Gut Microbiome Shotgun Metagenomic Sequencing in Survivors of Acute Lymphoblastic Leukemia Compared to Siblings

Gut Microbiome Shotgun Metagenomic Sequencing in Survivors of Acute Lymphoblastic Leukemia Compared to Siblings | Authorea try { document.documentElement.classList.add('js'); } catch (e) { } var _gaq = _gaq || []; _gaq.push(['_setAccount', 'G-8VDV14Y67G']); _gaq.push(['_trackPageview']); (function() { var ga = document.createElement('script'); ga.type = 'text/javascript'; ga.async = true; ga.src = ('https:' == document.location.protocol ? 'https://ssl' : 'http://www') + '.google-analytics.com/ga.js'; var s = document.getElementsByTagName('script')[0]; s.parentNode.insertBefore(ga, s); })(); Skip to main content Preprints Collections Wiley Open Research IET Open Research Ecological Society of Japan All Collections About About Authorea FAQs Contact Us Quick Search anywhere Search for preprint articles, keywords, etc. Search Search ADVANCED SEARCH SCROLL This is a preprint and has not been peer reviewed. Data may be preliminary. 18 March 2025 V1 Latest version Share on Gut Microbiome Shotgun Metagenomic Sequencing in Survivors of Acute Lymphoblastic Leukemia Compared to Siblings Authors : Roma Bhuta 0000-0002-4013-632X [email protected] , Jason Shapiro , Xochitl Morgan , Bradley DeNardo 0000-0002-3721-3523 , and Thomas Kuntz Authors Info & Affiliations https://doi.org/10.22541/au.174228170.06111907/v1 211 views 134 downloads Contents Abstract Supplementary Material Information & Authors Metrics & Citations View Options References Figures Tables Media Share Abstract Background: The gut microbiome maintains human health through homeostasis and immune function. Disruptions may be caused by various aspects of leukemia therapy and result in long-term changes that contribute to late effects seen in survivors. We previously reported on significant differences in composition of the gut microbiome in pediatric ALL survivors compared to healthy siblings using 16S sequencing. There remains limited data on the microbial taxa and functional profiles that drive such changes. Procedure: Gut microbiome shotgun metagenomic sequencing was completed on stool DNA samples obtained from 9 survivors of childhood ALL and 10 healthy sibling controls. Stool samples were collected a minimum of 6 months after completion of chemotherapy. Results: Within survivors, beta diversity was significant when looking at time elapsed since chemotherapy. Survivors’ microbiomes were more similar to siblings further from treatment but only two families had sample pairs as similar as untreated siblings. The functional potential of gluconate-5-dehydrogenase enzyme (Ga5DH) decreased significantly with time from treatment. The species Faecalibacterium prausnitzii was identified as the major contributor in most samples to the relative abundance of Ga5DH in subjects. Conclusions: Shotgun metagenomic sequencing demonstrated time from treatment has a significant effect on microbial composition. Increased time from chemotherapy corresponds to microbiomes more similar to siblings. However, microbial dysbiosis may be long lasting given the limited number of families who demonstrated microbiome pairs that were as similar in survivors as their siblings. Additional studies are needed to investigate the role of Ga5DH in the gut microbiome in survivors of ALL. Title: Gut Microbiome Shotgun Metagenomic Sequencing in Survivors of Acute Lymphoblastic Leukemia Compared to Siblings Authors : 1. Roma Bhuta 2. Jason Shapiro 3. Xochitl Morgan 4. Bradley DeNardo 5. Thomas Kuntz Affiliations: 1. Division of Pediatric Hematology-Oncology, Hasbro Children’s, The Warren Alpert Medical School of Brown University, Providence, Rhode Island 2. Division of Pediatric Gastroenterology, Hasbro Children’s, Nutrition and Liver Diseases, The Warren Alpert Medical School of Brown University, Providence, Rhode Island 3. Harvard Chan Microbiome Analysis Core, Department of Biostatistics, Harvard TH Chan School of Public Health, Boston MA 4. Division of Pediatric Hematology-Oncology, Hasbro Children’s, The Warren Alpert Medical School of Brown University, Providence, Rhode Island 5. Harvard Chan Microbiome Analysis Core, Department of Biostatistics, Harvard TH Chan School of Public Health, Boston MA Corresponding Author: Roma Bhuta, DO Division of Pediatric Hematology-Oncology, Hasbro