bioRxiv Science⌕ Search

Biology subjects

Ipe, J.

Publications and source records attributed to Ipe, J..

3 recordsLinked to original sources

Immediate and Long-Term Electrophysiological Biomarkers of Antidepressant Behavioral Effects After Subanesthetic Ketamine and Medial Prefrontal Cortex Deep Brain Stimulation Treatment

1Ketamine and medial prefrontal cortex (mPFC) deep brain stimulation (DBS) are emerging depression treatments, however their mechanisms and efficacy biomarkers have not been established. This study investigates immediate and long-term mPFC electrophysiological correlates of their antidepressant effects, and explores the effects of combinatorial treatment. Local field potential electrodes were implanted into the mPFC of male Sprague Dawley rats, then corticosterone was administered for 21 days, except in the control group (n = 8). In the last week of depression induction, the remaining groups received mPFC DBS (n = 8), ketamine (n = 9), both (n = 9), or neither (n = 8). All groups then performed a behavioral assay. We found that DBS treatment immediately suppressed relative low gamma power and improved cognition in the long-term, which correlated with rescued apathy-like behavior in the Groom Test (GT). In contrast, ketamine treatment suppressed sample entropy in the long-term, which correlated with its immediate decrease in sample entropy as well as rescued GT behavior. In the combinatorial group these changes were inhibited, which correlated with a failure to fully rescue GT behavior. Despair-like behavior in the Forced Swim Test was rescued by all three treatments, which correlated with increased relative high frequency oscillation (HFO) power immediately after ketamine and combinatorial treatment. Our findings support the utility of low gamma and HFO power, sample entropy, and cognitive tests in the search for mechanisms and biomarkers of depression and novel treatments. 2 Significance StatementThere is growing interest in psychedelic pharmacological agents, such as ketamine, and direct neurostimulation techniques, like deep brain stimulation, as treatments for depression. However, their mechanisms are poorly understood, particularly those underlying their sustained benefits. Our study addresses this by correlating behavioral rescue with immediate and long-term changes in the function of the medial prefrontal cortex. These antidepressant correlates may represent therapeutically relevant functional changes, and therefore also have potential as biomarkers. Clinically, such biomarkers enable predicting and monitoring of treatment efficacy, and thereby contribute to the personalization of care. In research, utilizing these translatable biomarkers enhances the etiological validity of disease models, and enables the development of more targeted, next-generation treatment strategies.

neuroscience↗

Allele-Specific Expression and High-Throughput Reporter Assay Reveal Functional Variants in Human Brains with Alcohol Use Disorders

Transcriptome studies can identify genes whose expression differs between alcoholics and controls. To test which variants associated with alcohol use disorder (AUDs) may cause expression differences, we integrated deep RNA-seq and genome-wide association studies (GWAS) data from four postmortem brain regions of 30 AUDs subjects and 30 controls (social/non-drinkers) and analyzed allele-specific expression (ASE). We identified 90 genes with differential ASE in subjects with AUDs compared to controls. Of these, 61 genes contained 437 single nucleotide polymorphisms (SNPs) in the 3 untranslated regions (3UTR) with at least one heterozygote among the subjects studied. Using a modified PASSPORT-seq (parallel assessment of polymorphisms in miRNA target-sites by sequencing) assay, we identified 25 SNPs that showed affected RNA levels in a consistent manner in two neuroblastoma cell lines, SH-SY5Y and SK-N-BE(2). Many of these are in binding sites of miRNAs and RNA binding proteins, indicating that these SNPs are likely causal variants of AUD-associated differential ASE.

neuroscience↗

RegSNPs-Intron: A Computational Framework For Prioritizing Intronic Single Nucleotide Variants in Human Genetic Disease

A large number of single nucleotide variants (SNVs) in the human genome are known to be responsible for inherited disease. An even larger number of SNVs, particularly those located in introns, have yet to be investigated for their pathogenic potential. Using known pathogenic and neutral intronic SNVs (iSNVs), we developed the regSNPs-intron algorithm based on a random forest classifier that integrates RNA splicing, protein structure and evolutionary conservation features. regSNPs-intron showed high accuracy in computing disease-causing probabilities of iSNVs. Using a high-throughput functional reporter assay called ASSET-seq (ASsay for Splicing using ExonTrap and sequencing), we validated regSNPs-intron predictions by measuring the impact of iSNVs on splicing outcome. Together, regSNPs-intron and ASSET-seq enable effective prioritization of iSNVs for disease pathogenesis. regSNPs-intron is available at https://regsnps-intron.ccbb.iupui.edu.

bioinformatics↗