Non-targeted Metabolomics Study Research Articles

1H NMR and UHPLC/Q-Orbitrap-MS-Based Metabolomics Combined with 16S rRNA Gut Microbiota Analysis Revealed the Potential Regulation Mechanism of Nuciferine in Hyperuricemia Rats.

Hyperuricemia seriously jeopardizes human health by increasing the risk of several diseases, such as gout and stroke. Nuciferine is able to alleviate hyperuricemia significantly. However, the underlying metabolic regulation mechanism remains unknown. To understand the metabolic effects of nuciferine on hyperuricemia by establishing a rat model of rapid hyperuricemia, 1H NMR and liquid chromatography-mass spectrometry were used to conduct nontargeted metabolomics studies. A total of 21 metabolites were authenticated in plasma and urine to be closely related with hyperuricemia, which were mainly correlated to the six metabolic pathways. Moreover, 16S rRNA analysis indicated that diversified intestinal microorganisms are closely related to changes in differential metabolites, especially bacteria from Firmicutes and Bacteroidetes. We propose that indoxyl sulfate and N-acetylglutamate in urine may be the potential biomarkers besides uric acid for early diagnosis and prevention of hyperuricemia. Gut microbiological analysis found that changes in the gut microbiota are closely related to these metabolites.

Non-targeted urine metabolomics and associations with prevalent and incident type 2 diabetes

Better risk prediction and new molecular targets are key priorities in type 2 diabetes (T2D) research. Little is known about the role of the urine metabolome in predicting the risk of T2D. We aimed to use non-targeted urine metabolomics to discover biomarkers and improve risk prediction for T2D. Urine samples from two community cohorts of 1,424 adults were analyzed by ultra-performance liquid chromatography/mass spectrometry (UPLC-MS). In a discovery/replication design, three out of 62 annotated metabolites were associated with prevalent T2D, notably lower urine levels of 3-hydroxyundecanoyl-carnitine. In participants without diabetes at baseline, LASSO regression in the training set selected six metabolites that improved prediction of T2D beyond established risk factors risk over up to 12 years' follow-up in the test sample, from C-statistic 0.866 to 0.892. Our results in one of the largest non-targeted urinary metabolomics study to date demonstrate the role of the urine metabolome in identifying at-risk persons for T2D and suggest urine 3-hydroxyundecanoyl-carnitine as a biomarker candidate.

Metabolome of canine and human saliva: a non-targeted metabolomics study

IntroductionSaliva metabolites are suggested to reflect the health status of an individual in humans. The same could be true with the dog (Canis lupus familiaris), an important animal model of human disease, but its saliva metabolome is unknown. As a non-invasive sample, canine saliva could offer a new alternative material for research to reveal molecular mechanisms of different (patho)physiological stages, and for veterinary medicine to monitor dogs’ health trajectories.ObjectivesTo investigate and characterize the metabolite composition of dog and human saliva in a non-targeted manner.MethodsStimulated saliva was collected from 13 privately-owned dogs and from 14 human individuals. We used a non-targeted ultra-high-performance liquid chromatography-quadrupole time-of-flight mass spectrometry (UHPLC-qTOF-MS) method to measure metabolite profiles from saliva samples.ResultsWe identified and classified a total of 211 endogenous and exogenous salivary metabolites. The compounds included amino acids, amino acid derivatives, biogenic amines, nucleic acid subunits, lipids, organic acids, small peptides as well as other metabolites, like metabolic waste molecules and other chemicals. Our results reveal a distinct metabolite profile of dog and human saliva as 25 lipid compounds were identified only in canine saliva and eight dipeptides only in human saliva. In addition, we observed large variation in ion abundance within and between the identified saliva metabolites in dog and human.ConclusionThe results suggest that non-targeted metabolomics approach utilizing UHPLC-qTOF-MS can detect a wide range of small compounds in dog and human saliva with partially overlapping metabolite composition. The identified metabolites indicate that canine saliva is potentially a versatile material for the discovery of biomarkers for dog welfare. However, this profile is not complete, and dog saliva needs to be investigated in the future with other analytical platforms to characterize the whole canine saliva metabolome. Furthermore, the detailed comparison of human and dog saliva composition needs to be conducted with harmonized study design.

