center for Using PMA-seq to develop data-driven best practices in microbiome informatics & fecal microbiota transplantations therapeutics Nathaniel D Chu1, Mark B Smith2, Allison R Perrotta1, Zain Kassam2, & Eric J Alm1 (1) Biological Engineering, Massachusetts Institute of Technology, Cambridge, MA; (2) OpenBiome, Medford, MA How can we evaluate and improve the quality, composition, and therapeutic value of fecal transplants? The gut microbiome is critical to human health METHODS by using propodium monoazide to Dysbiosis in the gut microbiome leads to disease PMA-seq identifies living bacteria remove DNA that is not protected by an intact membrane (i.e., in a living cell). In combination with 16S genetic sequencing, we can identify living and dead bacteria in Gastrointestinal infections a microbial sample. e.g. Clostridium difficile Dead cell Autoimmune diseases e.g. Inflammatory bowel disease Living cell Metabolic diseases PMA-Propodium Monoazide e.g. Obesity, diabetes DNA Fecal transplants can treat gut microbiome dysbioses 120 130 Symbiosis GA T A AAT CT GG T C T T ATTT CC But we lack a method to evaluate fecal transplants Sequencing RESULTS Oxygen exposure alters the living bacteria in fecal transplants, Freeze-thaw cycles and "lag time" including killing anti-inflammatory microbes do not effect bacterial composition PMA-seq identifies changes in bacterial community composition with oxygen exposure Bilophila Relative abundances of common gut bacteria Freeze-thaw Cycles Sutterella Erysipelotrichaceae, unassigned are altered by stool processing methods Haemophilus Phascolarctobacterium Sutterella Megasphaera ●● Megamonas Erysipelotrichaceae, unassigned 20% ● Megamonas ● Phascolarctobacterium ● ● Ruminococcus 15% Megasphaera ● ●● ● Oscillospira ● ● Megamonas ● ● ● ●● Faecalibacterium 10% ●● ● Ruminococcus ● ● Ruminococcaceae, unassigned ● Faecalibacterium 5% ●● ● Lachnospira ● Ruminococcaceae, unassigned Lachnobacterium Lachnospira Dorea Dorea 16% ● Faecalibacterium ● Coprococcus ● ● Oxygen Sensitive Coprococcus ● Blautia ● Blautia 12% ● ● Lachnospiraceae, unassigned ● Lachnospiraceae, unassigned ● ● ● Clostridium ●● ● Clostridium 8% Clostridiaceae, unassigned Clostridiaceae, unassigned ● ●● ● ● Clostridiales, unassigned ● ● ● ● Clostridiales, unassigned ● ●● Barnesiellaceae, unassigned 4% ●● Barnesiellaceae, unassigned Rikenellaceae, unassigned Parabacteroides Parabacteroides Ruminococcaceae, unassigned genus Bacteroides ● Bacteroides ●● Bifidobacterium 4.5% ●● ● Bifidobacterium 4% ANC-Anaerobic preparation ● ● ● ● ● with cysteine 3.5% ● ● ● ● ● ● ● ● ANA-Anaerobic preparation ● ● Bars represent relative abundance of each bacterial genus ● ● ● AEC-Aerobic preparation 3% ● ● ● Replicates Replicates Replicates ● Bars represent relative abundance of each bacterial genus with cysteine 2.5% ● ANC ANA AEC AER ARS ANC ANA AEC AER ARS AER-Aerobic preparation (current standard) 1 Cycle Lachnospira 0 Cycles 5 Cycles ARS-Aerobic preparation ●● 20 Cycles ● Oxygen exposure Oxygen exposure with gas sparge 6% ● ● ● ● ● Standard 16S sequencing PMA-seq ● 4% ● ●● ● ● Lag Time 2% ●● ●● ● Oxygen sensitive species include many Clostridia, such as the ● ●● ●●● Percent abundance ●● anti-inflammatory species Faecalibacterium prausnitzii. Bacteria Sutterella Lachnospiraceae, unassigned Holdemania 20% Erysipelotrichaceae, unassigned of the phylum Bacteroidetes