Identifying Variables Influencing Traditional Food Solid-State Fermentation by Statistical Modeling.

Solid-state fermentation is widely used in traditional food production, but most of the complex processes involved were designed and are carried out without a scientific basis. Often, mathematical models can be established to describe mass and heat transfer with the assistance of chemical engineering tools. However, due to the complex nature of solid-state fermentation, mathematical models alone cannot explain the many dynamic changes that occur during these processes. For example, it is hard to identify the most important variables influencing product yield and quality fluctuations. Here, using solid-state fermentation of Chinese liquor as a case study, we established statistical models to correlate the final liquor yield with available industrial data, including the starting content of starch, water and acid; starting temperature; and substrate temperature profiles throughout the process. Models based on starting concentrations and temperature profiles gave unsatisfactory yield predictions. Although the most obvious factor is the starting month, ambient temperature is unlikely to be the direct driver of differences. A lactic-acid-inhibition model indicates that lactic acid from lactic acid bacteria is likely the reason for the reduction in yield between April and December. Further integrated study strategies are necessary to confirm the most crucial variables from both microbiological and engineering perspectives. Our findings can facilitate better understanding and improvement of complex solid-state fermentations.

Selective ionization of oxidized lipid species using different solvent additives in flow injection mass spectrometry.

Lipid oxidation in food products is a crucial problem that causes undesirable changes in the food's flavor, texture, and nutritional value. It should be carefully monitored as it can lead to the formation of potentially toxic compounds and in that way reduce the shelf life of the product. Liquid chromatography coupled to mass spectrometry is a powerful tool to monitor the formation of oxidized lipids. However, the presence of lipid species in both their non-oxidized and oxidized forms at distinctly different concentrations can hinder the detection and identification of the less abundant oxidized species, due to coelution. In this study, a flow injection mass spectrometry approach was used to selectively ionize oxidized triacylglycerols versus their non-oxidized precursors. Three mobile phase additives were investigated (ammonium formate, sodium acetate, and sodium iodide) at three different concentrations, and ion source settings (i.e., sheath gas temperature, capillary voltage, and nozzle voltage) were optimized. A fractional factorial design was conducted to examine not only the direct effect of the operating parameters on the selectivity of ionization for the oxidized lipid species, but also to assess their combined effect. Overall, selective ionization of oxidized versus non-oxidized lipid species was favored by the use of sodium-containing solvent additives. The application of specific ion source settings resulted in an increased ionization selectivity, with sheath gas temperature and capillary voltage having the most significant influence. A selectivity factor as high as 120 could be reached by combining 0.1 mg/mL sodium-containing additives, with 250 °C sheath gas temperature and 5000 V capillary voltage. These findings will contribute to future studies on fast detection and relative quantification of low abundant oxidized triacylglycerols and their possible impact on human health.

Quantifying techno-functional properties of ingredients from multiple crops using machine learning.

Food ingredients with a low degree of refining consist of multiple components. Therefore, it is essential to formulate food products based on techno-functional properties rather than composition. We assessed the potential of quantifying techno-functional properties of ingredient blends from multiple crops as opposed to single crops. The properties quantified were gelation, viscosity, emulsion stability, and foaming capacity of ingredients from yellow pea and lupine seeds. The relationships were quantified using spline regression, random forest, and neural networks. Suitable models were picked based on model accuracy and physical feasibility of model predictions. A single model to quantify the properties of both crops could be created for each techno-functional property, albeit with a trade-off of higher prediction errors as compared to models based on individual crops. A reflection on the number of observations in each dataset showed that they could be reduced for some properties.

Improved LC-MS identification of short homologous peptides using sequence-specific retention time predictors.

