126 research outputs found
High-throughput analysis reveals novel maternal germline RNAs crucial for primordial germ cell preservation and proper migration
No full textDuring oogenesis, hundreds of maternal RNAs are selectively localized to the animal or vegetal pole, including determinants of somatic and germline fates. Although microarray analysis has identified localized determinants, it is not comprehensive and is limited to known transcripts. Here, we utilized high-throughput RNA sequencing analysis to comprehensively interrogate animal and vegetal pole RNAs in the fully grown Xenopus laevis oocyte. We identified 411 (198 annotated) and 27 (15 annotated) enriched mRNAs at the vegetal and animal pole, respectively. Ninety were novel mRNAs over 4-fold enriched at the vegetal pole and six were over 10-fold enriched at the animal pole. Unlike mRNAs, microRNAs were not asymmetrically distributed. Whole-mount in situ hybridization confirmed that all 17 selected mRNAs were localized. Biological function and network analysis of vegetally enriched transcripts identified protein-modifying enzymes, receptors, ligands, RNA-binding proteins, transcription factors and co-factors with five defining hubs linking 47 genes in a network. Initial functional studies of maternal vegetally localized mRNAs show that sox7 plays a novel and important role in primordial germ cell (PGC) development and that ephrinB1 (efnb1) is required for proper PGC migration. We propose potential pathways operating at the vegetal pole that highlight where future investigations might be most fruitful.Fil: Owens, Dawn A.. University of Miami; Estados UnidosFil: Butler, Amanda M.. University of Miami; Estados UnidosFil: Agüero, Tristán Horacio. University of Miami; Estados Unidos. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Tucumán. Instituto Superior de Investigaciones Biológicas. Universidad Nacional de Tucumán. Instituto Superior de Investigaciones Biológicas; ArgentinaFil: Newman, Karen M.. University of Miami; Estados UnidosFil: Van Booven, Derek. University of Miami; Estados UnidosFil: King, Mary Lou. University of Miami; Estados Unido
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Abstract 187: Automated deep-learning system for Gleason grading of prostate cancer using digital pathology and genomic signatures
No full textAbstract PurposeTo use deep learning techniques to aid the automation of prostate cancer grading from histologic samples, thereby improving diagnostic accuracy and treatment selection. IntroductionAmong the most valuable tools in the evaluation of prostate cancer is the Gleason Score (GS) that is assigned by a pathologist to prostatic biopsies. This score represents the aggressiveness of the tumor and ranges from 3 to 5 (least to most aggressive) in a primary and secondary pattern. Here we use deep learning techniques to automate the classification of prostate samples by GS. By automating this process, we are able to reduce the human subjectivity and potential error of Gleason grading and are able to create a pipeline by which we can integrate gene expression to quickly determine cancer progression in individual patients. Methods733 prostate tissue slide images were downloaded along with clinical information on 500 individuals from the PRAD study contained in The Cancer Genome Atlas (TCGA). We used the Xception network architecture within the Keras software package and TensorFlow backend in Python. We used 120 images that were scored by a board-certified pathologist as training images (with minimal data augmentation) to create a refined deep convolutional neural network to automate Gleason grading to identify cancer regions of interest on test training slides. We then used this model to score new unannotated by a different pathologist blindly to gauge accuracy. Additionally, we have looked at gene expression by combining patients within a specific GS, and compared profiles within each group with gene expression software. From this we were able to select a set of unique markers to identify new patients by GS as well as identify the amount of genetic variance within a given dataset. Results We compared the model results of the Gleason grading from 10 images with the score that was reported in the TCGA and found that the system was able to correctly identify cancer regions at 93% and severity at 55%. We were able to identify 6,411 differentially expressed genes (DEG) between normal adjacent tissue and GS6, 186 DEGs between GS6 and GS7, 1,855 DEGs between GS7 and GS8, and 603 DEGs between GS8 and GS9. ConclusionsDeep learning can be applied to determine the histologic severity of tissue images. Given future modifications to improve the accuracy of our current model, we can be hopeful of producing an automated software workflow that will aid in identifying tumor areas, determining their severity, and influencing treatment decisions. Citation Format: Derek Van Booven, Victor Sandoval, Oleksander Kryvenko, Madhumita Parmar, Andres Briseño, Himanshu Arora. Automated deep-learning system for Gleason grading of prostate cancer using digital pathology and genomic signatures [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2021; 2021 Apr 10-15 and May 17-21. Philadelphia (PA): AACR; Cancer Res 2021;81(13_Suppl):Abstract nr 187
