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Simple Summary: Breast cancer tumors are considered to have intratumoral, as well as human epidermal growth factor receptor 2 (HER2), heterogeneity. Tumors with high intratumoral heterogeneity (ITH) have demonstrated therapeutic resistance. However, studies on cancer heterogeneity as a prognostic factor in breast cancer have been limited. Therefore, we evaluated HER2 ITH, which was manifested by the shape of HER2 fluorescence in situ hybridization amplification distributed histograms (HER2 FISH distributions) with the HER2 gene copy number within a tumor sample. We aimed to determine whether high HER2 heterogeneity is clinically significant for poor prognosis due to resistance to postoperative adjuvant therapy with HER2-targeted agents in primary breast cancer. Indeed, we were able to show herein that high HER2 heterogeneity is significantly associated with poorer prognosis in patients with HER2-positive breast cancer. These results indicated that high HER2 heterogeneity is a factor predicting poor prognosis in patients with HER2-positive breast cancer. Our present observation seemed to be clinically important, because it is expected that our HER2 FISH distribution analysis of heterogeneity might be a convenient and clinically useful method for the prognosis prediction of patients after HER2 adjuvant therapy. Purpose: Breast cancer tumors frequently have intratumoral heterogeneity (ITH). Tumors with high ITH cause therapeutic resistance and have human epidermal growth factor receptor 2 (HER2) heterogeneity in response to HER2-targeted therapies. This study aimed to investigate whether high HER2 heterogeneity levels were clinically related to a poor prognosis for HER2-targeted adjuvant therapy resistance in primary breast cancers. Methods: This study included patients with primary breast cancer (n = 251) treated with adjuvant HER2-targeted therapies. HER2 heterogeneity was manifested by the shape of HER2 fluorescence in situ hybridization amplification (FISH) distributed histograms with the HER2 gene copy number within a tumor sample. Each tumor was classified into a biphasic grade graph (high heterogeneity [HH]) group or a monophasic grade graph (low heterogeneity [LH]) group based on heterogeneity. Both groups were evaluated for disease-free survival (DFS) and overall survival (OS) for a median of ten years of annual follow-up. Results: Of 251 patients with HER2-positive breast cancer, 46 (18.3%) and 205 (81.7%) were classified into the HH and LH groups, respectively. The HH group had more distant metastases and a poorer prognosis than the LH group (DFS: p < 0.001 (HH:63% vs. LH:91% at 10 years) and for the OS: p = 0.012 (HH:78% vs. LH:95% at 10 years). Conclusions: High HER2 heterogeneity is a poor prognostic factor in patients with HER2-positive breast cancer. A novel approach to heterogeneity, which is manifested by the shape of HER2 FISH distributions, might be clinically useful in the prognosis prediction of patients after HER2 adjuvant therapy. [ABSTRACT FROM AUTHOR]

The increase in ecosystem biodiversity can be perceived as one of the universal processes converting energy into information across a wide range of living systems. This study delves into the dynamics of living systems, highlighting the distinction between ex post adaptation, typically associated with natural selection, and its proactive counterpart, ex ante adaptability. Through coalescence experiments using synthetic ecosystems, we (i) quantified ecosystem stability, (ii) identified correlations between some biodiversity indexes and the stability, (iii) proposed a mechanism for increasing biodiversity through moderate inter-ecosystem interactions, and (iv) inferred that the information carrier of ecosystems is species composition, or merged genomic information. Additionally, it was suggested that (v) changes in ecosystems are constrained to a low-dimensional state space, with three distinct alteration trajectories—fluctuations, rapid environmental responses, and long-term changes—converging into this state space in common. These findings suggest that daily fluctuations may predict broader ecosystem changes. Our experimental insights, coupled with an exploration of living systems' information dynamics from an ecosystem perspective, enhance our predictive capabilities for natural ecosystem behavior, providing a universal framework for understanding a broad spectrum of living systems. [ABSTRACT FROM AUTHOR]

Abstract Background Bayesian networks (BNs) have been widely used to estimate gene regulatory networks. Many BN methods have been developed to estimate networks from microarray data. However, two serious problems reduce the effectiveness of current BN methods. The first problem is that BN-based methods require huge computational time to estimate large-scale networks. The second is that the estimated network cannot have cyclic structures, even if the actual network has such structures. Results In this paper, we present a novel BN-based deterministic method with reduced computational time that allows cyclic structures. Our approach generates all the combinational triplets of genes, estimates networks of the triplets by BN, and unites the networks into a single network containing all genes. This method decreases the search space of predicting gene regulatory networks without degrading the solution accuracy compared with the greedy hill climbing (GHC) method. The order of computational time is the cube of number of genes. In addition, the network estimated by our method can include cyclic structures. Conclusions We verified the effectiveness of the proposed method for all known gene regulatory networks and their expression profiles. The results demonstrate that this approach can predict regulatory networks with reduced computational time without degrading the solution accuracy compared with the GHC method.

