This is an open access article under the terms of the Creat ive Commo ns Attri butio n-NonCo mmerc ial-NoDerivs License, which permits use and distribution in any medium, provided the original work is properly cited, the use is non-commercial and no modifications or adaptations are made.
Cross-sectional epidemiological studies have shown that the incidence of several nervous system diseases is more frequent in epilepsy patients than in the general population. Some comorbidities [e.g. Alzheimer’s disease (AD) and Parkinson’s disease] are also risk factors for the development of seizures; suggesting they may share pathophysiological mechanisms with epilepsy. A literature-based approach was used to identify gene overlap between epilepsy and its comorbidities as a proxy for a shared genetic basis for disease, or genetic pleiotropy, as a first effort to identify shared mechanisms. While the results identified neurological disorders as the group of diseases with the highest gene overlap, this analysis was insufficient for identifying putative common mechanisms shared across epilepsy and its comorbidities. This motivated the use of a dedicated literature mining and knowledge assembly approach in which a cause-and-effect model of epilepsy was captured with Biological Expression Language. After enriching the knowledge assembly with information surrounding epilepsy, its risk factors, its comorbidities, and anti-epileptic drugs, a novel comparative mechanism enrichment approach was used to propose several downstream effectors (including the GABA receptor, GABAergic pathways, etc.) that could explain the therapeutic effects carbamazepine in both the contexts of epilepsy and AD. We have made the Epilepsy Knowledge Assembly available at https://www.scai.fraunhofer.de/content/dam/scai/de/downloads/bioinformatik/epilepsy.bel and queryable through NeuroMMSig at http://neurommsig.scai.fraunhofer.de. The source code used for analysis and tutorials for reproduction are available on GitHub at https://github.com/cthoyt/epicom.
Cross-sectional epidemiological studies have shown that the incidence of several nervous system diseases is more frequent in epilepsy patients than in the general population. Some comorbidities (e.g., Alzheimer's disease and Parkinson's disease) are also risk factors for the development of seizures; suggesting they may share pathophysiological mechanisms with epilepsy.A literature-based approach was used to identify gene overlap between epilepsy and its comorbidities as a proxy for a shared genetic basis for disease, or genetic pleiotropy, as a first effort to identify shared mechanisms. While the results identified neurological disorders as the group of diseases with the highest gene overlap, this analysis was insufficient for identifying putative common mechanisms shared across epilepsy and its comorbidities. This motivated the use of a dedicated literature mining and knowledge assembly approach in which a cause-and-effect model of epilepsy was captured with Biological Expression Language.After enriching the knowledge assembly with information surrounding epilepsy, its risk factors, its comorbidities, and antiepileptic drugs, a novel comparative mechanism enrichment approach was used to propose several downstream effectors (including the GABA receptor, GABAergic pathways, etc.) that could explain the therapeutic effects carbamazepine in both the contexts of epilepsy and AD.We have made the Epilepsy Knowledge Assembly available at
During embryogenesis, the fetal liver becomes the main hematopoietic organ, where stem and progenitor cells as well as immature and mature immune cells form an intricate cellular network. Hematopoietic stem cells (HSCs) reside in a specialized niche, which is essential for their proliferation and differentiation. However, the cellular and molecular determinants contributing to this fetal HSC niche remain largely unknown. Macrophages are the first differentiated hematopoietic cells found in the developing liver, where they are important for fetal erythropoiesis by promoting erythrocyte maturation and phagocytosing expelled nuclei. Yet, whether macrophages play a role in fetal hematopoiesis beyond serving as a niche for maturing erythroblasts remains elusive. Here, we investigate the heterogeneity of macrophage populations in the fetal liver to define their specific roles during hematopoiesis. Using a single-cell omics approach combined with spatial proteomics and genetic fate-mapping models, we found that fetal liver macrophages cluster into distinct yolk sac-derived subpopulations and that long-term HSCs are interacting preferentially with one of the macrophage subpopulations. Fetal livers lacking macrophages show a delay in erythropoiesis and have an increased number of granulocytes, which can be attributed to transcriptional reprogramming and altered differentiation potential of long-term HSCs. Together, our data provide a detailed map of fetal liver macrophage subpopulations and implicate macrophages as part of the fetal HSC niche.
27The unfolded protein response (UPR) is associated with the hepatic metabolic function, 28 yet it is not well understood how endoplasmic reticulum (ER) disturbance might 29 influence metabolic homeostasis. Here, we describe the physiological function of 30Cysteine-rich with EGF-like domains 2 (Creld2), previously characterized as a 31 downstream target of the ER-stress signal transducer Atf6. Creld2 enhances protein 32 folding and degradation through its interaction with proteins involved in UPR, thereby, 33 promoting tolerance of chronic stress and recovery from acute stress. Creld2-34 deficiency leads to a dysregulated UPR, and causes the development of hepatic 35 steatosis in male mice, while females are protected. We observed this sex dimorphism 36 also in humans with fatty liver disease, with only males showing an accumulation of 37 CRELD2 protein in the liver. These results reveal a Creld2 function at the intersection 38 between UPR and metabolic homeostasis and suggest a mechanism in which chronic 39 ER stress underlies fatty liver disease in males. 40 41 marker for kidney disease in urine 5 and for prosthetic joint infections in synovial fluid 53 6 . However, the physiological role of Creld2 in vivo remains unknown. 54Several in vitro studies have identified Creld2 as an ER-stress inducible gene, 55 whose expression is regulated by activating transcription factor 6 (Atf6) 7,8 . ER stress 56 is characterized by an accumulation of proteins in the ER lumen. Consequently, cells 57 activate an unfolded protein response (UPR), a signaling network that collectively aims 58 at decreasing ER protein load by broad inhibition of protein synthesis and at the same 59 time promotes the activation and production of proteins that increase protein folding 60 capacity and degradation. The latter include chaperones, shuttling proteins that 61 promote secretion of proteins out of the ER and ER-associated protein degradation 62 (ERAD) components 9 . 63The UPR is controlled by three sensors: Atf6, inositol requiring enzyme 1 (Ire1), 64 and protein kinase RNA-activated (PKR)-like ER kinase (Perk). The ER luminal 65 domains of the ER stress sensors are bound and thereby inactivated by the chaperone 66Grp78 (glucose-regulated protein 78, also known as heat shock protein A5 (Hspa5)). 67Upon ER stress Grp78 is sequestered by the accumulation of proteins in the ER lumen, 68 causing the activation of the three sensors. Perk dimerizes and is activated by trans-69 autophosphorylation of its kinase domains, leading to translational inhibition through 70 eIF2a phosphorylation. This directly enhances the translation of DNA-damage-71 inducible 34 (Gadd34) and CAAT/enhancer-binding protein (C/EBP) homologous 72 protein (Chop). While Gadd34 serves as a feedback loop to dephosphorylate eIF2α 73 allowing the cell to recover from translational inhibition, increased Chop expression 74 may trigger cell death due to unresolved ER stress. The kinase Ire1 undergoes 75 autophosphorylation resulting in splicing of the X-box binding protein 1 (sXbp1) ...
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