Gut microbiome-meta-transcriptome analysis reveals that pyruvate and amino acid metabolism bacterial genes are involved in hyperuricemia and gout in humans

Gut microbiome-meta-transcriptome analysis reveals that pyruvate and amino acid metabolism bacterial genes are involved in hyperuricemia and gout in humans | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Article Gut microbiome-meta-transcriptome analysis reveals that pyruvate and amino acid metabolism bacterial genes are involved in hyperuricemia and gout in humans Gabriela Angélica Martínez-Nava, Efren Altamirano-Molina, Janitzia Vázquez-Mellado, and 24 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-5411102/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 22 Mar, 2025 Read the published version in Scientific Reports → Version 1 posted 10 You are reading this latest preprint version Abstract Background Several pathologies with metabolic origin, such as hyperuricemia and gout, have been associated with the gut microbiota taxonomic profile. However, there is no evidence of which bacterial genes are being expressed in the gut microbiome, and of their potential effects on hyperuricemia and gout. Materials and Methods We sequenced the RNA of 26 fecal samples from 10 healthy normouricemic controls, 10 with asymptomatic hyperuricemia (AH), and six gout patients. The coding sequences were mapped to KEGG orthologues (KO). We compared the expression levels using generalized linear models and validated the expression of three KO in a larger sample by qRT-PCR. Results A distinct genetic expression pattern was identified among groups. AH individuals and gout patients showed an over-expression of KOs mainly related to pyruvate metabolism (Log2foldchange > 23, p -adj ≤ 3.56x10 − 9 ), the pentose pathway (Log2foldchange > 24, p -adj 22, p -adj < 1.25x10 − 7 ). AH subjects had lower expression of KO related to glycine metabolism (Log2foldchange=-18, p -adj < 1.72x10 − 6 ) than controls. Gout patients had lower expression (Log2foldchange=-22.42, p -adj < 3.31x10 − 16 ) of a KO involved in phenylalanine biosynthesis, in comparison to controls and AH subjects. The over-expression seen for the KO related to pyruvate metabolism and the pentose pathway in gout patients´ microbiome was validated. Conclusions There is a differential gene expression pattern in the gut microbiome of normouricemic individuals, AH subjects and gout patients. These differences are mainly located in metabolic pathways involved in acetate precursors and bioavailability of amino acids. meta-transcriptome gut microbiome hyperuricemia gout Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 1. BACKGROUND Hyperuricemia is defined as the constant state of urate levels in serum over 7 mg/dL, and is associated not only with gout, but with other pathologies such as type 2 diabetes (T2D), metabolic syndrome (MSx), and hypertension [ 1 – 3 ]. Normally humans produce on average 620 mg ± 75 mg of urate per day, of which 70% is excreted through the kidneys and 30% is excreted at the intestinal level [ 4 ]. The association of the gut microbiota with urate serum levels was made clear in 2014 [ 5 ] and several observational studies have demonstrated the association between the gut microbiota and gout diagnosis, as well as with the presence of asymptomatic hyperuricemia (AH) [ 6 – 10 ]. However, direct functional evidence of gut microbiome regulation of urate serum levels is scarce, and based primarily in animal models. In murine models of hyperuricemia, it has been observed that a microbiota enriched with purine-degrading bacteria reduces the urate levels in serum. Moreover, upon transplanting such microbiota to germ-free mice, they are able to modulate urate levels in the intestine and at a systemic level [ 11 , 12 ]. A study on a murine model demonstrated that the use of uricolytic bacteria-based probiotics helps prevent oxonic acid-induced hyperuricemia [ 13 ]. Murine models of hyperuricemia have also reported groups of bacterial genes present in the gut microbiota that code for enzymes involved in purine [ 12 ] and urate catabolism [ 14 ]. Functional predictions drawing from 16S and shotgun metagenomic analyses suggest that gut dysbiosis in gout or AH patients affects urate homeostasis and the inflammatory process [ 8 , 10 , 15 , 16 ]. A previous study performed in Chinese gout patients evaluated the concentration of several fecal metabolites and found differences in the concentrations of metabolites related to purine metabolism, such as glycine and aspartate, and those related to inflammatory processes, such as taurine, acetate, succinate, valine, and methionine [ 7 ]. Despite these evidences, it is still unknown which genes are expressed in the human gut microbiome and which could be involved in AH and gout. Therefore, our objective was to evaluate the gut microbiota meta-transcriptome of gout patients and subjects with and without AH. 2. MATERIALS AND METHODS 2.1 Study Design and Population We performed a cross-sectional study using samples obtained from a biologic bank that was generated in a previous study (INR-28/15) [ 8 ]. A total of 26 fecal samples were included: 10 from normouricemic controls, 10 from AH subjects, and 6 from gout patients. Samples selected for the meta-transcriptome analysis were paired by age, gender, and body mass index (BMI). Controls were blood donors from the blood bank of the Luis Guillermo Ibarra Ibarra National Rehabilitation Institute (INRLGII). In order to be included they had to have urate levels under 7 mg/dL, BMI < 25kg/m 2 , and not to have any disease diagnosis. AH subjects were also blood donors, but with urate levels over 7 mg/dL, BMI < 25kg/m 2 , and no previous clinic history of any gout attack. The gout patients were recruited in the Rheumatology Department of the “Dr. Eduardo Liceaga” General Hospital of Mexico and of the INRLGII. The inclusion criteria for gout patients included no clinical history of diagnosis of T2D, MSx, hypertension, any other rheumatic disease, chronic kidney disease, or kidney transplant. All participants that consumed antibiotics, antivirals, or antiparasitic drugs three months before sampling were excluded. Each participant signed an informed consent letter before sampling. This study was conducted under the criteria set forth in the Declaration of Helsinki and was approved by the Ethics and Research Committees of the participating institutions (INR:30/20-SP1 - HGM:DI/18/404-A/03/004). 2.2 Sample Processing and Sequencing An aliquot of 250 mg of feces of each individual was preserved in 2 mL of RNAlatter (Ambion, Thermo Fisher Scientific Massachusetts, USA) and stored at -80° C until it was processed. Total RNA was obtained with a commercial kit (RNeasy PowerMicrobiome Kit, QIAGEN, Germany), quantified by fluorometry, and its quality evaluated with chip electrophoresis in an Agilent 2100 bioanalyzer (Agilent Technologies, EUA). The Illumina Stranded Total RNA Prep kit, in combination with the Ribo-Zero Plus kit (Illumina, EUA) were used to prepare the libraries. The meta-transcriptome was sequenced in two batches in the NextSeq equipment (Illumina, EUA) in the Sequencing Department of the National Genomic Medicine Institute (INMEGEN). 2.3 Meta-transcriptome Bioinformatics Analysis The sequences were filtered considering their quality and merged with the Fastp [ 17 ] and Flash [ 18 ] tools, respectively. We performed an integral search for ribosomal RNA and non-coding RA (ncRNA) with the SortmeRNA [ 19 ] and INFERNAL [ 20 ] algorithms using the Silva database (SILVAv.138 SSU ref NR99, SILVAv.138 LSU Ref NR99) and a subgroup of RFAM [ 21 ] (CL00111, CL00112, CL00001, CL00002, and CL00003). Once all ncRNA were removed, we performed a search for the coding fragments using the FragGeneScan program [ 22 ], accepting an error rate of 1%. We did the functional annotation of the predicted coding fragments in HUMAnN3 [ 23 ], based on the results of a previous taxonomic analysis with an approach of genetic markers with merged and filtered sequences using MetaPhlan2. The HUMAnN3 software allowed us to identify the abundance of gene families, pathways, and coverage by bacterial genus. With the gene family abundance and pathways data, each identified element was assigned orthologues from the Kyoto Encyclopedia of Genes and Genomes (KEGG) (KO). 2.4 Meta-transcriptome Statistical Analysis With the abundance of KO obtained from HUMANn3 in readings per kilobase, we started by evaluating their potential to discriminate samples into their proper group, using a t-SNE non-linear dimensionality reduction algorithm. Then, we evaluated the differential expression among the study groups using a Gamma-Poisson distribution with DeSeq2 R package [ 24 ]. Genes with less than 10 readings in the whole sample were excluded from this analysis. For additional analyses, we transformed the data from the raw count using the regularized logarithm method. Finally, with the KO that showed a significant difference (adjusted p -value ( p -adj) < 0.05) among the study groups and a high magnitude effect (log 2 fold change (FC)≥|10|) we carried out a multivariate association test using generalized linear models implemented in the MTX model [ 25 ] in order to verify their association adjusting by significant covariates and to evaluate their association with urate levels. We adjusted the p -values for all analyses by multiple comparisons using the Benjamini-Hochberg method. 