A Molecular Electrostatic Potential Mapping Study of Some Fluoroquinolone Anti-Bacterial Agents

Molecular electrostatic potential (MEP) maps of some fluoroquinolones having varying degrees of activity against the bacterium Staphylococcus Aureus have been studied using the optimized hybridization displacement charges (HDC) combined with Löwdin charges obtained by the AM1 method. The roles of different substitutions at the N₁-position in the parent quinolone ring have been studied. The conformation of the carboxylic group attached to the quinolone ring was shown to be such that there is an intramolecular hydrogen bonding between the hydrogen atom of this group and the oxygen atom of the carbonyl group of the quinolone moiety. The carbonyl oxygen atom of the quinolone moiety, hydroxyl oxygen atom of the carboxylic group and the terminal nitrogen atom of the piperazin ring attached to the quinolone ring appear to be involved in the action of the drugs through electrostatic interactions while the N₁-alkyl substituents seem to be involved in the same through hydrophobic interactions.     

Winter ecology of the Arunachal macaque Macaca munzala in Pangchen Valley,western Arunachal Pradesh,northeastern India

The newly described Arunachal macaque Macaca munzala occurs largely in sub‐tropical to temperate environments at elevations of c. 1,800–3,000 m in Arunachal Pradesh, northeastern India. We studied its over‐wintering strategy by comparing the diet, ranging, and behavior of a troop of 24 individuals during winter and spring (December 2005 to May 2006) through instantaneous scan sampling (3,002 records, 448 scans, 112 hr of observation). We also monitored the phenology of food plants. The macaques spent more time (41–66%) feeding in the winter than in spring (33–51%), whereas time spent moving and resting was greater in spring. The diet composed largely of plants, with animal matter being eaten rarely. The number of plant species in the diet increased from 18 to 25 whereas food types rose from 18 to 36 from winter to spring, respectively. Although only two species formed 75% of the winter diet, seven species comprised this proportion in spring. Availability of fruits and young leaves increased in spring; the troop moved more and utilized a larger part of its range during this time. Seasonal changes in behavior could be explained by the scarcity of food and the costs of thermoregulation in winter. Our study suggests that the Arunachal macaque inhabits a highly seasonal environment and has an over‐wintering strategy that includes subsisting on a high‐fiber diet by increasing the time spent feeding, and minimizing energy expenditure by reducing the time spent moving. Am. J. Primatol. 71:939–947, 2009. © 2009 Wiley‐Liss, Inc.     

Defining the Minimal Important Difference for the Visual Analogue Scale Assessing Dyspnea in Patients with Malignant Pleural Effusions

Background

The minimal important difference (MID) is essential for interpreting the results of randomised controlled trials (RCTs). Despite a number of RCTs in patients with malignant pleural effusions (MPEs) which use the visual analogue scale for dyspnea (VASD) as an outcome measure, the MID has not been established.

Methods

Patients with suspected MPE undergoing a pleural procedure recorded their baseline VASD and their post-procedure VASD (24 hours after the pleural drainage), and in parallel assessed their breathlessness on a 7 point Likert scale.

Findings

The mean decrease in VASD in patients with a MPE reporting a ‘small but just worthwhile decrease’ in their dyspnea (i.e. equivalent to the MID) was 19mm (95% CI 14-24mm). The mean drainage volume required to produce a change in VASD of 19mm was 760ml.

Interpretation

The mean MID for the VASD in patients with a MPE undergoing a pleural procedure is 19mm (95% CI 14-24mm). Thus choosing an improvement of 19mm in the VASD would be justifiable in the design and analysis of future MPE studies.     