Peptides are an important group of compounds contributing to the desired, as well as the undesired taste of a food product. Their taste impressions can include aspects of sweetness, bitterness, savoury, umami and many other impressions depending on the amino acids present as well as their sequence. Identification of short peptides in foods is challenging. We developed a method to assign identities to short peptides including homologous structures, i.e. peptides containing the same amino acids with a different sequence order, by accurate prediction of the retention times during reversed phase separation. To train the method, a large set of well-defined short peptides with systematic variations in the amino acid sequence was prepared by a novel synthesis strategy called 'swapped-sequence synthesis'. Additionally, several proteins were enzymatically digested to yield short peptides. Experimental retention times were determined after reversed phase separation and peptide MS2 data was acquired using a high-resolution mass spectrometer operated in data-dependent acquisition mode (DDA). A support vector regression model was trained using a combination of existing sequence-independent peptide descriptors and a newly derived set of selected amino acid index derived sequence-specific peptide (ASP) descriptors. The model was trained and validated using the experimental retention times of the 713 small food-relevant peptides prepared. Whilst selecting the most useful ASP descriptors for our model, special attention was given to predict the retention time differences between homologous peptide structures. Inclusion of ASP descriptors greatly improved the ability to accurately predict retention times, including retention time differences between 157 homologous peptide pairs. The final prediction model had a goodness-of-fit (Q2) of 0.94; moreover for 93% of the short peptides, the elution order was correctly predicted.

Identification of plant polysaccharides by MALDI-TOF MS fingerprinting after periodate oxidation and thermal hydrolysis.

An autoclave treatment at 121 °C on periodate-oxidized plant polysaccharides and mixes thereof was investigated for the release of oligosaccharides to obtain a generic polysaccharide depolymerization method for polysaccharides fingerprinting. Matrix-Assisted Laser Desorption Ionization Time-Of-Flight Mass Spectrometry (MALDI-TOF MS) analysis of the oligosaccharides released showed that each polysaccharide had a unique oligosaccharides profile, even the same type of polysaccharide derived from different sources and/or having different fine structures (e.g. class of (arabino)xylans, galactomannans, glucans, or pectic materials). Various polysaccharide classes present in a polysaccharide mix could be identified based on significantly different (p < 0.05) marker m/z values present in the mass spectrum. Principal component analysis and hierarchical cluster analysis of the obtained MALDI-TOF MS data highlighted the structural heterogeneity of the polysaccharides studied, and clustered polysaccharide samples with resembling oligosaccharide profiles. Our approach represents a step further towards a generic and accessible identification of plant polysaccharides individually or in a mixture.

Special Issue: Development and Application of Statistical Methods for Analyzing Metabolomics Data.

In the last decade, the field of metabolomics has developed tremendously: it is now possible to routinely measure a wide range of metabolites for many specimens at reduced costs, opening the door to many exciting experiments [...].

Insights into the molecular properties underlying antibacterial activity of prenylated (iso)flavonoids against MRSA.

High resistance towards traditional antibiotics has urged the development of new, natural therapeutics against methicillin-resistant Staphylococcus aureus (MRSA). Prenylated (iso)flavonoids, present mainly in the Fabaceae, can serve as promising candidates. Herein, the anti-MRSA properties of 23 prenylated (iso)flavonoids were assessed in-vitro. The di-prenylated (iso)flavonoids, glabrol (flavanone) and 6,8-diprenyl genistein (isoflavone), together with the mono-prenylated, 4'-O-methyl glabridin (isoflavan), were the most active anti-MRSA compounds (Minimum Inhibitory Concentrations (MIC) ≤ 10 µg/mL, 30 µM). The in-house activity data was complemented with literature data to yield an extended, curated dataset of 67 molecules for the development of robust in-silico prediction models. A QSAR model having a good fit (R2adj 0.61), low average prediction errors and a good predictive power (Q2) for the training (4% and Q2LOO 0.57, respectively) and the test set (5% and Q2test 0.75, respectively) was obtained. Furthermore, the model predicted well the activity of an external validation set (on average 5% prediction errors), as well as the level of activity (low, moderate, high) of prenylated (iso)flavonoids against other Gram-positive bacteria. For the first time, the importance of formal charge, besides hydrophobic volume and hydrogen-bonding, in the anti-MRSA activity was highlighted, thereby suggesting potentially different modes of action of the different prenylated (iso)flavonoids.

