Reduced calorie diet combined with NNMT inhibition establishes a distinct microbiome in DIO mice

Treatment with a nicotinamide N-methyltransferase inhibitor (NNMTi; 5-amino-1-methylquinolinium) combined with low-fat diet (LD) promoted dramatic whole-body adiposity and weight loss in diet-induced obese (DIO) mice, rapidly normalizing these measures to age-matched lean animals, while LD switch alone was unable to restore these measures to age-matched controls in the same time frame. Since mouse microbiome profiles often highly correlate with body weight and fat composition, this study was designed to test whether the cecal microbiomes of DIO mice treated with NNMTi and LD were comparable to the microbiomes of age-matched lean counterparts and distinct from microbiomes of DIO mice maintained on a high-fat Western diet (WD) or subjected to LD switch alone. There were minimal microbiome differences between lean and obese controls, suggesting that diet composition and adiposity had limited effects. However, DIO mice switched from an obesity-promoting WD to an LD (regardless of treatment status) displayed several genera and phyla differences compared to obese and lean controls. While alpha diversity measures did not significantly differ between groups, beta diversity principal coordinates analyses suggested that mice from the same treatment group were the most similar. K-means clustering analysis of amplicon sequence variants by animal demonstrated that NNMTi-treated DIO mice switched to LD had a distinct microbiome pattern that was highlighted by decreased Erysipelatoclostridium and increased Lactobacillus relative abundances compared to vehicle counterparts; these genera are tied to body weight and metabolic regulation. Additionally, Parasutterella relative abundance, which was increased in both the vehicle- and NNMTi-treated LD-switched groups relative to the controls, significantly correlated with several adipose tissue metabolites’ abundances. Collectively, these results provide a novel foundation for future investigations.

Animals Animal experiments were performed with adherence to all national and local guidelines and regulations, the Guide for the Care and Use of Laboratory Animals 25 , and the ARRIVE guidelines, and with the approval of the Institutional Animal Care and Use Committee at The University of Texas Medical Branch (UTMB). Male, 18-week-old C57BL/6J mice, maintained on a 60% high-fat diet (HFD; 60 kcal% fat, 20 kcal% carbohydrate, 20 kcal% protein, Research Diets, Inc. OpenSource Diets formula D12492; DIO mice, JAX cat no. 380050) or a low-fat (lean) diet (LD; 10 kcal% fat, 70 kcal% carbohydrate, 20 kcal% protein, Research Diets, Inc. OpenSource Diets formula D12450B; DIO control mice, JAX cat no. 380056) from 6 to 18 weeks of age, and co-housed with counterparts on the same diet, were purchased from The Jackson Laboratory (Bar Harbor, ME, USA). Upon arrival at UTMB, the 18-week-old mice were single-housed and provided ad libitum access to water and a Western diet (WD; 45 kcal% fat, 35 kcal% carbohydrate, 20 kcal% protein, Research Diets, Inc. OpenSource Diets formula D12451) or, if they were previously on an LD, they were maintained continued on LD (termed lean control mice). Mice were maintained in one controlled colony room, kept at 21–23 °C and 45–50% humidity with a 12-h light–dark cycle (lights on 6 a.m.–6 p.m. Central Standard Time [CST]). Cages were exchanged for new sanitized cages filled with irradiated bedding every 2 weeks. Water was changed weekly. Up to two pieces of enrichment were included in each cage. The DIO mice were randomized into three treatment groups balanced by baseline body weight and body composition (fat and lean mass) measures. Two of the three groups were transitioned from WD to LD for 5 days before the start of the study. All mice received 2 days of 10 mL/kg body weight sterile saline subcutaneous injections before beginning the treatment phase to allow for acclimation to the handling and stress associated with injections. Control groups of mice were maintained on LD or WD and given sham (10 mL/kg body weight sterile saline) injections throughout the study (termed LD/LD-V, or LD control, and WD/WD-V, or WD control, groups; n = 6–8 mice/group). One group of mice that were switched from WD to LD received these same sterile saline injections for the remainder of the study (termed the WD/LD-V group; n = 8), while a second group received 5A-1MQ treatment at 32 mg/kg of active pharmaceutical ingredient (injections of 4 mg 5A-1MQ monochloride salt/mL sterile saline dosed at 10 mL/kg body weight) after being switched from WD to LD (termed the WD/LD-T group; n = 8). Subcutaneous injection was chosen as 5A-1MQ specifically has poor oral bioavailability in mice, a