2.5 Validation of KO differential expression levels We performed a new sampling process, similarly as the one previously describe for the sequenced samples. The only thing made differently was the location at where the AH subject were enrolled. For this phase of the study, we recruited AH individuals from an outgoing cohort carried out in the Instituto Nacional de Cardiología Ignacio Chávez and for their inclusion they must had urate levels above 7mg/dL in two consecutive measurements separated by one year, no clinic history of any gout attack, and must not had any disease diagnosis Supplementary Table 1. As done for the meta-transcriptome analysis, all participants did not take any antibiotics, antiparasitic or antiviral drugs for at least three months previous of the sampling and did not have diagnosis of any gastrointestinal diseases. All participants signed the informed consent letter approved by the INRLGII ethics committee. We obtained total RNA from 272 fecal samples (100 from normouricemic subjects, 94 from subjects with AH, and 78 from patients with gout) using the same commercial kit as the one used for the sequenced samples. From 1ug of total RNA we synthesized the cDNA using the High Capacity cDNA reverse transcription kit (Applied Biosystems, USA). Upon orthologues selection based in their effect magnitude observed in the bivariated meta-transcriptome analysis, we performed global alignments using ClustalΩ [ 26 ] with all the orthologues sequences from bacteria genus identified in the initial meta-transcriptome analysis with HUMAnN3. With the global alignment we define a consensus sequence using and Emboss Cons software [ 26 ]. We designed specific oligos using PrimerQuest tool software (IDT, USA) from each orthologue consensus sequence to evaluate its expression by real-time PCR in a Rotor Gene-Q instrument (QIAGEN, Germany). The rpoB gene was used as a housekeeping gene [ 27 ] and all assays were carried out in duplicate. Designed primers´ information, as well as their cycling conditions are described in Supplementary Table 2. We compared the relative expression units (REU), calculated by 2 −Δct method, of each orthologue between groups using Dunn´s test and performed multivariated linear regression models to evaluate the association of the orthologues’ REU adjusting for confounding variables. 3. RESULTS Age and BMI were not statistically different among the study groups of the sequenced samples; urate levels were significantly higher in AH subjects than in gout patients and control individuals ( p < 0.01). The levels of triglycerides were statistically different as well ( p < 0.001) among the study groups, but gout patients had the highest levels, followed by controls and AH subjects (Table 1 ). Table 1 Features of the study population for sequencing analysis and validation by group. Meta-transcriptome sequencing analysis Variable Controls (n = 10) AH (n = 10) Gout (n = 6) p Age (years) Average (± SD) 42.7 (± 7.69) 41.22 (± 8.45) 45.67 (± 11.55) 0.65* BMI (kg/m 2 ) Median (IQR) 25.48 (3.30) 27.04 (1.97) 24.88 (3.18) 0.29** Urate (mg/dL) Average (± SD) 5.63 (± 0.71) 8.43 (± 1.87) 8.29 (± 1.43) < 0.01* Glucose (mg/dL) Median (IQR) 89.47 (15.37) 94.44 (14.10) 93.70 (16.08) 0.72** Triglycerides (mg/dL) Median (IQR) 155.91 (22.08) 125.68 (42.94) 187.20 (96.32) 0.01** Cholesterol (mg/dL) Average (± SD) 148.39 (± 46.48) 150.82 (± 31.87) 149.41 (± 39.59) 0.99* After filtering by quality and merging the paired readings, we calculated a median of 71.86 million readings per sample with an interquartile range (IQR) of 10.88. The number of readings was uniform among study groups ( p = 0.95). A high percentage of the filtered sequences were identified as coding sequences ( p 50 = 90.67%, IQR = 6.10), and was homogenous among study groups ( p = 0.30). Of these predicted coding sequences, an average of 93.84% (± 2.53%) was successfully mapped to a KO with the HUMANn3 algorithm. We observed that the KO expression profile was able to discriminate the samples from the gout patients and controls, but the AH subject sample group was not clearly separated (Fig. 1 ). Nevertheless, we observed significant differences among the expression of several KO in the study groups, including the AH subjects. 3.1 KEGG Orthologues Expressed Differentially among Groups We observed statistically significant differences in the expression of 155 KO among the study groups (Fig. 2 A-C). Of those, 67 KO had a higher effect magnitude (Supplementary Table 3, Fig. 3 ), of which 46 KO were mapped to at least one metabolic pathway (carbon metabolism, glycolysis/gluconeogenesis, amino acid biosynthesis, pyruvate metabolism, starch and sucrose metabolism, among others) and 19 were mapped to transporters of the ABC family (Fig. 2 D). When comparing the expression observed in the microbiome of AH subjects and healthy controls, 62 KO had a log 2 FC ≥ |10| and a p -adj < 0.001. More than 80% of the KO (88.89%) detected as over-expressed in AH subjects were also over-expressed in gout patients in comparison to the controls. Among them, 12 KO were mapped to metabolic pathways, such as metabolism of pyruvate, different amino acids (aa.), purines, butanoate, and the pentose pathway; whereas 8 were mapped to transporters, including transporters of the ABC family, and a xanthine permease (Fig. 2 D). All the KO under-expressed in the AH subjects when compared to the controls were also under-expressed when compared to the gout patients (Fig. 2 D). Among these, an orthologue from the glycine cleavage system (GCS) drew our attention (K00282) due to its involvement in glycine metabolism and its potential function in purine formation. Among the gout patients, we identified 60 KO with a higher effect magnitude and a p -adj < 0.001 (Fig. 2 B), compared to the controls and AH subjects. We observed that three KO with an elevated, highly significant ( p -adj < 1 x 10 − 8 ) log 2 FC were identified in gout patients compared with the controls, and significantly different as well when compared to the AH subjects. Two of them (K00161, and K02221) were over-expressed in gout patients, whereas the third one, which is involved in biosynthesis pathways of phenylalanine, tyrosine, and tryptophan (K14170), was under-expressed in comparison to the AH subjects (Fig. 2 D). 3.2 Association with UA Levels We performed a multivariate generalized linear model analysis introducing urate levels and found 42 significantly associated KO ( p- value < 0.05 and q- value < 0.05). Most of them were associated with gout, and only the orthologue K16509, a spX regulating protein, was also associated with AH. Of the KO associated with gout, 24 were assigned to enzymes (6 oxidoreductases, 5 transferases, 5 hydrolases, 4 lyases, 2 ligases, 1 isomerase, and 1 translocase), 8 to transporters, including the 5 orthologues mapped to the ABC family transporters and the xanthine permease we previously mentioned (Fig. 4 ). As observed in previous analyses, two KO involved in pyruvate metabolism (pyruvate dehydrogenase E1 component, sub-units α and β) were significantly associated with gout ( q = 0.043 and q = 0.001, respectively), as well as the GMP reductase orthologue ( q = 0.02), xanthine permease ( q = 0.003), and xylulose 5-phosphate phosphoketolase ( q = 0.001). Moreover, we found a significant association ( q = 0.01) with acetolactate decarboxylase, an enzyme involved in the 2-oxocarboxylic acid metabolism, in which pyruvate is an intermediate metabolite (Fig. 4 ). 3.3 Validation For the time being, we have chosen to validate four of the orthologues that exhibit the largest effect magnitude observed in afore mention meta-transcriptome analysis and that participate in interconnected metabolic pathways (Supplementary table 3). We validated pyruvate dehydrogenase E1 component α (K00161) and β (K00162) sub-units; xylulose 5-phosphate phosphoketolase (K01621); and chorismate mutase (K14170) orthologues relative expression. The main traits of the population for the validation analysis are describe in Supplementary Table 1. Features that were significantly different among groups were considered as confounding factors at the multivariated analysis. We observed a clear over-expression of three of these orthologues (K00161, K00162 and K01621) in the gut microbiome of patients with gout compared to normouricemic subjects and to subjects with AH (p = 0.0001) (Fig. 5 A-C). However, we were unable to detect a statistically significant difference between AH and normouricemic subjects as seen in the bivariated meta-transcriptome analysis for the K00161 and K01621 orthologues. For the K14170 orthologue log(RUE) we observed an over-expression in the gut microbiome of patients with AH compared to normouricemic subjects (p = 0.0001), nevertheless the comparison between each of these two groups and gout patients log(REU) was not significant as previously described (Fig. 5 D). Finally, by performing regression models adjusted by age, BMI, urate, glucose, cholesterol and triglycerides, we validated the association of the over-expression of K00161, K00162 and K01621 with the diagnosis of gout, and the over expression of K14170 with AH status. We found that patients with gout have in average 4.49 (95%CI = 3.07–5.92; p < 0.0001), 4.50 (95%CI = 2.84–6.16; p < 0.0001) and 4.36 (95%CI = 2.36–6.36; p < 0.0001) more of the K00161, K00162 and K01621 orthologues log(REU), respectively, than the normouricemic subjects regardless of age, BMI, urate, glucose, cholesterol and triglycerides levels. As for the K14170 orthologue expression, we observed that the microbiome of AH subjects expressed 1.21 (95%CI = 2.84–6.16; p = 0.04) log(REU) more of K14170 orthologue than normouricemics´ gut microbiome. 