Impact of Xylanase and Glucanase on Oligosaccharide Formation, Carbohydrate Fermentation Patterns, and Nutrient Utilization in the Gastrointestinal Tract of Broilers.

This study aimed at determining how the degradation of cereal non-starch polysaccharides (NSP) by dietary enzymes during feed digestion can influence nutrient digestibility and NSP fermentability in broilers. Ninety-six one-day-old male broilers were assigned to 4 different treatments: control and enzyme-supplemented wheat-based (WC, WE) or maize-based (MC, ME) treatments. Enzyme supplementation with endo-xylanase and endo-glucanase occurred from day 20 onwards. On day 28, digesta samples were collected. Nutrient digestibility, NSP recovery, oligosaccharide profile, and short-chain fatty acids (SCFA) content were determined. Enzyme supplementation in WE resulted in a higher starch (3%; p = 0.004) and protein (5%; p = 0.002) digestion in the ileum compared to WC. Xylanase activity in WE led to in situ formations of arabinoxylan-oligosaccharides consisting of 5 to 26 pentose units in the ileum. This coincided with decreased arabinose (p = 0.059) and xylose (p = 0.036) amounts in the ceca and higher acetate (p = 0.014) and butyrate (p = 0.044) formation in WE compared to WC. Conversely, complete total tract recovery of arabinoxylan in MC and ME suggested poor maize NSP fermentability. Overall, enzyme action improved nutrient digestibility and arabinoxylan fermentability in the wheat-based diet. The lower response of the maize-based diet to enzyme treatment may be related to the recalcitrance of maize arabinoxylan as well as to the high nutritive value of maize.

LC-MS based plant metabolic profiles of thirteen grassland species grown in diverse neighbourhoods.

In plants, secondary metabolite profiles provide a unique opportunity to explore seasonal variation and responses to the environment. These include both abiotic and biotic factors. In field experiments, such stress factors occur in combination. This variation alters the plant metabolic profiles in yet uninvestigated ways. This data set contains trait and mass spectrometry data of thirteen grassland species collected at four time points in the growing season in 2017. We collected above-ground vegetative material of seven grass and six herb species that were grown in plant communities with different levels of diversity in the Jena Experiment. For each sample, we recorded visible traits and acquired shoot metabolic profiles on a UPLC-ESI-Qq-TOF-MS. We performed the raw data pre-processing in Galaxy-W4M and prepared the data for statistical analysis in R by applying missing data imputation, batch correction, and validity checks on the features. This comprehensive data set provides the opportunity to investigate environmental dynamics across diverse neighbourhoods that are reflected in the metabolomic profile.

Revealing the main factors and two-way interactions contributing to food discolouration caused by iron-catechol complexation.

Fortification of food with iron is considered to be an effective approach to counter the global health problem caused by iron deficiency. However, reactivity of iron with the catechol moiety of food phenolics leads to discolouration and impairs bioavailability. In this study, we investigated the interplay between intrinsic and extrinsic factors on food discolouration caused by iron-catechol complexation. To this end, a three-level fractional factorial design was implemented. Absorbance spectra were analysed using statistical methods, including PCA, HCA, and ANOVA. Furthermore, a direct link between absorbance spectra and stoichiometry of the iron-catechol complexes was confirmed by ESI-Q-TOF-MS. All statistical methods confirm that the main effects affecting discolouration were type of iron salt, pH, and temperature. Additionally, several two-way interactions, such as type of iron salt × pH, pH × temperature, and type of iron salt × concentration significantly affected iron-catechol complexation. Our findings provide insight into iron-phenolic complexation-mediated discolouration, and facilitate the design of iron-fortified foods.

