Optimized experimental design for studying responses of the human gut microbiota to repeated antibiotic exposures.

<p>Each panel (<b>A–C</b>) depicts an optimized design for one subject in the study. (<b>A</b>) Subject D. (<b>B</b>) Subject E. (<b>C</b>) Subject F. The rows in each panel depict the time-points from the optimal (“opt”) and original (“org”) designs. Times are in days from the start of the experiments. Time-points that overlap in both designs are shown in green boxes. Time-points that are unique to either of the optimal or original designs are depicted with colored numbers (optimal = purple, original = orange). The two antibiotic exposure intervals are shown as blue rectangles.</p

Relaxation Time Distributions for responses of human gut commensals to repeated antibiotic exposures.

<p>Each relaxation time constant characterizes the time for a reference operational taxonomic unit (refOTU) to reach an equilibrium relative abundance level in the ecosystem after an antibiotic pulse. Probability density functions were estimated for either the first post-antibiotic exposure interval (solid blue line, “1^st relaxation time”) or the second post-antibiotic exposure interval (dashed red line, “2^nd relaxation time”). A smoothing kernel algorithm was used to estimate probability density functions, using relaxation time constants from refOTUs from all subjects (756 time constants for each post-antibiotic exposure interval).</p

SignatureDiversity scores for gut microbiota of three human subjects treated twice with antibiotics.

<p>(<b>A</b>) Intra-signature diversity scores for equilibrium levels (SD1_μ), which measure the expected fraction of reference operational taxonomic units (refOTUs) that change equilibrium levels in response to one or more of the antibiotic treatments. (<b>B</b>) Intra-signature diversity scores for relaxation times (SD1_λ), which measure the expected fraction of refOTUs that exhibit different relaxation time constants after the antibiotic treatments. (<b>C</b>) Intra-ecosystem signature diversity scores (SD2), which measure the expected equivalent number of prototype signatures per 100 refOTUs. (<b>D</b>) The inter-ecosystem signature diversity score (SD3), which measures the degree of sharing of prototype signatures across host ecosystems, is a ratio of the SD2^Dto the SD2^I score. The SD2^Dscore is computed on a hypothetical combined ecosystem, in which refOTUs from different subjects probabilistically share prototype signatures. The SD2^I score is a weighted average of SD2 scores computed on each subject separately.</p

Consensus Signature Groups of human gut commensals ordered by relaxation time after first antibiotic pulse.

<p>Each panel (<b>A–M</b>) depicts a Consensus Signature Group (CSG), with signatures conformed to a common time-scale and amplitude to facilitate comparison across subjects and CSGs. Displayed CSGs are those containing at least one reference operational taxonomic units (refOTUs) shared among all subjects, and significantly enriched for at least one taxonomic label at the family or genus level (false discovery rate <0.05, hypergeometric tests). The horizontal axis indicates time in days and the vertical axis indicates normalized signature amplitude. Red dashed lines depict median inferred signatures, and shaded red areas indicate 95% credible intervals. Horizontal blue lines depict antibiotic exposure windows. Numbers above plots indicate relaxation time constants in units of days.(<b>A–H</b>) are CSGs showing decreases in relative abundance during the first antibiotic pulse, and (<b>I–M</b>) are CSGs showing increases in relative abundance during the first antibiotic pulse.</p

Schematics of microbial ecosystems illustrating Signature Diversity scores at multiple levels of resolution.

<p>The panels depict examples of simplified microbial ecosystems measured over time, to illustrate three levels of Signature Diversity (SD) scores computed by the Microbial Counts Trajectories Infinite Mixture Model Engine (MC-TIMME) framework. (<b>A</b>) Intra-signature diversity (SD1), characterizes the dimensionality of each prototype signature. The top panel depicts a prototype signature with a lower SD1 score than the prototype signature in the bottom panel, which exhibits different equilibrium levels and relaxation time constants on each of the shaded intervals. (<b>B–C</b>) Intra-ecosystem signature diversity (SD2), characterizes the extent of prototype signature sharing among taxa within a host ecosystem. Host ecosystem (B) has a lower SD2 score than (C), because all taxa in (B) share the same prototype signature. (<b>D–E</b>) Inter-ecosystem signature diversity (SD3), characterizes the extent of prototype signatures haring across ecosystems. Each panel depicts two ecosystems. The two ecosystems in (D) have a lower SD3 score than those in (E), because more prototype signatures are shared between the ecosystems in D.</p

Schematic of the Microbial Counts Trajectories Infinite Mixture Model Engine (MC-TIMME) generative probabilistic model.

