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Aim The maternal diet during pregnancy and lactation may affect the long-term health of the offspring. Our aim was to study how a fish or meat diet perinatal and after weaning affects body composition, insulin sensitivity and the profile of n-3 and n-6 polyunsaturated fatty acids ( PUFAs) in breast milk, fat depots, skeletal muscle and liver in male adult mice offspring. Methods During gestation and lactation, C57 BL/6 dams were fed a herring- or beef-based diet. Half of the pups in each group changed diets after weaning. In offspring, body composition measured by DEXA, plasma lipid profile and insulin sensitivity measured by euglycemic clamp or QUICKI were monitored to adulthood. Analysis of total FAs by GC- MS were performed in the diet, breast milk and in different tissues. Results At 9 week of age, offspring of herring-fed dams had less body fat than offspring of beef-fed dams. Mice fed herring after weaning had increased insulin sensitivity at 15 week of age, reduced total plasma cholesterol and triglyceride levels, and compared with beef-fed mice, larger interscapular brown adipose tissue depots. The FA composition of the maternal diet was mirrored in breast milk, and the herring diet significantly affected the FA profile of different tissues, leading to an increased content of n-3 PUFAs. Conclusion A herring-based maternal diet reduces body fat in the offspring, but the insulin sensitivity, plasma lipids and amount of brown adipose tissue are affected by the offspring's own diet; the herring diet is more beneficial than the beef diet. [ABSTRACT FROM AUTHOR]

Abstract Background Wax ester synthases (WSs) can synthesize wax esters from alcohols and fatty acyl coenzyme A thioesters. The knowledge of the preferred substrates for each WS allows the use of yeast cells for the production of wax esters that are high-value materials and can be used in a variety of industrial applications. The products of WSs include fatty acid ethyl esters, which can be directly used as biodiesel. Results Here, heterologous WSs derived from five different organisms were successfully expressed and evaluated for their substrate preference in Saccharomyces cerevisiae. We investigated the potential of the different WSs for biodiesel (that is, fatty acid ethyl esters) production in S. cerevisiae. All investigated WSs, from Acinetobacter baylyi ADP1, Marinobacter hydrocarbonoclasticus DSM 8798, Rhodococcus opacus PD630, Mus musculus C57BL/6 and Psychrobacter arcticus 273-4, have different substrate specificities, but they can all lead to the formation of biodiesel. The best biodiesel producing strain was found to be the one expressing WS from M. hydrocarbonoclasticus DSM 8798 that resulted in a biodiesel titer of 6.3 mg/L. To further enhance biodiesel production, acetyl coenzyme A carboxylase was up-regulated, which resulted in a 30% increase in biodiesel production. Conclusions Five WSs from different species were functionally expressed and their substrate preference characterized in S. cerevisiae, thus constructing cell factories for the production of specific kinds of wax ester. WS from M. hydrocarbonoclasticus showed the highest preference for ethanol compared to the other WSs, and could permit the engineered S. cerevisiae to produce biodiesel.

Data on airborne microorganisms, particularly in Southeast Asia, are more limited compared to chemical data. This study is the first to examine the community and diversity of microorganisms on PM 2.5 in an urban area of Northern Thailand during both smoke haze and non-smoke haze periods of 2020. This study evaluated the composition of airborne bacteria and fungi and analyzed their association with the chemical composition of PM 2.5 and meteorological variables. Significantly higher concentrations of PM 2.5 and more chemical compounds were observed during the smoke haze period compared to the non-smoke haze period. Increased PM 2.5 concentrations significantly altered both bacterial and fungal communities. The diversity and richness of airborne bacteria increased, whereas those of fungi decreased. The level of PM 2.5 concentration (the carrier), the chemical composition of PM 2.5 (the resources for survival), and the local meteorological conditions (relative humidity (RH)) were associated with the differences in bacterial and fungal populations. In addition, air originating from the west of the receptor site, influenced by both terrestrial and marine air mass routes, contributed to higher bacterial diversity and richness during the smoke haze period. In contrast, fungal diversity and richness were greater when the air came from the southwest, following a marine route. However, the primary health concern is pathogens, which were present in both periods (such as Clostridium, Aspergillus, and Cladosporium) and were especially abundant during smoke haze periods. This study highlights those airborne microorganisms, along with the particles and their chemical composition, are important components that can impact health, including that of humans, animals, and the environment., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 Elsevier Ltd. All rights reserved.)

Kratom, Mitragyna speciosa, is one of the most popular herbs in the West and Southeast Asia. A number of previous works have focused on bioactive alkaloids in this plant; however, non-alkaloids have never been investigated for their biological activities. Antiviral and virucidal assays of a methanol leaf extract of Kratom, M. speciosa, revealed that a crude extract displayed virucidal activity against the SARS-CoV-2. Activity-guided isolation of a methanol leaf extract of Kratom led to the identification of B-type procyanidin condensed tannins of (-)-epicatechin as virucidal compounds against SARS-CoV-2. The fraction containing condensed tannins exhibited virucidal activity with an EC 50 value of 8.38 μg/mL and a selectivity index (SI) value >23.86. LC-MS/MS analysis and MALDI-TOF MS identified the structure of the virucidal compounds in Kratom as B-type procyanidin condensed tannins, while gel permeation chromatograph (GPC) revealed weight average molecular weight of 238,946 Da for high molecular-weight condensed tannins. In addition to alkaloids, (-)-epicatechin was found as a major component in the leaves of M. speciosa, but it did not have virucidal activity. Macromolecules of (-)-epicatechin, i.e., procyanidin condensed tannins, showed potent virucidal activity against SARS-CoV-2, suggesting that the high molecular weights of these polyphenols are important for virucidal activity., Competing Interests: Declaration of competing interest The authors declare no competing financial interest., (Copyright © 2024 Elsevier B.V. All rights reserved.)

Short-chain fatty acids (SCFAs) are involved in important physiological processes such as gut health and immune response, and changes in SCFA levels can be indicative of disease. Despite the importance of SCFAs in human health and disease, reference values for fecal and plasma SCFA concentrations in healthy individuals are scarce. To address this gap in current knowledge, we developed a simple and reliable derivatization-free GC-TOFMS method for quantifying fecal and plasma SCFAs in healthy individuals. We targeted six linear- and seven branched-SCFAs, obtaining method recoveries of 73-88% and 83-134% in fecal and plasma matrices, respectively. The developed methods are simpler, faster, and more sensitive than previously published methods and are well suited for large-scale studies. Analysis of samples from 157 medically confirmed healthy individuals showed that the total SCFAs in the feces and plasma were 34.1 ± 15.3 µmol/g and 60.0 ± 45.9 µM, respectively. In fecal samples, acetic acid (Ace), propionic acid (Pro), and butanoic acid (But) were all significant, collectively accounting for 89% of the total SCFAs, whereas the only major SCFA in plasma samples was Ace, constituting of 93% of the total plasma SCFAs. There were no statistically significant differences in the total fecal and plasma SCFA concentrations between sexes or among age groups. The data revealed, however, a positive correlation for several nutrients, such as carbohydrate, fat, iron from vegetables, and water, to most of the targeted SCFAs. This is the first large-scale study to report SCFA reference intervals in the plasma and feces of healthy individuals, and thereby delivers valuable data for microbiome, metabolomics, and biomarker research., Competing Interests: The authors declare that they have no competing interest with the contents of this article., (© 2024 The Authors.)

