Changes in gut microbiome taxonomic composition and their relationship to biosynthetic and metabolic pathways of B vitamins in children with multiple sclerosis

Cover Page

Cite item

Full Text

Open Access Open Access
Restricted Access Access granted
Restricted Access Subscription Access

Abstract

Multiple sclerosis (MS) is a chronic inflammatory autoimmune disease characterised by progressive demyelination leading to the death of neurons in the central nervous system. The disease usually manifests in people aged 20–40 years, but in recent years there has been an increase in the number of cases with childhood MS debut. We assume that this may be related to the peculiarities of the taxonomic composition of the intestinal microbiota and its ability to produce B vitamins. Purpose: To identify changes in the composition of the gut microbiome in the debut of multiple sclerosis in children and adults and to assess the potential of the gut microbiome to metabolise and synthesise B vitamins. Fifteen children (9–17 years), 15 adults with MS manifested in childhood and 14 adults over 37 years of age with MS duration less than 1 year participated in the study. The composition of the intestinal microbiome was determined by sequencing the 16S rRNA gene on the Illumina platform with universal primers for the 16S rRNA V3-V4 variable region. The PICRUST algorithm using the KEGG reference genome database was used to predict the presence of B vitamin metabolic pathways in the intestinal microbiome. Children in MS debut were found to have specific microbiome changes different from those in adults. These changes include a decrease in alpha diversity as well as a reduction in dominant phylum and an increase in p_Verrucomicrobiota and p_Mycoplasmatota, which was accompanied by a decrease in the number of bacterial genes involved in the pathways of metabolism and synthesis of vitamins B1, B2, B3, B5 and B12. Such changes may be associated with early manifestation of MS symptoms in children. The findings highlight the importance of further study of the influence of the intestinal microbiome and its metabolic potential on the development and progression of MS, especially in childhood, and may contribute to the development of modern more effective methods of treatment and prevention of this demyelinating disease.

Full Text

Restricted Access

About the authors

I. N. Abdurasulova

Institute of Experimental Medicine

Author for correspondence.
Email: i_abdurasulova@mail.ru
Russian Federation, St. Petersburg

E. A. Chernyavskaya

Institute of Experimental Medicine

Email: i_abdurasulova@mail.ru
Russian Federation, St. Petersburg

V. A. Nikitina

Institute of Experimental Medicine

Email: i_abdurasulova@mail.ru
Russian Federation, St. Petersburg

A. B. Ivanov

National Research University ITMO

Email: i_abdurasulova@mail.ru
Russian Federation, St. Petersburg

V. I. Lioudyno

Institute of Experimental Medicine

Email: i_abdurasulova@mail.ru
Russian Federation, St. Petersburg

A. A. Nartova

Institute of Experimental Medicine

Email: i_abdurasulova@mail.ru
Russian Federation, St. Petersburg

A. V. Matsulevich

Institute of Experimental Medicine

Email: i_abdurasulova@mail.ru
Russian Federation, St. Petersburg

E. Yu. Skripchenko

Pediatric Research and Clinical Centre for Infectious Diseases under the Federal Medical Biological Agency