Metabolomics of Rat Brain After Treatment with Phenelzine: High-Resolution Mass Spectrometric Demonstration of Increased Brain Levels of N-Acetyl Amino Acids

Background: Phenelzine (PLZ) is a non-specific monoamine oxidase inhibitor that has demonstrated clinical efficacy in patients with treatment resistant depression. The mechanism of action with regard to this efficacy is complicated in that its metabolite, β-phenylethylidenehydrazine (PEH), is an inhibitor of amino acid transaminases resulting in dramatic brain elevations of GABA, alanine, ornithine and tyrosine. The full neurochemical profile of PLZ and PEH remain to be explored. Objective: To undertake a non-targeted metabolomics study of phenelzine on rat brain neurochemistry. Methods: We undertook a high-resolution mass spectrometric metabolomics analysis of rat cortical brain 1 and 12 hours after intraperitoneal dosing with PLZ or PEH. Tandem mass spectrometry was utilized to obtain relative quantitation data. Results: N-acetyl amino acids were found to be elevated in cortical brain tissue following either PLZ or PEH treatments. Conclusions: Our data indicate PLZ treatment significantly augments brain levels of N-acetyl amino acids and that this may involve inhibition of deacylases by PEH and/or induction of N-amino acid acetyltransferases.

CPVA: a web-based metabolomic tool for chromatographic peak visualization and annotation.

Liquid chromatography-mass spectrometry-based non-targeted metabolomics is routinely performed to qualitatively and quantitatively analyze a tremendous amount of metabolite signals in complex biological samples. However, false-positive peaks in the datasets are commonly detected as metabolite signals by using many popular software, resulting in non-reliable measurement. To reduce false-positive calling, we developed an interactive web tool, termed CPVA, for visualization and accurate annotation of the detected peaks in non-targeted metabolomics data. We used a chromatogram-centric strategy to unfold the characteristics of chromatographic peaks through visualization of peak morphology metrics, with additional functions to annotate adducts, isotopes and contaminants. CPVA is a free, user-friendly tool to help users to identify peak background noises and contaminants, resulting in decrease of false-positive or redundant peak calling, thereby improving the data quality of non-targeted metabolomics studies. The CPVA is freely available at http://cpva.eastus.cloudapp.azure.com. Source code and installation instructions are available on GitHub: https://github.com/13479776/cpva. Supplementary data are available at Bioinformatics online.

Paraben Exposure Related To Purine Metabolism and Other Pathways Revealed by Mass Spectrometry-Based Metabolomics.

Parabens are widely used as common preservatives in the pharmaceutical and cosmetic industries. Exposure to parabens has been found to be associated with metabolic alterations of human and an increased risk of metabolic disease, such as diabetes. However, limited information is available about metabolic pathways related to paraben exposure. In this study, three parabens were determined in the urine samples of 88 pregnant women by using ultrahigh-performance liquid chromatography coupled with triple quadrupole mass spectrometry (UHPLC-QqQ MS). The samples were divided into different groups based on tertile distribution of urinary paraben concentrations. Metabolic profiling of the 88 urine samples was performed by using UHPLC coupled with Orbitrap high-resolution MS. Differential metabolites were screened by comparing the profiles of urine samples from different paraben-exposure groups. The identified metabolites included purines, acylcarnitines, etc., revealing that metabolic pathways such as purine metabolism, fatty acid β-oxidation, and other pathways were disturbed by parabens. Eighteen and three metabolites were correlated (Spearman correlation analysis, p < 0.05) with the exposure levels of methyparaben and propylparaben, respectively. This is the first MS-based nontargeted metabolomics study on pregnant women with paraben exposure. The findings reveal the potential health risk of exposure to parabens and might help one to understand the link between paraben exposure and some metabolic diseases.

High-throughput non-targeted metabolomics study of the effects of perfluorooctane sulfonate (PFOS) on the metabolic characteristics of A. thaliana leaves

The ecotoxicity of perfluorooctane sulfonate (PFOS) is complex and has been reported in animals (including fish and mice), but the effects of PFOS in plants, especially the toxic mechanisms, have rarely been studied. High-throughput nontargeted metabolomics methods for comprehensive assessment were selected to study changes in metabolic characteristics in Arabidopsis thaliana leaves by exposure to different concentrations of PFOS throughout the growth period (30 days). All the metabolites were analyzed by PCA and OPLS-DA methods, by the cutoff of VIP and p-value, 53 biomarkers were found and significantly regulated, all amino acids except glutamate were inhibited and probably associated with binding to protein, auxin and cytokinin of phytohormones were significantly down-regulated. In response mechanism to oxidative stress from PFOS, the phenylpropanoid pathway were fully activated to form several polyphenols and further enhanced into several flavonoids against the reactive oxygen species (ROS) as the primary defend pathway, in addition, ascorbate, trehalose and nicotinamide also were activated and help decrease the damage from oxidative stress. These results provide insights into the mechanism underlying the phytotoxicity of PFOS.