Abstract 6053: Increased nitric oxide augments the action of CSF-1R inhibition against tumor associated macrophages in castration resistant prostate cancer
No full textAbstract Introduction and Objectives: Prostate cancer (PCa) is the most common non-skin cancer among American men, and growing evidence suggests that targeting the tumor microenvironment (TME) could be essential in combating the progression of metastasis and resistance development in cancer. In this context, the colony-stimulating factor 1 (CSF1)/colony-stimulating factor 1 receptor (CSF1R) axis has gained the most attention, and various approaches targeting either the ligands or the receptor are currently in clinical development. However, as a class, CSF1R inhibitors have proved mostly disappointing in early clinical trials when used as monotherapy. Researchers believe their true potential can be tapped by combining them with other anticancer drugs. Sensitization to CSF-1R blockage therapy is tumor microenvironment (TME) driven. In one of our studies, we demonstrated that increased nitric oxide (NO) reduces tumor burden in murine models for CRPC by targeting TME . Therefore, in the present study, we evaluated the hypothesis that Increased Nitric oxide augments the action of CSF-1R inhibition against tumor associated macrophages in castration resistant prostate cancer. Methods: The castrated SCID mice were treated with CSF1R inhibitor (GS2580) at the dosage of 40mg/kg/day IP or/and GSNO at the dosage of 10mg/kg/day IP. After 4 weeks mice were humanely sacrificed. Tumor RNA and proteins to analyze the markers that are important for prostate cancer progression using qPCR, western blot and cytokine antibody array. RNA library was generated, and RNA sequencing was performed using RNA isolated from tumors. Molecular analyses were performed using standard procedures. GraphPad Prism (GraphPad Software) was used for statistical analysis. All data were presented as the means ± SEM. Results: Mice which recieved CSF1R inhibition showed significant reduction in tumor burden (p<0.05), however, in over 50% of the mice, the expression of markers like AR, pERK, p-GSK, and VEGF was found to be increased. Next, to study if increased Nitric oxide levels are able to augment the action of CSF1Ri against CRPC, we studied the effects of GSNO monotherapy, CSF1Ri monotherapy and GSNO+CSF1Ri combination on overall tumor burden. Results revealed that the most significant reduction in tumor burden were in mice that recieved the combination of GSNO-CSF1Ri, compared to GSNO or CSF1Ri monotherpies. Furthermore, RNA sequencing analysis demonstrated that the combination therapy is capable of targeting (suppressing) tumor immunology (signature of interferon-alpha, gama, and Myc signaling were significantly reduced). This was further supported by cytokine antibody array and immunostaning which showed that several cytokines like CXCL5, FGF4, IGFBP-3, MCP-4, IL-6, TNFalpha, expression of CRRPC markers- AR, ARV7, PSA, TMRPSS2, p-GSK, p-ERK, p90RSK, and markers of anti-inflamatorry macrophages (F4/80, CD206 etc) were suppressed in tumor proteins from mice which received combination therapy. Conclusions: Our findings suggest that CSF1R inhibition induced changes against CRPC are augmented in the presence of increased NO levels therefore demonstrating the therapeutic potential of increased NO levels against CRPC. Citation Format: Yash Soni, Manish Kuchakulla, Rehana Qureshi, Van Booven Derek J, Joshua M. Hare, Ranjith Ramasamy, Himanshu Arora. Increased nitric oxide augments the action of CSF-1R inhibition against tumor associated macrophages in castration resistant prostate cancer [abstract]. In: Proceedings of the Annual Meeting of the American Association for Cancer Research 2020; 2020 Apr 27-28 and Jun 22-24. Philadelphia (PA): AACR; Cancer Res 2020;80(16 Suppl):Abstract nr 6053
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ChatGPT and healthcare—current and future prospects
No full textChatGPT is an artificial intelligence (AI) chatbot developed by OpenAI. It was initially built on a family of large language models (LLMs) known as GPT-3, which was released in June 2020. GPT stands for Generative Pretrained Transformer language model, designed to generate human-like text based on the input they receive. LLMs are a particular use case within natural language processing (NLP). NLP delves into how computers interact with human language. This entails examining and interpreting natural language data, whether in text or speech form, to empower machines to comprehend, produce, and engage with human language. ChatGPT is specifically fine-tuned to improve its performance in conversational contexts. Because it has been trained on hundreds of billions of words on a vast amount of data, including books, articles, websites, and social media, ChatGPT can create responses that make it seem like, in its own words, “a friendly and intelligent robot.” Some key capabilities of ChatGPT include its ability to understand and respond to complex and open-ended questions, to handle multiple turns in a conversation, and to maintain coherence and consistency in its responses. ChatGPT’s success in becoming the fastest-growing app of all time, with 100 million users in just two months, can be attributed to its ability to provide responses that are remarkably similar to those of humans, and often highly accurate, across a wide range of questions
