https://doi.org/10.5061/dryad.8pk0p2nvh

This data set contains the following:

1. RNAi Screen - Glycerol Release: RNA interference screening experiment, in adipocytes differentiated from CD55+/DPP4+ progenitor cells, was performed with the siGENOME® SMARTpool® siRNA Human Transcription Factor Library (Dharmacon, Lafayette, CO, USA). Lipolysis was assessed by fluorometric detection of glycerol in phenol-red-free media (ThermoFisher Scientific, 21041-033) collected from cells after 72 hours after siRNA transfection. Cytotoxicity was assessed by measuring LDH activity using the Cytotoxicity Detection KitPLUS 548 (Roche, 4744926001) according to the manufacturer’s instructions. Glycerol release and LDH data were analyzed using R v4.2.2. Outliers were identified using the IQR method, additionally triplicates with CV > 20% were excluded from further analysis. The reproducibility of the glycerol release assays was assessed by calculation of the Pearson correlation coefficient between the experimental replicates. Data were normalized and scaled using the robust z-score method. Replicates with LDH release with rz-score > |5| were excluded from the statistical analysis. Targets that significantly affect glycerol release (“hits”) were identified using a One-way ANOVA with Dunnet’s post-hoc test.
2. Meta-analysis of the relationship between ZNF189 mRNA clinical parameters \ To assess the relationship between ZNF189 mRNA levels and metabolic parameters, we performed a meta-analysis of its expression in subcutaneous WAT samples across nine published clinical cohorts. For the meta-analysis of the relationship between ZNF189 mRNA and clinical parameters, both random and common effects from Spearman correlations are presented.
3. Lipidomics\ Samples were analyzed in both positive and negative ion modes with a resolution of Rm/z=200=500000 for MS and Rm/z=200=30000 for MS/MS experiments. MS/MS was triggered by an inclusion list encompassing corresponding MS mass ranges scanned in 1Da increments. Both MS and MS/MS data were combined to monitor CE, DAG, and TAG ions as ammonium adducts; PC, PC O-, as acetate adducts; and CL, PA, PE, PE O-, PG, PI, and PS as deprotonated anions. MS only was used to monitor LPA, LPE, LPE O-,LPI, and LPS as deprotonated anions; Cer, HexCer, SM, LPC, and LPC O- as acetate adducts. Data were analyzed with in-house developed lipid identification software, based on LipidXplorer. Data post-processing and normalization were performed using an in-house developed data management system. Only lipid identifications with a signal-to-noise ratio >and a signal intensity five-fold higher than in corresponding blank samples were considered for further data analysis. Lipid species were included in statistical analysis if they had detectable values across all 6 replicates for both siRNA conditions. Principal components analysis was performed with the FactoMineR package in R. Confidence ellipses were calculated for a 0.95 confidence level. Species-level differences in lipids were assessed by pairwise comparisons using eBayes with the limma package in R, with FDR correction.

Description of the data and file structure

1.RNAi Screen - Glycerol Release: Data for glycerol release levels and LDH activity, as well as the corresponding standard curves are stored as csv files. R Scripts are available for analyzing the data.

Description of csv files:

