Sugar-rich foods exacerbate antibiotic-induced microbiome disruption

The intestinal microbiota modulates host metabolism and immunity, and perturbations of microbiome composition are linked to various disease states². Although a precise definition of a homeostatic human microbiome composition remains unclear^3,4, a number of disease states share dysbiotic patterns in microbial community compositions, often characterized by loss of diversity and expansion of facultative anaerobes⁵. Understanding the factors that influence microbiome disruption and dysbiosis is key to deciphering the interactions between host and commensal organisms and to designing therapeutic strategies that target the microbiome.

Host and environmental factors shape intestinal microbiome compositions; in particular, a major effect of diet has been observed both in mice and humans^1,6,7,8,9,10. However, most of these human analyses were either in small dietary-intervention trials of individuals with chronic conditions or of healthy volunteers with unperturbed, stable microbiomes. Severe perturbations, such as those that occur during acute illness or intensive medical treatment are less well understood^4,11. Controlled studies in mice and humans have indicated that the recovery of mouse microbiome composition after antibiotics can be impaired or facilitated by withholding or augmenting dietary fibre, respectively^10,12,13. Moreover, while some studies suggest that dietary perturbations exert effects on microbial composition within hours^14,15,16,17, most human studies have correlated faecal microbiome compositions with long-term habitual diet^7,18,19,20 or variations on months-long timescales^21,22 and relied on recall-based surveys, the imprecision and limitations of which have been well described^23,24. Taken together, while diet is assumed to be a major determinant of microbiome composition, an understanding of precise diet–microbiota interactions in humans under real-world disease and treatment conditions is lacking.

Patients with blood cancers who undergo allogeneic haematopoietic cell transplantation (allo-HCT) are typically hospitalized for several weeks, during which they receive chemotherapy and, frequently, antibiotics. During this time, they exhibit substantial changes in nutritional intake²⁵, as well as severe microbiome perturbation^4,26, including loss of α-diversity and expansion of facultative anaerobe organisms, most often Enterococcus²⁷. These shifts are associated with adverse clinical outcomes, including mortality^26,27. These microbial–ecological disruptions are driven largely by antibiotics^4,28, as well as by intestinal inflammation induced by chemotherapy, irradiation and graft-versus-host disease (GVHD)^29,30. Here we hypothesize that nutrition contributes to these microbiome dynamics, and we reasoned that microbiome perturbations offer an opportunity to reveal these relationships by tracking microbiome composition alongside precise dietary records.

The weeks during which patients undergoing allo-HCT are hospitalized facilitate frequent collection of both stool specimens and high-precision dietary intake data; considering these data in light of such therapy-induced perturbations enables causal-inference approaches^31,32. We therefore aimed to quantify associations between dietary intake and microbiome composition to reveal mechanistic insights into diet-inducible microbiome modulation. For this, we collected daily dietary intake data, paired with longitudinally collected faecal samples. We profiled faecal microbiome compositions and revealed associations between key microbiome features and dietary components. Intake of sweets during antibiotic exposure predicted lowered faecal microbiome α-diversity, as well as increased Enterococcus abundance, suggesting a model whereby simple dietary sugars favour the expansion of pathobionts that persisted through antibiotic treatment. We validated this prediction in a mouse model in which dietary sugar supplementation exacerbated and prolonged antibiotic-induced Enterococcus blooms. This study advances our understanding of microbiome dynamics outside of the steady state and identifies potential interventional approaches; for example, short-term sugar avoidance is a strategy that could be readily evaluated in HCT and across patient cohorts treated with various antibiotics.

Allo-HCT begins with an intensive course of chemotherapy conditioning that can cause painful mouth and throat lesions (mucositis), nausea and poor appetite (Fig. 1a). Consequently, patients often have a poor diet and lose weight during a period of increased caloric needs²⁵. These symptoms typically persist until the graft is established in the bone marrow (engraftment) and produces neutrophils—observed in this cohort after a median of 12 days (range, 8–37 days). The severe degree of nutritional perturbation is underscored by the observation that a subset of patients in the cohort had such a low dietary intake that they required nutrition support, either through intravenous infusion (total parenteral nutrition (TPN); n = 23 patients for a median of 11 days; range, 2–63 days) or a gastric tube (enteral nutrition, EN; n = 5 patients for a median of 76 days; range, 5–156 days).

We recorded the precise intake of food items consumed during 9,419 meals (Fig. 1b) consumed by 173 allo-HCT recipients with blood cancers (Supplementary Table 1; range, 8–128 meals per patient) and also collected faecal samples during this period (Fig. 1c). We mapped meal records to a hierarchical food nomenclature (Food and Nutrient Database for Dietary Studies, FNDDS) that classifies foods into nested categories, resulting in nine distinct food groups. A food taxonomy constructed from these categories (Fig. 1d) facilitated analysis of specific food types at various hierarchical aggregation levels, as well as the application of metrics from ecology to summarize nutritional intake data⁸. Thus, the high-frequency, granular dietary intake data in this study takes the form of, for example, ‘1 day before the faecal sample was collected on day −5 relative to HCT, the patient consumed 142 g of baked, broiled or roasted chicken breast’, which is distinct from the resolution offered by typical food-frequency instruments (for example, ‘chicken two times per week’).

To visualize the high-dimensional dietary dataset, we applied the TaxUMAP algorithm³ such that each point represents a patient’s total food consumption on a single day; gradients across dietary TaxUMAP space indicated that patients consumed more calories early during transplantation in the form of more complex meals (Fig. 1e–h), an observation that was confirmed in plots of daily caloric intake (Fig. 1i; P < 0.01) and dietary α-diversity over time (Fig. 1j; P < 0.01).

After filtering to include evaluable microbiome samples with matching dietary data, we included 1,009 stool samples profiled by 16S rRNA gene sequencing from a subset of 158 patients (median, 5 samples per patient, range 1–28; Fig. 1c, Extended Data Fig. 1a and Supplementary Table 2). A temporal decline was observed in faecal microbiome α-diversity (Fig. 1k), as seen in other allo-HCT cohorts²⁶, that mirrored the trends in caloric intake and dietary α-diversity (Fig. 1i,j). Intake of the macronutrients carbohydrates, fat, fibres, protein and sugars all declined from hospitalization until day 12 (the median time to neutrophil engraftment in this cohort) (Fig. 1m; false-discovery rate (FDR)-adjusted q < 0.001). The average intake of all specific FNDDS food-group items except for ‘sugars, sweets and beverages’ also declined over this period (Fig. 1l; FDR-adjusted q < 0.01).

Shifts in microbiome composition (dissimilarity from baseline) were moderately, albeit significantly, associated with shifts in dietary composition from each patient’s baseline (Fig. 1n,o; slope = 0.6, P < 0.001). Substantial variability in microbiome perturbation remained unexplained by diet alone, suggesting the influence of other factors (Extended Data Fig. 1b). Despite these global dietary trends, intrapatient and interpatient dietary intake was highly variable (Supplementary Table 6), calling for a granular investigation of dietary association with microbiome disruption patterns.

We developed a Bayesian model to quantify the contribution of dietary intake to microbiome composition in the context of potentially confounding clinical variables. We chose a dietary exposure period of 2 days preceding each faecal sample, as this window provided a reasonable correlation between microbiome composition and dietary composition (Fig. 2a) and was consistent with a previous report⁸. The model considers treatment regimen (conditioning) intensity³⁰ and exposures during the 2 days before sample collection: nutrition-support events (TPN and EN), antibiotics and dietary intake as fixed-effect predictors of microbiome composition; and an interaction term between diet and broad-spectrum-antibiotic exposure, which captures an additional dietary effect on the microbiome when antibiotics are administered (Fig. 2b).

