Delayed Impact of Ionizing Radiation Depends on Sex: Integrative Metagenomics and Metabolomics Analysis of Rodent Colon Content
Abstract
1. Introduction
2. Results
2.1. Sex-Biased Bacterial Diversity Profile
2.2. Differentially Abundant Taxa Under Bacterial Kingdom
2.3. Differentially Perturbed Networks Linked to the Bacteria in DCC
2.4. Differentially Expressed Metabolites Derived from DCC as Markers of Host–Microbiome Association
3. Discussion
4. Materials and Methods
4.1. Animals
4.2. Whole-Body Gamma Irradiation
4.3. Ethics Statement and Veterinary Care Following Radiation
4.4. Post-Euthanasia Sample Collection
4.5. 16S rRNA Sample Processing Using Descending Colon Contents
4.6. 16S rRNA Metagenomics and Functional Network Analysis
4.7. Global Metabolomics Assay of DCC
4.8. Functional Metabolomics Analysis
Supplementary Materials
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Acknowledgments
Conflicts of Interest
References
- Zheng, Y.; Gao, W.; Spratt, D.E.; Sun, Y.; Xing, L. Management of gastrointestinal perforation related to radiation. Int. J. Clin. Oncol. 2020, 25, 1010–1015. [Google Scholar] [CrossRef]
- Magne, F.; Gotteland, M.; Gauthier, L.; Zazueta, A.; Pesoa, S.; Navarrete, P.; Balamurugan, R. The Firmicutes/Bacteroidetes Ratio: A Relevant Marker of Gut Dysbiosis in Obese Patients? Nutrients 2020, 12, 1474. [Google Scholar] [CrossRef] [PubMed]
- Sheflin, A.M.; Whitney, A.K.; Weir, T.L. Cancer-promoting effects of microbial dysbiosis. Curr. Oncol. Rep. 2014, 16, 406. [Google Scholar] [CrossRef] [PubMed]
- Biragyn, A.; Ferrucci, L. Gut dysbiosis: A potential link between increased cancer risk in ageing and inflammaging. Lancet Oncol. 2018, 19, e295–e304. [Google Scholar] [CrossRef]
- Lau, K.; Srivatsav, V.; Rizwan, A.; Nashed, A.; Liu, R.; Shen, R.; Akhtar, M. Bridging the Gap between Gut Microbial Dysbiosis and Cardiovascular Diseases. Nutrients 2017, 9, 859. [Google Scholar] [CrossRef]
- de Oliveira, G.L.V.; Cardoso, C.R.B.; Taneja, V.; Fasano, A. Editorial: Intestinal Dysbiosis in Inflammatory Diseases. Front. Immunol. 2021, 12, 727485. [Google Scholar] [CrossRef]
- Sarkar, A.; Harty, S.; Lehto, S.M.; Moeller, A.H.; Dinan, T.G.; Dunbar, R.I.M.; Cryan, J.F.; Burnet, P.W.J. The Microbiome in Psychology and Cognitive Neuroscience. Trends Cogn. Sci. 2018, 22, 611–636. [Google Scholar] [CrossRef]
- Parker, A.; Fonseca, S.; Carding, S.R. Gut microbes and metabolites as modulators of blood-brain barrier integrity and brain health. Gut Microbes 2020, 11, 135–157. [Google Scholar] [CrossRef] [PubMed]
- Acharjee, A.; Singh, U.; Choudhury, S.P.; Gkoutos, G.V. The diagnostic potential and barriers of microbiome based therapeutics. Diagnosis 2022, 9, 411–420. [Google Scholar] [CrossRef]
- Gulliver, E.L.; Young, R.B.; Chonwerawong, M.; D’Adamo, G.L.; Thomason, T.; Widdop, J.T.; Rutten, E.L.; Rossetto Marcelino, V.; Bryant, R.V.; Costello, S.P.; et al. Review article: The future of microbiome-based therapeutics. Aliment. Pharmacol. Ther. 2022, 56, 192–208. [Google Scholar] [CrossRef]
- Pant, A.; Das, B. Microbiome-based therapeutics: Opportunity and challenges. Prog. Mol. Biol. Transl. Sci. 2022, 191, 229–262. [Google Scholar] [CrossRef] [PubMed]
- Sorbara, M.T.; Pamer, E.G. Microbiome-based therapeutics. Nat. Rev. Microbiol. 2022, 20, 365–380. [Google Scholar] [CrossRef] [PubMed]
- Ghosh, S.; Molcan, E.; DeCoffe, D.; Dai, C.; Gibson, D.L. Diets rich in n-6 PUFA induce intestinal microbial dysbiosis in aged mice. Br. J. Nutr. 2013, 110, 515–523. [Google Scholar] [CrossRef]
- Mariat, D.; Firmesse, O.; Levenez, F.; Guimaraes, V.; Sokol, H.; Dore, J.; Corthier, G.; Furet, J.P. The Firmicutes/Bacteroidetes ratio of the human microbiota changes with age. BMC Microbiol. 2009, 9, 123. [Google Scholar] [CrossRef]
- McGee, J.S.; Huttenhower, C. Of mice and men and women: Sexual dimorphism of the gut microbiome. Int. J. Women’s Dermatol. 2021, 7, 533–538. [Google Scholar] [CrossRef]
