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Transcription factors activate or repress target gene expression or switch between activation and repression. In animals and yeast, Groucho/Tupl corepressor proteins are recruited by diverse transcription factors to induce context-specific transcriptional repression. Two groups of Groucho/Tupl-like corepressors have been described in plants. LEUNIG and LEUNIG_ HOMOLOG constitute one group and TOPLESS (TPL) and the four TPL-related (TPR) corepressors form the other. To discover the processes in which TPL and the TPR corepressors operate, high-throughput yeast two-hybrid approaches were used to identify interacting proteins. We found that TPL/TPR corepressors predominantly interact directly with specific transcription factors, many of which were previously implicated in transcriptional repression. The interacting transcription factors reveal that the TPL/TPR family has been coopted multiple times to modulate gene expression in diverse processes, including hormone signaling, stress responses, and the control of flowering time, for which we also show biological validation. The interaction data suggest novel mechanisms for the involvement of TPL/TPR corepressors in auxin and jasmonic acid signaling.A number of short repression domain (RD) sequences have previously been identified in Arabidopsis (Arabidopsis thaliana) transcription factors. All known RD sequences were enriched among the TPL/TPR inter actors, and novel TPL-RD interactions were identified. We show that the presence of RD sequences is essential for TPL/TPR recruitment. These data provide a framework for TPL/TPR-dependent transcriptional repression. They allow for predictions about new repressive transcription factors, corepressor interactions, and repression mechanisms and identify a wide range of plant processes that utilize TPL/TPR-mediated gene repression.

Esophageal squamous-cell carcinoma (ESCC), one of the most prevalent and lethal malignant disease, has a complex but unknown tumor ecosystem. Here, we investigate the composition of ESCC tumors based on 208,659 single-cell transcriptomes derived from 60 individuals. We identify 8 common expression programs from malignant epithelial cells and discover 42 cell types, including 26 immune cell and 16 nonimmune stromal cell subtypes in the tumor microenvironment (TME), and analyse the interactions between cancer cells and other cells and the interactions among different cell types in the TME. Moreover, we link the cancer cell transcriptomes to the somatic mutations and identify several markers significantly associated with patients’ survival, which may be relevant to precision care of ESCC patients. These results reveal the immunosuppressive status in the ESCC TME and further our understanding of ESCC. Esophageal squamous-cell carcinomas (ESCC) have poor prognosis, and detailed molecular profiles are necessary to identify prognostic markers. Here the authors analyse 60 ESCC patient samples using scRNA-seq, TCR-seq and genomics; they find mucosal immunity markers associated with survival and immunosuppressive microenvironments.

Breast cancer is the most common cancer in women. Breast masses are one of the distinctive signs for diagnosing breast cancer, and ultrasound is widely used for screening as a non-invasive and effective method for breast examination. In this study, we used the Mendeley and BUSI datasets, comprising 250 images (100 benign, 150 malignant) and 780 images (133 normal, 487 benign, 210 malignant), respectively. The datasets were split into 80% for training and 20% for validation. The accurate measurement and characterization of different breast tumors play a crucial role in guiding clinical decision-making. The area and shape of the different breast tumors detected are critical for clinicians to make accurate diagnostic decisions. In this study, a deep learning method for mass segmentation in breast ultrasound images is proposed, which uses densely connected U-net with attention gates (AGs) as well as channel attention modules and scale attention modules for accurate breast tumor segmentation.The densely connected network is employed in the encoding stage to enhance the network’s feature extraction capabilities. Three attention modules are integrated in the decoding stage to better capture the most relevant features. After validation on the Mendeley and BUSI datasets, the experimental results demonstrate that our method achieves a Dice Similarity Coefficient (DSC) of 0.8764 and 0.8313, respectively, outperforming other deep learning approaches. The source code is located at github.com/zhanghaoCV/plos-one .