Reciprocal cybrids reveal how organellar genomes affect plant phenotypes.

Assessment of the impact of variation in chloroplast and mitochondrial DNA (collectively termed the plasmotype) on plant phenotypes is challenging due to the difficulty in separating their effect from nuclear-derived variation (the nucleotype). Haploid-inducer lines can be used as efficient plasmotype donors to generate new plasmotype-nucleotype combinations (cybrids)1. We generated a panel comprising all possible cybrids of seven Arabidopsis thaliana accessions and extensively phenotyped these lines for 1,859 phenotypes under both stable and fluctuating conditions. We show that natural variation in the plasmotype results in both additive and epistatic effects across all phenotypic categories. Plasmotypes that induce more additive phenotypic changes also cause more epistatic effects, suggesting a possible common basis for both additive and epistatic effects. On average, epistatic interactions explained twice as much of the variance in phenotypes as additive plasmotype effects. The impact of plasmotypic variation was also more pronounced under fluctuating and stressful environmental conditions. Thus, the phenotypic impact of variation in plasmotypes is the outcome of multi-level nucleotype-plasmotype-environment interactions and, as such, the plasmotype is likely to serve as a reservoir of variation that is predominantly exposed under certain conditions. The production of cybrids using haploid inducers is a rapid and precise method for assessment of the phenotypic effects of natural variation in organellar genomes. It will facilitate efficient screening of unique nucleotype-plasmotype combinations to both improve our understanding of natural variation in these combinations and identify favourable combinations to enhance plant performance.

Biomarkers for grain yield stability in rice under drought stress.

Crop yield stability requires an attenuation of the reduction of yield losses caused by environmental stresses such as drought. Using a combination of metabolomics and high-throughput colorimetric assays, we analysed central metabolism and oxidative stress status in the flag leaf of 292 indica rice (Oryza sativa) accessions. Plants were grown in the field and were, at the reproductive stage, exposed to either well-watered or drought conditions to identify the metabolic processes associated with drought-induced grain yield loss. Photorespiration, protein degradation, and nitrogen recycling were the main processes involved in the drought-induced leaf metabolic reprogramming. Molecular markers of drought tolerance and sensitivity in terms of grain yield were identified using a multivariate model based on the values of the metabolites and enzyme activities across the population. The model highlights the central role of the ascorbate-glutathione cycle, particularly dehydroascorbate reductase, in minimizing drought-induced grain yield loss. In contrast, malondialdehyde was an accurate biomarker for grain yield loss, suggesting that drought-induced lipid peroxidation is the major constraint under these conditions. These findings highlight new breeding targets for improved rice grain yield stability under drought.

Application of Volatile Organic Compound Analysis in a Nutritional Intervention Study: Differential Responses during Five Hours Following Consumption of a High- and a Low-Fat Dairy Drink.

SCOPE: Exhaled volatile organic compounds (VOCs) are a possible relevant target for noninvasive assessment of metabolic responses. Using a breathomics approach, it is aimed to explore whether lipid intake influences VOC profiles in exhaled air, and to obtain insight in intra- and interindividual variations. METHODS AND RESULTS: Three human interventions are performed. In the first, 12 males consume a high-fat drink on three study days. In the second, 12 males receive a high- and a low-fat drink on 6 days. In the third, three volunteers consume the high-fat drink again for tentative compound identification. Participants are asked to exhale, for 5 h postprandial with 15-20 min intervals, into a proton-transfer-reaction mass spectrometer, and VOCs in exhaled air are measured. Consumption of a drink alters the VOC profile, with considerable interindividual variation and quantitative intraindividual differences between days. Consumption of two different drinks results in a distinct VOC profile, caused by several specific m/z values. Most of these compounds are identified as being related to ketone body formation and lipid oxidation, showing an increase in high- versus low-fat drink. CONCLUSION: Exhaled VOCs have the potential to assess differences in metabolic responses induced by nutrition, especially when day-to-day variation can be minimized.

