Gut microbiota changes are associated with prolonged neutropenia after induction treatment of childhood acute lymphoblastic leukemia

doi:10.21203/rs.3.rs-4266171/v1

Download PDF

Article

Gut microbiota changes are associated with prolonged neutropenia after induction treatment of childhood acute lymphoblastic leukemia

https://doi.org/10.21203/rs.3.rs-4266171/v1

This work is licensed under a CC BY 4.0 License

You are reading this latest preprint version

Prolonged neutrophil recovery during acute lymphoblastic leukemia (ALL) treatment increases infection risk and delays chemotherapy. Emerging evidence implicates the gut microbiota in neutrophil reconstitution after chemotherapy. We explored the interplay between the gut microbiota and neutrophil dynamics, including neutrophil chemoattractants, in 51 children with newly-diagnosed ALL. Daily absolute neutrophil count (ANC), weekly plasma chemokines (CXCL1 and CXCL8), granulocyte colony-stimulating factor (G-CSF), and fecal samplings were monitored until day 29 during ALL induction treatment. Fecal sequencing by 16S rRNA revealed an overall significant reduction in bacterial diversity and Enterococcus overgrowth throughout the induction treatment. Prolonged neutropenia (ANC < 0.5x10⁹ cells/L at day 36) and elevated chemokines levels were associated with decreased abundance of genera from the Ruminococcaceae and Lachnospiraceae families, decreased Veillonella genus, and Enterococcus overgrowth from diagnosis and throughout induction treatment. G-CSF was upregulated in response to neutropenia but unrelated to microbiota changes.

Overall, this study reveals that diminished abundance of specific intestinal commensals and Enterococcus overgrowth are associated with delayed neutrophil reconstitution and increased chemokine signaling. These findings enhance our understanding of the mechanisms behind the huge variability in neutrophil reconstitution post-chemotherapy, emphasizing the need for gut microbiota-sparing strategies to minimize the impact of gut dysbiosis on immune recovery.

Health sciences/Risk factors

Biological sciences/Immunology/Chemokines

Biological sciences/Immunology/Innate immunity

Health sciences/Diseases/Haematological diseases/Haematological cancer/Leukaemia/Acute lymphocytic leukaemia

Health sciences/Medical research/Clinical trial design

During the past decades intensification of chemotherapy treatment has significantly improved survival rates in pediatric acute lymphoblastic leukemia (ALL)(1). However, this progress has come with a cost of prolonged immunosuppression, with increased risk of infections and delay in subsequent antineoplastic treatment, thus jeopardizing the control of the leukemia(2, 3).

Neutrophils play a pivotal role in the innate immune system as a first line defense, being crucial for combating bacterial infections through early recruitment to sites of infections and tissue damage(4). Understanding the factors driving neutrophil recovery after chemotherapy is essential in devising improved strategies to limit infectious complications and treatment delays in ALL treatment.

Emerging evidence suggests a key role of the gut microbiota in regulating neutrophil production and migration to extravascular tissues(5). Experimental mice studies have substantiated these findings, demonstrating that certain gut bacteria facilitates neutrophil recovery following chemotherapy-induced neutropenia(5, 6). In clinical settings, extensive antibiotics use has consistently been linked to an increased risk of prolonged neutropenia in various patient groups(7, 8). Although the underlying mechanisms behind prolonged neutropenia are poorly understood, they may involve loss of the gut microbiota-induced stimulation of granulopoiesis within the bone marrow as well as increased extravascular migration of neutrophils due to overgrowth of pro-inflammatory bacteria(5, 9–11).

While neutrophil production is controlled by colony-stimulating factors, among those granulocyte colony-stimulating factor (G-CSF)(12), the recruitment of circulating neutrophils is regulated by chemokines, a superfamily of immune-modulatory small proteins, the primaries and most well-described being CXCL1 and CXCL8(13, 14). These are released from antigen-presenting cells and damaged tissues, including the gut, independently of the peripheral blood neutrophil count(15).

This study aimed to investigate the dynamics of the relationship between gut microbiota composition and neutropenia in children undergoing ALL induction treatment. Moreover, we explored associations between the microbiota and signaling proteins controlling neutrophil production and migration.

Study population and treatment

In this prospective, observational multicenter study, 51 children with newly diagnosed ALL treated according to the Nordic Society of Pediatric Hematology and Oncology ALL 2008 protocol were included at Rigshospitalet, University Hospital of Copenhagen, H.C. Andersen Children’s Hospital, Odense University Hospital and Aarhus University Hospital, Denmark in 2015–2018. Patients received a 29-day induction chemotherapy treatment as previously described(16). The cohort of this study has been previously published as part of larger studies(17–19).

All patients received trimethoprim-sulfamethoxazole twice weekly as prophylaxis for Pneumocystis jirovecii infection. Positive blood cultures and antibiotic administrations were registered from the patients’ medical records.

Out of 51 families, 36 were screened for siblings’ eligibility whereof 12 refused participation and five did not meet the inclusion criteria(18). Therefore, 19 siblings were included. The siblings shared the same parents and housing as the patients.

Quantification of neutrophil and neutrophil signaling markers

Peripheral blood absolute neutrophil count (ANC) was determined daily part of the clinical routine by flow cytometry (Sysmex XN, Norderstedt, Germany).

