Genetic variation in insulin-like growth factor signaling genes and breast cancer risk among BRCA1 and BRCA2 carriers

Abstract Introduction Women who carry mutations in BRCA1 and BRCA2 have a substantially increased risk of developing breast cancer as compared with the general population. However, risk estimates range from 20 to 80%, suggesting the presence of genetic and/or environmental risk modifiers. Based on extensive in vivo and in vitro studies, one important pathway for breast cancer pathogenesis may be the insulin-like growth factor (IGF) signaling pathway, which regulates both cellular proliferation and apoptosis. BRCA1 has been shown to directly interact with IGF signaling such that variants in this pathway may modify risk of cancer in women carrying BRCA mutations. In this study, we investigate the association of variants in genes involved in IGF signaling and risk of breast cancer in women who carry deleterious BRCA1 and BRCA2 mutations. Methods A cohort of 1,665 adult, female mutation carriers, including 1,122 BRCA1 carriers (433 cases) and 543 BRCA2 carriers (238 cases) were genotyped for SNPs in IGF1, IGF1 receptor (IGF1R), IGF1 binding protein (IGFBP1, IGFBP2, IGFBP5), and IGF receptor substrate 1 (IRS1). Cox proportional hazards regression was used to model time from birth to diagnosis of breast cancer for BRCA1 and BRCA2 carriers separately. For linkage disequilibrium (LD) blocks with multiple SNPs, an additive genetic model was assumed; and for single SNP analyses, no additivity assumptions were made. Results Among BRCA1 carriers, significant associations were found between risk of breast cancer and LD blocks in IGF1R (global P = 0.011 for LD block 2 and global P = 0.012 for LD block 11). Among BRCA2 carriers, an LD block in IGFBP2 (global P = 0.0145) was found to be associated with the time to breast cancer diagnosis. No significant LD block associations were found for the other investigated genes among BRCA1 and BRCA2 carriers. Conclusions This is the first study to investigate the role of genetic variation in IGF signaling and breast cancer risk in women carrying deleterious mutations in BRCA1 and BRCA2. We identified significant associations in variants in IGF1R and IRS1 in BRCA1 carriers and in IGFBP2 in BRCA2 carriers. Although there is known to be interaction of BRCA1 and IGF signaling, further replication and identification of causal mechanisms are needed to better understand these associations

Global Metabolic Responses to Salt Stress in Fifteen Species

Cells constantly adapt to unpredictably changing extracellular solute concentrations. A cornerstone of the cellular osmotic stress response is the metabolic supply of energy and building blocks to mount appropriate defenses. Yet, the extent to which osmotic stress impinges on the metabolic network remains largely unknown. Moreover, it is mostly unclear which, if any, of the metabolic responses to osmotic stress are conserved among diverse organisms or confined to particular groups of species. Here we investigate the global metabolic responses of twelve bacteria, two yeasts and two human cell lines exposed to sustained hyperosmotic salt stress by measuring semiquantitative levels of hundreds of cellular metabolites using nontargeted metabolomics. Beyond the accumulation of osmoprotectants, we observed significant changes of numerous metabolites in all species. Global metabolic responses were predominantly species-specific, yet individual metabolites were characteristically affected depending on species’ taxonomy, natural habitat, envelope structure or salt tolerance. Exploiting the breadth of our dataset, the correlation of individual metabolite response magnitudes across all species implicated lower glycolysis, tricarboxylic acid cycle, branched-chain amino acid metabolism and heme biosynthesis to be generally important for salt tolerance. Thus, our findings place the global metabolic salt stress response into a phylogenetic context and provide insights into the cellular phenotype associated with salt tolerance.ISSN:1932-620

Correlation analysis of metabolite responses with salt tolerance.

<p>(A) Correlation of metabolite fold-changes in response to strong salt stress with IC₅₀ values of the different organisms was assessed by Pearson’s correlation coefficient R. For each metabolite, the upper quartile x_0.75 of absolute log₂ fold-changes across all species was plotted against R. Only metabolites detected in more than 10 species were considered. Metabolites with |R| > 0.5 and x_0.75 > 1 are highlighted in blue (anticorrelating metabolites) and pink (correlating metabolites), and the names of representative compounds are listed. The full correlation data is provided in <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0148888#pone.0148888.s002" target="_blank">S2 Data</a>. (B) Visualization of metabolites correlating with salt tolerance on the KEGG metabolic pathway map using PathwayProjector [<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0148888#pone.0148888.ref047" target="_blank">47</a>]. Color intensity of metabolites indicates strength of positive (pink) or negative (blue) correlation, and size indicates x_0.75. Key pathways are highlighted and labeled. (C) Correlation of fold-change with salt tolerance for selected compounds in lower glycolysis; (D) in cysteine and methionine metabolism; (E) in branched-chain amino acid metabolism; and (F) in heme biosynthesis. In panels C to F mean and standard deviation of four (microbes) or three (human cell lines) replicates are shown. Note that metabolite annotations are based on accurate mass and can be ambiguous; refer to <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0148888#pone.0148888.s001" target="_blank">S1 Data</a> for complete annotations.</p

Phylogenetic relationship between species is insufficient to explain differences in metabolic salt stress responses.

