Abstract:Motivation
Human diseases are characterized by multiple features such as their pathophysiological, molecular, and genetic changes. The rapid expansion of such multi-modal disease-omics space provides an opportunity to re-classify diverse human diseases and to uncover their latent molecular similarities, which could be exploited to repurpose a therapeutic-target for one disease to another.
Results
Herein, we probe this underex… Show more
“…The molecular relatedness of IPF to non-respiratory/non-pulmonary diseases were identified by using the multi-modal generative topic modeling method that we developed and previously reported 29 . The overall design is summarized in Fig.…”
Section: Resultsmentioning
confidence: 99%
“…1 , and it works as follows: Figure 1 General overview of the multi-modal generative topic modeling approach for IPF. The previously developed method 29 is adapted to IPF. …”
Section: Resultsmentioning
confidence: 99%
“…The multi-modal generative topic modeling generates (i.e., predicts) the features of the missing modalities. The performance was evaluated by calculating the area under the receiver operating characteristic curve (AUC) values as previously described 29 and they were found to be above 0.8 for all three modalities (Supplementary Fig. S1 ).…”
Section: Resultsmentioning
confidence: 99%
“…The multi-modal generative topic modeling approach is as previously described 29 . This topic modeling approach exploits the similarities among diseases on the basis of their multi-modal omics features.…”
Section: Methodsmentioning
confidence: 99%
“…The organs and cells where the IPF-features are expressed were identified by organ/cell enrichment analyses using THE HUMAN PROTEIN ATLAS v 21.1 23 – 25 , as previously described 29 . Briefly, we generated a 2 × 2 contingency table showing the number of the genes of interest that are associated with the target organ(s)/cell(s), and performed chi-square test of independence by using the contingency table.…”
Idiopathic pulmonary fibrosis (IPF) is a chronic and progressive disease characterized by complex lung pathogenesis affecting approximately three million people worldwide. While the molecular and cellular details of the IPF mechanism is emerging, our current understanding is centered around the lung itself. On the other hand, many human diseases are the products of complex multi-organ interactions. Hence, we postulate that a dysfunctional crosstalk of the lung with other organs plays a causative role in the onset, progression and/or complications of IPF. In this study, we employed a generative computational approach to identify such inter-organ mechanism of IPF. This approach found unexpected molecular relatedness of IPF to neoplasm, diabetes, Alzheimer’s disease, obesity, atherosclerosis, and arteriosclerosis. Furthermore, as a potential mechanism underlying this relatedness, we uncovered a putative molecular crosstalk system across the lung and the liver. In this inter-organ system, a secreted protein, kininogen 1, from hepatocytes in the liver interacts with its receptor, bradykinin receptor B1 in the lung. This ligand–receptor interaction across the liver and the lung leads to the activation of calmodulin pathways in the lung, leading to the activation of interleukin 6 and phosphoenolpyruvate carboxykinase 1 pathway across these organs. Importantly, we retrospectively identified several pre-clinical and clinical evidence supporting this inter-organ mechanism of IPF. In conclusion, such feedforward and feedback loop system across the lung and the liver provides a unique opportunity for the development of the treatment and/or diagnosis of IPF. Furthermore, the result illustrates a generative computational framework for machine-mediated synthesis of mechanisms that facilitates and complements the traditional experimental approaches in biomedical sciences.
