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Sci Rep
Sci Rep
Scientific Reports
2045-2322
Nature Publishing Group UK London

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68645
10.1038/s41598-024-68645-8
Article
d-Glutamate production by stressed Escherichia coli gives a clue for the hypothetical induction mechanism of the ALS disease
Monselise Edna Ben-Izhak bened@post.bgu.ac.il

1
Vyazmensky Maria 1
Scherf Tali 2
Batushansky Albert 3
Fishov Itzhak fishov@bgu.ac.il

1
1 https://ror.org/05tkyf982 grid.7489.2 0000 0004 1937 0511 Department of Life Science, Bergman Campus, Ben-Gurion University of the Negev, 8441901 Beer-Sheva, Israel
2 https://ror.org/0316ej306 grid.13992.30 0000 0004 0604 7563 Department of Chemical Research Support, The Weizmann Institute of Science, 7610001 Rehovot, Israel
3 https://ror.org/05tkyf982 grid.7489.2 0000 0004 1937 0511 Ilse Katz Institute for Nanoscale Science & Technology, Marcus Campus, Ben-Gurion University of the Negev, 8410501 Beer-Sheva, Israel
6 8 2024
6 8 2024
2024
14 1824714 3 2024
25 7 2024
© The Author(s) 2024, corrected publication 2024
2024
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In the search for the origin of Amyotrophic Lateral Sclerosis disease (ALS), we hypothesized earlier (Monselise, 2019) that d-amino acids produced by stressed microbiome may serve as inducers of the disease development. Many examples of d-amino acid accumulation under various stress conditions were demonstrated in prokaryotic and eukaryotic cells. In this work, wild-type Escherichia coli, members of the digestive system, were subjected to carbon and nitrogen starvation stress. Using NMR and LC–MS techniques, we found for the first time that d-glutamate accumulated in the stressed bacteria but not in control cells. These results together with the existing knowledge, allow us to suggest a new insight into the pathway of ALS development: d-glutamate, produced by the stressed microbiome, induces neurobiochemical miscommunication setting on C1q of the complement system. Proving this insight may have great importance in preventive medicine of such MND modern-age diseases as ALS, Alzheimer, and Parkinson.

Keywords

d-glutamate and d-glutamate racemase
Mitochondria
Eukaryotic and prokaryotic communication
Evolutionary approach
Subject terms

Neuroscience
Diseases
Microbiology
Bacteriology
The research budget for this work was established from the personal resources of E. B. I. M. to a BGU account named "The budget for research in the field of ALS by the name of late Moshe Melech Ben Izhak".issue-copyright-statement© Springer Nature Limited 2024
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pmcIntroduction

d-amino acids are indispensable for bacterial growth as components of cell wall peptidoglycans. They are incorporated into the peptidoglycan monomeric units by the MurD enzyme1,2. The interconversion between d- and l-glutamate is achieved by the Glutamate Racemase enzyme3, specific for bacteria. This enzyme appears to be the primary source of d-glutamate for cell-wall biosynthesis, making it a potentially attractive target for antimicrobial drug design4,5.

Advanced analytical techniques that detect chiral amino acids, e.g.6 (and see7 for a review), have demonstrated the presence of several d-amino acids in mammals as well, including humans8,9, with identified physiological functions comprising regulatory roles (reviewed in10). Particularly, d-serine regulates nervous signaling in the cerebral cortex and participates in memorization and learning; d-aspartate is often present in the central nervous system (CNS), neuroendocrine, and endocrine systems and plays physiological roles in the regulation of hormone secretion and steroidogenesis11–14. The exogenous d-amino acids are thought to be metabolized by dietary enzymes and bacterial flora15–17. In eukaryotic cells, d-glutamate is metabolized within the mitochondria and chloroplasts (ancient prokaryote)18,19. d-Amino acids are increasingly being recognized as important signaling molecules in the central nervous system in mammals, including humans20,21.

Abnormal levels of d-amino acids have been associated with the pathogenesis of different diseases, including schizophrenia and amyotrophic lateral sclerosis (ALS), indicating that d-amino acid levels hold potential as diagnostic markers22–26 Table 1 summarizes 19 examples of d-amino acids' appearance under various stress or disease conditions, indicating that this phenomenon is common for prokaryotic and eukaryotic cells.Table 1 d-amino acid accumulation, as signaling agents, under various stress or disease conditions-a universal phenomenon.

