Search NASA⌕ Search

SEARCH · Search NASA

Results for “Metabolite”

Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

At least 271 records · Page 15
All sourcesNASA NTRSDOE OSTI
DOE OSTI2020

Improved Annotation of Untargeted Metabolomics Data through Buffer Modifications That Shift Adduct Mass and Intensity

Annotation of untargeted high-resolution full-scan LC-MS metabolomics data remains challenging due to individual metabolites generating multiple LC-MS peaks arising from isotopes, adducts and fragments. Adduct annotation is a particular challenge, as the same mass difference between peaks can arise from adduct formation, fragmentation, or different biological species. To address this, here we describe a Buffer Modification Workflow (BMW), in which the same sample is run by LC-MS in both liquid chromatography solvent with 14 NH 3 -acetate buffer, and in solvent with the buffer modified with 15 NH 3 -formate. Buffer switching results in characteristic mass and signal intensity changes for adduct peaks, facilitating their annotation. This relatively simple and convenient chromatography modification annotated yeast metabolomics data with similar effectiveness to growing the yeast in isotope-labeled media. Application to mouse liver data annotated both known metabolite and known adduct peaks with 95% accuracy. Altogether, it identified 26% of ~ 27,000 liver LC-MS features as putative metabolites, of which ~ 2600 showed HMDB or KEGG database formula match. This workflow is well-suited to biological samples that cannot be readily isotope labeled, including plants, mammalian tissues, and tumors.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2021

Smartphone-based Dual-channel Immunochromatographic Test Strip with Polymer Quantum Dot Labels for Simultaneous Detection of Cypermethrin and 3-phenoxybenzoic acid

Currently, gas chromatography-mass spectrometry (GC-MS) and liquid chromatography-mass spectrometry (LC-MS) are the primary methods used to detect pesticides and their metabolites for biomonitoring of exposure. Although GC-MS and LC-MS can provide accurate and sensitive measurements, these techniques are not suitable for point-of-care or in-field biomonitoring applications. The objective of this work is to develop a smartphone-based dual-channel immunochromatographic test strip (ICTS) for on-site biomonitoring of exposure to cypermethrin by simultaneous detection of cypermethrin and its metabolite, 3-phenoxybenzoic acid (3-PBA). Polymer carbon dots (PCDs) with ultra-high fluorescent brightness were synthesized and used as signal amplifier in ICTS assay. Cypermethrin (a representative pyrethroid pesticide) and its major metabolite 3-PBA were simultaneously detected to provide more comprehensive analysis of cypermethrin exposure. After competitive immunoreactions between the target sample and the coating antigens preloaded on the test line, the tracer antibody (PCDs conjugated antibody) was quantitatively captured on the test lines. The captured PCDs were inversely proportional to the amount of the target compound in sample. The red fluorescence on the test line then was recorded by a smartphone-based device capable of conducting image analysis and recording. Under optimal conditions, the sensor showed excellent linear responses for detecting cypermethrin and 3-PBA ranging from 1 ng/mL to 100 ng/mL and from 0.1 ng/mL to 100 ng/mL, the limits of detection were calculated to be ~0.35 ng/mL for cypermethrin and ~0.04 ng/mL for 3-PBA, respectively. The results demonstrate that the ICTS device is promising for accurate point-of-care biomonitoring of pesticide exposure.

Zhao, Yuting↗
DOE OSTI2022

13 C-Isotope-Assisted Assessment of Metabolic Quenching During Sample Collection from Suspension Cell Cultures

Metabolomics and fluxomics are core approaches to directly profile and interrogate cellular metabolism in response to various genetic or environmental perturbations. In order to accurately measure the abundance and isotope enrichment of intracellular metabolites, cell culture samples must be rapidly harvested and cold-quenched to preserve the in vivo metabolic state of the cells at the time of sample collection. When dealing with suspension cultures, this process is complicated by the need to separate the liquid culture media from cellular biomass prior to metabolite extraction. Here, we examine the efficacy of several commonly used metabolic quenching methods, using the model cyanobacterium Synechocystis sp. PCC 6803 as an example. Multiple 13 C-labeled compounds, including 13 C-bicarbonate, 13 C-glucose and 13 C-glutamine, were used as tracers during the sample collection and cold-quenching process to assess the extent of metabolic turnover after cells were harvested from culture flasks. We show that the combination of rapid filtration followed by 100% cold (–80°C) methanol quenching exhibits the highest quenching efficiency, while mixing cell samples with a partially frozen 30% methanol slurry (–24°C) followed by centrifugation is slightly less effective at quenching metabolism but enables less laborious sample processing. By contrast, rapidly mixing the cells with a saline ice slurry (~0°C) is less effective as indicated by high isotope-labeling rates after sample harvest, while mixing the cells with 60% cold methanol (–65°C) prior to centrifugation causes significant metabolite loss. Furthermore, this study demonstrates a rigorous, quantitative, and broadly applicable method for assessing the metabolic quenching efficacy of protocols used for sample collection in metabolomics and fluxomics studies.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2021