Children’s Hospital, The Warren Alpert Medical School of Brown University Multiphasic Building 539 Eddy Street Providence, Rhode Island 02903 [email protected] phone: 401-444-5171 fax: 401-444-8845 Word Count: Abstract: 250 Main Text: 2409 Number of Tables: 1 Number of Figures: 4 Number of Supplemental Figures: 1 Running Title : Shotgun metagenomic sequencing in Survivors of Childhood ALL Keywords: Metagenomics, Gut Microbiome, Pediatric, Children, Cancer Ga5DH Gluconate-5-dehydrogenase PCoA Principal coordinate analysis Abstract Background: The gut microbiome maintains human health through homeostasis and immune function. Disruptions may be caused by various aspects of leukemia therapy and result in long-term changes that contribute to late effects seen in survivors. We previously reported on significant differences in composition of the gut microbiome in pediatric ALL survivors compared to healthy siblings using 16S sequencing. There remains limited data on the microbial taxa and functional profiles that drive such changes. Procedure: Gut microbiome shotgun metagenomic sequencing was completed on stool DNA samples obtained from 9 survivors of childhood ALL and 10 healthy sibling controls. Stool samples were collected a minimum of 6 months after completion of chemotherapy. Results: Within survivors, beta diversity was significant when looking at time elapsed since chemotherapy. Survivors’ microbiomes were more similar to siblings further from treatment but only two families had sample pairs as similar as untreated siblings. The functional potential of gluconate-5-dehydrogenase enzyme (Ga5DH) decreased significantly with time from treatment. The species Faecalibacterium prausnitzii was identified as the major contributor in most samples to the relative abundance of Ga5DH in subjects. Conclusions: Shotgun metagenomic sequencing demonstrated time from treatment has a significant effect on microbial composition. Increased time from chemotherapy corresponds to microbiomes more similar to siblings. However, microbial dysbiosis may be long lasting given the limited number of families who demonstrated microbiome pairs that were as similar in survivors as their siblings. Additional studies are needed to investigate the role of Ga5DH in the gut microbiome in survivors of ALL. Introduction The treatment of pediatric acute lymphoblastic leukemia (ALL) has made many advances over the last several decades with 5-year survival rates now above 90% for most patients. 1 With improved risk stratification and targeted therapy resulting in increased overall survival, minimizing late effects of treatment and maximizing survivorship-related mortality represents vital aspects of long-term care. Fifty percent of survivors of childhood ALL report at least once chronic medical condition and are 2.8 times more likely than siblings to describe having multiple chronic medical conditions. 2 Late effects of therapy include subsequent malignancy as well as musculoskeletal, cardiovascular, and neurologic diseases. 2,3 Durable alternations of the intestinal microbiota may contribute to some of the late effects seen in survivors of childhood ALL. The gut microbiome is an integral part of maintaining human health through homeostasis and immune function. 4 It is impacted by a variety of factors, many of which overlap with standard pediatric ALL therapy including diet, medications, environmental exposures, and infection. 5,6 Microbial dysbiosis, or imbalance of the gut microbiota following intestinal disruption, can contribute to a variety of pathologies, including cardiovascular disease, gastrointestinal disease, neurologic disease, endocrinopathies, and malignancy. 7,8 We previously reported on 16S rRNA gene sequencing performed on gut microbiome samples from nine survivors of ALL compared to 10 healthy sibling controls. A significant difference was seen in the composition of the gut microbiome of pediatric ALL survivors compared to healthy siblings. Differential microbiome species selectively enriched in ALL survivors supported the hypothesis that survivors of ALL may have durable, long-term changes in their gut microbiome. 