Email: i_abdurasulova@mail.ru
Russian Federation, St. Petersburg

G. N. Bisaga

Almazov National Medical Research Centre

Email: i_abdurasulova@mail.ru
Russian Federation, St. Petersburg

V. I. Ulyantsev

National Research University ITMO

Email: i_abdurasulova@mail.ru
Russian Federation, St. Petersburg

A. V. Dmitriev

Institute of Experimental Medicine

Email: i_abdurasulova@mail.ru
Russian Federation, St. Petersburg

References

  1. Van den Elsen PJ, van Eggermond MCJA, Puentes F, van der Valk P, Baker D, Amor S (2014) The epigenetics of multiple sclerosis and other related disorders. Mult Scler Rel Dis 3(2): 163–175. https://doi.org/10.1016/j.msard.2013.08.007
  2. Loma I, Heyman R (2011) Multiple sclerosis: pathogenesis and treatment. Curr Neuropharmacol 9(3): 409–416. https://doi.org/ 10.2174/157015911796557911
  3. Simpson SJr, Blizzard L, Otahal P, Van der Mei I, Taylor B (2011) Latitude is significantly associated with the prevalence of multiple sclerosis: a meta-analysis. J Neurol Neurosurg Psychiatry 82(10): 1132–1141. https://doi.org/ 10.1136/jnnp.2011.240432
  4. Simpson SJr, Taylor B, Blizzard L, Ponsonby AL, Pittas F, Tremlett H, Dwyer T, Gies P, van der Mei I (2010) Higher 25-hydroxyvitamin D is associated with lower relapse risk in multiple sclerosis. Ann Neurol 68(2): 193–203. https://doi.org/ 10.1002/ana.22043
  5. Trojano M, Lucchese G, Graziano G, Taylor BV, Simpson SJr, Lepore V, Grand’maison F, Duquette P, Izquierdo G, Grammond P, Amato MP, Bergamaschi R, Giuliani G, Boz C, Hupperts R, Van Pesch V, Lechner-Scott J, Cristiano E, Fiol M, Oreja-Guevara C, Saladino ML, Verheul F, Slee M, Paolicelli D, Tortorella C, D’Onghia M, Iaffaldano P, Direnzo V, Butzkueven H, Group MSS, the New Zealand MSPSG (2012) Geographical variations in sex ratio trends over time in multiple sclerosis. PloS one 7(10): e48078. https://doi.org/10.1371/journal.pone.0048078
  6. Wu GF, Alvarez E (2011) The immunopathophysiology of multiple sclerosis. Neurol Clin 29(2): 257–278. https://doi.org/ 10.1016/j.ncl.2010.12.009
  7. Wiendl H, Gold R, Zipp F (2021) Multiple sclerosis therapy consensus group (MSTCG): answers to the discussion questions. Neurologic Res Practice 3: 44. https://doi.org/10.1186/s42466-021-00140-1
  8. Ельчанинова ЕЮ, Смагина ИВ (2017) Невроло Журн (2): 64–71. [El’chaninova EYu, Smagina IV (2017) Nevrol Zh (2): 64–71. (In Russ)]. https://doi.org/10.8821/1560-9545-2017-22-2-64-71
  9. Смагина ИВ, Ельчанинова ЕЮ, Раевских ВМ Связь антропогенных и биотических факторов с риском педиатрического рассеянного склероза (2020) Медико-фармацевт Журн “Пульс” 22(9): 42–51. [Smagina IV, Elchaninova EY, Raevskikh VM (2020) Association of anthropogenic and biotic factors with the risk of pediatric multiple sclerosis. Med Pharmaceut J “Pulse” 22(9): 42–51. (In Russ)]. https://doi.org/10.26787/nydha-2686-6838-2020-22-9- 42-51
  10. Bar-Or А, Hintzen RQ, Dale RC, Rostasy K, Brück W, Chitnis T (2016) Immunopathophysiology of pediatric CNS inflammatory demyelinating diseases. Neurology 87 (9 Suppl 2): S12–19. https://doi.org/10.1212/WNL.0000000000002821
  11. Nemazannikova N, Mikkleson K, Stojanovska L, Blatch GL, Apostolopoulos V (2018) Is there a link between vitamin B and multiple sclerosis? Med Chem 14(2): 170. https://doi.org/10.2174/1573406413666170906123857
  12. Klenner FB (1973) Response of Peripheral and Central Nerve Pathology to Mega-Doses of the Vitamin B-Complex and Other Metabolites. J Appl Nutr.
  13. Nijst TQ, Wevers RA, Schoonderwaldt HC, Hommes OR, de Haan AF (1990) Vitamin B12 and folate concentrations in serum and cerebrospinal fluid of neurological patients with special reference to multiple sclerosis and dementia. J Neurol Neurosurg Psychiatry 53(11): 951. https://doi.org/10.1136 / jnnp.53.11.951
  14. Comerford KB (2013) Recent developments in multivitamin/mineral research. Advanc Nutrit 4(6): 644–656. https://doi.org/ 10.3945/an.113.004523