High Temperature Acclimation of Leaf Gas Exchange, Photochemistry, and Metabolomic Profiles in Populus trichocarpa

High temperatures alter the thermal sensitivities of numerous physiological and biochemical processes that impact tree growth and productivity. Foliar and root applications of methanol have been implicated in plant acclimation to high temperature via the C 1 pathway. Here, we characterized temperature acclimation at 35°C of leaf gas exchange, chlorophyll fluorescence and extractable metabolites of potted Populus trichocarpa saplings and examined potential influences of mM concentrations of methanol added during soil watering over a two-month period. Relative to plants grown under the low growth temperature (LGT), high growth temperature (HGT) plants showed a suppression of leaf water use and carbon cycling including transpiration (E), net photosynthesis (P n ), an estimate of photorespiration (R p ), and dark respiration (R d ), attributed to reductions in stomatal conductance and direct negative effects on gas exchange and photosynthetic machinery. In contrast, HGT plants showed an upregulation of non-photochemical quenching (NPQ t ), the optimum temperature for ETR, and leaf isoprene emissions at 40°C. A large number of metabolites (867) were induced under HGT, many implicated in flavonoid biosynthesis highlighting a potentially protective role for these compounds. Methanol application did not significantly alter leaf gas exchange but slightly reduced the suppression of R d and R p by the high growth temperature while slightly impairing ETR, Fv’/Fm’, and q p . However, we were unable to determine if soil methanol was sufficiently taken up by the plant to have a direct effect on foliar processes. A small number of extracted leaf tissue metabolites (55 out of 10,015) showed significantly altered abundances under LGT and methanol treatments relative to water controls, and this increased in compound number (222) at the HGT. The results demonstrate the large physiological and biochemical impacts of high growth temperature on poplar seedlings and highlights the enhancement of the optimum temperature of ETR as a rapid thermal acclimation mechanism. Finally, although no large effect on leaf physiology was observed, the results are consistent with methanol both impairing photochemistry of the light reactions via formaldehyde toxicity, and stimulating photosynthesis and dark respiration through formate oxidation to CO 2 .

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2022

Metabolomics Analysis of Bacterial Pathogen Burkholderia thailandensis and Mammalian Host Cells in Co-culture

The Tier 1 HHS/USDA Select Agent Burkholderia pseudomallei is a bacterial pathogen that is highly virulent when introduced into the respiratory tract and intrinsically resistant to many antibiotics. Transcriptomic- and proteomic-based methodologies have been used to investigate mechanisms of virulence employed by B. pseudomallei and Burkholderia thailandensis, a convenient surrogate; however, analysis of the pathogen and host metabolomes during infection is lacking. Changes in the metabolites produced can be a result of altered gene expression and/or post-transcriptional processes. Thus, metabolomics complements transcriptomics and proteomics by providing a chemical readout of a biological phenotype, which serves as a snapshot of an organism’s physiological state. However, the poor signal from bacterial metabolites in the context of infection poses a challenge in their detection and robust annotation. In this work, we coupled mammalian cell culture-based metabolomics with feature-based molecular networking of mono- and co-cultures to annotate the pathogen’s secondary metabolome during infection of mammalian cells. These methods enabled us to identify several key secondary metabolites produced by B. thailandensis during infection of airway epithelial and macrophage cell lines. Additionally, the use of in silico approaches provided insights into shifts in host biochemical pathways relevant to defense against infection. Using chemical class enrichment analysis, for example, we identified changes in a number of host-derived compounds including immune lipids such as prostaglandins, which were detected exclusively upon pathogen challenge. Taken together, our findings indicate that co-culture of B. thailandensis with mammalian cells alters the metabolome of both pathogen and host and provides a new dimension of information for in-depth analysis of the host–pathogen interactions underlying Burkholderia infection.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2020