Entry No	Type of stress/disease	Organism	The d-amino acid accumulated	References	
Prokaryotic	
1	Density	Bacteria

Bacillus anthracis (germination of fresh spore is inhibited in a density-dependent manner by d-alanine)

	d-Alanine	27	
2	Hyperthermal stress	Archaea

Pyrobaculum islandicum

Methanosarcina barkeri Halobacterium salinarium

	d-Alanine	28	
3	Vibrio cholerae mrcA mutant	Bacteria

a mutant in Vibrio cholerae mrcA, and Bacillus subtilisgenerated

	d-Methionine d-Leucine

d-Tryptophan d-Phenyl alanine

	29	
Eukaryotic	
4	Osmotic stress	Parasitic protozoan

Leishmania amazonensis

	d-Alanine	30	
5	Hypersalinity acclimation	Crustaceans Aquatic invertebrates

Penaeus japonicus

Procambarus clarkia

Juasus lalandi

Chionoecetes opili

Eriocheir japonicus

	d-Alanine	31	
6	Changes in external salinity	A brackish-water mollusc,

Corbicula japonica

	d-Alanine	32	
7	Hypersalinity acclimation	Mollusks Aquatic invertebrates

Scapharca broughtonii

Crassostrea gigas

Patinopecten yessoensis

Meretrix lusoria

Ruditapes philippinarum

Pseudocardium sachalinensis

Tresus keenae

	d-Alanine	31	
8	Hypertonic or Hypotonic stress	Mollusks aquatic invertebrates

Lucinoma aequizonata

	d-Alanine	33	
9	Herbicides	Plant

Nicotiana tabacum

	d-Alanine	34	
10	Ultraviolet

radiation

	Duckweed plants

Landoltia punctata

	d-Alanine	6	
11	Amino acid deprivation	Plant

Arabidopsis thaliana

	d-Alanine	35	
12	Tidal freshwater marshes	Plant

Phragmites australis

	d-Alanine	36	
13	Most exposed to chronic mild stress (CMS), also some of them with Alzheimer's disease (AD)	Male Wistar Rats

mammalian tissues

frontal cortex

	d-Glutamate	37	
14	Mutant ddY/DAO− mice lacking  d-amino-acid oxidase	Mouse

mammalian tissues

in the pituitary and pineal glands

	d-Serine

d-Alanine

d-Proline

d-Asparagine

d-Serine

d-Leucine

	38–41	
15	Treated with drugs employed for therapy of mood/anxiety and subjected to food shock stress	Rat

mammalian tissues

	d-Glutamate	42	
16	Adult male-aging	Rat

mammalian tissues

salivary glands

CNS anterior pituitary gland

and in the pancreas

Islets of Langerhans of rat pancreas

	d-Alanine

d-Asparagine

d-Alanine

	43–45	
17	Renal–kidney disease-	Human

Homo sapiens

mammalian tissues

	d-Serine

d-Alanine

d-Proline

	46	
18	Alzheimer’s disease (AD)	Human	d- Serine

d-Alanine

d-Proline

d-Glutamate

	25	
19	Motor Neuron Disease (MND)/Amyotrophic Lateral Sclerosis (ALS)	Human	d-Asparagine

d-Glutamate

	47	

Previously, we hypothesized that the stressed microbiome may produce elevated levels of d-amino acids48. Emerging evidence has demonstrated that the gut microbiome (GM) plays an essential role in the pathogenesis of human diseases in distal organs49–54. An increasing number of studies suggested that GM can modulate nervous, endocrine, and immune communication through the gut-brain axis which takes part in the occurrence and development of central nervous system diseases. The relationship between GM and neurodegenerative disease has recently gained a lot of attention in the medical community, especially in Parkinson's and Alzheimer's diseases, and ALS48,55–60. A comparison between ALS and a healthy group revealed a variation in the intestinal microbial composition with a higher abundance of E. coli and enterobacteria and a low abundance of total yeast in patients61. Notably, elevated levels of d-glutamate were found in the gut microbiota of Alzheimer’s disease patients60.