Esophageal squamous cell carcinoma (ESCC) is prevalent in some geographical regions of the world. ESCC development presents a multistep pathogenic process from inflammation to invasive cancer; however, what is critical in these processes and how they evolve is largely unknown, obstructing early diagnosis and effective treatment. Here, we create a mouse model mimicking human ESCC development and construct a single-cell ESCC developmental atlas. We identify a set of key transitional signatures associated with oncogenic evolution of epithelial cells and depict the landmark dynamic tumorigenic trajectories. An early downregulation of CD8 + response against the initial tissue damage accompanied by the transition of immune response from type 1 to type 3 results in accumulation and activation of macrophages and neutrophils, which may create a chronic inflammatory environment that promotes carcinogen-transformed epithelial cell survival and proliferation. These findings shed light on how ESCC is initiated and developed. The multistep processes involved in the evolution of inflammation to invasive esophageal squamous cell carcinoma (ESCC) is unclear. Here, the authors report a mouse model of ESCC and the role of interplay between carcinogen-transformed epithelial cells and their microenvironment in ESCC development.

Somatic mutations that accumulate in normal tissues are associated with ageing and disease 1 , 2 . Here we performed a comprehensive genomic analysis of 1,737 morphologically normal tissue biopsies of 9 organs from 5 donors. We found that somatic mutation accumulations and clonal expansions were widespread, although to variable extents, in morphologically normal human tissues. Somatic copy number alterations were rarely detected, except for in tissues from the oesophagus and cardia. Endogenous mutational processes with the SBS1 and SBS5 mutational signatures are ubiquitous among normal tissues, although they exhibit different relative activities. Exogenous mutational processes operate in multiple tissues from the same donor. We reconstructed the spatial somatic clonal architecture with sub-millimetre resolution. In the oesophagus and cardia, macroscopic somatic clones that expanded to hundreds of micrometres were frequently seen, whereas in tissues such as the colon, rectum and duodenum, somatic clones were microscopic in size and evolved independently, possibly restricted by local tissue microstructures. Our study depicts a body map of somatic mutations and clonal expansions from the same individual. Laser-capture microdissection and mini-bulk exome sequencing are combined to analyse somatic mutations in morphologically normal tissues from nine organs from five donors, revealing variation in mutation burdens, mutational signatures and clonal expansions.

Adenocarcinoma at the gastroesophageal junction (ACGEJ) has dismal clinical outcomes, and there are currently few specific effective therapies because of limited knowledge on its genomic and transcriptomic alterations. The present study investigates genomic and transcriptomic changes in ACGEJ from Chinese patients and analyzes their drug vulnerabilities and associations with the survival time. Here we show that the major genomic changes of Chinese ACGEJ patients are chromosome instability promoted tumorigenic focal copy-number variations and COSMIC Signature 17-featured single nucleotide variations. We provide a comprehensive profile of genetic changes that are potentially vulnerable to existing therapeutic agents and identify Signature 17-correlated IFN-α response pathway as a prognostic marker that might have practical value for clinical prognosis of ACGEJ. These findings further our understanding on the molecular biology of ACGEJ and may help develop more effective therapeutic strategies. Adenocarcinoma at the gastroesophageal junction has a dismal prognosis and few drug options. Here, the authors present genomic and transcriptomic features and potential therapeutic targets and prognostic biomarkers of Chinese and Caucasian tumours, and reveal the molecular similarities.

Ovarian cancer is a highly lethal malignancy in the field of oncology. Generally speaking, the segmentation of ovarian medical images is a necessary prerequisite for the diagnosis and treatment planning. Therefore, accurately segmenting ovarian tumors is of utmost importance. In this work, we propose a hybrid network called PMFFNet to improve the segmentation accuracy of ovarian tumors. The PMFFNet utilizes an encoder-decoder architecture. Specifically, the encoder incorporates the ViTAEv2 model to extract inter-layer multi-scale features from the feature pyramid. To address the limitation of fixed window size that hinders sufficient interaction of information, we introduce Varied-Size Window Attention (VSA) to the ViTAEv2 model to capture rich contextual information. Additionally, recognizing the significance of multi-scale features, we introduce the Multi-scale Feature Fusion Block (MFB) module. The MFB module enhances the network’s capacity to learn intricate features by capturing both local and multi-scale information, thereby enabling more precise segmentation of ovarian tumors. Finally, in conjunction with our designed decoder, our model achieves outstanding performance on the MMOTU dataset. The results are highly promising, with the model achieving scores of 97.24%, 91.15%, and 87.25% in mACC, mIoU, and mDice metrics, respectively. When compared to several Unet-based and advanced models, our approach demonstrates the best segmentation performance.

