Rabbit Anti-Human SLC1A3 Polyclonal Antibody

Glutamate is the main excitatory neuro transmitter in both the peripheral and central auditory systems. Changes of glutamate and glutamate-related genes with age may be an important factor in the pathogenesis of age-related hearing loss-presbycusis. in this study, changes in glutamate-related mRNA gene expression in the CBA mouse inferior colliculus with age and hearing loss were examined and correlations were sought between these changes and functional hearing measures, such as the auditory brainstem response (ABR) and distortion product otoacoustic emissions (DPOAEs). Gene expression of 68 glutamate-related genes was investigated using both genechip microarray and real-time PCR (qPCR) molecular techniques for four different age/hearing loss CBA mouse subject groups. Two genes showed consistent differences between groups for both the genechip and qPCR. Pyrroline-5-carboxylate synthetase enzyme (Pycs) showed down-regulation with age and a high-affinity glutamate transporter (Slc1a3) showed up-regulation with age and hearing loss. Since Pycs plays a role in converting glutamate to proline, its deficiency in old age may lead to both glutamate increases and proline deficiencies in the auditory midbrain, playing a role in the subsequent inducement of glutamate toxicity and loss of proline neuroprotective effects. The up-regulation of Slc1a3 gene expression may reflect a cellular compensatory mechanism to protect against age-related glutamate or calcium excitoxicity. (c) 2006 Elsevier B.V. All rights reserved.

Background: Measuring allele-specific RNA expression provides valuable insights into cis-acting genetic and epigenetic regulation of gene expression. Widespread adoption of high-throughput sequencing technologies for studying RNA expression (RNA-Seq) permits measurement of allelic RNA expression imbalance (AEI) at heterozygous single nucleotide polymorphisms (SNPs) across the entire transcriptome, and this approach has become especially popular with the emergence of large databases, such as GTEx. However, the existing binomial-type methods used to model allelic expression from RNA-seq assume a strong negative correlation between reference and variant allele reads, which may not be reasonable biologically. Results: Here we propose a new strategy for AEI analysis using RNA-seq data. Under the null hypothesis of no AEI, a group of SNPs (possibly across multiple genes) is considered comparable if their respective total sums of the allelic reads are of similar magnitude. Within each group of "comparable" SNPs, we identify SNPs with AEI signal by fitting a mixture of folded Skellam distributions to the absolute values of read differences. By applying this methodology to RNA-Seq data from human autopsy brain tissues, we identified numerous instances of moderate to strong imbalanced allelic RNA expression at heterozygous SNPs. Findings with SLC1A3 mRNA exhibiting known expression differences are discussed as examples. Conclusion: The folded Skellam mixture model searches for SNPs with significant difference between reference and variant allele reads (adjusted for different library sizes), using information from a group of "comparable" SNPs across multiple genes. This model is particularly suitable for performing AEI analysis on genes with few heterozygous SNPs available from RNA-seq, and it can fit over-dispersed read counts without specifying the direction of the correlation between reference and variant alleles.