- Sarkar, A.; Yoo, J.Y.; Valeria Ozorio Dutra, S.; Morgan, K.H.; Groer, M. The Association between Early-Life Gut Microbiota and Long-Term Health and Diseases. J. Clin. Med. 2021, 10, 459. [Google Scholar] [CrossRef]
- Kashtanova, D.A.; Popenko, A.S.; Tkacheva, O.N.; Tyakht, A.B.; Alexeev, D.G.; Boytsov, S.A. Association between the gut microbiota and diet: Fetal life, early childhood, and further life. Nutrition 2016, 32, 620–627. [Google Scholar] [CrossRef]
- Wang, M.; Monaco, M.H.; Donovan, S.M. Impact of early gut microbiota on immune and metabolic development and function. Semin. Fetal Neonatal Med. 2016, 21, 380–387. [Google Scholar] [CrossRef]
- Sender, R.; Fuchs, S.; Milo, R. Revised estimates for the number of human and bacteria cells in the body. PLoS Biol. 2016, 14, e1002533. [Google Scholar] [CrossRef]
- Punzon-Jimenez, P.; Labarta, E. The impact of the female genital tract microbiome in women health and reproduction: A review. J. Assist. Reprod. Genet. 2021, 38, 2519–2541. [Google Scholar] [CrossRef]
- Srinivasan, S.; Hua, X.; Wu, M.C.; Proll, S.; Valint, D.J.; Reed, S.D.; Guthrie, K.A.; LaCroix, A.Z.; Larson, J.C.; Pepin, R.; et al. Impact of Topical Interventions on the Vaginal Microbiota and Metabolome in Postmenopausal Women: A Secondary Analysis of a Randomized Clinical Trial. JAMA Netw. Open 2022, 5, e225032. [Google Scholar] [CrossRef] [PubMed]
- Fu, M.; Zhang, X.; Liang, Y.; Lin, S.; Qian, W.; Fan, S. Alterations in Vaginal Microbiota and Associated Metabolome in Women with Recurrent Implantation Failure. mBio 2020, 11, e03242-19. [Google Scholar] [CrossRef] [PubMed]
- Kovalchuk, O.; Ponton, A.; Filkowski, J.; Kovalchuk, I. Dissimilar genome response to acute and chronic low-dose radiation in male and female mice. Mutat. Res. 2004, 550, 59–72. [Google Scholar] [CrossRef] [PubMed]
- Kovalchuk, O.; Burke, P.; Besplug, J.; Slovack, M.; Filkowski, J.; Pogribny, I. Methylation changes in muscle and liver tissues of male and female mice exposed to acute and chronic low-dose X-ray-irradiation. Mutat. Res. 2004, 548, 75–84. [Google Scholar] [CrossRef]
- De Courcy, L.; Bezak, E.; Marcu, L.G. Gender-dependent radiotherapy: The next step in personalised medicine? Crit. Rev. Oncol. Hematol. 2020, 147, 102881. [Google Scholar] [CrossRef]
- Collado, M.C.; Isolauri, E.; Laitinen, K.; Salminen, S. Effect of mother’s weight on infant’s microbiota acquisition, composition, and activity during early infancy: A prospective follow-up study initiated in early pregnancy. Am. J. Clin. Nutr. 2010, 92, 1023–1030. [Google Scholar] [CrossRef]
- Mokhtari, P.; Metos, J.; Anandh Babu, P.V. Impact of type 1 diabetes on the composition and functional potential of gut microbiome in children and adolescents: Possible mechanisms, current knowledge, and challenges. Gut Microbes 2021, 13, 1926841. [Google Scholar] [CrossRef]
- Jungles, K.N.; Jungles, K.M.; Greenfield, L.; Mahdavinia, M. The Infant Microbiome and Its Impact on Development of Food Allergy. Immunol. Allergy Clin. N. Am. 2021, 41, 285–299. [Google Scholar] [CrossRef]
- Mathews, T.; Hayer, S.S.; Dinkel, D.; Hanish, A.; Poppert Cordts, K.M.; Rasmussen, H.; Moore, T. Maternal-Child Microbiome and Impact on Growth and Neurodevelopment in Infants and Children: A Scoping Review. Biol. Res. Nurs. 2023, 25, 454–468. [Google Scholar] [CrossRef]
- Dilalla, V.; Chaput, G.; Williams, T.; Sultanem, K. Radiotherapy side effects: Integrating a survivorship clinical lens to better serve patients. Curr. Oncol. 2020, 27, 107–112. [Google Scholar] [CrossRef]
- Guo, H.; Chou, W.C.; Lai, Y.; Liang, K.; Tam, J.W.; Brickey, W.J.; Chen, L.; Montgomery, N.D.; Li, X.; Bohannon, L.M.; et al. Multi-omics analyses of radiation survivors identify radioprotective microbes and metabolites. Science 2020, 370, eaay9097. [Google Scholar] [CrossRef] [PubMed]
- Chakraborty, N.H.A.; Campbell, R.; Holmes-Hampton, G.; Kumar, V.P.; Moyler, C.; Gautam, A.; Hammamieh, R.; Ghosh, S.P. Ionizing Radiation Dose Differentially Affects the Host–Microbe Relationship over Time. Microorganisms 2024, 12, 1995. [Google Scholar] [CrossRef] [PubMed]
