Untersuchung des Dickdarm-Mikrobioms in der klinischen Forschung

ZUSAMMENFASSUNG

Gegenstand und Ziel: Analysen des Dickdarm-Mikrobioms sind ein international zunehmend intensiver untersuchter Forschungsschwerpunkt. Die zahlreichen Schwierigkeiten solcher Studien sind oft nicht bekannt, worauf hier vermehrt aufmerksam gemacht werden soll. Antidepressiva und Antibiotika zeigen teils ähnliche Wirkmechanismen, weshalb erneut psychiatrische Indikationen für Antibiotika geprüft werden. Vor diesem Hintergrund zeigen wir Daten einer Feldbeobachtung an der Universität Regensburg.

Material und Methoden: 179 Teilnehmer spendeten eine Stuhlprobe und füllten das Regensburg Microbiome Questionnaire (ReMBiQ) aus. Das bakterielle Mikrobiom aller Stuhlproben wurde mittels 16S-rRNA-Hochdurchsatz-Sequenzierung analysiert. Anhand des ReMBiQ wurde eine Subkohorte ausgewählt: Zu einer Teilnehmerin mit seit 18 Monaten behandelter MDR-Tuberkulose wurden 4 gesunde Kontrollen anhand zahlreicher und für das Dickdarm-Mikrobiom relevanter Kriterien ausgewählt.

Ergebnisse: Mikrobiomtypische Parameter wie Alpha- und Beta-Diversität grenzten die Teilnehmerinnen innerhalb der Subkohorte klar voneinander ab. Hierbei half auch die Messung absoluter Bakterienmengen mittels spike-in-based calibration to total microbial load (SCML). Insgesamt fiel im Vergleich insbesondere eine deutliche Zunahme der Gattung Bacteroides auf, die mit 94,5 % dominierte.

Schlussfolgerungen: Unterschiedliche Ergebnisse bei gleichen oder ähnlichen, starken Eingriffen im Dickdarm, verdeutlichen die Notwendigkeit strenger methodischer Standards. Dies gilt insbesondere für die Beobachtung vergleichsweise kleiner Effektgrößen in den Neurowissenschaften.

Klinische Relevanz: Die neurowissenschaftliche Bewertung des Dickdarm-Mikrobioms unter kombiniert antidepressiv-antimikrobieller Therapie wird wichtige Hinweise auf die Wirksamkeit neuer Therapieformen liefern.

ABSTRACT

Objective: Analyzing the gut microbiome is a highly demanded research topic all over the world. Its application however comes with many challenges that are often little known. To create an awareness for these obstacles is one of the aims of this work. Antidepressants and antibiotics show partly similar mechanisms of action. Hence, antibiotics are, once again, tested for clinical use in psychiatry. In this light, data from a field observation study at Regensburg University are presented here.

Material and Methods: 179 participants provided a stool sample and completed the Regensburg Microbiome Questionnaire (ReMBiQ). 16S-rRNA high-throughput sequencing was performed to analyse the bacterial microbiome from collected stool samples. According to ReMBiQ information, a subgroup was selected. Within this group was a participant who had been treated for MDR-tuberculosis for 18 months at the time of sampling and four healthy controls who had been strictly matched along several criteria relevant to the gut microbiome.

Results: Alpha and beta diversity, which are typical measures for investigating the microbiome, showed clear separation of participants within the selected subgroup. Quantification of absolute bacterial abundances was achieved by spike-in-based calibration to total microbial load (SCML) and contributed relevant information. Overall, the genus Bacteroides were dominant at 94.5 %.

Conclusions: Even strong equal or similar interventions in the gut yield differing results. This calls for strict methodological standards, especially when the goal is to capture comparably small effects with experimental designs in neuroscience.

Clinical Relevance: Neuroscientific investigations of the gut microbiome in clinical studies applying combined therapies with antidepressants and antimicrobials will provide helpful insights into efficacy of novel therapy approaches.