Enhanced nutrient uptake is sufficient to drive emergent cross-feeding between bacteria in a synthetic community

Abstract Interactive microbial communities are ubiquitous, influencing biogeochemical cycles and host health. One widespread interaction is nutrient exchange, or cross-feeding, wherein metabolites are transferred between microbes. Some cross-fed metabolites, such as vitamins, amino acids, and ammonium (NH4+), are communally valuable and impose a cost on the producer. The mechanisms that enforce cross-feeding of communally valuable metabolites are not fully understood. Previously we engineered a cross-feeding coculture between N2-fixing Rhodopseudomonas palustris and fermentative Escherichia coli. Engineered R. palustris excretes essential nitrogen as NH4+ to E. coli, while E. coli excretes essential carbon as fermentation products to R. palustris. Here, we sought to determine whether a reciprocal cross-feeding relationship would evolve spontaneously in cocultures with wild-type R. palustris, which is not known to excrete NH4+. Indeed, we observed the emergence of NH4+ cross-feeding, but driven by adaptation of E. coli alone. A missense mutation in E. coli NtrC, a regulator of nitrogen scavenging, resulted in constitutive activation of an NH4+ transporter. This activity likely allowed E. coli to subsist on the small amount of leaked NH4+ and better reciprocate through elevated excretion of fermentation products from a larger E. coli population. Our results indicate that enhanced nutrient uptake by recipients, rather than increased excretion by producers, is an underappreciated yet possibly prevalent mechanism by which cross-feeding can emerge.

Fritts, Ryan K.↗
DOE OSTI2020

Using metacommunity ecology to understand environmental metabolomes

Abstract Environmental metabolomes are fundamentally coupled to microbially-linked biogeochemical processes within ecosystems. However, significant gaps exist in our understanding of their spatiotemporal organization, limiting our ability to uncover transferrable principles and predict ecosystem function. We propose that a theoretical paradigm, which integrates concepts from metacommunity ecology, is necessary to reveal underlying mechanisms governing metabolomes. We call this synthesis between ecology and metabolomics ‘meta-metabolome ecology’ and demonstrate its utility using a mass spectrometry dataset. We developed three relational metabolite dendrograms using molecular properties and putative biochemical transformations and performed ecological null modeling. Based upon null modeling results, we show that stochastic processes drove molecular properties while biochemical transformations were structured deterministically. We further suggest that potentially biochemically active metabolites were more deterministically assembled than less active metabolites. Understanding variation in the influences of stochasticity and determinism provides a way to focus attention on which meta-metabolomes and which parts of meta-metabolomes are most likely to be important to consider in mechanistic models. We propose that this paradigm will allow researchers to study the connections between ecological systems and their molecular processes in previously inaccessible detail.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2023

The core metabolome and root exudation dynamics of three phylogenetically distinct plant species

Abstract Root exudates are plant-derived, exported metabolites likely shaping root-associated microbiomes by acting as nutrients and signals. However, root exudation dynamics are unclear and thus also, if changes in exudation are reflected in changes in microbiome structure. Here, we assess commonalities and differences between exudates of different plant species, diurnal exudation dynamics, as well as the accompanying methodological aspects of exudate sampling. We find that exudates should be collected for hours rather than days as many metabolite abundances saturate over time. Plant growth in sterile, nonsterile, or sugar-supplemented environments significantly alters exudate profiles. A comparison of Arabidopsis thaliana , Brachypodium distachyon , and Medicago truncatula shoot, root, and root exudate metabolite profiles reveals clear differences between these species, but also a core metabolome for tissues and exudates. Exudate profiles also exhibit a diurnal signature. These findings add to the methodological and conceptual groundwork for future exudate studies to improve understanding of plant-microbe interactions.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2021