phenomenon that does not occur in rats 26 . This dose was chosen based on a previous dose-escalation study and subsequent work demonstrating its efficacy to drive weight loss in a short period of time 8 . Study design, and diet and treatment groups are diagrammed in Fig. 1 a. Daily doses were given between approximately 4–6 PM CST. Food intake and body weight were measured twice weekly. Body composition was determined weekly with an EchoMRI 4in1-500 Body Composition Analyzer (EchoMRI Whole Body Composition Analyzer; EchoMRI LLC, Houston, TX, USA). Injection volumes were calculated using the most recently recorded animal weight and adjusted weekly; for more details, see Sampson et al., 2021 13 . Mice were euthanized in a non-fasted state over 2 days during their sleep cycle; six animals from the LD control, WD control, and WD/LD-V groups and five animals from the WD/LD-T group were euthanized the first day. The remaining two LD/LD-V, two WD/LD-V, and three WD/LD-T mice were euthanized 2 days later. One individual per group was euthanized before addressing the next animal in each group to render differences arising from the time of day at euthanization (e.g., cecal mass changes resulting from differences in time post-food consumption) comparable across all groups. Figure 1 Diet and NNMTi treatment status substantially altered body weight and fat mass. a After weaning, mice were placed on a high-fat diet (HFD, 60% fat) for 12 weeks, transitioned to Western diet (WD, 45% fat) for 4 weeks, and then randomized to WD or to lean diet (LD, 10% fat) and vehicle (-V) or NNMTi treatment (-T) with 5-amino-1-methylquinolinium (5A-1MQ) for approximately 7 weeks prior to study termination and cecal sample collection; a control group remained on LD throughout the study. b,c Body weight and fat mass were significantly different between the diet-induced obesity model and the lean control group at study start (22.5 weeks of age); at the end of the study, body weight and fat mass were lower in the WD/LD-V group than the WD/WD-V control group, and in the NNMTi-treated group these measures were even lower and statistically indistinguishable from the LD/LD-V control group when a correction for multiple comparisons was not run (n = 6–8). Graphs depict mean

The study's primary aim was to determine whether 5A-1MQ treatment in combination with a switch from WD to LD rendered the intestinal microbiome comparable to that of mice continuously maintained on LD, and distinct from that of DIO mice switched to LD but given saline injections or mice maintained continuously on WD. Microbiome 16S V1-V3 rRNA gene sequencing, alignment, and initial filtering were performed by BGI. Sequencing was performed using a HiSeq 2500 (Illumina), with a read length of 300 bp paired-end reads and at least 25,000 tags per sample. Reads with an average quality < 20 over a 25 bp sliding window were truncated (phred algorithm 32 , 33 ), and the trimmed reads with < 75% of their original length were removed along with their respective pair. Additionally, reads contaminated by the adapter (reads with ≥ 15 bases overlapping between the reads and the adapter with up to 3 bases mismatching), reads with ambiguous bases, and reads with ≥ 10 identical consecutive bases were removed along with each of their respective paired reads. Cleaned reads were assigned to their corresponding samples by requiring 0 base mismatches to the barcode sequences, as determined by in-house (BGI) scripts. For paired-end reads, a consensus sequence was created using Fast Length Adjustment of SHort reads 34 (v1.2.11) with minimal overlapping length (15 bp) and a mismatching ratio of the overlapped region ≤ 0.1; paired-end reads not meeting this criteria were removed. Combining the paired-end reads with the tags based on overlapping regions generated 3,360,747 tags identified in total for all samples, with an average of 134,429 tags/sample. Sequences with less than four consecutive bases matching the tags at the 3′ end and > 2 mismatches in the bases for the remaining part of the primer were removed, and this resulted in a total of 3,332,352 tags, averaging 133,294 tags/sample with an average length of 481 bp. These sequences were then run through an R script ( Supplementary Materials , bash_DADA2 + IDTAXA_workflow_Illumina-PairedEnd_V1V3.R) which trimmed reverse reads to 260 bp, removed forward reads exceeding a maximum expected error of three, and truncated reverse reads exceeding a maximum expected error of six. Filtered reads were then paired-end merged again, and chimeras and non-bacterial sequences were discarded from the data. Paired-end FASTQ files were then run through the DADA2 pipeline (v1.8) to generate an amplicon sequence variant (ASV) table and assign taxonomy. DECIPHER (v2.6.0) was used to align sequences, and FastTree (v2.1.3) was used to generate a phylogenetic tree. Two analyses were performed to determine the species comprising the genera. The first method used a Basic Local Alignment Search Tool Nucleotide (BLASTn; v2.7.1; script in Supplementary Materials Scripts .zip file, BLASTn Query) search 35 , 36 ; this assigned the best hit for each of the ASVs and then provided the percentage of identical matches between the sequence of interest and the top hit as well as an e-value (i.e., the number of expected hits with a similar quality that could be found by chance). Unfortunately, the e-value is affected by the database size and several gaps and mismatches could be present without dramatically influencing the e-value. Consequently, a second method, Bayesian Latent Class Analysis-based Taxonomic Classification (BLCA; Python v3; https://github.com/qunfengdong/BLCA ; accessed November 25, 2020; script in Supplementary Materials Scripts .zip file, BLCA), was used that output bootstrap confidence intervals determined using Bayesian posterior probability and weighing the similarity between database hits relative to the query sequence. In this second approach, higher similarity resulted in greater weight and, consequently, greater contribution to taxonomic assignment. The species results of these two methods were cross-compared and a result was only considered definitive if it was identified using both methodologies. The BIOM file output from the DADA2 pipeline and the DECIPHER-aligned FastTree phylogenetic tree were input into QIIME (v1.9.1) and the scripts in the Supplementary Materials (QIIME Scripts compilation) were used to establish the alpha diversity metrics of Chao1 richness estimate, Simpson’s evenness measure E, Shannon diversity index H, Simpson’s diversity index, and Faith’s phylogenetic diversity metric (whole tree) as well as the beta diversity measures of unweighted and weighted UniFrac analyses (the latter of which factors in ASV abundance 37 ) and Bray–Curtis dissimilarity analysis. Principle coordinates analyses (PCoAs) were generated based on the distance and dissimilarity matrices. From the ASV table (Supplementary Table S1 ), sums of ASVs within each respective phylum or genus for each sample were calculated to establish the abundance of each phylum or genus per sample. The sums of the number of unique ASVs per sample were compared across groups, with the unclassified sequences e

Since a secondary aim of this study was to examine potential relationships between the cecal microbiome and adipose tissue metabolome, a Python (v3.8) tool, which used modules from SciPy 39 , was created to expedite the correlational analyses between the cecal microbiome and the previously-published epididymal white adipose tissue (EWAT) metabolome 13 . The tool followed the decision tree in Supplementary Fig. S1 . It used a Shapiro–Wilk test to assess normality and an F-test to assess homoscedasticity, and attempted log-transformation to resolve any lack thereof 40 . This novel tool, termed the Spearman’s-Pearson’s Decider, which expedited the processes needed to decide between a parametric or non-parametric analysis, is freely available at GitHub ( https://github.com/JerrinWiley/Spearmans_Pearsons_Decider ; script and ReadMe in Supplementary Materials Scripts .zip file under Spearmans_Pearsons_Decider and ReadMe for Spearmans_Pearsons_Decider, respectively). An assumption of parametric analyses that was not tested by the tool was a lack of outliers; this was not included in the tool since the definition of outliers is subjective. However, outliers were analyzed for these correlations using the interquartile range (IQR) method; quartiles were computed in Microsoft Excel and the upper range for outliers was set at quartile 3 + (1.5 × the IQR), and the lower range was set at quartile 1 − (1.5 × the IQR). Outliers were then removed, and the data were run through the Spearman’s-Pearson’s Decider again. Finally, the correlation coefficient for each combination of genus and metabolite with outliers was compared to the coefficient without outliers for the same combination; correlation coefficients that changed > 0.2 were noted as being influenced by the presence of outliers; however, these results were not excluded from corrections for multiple comparisons or the heat map generated with the data. Benjamini–Hochberg false-discovery rate corrections (set to 5%) were applied to the results with and without outliers individually. For each analysis, the statistical test used and n for each group are found in Supplemental Tables 2 , 3 , or 5 ; all tests were two-tailed and α = 0.05 for each analysis. When comparisons were made, all relevant treatment groups were included.