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MP40-10 CREATING HIGH QUALITY SYNTHETIC GENITOURINARY TISSUE IMAGES FROM HISTOLOGY REPOSITORIES TO OVERCOME LIMITATIONS WITH THE USE OF CLINICAL TRIAL DATA IN AI MODELS
No full textSUPERmerge: ChIP-seq coverage island analysis algorithm for broad histone marks
No full textAbstractSUPERmerge is a ChIP-seq read pileup analysis and annotation algorithm for investigating alignment (BAM) files of diffuse histone modification ChIP-seq datasets with broad chromatin domains at a single base pair resolution level. SUPERmerge allows flexible regulation of a variety of read pileup parameters, thereby revealing how read islands aggregate into areas of coverage across the genome and what annotation features they map to within individual biological replicates. SUPERmerge is especially useful for investigating low sample size ChIP-seq experiments in which epigenetic histone modifications (e.g., H3K9me1, H3K27me3) result in inherently broad peaks with a diffuse range of signal enrichment spanning multiple consecutive genomic loci and annotated features.</jats:p
Epigenomic and metabolic responses of hypothalamic POMC neurons to gestational nicotine exposure in adult offspring
No full textEpidemiological and animal studies have reported that prenatal nicotine exposure (PNE) leads to obesity and type-2 diabetes in offspring. Central leptin-melanocortin signaling via hypothalamic arcuate proopiomelanocortin (POMC) neurons is crucial for the regulation of energy and glucose balance. Furthermore, hypothalamic POMC neurons were recently found to mediate the anorectic effects of nicotine through activation of acetylcholine receptors. Here, we hypothesized that PNE impairs leptin-melanocortinergic regulation of energy balance in first-generation offspring by altering expression of long non-coding RNAs (lncRNAs) putatively regulating development and/or function of hypothalamic POMC neurons.
C57BL/6J females were exposed ad libitum to nicotine through drinking water and crossed with C57BL/6J males. Nicotine exposure was sustained during pregnancy and discontinued at parturition. Offspring development was monitored from birth into adulthood. From the age of 8 weeks, central leptin-melanocortin signaling, diabetes, and obesity susceptibility were assessed in male offspring fed a low-fat or high-fat diet for 16 weeks. Nicotine-exposed and non-exposed C57BL/6J females were also crossed with C57BL/6J males expressing the enhanced green fluorescent protein specifically in POMC neurons. Transgenic male offspring were subjected to laser microdissections and RNA sequencing (RNA-seq) analysis of POMC neurons for determination of nicotine-induced gene expression changes and regulatory lncRNA/protein-coding gene interactions.
Contrary to expectation based on previous studies, PNE did not impair but rather enhanced leptin-melanocortinergic regulation of energy and glucose balance via POMC neurons in offspring. RNA-seq of laser microdissected POMC neurons revealed only one consistent change, upregulation of Gm15851, a lncRNA of yet unidentified function, in nicotine-exposed offspring. RNA-seq further suggested 82 cis-regulatory lncRNA/protein-coding gene interactions, 19 of which involved coding genes regulating neural development and/or function, and revealed expression of several previously unidentified metabolic, neuroendocrine, and neurodevelopment pathways in POMC neurons.
PNE does not result in obesity and type 2 diabetes but instead enhances leptin-melanocortinergic feeding and body weight regulation via POMC neurons in adult offspring. PNE leads to selective upregulation of Gm15851, a lncRNA, in adult offspring POMC neurons. POMC neurons express several lncRNAs and pathways possibly regulating POMC neuronal development and/or function
HeatmapGenerator: high performance RNAseq and microarray visualization software suite to examine differential gene expression levels using an R and C++ hybrid computational pipeline
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Chapter 7 - Adversarial networks—enhancing current methodology with new models
No full textGenerative adversarial networks (GANs), introduced by Goodfellow et al. in 2014, have revolutionized the fields of machine learning and artificial intelligence. These networks have the unique ability to generate authentic synthetic data based on a given training dataset. The applications of GANs span across diverse domains, including the generation of new images, augmentation of existing datasets, imputation of missing data, and the creation of innovative models, such as completing 3D models from 2D data. Their utility extends to fields ranging from forensic science to biomedical research, where GANs have shown remarkable success in enhancing data-driven tasks. This chapter aims to provide an in-depth exploration of the role and impact of GANs across various sectors, emphasizing their practical applications and the underlying methodologies that drive their success
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