- Wilson, B.R. Survival Studies of Whole-Body X-Irradiated Germfree (Axenic) Mice. Radiat. Res. 1963, 20, 477–483. [Google Scholar] [CrossRef] [PubMed]
- Crawford, P.A.; Gordon, J.I. Microbial regulation of intestinal radiosensitivity. Proc. Natl. Acad. Sci. USA 2005, 102, 13254–13259. [Google Scholar] [CrossRef]
- Wishart, D.S. Metabolomics for Investigating Physiological and Pathophysiological Processes. Physiol. Rev. 2019, 99, 1819–1875. [Google Scholar] [CrossRef]
- Ramautar, R.; Berger, R.; van der Greef, J.; Hankemeier, T. Human metabolomics: Strategies to understand biology. Curr. Opin. Chem. Biol. 2013, 17, 841–846. [Google Scholar] [CrossRef]
- Wachsmuth, H.R.; Weninger, S.N.; Duca, F.A. Role of the gut-brain axis in energy and glucose metabolism. Exp. Mol. Med. 2022, 54, 377–392. [Google Scholar] [CrossRef]
- Chakraborty, N. Metabolites: A converging node of host and microbe to explain meta-organism. Front. Microbiol. 2024, 15, 1337368. [Google Scholar] [CrossRef]
- Krautkramer, K.A.; Fan, J.; Backhed, F. Gut microbial metabolites as multi-kingdom intermediates. Nat. Rev. Microbiol. 2021, 19, 77–94. [Google Scholar] [CrossRef]
- Kanehisa, M.; Goto, S. KEGG: Kyoto encyclopedia of genes and genomes. Nucleic Acids Res. 2000, 28, 27–30. [Google Scholar] [CrossRef]
- Metabolite Statistics. Available online: https://hmdb.ca/statistics (accessed on 4 April 2025).
- Holmes-Hampton, G.P.; Soni, D.K.; Kumar, V.P.; Biswas, S.; Wuddie, K.; Biswas, R.; Ghosh, S.P. Time- and sex-dependent delayed effects of acute radiation exposure manifest via miRNA dysregulation. iScience 2024, 27, 108867. [Google Scholar] [CrossRef] [PubMed]
- Rath, S.; Heidrich, B.; Pieper, D.H.; Vital, M. Uncovering the trimethylamine-producing bacteria of the human gut microbiota. Microbiome 2017, 5, 54. [Google Scholar] [CrossRef]
- Ahmed, H.; Leyrolle, Q.; Koistinen, V.; Karkkainen, O.; Laye, S.; Delzenne, N.; Hanhineva, K. Microbiota-derived metabolites as drivers of gut-brain communication. Gut Microbes 2022, 14, 2102878. [Google Scholar] [CrossRef]
- Wang, S.P.; Rubio, L.A.; Duncan, S.H.; Donachie, G.E.; Holtrop, G.; Lo, G.; Farquharson, F.M.; Wagner, J.; Parkhill, J.; Louis, P.; et al. Pivotal Roles for pH, Lactate, and Lactate-Utilizing Bacteria in the Stability of a Human Colonic Microbial Ecosystem. mSystems 2020, 5, e00645-20. [Google Scholar] [CrossRef]
- Mou, Z.; Yang, Y.; Hall, A.B.; Jiang, X. The taxonomic distribution of histamine-secreting bacteria in the human gut microbiome. BMC Genom. 2021, 22, 695. [Google Scholar] [CrossRef] [PubMed]
- Otaru, N.; Ye, K.; Mujezinovic, D.; Berchtold, L.; Constancias, F.; Cornejo, F.A.; Krzystek, A.; de Wouters, T.; Braegger, C.; Lacroix, C.; et al. GABA Production by Human Intestinal Bacteroides spp.: Prevalence, Regulation, and Role in Acid Stress Tolerance. Front. Microbiol. 2021, 12, 656895. [Google Scholar] [CrossRef]
- Yano, J.M.; Yu, K.; Donaldson, G.P.; Shastri, G.G.; Ann, P.; Ma, L.; Nagler, C.R.; Ismagilov, R.F.; Mazmanian, S.K.; Hsiao, E.Y. Indigenous bacteria from the gut microbiota regulate host serotonin biosynthesis. Cell 2015, 161, 264–276. [Google Scholar] [CrossRef]
- Fung, T.C.; Vuong, H.E.; Luna, C.D.G.; Pronovost, G.N.; Aleksandrova, A.A.; Riley, N.G.; Vavilina, A.; McGinn, J.; Rendon, T.; Forrest, L.R.; et al. Intestinal serotonin and fluoxetine exposure modulate bacterial colonization in the gut. Nat. Microbiol. 2019, 4, 2064–2073. [Google Scholar] [CrossRef]
- Kurland, I.J.; Broin, P.O.; Golden, A.; Su, G.; Meng, F.; Liu, L.; Mohney, R.; Kulkarni, S.; Guha, C. Integrative Metabolic Signatures for Hepatic Radiation Injury. PLoS ONE 2015, 10, e0124795. [Google Scholar] [CrossRef]
- Kasahara, K.; Kerby, R.L.; Zhang, Q.; Pradhan, M.; Mehrabian, M.; Lusis, A.J.; Bergstrom, G.; Backhed, F.; Rey, F.E. Gut bacterial metabolism contributes to host global purine homeostasis. Cell Host Microbe 2023, 31, 1038–1053. [Google Scholar] [CrossRef]