were more oxygen tolerant ● ● ● ●● ● ● ● Phascolarctobacterium ● ● Total Relative Average Change with ● ● Taxonomy ● ● ● Megasphaera 15% ● ● ● Abundance oxygen exposure ● ● ● Megamonas ● ● –50% change ●● ● Firmicutes: Erysipelotrichi: Erysipelotrichales: Erysipelotrichaceae: unassigned 0.91% -45.78% ● Ruminococcus Firmicutes: Clostridia: Clostridiales: Veillonellaceae: Megamonas 12.42% -37.98% 10% Oscillospira Firmicutes: Clostridia: Clostridiales: Clostridiaceae: unassigned 0.46% -35.95% Faecalibacterium 0% change 5% Ruminococcaceae, unassigned Firmicutes: Clostridia: Clostridiales: Veillonellaceae: Megasphaera 0.38% -35.41% Oxygen tolerant Roseburia Firmicutes: Clostridia: Clostridiales: Ruminococcaceae: unassigned 3.04% -31.42% Lachnospira Firmicutes: Clostridia: Clostridiales: Lachnospiraceae: Lachnospira 3.31% -27.97% +75% change Bacteroides Lachnobacterium Firmicutes: Clostridia: Clostridiales: Clostridiaceae: Clostridium 0.39% -14.95% ● ● ● ● Dorea 40% ● Firmicutes: Clostridia: Clostridiales: Ruminococcaceae: Faecalibacterium 9.08% -13.81% ● ●● ● Coprococcus ●● ● Firmicutes: Clostridia: Clostridiales: Lachnospiraceae: unassigned 15.81% -13.13% ● ● 35% ● Blautia Actinobacteria: Actinobacteria: Bifidobacteriales: Bifidobacteriaceae: Bifidobacterium 1.79% -11.71% Lachnospiraceae, unassigned ● ● Firmicutes: Clostridia: Clostridiales: Ruminococcaceae: Ruminococcus 1.45% -10.35% 30% ● ● Clostridium ● Firmicutes: Clostridia: Clostridiales: Lachnospiraceae: Blautia ● Clostridiaceae, unassigned 1.06% -4.72% ● ● ● 25% ● ● Firmicutes: Clostridia: Clostridiales: unassigned: unassigned 2.70% -1.37% ● ● Clostridiales, unassigned Barnesiellaceae, unassigned Firmicutes: Clostridia: Clostridiales: Lachnospiraceae: Coprococcus 3.02% -0.16% ANC ANA AEC AER ARS ANC ANA AEC AER ARS Rikenellaceae, unassigned Firmicutes: Clostridia: Clostridiales: Ruminococcaceae: Oscillospira 1.14% +5.70% Parabacteroides Firmicutes: Clostridia: Clostridiales: Veillonellaceae: Phascolarctobacterium 1.22% +35.56% Bacteroides Proteobacteria: Betaproteobacteria: Burkholderiales: Alcaligenaceae: Sutterella Oxygen exposure Oxygen exposure 4.22% +39.18% Bifidobacterium Bacteroidetes: Bacteroidia: Bacteroidales: Bacteroidaceae: Bacteroides 33.66% +42.37% Replicates Firmicutes: Clostridia: Clostridiales: unassigned: unassigned Stool sample #1 Stool sample #2 Bars represent relative abundance of each bacterial genus 0.33% +47.51% 0hr 0.5hr 1hr 3hr 7hr Bacteroidetes: Bacteroidia: Bacteroidales: Porphyromonadaceae: Parabacteroides 1.16% +60.14% Bacteroidetes: Bacteroidia: Bacteroidales: Barnesiellaceae: unassigned 0.77% +65.41% Lag time – time between defecation and sample processing Bacteroidetes: Bacteroidia: Bacteroidales: Rikenellaceae: unassigned 0.84% +75.88% CONCLUSIONS CLINICAL APPLICATIONS PMA-seq is an effective tool for identifying living fecal microbes Use PMA-seq to provide more reliable, quality controlled treatments Current practices alter the microbial content of fecal samples Reduction in anti-inflammatory bacteria may be a factor the modest efficacy of fecal Change fecal transplant procedures to maximize treatment sucess transplants in treating inflammatory diseases PMA-seq can inform best practices and, eventually, serve as a screen to evaluate Can we target specific fecal microbiota compositions to treat certain diseases? the suitability of clinical fecal material.