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.

Genetic identification of four Malaysian mackerel species off Coast of Peninsular Malaysia based on molecular marker

Random Amplified Polymorphic DNA (RAPD) markers and cytochrome b (Cyt-b) gene sequences were utilized to fingerprint and construct phylogenetic relationships among four species of mackerel commonly found in the Straits of Malacca namely Rastrelliger kanagurta, R. brachysoma, Decapterus maruadsi and D. russelli. The UPGMA dendogram and genetic distance clearly showed that the individuals clustered into their own genus and species except for the Decapterus. These results were also supported by partial mtDNA cytochrome b gene sequences (279 bp) which found monotypic sequence for all Decapterus studied. Cytochrome b sequence phylogeny generated through Neighbor Joining (NJ) method was congruent with RAPD data. Results showed clear discrimination between both genera with average nucleotide divergence about 25.43%. This marker also demonstrated R. brachysoma and R. kanagurta as distinct species separated with average nucleotide divergence about 2.76%. However, based on BLAST analysis, this study indicated that the fish initially identified as D. maruadsi was actually D. russelli. The results highlighted the importance of genetic analysis for taxonomic validation, in addition to morphological traits.

A Bayesian method for identification of stock mixtures from molecular marker data

Molecular markers have been demonstrated to be useful for the estimation of stock mixture proportions where the origin of individuals is determined from baseline samples. Bayesian statistical methods are widely recognized as providing a preferable strategy for such analyses. In general, Bayesian estimation is based on standard latent class models using data augmentation through Markov chain Monte Carlo techniques. In this study, we introduce a novel approach based on recent developments in the estimation of genetic population structure. Our strategy combines analytical integration with stochastic optimization to identify stock mixtures. An important enhancement over previous methods is the possibility of appropriately handling data where only partial baseline sample information is available. We address the potential use of nonmolecular, auxiliary biological information in our Bayesian model.