This work aimed to examine d-glutamate accumulation in stressed gut microbiome as predicted in our hypothesis48. However, the gut microbiome is extremely complex, and its composition and corresponding functionality are very diverse and dynamic even in healthy humans (see e.g.62). Various in vitro experimental microbiome models were explored, but none of them can provide unambiguous results (for a review see e.g.63). To this end, we searched for an answer to a simple question: is an accumulation of d-glutamate by a wil d-type bacterium possible in stress conditions? The bacterium chosen for this purpose was the well-studied E. coli B/r H266 under nutritional starvation. Using Nuclear Magnetic Resonance (NMR) spectroscopy and liqui d-chromatography mass-spectrometry (LC–MS) techniques, we found for the first time that d-glutamate accumulated in the stressed bacteria but not in control cells. In the Discussion section, we consider how this accumulation may lead to ALS development via the complementary immune system response in the frame of our hypothesis.

Results

Chiral recognition plays an important role in many fundamental interactions of living systems. Different spectroscopic methods such as fluorescence spectroscopy, mass spectrometry, and nuclear magnetic resonance (NMR) spectroscopy, have been applied to achieve the analysis of chiral enantiomeric compound6,64,65. In this study, we employed targeted analysis of amino acids by liqui d-chromatography mass-spectrometry (LC–MS) and NMR spectroscopy. Since both techniques are achiral methods, chiral enantiomeric discrimination requires the formation of diastereoisomeric entities, which can then be distinguished (see Materials and Methods section).

Accumulation of d-Glutamate in starved E. coli revealed by LC–MS analysis

Derivatization of the l- and d-glutamic acid with a chiral reagent (S)-NIFE66,67 allows the separation of derivatized isomers chromatographically. The identity of the targets was confirmed by the exact mass-to-charge ratio recorded by a high-resolution mass spectrometer that also allows monitoring of isotopic composition, further supporting the identification. The results of the analysis are presented in Fig. 1. The data reveals the presence of l- and d-glutamate in the samples under stress conditions, while the control samples had l-glutamate only (Fig. 1E–G). Remarkably, 10-h-long stress showed a visibly stronger accumulation of d-glutamate compared to 24 h of stress. Considering the large difference in abundance between the two forms of glutamate in the biological samples, it was impossible to quantify them precisely using LC–MS. However, relative quantification of metabolites was performed by comparative analysis, calculating the peak's areas. The results support the visual observation of higher d-glutamate content after 10 h compared to 24 h of stress. Thus, the ratio between d- and l-glutamate after 10 h was 1:6, while it was 1:12 only after 24 h. These results were consistent among the analysis of three sets of samples from three independent preparations, containing two duplicates of unstressed E. coli (control), 10-h stressed cells, and 24-h stressed cells each.Figure 1 The results of LC–MS analysis of glutamic acids enantiomers. (A–D) Separation method development and verification; (E–G) representative samples of the control and the stress conditions. Chromatographic peaks framed in green (retention time 10.25 min) are l-glutamic acid (regular and d5-labeled stable isotope as internal standard), and peaks framed in blue (retention time 10.55 min) are d-glutamic acid. The mass-spectrum values of the corresponding peaks are presented in the plot.

d-Glutamate detection in E. coli cells by NMR spectroscopy

NMR spectroscopy is also an achiral technique that requires the creation of diastereoisomeric entities, whereby spectral differences between an antipodal pair can be recognized. For enantiomeric discrimination, an optically pure chiral reagent (chiral auxiliary) is required to convert the mixture of enantiomers into a diastereomeric mixture through in-situ formation of nonequivalent diastereomeric complexes with substrate enantiomers. NMR resonances of diastereomers are anisochronous and, therefore, can often be distinguished in the NMR spectrum. Over many years, a variety of chiral NMR auxiliaries have been introduced, e.g. tartaric acid, which is a bidentate ligand with two chiral centers forming a seven-membered chelate ring, as well as different shift reagents. In this study, d-tartrate was added to the chiral sample, and the complex was formed in situ. In an attempt to differentiate and increase chemical shift differences between d- and l-glutamate, several chiral auxiliary reagents were tested, under different experimental conditions. Aizawa et al.68 had used Somarium Lantanide shift reagent in the presence of chiral Tartrate, pH 8. Unfortunately, under these conditions, our biological samples had precipitated, and no NMR signal was detected. Therefore, it was decided to use d-tartaric acid solely, testing different pH conditions. Best results were obtained using a threefold excess of d-tartaric acid and pH 7, conditions that were eventually used in this study.