EDTA anti-coagulated blood was collected from each patient on treatment days 8, 15, 22, and 29 (± 2 days). Ten unrelated healthy young blood donors served as controls (aged 20–31 years, 50% males). Concentrations of CXCL1, CXCL8 and G-CSF were measured using the Bio-Plex Pro Human Chemokine Assay (Bio-Rad, Hercules, California, USA) on the Luminex platform (Luminex Corporation, Austin, Texas, USA), according to the manufacturer's instructions, as previously described(17).

Fecal samples collection

Fecal samples were collected on treatment day 1 (range − 2 − 5), 8 (5–11), 15 (13–18), 22 (20–25) and 29 (27–34), for a total of 185 samples (median 4, range 1–5, per patient). The number of samples available for each time-point is reported in Supplementary Table 1. The fecal samples were stored at -20°C immediately after collection and transferred to − 80°C within 2–5 days. Siblings had fecal samples collected at 2–62 days from the patients’ start of chemotherapy. None of the siblings had received antibiotics the month prior to sampling.

DNA extraction and 16S rRNA gene amplicon sequencing

DNA extraction and 16S rRNA gene amplicon sequencing was performed as previously described(18). In short, DNA was extracted from fecal samples and 11 blank controls using the Qiagen® QIAamp Fast DNA Stool mini kit, modified to include a bead-beating step (Qiagen® PowerBead Tubes, Garnet 0.70 mm). High-throughput amplicon sequencing of the V3–V4 region of the 16S rRNA gene was conducted by Illumina® MiSeq technology (Illumina Inc., San Diego, CA). Samples were re-sequenced if the read count after chimera removal (described below) was < 9 000.

Sequencing data processing

Primer sequences were removed with cutadapt (version 1.16)(20) at a tolerated maximum error rate of 15%. Reads were further processed using the R package DADA2 (version 1.8) for inference of high-resolution amplicon sequence variants (ASV)(21). Forward and reverse reads were truncated at 280 and 200 bp, respectively, to allow overlap for merging. Chimeras were identified sample-wise and removed from the whole data set (removeBimeraDenovo() function, method “consensus”). Taxonomy was assigned with SILVA reference database version 132 formatted for DADA2(22). No technical batch effects by sequencing run, extraction round, extraction kit or extraction date were detected by Principal Coordinates Analysis (PCoA) with different distance metrics (Bray-Curtis, Jaccard, Raup-Crick, Chao and Horn-Morisita) using the R package phyloseq. A total of 266 putative contaminant ASVs were removed using the R package decontam, following the “combined” method with probability threshold 0.07(23) and manually assessing borderline sequences. Subsequently, only ASVs with at least five reads in at least two samples were selected for further analyses by using the kOverA() function in the R package genefilter. After this step, median sequencing depth was 20 649 reads (range 5 038–326 811).

Ethics statement

The study was approved by the Scientific Ethics committee of the Capital Region of Denmark (H-16016942) and the Danish Data Protection Agency (RH-2016-214) and conducted in accordance with the Declaration of Helsinki. Oral and written informed consent was obtained from parents/legal guardians.

Statistics

Statistical analyses and creation of graphs were performed with the program R (Version 3.4.0, R Foundation for Statistical Computing, Vienna, Austria)(24). Sequencing data were integrated with the R package phyloseq and its dependencies.

The within-sample bacterial diversity (α-diversity) was calculated as number of observed ASVs at species level (richness) and the Shannon diversity index (accounting for both richness and evenness of the ASVs present), and the between-sample diversity metrics (β-diversity) were computed with Bray-Curtis dissimilarity at ASV level.

A mixed model with an unstructured variance–covariance matrix was applied to compare chemokine levels, α-diversity and relative abundance of the microbial community on genus-level over time and between groups. Interaction between time-point and variables was tested by likelihood-ratio test. Chemokine levels, the number of observed species and relative abundances were log-transformed using the lowest nonzero value as a pseudocount.

Correlation analyses were performed using Spearman’s rank order correlation analysis. Logistic regression models were applied when assessing risk of prolonged neutropenia.

The differences in β-diversity were visualized with PCoA plots, which were calculated based on all the samples visualized in the specific plot. β-diversity was tested for inference using permutational multivariate analysis of variance (PERMANOVA) (adonis2 from the package vegan with 999 permutations).

A supervised sparse partial least squares (sPLS) regression model was used to estimate the predictive power of multiple bacterial genera together in relation to prolonged neutropenia. Relative abundances were used as input features after filtering (day 1 samples, genera with prevalence > 25%). We tuned the sPLS models with a range of included genera (1, 2, 4, 8, 16, 25, 40, 50, and 65) to be kept for 1–5 components. We selected the optimum number of input variables (bacterial genera) using 5-times repeated 10-fold cross-validation of the area under the curve (AUC) statistic to avoid overfitting. The final model was chosen by the highest median AUC value and included the total number of included genera (n = 65) and 1 component. The predicted values of left out folds were combined to a PLS score. Further validation of the set of selected genera through a data-splitting approach was not feasible due to the relatively small data set. Finally, the relative abundance of all genera with a prevalence > 25% of day 1 samples were compared between the prolonged neutropenia group with the univariate Wilcoxon rank-sum test. Multiple test correction was performed using the Benjamini-Hochberg False Discovery Rate (FDR) method.

Clinical characteristics of the 51 included patients are listed in Table 1.

