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Search Results: 1 - 10 of 1996 matches for " 'omics technologies "
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Omics Technologies Reveal Abundant Natural Variation in Metabolites and Transcripts among Conventional Maize Hybrids  [PDF]
Xiaofeng S. Yang, Jeffrey M. Staub, Anand Pandravada, Susan G. Riordan, Yongpan Yan, Gary A. Bannon, Susan J. Martino-Catt
Food and Nutrition Sciences (FNS) , 2013, DOI: 10.4236/fns.2013.43044
Abstract:

In this report we have evaluated metabolite and RNA profiling technologies to begin to understand the natural variation in these biomolecules found in commercial-quality, conventional (non-GM) maize hybrids. Our analyses focus on mature grain, the article of commerce that is most typically subjected to the rigorous studies involved in the comparative safety assessment of GM products. We have used a population of conventionally-bred maize hybrids that derive from closely related inbred parents grown under standard field conditions across geographically similar locations. This study highlights the large amount of natural variation in metabolites and transcripts across conventional maize germplasm grown under normal field conditions, and underscores the critical need for further extensive studies before these technologies can be seriously considered for utility in the comparative safety assessment of GM crops.

Harnessing the potential clinical use of medicinal plants as anti-diabetic agents
Campbell-Tofte JI, M lgaard P, Winther K
Botanics: Targets and Therapy , 2012, DOI: http://dx.doi.org/10.2147/BTAT.S17302
Abstract: rnessing the potential clinical use of medicinal plants as anti-diabetic agents Review (1726) Total Article Views Authors: Campbell-Tofte JI, M lgaard P, Winther K Published Date August 2012 Volume 2012:2 Pages 7 - 19 DOI: http://dx.doi.org/10.2147/BTAT.S17302 Received: 24 January 2012 Accepted: 23 April 2012 Published: 22 August 2012 Joan IA Campbell-Tofte,1 Per M lgaard,2 Kaj Winther1 1Department of Clinical Biochemistry, Frederiksberg University Hospital, Frederiksberg, Denmark; 2Department of Medicinal Chemistry, Faculty of Pharmaceutical Sciences, University of Copenhagen, Copenhagen, Denmark Abstract: Diabetes is a metabolic disorder arising from complex interactions between multiple genetic and/or environmental factors. The characteristic high blood sugar levels result from either lack of the hormone insulin (type 1 diabetes, T1D), or because body tissues do not respond to the hormone (type 2 diabetes, T2D). T1D patients currently need exogenous insulin for life, while for T2D patients who do not respond to diet and exercise regimes, oral anti-diabetic drugs (OADs) and sometimes insulin are administered to help keep their blood glucose as normal as possible. As neither the administration of insulin nor OADs is curative, many patients develop tissue degenerative processes that result in life-threatening diabetes comorbidities. Several surveys of medicinal plants used as anti-diabetic agents amongst different peoples have been published. Some of this interest is driven by the ongoing diabetes pandemic coupled with the inadequacies associated with the current state of-the-art care and management of the syndrome. However, there is a huge cleft between traditional medicine and modern (Western) medicine, with the latter understandably demanding meaningful and scientific validation of anecdotal evidence for acceptance of the former. The main problems for clinical evaluation of medicinal plants with promising anti-diabetic properties reside both with the complexity of components of the plant materials and with the lack of full understanding of the diabetes disease etiology. This review is therefore focused on why research activities involving an integration of Systems Biology-based technologies of pharmacogenomics, metabolomics, and bioinformatics with standard clinical data, should be used for cost-effective validation of the safety and anti-diabetic efficacy of promising medicinal plants. The application of such approaches to studying entire mixtures of plant materials will ensure proper elucidation of novel therapies with improved mechanisms of action, as well as facilitate a personalized clinical use of medicinal plants as anti-diabetic agents.
The plant microbiome and its importance for plant and human health
Gabriele Berg,Martin Grube,Michael Schloter,Kornelia Smalla
Frontiers in Microbiology , 2014, DOI: 10.3389/fmicb.2014.00491