9 However, given the limited sample size, additional analysis and classification of the differences was unable to be performed. Shotgun metagenomic sequencing assesses all DNA within a sample and can provide additional information regarding microbial and gene content relative to targeted sequencing of 16S rRNA gene hypervariable regions. Such analysis allows for improved taxonomic resolution compared to 16S sequencing alone and can provide more detailed identification and relative abundance of the microbial taxa as well as their microbiome functional profiles. 10,11 Given the value of such additional information, we sought to better understand the microbial differences previously identified in survivors of pediatric ALL compared to healthy siblings using shotgun metagenomic analysis. Methods: Gut microbiome shotgun metagenomic sequencing was completed on DNA samples obtained from a previously completed, institutional review board approved, single-center cohort study. 9 Stool samples were collected to assess the gut microbiome composition of survivors of pediatric and adolescent pre-B-cell ALL compared to their siblings. Eligible patients were between the ages of 3 and 30, had a history of pre-B-cell ALL currently in remission, and were at least 6 months from the completion of therapy. All participants were required to have at least one biological sibling who lived in the same home full-time. Exclusion criteria included history of bone marrow transplant and known autoimmune conditions or inflammatory bowel disease. Individuals with use of antibiotics or a diagnosis of gastrointestinal illness (defined as vomiting and/or diarrhea) within 30 days of enrollment were excluded as well as those taking medications known to impact the gut microbiome such as immunosuppressive agents, nonsteroidal anti-inflammatory drugs, and proton pump inhibitors. Three stool samples were collected from each study participant, each a minimum of 24 hours apart via a standardized method. Detailed description of methods and results from these analyses were previously published. 9 Metagenomic sequencing was completed by Novogene Corporation, Inc for quality control of the DNA samples, library construction, and sequencing. Sequencing libraries were generated using NEBNext® Ultra™DNA Library Prep Kit for Illumina (NEB, USA) and sequenced on an Illumina HiSeq platform, following manufacturer’s recommendations. Sequencing data was processed using the bioBakery meta’omics workflow, using the standard pipeline with default parameters. 12 KneadData v0.12.0 was used for quality control of raw reads, including trimming, adapter removal, and removal of off target reads from the default hg37 and human contamination database. No samples fell under a 10 million final read quality control cutoff. Species-level taxonomic profiles were generated using MetaPhlAn v3.0.14 and the CHOCOPhlAn v30 database. 13 Functional profiling was performed using HUMAnN v3.0.0 with UniRef gene families grouped into enzyme commission number (EC) for downstream analysis. 14 Data was analyzed using R 4.3.0. The vegan package was used for calculation of ecological diversity measurements and whole-community statistical tests. Alpha and beta diversity were quantified as Shannon diversity and Bray-Curtis dissimilarity, respectively. Permutation-based ANOVA was performed using the adonis2 function. The MaAsLin2 v1.18.0 package was used for linear modeling, for which species were filtered at a threshold of minimum 0.1% relative abundance in over 10% of samples. 15 Total sum scaling was used, and species were log transformed with a pseudocount of half the minimum non-zero value for variance stabilization. Resulting p-values from linear modeling were adjusted for false discovery using the Benjamini and Hochberg method via the p.adjust function in R, and q-values < 0.25 were reported as significant. Sequencing data is deposited in Sequencing Read Archive as BioProject PRJNA1235679. Results Patient Information Nine survivors of ALL and 10 siblings were enrolled on the initial pilot study (Table 1) 9 . All survivors had a history of pre B cell ALL and were treated according to standard chemotherapy protocols at the time of diagnosis. There was a fairly equal sex distribution (4 females, 5 males). At the time of stool collection, the median age of survivors was 11 years (4-19 years), and the median age of siblings was 12.5 years (5-19). Fifty-seven stool samples were collected. Median time from end of leukemia therapy to stool collection was 24 months (6.5-114 months). Species Composition A total of 247 species were initially identified by MetaPhlAn v3.0.14. 