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Extended Data Fig. 5 | Detecting shifts in longitudinal microbiome
multi-omics. a, Distributions of Bray–Curtis dissimilarities as a function
of time difference between samples for protein profiles, species-level
transcriptional activity (see Methods), and species-level taxonomy
(though excluding subjects with dysbiosis at any time point), otherwise as
in Fig. 3a. Removing subjects with dysbiotic samples removes the extreme
dissimilarities (near 1) observed in IBD subjects. Boxplots show median
and lower/upper quartiles; whiskers show inner fences. b, Distribution
of Bray–Curtis dissimilarities between samples from the same subject,
two weeks apart versus those from different individuals, allowing us
to define a ‘shift’ in the microbiome as a change more likely to have
been drawn from the between-subject distribution than within-subject
distances (corresponding to Bray–Curtis > 0.54). c, Relative abundance
differences of the top ten microorganisms that contributed to each of the
183 detected taxonomic shifts among any two within-subject subsequent
time points. Shifts are typically reciprocal (that is, losing a microorganism
and regaining it later, or vice versa), and microorganism with frequent
high-abundance shifts generally correspond to frequent contributors in
Fig. 3b. Sample ordering is from a hierarchical clustering using average
linkage followed by optimal leaf ordering^101. d, As in Fig. 3c, but for E. coli
(n = 322 samples from 24 subjects; two-tailed Wilcoxon test of the
absolute differences in relative abundances between consecutive time
points P = 2.2 × 10 −^4 for non-IBD to UC, and P = 0.029 for non-IBD
to CD), which is frequently implicated in gut inflammation. e, As in
b, but showing Bray–Curtis dissimilarities of metabolomic profiles. Here,
22% (96 out of 440) of sample pairs exceed the shift threshold, whereas
13% (183 out of 1,413) exceed the threshold in b. If metagenomic profiles
are sub-sampled to match the metabolomics samples, this increases to
14% (57 out of 398) of sample pairs, showing that if we increased the
sampling rate, this measurement type would be likely to shift more than
the metagenomes. f, As in Fig. 3b, but showing the primary contributors
to metabolomic shifts, that is, the metabolite with the largest change in
relative abundance during a shift. Note that other metabolites may still
experience large changes in abundance (for example, for this reason, urate
was not a primary contributor to any non-IBD shifts, though large changes
are visible for one non-IBD individual in Fig. 3e). The full table of detected
metabolomic shifts is given in Supplementary Table 30. Violin plot shows
the density of points around that intake frequency; bandwidth chosen
automatically by Silverman’s method^102.