Abstract: To study plant-associated microorganisms has a long history that reaches back to Lorenz Hiltner’s definition of the rhizosphere in 1904 (Hartmann et al., 2008). Today, we know that microorganisms colonizing plant surfaces and inner tissues play an eminent role in shaping of our planet – from our natural vegetation to intense agricultural production systems up to human health. Plant-associated microorganisms have to be considered as key drivers for plant health, productivity, community composition and ecosystem functioning. For this e-book “The plant microbiome and its importance for plant and human health” we collected 18 articles, including reviews, original and opinion articles that highlight the current knowledge regarding plant microbiomes, their specificity, diversity and function as well as all aspects studying the management of plant microbiomes to improve plant performance and health. The contribution of the single articles of this research topic to these questions is discussed in detail in the mini-review and 1st chapter of the book by Berg et al., (2014a). Overall the presented articles confirm that the plant-associated microbiome has greatly expanded the metabolic repertoire of plants and often increase resource uptake and provide novel nutritional and defense pathways. Thus the plant microbiome has a direct impact on plant functional traits, such as leaf longevity, specific leaf area, leaf nutrient levels, and shoot/root ratio. By providing novel nutritional and defense pathways and by modifying biochemical pathways, the plant associated microbiome can enhance or decrease species coexistence and consequently influence not only a single plant but complete ecosystems. Thus future breeding strategies may take the importance of plant-microbe interactions more into account than in the past, to obtain plants that generate high yields and are more tolerate to the constraints of global change. Studies related to raw-eaten vegetables are a special show case in this e-book. Here the plant-associated microbiome does not only influence plant performance but strongly contributes to human health. As those microbes are also part of our diet they can either improve human health (Blaser et al., 2013) or cause heavy outbreaks of infectious diseases by transferring possible pathogens (van Oberbeck et al., 2014). Interestingly, the gathered manuscripts indicate that microbiomes of different environments are not isolated but show interplay. For example, the microbiome of vegetables, humans as well as build environment such as hospitals seems to be well-connected
Beneficial effects of plant-associated microbes on indoor microbiomes and human health?
Gabriele Berg,Alexander Mahnert,Christine Moissl-Eichinger
Frontiers in Microbiology , 2014, DOI: 10.3389/fmicb.2014.00015
Abstract:
Fungome: Annotating proteins implicated in fungal pathogenesis
Ranganath Gudimella,Sivaramaiah Nallapeta,Pritish Varadwaj,Prashanth Suravajhala
Bioinformation , 2010,
Abstract: Sequencing genomes of different pathogenic fungi produced plethora of genetic information. This “omics” data might be of great interest to probe strain diversity, identify virulence factors and complementary genes in other fungal species, and importantly in predicting the role of proteins specific to pathogenesis in humans. We propose a component called “fungome” for those fungal proteins implicated in pathogenesis which, we believe, will allow researchers to improve the annotation of fungal proteins.
Facing a Shift in Paradigm at the Bedside?  [PDF]
Borja Vargas, Manuel Varela
International Journal of Clinical Medicine (IJCM) , 2013, DOI: 10.4236/ijcm.2013.41008
Abstract:

Our entire medical framework is based on the concept of disease, understood as a qualitative departure from normality (health) with a structural substrate (lesion), and usually an identifiable cause (aetiology). This paradigm is loaded with problems, some of which are discussed in the text. Nevertheless, we study, diagnose and treat diseases, and while often painfully conscious of the dysfunctionalities of this scheme, we can hardly imagine how we could practice medicine otherwise. However, most of the recent developments in basic sciences, and most notably in Immunology, Genetics and -omics, are inconsistent with this “health/disease” paradigm. The emerging scenario is that of complex networks, more in the spirit of Systems Biology. In these settings the qualitative difference between health and disease loses its meaning, and the whole discourse becomes progressively irreducible to our conventional clinical categories. As clinical research stagnates while basic sciences thrive, this gap is widening, and a change in the prevailing paradigm seems unavoidable. However, all our clinical judgments (including Bayesian reasoning and Evidence Based Medicine) are rooted in the disease/health dichotomy, and one can hardly conceive how they could work without it. The shift in paradigm will not be easy, and certain turmoil is to be expected.