Abstract Background Both large deletions in genome and heat shock stress would lead to alterations in the gene expression profile; however, whether there is any potential linkage between these disturbances to the transcriptome have not been discovered. Here, the relationship between the genomic and environmental contributions to the transcriptome was analyzed by comparing the transcriptomes of the bacterium Escherichia coli (strain MG1655 and its extensive genomic deletion derivative, MDS42) grown in regular and transient heat shock conditions. Results The transcriptome analysis showed the following: (i) there was a reorganization of the transcriptome in accordance with preferred chromosomal periodicity upon genomic or heat shock perturbation; (ii) there was a considerable overlap between the perturbed regulatory networks and the categories enriched for differentially expressed genes (DEGs) following genome reduction and heat shock; (iii) the genes sensitive to genome reduction tended to be located close to genomic scars, and some were also highly responsive to heat shock; and (iv) the genomic and environmental contributions to the transcriptome displayed not only a positive correlation but also a negatively compensated relationship (i.e., antagonistic epistasis). Conclusion The contributions of genome reduction and heat shock to the Escherichia coli transcriptome were evaluated at multiple levels. The observations of overlapping perturbed networks, directional similarity in transcriptional changes, positive correlation and epistatic nature linked the two contributions and suggest somehow a crosstalk guiding transcriptional reorganization in response to both genetic and environmental disturbances in bacterium E. coli.

Muscle stem cells, also called muscle satellite cells (MuSCs), are responsible for skeletal muscle regeneration and are sustained in an undifferentiated and quiescent state under steady conditions. The calcitonin receptor (CalcR)‐protein kinase A (PKA)‐Yes‐associated protein 1 (Yap1) axis is one pathway that maintains quiescence in MuSCs. Although CalcR signaling in MuSCs has been identified, the critical CalcR signaling targets are incompletely understood. Here, we show the relevance between the ectopic expression of delta‐like non‐canonical Notch ligand 1 (Dlk1) and the impaired quiescent state in CalcR‐conditional knockout (cKO) MuSCs. Dlk1 expression was rarely detected in both quiescent and proliferating MuSCs in control mice, whereas Dlk1 expression was remarkably increased in CalcR‐cKO MuSCs at both the mRNA and protein levels. It is noteworthy that all Ki67+ non‐quiescent CalcR‐cKO MuSCs express Dlk1, and non‐quiescent CalcR‐cKO MuSCs are enriched in the Dlk1+ fraction by cell sorting. Using mutant mice, we demonstrated that PKA‐activation or Yap1‐depletion suppressed Dlk1 expression in CalcR‐cKO MuSCs, which suggests that the CalcR‐PKA‐Yap1 axis inhibits the expression of Dlk1 in quiescent MuSCs. Moreover, the loss of Dlk1 rescued the quiescent state in CalcR‐cKO MuSCs, which indicates that the ectopic expression of Dlk1 disturbs quiescence in CalcR‐cKO. Collectively, our results suggest that ectopically expressed Dlk1 is responsible for the impaired quiescence in CalcR‐cKO MuSCs. [ABSTRACT FROM AUTHOR]

Specific conditions, such as exposure to cold, can induce the production of brown-like adipocytes in white adipose tissue. These adipocytes express high levels of uncoupling protein 1 (UCP1) and energy expended by generating heat. Thus, these are a potential target for the prevention or treatment of obesity. The present study involved a comprehensive analysis of the adipose tissue to understand the relationship between long non-coding RNA (lncRNA) 2310069B03Rik and UCP1. Cold exposure increased both lncRNA 2310069B03Rik and Ucp1 expression in inguinal white adipose tissue (iWAT). However, overexpression of lncRNA 2310069B03Rik suppressed the Ucp1 mRNA expression and the promoter activity of UCP1 in the iWAT primary adipocytes. In addition, compared to the early induction of Ucp1 expression by cold stimulation, the induction of lncRNA 2310069B03Rik expression was later. These results suggest that lncRNA 2310069B03Rik functions as a suppression factor of Ucp1 expression. Long non-coding RNA 2310069B03Rik functions as a suppression factor of Ucp1 expression. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Fusion genes are involved in cancer, and their detection using RNA-Seq is insufficient given the relatively short reading length. Therefore, we proposed a shifted short-read clustering (SSC) method, which focuses on overlapping reads from the same loci and extends them as a representative sequence. To verify their usefulness, we applied the SSC method to RNA-Seq data from four types of cell lines (BT-474, MCF-7, SKBR-3, and T-47D). As the slide width of the SSC method increased to one, two, five, or ten bases, the read length was extended from 201 bases to 217 (108%), 234 (116%), 282 (140%), or 317 (158%) bases, respectively. Furthermore, fusion genes were investigated using STAR-Fusion, a fusion gene detection tool, with and without the SSC method. When one base was shifted by the SSC method, the reads mapped to multiple loci decreased from 9.7% to 4.6%, and the sensitivity of the fusion gene was improved from 47% to 54% on average (BT-474: from 48% to 57%, MCF-7: 49% to 53%, SKBR-3: 50% to 57%, and T-47D: 43% to 50%) compared with original data. When the reads are shifted more, the positive predictive value was also improved. The SSC method could be an effective method for fusion gene detection. [ABSTRACT FROM AUTHOR]

