Osteoclasts are a particularly important type of cell involved in bone maintenance. These cells absorb old or damaged bone and digest it, allowing the body to reuse important materials like calcium and giving way to new bones. As one might expect, various bone diseases arise when osteoclasts do not fulfill their role properly. Scientists have been investigating the mechanisms that regulate the proliferation and differentiation of precursor cells into osteoclasts.

In a published in 2020, researchers from Tokyo University of Science (TUS) led by Professor Tadayoshi Hayata revealed that the cytoplasmic polyadenylation element-binding protein 4 (Cpeb4) is essential in osteoclast differentiation. (Differentiation is the process by which cells develop into particular cell types, such as osteoclasts.) The researchers also discovered that this protein, which regulates the stability and translation of messenger RNA (mRNA) molecules, transported into specific structures within the nucleus of the cell when osteoclast differentiation was induced. However, just how this relocation occurs and what Cpeb4 exactly does within these nuclear structures still remains a mystery.

Now, in a recent published in the Journal of Cellular Physiology on January 29, 2024, Hayata and Yasuhiro Arasaki, also from from TUS, tackled these knowledge gaps. Interested in the intricate and complex process of osteoclast differentiation, they sought to more thoroughly understand how the “life cycle” of mRNA, i.e., mRNA metabolism, is involved.

How was the study conducted?

First, the researchers introduced strategic modifications into Cpeb4 proteins and performed a series of experiments in cell cultures. They found that the localization of Cpbe4 in the abovementioned nuclear bodies occurred owing to its ability to bind to RNA molecules.

Afterwards, seeking to understand the role of Cpeb4 in the nucleus, the researchers demonstrated that Cpeb4 co-localized with certain mRNA splicing factors. These proteins are involved in the process of mRNA splicing, which is a key step in mRNA metabolism. Put simply, it enables a cell to produce diverse mature mRNA molecules (and eventually proteins) from a single gene. 

Through RNA sequencing and gene analysis in Cpeb4-depleted cells, the researchers found that Cpeb4 alters the expression of multiple genes associated with splicing events in freshly differentiated osteoclasts.

Finally, through further experiments, they concluded that Cpeb4 only altered the splicing patterns of Id2 mRNA, an important protein known to regulate osteoclast differentiation and development.

What is the significance of these findings?

Overall, this study sheds important light on the mechanisms that regulate osteoclast differentiation. “Through this research, we were able to identify important factors involved in regulating mRNA splicing during the osteoclast differentiation process and obtained new knowledge regarding the control of mRNA splicing during osteoclast differentiation,” Hayata commented.

While the contribution of Cpeb4 is smaller than that of RANKL, a signaling factor that induces osteoclast differentiation, targeting Cpeb4 may have the advantage of reducing the side effects of existing drugs as excessive inhibition of osteoclast differentiation with RANKL inhibitory antibodies would halt bone remodeling.

Importantly, the results contribute to a more detailed understanding of how bones are maintained. “Although we used cultured mouse cells in our study, there are also research reports that show a correlation between variations in the Cpeb4 gene and bone density in humans,” Hayata said. “We hope that our findings will help clarify the relationship between these two in the near future.”

Most importantly, the findings of the present study may prove to be a crucial stepping stone for advancing diagnostic techniques and treatments for bone and joint diseases. Use of genome-wide association study has evidenced a correlation between single nucleotide polymorphisms in introns of the Cpeb4 gene region and the estimated bone density. Therefore, it is possible that Cpeb4 expression and activity can be used as diagnostic criteria.

However, the researchers note that it is unclear whether Cpeb4 actually regulates bone metabolism in vivo. Therefore, clarification of the molecular basis of Cpeb4 in bone metabolism in mice would help to establish a therapeutic approach. Additionally, recent studies have reported that Cpeb4 is expressed in various cancer cells and contributes to cancer cell survival. In cancer, Cpeb4 contributes to mRNA stability, although splicing regulation may exist.

“The discovery of part of the mechanisms by which Cpeb4 controls osteoclast differentiation could lead to the elucidation of pathologies, including osteoporosis and rheumatoid arthritis, and ultimately become the foundation for the development of new therapeutic drugs,” a hopeful Hayata concluded.

We too hope these efforts will pave the way for a brighter future for the millions of people suffering from osteoporosis and similar disorders, enabling them to live more active and fulfilling lives. 

For more information, you can read the original paper .

The views expressed in this article are the author’s own and do not necessarily reflect 51�Թ�’s editorial policy.

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To comply with these laws, experienced fishermen have learned how to distinguish males from females through visual inspection. While it is relatively straightforward to distinguish them by looking at the underside of the crabs, doing so by looking at their shell side is much more challenging. Unfortunately, when captured crabs settle on board a ship, they almost always do so with their shell side pointing up, and picking them up and flipping them individually to determine their sex is time-consuming.

Could this be yet another task artificial intelligence (AI) may excel at?

How AI can sort crabs by sex

In a recent study, a research team from Japan, including Professor Shin-ichi Satake from Tokyo University of Science (TUS), Japan, sought to answer this question using deep learning. Their latest , published in Scientific Reports, is co-authored by Associate Professor Yoshitaka Ueki and Professor Ken Takeuchi from TUS and Assistant Professor Kenji Toyota and Professor Tsuyoshi Ohira from Kanagawa University.

The researchers implemented three deep convolutional neural networks based on three well-established image classification algorithms: AlexNet, VGG-16 and ResNet-50. To train and test these models, they used 120 images of horsehair crabs captured in Hokkaido. Half of them were males, and the other half were females.

A notable advantage of these models is that they are “explainable AI.” Simply put, this means that the model does not operate as a black box. Given an image of a crab, one can see which specific regions of the image were relevant for the algorithm in making its classification decision. This can reveal subtle differences between the males and females that could be useful for manual classification.

The test results were quite promising in terms of accuracy and performance metrics, as Prof. Satake highlights: “Even though gender classification was virtually impossible by human visual inspection on the shell side, the proposed deep learning models enabled male and female classification with high precision, achieving an F-1 measure of approximately 95% and similarly high accuracy values.” This means that the AI approach vastly outperformed humans and provided consistent, reliable classification. 

The model reveals how human crabbers identify males and females

Interestingly, when observing the heatmaps, which represented the regions the models focused on for classification, the team found significant differences between the sexes. For one, the heatmap was enhanced near the genitalia shape on the abdomen side. When classifying males, the algorithms focused on the lower part of the carapace. In contrast, when classifying females, the algorithms focused on the upper portion of the carapace. This could provide useful information not only for the development of future AI sex classification models for crabs but also shed light on how experienced fishermen can tell males from females even when looking at their shell side.

Considering that being captured can be a great source of stress for crabs, being able to quickly tell females apart without flipping them before release could help prevent health or reproductive problems for these crabs. Thus, deep learning could potentially be an important tool for enhancing conservation and farming efforts. “The fact that deep learning can discriminate male and female crabs is an important finding not only for the conservation of these important marine resources but also for the development of efficient aquaculture techniques,” remarks Prof. Satake.

Notably, implementing AI classification techniques directly on ships could reduce the amount of manual work and make crab fishing more cost-effective. Moreover, the proposed models could be retrained and repurposed for the gender classification of other species of crabs, such as the blue crab or the Dungeness crab. 

Overall, this study showcases how AI can be leveraged in creative ways to not only make people’s work more efficient but also have a direct positive effect on conservation, responsible fishing, and sustainability of crab aquaculture.

For more information, you can read the original paper .

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