Department of Food Science
Fecal Microbiota signatures in South African Infants with Respiratory, Gastrointestinal and Other Diseases
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Research Motivation
Research Questions ?
Baseline Data & Study Design
Globally, 5.4 million children under five years of age die in 2017, roughly half of them lived in sub- Saharan Africa (UNICEF, 2018), and the leading cause is respiratory and gastrointestinal diseases. 34 infants (Mean age: 16 ± 8 months; 17 male and 17 female) hospitalized @ Cecilia Makiwane Hospital, East London, South Africa
Methodology Qiime DADA2 Table
Baseline data Medical history and clinical blood parameters of the infants were collected from hospital records and through a structured verbal interview with the parents and doctor.
Fecal Sample Collection A sterile 13 ml stool tube with DNA/RNA shield was used @ the time of infants hospitalization.
Qiime Workflow Qiime version 2019.1 Casava 1.8 single-end demultiplexed fastq
Quality Filtering Taxonomic Profile Clustering & Identifying chimeras
Generating OTU table
16s Metagenome Analysis
Classify OTUs FASTA format OTU table
Alpha Diversity
•Sample ID input filtered denoised non-chimeric denoised •Sample-1 Sample 8724 ID 8061input 8061 filtered 7210 •Sample-2 Sample-1 5258 4984 8724 4984 4932 8061 8061 •Sample-3 11989 11474 11474 11346 Sample-2 5258 4984 4984 •Sample-4 5248 5056 5056 4831 11989 11474 •Sample-5 Sample-3 7066 6658 6658 11474 6495 •Sample-6 Sample-4 2765 2631 5248 2631 2444 5056 5056 •Sample-7 9470 9163 9163 8842 Sample-5 7066 6658 6658 •Sample-8 9517 9072 9072 8939 2631 2631 •Sample-9 Sample-6 4121 3657 2765 3657 3085 •Sample-10 Sample-7 5095 4658 9470 4658 4270 9163 9163 •Sample-11 9379 8640 8640 7199 9072 9072 •Sample-12 Sample-8 17047 164739517 16473 14348 3657 3657 •Sample-13 Sample-9 8172 7839 4121 7839 7779 •Sample-14 Sample-10 5135 4865 5095 4865 4865 4658 4658 •Sample-15 3939 3749 3749 3622 Sample-11 9379 8640 8640 •Sample-16 987 931 931 931 •Sample-17 Sample-12 7496 7212 7212 16473 6553 17047 16473 •Sample-18 12565 12130 12130 11165 Sample-13 8172 7839 7839 •Sample-19 10112 8973 8973 7943 4865 4865 •Sample-20 Sample-14 4770 4338 5135 4338 4255 •Sample-21 Sample-15 15037 138213939 13821 13479 3749 3749 •Sample-22 21648 20898 20898 18848 Sample-16 987 931 931 •Sample-23 6784 6557 6557 6421 7212 7212 •Sample-24 Sample-17 3964 3818 7496 3818 3726 •Sample-25 Sample-18 6765 6351 6351 12565 •Sample-26 4149 3968 3968 Sample-19 10112 •Sample-27 15836 15364 15364 •Sample-28 Sample-20 19572 188354770 18835 •Sample-29 Sample-21 7833 7086 7086 15037 •Sample-31 11354 10344 10344 21648 •Sample-32 Sample-22 4832 4414 4414 Sample-23 •Sample-33 4440 4294 6784 4294 •Sample-34 Sample-24 9060 8788 3964 8788 •Sample-35 7880 7565 7565 Sample-25 6765
Beta Diversity ANCOM Analysis
V3-V4 16S rRNA gene fragments through Illumina Miseq sequencing platform @ Inqaba Biotechnical Industries (Pty) Ltd., South Africa.
non-chimeric 7210 4932 11346 4831 6495 2444 8842 8939 3085 4270 7199 14348 7779 4865 3622 931 6553
6098 12130 3911 8973 15095 4338 16179 6483 13821 10004 20898 4401 6557 4294 8603 3818 6691 6351
12130
11165
8973
7943
4338
4255
13821
13479
20898
18848
6557
6421
3818
3726
6351
6098
Sample-26
4149
3968
3968
3911
Sample-27
15836
15364
15364
15095
Sample-28
19572
18835
18835
16179
Sample-29
7833
7086
7086
6483
Sample-31
11354
10344
10344
10004
Sample-32
4832
4414
4414
4401
Sample-33
4440
4294
4294
4294
Sample-34
9060
8788
8788
8603
Sample-35
7880
7565
7565
6691
Results & Discussion
Figure 5 - Beta Diversity Fig. 5 Two-dimensional PCoA plots of beta diversity based on the unweighted UniFrac distance matrix of the 34 IFM. Each dot represents a sample point. Box plot next to the PCoA graph shows the significance. A. Groups categorized by disease conditions (GD – Gastrointestinal Disease, RD -Respiratory Disease and OD – Other Diseases); B. Groups categorized based on deworming therapy. Discussion: Significant differences between the IFM of infants among the disease conditions (95% CI, 0.44 – 0.86; P < .012) (Figure 3A) and deworming therapy (95% CI, 0.40 – 0.90; P = .033) (Figure 3B) was found.
