Desialylation in physiological and pathological processes: New target for diagnostic and therapeutic development

Wei, Mohui; Wang, Peng George

doi:10.1016/bs.pmbts.2018.12.001

Cited by 28 publications

(31 citation statements)

References 149 publications

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“…The result demonstrates a significant downregulation in several pathways from control to the DLF category of the disease group (p < 0.01). To validate if the identified pathways are reported to have association with type 2 diabetes, we tested and found that most of our findings are consistent with the published literature[78][79][80][81][82][83][84][85][86][87]. However, here we also report three pathways to have strong…”

supporting

confidence: 85%

Prediction and analysis of Metagenomic operons via MetaRon: a Pipeline for Prediction of Metagenomic OpeRons

Zaidi

Kayani

Zhang

et al. 2020

Preprint

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Background: Efficient regulation of bacterial genes against the environmental stimulus results in unique operonic organizations. Lack of complete reference and functional information makes metagenomic operon prediction challenging and therefore opens new perspectives on the interpretation of the host-microbe interactions. Methods: Here we present MetaRon (pipeline for the prediction of Metagenomic operons), an open-source pipeline explicitly designed for the metagenomic shotgun sequencing data. It recreates the operonic structure without functional information. MetaRon identifies closely packed co-directional gene clusters with a promoter upstream and downstream of the first and last gene, respectively. Promoter prediction marks the transcriptional unit boundary (TUB) of closely packed co-directional gene clusters.Results: Escherichia coli (E. coli) K-12 MG1655 presents a gold standard for operon prediction. Therefore, MetaRon was initially implemented on two simulated illumina datasets: (1) E. coli MG1655 genome (2) a mixture of E. coli MG1655, Mycobacterium tuberculosis H37Rv and Bacillus subtilis str. 168 genomes. Operons were predicted in the single genome and mixture of genomes with a sensitivity of 97.8% and 93.7%, respectively. In the next phase, operons predicted from E. coli c20 draft genome isolated from chicken gut metagenome achieved a sensitivity of 94.1%. Lastly, the application of MetaRon on 145 paired-end gut metagenome samples identified 1,232,407 unique operons. Conclusion: MetaRon removes two notable limitations of existing methods: (1) dependency on functional information, and (2) liberates the users from enormous metagenomic data management. Current study showed the idea of using operons as subset to represent the whole-metagenome in terms of secondary metabolites and demonstrated its effectiveness in explaining the occurrence of a disease condition. This will significantly reduce the hefty whole-metagenome data to a small more precise data set. Furthermore, metabolic pathways from the operonic sequences were identified in association with the occurrence of type 2 diabetes (T2D). Presumably, this is the first organized effort to predict metagenomic operons and perform a detailed analysis in association with a disease, in this case T2D. The application of MetaRon to metagenome data at diverse scale will be beneficial to understand the gene regulation and therapeutic metagenomics.

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supporting

confidence: 85%

Prediction and analysis of Metagenomic operons via MetaRon: a Pipeline for Prediction of Metagenomic OpeRons

Zaidi

Kayani

Zhang

et al. 2020

Preprint

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“…One could therefore hypothesize that the decrease in Sia during mitochondrial stress, like in our study, may be a result of most of the Sia being destroyed by scavenging the increased ROS during the oxidative stress. Moreover, the increased oxidative stress has been associated with an increase in sialidase activity (Wei and Wang 2019), which means less Sia linked to proteins and lipids.…”

Section: Discussionmentioning

confidence: 99%

Time and space-resolved quantification of plasma membrane sialylation for measurements of cell function and neurotoxicity

et al. 2019

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While there are many methods to quantify the synthesis, localization, and pool sizes of proteins and DNA during physiological responses and toxicological stress, only few approaches allow following the fate of carbohydrates. One of them is metabolic glycoengineering (MGE), which makes use of chemically modified sugars (CMS) that enter the cellular biosynthesis pathways leading to glycoproteins and glycolipids. The CMS can subsequently be coupled (via bio-orthogonal chemical reactions) to tags that are quantifiable by microscopic imaging. We asked here, whether MGE can be used in a quantitative and time-resolved way to study neuronal glycoprotein synthesis and its impairment. We focused on the detection of sialic acid (Sia), by feeding human neurons the biosynthetic precursor N-acetyl-mannosamine, modified by an azide tag. Using this system, we identified non-toxic conditions that allowed live cell labeling with high spatial and temporal resolution, as well as the quantification of cell surface Sia. Using combinations of immunostaining, chromatography, and western blotting, we quantified the percentage of cellular label incorporation and effects on glycoproteins such as polysialylated neural cell adhesion molecule. A specific imaging algorithm was used to quantify Sia incorporation into neuronal projections, as potential measure of complex cell function in toxicological studies. When various toxicants were studied, we identified a subgroup (mitochondrial respiration inhibitors) that affected neurite glycan levels several hours before any other viability parameter was affected. The MGE-based neurotoxicity assay, thus allowed the identification of subtle impairments of neurochemical function with very high sensitivity.

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“…Sialidases/neuraminidases are glycoside hydrolase enzymes. They participate in the cleavage of terminal Sias from glycoproteins and glycolipids, a process named desialylation [ 68 ]. In mammalian cells, there are four types of sialidases, sialidase 1–4.…”

Section: Sialic Acids (Sias): Structure Diversity Biosynthesis Rmentioning

confidence: 99%

“…However, NEU1 has negligible activity against gangliosides, which are the main substrates of NEU3 [ 70 ]. Growing evidence suggests that mammalian sialidases play an important role in the development and function of the CNS [ 54 , 68 , 70 ].…”

Section: Sialic Acids (Sias): Structure Diversity Biosynthesis Rmentioning

confidence: 99%

Control of Innate Immunity by Sialic Acids in the Nervous Tissue

Liao

Klaus

Neumann

2020

IJMS

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Sialic acids (Sias) are the most abundant terminal sugar residues of glycoproteins and glycolipids on the surface of mammalian cells. The nervous tissue is the organ with the highest expression level of Sias. The ‘sialylation’ of glycoconjugates is performed via sialyltransferases, whereas ‘desialylation’ is done by sialidases or is a possible consequence of oxidative damage. Sialic acid residues on the neural cell surfaces inhibit complement and microglial activation, as well as phagocytosis of the underlying structures, via binding to (i) complement factor H (CFH) or (ii) sialic acid-binding immunoglobulin-like lectin (SIGLEC) receptors. In contrast, activated microglial cells show sialidase activity that desialylates both microglia and neurons, and further stimulates innate immunity via microglia and complement activation. The desialylation conveys neurons to become susceptible to phagocytosis, as well as triggers a microglial phagocytosis-associated oxidative burst and inflammation. Dysfunctions of the ‘Sia–SIGLEC’ and/or ‘Sia–complement’ axes often lead to neurological diseases. Thus, Sias on glycoconjugates of the intact glycocalyx and its desialylation are major regulators of neuroinflammation.

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Desialylation in physiological and pathological processes: New target for diagnostic and therapeutic development

Cited by 28 publications

References 149 publications

Prediction and analysis of Metagenomic operons via MetaRon: a Pipeline for Prediction of Metagenomic OpeRons

Prediction and analysis of Metagenomic operons via MetaRon: a Pipeline for Prediction of Metagenomic OpeRons

Time and space-resolved quantification of plasma membrane sialylation for measurements of cell function and neurotoxicity

Control of Innate Immunity by Sialic Acids in the Nervous Tissue

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