Age-related Increase of CD38 Directs Osteoclastogenic Potential of Monocytic Myeloid-Derived Suppressor Cells through Mitochondrial Dysfunction
An aged immune system undergoes substantial changes where myelopoiesis dominates within the bone marrow. Monocytic-MDSCs (M-MDSCs) have been found to play an important role in osteoclastogenesis and bone resorption. In this study, we sought to provide a more comprehensive understanding of the osteoclastogenic potential of bone marrow M-MDSCs during normal aging through transcriptomic and metabolic changes. Using young mature and aged mice, detailed immunophenotypic analyses of myeloid cells revealed that the M-MDSCs were not increased in bone marrow, however M-MDSCS were significantly expanded in peripheral tissues. Although aged mice exhibited a similar number of M-MDSCs in bone marrow, these M-MDSCs had significantly higher osteoclastogenic potential and greater bone resorption capacity. Intriguingly, osteoclast progenitors from aged bone marrow M-MDSCs exhibited greater mitochondrial respiration rate and glucose metabolism. Further, transcriptomic analyses revealed the upregulation of mitochondrial oxidative phosphorylation and glucose metabolism genes. Interestingly, there was a 3-fold increase in Cd38 mRNA gene expression, consistent with the Mouse Aging Cell Atlas transcriptomic database. CD38 regulates NAD+ availability, and 78c, a small molecule inhibitor of CD38, reduced the mitochondrial oxygen consumption rate and glucose metabolism and inhibited the osteoclastogenic potential of aged mice bone marrow-derived M-MDSCs. These results indicate that the age-related increase in Cd38 expression in M-MDSCs bias the transcriptome of M-MDSCs towards osteoclastogenesis. This enhanced understanding of the mechanistic underpinnings of M-MDSCs and their osteoclastogenesis during aging could lead to new therapeutic approaches for age-related bone loss and promote healthy aging. Overall design: Total RNA was obtained from sorted CD11b+Ly6G-Ly6Chi M-MDSCs from young and old mice using RNeasy Mini Kit (Qiagen, USA). The whole-genome sequencing was performed using NovaSeq 6000 System (Illumina Inc, San Diego, CA). The FastQC and FastQ were used to review the sequencing quality and detect potential contamination, respectively. Bioconductor package DESeq2 version 1.32.0. was used to detect the differentially expressed genes.