Intercellular communication signals in the tumor microenvironment are closely related to behaviors such as cancer cell proliferation and immune evasion. However, the specific roles of intercellular signaling pathways in intrahepatic cholangiocarcinoma (ICC) have not yet been fully characterized. In this study, we analyzed publicly available single-cell RNA sequencing (scRNA-seq) data derived from paired samples of two intrahepatic cholangiocarcinoma (ICC) tissues and two adjacent normal tissues, thoroughly examining their cellular composition. InferCNV analysis was employed to compare tumor cells and normal cells, and pseudotime analysis was used to identify the growth and differentiation trajectories of the cells. Additionally, intercellular communication analysis was conducted to elucidate the communication networks between cells. Our analysis delineated the cellular ecosystem of ICC, identifying cell subclusters with shared characteristics between ICC and normal tissues. Notably, we characterized a distinct C7-E-T subcluster that exhibited high expression of CXCR4 and BPTF, markers associated with cancer stem cells (CSCs). Further investigation revealed that the MIF intercellular signaling pathway promotes the progression of ICC by activating intracellular signals in the MYC pathway. This study highlights the dysregulation of intercellular signaling pathways within tumor clusters, which influences the onset and progression of ICC. The cancer stem cell subpopulation (CXCR4 hi BPTF hi E-T) exerts a significant influence on ICC progression by secreting relevant signaling molecules via the MIF signaling pathway.

Background Schizophrenia places a great humanistic and financial burden to patients, families, and societies, and the burden is substantially impacted by comorbid conditions. This study aimed to estimate the lifetime prevalence of schizophrenia and to assess the health-related quality of life (HRQoL), work productivity, and indirect cost among schizophrenia patients with and without comorbidities (depressive symptoms, sleep disturbances, and anxiety problems). Methods This is a secondary analysis of existing data collected in 2019 from the Japan National Health and Wellness Survey. The schizophrenia patients were categorized based on their Patient Health Questionnaire-9 score, self-reported experience of sleep disturbances, and anxiety problems. The lifetime prevalence was estimated using the total number of diagnosed schizophrenia patients as the numerator and the total number of respondents as the denominator. The HRQoL was evaluated through the Short Form 12-Item (version 2) Health Survey and EuroQoL 5-dimensions scale. Work productivity and annual indirect costs were evaluated through the Work Productivity and Activity Impairment instrument and monthly wage rates. Multivariate analyses included the comparison of outcomes using generalized linear models. Results The study was conducted with 178 schizophrenia patients with an average age of 42.7 years old and an estimated lifetime prevalence of 0.59% (95% CI: 0.51%, 0.68%). Patients who experienced sleep disturbances, more severe depressive symptoms, and anxiety problems had lower HRQoL, higher levels of absenteeism, presenteeism, total work productivity and activity impairment, and almost twice more indirect costs, compared to those without these conditions. Conclusion Comorbid conditions among patients with schizophrenia impact significantly on their quality of life, work productivity as well as indirect costs.

Taking a strategic fit perspective, we investigate the relationships between task environment (environmental dynamism), institutional environment (state ownership and institutional development), and innovation strategy (cooperative strategy) in the context of China. Using a longitudinal data set of Chinese manufacturing firms from 2012 to 2017, we confirm that environmental dynamism positively drives firms’ decisions to cooperate for innovation. This driving effect is stronger for state-owned firms and firms located in regions with limited developed institutions. Overall, no one-size-fits-all innovation strategy exists, and the appropriateness of a strategy depends on the coalignment of a series of external and internal factors. The theoretical and practical implications of this finding are explored.