A Localized-Statistic-Based Approach for Biomarker Identification of Omics Data  [PDF]
Kuan Zhang, He Chen, Yongtao Li
Engineering (ENG) , 2013, DOI: 10.4236/eng.2013.510B089
Abstract:

Omics data provides an essential means for molecular biology and systems biology to capture the systematic properties of inner activities of cells. And one of the strongest challenge problems biological researchers have faced is to find the methods for discovering biomarkers for tracking the process of disease such as cancer. So some feature selection methods have been widely used to cope with discovering biomarkers problem. However omics data usually contains a large number of features, but a small number of samples and some omics data have a large range distribution, which make feature selection methods remains difficult to deal with omics data. In order to overcome the problems, wepresent a computing method called localized statistic of abundance distribution based on Gaussian window(LSADBGW) to test the significance of the feature. The experiments on three datasets including gene and protein datasets showed the accuracy and efficiency of LSADBGW for feature selection.

DATA ANNOTATION AND RELATIONS MODELING FOR INTEGRATED OMICS IN CLINICAL RESEARCH
Arno Lukas,Bernd Mayer
The IIOAB Journal , 2010,
Abstract: Omics has massively permeated translational clinical research with numerous diseases being covered by Omics studies from the genome to the metabolome level. Integrating these disease specific Omics tracks appears a logical next step for building the fundament of Systems Biology and Systems Medicine. Here, coherence of individual Omics tracks regarding clinical hypothesis, samples and clinical descriptors, and finally data handling and integration become pivotal. We present a data integration, annotation and relations modeling concept for heterogeneous Omics data and workflows. With molecular features at the center of all Omics we link the result profiles from different Omics tracks characterizing a specific disease phenotype to a common human molecular reference network for allowing a seamless integration and subsequent support in interpretation of Omics screening results. Our concept rests on data structures for representing objects specified by metadata and content. For handling diverse Omics tracks a flexible structure for content is proposed allowing data representation at different levels of granularity as demanded by the type of Omics and specific type of data. Content on the molecular level includes deep annotation of molecular features on gene and protein level. Based on this annotation pair-wise relations between molecular objects are built, traversing the molecular annotation into a network of relations (molecular feature graph). Such a relation network is also built on the Omics data level, combining explicit relations derived from study setup and implicit relations generated by mining metadata and content (Omics data graph). Finally both graphs are merged utilizing the molecular feature level as common denominator, enabling a persistent integration and subsequently interpretation of Omics profiling results in the realm of a given clinical hypothesis. We present a case study on integrating transcriptomics and proteomics data on chronic kidney disease for demonstrating the feasibility of this concept.
A new era in cardiogenetics
Giuseppe Limongelli
Cardiogenetics , 2011, DOI: 10.4081/cardiogenetics.2011.e1
Abstract: Unless you have insatiable curiosity about what you are studying, and the willingness to work hard to answer the questions you have posed, I think it’s unlikely that you will be successful Eugene Braunwald (Circ. Res. 2010;106;1786-1788) Welcome to Cardiogenetics. Why Cardiogenetics? When I received the invitation to join this project my first impulse was to ask: why do we need a new journal? But the answer was there all along. Cardiogenetics is more than just the name of a new journal. Cardiogenetics is an idea.
On new challenge for the Bioinformatics
Susan Costantini,Ida Autiero,Giovanni Colonna
Bioinformation , 2008,
Abstract: The living organisms may be studied as a whole complex system. The “omics sciences” tend at understanding and describing the global information of genes, mRNA, proteins, and metabolites. The aim of the Bioinformatics should be that of developing methods not only able to study the individual components of a system, but also to represent and simulate the relationships between all these components.
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