  15. Mikkelsen K, Stojanovska L, Apostolopoulos V (2016) The Effects of Vitamin B in Depression. Curr Med Chem 23(38): 4317–4337. https://doi.org/10.2174/0929867323666160920110810
  16. Mikkelsen K, Stojanovska L, Tangalakis K, Bosevski M, Apostolopoulos V (2016) Cognitive decline: A vitamin B perspective. Maturitas 93: 108–113. https://doi.org/10.1016/j.maturitas.2016.08.001
  17. Mikkelsen K, Stojanovska L, Prakash M, Apostolopoulos V (2017) The effects of vitamin B on the immune/cytokine network and their involvement in depression. Maturitas 96: 58–71. https://doi.org/10.1016/j.maturitas.2016.11.012
  18. Abdurasulova IN (2022) Role of the intestinal microbiota in the pathogenesis of multiple sclerosis. Part 1. Clinical and experimental evidence for the involvement of the gut microbiota in the development of multiple sclerosis. Medical Academic J 22(2): 9–36. https://doi.org/10.17816/MAJ108241
  19. O’Connor EM (2013) The role of gut microbiota in nutritional status. Curr Opin Clin Nutr Metab Care 16: 509–516. https://doi.org/10.1097/MCO.0b013e3283638eb3
  20. Berding K, Donovan SM (2016) Microbiome and nutrition in autism spectrum disorder: current knowledge and research needs. Nutr Rev 74: 723–736. https://doi.org/10.1093/nutrit/nuw048
  21. Maruvada P, Leone V, Kaplan LM, Chang EB (2017) The human microbiome and obesity: moving beyond associations. Cell Host Microbe 22: 589–599. https://doi.org/10.1016/j.chom.2017.10.005
  22. Rossi M, Amarett A, Raimondi S (2011) Folate production by probiotic bacteria. Nutrients 3: 118–134. https://doi.org/10.3390/nu3010118
  23. Morowitz MJ, Carlisle EM, Alverdy JC (2011) Contributions of intestinal bacteria to nutrition and metabolism in the critically ill. Surg Clin North Am 91: 771–785. https://doi.org/10.1016/j.suc.2011.05.001
  24. Magnúsdóttir S, Ravcheev D, de Crecy-Lagard V, Thiele I (2015) Systematic genome assessment of B-vitamin biosynthesis suggests co-operation among gut microbes. Front Genet 6: 148. https://doi.org/10.3389/fgene.2015.00148
  25. Steinert RE, Lee Y-K, Sybesma W (2019) Vitamins for the Gut Microbiome. Trends Mol Med 26(2): 137. https://doi.org/10.1016/j.molmed.2019.11.005
  26. Ling Z, Cheng Y, Yan X, Shao L, Liu X, Zhou D, Zhang L, Yu K, Zhao L (2020) Alterations of the Fecal Microbiota in Chinese Patients with Multiple Sclerosis. Front Immunol 11: 590783. https://doi.org/10.3389/fimmu.2020.590783
  27. Efimova D, Tyakht A, Popenko A, Vasilyev A, Altukhov I, Dovidchenko N, Odintsova V, Klimenko N, Loshkarev R, Pashkova M, Elizarova A, Voroshilova V, Slavskii S, Pekov Y, Filippova E, Shashkova T, Levin E, Alexeev D (2018) Knomics-Biota – a system for exploratory analysis of human gut microbiota data. BioData Mining 11: 25. https://doi.org/10.1186/s13040-018-0187-3
  28. Edgar RC (2010) Search and clustering orders of magnitude faster than BLAST. Bioinformatics 26 (19): 2460–2461. https://doi.org/ 10.1093/bioinformatics/btq461
  29. DeSantis TZ, Hugenholtz P, Larsen N, Rojas M, Brodie EL, Keller K, Huber T, Dalevi D, Hu P, Andersen GL (2006) Greengenes, a chimera-checked 16S rRNA gene database and workbench compatible with ARB. Appl Environ Microbiol 72(7): 5069–5072. https://doi.org/10.1128/AEM.03006–05
  30. Shannon CE, Weaver W The mathematical theory of communication, by CE Shannon (and recent contributions to the mathematical theory of communication). W. Weaver. University of illinois Press. 1949.
  31. Chao A (1984) Nonparametric estimation of the number of classes in a population. Scand J Statistics 11(4): 265–270.
  32. Lozupone CA, Hamady M, Kelley ST, Knight R (2007) Quantitative and qualitative β diversity measures lead to different insights into factors that structure microbial communities. Appl Environ Microbiol 73(5): 1576–1585. https://doi.org/10.1128/AEM.01996–06