The collision cross-sections (CCS) measurement using ion mobility spectrometry (IMS) in combination with mass spectrometry (MS) offers a great opportunity to increase confidence in metabolite identification. However, owing to the lack of sensitivity and resolution, IMS has an analytical challenge in studying the CCS values of very low-molecular-weight metabolites (VLMs ≤ 250 Da). Here, we describe an analytical method using ultrahigh-performance liquid chromatography (UPLC) coupled to a traveling wave ion mobility-quadrupole-time-of-flight mass spectrometer optimized for the measurement of VLMs in human urine samples. The experimental CCS values, along with mass spectral properties, were reported for the 174 metabolites. The experimental data included the mass-to-charge ratio ( m / z ), retention time (RT), tandem MS (MS/MS) spectra, and CCS values. Among the studied metabolites, 263 traveling wave ion mobility spectrometry (TWIMS)-derived CCS values ( TW CCS N2 ) were reported for the first time, and more than 70% of these were CCS values of VLMs. The TW CCS N2 values were highly repeatable, with inter-day variations of <1% relative standard deviation (RSD). The developed method revealed excellent TW CCS N2 accuracy with a CCS difference (ΔCCS) within ±2% of the reported drift tube IMS (DTIMS) and TWIMS CCS values. The complexity of the urine matrix did not affect the precision of the method, as evidenced by ΔCCS within ±1.92%. According to the Metabolomics Standards Initiative, 55 urinary metabolites were identified with a confidence level of 1. Among these 55 metabolites, 53 (96%) were VLMs. The larger number of confirmed compounds found in this study was a result of the addition of TW CCS N2 values, which clearly increased metabolite identification confidence., Competing Interests: The authors declare that there are no conflicts of interest., (© 2023 The Author(s).)

The rotationally averaged collision cross-section (CCS) determined by ion mobility-mass spectrometry (IM-MS) facilitates the identification of various biomolecules. Although machine learning (ML) models have recently emerged as a highly accurate approach for predicting CCS values, they rely on large data sets from various instruments, calibrants, and setups, which can introduce additional errors. In this study, we identified and validated that ion's polarizability and mass-to-charge ratio ( m / z ) have the most significant predictive power for traveling-wave IM CCS values in relation to other physicochemical properties of ions. Constructed solely based on these two physicochemical properties, our CCS prediction approach demonstrated high accuracy (mean relative error of <3.0%) even when trained with limited data (15 CCS values). Given its ability to excel with limited data, our approach harbors immense potential for constructing a precisely predicted CCS database tailored to each distinct experimental setup. A Python script for CCS prediction using our approach is freely available at https://github.com/MSBSiriraj/SVR&#95;CCSPrediction under the GNU General Public License (GPL) version 3.

In classic semiquantitative metabolomics, metabolite intensities are affected by biological factors and other unwanted variations. A systematic evaluation of the data processing methods is crucial to identify adequate processing procedures for a given experimental setup. Current comparative studies are mostly focused on peak area data but not on absolute concentrations. In this study, we evaluated data processing methods to produce outputs that were most similar to the corresponding absolute quantified data. We examined the data distribution characteristics, fold difference patterns between 2 metabolites, and sample variance. We used 2 metabolomic datasets from a retail milk study and a lupus nephritis cohort as test cases. When studying the impact of data normalization, transformation, scaling, and combinations of these methods, we found that the cross-contribution compensating multiple standard normalization (ccmn) method, followed by square root data transformation, was most appropriate for a well-controlled study such as the milk study dataset. Regarding the lupus nephritis cohort study, only ccmn normalization could slightly improve the data quality of the noisy cohort. Since the assessment accounted for the resemblance between processed data and the corresponding absolute quantified data, our results denote a helpful guideline for processing metabolomic datasets within a similar context (food and clinical metabolomics). Finally, we introduce Metabox 2.0, which enables thorough analysis of metabolomic data, including data processing, biomarker analysis, integrative analysis, and data interpretation. It was successfully used to process and analyze the data in this study. An online web version is available at http://metsysbio.com/metabox., (© The Author(s) 2024. Published by Oxford University Press GigaScience.)

Hericium erinaceus is an edible mushroom with diverse pharmaceutical applications. Although this mushroom is an attractive source of natural products for cancer treatment, little is known about the bioactive compounds from this mushroom, which may possess antibreast cancer activity. Here, we report the isolation and structure elucidation of new compounds, 5'-hydroxyhericenes A-D ( 1-4 ) as an inseparable mixture, together with known compounds ( 5-16 ) from the fruiting body of H. erinaceus . Based on NMR spectroscopic data and MS fragmentation analysis, the structure of a previously reported natural product, 3-[2,3-dihydroxy-4-(hydroxymethyl)tetrahydrofuran-1-yl]-pyridine-4,5-diol ( 5 ), should be revised to adenosine ( 6 ). Compounds 1-4 inhibit xanthine oxidase activity, while compounds 6 , 9 , and 10 scavenge reactive oxygen species generated by xanthine oxidase. Moreover, hericerin ( 13 ) exhibits strong growth inhibitory activity against T47D breast cancer cells and, to a lesser extent, against MDA-MB-231 breast cancer and MRC-5 normal embryonic cells. Exposure of T47D and MDA-MB-231 cells slightly increased PARP cleavage, suggesting that the growth inhibitory effect of hericerin may be mediated through nonapoptotic pathways. Our results suggest that the bioactive compounds of mushroom H. erinaceus hold promise as antibreast cancer agents., Competing Interests: The authors declare no competing financial interest., (© 2023 The Authors. Published by American Chemical Society.)

The gut microbiota exert a profound influence on human health and metabolism, with microbial metabolites playing a pivotal role in shaping host physiology. This study investigated the impact of prolonged egg supplementation on insulin-like growth factor 1 (IGF-1) and circulating short-chain fatty acids (SCFAs). In a subset of a cluster-randomized trial, participants aged 8-14 years were randomly assigned into three groups: (1) Whole Egg (WE)-consuming 10 additional eggs per week [ n = 24], (2) Protein Substitute (PS)-consuming yolk-free egg substitute equivalent to 10 eggs per week [ n = 25], and (3) Control Group (C) [ n = 26]. At week 35, IGF-1 levels in WE significantly increased (66.6 ± 27.7 ng/mL, p < 0.05) compared to C, with positive SCFA correlations, except acetate. Acetate was stable in WE, increasing in PS and C. Significant propionate differences occurred between WE and PS (14.8 ± 5.6 μmol/L, p = 0.010). WE exhibited notable changes in the relative abundance of the Bifidobacterium and Prevotella genera. Strong positive SCFA correlations were observed with MAT-CR-H4-C10 and Libanicoccus , while Roseburia, Terrisporobacter, Clostridia&#95;UCG-014 , and Coprococcus showed negative correlations. In conclusion, whole egg supplementation improves growth factors that may be related to bone formation and growth; it may also promote benefits to gut microbiota but may not affect SCFAs.