Relative Abundance and Plasmodium Infection Rates of Malaria Vectors in and around Jabalpur,a Malaria Endemic Region in Madhya Pradesh State,Central India

BackgroundThis study was undertaken in two Primary Health Centers (PHCs) of malaria endemic district Jabalpur in Madhya Pradesh (Central India).MethodsIn this study we had investigated the relative frequencies of the different anopheline species collected within the study areas by using indoor resting catches, CDC light trap and human landing methods. Sibling species of malaria vectors were identified by cytogenetic and molecular techniques. The role of each vector and its sibling species in the transmission of the different Plasmodium species was ascertained by using sporozoite ELISA.ResultsA total of 52,857 specimens comprising of 17 anopheline species were collected by three different methods (39,964 by indoor resting collections, 1059 by human landing and 11,834 by CDC light trap). Anopheles culicifacies was most predominant species in all collections (55, 71 and 32% in indoor resting, human landing and light trap collections respectively) followed by An. subpictus and An. annularis. All five sibling species of An. culicifacies viz. species A, B, C, D and E were found while only species T and S of An. fluviatilis were collected. The overall sporozoite rate in An. culicifacies and An. fluviatilis were 0.42% (0.25% for P. falciparum and 0.17% for P. vivax) and 0.90% (0.45% for P. falciparum and 0.45% for P. vivax) respectively. An. culicifacies and An. fluviatilis were found harbouring both P. vivax variants VK-210 and VK-247, and P. falciparum. An. culicifacies sibling species C and D were incriminated as vectors during most part of the year while sibling species T of An. fluviatilis was identified as potential vector in monsoon and post monsoon season.ConclusionsAn. culicifacies species C (59%) was the most abundant species followed by An. culicifacies D (24%), B (8.7%), E (6.7%) and A (1.5%). Among An. fluviatilis sibling species, species T was common (99%) and only few specimens of S were found. Our study provides crucial information on the prevalence of An. culicifacies and An. fluviatilis sibling species and their potential in malaria transmission which will assist in developing strategic control measures against these vectors.     

Protectome Analysis: A New Selective Bioinformatics Tool for Bacterial Vaccine Candidate Discovery