Several alpha diversity measures were analyzed since bacterial evenness and richness can be incorporated to varying degrees in different measures 41 . Bacterial richness measured by the unique ASV counts observed did not significantly differ between treatment groups (Fig. 2 a). Additionally, no differences between treatment groups were identified using Simpson’s evenness measure E (Fig. 2 b) or when richness and evenness were combined in the Shannon–Wiener Diversity Index H (Fig. 2 c). No significant differences between treatment groups were observed using the Chao1 richness estimate 42 , which accounts for ASV rareness in the richness measure (Supplementary Fig. S2 ). Furthermore, there were no differences between treatment groups for Simpson’s Diversity Index (Supplementary Fig. S2 ) or phylogenetic diversity as measured by Faith’s formula applied to the whole phylogenetic tree (Supplementary Fig. S2 ). Although the mean values for each of the alpha diversity measures were often largest for the 5A-1MQ-treated group switched from WD to LD relative to all other groups (Fig. 2 ; Supplementary Fig. S2 ), this trend did not rise to the level of statistical significance. Figure 2 Alpha diversity measures did not differ significantly across treatment groups, and unweighted UniFrac beta diversity analysis exhibited partial clustering by treatment group. Observed unique ASV count ( a ), Simpson’s Evenness Measure E ( b ), and Shannon Diversity Index H ( c ) were all statistically indistinguishable between groups; graphs depict mean + /− SEM. Unweighted UniFrac analysis is presented as a PCoA ( d–f ) and exhibits distinct clustering, with a large separation of the LD control group from the other groups.

Six distinct phyla were identified in the cecal microbiomes, but not all were present in each sample. Sequences that could not be definitively assigned to a phylum were labeled “unclassified” (Fig. 3 a; Supplementary Table S3 ). Of these seven groups, the Firmicutes , Proteobacteria , and Verrucomicrobia phyla demonstrated a significant main effect of treatment group on relative abundance after Benjamini–Hochberg correction for multiple comparisons, while Bacteroidetes and Actinobacteria demonstrated significant main effects of treatment group on relative abundance before Benjamini–Hochberg correction. Post-hoc comparisons showed that the Firmicutes phylum’s relative abundance was significantly higher in WD control mice compared to mice in the vehicle- and 5A-1MQ-treated groups switched from WD to LD (Fig. 3 b). In contrast, Proteobacteria relative abundance was significantly higher in both the vehicle- and 5A-1MQ-treated groups switched from WD to LD, compared to both the LD and WD control groups (Fig. 3 c). Verrucomicrobia relative abundance was significantly higher in vehicle-treated mice switched from WD to LD compared to both the LD and WD control groups (Fig. 3 d). Actinobacteria showed a significantly greater relative abundance in WD control mice compared to all other groups (Fig. 3 e). Finally, Bacteroidetes relative abundance was significantly increased in 5A-1MQ-treated mice switched from WD to LD relative to WD control mice (Fig. 3 f), and its relative abundance was lowest in WD control mice compared to all other groups. Figure 3 Microbial phyla exhibit dramatic shifts predominantly mediated by diet switch as opposed to diet composition. ( a ) Relative abundance for each of the phyla identified in the mouse cecal samples as well as the remaining sequences which could not be assigned (unclassified). ( b–d ) Three bacterial phyla demonstrated significant differences following Benjamini–Hochberg correction for multiple comparisons: Firmicutes , Proteobacteria , and Verrucomicrobia. ( e–f ) Actinobacteria and Bacteroidetes both demonstrated significant differences before (but not after) Benjamini–Hochberg correction. ( g ) The ratio of Firmicutes:Bacteroidetes was significantly lower in the WD/LD-T mice compared