Genomic and non genomic effects of elevated concentration of anabolic steroids in human neuronal cells

Nandrolone and other anabolic androgenic steroids (AAS) at elevated concentration can alter the expression and function of neurotransmitter systems and contribute to neuronal cell death. This effect can explain the behavioural changes, drug dependence and neuro degeneration observed in steroid abuser. Nandrolone treatment (10-8M–10-5M) caused a time- and concentration-dependent downregulation of mu opioid receptor (MOPr) transcripts in SH-SY5Y human neuroblastoma cells. This effect was prevented by the androgen receptor (AR) antagonist hydroxyflutamide. Receptor binding assays confirmed a decrease in MOPr of approximately 40% in nandrolonetreated cells. Treatment with actinomycin D (10-5M), a transcription inhibitor, revealed that nandrolone may regulate MOPr mRNA stability. In SH-SY5Y cells transfected with a human MOPr luciferase promoter/reporter construct, nandrolone did not alter the rate of gene transcription. These results suggest that nandrolone may regulate MOPr expression through post-transcriptional mechanisms requiring the AR. Cito-toxicity assays demonstrated a time- and concentration dependent decrease of cells viability in SH-SY5Y cells exposed to steroids (10-6M–10-4M). This toxic effects is independent of activation of AR and sigma-2 receptor. An increased of caspase-3 activity was observed in cells treated with Nandrolone 10-6M for 48h. Collectively, these data support the existence of two cellular mechanisms that might explain the neurological syndromes observed in steroids abuser.

A machine learning based method to detect genomic imbalances exploiting X chromosome exome reads

Whole Exome Sequencing (WES) is rapidly becoming the first-tier test in clinics, both thanks to its declining costs and the development of new platforms that help clinicians in the analysis and interpretation of SNV and InDels. However, we still know very little on how CNV detection could increase WES diagnostic yield. A plethora of exome CNV callers have been published over the years, all showing good performances towards specific CNV classes and sizes, suggesting that the combination of multiple tools is needed to obtain an overall good detection performance. Here we present TrainX, a ML-based method for calling heterozygous CNVs in WES data using EXCAVATOR2 Normalized Read Counts. We select males and females’ non pseudo-autosomal chromosome X alignments to construct our dataset and train our model, make predictions on autosomes target regions and use HMM to call CNVs. We compared TrainX against a set of CNV tools differing for the detection method (GATK4 gCNV, ExomeDepth, DECoN, CNVkit and EXCAVATOR2) and found that our algorithm outperformed them in terms of stability, as we identified both deletions and duplications with good scores (0.87 and 0.82 F1-scores respectively) and for sizes reaching the minimum resolution of 2 target regions. We also evaluated the method robustness using a set of WES and SNP array data (n=251), part of the Italian cohort of Epi25 collaborative, and were able to retrieve all clinical CNVs previously identified by the SNP array. TrainX showed good accuracy in detecting heterozygous CNVs of different sizes, making it a promising tool to use in a diagnostic setting.