The involvement of the gut microbiota in postoperative cognitive dysfunction based on integrated metagenomic and metabolomics analysis

ABSTRACT Cognitive dysfunction is a common symptom experienced by elderly individuals after surgery, resulting in memory problems, difficulties with logical thinking, hallucinations, delusions, and an increased risk of dementia. Despite its prevalence, the underlying cause of postoperative cognitive dysfunction remains unclear. Recent research has uncovered a link between neurodegenerative diseases and the gut microbiota, indicating the need for further investigation into the role of the intestinal flora in postoperative cognitive dysfunction. To address this research gap, we conducted behavioral tests, gene and protein analyses, metagenomics, and non-targeted metabolomics to compare the gut microbiota and metabolomics of mice exposed to anesthesia/surgery that exhibited cognitive impairment with those of age-matched control mice. Our goal was to identify possible correlations between these factors. We found that mice experiencing postoperative cognitive dysfunction had a distinct microbial composition, neuroinflammation, and synaptic damage compared to the control group. Specifically, we observed significant increases in the relative abundances of Bacteroidetes unclassified, Bacteroides acidifaciens, Rikenellaceae bacterium, Muribaculaceae bacterium Isolate-104 HZI, Muribaculaceae bacterium Isolate-110 HZI, and Mucispirillum schaedleri in aged mice exposed to anesthesia and surgery, while the relative abundances of Lachnospiraceae bacterium A2, Lachnospiraceae bacterium A4, Lachnospiraceae bacterium, Blautia, Lachnoclostridium bacterium MD355, Eubacterium rectale, Ruminococcus sp. 1xD21-23, and Butyrivibrio were significantly decreased. Additionally, metabolites, such as thiamine, spermidine, and long-chain unsaturated fatty acids, were down-regulated compared to the control group. These findings suggest that the intestinal metabolic abnormalities observed in elderly mice exposed to anesthesia/surgery may be regulated by the intestinal microbiota, specifically the Lachnospiraceae, Lachnoclostridium, Butyrivibrio, and Eubacterium. Therefore, our study highlights the potential of manipulating the gut microbiota to modulate the host metabolism in order to prevent and manage postoperative cognitive dysfunction. IMPORTANCE As the population ages and medical technology advances, anesthesia procedures for elderly patients are becoming more common, leading to an increased prevalence of postoperative cognitive dysfunction. However, the etiology and correlation between the gut microbiota and cognitive dysfunction are poorly understood, and research in this area is limited. In this study, mice with postoperative cognitive dysfunction were found to have reduced levels of fatty acid production and anti-inflammatory flora in the gut, and Bacteroides was associated with increased depression, leading to cognitive dysfunction and depression. Furthermore, more specific microbial species were identified in the disease model, suggesting that modulation of host metabolism through gut microbes may be a potential avenue for preventing postoperative cognitive dysfunction.

4. The grammar and spelling need to be polished.

Reviewer #2 (Comments for the Author):
This study provides a significant reference for understanding cognitive impairments and emotional abnormalities in elderly individuals following surgery.Through a multi-tiered approach, the study comprehensively analyzes the underlying biological mechanisms associated with these changes.In the elderly population, the decline in cognitive function and the emergence of depressive symptoms after surgery have become common concerns.However, the fundamental causes of this phenomenon remain elusive.In this study, the authors have adeptly employed a diverse array of experimental methodologies, ranging from behavioral testing to molecular analysis, as well as metabolomics.These perspectives collectively offer a comprehensive and multifaceted exploration, delving deeper into the cognitive impairments and depressive behaviors exhibited by aged mice post anesthesia/surgery.By correlating observations of cognitive alterations with gut microbiota and metabolites, the authors establish a link between cognitive changes and intestinal microbial metabolites in elderly mice subjected to anesthesia/surgery.This not only opens new avenues for further research but also holds promise in guiding future intervention strategies and therapeutic approaches.

Strengths:
The authors have adopted a multi-faceted approach, furnishing a diverse and rich dataset brimming with details.This dataset includes behavioral testing, genetic analysis, and metabolomics, collectively shedding light on the cognitive impairments and depressive behaviors manifested by aged mice post anesthesia/surgery.Furthermore, the correlation of cognitive changes with microbial metabolites provides a novel view of investigation.