Schlüsselwörter

Dickdarm-Mikrobiota - Mikrobiomanalytik - Antibiotika - Major Depression

Key words

Gut microbiota - microbiome analysis - antibiotics - major depression

• Literatur

• 1 Maier L, Pruteanu M, Kuhn M. et al Extensive impact of non-antibiotic drugs on human gut bacteria. Nature 2018; 555 7698 623-628
• 2 Valles-Colomer M, Falony G, Darzi Y. et al The neuroactive potential of the human gut microbiota in quality of life and depression. Nat Microbiol 2019; 4 (04) 623-32
• 3 Kelly BJ, Gross R, Bittinger K. et al Power and sample-size estimation for microbiome studies using pairwise distances and PERMANOVA. Bioinformatics 2015; 31 (15) 2461-2468
• 4 Mattiello F, Verbist B, Faust K. et al A web application for sample size and power calculation in case-control microbiome studies. Bioinformatics 2016; 32 (13) 2038-2040
• 5 Xia Y, Sun J, Chen D-G. Power and Sample Size Calculations for Microbiome Data. Statistical Analysis of Microbiome Data with R. ICSA Book Series in Statistics. Singapore, Taiwan: Springer Nature Singapore; 2018
• 6 Hiergeist A, Reischl U. Priority Program Intestinal Microbiota Consortium/quality assessment p, Gessner A. Multicenter quality assessment of 16 S ribosomal DNA-sequencing for microbiome analyses reveals high inter-center variability. Int J Med Microbiol 2016; 306 (05) 334-342
• 7 Stammler F, Glasner J, Hiergeist A. et al Adjusting microbiome profiles for differences in microbial load by spike-in bacteria. Microbiome 2016; 4 (01) 28
• 8 Robitzek EH, Selikoff IJ, Ornstein GG. Chemotherapy of human tuberculosis with hydrazine derivatives of isonicotinic acid; preliminary report of representative cases. Q Bull Sea View Hosp 1952; 13 (01) 27-51
• 9 Loomer HP, Saunders JC, Kline NS. A clinical and pharmacodynamic evaluation of iproniazid as a psychic energizer. Psychiatr Res Rep Am Psychiatr Assoc 1957; 8: 129-141
• 10 Levine J, Cholestoy A, Zimmerman J. Possible antidepressant effect of minocycline. Am J Psychiatry 1996; 153 (04) 582
• 11 Schmidtner A, Slattery DA, Glasner J. et al Minocycline alters behavior, microglia and the gut microbiome in a trait-anxiety-dependent manner. Transl Psychiatry 2019; 9 (01) 223
• 12 Macedo D, Filho A, Soares de Sousa CN. et al Antidepressants, antimicrobials or both? Gut microbiota dysbiosis in depression and possible implications of the antimicrobial effects of antidepressant drugs for antidepressant effectiveness. J Affect Disord 2017; 208: 22-32
• 13 Kupis J, Johnson S, Hallihan G. et al Assessing the Usability of the Automated Self-Administered Dietary Assessment Tool (ASA24) among Low-Income Adults. Nutrients 2019; 11: 1
• 14 Subar AF, Thompson FE, Potischman N. et al Formative research of a quick list for an automated self-administered 24-hour dietary recall. J Am Diet Assoc 2007; 107 (06) 1002-1007
• 15 Minerbi A, Gonzalez E, Brereton NJB. et al Altered microbiome composition in individuals with fibromyalgia. Pain 2019; 160 (11) 2589-602
• 16 McDonald D, Hyde E, Debelius JW. et al American Gut: an Open Platform for Citizen Science Microbiome Research. mSystems 2018; 3: 3
• 17 Rognes T, Flouri T, Nichols B. et al VSEARCH: a versatile open source tool for metagenomics. Peer J 2016; 4: e2584
• 18 Pruesse E, Quast C, Knittel K. et al SILVA: a comprehensive online resource for quality checked and aligned ribosomal RNA sequence data compatible with ARB. Nucleic Acids Res 2007; 35 (21) 7188-7196
• 19 Team RDC. R: A Language and Enviroment for Statistical Computing. Vienna, Austria: R Foundation for Statistical Computing. 2018 https://www.R-project.org
• 20 Callahan B, Proctor D, Relman D. et al Reproducible Research Workflow in R for the Analysis of Personalized Human Microbiome Data. Pac Symp Biocomput 2016; 21: 183-194
• 21 McMurdie PJ, Holmes S. phyloseq: an R package for reproducible interactive analysis and graphics of microbiome census data. PLoS One 2013; 8 (04) e6121
• 22 Lozupone C, Knight R. UniFrac: a new phylogenetic method for comparing microbial communities. Appl Environ Microbiol 2005; 71 (12) 8228-8235
• 23 Lewis SJ, Heaton KW. Stool form scale as a useful guide to intestinal transit time. Scand J Gastroenterol 1997; 32 (09) 920-924
• 24 Buttigieg PL, Ramette A. A guide to statistical analysis in microbial ecology: a community-focused, living review of multivariate data analyses. FEMS Microbiol Ecol 2014; 90 (03) 543-550
• 25 Ramette A. Multivariate analyses in microbial ecology. FEMS Microbiol Ecol 2007; 62 (02) 142-60
• 26 Palleja A, Mikkelsen KH, Forslund SK. et al Recovery of gut microbiota of healthy adults following antibiotic exposure. Nat Microbiol 2018; 3 (11) 1255-1265
• 27 Dubourg G, Lagier JC, Armougom F. et al The gut microbiota of a patient with resistant tuberculosis is more comprehensively studied by culturomics than by metagenomics. Eur J Clin Microbiol Infect Dis 2013; 32 (05) 637-645
• 28 Luo M, Liu Y, Wu P. et al Alternation of Gut Microbiota in Patients with Pulmonary Tuberculosis. Front Physiol 2017; 8: 822
• 29 Wipperman MF, Fitzgerald DW, Juste MAJ. et al Antibiotic treatment for Tuberculosis induces a profound dysbiosis of the microbiome that persists long after therapy is completed. Sci Rep 2017; 7 (01) 10767
• 30 Dinan TG, Stanton C, Cryan JF. Psychobiotics: a novel class of psychotropic. Biol Psychiatry 2013; 74 (10) 720-726
• 31 Suez J, Zmora N, Zilberman-Schapira G. et al Post-Antibiotic Gut Mucosal Microbiome Reconstitution Is Impaired by Probiotics and Improved by Autologous FMT. Cell 2018; 174 (06) 1406-23e16
• 32 Costea PI, Zeller G, Sunagawa S. et al Towards standards for human fecal sample processing in metagenomic studies. Nat Biotechnol 2017; 35 (11) 1069-1076
• 33 Sinha R, Abu-Ali G, Vogtmann E. et al Assessment of variation in microbial community amplicon sequencing by the Microbiome Quality Control (MBQC) project consortium. Nat Biotechnol 2017; 35 (11) 1077-1087