New generation vaccines are in demand to include only the key antigens sufficient to confer protective immunity among the plethora of pathogen molecules. In the last decade, large-scale genomics-based technologies have emerged. Among them, the Reverse Vaccinology approach was successfully applied to the development of an innovative vaccine against Neisseria meningitidis serogroup B, now available on the market with the commercial name BEXSERO® (Novartis Vaccines). The limiting step of such approaches is the number of antigens to be tested in in vivo models. Several laboratories have been trying to refine the original approach in order to get to the identification of the relevant antigens straight from the genome. Here we report a new bioinformatics tool that moves a first step in this direction. The tool has been developed by identifying structural/functional features recurring in known bacterial protective antigens, the so called “Protectome space,” and using such “protective signatures” for protective antigen discovery. In particular, we applied this new approach to Staphylococcus aureus and Group B Streptococcus and we show that not only already known protective antigens were re-discovered, but also two new protective antigens were identified.Although vaccines based on attenuated pathogens as pioneered by Luis Pasteur have been shown to be extremely effective, safety and technical reasons recommend that new generation vaccines include few selected pathogen components which, in combination with immunostimulatory molecules, can induce long lasting protective responses. Such approach implies that the key antigens sufficient to confer protective immunity are singled out among the plethora of pathogen molecules. As it turns out, the search for such protective antigens can be extremely complicated.Genomic technologies have opened the way to new strategies in vaccine antigen discovery (1, 2, 3). Among them, Reverse Vaccinology (RV)¹ has proved to be highly effective, as demonstrated by the fact that a new Serogroup B Neisseria meningitidis (MenB) vaccine, incorporating antigens selected by RV, is now available to defeat meningococcal meningitis (4, 5). In essence, RV is based on the simple assumption that cloning all annotated proteins/genes and screening them against a robust and reliable surrogate-of-protection assay must lead to the identification of all protective antigens. Because most of the assays available for protective antigen selection involve animal immunization and challenge, the number of antigens to be tested represents a severe bottleneck of the entire process. For this reason, despite the fact that RV is a brute force, inclusive approach (“test-all-to-lose-nothing” type of approach) in their pioneered work of MenB vaccine discovery, Pizza and co-workers did not test the entire collection of MenB proteins but rather restricted their analysis to the ones predicted to be surface-localized. This was based on the evidence that for an anti-MenB vaccine to be protective bactericidal antibodies must be induced, a property that only surface-exposed antigens have. For the selection of surface antigens Pizza and co-workers mainly used PSORT and other available tools like MOTIFS and FINDPATTERNS to find proteins carrying localization-associated features such as transmembrane domains, leader peptides, and lipobox and outer membrane anchoring motifs. At the end, 570 proteins were selected and entered the still very labor intensive screening phase. Over the last few years, our laboratories have been trying to move to more selective strategies. Our ultimate goal, we like to refer to as the “Holy Grail of Vaccinology,” is to identify protective antigens by “simply” scanning the genome sequence of any given pathogen, thus avoiding time consuming “wet science” and “move straight from genome to the clinic” (6).With this objective in mind, we have developed a series of proteomics-based protocols that, in combination with bioinformatics tools, have substantially reduced the number of antigens to be tested in the surrogate-of-protection assays (7, 8). In particular, we have recently described a three-technology strategy that allows to narrow the number of antigens to be tested in the animal models down to less than ten (9). However, this strategy still requires high throughput experimental activities. Therefore, the availability of in silico tools that selectively and accurately single out relevant categories of antigens among the complexity of pathogen components would greatly facilitate the vaccine discovery process.In the present work, we describe a new bioinformatics approach that brings an additional contribution to our “from genome to clinic” goal. The approach has been developed on the basis of the assumption that protective antigens are protective in that they have specific structural/functional features (“protective signatures”) that distinguish them from immunologically irrelevant pathogen components. These features have been identified by using existing databases and prediction tools, such as PFam and SMART. Our approach focuses on protein biological role rather than its localization: it is completely protein localization unbiased, and lead to the identification of both surface-exposed and secreted antigens (which are the majority in extracellular bacteria) as well as cytoplasmic protective antigens (for instance, antigens that elicit interferon γ producing CD4+ T cells, thus potentiating the killing activity of phagocytic cells toward intracellular pathogens). Should these assumptions be valid, PS could be identified if: (1) all known protective antigens are compiled to create what we refer to as “the Protectome space,” and (2) Protectome is subjected to computer-assisted scrutiny using selected tools. Once signatures are identified, novel protective antigens of a pathogen of interest should be identifiable by scanning its genome sequence in search for proteins that carry one or more protective signatures. A similar attempt has been reported (10), where the discrimination of protective antigens versus nonprotective antigens was tried using statistical methods based on amino acid compositional analysis and auto cross-covariance. This model was implemented in a server for the prediction of vaccine candidates, that is, Vaxijen (www.darrenflower.info/Vaxijen); however, the selection criteria applied are still too general leading to a list of candidates that include ca. 30% of the total genome ORFs very similarly to the number of antigens predicted by classical RV based on the presence of localization signals.Here we show that Protectome analysis unravels specific signatures embedded in protective antigens, most of them related to the biological role/function of the proteins. These signatures narrow down the candidate list to ca. 3% of the total ORFs content and can be exploited for protective antigen discovery. Indeed, the strategy was validated by demonstrating that well characterized vaccine components could be identified by scanning the genome sequence of the corresponding pathogens for the presence of the PS. Furthermore, when the approach was applied to Staphylococcus aureus and Streptococcus agalactiae (Group B Streptococcus, GBS) not only already known protective antigens were rediscovered, but also two new protective antigens were identified.     

Comparative Genomic Analysis of Buffalo (Bubalus bubalis) NOD1 and NOD2 Receptors and Their Functional Role in In-Vitro Cellular Immune Response

Nucleotide binding and oligomerization domain (NOD)-like receptors (NLRs) are innate immune receptors that recognize bacterial cell wall components and initiate host immune response. Structure and function of NLRs have been well studied in human and mice, but little information exists on genetic composition and role of these receptors in innate immune system of water buffalo—a species known for its exceptional disease resistance. Here, a comparative study on the functional domains of NOD1 and NOD2 was performed across different species. The NOD mediated in-vitro cellular responses were studied in buffalo peripheral blood mononuclear cells, resident macrophages, mammary epithelial, and fibroblast cells. Buffalo NOD1 (buNOD1) and buNOD2 showed conserved domain architectures as found in other mammals. The domains of buNOD1 and buNOD2 showed analogy in secondary and tertiary conformations. Constitutive expressions of NODs were ubiquitous in different tissues. Following treatment with NOD agonists, peripheral lymphocytes showed an IFN-γ response along-with production of pro-inflammatory cytokines. Alveolar macrophages and mammary epithelial cells showed NOD mediated in-vitro immune response through NF-κB dependent pathway. Fibroblasts showed pro-inflammatory cytokine response following agonist treatment. Our study demonstrates that both immune and non-immune cells could generate NOD-mediated responses to pathogens though the type and magnitude of response depend on the cell types. The structural basis of ligand recognition by buffalo NODs and knowledge of immune response by different cell types could be useful for development of non-infective innate immune modulators and next generation anti-inflammatory compounds.     

