Side-population of HBM.

<p>(a) Sorting profile of side-population (SP) and non side-population (NSP) from breast milk. (b) Scatter plots showing no relationship between the percentage of SP and duration of breastfeeding and (c) age of mother. (d) Immunocytochemistry illustrating expression of nestin exclusively only on SP (top left) and expression of CK 18 exclusively on NSP (bottom right).</p

Cellular concentration in human breast milk did not vary in relation to the duration of breastfeeding.

<p>Cellular concentration in human breast milk did not vary in relation to the duration of breastfeeding.</p

RT-PCR on Messenger RNA (mRNA) of milk samples from three individuals.

<p>(a) mRNA of CD133 and CD34 were present in WCP of HBM (Lane 1–3). (b) Osteonectin (ON), alkaline phosphatase (ALP) and osteopontin (OP) (Lane 1–3) as well as (c) musashi-1 (Msi), nestin (NES) and neurofilament-M (NFM) in observed in WCP of HBM (Lane 1–3). (d) Messenger RNA of CK5, 14 and 18 were present in WCP of HBM (Lane 1–3). The negative controls in Lane 4, were MCF-7 for hematopoietic, mesenchymal and neural markers <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0014421#pone.0014421-Calvanese1" target="_blank">[49]</a> and mononuclear cells in peripheral blood for epithelial cell markers of CK5 and CK14. Positive controls (Lane 5) are cells isolated from umbilical cord blood (a), fetal MSC (b), cells from snap-frozen fetal brain (c) and MCF-7 (d) respectively.</p

Antigens expressed on CD133+ and CD133- cells in HBM.

<p>Antigens expressed on CD133+ and CD133- cells in HBM.</p

Mesenchymal Culture of Cells.

<p>(a, b) Adherent colonies of fetal MSC emerged after low density seeding at 4 cells per cm². Osteogenic induction of fetal MSC resulted in the deposition of extracellular calcium crystals staining positive with Alizarin red (c) and Von Kossa (d).CFU-F assays of SP and NSP as well as Stro1 positive and negative fractions of HBM did not establish any colonies as shown by the absence of colonies (e, h). WCP (f) and Stro-1 positive cells (g) cultured in D10 remained as non-adherent cells (red arrows) which did not undergo any proliferation in culture.</p

Antigens expressed on the cells in HBM.

<p>Antigens expressed on the cells in HBM.</p

CD133 Sorting.

<p>(a) Flow cytometry of CD133 staining on cellular component of HBM (i), as compared to the isotype control (ii). (b, c) Three was no relationship between the frequency of CD133 cells and the duration of breastfeeding, nor the age of the mother. (d) CD133+ cells from cord blood formed multi-lineage colonies on CFC assays.</p

Keystone species in pregnancy gingivitis: A snapshot of oral microbiome during pregnancy and postpartum period

It is well known that pregnancy is under the constant influence of hormonal, metabolic and immunological factors and this may impact the oral microbiota toward pregnancy gingivitis. However, it is still not clear how the oral microbial dysbiosis can modulate oral diseases as oral microbiome during pregnancy is very poorly characterized. In addition, the recent revelation that placental microbiome is akin to oral microbiome further potentiates the importance of oral dysbiosis in adverse pregnancy outcomes. Hence, leveraging on the 16S rRNA gene sequencing technology, we present a snapshot of the variations in the oral microbial composition with the progression of pregnancy and in the postpartum period and its association with pregnancy gingivitis. Despite the stability of oral microbial diversity during pregnancy and postpartum period, we observed that the microbiome makes a pathogenic shift during pregnancy and reverts back to a healthy microbiome during the postpartum period. Co-occurrence network analysis provided a mechanistic explanation of the pathogenicity of the microbiome during pregnancy and predicted taxa at hubs of interaction. Targeting the taxa which form the ecological guilds in the underlying microbiome would help to modulate the microbial pathogenicity during pregnancy, thereby alleviating risk for oral diseases and adverse pregnancy outcomes. Our study has also uncovered the possibility of novel species in subgingival plaque and saliva as the key players in the causation of pregnancy gingivitis. The keystone species hold the potential to open up avenues for designing microbiome modulation strategies to improve host health during pregnancy

E2F1 is over-expressed in Wharton’s jelly MSCs from SGA background.

<p>(A) Boxplot illustrating the relative E2F1 gene expression in 9 SGA and 5 AGA lines. Total mRNA expression was quantified by real-time RT-qPCR. E2F1 expression levels were normalized against those of β-actin. The data represent mean ± SEM of at least 3 independent experiments. The p-value was calculated using an unpaired t-test. (B) E2F1 and β-actin protein expression levels in 9 SGA- and 5 AGA-isolated MSCs. (C) Basal and drug-induced mitochondrial OCR in the MSCs was measured after transient transfection. Solid and dotted lines refer to the OCR traces before and after E2F1 depletion, respectively in the 6 representative MSC lines (MSC-01, MSC-56, MSC-75, MSC-44, MSC-57 and MSC-60). Results represent mean ± SEM of at least 3 independent experiments. (D) Venn diagrams depicting the number of genes affected by E2F1 depletion, either unique to or common between SGA and AGA groups. Top and bottom panels show down- and upregulated genes upon siE2F1 treatment from RNA-seq, respectively. (E) Scatterplot correlating gene expression upon E2F1 suppression in SGA and AGA lines. Dotted lines indicate the log2-fold change of 0.38 approximately for both axes. Blue, red and green dots represent genes which were significantly affected in the presence of E2F1 knockdown in SGA only, AGA only and both groups, respectively. Black dots represent the remaining genes expressed in the transfected cell lines. Linear relationship was determined by a Pearson correlation coefficient (R). (F) Barcharts showing the proportion of E2F1 up- (top) and downregulated (bottom) genes which are occupied by E2F1 at gene TSS exclusively in SGA or AGA-derived MSCs or in both groups of cell lines.</p

Active H3K27ac and H3K4me3 marks, but not E2F1, are enriched in SGA-derived MSCs at promoters of upregulated DEGs.

<p>(A) Heatmaps illustrating E2F1 ChIP-seq binding (left), E2F1 de novo motif enrichment (middle) and siE2F1 RNA-seq gene expression modulations (right) for all DEGs sorted according to the basal RNA-seq fold change expression difference between both groups of MSCs with SGA-upregulated genes on top followed by AGA-upregulated genes below. For the motif enrichment heatmap, the color intensity corresponds to the highest scoring motif within a 100bp window. For both binding and motif enrichments, signal intensities were plotted around the TSS ±5 kb of each DEG. (B) Boxplots comparing ChIP-seq intensities of E2F1 and five histone modifications (H3K27ac, H3K27me3, H3K4me1, H3K4me3 and H3K36me3) in MSCs established from SGA and AGA neonates. The p-values were calculated using a Wilcoxon’s test. (C) Scatterplots illustrating ChIP-seq intensities of E2F1 (left), H3K27ac (middle) and H3K4me3 (right) between SGA and AGA-derived MSCs at both groups of DEGs. Top: DEGs with higher basal gene expression in SGA-derived MSCs; bottom: DEGs with higher basal gene expression in AGA-derived MSCs. The p-values were calculated using a two-tailed binomial test.</p