Small molecules derived from gut microbiota have been increasingly investigated to better understand the functional roles of the human gut microbiome. Microbial metabolites of aromatic amino acids (AAA) have been linked to many diseases, such as metabolic disorders, chronic kidney diseases, inflammatory bowel disease, diabetes, and cancer. Important microbial AAA metabolites are often discovered via global metabolite profiling of biological specimens collected from humans or animal models. Subsequent metabolite identity confirmation and absolute quantification using targeted analysis enable comparisons across different studies, which can lead to the establishment of threshold concentrations of potential metabolite biomarkers. Owing to their excellent selectivity and sensitivity, hyphenated mass spectrometry (MS) techniques are often employed to identify and quantify AAA metabolites in various biological matrices. Here, we summarize the developments over the past five years in MS-based methodology for analyzing gut microbiota-derived AAA. Sample preparation, method validation, analytical performance, and statistical methods for correlation analysis are discussed, along with future perspectives., Competing Interests: None., (© 2023 The Authors.)

Protein-energy malnutrition still impacts children's growth and development. We investigated the prolonged effects of egg supplementation on growth and microbiota in primary school children. For this study, 8-14-year-old students (51.5% F) in six rural schools in Thailand were randomly assigned into three groups: (1) whole egg (WE), consuming 10 additional eggs/week ( n = 238) ( n = 238); (2) protein substitute (PS), consuming yolk-free egg substitutes equivalent to 10 eggs/week ( n = 200); and (3) control group (C, ( n = 197)). The outcomes were measured at week 0, 14, and 35. At the baseline, 17% of the students were underweight, 18% were stunted, and 13% were wasted. At week 35, compared to the C group the weight and height difference increased significantly in the WE group (3.6 ± 23.5 kg, p < 0.001; 5.1 ± 23.2 cm, p < 0.001). No significant differences in weight or height were observed between the PS and C groups. Significant decreases in atherogenic lipoproteins were observed in the WE, but not in PS group. HDL-cholesterol tended to increase in the WE group (0.02 ± 0.59 mmol/L, ns ). The bacterial diversity was similar among the groups. The relative abundance of Bifidobacterium increased by 1.28-fold in the WE group compared to the baseline and differential abundance analysis which indicated that Lachnospira increased and Varibaculum decreased significantly. In conclusion, prolonged whole egg supplementation is an effective intervention to improve growth, nutritional biomarkers, and gut microbiota with unaltered adverse effects on blood lipoproteins.

Cancer progression is linked to gene-environment interactions that alter cellular homeostasis. The use of biomarkers as early indicators of disease manifestation and progression can substantially improve diagnosis and treatment. Large omics datasets generated by high-throughput profiling technologies, such as microarrays, RNA sequencing, whole-genome shotgun sequencing, nuclear magnetic resonance, and mass spectrometry, have enabled data-driven biomarker discoveries. The identification of differentially expressed traits as molecular markers has traditionally relied on statistical techniques that are often limited to linear parametric modeling. The heterogeneity, epigenetic changes, and high degree of polymorphism observed in oncogenes demand biomarker-assisted personalized medication schemes. Deep learning (DL), a major subunit of machine learning (ML), has been increasingly utilized in recent years to investigate various diseases. The combination of ML/DL approaches for performance optimization across multi-omics datasets produces robust ensemble-learning prediction models, which are becoming useful in precision medicine. This review focuses on the recent development of ML/DL methods to provide integrative solutions in discovering cancer-related biomarkers, and their utilization in precision medicine., Competing Interests: None., (© 2023 The Authors.)

Background: Massive parallel sequencing technologies have enabled the elucidation of plant phylogenetic relationships from chloroplast genomes at a high pace. These include members of the family Rhamnaceae. The current Rhamnaceae phylogenetic tree is from 13 out of 24 Rhamnaceae chloroplast genomes, and only one chloroplast genome of the genus Ventilago is available. Hence, the phylogenetic relationships in Rhamnaceae remain incomplete, and more representative species are needed., Results: The complete chloroplast genome of Ventilago harmandiana Pierre was outlined using a hybrid assembly of long- and short-read technologies. The accuracy and validity of the final genome were confirmed with PCR amplifications and investigation of coverage depth. Sanger sequencing was used to correct for differences in lengths and nucleotide bases between inverted repeats because of the homopolymers. The phylogenetic trees reconstructed using prevalent methods for phylogenetic inference were topologically similar. The clustering based on codon usage was congruent with the molecular phylogenetic tree. The groups of genera in each tribe were in accordance with tribal classification based on molecular markers. We resolved the phylogenetic relationships among six Hovenia species, three Rhamnus species, and two Ventilago species. Our reconstructed tree provides the most complete and reliable low-level taxonomy to date for the family Rhamnaceae. Similar to other higher plants, the RNA editing mostly resulted in converting serine to leucine. Besides, most genes were subjected to purifying selection. Annotation anomalies, including indel calling errors, unaligned open reading frames of the same gene, inconsistent prediction of intergenic regions, and misannotated genes, were identified in the published chloroplast genomes used in this study. These could be a result of the usual imperfections in computational tools, and/or existing errors in reference genomes. Importantly, these are points of concern with regards to utilizing published chloroplast genomes for comparative genomic analysis., Conclusions: In summary, we successfully demonstrated the use of comprehensive genomic data, including DNA and amino acid sequences, to build a reliable and high-resolution phylogenetic tree for the family Rhamnaceae. Additionally, our study indicates that the revision of genome annotation before comparative genomic analyses is necessary to prevent the propagation of errors and complications in downstream analysis and interpretation., (© 2023. The Author(s).)