4. DISCUSSION Hyperuricemia is a prevalent metabolic disorder, and is considered a necessary factor, although insufficient, for gout [ 1 ]. Only 18% of AH subjects is estimated to develop an acute gout attack at one point in their life [ 28 ]. Hyperuricemia has also been associated with several pathologies that pose a public health problem, like T2D, hypertension and MSx [ 1 – 3 ]. Even though there is a treatment for hyperuricemia, the decision of whether to medicate AH subjects or start treating them after the first acute gout attack is controversial [ 29 ]. It is clear that the gut microbiota plays a role in urate homeostasis in humans, which is why researchers all over the world have centered their attention in it as a potential therapeutic strategy that could help solve the controversy about treatment for AH subjects [ 30 , 31 ]. In this study, we have showed that the gene expression profile of the gut microbiome is clearly different between gout patients and normouricemic individuals. However, this difference is not as evident between AH subjects and gout patients. This may reflect the percentage of AH subjects that will never develop gout. Nevertheless, there were significant differences in the expression of several orthologues mainly involved in pathways related to the metabolism of purines, pyruvate, and different aa; particularly glycine, phenylalanine and tryptophane metabolism. Interestingly, the orthologues that showed the largest effect in the bivariated meta-transcriptome analysis were those belonging to the pyruvate metabolism. Fortunately, we were able to validate the over-expression of these orthologues in gout patients´ microbiome (subunits α and β of pyruvate dehydrogenase). This enzyme is responsible for decarboxylating pyruvate to produce acetyl-CoA, which is a precursor of short chain fatty acids (SCFA), including acetate [ 32 ]. Likewise, we validated the over-expression of an orthologue coding for a phosphoketolase in gout patients´ gut microbiome, which is involved in the production of acetyl phosphate from fructose 6-phosphate and D-xylulose 5-phosphate, which in turn may also produce acetate as a resulting metabolite from ATP generation [ 33 , 34 ]. This suggests that the microbiome of gout patients has a high capacity to produce acetate trough the metabolism of pyruvate and the phosphoketolase pathway. These results are in accordance with our previous report where we observed a higher concentration of genes coding for pyruvate oxidase in the gut microbiome of gout patients when compared to AH subjects [ 10 ]. This is an enzyme that allows certain bacteria to metabolize pyruvate in a non-conventional pathway and thus produce acetate [ 10 ]. Furthermore, an increase in the quantity of acetate in feces and acetate-producing bacteria has been previously associated in the gut microbiota of gout patients [ 10 , 35 ]. Additionally, it has even been demonstrated in a murine model of gout that this SCFA is necessary to develop the inflammatory response against monosodium urate crystals [ 36 ]. Moreover, acetate supplementation in mice favors the polarization of the cell immune response to Th17 in the presence of an infectious agent [ 37 ]. Our sample size for the meta-transcriptome analysis did not allow us to have enough power to observe significant associations in the generalized linear models adjusting by triglyceride levels. Nevertheless, we were able to evaluate the association of the bacterial gene expression with the urate levels adjusting by the main confounding factors from the study design, since our study population was under a very strict control in terms of comorbidities, and coupled by age, BMI, and gender. Additionally, we performed a validation analysis of the four orthologues with the largest effect in the meta-transcriptome analysis, and the association seen with gout remained significant for three of them, even in the multivariate analysis adjusting not only by triglycerides, but also by other potential confounding factors. Previous studies have shown that there is a difference in the concentration of certain aa. in the serum of gout patients and hyperuricemia subjects; and they have even been proposed as potential biomarkers of the disease [ 38 ]. Therefore, it would be highly relevant to further explore the expression of the KO related to glycine (glycine cleavage system P protein (glycine dehydrogenase subunit 1, K00282) phenylalanine in the gut microbiome of AH individuals and gout patients. This might provide further evidence in order to suggest the modulation of these metabolic pathways in the gut microbiome as a potential adjuvant therapy for hyperuricemia and gout. 5. CONCLUSIONS Based on our results, we can conclude that there is a differential gene expression pattern in the gut microbiome of normouricemic individuals, AH subjects, and gout patients. These differences are focused on orthologues from metabolic pathways involved in the production of acetate precursors and which could be potential targets for microbiome modulation therapy for gout. Declarations 6.1 Ethics approval The present study was conducted under the principles set forth in the Declaration of Helsinki and was approved by the Ethics and Research Committee of the Instituto Nacional de Rehabilitación Luis Guillermo Ibarra Ibarra (INR30/20-SP1). 6.2 Accordance to participate Informed consent was obtained from all individual participants included in the study. 6.3 Declaration of competing interest The authors declare that no conflict of interest exists. 6.4 Availability of data and materials Raw data supporting this study are not openly available due to ethical restrictions. However, it is available upon reasonable request to the corresponding author. For the peer review process data have been deposited at the following link. https://drive.google.com/drive/folders/1y8ilmmugcLQLGPofWroxPyl_3MoCoSeP?usp=drive_link 6.5 Financial support This study received funding from the Consejo Nacional de Humanidades, Ciencias y Tecnologías (CONAHCyT) of Mexico through the 2019 science frontiers tender [FORDECYT-PRONACES/87754/2020]. The CONAHCyT did not participate in the conceptualization, design, data collection, analysis, decision to publish, or preparation of the manuscript. 6.6 Author contributions: credit GAMN : Conceptualization, Methodology, Formal analysis, Investigation Funding acquisition, Writing - Original Draft, Visualization, Project administration; EAM : Validation, Investigation, Writing - Review & Editing, Visualization; JVM : Methodology, Investigation, Writing - Review & Editing; CCS : Software, Writing - Review & Editing; JD : Software, Formal analysis, Writing - Review & Editing; CLP : Data Curation, Writing - Review & Editing; BHL : Investigation, Data Curation, Writing - Review & Editing; LEMG : Investigation, Data Curation, Writing - Review & Editing; CMA : Investigation, Writing - Review & Editing; DLGG : Investigation, Data Curation, Writing - Review & Editing; SVG : Validation, Investigation, Writing - Review & Editing; CSA : Investigation, Writing - Review & Editing; MCCR : Validation, Investigation, Writing - Review & Editing; MMG : Validation, Investigation Writing - Review & Editing; GGE : Validation, Data Curation, Writing - Review & Editing; LMAG : Methodology, Investigation, & Validation Writing - Review & Editing; YZC : Investigation, Writing - Review & Editing; KMF : Investigation, Writing - Review & Editing; JFT : Investigation, Writing - Review & Editing; ABG : Investigation, Writing - Review & Editing; YCOO : Investigation, Writing - Review & Editing; ALM : Investigation, Writing - Review & Editing; EOMS : Investigation, Writing - Review & Editing, AFB : Investigation, Writing - Review & Editing; BPG : Methodology, Investigation, Writing - Review & Editing ; CP : Methodology, Investigation, Writing - Review & Editing; ALR : Conceptualization, Investigation, Writing - Review & Editing: Supervision. 6.7 Acknowledgments We want to thank the Sequencing Department of INMEGEN (USEC) for their support to sequence the meta-transcriptome, as well as the staff from the Computational Medicine Platform, Fundación Progreso y Salud (FPS) at the Virgen del Rocío Hospital for their valuable support to perform the bioinformatics analysis. 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Supplementary Files Supplementarymaterial.docx APPENDIX A Cite Share Download PDF Status: Published Journal Publication published 22 Mar, 2025 Read the published version in Scientific Reports → Version 1 posted Editorial decision: Revision requested 03 Feb, 2025 Reviews received at journal 27 Jan, 2025 Reviewers agreed at journal 12 Jan, 2025 Reviews received at journal 12 Jan, 2025 Reviewers agreed at journal 12 Jan, 2025 Reviewers invited by journal 11 Jan, 2025 Editor assigned by journal 25 Dec, 2024 Editor invited by journal 14 Nov, 2024 Submission checks completed at journal 12 Nov, 2024 First submitted to journal 07 Nov, 2024 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. 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15:38:14","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-5411102/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-5411102/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1038/s41598-025-93899-1","type":"published","date":"2025-03-22T15:57:22+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":71634544,"identity":"6ee918cb-6cd2-4751-9208-d543169ed0dc","added_by":"auto","created_at":"2024-12-17 09:56:07","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":505189,"visible":true,"origin":"","legend":"\u003cp\u003et-SNE non-linear dimensionality reduction analysis to identify the study groups from the gut microbiome expression profile at the KEGG orthologue (KO) level.