Cognitive analysis of metabolomics data for systems biology

Cognitive computing is revolutionizing the way big data are processed and integrated, with artificial intelligence (AI) natural language processing (NLP) platforms helping researchers to efficiently search and digest the vast scientific literature. Most available platforms have been developed for biomedical researchers, but new NLP tools are emerging for biologists in other fields and an important example is metabolomics. NLP provides literature-based contextualization of metabolic features that decreases the time and expert-level subject knowledge required during the prioritization, identification and interpretation steps in the metabolomics data analysis pipeline. Here, we describe and demonstrate four workflows that combine metabolomics data with NLP-based literature searches of scientific databases to aid in the analysis of metabolomics data and their biological interpretation. Additionally, the four procedures can be used in isolation or consecutively, depending on the research questions. The first, used for initial metabolite annotation and prioritization, creates a list of metabolites that would be interesting for follow-up. The second workflow finds literature evidence of the activity of metabolites and metabolic pathways in governing the biological condition on a systems biology level. The third is used to identify candidate biomarkers, and the fourth looks for metabolic conditions or drug-repurposing targets that the two diseases have in common. The protocol can take 1–4 h or more to complete, depending on the processing time of the various software used.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2020

Metabolomics and the pig model reveal aberrant cardiac energy metabolism in metabolic syndrome

Abstract Although metabolic syndrome (MS) is a significant risk of cardiovascular disease (CVD), the cardiac response (MR) to MS remains unclear due to traditional MS models’ narrow scope around a limited number of cell-cycle regulation biomarkers and drawbacks of limited human tissue samples. To date, we developed the most comprehensive platform studying MR to MS in a pig model tightly related to human MS criteria. By incorporating comparative metabolomic, transcriptomic, functional analyses, and unsupervised machine learning (UML), we can discover unknown metabolic pathways connections and links on numerous biomarkers across the MS-associated issues in the heart. For the first time, we show severely diminished availability of glycolytic and citric acid cycle (CAC) pathways metabolites, altered expression, GlcNAcylation, and activity of involved enzymes. A notable exception, however, is the excessive succinate accumulation despite reduced succinate dehydrogenase complex iron-sulfur subunit b (SDHB) expression and decreased content of precursor metabolites. Finally, the expression of metabolites and enzymes from the GABA-glutamate, GABA-putrescine, and the glyoxylate pathways significantly increase, suggesting an alternative cardiac means to replenish succinate and malate in MS. Our platform discovers potential therapeutic targets for MS-associated CVD within pathways that were previously unknown to corelate with the disease.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2021

Machine learning approaches to predict gestational age in normal and complicated pregnancies via urinary metabolomics analysis

The elucidation of dynamic metabolomic changes during gestation is particularly important for the development of methods to evaluate pregnancy status or achieve earlier detection of pregnancy-related complications. Some studies have constructed models to evaluate pregnancy status and predict gestational age using omics data from blood biospecimens; however, less invasive methods are desired. Here we propose a model to predict gestational age, using urinary metabolite information. In our prospective cohort study, we collected 2741 urine samples from 187 healthy pregnant women, 23 patients with hypertensive disorders of pregnancy, and 14 patients with spontaneous preterm birth. Using gas chromatography-tandem mass spectrometry, we identified 184 urinary metabolites that showed dynamic systematic changes in healthy pregnant women according to gestational age. A model to predict gestational age during normal pregnancy progression was constructed; the correlation coefficient between actual and predicted weeks of gestation was 0.86. The predicted gestational ages of cases with hypertensive disorders of pregnancy exhibited significant progression, compared with actual gestational ages. This is the first study to predict gestational age in normal and complicated pregnancies by using urinary metabolite information. Minimally invasive urinary metabolomics might facilitate changes in the prediction of gestational age in various clinical settings.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2023

Anti-fungal bioactive terpenoids in the bioenergy crop switchgrass (Panicum virgatum) may contribute to ecotype-specific microbiome composition

Abstract Plant derived bioactive small molecules have attracted attention of scientists across fundamental and applied scientific disciplines. We seek to understand the influence of these phytochemicals on rhizosphere and root-associated fungi. We hypothesize that – consistent with accumulating evidence that switchgrass genotype impacts microbiome assembly – differential terpenoid accumulation contributes to switchgrass ecotype-specific microbiome composition. An initial in vitro Petri plate-based disc diffusion screen of 18 switchgrass root derived fungal isolates revealed differential responses to upland- and lowland-isolated metabolites. To identify specific fungal growth-modulating metabolites, we tested fractions from root extracts on three ecologically important fungal isolates – Linnemania elongata, Trichoderma sp. and Fusarium sp. Saponins and diterpenoids were identified as the most prominent antifungal metabolites. Finally, analysis of liquid chromatography-purified terpenoids revealed fungal inhibition structure – activity relationships (SAR). Saponin antifungal activity was primarily determined by the number of sugar moieties – saponins glycosylated at a single core position were inhibitory whereas saponins glycosylated at two core positions were inactive. Saponin core hydroxylation and acetylation were also associated with reduced activity. Diterpenoid activity required the presence of an intact furan ring for strong fungal growth inhibition. These results inform future breeding and biotechnology strategies for crop protection with reduced pesticide application.