13 The 15 species with highest mean relative abundance were plotted and inspected to assess the sample microbial compositions and its consistency between replicates (Figure 1). Four samples were omitted from analyses. Sample RB48 (one stool sample from patient 9) had a taxonomic composition vastly different from the other two samples from the same patient, suggesting a quality issue. The taxonomic composition of three stool samples from Patient 1 was an extreme outlier compared to all other samples, possibly due to underlying diagnosis of Down Syndrome. Thus, sample RB48 and the samples from Patient 1 and their corresponding sibling were omitted from subsequent analyses. A principal coordinate analysis (PCoA) plot was created to demonstrate the relationship between survivor and sibling samples (Figure 2). Alpha and Beta Diversity Alpha diversity, or within sample evenness and richness, was assessed using the inverse Simpson index. There was no significant difference between survivors and siblings when tested by linear model, controlling for subject and family as random effects (p = 0.635). Time from treatment also did not significantly affect alpha diversity among survivors (p = 0.309). Beta diversity, or between-sample dissimilarity, was quantified using Bray-Curtis dissimilarity. Survivor vs. sibling status did not significantly affect beta diversity in a model controlling for family and permuting metadata blocked by subject (R 2 = 0.034, p = 0.832, permutation-based ANOVA). 16 Within survivors, time elapsed since chemotherapy was significant (R 2 = 0.249, p = 0.019, permutation-based ANOVA). Bray-Curtis dissimilarity between survivors and siblings within the same family was used as a metric of the degree of dysbiosis of survivor microbiomes. Linear modeling was used to assess the relationship between time since chemotherapy and difference between survivor and sibling microbiomes (coef = -0.0021, p = 5.36e-05). There was a downward trend, indicating that survivors’ microbiomes were more similar to siblings at timepoints further from treatment (Figure 3). While a robust measure of expected familial dissimilarity within healthy controls was not available, one pair of siblings in a single family had a mean dissimilarity of 0.36, which is comparable to the samples with the longest elapsed time since chemotherapy. Taxonomic and functional associations The association between survivor status and the relative abundance of each microbiome feature (i.e. species and EC) was tested using the MaAsLin2 framework. Linear mixed models were constructed with patient ID and family ID as random effects to account for repeated sampling. No significant associations were observed. When analyzing months from chemotherapy in survivors, the functional potential of gluconate-5-dehydrogenase enzyme (Ga5DH) was found to decrease significantly with time since treatment (q = 0.025) (Figure 4). The species Faecalibacterium prausnitzii was identified as the major contributor in most samples to the relative abundance of Ga5DH in subjects (Supplemental Figure 1). However, the overall abundance of Faecalibacterium prausnitzii was not associated with time from treatment (p = 0.08). Discussion This study compared the intestinal microbiota of pediatric ALL survivors and healthy siblings via deep sequencing with shotgun metagenomic analysis. Prior studies have been limited and relied on 16S rRNA sequencing to demonstrate that certain gut microbiome changes can be seen in survivors after treatment of ALL. 17-19 Thomas et al. found that members of Ruminococcaceae, Lachnospiraceae , and Faecalibacterium were significantly depleted in survivors compared to siblings. 17 Chua et al. also reported a decrease in Faecalibacterium and alterations in immune regulation in survivors of ALL. 19 Our previously published work added to these studies and demonstrated a significant difference in alpha and beta diversity in survivors of ALL compared to their siblings. 