\u003c/p\u003e","description":"","filename":"Figure1.png","url":"https://assets-eu.researchsquare.com/files/rs-5411102/v1/ab85e514be98273fe57c0211.png"},{"id":71634541,"identity":"fbfe9029-3267-4743-8302-3b63f8431ba7","added_by":"auto","created_at":"2024-12-17 09:56:07","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":3095335,"visible":true,"origin":"","legend":"\u003cp\u003eIdentified orthologues from the Kyoto Encyclopedia of Genes and Genomes (KO) with a differential expression when comparing A) AH subjects and normouricemic controls, B) gout patients and normouricemic controls, C) AH subjects and gout patients. The blue dots represent the KO with an adjusted \u003cem\u003ep\u003c/em\u003evalue \u0026lt;0.05 and the red dots represent the KO with a Log2 (fold change) ≥ |10| and an adjusted \u003cem\u003ep\u003c/em\u003e \u0026gt;0.001. D) Venn diagram showing the KO that were identified in the different comparisons carried out with a Log2 (fold change) ≥ |10| and an adjusted \u003cem\u003ep\u003c/em\u003e \u0026gt; 0.001. For the 55 orthologues over-expressed both in AH subjects and gout patients, only the most representative KO mapped to metabolic pathways are shown. KO ID: identification number for the KEGG orthologue.\u003c/p\u003e","description":"","filename":"Figure2.png","url":"https://assets-eu.researchsquare.com/files/rs-5411102/v1/41011627d48e005e39ce7dc9.png"},{"id":71634540,"identity":"5e32a040-e339-4d4d-818e-86d04da6a2ed","added_by":"auto","created_at":"2024-12-17 09:56:07","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":322253,"visible":true,"origin":"","legend":"\u003cp\u003eNumber of readings standardized with the regularized logarithm function of the KEGG orthologues that had a differential expression between the study groups (Log2 (fold change) ≥ |10| and an adjusted \u003cem\u003ep\u003c/em\u003e \u0026gt; 0.001).\u003c/p\u003e","description":"","filename":"Figure3.png","url":"https://assets-eu.researchsquare.com/files/rs-5411102/v1/3115fda6a15c7aa5f268bccc.png"},{"id":71634547,"identity":"80152bd6-4095-4434-b918-4d520157161d","added_by":"auto","created_at":"2024-12-17 09:56:07","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":1408440,"visible":true,"origin":"","legend":"\u003cp\u003eKEGG orthologues that were significantly associated (\u003cem\u003ep\u003c/em\u003e \u0026lt; 0.05 and q \u0026lt; 0.05) in gout patients in the multivariate model introducing urate levels. The chart shows the β coefficient and estimated standard error (error bars). The red bars indicate a β coefficient \u0026gt; 4, the orange bars indicate a β coefficient \u0026gt; 2, the yellow bars indicate a β coefficient ≥ 1, and the green bars indicate a β coefficient \u0026lt; 1.\u003c/p\u003e","description":"","filename":"Figure4.png","url":"https://assets-eu.researchsquare.com/files/rs-5411102/v1/23733338eff4ab4c7ba2f614.png"},{"id":71635060,"identity":"08b469a5-861c-467c-b8c3-b5ea5d69c09d","added_by":"auto","created_at":"2024-12-17 10:04:07","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":102750,"visible":true,"origin":"","legend":"\u003cp\u003eLogarithm of relative expression units (REU) of K00161 (A), K00162 (B), K001621 (C) and K14170 (D) by study groups. \u003cem\u003ep\u003c/em\u003e-value obtained from\u003cstrong\u003e \u003c/strong\u003eDunn's pairwise comparison.\u003c/p\u003e","description":"","filename":"Fig.5SR.png","url":"https://assets-eu.researchsquare.com/files/rs-5411102/v1/68d2c3e5b76df22d981542c1.png"},{"id":79121349,"identity":"02220082-2d81-490d-abd7-33c40820e04a","added_by":"auto","created_at":"2025-03-24 16:12:13","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":6857440,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-5411102/v1/15ff71b2-3f8a-468c-83cb-872dd5a9c58e.pdf"},{"id":71634546,"identity":"e829af69-2843-4e81-82b0-ba1a51066c2a","added_by":"auto","created_at":"2024-12-17 09:56:07","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":35714,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eAPPENDIX A\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"Supplementarymaterial.docx","url":"https://assets-eu.researchsquare.com/files/rs-5411102/v1/3909936d2482fbc59933469c.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Gut microbiome-meta-transcriptome analysis reveals that pyruvate and amino acid metabolism bacterial genes are involved in hyperuricemia and gout in humans","fulltext":[{"header":"1. BACKGROUND","content":"\u003cp\u003eHyperuricemia is defined as the constant state of urate levels in serum over 7 mg/dL, and is associated not only with gout, but with other pathologies such as type 2 diabetes (T2D), metabolic syndrome (MSx), and hypertension [\u003cspan additionalcitationids=\"CR2\" citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eNormally humans produce on average 620 mg\u0026thinsp;\u0026plusmn;\u0026thinsp;75 mg of urate per day, of which 70% is excreted through the kidneys and 30% is excreted at the intestinal level [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThe association of the gut microbiota with urate serum levels was made clear in 2014 [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e] and several observational studies have demonstrated the association between the gut microbiota and gout diagnosis, as well as with the presence of asymptomatic hyperuricemia (AH) [\u003cspan additionalcitationids=\"CR7 CR8 CR9\" citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. However, direct functional evidence of gut microbiome regulation of urate serum levels is scarce, and based primarily in animal models.\u003c/p\u003e \u003cp\u003eIn murine models of hyperuricemia, it has been observed that a microbiota enriched with purine-degrading bacteria reduces the urate levels in serum. Moreover, upon transplanting such microbiota to germ-free mice, they are able to modulate urate levels in the intestine and at a systemic level [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e, \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. A study on a murine model demonstrated that the use of uricolytic bacteria-based probiotics helps prevent oxonic acid-induced hyperuricemia [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. Murine models of hyperuricemia have also reported groups of bacterial genes present in the gut microbiota that code for enzymes involved in purine [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e] and urate catabolism [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eFunctional predictions drawing from 16S and shotgun metagenomic analyses suggest that gut dysbiosis in gout or AH patients affects urate homeostasis and the inflammatory process [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e, \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]. A previous study performed in Chinese gout patients evaluated the concentration of several fecal metabolites and found differences in the concentrations of metabolites related to purine metabolism, such as glycine and aspartate, and those related to inflammatory processes, such as taurine, acetate, succinate, valine, and methionine [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eDespite these evidences, it is still unknown which genes are expressed in the human gut microbiome and which could be involved in AH and gout. Therefore, our objective was to evaluate the gut microbiota meta-transcriptome of gout patients and subjects with and without AH.\u003c/p\u003e"},{"header":"2. MATERIALS AND METHODS","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e2.1 Study Design and Population\u003c/h2\u003e \u003cp\u003eWe performed a cross-sectional study using samples obtained from a biologic bank that was generated in a previous study (INR-28/15) [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. A total of 26 fecal samples were included: 10 from normouricemic controls, 10 from AH subjects, and 6 from gout patients. Samples selected for the meta-transcriptome analysis were paired by age, gender, and body mass index (BMI). Controls were blood donors from the blood bank of the Luis Guillermo Ibarra Ibarra National Rehabilitation Institute (INRLGII). In order to be included they had to have urate levels under 7 mg/dL, BMI\u0026thinsp;\u0026lt;\u0026thinsp;25kg/m\u003csup\u003e2\u003c/sup\u003e, and not to have any disease diagnosis. AH subjects were also blood donors, but with urate levels over 7 mg/dL, BMI\u0026thinsp;\u0026lt;\u0026thinsp;25kg/m\u003csup\u003e2\u003c/sup\u003e, and no previous clinic history of any gout attack. The gout patients were recruited in the Rheumatology Department of the \u0026ldquo;Dr. Eduardo Liceaga\u0026rdquo; General Hospital of Mexico and of the INRLGII. The inclusion criteria for gout patients included no clinical history of diagnosis of T2D, MSx, hypertension, any other rheumatic disease, chronic kidney disease, or kidney transplant. All participants that consumed antibiotics, antivirals, or antiparasitic drugs three months before sampling were excluded.\u003c/p\u003e \u003cp\u003e Each participant signed an informed consent letter before sampling. This study was conducted under the criteria set forth in the Declaration of Helsinki and was approved by the Ethics and Research Committees of the participating institutions (INR:30/20-SP1 - HGM:DI/18/404-A/03/004).