Laura G. Dubois

Breast cancer (BC) is among the most frequently diagnosed malignant cancers in women in the United States. Diet and nutrition supplementation are closely related to BC onset and progression, and inulin is commercially available as a health supplement to improve gut health. However, little is known with respect to inulin intake for BC prevention. We investigated the effect of an inulin-supplemented diet on the prevention of estrogen receptor-negative mammary carcinoma in a transgenic mouse model. Plasma short-chain fatty acids were measured, the gut microbial composition was analyzed, and the expression of proteins related to cell cycle and epigenetics-related genes was measured. Inulin supplementation greatly inhibited tumor growth and significantly delayed tumor latency. The mice that consumed inulin had a distinct microbiome and higher diversity of gut microbial composition compared to the control. The concentration of propionic acid in plasma was significantly higher in the inulin-supplemented group. The protein expression of epigenetic-modulating histone deacetylase 2 (Hdac2), Hdac8, and DNA methyltransferase 3b decreased. The protein expression of factors related to tumor cell proliferation and survival, such as Akt, phospho-PI3K, and NF-kB, also decreased with inulin administration. Furthermore, sodium propionate showed BC prevention effect in vivo through epigenetic regulations. These studies suggest that modulating microbial composition through inulin consumption may be a promising strategy for BC prevention.

Background: In sickle cell disease (SCD), the mitogen-activated protein kinase (MAPK) ERK1/2 is constitutively active and can be inducible by agonist-stimulation only in sickle but not in normal human red blood cells (RBCs). ERK1/2 is involved in activation of ICAM-4-mediated sickle RBC adhesion to the endothelium. However, other effects of the ERK1/2 activation in sickle RBCs leading to the complex SCD pathophysiology, such as alteration of RBC hemorheology are unknown. Results: To further characterize global ERK1/2-induced changes in membrane protein phosphorylation within human RBCs, a label-free quantitative phosphoproteomic analysis was applied to sickle and normal RBC membrane ghosts pre-treated with U0126, a specific inhibitor of MEK1/2, the upstream kinase of ERK1/2, in the presence or absence of recombinant active ERK2. Across eight unique treatment groups, 375 phosphopeptides from 155 phosphoproteins were quantified with an average technical coefficient of variation in peak intensity of 19.8%. Sickle RBC treatment with U0126 decreased thirty-six phosphopeptides from twenty-one phosphoproteins involved in regulation of not only RBC shape, flexibility, cell morphology maintenance and adhesion, but also glucose and glutamate transport, cAMP production, degradation of misfolded proteins and receptor ubiquitination. Glycophorin A was the most affected protein in sickle RBCs by this ERK1/2 pathway, which contained 12 unique phosphorylated peptides, suggesting that in addition to its effect on sickle RBC adhesion, increased glycophorin A phosphorylation via the ERK1/2 pathway may also affect glycophorin A interactions with band 3, which could result in decreases in both anion transport by band 3 and band 3 trafficking. The abundance of twelve of the thirty-six phosphopeptides were subsequently increased in normal RBCs co-incubated with recombinant ERK2 and therefore represent specific MEK1/2 phospho-inhibitory targets mediated via ERK2. Conclusions: These findings expand upon the current model for the involvement of ERK1/2 signaling in RBCs. These findings also identify additional protein targets of this pathway other than the RBC adhesion molecule ICAM-4 and enhance the understanding of the mechanism of small molecule inhibitors of MEK/1/2/ERK1/2, which could be effective in ameliorating RBC hemorheology and adhesion, the hallmarks of SCD.

Label-free quantitative proteomics holds a great deal of promise for the future study of both medicine and biology. However, the data generated is extremely intricate in its correlation structure, and its proper analysis is complex. There are issues with missing identifications. There are high levels of correlation between many, but not all, of the peptides derived from the same protein. Additionally, there may be systematic shifts in the sensitivity of the machine between experiments or even through time within the duration of a single experiment. We describe a hierarchical model for analyzing unbiased, label-free proteomics data which utilizes the covariance of peptide expression across samples as well as MS/MS-based identifications to group peptides—a strategy we call metaprotein expression modeling. Our metaprotein model acknowledges the possibility of misidentifications, post-translational modifications and systematic differences between samples due to changes in instrument sensitivity or differences in total protein concentration. In addition, our approach allows us to validate findings from unbiased, label-free proteomics experiments with further unbiased, label-free proteomics experiments. Finally, we demonstrate the clinical/translational utility of the model for building predictors capable of differentiating biological phenotypes as well as for validating those findings in the context of three novel cohorts of patients with Hepatitis C. Mass-spectrometry proteomics is quickly becoming a powerful tool for studying biological and translational questions. Making use of all of the information contained in a particular set of data will be critical to the success of those endeavors. Our proposed model represents an advance in the ability of statistical models of proteomic data to identify and utilize correlation between features. This allows validation of predictors without translation to targeted assays in addition to informing the choice of targets when it is appropriate to generate those assays.