9 Shotgun metagenomic sequencing allowed for better understanding of the differences between our two cohorts with higher species resolution, assessment of functionality, and improved accuracy due to less amplification during library preparation. While survivor status alone was not a significant predictor of either overall microbiome community or individual features, there was evidence that the time from treatment does have a significant effect. Because longitudinal samples were not available, this was assessed as microbiome differences between survivors and siblings as well as time from treatment associations within the survivor cohort. For within-family comparisons, the Bray-Curtis dissimilarities for each survivor sample against each sibling sample were used as an approximation of deviation from a baseline state. It is expected that familial transmission, particularly in children, leads to substantially lower dissimilarities compared to strangers; therefore, large differences between survivors and siblings may indicate treatment-induced dysbiosis. 20 Upper and lower reference points were established by comparing unrelated study participants and a pair of untreated siblings, respectively. Our study demonstrated that longer duration from chemotherapy exposure is associated with increasing similarity of subjects’ microbiome composition compared to their siblings. However, only two families had sample pairs as similar as untreated siblings, one of which was the survivor sampled furthest from treatment. For some survivors closest to treatment, the difference between sibling microbiomes was approximately as large as the difference between unrelated study participants. This supports the notion that microbial dysbiosis may persist after completion of chemotherapy, much longer than expected for antibiotic treatment alone, on the timescale of several years for some patients. 21 Microbiome feature associations with time within the survivor cohort should provide reasonable estimates of longitudinal trends, particularly if a feature is relatively conserved across healthy subjects. This is more likely in functional profiles than in species. 22 Using sibling comparators as a survivor baseline in interaction models was precluded by limited sample size. The relative abundance of Ga5DH enzyme was found to have a significant negative association with time from end of therapy. Ga5DH catalyzes a reversible reduction of 5-ketogluconate to D-gluconate. 23 D-gluconate is an importance source of carbon for many microorganisms and may play a role in bacterial survival and virulence. 24 Additional studies are needed to verify a within-patient decrease in Ga5DH and investigate its role in the gut microbiome in survivors of ALL. Further work will be needed to better understand if there are clinical implications of Ga5DH disruption in survivors of ALL. Our study had several strengths and limitations. Our design pairing survivors with healthy siblings allowed for the control of various factors known to impact the gut microbiome such as genetics, environment, and diet. By usings siblings as the comparator group, a per-patient microbiome baseline could be created where one would expect the microbiome of survivors to regress to over time after treatment. Collecting 3 different samples from each participant also allowed for better assessment of outliers and technical artifacts. Shotgun metagenomic sequencing allowed for more precise taxonomic assignments than previous 16S rRNA studies, as well as functional profiling. Our primary limitation was sample size precluding further powered analyses to expand on some of the perturbations seen. Samples from one patient (patient ID1) in our survivorship cohort were removed due to a significant differences in the taxonomic composition compared to other participants, likely due to underlying Down Syndrome. Their corresponding sibling was also removed from analyses. One sample from patient 10 (labeled RB48) was also omitted from analyses as its taxonomic composition varied significantly compared to the other samples from that same patient, raising concern for a technical or experimental error as such variations would not be expected in samples collected within 1 month of one another. By allowing samples to be taken at different times after the completion of chemotherapy, we were less powered to see consistent associations between subjects and treatment history at specific time points. Conversely, this allowed for better assessment of treatment recovery, albeit not as well as true longitudinal sampling. General aspects of microbiome recovery from chemotherapy are not currently well described because of the difficulty of sample acquisition. Longitudinal