Adv Exp Med Biol
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Fecal microbiota transplantation (FMT) is a rather straightforward therapy that manipulates the human gastrointestinal (GI) microbiota, by which a healthy donor microbiota is transferred into an existing but disturbed microbial ecosystem. This is a natural process that occurs already at birth; infants are rapidly colonized by a specific microbial community, the composition of which strongly depends on the mode of delivery and which therefore most likely originates from the mother (Palmer et al. 2007; Tannock et al. 1990). Since this early life microbial community already contains most, if not all, of the predominantly anaerobic microbes that are only found in the GI tract, it is reasonable to assume that early life colonization is the ultimate natural fecal transplantation.
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Biological systems function via intricate cellular processes and networks in which RNAs, metabolites, proteins and other cellular compounds have a precise role and are exquisitely regulated (Kumar and Mann, FEBS Lett 583(11):1703-1712, 2009). The development of high-throughput technologies, such as the Next Generation DNA Sequencing (NGS) and DNA microarrays for sequencing genomes or metagenomes, have triggered a dramatic increase in the last few years in the amount of information stored in the GenBank and UniProt Knowledgebase (UniProtKB). GenBank release 210, reported in October 2015, contains 202,237,081,559 nucleotides corresponding to 188,372,017 sequences, whilst there are only 1,222,635,267,498 nucleotides corresponding to 309,198,943 sequences from Whole Genome Shotgun (WGS) projects. ⋯ Meanwhile, UniProtKB/TrEMBL (release 2015_12 of December 9 2015) contains 1,838,851,8871 amino acids corresponding to 555,270,679 entries. Proteomics has also improved our knowledge of proteins that are being expressed in cells at a certain time of the cell cycle. It has also allowed the identification of molecules forming part of multiprotein complexes and an increasing number of posttranslational modifications (PTMs) that are present in proteins, as well as the variants of proteins expressed.
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Lung cancer is the leading cause of cancer-related deaths worldwide with a 5-year overall survival rate of less than 20 %. Considering the treatments currently available, this statistics is shocking. A possible explanation for the disconnect between sophisticated treatments and the survival rate can be related to the post-treatment enrichment of Cancer Stem Cells (CSCs), which is one of a sub-set of drug resistant tumor cells with abilities of self-renewal, cancer initiation, and further maintenance of tumors. ⋯ Through the processes of EMT, epithelial cells lose their epithelial phenotype and gain mesenchymal properties, rendering EMT phenotypic cells acquire drug-resistance. In this chapter, we will further discuss the role of microRNAs (miRNAs) especially because miRNA-based therapies are becoming attractive target with respect to therapeutic resistance and CSCs. Finally, the potential role of the natural agents and synthetic derivatives of natural compounds with anti-cancer activity, e.g. curcumin, CDF, and BR-DIM is highlighted in overcoming therapeutic resistance, suggesting that the above mentioned agents could be important for better treatment of lung cancer in combination therapy.
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Shotgun proteomics is a high throughput technique for protein identification able to identify up to several thousand proteins from a single sample. In order to make sense of this large amount of data, proteomics analysis software is needed, aimed at making the data intuitively accessible to beginners as well as experienced scientists. This chapter provides insight on where to start when analyzing shotgun proteomics data, with a focus on explaining the most common pitfalls in protein identification analysis and how to avoid them. Finally, the move to seeing beyond the list of identified proteins and to putting the results into a bigger biological context is discussed.
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Quantification of individual proteins and even entire proteomes is an important theme in proteomics research. Quantitative proteomics is an approach to obtain quantitative information about proteins in a sample. Compared to qualitative or semi-quantitative proteomics, this approach can provide more insight into the effects of a specific stimulus, such as a change in the expression level of a protein and its posttranslational modifications, or to a panel of proposed biomarkers in a given disease state. ⋯ As the theory and technological aspects underlying the proteomics methodologies will be extensively described in Chap. 20 , and protein identification as a prerequisite of quantification has been discussed in Chap. 17 , we will focus on the quantitative proteomics bioinformatics algorithms and software tools in this chapter. Our goal is to provide researchers and newcomers a rational framework to select suitable bioinformatics tools for data analysis, interpretation, and integration in protein quantification. Before doing so, a brief overview of quantitative proteomics is provided.