09 BIOMASS FUELS↗
DOE OSTI2023

Reassessing the claimed cytokinin-substituting activity of dehydrodiconiferyl alcohol glucoside

Dehydrodiconiferyl alcohol glucoside (DCG) is a phenylpropanoid-derived plant metabolite with reported cytokinin-substituting and cell-division-promoting activity. Despite its claimed activity, DCG did not trigger morphological changes in Arabidopsis seedlings nor did it alter transcriptional shifts in cell division and cytokinin-responsive genes. In reinvestigating the bioactivity of DCG in its original setting, the previously described stimulation of tobacco callus formation could not be confirmed. No evidence was found that DCG is actually taken up by plant cells, which could explain the absence of any observable activity in the performed experiments. The DCG content in plant tissue increased when feeding explants with the DCG aglycone dehydrodiconiferyl alcohol, which is readily taken up and converted to DCG by plant cells. Despite the increased DCG content, no activity for this metabolite could be demonstrated. Furthermore, our results therefore demand a reevaluation of the often-quoted cytokinin-substituting and cell-division-promoting activity that has previously been attributed to this metabolite.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2021

MS2Planner: improved fragmentation spectra coverage in untargeted mass spectrometry by iterative optimized data acquisition

Motivation: Untargeted mass spectrometry experiments enable the profiling of metabolites in complex biological samples. The collected fragmentation spectra are the metabolite’s fingerprints that are used for molecule identification and discovery. Two main mass spectrometry strategies exist for the collection of fragmentation spectra: data-dependent acquisition (DDA) and data-independent acquisition (DIA). In the DIA strategy, all the metabolites ions in predefined mass-to-charge ratio ranges are co-isolated and co-fragmented, resulting in multiplexed fragmentation spectra that are challenging to annotate. In contrast, in the DDA strategy, fragmentation spectra are dynamically and specifically collected for the most abundant ions observed, causing redundancy and sub-optimal fragmentation spectra collection. Yet, DDA results in less multiplexed fragmentation spectra that can be readily annotated. Results: We introduce the MS2Planner workflow, an Iterative Optimized Data Acquisition strategy that optimizes the number of high-quality fragmentation spectra over multiple experimental acquisitions using topological sorting. Our results showed that MS2Planner increases the annotation rate by 38.6% and is 62.5% more sensitive and 9.4% more specific compared to DDA. Availability and implementation MS2Planner code is available at https://github.com/mohimanilab/MS2Planner. The generation of the inclusion list from MS2Planner was performed with python scripts available at https://github.com/lfnothias/IODA_MS.

47 OTHER INSTRUMENTATION↗
DOE OSTI2025

Bacterial and fungal composition and exometabolites control the development and persistence of soil water repellency

Soil water repellency (SWR), the reduced affinity of soil for water, is a phenomenon that affects soils globally. With worsening climate change, SWR is expected to increase emphasizing the need to understand the mechanisms driving SWR development and persistence. The importance of the soil microbes in SWR has been postulated for decades, but limited research has been conducted into whole-community interactions and the role of community metabolic activity. To address this gap in knowledge, we investigated the direct effect of microbial community composition, activity, and diversity, as well as their associated metabolites on the development and persistence of SWR by inoculating microcosms containing model soils with 15 different microbial communities and quantified respiration and SWR over time. Six communities that consistently produced either a hydrophobic or hydrophilic phenotype were characterized using metagenomics and metabolomics to determine the impact of microbial and metabolite composition and diversity on SWR. We identified several bacterial genera with significant changes in abundance between SWR phenotypes including Nocardiopsis and Kocuria in hydrophilic and Streptomyces and Cutibacterium in hydrophobic. We discovered that hydrophilic communities were more positively connected when compared to hydrophobic communities, which could be due to an increase in defense mechanism genes. Additionally, we identified specific metabolites associated with hydrophilic and hydrophobic phenotypes including an increase in the osmolyte ectoine in hydrophilic and an increase in plant-derived decomposition products in hydrophobic communities. Finally, our research suggests that fungi, previously thought to cause hydrophobicity, may actually contribute to hydrophilicity through their preferential consumption of hydrophobic compounds.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2021