  33. Langille MG, Zaneveld J, Caporaso JG, McDonald D, Knights D, Reyes JA, Clemente JC, Burkepile DE, Vega Thurber RL, Knight R, Beiko RG, Huttenhower C (2013) Predictive functional profiling of microbial communities using 16S rRNA marker gene sequences. Nat Biotechnol 31(9): 814–821. https://doi.org/10.1038/nbt.2676
  34. Kanehisa M, Goto S (2000) KEGG: kyoto encyclopedia of genes and genomes. Nucleic Acids Res 28(1): 27–30. https://doi.org/10.1093/nar/28.1.27
  35. Charlier F, Weber M, Izak D, Harkin E, Magnus M, Lalli J, Fresnais L, Chan M, Markov N, Amsalem O, Proost S, Krasoulis A, getzze, Repplinger S (2022) Statannotations (v0.6). Zenodo. https://doi.org/10.5281/zenodo.7213391
  36. Hunter JD (2007) Matplotlib: A 2D Graphics Environment. Computing Sci Engineering 9(3): 90–95. https://doi.org/10.1109/MCSE.2007.55
  37. Van Rossum G, de Boer J (1991) Interactively Testing Remote Servers Using the Python Programming Language. CWI Quarterly 4(4): 283–303.
  38. OpenEpi Epidemiologic Statistics [Electronic resource]. URL: http://www.openepi.com (accessed: 14.04.2023).
  39. Medstatistic [Electronic resource]. URL: http://www.medstatistic.ru (accessed: 14.04.2023.
  40. Harris CR, Millman KJ, van der Walt SJ, Gommers R, Virtanen P, Cournapeau D, Wieser E, Taylor J, Berg S, Smith NJ, Kern R, Picus M, Hoyer S, van Kerkwijk MH, Brett M, Haldane A, Del Río JF, Wiebe M, Peterson P, Gérard-Marchant P, Sheppard K, Reddy T, Weckesser W, Abbasi H, Gohlke C, Oliphant TE (2020) Array programming with NumPy. Nature 585(7825): 357–362. https://doi.org/10.1038/s41586-020-2649-2
  41. Virtanen P, Gommers R, Oliphant TE, Haberland M, Reddy T, Cournapeau D, Burovski E, Peterson P, Weckesser W, Bright J, van der Walt SJ, Brett M, Wilson J, Millman KJ, Mayorov N, Nelson ARJ, Jones E, Kern R, Larson E, Carey CJ, Polat İ, Feng Y, Moore EW, VanderPlas J, Laxalde D, Perktold J, Cimrman R, Henriksen I, Quintero EA, Harris CR, Archibald AM, Ribeiro AH, Pedregosa F, van Mulbregt P, SciPy 1.0 Contributors (2020) SciPy 1.0: fundamental algorithms for scientific computing in Python. Nat Methods 17(3): 261–272. https://doi.org/10.1038/s41592-019-0686-2
  42. McKinney W (2010) Data Structures for Statistical Computing in Python. Proceed Python Sci Conf (SciPy 2010): 56–61.
  43. Jhangi S, Gandhi R, Cox LM, Li N, von Glehn F, Yan R, Patel B, Mazzola MA, Liu S, Glanz BL, Cook S, Tankou S, Stuart F, Melo K, Nejad P, Smith K, Topçuolu BD, Holden J, Kivisäkk P, Chitnis T, De Jager PL, Quintana FJ, Gerber GK, Bry L, Weiner HL (2016) Alterations of the human gut microbiome in multiple sclerosis. Nat Commun 7: 12015. https://doi.org/10.1038/ncomms12015
  44. Takewaki D, Suda W, Sato W, Takayasu L, Kumar N, Kimura K, Kaga N, Mizuno T, Miyake S, Hattori M, Yamamura T (2020) Alterations of the gut ecological and functional microenvironment in different stages of multiple sclerosis. PNAS117 (36): 22402–22412. https://doi.org/10.1073/pnas.2011703117
  45. Tremlett H, Fadrosh D, Faruqi AA, Zhu F, Hart J, Roalstad S, Graves J, Lynch S, Waubant E; US Network of Pediatric MS Centers (2016) Gut microbiome in early pediatric multiple sclerosis: a case-control study. Eur J Neurol 23(8): 1308–1321. https://doi.org/10.1111/ene.13026
  46. Tremlett H, Fadrosh DW, Faruqi AA, Hart J, Roalstad S, Graves J, Lynch S, Waubant E; US Network of Pediatric MS Centers (2016) Gut microbiota composition and relapse risk in pediatric MS: A pilot study. J Neurol Sci 363: 153–157. https://doi.org/10.1016/j.jns.2016.02.042
  47. Miyake S, Kim S, Suda W, Oshima K, Nakamura M, Matsuoka T, Chihara N, Tomita A, Sato W, Kim S-W, Morita H, Hattori M, Yamamura T (2015) Dysbiosis in the Gut Microbiota of Patients with Multiple Sclerosis, with a Striking Depletion of Species Belonging to Clostridia XIVa and IV Cluster. PLoS One 10(9): e0137429. https://doi.org/10.1371/journal.pone.0137429
  48. Galluzzo P, Capri FC, Vecchioni L, Realmuto S, Scalisi L, Cottone S, Nuzzo D, Alduina R (2021) Comparison of the intestinal microbiome of Italian patients with multiple sclerosis and their household relatives. Life (Basel) 11(7): 620. https://doi.org/10.3390/life11070620