Feasibility of Localized Metabolomics in the Study of Pancreatic Islets and Diabetes.

(1) Background: Disruption of insulin production by native or transplanted pancreatic islets caused by auto/allo-immunity leads to hyperglycemia, a serious health condition and important therapeutic challenge due to the lifelong need for exogeneous insulin administration. Early metabolic biomarkers can prompt timely interventions to preserve islet function, but reliable biomarkers are currently lacking. We explored the feasibility of “localized metabolomics” where initial biomarker discovery is made in aqueous humor samples for further validation in the circulation. (2) Methods: We conducted non-targeted metabolomic studies in parallel aqueous humor and plasma samples from diabetic and nondiabetic mice. Metabolite levels and associated pathways were compared in both compartments as well as to an earlier longitudinal dataset in hyperglycemia-progressor versus non-progressor non-obese diabetic (NOD) mice. (3) Results: We confirmed that aqueous humor samples can be used to assess metabolite levels. About half of the identified metabolites had well-correlated levels in the aqueous humor and plasma. Several plasma metabolites were significantly different between diabetic and nondiabetic animals and between males and females, and many of them were correlated with the aqueous humor. (4) Conclusions: This study provides proof-of-concept evidence that aqueous humor samples enriched with islet-related metabolites and representative of the immediate islet microenvironment following intraocular islet transplant can be used to assess metabolic changes that could otherwise be overlooked in the general circulation. The findings support localized metabolomics, with and without intraocular islet transplant, to identify biomarkers associated with diabetes and islet allograft rejection.

Serum metabonomics study of pregnant women with gestational diabetes mellitus based on LC-MS.

ObjectiveThrough metabolomics method, the objective of the paper is to differentially screen serum metabolites of GDM patients and healthy pregnant women, to explore potential biomarkers of GDM and analyze related pathways, and to explain the potential mechanism and biological significance of GDM.MethodsThe serum samples from 30 GDM patients and 30 healthy pregnant women were selected to conduct non-targeted metabolomics study by liquid chromatography-mass spectrometry. The differential metabolites between the two groups were searched and the metabolic pathway was analyzed by KEGG database.ResultsMultivariate statistical analysis found that serum metabolism in GDM patients was different significantly from healthy pregnant women, 36 differential metabolites and corresponding metabolic pathways were identified in serum, which involved several metabolic ways like, fatty acid metabolism, butyric acid metabolism, bile secretion, and amino acid metabolism.ConclusionThe discovery of these biomarkers provided a new theoretical basis and experimental basis for further study of the early diagnosis and pathogenesis of GDM. At the same time, LC-MS-based serum metabolomics methods also showed great application values in disease diagnosis and mechanism research.

PFOA and PFOS promote diabetic renal injury in vitro by impairing the metabolisms of amino acids and purines

BackgroundEnvironmental pollutants, perfluorooctanoic acid (PFOA) and perfluorooctane sulfonate (PFOS), are common surfactants in various consumer products. Epidemiological studies have demonstrated the association of diabetic kidney diseases with PFOA and PFOS. However, mechanisms of metabolic alterations involved are still unclear. MethodsConsidering their involvement of glomerular hemodynamics, rat mesangial cells (MCs) are used as an in vitro model of diabetic kidney diseases for exposure to PFOS/PFOA under diabetic condition. Non-targeted metabolomics studies based on liquid chromatography-high resolution mass spectrometry were conducted to determine how PFOA/PFOS promoted fibrotic and proinflammatory responses in the MCs under diabetic condition. ResultsExposure of PFOA/PFOS (10 μM) increased oxidative stress and the levels of fibrotic and proinflammatory markers in MCs under diabetic condition. We demonstrated for the first time that PFOA and PFOS altered amino acid biosynthesis, citrate cycle, and purine metabolism in MCs under diabetic condition. Compared with diabetic condition, the exposure of PFOA and PFOS under diabetic condition more significantly altered the levels of 13 intracellular metabolites, including L-tyrosine, L-phenylalanine, L-arginine, L-tryptophan, AMP, ADP, UMP, inosine, and hypoxanthine, which have been reported to be related to kidney injury. In addition, PFOA/PFOS treatment significantly altered the expression levels of key enzymes involved in these metabolisms. Treatment with L-tyrosine, L-phenylalanine, L-arginine, and L-tryptophan reduced the levels of fibrotic and inflammatory markers induced by PFOA/PFOS. ConclusionOur results suggest that under diabetic condition, exposure of PFOA or PFOS aggravated diabetic kidney injury in vitro by impairing metabolisms of amino acids and purines to induce more fibrosis and inflammation in MCs.