One-dimensional (1D) 1H NMR spectra, as well as 2D 1H-13C Heteronuclear Single Quantum Correlation (HSQC) NMR spectra, were used to characterize and identify the presence of glutamate in stressed cell extract. A comparison of the 1H and 13C chemical shifts between standard samples of d- and l-glutamate reveals slight differences between the NMR signals of the two glutamate enantiomers. The superposition of the 1D 1H NMR spectra of d-glutamate (red), l-glutamate (blue), and a mixture of d- and l-glutamate, 1:15 (green), is displayed in Fig. 2. (This 1:15 ratio was chosen as the desired lowest detection level based on results obtained by LC–MS for the biological samples. Spectra of 1:1, and 1:2 mixtures are shown in the Fig. S1 of Supplementary Information.). The largest chemical shift difference is observed for the Hβ signals (~ 2.36 ppm; see Fig. 2 insert/enlargement). Chemical shifts of the 1:15 d:l-glutamate mixture (Fig. 2) further support the chemical exchange between the two forms, revealing weighted average chemical shifts of d- and l-glutamate.Figure 2 Superposition of 1D 1H NMR spectra of d-Glu (red), l-Glu (blue) and a mixture of d- & l-Glu, 1:15 (green), all dissolved in 10/90% D2O/H2O, pH 7. The inserts present the enlarged signals of Hβ (~ 2.10 ppm), Hγ (~ 2.36 ppm), and Hα (~ 3.70 ppm).

13C chemical shifts differences between d- and l-glutamate followed the trend observed for the 1H shift differences. To simultaneously track 1H and 13C shift differences, a 2D 1H–13C Heteronuclear Single Quantum Correlation (HSQC) NMR spectrum was recorded. Figure 3 presents the aliphatic region of the 2D 1H–13C correlation spectrum of stressed cell extract (red), superimposed on the corresponding spectrum of a 1:15 mixture of d- and l-glutamate (blue). For clarity, the 1D 1H NMR spectrum of the latter mixture sample is shown as well (green). The chemical shifts of the 1:15 mixture signals nicely fit the signals of the stressed cell extract, clearly supporting and identifying the presence of glutamate in the cell extract sample.Figure 3 The aliphatic spectral region of 2D 1H–13C correlation NMR Spectrum of stressed cell extracts (red), superimposed on the corresponding spectrum of a mixture of d- and l-Glu, 1:15 (blue). For clarity, the 1D 1H NMR spectrum of the latter is shown in the bottom (green). H/C correlation signals arising from the α, β and γ positions of glutamic acid are marked.

Discussion

Our efforts to optimize the conditions allowing an effective resolution of NMR signals of the glutamate enantiomers led to the desired results: the presence of d-glutamate in the 24-h starved E. coli was unambiguously detected (Figs. 2 and 3). The similarly optimized LC–MS spectroscopy not only successfully detected d-glutamate presence in the biological samples, but also provided valuable quantitative data. We note here that some of the samples subjected to the stress conditions did not demonstrate a remarkable accumulation of d-glutamate in the range of 1:6 or 1:12 (after 10 and 24-h starvation, respectively) compared to l-glutamate as shown in Fig. 1. As well, E. coli as other bacterial species are known to adapt gradually to nutritional stress69,70. In light of this, we assume that the decrease in the d:l glutamate enantiomers ratio may reflect the adaptation process during long starvation. Nevertheless, the reliability of the results was confirmed by the consistent detection of d-glutamate in the samples in each of the three independent sets of bacterial samples. We therefore may use the highest detected d:l ratio of 1:6 to estimate the d-glutamate concentration in starved cells. The reported l-glutamate content in E. coli cells normally growing at the same conditions71,72 is 64 µmol/g of dry weight. Taking the average cell volume of 3 fl, and dry weight of a single cell of 470 fg, the d-glutamate concentration within an average E. coli cell can be calculated as about 50 µM. This value looks reasonable and essential if d-glutamate is released from the starved cells that eventually lyse.

