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The lipid status in patients with ulcerative colitis : Sphingolipids are disease-dependent regulated
(2019)
The factors that contribute to the development of ulcerative colitis (UC), are still not fully identified. Disruption of the colon barrier is one of the first events leading to invasion of bacteria and activation of the immune system. The colon barrier is strongly influenced by sphingolipids. Sphingolipids impact cell–cell contacts and function as second messengers. We collected blood and colon tissue samples from UC patients and healthy controls and investigated the sphingolipids and other lipids by LC-MS/MS or LC-QTOFMS. The expression of enzymes of the sphingolipid pathway were determined by RT-PCR and immunohistochemistry. In inflamed colon tissue, the de novo-synthesis of sphingolipids is reduced, whereas lactosylceramides are increased. Reduction of dihydroceramides was due to posttranslational inhibition rather than altered serine palmitoyl transferase or ceramide synthase expression in inflamed colon tissue. Furthermore, in human plasma from UC-patients, several sphinglipids change significantly in comparison to healthy controls. Beside sphingolipids free fatty acids, lysophosphatidylcholines and triglycerides changed significantly in the blood of colitis patients dependent on the disease severity. Our data indicate that detraction of the sphingolipid de novo synthesis in colon tissue might be an important trigger for UC. Several lipids changed significantly in the blood, which might be used as biomarkers for disease control; however, diet-related variabilities need to be considered.
Natural products (NPs) from microorganisms have been important sources for discovering new therapeutic and chemical entities. While their corresponding biosynthetic gene clusters (BGCs) can be easily identified by gene-sequence-similarity-based bioinformatics strategies, the actual access to these NPs for structure elucidation and bioactivity testing remains difficult. Deletion of the gene encoding the RNA chaperone, Hfq, results in strains losing the production of most NPs. By exchanging the native promoter of a desired BGC against an inducible promoter in Δhfq mutants, almost exclusive production of the corresponding NP from the targeted BGC in Photorhabdus, Xenorhabdus and Pseudomonas was observed including the production of several new NPs derived from previously uncharacterized non-ribosomal peptide synthetases (NRPS). This easyPACId approach (easy Promoter Activated Compound Identification) facilitates NP identification due to low interference from other NPs. Moreover, it allows direct bioactivity testing of supernatants containing secreted NPs, without laborious purification.
Blood-pressure-lowering drugs are proposed to foster SARS-CoV-2 infection by pharmacological upregulation of angiotensin-converting enzyme 2 (ACE2), the binding partner of the virus spike (S) protein, located on the surface of the host cells. Conversely, it is postulated that angiotensin–renin system antagonists may prevent lung damage caused by SARS-CoV-2 infection, by reducing angiotensin II levels, which can induce permeability of lung endothelial barrier via its interaction with the AT1 receptor (AT1R). Methods: We have investigated the influence of the ACE inhibitors (lisinopril, captopril) and the AT1 antagonists (telmisartan, olmesartan) on the level of ACE2 mRNA and protein expression as well as their influence on the cytopathic effect of SARS-CoV-2 and on the cell barrier integrity in a Caco-2 cell model. Results: The drugs revealed no effect on ACE2 mRNA and protein expression. ACE inhibitors and AT1R antagonist olmesartan did not influence the infection rate of SARS-CoV-2 and were unable to prevent the SARS-CoV-2-induced cell barrier disturbance. A concentration of 25 µg/mL telmisartan significantly reduced the virus replication rate. Conclusion: ACE inhibitors and AT1R antagonist showed neither beneficial nor detrimental effects on SARS-CoV-2-infection and cell barrier integrity in vitro at pharmacologically relevant concentrations.
