Osteoclastic bone resorption and osteoblastic bone formation/replenishment are closely coupled in bone metabolism. Anabolic parathyroid hormone (PTH), which is commonly used for treating ...osteoporosis, shifts the balance from osteoclastic to osteoblastic, although it is unclear how these cells are coordinately regulated by PTH. Here, we identify a serine protease inhibitor, secretory leukocyte protease inhibitor (SLPI), as a critical mediator that is involved in the PTH-mediated shift to the osteoblastic phase. Slpi is highly upregulated in osteoblasts by PTH, while genetic ablation of Slpi severely impairs PTH-induced bone formation. Slpi induction in osteoblasts enhances its differentiation, and increases osteoblast-osteoclast contact, thereby suppressing osteoclastic function. Intravital bone imaging reveals that the PTH-mediated association between osteoblasts and osteoclasts is disrupted in the absence of SLPI. Collectively, these results demonstrate that SLPI regulates the communication between osteoblasts and osteoclasts to promote PTH-induced bone anabolism.
Bone homeostasis is regulated by communication between bone-forming mature osteoblasts (mOBs) and bone-resorptive mature osteoclasts (mOCs). However, the spatial-temporal relationship and mode of ...interaction in vivo remain elusive. Here we show, by using an intravital imaging technique, that mOB and mOC functions are regulated via direct cell-cell contact between these cell types. The mOBs and mOCs mainly occupy discrete territories in the steady state, although direct cell-cell contact is detected in spatiotemporally limited areas. In addition, a pH-sensing fluorescence probe reveals that mOCs secrete protons for bone resorption when they are not in contact with mOBs, whereas mOCs contacting mOBs are non-resorptive, suggesting that mOBs can inhibit bone resorption by direct contact. Intermittent administration of parathyroid hormone causes bone anabolic effects, which lead to a mixed distribution of mOBs and mOCs, and increase cell-cell contact. This study reveals spatiotemporal intercellular interactions between mOBs and mOCs affecting bone homeostasis in vivo.
Bone morphogenetic protein (BMP)-2 plays a central role in bone-tissue engineering because of its potent bone-induction ability. However, the process of BMP-induced bone formation in vivo remains ...poorly elucidated. Here, we aimed to establish a method for intravital imaging of the entire process of BMP-2-induced ectopic bone formation. Using multicolor intravital imaging in transgenic mice, we visualized the spatiotemporal process of bone induction, including appearance and motility of osteoblasts and osteoclasts, angiogenesis, collagen-fiber formation, and bone-mineral deposition. Furthermore, we investigated how PTH1-34 affects BMP-2-induced bone formation, which revealed that PTH1-34 administration accelerated differentiation and increased the motility of osteoblasts, whereas it decreased morphological changes in osteoclasts. This is the first report on visualization of the entire process of BMP-2-induced bone formation using intravital imaging techniques, which, we believe, will contribute to our understanding of ectopic bone formation and provide new parameters for evaluating bone-forming activity.
Epithelial-mesenchymal transition (EMT) has been recognized as playing a crucial role in cancer progression. Among the studies on EMT, biomarker detection has been one of the important topics to ...understand the biology and mechanism of EMT related to tumor progression and treatment resistance. The existing methods often identified differentially-expressed genes as potential markers by ranking all genes by their variances. This paper proposes a novel method to detect markers for respective lineages in the EMT process.
Our method consists of three steps: first, perform trajectory inference to identify the lineage of transitional processes in EMT progression, and secondly, identify the lineage for EMT reversion in addition to EMT progression, and thirdly detect biomarkers for both of the EMT progression and reversion lineages with differential expression analysis. Furthermore, to elucidate the heterogeneity of the EMT process, we performed a clustering analysis of the cells in the EMT progression and reversion conditions. We then explored branching trajectories that order clusters using time information of the time-course samples. Using this method, we successfully detected two potential biomarkers related to EMT, phospholipid phosphatase 4 (PLPP4) and lymphotoxin-beta (LTB), which have not been detected by the existing method.
In this study, we propose a method for the detection of biomarkers of EMT based on trajectory inference with single-cell RNA-seq data. The performance of the method is demonstrated by the detection of potential biomarkers related to EMT.
Horizontal gene transfer is one of the main mechanisms contributing to microbial genome diversification. To clarify the overall picture of interspecific gene flow among prokaryotes, we developed a ...new method for detecting horizontally transferred genes and their possible donors by Bayesian inference with training models for nucleotide composition. Our method gives the average posterior probability (horizontal transfer index) for each gene sequence, with a low horizontal transfer index indicating recent horizontal transfer. We found that 14% of open reading frames in 116 prokaryotic complete genomes were subjected to recent horizontal transfer. Based on this data set, we quantitatively determined that the biological functions of horizontally transferred genes, except mobile element genes, are biased to three categories: cell surface, DNA binding and pathogenicity-related functions. Thus, the transferability of genes seems to depend heavily on their functions.
