Autophagic Marker MAP1LC3B Expression Levels Are Associated with Carotid Atherosclerosis Symptomatology

Objectives: The mechanism by which atheroma plaque becomes unstable is not completely understood to date but analysis of differentially expressed genes in stable versus unstable plaques may provide clues. This will be crucial toward disclosing the mechanistic basis of plaque instability, and may help to identify prognostic biomarkers for ischaemic events. The objective of our study was to identify differences in expression levels of 59 selected genes between symptomatic patients (unstable plaques) and asymptomatic patients (stable plaques). Methods: 80 carotid plaques obtained by carotid endarterectomy and classified as symptomatic (>70% stenosis) or asymptomatic (>80% stenosis) were used in this study. The expression levels of 59 genes were quantified by qPCR on RNA extracted from the carotid plaques obtained by endarterectomy and analyzed by means of various bioinformatic tools. Results: Several genes associated with autophagy pathways displayed differential expression levels between asymptomatic and symptomatic (i.e. MAP1LC3B, RAB24, EVA1A). In particular, mRNA levels of MAP1LC3B, an autophagic marker, showed a 5-fold decrease in symptomatic samples, which was confirmed in protein blots. Immune system-related factors and endoplasmic reticulum-associated markers (i.e. ERP27, ITPR1, ERO1LB, TIMP1, IL12B) emerged as differently expressed genes between asymptomatic and symptomatic patients. Conclusions: Carotid atherosclerotic plaques in which MAP1LC3B is underexpressed would not be able to benefit from MAP1LC3B-associated autophagy. This may lead to accumulation of dead cells at lesion site with subsequent plaque destabilization leading to cerebrovascular events. Identified biomarkers and network interactions may represent novel targets for development of treatments against plaque destabilization and thus for the prevention of cerebrovascular events.

Novel equation to determine the hepatic triglyceride concentration in humans by MRI: diagnosis and monitoring of NAFLD in obese patients before and after bariatric surgery

Background: Non-alcoholic fatty liver disease (NAFLD) is caused by abnormal accumulation of lipids within liver cells. Its prevalence is increasing in developed countries in association with obesity, and it represents a risk factor for non-alcoholic steatohepatitis (NASH), cirrhosis and hepatocellular carcinoma. Since NAFLD is usually asymptomatic at diagnosis, new non-invasive approaches are needed to determine the hepatic lipid content in terms of diagnosis, treatment and control of disease progression. Here, we investigated the potential of magnetic resonance imaging (MRI) to quantitate and monitor the hepatic triglyceride concentration in humans. Methods: A prospective study of diagnostic accuracy was conducted among 129 consecutive adult patients (97 obesity and 32 non-obese) to compare multi-echo MRI fat fraction, grade of steatosis estimated by histopathology, and biochemical measurement of hepatic triglyceride concentration (that is, Folch value). Results: MRI fat fraction positively correlates with the grade of steatosis estimated on a 0 to 3 scale by histopathology. However, this correlation value was stronger when MRI fat fraction was linked to the Folch value, resulting in a novel equation to predict the hepatic triglyceride concentration (mg of triglycerides/g of liver tissue = 5.082 + (432.104 * multi-echo MRI fat fraction)). Validation of this formula in 31 additional patients (24 obese and 7 controls) resulted in robust correlation between the measured and estimated Folch values. Multivariate analysis showed that none of the variables investigated improves the Folch prediction capacity of the equation. Obese patients show increased steatosis compared to controls using MRI fat fraction and Folch value. Bariatric surgery improved MRI fat fraction values and the Folch value estimated in obese patients one year after surgery. Conclusions: Multi-echo MRI is an accurate approach to determine the hepatic lipid concentration by using our novel equation, representing an economic non-invasive method to diagnose and monitor steatosis in humans.