- Fukuda, T.; Majumder, K.; Zhang, H.; Turner, P.V.; Matsui, T.; Mine, Y. Adenine Inhibits TNF-alpha Signaling in Intestinal Epithelial Cells and Reduces Mucosal Inflammation in a Dextran Sodium Sulfate-Induced Colitis Mouse Model. J. Agric. Food Chem. 2016, 64, 4227–4234. [Google Scholar] [CrossRef] [PubMed]
- Wlodarska, M.; Luo, C.; Kolde, R.; d’Hennezel, E.; Annand, J.W.; Heim, C.E.; Krastel, P.; Schmitt, E.K.; Omar, A.S.; Creasey, E.A.; et al. Indoleacrylic Acid Produced by Commensal Peptostreptococcus Species Suppresses Inflammation. Cell Host Microbe 2017, 22, 25–37. [Google Scholar] [CrossRef] [PubMed]
- Pan, H.; Zhou, M.; Ju, Z.; Luo, J.; Jin, J.; Shen, L.; Zhou, P.; Huang, R. Potential role of gut microbiota-LCA-INSR axis in high fat-diet-induced non-alcoholic fatty liver dysfunction: From perspective of radiation variation. Curr. Res. Food Sci. 2022, 5, 1685–1700. [Google Scholar] [CrossRef] [PubMed]
- Gao, Y.; Meng, L. Significant correlation between glucose metabolism status and acute radiation enteritis resulting from concurrent chemoradiotherapy in rectal cancer. Am. J. Transl. Res. 2023, 15, 4228–4236. [Google Scholar]
- Silva, Y.P.; Bernardi, A.; Frozza, R.L. The Role of Short-Chain Fatty Acids From Gut Microbiota in Gut-Brain Communication. Front. Endocrinol. 2020, 11, 25. [Google Scholar] [CrossRef]
- Macfarlane, S.; Macfarlane, G.T. Regulation of short-chain fatty acid production. Proc. Nutr. Soc. 2003, 62, 67–72. [Google Scholar] [CrossRef]
- Carling, D. AMPK signalling in health and disease. Curr. Opin. Cell Biol. 2017, 45, 31–37. [Google Scholar] [CrossRef]
- Ahmadi, S.; Wang, S.; Nagpal, R.; Wang, B.; Jain, S.; Razazan, A.; Mishra, S.P.; Zhu, X.; Wang, Z.; Kavanagh, K.; et al. A human-origin probiotic cocktail ameliorates aging-related leaky gut and inflammation via modulating the microbiota/taurine/tight junction axis. JCI Insight 2020, 5, e132055. [Google Scholar] [CrossRef]
- Li, Y.; Wang, Y.; Wu, P. 5′-Methylthioadenosine and Cancer: Old molecules, new understanding. J. Cancer 2019, 10, 927–936. [Google Scholar] [CrossRef]
- Bettio, L.E.; Gil-Mohapel, J.; Rodrigues, A.L. Guanosine and its role in neuropathologies. Purinergic Signal 2016, 12, 411–426. [Google Scholar] [CrossRef]
- Kiang, J.G.; Cannon, G.; Olson, M.G.; Smith, J.T.; Anderson, M.N.; Zhai, M.; Umali, M.V.; Ho, K.; Ho, C.; Cui, W.; et al. Female Mice are More Resistant to the Mixed-Field (67% Neutron + 33% Gamma) Radiation-Induced Injury in Bone Marrow and Small Intestine than Male Mice due to Sustained Increases in G-CSF and the Bcl-2/Bax Ratio and Lower miR-34a and MAPK Activation. Radiat. Res. 2022, 198, 120–133. [Google Scholar] [CrossRef] [PubMed]
- Chmielewski-Stivers, N.; Petit, B.; Ollivier, J.; Monceau, V.; Tsoutsou, P.; Quintela Pousa, A.; Lin, X.; Limoli, C.; Vozenin, M.C. Sex-Specific Differences in Toxicity Following Systemic Paclitaxel Treatment and Localized Cardiac Radiotherapy. Cancers 2021, 13, 3973. [Google Scholar] [CrossRef] [PubMed]
- Cosar, R.; Ozen, A.; Tastekin, E.; Sut, N.; Cakina, S.; Demir, S.; Parlar, S.; Nurlu, D.; Kavuzlu, Y.; Kocak, Z. Does Gender Difference Effect Radiation-Induced Lung Toxicity? An Experimental Study by Genetic and Histopathological Predictors. Radiat. Res. 2022, 197, 280–288. [Google Scholar] [CrossRef]
- Daisley, B.A.; Koenig, D.; Engelbrecht, K.; Doney, L.; Hards, K.; Al, K.F.; Reid, G.; Burton, J.P. Emerging connections between gut microbiome bioenergetics and chronic metabolic diseases. Cell Rep. 2021, 37, 110087. [Google Scholar] [CrossRef]
- Hori, T.; Matsuda, K.; Oishi, K. Probiotics: A Dietary Factor to Modulate the Gut Microbiome, Host Immune System, and Gut-Brain Interaction. Microorganisms 2020, 8, 1401. [Google Scholar] [CrossRef]
- Mayer, E.A.; Tillisch, K.; Gupta, A. Gut/brain axis and the microbiota. J. Clin. Investig. 2015, 125, 926–938. [Google Scholar] [CrossRef]
- Baczkowski, A.J.; Joanes, D.N.; Shamia, G.M. Range of validity of alpha and beta for a generalized diversity index H (alpha, beta) due to Good. Math. Biosci. 1998, 148, 115–128. [Google Scholar] [CrossRef]
