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Microbes & Immunity Carotene and immunity to COVID-19 vaccine

5’-CCTAYGGGRBGCASCAG and reverse primer Agilent Technologies, USA), and a 7693A automatic liquid
5’-GGACTACNNGGGTATCTAAT. The DNA samples sampler (G4513A, Agilent Technologies, USA). Hydroguard
were sent to an accredited laboratory (NovogeneAIT FS water-resistant guard column (5 m length, 0.25 mm ID,
Genomics, Singapore) for library preparation, sequencing, 10079, Restek, USA) was used in combination with a high
and bioinformatics analysis. polarity, DB-FATWAX Ultra Inert PEG column (30 m length,
0.25 mm ID, 0.25 film thickness, G3903-63008, Agilent
2.10.2. Bioinformatics analysis pipeline
Technologies, USA). Data were analyzed using the Enhanced
The raw FASTQ files from sequencing were analyzed using ChemStation software (version E.02.02.1431, Agilent, USA).
the bioinformatics methods outlined in Table 2.
2.12. Statistical analysis
2.11. Determination of SCFA levels in fecal samples All statistical analyses were performed using Prism 10.3.1
The levels of SCFAs in fecal samples were identified and (GraphPad, USA). Comparisons between two groups
determined using gas chromatography-mass spectrometry were evaluated with a two-tailed unpaired t-test with
(GC-MS) (Agilent 7890A Gas Chromatograph, Agilent, Welch’s correction, while multiple group comparisons were
USA). The water-methanol (80:20, v/v, pH 1.5 – 2.5) diluent, conducted using analysis of variance (ANOVA) with Tukey
standard stock, and internal standards were prepared post hoc test. A p<0.05 was considered statistically significant.
according to Gray et al. with slight modifications. Stock
24
solutions of acetic acid (30.0 µL), propionic acid (30.3 µL), 3. Results
isobutyric acid (39.0 µL), and butyric acid (32.7 µL) This section outlines the results of the study.
were prepared in acidified diluent to achieve the final
concentrations of 52.60 mM, 39.43 mM, 42.05 mM, and 3.1. Modulation of immune responses by carotene
36.80 mM, respectively. The internal standard (4-methyl supplementation
valeric acid) was spiked into all calibration standards at a The effect of carotene supplementation under various
concentration of 7.9 mM. The calibration curves of all SCFA
standards were then established to allow quantification of conditions is outlined in the following subsections.
SCFA levels in fecal samples. 3.1.1. Effect of carotene supplementation on
The fecal samples were withdrawn from −80°C storage, lymphocyte subsets
thawed, and homogenized in 1,993 µL of the water-methanol The data from the flow cytometry analysis revealed no
diluent with 7 µL of phosphoric acid (85% w/w) and mixed significant differences in CD3 T lymphocytes, CD4 Th
+
+
thoroughly for 10 min using a vortex (Vortex-Genie 2, Scientific cells, CD8 CTLs, and B cells between the baseline groups
+
Industries, USA). The fecal suspensions were centrifuged and carotene supplementation groups (Figure 2 and
(12,000 rpm for 10 min), and 1,800 µL of the supernatant was Table A1).
transferred to 2 mL GC vials. The 46.7 µL of internal standard
(7.9 mM) was added to each vial before analysis. 3.1.2. Effect of carotene supplementation on
SARS-CoV-2-specific antibody production
SCFA s were quantified using an Agilent 8890 gas
chromatograph (G3542A, Agilent Technologies, USA) The plasma samples collected on days 42 and 70 of the
paired with a 5977C mass selective detector (G7077C, intervention were used to determine the SARS-CoV-2-specific

Table 2. Methods used for bioinformatics analysis
Data processing Description Methods/packages
Sequence assembly Merge paired-end reads BBMap
Data split Trimming primer sequences QIIME
Amplicon sequence variant (ASV) denoise Reconstruct ASVs from noisy amplicon sequencing reads DADA2 in QIIME
Taxonomy classification Classify pre-processed reads to the respective taxonomy QIIME using silva138 AB V3–V4 classifier
Generation of phyloseq object Export QIIME artifact into phyloseq QIIME2R
Heatmap Visualization of microbiome compositions pheatmap in R
Alpha diversity Estimation of alpha diversity Tidyverse in R
Beta diversity Estimation of beta diversity mia and miaViZ in R
Differential abundance analysis Identify differentially abundant microbes LEfSe, DESeq2, and corncob in R

Volume 2 Issue 3 (2025) 76 doi: 10.36922/MI025110021