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Species of the genus Wolffia are traditionally used as human food in some of the Asian countries. Therefore, all 11 species of this genus, identified by molecular barcoding, were investigated for ingredients relevant to human nutrition. The total protein content varied between 20 and 30% of the freeze-dry weight, the starch content between 10 and 20%, the fat content between 1 and 5%, and the fiber content was ~25%. The essential amino acid content was higher or close to the requirements of preschool-aged children according to standards of the World Health Organization. The fat content was low, but the fraction of polyunsaturated fatty acids was above 60% of total fat and the content of n-3 polyunsaturated fatty acids was higher than that of n-6 polyunsaturated fatty acids in most species. The content of macro- and microelements (minerals) not only depended on the cultivation conditions but also on the genetic background of the species. This holds true also for the content of tocopherols, several carotenoids and phytosterols in different species and even intraspecific, clonal differences were detected in Wolffia globosa and Wolffia arrhiza. Thus, the selection of suitable clones for further applications is important. Due to the very fast growth and the highest yield in most of the nutrients, Wolffia microscopica has a high potential for practical applications in human nutrition.
Background: Native breast milk composition displays significant inter- and intra-individual variation which persists after standard fortification with fixed doses and challenges target fortification. This study aims to analyze the macronutrient composition of different commercially available fortifiers and the effect of different fortification strategies on nutritional intake of preterm infants.
Methods: In 103 preterm infants, native breast milk samples were collected from 24-h feeding batches (n = 3,338) and fat, protein and carbohydrate contents were analyzed. Nutrient content was compared for breast milk that had undergone either (i) standard fortification, (ii) targeted fortification, (iii) selective batching according to breast milk composition, or (iv) partial lyophilization. For (i) eight commercially available standard fortifiers were tested. Targeted fortification (ii) involved the addition of single component modulars of either protein, fat or carbohydrates to standard fortified breast milk. Using a mathematical growth model, the combined effect of protein, fat and carbohydrate intake on growth was assessed. The best composition of standard fortifiers as the initial step for target fortification was explored assuming three clinical scenarios for milk analysis.
Results: Macronutrient content was highly variable between native breast milk samples, and this variation was still present after standard fortification, however at elevated macronutrient levels. Standard fortification, breast milk batching, as well as partial lyophilization of human milk resulted in deficient and imbalanced enteral intakes in a significant proportion of infants. Target fortification reduced this variation in a, respectively, higher percentage of samples. The effect size was dependent on the number of measurements per week. The optimum composition of standard fortifiers was dependent on the clinical scenario (measurement frequency) for target fortification.
Conclusions: To provide precise and accurate intakes of macronutrients, breast milk should be target fortified. Standard fortified breast milk can result in excess above recommended intakes of some macronutrients which limits the efficiency of target fortification. Standard fortifiers with improved composition are needed for target fortification.
Approaches to the Analysis of Proteomics and Transcriptomics Data based on Statistical Methodology
(2014)
Recent developments in genomics and molecular biology led to the generation of an enormous amount of complex data of different origin. This is demonstrated by a number of published results from microarray experiments in Gene Expression Omnibus. The number was growing in exponential pace over the last decade. The challenge of interpreting these vast amounts of data from different technologies led to the development of new methods in the fields of computational biology and bioinformatics. Researchers often want to represent biological phenomena in the most detailed and comprehensive way. However, due to the technological limitations and other factors like limited resources this is not always possible. On one hand, more detailed and comprehensive research generates data of high complexity that is very often difficult to approach analytically, however, giving bioinformatics a chance to draw more precise and deeper conclusions. On the other hand, for low-complexity tasks the data distribution is known and we can fit a mathematical model. Then, to infer from this mathematical model, researchers can use well-known and standard methodologies. In return for using standard methodologies, the biological questions we are answering might not be unveiling the whole complexity of the biological meaning. Nowadays it is a standard that a biological study involves generation of large amounts of data that needs to be analyzed with a statistical inference. Sometimes data challenge researchers with low complexity task that can be performed with standard and popular methodologies as in Proteomic analysis of mouse oocytes reveals 28 candidate factors of the "reprogrammome". There, we established a protocol for proteomics data that involves preprocessing of the raw data and conducting Gene Ontology overrepresentation analysis utilizing hypergeometric distribution. In cases, where the data complexity is high and there are no published frameworks a researcher could follow, randomization can be an approach to exploit. In two studies by The mouse oocyte proteome escapes maternal aging and CellFateScout - a bioinformatics tool for elucidating small molecule signaling pathways that drive cells in a specific direction we showed how randomization can be performed for distinct complex tasks. In The mouse oocyte proteome escapes maternal aging we constructed a random sample of semantic similarity score between oocyte transcriptome and random transcriptome subset of oocyte proteome size. Therefore, we could calculate whether the proteome is representative of the trancriptome. Further, we established a novel framework for Gene Ontology overrepresentation that involves randomization testing. Every Gene Ontology term is tested whether randomly reassigning all gene labels of belonging to or not belonging to this term will decrease the overall expression level in this term. In CellFateScout - a bioinformatics tool for elucidating small molecule signaling pathways that drive cells in a specific direction we validated CellFateScout against other well-known bioinformatics tools. We stated the question whether our plugin is able to predict small molecule effects better in terms of expression signatures. For this, we constructed a protocol that uses randomization testing. We assess here if the small molecule effect described as a (set of) active signaling pathways, as detected by our plugin or other bioinformatics tools, is significantly closer to known small molecule targets than a random path.