Since d-glutamate is an essential component in pathway of the peptidoglycan synthesis in bacteria, its accumulation is expected in any case of l-glutamate availability and arrested growth (e. g. about a tenfold increase in glutamate levels in E. coli cells with the transiently paused growth71,73. The constitutive glutamate racemase may readily convert l-glutamate to Denantiomer at these physiological conditions. This kind of stress-induced accumulation of d-glutamate may occur in all peptidoglycan-synthesizing bacteria populating the microbiome of the digestive system. The excretion of amino acids from bacterial cells under stress, or even at normal growth conditions74, was demonstrated although we are not aware of the chiral specificity of the carrier.

What may be a consequence of such d-glutamate accumulation in the stressed microbiome? A hectic modern lifestyle may affect human health by causing stress to the gut microbiota. As a result, the gut microbiota releases their signaling agents, d-amino acids, as their distress beacons (see for a review10. Another source of d-glutamate in the digestive system arises from consumed industrial food that contains a high percentage of d-glutamate60,75–78. Further, d-amino acids may travel via the bloodstream throughout the entire circulatory system setting on a neurobiochemical chain reaction by exciting the Complement immune system C1q and disrupting the signaling in CNS (Fig. 4).Figure 4 Scheme illustrating the connection between a stressed gut microbiota releasing d-glutamate as a communication signal, and C1q immune reaction (A) and the resulting impact on motor neurons (B). Mitochondria are of particular importance in neurons, which have high metabolic requirements. ALS-associated mitochondrial dysfunction comes in many guises, including defective oxidative phosphorylation, reactive oxygen species (ROS) production, impaired calcium buffering capacity, and defective mitochondrial dynamics79. Mitochondrial dysfunction is one of the earliest pathophysiological events in ALS80 The mitochondrial ultrastructure is a useful tool for assessing mitochondrial quality81. Aggregated mitochondria, with a swollen and vacuolated appearance, are one of the first changes82–84. The activity of the complexes involved in the electron transport chain is decreased in ALS. This results in decreased ATP generation, and increased generation of ROS leading to oxidative damage to DNA, RNA and mRNA80. In synaptic pruning, microglia-derived C1q may play an essential source of excessive synapse removal leading to pathological conditions85–87.

The complement component 1q, or briefly C1q, is a pattern recognition protein as it can identify various structures and ligands on microbial surfaces, apoptotic cells, or indirectly via antibodies and C-reactive protein85,88–91. The immune system can react to elevated levels of d-glutamate by initiating the classical pathway of complement activation, which can help to eliminate the bacterial d-amino acid92 (Fig. 4). When C1q is introduced to components of a potential pathogen, it may trigger the production and activation of more C1q as part of the immune response93. Clinical studies94 reveal that antibodies specific to human C1q cause a slowdown in ALS progression by reducing C1q activity/levels. Besides its role in innate immunity, C1q has a function in neurodevelopment, where it marks synapses for pruning by glia95. Aberrant activation of C1q in Alzheimer’s disease and related conditions leads to the removal of healthy synapses and contributes to dementia and loss of function96. Neutralizing C1q with ANX005 aims to limit complement-mediated neurodegeneration and preserve synapses94.

We are trying to locate and understand the source of neurobiochemical miscommunication occurring in neurodegenerative diseases such as ALS. ALS, commonly known as Lou Gehrig’s disease, is characterized by progressive degeneration of both upper and lower motor neurons, resulting in muscle atrophy, gradual paralysis, and death, usually resulting from respiratory failure. Sporadic ALS has a worldwide prevalence of 6–8 in 100,000. The average age of onset is between 55 and 65 years of age. The average survival period is 2–5 years from diagnosis97–99. Structurally altered and aggregated mitochondria, with a swollen and vacuolated appearance, are one of the first changes observed in ALS patient motor neurons100, suggesting direct involvement in disease pathogenesis80. Unfortunately, there are very few, if any, effective treatments for this disease, and of its origins. One of the mechanisms leading to nerve cell damage is the elevated Glutamate in the bloodstream. Accordingly, the main component of medicine Rilutek (riluzole) affects presynaptic sodium channels causing a reduction in the release of Glutamate101,102.

In this study, revealing the increased levels of d-glutamate under stress conditions we have proved the first step in the hypothesis suggested earlier103. Further studies are required to examine the next steps in the proposed scheme of ALS disease development (Fig. 4), inspiring possible treatment or prevention. We believe it may be important in preventive medicine for many other modern-age MND diseases e.g., Alzheimer and Parkinson.