<p>Observed data of time-series of sequencing counts for reference operational taxonomic units (refOTUs) are assumed to arise from a multi-level generative probabilistic mixture model.(<b>A</b>)Infinite mixture over latent prototype signatures (green, red and blue solid lines),which specify models of dynamics continuous in both time and amplitude. The horizontal axis for each prototype signature represents time, and the vertical axis represents amplitude. Prototype signatures may adapt their dimensionality, which is shown increasing from left to right. The variables π_i and associated shaded bars represent prior probabilities for choosing among prototype signatures. (<b>B</b>)For each refOTU, a prototype signature is probabilistically chosen and sampled at discrete observed time-points. (<b>C</b>)Experiment and refOTU specific variables are added to the selected prototype signature to create an individual signature. (<b>D</b>) Observed data, consisting of sequencing counts, is generated through a discrete-valued noise model parameterized by individual signatures generated in step C.</p

Sequence and culture-based signatures for three <i>Lactobacillus</i> species in ileum and cecum.

<p>Horizontal axis indicates days post-inoculation with <i>Citrobacter rodentium</i>. For sequence-based signatures, vertical axis indicates the number of normalized sequencing counts for the Operational Taxonomic Unit (OTU). For culture-based signatures, vertical axis indicates log₁₀ Colony Forming Units (CFUs) per gram of input tissue. Dashed lines indicate the inferred median signature shape for each trajectory. Shaded regions indicate the 95% credible interval. <b>(A, G)</b> Sequence and <b>(D, J)</b> culture-based signatures for <i>Lactobaillus johnsonii</i>. <b>(B, H)</b> Sequence and <b>(E, K)</b> culture-based signatures for <i>Lactobacillus murinus</i>. <b>(C, I)</b> Sequence and <b>(F, L)</b> culture-based signatures for <i>Lactobacillus reuteri</i>.</p

Summary of sequenced samples and Good's coverage estimates.

<p>Numbers of reads are listed in thousands, and are after all preprocessing quality control and filtering steps. The average number of reads across all samples was ≈2,700. Good's coverage is a nonparametric estimate of the proportion of classes (i.e., OTUs) that are observed in a sample out of the total number of classes inferred to be present in the population. Labels in the top table row: T  =  time-point, ctrl  =  control, uninfected mice; inf  =  infected mice.</p

Consensus Signature Groups systematically characterize patterns of time-dependent microbiota changes in response to infection.

<p>Consensus Signature Groups (CSGs) represent sets of taxa that share similar dynamics within a tissue, providing a means to identify common behaviors among taxa regardless of their phylogenetic relationships. Representative signatures of individual Operational Taxonomic Units (OTUs) from CSGs are shown. Horizontal axis indicates days post-inoculation with the pathogen; vertical axis shows normalized sequencing counts for the OTU. Dashed or dotted lines indicate median signature shapes for OTUs. Shaded regions indicate 95% credible intervals for signatures; regions of overlap indicate time-periods during which changes were not detected. Phases of infection are E  =  early, A  =  acute, R  =  recovery, C  =  convalescence. (<b>A</b>) The pathogen, <i>Citrobacter rodentium</i> (OTU#6) in colon. (<b>B</b>) <i>Mucispirillum</i> (OTU#1) in colon, rapidly decreases and does not return to baseline until the convalescent phase. (<b>C</b>) <i>Parabacteroides</i> (OTU#8) in colon, decreases during early infection, but returns to baseline by the recovery phase. (<b>D</b>) <i>Parabacteroides</i> (OTU#8) in cecum had no detectable change between cohorts. (<b>E–F</b>) Two <i>Lactobacilli</i> in ileum, showing different dynamics: OTU#3 increases during acute infection, while OTU#13 decreases. (<b>G–H</b>) <i>Clostridium</i> (OTU#24) in ileum and cecum, has a delayed increase that persists into the convalescent phase. (<b>I–J</b>) Representative OTUs in colon and ileum showing no detectable changes between cohorts.</p

Filtering steps in bioinformatics pipeline and remaining sequencing reads.

<p>Filtering steps in bioinformatics pipeline and remaining sequencing reads.</p