- Eeckhaut, V.; Van Immerseel, F.; Croubels, S.; De Baere, S.; Haesebrouck, F.; Ducatelle, R.; Louis, P.; Vandamme, P. Butyrate production in phylogenetically diverse Firmicutes isolated from the chicken caecum. Microb. Biotechnol. 2011, 4, 503–512. [Google Scholar] [CrossRef]
- Spivak, I.; Fluhr, L.; Elinav, E. Local and systemic effects of microbiome-derived metabolites. EMBO Rep. 2022, 23, e55664. [Google Scholar] [CrossRef]
- Fernandez-Veledo, S.; Vendrell, J. Gut microbiota-derived succinate: Friend or foe in human metabolic diseases? Rev. Endocr. Metab. Disord. 2019, 20, 439–447. [Google Scholar] [CrossRef]
- Abedi, E.; Hashemi, S.M.B. Lactic acid production—Producing microorganisms and substrates sources-state of art. Heliyon 2020, 6, e04974. [Google Scholar] [CrossRef] [PubMed]
- Duncan, S.H.; Louis, P.; Flint, H.J. Lactate-utilizing bacteria, isolated from human feces, that produce butyrate as a major fermentation product. Appl. Environ. Microbiol. 2004, 70, 5810–5817. [Google Scholar] [CrossRef] [PubMed]
- Abot, A.; Fried, S.; Cani, P.D.; Knauf, C. Reactive Oxygen Species/Reactive Nitrogen Species as Messengers in the Gut: Impact on Physiology and Metabolic Disorders. Antioxid. Redox Signal. 2022, 37, 394–415. [Google Scholar] [CrossRef]
- Dutta, S.; Sengupta, P. Men and mice: Relating their ages. Life Sci. 2016, 152, 244–248. [Google Scholar] [CrossRef] [PubMed]
- Kumar, V.P.; Holmes-Hampton, G.P.; Biswas, S.; Stone, S.; Sharma, N.K.; Hritzo, B.; Guilfoyle, M.; Eichenbaum, G.; Guha, C.; Ghosh, S.P. Mitigation of total body irradiation-induced mortality and hematopoietic injury of mice by a thrombopoietin mimetic (JNJ-26366821). Sci. Rep. 2022, 12, 3485. [Google Scholar] [CrossRef]
- Gautam, A.; Kumar, R.; Chakraborty, N.; Muhie, S.; Hoke, A.; Hammamieh, R.; Jett, M. Altered fecal microbiota composition in all male aggressor-exposed rodent model simulating features of post-traumatic stress disorder. J. Neurosci. Res. 2018, 96, 1311–1323. [Google Scholar] [CrossRef]
- Hoke, A.; Chakraborty, N.; Gautam, A.; Hammamieh, R.; Jett, M. Acute and Delayed Effects of Stress Eliciting Post-Traumatic Stress-Like Disorder Differentially Alters Fecal Microbiota Composition in a Male Mouse Model. Front. Cell Infect. Microbiol. 2022, 12, 810815. [Google Scholar] [CrossRef]
- Klindworth, A.; Pruesse, E.; Schweer, T.; Peplies, J.; Quast, C.; Horn, M.; Glöckner, F.O. Evaluation of general 16S ribosomal RNA gene PCR primers for classical and next-generation sequencing-based diversity studies. Nucleic Acids Res. 2013, 41, e1. [Google Scholar] [CrossRef]
- Karl, J.P.; Margolis, L.M.; Madslien, E.H.; Murphy, N.E.; Castellani, J.W.; Gundersen, Y.; Hoke, A.V.; Levangie, M.W.; Kumar, R.; Chakraborty, N.; et al. Changes in intestinal microbiota composition and metabolism coincide with increased intestinal permeability in young adults under prolonged physiological stress. Am. J. Physiol. Gastrointest. Liver Physiol. 2017, 312, G559–G571. [Google Scholar] [CrossRef]
- Callahan, B.J.; McMurdie, P.J.; Rosen, M.J.; Han, A.W.; Johnson, A.J.; Holmes, S.P. DADA2: High-resolution sample inference from Illumina amplicon data. Nat. Methods 2016, 13, 581–583. [Google Scholar] [CrossRef]
- Gentleman, R.C.; Carey, V.J.; Bates, D.M.; Bolstad, B.; Dettling, M.; Dudoit, S.; Ellis, B.; Gautier, L.; Ge, Y.; Gentry, J.; et al. Bioconductor: Open software development for computational biology and bioinformatics. Genome Biol. 2004, 5, R80. [Google Scholar] [CrossRef] [PubMed]
- Bolyen, E.; Rideout, J.R.; Dillon, M.R.; Bokulich, N.A.; Abnet, C.C.; Al-Ghalith, G.A.; Alexander, H.; Alm, E.J.; Arumugam, M.; Asnicar, F.; et al. Reproducible, interactive, scalable and extensible microbiome data science using QIIME 2. Nat. Biotechnol. 2019, 37, 852–857. [Google Scholar] [CrossRef] [PubMed]
- Simpson, E.H. Measurement of diversity. Nature 1949, 163, 688. [Google Scholar] [CrossRef]
