Integrating High-Resolution Mass Spectral Data, Bioassays and Computational Models to Annotate Bioactives in Botanical Extracts: Case Study Analysis of C. asiatica Extract Associates Dicaffeoylquinic Acids with Protection against Amyloid-ß Toxicity.

Rapid screening of botanical extracts for the discovery of bioactive natural products was performed using a fractionation approach in conjunction with flow-injection high-resolution mass spectrometry for obtaining chemical fingerprints of each fraction, enabling the correlation of the relative abundance of molecular features (representing individual phytochemicals) with the read-outs of bioassays. We applied this strategy for discovering and identifying constituents of Centella asiatica (C. asiatica) that protect against Aß cytotoxicity in vitro. C. asiatica has been associated with improving mental health and cognitive function, with potential use in Alzheimer's disease. Human neuroblastoma MC65 cells were exposed to subfractions of an aqueous extract of C. asiatica to evaluate the protective benefit derived from these subfractions against amyloid ß-cytotoxicity. The % viability score of the cells exposed to each subfraction was used in conjunction with the intensity of the molecular features in two computational models, namely Elastic Net and selectivity ratio, to determine the relationship of the peak intensity of molecular features with % viability. Finally, the correlation of mass spectral features with MC65 protection and their abundance in different sub-fractions were visualized using GNPS molecular networking. Both computational methods unequivocally identified dicaffeoylquinic acids as providing strong protection against Aß-toxicity in MC65 cells, in agreement with the protective effects observed for these compounds in previous preclinical model studies.

Comparative liquid chromatography/tandem mass spectrometry lipidomics analysis of macaque heart tissue flash-frozen or embedded in optimal cutting temperature polymer (OCT): Practical considerations.

RATIONALE: Biobanks of patient tissues have emerged as essential resources in biomedical research. Optimal cutting temperature compound (OCT) blends have shown to provide stability to the embedded tissue and are compatible with spectroscopic methods, such as infrared (IR) and Raman spectroscopy. Data derived from omics-methods are only useful if tissue damage caused by storage in OCT blends is minimal and well understood. In this context, we investigated the suitability of OCT storage for heart tissue destined for liquid chromatography/tandem mass spectrometry (LC/MS/MS) lipidomic studies. METHODS: To determine the compatibility of OCT storage with LC/MS/MS lipidomics studies. The lipid profiles of macaque heart tissue snap-frozen in liquid nitrogen or stored in an OCT blend were evaluated. RESULTS: We have evaluated a lipid extraction protocol suitable for OCT-embedded tissue that is compatible with LC/MS/MS. We annotated and evaluated the profiles of 306 lipid species from tissues stored in OCT or liquid nitrogen. For most of the lipid species (95.4%), the profiles were independent of the storage conditions. However, 4.6% of the lipid species; mainly plasmalogens, were affected by the storage method. CONCLUSIONS: This study shows that OCT storage is compatible with LC-MS/MS lipidomics of heart tissue, facilitating the use of biobanked tissue samples for future studies.

Integration of mass spectral fingerprinting analysis with precursor ion (MS1) quantification for the characterisation of botanical extracts: application to extracts of Centella asiatica (L.) Urban.

INTRODUCTION: The phytochemical composition of plant material governs the bioactivity and potential health benefits as well as the outcomes and reproducibility of laboratory studies and clinical trials. OBJECTIVE: The objective of this work was to develop an efficient method for the in-depth characterisation of plant extracts and quantification of marker compounds that can be potentially used for subsequent product integrity studies. Centella asiatica (L.) Urb., an Ayurvedic herb with potential applications in enhancing mental health and cognitive function, was used as a case study. METHODS: A quadrupole time-of-flight analyser in conjunction with an optimised high-performance liquid chromatography (HPLC) separation was used for in-depth untargeted fingerprinting and post-acquisition precursor ion quantification to determine levels of distinct phytochemicals in various C. asiatica extracts. RESULTS: We demonstrate the utility of this workflow for the characterisation of extracts of C. asiatica. This integrated workflow allowed the identification or tentative identification of 117 compounds, chemically interconnected based on Tanimoto chemical similarity, and the accurate quantification of 24 phytochemicals commonly found in C. asiatica extracts. CONCLUSION: We report a phytochemical analysis method combining liquid chromatography, high resolution mass spectral data acquisition, and post-acquisition interrogation that allows chemical fingerprints of botanicals to be obtained in conjunction with accurate quantification of distinct phytochemicals. The variability in the composition of specialised metabolites across different C. asiatica accessions was substantial, demonstrating that detailed characterisation of plant extracts is a prerequisite for reproducible use in laboratory studies, clinical trials and safe consumption. The methodological approach is generally applicable to other botanical products.

Decoding the neural representation of story meanings across languages.

Drawing from a common lexicon of semantic units, humans fashion narratives whose meaning transcends that of their individual utterances. However, while brain regions that represent lower-level semantic units, such as words and sentences, have been identified, questions remain about the neural representation of narrative comprehension, which involves inferring cumulative meaning. To address these questions, we exposed English, Mandarin, and Farsi native speakers to native language translations of the same stories during fMRI scanning. Using a new technique in natural language processing, we calculated the distributed representations of these stories (capturing the meaning of the stories in high-dimensional semantic space), and demonstrate that using these representations we can identify the specific story a participant was reading from the neural data. Notably, this was possible even when the distributed representations were calculated using stories in a different language than the participant was reading. Our results reveal that identification relied on a collection of brain regions most prominently located in the default mode network. These results demonstrate that neuro-semantic encoding of narratives happens at levels higher than individual semantic units and that this encoding is systematic across both individuals and languages. Hum Brain Mapp 38:6096-6106, 2017. © 2017 Wiley Periodicals, Inc.

