Additional file 1 of Integration of transcription regulation and functional genomic data reveals lncRNA SNHG6’s role in hematopoietic differentiation and leukemia
Creators: name: Hazan, Joshua M.
name: Amador, Raziel
name: Ali-Nasser, Tahleel
name: Lahav, Tamar
name: Shotan, Stav Roni
name: Steinberg, Miryam
name: Cohen, Ziv
name: Aran, Dvir
name: Meiri, David
name: Assaraf, Yehuda G.
name: Guigó, Roderic
name: Bester, Assaf C.
Publisher: name: figshare
Year: 2024
Subjects: subject: Medicine
subject: Cell Biology
subject: Genetics
subject: FOS: Biological sciences
subject: Environmental Sciences not elsewhere classified
subject: Biological Sciences not elsewhere classified
subject: Information Systems not elsewhere classified
subject: Developmental Biology
subject: Cancer
subject: Hematology
subject: Plant Biology

description: Additional file 1: Fig. S1. Building the machine learning algorithm. a Process followed for model training. The functional screening based on CRISPRi and the ENCODE Transcription Factor datasets were split into 90% for the training set (adopting a stratified cross-validation) and 10% for the testing set; along with binary labels indicating whether the lncRNA locus is either a hit or not hit. b XGBoost first residual-tree. Tree nodes are represented as rounded grey boxes, and squared white boxes are the tree leafs. c ROC curves comparing XGBoost, balanced random forest, and logistic regression models. d XGBoost (upper) and balanced random forest (lower) confusion matrices. Models were trained on 90% of the data, and ROC curves and confusion matrices show predictive value on the remaining 10%. Percentages from confusion matrices are row-normalized. e, f Under-sampling PCA. PCA of random under-sampling of the majority class (i.e. not hit) without (e) and with (f) replacement, plotting the complete dataset (upper-left plot) plus 8 sampling strategies. PCA values based on 130 numeric features showing the removed not hit transcripts. Red dots: hit; grey dots: not hit. g Recursive feature elimination. Iteratively, one feature was removed to train a new model, removing the least important. Red and black lines denote the optimal number of features (n = 71) and sensitivity value using 143 features, respectively. h SHAP dependence plot for the TSS PC distance feature. Each blue dot denotes a lncRNA. Positive odd values (above dashed line) contribute towards prediction of hits. i Detailed explanation for the INFLAMeR score of SNHG6. The final INFLAMeR score of SNHG6 was 0.504. Fig. S2. sgRNA control samples show distinct clustering after batch effect correction. After batch effect correction using the NOISeq package, the samples were clustered according to the target KD; however, the sgRNA control samples were distinctly clustered from all lncRNA KD samples. Fig. S3. Confirming the change in lncRNA expression after knockdown (KD), knockout (KO), and overexpression by qPCR. a KD of thirty-nine lncRNAs predicted to be functional (red) and seven lncRNAs predicted to be non-functional (blue). b KO of eight top performing lncRNAs. c Confirming overexpression of ten top performing lncRNAs following stable transduction of a lentiviral plasmid containing the lncRNA sequence. Expression is given relative to that in samples transduced with a non-targeting control sgRNA. Fig. S4. There was no correlation between INFLAMeR score and the essentiality of neighboring protein-coding (PC) genes. Genes with a dependency score &gt; 1 (red line) are considered essential. Green line represents the linear regression with 95% confidence interval (grey). Fig. S5. Knockdown of top performing lncRNAs affects cell cycle, apoptosis, and DNA damage. Knockdown of the indicated lncRNAs caused dysregulation of the cell cycle (a), increased apoptosis rates (b), and increased rates of DNA damage (c), as indicated by increased levels of γH2AX. *p &lt; 0.05, **p &lt; 0.01, ***p &lt; 0.001, ****p &lt; 0.0001 vs. sgRNA control (n = 3). Fig. S6. Knockout of top performing lncRNAs affects cell cycle and apoptosis. Functional knockout of the indicated lncRNAs by TSS deletion caused dysregulation of the cell cycle (a) and increased apoptosis rates (b), replicating the results seen after knockdown (See Fig. S4). *p &lt; 0.05, **p &lt; 0.01, ***p &lt; 0.001, ****p &lt; 0.0001 vs. sgRNA control (n = 3). Fig. S7. Determining the IC50 for daunorubicin (DNR) of selected samples. K562 cells from the indicated samples were incubated with the indicated concentrations of DNR for 72 h and their viability was determined using the XTT assay relative to that in untreated cells. Values are given as the mean ± SD for n = 3 biological replicates. The black line in each curve represents the non-targeting sgRNA control sample. * p &lt; 0.05, **p &lt; 0.01, ***p &lt; 0.001 vs. sgRNA control. Fig. S8. Differentially expressed genes from each sample. KD of the indicated lncRNAs generally led to a higher proportion of downregulated genes (blue) compared to upregulated genes (red). Fig. S9. KD of the indicated lncRNAs affected the expression of genes across the genome. Red represents upregulation, blue represents downregulation. Fig. S10. Overexpression of top performing lncRNAs rescued cell cycle regulation and apoptosis rates. Rescuing the expression of the indicated lncRNAs after knockdown partially or fully restored cell cycle regulation (a) and reduced apoptosis rates (b) for most of the samples (See Fig. S4). *p &lt; 0.05, **p &lt; 0.01 vs. sgRNA control (n = 3). Fig. S11. SNHG6 subcellular localization. The enrichment of SNHG6 was measured by qPCR in the nuclear/cytoplasmic fractions of K562 cells. MALAT1 and GAPDH were used as nuclear and cytoplasmic controls, respectively. Fig. S12. Gene ontology analysis for the transcription factors that bind the promoter of SNHG6 in K562. The top 50 pathways are shown. Fig. S13. Measuring the levels of myeloid differentiation markers by flow cytometry. a Erythrocyte differentiation was assessed based on glycophorin A (GPA) levels using immunostaining with flow cytometry in cells incubated with 30 µM hemin for 72 h. b Megakaryocyte differentiation was measured based on CD41/CD61 levels using immunostaining with flow cytometry in cells incubated with 0.2 nM PMA for 72 h. Table S5. The 143 features included in the initial algorithm. Table S6. Cost-sensitive model metrics. Table S7. Under-sampling strategies without replacement. Table S8. Under-sampling strategies with replacement. Table S9. Model performance comparison. Table S10. Performance of 10-fold cross-validation (CV).