Table 1

Patient and disease characteristics, N = 51
Patient characteristics
Age, median years (range)	3.7 (1.1–17.3)
Sex, n (%)
Male/Female	38 (75) / 13 (25)
ALL phenotype, n (%)
Pre-B-cell ALL	43 (84)
T-cell ALL	7 (14)
Other¹	1 (2)
Induction, n (%)
Non high-risk	42 (82)
High-risk	5 (10)
Other²	4 (8)
Treating center, n (%)
Odense University Hospital	15 (29)
Rigshospitalet, Copenhagen University Hospital	33 (65)
Aarhus University Hospital	3 (6)
¹Blastic plasmacytoid dendritic cell acute leukemia treated with NOPHO ALL2008 protocol.
²Two patients start high-risk block therapy before the end of induction treatment and two Ph + ALL patients receive non-high risk induction treatment with Imatinib from Day 15.
Abbreviations: ALL, acute lymphoblastic leukemia

Neutropenia, bacteremia, and antibiotics

Median peripheral blood ANC declined during induction treatment reaching nadir at day 15 (0.08x10⁹ cells/L, range: 0.0-0.6) (Fig. 1A) and then gradually recovered towards day 29 (median: 0.7x10⁹ cells/L, range: 0.0–10.0). At diagnosis, before start of chemotherapy, 24 (47%) patients exhibited signs of leukemia-induced bone marrow suppression with neutropenia (ANC < 0.5x10⁹ cells/L). At day 15, following initiation of chemotherapy, 44 (91%) of the patients had neutropenia. Prolonged neutropenia, defined as neutropenia persisting at treatment day 36 (day of scheduled start of consolidation therapy), was observed in 18 (35%) patients. The median duration of neutropenia within the first 36 days was 24 days (range 0–36).

Due to suspected bacterial infection, a total of 47 patients (92%) received antibiotic treatment during the induction period, being initiated prior to day 1 in 44 patients (86%) and with a median duration of 17 days (range: 0–29 days). Bacteremia was confirmed in 13 (27%) patients on median day 13 (range 5–20). Details regarding antibiotics and microbial finding are provided in supplementary Tables 2 and 3.

Prolonged neutropenia was not associated with sex, age, ALL phenotype, induction regimen, ANC at treatment initiation (day 1), duration of antibiotics or documented bacteremia during the induction period (all p > 0.05).

Neutrophil signaling markers

Plasma levels of CXCL1, CXCL8 and G-CSF were significantly increased in the patients compared with the healthy controls, reaching a peak at day 15 (Fig. 1B-D). Older patients had lower levels of CXCL1 (16% decrease per year, 95%CI: -10 - -21%, p = 0.01) and CXCL8 (9% decrease per year, 95%CI: 5–14%, p < 0.001) during the induction treatment but not G-CSF (p = 0.08). Levels of CXCL1, CXCL8 or G-CSF were not associated with sex, ALL phenotype, induction regimen, or duration of antibiotics (all p > 0.05).

Patients with prolonged neutropenia had overall significantly higher levels of CXCL1 (177% higher, 95%CI: 48–417%, p = 0.002), CXCL8 (122%, 95%CI: 46–239%, p < 0.001) and G-CSF (56% higher, 95%CI: 2-138%, p = 0.04) during the induction period than patients without prolonged neutropenia (Fig. 1G-I). In separate logistic regression models adjusting for age, higher levels of CXCL1 day 29 and CXCL8 day 22 and day 29 remained associated with increased risk of prolonged neutropenia (Odds ratios: 2.3–2.7 per doubling of chemokine level, all p < 0.01). Similarly, ANC correlated inversely with same-day levels of both chemokines and G-CSF at all measured time points (rho=-0.48 - -0.71, all p < 0.01).

Gut microbiota α-diversity, neutropenia and neutrophil signaling markers

The overall α-diversity decreased after day 1, reaching nadir at day 8, and remained significantly reduced at day 29 or only until day 22 according to observed species (Fig. 1E) and Shannon index (Supplementary Fig. 1), respectively. Patients receiving antibiotics on day 1 had overall lower α-diversity during induction treatment than patients not receiving antibiotics on day 1 (observed species: 43% reduced, 95%CI: 23–58%, p < 0.001), and older patients had marginal higher α-diversity measures (observed species: 4% increase per year, 95%CI: 2–7%, p = 0.002).

Low α-diversity at day 1 was associated with increased risk of prolonged neutropenia (Odds ratio = 0.2 per doubling of observed species, 95%CI: 0.05–0.7, p = 0.02) (Fig. 1J) and this remained significant after adjusting for patient age and antibiotic treatment day 1 (Supplementary Table 4 incl. results of additional diversity measures). Additionally, α-diversity at treatment day 15 was positively correlated to ANC levels at day 15 (rho = 0.49, p = 0.004) and day 22 (rho = 0.43, p = 0.03) and negatively correlated with number of days with neutropenia (rho=-0.43, p = 0.01). α-diversity metrics at treatment day 15 were negatively correlated with CXCL1 and CXCL8 levels on day 15 and 22 (Fig. 2A-B).

No significant associations between microbiota diversity metrics and levels of G-CSF were found.