The combination of ion mobility mass spectrometry (IM-MS) and chromatography is a valuable tool for identifying compounds in natural products. In this study, using an ultra-performance liquid chromatography system coupled to a high-resolution quadrupole/traveling wave ion mobility spectrometry/time-of-flight MS (UPLC-TWIMS-QTOF), we have established and validated a comprehensive TW CCS N2 and MS database for 112 plant specialized metabolites. The database included 15 compounds that were isolated and purified in-house and are not commercially available. We obtained accurate m / z , retention times, fragment ions, and TWIMS-derived CCS ( TW CCS N2 ) values for 207 adducts (ESI + and ESI - ). The database included novel 158 TW CCS N2 values from 79 specialized metabolites. In the presence of plant matrix, the CCS measurement was reproducible and robust. Finally, we demonstrated the application of the database to extend the metabolite coverage of Ventilago harmandiana Pierre. In addition to pyranonaphthoquinones, a group of known specialized metabolites in V. harmandiana , we identified flavonoids, xanthone, naphthofuran, and protocatechuic acid for the first time through targeted analysis. Interestingly, further investigation using IM-MS of unknown features suggested the presence of organonitrogen compounds and lipid and lipid-like molecules, which is also reported for the first time. Data are available on the MassIVE (https://massive.ucsd.edu, data set identifier MSV000090213).

Two dimensional GC (GC × GC)-time-of-flight mass spectrometry (TOFMS) has been used to improve accurate metabolite identification in the chemical industry, but this method has not been applied as readily in biomedical research. Here, we evaluated and validated the performance of high resolution GC × GC-TOFMS against that of GC-TOFMS for metabolomics analysis of two different plasma matrices, from healthy controls (CON) and diabetes mellitus (DM) patients with kidney failure (DM with KF). We found GC × GC-TOFMS outperformed traditional GC-TOFMS in terms of separation performance and metabolite coverage. Several metabolites from both the CON and DM with KF matrices, such as carbohydrates and carbohydrate-conjugate metabolites, were exclusively detected using GC × GC-TOFMS. Additionally, we applied this method to characterize significant metabolites in the DM with KF group, with focused analysis of four metabolite groups: sugars, sugar alcohols, amino acids, and free fatty acids. Our plasma metabolomics results revealed 35 significant metabolites (12 unique and 23 concentration-dependent metabolites) in the DM with KF group, as compared with those in the CON and DM groups (N = 20 for each group). Interestingly, we determined 17 of the 35 (14/17 verified with reference standards) significant metabolites identified from both the analyses were metabolites from the sugar and sugar alcohol groups, with significantly higher concentrations in the DM with KF group than in the CON and DM groups. Enrichment analysis of these 14 metabolites also revealed that alterations in galactose metabolism and the polyol pathway are related to DM with KF. Overall, our application of GC × GC-TOFMS identified key metabolites in complex plasma matrices., Competing Interests: Conflict of interest The authors declare that they have no conflicts of interest with the contents of this article., (Copyright © 2022 The Authors. Published by Elsevier Inc. All rights reserved.)

Sweet pickled mango named Ma-Muang Bao Chae-Im (MBC), a delicacy from the Southern part of Thailand, has a unique aroma and taste. The employed immersion processes (brining 1, brining 2, and immersion in a hypertonic sugar solution, sequentially) in the MBC production process bring changes to the unripe mango, which indicate the occurrence of metabolic profiles alteration during the production process. This occurrence was never been explored. Thus, this study investigated metabolic profile alteration during the MBC production process. The untargeted metabolomics profiling method was used to reveal the changes in volatile and non-volatile metabolites. Headspace solid-phase micro-extraction tandem with gas chromatography quadrupole time of flight (GC/QTOF) was employed for the volatile analysis, while metabolites derivatization for non-volatile analysis. In conclusion, a total of 82 volatile and 41 non-volatile metabolites were identified during the production process. Terpenes, terpenoids, several non-volatile organic acids, and sugars were the major mango metabolites that presented throughout the process. Gamma-aminobutyric acid (GABA) was only observed during the brining processes, which suggested the microorganism's stress response mechanism to an acidic environment and high chloride ions in brine. Esters and alcohols were abundant during the last immersion process, which had an important role in MBC flavor characteristics. The knowledge of metabolites development during the MBC production process would be beneficial for product development and optimization., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2022 Indrati, Phonsatta, Poungsombat, Khoomrung, Sumpavapol and Panya.)

Two-dimensional gas chromatography-time-of-flight mass spectrometry (GC × GC-TOFMS) provides a large amount of molecular information from biological samples. However, the lack of a comprehensive compound library or customizable bioinformatics tool is currently a challenge in GC × GC-TOFMS data analysis. We present an open-source deep learning (DL) software called contour regions of interest (ROI) identification, simulation and untargeted metabolomics profiler (CRISP). CRISP integrates multiple customizable deep neural network architectures for assisting the semi-automated identification of ROIs, contour synthesis, resolution enhancement and classification of GC × GC-TOFMS-based contour images. The approach includes the novel aggregate feature representative contour (AFRC) construction and stacked ROIs. This generates an unbiased contour image dataset that enhances the contrasting characteristics between different test groups and can be suitable for small sample sizes. The utility of the generative models and the accuracy and efficacy of the platform were demonstrated using a dataset of GC × GC-TOFMS contour images from patients with late-stage diabetic nephropathy and healthy control groups. CRISP successfully constructed AFRC images and identified over five ROIs to create a deepstacked dataset. The high fidelity, 512 × 512-pixels generative model was trained as a generator with a Fréchet inception distance of <47.00. The trained classifier achieved an AUROC of >0.96 and a classification accuracy of >95.00% for datasets with and without column bleed. Overall, CRISP demonstrates good potential as a DL-based approach for the rapid analysis of 4-D GC × GC-TOFMS untargeted metabolite profiles by directly implementing contour images. CRISP is available at https://github.com/vivekmathema/GCxGC-CRISP., (© The Author(s) 2022. Published by Oxford University Press.)

The current gold standard for classifying lupus nephritis (LN) progression is a renal biopsy, which is an invasive procedure. Undergoing a series of biopsies for monitoring disease progression and treatments is unlikely suitable for patients with LN. Thus, there is an urgent need for non-invasive alternative biomarkers that can facilitate LN class diagnosis. Such biomarkers will be very useful in guiding intervention strategies to mitigate or treat patients with LN. Urine samples were collected from two independent cohorts. Patients with LN were classified into proliferative (class III/IV) and membranous (class V) by kidney histopathology. Metabolomics was performed to identify potential metabolites, which could be specific for the classification of membranous LN. The ratio of picolinic acid (Pic) to tryptophan (Trp) ([Pic/Trp] ratio) was found to be a promising candidate for LN diagnostic and membranous classification. It has high potential as an alternative biomarker for the non-invasive diagnosis of LN., Competing Interests: The authors declare no competing interests., (© 2021 The Author(s).)

