Calreticulin Deficiency Disturbs Ribosome Biogenesis and Results in Retardation in Embryonic Kidney Development

Establishing protein expression profiles involved in tooth development using a proteomic approach

Various growth and transcription factors are involved in tooth development and developmental abnormalities; however, the protein dynamics do not always match the mRNA expression level. Using a proteomic approach, this study comprehensively analyzed protein expression in epithelial and mesenchymal tissues of the tooth germ during development. First molar tooth germs from embryonic day 14 and 16 Crlj:CD1 (ICR) mouse embryos were collected and separated into epithelial and mesenchymal tissues by laser microdissection. Mass spectrometry of the resulting proteins was carried out, and three types of highly expressed proteins [ATP synthase subunit beta (ATP5B), receptor of activated protein C kinase 1 (RACK1), and calreticulin (CALR)] were selected for immunohistochemical analysis. The expression profiles of these proteins were subsequently evaluated during all stages of amelogenesis using the continuously growing incisors of 3-week-old male ICR mice. Interestingly, these three proteins were specifically expressed depending on the stage of amelogenesis. RACK1 was highly expressed in dental epithelial and mesenchymal tissues during the proliferation and differentiation stages of odontogenesis, except for the pigmentation stage, whereas ATP5B and CALR immunoreactivity was weak in the enamel organ during the early stages, but became intense during the maturation and pigmentation stages, although the timing of the increased protein expression was different between the two. Overall, RACK1 plays an important role in maintaining the cell proliferation and differentiation in the apical end of incisors. In contrast, ATP5B and CALR are involved in the transport of minerals and the removal of organic materials as well as matrix deposition for CALR.

Network Embedding Across Multiple Tissues and Data Modalities Elucidates the Context of Host Factors Important for COVID-19 Infection

COVID-19 is a heterogeneous disease caused by SARS-CoV-2. Aside from infections of the lungs, the disease can spread throughout the body and damage many other tissues, leading to multiorgan failure in severe cases. The highly variable symptom severity is influenced by genetic predispositions and preexisting diseases which have not been investigated in a large-scale multimodal manner. We present a holistic analysis framework, setting previously reported COVID-19 genes in context with prepandemic data, such as gene expression patterns across multiple tissues, polygenetic predispositions, and patient diseases, which are putative comorbidities of COVID-19. First, we generate a multimodal network using the prior-based network inference method KiMONo. We then embed the network to generate a meaningful lower-dimensional representation of the data. The input data are obtained via the Genotype-Tissue Expression project (GTEx), containing expression data from a range of tissues with genomic and phenotypic information of over 900 patients and 50 tissues. The generated network consists of nodes, that is, genes and polygenic risk scores (PRS) for several diseases/phenotypes, as well as for COVID-19 severity and hospitalization, and links between them if they are statistically associated in a regularized linear model by feature selection. Applying network embedding on the generated multimodal network allows us to perform efficient network analysis by identifying nodes close by in a lower-dimensional space that correspond to entities which are statistically linked. By determining the similarity between COVID-19 genes and other nodes through embedding, we identify disease associations to tissues, like the brain and gut. We also find strong associations between COVID-19 genes and various diseases such as ischemic heart disease, cerebrovascular disease, and hypertension. Moreover, we find evidence linking PTPN6 to a range of comorbidities along with the genetic predisposition of COVID-19, suggesting that this kinase is a central player in severe cases of COVID-19. In conclusion, our holistic network inference coupled with network embedding of multimodal data enables the contextualization of COVID-19-associated genes with respect to tissues, disease states, and genetic risk factors. Such contextualization can be exploited to further elucidate the biological importance of known and novel genes for severity of the disease in patients.

Calreticulin Shortage Results in Disturbance of Calcium Storage, Mitochondrial Disease, and Kidney Injury

Renal Ca²⁺ reabsorption plays a central role in the fine-tuning of whole-body Ca²⁺ homeostasis. Here, we identified calreticulin (Calr) as a missing link in Ca²⁺ handling in the kidney and showed that a shortage of Calr results in mitochondrial disease and kidney pathogenesis. We demonstrated that Calr^+/− mice displayed a chronic physiological low level of Calr and that this was associated with progressive renal injury manifested in glomerulosclerosis and tubulointerstitial damage. We found that Calr^+/− kidney cells suffer from a disturbance in functionally active calcium stores and decrease in Ca²⁺ storage capacity. Consequently, the kidney cells displayed an abnormal activation of Ca²⁺ signaling and NF-κB pathways, resulting in inflammation and wide progressive kidney injury. Interestingly, the disturbance in the Ca²⁺ homeostasis and signaling in Calr^+/− kidney mice cells triggered severe mitochondrial disease and aberrant mitophagy, resulting in a high level of oxidative stress and energy shortage. These findings provide novel mechanistic insight into the role of Calr in kidney calcium handling, function, and pathogenesis.