Heritability of Oral Microbiota and Immune Responses to Oral Bacteria

Maintaining a symbiotic oral microbiota is essential for oral and dental health, and host genetic factors may affect the composition or function of the oral microbiota through a range of possible mechanisms, including immune pathways. The study included 836 Swedish twins divided into separate groups of adolescents (n= 418) and unrelated adults (n= 418). Oral microbiota composition and functions of non-enzymatically lysed oral bacteria samples were evaluated using 16S rRNA gene sequencing and functional bioinformatics tools in the adolescents. Adaptive immune responses were assessed by testing for serum IgG antibodies against a panel of common oral bacteria in adults. In the adolescents, host genetic factors were associated with both the detection and abundance of microbial species, but with considerable variation between species. Host genetic factors were associated with predicted microbiota functions, including several functions related to bacterial sucrose, fructose, and carbohydrate metabolism. In adults, genetic factors were associated with serum antibodies against oral bacteria. In conclusion, host genetic factors affect the composition of the oral microbiota at a species level, and host-governed adaptive immune responses, and also affect the concerted functions of the oral microbiota as a whole. This may help explain why some people are genetically predisposed to the major dental diseases of caries and periodontitis

Allosteric Regulation Of the Hsp90 Dynamics and Stability By Client Recruiter Cochaperones: Protein Structure Network Modeling

The fundamental role of the Hsp90 chaperone in supporting functional activity of diverse protein clients is anchored by specific cochaperones. A family of immune sensing client proteins is delivered to the Hsp90 system with the aid of cochaperones Sgt1 and Rar1 that act cooperatively with Hsp90 to form allosterically regulated dynamic complexes. In this work, functional dynamics and protein structure network modeling are combined to dissect molecular mechanisms of Hsp90 regulation by the client recruiter cochaperones. Dynamic signatures of the Hsp90-cochaperone complexes are manifested in differential modulation of the conformational mobility in the Hsp90 lid motif. Consistent with the experiments, we have determined that targeted reorganization of the lid dynamics is a unifying characteristic of the client recruiter cochaperones. Protein network analysis of the essential conformational space of the Hsp90-cochaperone motions has identified structurally stable interaction communities, interfacial hubs and key mediating residues of allosteric communication pathways that act concertedly with the shifts in conformational equilibrium. The results have shown that client recruiter cochaperones can orchestrate global changes in the dynamics and stability of the interaction networks that could enhance the ATPase activity and assist in the client recruitment. The network analysis has recapitulated a broad range of structural and mutagenesis experiments, particularly clarifying the elusive role of Rar1 as a regulator of the Hsp90 interactions and a stability enhancer of the Hsp90-cochaperone complexes. Small-world organization of the interaction networks in the Hsp90 regulatory complexes gives rise to a strong correspondence between highly connected local interfacial hubs, global mediator residues of allosteric interactions and key functional hot spots of the Hsp90 activity. We have found that cochaperone-induced conformational changes in Hsp90 may be determined by specific interaction networks that can inhibit or promote progression of the ATPase cycle and thus control the recruitment of client proteins

The Pseudomonas aeruginosa biofilm matrix and cells are drastically impacted by gas discharge plasma treatment: A comprehensive model explaining plasma-mediated biofilm eradication

Biofilms are microbial communities encased in a protective matrix composed of exopolymeric substances including exopolysaccharides, proteins, lipids, and extracellular DNA. Biofilms cause undesirable effects such as biofouling, equipment damage, prostheses colonization, and disease. Biofilms are also more resilient than free-living cells to regular decontamination methods and therefore, alternative methods are needed to eradicate them. The use of non-thermal atmospheric pressure plasmas is a good alternative as plasmas contain reactive species, free radicals, and UV photons well-known for their decontamination potential against free microorganisms. Pseudomonas aeruginosa biofilms colonize catheters, indwelling devices, and prostheses. Plasma effects on cell viability have been previously documented for P. aeruginosa biofilms. Nonetheless, the effect of plasma on the biofilm matrix has received less attention and there is little evidence regarding the changes the matrix undergoes. The aim of this work was to study the effect plasma exerts mostly on the P. aeruginosa biofilm matrix and to expand the existing knowledge about its effect on sessile cells in order to achieve a better understanding of the mechanism/s underlying plasma-mediated biofilm inactivation. We report a reduction in the amount of the biofilm matrix, the loss of its tridimensional structure, and morphological changes in sessile cells at long exposure times. We show chemical and structural changes on the biofilm matrix (mostly on carbohydrates and eDNA) and cells (mostly on proteins and lipids) that are more profound with longer plasma exposure times. We also demonstrate the presence of lipid oxidation products confirming cell membrane lipid peroxidation as plasma exposure time increases. To our knowledge this is the first report providing detailed evidence of the variety of chemical and structural changes that occur mostly on the biofilm matrix and sessile cells as a consequence of the plasma treatment. Based on our results, we propose a comprehensive model explaining plasma-mediated biofilm inactivation.Fil: Soler Arango, Juliana. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - La Plata. Centro de Investigación y Desarrollo en Fermentaciones Industriales. Universidad Nacional de La Plata. Facultad de Ciencias Exactas. Centro de Investigación y Desarrollo en Fermentaciones Industriales; ArgentinaFil: Fígoli, Cecilia Beatríz. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - La Plata. Centro de Investigación y Desarrollo en Fermentaciones Industriales. Universidad Nacional de La Plata. Facultad de Ciencias Exactas. Centro de Investigación y Desarrollo en Fermentaciones Industriales; ArgentinaFil: Muraca, Giuliana Sabrina. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - La Plata. Centro de Investigación y Desarrollo en Fermentaciones Industriales. Universidad Nacional de La Plata. Facultad de Ciencias Exactas. Centro de Investigación y Desarrollo en Fermentaciones Industriales; ArgentinaFil: Bosch, María Alejandra. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - La Plata. Centro de Investigación y Desarrollo en Fermentaciones Industriales. Universidad Nacional de La Plata. Facultad de Ciencias Exactas. Centro de Investigación y Desarrollo en Fermentaciones Industriales; ArgentinaFil: Brelles Mariño, Graciela. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - La Plata. Centro de Investigación y Desarrollo en Fermentaciones Industriales. Universidad Nacional de La Plata. Facultad de Ciencias Exactas. Centro de Investigación y Desarrollo en Fermentaciones Industriales; Argentin