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e2.2 Sample Processing and Sequencing\u003c/h2\u003e \u003cp\u003eAn aliquot of 250 mg of feces of each individual was preserved in 2 mL of RNAlatter (Ambion, Thermo Fisher Scientific Massachusetts, USA) and stored at -80\u0026deg; C until it was processed. Total RNA was obtained with a commercial kit (RNeasy PowerMicrobiome Kit, QIAGEN, Germany), quantified by fluorometry, and its quality evaluated with chip electrophoresis in an Agilent 2100 bioanalyzer (Agilent Technologies, EUA). The Illumina Stranded Total RNA Prep kit, in combination with the Ribo-Zero Plus kit (Illumina, EUA) were used to prepare the libraries.\u003c/p\u003e \u003cp\u003eThe meta-transcriptome was sequenced in two batches in the NextSeq equipment (Illumina, EUA) in the Sequencing Department of the National Genomic Medicine Institute (INMEGEN).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003e2.3 Meta-transcriptome Bioinformatics Analysis\u003c/h2\u003e \u003cp\u003eThe sequences were filtered considering their quality and merged with the Fastp [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e] and Flash [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e] tools, respectively. We performed an integral search for ribosomal RNA and non-coding RA (ncRNA) with the SortmeRNA [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e] and INFERNAL [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e] algorithms using the Silva database (SILVAv.138 SSU ref NR99, SILVAv.138 LSU Ref NR99) and a subgroup of RFAM [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e] (CL00111, CL00112, CL00001, CL00002, and CL00003). Once all ncRNA were removed, we performed a search for the coding fragments using the FragGeneScan program [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e], accepting an error rate of 1%.\u003c/p\u003e \u003cp\u003eWe did the functional annotation of the predicted coding fragments in HUMAnN3 [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e], based on the results of a previous taxonomic analysis with an approach of genetic markers with merged and filtered sequences using MetaPhlan2. The HUMAnN3 software allowed us to identify the abundance of gene families, pathways, and coverage by bacterial genus. With the gene family abundance and pathways data, each identified element was assigned orthologues from the Kyoto Encyclopedia of Genes and Genomes (KEGG) (KO).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003e2.4 Meta-transcriptome Statistical Analysis\u003c/h2\u003e \u003cp\u003eWith the abundance of KO obtained from HUMANn3 in readings per kilobase, we started by evaluating their potential to discriminate samples into their proper group, using a t-SNE non-linear dimensionality reduction algorithm. Then, we evaluated the differential expression among the study groups using a Gamma-Poisson distribution with DeSeq2 R package [\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]. Genes with less than 10 readings in the whole sample were excluded from this analysis. For additional analyses, we transformed the data from the raw count using the regularized logarithm method. Finally, with the KO that showed a significant difference (adjusted \u003cem\u003ep\u003c/em\u003e-value (\u003cem\u003ep\u003c/em\u003e-adj)\u0026thinsp;\u0026lt;\u0026thinsp;0.05) among the study groups and a high magnitude effect (log\u003csub\u003e2\u003c/sub\u003e fold change (FC)\u0026ge;|10|) we carried out a multivariate association test using generalized linear models implemented in the MTX model [\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e] in order to verify their association adjusting by significant covariates and to evaluate their association with urate levels. We adjusted the \u003cem\u003ep\u003c/em\u003e-values for all analyses by multiple comparisons using the Benjamini-Hochberg method.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003e2.5 Validation of KO differential expression levels\u003c/h2\u003e \u003cp\u003eWe performed a new sampling process, similarly as the one previously describe for the sequenced samples. The only thing made differently was the location at where the AH subject were enrolled. For this phase of the study, we recruited AH individuals from an outgoing cohort carried out in the Instituto Nacional de Cardiolog\u0026iacute;a Ignacio Ch\u0026aacute;vez and for their inclusion they must had urate levels above 7mg/dL in two consecutive measurements separated by one year, no clinic history of any gout attack, and must not had any disease diagnosis Supplementary Table\u0026nbsp;1. As done for the meta-transcriptome analysis, all participants did not take any antibiotics, antiparasitic or antiviral drugs for at least three months previous of the sampling and did not have diagnosis of any gastrointestinal diseases. All participants signed the informed consent letter approved by the INRLGII ethics committee.\u003c/p\u003e \u003cp\u003eWe obtained total RNA from 272 fecal samples (100 from normouricemic subjects, 94 from subjects with AH, and 78 from patients with gout) using the same commercial kit as the one used for the sequenced samples. From 1ug of total RNA we synthesized the cDNA using the High Capacity cDNA reverse transcription kit (Applied Biosystems, USA).\u003c/p\u003e \u003cp\u003eUpon orthologues selection based in their effect magnitude observed in the bivariated meta-transcriptome analysis, we performed global alignments using ClustalΩ [\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e] with all the orthologues sequences from bacteria genus identified in the initial meta-transcriptome analysis with HUMAnN3. With the global alignment we define a consensus sequence using and Emboss Cons software [\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e]. We designed specific oligos using PrimerQuest tool software (IDT, USA) from each orthologue consensus sequence to evaluate its expression by real-time PCR in a Rotor Gene-Q instrument (QIAGEN, Germany). The rpoB gene was used as a housekeeping gene [\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e] and all assays were carried out in duplicate. Designed primers\u0026acute; information, as well as their cycling conditions are described in Supplementary Table\u0026nbsp;2.\u003c/p\u003e \u003cp\u003eWe compared the relative expression units (REU), calculated by 2\u003csup\u003e\u0026minus;Δct\u003c/sup\u003e method, of each orthologue between groups using Dunn\u0026acute;s test and performed multivariated linear regression models to evaluate the association of the orthologues\u0026rsquo; REU adjusting for confounding variables.\u003c/p\u003e \u003c/div\u003e"},{"header":"3. RESULTS","content":"\u003cp\u003eAge and BMI were not statistically different among the study groups of the sequenced samples; urate levels were significantly higher in AH subjects than in gout patients and control individuals (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.01). The levels of triglycerides were statistically different as well (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001) among the study groups, but gout patients had the highest levels, followed by controls and AH subjects (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eFeatures of the study population for sequencing analysis and validation by group.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colspan=\"5\" nameend=\"c6\" namest=\"c2\"\u003e \u003cp\u003eMeta-transcriptome sequencing analysis\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVariable\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eControls\u003c/p\u003e \u003cp\u003e(n\u0026thinsp;=\u0026thinsp;10)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eAH\u003c/p\u003e \u003cp\u003e(n\u0026thinsp;=\u0026thinsp;10)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eGout\u003c/p\u003e \u003cp\u003e(n\u0026thinsp;=\u0026thinsp;6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e \u003cp\u003e\u003cem\u003ep\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge (years)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c6\" namest=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAverage (\u0026plusmn;\u0026thinsp;SD)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e42.7 (\u0026plusmn;\u0026thinsp;7.69)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e41.22 (\u0026plusmn;\u0026thinsp;8.45)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e45.67 (\u0026plusmn;\u0026thinsp;11.55)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.65*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c6\" namest=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBMI (kg/m\u003csup\u003e2\u003c/sup\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c6\" namest=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMedian (IQR)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e25.48 (3.30)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e27.04 (1.97)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e24.88 (3.18)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.29**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c6\" namest=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUrate (mg/dL)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c6\" namest=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAverage (\u0026plusmn;\u0026thinsp;SD)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e5.63 (\u0026plusmn;\u0026thinsp;0.71)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e8.43 (\u0026plusmn;\u0026thinsp;1.87)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e8.29 (\u0026plusmn;\u0026thinsp;1.43)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.01*\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c6\" namest=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGlucose (mg/dL)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c6\" namest=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMedian (IQR)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e89.47 (15.37)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e94.44 (14.10)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e93.70 (16.08)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.72**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c6\" namest=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTriglycerides (mg/dL)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c6\" namest=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMedian (IQR)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e155.91 (22.08)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e125.68 (42.94)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e187.20 (96.32)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e0.01**\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c6\" namest=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCholesterol (mg/dL)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c6\" namest=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAverage (\u0026plusmn;\u0026thinsp;SD)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e148.39 (\u0026plusmn;\u0026thinsp;46.48)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e150.82 (\u0026plusmn;\u0026thinsp;31.87)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e149.41 (\u0026plusmn;\u0026thinsp;39.59)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.99*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c6\" namest=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e\u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eAfter filtering by quality and merging the paired readings, we calculated a median of 71.86\u0026nbsp;million readings per sample with an interquartile range (IQR) of 10.88. The number of readings was uniform among study groups (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.95). A high percentage of the filtered sequences were identified as coding sequences (\u003cem\u003ep\u003c/em\u003e50\u0026thinsp;=\u0026thinsp;90.67%, IQR\u0026thinsp;=\u0026thinsp;6.10), and was homogenous among study groups (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.30). Of these predicted coding sequences, an average of 93.84% (\u0026plusmn;\u0026thinsp;2.53%) was successfully mapped to a KO with the HUMANn3 algorithm.\u003c/p\u003e \u003cp\u003eWe observed that the KO expression profile was able to discriminate the samples from the gout patients and controls, but the AH subject sample group was not clearly separated (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). Nevertheless, we observed significant differences among the expression of several KO in the study groups, including the AH subjects.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003e3.1 KEGG Orthologues Expressed Differentially among Groups\u003c/h2\u003e \u003cp\u003eWe observed statistically significant differences in the expression of 155 KO among the study groups (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eA-C). Of those, 67 KO had a higher effect magnitude (Supplementary Table\u0026nbsp;3, Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e), of which 46 KO were mapped to at least one metabolic pathway (carbon metabolism, glycolysis/gluconeogenesis, amino acid biosynthesis, pyruvate metabolism, starch and sucrose metabolism, among others) and 19 were mapped to transporters of the ABC family (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eD).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eWhen comparing the expression observed in the microbiome of AH subjects and healthy controls, 62 KO had a log\u003csub\u003e2\u003c/sub\u003e FC \u0026ge; |10| and a \u003cem\u003ep\u003c/em\u003e-adj\u0026thinsp;\u0026lt;\u0026thinsp;0.001. More than 80% of the KO (88.89%) detected as over-expressed in AH subjects were also over-expressed in gout patients in comparison to the controls. Among them, 12 KO were mapped to metabolic pathways, such as metabolism of pyruvate, different amino acids (aa.), purines, butanoate, and the pentose pathway; whereas 8 were mapped to transporters, including transporters of the ABC family, and a xanthine permease (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eD).\u003c/p\u003e \u003cp\u003eAll the KO under-expressed in the AH subjects when compared to the controls were also under-expressed when compared to the gout patients (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eD). Among these, an orthologue from the glycine cleavage system (GCS) drew our attention (K00282) due to its involvement in glycine metabolism and its potential function in purine formation.\u003c/p\u003e \u003cp\u003eAmong the gout patients, we identified 60 KO with a higher effect magnitude and a \u003cem\u003ep\u003c/em\u003e-adj\u0026thinsp;\u0026lt;\u0026thinsp;0.001 (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eB), compared to the controls and AH subjects. We observed that three KO with an elevated, highly significant (\u003cem\u003ep\u003c/em\u003e-adj\u0026thinsp;\u0026lt;\u0026thinsp;1 x 10\u003csup\u003e\u0026minus;\u0026thinsp;8\u003c/sup\u003e) log\u003csub\u003e2\u003c/sub\u003e FC were identified in gout patients compared with the controls, and significantly different as well when compared to the AH subjects. Two of them (K00161, and K02221) were over-expressed in gout patients, whereas the third one, which is involved in biosynthesis pathways of phenylalanine, tyrosine, and tryptophan (K14170), was under-expressed in comparison to the AH subjects (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eD).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003e3.2 Association with UA Levels\u003c/h2\u003e \u003cp\u003eWe performed a multivariate generalized linear model analysis introducing urate levels and found 42 significantly associated KO (\u003cem\u003ep-\u003c/em\u003evalue\u0026thinsp;\u0026lt;\u0026thinsp;0.05 and \u003cem\u003eq-\u003c/em\u003evalue\u0026thinsp;\u0026lt;\u0026thinsp;0.05). Most of them were associated with gout, and only the orthologue K16509, a spX regulating protein, was also associated with AH. Of the KO associated with gout, 24 were assigned to enzymes (6 oxidoreductases, 5 transferases, 5 hydrolases, 4 lyases, 2 ligases, 1 isomerase, and 1 translocase), 8 to transporters, including the 5 orthologues mapped to the ABC family transporters and the xanthine permease we previously mentioned (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e). As observed in previous analyses, two KO involved in pyruvate metabolism (pyruvate dehydrogenase E1 component, sub-units α and β) were significantly associated with gout (\u003cem\u003eq\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.043 and \u003cem\u003eq\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.001, respectively), as well as the GMP reductase orthologue (\u003cem\u003eq\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.02), xanthine permease (\u003cem\u003eq\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.003), and xylulose 5-phosphate phosphoketolase (\u003cem\u003eq\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.001). Moreover, we found a significant association (\u003cem\u003eq\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.01) with acetolactate decarboxylase, an enzyme involved in the 2-oxocarboxylic acid metabolism, in which pyruvate is an intermediate metabolite (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003e3.3 Validation\u003c/h2\u003e \u003cp\u003eFor the time being, we have chosen to validate four of the orthologues that exhibit the largest effect magnitude observed in afore mention meta-transcriptome analysis and that participate in interconnected metabolic pathways (Supplementary table 3). We validated pyruvate dehydrogenase E1 component α (K00161) and β (K00162) sub-units; xylulose 5-phosphate phosphoketolase (K01621); and chorismate mutase (K14170) orthologues relative expression. The main traits of the population for the validation analysis are describe in Supplementary Table\u0026nbsp;1. Features that were significantly different among groups were considered as confounding factors at the multivariated analysis.\u003c/p\u003e \u003cp\u003eWe observed a clear over-expression of three of these orthologues (K00161, K00162 and K01621) in the gut microbiome of patients with gout compared to normouricemic subjects and to subjects with AH (p\u0026thinsp;=\u0026thinsp;0.0001) (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eA-C). However, we were unable to detect a statistically significant difference between AH and normouricemic subjects as seen in the bivariated meta-transcriptome analysis for the K00161 and K01621 orthologues.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eFor the K14170 orthologue log(RUE) we observed an over-expression in the gut microbiome of patients with AH compared to normouricemic subjects (p\u0026thinsp;=\u0026thinsp;0.0001), nevertheless the comparison between each of these two groups and gout patients log(REU) was not significant as previously described (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eD).