Summary of Study ST002535

This data is available at the NIH Common Fund's National Metabolomics Data Repository (NMDR) website, the Metabolomics Workbench, https://www.metabolomicsworkbench.org, where it has been assigned Project ID PR001631. The data can be accessed directly via it's Project DOI: 10.21228/M8W43G This work is supported by NIH grant, U2C- DK119886.

See: https://www.metabolomicsworkbench.org/about/howtocite.php

This study contains a large results data set and is not available in the mwTab file. It is only available for download via FTP as data file(s) here.

Study ID	ST002535
Study Title	Relationships between the gut microbiome and cognitive impairment in residents of long-term aged care.
Study Summary	Ageing-associated cognitive decline affects more than half of those in long-term residential aged care. Emerging evidence suggests that gut microbiome-host interactions influence the effects of modifiable risk factors. We investigated the relationship between gut microbiome characteristics and severity of cognitive impairment (CI) in 159 residents of long-term aged care. Severe CI was associated with a significantly increased abundance of proinflammatory bacterial species, including Methanobrevibacter smithii and Alistipes finegoldii, and decreased relative abundance of beneficial bacterial clades. Severe CI was associated with increased microbial capacity for methanogenesis, and reduced capacity for synthesis of short-chain fatty acids, neurotransmitters glutamate and gamma-aminobutyric acid, and amino acids required for neuro-protective lysosomal activity. These relationships were independent of age, sex, antibiotic exposure, and diet. Our findings implicate multiple gut microbiome-brain pathways in ageing-associated cognitive decline, including inflammation, neurotransmission, and autophagy, and highlight the potential to predict and prevent cognitive decline through microbiome-targeted strategies.
Institute	South Australian Health and Medical Research Institute
Last Name	Shoubridge
First Name	Andrew
Address	North Terrace, Adelaide, South Australia, 5000, Australia
Email	andrew.shoubridge@sahmri.com
Phone	+61405041977
Submit Date	2023-03-26
Raw Data Available	Yes
Raw Data File Type(s)	mzML
Analysis Type Detail	LC-MS
Release Date	2023-04-04
Release Version	1

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Sample Preparation:

Sampleprep ID:	SP002641
Sampleprep Summary:	SCFA analysis was performed using an Agilent 6490 series triple quadrupole mass spectrometer (Agilent Technologies) with chromatographic separation on an Agilent 1200 series high-performance liquid chromatography system (HPLC) (Agilent Technologies). SCFAs were extracted by adding 360μL of 50% acetonitrile with 10μM 4-methylvaleric acid internal standard to 40μL of biological sample supernatant. Samples were then vortexed for 30 seconds, incubated at 10oC for 30 minutes at 950RPM, centrifuged at 14,000RPM for five minutes at 4oC, followed by supernatant collection. Derivatisation for SCFA analysis was performed by first adding 20μL of 20μM 13C6-nitrophenylhydrazine as internal standard to 40μL of the extracted supernatant, followed by 20μL each of 200mM nitrophenylhydrazine and 120mM 1-ethyl-3-(3-dimethylaminopropyl)carbodiimide (EDC), incubated at 40oC for 30 minutes at 950RPM, quenched with 20μL of 200mM quinic acid, and incubated at 40oC for a further 30 minutes at 950RPM. Lastly, the samples were reconstituted with 1.9mL of 15% acetonitrile and 1μL was injected onto the column. Pooled biological quality controls (PBQCs) were created by pooling extracts (20μL) from individual biological samples and injected onto the column in five sample intervals. A reagent and procedural blank of the original sample preservation buffer was included for analysis to perform background correction. Polar metabolite analysis was performed using an Agilent 6545 series quadrupole time-of-flight mass spectrometer (Agilent Technologies) with chromatographic separation on an Agilent 1200 series HPLC system (Agilent Technologies). Metabolite extraction was performed by first adding a solvent mixture of acetonitrile, methanol and water to 20μL of biological sample, followed by vortexing, sonication and agitation. Samples were then centrifuged and supernatant collected and mixed with an internal standard mixture containing 13C5, 15N-valine, 13C6-leucine, and 13C6-sorbitol, and 14μL of sample was injected onto the column. Samples were injected in a randomised order and PBQCs were injected onto the column in five sample intervals. Data matrices were imported to the web-based platform MetaboAnalyst (v5.0) for quality control checks by multivariate statistics. SCFA data were normalised to internal standards, and polar metabolite data were log-transformed and median-normalised.

Sampleprep ID:

SP002641

Sampleprep Summary:

SCFA analysis was performed using an Agilent 6490 series triple quadrupole mass spectrometer (Agilent Technologies) with chromatographic separation on an Agilent 1200 series high-performance liquid chromatography system (HPLC) (Agilent Technologies). SCFAs were extracted by adding 360μL of 50% acetonitrile with 10μM 4-methylvaleric acid internal standard to 40μL of biological sample supernatant. Samples were then vortexed for 30 seconds, incubated at 10oC for 30 minutes at 950RPM, centrifuged at 14,000RPM for five minutes at 4oC, followed by supernatant collection. Derivatisation for SCFA analysis was performed by first adding 20μL of 20μM 13C6-nitrophenylhydrazine as internal standard to 40μL of the extracted supernatant, followed by 20μL each of 200mM nitrophenylhydrazine and 120mM 1-ethyl-3-(3-dimethylaminopropyl)carbodiimide (EDC), incubated at 40oC for 30 minutes at 950RPM, quenched with 20μL of 200mM quinic acid, and incubated at 40oC for a further 30 minutes at 950RPM. Lastly, the samples were reconstituted with 1.9mL of 15% acetonitrile and 1μL was injected onto the column. Pooled biological quality controls (PBQCs) were created by pooling extracts (20μL) from individual biological samples and injected onto the column in five sample intervals. A reagent and procedural blank of the original sample preservation buffer was included for analysis to perform background correction. Polar metabolite analysis was performed using an Agilent 6545 series quadrupole time-of-flight mass spectrometer (Agilent Technologies) with chromatographic separation on an Agilent 1200 series HPLC system (Agilent Technologies). Metabolite extraction was performed by first adding a solvent mixture of acetonitrile, methanol and water to 20μL of biological sample, followed by vortexing, sonication and agitation. Samples were then centrifuged and supernatant collected and mixed with an internal standard mixture containing 13C5, 15N-valine, 13C6-leucine, and 13C6-sorbitol, and 14μL of sample was injected onto the column. Samples were injected in a randomised order and PBQCs were injected onto the column in five sample intervals. Data matrices were imported to the web-based platform MetaboAnalyst (v5.0) for quality control checks by multivariate statistics. SCFA data were normalised to internal standards, and polar metabolite data were log-transformed and median-normalised.