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Publications
Deconvolving X-ray Galaxy Cluster Spectra Using a Recurrent Inference Machine
Recent advances in machine learning algorithms have unlocked new insights in observational astronomy by allowing astronomers to probe new fr… (voir plus)ontiers. In this article, we present a methodology to disentangle the intrinsic X-ray spectrum of galaxy clusters from the instrumental response function. Employing state-of-the-art modeling software and data mining techniques of the Chandra data archive, we construct a set of 100,000 mock Chandra spectra. We train a recurrent inference machine (RIM) to take in the instrumental response and mock observation and output the intrinsic X-ray spectrum. The RIM can recover the mock intrinsic spectrum below the 1-
For a long time, the pancreas was thought to have separate cellular compartments that functioned distinctly from one another. The endocrine … (voir plus)pancreas (islets of Langerhans) regulates glucose homeostasis, while the exocrine pancreas (acini and ducts) produces and secretes digestive enzymes. However, it has recently become clear that the endocrine and exocrine compartments communicate with one another, and dysfunction in one leads to dysfunction in the other, resulting in diabetes or pancreatitis. However, whether and how the endocrine pancreas drives the development of pancreatic ductal adenocarcinoma (PDAC), an exocrine tumor, remains unresolved. Strikingly, we found that genetic ablation of insulin-producing islet beta (β) cells (Akita) in a faithful Kras/Trp53-driven PDAC model (KPC: Kras LSL-G12D /+; Trp 53172 /+; Pdx1-Cre) suppressed PDAC progression. Conversely, obesity-induced β cell hormone dysregulation promoted Kras-driven PDAC development. Single-cell RNA sequencing (scRNA-seq) analysis of wild-type and obese mice (high-fat diet-fed and leptin-deficient (Lep ob/ob )) revealed increased expression of the peptide hormone cholecystokinin (CCK) in a subset of β cells concordant with increasing obesity, and transgenic β cell overexpression of CCK was sufficient to promote exocrine tumorigenesis in KC mice. Combined in silico (pseudotime (TrajectoryNET) and archetypal (AANet) analysis) and experimental (CreER) lineage tracing demonstrated that CCK-expressing β cells originated from a pre-existing immature β cell population (virgin β cells). Grainger causality analysis of transcriptional networks uncovered a stress-induced JNK-cJun pathway that promotes CCK expression β cells, which we confirmed using JNK inhibitors in β cell models. Together, our findings identify cellular and molecular mechanisms of β cell adaptation to obesity that contribute to obesity-driven pancreatic cancer. Furthermore, we define a critical role for endocrine-exocrine signaling in PDAC progression and stress-induced β cell pathways which could be leveraged to target the endocrine pancreas to subvert exocrine tumorigenesis.
Citation Format: Cathy Garcia, Aarthi Venkat, Daniel McQuaid, Sherry Agabiti, Alex Tong, Rebecca Cardone, Richard Kibbey, Smita Krishnaswamy, Mandar Muzumdar. Endocrine beta-cell stress promotes pancreatic ductal adenocarcinoma through endocrine-exocrine cell crosstalk [abstract]. In: Proceedings of the AACR Special Conference in Cancer Research: Advances in Pancreatic Cancer Research; 2024 Sep 15-18; Boston, MA. Philadelphia (PA): AACR; Cancer Res 2024;84(17 Suppl_2):Abstract nr PR-05.
For a long time, the pancreas was thought to have separate cellular compartments that functioned distinctly from one another. The endocrine … (voir plus)pancreas (islets of Langerhans) regulates glucose homeostasis, while the exocrine pancreas (acini and ducts) produces and secretes digestive enzymes. However, it has recently become clear that the endocrine and exocrine compartments communicate with one another, and dysfunction in one leads to dysfunction in the other, resulting in diabetes or pancreatitis. However, whether and how the endocrine pancreas drives the development of pancreatic ductal adenocarcinoma (PDAC), an exocrine tumor, remains unresolved. Strikingly, we found that genetic ablation of insulin-producing islet beta (β) cells (Akita) in a faithful Kras/Trp53-driven PDAC model (KPC: Kras LSL-G12D /+; Trp 53172 /+; Pdx1-Cre) suppressed PDAC progression. Conversely, obesity-induced β cell hormone dysregulation promoted Kras-driven PDAC development. Single-cell RNA sequencing (scRNA-seq) analysis of wild-type and obese mice (high-fat diet-fed and leptin-deficient (Lep ob/ob )) revealed increased expression of the peptide hormone cholecystokinin (CCK) in a subset of β cells concordant with increasing obesity, and transgenic β cell overexpression of CCK was sufficient to promote exocrine tumorigenesis in KC mice. Combined in silico (pseudotime (TrajectoryNET) and archetypal (AANet) analysis) and experimental (CreER) lineage tracing demonstrated that CCK-expressing β cells originated from a pre-existing immature β cell population (virgin β cells). Grainger causality analysis of transcriptional networks uncovered a stress-induced JNK-cJun pathway that promotes CCK expression β cells, which we confirmed using JNK inhibitors in β cell models. Together, our findings identify cellular and molecular mechanisms of β cell adaptation to obesity that contribute to obesity-driven pancreatic cancer. Furthermore, we define a critical role for endocrine-exocrine signaling in PDAC progression and stress-induced β cell pathways which could be leveraged to target the endocrine pancreas to subvert exocrine tumorigenesis.