- Chao, A. Non-Parametric Estimation of the Number of Classes in a Population. Scand. J. Stat. 1984, 11, 265–270. [Google Scholar]
- Shannon, C.E.; Weaver, W. The Mathematical Theory of Communication; University of Illonois Press: Champaign, IL, USA, 1949. [Google Scholar]
- Lozupone, C.A.; Hamady, M.; Kelley, S.T.; Knight, R. Quantitative and qualitative beta diversity measures lead to different insights into factors that structure microbial communities. Appl. Environ. Microbiol. 2007, 73, 1576–1585. [Google Scholar] [CrossRef]
- Anderson, M.J. A new method for non-parametric multivariate analysis of variance. Austral Ecol. 2001, 26, 32–46. [Google Scholar]
- Bokulich, N.A.; Kaehler, B.D.; Rideout, J.R.; Dillon, M.; Bolyen, E.; Knight, R.; Huttley, G.A.; Gregory Caporaso, J. Optimizing taxonomic classification of marker-gene amplicon sequences with QIIME 2’s q2-feature-classifier plugin. Microbiome 2018, 6, 90. [Google Scholar] [CrossRef]
- Pedregosa, F.; Varoquaux, G.; Gramfort, A.; Michel, V.; Thirion, B.; Grisel, O.; Blondel, M.; Prettenhofer, P.; Weiss, R.; Dubourg, V.; et al. Scikit-learn: Machine learning in python. J. Mach. Learn. Res. 2011, 12, 2825–2830. [Google Scholar]
- Caspi, R.; Altman, T.; Billington, R.; Dreher, K.; Foerster, H.; Fulcher, C.A.; Holland, T.A.; Keseler, I.M.; Kothari, A.; Kubo, A.; et al. The MetaCyc database of metabolic pathways and enzymes and the BioCyc collection of Pathway/Genome Databases. Nucleic Acids Res. 2014, 42, D459–D471. [Google Scholar] [CrossRef]
- Douglas, G.M.; Maffei, V.J.; Zaneveld, J.R.; Yurgel, S.N.; Brown, J.R.; Taylor, C.M.; Huttenhower, C.; Langille, M.G.I. PICRUSt2 for prediction of metagenome functions. Nat. Biotechnol. 2020, 38, 685–688. [Google Scholar] [CrossRef]
- Barbera, P.; Kozlov, A.M.; Czech, L.; Morel, B.; Darriba, D.; Flouri, T.; Stamatakis, A. EPA-ng: Massively Parallel Evolutionary Placement of Genetic Sequences. Syst. Biol. 2019, 68, 365–369. [Google Scholar] [CrossRef] [PubMed]
- Czech, L.; Barbera, P.; Stamatakis, A. Genesis and Gappa: Processing, analyzing, and visualizing phylogenetic (placement) data. Bioinformatics 2020, 36, 3263–3265. [Google Scholar] [CrossRef]
- Louca, S.; Doebeli, M. Efficient comparative phylogenetics on large trees. Bioinformatics 2018, 34, 1053–1055. [Google Scholar] [CrossRef]
- Ye, Y.; Doak, T.G. A parsimony approach to biological pathway reconstruction/inference for genomes and metagenomes. PLoS Comput. Biol. 2009, 5, e1000465. [Google Scholar] [CrossRef]
- Chakraborty, N.; Zamarioli, A.; Gautam, A.; Campbell, R.; Mendenhall, S.K.; Childress, P.J.; Dimitrov, G.; Sowe, B.; Tucker, A.; Zhao, L.; et al. Gene-metabolite networks associated with impediment of bone fracture repair in spaceflight. Comput. Struct. Biotechnol. J. 2021, 19, 3507–3520. [Google Scholar] [CrossRef]
- Chakraborty, N.; Gautam, A.; Holmes-Hampton, G.P.; Kumar, V.P.; Biswas, S.; Kumar, R.; Hamad, D.; Dimitrov, G.; Olabisi, A.O.; Hammamieh, R.; et al. microRNA and Metabolite Signatures Linked to Early Consequences of Lethal Radiation. Sci. Rep. 2020, 10, 5424. [Google Scholar] [CrossRef]
- Libiseller, G.; Dvorzak, M.; Kleb, U.; Gander, E.; Eisenberg, T.; Madeo, F.; Neumann, S.; Trausinger, G.; Sinner, F.; Pieber, T.; et al. IPO: A tool for automated optimization of XCMS parameters. BMC Bioinform. 2015, 16, 118. [Google Scholar] [CrossRef]
- Ghosh, S.P.; Perkins, M.W.; Hieber, K.; Kulkarni, S.; Kao, T.C.; Reddy, E.P.; Reddy, M.V.; Maniar, M.; Seed, T.; Kumar, K.S. Radiation protection by a new chemical entity, Ex-Rad: Efficacy and mechanisms. Radiat. Res. 2009, 171, 173–179. [Google Scholar] [CrossRef]
- Kumar, V.P.; Biswas, S.; Holmes-Hampton, G.P.; Sheleg, M.; Stone, S.; Legesse, B.; Ofir, R.; Ghosh, S.P. Pre-Administration of PLX-R18 Cells Protects Mice from Radiation-Induced Hematopoietic Failure and Lethality. Genes 2022, 13, 1756. [Google Scholar] [CrossRef]
Diversity | Index | Cohort | Sex | RD | TSI | Sex × RD | RD × TSI | Sex × TSI | Sex × RD × TSI |