  49. Cosorich I, Dalla-Costa G, Sorini C, Ferrarese R, Messina MJ, Dolpady J, Radice E, Mariani A, Testoni PA, Canducci F, Comi G, Martinelli V, Falcone M (2017) High frequency of intestinal TH17 cells correlates with microbiota alterations and disease activity in multiple sclerosis. Sci Adv 3(7): e1700492. https://doi.org/10.1126/sciadv.1700492
  50. Pellizoni FP, Leite AZ, de Campos Rodrigues N, Ubaiz MJ, Gonzaga MI, Takaoka NNC, Mariano VS, Omori WP, Pinheiro DG, Junior EM, Gomes E, de Oliveira GLV (2021) Detection of Dysbiosis and Increased Intestinal Permeability in Brazilian Patients with Relapsing-Remitting Multiple Sclerosis. Int J Environ Res Public Health 18(9): 4621. https://doi.org/10.3390/ijerph18094621
  51. Horton MK, McCauley K, Fadrosh D, Fujimura K, Graves J, Ness J, Wheeler Y, Gorman MP, Benson LA, Weinstock-Guttman B, Waldman A, Rodriguez M, Tillema JM, Krupp L, Belman A, Mar S, Rensel M, Chitnis T, Casper TC, Rose J, Hart J, Shao X, Tremlett H, Lynch SV, Barcellos LF, Waubant E; U.S. Network of Pediatric MS Centers (2021) Gut microbiome is associated with multiple sclerosis activity in children. Ann Clin Transl Neurol 8(9): 1867–1883. https://doi.org/10.1002/acn3.51441
  52. Castillo-Álvarez F, Pérez-Matute P, Oteo JA, Marzo-Sola ME (2021) The influence of interferon β-1b on gut microbiota composition in patients with multiple sclerosis. Neurologia (Engl Ed) 36(7): 495–503. https://doi.org/10.1016/j.nrleng.2020.05.006
  53. Oezguen N, Yalcinkaya N, Kücükali CI, Dahdouli M, Hollister EB, Luna RA, Türkoglu R, Kürtüncü M, Eraksoy M, Savidge TC, Tüzün E (2019) Microbiota stratification identifies disease-specific alterations in neuro-Behçet’s disease and multiple sclerosis. Clin Exp Rheumatol 37 Suppl 121(6): 58–66.
  54. Tremlett H, Waubant E (2018) Gut microbiome and pediatric multiple sclerosis. Multiple Sclerosis Journal 24(1): 64–68. https://doi.org/10.1177/1352458517737369
  55. Абдурасулова ИН, Мацулевич АВ, Грефнер НМ, Негореева ИГ, Бисага ГН (2023) Повышенный уровень Bifidobacterium в составе кишечной микробиоты – маркер неблагоприятного течения рассеянного склероза. Журн Неврологии и психиатрии им СС Корсакова 123 (7, Вып. 2): 136–137. [Abdurasulova IN, Matsulevich AV, Grefner NM, Negoreeva IG, Bisaga GN (2023) Increased level of Bifidobacterium in intestinal microbiota is a marker of unfavourable course of multiple sclerosis. Zhurn Neurologii i Psychiatriiia im SS Korsakov 123(7, Issue 2): 136–137. (In Russ)]. https://doi.org/10.17116/jnevro2023123072136
  56. Forbes JD, Chen C-Y, Knox NC, Marrie R-A, El-Gabalawy H, de Kievit T, Alfa M, Bernstein CN, Van Domselaar G (2018) A comparative study of the gut microbiota in immune-mediated inflammatory diseases – does a common dysbiosis exist? Microbiome 6(1): 221. https://doi.org/10.1186/s40168-018-0603-4
  57. Cox LM, Maghzi AH, Liu S, Tankou SK, Dhang FH, Willocq V, Song A, Wasén C, Tauhid S, Chu R, Anderson MC, De Jager PL, Polgar-Turcsanyi M, Healy BC, Glanz BI, Bakshi R, Chitnis T, Weiner HL (2021) The Gut Microbiome in Progressive Multiple Sclerosis. Ann Neurol 89(6): 1195–1211. https://doi.org/10.1002/ana.26084
  58. Kozhieva M, Naumova N, Alikina T, Boyko A, Vlassov V, Kabilov MR (2019) Primary progressive multiple sclerosis in a Russian cohort: relationship with gut bacterial diversity. BMC Microbiology 19(1): 309. https://doi.org/10.1186/s12866-019-1685-2
  59. Zeng Q, Junli Gong, Liu X, Chen C, Sun X, Li H, Zhou Y, Cui C, Wang Y, Yang Y, Wu A, Shu Y, Hu X, Lu Z, Zheng SG, Qiu W, Lu Y (2019) Gut dysbiosis and lack of short chain fatty acids in a Chinese cohort of patients with multiple sclerosis. Neurochem Int. 129:104468. https://doi.org/10.1016/j.neuint.2019.104468
  60. Reynders T, Devolder L, Valles-Colomer M, Van Remoortel A, Joossens M, De Keyser J, Nagels G, D’hooghe M, Raes J (2020) Gut microbiome variation is associated to Multiple Sclerosis phenotypic subtypes. Ann Clin Transl Neurol 7(4): 406–419. https://doi.org/10.1002/acn3.51004
