RESUMEN
Increasing evidence indicates that the gut microbiome (GM) plays an important role in dyslipidemia. To date, however, no in-depth characterization of the associations between GM with lipoproteins distributions (LPD) among adult individuals with diverse BMI has been conducted. To determine such associations, we studied blood-plasma LPD, fecal short-chain fatty acids (SCFA) and GM of 262 Danes aged 19-89 years. Stratification of LPD segregated subjects into three clusters displaying recommended levels of lipoproteins and explained by age and body-mass-index. Higher levels of HDL2a and HDL2b were associated with a higher abundance of Ruminococcaceae and Christensenellaceae. Increasing levels of total cholesterol and LDL-1 and LDL-2 were positively associated with Lachnospiraceae and Coriobacteriaceae, and negatively with Bacteroidaceae and Bifidobacteriaceae. Metagenome-sequencing showed a higher abundance of biosynthesis of multiple B-vitamins and SCFA metabolism genes among healthier LPD profiles. Metagenomic-assembled genomes (MAGs) affiliated to Eggerthellaceae and Clostridiales were contributors of these genes and their relative abundance correlated positively with larger HDL subfractions. The study demonstrates that differences in composition and metabolic traits of the GM are associated with variations in LPD among the recruited subjects. These findings provide evidence for GM considerations in future research aiming to shed light on mechanisms of the GM-dyslipidemia axis.
RESUMEN
Nowadays, hyphenated chemical analysis methods like GC/MS, LC/MS, or HPLC with UV/Vis diode array detection are widely used. These methods produce a data matrix of mixtures measured during the analytical process. When a set of samples is to be analyzed with one data matrix per sample, the data is often presumed to have "trilinear" structure if the profile for each compound does not change shape or position from one sample to the other. By applying this information as a trilinearity constraint in Self Modeling Curve Resolution (SMCR) methods, overlapping peaks related to the pure compounds of interest can be resolved in a unique way. In practice, many systems have non-trilinear behavior due to deviation from ideal response, for example, a sample matrix effect or changes in instrumental response (e.g., shifts or changes in the shape of chromatographic peaks). In such cases, the trilinear model is not valid because every analyte does not have the same peak shape or position in every sample. In such cases, the unique profiles obtained by strictly enforced trilinearity constraints will not necessarily produce true profiles because the data set does not follow the assumed trilinear behavior. In this work, we introduce "soft-trilinearity constraints" to permit peak profiles of given components to have small deviations in their shape and position in different samples. The advantages and disadvantages of this approach are compared to other methods like PARAFAC2. We illustrate the influence of soft-trilinearity constraints on the accuracy of SMCR results for the case of a 3-component simulated system and an experimental data set. The results show that implementing soft-trilinearity constraints reduces the range of possible solutions considerably compared to the application of constraints such as just non-negativity. In addition, we show that the application of hard-trilinearity constraints can lead to solutions that are completely wrong or exclude the opportunity of a possible solution at all.
RESUMEN
Self Modeling Curve Resolution (SMCR) is a class of techniques concerned with estimating pure profiles underlying a set of measurements on chemical systems. In general, the estimated profiles are ambiguous (non-unique) except if some special conditions fulfilled. Implementing the adequate information can reduce the so-called rotational ambiguity effectively, and in the most desirable cases lead to the unique solution. Therefore, studies on circumstances resulting in unique solution are of particular importance. The conditions of unique solution can particularly be studied based on duality principle. In bilinear chemical (e.g., spectroscopic) data matrix, there is a natural duality between its row and column vector spaces using minimal constraints (non-negativity of concentrations and absorbances). In this article, the conditions of the unique solution according to duality concept and using zero concentration region information is intended to show. A simulated dataset of three components and an experimental system with synthetic mixtures containing three amino acids tyrosine, phenylalanine and tryptophan are analyzed. It is shown that in the presence of sufficient information, the reliable unique solution is obtained that is valuable in analytical qualification and for quantitative verification analysis.
