Loss of SNORA73 reprograms cellular metabolism and protects against steatohepatitis

Dyslipidemia and resulting lipotoxicity are pathologic signatures of metabolic syndrome and type 2 diabetes. Excess lipid causes cell dysfunction and induces cell death through pleiotropic mechanisms that link to oxidative stress. However, pathways that regulate the response to metabolic stress are not well understood. Herein, we show that disruption of the box H/ACA SNORA73 small nucleolar RNAs encoded within the small nucleolar RNA hosting gene 3 (Snhg3) causes resistance to lipid-induced cell death and general oxidative stress in cultured cells. This protection from metabolic stress is associated with broad reprogramming of oxidative metabolism that is dependent on the mammalian target of rapamycin signaling axis. Furthermore, we show that knockdown of SNORA73 in vivo protects against hepatic steatosis and lipid-induced oxidative stress and inflammation. Our findings demonstrate a role for SNORA73 in the regulation of metabolism and lipotoxicity.

For manuscripts utilizing custom algorithms or software that are central to the research but not yet described in published literature, software must be made available to editors and reviewers. We strongly encourage code deposition in a community repository (e.g. GitHub). See the Nature Research guidelines for submitting code & software for further information.

Data
Policy information about availability of data All manuscripts must include a data availability statement. This statement should provide the following information, where applicable: For biochemical and cell biological analyses, Methods state, "results are expressed as mean ± SEM for a minimum of 3 independent experiments. For directed comparisons, the statistical significance of differences in mean values was determined by a two-tailed or paired Student's t-test using GraphPad Prism 8 for Mac OS X (version 8.0a). p values were adjusted for multiple comparisons using the Holm-Sidak method where indicated. For all tests, p < 0.05 was considered significant." For RNA sequence analyses, Quality of RNA-seq reads were checked with fastqc, which computes various quality metrics for the raw reads. Reads were trimmed for adapters and filtered by sequencing Phred quality (>= Q15) by using fastp and aligned to mouse rRNA by using bowtie (reference 77,78) . Unmapped reads were extracted by using samtools (reference 79). Reads were aligned to the mouse transcriptome (Ensembl version 102) using kallisto (version 0.46.2) and transcript counts were converted to gene counts using tximport (reference 80,81). Counts were normalized by weighted trimmed mean of M values if necessary (reference 82). Read counts were transformed to log 2 counts per million, their mean-variance relationship was projected, and their observational-level weights were computed with Voom (reference 83). Low expressing genes were filtered out by keeping genes that have counts per million (CPM) more than 0.5 in at least 4 samples. Differential gene expression was determined by performing linear modeling using limma (version 3.46.0, reference 83). Statistical significance was examined by using p-value adjusted for multiple tests (by the Benjamini-Hochberg False Discovery Rate (FDR)), and genes below FDR < 0.05 were accepted as statistically significant. Gene Ontology and KEGG pathways enrichment analyses were performed using ToppFun and DAVID. ToppFun collects the content from multiple databases, including KEGG, WikipPathways and REACTOME (reference 84). Bonferroni FDR correction was applied to the analyses to select the most relevant terms.
The data supporting the findings of this study are available within the paper and its supplementary information files. RNA-sequencing data has been deposited in the Gene Expression Omnibus, accession GSE179228.
Sample size calculations were not performed. For cell-based studies sample size was 3-5, based upon pilot experiments that determined effect size. For some animal studies larger sample sizes were necessary because of physiological variability, based on preliminary observations of responses of C57BL6/J mice to the various diets used.
Testing for outlier values was performed by Grubbs or Rout test as appropriate and outlier values were excluded as described in Methods and figure legends. Following initial analysis of 5 GFP LNA and 5 SNORA73 LNA treated livers by principal component analyses, 1 sample from each group was excluded because it failed to cluster with others in the group, leaving n = 4 for each group.
For cell-based studies, at least 3 independent experiments were performed . All findings were replicated. Attempts at replication in which control samples failed to demonstrate established findings were not further analyzed. For mouse studies all measurements are from different animals and all findings were replicated.
Animals were randomly assigned to diets by coin flip.
Where possible analyses were blinded (e.g., histology, analysis of tissue specimens, mass spectrometry). However, it was not possible to blind some analyses of cellular phenotypes due to limited number of persons working on studies at any one time. Note that full information on the approval of the study protocol must also be provided in the manuscript.