P-496: The influence of the gut microbiome on the development of multiple myeloma.

Introduction: Recently, much attention has been paid to the analysis of the gut microbiome of patients with multiple myeloma (MM). Unfortunately, the role of the gut microbiome in the development of MM remains unclear.

Methods: The study analysed stool samples from 19 NDMM (Newly Diagnosed Multiple Myeloma) individuals and stool samples from 20 healthy volunteers.
16S ribosomal RNA (rRNA) gene sequencing was performed to profile microbial communities.In the first step, sets of amplicons of hypervariable regions of the 16S rRNA gene, V3-V4 and V7-V9 were prepared.These amplified fragments were purified on magnetic beads. Paired-end (2x276) sequencing of the 10 pM DNA library was performed on a MiSeq sequencer (Illumina) using MiSeq Reagent Kit v3 (600 cycles) (Illumina) yielding data in FASTQ format. Bioinformatics analysis of microbiomes was performed in QIIME2 2021.8. To measure alpha-diversity, four metrics were calculated: Shannon’s diversity index, Observed Features, Faith’s Phylogenetic Diversity and Pielou’s Evenness. The Kruskal-Wallis test was performed to test the group significance. For beta-diversity metrics that were calculated: Jaccard distance,Bray‐Curtis dissimilarity, unweighted UniFrac, and weighted UniFrac.The data matrices from these metrics were used to perform Principal Coordinate Analysis (PCoA).To test if there are significant differences between patients and healthy controls in the beta-diversity, the PERMANOVA test with 999 permutations was performed. A p-value < 0.05 was considered statistically significant.The ANCOM (Analysis of Composition of Microbiomes) tool was employed to determine which taxa varied in relative abundance.

Results: In the beta-diversity analysis, statistically significant differences were obtained in all 4 metrics: Jaccard distance (p= 0.001), Bray‐Curtis dissimilarity (p=0.001), unweighted UniFrac (p= 0.011), and weighted UniFrac (p=0.029).
In the alpha-diversity analysis, no statistical significance was obtained in the measured metrics.
In the ANCOM analysis, statistically significant differences in relative abundance between the analysed groups were found in bacteria belonging to the following families: Peptostreptococcaceae (p=0.016), Staphylococaceae (p=0.012),Lachnospiraceae (p < 0.016). In addition, differences in relative abundance were found in bacteria belonging to the genus: Lachnospiraceae UCG-008 (p< < 0.001), Erysipelotrichaceae UCG-003 (p=0.002), Anaerostips (p< < 0.001).
The relative abundance of these bacteria was significantly lower in the study group compared to the control group.The mentioned families and groups of bacteria participate, among others, in the metabolism of short-chain fatty acids (SCFAs) and thus affect the host's immune system.

Conclusions: In the microbiome of NDMM and healthy people, there are statistically significant differences in relative abundance between bacteria involved in the metabolism of SCFAs. The relative abundance of bacteria involved in SCFAs metabolism was significantly lower in the NDMM group.