designs require a long sampling time so many studies are either ongoing or utilize older microbiome sequencing and analysis methodologies. Increasing sample size and length of time from end of therapy to stool collection will be critical to better understand the changes we demonstrated within our study. While the cure rate of pediatric ALL is high in children, the multimodal treatment regimen needed to maximize survival is intense and associated with long-term effects. Microbial dysbiosis from various treatment exposures may represent a pathologic mechanism for the development of long-term side effects of such therapy. The microbiome is known to have important developmental impacts, especially with regards to immune development and function. Early life perturbations in the microbiome have been demonstrated to have long lasting effects, so disruptions are particularly concerning if recovery of the microbiome is slow compared to conditions such as illness and antibiotic usage. 25 Furthermore, survivors’ microbiomes might never return to their pre-treatment state, instead developing a new equilibrium that is either more or less healthy. 26 More data is required to understand how microbial dysbiosis is associated with specific multi-modal ALL treatment, if the microbial dysbiosis eventually resolves with prolonged time off- therapy, and whether interventions can be utilized to resolve persistent microbial dysbiosis. The current results are a strong step in determining this relationship, especially in a population that remains poorly understood. Conflict of Interest The authors have no conflicts of interest. Acknowledgments We acknowledge the work of David Nelson PhD, Ying Zhang PhD, Jing Wang PhD, and Janet Atoyan MS in the initial stool sample processing, extraction of DNA, and 16S analysis. References 1. Inaba H and Mulligham CG. Pediatric acute lymphoblastic leukemia. Haematologica. 2020; 105(11):2524-2539. 2. Mody R, Li S, Dover DC, et al. Twenty-five-year follow-up among survivors of childhood acute lymphoblastic leukemia: a report from the Childhood Cancer Survivor Study. Blood. 2008;111:5515-5523. 3. Liu W, Cheung YT, Conklin HM, et al. Evolution of Neurocognitive Function in Long-term Survivors of Childhood Acute Lymphoblastic Leukemia Treated with Chemotherapy Only. J Cacner Surviv. 2018; 12(3): 398-406. 4. Hou K, Wu ZX, Chen XY, et al. Microbiota in health and diseases. Signal Transduction and Targeted Therapy. 2022; 7:135. 5. Duerkop BA, Vaishnava S, Hooper LV. Immune Responses to the Microbiota at the Intestinal Mucosal Surface. Immunity. 2009; 31(3):368-76. 6. Kostic AD, Xavier RJ, and Gevers D. The Microbiome in Inflammatory Bowel Diseases: Current Status and the Future Ahead. Gastroenterology. 2014; 146(6): 1489-1499. 7. deVos WM, Tilg H, Van Hul M, et al. Gut microbiome and health: mechanistic insights. Gut. 2022;71:1020-1032. 8. Valdes AM, Walter J, Segal E, et al. Role of the gut microbiota in nutrition and health. BMJ. 2018; 316:k2179. 9. Bhuta R, DeNardo B, Wang J, et al. Durable changes in the gut microbiome in survivors of childhood acute lymphoblastic leukemia. Pediatr Blood Cancer. 2021; 68(12):e29308. 10. Durazzi F, Sala C, Castellani, et al. Comparison between 16S rRNA and shotgun sequencing data for the taxonomic characterization of the gut microbiota. Sci Rep. 2021; 11(1):3030. 11. Peterson D, Bonham KS, Rowland S, et al. Comparative Analysis of 16S rRNA Gene and Metagenome Sequencing in Pediatric Gut Microbiomes. Front Microbiol. 2021; 12:670336. 12. Beghini F, McIver LJ, Blanco-Miguez A, et al. Integrating taxonomic, functional, and strain-level profiling of diverse microbial communities with bioBakery 3. Elife. 2021; 10:e65088. 13. Tin Truong D, Franzosa EA, Tickle T, et al. MetaPhlAn2 for enhanced metagenomic taxonomic profiling. Nat Methods. 2015; 12(10):902-3. 14. Franzosa EA, McIver LJ, Rahnavard G, et al. Species-level functional profiling of metagenomes and metatranscriptomes. Nat Methods. 2018; 15(11):962-968. 15. Mallick H, Rahnavard A, McIver LJ, et al. Multivariable association discovery in population-scale meta-omics studies. PLoS Comput Biol. 2021; 17(11):e1009442. 16. Llyod-Price J, Arze C, Ananthakrishnan AN, et al. Multi-omics of the gut microbial ecosystem in inflammatory bowel diseases. Nature. 3019; 569(7758):655-662. 