\u003c/p\u003e \u003cp\u003eFinally, by performing regression models adjusted by age, BMI, urate, glucose, cholesterol and triglycerides, we validated the association of the over-expression of K00161, K00162 and K01621 with the diagnosis of gout, and the over expression of K14170 with AH status. We found that patients with gout have in average 4.49 (95%CI\u0026thinsp;=\u0026thinsp;3.07\u0026ndash;5.92; p\u0026thinsp;\u0026lt;\u0026thinsp;0.0001), 4.50 (95%CI\u0026thinsp;=\u0026thinsp;2.84\u0026ndash;6.16; p\u0026thinsp;\u0026lt;\u0026thinsp;0.0001) and 4.36 (95%CI\u0026thinsp;=\u0026thinsp;2.36\u0026ndash;6.36; p\u0026thinsp;\u0026lt;\u0026thinsp;0.0001) more of the K00161, K00162 and K01621 orthologues log(REU), respectively, than the normouricemic subjects regardless of age, BMI, urate, glucose, cholesterol and triglycerides levels. As for the K14170 orthologue expression, we observed that the microbiome of AH subjects expressed 1.21 (95%CI\u0026thinsp;=\u0026thinsp;2.84\u0026ndash;6.16; p\u0026thinsp;=\u0026thinsp;0.04) log(REU) more of K14170 orthologue than normouricemics\u0026acute; gut microbiome.\u003c/p\u003e \u003c/div\u003e"},{"header":"4. DISCUSSION","content":"\u003cp\u003eHyperuricemia is a prevalent metabolic disorder, and is considered a necessary factor, although insufficient, for gout [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. Only 18% of AH subjects is estimated to develop an acute gout attack at one point in their life [\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e]. Hyperuricemia has also been associated with several pathologies that pose a public health problem, like T2D, hypertension and MSx [\u003cspan additionalcitationids=\"CR2\" citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. Even though there is a treatment for hyperuricemia, the decision of whether to medicate AH subjects or start treating them after the first acute gout attack is controversial [\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e]. It is clear that the gut microbiota plays a role in urate homeostasis in humans, which is why researchers all over the world have centered their attention in it as a potential therapeutic strategy that could help solve the controversy about treatment for AH subjects [\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e, \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eIn this study, we have showed that the gene expression profile of the gut microbiome is clearly different between gout patients and normouricemic individuals. However, this difference is not as evident between AH subjects and gout patients. This may reflect the percentage of AH subjects that will never develop gout. Nevertheless, there were significant differences in the expression of several orthologues mainly involved in pathways related to the metabolism of purines, pyruvate, and different aa; particularly glycine, phenylalanine and tryptophane metabolism.\u003c/p\u003e \u003cp\u003eInterestingly, the orthologues that showed the largest effect in the bivariated meta-transcriptome analysis were those belonging to the pyruvate metabolism. Fortunately, we were able to validate the over-expression of these orthologues in gout patients\u0026acute; microbiome (subunits \u003cem\u003eα and β\u003c/em\u003e of pyruvate dehydrogenase). This enzyme is responsible for decarboxylating pyruvate to produce acetyl-CoA, which is a precursor of short chain fatty acids (SCFA), including acetate [\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e]. Likewise, we validated the over-expression of an orthologue coding for a phosphoketolase in gout patients\u0026acute; gut microbiome, which is involved in the production of acetyl phosphate from fructose 6-phosphate and D-xylulose 5-phosphate, which in turn may also produce acetate as a resulting metabolite from ATP generation [\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e, \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e]. This suggests that the microbiome of gout patients has a high capacity to produce acetate trough the metabolism of pyruvate and the phosphoketolase pathway.\u003c/p\u003e \u003cp\u003eThese results are in accordance with our previous report where we observed a higher concentration of genes coding for pyruvate oxidase in the gut microbiome of gout patients when compared to AH subjects [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. This is an enzyme that allows certain bacteria to metabolize pyruvate in a non-conventional pathway and thus produce acetate [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. Furthermore, an increase in the quantity of acetate in feces and acetate-producing bacteria has been previously associated in the gut microbiota of gout patients [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e, \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e]. Additionally, it has even been demonstrated in a murine model of gout that this SCFA is necessary to develop the inflammatory response against monosodium urate crystals [\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e]. Moreover, acetate supplementation in mice favors the polarization of the cell immune response to Th17 in the presence of an infectious agent [\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eOur sample size for the meta-transcriptome analysis did not allow us to have enough power to observe significant associations in the generalized linear models adjusting by triglyceride levels. Nevertheless, we were able to evaluate the association of the bacterial gene expression with the urate levels adjusting by the main confounding factors from the study design, since our study population was under a very strict control in terms of comorbidities, and coupled by age, BMI, and gender. Additionally, we performed a validation analysis of the four orthologues with the largest effect in the meta-transcriptome analysis, and the association seen with gout remained significant for three of them, even in the multivariate analysis adjusting not only by triglycerides, but also by other potential confounding factors.\u003c/p\u003e \u003cp\u003ePrevious studies have shown that there is a difference in the concentration of certain aa. in the serum of gout patients and hyperuricemia subjects; and they have even been proposed as potential biomarkers of the disease [\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e]. Therefore, it would be highly relevant to further explore the expression of the KO related to glycine (glycine cleavage system P protein (glycine dehydrogenase subunit 1, K00282) phenylalanine in the gut microbiome of AH individuals and gout patients. This might provide further evidence in order to suggest the modulation of these metabolic pathways in the gut microbiome as a potential adjuvant therapy for hyperuricemia and gout.\u003c/p\u003e"},{"header":"5. CONCLUSIONS","content":"\u003cp\u003eBased on our results, we can conclude that there is a differential gene expression pattern in the gut microbiome of normouricemic individuals, AH subjects, and gout patients. These differences are focused on orthologues from metabolic pathways involved in the production of acetate precursors and which could be potential targets for microbiome modulation therapy for gout.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003e6.1 Ethics approval\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe present study\u0026nbsp;was conducted under the principles set forth in the Declaration of Helsinki and was approved by the Ethics and Research Committee of the Instituto Nacional de Rehabilitaci\u0026oacute;n Luis Guillermo Ibarra Ibarra (INR30/20-SP1).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e6.2\u003c/strong\u003e\u003cstrong\u003eAccordance to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eInformed consent was obtained from all individual participants included in the study.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e6.3 Declaration of competing interest\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that no conflict of interest exists.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e6.4\u003c/strong\u003e\u003cstrong\u003eAvailability of data and materials\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eRaw data supporting this study are not openly available due to ethical restrictions. However, it is available upon reasonable request to the corresponding author. For the peer review process data have been deposited at the following link. https://drive.google.com/drive/folders/1y8ilmmugcLQLGPofWroxPyl_3MoCoSeP?usp=drive_link\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e6.5 Financial support\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study received funding from the Consejo Nacional de Humanidades, Ciencias y Tecnolog\u0026iacute;as (CONAHCyT) of Mexico through the 2019 science frontiers tender [FORDECYT-PRONACES/87754/2020]. The CONAHCyT did not participate in the conceptualization, design, data collection, analysis, decision to publish, or preparation of the manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e6.6 Author contributions: credit\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eGAMN\u003c/strong\u003e: Conceptualization, Methodology, Formal analysis, Investigation Funding acquisition, Writing - Original Draft, Visualization, Project administration; \u003cstrong\u003eEAM\u003c/strong\u003e: Validation, Investigation, Writing - Review \u0026amp; Editing, Visualization; \u003cstrong\u003eJVM\u003c/strong\u003e: Methodology, Investigation, Writing - Review \u0026amp; Editing; \u003cstrong\u003eCCS\u003c/strong\u003e: Software, Writing - Review \u0026amp; Editing; \u0026nbsp; \u003cstrong\u003eJD\u003c/strong\u003e: Software, Formal analysis, Writing - Review \u0026amp; Editing; \u003cstrong\u003eCLP\u003c/strong\u003e: Data Curation, Writing - Review \u0026amp; Editing; \u003cstrong\u003eBHL\u003c/strong\u003e: Investigation, Data Curation, Writing - Review \u0026amp; Editing; \u003cstrong\u003eLEMG\u003c/strong\u003e: Investigation, Data Curation, Writing - Review \u0026amp; Editing; \u003cstrong\u003eCMA\u003c/strong\u003e: Investigation, Writing - Review \u0026amp; Editing; \u003cstrong\u003eDLGG\u003c/strong\u003e: Investigation, Data Curation, Writing - Review \u0026amp; Editing; \u003cstrong\u003eSVG\u003c/strong\u003e: Validation, Investigation, Writing - Review \u0026amp; Editing; \u003cstrong\u003eCSA\u003c/strong\u003e: Investigation, Writing - Review \u0026amp; Editing; \u003cstrong\u003eMCCR\u003c/strong\u003e: Validation, Investigation, Writing - Review \u0026amp; Editing; \u003cstrong\u003eMMG\u003c/strong\u003e: Validation, Investigation Writing - Review \u0026amp; Editing; \u003cstrong\u003eGGE\u003c/strong\u003e: Validation, Data Curation, Writing - Review \u0026amp; Editing; \u003cstrong\u003eLMAG\u003c/strong\u003e: Methodology, Investigation, \u0026amp; Validation Writing - Review \u0026amp; Editing; \u003cstrong\u003eYZC\u003c/strong\u003e: Investigation, Writing - Review \u0026amp; Editing; \u003cstrong\u003eKMF\u003c/strong\u003e: Investigation, Writing - Review \u0026amp; Editing; \u003cstrong\u003eJFT\u003c/strong\u003e: Investigation, Writing - Review \u0026amp; Editing; \u003cstrong\u003eABG\u003c/strong\u003e: Investigation, Writing - Review \u0026amp; Editing; \u003cstrong\u003eYCOO\u003c/strong\u003e: Investigation, Writing - Review \u0026amp; Editing; \u003cstrong\u003eALM\u003c/strong\u003e: Investigation, Writing - Review \u0026amp; Editing; \u003cstrong\u003eEOMS\u003c/strong\u003e: Investigation, Writing - Review \u0026amp; Editing, \u003cstrong\u003eAFB\u003c/strong\u003e: Investigation, Writing - Review \u0026amp; Editing; \u003cstrong\u003eBPG\u003c/strong\u003e: Methodology, Investigation, Writing - Review \u0026amp; Editing ; \u003cstrong\u003eCP\u003c/strong\u003e: Methodology, Investigation, Writing - Review \u0026amp; Editing; \u003cstrong\u003eALR\u003c/strong\u003e: Conceptualization, Investigation, Writing - Review \u0026amp; Editing: Supervision.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e6.7 Acknowledgments\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe want to thank the Sequencing Department of INMEGEN (USEC) for their support to sequence the meta-transcriptome, as well as the staff from the Computational Medicine Platform, Fundaci\u0026oacute;n Progreso y Salud (FPS) at the Virgen del Roc\u0026iacute;o Hospital for their valuable support to perform the bioinformatics analysis.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eWe also want to thank Patricia Alejandra Mart\u0026iacute;nez-Nava for her valuable support in the translation and style correction of the manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e6.8 Consent for publication:\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eN/A\u003c/p\u003e\u003cp\u003e\u003cstrong\u003e7. APPENDIX A\u003c/strong\u003e. Supplementary Table 1, 2 and 3\u003c/p\u003e\n"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eDalbeth N, Choi HK, Joosten LAB, Khanna PP, Matsuo H, Perez-Ruiz F, et al. Gout. Nat Rev Dis Prim 2019;5:2039\u0026ndash;52. doi:10.1038/s41572-019-0115-y.\u003c/li\u003e\n\u003cli\u003eLv S, Liu W, Zhou Y, Liu Y, Shi D, Zhao Y, et al. Hyperuricemia and severity of coronary artery disease: An observational study in adults 35 years of age and younger with acute coronary syndrome. Cardiol J 2019;26:275. doi:10.5603/CJ.A2018.0022.\u003c/li\u003e\n\u003cli\u003eStamp LK, Chapman PT. Gout and its comorbidities: Implications for therapy. Rheumatol (United Kingdom) 2013;52:34\u0026ndash;44. doi:10.1093/rheumatology/kes211.\u003c/li\u003e\n\u003cli\u003eBenn CL, Dua P, Gurrell R, Loudon P, Pike A, Ian Storer R, et al. Physiology of hyperuricemia and urate-lowering treatments. 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Open-source sequence clustering methods improve the state of the art 2014;1:1\u0026ndash;16. doi:10.1128/mSystems.00003-15.Editor.\u003c/li\u003e\n\u003cli\u003eNawrocki EP, Eddy SR. Infernal 1.1: 100-fold faster RNA homology searches. Bioinformatics 2013;29:2933\u0026ndash;5. doi:10.1093/bioinformatics/btt509.\u003c/li\u003e\n\u003cli\u003eKalvari I, Nawrocki EP, Ontiveros-Palacios N, Argasinska J, Lamkiewicz K, Marz M, et al. Rfam 14: expanded coverage of metagenomic, viral and microRNA families. Nucleic Acids Res 2021;49:D192\u0026ndash;200. doi:10.1093/NAR/GKAA1047.\u003c/li\u003e\n\u003cli\u003eLiu Y, Guo J, Hu G, Zhu H. Gene prediction in metagenomic fragments based on the SVM algorithm. BMC Bioinformatics 2013;14 Suppl 5. doi:10.1186/1471-2105-14-S5-S12.\u003c/li\u003e\n\u003cli\u003eBeghini F, McIver LJ, Blanco-M\u0026iacute;guez A, Dubois L, Asnicar F, Maharjan S, et al. 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The biomarkers discovery of hyperuricemia and gout: proteomics and metabolomics. PeerJ 2023;11. doi:10.7717/peerj.14554.\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"meta-transcriptome, gut microbiome, hyperuricemia, gout","lastPublishedDoi":"10.21203/rs.3.rs-5411102/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-5411102/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e \u003cp\u003eSeveral pathologies with metabolic origin, such as hyperuricemia and gout, have been associated with the gut microbiota taxonomic profile. However, there is no evidence of which bacterial genes are being expressed in the gut microbiome, and of their potential effects on hyperuricemia and gout.\u003c/p\u003e\u003ch2\u003eMaterials and Methods\u003c/h2\u003e \u003cp\u003eWe sequenced the RNA of 26 fecal samples from 10 healthy normouricemic controls, 10 with asymptomatic hyperuricemia (AH), and six gout patients. The coding sequences were mapped to KEGG orthologues (KO). We compared the expression levels using generalized linear models and validated the expression of three KO in a larger sample by qRT-PCR.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eA distinct genetic expression pattern was identified among groups. AH individuals and gout patients showed an over-expression of KOs mainly related to pyruvate metabolism (Log2foldchange\u0026thinsp;\u0026gt;\u0026thinsp;23, \u003cem\u003ep\u003c/em\u003e-adj\u0026thinsp;\u0026le;\u0026thinsp;3.56x10\u003csup\u003e\u0026minus;\u0026thinsp;9\u003c/sup\u003e), the pentose pathway (Log2foldchange\u0026thinsp;\u0026gt;\u0026thinsp;24, \u003cem\u003ep\u003c/em\u003e-adj\u0026thinsp;\u0026lt;\u0026thinsp;1.10x10\u003csup\u003e\u0026minus;\u0026thinsp;12\u003c/sup\u003e) and purine metabolism (Log2foldchange\u0026thinsp;\u0026gt;\u0026thinsp;22, \u003cem\u003ep\u003c/em\u003e-adj\u0026thinsp;\u0026lt;\u0026thinsp;1.25x10\u003csup\u003e\u0026minus;\u0026thinsp;7\u003c/sup\u003e). AH subjects had lower expression of KO related to glycine metabolism (Log2foldchange=-18, \u003cem\u003ep\u003c/em\u003e-adj\u0026thinsp;\u0026lt;\u0026thinsp;1.72x10\u003csup\u003e\u0026minus;\u0026thinsp;6\u003c/sup\u003e) than controls. Gout patients had lower expression (Log2foldchange=-22.42, \u003cem\u003ep\u003c/em\u003e-adj\u0026thinsp;\u0026lt;\u0026thinsp;3.31x10\u003csup\u003e\u0026minus;\u0026thinsp;16\u003c/sup\u003e) of a KO involved in phenylalanine biosynthesis, in comparison to controls and AH subjects. The over-expression seen for the KO related to pyruvate metabolism and the pentose pathway in gout patients\u0026acute; microbiome was validated.\u003c/p\u003e\u003ch2\u003eConclusions\u003c/h2\u003e \u003cp\u003eThere is a differential gene expression pattern in the gut microbiome of normouricemic individuals, AH subjects and gout patients. These differences are mainly located in metabolic pathways involved in acetate precursors and bioavailability of amino acids.\u003c/p\u003e","manuscriptTitle":"Gut microbiome-meta-transcriptome analysis reveals that pyruvate and amino acid metabolism bacterial genes are involved in hyperuricemia and gout in humans","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-12-17 09:56:02","doi":"10.21203/rs.3.rs-5411102/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2025-02-03T12:20:39+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-01-28T03:42:53+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"128097933534979162011432191127580056397","date":"2025-01-13T01:43:44+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-01-12T21:47:41+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"281992406016873565529143293931328156209","date":"2025-01-12T21:17:37+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-01-12T00:16:03+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2024-12-25T15:37:24+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2024-11-14T16:17:00+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2024-11-12T13:48:45+00:00","index":"","fulltext":""},{"type":"submitted","content":"Scientific Reports","date":"2024-11-07T15:27:26+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"d8df4e46-0e41-4aa5-9188-b845122e327b","owner":[],"postedDate":"December 17th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2025-03-24T16:10:26+00:00","versionOfRecord":{"articleIdentity":"rs-5411102","link":"https://doi.org/10.1038/s41598-025-93899-1","journal":{"identity":"scientific-reports","isVorOnly":false,"title":"Scientific Reports"},"publishedOn":"2025-03-22 15:57:22","publishedOnDateReadable":"March 22nd, 2025"},"versionCreatedAt":"2024-12-17 09:56:02","video":"","vorDoi":"10.1038/s41598-025-93899-1","vorDoiUrl":"https://doi.org/10.1038/s41598-025-93899-1","workflowStages":[]},"version":"v1","identity":"rs-5411102","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-5411102","identity":"rs-5411102","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}