The accurate quantification of triterpenoids in Ganoderma lucidum mushroom in the mycelium stage is challenging due to their low concentrations, interference from other possible isomers, and the complex matrix. Here, a high-resolution quadrupole-time-of-flight mass spectrometry "multiple reaction monitoring" with target enhancement (HR-QTOF-MRM) method was developed to quantify seven target triterpenoids in G. lucidum . The performance of this method was compared against an optimized QQQ-MRM method. The HR-QTOF-MRM was shown to be capable of distinguishing target triterpenoids from interferent peaks in the presence of matrices. The HR-QTOF-MRM LOD and LLOQ values were found to be one to two times lower than those derived from the QQQ-MRM method. Intraday and interday variabilities of the HR-QTOF-MRM demonstrated better reproducibility than the QQQ-MRM. In addition, excellent recoveries of the analytes ranging from 80 to 117% were achieved. Spiking experiments were carried out to verify and compare the quantitative accuracy of the two methods. The HR-QTOF-MRM method provided better percent accuracy, ranging from 84% to 99% (<3% RSD), compared with the range of 69 to 114% (<4%RSD) given by the QQQ-MRM method. These results demonstrate that the new HR-QTOF-MRM mode is able to improve sensitivity, reproducibility, and accuracy of trace level analysis of triterpenoids in the complex biological samples. The triterpenoid concentrations were in the range of nondetect to 0.06-6.72 mg/g of dried weight in fruiting body and to 0.0009-0.01 mg/g of dried weight in mycelium.

Deep learning (DL), an emerging area of investigation in the fields of machine learning and artificial intelligence, has markedly advanced over the past years. DL techniques are being applied to assist medical professionals and researchers in improving clinical diagnosis, disease prediction and drug discovery. It is expected that DL will help to provide actionable knowledge from a variety of 'big data', including metabolomics data. In this review, we discuss the applicability of DL to metabolomics, while presenting and discussing several examples from recent research. We emphasize the use of DL in tackling bottlenecks in metabolomics data acquisition, processing, metabolite identification, as well as in metabolic phenotyping and biomarker discovery. Finally, we discuss how DL is used in genome-scale metabolic modelling and in interpretation of metabolomics data. The DL-based approaches discussed here may assist computational biologists with the integration, prediction and drawing of statistical inference about biological outcomes, based on metabolomics data., (© The Author(s) 2020. Published by Oxford University Press. All rights reserved. For Permissions, please email: journals.permissions@oup.com.)

Pyranonaphthoquinones (PNQs) are important structural scaffolds found in numerous natural products. Research interest in these specialized metabolites lies in their natural occurrence and therapeutic activities. Nonetheless, research progress has thus far been hindered by the lack of analytical standards and analytical methods for both qualitative and quantitative analysis. We report here that various parts of Ventilago harmandiana are rich sources of PNQs. We developed an ultraperformance liquid chromatography-electrospray ionization multiple reaction monitoring/mass spectrometry method to quantitatively determine six PNQs from leaves, root, bark, wood, and heartwood. The addition of standards in combination with a stable isotope of salicylic acid-D 6 was used to overcome the matrix effect with average recovery of 82% ± 1% ( n = 15). The highest concentration of the total PNQs was found in the root (11,902 μg/g dry weight), whereas the lowest concentration was found in the leaves (28 μg/g dry weight). Except for the root, PNQ-332 was found to be the major compound in all parts of V. harmandiana , accounting for ∼48% of the total PNQs quantified in this study. However, PNQ-318A was the most abundant PNQ in the root sample, accounting for 27% of the total PNQs. Finally, we provide novel MS/MS spectra of the PNQs at different collision induction energies: 10, 20, and 40 eV (POS and NEG). For structural elucidation purposes, we propose complete MS/MS fragmentation pathways of PNQs using MS/MS spectra at collision energies of 20 and 40 eV. The MS/MS spectra along with our discussion on structural elucidation of these PNQs should be very useful to the natural products community to further exploring PNQs in V. harmandiana and various other sources., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2021 Limjiasahapong, Kaewnarin, Jariyasopit, Hongthong, Nuntasaen, Robinson, Nookaew, Sirivatanauksorn, Kuhakarn, Reutrakul and Khoomrung.)

In the past few years, deep learning has been successfully applied to various omics data. However, the applications of deep learning in metabolomics are still relatively low compared to others omics. Currently, data pre-processing using convolutional neural network architecture appears to benefit the most from deep learning. Compound/structure identification and quantification using artificial neural network/deep learning performed relatively better than traditional machine learning techniques, whereas only marginally better results are observed in biological interpretations. Before deep learning can be effectively applied to metabolomics, several challenges should be addressed, including metabolome-specific deep learning architectures, dimensionality problems, and model evaluation regimes., (© 2020 The Author(s).)

Interferons are commonly utilized in the treatment of chronic hepatitis B virus (HBV) infection but are not effective for all patients. A deep understanding of the limitations of interferon treatment requires delineation of its activity at multiple "omic" levels. While myriad studies have characterized the transcriptomic effects of interferon treatment, surprisingly, few have examined interferon-induced effects at the proteomic level. To remedy this paucity, we stimulated HepG2 cells with both IFN-α and IFN-λ and performed proteomic analysis versus unstimulated cells. Alongside, we examined the effects of HBV transfection in the same cell line, reasoning that parallel IFN and HBV analysis might allow determination of cases where HBV transfection counters the effects of interferons. More than 6000 proteins were identified, with multiple replicates allowing for differential expression analysis at high confidence. Drawing on a compendium of transcriptomic data, as well as proteomic half-life data, we suggest means by which transcriptomic results diverge from our proteomic results. We also invoke a recent multiomic study of HBV-related hepatocarcinoma (HCC), showing that despite HBV's role in initiating HCC, the regulated proteomic landscapes of HBV transfection and HCC do not strongly align. Special focus is applied to the proteasome, with numerous components divergently altered under IFN and HBV-transfection conditions. We also examine alterations of other protein groups relevant to HLA complex peptide display, unveiling intriguing alterations in a number of ubiquitin ligases. Finally, we invoke genome-scale metabolic modeling to predict relevant alterations to the metabolic landscape under experimental conditions. Our data should be useful as a resource for interferon and HBV researchers., Competing Interests: The authors declare no competing financial interest., (Copyright © 2020 American Chemical Society.)

Phospholipids, the most abundant membrane lipid components, are crucial in maintaining membrane structures and homeostasis for biofunctions. As a structurally diverse and tightly regulated system involved in multiple organelles, phospholipid metabolism is complicated to manipulate. Thus, repurposing phospholipids for lipid-derived chemical production remains unexplored. Herein, we develop a Saccharomyces cerevisiae platform for de novo production of oleoylethanolamide, a phospholipid derivative with promising pharmacological applications in ameliorating lipid dysfunction and neurobehavioral symptoms. Through deregulation of phospholipid metabolism, screening of biosynthetic enzymes, engineering of subcellular trafficking and process optimization, we could produce oleoylethanolamide at a titer of 8,115.7 µg l -1 and a yield on glucose of 405.8 µg g -1 . Our work provides a proof-of-concept study for systemically repurposing phospholipid metabolism for conversion towards value-added biological chemicals, and this multi-faceted framework may shed light on tailoring phospholipid metabolism in other microbial hosts.