“…The molecular relatedness of IPF to non-respiratory/non-pulmonary diseases were identified by using the multi-modal generative topic modeling method that we developed and previously reported 29 . The overall design is summarized in Fig.…”
Section: Resultsmentioning
confidence: 99%
“…1 , and it works as follows: Figure 1 General overview of the multi-modal generative topic modeling approach for IPF. The previously developed method 29 is adapted to IPF. …”
Section: Resultsmentioning
confidence: 99%
“…The multi-modal generative topic modeling generates (i.e., predicts) the features of the missing modalities. The performance was evaluated by calculating the area under the receiver operating characteristic curve (AUC) values as previously described 29 and they were found to be above 0.8 for all three modalities (Supplementary Fig. S1 ).…”
Section: Resultsmentioning
confidence: 99%
“…The multi-modal generative topic modeling approach is as previously described 29 . This topic modeling approach exploits the similarities among diseases on the basis of their multi-modal omics features.…”
Section: Methodsmentioning
confidence: 99%
“…The organs and cells where the IPF-features are expressed were identified by organ/cell enrichment analyses using THE HUMAN PROTEIN ATLAS v 21.1 23 – 25 , as previously described 29 . Briefly, we generated a 2 × 2 contingency table showing the number of the genes of interest that are associated with the target organ(s)/cell(s), and performed chi-square test of independence by using the contingency table.…”
Idiopathic pulmonary fibrosis (IPF) is a chronic and progressive disease characterized by complex lung pathogenesis affecting approximately three million people worldwide. While the molecular and cellular details of the IPF mechanism is emerging, our current understanding is centered around the lung itself. On the other hand, many human diseases are the products of complex multi-organ interactions. Hence, we postulate that a dysfunctional crosstalk of the lung with other organs plays a causative role in the onset, progression and/or complications of IPF. In this study, we employed a generative computational approach to identify such inter-organ mechanism of IPF. This approach found unexpected molecular relatedness of IPF to neoplasm, diabetes, Alzheimer’s disease, obesity, atherosclerosis, and arteriosclerosis. Furthermore, as a potential mechanism underlying this relatedness, we uncovered a putative molecular crosstalk system across the lung and the liver. In this inter-organ system, a secreted protein, kininogen 1, from hepatocytes in the liver interacts with its receptor, bradykinin receptor B1 in the lung. This ligand–receptor interaction across the liver and the lung leads to the activation of calmodulin pathways in the lung, leading to the activation of interleukin 6 and phosphoenolpyruvate carboxykinase 1 pathway across these organs. Importantly, we retrospectively identified several pre-clinical and clinical evidence supporting this inter-organ mechanism of IPF. In conclusion, such feedforward and feedback loop system across the lung and the liver provides a unique opportunity for the development of the treatment and/or diagnosis of IPF. Furthermore, the result illustrates a generative computational framework for machine-mediated synthesis of mechanisms that facilitates and complements the traditional experimental approaches in biomedical sciences.
Idiopathic pulmonary fibrosis (IPF) is a chronic and progressive disease characterized by complex lung pathogenesis affecting approximately three million people worldwide. While the molecular and cellular details of the IPF mechanism is emerging, our current understanding is centered around the lung itself. On the other hand, many human diseases are the products of complex multi-organ interactions. Hence, we postulate that a dysfunctional crosstalk of the lung with other organs plays a causative role in the onset, progression and/or complications of IPF. In this study, we employed a generative computational approach to identify such inter-organ mechanism of IPF. The approach works as follows: 1) To find unexpected relatedness of IPF to other diseases of non-lung organs and to identify molecular features that define such relatedness, 2) To identify differentially expressed genes between the lung tissues of IPF vs. those of non-IPF pulmonary disease patients, 3) To detect ligand-receptor relationships across multiple organs and their upstream and downstream signaling pathways in 1) and 2), 4) To generate a map of the inter-organ IPF mechanism with the molecular and cellular resolution. This approach found unexpected molecular relatedness of IPF to neoplasm, diabetes, Alzheimer′s disease, obesity, atherosclerosis, and arteriosclerosis. Furthermore, as a potential mechanism underlying this relatedness, we uncovered a putative molecular crosstalk system across the lung and the liver. In this inter-organ system, a secreted protein, kininogen 1, from hepatocytes in the liver interacts with its receptor, bradykinin receptor B1 in the lung. This ligand-receptor interaction across the liver and the lung leads to the activation of calmodulin pathways in the lung, leading to the activation of interleukin 6 and phosphoenolpyruvate carboxykinase 1 pathway across these organs. Furthermore, we retrospectively identified several pre-clinical and clinical evidence supporting this inter-organ mechanism of IPF. In conclusion, such feedforward and feedback system across the lung and the liver provides a unique opportunity for the development of the treatment and/or diagnosis of IPF. Furthermore, the result illustrates a generative computational framework for machine-mediated synthesis of mechanisms that facilitates and complements the traditional experimental approaches in biomedical sciences.
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