All 173 patients received at least one antibiotic during their dietary collection period. They were typically treated initially with prophylactic antibiotics (fluoroquinolones and intravenous vancomycin). Importantly, 138 out of 173 (80%) patients were switched from prophylactic antibiotics to broader-spectrum antibiotics during this period when they developed fever or other signs of potential infection (most commonly piperacillin–tazobactam, cefepime, linezolid, or a carbapenem for neutropenic fever or bloodstream infection; and metronidazole or oral vancomycin for Clostridioides difficile diarrhoea). As they are major drivers of dysbiosis in HCT^4,28,33, we considered exposure to these broad-spectrum antibiotics explicitly in the model (Extended Data Fig. 2). To accommodate other exposures not explicitly captured that might vary between patients and over time, we included varying intercepts for the week relative to transplantation and varying intercepts per patient (Extended Data Fig. 1c) to correct for differences between patients and to account for repeated samples from the same individuals.

We applied this model to 1,009 stool samples from 158 patients to quantify associations of antibiotics, clinical parameters and dietary food groups with microbiome α-diversity, a microbiome feature that we have previously correlated with mortality in this population²⁶. Consistent with our previous report³⁰, faecal samples from recipients of the mildest conditioning regimen (non-ablative) had the highest average α-diversity (Fig. 2c), and exposure to broad-spectrum antibiotics in the previous 2 days was negatively associated with bacterial α-diversity, as expected (Fig. 2d; abx term, mean = −0.21, 95% credible interval (CI) = −0.38 to −0.05). Notably, although intake of food items categorized as sugars, sweets and beverages (hereafter, sweets) alone was not associated with faecal microbiome α-diversity, their intake before the collection of antibiotic-exposed samples was associated with the largest diet-mediated decrease in α-diversity: for every 100 g increase of intake after broad-spectrum-antibiotic exposure, the model predicted an additional 24.1% decrease (95% CI = 7.7–38.1%) in mean α-diversity compared with samples without previous antibiotic exposure (Fig. 2d). Combined exposure to sweets and antibiotics was also associated with a shift toward compositions enriched for features such as community domination, for example, by Enterococcus faecium (Extended Data Fig. 3). These associations suggested that dietary sugars could synergize to exacerbate the impact of antibiotics.

To verify this independently of the FNDDS classification of food items, we summarized the recorded meals by their macronutrient composition, performing an equivalent analysis on the now differently encoded dietary data. This confirmed that intake of sugars during antibiotic exposure (abx × sugars interaction term) was significantly additive to the antibiotic-associated α-diversity reduction (Fig. 2e), with a further mean decrease of 20.6% per 100 g (95% CI = 2–35.6% decrease). Although protein was omitted from the main macronutrient model as it was highly collinear with fat (r = 0.82, P < 0.001), a sensitivity analysis with terms for each of the major macronutrient categories confirmed the interaction between antibiotics and sugar intake predicting low α-diversity (Extended Data Fig. 4a,b).

These associations were surprising because sugar-enriched oral nutritional supplements are commonly recommended to transplant recipients³⁴ (Extended Data Fig. 4d). A higher-resolution model suggested that key contributors to the relationship between sugar-enriched foods, antibiotic exposure and low microbial α-diversity included formulated nutritional beverages (such as sports drinks and nutritional shakes; Extended Data Fig. 4e), as well as added, as opposed to naturally occurring, sugars (Extended Data Fig. 5a–d). To ascertain these findings, we examined several potential confounders in variants of the model. The interaction between sweets consumption and antibiotics remained a significant predictor of low microbiota α-diversity in models that considered underlying malignant disease, comorbidities or intensive analgesic exposure as an indicator of the most severely ill states (Extended Data Fig. 5e). We also confirmed the association was not simply explained by certain confounding factors, as it was still observed in subset analyses that excluded patients exposed to EN or TPN (Extended Data Fig. 5f), as well as in a subset that excluded samples that were collected during treatment of confirmed infections (Extended Data Fig. 5g). Finally, a similar finding was also observed in a subset analysis of samples that were collected early in transplantation, before the onset of severe mucositis, which can limit consumption of a regular diet (Extended Data Fig. 5h).

Having observed a robust relationship between microbiome disruption and concurrent exposure to antibiotics and sweets, we also analysed the raw data without adjustments for the many clinical variables encoded in the Bayesian model. Indeed, a statistically significant inverse relationship was apparent between grams of sweets consumed and faecal microbiota α-diversity in samples from patients who were exposed to antibiotics in the 2 days before sampling (Spearman correlation = −0.2, P < 0.01), although substantial variation remained. However, this relationship was not observed in the absence of antibiotic exposure (Spearman correlation = 0.04, P = 0.28) (Fig. 2f). Consistent with this, marginal-effects plots from the Bayesian model revealed an independent relationship between food group sweets intake and microbiota α-diversity only during periods of antibiotic exposure (Fig. 2g); the analogous model of macronutrients as dietary predictors also demonstrated a diverging effect of sugar intake on microbiota α-diversity dependent on antibiotic exposure (Fig. 2h). We also confirmed that these results did not depend on the way in which foods were classified (Extended Data Figs. 4f,g and 6a,b), nor driven by over-represented patients (Extended Data Fig. 5l), nor were they explained simply by total caloric intake in the 2 days before faecal sampling (Extended Data Fig. 4c).

As microbiome disruption is associated with poor prognoses after transplantation^26,27,28, we next examined whether the synergy between broad-spectrum antibiotics and high dietary sweets intake is predictive of clinical outcomes. We first dichotomized the cohort at the median sugar intake, normalized to total caloric intake and averaged across HCT days −7 to 12 (Supplementary Table 3). Among those who consumed an above-median intake of sugar, the duration of broad-spectrum antibiotic exposure correlated with a higher mortality risk (Fig. 3a; hazard ratio (HR) = 1.12, 95% confidence interval = 1.01–1.23, P = 0.027, for each additional day of antibiotic exposure in a model adjusted for conditioning intensity, graft source and GVHD prophylaxis; Supplementary Table 4 and Extended Data Fig. 6j). This was not observed in the below-median sugar-intake group (HR = 1.07, 95% confidence interval = 0.96–1.19, P = 0.23). A concordant result was observed in a separate analysis based on dietary timeseries by defining two clusters of dietary patterns using latent trajectory class analysis (Supplementary Table 5). Cluster 1 was characterized by lower average daily food intake (914 kcal per day less, P < 0.001) as compared to more sustained higher intake overall in cluster 2 (Extended Data Fig. 6e). Cluster 1 also had a higher proportion of sugar in the diet (10.5% higher, P < 0.001) compared with cluster 2 (Extended Data Fig. 6f). The duration of antibiotic exposure was significantly associated with higher mortality risk in cluster 1 (HR = 1.11, CI = 1.01–1.22, P = 0.024, for each additional day of antibiotic exposure) but not cluster 2 (HR = 1.06, CI = 0.93–1.21, P = 0.36) in a multivariable model accounting for conditioning intensity, graft source and GVHD prophylaxis (Fig. 3b and Supplementary Table 6). Patients in cluster 1 also had longer hospitalizations after neutrophil engraftment (Extended Data Fig. 6i). Thus, mortality risk after allo-HCT correlated with duration of antibiotic exposure in patients who consumed dietary patterns characterized by relative sugar enrichment and overall lower intake.

We next examined whether sugar intake during antibiotic-induced microbiome perturbations might favour blooms of certain bacteria²⁶. Although a variety of taxa was correlated with food-group and antibiotic exposures (Extended Data Fig. 7a), we examined the genus Enterococcus and, specifically, E. faecium as the species that expanded most prominently during microbiome disruption (r = −0.34, FDR-adjusted P < 0.001; Fig. 4a and Extended Data Fig. 7b–d), consistent with our previous report²⁷. Notably, Enterococcus is an important cause of antibiotic-resistant bloodstream infections and is a microbiome surrogate of adverse clinical outcomes after HCT, including GVHD and mortality, and it exacerbates GVHD disease in mice^4,27,35. After antibiotic exposure, each 100 g of additional sweets intake was associated with a 0.89 U increase in the centred log ratio (CLR)-transformed abundance of E. faecium (95% CI = 0.22–1.58), compared with samples without previous antibiotic exposure (Fig. 4b and Extended Data Fig. 7e,f).

