I took an ethnopharmacological approach to understand how Italian medicinal plants used for the treatment of SSTIs affect MRSA growth and virulence. My hypothesis was that plants used in folk remedies for SSTI would exhibit lower cytotoxicity and greater inhibition of bacterial growth, biofilm formation and toxin production in MRSA than plants used for remedies unrelated to the skin or for plants with no ethnomedical application. The field portion of my research was conducted in the Vulture-Alto Bradano area of southern Italy. I collected 104 plant species and created 168 crude extracts. In the lab, I screened samples for activity against MRSA in a battery of bioassays. Growth inhibition was analyzed using broth microtiter assays for determination of the minimum inhibitory concentration. Interference with quorum-sensing (QS) processes, which mediate pathogenicity, was quantified through RP-HPLC of δ-toxin production. Interference with biofilm formation and adherence was assessed using staining methods. The mammalian cytotoxicity of natural products was analyzed using MTT cell proliferation assay techniques.^ Although bacteriostatic activity was limited, extracts from six plants used in Italian folk medicine (Arundo donax, Ballota nigra, Juglans regia, Leopoldia comosa, Marrubium vulgare, and Rubus ulmifolius ) significantly inhibited biofilm formation and adherence. Moreover, plants used to treat SSTI demonstrated significantly greater anti-biofilm activity when compared to plants with no ethnomedical application. QSI activity was evident in 90% of the extracts tested and extracts from four plants ( Ballota nigra, Castanea saliva, Rosmarinus officinalis, and Sambucus ebulus) exhibited a significant dose-dependent response. Some of the plant remedies for SSTI identified in this study can be validated due to anti-MRSA activity.^

Relationship between super antigenicity, antimicrobial resistance and origin of Staphylococcus aureus isolated

Introduction: Staphylococcus aureus is a pathogen that causes food poisoning as well as hospital and community acquired infections. Objective: Establish the profile of superantigen genes among hospital isolates in relation to clinical specimen type, susceptibility to antibiotics and hospital or community acquisition. Methods: Eighty one isolates obtained from patients at Colombian hospital, were classified by antimicrobial susceptibility, specimen type and hospital or community acquired . The PCR uniplex and multiplex was used for detection of 22 superantigen genes (18 enterotoxins, tsst-1 and three exfoliative toxins). Results: Ninety five point one percent of isolates harbored one or more of the genes with an average of 5.6 genes. Prevalence of individual genes was variable and the most prevalent was seg (51.9%). Thirty nine genotypes were obtained, and the genotype gimnou (complete egc cluster) was the most prevalent alone (16.0%) and in association with other genes (13.6%). The correlation between presence of superantigens and clinical specimen or antimicrobial susceptibility showed no significant difference. But there was significant difference between presence of superantigens and the origin of the isolates, hospital or community acquired (p= 0.049). Conclusions: The results show the variability of the superantigen genes profile in hospital isolates and shows no conclusive relationship with the clinical sample type and antimicrobial susceptibility, but there was correlation with community and hospital isolates. The analysis of the interplay between virulence, epidemic and antibiotic resistance of bacterial populations is needed to predict the future of infectious diseases.

Detecção de genes de enterotoxinas e da toxina-1 da Síndrome do Choque Tóxico de Staphylococcus aureus isolados de mastites no Distrito Federal e Entorno

Dissertação (mestrado)—Universidade de Brasília, Faculdade de Agronomia e Medicina Veterinária, Programa de Pós-Graduação em Saúde Animal, 2016.

An investigation of contact transmission of methicillin-resistant staphylococcus aureus

Hand hygiene is critical in the healthcare setting and it is believed that methicillin-resistant Staphylococcus aureus (MRSA), for example, is transmitted from patient to patient largely via the hands of health professionals. A study has been carried out at a large teaching hospital to estimate how often the gloves of a healthcare worker are contaminated with MRSA after contact with a colonized patient. The effectiveness of handwashing procedures to decontaminate the health professionals' hands was also investigated, together with how well different healthcare professional groups complied with handwashing procedures. The study showed that about 17% (9–25%) of contacts between a healthcare worker and a MRSA-colonized patient results in transmission of MRSA from a patient to the gloves of a healthcare worker. Different health professional groups have different rates of compliance with infection control procedures. Non-contact staff (cleaners, food services) had the shortest handwashing times. In this study, glove use compliance rates were 75% or above in all healthcare worker groups except doctors whose compliance was only 27%.