17. Thomas R, Wong WSW, Saadon R, et al. Gut microbial composition difference between pediatric ALL survivors and siblings. Pediatr Hematol Oncol. 2020; 37(6): 475-488. 18. Chua LL, Rajasuriar R, Ai Lian Liam Y, et al. Temporal changes in gut microbiota profile in children with acute lymphoblastic leukemia prior to commencement-, during-, and post- cessation of chemotherapy. BMC Cancer. 2020; 20(1):151. 19. Chua LL, Rajasuriar R, Safiq Azanan M, et al. Reduced microbial diversity in adult survivors of childhood acute lymphoblastic leukemia and microbial associations with increased immune activation. Microbiome. 2017; 5(1):35. 20. Zhao L, Chen W, Ge Y, et al. Putative Familial Transmissible Bacteria of Various Body Niches Link with Home Environment and Children’s Immune Health. Microbiol Spectr. 2021; 9(3):e0087221. 21. Elvers KT, Wilson VJ, Hammond A, et al. Antibiotic-induced changes in the human gut microbiota for the most commonly prescribed antibiotics in primary care in the UK: a systemic review. BMJ Open. 2020; 10(9):e035677. 22. Human Microbiome Project Consortium. Structure, function and diversity of the healthy human microbiome. Nature. 2012;486(7402):207-14. 23. Zhang Q, Peng H, Gao F, et al. Structural insight into the catalytic mechanism of gluconate 5-dehydrogenase from Streptococcus suis: Crystal structures of the substrate-free and quaternary complex enzymes. Protein Sci. 2009;18(2):294-303. 24. Shi Z, Xuan C, Han H, et al. Gluconate 5-dehydrogenase (Ga5DH) participates in Streptococcus suis cell division. Protein Cell. 2014; 5(10):761-9. 25. Saeed NK, Al-Beltagi M, Bediwy AS, et al. Gut microbiota in various childhood disorders: Implication and indications. World J Gastroenterol. 2022;28(18):1875-1901. 26. Vangay P, Ward T, Gerber JS, et al. Antibiotics, pediatric dysbiosis, and disease. Cell Host Microbe. 2015;17(5):553-64. Legends TABLE 1 Patient demographics Nine survivors of childhood ALL and 10 siblings were enrolled on this study. Median time from end of therapy was 24 months. FIGURE 1 Taxonomic composition of samples Bacterial separation based on taxonomic family level are represented by color variation. One hundred twenty two species and 60 genera were present after basic filtering. The 15 species with the highest mean relative abundance were plotted for each stool sample. FIGURE 2 PCoA plot of survivors of ALL and their siblings Samples largely cluster by survivor and family with small separations between points from the same subject, which is expected from the bar plots and experimental design. FIGURE 3 Beta diversity between survivors and siblings within families The downward trend indicated microbiomes of survivors were more similar to siblings at times further from healthy siblings. FIGURE 4 Abundance of Gluconate-5-dehydrogenase enzyme in survivors, as a function of time since chemotherapy The functional potential of gluconate-5-dehydrogenase in survivors decreases the further away from chemotherapy. SUPPLEMENTAL FIGURE S1 Contributions of microbial species to measured gluconate-5-dehydrogenase The proportion of relative abundance of gluconate-5-dehydrogenase was plotted based on survivor status. The genus Faecalibacterium was the major contributor in most samples. Supplementary Material File (table 1 demographics.docx) Download 27.64 KB Information & Authors Information Version history V1 Version 1 18 March 2025 Copyright This work is licensed under a Non Exclusive No Reuse License. Keywords all late effects of cancer treatment oncology Authors Affiliations Roma Bhuta 0000-0002-4013-632X [email protected] Brown University Warren Alpert Medical School View all articles by this author Jason Shapiro Brown University Warren Alpert Medical School View all articles by this author Xochitl Morgan Harvard T H Chan School of Public Health View all articles by this author Bradley DeNardo 0000-0002-3721-3523 Brown University Warren Alpert Medical School View all articles by this author Thomas Kuntz Harvard T H Chan School of Public Health View all articles by this author Metrics & Citations Metrics Article Usage 211 views 134 downloads .FvxKWukQNSOunydq8rnd { width: 100px; } Citations Download citation Roma Bhuta, Jason Shapiro, Xochitl Morgan, et al. Gut Microbiome Shotgun Metagenomic Sequencing in Survivors of Acute Lymphoblastic Leukemia Compared to Siblings. Authorea . 18 March 2025. 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