Material and methods

Strains and media

A wild strain Escherichia coli B/r H266104 was grown in minimal salt M9 medium (Formedium LTD, Hunstanton, UK) supplemented with 0.2% glucose, 1 mM MgSO4, 0.1 mM CaCl2, and 0.1 µg/ml Thiamine (B1) (Sigma Germany), in Erlenmeyer flasks with shaking at 37 °C, for 20 h. Cells were collected by filtration using sterile Polycarbonate filters (0.4 µ pore size, 47 mm diameter) and resuspended to an OD600 of 1.8 in "Stress medium": growth medium without NH4Cl and glucose, for incubation periods of either 10 h or 24 h in a shaker at 37 °C. The cells were harvested by centrifugation (1500×G for 15 min at 24 °C) and the pellet was stored at − 80 °C until extraction for NMR and UPLC studies.

LC–MS analysis

Targeted LC–MS analysis is a widely used technique in biological studies. It is based on separating components in a complex mixture by liquid chromatography and detecting their mass-to-charge ratio by mass-spectrometry. However, direct chromatography of enantiomers having identical molecular weight will also have the same retention time, making simple LC–MS ineffective. This challenge can be overcome using LC column with a chiral stationary phase105. This method requires a precise selection of specific columns, and multiple optimizations for specific compounds and still is not widely accessible for biological samples where the difference between concentrations of L- and d-amino acids can be orders of magnitude. In this study, a chemical derivatization followed by a relatively simple reverse-phase LC–MS technique demonstrated a better alternative.

Sample preparation for the LC–MS

Extraction was started by adding 2 ml of pre-chilled LC–MS grade methanol to the frozen pellet. The mixture was vortexed, thawed on ice, vortexed again, and sonicated for 5 min in the ultrasound bath. Next, 1 ml of LC–MS grade chloroform and 1 ml of LC–MS grade water were consequently added. The mixture was vigorously shaken for 10 min and centrifuged for 10 min at 14,000 rpm. The supernatant was transferred to the new Eppendorf tubes and completely evaporated in the SpeedVac for 8 h106. The dry pellet was reconstituted in 50 µl of LC–MS grade water and subjected to derivatization.

Chiral derivatization with (S)-N-(4-nitrophenoxycarbonyl) phenylalanine methoxyethyl ester, (S)-NIFE, was performed based on the previously published works with modifications66,67. The derivatization process conjugate (S)-NIFE and amino acid (glutamate) radical, changing the targeted molecular weight (Fig. 5). Briefly, 10 µl of the sample were mixed with 10 µl of the internal standard (d5-l-glutamate 10 µg/ml) and 20 µl of 0.15 M sodium tetraborate, briefly vortexed, and then 30 µl of 2.5 mg/ml (S)-NIFE in acetonitrile were added. The mixture was incubated for 40 min at 22 °C on the slowly rotating thermos-shaker, and then neutralized by adding 6 µl of 4 N HCl and diluted with 24 µl of water to the total volume of 100 µl. The samples were centrifuged for 5 min at 14,000 rpm, and 60 µl from the top were transferred to the LC–MS vials.Figure 5 A scheme of chiral derivatization of glutamic acid with (S)-NIFE reagent.

LC–MS setup

The Waters Ultra Performance Liquid Chromatography (UPLC) system coupled with Thermo Exploris 240 mass-spectrometer was used for the analysis. A separation of the derivatized compounds was achieved on Waters BEH C18 column (1.7 µm, 2.1 × 50 mm) using the following gradient of the mobile phase A (0.1% formic acid) and mobile phase B (acetonitrile): 0 min 95% A, 15 min 70% A, 15.5–17.5 min 0% A, 18–21 min 95% A. The injection volume was 2 µl, the column temperature was kept at 40 °C, and the flow rate was 0.2 ml/min. The total run time was 21 min.

High-resolution mass spectra were acquired using electron spray ionization (ESI) in positive full scan mode (70–700 m/z) with a resolution of 24,000 full width at half-maximum (FWHM). The MS parameters (ion spray voltage, sheath gas, aux gas, sweep gas, ion transfer tube temperature, and vaporizer temperature) were set up according to the manufacturer's recommendations. Data were acquired under the control of Thermo Xcalibur software version 4.5.455 (Thermo Fisher Scientific) (https://www.thermofisher.com) which was used for data analysis as well, extracting targeted ions (EIC) of the derivatized targets (l- and d-glutamate) and their internal standard (d5-l-glutamate), and relative targets quantification.

