Our IPA analysis found three interaction networks to be significantly associated with nonmeat consumption (IPA network score ≥20). These IPA networks were identified as functioning with cancer, organismal injury and abnormalities, and tumor morphology (score=31, focus molecules=11) (Fig. 1a), cellular function, maintenance, cellular movement, cell death and survival (score=31, focus molecules=15) (Fig. 1b), and drug metabolism, molecular transport, and small molecule biochemistry (score=26, focus molecules=13) (Fig. 1c).
Among genes identified as significantly differentially expressed between high and low meat-related intake categories, 64 genes were differentially expressed with red meat, two genes were differentially expressed with processed meat, and three genes were differentially expressed with a western dietary pattern (Table 4). Of these, eight genes were differentially expressed by the level of red meat and LSAMP was differentially expressed by the level of western dietary pattern when using a more stringent selection criterion of a maximal FDR of less than 0.05. The majority of these genes were downregulated by red meat consumption; of all of the genes identified, only IL22RA1, SLC17A4, SLC25A25, MUC17, ZFY, FRK, PEX11G, NEDD9, NR3C2, and RPS4Y1 were upregulated by red meat consumption. The two genes identified as being differentially expressed for western dietary pattern were also differentially expressed for red meat; C3 was downregulated by red meat and processed meat.
In IPA analysis, three interaction networks were significantly associated (score≥20) with meat-related variables (i.e. red meat, processed meat, and western dietary pattern). These IPA networks were identified as hematological system developmental function, nervous system development and function, tissue morphology (IPA network score=31, focus molecules 22) (Fig. 2a); connective tissue disorders, organismal injury and abnormalities, cardiovascular system development and function (score=29, focus molecules=14) (Fig. 2b); and cellular compromise, cellular function and maintenance, and protein degradation (score=21, focus molecules 11) (Fig. 2c). Supplement Table 1, Supplemental digital content 1, http://links.lww.com/FPC/B6 shows genes upregulated and downregulated in each of these networks. Some genes were differentially expressed between high-consumption and low-consumption categories for both meat and non-meat dietary variables. COL1A1 was downregulated for vegetables and meat intake; MUC5AC was downregulated for folate, vegetables, whole grains, and meat. Foods and nutrients analyzed that were not associated with any differential gene expression (FDR<0.1) were mutagen index, total dairy products, carbohydrates, trans-fatty acids, total dietary fat, dietary fiber, refined grain products, and sucrose. In addition, we observed no differences in gene expression by level of vigorous physical activity.
Further evaluation of the genes associated with nonmeat-related variables showed that genes associated with calcium were upregulated, two of the four genes associated with folate were upregulated, and the TXNDC17 gene associated with prudent dietary pattern was upregulated. The genes associated with calcium consumption were involved in transmembrane transport, regulation of transcription and translation, cell survival, and the complement system. Calcium plays a key role in muscle contraction and relaxation, blood coagulation, nerve transmission, and keratinocyte differentiation 49. The genes upregulated by high calcium consumption include FOXJ2, encoding for a transcription activator; NECAP1, whose product is involved in receptor-mediated endocytosis; and C3AR1, encoding for a C3A receptor. A prudent dietary pattern was associated with roughly three servings of milk per day in one study 20 and was associated with a decreased risk of cancer and the B cell receptor pathways 20. Our observed calcium-associated upregulation of tumor suppressors and a C3A receptor is consistent with these earlier findings.
We found that the majority of genes differentially expressed between intake levels of whole grains and vegetables were downregulated, especially for vegetable intake. The genes that are differentially expressed with level of whole grain and vegetable consumption are associated with NF-κB signaling, regulation of apoptosis, cytoskeleton dynamics, and carbohydrate metabolism. Carbohydrates are important modulators of insulin action on glucose metabolism 54, which is consistent with our findings of PFKL, ABCC3, and GMPPB downregulated expression with whole grain consumption, and HSPG2 and REG4, which were downregulated by high vegetable consumption. Both fiber and polyphenols attenuate inflammation 46. Phenolic compounds are major bioactive compounds in whole grains and polyphenols are known to interfere with the NF-κB signaling pathway 55, which is consistent with our data showing a downregulation of genes involved in the NF-κB signaling. Our data suggest that the previously described polyphenolic perturbation of nuclear signaling and anti-inflammatory effects 56 may be explained by the differential gene expression observed with both whole grain and vegetable levels of intake.
To the best of our knowledge, this is one of the few studies to examine the role of diet in gene expression using data from a population-based study and comprehensive gene expression data. Our study has a number of strengths including using RNA-Seq, which produces global gene expression data for each RNA sample and is an ideal method to carry out a discovery study such as ours 67,68. However, it is important to keep in mind that the gene expression profile is most relevant to current diet exposure. The time between tissue ascertainment and the referent period for diet in our study could be from several months to 3 or possibly 4 years. Although the lack of findings could indicate a disparity in time between exposure and tissue sample acquirement, finding associations would imply that the exposure is recent enough, in that dietary patterns are consistent over time, to alter the expression. In addition, we have utilized normal colonic mucosa; thus, genes would have to be expressed in colon tissue for detection. It should be recognized that different platforms carry different technical strengths and weaknesses that can influence results. Thus, it is essential to validate these findings in other populations using the methods that we used here as well as other platforms to better understand associations between diet and gene expression. We utilized DESeq2 to assess gene expression data adjusting for age and sex. We have previously shown associations between cigarette smoking and alcohol and gene expression in these samples 69. Similarly, we report statistically significant differential expression as an indicator of dysregulated genes; however, the level of dysregulation that is necessary to result in functional significance is not clear.
Although this study was carried out in a rigorous manner, there are limitations. Our dietary data were collected shortly after diagnosis. Although our dietary questionnaire allowed for reporting of over 800 food items and we implemented extremely rigid QC procedures, it is possible that recall could have been influenced by the disease status. Although this is a possibility, it should be noted that our risk estimates for diet and colon cancer are almost identical to those reported by large cohort studies 70. In addition, these data reflect foods consumed in the early 1990s. Although we believe that individuals still eat the foods that we report here, different dietary patterns may be more important today than the western and prudent diet that we report.
This study was funded by NCI grants CA48998. The contents of this manuscript are solely the responsibility of the authors and do not necessarily represent the official view of the National Cancer Institute. The authors would like to acknowledge the contributions of Dr Bette Caan, Judy Morse, and Donna Schaffer, the Kaiser Permanente Medical Research Program, Sandra Edwards for data collection and organization, Jennifer Herrick for data management, Erica Wolff and Michael Hoffman for RNA extraction, Wade Samowitz for slide review, and Brett Milash at the Bioinformatics Core Facility at the University of Utah.
Author contributions: A.P. compiled and interpreted the data and wrote the manuscript; D.P. carried out statistical analysis, edited the manuscript, and approved the final version of manuscript; L.M. carried out bioinformatics analysis and approved the final manuscript; and R.W. oversaw RNAseq data collection and approved the final manuscript. M.S. obtained funding, collected data for study, assisted in data interpretation and analysis, edited, and approved the final manuscript.
There are no conflicts of interest.
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