Modeling strategies to study metabolic pathways in progression to type 1 diabetes – Challenges and opportunities

Towards systems immunology of critical illness at scale: from single cell 'omics to digital twins

Single-cell 'omics methodology has yielded unprecedented insights based largely on data-centric informatics for reducing, and thus interpreting, massive datasets. In parallel, parsimonious mathematical modeling based on abstractions of pathobiology has also yielded major insights into inflammation and immunity, with these models being extended to describe multi-organ disease pathophysiology as the basis of 'digital twins' and in silico clinical trials. The integration of these distinct methods at scale can drive both basic and translational advances, especially in the context of critical illness, including diseases such as COVID-19. Here, I explore achievements and argue the challenges that are inherent to the integration of data-driven and mechanistic modeling approaches, highlighting the potential of modeling-based strategies for rational immune system reprogramming.

Combined treatment with niclosamide ethanolamine and artemether combination improves type 1 diabetes via the targeting of liver mitochondria

Type 1 diabetes (T1D) is characterized by dysregulated blood glucose and liver metabolism. In previous studies, niclosamide ethanolamine salt (NEN) and artemether (Art) displayed significant hypoglycemic effects. However, their combined therapeutic effects on the liver in T1D have remained elusive. In the present study, T1D mice were established and randomly allocated into groups. Following treatment, the physiological and metabolic parameters, including liver function, glycogen content, glucose-6-phosphatase (G6Pase) protein expression levels, mitochondrial biogenesis and mitochondrial metabolism were analyzed. Compared with the NEN or Art treatments alone, their combination improved glycometabolism and the symptoms of diabetes. Combined treatment with NEN and Art also significantly ameliorated liver injury and increased liver glycogen storage. Furthermore, combinatorial treatment significantly downregulated hepatic G6Pase protein expression levels and regulated mitochondrial biogenesis. NEN and Art increased the respiratory exchange rate and reduced mitochondrial phosphoenolpyruvate carboxykinase and branched-chain α-keto acid dehydrogenase complex protein expression levels, whereby the effects were obviously enhanced by their application as a combined treatment. In conclusion, the present study confirmed that combined treatment with NEN and Art improved glycometabolism and liver function in T1D mice and the therapeutic effects may be partially associated with the regulation of liver mitochondria.

Roles of microRNAs in chronic pediatric diseases and their use as potential biomarkers: A review

MicroRNAs (miRNAs) are small non-coding highly conserved RNA molecules that can act as master regulators of gene expression in a sequence-specific manner either by translation repression or mRNA degradation, influencing a wide range of biologic processes that are essential for the maintenance of cellular homeostasis. Chronic pediatric diseases are the leading cause of death worldwide among children and the recent evidence indicates that aberrant miRNA expression significantly contributes to the development of chronic pediatric diseases. This review focuses on the role of miRNAs in five major chronic pediatric diseases including bronchial asthma, congenital heart diseases, cystic fibrosis, type 1 diabetes mellitus, and epilepsy, and their potential use as novel biomarkers for the diagnosis and prognosis of these disorders.

Reckoning the Dearth of Bioinformatics in the Arena of Diabetic Nephropathy (DN)—Need to Improvise

Diabetic nephropathy (DN) is a recent rising concern amongst diabetics and diabetologist. Characterized by abnormal renal function and ending in total loss of kidney function, this is becoming a lurking danger for the ever increasing population of diabetics. This review touches upon the intensity of this complication and briefly reviews the role of bioinformatics in the area of diabetes. The advances made in the area of DN using proteomic approaches are presented. Compared to the enumerable inputs observed through the use of bioinformatics resources in the area of proteomics and even diabetes, the existing scenario of skeletal application of bioinformatics advances to DN is highlighted and the reasons behind this discussed. As this review highlights, almost none of the well-established tools that have brought breakthroughs in proteomic research have been applied into DN. Laborious, voluminous, cost expensive and time-consuming methodologies and advances in diagnostics and biomarker discovery promised through beckoning bioinformatics mechanistic approaches to improvise DN research and achieve breakthroughs. This review is expected to sensitize the researchers to fill in this gap, exploiting the available inputs from bioinformatics resources.