Lichen-forming fungi are symbiotic organisms that synthesize unique natural products with potential for new drug leads. Here, we explored the pharmacological activity of six lichen extracts (Evernia prunastri, Pseudevernia furfuracea, Umbilicaria pustulata, Umbilicaria crustulosa, Flavoparmelia caperata, Platismatia glauca) in the context of cancer and inflammation using a comprehensive set of 11 functional and biochemical in vitro screening assays. We assayed intracellular Ca2+ levels and cell migration. For cancer, we measured tumor cell proliferation, cell cycle distribution and apoptosis, as well as the angiogenesis-associated proliferation of endothelial cells (ECs). Targeting inflammation, we assayed leukocyte adhesion onto ECs, EC adhesion molecule expression, as well as nitric oxide production and prostaglandin (PG)E2 synthesis in leukocytes. Remarkably, none of the lichen extracts showed any detrimental influence on the viability of ECs. We showed for the first time that extracts of F. caperata induce Ca2+ signaling. Furthermore, extracts from E. prunastri, P. furfuracea, F. caperata, and P. glauca reduced cell migration. Interestingly, F. caperata extracts strongly decreased tumor cell survival. The proliferation of ECs was significantly reduced by E. prunastri, P. furfuracea, and F. caperata extracts. The extracts did not inhibit the activity of inflammatory processes in ECs. However, the pro-inflammatory activation of leukocytes was inhibited by extracts from E. prunastri, P. furfuracea, F. caperata, and P. glauca. After revealing the potential biological activities of lichen extracts by an array of screening tests, a correlation analysis was performed to evaluate particular roles of abundant lichen secondary metabolites, such as atranorin, physodic acid, and protocetraric acid as well as usnic acid in various combinations. Overall, some of the lichen extracts tested in this study exhibit significant pharmacological activity in the context of inflammation and/or cancer, indicating that the group lichen-forming fungi includes promising members for further testing.
Dysregulation of lysophosphatidic acids in multiple sclerosis and autoimmune encephalomyelitis
(2017)
Bioactive lipids contribute to the pathophysiology of multiple sclerosis. Here, we show that lysophosphatidic acids (LPAs) are dysregulated in multiple sclerosis (MS) and are functionally relevant in this disease. LPAs and autotaxin, the major enzyme producing extracellular LPAs, were analyzed in serum and cerebrospinal fluid in a cross-sectional population of MS patients and were compared with respective data from mice in the experimental autoimmune encephalomyelitis (EAE) model, spontaneous EAE in TCR1640 mice, and EAE in Lpar2 -/- mice. Serum LPAs were reduced in MS and EAE whereas spinal cord LPAs in TCR1640 mice increased during the ‘symptom-free’ intervals, i.e. on resolution of inflammation during recovery hence possibly pointing to positive effects of brain LPAs during remyelination as suggested in previous studies. Peripheral LPAs mildly re-raised during relapses but further dropped in refractory relapses. The peripheral loss led to a redistribution of immune cells from the spleen to the spinal cord, suggesting defects of lymphocyte homing. In support, LPAR2 positive T-cells were reduced in EAE and the disease was intensified in Lpar2 deficient mice. Further, treatment with an LPAR2 agonist reduced clinical signs of relapsing-remitting EAE suggesting that the LPAR2 agonist partially compensated the endogenous loss of LPAs and implicating LPA signaling as a novel treatment approach.
Based on increasing evidence suggesting that MS pathology involves alterations in bioactive lipid metabolism, the present analysis was aimed at generating a complex serum lipid-biomarker. Using unsupervised machine-learning, implemented as emergent self-organizing maps of neuronal networks, swarm intelligence and Minimum Curvilinear Embedding, a cluster structure was found in the input data space comprising serum concentrations of d = 43 different lipid-markers of various classes. The structure coincided largely with the clinical diagnosis, indicating that the data provide a basis for the creation of a biomarker (classifier). This was subsequently assessed using supervised machine-learning, implemented as random forests and computed ABC analysis-based feature selection. Bayesian statistics-based biomarker creation was used to map the diagnostic classes of either MS patients (n = 102) or healthy subjects (n = 301). Eight lipid-markers passed the feature selection and comprised GluCerC16, LPA20:4, HETE15S, LacCerC24:1, C16Sphinganine, biopterin and the endocannabinoids PEA and OEA. A complex classifier or biomarker was developed that predicted MS at a sensitivity, specificity and accuracy of approximately 95% in training and test data sets, respectively. The present successful application of serum lipid marker concentrations to MS data is encouraging for further efforts to establish an MS biomarker based on serum lipidomics.