Spatiotemporal Dynamics of Cerebral Vascular Permeability in Type 2 Diabetes-Related Cerebral Microangiopathy

AimsDiabetes-related cerebral microangiopathy can manifest as cerebral small vessel disease (CSVD) and exhibit cognitive decline. To find the early change of function in advance, this study examined the spatiotemporal dynamics of cerebral vascular permeability (Ktrans) in the progression of type 2 diabetes mellitus (T2DM).MethodsKtrans was cross-sectionally measured in T2DM and non-diabetes groups with or without CSVD using dynamic contrast-enhanced MRI (DCE-MRI).ResultsIn all patients with T2DM, the Ktrans of white matter (WM) was increased, whereas the Ktrans of gray matter (GM) was increased only in T2DM with CSVD. The involvement of WM was earlier than GM and was before the CSVD features could be visualized on MRI. Among the commonly available four CSVD items of MRI, microbleeds were the most sensitive, indicating the increased permeability in all patients. Increased Ktrans in T2DM was more associated with moderate WM hyperintensity but less with the presence of lacunae or multiple perivascular spaces, in contrast to patients without diabetes. The differential correlation suggested distinct mechanisms underlying diabetes-related CSVD and other CSVDs.ConclusionsThis study highlights the early development of cerebral microangiopathy with increased BBB leakage in T2DM, before the CSVD features can be visualized on MRI. The results may increase the proactivity of clinicians in recognizing the subsequent neurological comorbidities.

Disparity-filtered differential correlation network analysis: a case study on CRC metabolomics

Differential network analysis has become a widely used technique to investigate changes of interactions among different conditions. Although the relationship between observed interactions and biochemical mechanisms is hard to establish, differential network analysis can provide useful insights about dysregulated pathways and candidate biomarkers. The available methods to detect differential interactions are heterogeneous and often rely on assumptions that are unrealistic in many applications. To address these issues, we develop a novel method for differential network analysis, using the so-called disparity filter as network reduction technique. In addition, we propose a classification model based on the inferred network interactions. The main novelty of this work lies in its ability to preserve connections that are statistically significant with respect to a null model without favouring any resolution scale, as a hard threshold would do, and without Gaussian assumptions. The method was tested using a published metabolomic dataset on colorectal cancer (CRC). Detected hub metabolites were consistent with recent literature and the classifier was able to distinguish CRC from polyp and healthy subjects with great accuracy. In conclusion, the proposed method provides a new simple and effective framework for the identification of differential interaction patterns and improves the biological interpretation of metabolomics data.

Spatiotemporal Dynamics of Cerebral Vascular Permeability in Type 2 Diabetes-Related Cerebral Microangiopathy

Objective Diabetes-related cerebral microangiopathy can manifest as cerebral small vessel disease (CSVD) and exhibit cognitive decline. To find the early change of function in advance, this study examined the spatiotemporal dynamics of cerebral permeability (Ktrans) in the progression of diabetes-related CSVD. Methods Cerebral vascular permeability was crossectional measured in diabetic patients with or without CSVD, and non-diabetic patients with or without CSVD by using dynamic contrast-enhanced MRI (DCE-MRI). Results In all diabetic patients, the Ktrans of white matter (WM) was increased. However, the Ktrans of gray matter (GM) was only increased in those with CSVD. This suggested the earlier involvement of WM than GM and indicated the development of diabetes-related cerebral microangiopathy was prior to it could be visualized as features of CSVD on MRI. To broaden the application of cerebral permeability and overcome the limitations of DCE-MRI, the commonly available CSVD items of MRI were used to indicate the increase in Ktrans. Among all CSVD items, the presence of microbleeds was most correlated with the increased permeability in all patients. In contrast to non-diabetic patients, increased Ktrans in diabetes was more associated with moderate WM hyperintensity but less with the presence of lacunae or multiple perivascular spaces. The differential correlation suggested distinct mechanisms underlying diabetes-related CSVD and other CSVDs. Conclusions This study highlights the early development of cerebral microangiopathy in diabetes and broadens the applicability of cerebral permeability. The results may increase the proactivity of clinicians in recognizing the subsequent neurological comorbidities.

CorDiffViz: an R package for visualizing multi-omics differential correlation networks

Background Differential correlation networks are increasingly used to delineate changes in interactions among biomolecules. They characterize differences between omics networks under two different conditions, and can be used to delineate mechanisms of disease initiation and progression. Results We present a new R package, , that facilitates the estimation and visualization of differential correlation networks using multiple correlation measures and inference methods. The software is implemented in , and , and is available at https://github.com/sqyu/CorDiffViz. Visualization has been tested for the Chrome and Firefox web browsers. A demo is available at https://diffcornet.github.io/CorDiffViz/demo.html. Conclusions Our software offers considerable flexibility by allowing the user to interact with the visualization and choose from different estimation methods and visualizations. It also allows the user to easily toggle between correlation networks for samples under one condition and differential correlations between samples under two conditions. Moreover, the software facilitates integrative analysis of cross-correlation networks between two omics data sets.

