Role of Active Site Rigidity in Activity: MD Simulation and Fluorescence Study on a Lipase Mutant

Relationship between stability and activity of enzymes is maintained by underlying conformational flexibility. In thermophilic enzymes, a decrease in flexibility causes low enzyme activity while in less stable proteins such as mesophiles and psychrophiles, an increase in flexibility is associated with enhanced enzyme activity. Recently, we identified a mutant of a lipase whose stability and activity were enhanced simultaneously. In this work, we probed the conformational dynamics of the mutant and the wild type lipase, particularly flexibility of their active site using molecular dynamic simulations and time-resolved fluorescence techniques. In contrast to the earlier observations, our data show that active site of the mutant is more rigid than wild type enzyme. Further investigation suggests that this lipase needs minimal reorganization/flexibility of active site residues during its catalytic cycle. Molecular dynamic simulations suggest that catalytically competent active site geometry of the mutant is relatively more preserved than wild type lipase, which might have led to its higher enzyme activity. Our study implies that widely accepted positive correlation between conformation flexibility and enzyme activity need not be stringent and draws attention to the possibility that high enzyme activity can still be accomplished in a rigid active site and stable protein structures. This finding has a significant implication towards better understanding of involvement of dynamic motions in enzyme catalysis and enzyme engineering through mutations in active site

Successful Amelioration of Mitochondrial Optic Neuropathy Using the Yeast NDI1 Gene in a Rat Animal Model

Background: Leber’s hereditary optic neuropathy (LHON) is a maternally inherited disorder with point mutations in mitochondrial DNA which result in loss of vision in young adults. The majority of mutations reported to date are within the genes encoding the subunits of the mitochondrial NADH-quinone oxidoreductase, complex I. Establishment of animal models of LHON should help elucidate mechanism of the disease and could be utilized for possible development of therapeutic strategies. Methodology/Principal Findings: We established a rat model which involves injection of rotenone-loaded microspheres into the optic layer of the rat superior colliculus. The animals exhibited the most common features of LHON. Visual loss was observed within 2 weeks of rotenone administration with no apparent effect on retinal ganglion cells. Death of retinal ganglion cells occurred at a later stage. Using our rat model, we investigated the effect of the yeast alternative NADH dehydrogenase, Ndi1. We were able to achieve efficient expression of the Ndi1 protein in the mitochondria of all regions of retinal ganglion cells and axons by delivering the NDI1 gene into the optical layer of the superior colliculus. Remarkably, even after the vision of the rats was severely impaired, treatment of the animals with the NDI1 gene led to a complete restoration of the vision to the normal level. Control groups that received either empty vector or the GFP gene had no effects

The dominant Anopheles vectors of human malaria in the Asia-Pacific region: occurrence data, distribution maps and bionomic précis

<p>Abstract</p> <p>Background</p> <p>The final article in a series of three publications examining the global distribution of 41 dominant vector species (DVS) of malaria is presented here. The first publication examined the DVS from the Americas, with the second covering those species present in Africa, Europe and the Middle East. Here we discuss the 19 DVS of the Asian-Pacific region. This region experiences a high diversity of vector species, many occurring sympatrically, which, combined with the occurrence of a high number of species complexes and suspected species complexes, and behavioural plasticity of many of these major vectors, adds a level of entomological complexity not comparable elsewhere globally. To try and untangle the intricacy of the vectors of this region and to increase the effectiveness of vector control interventions, an understanding of the contemporary distribution of each species, combined with a synthesis of the current knowledge of their behaviour and ecology is needed.</p> <p>Results</p> <p>Expert opinion (EO) range maps, created with the most up-to-date expert knowledge of each DVS distribution, were combined with a contemporary database of occurrence data and a suite of open access, environmental and climatic variables. Using the Boosted Regression Tree (BRT) modelling method, distribution maps of each DVS were produced. The occurrence data were abstracted from the formal, published literature, plus other relevant sources, resulting in the collation of DVS occurrence at 10116 locations across 31 countries, of which 8853 were successfully geo-referenced and 7430 were resolved to spatial areas that could be included in the BRT model. A detailed summary of the information on the bionomics of each species and species complex is also presented.</p> <p>Conclusions</p> <p>This article concludes a project aimed to establish the contemporary global distribution of the DVS of malaria. The three articles produced are intended as a detailed reference for scientists continuing research into the aspects of taxonomy, biology and ecology relevant to species-specific vector control. This research is particularly relevant to help unravel the complicated taxonomic status, ecology and epidemiology of the vectors of the Asia-Pacific region. All the occurrence data, predictive maps and EO-shape files generated during the production of these publications will be made available in the public domain. We hope that this will encourage data sharing to improve future iterations of the distribution maps.</p

