Bree AJ, Puente EC, Daphna-Iken D, Fisher SJ. Diabetes increases brain damage caused by severe hypoglycemia. Am J Physiol Endocrinol Metab 297: E194 -E201, 2009. First published May 12, 2009 doi:10.1152/ajpendo.91041.2008.-Insulin-induced severe hypoglycemia causes brain damage. The hypothesis to be tested was that diabetes portends to more extensive brain tissue damage following an episode of severe hypoglycemia. Nine-week-old male streptozotocin-diabetic (DIAB; n ϭ 10) or vehicle-injected control (CONT; n ϭ 7) SpragueDawley rats were subjected to hyperinsulinemic (0.2 U⅐kg Ϫ1 ⅐min Ϫ1 ) severe hypoglycemic (10 -15 mg/dl) clamps while awake and unrestrained. Groups were precisely matched for depth and duration (1 h) of severe hypoglycemia (CONT 11 Ϯ 0.5 and DIAB 12 Ϯ 0.2 mg/dl, P ϭ not significant). During severe hypoglycemia, an equal number of episodes of seizure-like activity were noted in both groups. One week later, histological analysis demonstrated extensive neuronal damage in regions of the hippocampus, especially in the dentate gyrus and CA1 regions and less so in the CA3 region (P Ͻ 0.05), although total hippocampal damage was not different between groups. However, in the cortex, DIAB rats had significantly (2.3-fold) more dead neurons than CONT rats (P Ͻ 0.05). There was a strong correlation between neuronal damage and the occurrence of seizure-like activity (r 2 Ͼ 0.9). Separate studies conducted in groups of diabetic (n ϭ 5) and nondiabetic (n ϭ 5) rats not exposed to severe hypoglycemia showed no brain damage. In summary, under the conditions studied, severe hypoglycemia causes brain damage in the cortex and regions within the hippocampus, and the extent of damage is closely correlated to the presence of seizure-like activity in nonanesthetized rats. It is concluded that, in response to insulin-induced severe hypoglycemia, diabetes uniquely increases the vulnerability of specific brain areas to neuronal damage.Fluoro-Jade; insulin; seizure; streptozotocin HYPOGLYCEMIA IS THE MOST PREVALENT clinical complication in the daily management of insulin-treated people with diabetes, and hypoglycemia continues to be the limiting factor in the glycemic management of diabetes (15). Since severe hypoglycemia affects 40% of insulin-treated people with diabetes (49), concern regarding the hazardous potential for severe hypoglycemia to cause "brain damage" continues to be a very real barrier in striving to fully realize the benefits associated with intensive glycemic control (14). Animal models have unambiguously demonstrated that acute episodes of severe hypoglycemia [blood glucose (BG) Ͻ18 mg/dl] reproducibly induce neuronal damage, especially in the vulnerable neurons in the cortex and hippocampus (2,9,33,44,45,54). Deficits in learning and memory have been shown to be a direct consequence of this severe hypoglycemia-induced hippocampal neuronal damage (2,44,45). However, clincial studies in patients with diabetes have yielded variable results, since episodes of severe hypoglycemia have been shown to alter br...

Background Studies of T-cell immune responses against SARS-CoV-2 are important in understanding the immune status of individuals or populations. Here, we use a simple, cheap and rapid whole blood stimulation assay - an Interferon-Gamma Release Assay (IGRA) - to study T-cell immunity to SARS-CoV-2 in convalescent COVID-19 patients and in unexposed healthy contacts from Quito, Ecuador. Methods Interferon-gamma (INF-γ) production was measured in the heparinized blood of convalescent and unexposed subjects after stimulation for 24 hours with the SARS-CoV-2 Spike S1 protein, the Receptor Binding Domain (RBD) protein or the Nucleocapsid (NP) protein, respectively. The presence of IgG- RBD protein antibodies in both study groups was determined with an “in-house” ELISA. Results As measured with INF-γ production, 80% of the convalescent COVID-19 patients, all IgG-RBD seropositive, had a strong T-cell response. However, unexpectedly, 44% of healthy unexposed healthy controls, all IgG-RBD seronegative, had a strong virus-specific T-cell response with the COVID-19 IGRA, probably because of prior exposure to common cold-causing coronaviruses or other viral or microbial antigens. Conclusion and Discussion The high percentage of unexposed healthy subjects with a pre-existing immunity suggests that a part of the Ecuadorian population is likely to have SARS-CoV-2 reactive T cells. Given that the IGRA technique is simple, and can be easily scaled up for investigations where high numbers of patients are needed, this COVID-19 IGRA may serve to determine if the T-cell only response represents protective immunity to SARS-CoV-2 infection in a population-based study.