  61. Cantarel BL, Waubant E, Chehoud C, Kuczynski J, DeSantis TZ, Warrington J, Venkatesan A, Fraser CM, Mowry EM (2015) Gut microbiota in multiple sclerosis: possible influence of immunomodulators. J Investig Med 63(5): 729–734. https://doi.org/10.1097/JIM.000000000000192
  62. Абдурасулова ИН, Дмитриев АВ (2023) Витамины группы В: от гомеостаза к патогенезу и лечению рассеянного склероза. Усп физиол наук 54(1): 26–54. [Abdurasulova IN, Dmitriev AV (2023) Group B Vitamins: From Homeostasis to Pathogenesis and Treatment of Multiple Sclerosis. Uspechi Fiziologicheskich nauk 54(1): 26–54. (In Russ)]. https://doi.org/10.31857/S0301179823010034
  63. Fangmann D, Theismann E-M, Türk K, Schulte DM, Relling I, Hartmann K, Keppler JK, Knipp J-R, Rehman A, Heinsen F-A, Franke A, Lenk L, Freitag-Wolf S, Appel E, Gorb S, Brenner C, Seegert D, Waetzig GH, Rosenstiel P, Schreiber S, Schwarz K, Laudes M (2017) Targeted Microbiome Intervention by Microencapsulated Delayed-Release Niacin Beneficially Affects Insulin Sensitivity in Humans. Diabetes Care 41(3): 398–405. https://doi.org/10.2337/dc17-1967
  64. Steinert RE, Sadaghian Sadabad M, Harmsen HJ, Weber P (2016) The prebiotic concept and human health: A changing landscape with riboflavin as a novel prebiotic candidate? Eur J Clin Nutr 70(12): 1348–1353. https://doi.org/10.1038/ejcn.2016.119.
  65. Da Silva AVA, de Castro Oliveira SB, Di Rienzi SC, Brown-Steinke K, Dehan LM, Rood JK, Carreira VS, Le H, Maier EA, Betz KJ, Aihara E, Ley RE, Preidis GA, Shen L, Moore SR (2019) Murine Methyl Donor Deficiency Impairs Early Growth in Association with Dysmorphic Small Intestinal Crypts and Reduced Gut Microbial Community Diversity. Curr Dev Nutr 3(1): nzy070. https://doi.org/10.1093/cdn/nzy070
  66. Arumugam M, Raes J, Pelletier E, Le Paslier D, Yamada T, Mende DR, Fernandes GR, Tap J, Bruls T, Batto JM, Bertalan M, Borruel N, Casellas F, Fernandez L, Gautier L, Hansen T, Hattori M, Hayashi T, Kleerebezem M, Kurokawa K, Leclerc M, Levenez F, Manichanh C, Nielsen HB, Nielsen T, Pons N, Poulain J, Qin J, Sicheritz-Ponten T, Tims S, Torrents D, Ugarte E, Zoetendal EG, Wang J, Guarner F, Pedersen O, de Vos WM, Brunak S, Doré J, MetaHIT Consortium, Antolín M, Artiguenave F, Blottiere HM, Almeida M, Brechot C, Cara C, Chervaux C, Cultrone A, Delorme C, Denariaz G, Dervyn R, Foerstner KU, Friss C, van de Guchte M, Guedon E, Haimet F, Huber W, van Hylckama-Vlieg J, Jamet A, Juste C, Kaci G, Knol J, Lakhdari O, Layec S, Le Roux K, Maguin E, Mérieux A, Melo Minardi R, M’rini C, Muller J, Oozeer R, Parkhill J, Renault P, Rescigno M, Sanchez N, Sunagawa S, Torrejon A, Turner K, Vandemeulebrouck G, Varela E, Winogradsky Y, Zeller G, Weissenbach J, Ehrlich SD, Bork P (2011) Enterotypes of the human gut microbiome. Nature 473(7346): 174–180. https://doi.org/10.1038/nature09944
  67. Said HM (2011) Intestinal absorption of water-soluble vitamins in health and disease. Biochem J 437(3): 357–372. https://doi.org/10.1042/ BJ20110326.
  68. Costliow ZA, Degnan PH (2017) Thiamine Acquisition Strategies Impact Metabolism and Competition in the Gut Microbe Bacteroides thetaiotaomicron. mSystems 2(5): e00116–17. https://doi.org/10 .1128/mSystems.00116-17
  69. Park J, Hosomi K, Kawashima H, Chen Y, Mohsen A, Ohno H, Konishi K, Tanisawa K, Kifushi M, Kogawa M, Takeyama H, Murakami H, Kubota T, Miyachi M, Kunisawa J, Mizuguchi K (2022) Dietary vitamin B1 intake influences gut microbial community and the consequent production of short-chain fatty acids. Nutrients 14(10): 2078. https://doi.org/10.3390/nu14102078
  70. Herrick JA, Alexopoulos CJ (1943) A Further Note on the Production of Thiamine by Actinomyces. Bull Torrey Botanic Club 70(4): 369–371. https://doi.org/10.2307/2481558