These observations in patients undergoing intensive cancer treatment suggested a model by which antibiotic-induced microbiome disruption, as manifested by α-diversity loss and pathobiont expansion, could be exacerbated by the consumption of sweets. To test this hypothesis experimentally, we established a system in which microbiome disruption was induced in C57BL/6 mice with a single subcutaneous injection of the carbapenem antibiotic biapenem. This reproducibly induced a moderate expansion of endogenous enterococci by 2–3 orders of magnitude that peaked at day 3 and largely resolved by day 6, as assessed by dilution plating on enterococcal-selective agar (Fig. 4c). As predicted by the analysis of patient data, supplementation of standard diet with sucrose exacerbated the expansion of enterococci by 16.3-fold on day 3 (P = 0.048) and by 33.4-fold on day 6 (P = 0.001). Notably, sucrose treatment alone had no effect on enterococcal burden in the absence of antibiotics.

Recapitulating the patterns observed in patients, Enterococcus expansion was accompanied by a decline in α-diversity and a shift in global composition (P < 0.001; Extended Data Fig. 8a–f). We considered the possibility that sucrose supplementation might reduce the intake of chow, the fibre component of which might modulate the response of the microbiome to antibiotics^12,13. However, food consumption was not appreciably reduced by sucrose supplementation (Extended Data Fig. 8g,h), nor did these brief treatments significantly affect mouse weight (Extended Data Fig. 8m). Sucrose prolonged antibiotic-induced Enterococcus expansion in mice maintained on a fibre-free diet (Extended Data Fig. 8l), indicating a fibre-independent effect of sugar. Moreover, monosaccharide components of sucrose were sufficient on their own to augment the antibiotic-induced enterococcal bloom, as was blended fruit purée (Extended Data Fig. 9a–c). Sucrose supplementation had no significant effect on the kinetics of Enterococcus faecalis monocolonization in germ-free mice (P = 0.7, Extended Data Fig. 9d), suggesting that this sucrose effect requires the presence of a complex microbiota perturbed by antibiotics. Sucrose also prolonged the antibiotic-induced enterococcal expansion in mice treated with bone marrow transplants (Extended Data Fig. 9h). Collectively, we provide evidence from real-world patients that consumption of dietary sugars during or after antibiotic exposure decreases microbiome α-diversity and drives Enterococcus expansion, a microbiome–diet dynamic that we validated in vivo.

In patients undergoing allo-HCT, intake of sugar-rich foods during treatment with broad-spectrum antibiotics predicted microbiome disruptions that we previously linked to adverse outcomes: low α-diversity, and relative expansion of Enterococcus^26,27. Moreover, among patients in this cohort who consumed sugar-enriched diets, the duration of broad-spectrum antibiotic exposure predicted mortality risk. The increased mortality risk associated could also stem, in part, from lower food intake. The therapy-induced perturbations in this clinical setting, along with high-frequency serial dietary and faecal sampling, served as a natural experiment that enabled causal-inference approaches distinct from healthy-cohort or volunteer-feeding study designs^{1,8,9,10,12,15,18,32,36}. The hypothesis raised by this clinical observation—that sugars and antibiotics synergize to disrupt the microbiome—was tested experimentally in mice, in which we confirmed that sucrose exacerbated and prolonged antibiotic-induced expansion of endogenous Enterococcus. This is consistent with other reports that simple carbohydrates can induce dysbiosis and exacerbate experimental colitis or metabolic disease^{37,38,39,40,41}, and it expands the dietary determinants of antibiotic-induced microbiome perturbation beyond the well-described role of fibre^12,13.

Several mechanisms could underlie this synergy. Sucrose could have a positive effect on the growth of pathobionts such as Enterococcus⁴², or a suppressive effect on colonization resistance that is usually conferred by specific commensal organisms^43,44. Alternatively, as most simple sugars are absorbed in the small intestine, sucrose may modulate the host, which, in turn, shapes microbiota composition^39,45. Limitations in the analysis of the human cohort include the practice of near-universal prophylactic use of fluoroquinolones, so the reported interaction may more precisely reflect dietary intake in the context of escalation to broad-spectrum antibiotics, rather than exposure to any antibiotic per se. Furthermore, as the initiation of broad-spectrum antibiotics is clinically driven (for example, fever or suspected infection) there may be a confounding association with the severity of the condition necessitating antibiotic therapy. Although patients were invited to report dietary intake outside of the items provided by the hospital, records regarding outside food are incomplete. Moreover, transient symptoms can affect dietary preferences and introduce additional time-varying confounders.

Despite these limitations, our results identify dietary simple sugars as a modifiable amplifier of antibiotic-induced microbiome disruption. In the context of HCT, prospective trials are needed to test whether corresponding dietary modulation improves clinical outcomes. More broadly, limiting sugar intake during antibiotic therapy may help mitigate microbiome injury.

Patients

In this cohort study of adult allo-HCT recipients at Memorial Sloan Kettering Cancer Center in New York between 2017 and 2022, eligible participants had been admitted for Allo-HCT. Three times weekly, patients who had been recently admitted for allo-HCT were invited to participate in dietary-intake collection. Neutrophil engraftment was defined as the first of 3 days of neutrophil count ≥500 per μl. Five patients died without achieving engraftment and were excluded from the analysis of median time to engraftment. No patients were lost to follow-up. For mortality analysis, one patient died before landmark day 12 and was excluded from that analysis. The latest follow-up/data collection date was in April 2023.

Nutrition data collection and annotation

The hospital kitchen commercial computer system (Computrition) was configured to provide a printout that accompanied each meal tray to the bedside on which patients were asked to indicate, immediately after each meal, whether they consumed 0, 25%, 50%, 75% or 100% of each item they ordered for that meal. These instruments were collected by a dietician or other research team members three times weekly, during which missing entries were completed at the bedside along with encouragement to sustain motivation with the project. Consumption data were entered into the kitchen software, which was linked to the recipe and mass of each item. Data were manually vetted by a research dietitian for quality control (for example, grossly implausible kcal values resulting from sporadic typographic errors in the hospital kitchen records). Habitual dietary patterns before admission were not surveyed; the analysis was focused on inpatient dietary intake. Per-patient intercept terms were included in the Bayesian model (see below), which may capture interpatient variability in unmeasured parameters such as patient-specific pretransplant dietary habits.

An eight-digit food code was assigned to each unique food item according to the classification of the FNDDS⁴⁶. The numeric codes are structures such that subsequent numeral positions differentiate foods within larger groups. For example, in the food code 14109010 for Swiss cheese, the first digit, 1, denotes milk and milk products; the second digit, 4, denotes cheeses; and each subsequent digit conveys progressively higher resolution classifications of foods. We removed the water content from the food total weight before analysis. To compute the dehydrated weight of consumed foods, we subtracted the water content, derived from FNDDS water fractions, from the total food weight. However, for EN, which is administered as a liquid and lacks a standardized total weight in grams, we used an additive approach. The dehydrated weight for these enteral formulations was calculated by summing the total gram weight of their protein, fat and carbohydrate components. This approach allows a more accurate comparison of nutrient intake across individuals and different food types, as it eliminates the variability introduced by water content. Therefore, when we refer to each 100 g increase in sweets, we are referring to 100 g of the dehydrated weight of foods within the ‘sweets’ category. This ensures that we are comparing the actual nutrient content, including sugar, rather than the total weight or volume of the food consumed. The analysed dataset therefore listed the dehydrated weight in grams of FNDDS food items consumed in each meal on each day of hospitalization.