Consensus strategy was proved to be highly efficient in the recognition of gene-disease association. Therefore, the main objective of this study was to apply theoretical approaches to explore genes and communities directly involved in breast cancer (BC) pathogenesis. We evaluated the consensus between 8 prioritization strategies for the early recognition of pathogenic genes. A communality analysis in the protein-protein interaction (PPi) network of previously selected genes was enriched with gene ontology, metabolic pathways, as well as oncogenomics validation with the OncoPPi and DRIVE projects. The consensus genes were rationally filtered to 1842 genes. The communality analysis showed an enrichment of 14 communities specially connected with ERBB, PI3K-AKT, mTOR, FOXO, p53, HIF-1, VEGF, MAPK and prolactin signaling pathways. Genes with highest ranking were TP53, ESR1, BRCA2, BRCA1 and ERBB2. Genes with highest connectivity degree were TP53, AKT1, SRC, CREBBP and EP300. The connectivity degree allowed to establish a significant correlation between the OncoPPi network and our BC integrated network conformed by 51 genes and 62 PPi. In addition, CCND1, RAD51, CDC42, YAP1 and RPA1 were functional genes with significant sensitivity score in BC cell lines. In conclusion, the consensus strategy identifies both well-known pathogenic genes and prioritized genes that need to be further explored.

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BackgroundPreeclampsia is a multifactorial disease with unknown pathogenesis. Even when recent studies explored this disease using several bioinformatics tools, the main objective was not directed to pathogenesis. Additionally, consensus prioritization was proved to be highly efficient in the recognition of genes-disease association. However, not information is available about the consensus ability to early recognize genes directly involved in pathogenesis. Therefore our aim in this study is to apply several theoretical approaches to explore preeclampsia; specifically those genes directly involved in the pathogenesis.MethodsWe firstly evaluated the consensus between 12 prioritization strategies to early recognize pathogenic genes related to preeclampsia. A communality analysis in the protein-protein interaction network of previously selected genes was done including further enrichment analysis. The enrichment analysis includes metabolic pathways as well as gene ontology. Microarray data was also collected and used in order to confirm our results or as a strategy to weight the previously enriched pathways.ResultsThe consensus prioritized gene list was rationally filtered to 476 genes using several criteria. The communality analysis showed an enrichment of communities connected with VEGF-signaling pathway. This pathway is also enriched considering the microarray data. Our result point to VEGF, FLT1 and KDR as relevant pathogenic genes, as well as those connected with NO metabolism.ConclusionOur results revealed that consensus strategy improve the detection and initial enrichment of pathogenic genes, at least in preeclampsia condition. Moreover the combination of the first percent of the prioritized genes with protein-protein interaction network followed by communality analysis reduces the gene space. This approach actually identifies well known genes related with pathogenesis. However, genes like HSP90, PAK2, CD247 and others included in the first 1% of the prioritized list need to be further explored in preeclampsia pathogenesis through experimental approaches.Electronic supplementary materialThe online version of this article (doi:10.1186/s12920-017-0286-x) contains supplementary material, which is available to authorized users.

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BackgroundThe systemic information enclosed in microarray data encodes relevant clues to overcome the poorly understood combination of genetic and environmental factors in Parkinson’s disease (PD), which represents the major obstacle to understand its pathogenesis and to develop disease-modifying therapeutics. While several gene prioritization approaches have been proposed, none dominate over the rest. Instead, hybrid approaches seem to outperform individual approaches.MethodsA consensus strategy is proposed for PD related gene prioritization from mRNA microarray data based on the combination of three independent prioritization approaches: Limma, machine learning, and weighted gene co-expression networks.ResultsThe consensus strategy outperformed the individual approaches in terms of statistical significance, overall enrichment and early recognition ability. In addition to a significant biological relevance, the set of 50 genes prioritized exhibited an excellent early recognition ability (6 of the top 10 genes are directly associated with PD). 40 % of the prioritized genes were previously associated with PD including well-known PD related genes such as SLC18A2, TH or DRD2. Eight genes (CCNH, DLK1, PCDH8, SLIT1, DLD, PBX1, INSM1, and BMI1) were found to be significantly associated to biological process affected in PD, representing potentially novel PD biomarkers or therapeutic targets. Additionally, several metrics of standard use in chemoinformatics are proposed to evaluate the early recognition ability of gene prioritization tools.ConclusionsThe proposed consensus strategy represents an efficient and biologically relevant approach for gene prioritization tasks providing a valuable decision-making tool for the study of PD pathogenesis and the development of disease-modifying PD therapeutics.Electronic supplementary materialThe online version of this article (doi:10.1186/s12920-016-0173-x) contains supplementary material, which is available to authorized users.

The therapeutic effects of drugs are well known to result from their interaction with multiple intracellular targets. Accordingly, the pharma industry is currently moving from a reductionist approach based on a ‘ one-target fixation ’ to a holistic multitarget approach. However, many drug discovery practices are still procedural abstractions resulting from the attempt to understand and address the action of biologically active compounds while preventing adverse effects. Here, we discuss how drug discovery can benefit from the principles of evolutionary biology and report two real-life case studies. We do so by focusing on the desirability principle, and its many features and applications, such as machine learning-based multicriteria virtual screening.