Beta diversity, neutropenia and neutrophil signaling markers

Next, we examined the dissimilarities in microbial composition between fecal samples, known as β-diversity, throughout the induction course using PCoA (Fig. 3A). Already at day 1, the healthy siblings tended to form a rather homogenous group distinct from the patients, suggesting that they shared a similar gut microbial composition compared to the patients (p = 0.02 by Adonis 2) (Fig. 3B).

Given the notable similarity in β-diversity across treatment days among patients (Fig. 3A), we opted to focus our analysis of β-diversity in relation to outcomes on the final treatment day (day 29).

As shown in Fig. 3C, β-diversity at day 29 was significantly associated with prolonged neutropenia, and likewise, with day 29 chemokine levels dichotomized at the median (Fig. 3D-E), but not with G-CSF levels (p = 0.07, Fig. 3F). Similar patterns regarding neutropenia and chemokines were observed across all days, although only borderline significant (data not shown).

Abundances of specific genera and neutropenia

In analyses of the specific bacterial genera characterizing the gut microbiota, we included genera represented in more than 25% of the patient fecal samples.

First, we investigated the changes in relative abundances over time throughout the induction treatment. Among the 65 included bacterial genera, Enterococcus was enriched during the induction period compared with day 1, while only the commensal bacteria Lachnospira and Clostridium sensu stricto 1 were depleted when looking at all patients as one group.

However, patients with prolonged neutropenia had significantly lower relative abundance of Ruminococcacea UCG-002, Ruminococcacea UCG-003, Lachnospira, Roseburia, Lachnospiraceae UCG-004, Lachnospiraceae UCG-010, Collinsella, and Sutterella throughout the induction treatment, generally most pronounced at day 29, and experienced significantly greater overgrowth of Enterococcus during the induction period compared with the patients who recovered from neutropenia (Fig. 4).

To predict risk of prolonged neutropenia from the microbiota at day 1 (n = 12 (38%)), we implemented a multivariate approach using a sPLS regression model. Through this model we identified a pattern of various Ruminococcaece and Lachnospiraceae genera together with Veillonella, Sutterella, Collinsella, and Erysipelotrichaceae UCG-003 to be the bacterial genera with the lowest negative loadings while Enterococcus had the highest loading (Fig. 5A). This reflects a combined pattern of low vs high abundances of these specific genera at day 1 predicting the risk of prolonged neutropenia (median (range) cross-validated AUC = 0.68 (0.63–0.69), Fig. 5B).

Figure 5C shows relative abundances at day 1 of the genera with significant difference (Wilcoxon rank-sum univariate test, p < 0.05) in relative abundance between groups (prolonged neutropenia). Following FDR-correction none of these univariate analyses were statistically significant.

Abundances of genera and neutrophil signaling markers

We dichotomized patients based on whether their maximum levels of CXCL1, CXCL8, and G-CSF throughout the induction treatment were above or below the median. The genera with significant difference in relative abundances between those groups are shown in Fig. 6.

When adjusting for patient’s age, relative abundance of Enterococcus day 29 remained significantly increased in patients with above median levels of CXCL1 (p = 0.02), CXCL8 (p = 0.004) and G-CSF (p = 0.005), while Sutterella (p = 0.02), Anaerostipes (day 15: p = 0.003; day 29: p = 0.01), and Lachnospira (p = 0.001) all remained depleted throughout the induction period in patients with above median CXCL8 levels. Following FDR-correction for multiple testing none of these associations remained statistically significant.

This study delves into the intricate interplay between the gut microbiota and neutrophil recovery during the induction treatment of childhood ALL. Our findings demonstrate a profound association between the gut microbial composition and the duration of neutropenia, uncovering potential implications for the management and improvement of outcomes in patients undergoing chemotherapy.

One of the key observations in our study is the significant association between the loss of gut microbiota diversity and prolonged neutrophil recovery following chemotherapy-induced neutropenia. Notably, alterations in diversity were evident from the onset of chemotherapy, suggesting that the richness of the gut microbiota at diagnosis holds valuable insights into the potential immunosuppressive burden of intestinal dysbiosis. This points to a potential role of microbial richness as a predictive indicator for the immune reconstitution process, although the clinical utility as a biomarker appears less likely due to the large variation in the data.

We identified specific commensal bacteria, including Veillonella, members of the Lachnospiracea family (Coprococcus 1, Roseburia, Dorea) and the Ruminococcaceae family (UGG-002, UGG-003 and Subdogranulum), as potential contributors in neutrophil recovery dynamics. The absence of these commensals or an increased relative abundance of Enterococcus at initiation of chemotherapy treatment emerged predictive of the duration of chemotherapy-induced neutropenia. When analyzing the changes in microbiota composition throughout the induction period similar patterns were found with increased relative abundance of Enterococcus and a loss of various Lachnospiraceae and Rumonicoccaeae genera, during the induction treatment, being significantly associated with prolonged period of neutropenia.

While the study design did not allow for exploring events preceding hospital admission for leukemia, we speculate that antibiotic treatment prior to leukemia diagnosis and disease-induced diet changes could potentially explain the substantial variation in gut microbiota composition observed among patients already at the time of leukemia diagnosis. These speculations are substantiated when considering the significant difference in gut microbiota composition observed in the healthy siblings from the same home environment.

