Crosstalk between circadian rhythms and the microbiota

Circadian rhythms influence daily molecular oscillations in gene/protein expression and aspects of biology and physiology, including behaviour, body temperature and sleep–wake cycles. These circadian rhythms have been associated with a number of metabolic, immune and microbial changes that correlate with health and susceptibility to disease, including infection. While light is the main inducer of circadian rhythms, other factors, including the microbiota, can have important effects on peripheral rhythms. The microbiota have been of significant interest to many investigators over the past decade, with the development of molecular techniques to identify large numbers of species and their function. These studies have shown microbial associations with disease susceptibility, and some of these have demonstrated that alterations in microbiota cause disease. Microbial circadian oscillations impact host metabolism and immunity directly and indirectly. Interestingly, microbial oscillations also regulate host circadian rhythms, and the host circadian rhythms in turn modulate microbial composition. Thus, it is of considerable interest and importance to understand the crosstalk between circadian rhythms and microbiota and especially the microbial influences on the host. In this review, we aim to discuss the role of circadian microbial oscillations and how they influence host immunity. In addition, we discuss how host circadian rhythms can also modulate microbial rhythms. We also discuss potential connections between microbes and circadian rhythms and how these may be used therapeutically to maximize clinical success

Circadian rhythms and pancreas physiology: A review

Type 2 diabetes mellitus, obesity and metabolic syndrome are becoming more prevalent worldwide and will present an increasingly challenging burden on healthcare systems. These interlinked metabolic abnormalities predispose affected individuals to a plethora of complications and comorbidities. Furthermore, diabetes is estimated by the World Health Organization to have caused 1.5 million deaths in 2019, with this figure projected to rise in coming years. This highlights the need for further research into the management of metabolic diseases and their complications. Studies on circadian rhythms, referring to physiological and behavioral changes which repeat approximately every 24 hours, may provide important insight into managing metabolic disease. Epidemiological studies show that populations who are at risk of circadian disruption such as night shift workers and regular long-haul flyers are also at an elevated risk of metabolic abnormalities such as insulin resistance and obesity. Aberrant expression of circadian genes appears to contribute to the dysregulation of metabolic functions such as insulin secretion, glucose homeostasis and energy expenditure. The potential clinical implications of these findings have been highlighted in animal studies and pilot studies in humans giving rise to the development of circadian interventions strategies including chronotherapy (time-specific therapy), time-restricted feeding, and circadian molecule stabilizers/analogues. Research into these areas will provide insights into the future of circadian medicine in metabolic diseases. In this review, we discuss the physiology of metabolism and the role of circadian timing in regulating these metabolic functions. Also, we review the clinical aspects of circadian physiology and the impact that ongoing and future research may have on the management of metabolic disease

Inflammasomes and Type 1 Diabetes

Microbiota have been identified as an important modulator of susceptibility in the development of Type 1 diabetes in both animal models and humans. Collectively these studies highlight the association of the microbiota composition with genetic risk, islet autoantibody development and modulation of the immune responses. However, the signaling pathways involved in mediating these changes are less well investigated, particularly in humans. Importantly, understanding the activation of signaling pathways in response to microbial stimulation is vital to enable further development of immunotherapeutics, which may enable enhanced tolerance to the microbiota or prevent the initiation of the autoimmune process. One such signaling pathway that has been poorly studied in the context of Type 1 diabetes is the role of the inflammasomes, which are multiprotein complexes that can initiate immune responses following detection of their microbial ligands. In this review, we discuss the roles of the inflammasomes in modulating Type 1 diabetes susceptibility, from genetic associations to the priming and activation of the inflammasomes. In addition, we also summarize the available inhibitors for therapeutically targeting the inflammasomes, which may be of future use in Type 1 diabetes

Circadian rhythm modulation of microbes during health and infection

Circadian rhythms, referring to 24-h daily oscillations in biological and physiological processes, can significantly regulate host immunity to pathogens, as well as commensals, resulting in altered susceptibility to disease development. Furthermore, vaccination responses to microbes have also shown time-of-day-dependent changes in the magnitude of protective immune responses elicited in the host. Thus, understanding host circadian rhythm effects on both gut bacteria and viruses during infection is important to minimize adverse effects on health and identify optimal times for therapeutic administration to maximize therapeutic success. In this review, we summarize the circadian modulations of gut bacteria, viruses and their interactions, both in health and during infection. We also discuss the importance of chronotherapy (i.e., timespecific therapy) as a plausible therapeutic administration strategy to enhance beneficial therapeutic responses

Slideflow: Deep Learning for Digital Histopathology with Real-Time Whole-Slide Visualization