Foodtree construction

A food tree was constructed with the 622 unique FNDDS items consumed by the patients in this cohort, as done previously⁷ (https://github.com/knights-lab/Food_Tree). The tree spans nine broad FNDDS food groups, namely, grain products (abbreviated here as grains); vegetables; meat, poultry, fish and mixtures (abbreviated here at meats); milk and milk products (abbreviated here as milk); sugars, sweets and beverages (abbreviated here as sweets); fruits; dry beans, peas, other legumes, nuts and seeds (abbreviated here as legumes); fats, oils and salad dressings (abbreviated here as fats); and eggs. In some cases, food items not explicitly classified in FNDDS contained ingredients from multiple categories (such as milk-based mango smoothie). We addressed this by manually categorizing foods on the basis of which FNDDS food description they fit best to. For example, milk-based mango smoothie, despite having mango in it, fits the description of ‘11553110 fruit smoothie, with whole fruit and dairy’; it was therefore classified in the ‘milk and milk products’ group.

Dietary data analysis: TaxUMAP and diet α-diversity

The hierarchical organization of the FNDDS vocabulary facilitated application of α-diversities to diet data using Faith’s phylogenetic distance⁴⁷, which was implemented in Qiime2 (qiime2-2021.11)⁴⁸ using the qiime diversity alpha-phylogenetic function with the faith_pd metric. The food tree taxonomy was used, as well as the dehydrated weight consumption of the food item represented by food code per patient per day. The TaxUMAP method⁴ was used to visualize compositional similarities between the patients’ daily meals, similar to β-diversity. The fraction of each consumed food represented by a food code per patient per day was used to calculate the food tree taxonomy. Trends in microbiome and nutrition dynamics over time (Fig. 1i–k) were analysed by GEEs using the geeglm function in geepack (v.1.3.9) in R.

Human faecal microbiome analysis

Faecal sample inclusion criteria and flow through the study are detailed in Extended Data Fig. 1a. Samples were transported from the inpatient transplant unit to the laboratory through a pneumatic tube system at room temperature, promptly refrigerated and then aliquoted and frozen at −80 °C within 1 business day. Microbiome profiling by 16S rRNA sequencing was performed in the MSK Molecular Microbiology Facility as described⁴⁹. In brief, bacterial cell walls were disrupted, and nucleic acids isolated using silica bead-beating and phenol–chloroform extraction, and the V4–V5 variable region of the 16S rRNA gene was amplified. Amplicons were purified using either the Qiagen PCR Purification Kit (Qiagen) or AMPure magnetic beads (Beckman Coulter) and quantified using the Tapestation instrument (Agilent). No enrichment of microbial DNA was performed. DNA was pooled to equal final concentrations for each sample and then sequenced on the Illumina platform. Of the 1,009 human faecal samples in this study, 42 were sequenced more than once. The median Bray–Curtis distance between distinct microbiome profiles generated from these same samples was 0.01, which was substantially smaller than the distance between samples from the same patients collected on different days (median value, 0.54; P < 0.0001) and also smaller than the distance between samples from different patients (median value, 0.89; P < 0.0001). This suggests that much more variation in composition is explained by biological differences than by technical sequencing variation. The median read count was 132,978 and the range was from 1,196 to 8,698,524. The 16S sequencing data were analysed using the R package DADA2 (v.1.16.0) pipeline with the default parameters except for maxEE=2 and truncQ=2 in the filterandtrim() function⁵⁰, 16S FASTQ files were capped at 10⁵ reads per sample. ASVs were annotated according to NCBI 16S database using BLAST⁵¹. Microbiome α-diversity was evaluated using the inverse Simpson index—a summary statistic of both the richness and evenness of the bacterial community. Taxa abundances were summarized at the genus level.

Relationships between microbiome α-diversity and clinical factors such as conditioning intensity, exposures to antibiotics, sweets and parenteral nutrition were analysed mainly through Bayesian models that accounted for repeated sampling, time relative to transplantation and other patient-level variables (see the ‘Bayesian multilevel model’ section below). To facilitate visualization of relationships in the unadjusted raw dataset, α-diversity values in various sample subsets are plotted as beeswarm plots in Extended Data Fig. 1f.

To address taxonomic heterogeneity within the Enterococcus genus and the pattern that E. faecium is the most commonly dominating species in this dataset (Extended Data Fig. 7d), we constructed a hybrid taxonomic feature table for the β-diversity analysis in Extended Data Fig. 3. In this taxonomic feature table, the Enterococcus genus was partitioned into two distinct features: (1) Enterococcus ASV_1 (identified as E. faecium through paired shotgun sequencing in a subset of samples (Extended Data Fig. 7c,d), and (2) a composite feature containing all remaining non-ASV 1 Enterococcus reads combined. All other taxa were aggregated at the genus level. β-Diversity was calculated using the Bray–Curtis dissimilarity metric on this hybrid feature table. For visualization, samples were classified as dominated if the relative abundance of any single feature (including the split Enterococcus features) exceeded 30%.

Mouse 16S rRNA gene sequencing and analysis

Mouse stool samples were sequenced using the ZymoBIOMICS Targeted Sequencing Service (Zymo Research). DNA was extracted using the ZymoBIOMICS-96 MagBead DNA Kit (Zymo Research) on an automated platform. The V3–V4 region of the bacterial 16S rRNA gene was amplified using the Quick-16S NGS Library Prep Kit (Zymo Research) with custom primers designed for optimal coverage and sensitivity. PCR reactions were performed in real-time PCR machines to control cycles and limit chimera formation. The final pooled library was cleaned, quantified and sequenced on the Illumina NextSeq system with the P1 reagent kit (600 cycles) using a 30% PhiX spike-in. ZymoBIOMICS Microbial Community Standards were included as positive controls for DNA extraction and library preparation. Negative controls were also included to assess for contamination. Bioinformatics analysis was performed as described in the following sections.

ASV inference

Unique ASVs were inferred from raw reads using the DADA2 pipeline⁵⁰, with removal of potential sequencing errors and chimeric sequences.

Taxonomic classification

Taxonomy assignment was performed using Uclust from QIIME (v.1.9.1)⁵² with the Zymo Research Database as a reference. α-Diversity metrics were calculated using QIIME v.1.9.1. PCoA was performed using the vegan (v.2.5-7) package⁵³ to visualize β-diversity. Differential abundance analysis was performed using MaAsLin2 (v.1.24.1)⁵⁴ to identify differentially abundant taxa between groups. The following parameters were used: normalization = “TSS”, transform = “LOG”, analysis_method = “LM”, max_significance = 0.05, min_abundance = 0.0001, min_prevalence = 0.10. Volcano plots were generated to visualize the results of the differential abundance analysis. The x axis of the volcano plot represents the coefficient from the MaAsLin2 model, indicating the direction and magnitude of the effect. For example, E. faecalis (seq2) exhibited the most significant increase in relative abundance in the biapenem + sucrose group compared with the biapenem-only group (coefficient = 1.58, FDR-adjusted q < 0.05). This coefficient of 1.58 means an approximately threefold change (or a threefold increase) in relative abundance, and the y axis represents the −log₁₀[q], indicating the statistical significance of the association.

Whole-metagenomic shotgun sequencing

For whole-metagenomic shotgun sequencing, profiles were analysed from 333 human and 24 mouse samples. DNA extraction and sequencing was performed as previously described⁴⁹. Metagenomic sequencing data were preprocessed and taxonomic profiles were generated using vdblab-shotgun (https://github.com/vdblab/vdblab-shotgun/). In brief, the samples were deduplicated and quality-filtered using clumpify and bbduk from the BBTools package. Removal of potential human host or mouse DNA was performed with the two-step process described previously⁵⁵. Taxonomic profilers were generated with Metaphlan v.4.0.5, database version mpa_vJan21_CHOCOPhlAnSGB_202103 (ref. ⁵⁶).

