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Dietary inflammatory potential in relation to the gut microbiome: results from a cross-sectional study
- Jiali Zheng, Kristi L. Hoffman, Jiun-Sheng Chen, Nitin Shivappa, Akhil Sood, Gladys J. Browman, Danika D. Dirba, Samir Hanash, Peng Wei, James R. Hebert, Joseph F. Petrosino, Susan M. Schembre, Carrie R. Daniel
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- Journal:
- British Journal of Nutrition / Volume 124 / Issue 9 / 14 November 2020
- Published online by Cambridge University Press:
- 01 June 2020, pp. 931-942
- Print publication:
- 14 November 2020
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- Article
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Diet has direct and indirect effects on health through inflammation and the gut microbiome. We investigated total dietary inflammatory potential via the literature-derived index (Dietary Inflammatory Index (DII®)) with gut microbiota diversity, composition and function. In cancer-free patient volunteers initially approached at colonoscopy and healthy volunteers recruited from the medical centre community, we assessed 16S ribosomal DNA in all subjects who provided dietary assessments and stool samples (n 101) and the gut metagenome in a subset of patients with residual fasting blood samples (n 34). Associations of energy-adjusted DII scores with microbial diversity and composition were examined using linear regression, permutational multivariate ANOVA and linear discriminant analysis. Spearman correlation was used to evaluate associations of species and pathways with DII and circulating inflammatory markers. Across DII levels, α- and β-diversity did not significantly differ; however, Ruminococcus torques, Eubacterium nodatum, Acidaminococcus intestini and Clostridium leptum were more abundant in the most pro-inflammatory diet group, while Akkermansia muciniphila was enriched in the most anti-inflammatory diet group. With adjustment for age and BMI, R. torques, E. nodatum and A. intestini remained significantly associated with a more pro-inflammatory diet. In the metagenomic and fasting blood subset, A. intestini was correlated with circulating plasminogen activator inhibitor-1, a pro-inflammatory marker (rho = 0·40), but no associations remained significant upon correction for multiple testing. An index reflecting overall inflammatory potential of the diet was associated with specific microbes, but not overall diversity of the gut microbiome in our study. Findings from this preliminary study warrant further research in larger samples and prospective cohorts.
7 - Cancer proteomics
- from Part 1.2 - Analytical techniques: analysis of RNA
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- By Samir Hanash, Fred Hutchinson Cancer Research Center, Seattle,WA, USA, Ayumu Taguchi, Fred Hutchinson Cancer Research Center, Seattle,WA, USA
- Edited by Edward P. Gelmann, Columbia University, New York, Charles L. Sawyers, Memorial Sloan-Kettering Cancer Center, New York, Frank J. Rauscher, III
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- Book:
- Molecular Oncology
- Published online:
- 05 February 2015
- Print publication:
- 19 December 2013, pp 52-57
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Summary
Introduction
The functional consequences of genetic and epigenetic changes that occur during tumor development and progression are mediated through protein alterations, which in turn account for the hallmarks of cancer, including uncontrolled proliferation, and tissue invasion and metastasis. Our current knowledge of the proteome and the spectrum of protein changes that occur in cancer and their functional consequences remain limited (1). We are challenged by the complexity of the proteome stemming from numerous post-translational modifications and the multitude of subcellular compartments in which proteins reside or traffic. As a result, most proteomic investigations have tackled a particular feature or component of the proteome, whether in cells, tissues, or biological fluids (Table 7.1). The emphasis of cancer proteomic studies has been on the identification of diagnostic, prognostic, or predictive markers, the identification of novel therapeutic targets, elucidation of signaling pathways regulated by oncogenes, and other genetic alterations that occur in cancer. Some of the progress made to date and the technologies utilized are highlighted in this chapter.
Proteomic technologies: mass spectrometry
Currently the workhorse for proteomic discovery studies is mass spectrometry, which has evolved from a tool to identify and characterize isolated proteins or for mass peak profiling of more complex protein mixtures, as in the application of matrix-assisted laser desorption ionization (MALDI) to clinical samples, to a high-performance platform for interrogating proteomes by matching mass spectra to sequence databases to derive protein identifications (15). The parallel development of electrospray ionization mass spectrometry for protein identification coupled with various pre-fractionation and separation schemes has allowed quantitative analysis of an ever-increasing number of proteins from cells, tissues, and biological fluids. Mass spectrometers currently available have significantly increased sensitivity and scan speed (16). As a result, identification of the major protein form of virtually all proteins translated from expressed genes in a cancer cell population and the comprehensive analysis of the serum and plasma proteome across seven or more logs of protein abundance have become achievable (17). However, such coverage of the proteome using mass spectrometry is achieved with low throughput. The massive amount of data produced necessitate intense informatics and statistical analysis to identify protein alterations associated with a disease state such as cancer.