Smoothing a Discrete Hazard Function for the Number of Patients Colonized with Methicillin-Resistance Staphylococcus Aureus in an Intensive Care Unit

A new method for estimating the time to colonization of Methicillin-resistant Staphylococcus Aureus (MRSA) patients is developed in this paper. The time to colonization of MRSA is modelled using a Bayesian smoothing approach for the hazard function. There are two prior models discussed in this paper: the first difference prior and the second difference prior. The second difference prior model gives smoother estimates of the hazard functions and, when applied to data from an intensive care unit (ICU), clearly shows increasing hazard up to day 13, then a decreasing hazard. The results clearly demonstrate that the hazard is not constant and provide a useful quantification of the effect of length of stay on the risk of MRSA colonization which provides useful insight.

Using a longitudinal model to estimate the effect of methicillin-resistant staphylococcus aureus infection on length of stay in an intensive care unit

Healthcare-associated methicillin-resistant Staphylococcus aureus(MRSA) infection may cause increased hospital stay or, sometimes, death. Quantifying this effect is complicated because it is a time-dependent exposure: infection may prolong hospital stay, while longer stays increase the risk of infection. We overcome these problems by using a multinomial longitudinal model for estimating the daily probability of death and discharge. We then extend the basic model to estimate how the effect of MRSA infection varies over time, and to quantify the number of excess ICU days due to infection. We find that infection decreases the relative risk of discharge (relative risk ratio = 0.68, 95% credible interval: 0.54, 0.82), but is only indirectly associated with increased mortality. An infection on the first day of admission resulted in a mean extra stay of 0.3 days (95% CI: 0.1, 0.5) for a patient with an APACHE II score of 10, and 1.2 days (95% CI: 0.5, 2.0) for a patient with an APACHE II score of 30. The decrease in the relative risk of discharge remained fairly constant with day of MRSA infection, but was slightly stronger closer to the start of infection. These results confirm the importance of MRSA infection in increasing ICU stay, but suggest that previous work may have systematically overestimated the effect size.

Phylogenetically distinct Staphylococcus aureus lineage prevalent among indigenous communities in northern Australia

The aim was to determine the evolutionary position of the Staphylococcus aureus clonal complex 75 (CC75) that is prevalent in tropical northern Australia. Sequencing of gap, rpoB, sodA, tuf, and hsp60 and the multilocus sequence typing loci revealed a clear separation between conventional S. aureus and CC75 and significant diversity within CC75.

Nasal carriage of Staphylococcus aureus, including community-associated methicillin-resistant strains, in Queensland adults

Community-associated methicillin-resistant Staphylococcus aureus (CA-MRSA) infections are emerging in southeast Queensland, Australia, but the incidence of carriage of CA-MRSA strains is unknown. The aim of this study was to assess the nasal carriage rate of S. aureus, including CA-MRSA strains, in the general adult population of southeast Queensland. 396 patients presenting to general practices in two Brisbane suburbs and 303 volunteers randomly selected from the electoral rolls in the same suburbs completed a medical questionnaire and had nasal swabs performed for S. aureus. All isolates of S. aureus underwent antibiotic susceptibility testing and single-nucleotide polymorphism (SNP) and binary typing, including determination of Panton–Valentine leukocidin (PVL). The nasal carriage rate of methicillin-susceptible S. aureus (MSSA) was 202/699 (28%), a rate similar to that found in other community-based nasal carriage studies. According to multivariate analysis, nasal carriage of S. aureus was associated with male sex, young adult age group and Caucasian ethnicity. Only two study isolates (one MSSA and one CA-MRSA) carried PVL. The nasal carriage rate of MRSA was low, at 5/699 (0.7%), and only two study participants (0.3%) had CA-MRSA strains. CA-MRSA is an emerging cause of infection in southeast Queensland, but as yet the incidence of carriage of CA-MRSA in the general community is low.