To clarify the effects of a dipeptidyl peptidase‐4 (DPP‐4) inhibitor on whole‐body energy metabolism, we treated mice fed a high‐fat diet (HFD) with teneligliptin, a clinically available DPP‐4 inhibitor. Teneligliptin significantly prevented HFD‐induced obesity and obesity‐associated metabolic disorders. It also increased oxygen consumption rate and upregulated uncoupling protein 1 (UCP1) expression in both brown adipose tissue (BAT) and inguinal white adipose tissue (iWAT), suggesting that it enhances BAT function. Soluble DPP‐4 inhibited β‐adrenoreceptor‐stimulated UCP1 expression in primary adipocytes, and this inhibition was prevented in the presence of teneligliptin, or an extracellular signal‐related kinase inhibitor. These results indicate that soluble DPP‐4 inhibits β‐adrenoreceptor‐stimulated UCP1 induction and that chronic DPP‐4 inhibitor treatment may prevent obesity through the activation of BAT function. Proposed schema of the preventive effect of teneligliptin on obesity in adipocytes. sDPP‐4‐mediated ERK activation, acting through PAR2, suppresses β‐adrenoreceptor‐stimulated UCP1 upregulation in adipocytes. Teneligliptin treatment can prevent this suppression, suggesting that this mechanism might be related to teneligliptin‐induced activation of BAT function in vivo. Teneligliptin‐activated BAT function enhances energy expenditure, thereby preventing obesity. [ABSTRACT FROM AUTHOR]

Background: 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. Results: 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. Conclusions: 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. [ABSTRACT FROM AUTHOR]

Emerging bioimaging technologies enable us to capture various dynamic cellular activities . As large amounts of data are obtained these days and it is becoming unrealistic to manually process massive number of images, automatic analysis methods are required. One of the issues for automatic image segmentation is that image-taking conditions are variable. Thus, commonly, many manual inputs are required according to each image. In this paper, we propose a bone marrow cavity (BMC) segmentation method for bone images as BMC is considered to be related to the mechanism of bone remodeling, osteoporosis, and so on. To reduce manual inputs to segment BMC, we classified the texture pattern using wavelet transformation and support vector machine. We also integrated the result of texture pattern classification into the graph-cuts-based image segmentation method because texture analysis does not consider spatial continuity. Our method is applicable to a particular frame in an image sequence in which the condition of fluorescent material is variable. In the experiment, we evaluated our method with nine types of mother wavelets and several sets of scale parameters. The proposed method with graph-cuts and texture pattern classification performs well without manual inputs by a user. [ABSTRACT FROM AUTHOR]

Comprehensively understanding the dynamics of biological systems is one of the greatest challenges in biology. Vastly improved biological technologies have provided vast amounts of information that must be understood by bioinformatics and systems biology researchers. Gene regulations have been frequently modeled by ordinary differential equations or graphical models based on time-course gene expression profiles. The state-of-the-art computational approaches for analyzing gene regulations assume that their models are same throughout time-course experiments. However, these approaches cannot easily analyze transient changes at a time point, such as diauxic shift. We propose a score that analyzes the gene regulations at each time point. The score is based on the information gains of information criterion values. The method detects the shifts in gene regulatory networks (GRNs) during time-course experiments with single-time-point resolution. The effectiveness of the method is evaluated on the diauxic shift from glucose to lactose in Escherichia coli. Gene regulation shifts were detected at two time points: the first corresponding to the time at which the growth of E. coli ceased and the second corresponding to the end of the experiment, when the nutrient sources (glucose and lactose) had become exhausted. According to these results, the proposed score and method can appropriately detect the time of gene regulation shifts. The method based on the proposed score provides a new tool for analyzing dynamic biological systems. Because the score value indicates the strength of gene regulation at each time point in a gene expression profile, it can potentially infer hidden GRNs from time-course experiments. [ABSTRACT FROM AUTHOR]