  71. Soto-Martin E, Warnke I, Farquharson F, Christodoulou M, Horgan G, Derrien M, Faurie J-M, Flint HJ, Duncan SH, Louis P (2020) Vitamin biosynthesis by human gut butyrate-producing bacteria and cross-feeding in synthetic microbial communities. mBio 11(4): 20. https://doi.org/10.1128/mBio.00886-20
  72. Hillman ET, Kozik AJ, Hooker CA, Burnett JL, Heo Y, Kiesel VA, Nevins CJ, Oshiro JMKI, Robins MM, Thakkar RD, Wu ST, Lindemann SR (2020) Comparative genomics of the genus Roseburia reveals divergent biosynthetic pathways that may influence colonic competition among species. Microb Genom 6(7): mgen000399. https://doi.org/10.1099/mgen.0.000399
  73. Abdou E, Hazell AS (2015) Thiamine deficiency: An update of pathophysiologic mechanisms and future therapeutic considerations. Neurochem Res 40(2): 353–361. https://doi.org/10.1007/s11064-014-1430-z
  74. Bâ A (2008) Metabolic and structural role of thiamine in nervous tissues. Cell Mol Neurobiol 28(7): 923–931. https://doi.org/10.1007/s10571-008-9297-7
  75. Butterworth RF (2003) Thiamin deficiency and brain disorders. Nutr Res Rev 16(2): 277–284. https://doi.org/10.1079/NRR200367
  76. Hazell AS, Butterworth RF (2009) Update of cell damage mechanisms in thiamine deficiency: focus on oxidative stress, excitotoxicity and inflammation. Alcohol Alcohol 44(2): 141–147. https://doi.org/10.1093/alcalc/agn120
  77. Ji Z, Fan Z, Zhang Y, Yu R, Yang H, Zhou C, Luo J, Ke ZJ (2014) Thiamine deficiency promotes T cell infiltration in experimental autoimmune encephalomyelitis: the involvement of CCL2. J Immunol 193(5): 2157–2167. https://doi.org/10.4049/jimmunol.1302702
  78. Singh N, Gurav A, Sivaprakasam S, Brady E, Padia R, Shi H, Thangaraju M, Prasad PD, Manicassamy S, Munn DH, Lee JR, Offermanns S, Ganapathy V (2014) Activation of Gpr109a, receptor for niacin and the commensal metabolite butyrate, suppresses colonic inflammation and carcinogenesis. Immunity 40(1): 128–139. https://doi.org/10.1016/j.immuni.2013.12.007
  79. Lipszyc PS, Cremaschi GA, Zubilete MZ, Bertolino MLA, Capani F, Genaro AM, Wald MR (2013) Niacin modulates pro-inflammatory cytokine secretion. A potential mechanism involved in its anti-atherosclerotic effect. Open Cardiovasc Med J 7: 90–98. https://doi.org/10.2174/1874192401307010090
  80. Gazzaniga F, Stebbins R, Chang SZ, McPeek MA, Brenner C (2009) Microbial NAD metabolism: lessons from comparative genomics. Microbiol Mol Biol Rev 73(3): 529–541. https://doi.org/10.1128/MMBR.00042-08
  81. Kurnasov O, Goral V, Colabroy K, Gerdes S, Anantha S, Osterman A, Begley TP (2003) NAD biosynthesis: identification of the tryptophan to quinolinate pathway in bacteria. Chem Biol 10(12): 1195–1204. https://doi.org/10.1016/j.chembiol.2003.11.011
  82. Gao J, Xu K, Liu H, Liu G, Bai M, Peng C, Li T, Yin Y (2018) Impact of the gut microbiota on intestinal immunity mediated by tryptophan metabolism. Front Cell Infect Microbiol 8:13. https://doi.org/10.3389/fcimb.2018.00013
  83. Crittenden RG, Martinez NR, Playne MJ (2003) Synthesis and utilisation of folate by yoghurt starter cultures and probiotic bacteria. Int J Food Microbiol 80(3): 217–222. https://doi.org/10.1016/s0168-1605(02)00170-8
  84. Degnan PH, Barry NA, Mok KC, Taga ME, Goodman AL (2014) Human Gut Microbes Use Multiple Transporters to Distinguish Vitamin B12 Analogs and Compete in the Gut. Cell Host Microbe 15(1): 47–57. https://doi.org/10.1016/j.chom.2013.12.007
  85. Kelly CJ, Alexeev EE, Farb L, Vickery TW, Zheng L, Eric LC, Kitzenberg DA, Battista KD, Kominsky DJ, Robertson CE, Frank DN, Stabler SP, Colgan SP (2019) Oral vitamin B12 supplement is delivered to the distal gut, altering the corrinoid profil and selectively depleting Bacteroides in C57BL/6 mice. Gut Microbes 10(6): 654–662. https://doi.org/10.1080/19490976.2019.1597667
  86. Miller A, Korem M, Almog R, Galboiz Y (2005) Vitamin B12, demyelination, remyelination and repair in multiple sclerosis. J Neurol Sci 233(1–2): 93–97. https://doi.org/10.1016/j.jns.2005.03.009

