Multi-omics Investigations in Endocrine Systems and Their Clinical Implications.

Innovative techniques such as the "omics" can be a powerful tool for the understanding of intracellular pathways involved in homeostasis maintenance and identification of new potential therapeutic targets against endocrine-metabolic disorders. Over the last decades, proteomics has been extensively applied in the study of a wide variety of human diseases, including those involving the endocrine system. Among the most endocrine-related disorders investigated by proteomics in humans are diabetes mellitus and thyroid, pituitary, and reproductive system disorders. In diabetes, proteins implicated in insulin signaling, glucose metabolism, and ß-cell activity have been investigated. In thyroid diseases, protein expression alterations were described in thyroid malignancies and autoimmune thyroid illnesses. Additionally, proteomics has been used to investigate the variations in protein expression in adrenal cancers and conditions, including Cushing's syndrome and Addison's disease. Pituitary tumors and disorders including acromegaly and hypopituitarism have been studied using proteomics to examine changes in protein expression. Reproductive problems such as polycystic ovarian syndrome and endometriosis are two examples of conditions where alterations in protein expression have been studied using proteomics. Proteomics has, in general, shed light on the molecular underpinnings of many endocrine-related illnesses and revealed promising biomarkers for both their detection and treatment. The capacity of proteomics to thoroughly and objectively examine complex protein mixtures is one of its main benefits. Mass spectrometry (MS) is a widely used method that identifies and measures proteins based on their mass-to-charge ratio and their fragmentation pattern. MS can perform the separation of proteins according to their physicochemical characteristics, such as hydrophobicity, charge, and size, in combination with liquid chromatography. Other proteomics techniques include protein arrays, which enable the simultaneous identification of several proteins in a single assay, and two-dimensional gel electrophoresis (2D-DIGE), which divides proteins depending on their isoelectric point and molecular weight. This chapter aims to summarize the most relevant proteomics data from targeted tissues, as well as the daily rhythmic variation of relevant biomarkers in both physiological and pathophysiological conditions within the involved endocrine system, especially because the actual modern lifestyle constantly imposes a chronic unentrained condition, which virtually affects all the circadian clock systems within human's body, being also correlated with innumerous endocrine-metabolic diseases.

Zika Virus Infection Alters the Circadian Clock Expression in Human Neuronal Monolayer and Neurosphere Cultures.

Rhythmic regulations are virtually described in all physiological processes, including central nervous system development and immunologic responses. Zika virus (ZIKV), a neurotropic arbovirus, has been recently linked to a series of birth defects and neurodevelopmental disorders. Given the well-characterized role of the intrinsic cellular circadian clock within neurogenesis, cellular metabolism, migration, and differentiation among other processes, this study aimed to characterize the influence of ZIKV infection in the circadian clock expression in human neuronal cells. For this, in vitro models of human-induced neuroprogenitor cells (hiNPCs) and neuroblastoma cell line SH-SY5Y, cultured as monolayer and neurospheres, were infected by ZIKV, followed by RNA-Seq and RT-qPCR investigation, respectively. Targeted circadian clock components presented mRNA oscillations only after exogenous synchronizing stimuli (Forskolin) in SH-SY5Y monolayer culture. Interestingly, when these cells were grown as 3D-arranged neurospheres, an intrinsic oscillatory expression pattern was observed for some core clock components without any exogenous stimulation. The ZIKV infection significantly disturbed the mRNA expression pattern of core clock components in both neuroblastoma cell culture models, which was also observed in hiNPCs infected with different strains of ZIKV. The ZIKV-mediated desynchronization of the circadian clock expression in human cells might further contribute to the virus impairment of neuronal metabolism and function observed in adults and ZIKV-induced congenital syndrome. In vitro models of Zika virus (ZIKV) neuronal infection. Human neuroprogenitor cells were cultured as monolayer and neurospheres and infected by ZIKV. Monolayer-cultured cells received forskolin (FSK) as a coupling factor for the circadian clock rhythmicity, while 3D-arranged neurospheres showed an intrinsic oscillatory pattern in the circadian clock expression. The ZIKV infection affected the mRNA expression pattern of core clock components in both cell culture models. The ZIKV-mediated desynchronization of the circadian clock machinery might contribute to the impairment of neuronal metabolism and function observed in both adults (e.g., Guillain-Barré syndrome) and ZIKV-induced congenital syndrome (microcephaly). The graphical abstract has been created with Canva at the canva.com website.

CircadiPy: an open-source toolkit for analyzing chronobiology time series.

BACKGROUND: Chronobiology is the scientific field focused on studying periodicity in biological processes. In mammals, most physiological variables exhibit circadian rhythmicity, such as metabolism, body temperature, locomotor activity, and sleep. The biological rhythmicity can be statistically evaluated by examining the time series and extracting parameters that correlate to the period of oscillation, its amplitude, phase displacement, and overall variability. NEW METHOD: We have developed a library called CircadiPy, which encapsulates methods for chronobiological analysis and data inspection, serving as an open-access toolkit for the analysis and interpretation of chronobiological data. The package was designed to be flexible, comprehensive and scalable in order to assist research dealing with processes affected or influenced by rhythmicity. RESULTS: The results demonstrate the toolkit's capability to guide users in analyzing chronobiological data collected from various recording sources, while also providing precise parameters related to the circadian rhythmicity. COMPARISON WITH EXISTING METHODS: The analysis methodology from this proposed library offers an opportunity to inspect and obtain chronobiological parameters in a straightforward and cost-free manner, in contrast to commercial tools. CONCLUSIONS: Moreover, being an open-source tool, it empowers the community with the opportunity to contribute with new functions, analysis methods, and graphical visualizations given the simplified computational method of time series data analysis using an easy and comprehensive pipeline within a single Python object.