Diet-induced obesity reduces bone marrow T and B cells and promotes tumor progression in a transplantable Vk*MYC model of multiple myeloma.

Obesity is associated with an increased risk of developing multiple myeloma (MM). The molecular mechanisms causing this association is complex and incompletely understood. Whether obesity affects bone marrow immune cell composition in multiple myeloma is not characterized. Here, we examined the effect of diet-induced obesity on bone marrow immune cell composition and tumor growth in a Vk*MYC (Vk12653) transplant model of multiple myeloma. We find that diet-induced obesity promoted tumor growth in the bone marrow and spleen and reduced the relative number of T and B cells in the bone marrow. Our results suggest that obesity may reduce MM immune surveillance and thus may contribute to increased risk of developing MM.

IL-32 is induced by activation of toll-like receptors in multiple myeloma cells.

Multiple myeloma (MM) is a hematological cancer characterized by accumulation of malignant plasma cells in the bone marrow. The patients are immune suppressed and suffer from recurrent and chronic infections. Interleukin-32 is a non-conventional, pro-inflammatory cytokine expressed in a subgroup of MM patients with a poor prognosis. IL-32 has also been shown to promote proliferation and survival of the cancer cells. Here we show that activation of toll-like receptors (TLRs) promotes expression of IL-32 in MM cells through NFκB activation. In patient-derived primary MM cells, IL-32 expression is positively associated with expression of TLRs. Furthermore, we found that several TLR genes are upregulated from diagnosis to relapse in individual patients, predominantly TLRs sensing bacterial components. Interestingly, upregulation of these TLRs coincides with an increase in IL-32. Taken together, these results support a role for IL-32 in microbial sensing in MM cells and suggest that infections can induce expression of this pro-tumorigenic cytokine in MM patients.

Comprehensive small RNA-sequencing of primary myeloma cells identifies miR-105-5p as a predictor of patient survival.

BACKGROUND: Small RNAs (sRNAs), a heterogenous group of non-coding RNAs, are emerging as promising molecules for cancer patient risk stratification and as players in tumour pathogenesis. Here, we have studied microRNAs (miRNAs) and other sRNAs in relation to survival and disease severity in multiple myeloma. METHODS: We comprehensively characterised sRNA expression in multiple myeloma patients by performing sRNA-sequencing on myeloma cells isolated from bone marrow aspirates of 86 myeloma patients. The sRNA expression profiles were correlated with the patients' clinical data to investigate associations with survival and disease subgroups, by using cox proportional hazards (coxph) -models and limma-voom, respectively. A publicly available sRNA dataset was used as external validation (n = 151). RESULTS: We show that multiple miRNAs are differentially expressed between ISS Stage I and III. Interestingly, we observed the downregulation of seven different U2 spliceosomal RNAs, a type of small nuclear RNAs in severe disease stages. Further, by a discovery-based approach, we identified miRNA miR-105-5p as a predictor of poor overall survival (OS) in multiple myeloma. Multivariate analysis showed that miR-105-5p predict OS independently of established disease markers. CONCLUSIONS: Overexpression of miR-105-5p in myeloma cells correlates with reduced OS, potentially improving prognostic risk stratification in multiple myeloma.

Automated Quantification of Human Osteoclasts Using Object Detection.

A balanced skeletal remodeling process is paramount to staying healthy. The remodeling process can be studied by analyzing osteoclasts differentiated in vitro from mononuclear cells isolated from peripheral blood or from buffy coats. Osteoclasts are highly specialized, multinucleated cells that break down bone tissue. Identifying and correctly quantifying osteoclasts in culture are usually done by trained personnel using light microscopy, which is time-consuming and susceptible to operator biases. Using machine learning with 307 different well images from seven human PBMC donors containing a total of 94,974 marked osteoclasts, we present an efficient and reliable method to quantify human osteoclasts from microscopic images. An open-source, deep learning-based object detection framework called Darknet (YOLOv4) was used to train and test several models to analyze the applicability and generalizability of the proposed method. The trained model achieved a mean average precision of 85.26% with a correlation coefficient of 0.99 with human annotators on an independent test set and counted on average 2.1% more osteoclasts per culture than the humans. Additionally, the trained models agreed more than two independent human annotators, supporting a more reliable and less biased approach to quantifying osteoclasts while saving time and resources. We invite interested researchers to test their datasets on our models to further strengthen and validate the results.

Paired miRNA- and messenger RNA-sequencing identifies novel miRNA-mRNA interactions in multiple myeloma.

Multiple myeloma (MM) is an incurable cancer of terminally differentiated plasma cells that proliferate in the bone marrow. miRNAs are promising biomarkers for risk stratification in MM and several miRNAs are shown to have a function in disease pathogenesis. However, to date, surprisingly few miRNA-mRNA interactions have been described for and functionally validated in MM. In this study, we performed miRNA-seq and mRNA-seq on CD138 + cells isolated from bone marrow aspirates of 86 MM patients to identify novel interactions between sRNAs and mRNAs. We detected 9.8% significantly correlated miRNA-mRNA pairs of which 5.17% were positively correlated and 4.65% were negatively correlated. We found that miRNA-mRNA pairs that were predicted by in silico target-prediction algorithms were more negatively correlated than non-target pairs, indicating functional miRNA targeting and that correlation between miRNAs and mRNAs from patients can be used to identify miRNA-targets. mRNAs for negatively correlated miRNA-mRNA target pairs were associated with gene ontology terms such as autophagy, protein degradation and endoplasmic stress response, reflecting important processes in MM. Targets for two specific miRNAs, miR-125b-5p and miR-365b-3p, were functionally validated in MM cell line transfection experiments followed by RNA-sequencing and qPCR. In summary, we identified functional miRNA-mRNA target pairs by correlating miRNA and mRNA data from primary MM cells. We identified several target pairs that are of potential interest for further studies. The data presented here may serve as a hypothesis-generating knowledge base for other researchers in the miRNA/MM field. We also provide an interactive web application that can be used to exploit the miRNA-target interactions as well as clinical parameters associated to these target-pairs.

Monoclonal immunoglobulins promote bone loss in multiple myeloma.

Most patients with multiple myeloma develop a severe osteolytic bone disease. The myeloma cells secrete immunoglobulins, and the presence of monoclonal immunoglobulins in the patient's sera is an important diagnostic criterion. Here, we show that immunoglobulins isolated from myeloma patients with bone disease promote osteoclast differentiation when added to human preosteoclasts in vitro, whereas immunoglobulins from patients without bone disease do not. This effect was primarily mediated by immune complexes or aggregates. The function and aggregation behavior of immunoglobulins are partly determined by differential glycosylation of the immunoglobulin-Fc part. Glycosylation analyses revealed that patients with bone disease had significantly less galactose on immunoglobulin G (IgG) compared with patients without bone disease and also less sialic acid on IgG compared with healthy persons. Importantly, we also observed a significant reduction of IgG sialylation in serum of patients upon onset of bone disease. In the 5TGM1 mouse myeloma model, we found decreased numbers of lesions and decreased CTX-1 levels, a marker for osteoclast activity, in mice treated with a sialic acid precursor, N-acetylmannosamine (ManNAc). ManNAc treatment increased IgG-Fc sialylation in the mice. Our data support that deglycosylated immunoglobulins promote bone loss in multiple myeloma and that altering IgG glycosylation may be a therapeutic strategy to reduce bone loss.