---|---|---|---|---|---|---|---|---|---|
Alpha | Shannon | All | ** | NS | NS | *** | ** | NS | ** |
Male | -- | *** | NS | -- | * | -- | -- | ||
Female | -- | ** | NS | -- | ** | -- | -- | ||
Simpson | All | * | NS | NS | *** | * | NS | * | |
Male | -- | * | NS | -- | NS | -- | -- | ||
Female | -- | * | NS | -- | * | -- | -- | ||
Chao-1 | All | ** | * | NS | ** | * | NS | *** | |
Male | -- | *** | NS | -- | *** | -- | -- | ||
Female | -- | NS | NS | -- | *** | -- | -- | ||
Beta/PERMANOVA | All | *** | *** | * | *** | * | *** | *** | |
Male | -- | ** | NS | -- | ** | -- | -- | ||
Female | -- | *** | *** | -- | *** | -- | -- |
Phylum | Cohort | Sex | RD | TSI | Sex × RD | RD × TSI | Sex × TSI | Sex × RD × TSI |
---|---|---|---|---|---|---|---|---|
Bacteroidetes | All | NS | NS | NS | NS | NS | * | NS |
Male | -- | NS | NS | -- | * | -- | -- | |
Female | -- | NS | NS | -- | ** | -- | -- | |
Firmicutes | All | ** | NS | NS | NS | NS | ** | ** |
Male | -- | NS | * | -- | * | -- | -- | |
Female | -- | NS | NS | -- | NS | -- | -- | |
Proteobacteria | All | NS | NS | * | NS | NS | NS | NS |
Male | -- | NS | NS | -- | NS | -- | -- | |
Female | -- | NS | NS | -- | NS | -- | -- | |
Verrucomicrobia | All | *** | NS | NS | NS | NS | NS | *** |
Male | -- | NS | NS | -- | * | -- | -- | |
Female | -- | NS | * | -- | NS | -- | -- |
Female | Male | |||||||
---|---|---|---|---|---|---|---|---|
1 Month TSI | 6 Month TSI | 1 Month TSI | 6 Month TSI | |||||
Superfamilies of Networks | 7 Gy | 7.5 Gy | 7 Gy | 7.5 Gy | 7 Gy | 7.5 Gy | 7 Gy | 7.5 Gy |
Carbohydrate metabolism | 1/0 | 0/0 | 6/0 | 3/0 | 0/4 | 2/4 | 0/8 | 0/4 |
Lipid metabolism | 0/0 | 0/3 | 2/1 | 0/0 | 1/0 | 3/3 | 0/5 | 0/5 |
Amino acid metabolism | 5/0 | 0/8 | 38/4 | 10/0 | 1/17 | 9/27 | 1/35 | 4/22 |
SCFA biosynthesis | 0/0 | 0/2 | 9/0 | 4/0 | 0/4 | 1/6 | 0/8 | 0/66 |
Purine and pyrimidine homeostasis | 1/0 | 0/8 | 2/8 | 1/0 | 1/0 | 1/5 | 0/19 | 0/12 |
Bioenergy | 0/0 | 0/0 | 11/3 | 4/0 | 5/0 | 12/1 | 4/9 | 1/7 |
Sex × TSI × RD | ||
---|---|---|
Metabolite | Regulation Status | Study Relevance |
Phenyllactic Acid | ♀ + ♂: upregulated. | A microbial metabolite previously identified as the marker of radiation-induced liver injury [50]. Potential sex-/dose-/time-independent radiation marker. |
Adenine | ♀ + ♂: upregulated. | Adenine contributes to host purine homeostasis and supports the growth of Proteobacteria and Firmicutes [51]. Adenine inhibits intestinal epithelial mucosal inflammation [52]. As a purine base, it is a precursor of nucleic acid in intestinal cells and markers of DNA damage. Additionally, it is produced by E.coli. Potential sex-/dose-/time-independent radiation marker. |
Indoleacrylic Acid | ♀ + ♂: upregulated. | Intestinal microorganisms catabolize tryptophan to indoles, which are converted into indoleacrylic acid, an anti-inflammatory agent that helps in bolstering the intestinal barrier [53]. Potential sex-/dose-/time-independent radiation marker. |
Tetradecanedioic Acid | ♀: upregulated. ♂: downregulated, except 6 m-7 Gy upregulated. | Related to glucose metabolism and radiation exposure [54]. |
Glucose | ♀: upregulated. ♂: For all RD, 1 m downregulated; 6 m upregulated. | Radiation exposure is typically linked to the malabsorption of glucose [55]. |
Pyruvate | ♀: upregulated. ♂: For all RD, 1 m downregulated; 6 m upregulated. | Typically, a low abundance of SCFA that are shared among different microbes for cross-feeding [56,57]; hence, its high abundance is a marker of dysbiosis. |
Deoxyadenosine Triphosphate (dATP) | ♀: In 7 Gy, regulation status shifted to upregulated with time. Contrastingly, in 7.5 Gy, regulation status shifted to downregulated with time. ♂: Mostly downregulated. | Reduced dATP is a marker of mitochondrial dysfunction. |
Sex × RD | ||
Cytosine | ♀ + ♂: upregulated. | Markers of DNA damage. Potential sex-/dose-independent radiation marker. |
