In Vitro Pancreatic Lipase Inhibition by Marine Fungi Purpureocillium lilacinum Associated with Stylissa sp. Sponge as Anti-obesity Agent

This study aimed to evaluate the potential of marine fungus Purpureocillium lilacinum isolated from an Indonesian marine sponge Stylissa sp. as an anti-obesity agent through pancreatic lipase inhibition assay. The fungus was identified as P. lilacinum through morphological and molecular characteristics. The fungal extract’s inhibition activity and kinetics were evaluated using spectrophotometry and Lineweaver-Burk plots. Ethyl acetate and butanol were used for extraction. Both extracts showed pancreatic lipase inhibition in a concentration-dependent manner. Both crude extracts were then fractionated once. All fractionated extracts showed inhibitory activity above 50%, with the highest activity found in fraction 5 of ethyl acetate at 93.41% inhibition. The best fractionated extract had an IC50value of 220.60 µg.mL-1. The most active fraction of P. lilacinum had a competitive-type inhibitor behavior as shown by the value of Vmax not significantly changing from 388.80 to 382.62 mM pNP.min-1, and the Michaelis-Menten constant (KM) increased from 2.02 to 5.47 mM in the presence of 500 µg.mL-1 fractionated extract. Metabolite identification with LC-MS/MS QTOF suggested that galangin, kaempferol, and quercetin were responsible for the observed lipase inhibition.

Transcription Factors in Alkaloid Engineering

Plants produce a large variety of low-molecular-weight and specialized secondary compounds. Among them, nitrogen-containing alkaloids are the most biologically active and are often used in the pharmaceutical industry. Although alkaloid chemistry has been intensively investigated, characterization of alkaloid biosynthesis, including biosynthetic enzyme genes and their regulation, especially the transcription factors involved, has been relatively delayed, since only a limited number of plant species produce these specific types of alkaloids in a tissue/cell-specific or developmental-specific manner. Recent advances in molecular biology technologies, such as RNA sequencing, co-expression analysis of transcripts and metabolites, and functional characterization of genes using recombinant technology and cutting-edge technology for metabolite identification, have enabled a more detailed characterization of alkaloid pathways. Thus, transcriptional regulation of alkaloid biosynthesis by transcription factors, such as basic helix–loop–helix (bHLH), APETALA2/ethylene-responsive factor (AP2/ERF), and WRKY, is well elucidated. In addition, jasmonate signaling, an important cue in alkaloid biosynthesis, and its cascade, interaction of transcription factors, and post-transcriptional regulation are also characterized and show cell/tissue-specific or developmental regulation. Furthermore, current sequencing technology provides more information on the genome structure of alkaloid-producing plants with large and complex genomes, for genome-wide characterization. Based on the latest information, we discuss the application of transcription factors in alkaloid engineering.

Metabolomics Meets Nutritional Epidemiology: Harnessing the Potential in Metabolomics Data

Traditionally, nutritional epidemiology is the study of the relationship between diet and health and disease in humans at the population level. Commonly, the exposure of interest is food intake. In recent years, nutritional epidemiology has moved from a “black box” approach to a systems approach where genomics, metabolomics and proteomics are providing novel insights into the interplay between diet and health. In this context, metabolomics is emerging as a key tool in nutritional epidemiology. The present review explores the use of metabolomics in nutritional epidemiology. In particular, it examines the role that food-intake biomarkers play in addressing the limitations of self-reported dietary intake data and the potential of using metabolite measurements in assessing the impact of diet on metabolic pathways and physiological processes. However, for full realisation of the potential of metabolomics in nutritional epidemiology, key challenges such as robust biomarker validation and novel methods for new metabolite identification need to be addressed. The synergy between traditional epidemiologic approaches and metabolomics will facilitate the translation of nutritional epidemiologic evidence to effective precision nutrition.

PeakBot: Machine learning based chromatographic peak picking

MotivationChromatographic peak picking is among the first steps in software pipelines for processing LC-HRMS datasets in untargeted metabolomics applications. Its performance is crucial for the holistic detection of all metabolic features as well as their relative quantification for statistical analysis and metabolite identification. Unfortunately, random noise, non-baseline separated compounds and unspecific background signals complicate this task.ResultsA machine-learning framework entitled PeakBot was developed for detecting chromatographic peaks in LC-HRMS profile-mode data. It first detects all local signal maxima in a chromatogram, which are then extracted as super-sampled standardized areas (retention time vs. m/z). These are subsequently inspected by a custom-trained convolutional neural network that forms the basis of PeakBot’s architecture. The model reports if the respective local maximum is the apex of a chromatographic peak or not as well as its peak center and bounding box.In independent training and validation datasets used for development, PeakBot achieved a high performance with respect to discriminating between chromatographic peaks and background signals (F1 score of 0.99). A comparison of different sets of reference features showed that at least 100 reference features (including isotopologs) should be provided to achieve high-quality results for detecting new chromatographic peaks.PeakBot is implemented in Python (3.8) and uses the TensorFlow (2.4.1) package for machine-learning related tasks. It has been tested on Linux and Windows OSs.AvailabilityThe framework is available free of charge for non-commercial use (CC BY-NC-SA). It is available at https://github.com/christophuv/[email protected] informationSupplementary data are available at Bioinformatics online.