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Dostopno za:
DOBA, IJS, IZUM, KILJ, NUK, PILJ, PNG, SAZU, UILJ, UKNU, UL, UM, UPUK
Fusion genes are important targets and biomarkers for cancer therapy. Methods of accurately detecting fusion genes are needed in clinical practice. RNA-seq is widely used to detect active fusion ...genes. Long-read RNA-seq can sequence the full length of mRNA, and long-read RNA-seq is expected to detect fusion genes that cannot be detected by short-read RNA-seq. However, long-read RNA-seq has high basecalling error rates, and gap sequences may occur near the breakpoints of long reads that are not aligned to the genome. When gap sequences occur, it is impossible to identify the correct fusion gene or breakpoint using existing methods. To address these challenges in fusion gene detection, we introduce a novel algorithm, FUGAREC (fusion detection with gap re-alignment and breakpoint clustering). FUGAREC uniquely combines gap sequence re-alignment with breakpoint clustering. This approach not only enhances the detection of previously undetectable fusion genes but also significantly reduces false positives. We demonstrate that FUGAREC has high fusion gene detection performance on both simulated data and sequenced data of a breast cancer cell line.
Recent epigenetics research has demonstrated that chromatin conformation plays an important role in various aspects of gene regulation. Chromosome Conformation Capture (3C) technology makes it ...possible to analyze the spatial organization of chromatin in a cell. Several algorithms for three-dimensional reconstruction of chromatin structure from 3C experimental data have been proposed. Compared to other algorithms, ShRec3D, one of the most advanced algorithms, can reconstruct a chromatin model in the shortest time for high-resolution whole-genome experimental data. However, ShRec3D employs a graph shortest path algorithm, which introduces errors in the resulting model. We propose an improved algorithm that optimizes shortest path distances using a genetic algorithm approach. The proposed algorithm and ShRec3D were compared using in silico 3C experimental data. Compared to ShRec3D, the proposed algorithm demonstrated significant improvement relative to the similarity between the algorithm's output and the original model with a reasonable increase to calculation time.
Single-cell RNA-sequencing is a rapidly evolving technology that enables us to understand biological processes at unprecedented resolution. Single-cell expression analysis requires a complex data ...processing pipeline, and the pipeline is divided into two main parts: The quantification part, which converts the sequence information into gene-cell matrix data; the analysis part, which analyzes the matrix data using statistics and/or machine learning techniques. In the analysis part, unsupervised cell clustering plays an important role in identifying cell types and discovering cell diversity and subpopulations. Identified cell clusters are also used for subsequent analysis, such as finding differentially expressed genes and inferring cell trajectories. However, single-cell clustering using gene expression profiles shows different results depending on the quantification methods. Clustering results are greatly affected by the quantification method used in the upstream process. In other words, even if the original RNA-sequence data is the same, gene expression profiles processed by different quantification methods will produce different clusters. In this article, we propose a robust and highly accurate clustering method based on joint non-negative matrix factorization (joint-NMF) by utilizing the information from multiple gene expression profiles quantified using different methods from the same RNA-sequence data. Our joint-NMF can extract common factors among multiple gene expression profiles by applying each NMF under the constraint that one of the factorized matrices is shared among multiple NMFs. The joint-NMF determines more robust and accurate cell clustering results by leveraging multiple quantification methods compared to conventional clustering methods, which use only a single gene expression profile. Additionally, we showed the usefulness of discovering marker genes with the extracted features using our method.
This study explores the comparative accuracy of two state-of-the-art algorithms, YOLOv3 and faster region-based convolutional neural network (R-CNN), in detecting endoscopy artifacts using the ...EAD2019 dataset. YOLOv3, primarily used for real-time tasks, and Faster R-CNN, which employs a two-step object detection process, exhibit variable performance based on the object characteristics. The analysis performed in this study focuses on identifying the objects or classes where each algorithm performs better. We conduct experiments to support our findings. We introduce a novel metric that quantifies the difference in average pixel intensities inside and outside the bounding boxes of detected objects. This metric forms the basis of a proposed ensemble method, allowing the method to effectively utilize either YOLOv3 or Faster R-CNN, depending on the characteristics of each class. The proposed method demonstrates an improved average precision score compared to using either algorithm separately. This research provides valuable insights into object detection in endoscopy, potentially enhancing artifact detection accuracy in medical imaging.
Comprehensively understanding the dynamics of biological systems is among the biggest current challenges in biology and medicine. To acquire this understanding, researchers have measured the ...time-series expression profiles of cell lines of various organisms. Biological technologies have also drastically improved, providing a huge amount of information with support from bioinformatics and systems biology. However, the transitions between the activation and inactivation of gene regulations, at the temporal resolution of single time points, are difficult to extract from time-course gene expression profiles.
Our proposed method reports the activation period of each gene regulation from gene expression profiles and a gene regulatory network. The correctness and effectiveness of the method were validated by analyzing the diauxic shift from glucose to lactose in Escherichia coli. The method completely detected the three periods of the shift; 1) consumption of glucose as nutrient source, 2) the period of seeking another nutrient source and 3) consumption of lactose as nutrient source. We then applied the method to mouse adipocyte differentiation data. Cell differentiation into adipocytes is known to involve two waves of the gene regulation cascade, and sub-waves are predicted. From the gene expression profiles of the cell differentiation process from ES to adipose cells (62 time points), our method acquired four periods; three periods covering the two known waves of the cascade, and a final period of gene regulations when the differentiation to adipocytes was completed.
Our proposed method identifies the transitions of gene regulations from time-series gene expression profiles. Dynamic analyses are essential for deep understanding of biological systems and for identifying the causes of the onset of diseases such as diabetes and osteoporosis. The proposed method can greatly contribute to the progress of biology and medicine.
Celotno besedilo
Dostopno za:
DOBA, IZUM, KILJ, NUK, PILJ, PNG, SAZU, SIK, UILJ, UKNU, UL, UM, UPUK
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