Alum has been widely used as an adjuvant for human vaccines; however, the impact of Alum on host metabolism remains largely unknown. Herein, we applied mass spectrometry (MS) (liquid chromatography-MS)-based metabolic and lipid profiling to monitor the effects of the Alum adjuvant on mouse serum at 6, 24, 72, and 168 h post-vaccination. We propose a new strategy termed subclass identification and annotation for metabolomics for class-wise identification of untargeted metabolomics data generated from high-resolution MS. Using this approach, we identified and validated the levels of several lipids in mouse serum that were significantly altered following Alum administration. These lipids showed a biphasic response even 168 h after vaccination. The majority of the lipids were triglycerides (TAGs), where TAGs with long-chain unsaturated fatty acids (FAs) decreased at 24 h and TAGs with short-chain FAs decreased at 168 h. To our knowledge, this is the first report on the impact of human vaccine adjuvant Alum on the host metabolome, which may provide new insights into the mechanism of action of Alum.

Passive air sampling was conducted in Toronto and the Greater Toronto Area from 2016 to 2017 for 6 periods, in order to investigate ambient levels of polycyclic aromatic compounds (PACs) associated with different source types. The selected sampling sites (n = 8) cover geographical areas with varying source emissions including background, traffic, urban, industrial and residential sites. Passive air samples were analyzed for PACs which include PAHs, alkylated PAHs (alk-PAHs), dibenzothiophene and alkylated dibenzothiophenes (DBTs) and results for PAHs were used to calculate inhalation cancer risks using different approaches. The samples were also characterized for PAH derivatives including nitrated PAHs (NPAHs) and oxygenated PAHs (OPAHs). Concentrations of Σalk-PAHs and DBTs, which are known to be enriched in fossil fuels, as well as ΣNPAHs, were highest at a traffic site (MECP) located adjacent to the 18-lane Highway 401 that runs across Toronto. Except for an industrial site (HH/BU), PAC compositions were similar across the sampling sites with Σalk-PAHs being the most abundant class of PACs suggesting traffic emission was a major contributor to PACs in the atmosphere of Toronto. The industrial site exhibited a distinct chemical composition with ΣPAHs dominating over Σalk-PAHs and with elevated levels of fluoranthene, 9-nitroanthracene, and 9,10-anthraquinone, which likely reflects emissions from nearby industrial sources. MECP and HH/BU exhibited higher lifetime excess inhalation cancer risks indicating an association with traffic and industrial sources. The importance of the traffic sector as a source of PACs to ambient air is further supported by strong correlations of the ΣPAHs, Σalk-PAHs, DBTs, and ΣOPAHs with NO x . This study highlights the importance of traffic as an emission source of PACs to urban air and the relevance of PAC classes other than just unsubstituted PAHs that are important but currently not included in air quality guidelines or for assessing inhalation cancer risks., (Crown Copyright © 2019. Published by Elsevier Ltd. All rights reserved.)

Biological significance of 18-carbon polyunsaturated fatty acids, γ-linolenic acid (GLA; C18:3 n-6) and dihomo-γ-linolenic acid (DGLA; C20:3 n-6) has gained much attention in the systematic development of optimized strains for industrial applications. In this work, a n-6 PUFAs-producing strain of Aspergillus oryzae was generated by manipulating metabolic reactions in fatty acid modification and triacylglycerol biosynthesis. The codon-optimized genes coding for Δ 6 -desaturase and Δ 6 -elongase of Pythium sp., and diacylglycerol acyltransferase 2 (mMaDGAT2) of Mortierella alpina were co-transformed in a single vector into A. oryzae BCC14614, yielding strain TD6E6-DGAT2. Comparative phenotypic analysis showed that a 70% increase of lipid titer was found in the engineered strain, which was a result of a significant increase in triacylglycerol (TAG) content (52.0 ± 1.8% of total lipids), and corresponded to the increased size of lipid particles observed in the fungal cells. Interestingly, the proportions of GLA and DGLA in neutral lipids of the engineered strain were similar, with the highest titers obtained in the high C:N culture (29:0; 6% glucose) during the lipid-accumulating stage of growth. Time-course expression analysis of the engineered strain revealed transcriptional control of TAG biosynthesis through a co-operation between the native DGAT2 of A. oryzae and the transformed mMaDGAT2., (Copyright © 2019 Elsevier B.V. All rights reserved.)

Metabolite profiles from biological samples suffer from both technical variations and subject-specific variants. To improve the quality of metabolomics data, conventional data processing methods can be employed to remove technical variations. These methods do not consider sources of subject variation as separate factors from biological factors of interest. This can be a significant issue when performing quantitative metabolomics in clinical trials or screening for a potential biomarker in early-stage disease, because changes in metabolism or a desired-metabolite signal are small compared to the total metabolite signals. As a result, inter-individual variability can interfere subsequent statistical analyses. Here, we propose an additional data processing step using linear mixed-effects modelling to readjust an individual metabolite signal prior to multivariate analyses. Published clinical metabolomics data was used to demonstrate and evaluate the proposed method. We observed a substantial reduction in variation of each metabolite signal after model fitting. A comparison with other strategies showed that our proposed method contributed to improved classification accuracy, precision, sensitivity and specificity. Moreover, we highlight the importance of patient metadata as it contains rich information of subject characteristics, which can be used to model and normalize metabolite abundances. The proposed method is available as an R package lmm2met.

Background: The yeast Saccharomyces cerevisiae plays an essential role in the fermentation of lignocellulosic hydrolysates. Weak organic acids in lignocellulosic hydrolysate can hamper the use of this renewable resource for fuel and chemical production. Plasma-membrane remodeling has recently been found to be involved in acquiring tolerance to organic acids, but the mechanisms responsible remain largely unknown. Therefore, it is essential to understand the underlying mechanisms of acid tolerance of S. cerevisiae for developing robust industrial strains., Results: We have performed a comparative analysis of lipids and fatty acids in S. cerevisiae grown in the presence of four different weak acids. The general response of the yeast to acid stress was found to be the accumulation of triacylglycerols and the degradation of steryl esters. In addition, a decrease in phosphatidic acid, phosphatidylcholine, phosphatidylserine and phosphatidylethanolamine, and an increase in phosphatidylinositol were observed. Loss of cardiolipin in the mitochondria membrane may be responsible for the dysfunction of mitochondria and the dramatic decrease in the rate of respiration of S. cerevisiae under acid stress. Interestingly, the accumulation of ergosterol was found to be a protective mechanism of yeast exposed to organic acids, and the ERG1 gene in ergosterol biosynthesis played a key in ergosterol-mediated acid tolerance, as perturbing the expression of this gene caused rapid loss of viability. Interestingly, overexpressing OLE1 resulted in the increased levels of oleic acid (18:1n-9) and an increase in the unsaturation index of fatty acids in the plasma membrane, resulting in higher tolerance to acetic, formic and levulinic acid, while this change was found to be detrimental to cells exposed to lipophilic cinnamic acid., Conclusions: Comparison of lipid profiles revealed different remodeling of lipids, FAs and the unsaturation index of the FAs in the cell membrane in response of S. cerevisiae to acetic, formic, levulinic and cinnamic acid, depending on the properties of the acid. In future work, it will be necessary to combine lipidome and transcriptome analysis to gain a better understanding of the underlying regulation network and interactions between central carbon metabolism (e.g., glycolysis, TCA cycle) and lipid biosynthesis.