HIF-1α Plays a Critical Role in the Gestational Sidestream Smoke-Induced Bronchopulmonary Dysplasia in Mice

Rationale

Smoking during pregnancy increases the risk of bronchopulmonary dysplasia (BPD) and, in mice, gestational exposure to sidestream cigarette smoke (SS) induces BPD-like condition characterized by alveolar simplification, impaired angiogenesis, and suppressed surfactant protein production. Normal fetal development occurs in a hypoxic environment and nicotinic acetylcholine receptors (nAChRs) regulate the hypoxia-inducible factor (HIF)-1α that controls apoptosis and angiogenesis. To understand SS-induced BPD, we hypothesized that gestational SS affected alveolar development through HIF-1α.

Methods

Pregnant BALB/c mice were exposed to air (control) or SS throughout the gestational period and the 7-day-old lungs of the progeny were examined.

Results

Gestational SS increased apoptosis of alveolar and airway epithelial cells. This response was associated with increased alveolar volumes, higher levels of proapoptotic factors (FOXO3a, HIPK2, p53, BIM, BIK, and BAX) and the antiangiogenic factor (GAX), and lower levels of antiapoptotic factors (Akt-PI3K, NF-κB, HIF-1α, and Bcl-2) in the lung. Although gestational SS increased the cells containing the proangiogenic bombesin-like-peptide, it markedly decreased the expression of its receptor GRPR in the lung. The effects of SS on apoptosis were attenuated by the nAChR antagonist mecamylamine.

Conclusions

Gestational SS-induced BPD is potentially regulated by nAChRs and associated with downregulation of HIF-1α, increased apoptosis of epithelial cells, and increased alveolar volumes. Thus, in mice, exposure to sidestream tobacco smoke during pregnancy promotes BPD-like condition that is potentially mediated through the nAChR/HIF-1α pathway.     

Artificial Intelligence versus Statistical Modeling and Optimization of Cholesterol Oxidase Production by using Streptomyces Sp.

Cholesterol oxidase (COD) is a bi-functional FAD-containing oxidoreductase which catalyzes the oxidation of cholesterol into 4-cholesten-3-one. The wider biological functions and clinical applications of COD have urged the screening, isolation and characterization of newer microbes from diverse habitats as a source of COD and optimization and over-production of COD for various uses. The practicability of statistical/ artificial intelligence techniques, such as response surface methodology (RSM), artificial neural network (ANN) and genetic algorithm (GA) have been tested to optimize the medium composition for the production of COD from novel strain Streptomyces sp. NCIM 5500. All experiments were performed according to the five factor central composite design (CCD) and the generated data was analysed using RSM and ANN. GA was employed to optimize the models generated by RSM and ANN. Based upon the predicted COD concentration, the model developed with ANN was found to be superior to the model developed with RSM. The RSM-GA approach predicted maximum of 6.283 U/mL COD production, whereas the ANN-GA approach predicted a maximum of 9.93 U/mL COD concentration. The optimum concentrations of the medium variables predicted through ANN-GA approach were: 1.431 g/50 mL soybean, 1.389 g/50 mL maltose, 0.029 g/50 mL MgSO₄, 0.45 g/50 mL NaCl and 2.235 ml/50 mL glycerol. The experimental COD concentration was concurrent with the GA predicted yield and led to 9.75 U/mL COD production, which was nearly two times higher than the yield (4.2 U/mL) obtained with the un-optimized medium. This is the very first time we are reporting the statistical versus artificial intelligence based modeling and optimization of COD production by Streptomyces sp. NCIM 5500.