Evaluation of appendicitis risk prediction models in adults with suspected appendicitis

Background Appendicitis is the most common general surgical emergency worldwide, but its diagnosis remains challenging. The aim of this study was to determine whether existing risk prediction models can reliably identify patients presenting to hospital in the UK with acute right iliac fossa (RIF) pain who are at low risk of appendicitis. Methods A systematic search was completed to identify all existing appendicitis risk prediction models. Models were validated using UK data from an international prospective cohort study that captured consecutive patients aged 16–45 years presenting to hospital with acute RIF in March to June 2017. The main outcome was best achievable model specificity (proportion of patients who did not have appendicitis correctly classified as low risk) whilst maintaining a failure rate below 5 per cent (proportion of patients identified as low risk who actually had appendicitis). Results Some 5345 patients across 154 UK hospitals were identified, of which two‐thirds (3613 of 5345, 67·6 per cent) were women. Women were more than twice as likely to undergo surgery with removal of a histologically normal appendix (272 of 964, 28·2 per cent) than men (120 of 993, 12·1 per cent) (relative risk 2·33, 95 per cent c.i. 1·92 to 2·84; P < 0·001). Of 15 validated risk prediction models, the Adult Appendicitis Score performed best (cut‐off score 8 or less, specificity 63·1 per cent, failure rate 3·7 per cent). The Appendicitis Inflammatory Response Score performed best for men (cut‐off score 2 or less, specificity 24·7 per cent, failure rate 2·4 per cent). Conclusion Women in the UK had a disproportionate risk of admission without surgical intervention and had high rates of normal appendicectomy. Risk prediction models to support shared decision‐making by identifying adults in the UK at low risk of appendicitis were identified

A holistic approach to a context-aware IoT ecosystem with Adaptive Ubiquitous Middleware

The Internet of Things is envisioned to provide connectivity and communication between various devices all over the world. Based on the devices and applications involved, the nature of the network formed differs. Thus, an intelligent and holistic ecosystem needs to be created wherein clients, data sources, smart objects, and services can all co-exist and interact with each other. We present Adaptive Ubiquitous Middleware for context-aware IoT ecosystems, which considers the situational context of the applications, devices, or people and the contexts of the network formed and accordingly adapts the behavior of the ecosystem. Adaptive Ubiquitous Middleware is a multi-agent, multi-communication protocol-facilitated middleware that acts as an integration point for applications to access relevant context, share it with other applications, and have relevant services made available via a multi-communication protocol bridge. We also present an optimal service allocation model for a single service class that utilizes available computing resources and achieves a minimum average response time. The system implementation has been evaluated with two use cases to demonstrate its applicability, effectiveness, and generality. The evaluation of optimal service allocation demonstrates the service response is much faster in the proposed model

Leveraging context-awareness for Internet of Things ecosystem: Representation, organization, and management of context

Present-day devices are becoming increasingly smarter than their predecessors. From a simple passive light switch to an intelligent wristwatch, great strides have been made in networking smart devices, creating an autonomous ecosystem, the so-called Internet of Things. In an increasingly information-driven world, context-awareness supports the intended applications as well as their constituent devices, making them conscious of and adaptive to the specific scenario in real-time. Moreover, heterogeneous devices in the Internet of Things ecosystem peruse disparate data formats and semantics, giving rise to interoperability and information sharing challenges. Context modeling is a core feature that facilitates interoperability and information sharing between applications. Although generic context models exist, they do not consider pertinent dimensions of context to provide a generic vocabulary, and therefore, they cannot be extended to generalize situations commonly encountered in the Internet of Things environment. An extensible, generic modeling and representation of context is required to manage pertinent context dimensions in various ecosystems by being dynamically aware of the situation. This paper presents Context Model for Internet of Things, an extensible and generic ontology-based context modeling approach that provides relevant information at the right time. This work encompasses Context Ontology for Internet of Things, an ontology-based context organization approach, which provides an abstract and overarching vocabulary that fosters knowledge reusability and sharing. The proposed model has been implemented and evaluated with a use case to validate its adaptability, effectiveness, and viability. Our evaluation based on generality, effectiveness, and consistency shows that the proposed model can effectively represent, organize, and manage the context in different Internet of Things ecosystems