Most of the taxa we found associated with reduced risk of prolonged neutropenia, including Ruminococcacea spp., Coprococcus, Dorea, and Roseburia, are well-known to diminish inflammation by inhibiting the NFκB pathway(25–27), which is known to be activated during chemotherapy-induced gut barrier injury(28), and by producing short-chain fatty acids (SCFAs), especially butyrate(29–31). Butyrate plays a multifaceted role, both systemically with effects on the host immune system as well as locally, by stabilizing the gut mucosal barrier as an energy source for colonocytes(32). Furthermore, during chemotherapy treatment these SCFA-acid producing microbes may add to the integrity of the intestinal barrier through increased mucus production, improved tight junction integrity and reduced inflammation(33–36). Veillonella is, particularly, known to promote general gut health through SCFA-production by lactate fermentation(37).

While the detailed biological functions of many Ruminococcaceae genera remain incompletely understood, recent studies suggest their immunomodulatory properties(38–40). Specifically, an experimental mouse study associates Ruminococcaceae UCG-014 with granulopoiesis after chemotherapy-induced neutropenia, through a mechanism involving T-cell production of IL-17a, which subsequently stimulate G-CSF secretion(6).

In line with our results, Schluter et al. established a connection between the high abundance of Ruminococcaceae genera and rapid neutrophil engraftment following hematopoietic stem cell transplantation (HSCT)(41), while Ingham et al found high abundances of mainly Ruminococcaceae and Veillonella to be associated with improved adaptive immune cell reconstitution - both T cell subset and B cells - after pediatric HSCT(42). Notably, prior studies in adult HSCT patients have consistently revealed that the loss of the taxonomic families of Ruminococcaceae and Lachnospiraceae associates with both increased treatment-related mortality and low overall survival(43–47). Moreover, members of Ruminococcaceae were found associated with increased efficacy of CAR T-cell therapy(48). The role of Sutterella, which we also found associated with reduced risk of prolonged neutropenia, is poorly understood, although its capacity to adhere to intestinal epithelial cells also implies a potential immunomodulatory function for this genus(49).

Enterococcus, though generally considered harmless in healthy individuals, has emerged as a potential pro-inflammatory contributor in the context of gut microbiota dysbiosis(50). Through disturbed interaction with the host, which might be facilitated through reduced mucosal barrier integrity, these bacteria can produce substances and cell wall components acting as pathogen-associated molecular patterns to activate innate immune responses in the host(51), including the release of chemokines(11).

The well-documented expansion of Enterococcus following chemotherapy has been associated with adverse effects, such as bacteremia, systemic inflammation, and enterocyte damage(18, 52, 53). The present findings extend these insights and contribute to the claim that Enterococcus overgrowth stimulates pro-inflammatory responses, including the secretion of chemokines, thereby attracting neutrophils to extravascular tissue.

Consequently, overgrowth of Enterococcus and the depletion of barrier-stabilizing commensals, coupled with depressed bone marrow function, may reduce peripheral blood ANC. The resulting pro-inflammatory responses removing neutrophils from circulation, might extend the duration of neutropenia despite hematopoietic recovery in the bone marrow. In the present study this hypothesis was substantiated by the finding of a positive correlation between higher abundances of Enterococcus and elevated chemokine levels.

In immunocompetent individuals, enhanced extravascular neutrophil migration can be easily compensated by upregulated granulopoiesis mediated by colony-stimulating factors. However, in the present cohort of patients with ALL, this compensatory mechanism is comprised due to chemotherapy-induced bone marrow impairment, which may explain our observation of neutropenia despite elevated G-CSF levels.

While antibiotics lead to improved overall survival in cancer patients(54, 55), the use is accompanied by substantial alterations to the gut microbiota(56, 57). Long-term use of antibiotics being followed by neutropenia has been observed in patients with various infections(7, 8). This dual impact of antibiotics, acting as both a shield and a potential risk factor, has sparked considerable debate and emphasized the need for a more targeted use of antibiotics, especially during periods of increased vulnerability such as chemotherapy-induced neutropenia as highlighted in this study.

The complex interplay between the gut microbiota and neutrophils is suggested to be bidirectional(58). Nevertheless, in this study, the predictive value of the microbiota composition at treatment onset emphasized that particularly the loss of specific commensals drives the prolonged neutropenia, which, additionally, may explain previous observations of antibiotic-induced neutropenia(7). Additionally, the study design with time-serial sampling strengthens our ability to discern the chronological order in the relationship between gut microbiota changes and neutropenia. Yet, due to the observational nature of the present study, conclusions regarding causality cannot be drawn.

A notable limitation of our study is the sample size. Even though not maintaining statistical significance after adjusting for multiple testing, this exploratory observational study yields valuable insights into the potential role of specific gut bacteria in influencing prolonged neutropenia. Notably, our findings align with previous studies and are supported by hypotheses concerning the impact of SCFA-producing bacteria on the host immune system.

In conclusion, this study highlights the intricate relationships between gut microbiota composition and neutrophil recovery during the induction treatment of childhood ALL. Our results indicate that the perturbation of the gut microbiota may influence blood neutrophil counts by upregulating chemokine production leading to increased extravascular migration rather than suppressing growth-promoting signaling to the bone marrow. While the study does not establish causality, it lays the groundwork for future investigations exploring the mechanisms behind these associations and their clinical implications for developing and optimizing gut-sparring strategies to improve treatment outcomes in pediatric leukemia patients.

Data availability

The 16S rRNA gene sequences are available through the European Nucleotide Archive (ENA) at the European Bioinformatics Institute (EBI) under accession number PRJEB35526.