Machine learning (ML) is being ubiquitously incorporated into everyday products such as Internet search, email spam filters, product recommendations, image classification, and speech recognition. New approaches for highly integrated manufacturing and automation such as the Industry 4.0 and the Internet of things are also converging with ML methodologies. Many approaches incorporate complex artificial neural network architectures and are collectively referred to as deep learning (DL) applications. These methods have been shown capable of representing and learning predictable relationships in many diverse forms of data and hold promise for transforming the future of omics research and applications in precision medicine. Omics and electronic health record data pose considerable challenges for DL. This is due to many factors such as low signal to noise, analytical variance, and complex data integration requirements. However, DL models have already been shown capable of both improving the ease of data encoding and predictive model performance over alternative approaches. It may not be surprising that concepts encountered in DL share similarities with those observed in biological message relay systems such as gene, protein, and metabolite networks. This expert review examines the challenges and opportunities for DL at a systems and biological scale for a precision medicine readership.

The development of robust and efficient cell factories requires understanding of the metabolic changes triggered by the production of the targeted compound. Here we aimed to study how production of p-coumaric acid, a precursor of multiple secondary aromatic metabolites, influences the cellular metabolism of Saccharomyces cerevisiae. We evaluated the growth and p-coumaric acid production in batch and chemostat cultivations and analyzed the transcriptome and intracellular metabolome during steady state in low- and high-producers of p-coumaric acid in two strain backgrounds, S288c or CEN.PK. We found that the same genetic modifications resulted in higher production of p-coumaric acid in the CEN.PK background than in the S288c background. Moreover, the CEN.PK strain was less affected by the genetic engineering as was evident from fewer changes in the transcription profile and intracellular metabolites concentrations. Surprisingly, for both strains we found the largest transcriptional changes in genes involved in transport of amino acids and sugars, which were downregulated. Additionally, in S288c amino acid and protein biosynthesis processes were also affected. We systematically overexpressed or deleted genes with significant transcriptional changes in CEN.PK low and high-producing strains. The knockout of some of the downregulated transporters triggered a 20-50% improvement in the synthesis of p-CA in the CEN.PK high-producing strain. This study demonstrates the importance of transporters in the engineering of cell factories for production of small molecules., (Copyright © 2017 The Authors. Published by Elsevier Inc. All rights reserved.)

In recent years, interest in studies of traditional medicine in Asian and African countries has gradually increased due to its potential to complement modern medicine. In this review, we provide an overview of Thai traditional medicine (TTM) current development, and ongoing research activities of TTM related to metabolomics. This review will also focus on three important elements of systems biology analysis of TTM including analytical techniques, statistical approaches and bioinformatics tools for handling and analyzing untargeted metabolomics data. The main objective of this data analysis is to gain a comprehensive understanding of the system wide effects that TTM has on individuals. Furthermore, potential applications of metabolomics and systems medicine in TTM will also be discussed.

Tolerance of yeast to acid stress is important for many industrial processes including organic acid production. Therefore, elucidating the molecular basis of long term adaptation to acidic environments will be beneficial for engineering production strains to thrive under such harsh conditions. Previous studies using gene expression analysis have suggested that both organic and inorganic acids display similar responses during short term exposure to acidic conditions. However, biological mechanisms that will lead to long term adaptation of yeast to acidic conditions remains unknown and whether these mechanisms will be similar for tolerance to both organic and inorganic acids is yet to be explored. We therefore evolved Saccharomyces cerevisiae to acquire tolerance to HCl (inorganic acid) and to 0.3M L-lactic acid (organic acid) at pH 2.8 and then isolated several low pH tolerant strains. Whole genome sequencing and RNA-seq analysis of the evolved strains revealed different sets of genome alterations suggesting a divergence in adaptation to these two acids. An altered sterol composition and impaired iron uptake contributed to HCl tolerance whereas the formation of a multicellular morphology and rapid lactate degradation was crucial for tolerance to high concentrations of lactic acid. Our findings highlight the contribution of both the selection pressure and nature of the acid as a driver for directing the evolutionary path towards tolerance to low pH. The choice of carbon source was also an important factor in the evolutionary process since cells evolved on two different carbon sources (raffinose and glucose) generated a different set of mutations in response to the presence of lactic acid. Therefore, different strategies are required for a rational design of low pH tolerant strains depending on the acid of interest., (Copyright © 2016 International Metabolic Engineering Society. Published by Elsevier Inc. All rights reserved.)

Organic solvents are widely used in microbial fermentations to reduce gas stripping effects and capture hydrophobic or toxic compounds. Reliable quantification of biochemical products in these overlays is highly challenging and practically difficult. Here, we present a significant improvement of identification and quantification methods for farnesene produced by Saccharomyces cerevisiae in two-liquid-phase fermentations using GC-MS and GC-FID. By increasing the polarity of the stationary phase introducing a ZB-50 column (50%-phenyl-50%-dimethylsiloxane) peak intensity could be increased and solvent carryover could be minimized. Direct quantification of farnesene in dodecane was achieved by GC-FID whereas GC-MS demonstrated to be an excellent technique for identification of known and unknown metabolites. The GC-FID is a suitable technique for direct quantification of farnesene in complex matrices as shown by the good calibration curve (R(2)>0.998, N=5) within the tested concentration range of 1-50 µg/mL and the reproducibility of the intensity (intraday; <10% RSD at each concentration; N=5). The limit of detection (LOD) and limit of quantification (LOQ) of the method were 0.24 and 0.80 µg/mL, respectively. Furthermore, the FID method proved to be highly stable with regard to the intensity of the calibration (N=6) when the measurements were performed across 250 samples that were derived from a dodecane overlay., (Copyright © 2015 Elsevier B.V. All rights reserved.)

Baker's yeast Saccharomyces cerevisiae is an attractive cell factory for production of chemicals and biofuels. Many different products have been produced in this cell factory by reconstruction of heterologous biosynthetic pathways; however, endogenous metabolism by itself involves many metabolites of industrial interest, and de-regulation of endogenous pathways to ensure efficient carbon channelling to such metabolites is therefore of high interest. Furthermore, many of these may serve as precursors for the biosynthesis of complex natural products, and hence strains overproducing certain pathway intermediates can serve as platform cell factories for production of such products. Here we implement a modular pathway rewiring (MPR) strategy and demonstrate its use for pathway optimization resulting in high-level production of L-ornithine, an intermediate of L-arginine biosynthesis and a precursor metabolite for a range of different natural products. The MPR strategy involves rewiring of the urea cycle, subcellular trafficking engineering and pathway re-localization, and improving precursor supply either through attenuation of the Crabtree effect or through the use of controlled fed-batch fermentations, leading to an L-ornithine titre of 1,041±47 mg l(-1) with a yield of 67 mg (g glucose)(-1) in shake-flask cultures and a titre of 5.1 g l(-1) in fed-batch cultivations. Our study represents the first comprehensive study on overproducing an amino-acid intermediate in yeast, and our results demonstrate the potential to use yeast more extensively for low-cost production of many high-value amino-acid-derived chemicals.

