Hydrophilic interaction and reversed phase mixed-mode liquid chromatography coupled to high resolution tandem mass spectrometry for polar lipids analysis

A hydrophilic interaction liquid chromatography (HILIC) fused-core column (150 × 2.1 mm ID, 2.7 μm particle size) and a short reversed-phase liquid chromatography (RPLC) column (20 mm × 2.1 mm ID, 1.9 μm) were serially coupled to perform mixed-mode chromatography (MMC) on complex mixtures of phospholipids (PL). Mobile phase composition and gradient elution program were, preliminarily, optimized using a mixture of phosphatidylcholines (PC), phosphatidylethanolamines (PE), their corresponding lyso-forms (LPC and LPE), and sphingomyelins (SM). Thus a mixture of PC extracted from soybean was characterized by MMC coupled to electrospray ionization (ESI) high-resolution Fourier-transform mass spectrometry (FTMS) using an orbital trap analyzer. Several previously undiscovered PC, including positional isomers (i.e. 16:0/19:1 and 19:1/16:0) of PC 35:1 and skeletal isomers (i.e. 18:1/18:2 and 18:0/18:3) of PC 36:3 were identified. Therefore, high-resolution MS/MS spectra unveiled the occurrence of isomers for several overall side chain compositions. The proposed MMC-ESI-FTMS/MS approach revealed an unprecedented capability in disclosing complexity of an actual lipid extract, thus representing a very promising approach to lipidomics

The validity of an updated metabolic power algorithm based upon Di Prampero’s theoretical model in Elite soccer players

The aim of this study was to update the metabolic power (MP) algorithm (P.VO2, W·kg−1) related to the kinematics data (PGPS, W·kg−1) in a soccer-specific performance model. For this aim, seventeen professional (Serie A) male soccer players (.VO2max 55.7 ± 3.4 mL·min−1·kg−1) performed a 6 min run at 10.29 km·h−1 to determine linear-running energy cost (Cr). On a separate day, thirteen also performed an 8 min soccer-specific intermittent exercise protocol. For both procedures, a portable Cosmed K4b2 gas-analyzer and GPS (10 Hz) was used to assess the energy cost above resting (C). From this aim, the MP was estimated through a newly derived C equation (PGPSn) and compared with both the commonly used (PGPSo) equation and direct measurement (P.VO2). Both PGPSn and PGPSo correlated with P.VO2 (r = 0.66, p < 0.05). Estimates of fixed bias were negligible (PGPSn = −0.80 W·kg−1 and PGPSo = −1.59 W·kg−1), and the bounds of the 95% CIs show that they were not statistically significant from 0. Proportional bias estimates were negligible (absolute differences from one being 0.03 W·kg−1 for PGPSn and 0.01 W·kg−1 for PGPSo) and not statistically significant as both 95% CIs span 1. All variables were distributed around the line of unity and resulted in an under-or overestimation of PGPSn, while PGPSo routinely underestimated MP across ranges. Repeated-measures ANOVA showed differences over MP conditions (F1,38 = 16.929 and p < 0.001). Following Bonferroni post hoc test significant differences regarding the MP between PGPSo and P.VO2 /PGPSn (p < 0.001) were established, while no differences were found between P.VO2 and PGPSn (p = 0.853). The new approach showed it can help the coaches and the soccer trainers to better monitor external training load during the training seasons.© 2020 by the authors. Licensee MDPI, Basel, Switzerland

The GAIA theory: from Lovelock to Margulis. From a homeostatic to a cognitive autopoietic worldview

n/

Intrinsic Mitochondrial Membrane Potential and Associated Tumor Phenotype Are Independent of MUC1 Over-Expression

We have established previously that minor subpopulations of cells with stable differences in their intrinsic mitochondrial membrane potential (Δψm) exist within populations of mammary and colonic carcinoma cells and that these differences in Δψm are linked to tumorigenic phenotypes consistent with increased probability of participating in tumor progression. However, the mechanism(s) involved in generating and maintaining stable differences in intrinsic Δψm and how they are linked to phenotype are unclear. Because the mucin 1 (MUC1) oncoprotein is over-expressed in many cancers, with the cytoplasmic C-terminal fragment (MUC1 C-ter) and its integration into the outer mitochondrial membrane linked to tumorigenic phenotypes similar to those of cells with elevated intrinsic Δψm, we investigated whether endogenous differences in MUC1 levels were linked to stable differences in intrinsic Δψm and/or to the tumor phenotypes associated with the intrinsic Δψm. We report that levels of MUC1 are significantly higher in subpopulations of cells with elevated intrinsic Δψm derived from both mammary and colonic carcinoma cell lines. However, using siRNA we found that down-regulation of MUC1 failed to significantly affect either the intrinsic Δψm or the tumor phenotypes associated with increased intrinsic Δψm. Moreover, whereas pharmacologically mediated disruption of the Δψm was accompanied by attenuation of tumor phenotype, it had no impact on MUC1 levels. Therefore, while MUC1 over-expression is associated with subpopulations of cells with elevated intrinsic Δψm, it is not directly linked to the generation or maintenance of stable alterations in intrinsic Δψm, or to intrinsic Δψm associated tumor phenotypes. Since the Δψm is the focus of chemotherapeutic strategies, these data have important clinical implications in regard to effectively targeting those cells within a tumor cell population that exhibit stable elevations in intrinsic Δψm and are most likely to contribute to tumor progression

Cancer Biomarker Discovery: The Entropic Hallmark

Background: It is a commonly accepted belief that cancer cells modify their transcriptional state during the progression of the disease. We propose that the progression of cancer cells towards malignant phenotypes can be efficiently tracked using high-throughput technologies that follow the gradual changes observed in the gene expression profiles by employing Shannon's mathematical theory of communication. Methods based on Information Theory can then quantify the divergence of cancer cells' transcriptional profiles from those of normally appearing cells of the originating tissues. The relevance of the proposed methods can be evaluated using microarray datasets available in the public domain but the method is in principle applicable to other high-throughput methods. Methodology/Principal Findings: Using melanoma and prostate cancer datasets we illustrate how it is possible to employ Shannon Entropy and the Jensen-Shannon divergence to trace the transcriptional changes progression of the disease. We establish how the variations of these two measures correlate with established biomarkers of cancer progression. The Information Theory measures allow us to identify novel biomarkers for both progressive and relatively more sudden transcriptional changes leading to malignant phenotypes. At the same time, the methodology was able to validate a large number of genes and processes that seem to be implicated in the progression of melanoma and prostate cancer. Conclusions/Significance: We thus present a quantitative guiding rule, a new unifying hallmark of cancer: the cancer cell's transcriptome changes lead to measurable observed transitions of Normalized Shannon Entropy values (as measured by high-throughput technologies). At the same time, tumor cells increment their divergence from the normal tissue profile increasing their disorder via creation of states that we might not directly measure. This unifying hallmark allows, via the the Jensen-Shannon divergence, to identify the arrow of time of the processes from the gene expression profiles, and helps to map the phenotypical and molecular hallmarks of specific cancer subtypes. The deep mathematical basis of the approach allows us to suggest that this principle is, hopefully, of general applicability for other diseases