Acknowledgements

We express our deepest gratitude to the children and families for their contribution to the study. The study has received funding from The Danish Childhood Cancer Foundation, the Copenhagen University Hospital, Rigshospitalet, the A.P. Møller Foundation, the Dagmar Marshall Foundation and the Knud and Edith Eriksen Memorial Foundation. Sequence preprocessing was performed using the DeiC National Life Science Supercomputer at the Technical University of Denmark.

Author Contributions

MES conducted data acquisition, analysis, and interpretation, and drafted the manuscript. KM supervised the study from conception to data acquisition, analysis, and interpretation. UB and JS developed the bioinformatics pipeline and contributed to statistical analyses. SdP, SW, MR, HS, BAN, CE, and SP contributed to study implementation and data acquisition. All co-authors made significant contributions to data analysis and interpretation and provided intellectual input throughout the study.

Competing Interests

The authors declare no competing financial interests.

Schmiegelow K, Forestier E, Hellebostad M, Heyman M, Kristinsson J, Söderhäll S, et al. Long-term results of NOPHO ALL-92 and ALL-2000 studies of childhood acute lymphoblastic leukemia. Leukemia. 2010;24(2):345–54.
Afzal S, Ethier MC, Dupuis LL, Tang L, Punnett AS, Richardson SE, et al. Risk factors for infection-related outcomes during induction therapy for childhood acute lymphoblastic leukemia. Pediatr Infect Dis J. 2009;28(12):1064–8.
Inaba H, Pei D, Wolf J, Howard SC, Hayden RT, Go M, et al. Infection-related complications during treatment for childhood acute lymphoblastic leukemia. Ann Oncol. 2017;28(2):386–92.
Kolaczkowska E, Kubes P. Neutrophil recruitment and function in health and inflammation. Nat Rev Immunol. 2013;13(3):159–75.
Balmer ML, Schürch CM, Saito Y, Geuking MB, Li H, Cuenca M, et al. Microbiota-Derived Compounds Drive Steady-State Granulopoiesis via MyD88/TICAM Signaling. J Immunol. 2014;193(10):5273–83.
Chen X, Hashimoto D, Ebata K, Takahashi S, Shimizu Y, Shinozaki R, et al. Reactive granulopoiesis depends on T-cell production of IL-17A and neutropenia-associated alteration of gut microbiota. Proc Natl Acad Sci. 2022;119(48):e2211230119.
Holz JM, Chevtchenko AV, Aitullina A. Acute antibiotically induced neutropenia: A systematic review of case reports. Br J Clin Pharmacol. 2022;88(5):1978–84.
Solis K, Dehority W. Antibiotic-Induced Neutropenia During Treatment of Hematogenous Osteoarticular Infections in Otherwise Healthy Children. J Pediatr Pharmacol Ther JPPT. 2019;24(5):431–7.
Josefsdottir KS, Baldridge MT, Kadmon CS, King KY. Antibiotics impair murine hematopoiesis by depleting the intestinal microbiota. Blood. 2017;129(6):729–39.
Zhang D, Chen G, Manwani D, Mortha A, Xu C, Faith JJ, et al. Neutrophil ageing is regulated by the microbiome. Nature. 2015;525(7570):528–32.
Park OJ, Han JY, Baik JE, Jeon JH, Kang SS, Yun CH, et al. Lipoteichoic acid of Enterococcus faecalis induces the expression of chemokines via TLR2 and PAFR signaling pathways. J Leukoc Biol. 2013;94(6):1275–84.
Dwivedi P, Greis KD. Granulocyte Colony Stimulating Factor Receptor (G-CSFR) signaling in severe congenital neutropenia, chronic neutrophilic leukemia and related malignancies. Exp Hematol. 2017;46:9–20.
Rajarathnam K, Schnoor M, Richardson RM, Rajagopal S. How do chemokines navigate neutrophils to the target site: Dissecting the structural mechanisms and signaling pathways. Cell Signal. 2019;54:69–80.
Petri B, Sanz MJ. Neutrophil chemotaxis. Cell Tissue Res. 2018;371(3):425–36.
Kobayashi Y. The role of chemokines in neutrophil biology. Front Biosci J Virtual Libr. 2008;13:2400–7.
Toft N, Birgens H, Abrahamsson J, Bernell P, Griškevičius L, Hallböök H, et al. Risk group assignment differs for children and adults 1–45 year with acute lymphoblastic leukemia treated by the NOPHO ALL-2008 protocol. Eur J Haematol. 2013;90(5):404–12.
Weischendorff S, De Pietri S, Rathe M, Frandsen TL, Hasle H, Nielsen CH, et al. Markers of neutrophil chemotaxis for identification of blood stream infections in children with acute lymphoblastic leukemia undergoing induction treatment. Eur J Haematol. 2023;
De Pietri S, Ingham AC, Frandsen TL, Rathe M, Krych L, Castro-Mejía JL, et al. Gastrointestinal toxicity during induction treatment for childhood acute lymphoblastic leukemia: The impact of the gut microbiota. Int J Cancer. 2020;29:1–10.
Rathe M, De Pietri S, Wehner PS, Frandsen TL, Grell K, Schmiegelow K, et al. Bovine Colostrum Against Chemotherapy-Induced Gastrointestinal Toxicity in Children With Acute Lymphoblastic Leukemia: A Randomized, Double-Blind, Placebo-Controlled Trial. J Parenter Enter Nutr. 2019;00(0).
Martin M. Cutadapt removes adapter sequences from high-throughput sequencing reads. EMBnet.journal. 2011;17(1):10–2.
Callahan BJ, McMurdie PJ, Rosen MJ, Han AW, Johnson AJA, Holmes SP. DADA2: High-resolution sample inference from Illumina amplicon data. Nat Methods. 2016;13(7):581–3.
Callahan B. Silva taxonomic training data formatted for DADA2 (Silva version 132). Zenodo; 2018.
Davis NM, Proctor DM, Holmes SP, Relman DA, Callahan BJ. Simple statistical identification and removal of contaminant sequences in marker-gene and metagenomics data. Microbiome. 2018;6(1):226.
R Core Team. R: A Language and Environment for Statistical Computing [Internet]. Vienna, Austria: R Foundation for Statistical Computing; 2019. Available from: https://www.r-project.org/
Montassier E, Gastinne T, Vangay P, Al-Ghalith GA, Bruley Des Varannes S, Massart S, et al. Chemotherapy-driven dysbiosis in the intestinal microbiome. Aliment Pharmacol Ther. 2015;42(5):515–28.
Lakhdari O, Tap J, Béguet-Crespel F, Le Roux K, de Wouters T, Cultrone A, et al. Identification of NF-κB Modulation Capabilities within Human Intestinal Commensal Bacteria. J Biomed Biotechnol. 2011;2011:282356.
Manzo VE, Bhatt AS. The human microbiome in hematopoiesis and hematologic disorders. Blood. 2015;126(3):311–8.
Sonis ST. Pathobiology of mucositis. Nat Rev Cancer. 2004;20(1):11–5.
