TELEX HEBDOMADAIRE NR 95 DU 17.09.82 DESTINE A L'ENSEMBLE DES DELEGATIONS EXTERIEURES ET BUREAUX DE PRESS ET D'INFORMATION INDEPENDANTS DANS LES PAYS TIERS = WEEKLY MEMO NO. 95 FOR 17.09.82 TO FOREIGN DELEGATIONS AND PRESS BUREAUS OF THIRD COUNTRIES

<p>High-performance liquid chromatography (HPLC) results of (A) commercial surfactin sample, and (B) our extract surfactin of <i>B</i>. <i>subtilis</i> HH2 in LB medium. There were three main peaks (Peak A-C) of the extract and the surfactin standard in the same location.</p

Progressive changes in milk composition and increasing individuality with time.

<p>Hierarchical cluster analysis (HCA) dendrogram (A) and principal components analysis (PCA) score plots (B-D) of milk samples collected serially from three giant pandas; Li Li (LL), circles; Yuan Yuan (YY), stars; Xiao Ya Tou (XYT), diamonds. Multivariate analysis using SIMCA (see <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0143417#sec002" target="_blank">Materials and Methods</a>) allows horizontal sectioning of the dendrogram in (A) at different levels of clustering as indicated by the horizontal dotted lines. This yields progressively more detailed PCA score plots that first reveal similarities in metabolomes with time in all three individual giant pandas (as in (B), only two main coloured classifications), and then progressive disparities between the individuals when analysed at deeper resolutions (as in (C) and (D), increasing number of coloured classes). The data representing quality control repetitions (YY-19d-1, 2 and 3) are circled in red and indicate good repeatability. Score plots of PCA (B, C and D): x-axis PC1 = 19.4% and y-axis PC2 = 9.95%.</p

The dominant compounds in giant panda milk in Phase 1 of early lactation.

<p>The 50 most abundant liquid chromatography-high resolution mass spectrometry (LC-HRMS) signals in Phase 1 (the first week of lactation). Relative abundances were estimated from areas under LC peaks. The compounds are listed in the order of their LC retention times (Rt). Italicised names indicate non-proton adducts and complex ions identified by MZMine 2.10, and confirmed by manually checking the raw LC-HRMS data. The metabolite annotations are based on the Metabolomics Standards Initiative (MSI) identification levels; level 1, retention times matched with authentic standards (labelled as ST); level 2: identified by MS/MS (labelled as MS); level 3: accurate mass; and level 4 unknown. The metabolites identified at levels 1 and 2 are also labelled with the compound identifiers (CID) codes from the PubChem database.</p><p>The dominant compounds in giant panda milk in Phase 1 of early lactation.</p

Changes in milk metabolome delineating discrete phases in early lactation.

<p>S-plot of orthogonal partial least squares discriminant analysis (OPLS-DA) of 55 giant panda milk samples collected serially after birth from three giant pandas. In an S-plot, the x variable is the relative magnitude of a variable, and the y variable is the variable confidence/reliability. So, data points falling in the upper right or lower left corners of the plot represent those features that are least likely to be the result of spurious correlations. Peaks with low magnitude/intensity falling in the centre of the plot near 0 are close to the noise level and exhibit high risks for spurious correlations. The 50 most abundant compounds in the early phase (before day 7; Phase 1) and the 20 most abundant in later milk (after day 7; Phases 2 and 3, cumulative) are highlighted in red and blue, respectively. The inset graphs describe the time-dependent changes in abundance of two oligosaccharide isomers N262 and N351 (identified as diamonds in the S-plot), subsequently identified as 3’-sialyllactose and 6’-sialyllactose, respectively.</p

Compounds in giant panda milk tentatively correlated with cub growth rate.

<p>(A) OPLS loading plot for 21 serially collected samples from giant panda Yuan Yuan (YY). Components selected for testing for correlation with rate of cub body weight change are highlighted in red; see main text for selection criteria. X-variables, Pareto scaled LC-HRMS data; Y-variable, rate of body weight change over time of YY’s cub. (B) Body mass changes for YY’s cub over the milk sampling period (see also Fig A in <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0143417#pone.0143417.s001" target="_blank">S1 File</a>). (C, D, E, F) Changes in abundance with time of the compounds indicated with diamonds in (A) selected to illustrate diverse correlations with weight gain by the cub. Compound N258 showed positive correlation, whereas compound N254 showed negative correlation, and compound P318 exhibited a more complex pattern. Y-axes represent relative abundances of each compound as estimated from areas under peaks calculated from HILIC-HRMS data, x-axes represent time after birth. Putative initial identifications were N258, isoglobotriose; P318, methyl-imidazole acetate; N254, citrate; N338, 3-methyl-2-oxopentanate.</p

Data_Sheet_7_Evaluating a potential model to analyze the function of the gut microbiota of the giant panda.PDF

To contribute to the conservation of endangered animals, the utilization of model systems is critical to analyze the function of their gut microbiota. In this study, the results of a fecal microbial transplantation (FMT) experiment with germ-free (GF) mice receiving giant panda or horse fecal microbiota showed a clear clustering by donor microbial communities in GF mice, which was consistent with the results of blood metabolites from these mice. At the genus level, FMT re-established approximately 9% of the giant panda donor microbiota in GF mice compared to about 32% for the horse donor microbiota. In line with this, the difference between the panda donor microbiota and panda-mice microbiota on whole-community level was significantly larger than that between the horse donor microbiota and the horse-mice microbiota. These results were consistent with source tracking analysis that found a significantly higher retention rate of the horse donor microbiota (30.9%) than the giant panda donor microbiota (4.0%) in GF mice where the microbiota remained stable after FMT. Further analyzes indicated that the possible reason for the low retention rate of the panda donor microbiota in GF mice was a low relative abundance of Clostridiaceae in the panda donor microbiota. Our results indicate that the donor microbiota has a large effect on GF mice microbiota after FMT.</p