Deficiencies:
Minor revisions: In the abstract, emphasize the novelty and importance of the study and briefly describe the application advantages of mNGS technology and main findings.Additionally, provide quantitative indicators to support the significance of the results, such as pvalues or significance levels after FDR correction.
In the introduction, include relevant literature that highlights the existing knowledge on the relationship between gut microbiota and metabolites, and introduce previous studies to help readers better understand the scientific contribution and research motivation of this study.
Incorporate additional figures or visualizations in the results section to present data more intuitively.Ensure that the titles and captions of figures are clear and concise, facilitating reader comprehension and interpretation of the results.
Carefully proofread and make necessary corrections to grammar, spelling, punctuation, and other language errors in the manuscript.
Reviewer #3 (Comments for the Author): Dr. Zhang and colleagues have presented a comprehensive analysis integrating metagenomics and metabolomics to elucidate the role of the gut microbiota in postoperative cognitive dysfunction.This study explores the complex relationship between the gut microbial community and changes in cognitive function following surgical interventions, revealing the crucial significance of the gut microbiota in postoperative cognitive dysfunction.Major: 1.In the Results section, the description of the relationship between neuroinflammation, synaptic injury, gut microbiota, and changes in metabolites is not sufficiently clear.It would be helpful to provide more clarity and detail in explaining these connections.2.In the Methods section, it would be beneficial to explain the rationale for choosing to collect samples at 72 hours after anesthesia.This information would provide a better understanding of the experimental design and help readers interpret the results.3.Furthermore, including the incidence rate of postoperative cognitive dysfunction in the article would be valuable.This additional information would provide a better context for the findings and help assess the clinical relevance of the study.Minor: 1.In the abstract, emphasize the novelty and importance of the study, and briefly describe the application advantages of mNGS technology and main findings.Additionally, provide quantitative indicators to support the significance of the results, such as pvalues or significance levels after FDR correction.2.In the introduction, include relevant literature that highlights the existing knowledge on the relationship between gut microbiota and metabolites, and introduce previous studies to help readers better understand the scientific contribution and research motivation of this study.3.In the section of metabolomics analysis, provide detailed descriptions of the metabolomics platform, databases utilized, and the analytical process.When identifying significantly different metabolites, include structural identification results and provide supporting literature references to ensure the reliability and biological significance of the metabolites.4.Enhance the description of differences in microbial composition in the results section.Further annotate significantly distinct microbial taxa and provide statistical results of their relative abundances, such as using histograms or box plots to illustrate differences among samples.5.There appears to be a minor error on line 134.Please carefully revise the writing to ensure accuracy and coherence.6.In the discussion section, incorporate relevant literature to further explore the potential mechanisms underlying the relationship between aging, gut microbiota, and metabolites.Propose future research directions and potential applications.Additionally, clearly specify the limitations of the study, such as limited sample size or constraints of the mice model.7.Additionally, I recommend thoroughly checking the figure legends and data units to ensure consistency and accuracy throughout the manuscript.This will enhance the clarity and understanding of the results presented.8.Carefully proofread and make necessary corrections to grammar, spelling, punctuation, and other language errors in the manuscript.
Reviewer #4 (Comments for the Author): Well-written manuscript.Timely and important theme and area of study.Mention the 'n' of independent experiments or animals used in each figure legend wherever applicable and in the materials and methods.If 'n' is 3 or less the authors must add more experiments to increase the 'n' to at least 5 to 6.