Making Predictions and Handling Errors in Reconstructed Biological Networks

In this thesis we present methods for applying techniques from complex network theory to analyze and interpret inferred biological interactions. With the advent of high throughput technologies such as gene microarrays and genome-wide sequencing, it is now possible to measure the activity of every gene in a cancer cell population under different conditions. How to extract important interactions from these experiments remains an outstanding question. Here we present a method to identify these key interactions by focusing on short paths in a transcription factor network. We use a mutual information-based approach to infer the transcription factor network from gene expression microarrays, which measure perturbations in a Diffuse Large B Cell Lymphoma cell line. By focusing on the number of short paths between transcription factors and signature genes in the inferred network, we find a set of transcription factors whose biology is crucial to the continued survival of these lymphoma cells and also show that a subset of these factors have a distinct expression pattern in patient tumors as well. As many networks of interest are reconstructed from data containing errors, we introduce two simple models of false and missing links to characterize the effects of network misinformation on three commonly used centrality measures: degree centrality, betweenness centrality, and dynamical importance. We show that all three measures are especially robust to both false and missing links when the network has a power law in the tail of its degree distribution

The doctoral research abstracts. Vol:6 2014 / Institute of Graduate Studies, UiTM

Congratulations to Institute of Graduate Studies on the continuous efforts to publish the 6th issue of the Doctoral Research Abstracts which ranged from the discipline of science and technology, business and administration to social science and humanities. This issue captures the novelty of research from 52 PhD doctorates receiving their scrolls in the UiTM’s 81st Convocation. This convocation is very significant especially for UiTM since we are celebrating the success of 52 PhD graduands – the highest number ever conferred at any one time. To the 52 doctorates, I would like it to be known that you have most certainly done UiTM proud by journeying through the scholastic path with its endless challenges and impediments, and by persevering right till the very end. This convocation should not be regarded as the end of your highest scholarly achievement and contribution to the body of knowledge but rather as the beginning of embarking into more innovative research from knowledge gained during this academic journey, for the community and country. As alumni of UiTM, we hold you dear to our hearts. The relationship that was once between a student and supervisor has now matured into comrades, forging and exploring together beyond the frontier of knowledge. We wish you all the best in your endeavour and may I offer my congratulations to all the graduands. ‘UiTM sentiasa dihati ku’ Tan Sri Dato’ Sri Prof Ir Dr Sahol Hamid Abu Bakar , FASc, PEng Vice Chancellor Universiti Teknologi MAR

Predictive Modeling of Metagenomes

Human-associated microbial communities have been implicated in a variety of chronic diseases, including inflammatory bowel diseases, obesity, and autoimmune disorders like diabetes. Environmental communities are also important for bioconversion of waste products in biofuel production. However, microbiomes are highly complex systems involving mutualism and competition between many constituent organisms, and a variety of fundamental and interesting computational challenges remain in the modeling of pathogenicity and community-wide response to perturbations [1, 2]. In this thesis we discuss several computational and statistical approaches to predictive modeling of microbiome behavior using high-throughput metagenomic and transcriptomic sequencing data, including models that leverage biological structures such as phylogenies and gene ontologies to help extract features and constrain model complexity. We also demonstrate several applications of these approaches to real biological problems. We successfully apply predictive modeling to new studies of human-associated and environmental microbial communities in several interdisciplinary collaborations with colleagues at numerous institutions around the world. These include a prominent study of the species and genes present in diverse mammalian gut communities, a study of the effects of yogurt consumption on gut microbial taxa and gene expression (i.e. transcriptomics) in humans and mice, and a large cross-sectional global survey of the human gut microbiota in varied populations. We also develop SourceTracker, a Bayesian approach to predictive modeling of mixtures of microbial communities [3] with important applications in forensics, pollution studies, public health, and detection of sample contamination. This dissertation introduces predictive modeling of human-associated and environmental microbial communities, increasing our ability to understanding the diversity and distribution of the human microbiota, and especially the systematic changes that occur in different physiological and disease states. We expect this type of predictive modeling to have far-reaching effects on health and disease [4]

Interpretable statistics for complex modelling: quantile and topological learning

As the complexity of our data increased exponentially in the last decades, so has our need for interpretable features. This thesis revolves around two paradigms to approach this quest for insights. In the first part we focus on parametric models, where the problem of interpretability can be seen as a “parametrization selection”. We introduce a quantile-centric parametrization and we show the advantages of our proposal in the context of regression, where it allows to bridge the gap between classical generalized linear (mixed) models and increasingly popular quantile methods. The second part of the thesis, concerned with topological learning, tackles the problem from a non-parametric perspective. As topology can be thought of as a way of characterizing data in terms of their connectivity structure, it allows to represent complex and possibly high dimensional through few features, such as the number of connected components, loops and voids. We illustrate how the emerging branch of statistics devoted to recovering topological structures in the data, Topological Data Analysis, can be exploited both for exploratory and inferential purposes with a special emphasis on kernels that preserve the topological information in the data. Finally, we show with an application how these two approaches can borrow strength from one another in the identification and description of brain activity through fMRI data from the ABIDE project