Procrustes test

As we collected serial faecal samples and serial dietary intake data, a question arose as to how many previous days of dietary intake should be correlated with any given faecal sample. We therefore assessed concordance between microbiome and dietary datasets by Procrustes analysis, as done previously⁸ across dietary exposure windows of 1 to 5 days preceding each faecal sample. We considered dietary data in two alternative intake metrics: first, by macronutrients; and second, by specific named foods in FNDDS categories. For each window, food-group intake was represented by unweighted UniFrac distances on FNDDS food codes and macronutrient intake by Bray–Curtis distances on five macronutrients, each summarized as the mean daily intake over the window, and stool composition was represented by Bray–Curtis distances at the genus level. Each distance matrix was ordinated by PCoA, and the symmetric Procrustes correlation was computed with the Procrustes function from the vegan (v.2.5-7) package⁵³, with each ordination scaled to unit sum of squares so that the residual sum of squares of the symmetric Procrustes fit (M²) lies between 0 and 1. For 801 stool samples, each of the 5 preceding days was recorded, either as food intake or as verified zero food intake. For 16 of these, the most recent preceding day was a day of verified zero food intake, which gives an empty food composition and therefore no defined UniFrac distance; these were removed, leaving a final analysis set of 785 samples from 143 patients. The marginal gain is the difference in the Procrustes correlation (ΔProcrustes) between successive windows. Confidence intervals and P values were obtained by a delete-one-patient jackknife; the P value is a two-sided test of whether the gain from a given 1-day extension differs from the gain from the next 1-day extension, a difference that was largest and significant for the extension from 1 day to 2 days on food group intake data (Fig. 2a). Moreover, a 2-day window aligns with typical human gut transit time and is consistent with a previous report⁵⁷.

Assessing the relationships between dietary changes and the microbiome changes

To assess the relationship between dietary shifts and microbiome changes (Fig. 1n,o), we calculated dissimilarities from baseline for both dietary composition and faecal microbiome profiles. Dietary dissimilarity was calculated using unweighted UniFrac distances between food-code-level intake for the 2-day period preceding each stool sample. Microbiome dissimilarity was calculated using Bray–Curtis distances at the genus level. For both stool and diet, we calculated the distance between each sampling timepoint and that patient’s baseline. We then constructed a linear mixed-effects model to assess the association between diet distance and stool distance, while accounting for individual patient differences. This model allowed us to test the hypothesis that, on average, as the dissimilarity increases between a patient’s initial and subsequent dietary composition, so does the dissimilarity between their initial and subsequent stool microbiome composition.

Bayesian multilevel model

Bayesian multilevel models were used to analyse these data due to their advantages in handling repeated measurements and providing understanding of uncertainty compared to traditional generalized linear models.

Data preparation

For macronutrient analysis, for each stool sample the included dietary data were summarized as the previous 2-day average intake of sugars, fibres and fat in grams. For the food-group analysis, for each stool sample, the included dietary data were summarized as the previous 2-day average intake of grains, vegetables, meats, milk, sweets, fruits, legumes, fats and eggs. Weights in grams were divided by 100 so that the resulting coefficients represent the expected change in the outcome variable per 100 g intake of the dietary component. With respect to transplant characteristics, conditioning intensity exhibited considerable collinearity with graft source (peripheral blood stem cells versus bone marrow versus cord blood) and with GVHD prophylaxis regimen (Cramér’s V > 0.5, P < 0.001 for both comparisons). As we have previously reported associations between conditioning intensity and microbiome disruption³⁰, we included this variable in the model as a three-level factor variable comprising (in increasing order of intensity) nonmyeloablative (nonablative), reduced intensity conditioning (reduced) and myeloablative (ablative). An alternative variant of the model that included a three-way interaction between sweets, antibiotics and conditioning intensity confirmed that the main sweets–antibiotic interaction predicting low microbial diversity was observed in both ablative and reduced-intensity conditioning (Extended Data Fig. 4j). EN, TPN and patient-controlled analgesia were binary variables encoded as true if the patient was exposed in the 2 days preceding faecal collection and otherwise false. Likewise, if the stool sample was collected after exposure to microbiome-damaging antibiotics in the 2-day window before it, it was set to true; otherwise, false. The microbiome-perturbing antibiotics considered were piperacillin–tazobactam, carbapenems, cefepime, linezolid, oral vancomycin and metronidazole; these were most commonly used as empiric therapy (most commonly for neutropenic fever) or in a pathogen-directed manner (most commonly for bloodstream infections or C. difficile diarrhoea)²⁶. Prophylactic antibiotics fluoroquinolones^58,59 and intravenous vancomycin^60,61 were not considered.

Association of foods and antibiotics with microbiome diversity were also analysed as interaction terms, with the assumption that food correlations with microbiome may be different depending on whether the patients had antibiotics in the previous 2-day window or not. We considered repeated measurements and therefore per-patient differences in microbiome features across samples by implementing a per-patient varying intercept. We also considered additional exposures during hospitalization that we did not capture explicitly and could therefore confound the analyses. Thus, we also accounted for time by grouping samples taken during similar timepoints along the therapy course into weekly categorical time bins, as done previously^62,63; these time bins were included in the model as varying intercepts. Our reasoning for this implementation was that we lack a biologically informed prior model for the effect of time on the microbiome over the short course of several weeks, and because the a priori known causal effectors of microbiome changes such as antibiotic exposures and diet were explicitly included in the model. We partially pooled the data using a varying-effects implementation because we believe that, while the microbiome could vary over time due to unmeasured confounders during the highly planned and controlled HCT treatment regimens, the time bins do contain mutual information justifying their cross-talk during Bayesian inference. The time bins were implemented in the format of weeks relative to transplant; for example, the sampling window expressed in days as [−7,0) is the week before transplant, and [7,14) is the second week after transplant.

When the outcome was microbiome α-diversity, it was analysed as the natural-log-transformed inverse Simpson’s index. When the outcome was genus abundance (Extended Data Fig. 7a), it was analysed as CLR-transformed raw ASV counts of the genus after adding a pseudocount of 0.5 reads using the clr function in the compositions package (v.2.0-6)⁶⁴.

Model construction, sampling and result visualization

The brms (v.2.16.3)^65,66,67 and rstan (v.2.26.4)⁶⁸ packages were used to build and run the model, with the model formula:

for nutritional intake represented as the macronutrients, or:

for nutritional intake represented as the food groups.

In the above model formula, log(simpson_reciprocal) represents the natural logarithm of the inverse Simpson index α-diversity metric. The ave_ terms (such as ave_fruit, ave_meat) represent the average daily intake of each food group in the 2 days before the faecal sample collection. The interaction terms (such as ave_fruit:abx) assess how the effect of each food group on diversity differs depending on antibiotic exposure. The model also includes terms for conditioning intensity (intensity), EN, TPN and antibiotic exposure (abx). Finally, (1|pid) and (1|timebin) are varying intercept terms that account, respectively, for repeated measurements within patients (patient identifier) and for consistent temporal effects of hospitalization not captured by the explicitly modelled variables. This multilevel structure induces partial pooling, a regularization technique that adaptively weights information across participants^69,70. Partial pooling prevents participants with a high frequency of samples from dominating the population-level estimates (overfitting) while simultaneously improving estimates for participants with fewer samples by shrinking them toward the population mean, thereby robustly handling the unbalanced sampling structure inherent to clinical observational cohorts.

Three levels of the conditioning intensity were used as intercept terms with normal distributions with a mean of 2 and s.d. of 0.1 as priors, intercept food group effects in units of per 100 g with or without antibiotic exposure were set to normal distributions with a mean of 0 and s.d. of 1; the coefficient for the binary indicator for antibiotic exposure was set to a normal distributions with a mean of 0 and s.d. of 0.5; EN and TPN exposure were set to normal distributions with a mean of 0 and an s.d. of 0.1. Intercepts were a priori centred on the median diversity observed in previous HCT cohorts. We selected priors to induce a regularizing effect on the many coefficients, reducing the risk of overfitting, and we confirmed that the model was constrained to realistic data domains (Extended Data Fig. 1g). The inference parameters were set as follows: warmup = 1000, iter = 3000, control = list(adapt_delta = 0.99), cores = 16, chains = 2, seed = 123, which means that the model will sample 1,000 steps during warm up, and 3,000 iterations in two chains, and adapt_delta is raised to 0.99 instead of 0.8 to avoid divergent transitions.