NMR analysis

NMR sample preparation

Standard samples of d-glutamate, l-glutamate (Sigma), and their 1:1, 1:2, and 1:15 mixtures of 136 mM (in total) were prepared in the presence of threefold d-Tartaric acid, in 90%/10% H2O/D2O, pH 7. Chemical shift calibration standard, 3-(trimethylsilyl) propionic acid-2,2,3,3-d4 (TMSP) was added.

Biological samples were prepared from about 5 × 1010 cells collected from a liquid culture as described above. The concentrated cells were thawed and sonicated by Sonics Vibra Cell, pulse amplitude 35% for 2 min. Both control and stressed-cell samples were prepared in the presence of a threefold excess of d-Tartaric acid, in 90%/10% H2O/D2O, pH 7 using TMSP as a chemical shift calibration standard.

NMR spectroscopy

NMR experiments were conducted at 298 °K on a Bruker Avance NEO 600 MHz NMR spectrometer equipped with a 5-mm cryogenic triple-resonance HCN TCI probe. Data were processed and analyzed using TOPSPIN 4.0 (Bruker BioSpin, Germany). Spectra were referenced against internal sodium salt of 3-(trimethylsilyl) propionic acid-2,2,3,3-d4 (TMSP). One-dimensional 1H NMR spectra were acquired using solvent presaturation to suppress the solvent signal. Two-dimensional 1H–13C Heteronuclear Single Quantum Coherence (2D HSQC) spectra were recorded using 8192 (t2) × 256 (t1) data points. Multiplicity editing HSQC enables differentiating between methyl and methine groups that give rise to positive correlation, versus methylene groups that appear as negative peaks.

Supplementary Information

Supplementary Figure S1.

Abbreviations

GM Gut microbiota

ALS Amyotrophic lateral sclerosis

C1q Complement component 1q

LC–MS Liquid chromatography mass spectrometry

UPLC Ultra performance liquid chromatography

HSQC Heteronuclear single quantum correlation

NMR Nuclear magnetic resonance

Supplementary Information

The online version contains supplementary material available at 10.1038/s41598-024-68645-8.

Acknowledgements

We want to thank the late Prof. Daniel Kost and Aliza Levkovitz, for the 15N-NMR spectrometric research work done together for over 30 years. It was the basis that led to the present research work. We wish to thank Prof. Dudy Bar-Zvi, Dr. Amira Rudi, Engineer Itsik Cohen (Bruker Israel), Prof. Adrian Israelson, Prof. Sen-Ichi Aizawa, Prof. David Avnir, Prof. Yossi Paltiel, Prof Itzhak Mizrahi and, Dr. Galit Yehezkel for their helpful comments. We wish to thank for helpful assistance: Dorit van-Moppes, Eyal Ben-Yehuda and Alina Katz, Library Information Specialists and Oded Shaul Ben-Izhak for Graphic editing. Dr. Tali Scherf is the incumbent of the Monroy-Marks Research Fellow Chair, at the Weizmann Institute of Science.

This article is dedicated to my dear husband and best friend, the late Moshe Melech Ben-Izhak who courageously withstood ALS disease from March18th 2018 till January19th 2021 and inspired everyone around him. May you rest in peace as I am keeping my promise to you.

Author contributions

E.B.I.M.—developed the concept and the working hypothesis, designed and performed the research, and drew the explanatory scheme; M.V. and I.F.—assisted and advised in planning microbiology experiments; T.S.—performed and analyzed the NMR experiments, including graphical presentation; A.B.—developed and performed sample preparation for LC–MS, conducted and analyzed LC–MS measurements and prepared figures. All authors listed have made a substantial, direct, and intellectual contribution to the writing of the manuscript, and approved it for publication.

Data availability

The datasets used and/or analysed during the current study available from the corresponding author on reasonable request.

Competing interests

The authors declare no competing interests.

The original online version of this Article was revised: The Acknowledgements section in the original version of this Article was incomplete. Full information regarding the correction made can be found in the correction for this Article.

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Change history

9/6/2024

A Correction to this paper has been published: 10.1038/s41598-024-71813-5
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