A workflow for bacterial metabolic fingerprinting and lipid profiling: application to Ciprofloxacin challenged Escherichia coli

J. William Allwood1,4*†, Haitham AlRabiah1*, Elon Correa1, Andrew Vaughan1, Yun Xu1, Mathew Upton2,5, Royston Goodacre1, 3

1 Manchester Institute of Biotechnology, School of Chemistry, University of Manchester, 131 Princess Street, Manchester, M1 7DN, U.K.

2 School of Translational Medicine, Microbiology and Virology Unit, Stopford Building, University of Manchester, Oxford Road, Manchester, M13 9PL, U.K.

3 Manchester Centre for Integrative Systems Biology, Manchester Institute of Biotechnology, School of Chemistry, University of Manchester, 131 Princess Street, Manchester, M1 7DN, U.K.

4 School of Biosciences, University of Birmingham, Edgbaston, Birmingham, B15 2TT, U.K.

5 School of Biomedical and Healthcare Sciences, Plymouth University, Drake Circus, Plymouth, PL4 8AA, U.K.

* J. William Allwood and Haitham Alrabiah have contributed equally to this publication

† Corresponding author:

Supplementary Materials

Supplementary Table S1: Normalised sample reconstitution volumes applied in LC-MS analysis

Supplementary Table S2: UHPLC-MS lipidomics table of class averages and within class standard error, PLS co-efficient, ANOVA p-value, and putative metabolite identifications for ESI positive and negative mode data

Supplementary Table S3: UHPLC-MS ESI positive mode dataset top 50 PLS coefficients for control compared to Ciprofloxacin challenge for each respective isolate

Supplementary Table S4: UHPLC-MS ESI negative mode dataset top 50 PLS coefficients for control compared to Ciprofloxacin challenge for each respective isolate

Figure S1: UHPLC-MS Trend Plots of Lipids significantly altered in response to Ciprofloxacin: Common lipid responses between susceptible and resistant isolates. Error bars represent the standard error within the non averaged data for each experimental class

Supplementary Table S1: Normalised sample reconstitution volumes applied in LC-MS analysis

Sample number / Flask / Absorbance at OD 600nm (average of 3 technical replicates) / Class / LC-MS injection order / Normalised reconstitution volume (µL)*
1 / 161 C1 / 0.365 / 1 / 1 / 114.1
2 / 161 C2 / 0.376 / 1 / 9 / 117.5
3 / 161 C3 / 0.375 / 1 / 17 / 117.2
4 / 161 0.31 / 0.376 / 2 / 2 / 117.5
5 / 161 0.32 / 0.3695 / 2 / 10 / 115.5
6 / 161 0.33 / 0.357 / 2 / 18 / 111.6
7 / 171 C1 / 0.396 / 3 / 3 / 123.8
8 / 171 C2 / 0.36 / 3 / 11 / 112.5
9 / 171 C3 / 0.374 / 3 / 19 / 116.9
10 / 171 0.31 / 0.322 / 4 / 4 / 100.6
11 / 171 0.32 / 0.3215 / 4 / 12 / 100.5
12 / 171 0.33 / 0.412 / 4 / 20 / 128.8
13 / 160 C1 / 0.391 / 5 / 5 / 122.2
14 / 160 C2 / 0.362 / 5 / 13 / 113.1
15 / 160 C3 / 0.374 / 5 / 21 / 116.9
16 / 160 0.021 / 0.377 / 6 / 6 / 117.8
17 / 160 0.022 / 0.378 / 6 / 14 / 118.1
18 / 160 0.023 / 0.36 / 6 / 22 / 112.5
19 / 173 C1 / 0.3905 / 7 / 7 / 122
20 / 173 C2 / 0.387 / 7 / 15 / 120.9
21 / 173 C3 / 0.372 / 7 / 23 / 116.3
22 / 173 0.021 / 0.446 / 8 / 8 / 139.4
23 / 173 0.022 / 0.4345 / 8 / 16 / 135.8
24 / 173 0.023 / 0.3245 / 8 / 24 / 101.4

*(Minimum volume (100 µL)/Minimum OD (0.32)) × Sample OD.

Figure S1: UHPLC-MS Trend Plots of Lipids significantly altered in response to Ciprofloxacin: Common lipid responses between susceptible and resistant isolates. Error bars represent the standard error within the non averaged data for each experimental class