Performance analysis of adaptive lattice filters for FM signals and alpha-stable processes

The performance of an adaptive filter may be studied through the behaviour of the optimal and adaptive coefficients in a given environment. This thesis investigates the performance of finite impulse response adaptive lattice filters for two classes of input signals: (a) frequency modulated signals with polynomial phases of order p in complex Gaussian white noise (as nonstationary signals), and (b) the impulsive autoregressive processes with alpha-stable distributions (as non-Gaussian signals). Initially, an overview is given for linear prediction and adaptive filtering. The convergence and tracking properties of the stochastic gradient algorithms are discussed for stationary and nonstationary input signals. It is explained that the stochastic gradient lattice algorithm has many advantages over the least-mean square algorithm. Some of these advantages are having a modular structure, easy-guaranteed stability, less sensitivity to the eigenvalue spread of the input autocorrelation matrix, and easy quantization of filter coefficients (normally called reflection coefficients). We then characterize the performance of the stochastic gradient lattice algorithm for the frequency modulated signals through the optimal and adaptive lattice reflection coefficients. This is a difficult task due to the nonlinear dependence of the adaptive reflection coefficients on the preceding stages and the input signal. To ease the derivations, we assume that reflection coefficients of each stage are independent of the inputs to that stage. Then the optimal lattice filter is derived for the frequency modulated signals. This is performed by computing the optimal values of residual errors, reflection coefficients, and recovery errors. Next, we show the tracking behaviour of adaptive reflection coefficients for frequency modulated signals. This is carried out by computing the tracking model of these coefficients for the stochastic gradient lattice algorithm in average. The second-order convergence of the adaptive coefficients is investigated by modeling the theoretical asymptotic variance of the gradient noise at each stage. The accuracy of the analytical results is verified by computer simulations. Using the previous analytical results, we show a new property, the polynomial order reducing property of adaptive lattice filters. This property may be used to reduce the order of the polynomial phase of input frequency modulated signals. Considering two examples, we show how this property may be used in processing frequency modulated signals. In the first example, a detection procedure in carried out on a frequency modulated signal with a second-order polynomial phase in complex Gaussian white noise. We showed that using this technique a better probability of detection is obtained for the reduced-order phase signals compared to that of the traditional energy detector. Also, it is empirically shown that the distribution of the gradient noise in the first adaptive reflection coefficients approximates the Gaussian law. In the second example, the instantaneous frequency of the same observed signal is estimated. We show that by using this technique a lower mean square error is achieved for the estimated frequencies at high signal-to-noise ratios in comparison to that of the adaptive line enhancer. The performance of adaptive lattice filters is then investigated for the second type of input signals, i.e., impulsive autoregressive processes with alpha-stable distributions . The concept of alpha-stable distributions is first introduced. We discuss that the stochastic gradient algorithm which performs desirable results for finite variance input signals (like frequency modulated signals in noise) does not perform a fast convergence for infinite variance stable processes (due to using the minimum mean-square error criterion). To deal with such problems, the concept of minimum dispersion criterion, fractional lower order moments, and recently-developed algorithms for stable processes are introduced. We then study the possibility of using the lattice structure for impulsive stable processes. Accordingly, two new algorithms including the least-mean P-norm lattice algorithm and its normalized version are proposed for lattice filters based on the fractional lower order moments. Simulation results show that using the proposed algorithms, faster convergence speeds are achieved for parameters estimation of autoregressive stable processes with low to moderate degrees of impulsiveness in comparison to many other algorithms. Also, we discuss the effect of impulsiveness of stable processes on generating some misalignment between the estimated parameters and the true values. Due to the infinite variance of stable processes, the performance of the proposed algorithms is only investigated using extensive computer simulations.

Multivariate Markov Process Models for the transmission of methicillin-resistant Staphylococcus Aureus in a hospital ward

Methicillin-resistant Staphylococcus Aureus (MRSA) is a pathogen that continues to be of major concern in hospitals. We develop models and computational schemes based on observed weekly incidence data to estimate MRSA transmission parameters. We extend the deterministic model of McBryde, Pettitt, and McElwain (2007, Journal of Theoretical Biology 245, 470–481) involving an underlying population of MRSA colonized patients and health-care workers that describes, among other processes, transmission between uncolonized patients and colonized health-care workers and vice versa. We develop new bivariate and trivariate Markov models to include incidence so that estimated transmission rates can be based directly on new colonizations rather than indirectly on prevalence. Imperfect sensitivity of pathogen detection is modeled using a hidden Markov process. The advantages of our approach include (i) a discrete valued assumption for the number of colonized health-care workers, (ii) two transmission parameters can be incorporated into the likelihood, (iii) the likelihood depends on the number of new cases to improve precision of inference, (iv) individual patient records are not required, and (v) the possibility of imperfect detection of colonization is incorporated. We compare our approach with that used by McBryde et al. (2007) based on an approximation that eliminates the health-care workers from the model, uses Markov chain Monte Carlo and individual patient data. We apply these models to MRSA colonization data collected in a small intensive care unit at the Princess Alexandra Hospital, Brisbane, Australia.