Deep learning methods have emerged as powerful tools for analyzing histopathological images, but current methods are often specialized for specific domains and software environments, and few open-source options exist for deploying models in an interactive interface. Experimenting with different deep learning approaches typically requires switching software libraries and reprocessing data, reducing the feasibility and practicality of experimenting with new architectures. We developed a flexible deep learning library for histopathology called Slideflow, a package which supports a broad array of deep learning methods for digital pathology and includes a fast whole-slide interface for deploying trained models. Slideflow includes unique tools for whole-slide image data processing, efficient stain normalization and augmentation, weakly-supervised whole-slide classification, uncertainty quantification, feature generation, feature space analysis, and explainability. Whole-slide image processing is highly optimized, enabling whole-slide tile extraction at 40X magnification in 2.5 seconds per slide. The framework-agnostic data processing pipeline enables rapid experimentation with new methods built with either Tensorflow or PyTorch, and the graphical user interface supports real-time visualization of slides, predictions, heatmaps, and feature space characteristics on a variety of hardware devices, including ARM-based devices such as the Raspberry Pi

TLR5-deficiency controls dendritic cell subset development in an autoimmune diabetes-susceptible model

Introduction: The incidence of the autoimmune disease, type 1 diabetes (T1D), has been increasing worldwide and recent studies have shown that the gut microbiota are associated with modulating susceptibility to T1D. Toll-like receptor 5 (TLR5) recognizes bacterial flagellin and is widely expressed on many cells, including dendritic cells (DCs), which are potent antigen-presenting cells (APCs). TLR5 modulates susceptibility to obesity and alters metabolism through gut microbiota; however, little is known about the role TLR5 plays in autoimmunity, especially in T1D. Methods: To fill this knowledge gap, we generated a TLR5-deficient non-obese diabetic (NOD) mouse, an animal model of human T1D, for study. Results: We found that TLR5-deficiency led to a reduction in CD11c+ DC development in utero, prior to microbial colonization, which was maintained into adulthood. This was associated with a bias in the DC populations expressing CD103, with or without CD8α co-expression, and hyper-secretion of different cytokines, both in vitro (after stimulation) and directly ex vivo. We also found that TLR5-deficient DCs were able to promote polyclonal and islet antigen-specific CD4+ T cell proliferation and proinflammatory cytokine secretion. Interestingly, only older TLR5-deficient NOD mice had a greater risk of developing spontaneous T1D compared to wild-type mice. Discussion: In summary, our data show that TLR5 modulates DC development and enhances cytokine secretion and diabetogenic CD4+ T cell responses. Further investigation into the role of TLR5 in DC development and autoimmune diabetes may give additional insights into the pathogenesis of Type 1 diabetes

Long-Term Preservation of Cones and Improvement in Visual Function Following Gene Therapy in a Mouse Model of Leber Congenital Amaurosis Caused by Guanylate Cyclase-1 Deficiency

Leber congenital amaurosis (LCA) is a severe retinal dystrophy manifesting from early infancy as poor vision or blindness. Loss-of-function mutations in GUCY2D cause LCA1 and are one of the most common causes of LCA, accounting for 20% of all cases. Human GUCY2D and mouse Gucy2e genes encode guanylate cyclase-1 (GC), which is responsible for restoring the dark state in photoreceptors after light exposure. The Glicy2e(-/-) mouse shows partially diminished rod function, but an absence of cone function before degeneration. Although the cones appear morphologically normal, they exhibit mislocalization of proteins involved in phototransduction. In this study we tested the efficacy of an rAAV2/8 vector containing the human rhodopsin kinase promoter and the human GUCY2D gene. Following subretinal delivery of the vector in Glicy2e(-/-) mice, GC1 protein was detected in the rod and cone outer segments, and in transduced areas of retina cone transducin was appropriately localized to cone outer segments. Moreover, we observed a dose-dependent restoration of rod and cone function and an improvement in visual behavior of the treated mice. Most importantly, cone preservation was observed in transduced areas up to 6 months post injection. To date, this is the most effective rescue of the Glicy2e(-/-) mouse model of LCA and we propose that a vector, similar to the one used in this study, could be suitable for use in a clinical trial of gene therapy for LCA1

Uncertainty-Informed Deep Learning Models Enable High-Confidence Predictions for Digital Histopathology

A model's ability to express its own predictive uncertainty is an essential attribute for maintaining clinical user confidence as computational biomarkers are deployed into real-world medical settings. In the domain of cancer digital histopathology, we describe a novel, clinically-oriented approach to uncertainty quantification (UQ) for whole-slide images, estimating uncertainty using dropout and calculating thresholds on training data to establish cutoffs for low- and high-confidence predictions. We train models to identify lung adenocarcinoma vs. squamous cell carcinoma and show that high-confidence predictions outperform predictions without UQ, in both cross-validation and testing on two large external datasets spanning multiple institutions. Our testing strategy closely approximates real-world application, with predictions generated on unsupervised, unannotated slides using predetermined thresholds. Furthermore, we show that UQ thresholding remains reliable in the setting of domain shift, with accurate high-confidence predictions of adenocarcinoma vs. squamous cell carcinoma for out-of-distribution, non-lung cancer cohorts