The metabolism of proliferating cells shows common features even in evolutionary distant organisms such as mammals and yeasts, for example the requirement for anabolic processes under tight control of signaling pathways. Analysis of the rewiring of metabolism, which occurs following the dysregulation of signaling pathways, provides new knowledge about the mechanisms underlying cell proliferation. The key energy regulator in yeast Snf1 and its mammalian ortholog AMPK have earlier been shown to have similar functions at glucose limited conditions and here we show that they also have analogies when grown with glucose excess. We show that loss of Snf1 in cells growing in 2% glucose induces an extensive transcriptional reprogramming, enhances glycolytic activity, fatty acid accumulation and reliance on amino acid utilization for growth. Strikingly, we demonstrate that Snf1/AMPK-deficient cells remodel their metabolism fueling mitochondria and show glucose and amino acids addiction, a typical hallmark of cancer cells., (Copyright © 2015 Elsevier B.V. All rights reserved.)

Malic acid is a C₄ dicarboxylic acid that is currently mainly used in the food and beverages industry as an acidulant. Because of the versatility of the group of C₄ dicarboxylic acids, the chemical industry has a growing interest in this chemical compound. As malic acid will be considered as a bulk chemical, microbial production requires organisms that sustain high rates, yields, and titers. Aspergillus oryzae is mainly known as an industrial enzyme producer, but it was also shown that it has a very competitive natural production capacity for malic acid. Recently, an engineered A. oryzae strain, 2103a-68, was presented which overexpressed pyruvate carboxylase, malate dehydrogenase, and a malic acid transporter. In this work, we report a detailed characterization of this strain including detailed rates and yields under malic acid production conditions. Furthermore, transcript levels of the genes of interest and corresponding enzyme activities were measured. On glucose as carbon source, 2103a-68 was able to secrete malic acid at a maximum specific production rate during stationary phase of 1.87 mmol (g dry weight (DW))⁻¹ h⁻¹ and with a yield of 1.49 mol mol⁻¹. Intracellular fluxes were obtained using ¹³C flux analysis during exponential growth, supporting the success of the metabolic engineering strategy of increasing flux through the reductive cytosolic tricarboxylic acid (rTCA) branch. Additional cultivations using xylose and a glucose/xylose mixture demonstrated that A. oryzae is able to efficiently metabolize pentoses and hexoses to produce malic acid at high titers, rates, and yields.

The maternal diet during gestation and lactation affects the long-term health of the offspring. We sought to determine whether maternal and postweaning crossover isocaloric diets based on fish or meat affect the geometry, mineral density, and biomechanical properties of bone in mouse offspring in adulthood. During gestation and lactation, C57BL/6 dams were fed a herring- or beef-based diet. After weaning, half of the pups in each group were fed the same diet as their dams, and half were fed the other diet. Areal bone mineral density (aBMD) and bone mineral content (BMC) of the whole body and lumbar spine were measured in the offspring by dual X-ray absorptiometry at 9 and 21 weeks of age. At 22-26 weeks, tibia bone geometry (length, cortical volumetric (v) BMD, BMC, area and thickness) was analyzed by peripheral quantitative computed tomography, and the biomechanical properties of the tibia were analyzed by the three-point bending test. Plasma insulin-like growth factor-1 was analyzed at 12 weeks. In comparison to the maternal herring diet, the maternal beef diet increased aBMD and BMC in the whole body and lumbar spine of adult offspring, as well as cortical vBMD, BMC, bone area, and thickness at the mid-diaphyseal region of the tibia and the biomechanical properties of tibia strength. In contrast, a postweaning beef diet decreased aBMD in the lumbar spine and BMC in the whole body and lumbar spine compared with a postweaning herring diet, which instead increased plasma insulin-like growth factor-1 levels. The change from a maternal beef diet before weaning to a herring diet after weaning decreased body weight and increased the cortical area, vBMD, BMC, thickness, and strength of the tibia. These significant crossover effects indicate that a preweaning maternal beef diet and a postweaning herring diet are optimal for increasing BMC and bone strength in offspring in adulthood.

We here present simple and rapid methods for fast screening of yeast lipids in Saccharomyces cerevisiae. First we introduced a microwave-assisted technique for fast lipid extraction that allows the extraction of lipids within 10 min. The new method enhances extraction rate by 27 times, while maintaining product yields comparable to conventional methods (n = 14, P > 0.05). The recovery (n = 3) from spiking of synthetic standards were 92 ± 6% for cholesterol, 95 ± 4% for triacylglycerol, and 92 ± 4% for free fatty acids. Additionally, the new extraction method combines cell disruption and extraction in one step, and the approach, therefore, not only greatly simplifies sample handling but also reduces analysis time and minimizes sample loss during sample preparation. Second, we developed a chromatographic separation that allowed separation of neutral and polar lipids from the extracted samples within a single run. The separation was performed based on a three gradient solvent system combined with hydrophilic interaction liquid chromatography-HPLC followed by detection using a charged aerosol detector. The method was shown to be highly reproducible in terms of retention time of the analytes (intraday; 0.002-0.034% RSD; n = 10, interday; 0.04-1.35% RSD; n = 5) and peak area (intraday; 0.63-6% RSD; n = 10, interday; 4-12% RSD; n = 5).

We present a fast and accurate method for preparation of fatty acid methyl esters (FAMEs) using microwave-assisted derivatization of fatty acids present in yeast samples. The esterification of free/bound fatty acids to FAMEs was completed within 5 min, which is 24 times faster than with conventional heating methods. The developed method was validated in two ways: (1) through comparison with a conventional method (hot plate) and (2) through validation with the standard reference material (SRM) 3275-2 omega-3 and omega-6 fatty acids in fish oil (from the Nation Institute of Standards and Technology, USA). There were no significant differences (P>0.05) in yields of FAMEs with both validations. By performing a simple modification of closed-vessel microwave heating, it was possible to carry out the esterification in Pyrex glass tubes kept inside the closed vessel. Hereby, we are able to increase the number of sample preparations to several hundred samples per day as the time for preparation of reused vessels was eliminated. Pretreated cell disruption steps are not required, since the direct FAME preparation provides equally quantitative results. The new microwave-assisted derivatization method facilitates the preparation of FAMEs directly from yeast cells, but the method is likely to also be applicable for other biological samples.