Abstract:Gametogenesis is a complex process, which includes mitosis and meiosis and results in the production of ovum and sperm. The development of gametogenesis is dynamic and needs many different genes to work synergistically, but it is lack of global perspective research about this process. In this study, we detected the dynamic process of gametogenesis from the perspective of systems biology based on protein-protein interaction networks (PPINs) and functional analysis. Results showed that gametogenesis genes have s… Show more
“…Apoptosis is a strictly controlled programmed cell death with different biochemical and genetic mechanisms that play an important role in tissue homeostasis, and maintains the balance between cell survival and cell loss. 11 The impact of apoptosis in cancer has been the focus of research over several years [12][13][14] ; however, there are conflicting results regarding its prognostic value in BC. [15][16][17][18][19][20][21][22] The assessment of apoptosis as a prognostic marker in BC faces several limitations, including the diversity of its morphological appearances, the assessment subjectivity and the controversy regarding its prognostic value.…”
Combined proliferation and apoptosis index provides better risk stratification in breast cancerAims: Breast cancer (BC) risk stratification is critical for predicting behaviour and guiding management decision-making. Despite the well-established prognostic value of cellular proliferation in BC, the interplay between proliferation and apoptosis remains to be defined. In this study, we hypothesised that the combined proliferation and apoptosis indices can provide a more accurate in-vivo growth rate measure and a precise prognostic predictor. Methods and results: Apoptotic and mitotic figures were counted in whole slide images (WSI) generated from haematoxylin and eosin-stained sections of 1545 BC cases derived from two well-defined BC cohorts. Counts were carried out visually within defined areas. There was a significant correlation between mitosis and apoptosis scores. High apoptotic counts were associated with features of aggressive behaviour, including high grade, high pleomorphism score and hormonal receptor negativity. Although the mitotic index (MI) and apoptotic index (AI) were independent prognostic indicators, the prognostic value was synergistically higher when combined. BC patients with a high combined AI and MI had the shortest survival. Replacing the mitosis score with the mitosis-apoptosis index in the Nottingham grading system revealed that the modified grade with the new score had a higher significant association with BC-specific survival with a higher hazard ratio. Conclusion: Apoptotic figures count provides additional prognostic value in BC when combined with MI; such a combination can be implemented to assess the behaviour of BC and provides an accurate prognostic indicator. This can be considered when using artificial intelligence algorithms to assess proliferation in BC.
“…Apoptosis is a strictly controlled programmed cell death with different biochemical and genetic mechanisms that play an important role in tissue homeostasis, and maintains the balance between cell survival and cell loss. 11 The impact of apoptosis in cancer has been the focus of research over several years [12][13][14] ; however, there are conflicting results regarding its prognostic value in BC. [15][16][17][18][19][20][21][22] The assessment of apoptosis as a prognostic marker in BC faces several limitations, including the diversity of its morphological appearances, the assessment subjectivity and the controversy regarding its prognostic value.…”
Combined proliferation and apoptosis index provides better risk stratification in breast cancerAims: Breast cancer (BC) risk stratification is critical for predicting behaviour and guiding management decision-making. Despite the well-established prognostic value of cellular proliferation in BC, the interplay between proliferation and apoptosis remains to be defined. In this study, we hypothesised that the combined proliferation and apoptosis indices can provide a more accurate in-vivo growth rate measure and a precise prognostic predictor. Methods and results: Apoptotic and mitotic figures were counted in whole slide images (WSI) generated from haematoxylin and eosin-stained sections of 1545 BC cases derived from two well-defined BC cohorts. Counts were carried out visually within defined areas. There was a significant correlation between mitosis and apoptosis scores. High apoptotic counts were associated with features of aggressive behaviour, including high grade, high pleomorphism score and hormonal receptor negativity. Although the mitotic index (MI) and apoptotic index (AI) were independent prognostic indicators, the prognostic value was synergistically higher when combined. BC patients with a high combined AI and MI had the shortest survival. Replacing the mitosis score with the mitosis-apoptosis index in the Nottingham grading system revealed that the modified grade with the new score had a higher significant association with BC-specific survival with a higher hazard ratio. Conclusion: Apoptotic figures count provides additional prognostic value in BC when combined with MI; such a combination can be implemented to assess the behaviour of BC and provides an accurate prognostic indicator. This can be considered when using artificial intelligence algorithms to assess proliferation in BC.
“…Unraveling gene regulatory interactions, often represented as a gene regulatory network (GRN), plays a crucial role in studying biological processes under different conditions [1][2][3] , simulating knockdown and knockout experiments 4,5 , and identifying therapeutic drug targets 6,7 . Many algorithms have been proposed for GRN reconstruction using bulk or single-cell RNA sequencing data (scRNA-seq), alone [8][9][10][11][12][13][14] or with other modalities [15][16][17][18] .…”
We introduce GRouNdGAN, a gene regulatory network (GRN)-guided causal implicit generative model for simulating single-cell RNA-seq data, in-silico perturbation experiments, and benchmarking GRN inference methods. Through the imposition of a user-defined GRN in its architecture, GRouNdGAN simulates steady-state and transient-state single-cell datasets where genes are causally expressed under the control of their regulating transcription factors (TFs). Training on three experimental datasets, we show that our model captures non-linear TF-gene dependences and preserves gene identities, cell trajectories, pseudo-time ordering, and technical and biological noise, with no user manipulation and only implicit parameterization. Despite imposing rigid causality constraints, it outperforms state-of-the-art simulators in generating realistic cells. GRouNdGAN learns meaningful causal regulatory dynamics, allowing sampling from both observational and interventional distributions. This enables it to synthesize cells under conditions that do not occur in the dataset at inference time, allowing to perform in-silico TF knockout experiments. Our results show that in-silico knockout of cell type-specific TFs significantly reduces cells of that type being generated, even though GRouNdGAN was not trained on any perturbation datasets. Interactions imposed through the GRN are emphasized in the simulated datasets, resulting in GRN inference algorithms assigning them much higher scores than interactions not imposed but of equal importance in the experimental training dataset. Benchmarking various GRN inference algorithms reveals that GRouNdGAN effectively bridges the existing gap between simulated and biological data benchmarks of GRN inference algorithms, providing gold standard ground truth GRNs and realistic cells corresponding to the biological system of interest. Our results show that GRouNdGAN is a stable, realistic, and effective simulator with various applications in single-cell RNA-seq analysis.
“…Therefore, revealing the molecular mechanisms of spermatogenesis is essential for the understanding of male infertility. Spermatogenesis is a complex and dynamic process leading to the continuous production of sperm [6]. This process requires the successive and coordinated expression of thousands of genes, mixed with multi-level regulations from transcriptional, post-transcriptional and translational gene regulation [7].…”
Spermatogenesis is an important physiological process associated with male infertility. As a kind of post-transcriptional regulation, RNA editings (REs) change the genetic information at the mRNA level. But whether there are REs and what's the role of REs during the process are still unclear. In this study, we integrated published RNA-Seq datasets and established a landscape of RNA REs during the development of mouse spermatogenesis. Totally, 7530 editing sites occurred in 2012 genes among all types of male germ cells were found, these sites enrich on some regions of chromosomes, including chromosome 17 and both ends of chromosome Y. We also found about half of the REs in CDSs can cause amino acids changes. Some non-synonymous REs which exist in specific genes may play important roles in spermatogenesis. Finally, we verified a nonsynonymous A-to-I RNA editing site in Cog3 and a stoploss editing in Tssk6 during spermatogenesis. In short, we systematically analyzed the dynamic landscape of RNA editing at different stages of spermatogenesis.
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