Circulating fatty acids (FA) are associated with a multitude of chronic diseases. However, a major gap in establishing such relationships is the lack of accepted fatty acid reference ranges representing healthy individuals. Data on validated FA reference ranges would provide a better understanding of study baseline measures and aid in the evaluation and interpretation of pharmaceutical or dietary interventions. Reference ranges for plasma FA levels have been reported in a few small studies and on a limited number of FA. Therefore, we determined the average and percentiles of a broad set of 61 FA (C14 - C24:1) from plasma total lipids from an ethnically diverse population of healthy young Canadian males and females (Total n = 826). Plasma concentrations of some of the major FA ranged from 0.3 to 4.1 mmol/L for palmitic acid, 0.1 to 1.0 mmol/L for stearic acid, 0.03 to 3.2 mmol/L for oleic acid, 0.2 to 5.0 mmol/L for linoleic acid (LA), 12.0 to 186.9 μmol/L for α-linolenic acid, and 7.2 to 237.5 μmol/L for docosahexaenoic acid (DHA). Males had significantly higher plasma concentrations of γ-linolenic acid (GLA) and n-3 docosapentaenoic acid and lower concentrations of palmitoleic acid, LA and DHA than females. Comparison of FA concentrations between Caucasians, East Asians and South Asians revealed that South Asians had significantly lower levels of palmitoleic acid (p < 0.01) and oleic acid (p = 0.01) while East Asians had lower levels of GLA (p = 0.02) and dihomo-γ-linolenic acid (p = 0.03). Overall, these data provide a comprehensive set of quantitative values that profiles a small cohort of Canadians which highlights the utility of establishing validated FA reference ranges that may be used to understand how deficient, suboptimal, or excess amounts of a given FA may be associated with chronic disease.

Berek JS, et al. J Gynecol Oncol. 2023 Aug;34:e85. https://doi.org/10.3802/jgo.2023.34.e85

Different mutations found in endometrial and prostate tumors affecting the substrate-recognition domain of SPOP, a component of the E3 ubiquitin ligase complex, result in opposing degradation activity of BET proteins and response to BET inhibitors. This work, along with findings by Zhang et al. and Dai et al., highlights the divergent effects of recurrent mutations affecting different residues within the same functional domain of SPOP and provides scientific rationale to guide the administration of BET inhibitors in endometrial and prostate cancer patients harboring SPOP mutations. It is generally assumed that recurrent mutations within a given cancer driver gene elicit similar drug responses. Cancer genome studies have identified recurrent but divergent missense mutations affecting the substrate-recognition domain of the ubiquitin ligase adaptor SPOP in endometrial and prostate cancers. The therapeutic implications of these mutations remain incompletely understood. Here we analyzed changes in the ubiquitin landscape induced by endometrial cancer–associated SPOP mutations and identified BRD2, BRD3 and BRD4 proteins (BETs) as SPOP–CUL3 substrates that are preferentially degraded by endometrial cancer–associated SPOP mutants. The resulting reduction of BET protein levels sensitized cancer cells to BET inhibitors. Conversely, prostate cancer–specific SPOP mutations resulted in impaired degradation of BETs, promoting their resistance to pharmacologic inhibition. These results uncover an oncogenomics paradox, whereby mutations mapping to the same domain evoke opposing drug susceptibilities. Specifically, we provide a molecular rationale for the use of BET inhibitors to treat patients with endometrial but not prostate cancer who harbor SPOP mutations.

The biomedical community is developing new methods of data analysis to more efficiently process the massive data sets produced by microarray experiments. Systematic and global mathematical approaches that can be readily applied to a large number of experimental designs become fundamental to correctly handle the otherwise overwhelming data sets. The gene selection model presented herein is based on the observation that: (1) variance of gene expression is a function of absolute expression; (2) one can model this relationship in order to set an appropriate lower fold change limit of significance; and (3) this relationship defines a function that can be used to select differentially expressed genes. The model first evaluates fold change (FC) across the entire range of absolute expression levels for any number of experimental conditions. Genes are systematically binned, and those genes within the top X% of highest FCs for each bin are evaluated both with and without the use of replicates. A function is fitted through the top X% of each bin, thereby defining a limit fold change. All genes selected by the 5% FC model lie above measurement variability using a within standard deviation (SDwithin) confidence level of 99.9%. Real time-PCR (RT-PCR) analysis demonstrated 85.7% concordance with microarray data selected by the limit function. The FC model can confidently select differentially expressed genes as corroborated by variance data and RT-PCR. The simplicity of the overall process permits selecting model limits that best describe experimental data by extracting information on gene expression patterns across the range of expression levels. Genes selected by this process can be consistently compared between experiments and enables the user to globally extract information with a high degree of confidence.