To quantify the effect of dietary predictors in concert, we leveraged the full posterior to conduct marginal-effects analyses (Fig. 2g,h), as well as posterior predictive checks (Extended Data Fig. 1h). Prior and posterior predictions were visualized with ggplot (v.3.3.5)⁷¹, tidybayes (v.3.0.2)⁷² and ggpubr (v.0.4.0) packages.

When modelling taxon abundance, the formula was changed to:

with the same running parameters.

Instead of relying on P-value adjustments for multiple testing, our Bayesian approach with regularizing priors provides a conservative assessment of the associations between dietary components and taxon abundances. This approach leverages the full posterior probability distribution to quantify uncertainty, offering a more comprehensive understanding of the relationships.

To identify clinical and dietary correlates of global community shifts, we modelled the sample coordinates on the first two principal coordinates (PCoA 1 and PCoA 2) as continuous outcomes. We used the same Bayesian multilevel regression framework described above, with the formula:

This allowed us to estimate the ‘vector’ of community movement (along axis 1 or axis 2) associated with specific dietary–antibiotic interactions while controlling for repeated measures and clinical confounders.

The marginal effects of each food group consumption on the predicted diversity are calculated with the conditional_effects function from brms package. The method used in the function was posterior_epred. While visualizing the predicted diversity as the intake of one food group changes, the rest of the food groups intake are held at average level, with intensity of the conditioning regimen set to be nonablative with no TPN or EN exposure.

For analysis of the association between diet, antibiotic exposures and α-diversity considering the reason broad-spectrum antibiotics (Extended Data Fig. 5g), samples were classified according to the reason for broad-spectrum antibiotics in the 2-day window before faecal sample collection. First, by pathogen-directed treatment either for bloodstream infection (91 samples from 27 patients) or for C. difficile (101 samples from 17 patients). Samples exposed to broad-spectrum antibiotics without either of these documented sources of infection were then classified as ‘empiric only’ (247 samples from 73 patients). The remaining 570 samples (from 128 patients) were classified as no broad-spectrum exposure.

In silico analysis of ASV-to-species specificity within Enterococcus

To determine the theoretical species-level specificity of the Enterococcus ASVs, we compared them to a database of all possible V4–V5 amplicons derived from complete Enterococcus genomes in the RefSeq database. This analysis was performed using the SYTYCS pipeline (https://github.com/nickp60/sytycs). In brief, theoretical V4–V5 amplicons were identified from the genomes using PrimerSearch⁷³ with the 16S PCR primer sequences used herein (563F, AYTGGGYDTAAAGNG; and 926Rb, CCGTCAATTYHTTTRAGT). The resulting sequences were extracted using seqkit⁷⁴, oriented in the forward direction using vsearch⁷⁵, and then clustered at 100% identity, also with vsearch, to identify unique V4–V5 sequences. These unique sequences were then mapped back to their source genomes to determine which species could produce identical amplicons.

Clinical-outcome analysis

To identify dietary patterns among patients, we applied latent trajectory class analysis⁷⁶ to log-transformed daily intake of the macronutrients fat, fibre, protein and sugar between days −7 and 12 relative to HCT. Day −7 was selected as the starting point as most of the dietary data were collected after day −7 (Fig. 1b); day 12 was selected as the end point as this was the median engraftment day and demarcates the time at which most patients begin to recover. We used cubic natural splines with three nodes (defined at the 10th, 50th and 90th quantiles of the observed timepoints) to model the nonlinear trajectories of macronutrient intake. The optimal number of clusters was determined using the classification entropy extended quasi-likelihood information criterion. We tested whether there was a significant difference in the caloric intake, as well as macronutrient fraction of the daily diet (as proportion of fat, protein, sugar, fibre and other carbohydrates) between the two patient clusters with a GEE model. The model considered the effect of time (days relative to transplant) using natural splines to flexibly model the potential nonlinear changes in sugar fraction over time. Knots were placed at the 20th and 80th percentiles of the time variable. The model also accounted for the correlation between repeated measurements within each patient, with patient identifier as the clustering variable. The model was fitted using the geeglm function from the geepack (v.1.3.11) package in R⁷⁷. The statistical significance was defined as P < 0.05. Next, we incorporated the trajectory cluster labels as a covariate in a multivariable Cox proportional hazards model to assess the association between dietary patterns and mortality end points. The model was adjusted for potential confounders, including the duration of broad-spectrum antibiotic exposure (between day −7 and day 12), conditioning intensity and graft source. Moreover, we included an interaction term between macronutrient intake clusters and antibiotic exposure to evaluate the combined effect of these factors.

Mice

Female C57BL/6 mice 6–8 weeks of age were obtained from Jackson Laboratory, specifically from rooms RB03, RB04, EM07 and RB08, or based on the presence of endogenous Enterococcus. The animals were housed under conditions that included a 06:00 to 18:00 light–dark cycle, at a temperature of 72 °F with a set point ±2 °F and, relative humidity of 30–70%. Mice were single-housed in shoebox-style cages for 1–2 days before each experiment. Baseline faecal samples (day 1) were collected to confirm the presence of endogenous Enterococcus before any manipulation and were processed for enumeration and analysis as described below.

Stool collection and colony counting

Faecal pellets were collected in a biosafety hood directly from each mouse into a barcoded preweighed sterile tube and kept on ice until the faecal pellets were weighed and homogenized in 1 ml of PBS and serially diluted. Then, 20 μl of each dilution was plated onto Enterococcus-selective agar (BD, 212205) in six-well plates and incubated at 37 °C under ambient air for 24 h. Colonies from a well with a countable density were manually enumerated.

Antibiotic intervention and diet preparation

We had noticed that the carbapenem antibiotic biapenem tends to facilitate expansion of endogenous enterococci after subcutaneous injection. We therefore established a model in which 2 mg of biapenem is administered subcutaneously to singly housed C57BL/6 female mice sourced from Jackson laboratories. Mice were treated with a single subcutaneous injection of antibiotic (Biapenem (Sigma-Aldrich; SML0306, 2 mg in 100 ml PBS)) or vehicle (PBS). In addition to standard mouse chow, the animals were fed plain hydrogel in cups (HydroGel, ClearH2O, 70-01-502) with or without added sucrose (Sigma-Aldrich, S0389; 5% sucrose supplement: 1 g per 20 ml hydrogel; and control supplement (diet vehicle): 20 ml plain hydrogel, respectively) or 5% glucose (Sigma-Aldrich, G7021), 5% fructose (Sigma-Aldrich, F0127) where indicated. Cups were replenished every 48 h. In some experiments, sucrose was administered in the drinking water (25 g per 500 ml). We also explored delaying the administration of sugar by 1 or 2 days (Extended Data Fig. 8i–k).

Trapezoidal AUC

The trapezoidal AUC was calculated between days 1 and 3, as well as between days 3 and 6, for each mouse in the experimental setting, following the trapezoidal rule. The day-1 raw counts were subtracted from day 3 and day 6 for each mouse before applying the trapezoidal rule. The reported total trapezoidal AUCs for each treatment group were the sums of the above two separate time periods and they were compared using the Wilcoxon rank-sum test.

Chow consumption

Daily chow consumption in singly housed mice was calculated as the difference between the starting weight and the weight of the chow remaining in the hopper, divided by the number of days between measurements, providing an average daily consumption rate. The trapezoidal AUC was calculated for each mouse over the experimental period to account for repeated measurements. Differences in chow consumption between groups were assessed using the Wilcoxon rank-sum test.

Fibre-free diet experiment

Mice were fed a fibre-free diet (W.F. Fisher and Son; AIN-93G) 5 days before starting the interventions and throughout the whole experiment course, instead of the standard mouse chow used in our institutional vivarium (LabDiet PicoLab Rodent Diet 20, 5053) (diet composition is shown in Supplementary Table 7).