Adenosine Monophosphate (AMP) | ♀ + ♂: upregulated. | An overloaded AMP molecule, along with a depleted ATP concentration, is a signature of an energy-deprived condition [58]. In the present condition, we found a reduced concentration of dATP. Potential sex-/dose-independent radiation marker. |
1-Methyladenosine | ♀: upregulated. ♂: downregulated. |
Biomarker of tumors. Potential sex-specific radiation marker. |
Xanthosine | ♀: upregulated. ♂: downregulated. | Important marker of host purine homeostasis [51]. Potential sex-specific radiation marker. |
Taurine | ♀: switched from 7 Gy (downregulated) to 7.5 Gy (upregulated) ♂: upregulated. | A bile acid component that helps mitigate gut permeability [59], although a high load could cause gastrointestinal discomfort. |
Methylthioadenosine (MTA) | ♀: switched from 7 Gy (upregulated) to 7.5 Gy (downregulated) ♂: upregulated. | Participates in purine salvage pathway and suppresses tumorigenesis [60]. |
Guanosine | ♀: upregulated. ♂: switched from 7 Gy (upregulated) to 7.5 Gy (downregulated). | Neuroprotective agent against ischemic damage [61]. |
Pantothenate | ♀: switched from 7 Gy (upregulated) to 7.5 Gy (downregulated). ♂: switched from 7 Gy (downregulated) to 7.5 Gy (upregulated). | Agent to form coenzyme-A (CoA); hence, it is critical in the metabolism and synthesis of carbohydrates, proteins, and fats. |
Biofunctions | Female | Male | ||||||
---|---|---|---|---|---|---|---|---|
1 Month TSI | 6 Month TSI | 1 Month TSI | 6 Month TSI | |||||
7 Gy | 7.5 Gy | 7 Gy | 7.5 Gy | 7 Gy | 7.5 Gy | 7 Gy | 7.5 Gy | |
Concentration of lipid | 1.26 | −0.46 | 1.26 | −0.46 | 1.72 | 1.72 | −0.49 | 1.98 |
Quantity of amino acids | 0.30 | 0.91 | 0.30 | 0.91 | −0.61 | −0.61 | 0.61 | 1.83 |
Production of reactive oxygen species | 2.61 | 0.43 | 2.61 | 0.43 | 3.34 | 2.17 | −2.61 | 1.16 |
Insulin Secretion Signaling Pathway | 3.58 | 0.89 | 3.58 | 0.89 | 3.58 | 2.68 | −3.58 | 0.00 |
Synthesis of purine nucleotide | 3.00 | 2.00 | 3.00 | 2.00 | 2.00 | 1.00 | −2.00 | 2.00 |
Synthesis of nucleotide | 1.79 | 0.89 | 1.79 | 0.89 | 1.78 | 0.89 | −0.89 | 2.68 |
Cellular homeostasis | 1.09 | −1.81 | 1.09 | −1.81 | −1.49 | 0.27 | −1.09 | 0.41 |
Disclaimer/Publisher’s Note: The statements, opinions and data contained in all publications are solely those of the individual author(s) and contributor(s) and not of MDPI and/or the editor(s). MDPI and/or the editor(s) disclaim responsibility for any injury to people or property resulting from any ideas, methods, instructions or products referred to in the content. |
© 2025 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
Share and Cite
Chakraborty, N.; Holmes-Hampton, G.; Rusling, M.; Kumar, V.P.; Hoke, A.; Lawrence, A.B.; Gautam, A.; Ghosh, S.P.; Hammamieh, R. Delayed Impact of Ionizing Radiation Depends on Sex: Integrative Metagenomics and Metabolomics Analysis of Rodent Colon Content. Int. J. Mol. Sci. 2025, 26, 4227. https://doi.org/10.3390/ijms26094227
Chakraborty N, Holmes-Hampton G, Rusling M, Kumar VP, Hoke A, Lawrence AB, Gautam A, Ghosh SP, Hammamieh R. Delayed Impact of Ionizing Radiation Depends on Sex: Integrative Metagenomics and Metabolomics Analysis of Rodent Colon Content. International Journal of Molecular Sciences. 2025; 26(9):4227. https://doi.org/10.3390/ijms26094227
Chicago/Turabian StyleChakraborty, Nabarun, Gregory Holmes-Hampton, Matthew Rusling, Vidya P. Kumar, Allison Hoke, Alexander B. Lawrence, Aarti Gautam, Sanchita P. Ghosh, and Rasha Hammamieh. 2025. "Delayed Impact of Ionizing Radiation Depends on Sex: Integrative Metagenomics and Metabolomics Analysis of Rodent Colon Content" International Journal of Molecular Sciences 26, no. 9: 4227. https://doi.org/10.3390/ijms26094227
APA StyleChakraborty, N., Holmes-Hampton, G., Rusling, M., Kumar, V. P., Hoke, A., Lawrence, A. B., Gautam, A., Ghosh, S. P., & Hammamieh, R. (2025). Delayed Impact of Ionizing Radiation Depends on Sex: Integrative Metagenomics and Metabolomics Analysis of Rodent Colon Content. International Journal of Molecular Sciences, 26(9), 4227. https://doi.org/10.3390/ijms26094227