Association between plasma phospholipid saturated fatty acids and metabolic markers of lipid, hepatic, inflammation and glycaemic pathways in eight European countries: a cross-sectional analysis in the EPIC-InterAct study.

BACKGROUND: Accumulating evidence suggests that individual circulating saturated fatty acids (SFAs) are heterogeneous in their associations with cardio-metabolic diseases, but evidence about associations of SFAs with metabolic markers of different pathogenic pathways is limited. We aimed to examine the associations between plasma phospholipid SFAs and the metabolic markers of lipid, hepatic, glycaemic and inflammation pathways. METHODS: We measured nine individual plasma phospholipid SFAs and derived three SFA groups (odd-chain: C15:0 + C17:0, even-chain: C14:0 + C16:0 + C18:0, and very-long-chain: C20:0 + C22:0 + C23:0 + C24:0) in individuals from the subcohort of the European Prospective Investigation into Cancer and Nutrition (EPIC)-InterAct case-cohort study across eight European countries. Using linear regression in 15,919 subcohort members, adjusted for potential confounders and corrected for multiple testing, we examined cross-sectional associations of SFAs with 13 metabolic markers. Multiplicative interactions of the three SFA groups with pre-specified factors, including body mass index (BMI) and alcohol consumption, were tested. RESULTS: Higher levels of odd-chain SFA group were associated with lower levels of major lipids (total cholesterol (TC), triglycerides, apolipoprotein A-1 (ApoA1), apolipoprotein B (ApoB)) and hepatic markers (alanine transaminase (ALT), aspartate transaminase (AST), gamma-glutamyl transferase (GGT)). Higher even-chain SFA group levels were associated with higher levels of low-density lipoprotein cholesterol (LDL-C), TC/high-density lipoprotein cholesterol (HDL-C) ratio, triglycerides, ApoB, ApoB/A1 ratio, ALT, AST, GGT and CRP, and lower levels of HDL-C and ApoA1. Very-long-chain SFA group levels showed inverse associations with triglycerides, ApoA1 and GGT, and positive associations with TC, LDL-C, TC/HDL-C, ApoB and ApoB/A1. Associations were generally stronger at higher levels of BMI or alcohol consumption. CONCLUSIONS: Subtypes of SFAs are associated in a differential way with metabolic markers of lipid metabolism, liver function and chronic inflammation, suggesting that odd-chain SFAs are associated with lower metabolic risk and even-chain SFAs with adverse metabolic risk, whereas mixed findings were obtained for very-long-chain SFAs. The clinical and biochemical implications of these findings may vary by adiposity and alcohol intake

Identification and characterization of the expression of the translation initiation factor 4A (eIF4A) from Drosophila melanogaster

We have identified the initiation factor 4A (eIF4A) in a two-dimensional protein database of Drosophila wing imaginal discs. eIF4A, a member of the DEAD-box family of RNA helicases, forms the active eIF4F complex that in the presence of eIF4B and eIF4H unwinds the secondary structure of the 5’-UTR of mRNAs during translational initiation. Two-dimensional gel electrophoresis and microsequencing allowed us to purify eIF4A, and generate specific polyclonal antibodies. A combination of immunoblotting and labelling with [35S]methionine 1 [35S]cysteine revealed the existence of a single eIF4A isoform encoded by a previously reported gene that maps to chromosome 2L at 26A7-9. Expression of this gene yields two mRNA species, generated by alternative splicing in the 3’-untranslated region. The two mRNAs contain the same open reading frame and produce the identical eIF4A protein. No expression was detected of the eIF4A-related gene CG7483. We detected eIF4A protein expression in the wing imaginal discs of several Drosophila species, and in haltere, leg 1, leg 2, leg 3, and eyeantenna imaginal discs of D. melanogaster. Examination of eIF4A in tumor suppressor mutants showed significantly increased (>50%) expression in the wing imaginal discs of these larvae. We observed ubiquitous expression of eIF4A mRNA and protein during Drosophila embryogenesis. Yeast two-hybrid analysis demonstrated the in vivo interaction of Drosophila eIF4G with the N-terminal third of eIF4A

Identification and characterization of the expression of the translation initiation factor 4A (eIF4A) from Drosophila melanogaster

We have identified the initiation factor 4A (eIF4A) in a two-dimensional protein database of Drosophila wing imaginal discs. eIF4A, a member of the DEAD-box family of RNA helicases, forms the active eIF4F complex that in the presence of eIF4B and eIF4H unwinds the secondary structure of the 5’-UTR of mRNAs during translational initiation. Two-dimensional gel electrophoresis and microsequencing allowed us to purify eIF4A, and generate specific polyclonal antibodies. A combination of immunoblotting and labelling with [35S]methionine 1 [35S]cysteine revealed the existence of a single eIF4A isoform encoded by a previously reported gene that maps to chromosome 2L at 26A7-9. Expression of this gene yields two mRNA species, generated by alternative splicing in the 3’-untranslated region. The two mRNAs contain the same open reading frame and produce the identical eIF4A protein. No expression was detected of the eIF4A-related gene CG7483. We detected eIF4A protein expression in the wing imaginal discs of several Drosophila species, and in haltere, leg 1, leg 2, leg 3, and eyeantenna imaginal discs of D. melanogaster. Examination of eIF4A in tumor suppressor mutants showed significantly increased (>50%) expression in the wing imaginal discs of these larvae. We observed ubiquitous expression of eIF4A mRNA and protein during Drosophila embryogenesis. Yeast two-hybrid analysis demonstrated the in vivo interaction of Drosophila eIF4G with the N-terminal third of eIF4A

The <i>ddeq</i> Python library for point source quantification from remote sensing images (version 1.0)

Atmospheric emissions from anthropogenic hotspots, i.e., cities, power plants and industrial facilities, can be determined from remote sensing images obtained from airborne and space-based imaging spectrometers. In this paper, we present a Python library for data-driven emission quantification (ddeq) that implements various computationally light methods such as the Gaussian plume inversion, cross-sectional flux method, integrated mass enhancement method and divergence method. The library provides a shared interface for data input and output and tools for pre- and post-processing of data. The shared interface makes it possible to easily compare and benchmark the different methods. The paper describes the theoretical basis of the different emission quantification methods and their implementation in the ddeq library. The application of the methods is demonstrated using Jupyter notebooks included in the library, for example, for NO2 images from the Sentinel-5P/TROPOMI satellite and for synthetic CO2 and NO2 images from the Copernicus CO2 Monitoring (CO2M) satellite constellation. The library can be easily extended for new datasets and methods, providing a powerful community tool for users and developers interested in emission monitoring using remote sensing images.</p