<p>(A) Cladogram of analyzed species based on metabolite ion log₂ fold-changes upon exposure to low (IC₁₀, L), medium (IC₂₅, M) and high (IC₅₀, H) salt stress relative to unstressed controls (IC₀). Pairwise distances between samples were calculated using the Cityblock metric. Species labels are colored according to taxonomic classification as defined in <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0148888#pone.0148888.g001" target="_blank">Fig 1A</a>. (B) Correlation of pairwise distances between species based on metabolic salt stress responses with phylogenetic distances. Distances between metabolic responses to different salt stress severities were calculated based on metabolite ion log₂ fold-changes relative to IC₀ using the Cityblock metric, and phylogenetic distances based on the aligned small ribosomal subunit RNA sequences using the Jukes-Cantor measure. R indicates Pearson’s correlation coefficient.</p

Individual metabolite responses are influenced by species taxonomy, habitat, cell wall thickness and salt tolerance.

<p>Four-way analysis of variance (ANOVA) was performed on log₂ metabolite fold-changes in the different species upon high salt stress (IC₅₀). Shown are the mean and standard errors of the 40 most significant metabolites of each factor (all with ANOVA <i>P</i> < 0.01 and |log₂ fold-change| > 1 in at least one group). Metabolites are sorted from left to right by ascending <i>P</i>-value. (A) Grouping of species according to taxonomic classification. (B) Grouping according to habitat. (C) Grouping according to cell wall thickness. (D) Grouping according to salt tolerance (IC₅₀ < 500 mM NaCl = low; 500 ≤ IC₅₀ ≤ 1,000 mM = medium; IC₅₀ > 1,000 mM = high). Note that metabolite annotations are based on accurate mass and can be ambiguous; refer to <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0148888#pone.0148888.s001" target="_blank">S1 Data</a> for complete annotations.</p

Responses of known osmoprotectants to sustained hyperosmotic salt stress.

<p>(A) Fold changes relative to unstressed control conditions of 54 detected metabolites with confirmed osmoprotective activity listed in the DEOP database [<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0148888#pone.0148888.ref036" target="_blank">36</a>], as well as of additional relevant polyprenyl quinones. For each species, only osmoprotectants showing a significant response (|log₂ fold-change| ≥ 1, <i>P</i> < 0.05, multiple-testing corrected two-sided unpaired <i>t</i>-tests) in at least one stress intensity were considered. Duplicate names represent different ions of the same metabolite because accurate mass annotation was ambiguous; refer to <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0148888#pone.0148888.s001" target="_blank">S1 Data</a> for complete annotations. Osmoprotectants were grouped based on their main chemical characteristics. (B) Baseline abundances of osmoprotectants at IC₀ (unstressed control condition). Data are represented as Z-scores to compare relative abundances between species. Small Z-scores (|Z| < 1) are not colored.</p

Hyperosmotic salt stress elicits complex and predominantly species-specific global metabolic responses.

<p>(A) Principal component analysis (PCA) was performed based on log₂ metabolite ion fold-changes upon low (IC₁₀, L), medium (IC₂₅, M) or high (IC₅₀, H) salt stress relative to unstressed controls. For each species the three stress intensity points are connected by triangular patches for visualization purposes. Patches and labels are colored according to taxonomic classification as defined in <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0148888#pone.0148888.g001" target="_blank">Fig 1A</a>. (B) Loading plot of metabolites underlying the PCA shown in panel A. Selected metabolites with large coefficients are highlighted. Note that metabolite annotations are based on accurate mass and can be ambiguous; refer to <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0148888#pone.0148888.s001" target="_blank">S1 Data</a> for complete annotations. (C) Numbers of strongly and significantly responding metabolites in each analyzed species, grouped either by the lowest stress intensity under which a change was observed (gray bars) or by change direction (magenta and blue bars). (D) Histogram of the number of species in which metabolite ions were affected by the individual salt stress intensities (black, dark gray and light gray curves) or by at least one stress intensity (magenta curve).</p

Analysis of salt tolerance in fifteen diverse species.

<p>(A) Phylogenetic tree of analyzed species. Jukes-Cantor distances between bacteria are drawn to scale based on aligned 16S small ribosomal subunit RNA sequences. Distances between eukaryotes are not drawn to scale for visualization purposes. Organisms are colored based on taxonomic classification, and cell wall strengths and typical habitats are indicated. Further information about strains and cell lines is provided in <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0148888#pone.0148888.s007" target="_blank">S1 Table</a>. (B) Sustained hyperosmotic salt tolerance based on growth inhibition experiments. Salt tolerance is expressed as mean and standard deviation (n = 2) of concentrations inhibiting growth rates by 10% (IC₁₀), 25% (IC₂₅) and 50% (IC₅₀) compared to unstressed conditions. Species are grouped according to taxonomic classification, and the colored horizontal bars indicate the average IC₅₀ of each taxonomic group. (C) Comparison of salt tolerance between species colonizing different habitats. (D) Comparison of salt tolerance between species with different cell wall strengths. Differences between groups in panels B to D were not statistically significant (P > 0.05, unpaired two-tailed <i>t</i>-tests) for all comparisons except those with human cell lines.</p