Supplementary files

Supplementary Files
Action
1. JATS XML
Download
2. Fig. 1. Alpha (a), (b) and beta diversity (c) of the intestinal microbiome of the studied sample. (a) and (b) – Alpha diversity was assessed by the Chao1 index (a), which measures richness (number of species) and the Shannon Diversity Index (b) ( relative abundance of different species). The data is presented as a median and 95% confidence interval. The differences between the groups were assessed for each diversity index separately using the Mann–Whitney criterion, adjusted Bonferroni for multiple comparisons (* – p < 0.05, ** – p < 0.01). (c) – Beta diversity – The distance matrix between the indices of diversity was visualized using multidimensional scaling (MDS) with the analysis of the main coordinates. The differences between the groups were assessed using permutation multivariate analysis of variance.

Download (216KB)
Indexing metadata
3. Fig. 2. The ratio of different phylum in the structure of the intestinal microbiome (a) and the distribution of patients with different content of dominant phylum (b). (a) – Comparison of the ratio of phylum in different groups of patients and healthy volunteers was performed using the Pearson Chi-squared test; * – p < 0.05 – difference from the control groups, there are no differences between the PC groups. (b) – A comparison of the total proportion of dominant phylum (Firmicutes + Bacteroidota) was performed using the Pearson Chi-squared test.

Download (184KB)
Indexing metadata
4. Fig. 3. Changes in the intestinal microbiome at the class level. The data are presented as a median, the boundaries of the rectangles are 1-3 quartiles, the boundaries of the segments are 95% confidence interval. For comparison, we used a two-sided Mann–Whitney test with Bonferroni correction for the multiplicity of comparisons.

Download (171KB)
Indexing metadata
5. Fig. 4. Changes in the intestinal microbiome at the family level. The data are presented as a median, the boundaries of the rectangles are 1-3 quartiles, the boundaries of the segments are 95% confidence interval. For comparison, a two–way Mann-Whitney test with Bonferroni correction for the multiplicity of comparisons was used.

Download (317KB)
Indexing metadata
6. Fig. 5. Changes in the intestinal microbiome at the level of childbirth. The data is presented as a median, the boundaries of rectangles are 1-3 quartiles, the boundaries of segments are 95% confidence interval. A two-way test was used for comparison Mann–Whitney with Bonferroni correction for the multiplicity of comparisons (ns: p > 0.05; *: 0.01 < p ≤ 0.05; **: 0.001 < p ≤ 0.01).

Download (487KB)
Indexing metadata
7. Fig. 6. Changes in the potential representation of bacterial genes involved in the pathways of metabolism, synthesis and transport of vitamins of group B. The data are presented as a median, the boundaries of rectangles are 1-3 quartiles, the boundaries of segments are 95% confidence interval. For comparison, a two–way Mann-Whitney test with Bonferroni correction for the multiplicity of comparisons was used.

Download (462KB)
Indexing metadata
8. Fig. 7. Correlation between the representation of bacterial phylum and potential genes involved in the pathways of metabolism and synthesis of vitamins of group B. Cells with coefficients reflect reliable correlations: weak correlation – r ≤ 0.5 (not shown in the graph); average correlation – 0.5 < r ≤ 0.7, strong correlation – r > 0.7.

Download (233KB)
Indexing metadata
9. Figure 8. Correlation between the representation of bacterial genera altered in MS and potential genes involved in the pathways of metabolism and synthesis of vitamins of group B. Cells with coefficients reflect significant correlations: weak correlation – r ≤ 0.5 is not displayed on the graph, average correlation – 0.5 < r ≤ 0.7, strong correlation – r > 0.7.

Download (273KB)
Indexing metadata

Copyright (c) 2024 Russian Academy of Sciences