Metabotype variation in a field population of tansy plants influences aphid host selection.

It is well known that plant volatiles influence herbivores in their selection of a host plant; however, less is known about how the nonvolatile metabolome affects herbivore host selection. Metabolic diversity between intraspecific plants can be characterized using non-targeted mass spectrometry that gives us a snapshot overview of all metabolic processes occurring within a plant at a particular time. Here, we show that non-targeted metabolomics can be used to reveal links between intraspecific chemical diversity and ecological processes in tansy (Tanacetum vulgare). First, we show that tansy plants can be categorized into five subgroups based up on their metabolic profiles, and that these "metabotypes" influenced natural aphid colonization in the field. Second, this grouping was not due to induced metabolomic changes within the plant due to aphid feeding but rather resulted from constitutive differences in chemical diversity between plants. These findings highlight the importance of intraspecific chemical diversity within one plant population and provide the first report of a non-targeted metabolomic field study in chemical ecology.

The brominated flame retardant BDE 47 upregulates purine metabolism and mitochondrial respiration to promote adipocyte differentiation

Adipocyte differentiation is closely associated with obesity and obesity-induced metabolic disorders. Epidemiological studies have demonstrated the association of obesity with environmental pollutants, such as polybrominated diphenyl ethers (PBDEs), common flame retardants in various consumer products. However, their obesogenic effects and mechanism are underexplored. We employed non-targeted metabolomics studies based on liquid chromatography-high resolution mass spectrometry to determine how 2,2′,4,4′-tetra-brominated biphenyl ether (BDE 47), one of the main congeners of PBDEs detected in human tissue, promotes adipocyte differentiation of mouse preadipocyte 3 T3-L1 cells. The promoting effects of BDE 47 exposure (5 or 10 μM) on adipocyte differentiation were confirmed by enhancing lipid accumulation and expression levels of biomarkers of adipogenesis. For the first time, we demonstrated that BDE 47 upregulated purine metabolism and altered glutathione metabolism to promote oxidative stress and uric acid production in adipocytes. BDE 47 also elevated mitochondrial respiration and glycolysis in adipocytes to induce more ATP to combat oxidative stress. Antioxidant treatments, including the suppression of xanthine oxidase, inhibited the effects of BDE 47 on inducing oxidative stress and lipid accumulation. BDE 47 may be a potential environmental obesogen by providing a permissive oxidative environment to induce adipocyte differentiation.

A non-targeted metabolomics study on different glucose tolerance states

A non-targeted metabolomics method was employed to study metabolic characteristics in subjects with different glucose tolerance. Plasma samples of 120 participants with normal glucose tolerance (NGT), impaired glucose regulation (IGR), and type 2 diabetes (T2D) were collected. Gas chromatography/mass spectrometry (GC/MS) was used to profile and compare the plasma metabolome among the three groups. Through the use of multivariate statistical analysis, we found distinct metabolome change from NGT to IGR and to T2D. ANOVA found that the IGR and T2D groups had perturbations of monosaccharide and lipid metabolism, disorders of glucogenic amino acids, and branched-chain amino acid catabolism. Furthermore, we also found that the levels of 2-hydroxybutyrate and 2-ketoisocaproate were progressively increased with glucose tolerance severity. The results from this study help us better understand the relationship between plasma metabolism and glucose tolerance states and also suggest that 2-hydroxybutyrate and 2-ketoisocaproate may be closely associated with the development of T2D.