Ze X, Duncan SH, Louis P, Flint HJ. Ruminococcus bromii is a keystone species for the degradation of resistant starch in the human colon. ISME J. 2012;6(8):1535–43.
Rivière A, Selak M, Lantin D, Leroy F, De Vuyst L. Bifidobacteria and Butyrate-Producing Colon Bacteria: Importance and Strategies for Their Stimulation in the Human Gut. Front Microbiol. 2016;7:979.
Louis P, Flint HJ. Diversity, metabolism and microbial ecology of butyrate-producing bacteria from the human large intestine. FEMS Microbiol Lett. 2009;294(1):1–8.
Vliet MJ van, Harmsen HJM, Bont ESJM de, Tissing WJE. The Role of Intestinal Microbiota in the Development and Severity of Chemotherapy-Induced Mucositis. PLOS Pathog. 2010;6(5):e1000879.
Nogal A, Valdes AM, Menni C. The role of short-chain fatty acids in the interplay between gut microbiota and diet in cardio-metabolic health. Gut Microbes. 13(1):1897212.
Smith PM, Howitt MR, Panikov N, Michaud M, Gallini CA, Bohlooly-Y M, et al. The Microbial Metabolites, Short-Chain Fatty Acids, Regulate Colonic T _reg Cell Homeostasis. Science. 2013;341(6145):569–73.
Chang PV, Hao L, Offermanns S, Medzhitov R. The microbial metabolite butyrate regulates intestinal macrophage function via histone deacetylase inhibition. Proc Natl Acad Sci U S A. 2014;111(6):2247–52.
Salvi PS, Cowles RA. Butyrate and the Intestinal Epithelium: Modulation of Proliferation and Inflammation in Homeostasis and Disease. Cells. 2021;10(7):1775.
Zhang SM, Huang SL. The Commensal Anaerobe Veillonella dispar Reprograms Its Lactate Metabolism and Short-Chain Fatty Acid Production during the Stationary Phase. Microbiol Spectr. 2023;11(2):e0355822–e0355822.
Gu Y, Yan D, Wu M, Li M, Li P, Wang J, et al. Influence of the densities and nutritional components of bacterial colonies on the culture-enriched gut bacterial community structure. AMB Express. 2021;11(1):78.
Koh A, De Vadder F, Kovatcheva-Datchary P, Bäckhed F. From Dietary Fiber to Host Physiology: Short-Chain Fatty Acids as Key Bacterial Metabolites. Cell. 2016;165(6):1332–45.
Liu X, Chen Y, Zhang S, Dong L. Gut microbiota-mediated immunomodulation in tumor. J Exp Clin Cancer Res. 2021;40(1):221.
Schluter J, Peled JU, Taylor BP, Markey KA, Smith M, Taur Y, et al. The gut microbiota is associated with immune cell dynamics in humans. Nature. 2020;588(7837):303–7.
Ingham AC, Kielsen K, Mordhorst H, Ifversen M, Aarestrup FM, Müller KG, et al. Microbiota long-term dynamics and prediction of acute graft-versus-host disease in pediatric allogeneic stem cell transplantation. Microbiome. 2021;9(1):148.
Weber D, Oefner PJ, Hiergeist A, Koestler J, Gessner A, Weber M, et al. Low urinary indoxyl sulfate levels early after transplantation reflect a disrupted microbiome and are associated with poor outcome. Blood. 2015;126(14):1723–8.
Jenq RR, Taur Y, Devlin SM, Ponce DM, Goldberg JD, Ahr KF, et al. Intestinal Blautia is associated with reduced death from graft-versus-host disease. Biol Blood Marrow Transplant J Am Soc Blood Marrow Transplant. 2015;21(8):1373–83.
Ingham AC, Kielsen K, Cilieborg MS, Lund O, Holmes S, Aarestrup FM, et al. Specific gut microbiome members are associated with distinct immune markers in pediatric allogeneic hematopoietic stem cell transplantation. Microbiome. 2019;7(1):131.
Masetti R, Leardini D, Muratore E, Fabbrini M, D’Amico F, Zama D, et al. Gut microbiota diversity before allogeneic hematopoietic stem cell transplantation as a predictor of mortality in children. Blood. 2023;142(16):1387–98.
Taur Y, Jenq RR, Perales MA, Littmann ER, Morjaria S, Ling L, et al. The effects of intestinal tract bacterial diversity on mortality following allogeneic hematopoietic stem cell transplantation. Blood. 2014;124(7):1174–82.
Smith M, Dai A, Ghilardi G, Amelsberg KV, Devlin SM, Pajarillo R, et al. Gut microbiome correlates of response and toxicity following anti-CD19 CAR T cell therapy. Nat Med. 2022;28(4):713–23.
Hiippala K, Kainulainen V, Kalliomäki M, Arkkila P, Satokari R. Mucosal Prevalence and Interactions with the Epithelium Indicate Commensalism of Sutterella spp. Front Microbiol. 2016;7:1706.
Yuen GJ, Ausubel FM. Enterococcus Infection Biology: Lessons from Invertebrate Host Models. J Microbiol Seoul Korea. 2014;52(3):200–10.
Thurlow LR, Thomas VC, Fleming SD, Hancock LE. Enterococcus faecalis Capsular Polysaccharide Serotypes C and D and Their Contributions to Host Innate Immune Evasion. Infect Immun. 2009;77(12):5551–7.
Hakim H, Dallas R, Wolf J, Tang L, Schultz-Cherry S, Darling V, et al. Gut Microbiome Composition Predicts Infection Risk During Chemotherapy in Children With Acute Lymphoblastic Leukemia. Clin Infect Dis Off Publ Infect Dis Soc Am. 2018;67(4):541–8.
Taur Y, Xavier JB, Lipuma L, Ubeda C, Goldberg J, Gobourne A, et al. Intestinal Domination and the Risk of Bacteremia in Patients Undergoing Allogeneic Hematopoietic Stem Cell Transplantation. Clin Infect Dis Off Publ Infect Dis Soc Am. 2012;55(7):905–14.
Lehrnbecher T, Fisher BT, Phillips B, Beauchemin M, Carlesse F, Castagnola E, et al. Clinical Practice Guideline for Systemic Antifungal Prophylaxis in Pediatric Patients With Cancer and Hematopoietic Stem-Cell Transplantation Recipients. J Clin Oncol Off J Am Soc Clin Oncol. 2020;38(27):3205–16.
Robinson PD, Lehrnbecher T, Phillips R, Dupuis LL, Sung L. Strategies for Empiric Management of Pediatric Fever and Neutropenia in Patients With Cancer and Hematopoietic Stem-Cell Transplantation Recipients: A Systematic Review of Randomized Trials. J Clin Oncol Off J Am Soc Clin Oncol. 2016;34(17):2054–60.
Kamboj M, Sepkowitz KA. Nosocomial infections in patients with cancer. Lancet Oncol. 2009;10(6):589–97.
El-Mahallawy HA, El-Wakil M, Moneer MM, Shalaby L. Antibiotic resistance is associated with longer bacteremic episodes and worse outcome in febrile neutropenic children with cancer. Pediatr Blood Cancer. 2011;57(2):283–8.
Secombe KR, Coller JK, Gibson RJ, Wardill HR, Bowen JM. The bidirectional interaction of the gut microbiome and the innate immune system: Implications for chemotherapy-induced gastrointestinal toxicity. Int J Cancer. 2019;144(10):2365–76.

