Summary Interleukin-32 (IL-32) is a nonclassical cytokine expressed in cancers, inflammatory diseases, and infections. Its expression is regulated by two different oxygen sensing systems; HIF1α and cysteamine dioxygenase (ADO), indicating that IL-32 may be involved in the response to hypoxia. We here demonstrate that endogenously expressed, intracellular IL-32 interacts with components of the mitochondrial respiratory chain and promotes oxidative phosphorylation. Knocking out IL-32 in three myeloma cell lines reduced cell survival and proliferation in vitro and in vivo . High-throughput transcriptomic and MS-metabolomic profiling of IL-32 KO cells revealed that cells depleted of IL-32 had perturbations in metabolic pathways, with accumulation of lipids, pyruvate precursors, and citrate. IL-32 was expressed in a subgroup of myeloma patients with inferior survival, and primary myeloma cells expressing IL-32 had a gene signature associated with immaturity, proliferation, and oxidative phosphorylation. In conclusion, we demonstrate a previously unrecognized role of IL-32 in the regulation of plasma cell metabolism.

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 criterium. Here, we demonstrate that immunoglobulins isolated from myeloma patients with bone disease promote osteoclast differentiation when added to human pre-osteoclasts 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 Ig-Fc part. Glycosylation analyses revealed that patients with bone disease had significantly less galactose on 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 number of lesions and decreased CTX-1 levels, a marker for osteoclast activity, in mice treated with the sialic acid precursor, N-acetylmannosamine (ManNAc). ManNAc treatment increased IgG-Fc sialylation in the mice. Our data support that de-glycosylated immunoglobulins promote bone loss in multiple myeloma and that altering IgG glycosylation may be a therapeutic strategy to reduce bone loss.

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.

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.

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.