Variability of Serum Proteins in Chinese and Dutch Human Milk during Lactation.

To better understand the variability of the type and level of serum proteins in human milk, the milk serum proteome of Chinese mothers during lactation was investigated using proteomic techniques and compared to the milk serum proteome of Dutch mothers. This showed that total milk serum protein concentrations in Chinese human milk decreased over a 20-week lactation period, although with variation between mothers in the rate of decrease. Variation was also found in the composition of serum proteins in both colostrum and mature milk, although immune-active proteins, enzymes, and transport proteins were the most abundant for all mothers. These three protein groups account for many of the 15 most abundant proteins, with these 15 proteins covering more than 95% of the total protein concentrations, in both the Chinese and Dutch milk serum proteome. The Dutch and Chinese milk serum proteome were also compared based on 166 common milk serum proteins, which showed that 22% of the 166 serum proteins differed in level. These differences were observed mainly in colostrum and concern several highly abundant proteins. This study also showed that protease inhibitors, which are highly correlated to immune-active proteins, are present in variable amounts in human milk and could be relevant during digestion.

Transcriptome Analysis of CHO Cell Size Increase During a Fed-Batch Process.

In a Chinese Hamster Ovary (CHO) cell fed-batch process, arrest of cell proliferation and an almost threefold increase in cell size occurred, which is associated with an increase in cell-specific productivity. In this study, transcriptome analysis is performed to identify the molecular mechanisms associated with this. Cell cycle analysis reveals that the cells are arrested mainly in the G0 /G1 phase. The cell cycle arrest is associated with significant up-regulation of cyclin-dependent kinases inhibitors (CDKNs) and down-regulation of cyclin-dependent kinases (CDKs) and cyclins. During the cell size increase phase, the gene expression of the upstream pathways of mechanistic target of rapamycin (mTOR), which is related to the extracellular growth factor, cytokine, and amino acid conditions, shows a strongly synchronized pattern to promote the mTOR activity. The downstream genes of mTOR also show a synchronized pattern to stimulate protein translation and lipid synthesis. The results demonstrate that cell cycle inhibition and stimulated mTOR activity at the transcriptome level are related to CHO cell size increase. The cell size increase is related to the extracellular nutrient conditions through a number of cascade pathways, indicating that by rational design of media and feeds, CHO cell size can be manipulated during culture processes, which may further improve cell growth and specific productivity.

QSAR of 1,4-benzoxazin-3-one antimicrobials and their drug design perspectives.

Synthetic derivatives of 1,4-benzoxazin-3-ones have been shown to possess promising antimicrobial activity, whereas their natural counterparts were found lacking in this respect. In this work, quantitative structure-activity relationships (QSAR) of natural and synthetic 1,4-benzoxazin-3-ones as antimicrobials were established. Data published in literature were curated into an extensive dataset of 111 compounds. Descriptor selection was performed by a genetic algorithm. QSAR models revealed differences in requirements for activity against fungi, gram-positive and gram-negative bacteria. Shape, VolSurf, and H-bonding property descriptors were frequently picked in all models. The models obtained for gram-positive and gram-negative bacteria showed good predictive power (Q2Ext 0.88 and 0.85, respectively). Based on the models generated, an additional set of 1,4-benzoxazin-3-ones, for which no antimicrobial activity had been determined in literature, were evaluated in silico. Additionally, newly designed lead compounds with a 1,4-benzoxazin-3-one scaffold were generated in silico by varying the positions and combinations of substituents. Two of these were predicted to be up to 5 times more active than any of the compounds in the current dataset. The 1,4-benzoxazin-3-one scaffold was concluded to possess potential for the design of new antimicrobial compounds with potent antibacterial activity, a multitarget mode of action, and possibly reduced susceptibility to gram negatives' efflux pumps.