Comprehensive Strategy to Construct In-House Database for Accurate and Batch Identification of Small Molecular Metabolites.

Identification of the metabolites is an essential step in metabolomics study to interpret the regulatory mechanism of pathological and physiological processes. However, it is still difficult in LC-MS n-based studies because of the complexity of mass spectrometry, chemical diversity of metabolites, and deficiency of standards database. In this work, a comprehensive strategy is developed for accurate and batch metabolite identification in nontargeted metabolomics studies. First, a well-defined procedure was applied to generate reliable and standard LC-MS2 data, including tR, MS1, and MS2 information at a standard operational procedure. An in-house database including about 2000 metabolites was constructed and used to identify the metabolites in nontargeted metabolic profiling by retention time calibration using internal standards, precursor ion alignment and ion fusion, auto-MS2 information extraction and selection, and database batch searching and scoring. As an application example, a pooled serum sample was analyzed to deliver the strategy, and 202 metabolites were identified in the positive ion mode. It shows our strategy is useful for LC-MS n-based nontargeted metabolomics study.

Metabolites change of Scenedesmus obliquus exerted by AgNPs.

With increasing emission of silver nanoparticles (AgNPs) into the environment, it is important to understand the effects of ambient concentration of AgNPs. The biological effects of AgNPs on Scenedesmus obliquus, a ubiquitous freshwater microalgae, was evaluated. AgNPs exerted a minor inhibitory effect at low doses. Non-targeted metabolomic studies were conducted to understand and analyze the effect of AgNPs on algal cells from a molecular perspective. During the 48 hr of exposure to AgNPs, 30 metabolites were identified, of which nine had significant changes compared to the control group. These include d-galactose, sucrose, and d-fructose. These carbohydrates are involved in the synthesis and repair of cell walls. Glycine, an important constituent amino acid of glutathione, increased with AgNP exposure concentration increasing, likely to counteract an increased intracellular oxidative stress. These results provide a new understanding of the toxicity effects and mechanism of AgNPs. These metabolites could be useful biomarkers for future research, employed in the early detection of environmental risk from AgNPs.

Abstract P202: Non-targeted Metabolomics Study Identify Multiple Metabolites Associated With Body Mass Index

Background: Non-targeted metabolomics technology provides a powerful tool to examine the complex etiology of obesity and the underlying mechanisms for its health consequences. The current study aimed to examine the associations of metabolites, quantified by non-targeted metabolomics technology, with body mass index (BMI) in the Bogalusa Heart Study. Methods: The study included 1,261 participants (825 White and 436 Blacks) aged 34-58 years. General linear models were used to examine the associations of BMI with the 1,202 metabolites that passed rigorous quality control measures, adjusted for age, sex, smoking, drinking, education, and total physical activity in Blacks and Whites, separately. Weighted correlation network analysis (WGCNA) was used to build metabolites modules according to pair-wise correlations (signed positive correlation and unsigned correlation regardless of correlation direction) among all metabolites; partial correlation was used to assess the link between each module and BMI. Results: Six-eight metabolites showed Bonferroni-corrected (P &lt; 4.16E-5) associations with BMI in both Blacks and Whites. The most significant metabolite was glutamate (P = 1.52E-12 in Blacks and P = 1.47E-37 in Whites). Among the 46 significant metabolites with known identities, a majority were involved in pathways of lipids (22 metabolites) and amino acids (13 metabolites). Fifteen unsigned metabolites modules were identified and six of the modules showed significant correlation with BMI (absolute r = 0.11-0.14); ten signed metabolites modules were identified and five of them were significantly correlated with BMI (absolute r = 0.14-0.31). Conclusion: We have identified multiple metabolites robustly associated with BMI, which have novel biological implications for obesity and its health consequences.

How to model temporal changes in nontargeted metabolomics study? A Bayesian multilevel perspective.