Citation Format: Cathy Garcia, Aarthi Venkat, Daniel McQuaid, Sherry Agabiti, Alex Tong, Rebecca Cardone, Richard Kibbey, Smita Krishnaswamy, Mandar Muzumdar. Endocrine beta-cell stress promotes pancreatic ductal adenocarcinoma through endocrine-exocrine cell crosstalk [abstract]. In: Proceedings of the AACR Special Conference in Cancer Research: Advances in Pancreatic Cancer Research; 2024 Sep 15-18; Boston, MA. Philadelphia (PA): AACR; Cancer Res 2024;84(17 Suppl_2):Abstract nr PR-05.
Generative Flow Networks (GFlowNets), a class of generative models have recently emerged as a suitable framework for generating diverse and … (voir plus)high-quality molecular structures by learning from unnormalized reward distributions. Previous works in this direction often restrict exploration by using predefined molecular fragments as building blocks, limiting the chemical space that can be accessed. In this work, we introduce Atomic GFlowNets (A-GFNs), a foundational generative model leveraging individual atoms as building blocks to explore drug-like chemical space more comprehensively. We propose an unsupervised pre-training approach using offline drug-like molecule datasets, which conditions A-GFNs on inexpensive yet informative molecular descriptors such as drug-likeliness, topological polar surface area, and synthetic accessibility scores. These properties serve as proxy rewards, guiding A-GFNs towards regions of chemical space that exhibit desirable pharmacological properties. We further our method by implementing a goal-conditioned fine-tuning process, which adapts A-GFNs to optimize for specific target properties. In this work, we pretrain A-GFN on the ZINC15 offline dataset and employ robust evaluation metrics to show the effectiveness of our approach when compared to other relevant baseline methods in drug design.
Generative Flow Networks (GFlowNets), a class of generative models have recently emerged as a suitable framework for generating diverse and … (voir plus)high-quality molecular structures by learning from unnormalized reward distributions. Previous works in this direction often restrict exploration by using predefined molecular fragments as building blocks, limiting the chemical space that can be accessed. In this work, we introduce Atomic GFlowNets (A-GFNs), a foundational generative model leveraging individual atoms as building blocks to explore drug-like chemical space more comprehensively. We propose an unsupervised pre-training approach using offline drug-like molecule datasets, which conditions A-GFNs on inexpensive yet informative molecular descriptors such as drug-likeliness, topological polar surface area, and synthetic accessibility scores. These properties serve as proxy rewards, guiding A-GFNs towards regions of chemical space that exhibit desirable pharmacological properties. We further our method by implementing a goal-conditioned fine-tuning process, which adapts A-GFNs to optimize for specific target properties. In this work, we pretrain A-GFN on the ZINC15 offline dataset and employ robust evaluation metrics to show the effectiveness of our approach when compared to other relevant baseline methods in drug design.
A high-throughput phenotypic screen combined with an ultra-large-scale deep learning-based virtual screening reveals novel scaffolds of antibacterial compounds
A high-throughput phenotypic screen combined with an ultra-large-scale deep learning-based virtual screening reveals novel scaffolds of antibacterial compounds
The proliferation of multi-drug-resistant bacteria underscores an urgent need for novel antibiotics. Traditional discovery methods face chal… (voir plus)lenges due to limited chemical diversity, high costs, and difficulties in identifying structurally novel compounds. Here, we explore the integration of small molecule high-throughput screening with a deep learning-based virtual screening approach to uncover new antibacterial compounds. Leveraging a diverse library of nearly 2 million small molecules, we conducted comprehensive phenotypic screening against a sensitized Escherichia coli strain that, at a low hit rate, yielded thousands of hits. We trained a deep learning model, GNEprop, to predict antibacterial activity, ensuring robustness through out-of-distribution generalization techniques. Virtual screening of over 1.4 billion compounds identified potential candidates, of which 82 exhibited antibacterial activity, illustrating a 90X improved hit rate over the high-throughput screening experiment GNEprop was trained on. Importantly, a significant portion of these newly identified compounds exhibited high dissimilarity to known antibiotics, indicating promising avenues for further exploration in antibiotic discovery.
In many data-driven applications, higher-order relationships among multiple objects are essential in capturing complex interactions. Hypergr… (voir plus)aphs, which generalize graphs by allowing edges to connect any number of nodes, provide a flexible and powerful framework for modeling such higher-order relationships. In this work, we introduce hypergraph diffusion wavelets and describe their favorable spectral and spatial properties. We demonstrate their utility for biomedical discovery in spatially resolved transcriptomics by applying the method to represent disease-relevant cellular niches for Alzheimer’s disease.
In many data-driven applications, higher-order relationships among multiple objects are essential in capturing complex interactions. Hypergr… (voir plus)aphs, which generalize graphs by allowing edges to connect any number of nodes, provide a flexible and powerful framework for modeling such higher-order relationships. In this work, we introduce hypergraph diffusion wavelets and describe their favorable spectral and spatial properties. We demonstrate their utility for biomedical discovery in spatially resolved transcriptomics by applying the method to represent disease-relevant cellular niches for Alzheimer’s disease.