Proteomic and bioinformatic discovery of biomarkers for diabetic nephropathy

Diabetes is associated with numerous metabolic and vascular risk factors that contribute to a high rate of micro-vascular and macro-vascular disorders leading to mortality and morbidity from diabetic complications. In this case, the major cause of death in overall diabetic patients results from diabetic nephropathy (DN) or renal failure. The risk factors and mechanisms that correspond to the development of DN are not fully understood and so far, no specific and sufficient diagnostic biomarkers are currently available other than micro- or macroalbuminuria. Therefore, this review describes current and novel protein biomarkers in the context of DN as well as probable proteins biomarkers associated with pathological processes for the early stage of DN via proteomics data together with bioinformatics. In addition, the mechanisms involved in early development of diabetic vascular disorders and complications resulting from glucose induced oxidative stress will also be explored.

pH-sensitive peptide hydrogel for glucose-responsive insulin delivery

Glucose-responsive system is one of important options for self-regulated insulin delivery to treat diabetes, which has become an issue of great public health concern in the world. In this study, we developed a novel and biocompatible glucose-responsive insulin delivery system using a pH-sensitive peptide hydrogel as a carrier loaded with glucose oxidase, catalase and insulin. The peptide could self-assemble into hydrogel under physiological conditions. When hypoglycemia is encountered, neighboring alkaline amino acid side chains are significantly repulsed due to reduced local pH by the enzymatic conversion of glucose into gluconic acid. This is followed by unfolding of individual hairpins, disassembly and release of insulin. The glucose-responsive hydrogel system was characterized on the basis of structure, conformation, rheology, morphology, acid-sensitivity and the amount of consistent release of insulin in vitro and vivo. The results illustrated that our system can not only regulate the blood glucose levels in vitro but also in mice models having STZ-induced diabetes.

STATEMENT OF SIGNIFICANCE

In this report, we have shown the following significance supported by the experimental results. 1. We successfully developed, characterized and screened a novel pH-responsive peptide. 2. We successfully developed a novel and biocompatible pH-sensitive peptide hydrogel as glucose-responsive insulin delivery system loaded with glucose oxidase, catalase and insulin. 3. We successfully confirmed that the hydrogel platform could regulate the blood glucose level in vitro and in vivo. Overall, we have shown enough significance and novelty with this smart hydrogel platform in terms of biomaterials, peptide chemistry, self-assembly, hydrogel and drug delivery. So we believe this manuscript is suitable for Acta Biomaterialia.

Where genes meet environment-integrating the role of gut luminal contents, immunity and pancreas in type 1 diabetes

The rise in new cases of type 1 diabetes (T1D) in genetically susceptible individuals over the past half century has been attributed to numerous environmental "triggers" or promoters such as enteroviruses, diet, and most recently, gut bacteria. No single cause has been identified in humans, likely because there are several pathways by which one can develop T1D. There is renewed attention to the role of the gut and its immune system in T1D pathogenesis based largely on recent animal studies demonstrating that altering the gut microbiota affects diabetes incidence. Although T1D patients display dysbiosis in the gut microbiome, it is unclear whether this is cause or effect. The heart of this question involves several moving parts including numerous risk genes, diet, viruses, gut microbiota, timing, and loss of immune tolerance to β-cells. Most clinical trials have addressed only one aspect of this puzzle using some form of immune suppression, without much success. The key location where our genes meet and deal with the environment is the gastrointestinal tract. The influence of all of its major contents, including microbes, diet, and immune system, must be understood as part of the integrative biology of T1D before we can develop durable means of preventing, treating, or curing this disease. In the present review, we expand our previous gut-centric model based on recent developments in the field.

Solving Immunology?

Emergent responses of the immune system result from the integration of molecular and cellular networks over time and across multiple organs. High-content and high-throughput analysis technologies, concomitantly with data-driven and mechanistic modeling, hold promise for the systematic interrogation of these complex pathways. However, connecting genetic variation and molecular mechanisms to individual phenotypes and health outcomes has proven elusive. Gaps remain in data, and disagreements persist about the value of mechanistic modeling for immunology. Here, we present the perspectives that emerged from the National Institute of Allergy and Infectious Disease (NIAID) workshop 'Complex Systems Science, Modeling and Immunity' and subsequent discussions regarding the potential synergy of high-throughput data acquisition, data-driven modeling, and mechanistic modeling to define new mechanisms of immunological disease and to accelerate the translation of these insights into therapies.

Lipidomics: Techniques, Applications, and Outcomes Related to Biomedical Sciences

Lipidomics is a newly emerged discipline that studies cellular lipids on a large scale based on analytical chemistry principles and technological tools, particularly mass spectrometry. Recently, techniques have greatly advanced and novel applications of lipidomics in the biomedical sciences have emerged. This review provides a timely update on these aspects. After briefly introducing the lipidomics discipline, we compare mass spectrometry-based techniques for analysis of lipids and summarize very recent applications of lipidomics in health and disease. Finally, we discuss the status of the field, future directions, and advantages and limitations of the field.