Molecular network analyses implicate death-associated protein kinase 3 (DAPK3) as a key factor in colitis-associated dysplasia progression

Ulcerative colitis (UC) is a progressive disorder that elevates the risk of cancer development through a colitis-dysplasia-carcinoma sequence. Differential gene expression (DEGs) profiles of three UC clinical subtypes and healthy controls were developed for the GSE47908 microarray dataset [n = 15 (healthy controls), n = 20 (left-sided colitis), n = 19 (pancolitis), and n = 6 (colitis-associated dysplasia, CAD)] using limma R. Gene ontology (GO) enrichment analysis of DEGs revealed a shift in transcriptome landscape as UC progressed from left-sided colitis to pancolitis to CAD, from being immune-centric to being cytoskeleton-dependent. Hippo signaling (via Yes-associated protein, YAP) and Ephrin receptor signaling were the top canonical pathways progressively altered in concert with the pathogenic progression of UC. Molecular interaction network analysis of DEGs in left-sided colitis, pancolitis, and CAD revealed one pairwise line or edge that was topologically important to the network structure. This edge was found to be highly enriched in actin-based processes, and death-associated protein kinase 3 (DAPK3) was a critical member and sole protein kinase associated with this edge. DAPK3 is a regulator of actin-cytoskeleton reorganization that controls proliferation and apoptosis. Differential correlation analyses revealed a negative correlation for DAPK3-YAP in healthy controls which flipped to positive in left-sided colitis. With UC progression to CAD, the DAPK3-YAP correlation grew progressively more positive. In summary, DAPK3 was identified as a candidate gene involved in UC progression to dysplasia.

Identification of Estrogen Signaling in a Prioritization Study of Intraocular Pressure-Associated Genes

Elevated intraocular pressure (IOP) is the only modifiable risk factor for primary open-angle glaucoma (POAG). Herein we sought to prioritize a set of previously identified IOP-associated genes using novel and previously published datasets. We identified several genes for future study, including several involved in cytoskeletal/extracellular matrix reorganization, cell adhesion, angiogenesis, and TGF-β signaling. Our differential correlation analysis of IOP-associated genes identified 295 pairs of 201 genes with differential correlation. Pathway analysis identified β-estradiol as the top upstream regulator of these genes with ESR1 mediating 25 interactions. Several genes (i.e., EFEMP1, FOXC1, and SPTBN1) regulated by β-estradiol/ESR1 were highly expressed in non-glaucomatous human trabecular meshwork (TM) or Schlemm’s canal (SC) cells and specifically expressed in TM/SC cell clusters defined by single-cell RNA-sequencing. We confirmed ESR1 gene and protein expression in human TM cells and TM/SC tissue with quantitative real-time PCR and immunofluorescence, respectively. 17β-estradiol was identified in bovine, porcine, and human aqueous humor (AH) using ELISA. In conclusion, we have identified estrogen receptor signaling as a key modulator of several IOP-associated genes. The expression of ESR1 and these IOP-associated genes in TM/SC tissue and the presence of 17β-estradiol in AH supports a role for estrogen signaling in IOP regulation.

Immunometabolic signatures predict risk of progression to sepsis in COVID-19

Viral sepsis has been proposed as an accurate term to describe all multisystemic dysregulations and clinical findings in severe and critically ill COVID-19 patients. The adoption of this term may help the implementation of more accurate strategies of early diagnosis, prognosis, and in-hospital treatment. We accurately quantified 110 metabolites using targeted metabolomics, and 13 cytokines/chemokines in plasma samples of 121 COVID-19 patients with different levels of severity, and 37 non-COVID-19 individuals. Analyses revealed an integrated host-dependent dysregulation of inflammatory cytokines, neutrophil activation chemokines, glycolysis, mitochondrial metabolism, amino acid metabolism, polyamine synthesis, and lipid metabolism typical of sepsis processes distinctive of a mild disease. Dysregulated metabolites and cytokines/chemokines showed differential correlation patterns in mild and critically ill patients, indicating a crosstalk between metabolism and hyperinflammation. Using multivariate analysis, powerful models for diagnosis and prognosis of COVID-19 induced sepsis were generated, as well as for mortality prediction among septic patients. A metabolite panel made of kynurenine/tryptophan ratio, IL-6, LysoPC a C18:2, and phenylalanine discriminated non-COVID-19 from sepsis patients with an area under the curve (AUC (95%CI)) of 0.991 (0.986–0.995), with sensitivity of 0.978 (0.963–0.992) and specificity of 0.920 (0.890–0.949). The panel that included C10:2, IL-6, NLR, and C5 discriminated mild patients from sepsis patients with an AUC (95%CI) of 0.965 (0.952–0.977), with sensitivity of 0.993(0.984–1.000) and specificity of 0.851 (0.815–0.887). The panel with citric acid, LysoPC a C28:1, neutrophil-lymphocyte ratio (NLR) and kynurenine/tryptophan ratio discriminated severe patients from sepsis patients with an AUC (95%CI) of 0.829 (0.800–0.858), with sensitivity of 0.738 (0.695–0.781) and specificity of 0.781 (0.735–0.827). Septic patients who survived were different from those that did not survive with a model consisting of hippuric acid, along with the presence of Type II diabetes, with an AUC (95%CI) of 0.831 (0.788–0.874), with sensitivity of 0.765 (0.697–0.832) and specificity of 0.817 (0.770–0.865).