Proteomics and RNAseq of C1-RIBOTAC treatment in MDA-MB-231
C1-RIBOTAC is a small molecule RNA degrader developed in our lab that selectively binds to human pre-miR-155 in cells and recruits RnaseL to degrade the transcript. Some RNA-binding compounds were expected to elicit a biological response as they bind functional sites, but most binders would not be expected to affect biological function. In these latter cases, an alternative way to modulate RNA biology is by cleaving the target via a ribonuclease targeting chimera, where an RNA-binding molecule is appended to a heterocycle that binds and locally activates RNase L. Overlay of the substrate specificity for RNase L with the binding landscape of small molecules in this study revealed many favorable candidate binders that are inactive but could be potently bioactive when converted into a degrader. We provide proof-of-concept by design of a degrader of the precursor to disease-associated microRNA-155 (pre-miR-155) in multiple cell lines and disease settings. These studies illustrate that small molecule RNA-targeted degradation can be leveraged to convert avid, yet inactive, binding interactions into potent and specific modulators of RNA function. The uploaded datasets include global proteomics and total RNA-seq studies to evaluate the effect on C1-RIBOTAC on the proteome and transcriptome of MDA-MB-231 cells.
Steps to reproduce
C1-RIBOTAC was added to MDA-MB-231 cells at 100 nM and incubated for 48 hrs. LNA-miR-155 was added to MDA-MB-231 cells at 50 nM and incubated for 48 hrs. Total RNA was then isolated with miRNeasy Kit (QIAGEN), and quantified using a Qubit 2.0 Fluorometer (Invitrogen, Carlsbad, CA). RNA quality was evaluated by using an Agilent 2100 Bioanalyzer RNA nano chip (Agilent Technologies, Santa Clara, CA). Approximately 200 ng of total RNA was depleted of ribosomal RNA using NEBNext rRNA depletion module (E6310L, NEB) according to manufacturer’s recommendations. Library preparation from the rRNA-depleted RNA was performed using NEBNext Ultra II Directional RNA kit (E7760, NEB), also per manufacturer’s protocol. The final libraries were validated by a Bioanalyzer analysis, pooled to equimolar concentrations, and loaded onto the NextSeq 500 v2.5 flow cell. Libraries were sequenced with 2 x 40 bp paired-end chemistry. RNA-seq data were analyzed using STAR and DESeq2. For proteomcis, The cells were then re-suspended in 1 DPBS, lysed via sonication, and centrifuged. The supernatant was collected, and the protein concentration therein was measured using a Bradford assay (Bio-Rad). Protein samples (~20 g) were denatured with 6 M urea in 50 mM NH4HCO3, pH 8, reduced with 10 mM tris(2-carboxyethyl)phosphine hydrochloride (TCEP) for 30 min, and then alkylated with 25 mM iodoacetamide for 30 min in dark. The samples were diluted to 2 M urea solution with 50 mM NH4HCO3, pH 8, and then digested with trypsin (1 L of 0.5 g/μL) in the presence of 1 mM CaCl2 for 12 h at 37 °C. The digested samples were acidified with acetic acid, added to a final concentration of 5% (v/v), desalted over a self-packed C18 spin column, and dried. Data obtained from the LC-MS-MS run were analyzed as follows: MS data were analyzed with MaxQuant16 (V184.108.40.206) and searched against the human proteome (Uniprot) and a common list of contaminants (included in MaxQuant). The peptide search tolerance was set at 20 ppm, 10 ppm was used for the main peptide search, and fragment mass tolerance was set to 0.02 Da. The false discovery rate for peptides, proteins, and sites identification was set to 1%. The minimum peptide length was set to 6 amino acids, and peptide re-quantification and label-free quantification (MaxLFQ) were enabled. The minimal number of peptides per protein was set to 2. Methionine oxidation was searched as a variable modification, and carbamidomethylation of cysteines was searched as a fixed modification.