Analysis of time series data addresses the question on mechanisms underlying normal physiology and its alteration under pathological conditions. However, adding time variable to high-dimension, collinear, noisy data is a challenge in terms of mining and analysis. Here, we used Bayesian multilevel modeling for time series metabolomics in vivo study to model different levels of random effects occurring as a consequence of hierarchical data structure. A multilevel linear model assuming different treatment effects with double exponential prior, considering major sources of variability and robustness to outliers was proposed and tested in terms of performance. The treatment effect for each metabolite was close to zero suggesting small if any effect of cancer on metabolomics profile change. The average difference in 964 signals for all metabolites varied by a factor ranging from 0.8 to 1.25. The inter-rat variability (expressed as a coefficient of variation) ranged from 3-30% across all metabolites with median around 10%, whereas the inter-occasion variability ranged from 0-30% with a median around 5%. Approximately 36% of metabolites contained outlying data points. The complex correlation structure between metabolite signals was revealed. We conclude that kinetics of metabolites can be modeled using tools accepted in pharmacokinetics type of studies.

Modelling of Hydrophilic Interaction Liquid Chromatography Stationary Phases Using Chemometric Approaches.

Metabolomics is a powerful and widely used approach that aims to screen endogenous small molecules (metabolites) of different families present in biological samples. The large variety of compounds to be determined and their wide diversity of physical and chemical properties have promoted the development of different types of hydrophilic interaction liquid chromatography (HILIC) stationary phases. However, the selection of the most suitable HILIC stationary phase is not straightforward. In this work, four different HILIC stationary phases have been compared to evaluate their potential application for the analysis of a complex mixture of metabolites, a situation similar to that found in non-targeted metabolomics studies. The obtained chromatographic data were analyzed by different chemometric methods to explore the behavior of the considered stationary phases. ANOVA-simultaneous component analysis (ASCA), principal component analysis (PCA) and partial least squares regression (PLS) were used to explore the experimental factors affecting the stationary phase performance, the main similarities and differences among chromatographic conditions used (stationary phase and pH) and the molecular descriptors most useful to understand the behavior of each stationary phase.

Comprehensive Metabolomics Study To Assess Longitudinal Biochemical Changes and Potential Early Biomarkers in Nonobese Diabetic Mice That Progress to Diabetes.

A global nontargeted longitudinal metabolomics study was carried out in male and female NOD mice to characterize the time-profile of the changes in the metabolic signature caused by onset of type 1 diabetes (T1D) and identify possible early biomarkers in T1D progressors. Metabolomics profiling of samples collected at five different time-points identified 676 and 706 biochemicals in blood and feces, respectively. Several metabolites were expressed at significantly different levels in progressors at all time-points, and their proportion increased strongly following onset of hyperglycemia. At the last time-point, when all progressors were diabetic, a large percentage of metabolites had significantly different levels: 57.8% in blood and 27.8% in feces. Metabolic pathways most strongly affected included the carbohydrate, lipid, branched-chain amino acid, and oxidative ones. Several biochemicals showed considerable (>4×) change. Maltose, 3-hydroxybutyric acid, and kojibiose increased, while 1,5-anhydroglucitol decreased more than 10-fold. At the earliest time-point (6-week), differences between the metabolic signatures of progressors and nonprogressors were relatively modest. Nevertheless, several compounds had significantly different levels and show promise as possible early T1D biomarkers. They include fatty acid phosphocholine derivatives from the phosphatidylcholine subpathway (elevated in both blood and feces) as well as serotonin, ribose, and arabinose (increased) in blood plus 13-HODE, tocopherol (increased), diaminopimelate, valerate, hydroxymethylpyrimidine, and dulcitol (decreased) in feces. A combined metabolic signature based on these compounds might serve as an early predictor of T1D-progressors.

Time-resolved comparative metabolomes for Koji fermentation with brown-, white-, and giant embryo-rice

A time-resolved non-targeted metabolomic study towards evaluating the effects of three rice substrate types viz., white rice (WR), giant embryo rice (GER), and brown rice (BR), on Koji fermentation was performed. Notwithstanding the relatively higher proportions of metabolites in unfermented BR (BR 0>WR 0, GER 0), the relative levels of metabolic repertoire in BR Koji were marginally enhanced during 36h fermentation except those for phenolic acids, fatty acids, and vitamins. The WR Koji showed higher levels of lysophospholipids (LysoPC’s) and protease-released amino acids (WR 36>GER 36>BR 36). The higher β-glucosidase activity in GER Koji effected the increased levels of sugars and flavonoid aglycons complementing its higher antioxidant activity (GER Koji>BR Koji∼WR Koji). The present study holistically underpins the dynamic metabolomes and enzymatic states during rice Koji fermentation with varying substrate types. The present study finds applications in optimization of commercial Koji production.