Current Status of Endoplasmic Reticulum Stress in Type II Diabetes

Mustapha, Sagir; Mohammed, Mustapha; Azemi, Ahmad Khusairi; Jatau, Abubakar Ibrahim; Shehu, Aishatu; Mustapha, Lukman; Aliyu, M.; Danraka, Rabi’u Nuhu; Amin, Abdulbasit; Bala, Auwal Adam; Ahmad, Wan Amir Nizam Wan; Rasool, Aida Hanum Ghulam; Mustafa, Mohd Rais; Mokhtar, Siti Safiah

doi:10.20944/preprints202106.0227.v1

Cited by 11 publications

(5 citation statements)

References 103 publications

(132 reference statements)

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“…The UPR arises in T2D owing to the proinsulin synthesis rate, which exceeds the protein processing capacity of cells, leading to beta cell dysfunction and death 80,81 . Thus, we attempted further study of the enriched GP describing the UPR, and compared the distribution of UPR scores with the scores of other GPs associated with protein synthesis and processing ( Fig.…”

Section: Resultsmentioning

confidence: 99%

Biologically informed deep learning to infer gene program activity in single cells

Lotfollahi

Rybakov

Hrovatin

et al. 2022

Preprint

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The increasing availability of large-scale single-cell datasets has enabled the detailed description of cell states across multiple biological conditions and perturbations. In parallel, recent advances in unsupervised machine learning, particularly in transfer learning, have enabled fast and scalable mapping of these new single-cell datasets onto reference atlases. The resulting large-scale machine learning models however often have millions of parameters, rendering interpretation of the newly mapped datasets challenging. Here, we propose expiMap, a deep learning model that enables interpretable reference mapping using biologically understandable entities, such as curated sets of genes and gene programs. The key concept is the substitution of the uninterpretable nodes in an autoencoder's bottleneck by labeled nodes mapping to interpretable lists of genes, such as gene ontologies, biological pathways, or curated gene sets, for which activities are learned as constraints during reconstruction. This is enabled by the incorporation of predefined gene programs into the reference model, and at the same time allowing the model to learn de novo new programs and refine existing programs durin reference mapping. We show that the model retains similar integration performance as existing methods while providing a biologically interpretable framework for understanding cellular behavior. We demonstrate the capabilities of expiMap by applying it to 15 datasets encompassing five different tissues and species. The interpretable nature of the mapping revealed unreported associations between interferon signaling via the RIG-I/MDA5 and GPCRs pathways, with differential behavior in CD8+ T cells and CD14+ monocytes in severe COVID-19, as well as the role of annexins in the cellular communications between lymphoid and myeloid compartments for explaining patient response to the applied drugs. Finally, expiMap enabled the direct comparison of a diverse set of pancreatic beta cells from multiple studies where we observed a strong, previously unreported correlation between the unfolded protein response and asparagine N-linked glycosylation. Altogether, expiMap enables the interpretable mapping of single cell transcriptome data sets across cohorts, disease states and other perturbations.

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Section: Resultsmentioning

confidence: 99%

Biologically informed deep learning to infer gene program activity in single cells

Lotfollahi

Rybakov

Hrovatin

et al. 2022

Preprint

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“…Accordingly, impaired beta cell function together with elevated insulin resistance cause hyperglycemia and type 2 diabetes (T2D) (Zheng et al 2018). Beta cell dysfunction in T2D has been attributed to several mechanisms, often combined with each other, including reduced oxidative phosphorylation (Supale et al 2012; Dludla et al 2023), prolonged ER stress (Back and Kaufman 2012; Mustapha et al 2021; Shrestha 2021), chronic islet inflammation (Donath et al 2003; Donath et al 2013; Kulkarni et al 2022; Rohm et al 2022) and amyloidosis (Kahn et al 1999; Jurgens et al 2011) resulting in beta cell dedifferentiation (Talchai et al 2012; Son and Accili 2023) and death (Butler et al 2003). Yet, it is important to appreciate that a clear explanation for how beta cell dysfunction evolves during the progression from normoglycemia to T2D is still missing.…”

Section: Introductionmentioning

confidence: 99%

Post-transcriptional regulation of insulin mRNA storage by G3BP1/2⁺condensates in beta cells