Mouse transplantation model

C57BL/6 (donors) and BALB/c (recipients) mice were procured from The Jackson Laboratory housed at five mice per cage under standard specific-pathogen-free conditions (chow and water ad libitum, 12 h–12 h light–dark cycle). BMT was performed as previously described²⁸. In brief, after split-dose lethal irradiation with 900 cGy, mice received 5 × 10⁶ bone marrow cells that were depleted of T cells (anti-Thy-1.2 (BioXcell) and low-Tox-M rabbit complement (CEDERLANE Laboratories)) through retro-orbital injection. Mice were monitored daily for survival and weekly as described previously²⁸. Then, 3 days after transplant, the mice received 2 mg biapenem or injection vehicle subcutaneously. In addition to food pellets, mice received either control diet supplement of hydrogel (100 ml per cage) with or without 5 g (w/v) sucrose. Grafts were characterized by flow cytometry after staining for surface CD45, CD19, CD3, CD4 and CD8 for 20 min at 4 °C in PBS with 0.5% BSA and counterstaining with DAPI on the LSR-II Fortessa (BD Biosciences) system with FlowJo v.10.10.0 (Tree Star Software).

Monocolonization of germ-free mice

E. faecalis was isolated from a faecal pellet obtained from specific-pathogen-free C57BL/6 mice by plating on Enterococcus-selective agar under ambient air at 37 °C and 5% CO₂. A single colony was inoculated into Difco brain–heart infusion (BHI) broth (BD, 237500) and cultured overnight at 37 °C with shaking. A growth curve was generated to determine that optical density at 600 nm of 0.1 corresponded to about 10¹⁰ colony-forming units (CFU) per ml. For subsequent experiments, a fresh overnight culture was prepared under identical conditions, aliquots were frozen at −80 °C in 15% glycerol and thawed immediately before use for oral gavage. In the initial experiment, six germ-free female 8-week-old C57BL/6 mice housed in isocages at the Cornell University Comparative Cancer Biology Center, Belfer Research Building were orally gavaged with 10⁵ CFU E. faecalis in sterile PBS. Of these, three mice received 2.5% (w/v) sucrose in their autoclaved drinking water for the duration of the experiment. Four germ-free control mice received PBS gavage alone; two of these also received 2.5% sucrose in drinking water. In the repeat experiment, which did not include germ-free controls, two germ-free mice received E. faecalis with regular water and three germ-free mice received E. faecalis with 2.5% sucrose in drinking water. Faecal pellets were collected at the baseline (pre-gavage) and at 4, 8, 24, 48 and 144 h after gavage for colonized groups. For germ-free controls, faecal pellets were collected at the baseline and at 4, 48 and 144 h after gavage. Stool was plated and Enterococcus colonies were enumerated as described above for the colonized mice. To confirm germ-free status, stool from germ-free mice was plated onto BHI agar under both anaerobic and aerobic conditions and incubated for 5 days to monitor for bacterial growth.

Isolation of intestinal epithelium and RNA extraction

The small and large intestines were excised, opened longitudinally on a sterile, moistened paper towel and cut into approximately 1 cm segments. Tissue pieces were transferred to a new Petri dish containing PBS and residual mucus was gently removed from both sides using a coverslip (two gentle passes per side). Cleaned intestinal segments were then transferred into a 2 ml Eppendorf tube containing PBS and minced into small fragments. The fragments were moved to a 50 ml Falcon tube with PBS and shaken at 80 rpm for 10 min at 4 °C. The supernatant was carefully removed, and the tissue was incubated in 12 ml of 10 mM EDTA for 20 min at 80 rpm in a cold room. After allowing the fragments to settle, the supernatant was discarded, and 10 ml of PBS was added. The suspension was shaken vigorously by hand (approximately 40 times) to detach epithelial cells. The resulting cell suspension was passed through a 70-µm cell strainer, centrifuged at 350g for 10 min at 4 °C to remove debris and the supernatant was discarded. The epithelial tissue pellet was resuspended in 1 ml of RNAlater (Sigma-Aldrich, R0901) and stored at 4 °C until RNA extraction. Total RNA was extracted using the RNeasy Mini Kit (Qiagen, 74104) according to the manufacturer’s instructions, including on-column DNase treatment to remove genomic DNA contamination.

RNA-seq library preparation and sequencing

For RNA-sequencing (RNA-seq) analyses, the RNA integrity number was assessed using the Agilent Bioanalyzer. Sequencing libraries were prepared and sequenced on an Illumina NovaSeq X to generate 100 bp paired-end reads at the MSKCC Integrated Genomics Operations core facility.

RNA-seq data analysis

Raw sequencing reads (FASTQ) were processed using the Illumina DRAGEN (Dynamic Read Analysis for GENomics) Bio-IT Platform, v.4.2.7. The DRAGEN pipeline was executed in RNA-aware mode (--enable-rna=true) against the mouse reference genome GRCm39; duplicate reads were marked, and the resulting alignments were delivered as coordinate-sorted, indexed BAM files. Gene counts were generated using featureCounts (v.2.1.1) against the GENCODE mouse basic gene annotation, release vM37 (GRCm39; https://www.gencodegenes.org/mouse/release_M37.html). Bioinformatic analysis was performed in R (v.4.4.1). Genes with low expression were removed by prefiltering; genes were retained only if they had a counts-per-million value of greater than 2.5 in at least three samples. Principal component analysis was performed to visualize sample clustering using the DESeq2 package. To ensure homoscedasticity, raw read counts were normalized using variance stabilizing transformation. This transformation was applied in a design-blind manner (blind = TRUE) to provide an unbiased view of sample variance. The principal component analysis coordinates were calculated using the top 500 genes with the highest row variance. The differential expression analysis was performed using the DESeq2 package (v.1.44.0), which incorporates normalization based on size factors, dispersion estimation and fitting a negative binomial generalized linear model. The significance threshold is defined as FDR < 0.05. To correct for the high variance observed in genes with low read counts, log₂[FC] estimates were moderated using the adaptive shrinkage estimator (ashr v.2.2-63) before generating volcano plots. Gene-set enrichment analysis was performed using the fgsea package (v.1.30.0). Genes were preranked using a metric combining the log₂[FC] and P value from the DESeq2 results, calculated as: sign(log₂[FC]) × −log₁₀[P]. The Hallmark (H) gene set collection (v2025.1.Mm) from the Molecular Signatures Database (MSigDB) was obtained through the msigdbr package (v.25.1.1). Mouse gene symbols were mapped to Entrez IDs as required using the org.Mm.eg.db annotation database (v.3.19.1).

Ethics statement

Participants consented in writing to collection and analysis of biospecimens; specimens were analysed for this study with the approval of the Memorial Sloan Kettering Cancer Center Institutional Review Board under protocol 16-834, which has been previously published²⁶ but is not listed in a clinical trial registry. The study was conducted in accordance with the Declaration of Helsinki. Mouse experiments were conducted under the supervision of the Memorial Sloan Kettering Institutional Animal Care and Use Committee (protocols 23-03-006 and 99-07-025).

Reporting summary

Further information on research design is available in the Nature Portfolio Reporting Summary linked to this article.