An efficient LC-MS method for isomer separation and detection of sugars, phosphorylated sugars, and organic acids

Abstract Assessing central carbon metabolism in plants can be challenging due to the dynamic range in pool sizes, with low levels of important phosphorylated sugars relative to more abundant sugars and organic acids. Here, we report a sensitive liquid chromatography–mass spectrometry method for analysing central metabolites on a hybrid column, where both anion-exchange and hydrophilic interaction chromatography (HILIC) ligands are embedded in the stationary phase. The liquid chromatography method was developed for enhanced selectivity of 27 central metabolites in a single run with sensitivity at femtomole levels observed for most phosphorylated sugars. The method resolved phosphorylated hexose, pentose, and triose isomers that are otherwise challenging. Compared with a standard HILIC approach, these metabolites had improved peak areas using our approach due to ion enhancement or low ion suppression in the biological sample matrix. The approach was applied to investigate metabolism in high lipid-producing tobacco leaves that exhibited increased levels of acetyl-CoA, a precursor for oil biosynthesis. The application of the method to isotopologue detection and quantification was considered through evaluating 13C-labeled seeds from Camelina sativa. The method provides a means to analyse intermediates more comprehensively in central metabolism of plant tissues.

Plant Sciences↗
DOE OSTI2023

Imaging plant metabolism in situ

Abstract Mass spectrometry imaging (MSI) has emerged as an invaluable analytical technique for investigating the spatial distribution of molecules within biological systems. In the realm of plant science, MSI is increasingly employed to explore metabolic processes across a wide array of plant tissues, including those in leaves, fruits, stems, roots, and seeds, spanning various plant systems such as model species, staple and energy crops, and medicinal plants. By generating spatial maps of metabolites, MSI has elucidated the distribution patterns of diverse metabolites and phytochemicals, encompassing lipids, carbohydrates, amino acids, organic acids, phenolics, terpenes, alkaloids, vitamins, pigments, and others, thereby providing insights into their metabolic pathways and functional roles. In this review, we present recent MSI studies that demonstrate the advances made in visualizing the plant spatial metabolome. Moreover, we emphasize the technical progress that enhances the identification and interpretation of spatial metabolite maps. Within a mere decade since the inception of plant MSI studies, this robust technology is poised to continue as a vital tool for tackling complex challenges in plant metabolism.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2023

Antibacterial and antioxidant activities of Streptomyces sp. strain FR7 isolated from forest soil

Abstract Actinomycetes produce secondary metabolites with many bioactivities such as antimicrobial, which can be useful as alternatives against resistant bacterial strains. Therefore, the screening of new habitats is likely to provide new strains with high potential. In this work, the antimicrobial capacity was used to select Streptomyces sp. strains isolated from Raf Raf forest (Tunisia). From the strain displaying higher activity, FR7, an ethyl acetate extract was prepared under optimized culturing conditions (10 days at 30°C in ISP2 medium with initial pH 8), showing significant antimicrobial activity against Micrococcus luteus and Staphylococcus aureus (MIC = 5 μg ml−1), and Listeria monocytogenes and Pseudomonas aeruginosa (MIC = 20 μg ml−1). The extract displayed strong DPPH radical scavenging activity (IC50 = 1.3 μg ml−1) and protection of yeast cells from H2O2-induced oxidative stress determined by flow cytometry with dichlorofluorescein diacetate. The crude extract showed the presence of polyketides, with methylsalicylic acid as moiety, a large and diverse group of secondary metabolites with a wide range of bioactivities, including antioxidant and antibacterial. Based on 16S RNA gene sequences, strain FR7 was identified as belonging to genus Streptomyces with high resemblance to S. iakyrus. Streptomyces sp. FR7 has great potential as a source of antibacterial and antioxidant metabolites.

Weslati, Imen↗