Temporal dynamics and composition of ocular surface microbiota in C57BL/6J mice: uncovering a 12h ultradian rhythm

Abstract

Purpose: This study aimed to investigate the presence of rhythmic fluctuations in the composition, abundance, and functions of commensal core bacteria on the ocular surface of C57BL/6J mice.

Methods: Male C57BL/6J mice, aged 12 weeks, were subjected to a 12-hour light/12-hour dark cycle. Ocular surface tissue samples were collected at four time points (ZT) over a 24-hour period at six-hour intervals. The core ocular surface microbiota's oscillation cycles and frequencies were assessed using 16S rRNA gene sequencing targeting the V3-V4 region, along with the JTK_CYCLE algorithm. Functional predictions of these bacteria were conducted using PICRUSt2.

Results: Deep sequencing of the ocular surface microbiota highlighted the high abundance of commensal bacteria, with Proteobacteria, Actinobacteriota, and Firmicutes collectively constituting over 90% of the total sample abundance. Among the 22 core bacterial genera, 11 exhibited robust 12-hour rhythms, including Halomonas, Pelagibacterium, Pseudomonas, Nesterenkonia, norank_f_Hyphomonadaceae, Stenotrophomonas, Anoxybacillus, Acinetobacter, Zoogloea, Brevibacillus, and Ralstonia. Further taxonomic analysis indicated significant intra-cluster similarities and inter-cluster differences at the order, family, and genus levels during ZT0/12 and ZT6/18. Community interaction networks and functional prediction analyses revealed synchronized 12-hour rhythmic oscillations in neural, immune, metabolic, and other pathways associated with symbiotic bacteria.

Conclusion: This study demonstrates the presence of ultradian rhythmic oscillations in commensal bacteria on the ocular surface of normal C57BL/6J mice, with a 12-hour cycle. These findings suggest a crucial role for ultradian rhythms in maintaining ocular surface homeostasis in the host.

Keywords: 16S rRNA; mice; microbiota; ocular surface; rhythmic oscillations.

Figure 1

Experimental design and workflow. (A) Ocular surface tissues from mice were collected at 4 time points at 6-hour intervals during a light cycle; (B) 16S rRNA sequencing of microbiota in ocular surface tissues using the Illumina MiSeq platform; (C) Multiple bioinformatics analysis and JTK CYCLE rhythmicity identification were performed on the sequencing data.

Figure 2

Comparison of the ocular surface microbiota alpha diversity at four time points. (A) Schematic diagram of the microbiota sampling from the mouse ocular surface, periocular skin and oral mucosa. (B) Principal component analysis (PCA) score plots based on the OTU abundance of the three sites. (C) Refraction curves of the Sobs index based on the OTU level. (D) ANOSIM similarity analysis. The box “Between” refers to the differences between groups, and the others show the differences within their respective groups. R values represent the explanation of sample differences by grouping factors, and P values represent statistical differences. (E, F) Sobs index and Shannon index based on OTU level, which represent community abundance and diversity, respectively. Data are presented as mean ± SD, Student′s t-test, *P<0.05, **P < 0.01.

Figure 3

Composition of the ocular surface core flora at different taxonomic levels. Phylum (A-C), class (D-F), order (G-I) and family (J-L) -level community composition analysis, PCA (intergroup difference test: ANOSIM) and microbiota typing analysis on OS. In all samples, species with less than 1% abundance were classified as “others”.

Figure 4

Rhythmic oscillations of ocular surface genus-level bacteria. (A) The composition analysis of ocular surface core microbiota community. Species with less than 1% abundance in all samples were classified as “others”. (B) PCA analysis of mouse eye samples at 4 sampling time points. (C) ocular surface microflora typing analysis of bacterial genera. (D) Confidence line plot of 12-h rhythmic ocular surface bacterial genera based on Figure (A) Confidence: 0.9, JTK_CYCLE algorithm screening threshold: BH.Q < 0.05.

Figure 5

Ocular surface microbial interactions and rhythmic analysis of the KEGG pathway. (A) Microbial co-occurrence network analysis at the phylum level. Red lines show positive correlations and green lines indicate negative correlations. Each node represents a microorganism, and each edge indicates the interaction relationship between microorganisms. The area of the node represents the degree, and the degree is the number of nodes directly connected to this node. (B) PICRUSt2 was used to predict ocular surface microbial function at the third level of the KEGG pathway, followed by the JTK_CYCLE algorithm for rhythmic screening (BH.Q < 0.01).