DeepConsensus improves the accuracy of sequences with a gap-aware sequence transformer.

Circular consensus sequencing with Pacific Biosciences (PacBio) technology generates long (10-25 kilobases), accurate 'HiFi' reads by combining serial observations of a DNA molecule into a consensus sequence. The standard approach to consensus generation, pbccs, uses a hidden Markov model. We introduce DeepConsensus, which uses an alignment-based loss to train a gap-aware transformer-encoder for sequence correction. Compared to pbccs, DeepConsensus reduces read errors by 42%. This increases the yield of PacBio HiFi reads at Q20 by 9%, at Q30 by 27% and at Q40 by 90%. With two SMRT Cells of HG003, reads from DeepConsensus improve hifiasm assembly contiguity (NG50 4.9 megabases (Mb) to 17.2 Mb), increase gene completeness (94% to 97%), reduce the false gene duplication rate (1.1% to 0.5%), improve assembly base accuracy (Q43 to Q45) and reduce variant-calling errors by 24%. DeepConsensus models could be trained to the general problem of analyzing the alignment of other types of sequences, such as unique molecular identifiers or genome assemblies.

Computation-Assisted Identification of Bioactive Compounds in Botanical Extracts: A Case Study of Anti-Inflammatory Natural Products from Hops.

The slow pace of discovery of bioactive natural products can be attributed to the difficulty in rapidly identifying them in complex mixtures such as plant extracts. To overcome these hurdles, we explored the utility of two machine learning techniques, i.e., Elastic Net and Random Forests, for identifying the individual anti-inflammatory principle(s) of an extract of the inflorescences of the hops (Humulus lupulus) containing hundreds of natural products. We fractionated a hop extract by column chromatography to obtain 40 impure fractions, determined their anti-inflammatory activity using a macrophage-based bioassay that measures inhibition of iNOS-mediated formation of nitric oxide, and characterized the chemical composition of the fractions by flow-injection HRAM mass spectrometry and LC-MS/MS. Among the top 10 predictors of bioactivity were prenylated flavonoids and humulones. The top Random Forests predictor of bioactivity, xanthohumol, was tested in pure form in the same bioassay to validate the predicted result (IC50 7 µM). Other predictors of bioactivity were identified by spectral similarity with known hop natural products using the Global Natural Products Social Networking (GNPS) algorithm. Our machine learning approach demonstrated that individual bioactive natural products can be identified without the need for extensive and repetitive bioassay-guided fractionation of a plant extract.

Plant growth and metabolic changes in 'Super Hot' chili fruit (Capsicum annuum) exposed to supplemental LED lights.

Light-emitting diodes (LEDs) of different colors improve plant growth and increase levels of secondary metabolites. This study aimed to determine the effect of red, blue, and red + blue LEDs (1:1) on the secondary metabolites composition in chili, focusing on capsaicinoids, at the top and middle of the plant canopy in 'Super Hot' chili. The accumulated yield of the chili fruit was the highest for control, followed by blue, red and red + blue LEDs, with the top canopy giving twice more yield than the middle canopy. UPLC-MS/MS analysis of chili fruit's methanolic extracts was used to determine capsaicinoids levels. Blue LEDs significantly increased nordihydrocapsaicin, capsaicin, dihydrocapsaicin, homocapsaicin and homodihydrocapsaicin contents by 57 %, 43 %, 56 %, 28 %, and 54 %, respectively, compared to the control. Also, 24 tentatively annotated metabolites, including phenylalanine, cinnamate, and valine, which are involved in the biosynthesis of capsaicinoids, were semi-quantitatively evaluated to determine the impact of LED exposure on the biosynthetic pathway of capsaicinoids. Supplemental blue LED placed at the top and between the canopy may boost the levels of capsaicinoids in chili fruit grown in greenhouses.

Plasma Oxylipins: A Potential Risk Assessment Tool in Atherosclerotic Coronary Artery Disease.

Background: While oxylipins have been linked to coronary artery disease (CAD), little is known about their diagnostic and prognostic potential. Objective: We tested whether plasma concentration of specific oxylipins may discriminate among number of diseased coronary arteries and predict median 5-year outcomes in symptomatic adults. Methods: Using a combination of high-performance liquid chromatography (HPLC) and quantitative tandem mass spectrometry, we conducted a targeted analysis of 39 oxylipins in plasma samples of 23 asymptomatic adults with low CAD risk and 74 symptomatic adults (≥70% stenosis), aged 38-87 from the Greater Portland, Oregon area. Concentrations of 22 oxylipins were above the lower limit of quantification in >98% of adults and were compared, individually and in groups based on precursors and biosynthetic pathways, in symptomatic adults to number of diseased coronary arteries [(1) n = 31; (2) n = 23; (3) n = 20], and outcomes during a median 5-year follow-up (no surgery: n = 7; coronary stent placement: n = 24; coronary artery bypass graft surgery: n = 26; death: n = 7). Results: Plasma levels of six quantified oxylipins decreased with the number of diseased arteries; a panel of five oxylipins diagnosed three diseased arteries with 100% sensitivity and 70% specificity. Concentrations of five oxylipins were lower and one oxylipin was higher with survival; a panel of two oxylipins predicted survival during follow-up with 86% sensitivity and 91% specificity. Conclusions: Quantification of plasma oxylipins may assist in CAD diagnosis and prognosis in combination with standard risk assessment tools.