There is NO conflict of interest to disclose.

SupplementaryInformation.pdf

Download PDF

Editorial decision: revise
03 May, 2024
Review #2 received at journal
02 May, 2024
Reviewer #2 agreed at journal
19 Apr, 2024
Review #1 received at journal
17 Apr, 2024
Reviewer #1 agreed at journal
16 Apr, 2024
Reviewers invited by journal
15 Apr, 2024
Editor assigned by journal
15 Apr, 2024
Submission checks completed at journal
15 Apr, 2024
First submitted to journal
14 Apr, 2024

You are reading this latest preprint version

Gut microbiota changes are associated with prolonged neutropenia after induction treatment of childhood acute lymphoblastic leukemia

Status:

Version 1

Abstract

Figures

Introduction

Subjects and Methods

Study population and treatment

Quantification of neutrophil and neutrophil signaling markers

Fecal samples collection

DNA extraction and 16S rRNA gene amplicon sequencing

Sequencing data processing

Ethics statement

Statistics

Results

Neutropenia, bacteremia, and antibiotics

Neutrophil signaling markers

Gut microbiota α-diversity, neutropenia and neutrophil signaling markers

Beta diversity, neutropenia and neutrophil signaling markers

Abundances of specific genera and neutropenia

Abundances of genera and neutrophil signaling markers

Discussion

Declarations

References

Additional Declarations

Supplementary Files

Status:

Version 1