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Author notes

1. Marcel R. M. van den Brink

   Present address: City of Hope, Duarte, CA, USA

2. These authors contributed equally: Anqi Dai, Annamaria Ballweg

3. These authors jointly supervised this work: Marcel R.M. van den Brink, Jonas Schluter, Jonathan U. Peled

Authors and Affiliations

1. Adult Bone Marrow Transplantation Service, Department of Medicine, Memorial Sloan Kettering Cancer Center, New York, NY, USA

   Anqi Dai, Annamaria Ballweg, Madhumitha Rangesa, Peter A. Adintori, Mirae Baichoo, Nicholas R. Waters, Abrielle Swartz, Sukanya Sahu, Brianna Gipson, Sandeep S. Raj, Zoe Pierce, Kenton Wu, Justin Neuberger, Adam Warren, Marissa Lubin Buchan, Ana Gradissimo, Luigi A. Amoretti, Annelie G. Clurman, Daniel G. Brereton, Paul A. Giardina, Miguel-Angel Perales, Sergio A. Giralt & Jonathan U. Peled

2. Institute for Systems Genetics, NYU Langone Health Grossman School of Medicine, New York, NY, USA

   William Jogia, Caichen Duan, Chenzhen Zhang, Fanny Matheis, Alexis P. Sullivan & Jonas Schluter

3. Department of Microbiology, NYU Langone Health Grossman School of Medicine, New York, NY, USA

   William Jogia, Caichen Duan, Chenzhen Zhang, Fanny Matheis, Alexis P. Sullivan & Jonas Schluter

4. Department of Hematology and Hematopoietic Cell Transplantation, City of Hope National Medical Center, Los Angeles, CA, USA

   Tyler Funnell, Natalie Smith, John B. Slingerland, Antonio L. C. Gomes & Robert R. Jenq

5. Department of Epidemiology and Biostatistics, Memorial Sloan Kettering Cancer Center, New York, NY, USA

   Teng Fei

6. Departments of Genomic Medicine and Stem Cell Transplantation Cellular Therapy, Division of Cancer Medicine, University of Texas MD Anderson Cancer Center, Houston, TX, USA

   Eiko Hayase

7. Human Oncology & Pathogenesis Program, Sloan Kettering Institute, Memorial Sloan Kettering Cancer Center, New York, NY, USA

   Marina Burgos da Silva

8. Department of Pediatrics, Memorial Sloan Kettering Cancer Center, New York, NY, USA

   Oriana Miltiadous

9. Haematology Unit, Department of Cellular Therapy, Hematology and Laboratory Medicine, Azienda Ospedaliero-Universitaria Senese, Siena, Italy

   Corrado Zuanelli Brambilla

10. Food and Nutrition Services, Memorial Sloan Kettering Cancer Center, New York, NY, USA

    Marissa Lubin Buchan & Tatanisha K. Peets

11. Department of Biomedical Engineering, NYU Tandon School of Engineering, New York, NY, USA

    Caichen Duan

12. Division of Epidemiology and Community Health, School of Public Health, University of Minnesota, Minneapolis, MN, USA

    Abigail J. Johnson

13. Riddet Institute, Massey University, Palmerston North, New Zealand

    Abigail J. Johnson

14. Department of Computer Science and Engineering, University of Minnesota, Minneapolis, MN, USA

    Dan Knights

15. Biotechnology Institute, University of Minnesota, Saint Paul, MN, USA

    Dan Knights

16. Translational Science and Therapeutics Division, Fred Hutchinson Cancer Center, Seattle, WA, USA

    Kate A. Markey

17. Division of Medicine, University of Washington, Seattle, WA, USA

    Kate A. Markey

18. Weill Cornell Medical College, New York, NY, USA

    Miguel-Angel Perales, Sergio A. Giralt & Jonathan U. Peled

19. Laura and Isaac Perlmutter Cancer Center, NYU Langone Health, New York, NY, USA

    Jonas Schluter

Contributions

W.J., M.R. and P.A.A. contributed equally as joint second authors. M.L.B., R.R.J., K.A.M., J.S., M.R.M.v.d.B. and J.U.P. conceptualized the project. A.D., A.B., M.R., P.A.A., A.S., S.S., B.G., S.S.R., E.H., Z.P., K.W., N.S., J.N., A.W., M.L.B., T.K.P., A.G., L.A.A., J.B.S., A.G.C., D.G.B. and P.A.G. collected data. A.D., W.J., M.B., T. Funnell, T. Fei, N.R.W., E.H., C.D., C.Z., F.M., A.P.S., A.L.C.G., A.J.J., D.K., J.S., M.R.M.v.d.B. and J.U.P. were involved in data analysis. A.D., A.B., W.J., M.R., P.A.A., T. Funnell, T. Fei, N.R.W., B.G., S.S.R., M.B.d.S., C.Z.B., M.L.B., A.P.S., A.L.C.G., A.J.J., D.K., R.R.J., J.S. and J.U.P. contributed to methodology. J.S., M.R.M.v.d.B. and J.U.P. helped with funding acquisition. A.G., J.B.S., M.-A.P., S.A.G., M.R.M.v.d.B. and J.U.P. helped with project administration. J.S., M.R.M.v.d.B. and J.U.P. supervised the project. A.D., J.S. and J.U.P. wrote the original draft. A.B., W.J., M.B., Z.P., K.W., O.M., K.A.M., J.S., M.R.M.v.d.B. and J.U.P. helped with the review and editing of the draft.

Corresponding authors

Correspondence to Marcel R. M. van den Brink, Jonas Schluter or Jonathan U. Peled.

Competing interests

J.S. has filed intellectual property applications related to the microbiome (reference numbers US2025/0064882A1, PCT/US2025/023826), serves on an advisory board and holds equity of Jona Health, and is cofounder of Postbiotics Plus Research. J.U.P. reports research funding, intellectual property fees and travel reimbursement from Seres Therapeutics, and consulting fees from DaVolterra, CSL Behring, Crestone, MaaT Pharma and Canaccord Genuity and RA Capital; he serves on an advisory board of and holds equity in Postbiotics Plus Research; he serves on an advisory board of and holds equity in Prodigy Biosciences; he has filed intellectual property applications related to the microbiome (reference numbers 62/843,849, 62/977,908 and 15/756,845). Memorial Sloan Kettering Cancer Center (MSK) has financial interests relative to Seres Therapeutics. K.A.M. reports previous consulting for Incucyte and Crestone; she serves on an advisory board of and holds equity in Postbiotics Plus Research. M.R.M.v.d.B. has received research support and stock options from Seres Therapeutics and stock options from Notch Therapeutics, Pluto Therapeutics and TymoFox; he has received royalties from Wolters Kluwer; he has consulted, received honorarium from or participated in advisory boards for Seres Therapeutics, Vor Biopharma, WindMIL Therapeutics, Rheos Medicines, Merck, Magenta Therapeutics, Frazier Healthcare Partners, Nektar Therapeutics, Notch Therapeutics, Forty Seven, Priothera, Ceramedix, Lygenesis, Pluto Therapeutics, GlaskoSmithKline, Da Volterra, Garuda, Thymofox, smarNovartis (spouse), Synthekine (spouse), Beigene (spouse), Kite (spouse), MustangBio (spouse) and Cellectar (spouse); he has IP licensing with Seres Therapeutics and Juno Therapeutics; he holds a fiduciary role on the Foundation Board of the non-profit organization DKMS; and he is the chairman of the scientific advisory board for Smart Immune. M.-A.P. reports honoraria from Arog Pharmaceuticals, Bristol-Myers Squibb, Cartesian, Celgene, Incyte, Kite/Gilead, Merck, Miltenyi Biotec, Novartis, Omeros, OrcaBio, Ossium Health, Pierre Fabre, Sanofi and Takeda; he serves on data and safety monitoring boards for Cidara Therapeutics/ Mundipharma and Sellas Life Sciences; he has ownership interests in Omeros and OrcaBio; he has received institutional research support for clinical trials from Allogene, Genmab, Incyte, Kite/Gilead, Miltenyi Biotec, Novartis and Tr1x. R.R.J. reports patent royalties and stock options from Seres Therapeutics, and consulting fees from MaaT Pharma and Nestle; he serves on the scientific advisory board of Postbiotics Plus Research. S.A.G. reports research funding from Amgen, Johnson & Johnson, Takeda, Celgene, Actinuum, Sanofi, Miltenyi, Kite, EUSA and Incyte; he serves on advisory boards at Amgen, Bristol-Myers Squibb, Actinuum, Celgene, Johnson & Johnson, Miltenyi, Takeda, Novartis, EUSA Omeros, Sanofi, Kite and Spectrum Pharma. A.L.C.G. reports stock/shares from Seres Therapeutics and equity ownership at Xbiome. M.L.B. reports employment and equity ownership at Savor Health. The other authors declare no competing interests.

Peer review information

Nature thanks Andrew Chan, Mahesh Desai, Jeremiah Faith and the other, anonymous, reviewer(s) for their contribution to the peer review of this work.

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