QSAR-based molecular signatures of prenylated (iso)flavonoids underlying antimicrobial potency against and membrane-disruption in Gram positive and Gram negative bacteria.

Prenylated flavonoids and isoflavonoids are phytochemicals with remarkable antibacterial activity. In this study, 30 prenylated (iso)flavonoids were tested against Listeria monocytogenes and Escherichia coli (the latter in combination with an efflux pump inhibitor). Minimum inhibitory concentrations of the most active compounds ranged between 6.3-15.0 µg/mL. Quantitative structure-activity relationships (QSAR) analysis was performed and linear regression models were proposed with R2 between 0.77-0.80, average R2m between 0.70-0.75, Q2LOO between 0.66-0.69, and relatively low amount of descriptors. Shape descriptors (related to flexibility and globularity), together with hydrophilic/hydrophobic volume and surface area descriptors, were identified as important molecular characteristics related to activity. A 3D pharmacophore model explaining the effect of the prenyl position on the activity of compounds was developed for each bacterium. These models predicted active compounds with an accuracy of 71-88%. With regard to the mode of action, good antibacterial prenylated (iso)flavonoids with low relative hydrophobic surface area caused remarkable membrane permeabilization, whereas those with higher relative hydrophobic surface area did not. Based on the QSAR and membrane permeabilization studies, the mode of action of antibacterial prenylated (iso)flavonoids was putatively rationalized.

Transcriptome analysis for the scale-down of a CHO cell fed-batch process.

Transcriptome and metabolism analysis were performed to evaluate the scale-down of a CHO cell fed-batch process from a 10 L bioreactor to an ambr 15® (ambr) system. Two different agitation scale-down principles were applied, resulting in two different agitation rates in the ambr system: 1300 RPM based on the agitator tip speed, and 800 rpm based on the volumetric power input (P/V). Culture performance including cell growth, product titer, glycosylation, and specific consumption/production rates of metabolites was the same for both agitation rates in the ambr and was comparable to that of the 10 L system. The initial variation in gene expression between the inocula for the ambr and 10 L system was no longer present after three days of culture, indicating comparable culture conditions in both systems. Based on principal component analysis, changes in gene expression over time were similar between both scales with less than 6% variation. 2455 genes were uniquely regulated in the ambr system compared to 1604 genes in the 10 L system. Functional analysis of these genes did not reveal their relations with scale or cellular function. This study further strengthens that the ambr system gives representative culture performance for the 10 L bench-scale bioreactor.

A Multidisciplinary Phenotyping and Genotyping Analysis of a Mapping Population Enables Quality to Be Combined with Yield in Rice.

In this study a mapping population (F8) of ca 200 progeny from a cross between the commercial rice varieties Apo and IR64 has been both genotyped and phenotyped. A genotyping-by-sequencing approach was first used to identify 2,681 polymorphic SNP markers which gave dense coverage of the genome with a good distribution across all 12 chromosomes. The coefficient of parentage was also low, at 0.13, confirming that the parents are genetically distant from each other. The progeny, together with both parents, were grown under irrigated and water restricted conditions in a randomised block design. All grain was harvested to determine variation in yield across the population. The grains were then polished following standard procedures prior to performing the phenotyping analyses. A Gas Chromatography-Mass Spectrometry approach was used to determine the volatile biochemical profiles of each line and after data curation and processing, discriminatory metabolites were putatively identified based on in-house and commercial spectral libraries. These data were used to predict the potential role of these metabolites in determining differences in aroma between genotypes. A number of QTLs for yield and for individual metabolites have been identified. Following these combined multi-disciplinary analyses, it proved possible to identify a number of lines which appeared to combine the favourable aroma attributes of IR64 with the favourable (higher) yield potential of Apo. As such, these lines are excellent candidates to assess further as potential genotypes to work up into a new variety of rice which has both good yield and good quality, thus meeting the needs of both farmer and consumer alike.