to those maintained on WD, and there was a trend for a similar effect in the WD/LD-V mice. Graphs depict mean + /− SEM; unless specified, multiple comparisons were significant before and after FDR correction ( p < 0.05 and q < 0.05); further statistical details can be found in Supplementary Table S3 . a , significantly different than the lean control group (LD/LD-V); b , significantly different than the obese control group (WD/WD-V); ^ , p < 0.05 but q > 0.05. The ratio of Firmicutes to Bacteroidetes is a commonly-studied change with an increase often linked to HFD consumption/obesity in mice 43 , 44 and humans 45 , 46 (for review, see John and Mullin, 2016 47 ). In the current study, the ratio of Firmicutes to Bacteroidetes was significantly higher in WD control mice compared to both groups of mice transitioned from WD to LD (Fig. 3 g). Although there was a clear trend for a higher Firmicutes : Bacteroidetes ratio in the WD control group relative to the LD control group, this difference did not rise to the level of statistical significance. On average, the Firmicutes : Bacteroidetes ratio for the WD control group was more than 2-fold greater than the groups that ended the study on LD.

Several ASVs mapped with high confidence directly to a specific species (Supplementary Table S4 ), with considerably more species mapped using the BLASTn method compared to the BLCA method. Both methods generated the same species assignments for 60 ASVs, whereas 11 ASVs were mapped to dissimilar species by the different methods. Importantly, all ASVs within six of the genera mapped to identical species when analyzed by both methods. For Erysipelatoclostridium, every ASV mapped to [ Clostridium ] cocleatum . The only ASV identified as Lactococcus mapped to Lactococcus lactis . All ASVs for Parasutterella mapped to Parasutterella excrementihominis . Every ASV for Romboutsia mapped to Romboutsia ilealis ; the only ASV identified for Staphylococcus mapped to Staphylococcus xylosus , and the only ASV found for UBA1819 mapped to Ruthenibacterium lactatiformans . In six instances, ASVs that had not been assigned a genus by DECIPHER mapped consistently to a genera and species across the BLASTn and BLCA methods; the majority (i.e., four) of these ASVs mapped to Dubosiella newyorkensis while the remaining two ASVs mapped to Romboutsia ilealis .

Five clusters were used for k-means clustering, per the results of the sum of squared estimate of errors curve, on the ASV relative abundance data with clustering by animal (n = 25). The two largest clusters contained 18 and 4 animals (Fig. 6 ). The latter cluster contained exclusively 5A-1MQ-treated animals switched from WD to LD (the WD/LD-T group), representing ~ 60% of all WD/LD-T animals. The other three treatment groups were represented approximately equally in the largest cluster, with a maximum of one animal per treatment group outside the cluster; however, the largest cluster included only two mice from the 5A-1MQ-treated group switched from WD to LD. The lack of substantial overlap between these two predominant clusters suggests that the WD/LD-T group has a distinct pattern of ASV relative abundances in its microbiome compared to the other treatment groups. Figure 6 K-means clustering of the ASV relative abundance data by animal resulted in two primary clusters, one of which exclusively contained 5A-1MQ-treated mice transitioned from WD to LD (the WD/LD-T group). Cluster 1 contained only one vehicle-treated mouse transitioned from WD to LD (WD/LD-V). Cluster 2 contained four 5A-1MQ-treated mice transitioned from WD to LD (WD/LD-T), while Cluster 3 contained five LD control mice (LD/LD-V), five WD control mice (WD/WD-V), six WD/LD-V mice, and two WD/LD-T mice. Cluster 4 contained one LD/LD-V mouse and Cluster 5 contained one WD/LD-T mouse. Collectively, k-means clustering supported the conclusion that 5A-1MQ treatment elicits a unique ASV profile relative to the other treatment groups.