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Thank you for submitting your paper to Microbiology Spectrum.
Dear Prof. Dr. Diyan Li, We are grateful for the letter you sent and the reviewers' remarks about our manuscript, " The involvement of the gut microbiota in postoperative cognitive dysfunction based on integrated metagenomic and metabolomics analysis (03104-23)", which was submitted to Microbiology Spectrum.
These comments are all valuable and very helpful for revising and improving our manuscript, as well as the important guiding significance to our research.We have studied the comments carefully and made the necessary revisions, which we hope are completed to the Editors satisfaction.The editors' and reviewers' comments were responded point-by-point, and modified portions are marked in red in the revised manuscript.The main corrections in the manuscript and the responses to the editors' and reviewers' comments are as follows.
We are looking forward to hearing from you.Response: Thank you for your valuable and helpful comments.We apologize for our carelessness.Thank you for the reminder.We have added a note on sample size in the methods section of the main text.It is as follows: "The 18-month-old C57BL/6 mice were randomly divided into two groups: the control group (n = 6) received air treatment, and the experimental group (n = 5) underwent multiple anesthesia/surgery procedures".See page 18, line 457-459.
2. Detailed description of the mNGS technical are suggested to be provide, such as: sample collection, DNA extraction, primer sequences used in PCR amplification, and relevant parameters of the sequencing platform.Response: Thank you very much for your valuable comments.Based on your comments, we have explained and illustrated the mNGS technology as follows, which mainly includes sample collection, DNA extraction, DNA library building, and data analysis.In order to ensure the accuracy and reliability of the sequencing data from the source, we have a rigorous and reliable sample testing and quality control process, from DNA extraction to sequencing, and we strictly control the quality of the samples at each step to ensure the authenticity and reliability of the sequencing data.Specific details are described below: Sample collection: "The samples were divided into two groups: the control group (n = 6) and the multiple AS group (n = 5).According to the clinical incidence of postoperative cognitive dysfunction and literature records, we chose to collect samples 72 hours after surgery (6).The specific operation is as follows: Sterilize for 15 minutes, ventilate for 5 minutes, and alcohol wipe off the ultra-clean table.The abdominal massage method urges the animal to defecate on an ultra-clean bench, with at least five to six fecal samples.Put the samples into 1.5 mL sterile test tubes and quickly freeze them on dry ice.The samples should be stored at -80℃ within two hours after collection".See page 20, line 487-494.DNA extractions: "DNA from different samples was extracted using CTAB according to the manufacturer's instructions.The reagent, designed to uncover DNA from trace amounts of sample, has been shown to be effective for the preparation of DNA from most bacteria.Sample blanks consisted of unused swabs processed through DNA extraction and tested to contain no DNA amplicons.The total DNA was eluted in 50 µl of Elution buffer by a modification of the procedure described by the manufacturer (QIAGEN) and stored at -80°C until measurement in the PCR by LC-BIO Technologies (Hangzhou) Co., Ltd., Hangzhou, Zhejiang Province, China".DNA Library Construction: "A DNA library was constructed using the TruSeq Nano DNA LT Library Preparation Kit .DNA was fragmented by dsDNA Fragmentase (NEB, M0348S) by incubating at 37°C for 30 minutes.Library construction began with fragmented cDNA.Blunt-end DNA fragments were generated using a combination of fill-in reactions and exonuclease activity, and size selection was performed with the provided sample purification beads.An A-base was then added to the blunt ends of each strand, preparing them for ligation to the indexed adapters.Each adapter contained a T-base overhang for ligation of the adapter to the A-tailed fragmented DNA.These adapters contained the full complement of sequencing primer hybridization sites for single, paired-end, and indexed reads.Single or dual index adapters were ligated to the fragments and the ligated products were amplified with PCR using the following conditions: initial denaturation at 95°C for 3 minutes; 8 cycles of denaturation at 98°C for 15 seconds, annealing at 60°C for 15 seconds, and extension at 72°C for 30 seconds; and then a final extension at 72°C for 5 minutes".See page 20-21, line 495-511.
Data analysis: "The NovaSeq6000 platform (Illumina) was used for the high-throughput sequencing of the library after the quality control process had been completed.To further analyze the reads obtained from the sequencing, the original sequence reads were filtered to obtain effective reads.The raw data were first split using Cutadapt (v1.9), followed by the removal of low-quality data with Fqtrim (v0.94).Host sequences were then eliminated using Bowtie2 (v2.2.0) to enhance the accuracy and precision of the species and functional annotation results.After data pre-processing, de novo assembly was performed on a single sample using Megahit (v1.2.9), and the assembled contigs were utilized for coding sequence (CDS) prediction with MetaGeneMark (v3.26).Subsequently, clustering and de-duplication were carried out using CD-HIT (v4.6.1)based on the predicted results, resulting in a non-redundant UniGene set.Species annotation information was obtained by comparing the UniGene dataset with the NR_mate library.Similarly, functional annotations of individual genes were obtained, encompassing a range of databases and resources.The Kyoto Encyclopedia of Genes and Genomes (KEGG-release 87.7) was used to annotate pathways, and the Carbohydrate-Active Enzymes (CAZy-2022.0806)database was used to analyze enzymes related to carbohydrates.The abundance spectra of single-feature genes, their classification, and their functional annotation are based on the classification and functional annotation of these single-feature genes.Fisher's exact test (non-replicated group and replicated group) was used to analyze the differences at each classification, functional, or gene level.QIIME2 was used to calculate alpha and beta diversity, and R packages were used to visualize the data.Species that differed significantly from one another were further compared using linear discriminant analysis (LDA) effect size (LEfSe) analysis".See page 21-22, line 512-532.
3. The relationship between aging, gut microbiota, and metabolites should be discussed.The limitations of the sample size or the mice model in the study are also suggested to be talked over.
Response: We sincerely appreciate your valuable comments.We think it is a good suggestion.We have carefully reviewed the literature and added more information about the link between aging, gut flora and metabolites and provided references to support it in the discussion section of the revised manuscript.The details are as follows: "Ageing leads to a deterioration of cellular, tissue, and organ functions, accompanied by susceptibility to chronic inflammation in the central nervous system and gastrointestinal system, which are particularly sensitive to metabolic dysregulation.With ageing, there is an increase in the number of goblet cells, while the expression of alpha-defensins, lysozymes, and F4/80 mRNA, along with NOx levels and protein concentrations of tight junction proteins, decreases (31,32).These changes are associated with elevated intestinal permeability and increased levels of bacterial endotoxins (33).Several studies have linked these age-related modifications to alterations in the gut microbiome observed in elderly humans and animals (34)(35)(36).In a recent study, it was found that transplanting fecal matter from aged individuals into young mice leads to disruption of the intestinal epithelial barrier and heightened levels of inflammation, particularly in the retina and nervous system.
Neuroinflammation in older mice with young donor microbiota was reversed by enriching B vitamins and lipid synthesis pathways, implying an important role in gut microbial metabolism in aging (37).Additionally, postoperative cognitive dysfunction may possess multifactorial origins, such as an intraoperative inflammatory response, anesthesia, and aging, further increasing susceptibility.In this study, we aim to investigate the contribution of gut microbes to postoperative cognitive dysfunction in elderly patients".See page 12, line 275-290.
• Manuscript: A .DOC version of the revised manuscript • Figures: Editable, high-resolution, individual figure files are required at revision, TIFF or EPS files are preferred number of the samples and the detection methods employed in the study should be clarified.