Quezada,

Vasiljevic,

Pal

et al. 2024

Preprint

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Hyperglycemia upregulates insulin translation in pancreatic beta cells. Several RNA-binding proteins involved in this process have been identified, including G3BP1, a stress granule marker downregulated in islets of subjects with type 2 diabetes. We show that in mouse insulinoma MIN6-K8 cells exposed to fasting glucose levels G3BP1 and its paralog G3BP2 colocalize to cytosolic condensates with eIF3b and Ins1/2 mRNA. Upon glucose stimulation, the condensates dissolve and G3BP1/2, eIF3b, and insulin mRNAs redistribute throughout the cytosol. Intriguingly, G3BP1+ condensates in MIN6-K8 cells differ from sodium arsenate-induced stress granules in regards to eIF2α and AMPKα phosphorylation. Knockout of G3BP1 or G3BP2 prevented condensate assembly, but only G3BP1 deletion decreased the levels of Ins1/2 mRNA and proinsulin and impaired polysome formation. Like glucose, other insulin secretagogues such as Exendin-4 and palmitate, but not high KCl, prompted the dissolution of G3BP1+ condensates. G3BP1+/Ins mRNA+ condensates were also present in mouse and human beta cells from normoglycemic donors. Hence, G3BP1+ condensates represent a glucose-regulated compartment for the physiological storage and protection of insulin mRNA in resting beta cells.

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“…According to National Diabetes Federation (2019), about 19 million in Pakistan from age 20 to 79 have been suffering from diabetes. Type-II diabetes is the most commonly occurring type that accounts for up to 90% of the total [3].…”

Section: Introductionmentioning

confidence: 99%

Cognitive behavior therapy for diabetes distress, depression, health anxiety, quality of life and treatment adherence among patients with type-II diabetes mellitus: a randomized control trial

et al. 2023

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Objective Diabetes distress typically causes depressive symptoms; common comorbidity of diabetes unpleasantly affects patients’ medical and psychological functions. Psychotherapeutic interventions are effective treatments to treat depressive symptoms and to improve the quality of life in many chronic diseases including diabetes. The present study investigated the efficacy of cognitive behavior therapy (CBT) to treat depressive symptoms in patients with type 2 diabetes mellitus (T2DM) using experimental and waitlist control conditions. Materials and Methods A total of 130 diagnosed patients with T2DM were taken from outdoor patients services of different hospitals in Faisalabad. Ninety patients met the eligibility criteria and were randomly assigned to experimental (n = 45) and waitlist control (n = 45) conditions. All the patients completed clinical interviews and assessment measures at pre-and post-assessment stages (16 weeks intervals). Medical consultants at the respective hospitals diagnosed the patients on the base of their medical reports and then referred those patients to us. Then we used different scales to assess primary and secondary outcomes: Diabetes Distress Scale (DDS) and Patient Health Questionnaire (PHQ) to assess primary outcomes, and a Short Health Anxiety Inventory (SHAI), a Revised Version of the Diabetes Quality of Life Questionnaire (DQLQ), and a General Medication Adherence Scale (GMAS) were used to investigate secondary outcomes. Repeated measure ANOVA was used to analyze the results. Results The findings indicated that patients who received CBT got a significant reduction in their diabetes distress F(1,60) = 222.710, P < 0.001, η2 = .788), depressive symptoms F(1,60) = 94.436, P < 0.001, η2 = .611), health anxiety F(1,60) = 201.915, P < .0.001, η2 = 771), and a significant improvement in their quality of life F(1,60) = 83.352, P < 0.001, η2 = .581), treatment adherence F(1,60) = 67.579, P < 0.001, η2 = .566) and physical activity schedule F(1,60) = 164.245, P < .0.001, η2 = .736 as compared to the patients in waitlist control condition. Conclusion It is concluded that cognitive behavior therapy is an effective and promising intervention for depressive symptoms, diabetes distress, and health anxiety which also helps the person to promote quality of life, treatment adherence and physical activity.

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Current Status of Endoplasmic Reticulum Stress in Type II Diabetes

Cited by 11 publications

References 103 publications

Biologically informed deep learning to infer gene program activity in single cells

Biologically informed deep learning to infer gene program activity in single cells

Post-transcriptional regulation of insulin mRNA storage by G3BP1/2⁺condensates in beta cells

Cognitive behavior therapy for diabetes distress, depression, health anxiety, quality of life and treatment adherence among patients with type-II diabetes mellitus: a randomized control trial

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Current Status of Endoplasmic Reticulum Stress in Type II Diabetes

Cited by 11 publications

References 103 publications

Biologically informed deep learning to infer gene program activity in single cells

Biologically informed deep learning to infer gene program activity in single cells

Post-transcriptional regulation of insulin mRNA storage by G3BP1/2+condensates in beta cells

Cognitive behavior therapy for diabetes distress, depression, health anxiety, quality of life and treatment adherence among patients with type-II diabetes mellitus: a randomized control trial

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Post-transcriptional regulation of insulin mRNA storage by G3BP1/2⁺condensates in beta cells