Effective interventional treatments are critically needed to address ever-increasing rates of global obesity and obesity-linked comorbidities. NNMTi-treatment offers a promising therapeutic approach to address this unmet need 8 , 9 , 13 . There is growing evidence that the microbiome may be critical for establishing, maintaining, and reversing obesity 14 – 16 , and that both diet composition and adiposity each contribute to modulation of the mouse gut microbiome profile 49 . Consequently, it is important to understand how the microbiome responds to NNMTi-focused obesity treatments. Multiple mechanisms may interrelate NNMTi treatment with the microbiome. Since NNMT is expressed in adipose, hepatic, and gut tissues 50 – 52 , and these tissues heavily cross-talk with the gut microbiome 53 – 55 , NNMTi treatment or its resulting weight loss effects 8 , 9 may modulate the microbiome. Additionally, the NNMT substrate nicotinamide 12 is metabolized by the microbiome 56 , 57 , and thus NNMTi may modulate substrate availability, potentially influencing microbial metabolic capacity to drive the NNMTi-mediated weight loss. Given these potential mechanisms, this study was designed to test the hypothesis that DIO mice restored to the weight and adiposity of LD controls through NNMTi treatment and LD switch 13 have a similar microbiome profile to mice continuously maintained on LD. Interestingly, we observed that the cecal microbiome of previously-obese animals returned to the lean state through NNMTi treatment and LD switch was distinct from the cecal microbiome of continuously lean control animals. Although we expected the largest microbiome differences to occur in the lean control group relative to the obese control group, we found that the diet-switched groups exhibited a greater number of differences from the control groups than the control groups did from one another. Importantly, this proof-of-concept study showed that the relative abundance of Lactobacillus , a genus associated with weight loss 58 , was uniquely increased and Erysipelatoclostridium was distinctly decreased in mice given 5A-1MQ treatment and an LD switch compared to mice switched to LD and given vehicle treatment. Erysipelatoclostridium is a member of the Erysipelotrichaceae family, which has been linked to obesity and related metabolic issues 59 . The distinct microbiome profile of 5A-1MQ-treated mice was also supported by k-means clustering. Furthermore, the relative abundance of Akkermansia , a genus positively associated with improvements in obesity-related metabolic issues 60 , was increased in the vehicle-treated group that underwent diet switch relative to the WD control group, suggesting a potential for distinct microbiome-mediated mechanisms of weight loss subsequent to diet switch from those of diet switch in combination with 5A-1MQ treatment. However, the relative abundance of the genus Parasutterella , commonly decreased by HFD 61 , was increased in both the 5A-1MQ- and vehicle-treated mice switched from WD to LD, and the abundance of Parasutterella in all mice exhibited several correlations the EWAT metabolome. This may suggest that Parasutterella changes are linked to the diet switch or the series of diets the animals have experienced in their lifetime. A summary of these key results is found in Fig. 8 (graphic generated by ADW using creative commons- and Adobe-licensed images and PowerPoint). Figure 8 Key results summary. This figure was generated using a combination of open-source images, purchased Adobe stock photos, and PowerPoint. In the present study, the cecal microbiome differed significantly across treatment groups, despite tightly controlling for several variables known to alter the microbiome in rodents. For example, parameters that have been shown in other studies to alter cecal and fecal microbiomes (e.g., vendor source 62 , 63 ) were consistent across experimental groups in this study. Importantly, animals were singly housed to eliminate the exchange and cross-contamination of intestinal microbiomes, a factor that potentially biases studies with group-housed animals by minimizing microbiome variations for co-housed animals within a cohort. Unfortunately, singly housing is not commonly reported. For instance, the majority (58%) of mouse microbiome studies published in 2018–2019 did not report the number of mice per cage, and less than 20% of studies reported using singly-housed animals 64 . While singly-housed animals may experience stress that impacts metabolism, body weight, and adiposity 65 – 69 , the singly-housed animals in this study were treated identically (to the best of the experimenter’s ability) to minimize and equalize stress across all animals and reduce confounding variables. Alpha diversity measures, calculated using several distinct methods, did not significantly differ between treatment groups. However, groups switched from WD to LD (WD/LD-V, WD/LD-T) tended to show greater alpha diversity compared to t

The online version contains supplementary material available at 10.1038/s41598-021-03670-5.