“A rapid changes occurred between the fecal microbiota of the infants based on the deworming therapy and disease condition groups ”
Figure 1 Alpha Rarefaction Fig,1 indicates alpha rarefaction curves (Shannon, observed OTUs and Faith_PD) as a function of sampling depths. The alpha rarefaction curve using Qiime indicates the species richness of the samples has been fully observed and or sequenced. If the lines in the plot appear to level out at a sampling depth along the X-axis, there is no possibility of additional or missing feature (Bolyen et al., 2019)
“The alpha rarefaction plots reflects the species richness and evenness of the fecal samples among the categorized groups”
Figure 2 Taxonomical Classification
Figure 3 Alpha Diversity
Fig.2 (A) Bar chart showing percentage relative abundance of phylum in the infants with respiratory, gastrointestinal, and other diseases and Fig.2 (B) indicates the ANCOM differential abundance volcano plot. The clr (centred log ratio) transformed OTU table at the genus level with 0 values modified to 1 was used. Only species which reject the null hypothesis are labelled.
Fig.3 (A) indicates the alpha diversity (observed species OTUs) group significance boxplots among the categorized groups based on the fecal microbiota and Fig.3 (B) indicates the alpha diversity (Shannon index) group significance. The boxes denote interquartile ranges. The P and H value between the group categories are indicated below each boxplot. The dotted line inside the box represents the median. Outliers are shown with open circles.
Discussion: The top phyla of the infants with respiratory disease were Proteobacteria, followed by Firmicutes, which were equally abundant in gastrointestinal disease. In a fecal sample analysis with respiratory disorder, the Bacteroides-dominant microbiota cluster exhibited the lowest incidence of respiratory disease, and Proteobacteria-dominant profiles exhibited the highest incidence of respiratory disease (Duvallet et al., 2017)
Discussion: There were no significant variations in the observed OTU richness from 16s rRNA gene sequencing data among the infant fecal microbiota (IFM) of the categorized disease groups (P>0.05, Figure 2A). However, in the Shannon diversity analysis, the greater diversity, including better evenness, was observed between the category of disease conditions and the IFM (95% CI, 2.6 – 4.4; P=0.008, Figure 2B). The H value was 9.60, which indicated varying microbial diversity among the samples in the cohort of infants. This finding may be due to a constant level of microbiota immigration and elimination through host mucosal clearance in respiratory diseases such as bronchiolitis (O’Dwyer et al., 2016)
“The results from the taxonomy and ANCOM tests indicated that at the genus level, Escherichia and Klebsiella belonging to Proteobacteria differed significantly in its abundance levels among the disease groups”
“A remarkable difference was seen within the infants fecal microbiota among the disease categorized groups”
Figure 4 Pathobiome Profile Fig. 4 A stacked bar diagram representing the pathobiome of infants fecal microbiome with disease conditions. Pathogens with more than 1% were picked, and the percentage of pathogen cluster was calculated from the OTUs of total microbiota. Discussion: The OTU counts of Escherichia coli found abundant across all the groups. Next, to E. coli, Klebsiella pneumonia was the most abundant pathogen in RD group infants with a maximum of 33.06% and Enterococcus faecium with a maximum of 32.34% abundancy of OTUs in the GD group among the pathobiome. No common candidate pathogen associated with OD group was found. The mean relative abundance of E. coli (90%) OTUs was present in most of the OD subjects pathobiome.
“The pathogen cluster identified through the infants fecal microbiota highlights the common causative pathogens in disease conditions”
Figure 6 – Beta Diversity Fig. 6 Two-dimension PCoA plots of beta diversity based on the weighted UniFrac distance matrix of the 34 IFM categorized by (A) disease conditions (GD – Gastrointestinal Disease, RD -Respiratory Disease and OD – Other Diseases); (B) antibiotic intake and (C) vitamin A supplementation. Each dot represents a sample point. Box plot next to the PCoA plot given to show the significance. Discussion: In the case of weighted UniFrac distance matrix analysis at 95% CI, the significant difference was observed among the IFM in regards to antibiotic therapy (95% CI, 0.20 – 0.75; P = .007), vitamin A intake (95% CI, 0.10 – 0.80; P < .033), and disease conditions (95% CI, 0.10 – 0.79; P = .006)
“Vitamin A supplementation, antibiotic intake and disease condition based classifications have a diverse microbial community between the groups ”
Conclusion Acknowledgements 01 02 03
Supported by the Institute for Food, Nutrition and Well-being and Genomics Research Institute, University of Pretoria, South Africa Srini would like to thank the Vice-Chancellor's Postdoctoral Fellowship Programme, University of Pretoria, South Africa The authors would like to thank Dr. Surendra Vikram for helping in metagenome analysis