Giladi, E. | bioRxiv Science

bioRxiv2022

Alteration in long noncoding RNAs in response to oxidative stress and ladostigil in SH-SY5Y cells

Microglia activation causes neuroinflammation, which is a hallmark of neurodegenerative disorders, brain injury, and aging. Ladostigil, a bifunctional reagent with antioxidant and anti-inflammatory properties, reduced microglial activation and enhanced brain functioning in elderly rats. In this study, we studied SH-SY5Y, a human neuroblastoma cell line, and tested viability in the presence of hydrogen peroxide and Sin1 (3-morpholinosydnonimine), which generates reactive oxygen and nitrogen species (ROS/RNS). Both stressors caused significant apoptosis and necrotic cell death that was attenuated by ladostigil. Our results from RNA-seq experiments show that long non-coding RNAs (lncRNAs) account for 30% of all transcripts in SH-SY5Y cells treated with Sin1 for 24 hours. Altogether, we identify 94 differently expressed lncRNAs in the presence of Sin1, including MALAT1, a highly expressed lncRNA with anti-inflammatory and anti-apoptotic functions. Additional activities of Sin-1 upregulated lncRNAs include redox homeostasis (e.g., MIAT, GABPB1-AS1), energy metabolism (HAND2-AS1), and neurodegeneration (e.g., MIAT, GABPB1-AS1, NEAT1). Four lncRNAs implicated as enhancers were significantly upregulated in cells exposed to Sin1 and ladostigil. Finally, we show that H2O2 and Sin1 increased the expression of DJ-1, a redox sensor and modulator of Nrf2 (nuclear factor erythroid 2- related factor 2). Nrf2 (NFE2L2 gene) is a major transcription factor regulating antioxidant genes. In the presence of ladostigil, DJ-1 expression is restored to its baseline. The mechanisms governing SH-SY5Y cell survival and homeostasis are highlighted by the beneficial role of ladostigil in the crosstalk involving Nrf2, antioxidant transcription factor DJ-1, and lncRNAs. Stress-dependent induction of lncRNAs represents an underappreciated regulatory level that contributes to cellular homeostasis and the capacity of SH-SY5Y to cope with oxidative stress.

cell biology↗

bioRxiv2021

Improving replicability using interaction with laboratories: a multi-lab experimental assessment

Experimentation with mouse and rat models has become a central strategy for discovering mammalian gene function, and for preclinical testing of pharmacological treatments, yet the utility of any findings critically depends on their replicability in other laboratories. In previous publications we proposed a statistical approach for estimating the inter-laboratory replicability of novel discoveries made in a single laboratory. We demonstrated that previous phenotyping results from multi-lab databases can be used to derive a Genotype-by-Lab (GxL) adjustment factor to greatly enhance the replicability of the single-lab findings, for similarly measured phenotypes, even before making the effort of replicating these finding in additional laboratories. This demonstration, however, still raised several important questions that could only be answered by an additional large-scale prospective experiment: 1) Does GxL-adjustment work in single-lab experiments that were not intended to be standardized across laboratories, and with genotypes that were not included in the previous experiments? And 2) Can it be used to adjust the results of pharmacological experiments? We investigated these questions by attempting to replicate, across three laboratories, results from five single-lab studies in the Mouse Phenome Database (MPD), offering 212 comparisons, including 60 involving a pharmacological treatment: 18 mg/kg/day fluoxetine. In addition, we define and use a dimensionless GxL factor, by dividing the GxL variance by the standard deviation between animals within groups, as a more robust vehicle to transfer the adjustment from the multi-lab analysis to very different labs and genotypes. For genotype comparisons, GxL-adjustment reduced the rate of non-replicable discoveries from 60% to 12%, for the price of reducing the power to make replicable discoveries from 87% to 66%. In absolute numbers, the adjustment prevented 23 non-replicable discoveries for the price of missing only three replicated ones. Tools and data needed for deployment of this method across other mouse experiments are publicly available in MPD. Our results further point at some phenotypes as more prone to produce non-replicable results, while others, known to be more difficult to measure, are as likely to produce replicable results (once adjusted) such as the physiological measure, body weight.

animal behavior and cognition↗