• a. data_samples.csv: file containing raw relative flawrescence unit (RFU) values from glycerol release of the screen. This file can be used to extrapolate glycerol concentration levels (ug/ml) using a standard curve that is provided in the file described below. Rows correspond to different samples. Columns describe an index number, a GeneID for the gene Knock-down, the plate of the sample, the well of the sample within that plate, and the RFU values (Signal). Missing values are presented as empty cells and derive from the signal measurement, these values are recognized as NA values by the R script provided and handled accordingly during the analysis pipeline.
• b. data_standard_curves.csv: file containing raw relative flawrescence unit (RFU) values from the standard samples for the glycerol release of the screen. This file can be used to construct the standard curves needed for calculating glyceros release concentrations of the samples. These standard samples are from the same plates used in the data_samples.csv file. Rows correspond to different samples. Columns describe an index number, the well of the sample, the measured RFU signal, the respective plate, the patient, and the concentration of the standard sample (ug/mL). Missing values are presented as empty cells and derive from the signal measurement, these values are recognised as NA values by the R script provided and handled accordingly during the analysis pipeline.
• c. data_samples_screen2.csv: file containing raw relative flawrescence unit (RFU) values from glycerol release of the secondary RNAi screen and with the same structure as data_samples.csv. This file can be used to extrapolate glycerol concentration levels using a standard curve that is provided in the file described below. Missing values are presented as empty cells and derive from the signal measurement, these values are recognised as NA values by the R script provided and handled accordingly during the analysis pipeline.
• d. data_standard_curves_screen2.csv: data table with the same structure as data_samples.csv for a number of samples from the same experiment that were re-measured. This file can be used to construct the standard curves needed for calculating glyceros release concentrations of the samples. Missing values are presented as empty cells and derive from the signal measurement, these values are recognised as NA values by the R script provided and handled accordingly during the analysis pipeline.
• e. data_samples_remeasured.csv : data table with the same structure as data_samples.csv for a number of samples from the secondary screen experiment that were re-measured. This file can be used to extrapolate glycerol concentration levels using a standard curve that is provided in the file described below. Missing values are presented as empty cells and derive from the signal measurement, these values are recognised as NA values by the R script provided and handled accordingly during the analysis pipeline.
• f. data_standard_curves_remeasured.csv: data table with the same structure as data_standard_curves.csv for a number of samples from the same experiment that were re-measured. This file can be used to construct the standard curves needed for calculating glyceros release concentrations of the samples. Missing values are presented as empty cells and derive from the signal measurement, these values are recognised as NA values by the R script provided and handled accordingly during the analysis pipeline.
• g. ldh_screen2.csv: raw data from the LDH experiment for the secondary screen. Rows correspond to different samples. This file can be used to assess the cell viability after RNAi in the secondary screen. Columns describe an index number, a GeneID for the gene Knock-down, the plate of the sample, the well of the sample within that plate, and the absorbance values (Abs). Missing values are presented as empty cells and derive from the signal measurement, these values are recognised as NA values by the R script provided and handled accordingly during the analysis pipeline.

Description of R script files:

• a. Glycerol_analysis_Screen1: Open the file and install the libraries needed for the analysis as indicated in the first section of the code. In the import files, choose the corresponding csv as indicated by the comments in the code. The linear regression and standard fit section of the code are used to create the standard curves for the glycerol release measurements using a linear regression model and to fit the unknown samples to those curves to obtain concentration values (ug/ml). In the next section, descriptive statistics are used to describe the samples and identify outliers and batch effects. Next, data are normalized using robust z-scores, and descriptive statistics are used again to exclude outliers using IQR outlier elimination and identify samples with high CV. Replicate correlation is used as a quality control for the measurements. In the final section, once outliers are removed, one-way ANOVA followedby Dunnett's post-hoc test is performed.
• b. Glycerol_analysis_Screen2: Open the file and install the libraries needed for the analysis as indicated in the first section of the code. In the import files, choose the corresponding csv as indicated by the comments in the code. The linear regression and standard fit section of the code are used to create the standard curves for the glycerol release measurements using a linear regression model and to fit the unknown samples to those curves to obtain concentration values (ug/ml). In the next section, descriptive statistics are used to describe the samples and identify outliers and batch effects, and to compare the samples with screen1. Next, data are normalized using robust z-scores, and descriptive statistics are used again to exclude outliers using IQR outlier elimination and identify samples with high CV. In the next section LDH data are fitted using linear regression and samples with high LDH release are removed from further analysis. In the final section, once outliers are removed, one-way ANOVA followed by Dunnett's post-hoc test is performed.
• Important note: Although the two scripts follow the same analysis pipeline, the script for the secondary screen includes measurement of glycerol release levels for a number of samples, as well as an additional analysis of LDH release levels that is used to exclude samples from further analysis.

2. Meta-analysis of the relationship between ZNF189 mRNA clinical parameters

Data tables from each clinical dataset used in the meta-analysis are provided as txt file. A README file is included with further information about its dataset. In every txt file, columns 1 and 2 correspond to PDE1B and ZNF189 gene expression. The rest of the columns correspond to clinical parameters available in the dataset. These clinical parameter can be weight-to-hip ratio (WHR) and/or the HOMA-Insulin Resistance index (HOMA-IR). Rows correspond to different samples.

3. Lipidomics

Lipidomics data are provided as csv files. Rows correspond to different lipid species and they are described by the first column. The rest of the columns correspond to different samples measured. Missing values are presented as empty cells and derive from the signal measurement, these values are recognised as NA values by the R script provided and handled accordingly during the analysis pipeline. R Scripts are available for analyzing the data. Open the file and install the libraries needed for the analysis as indicated in the first section of the code. Import the file as indicated by the file path at the beginning of the code. In every step follow the instructions as indicated by the comments. The code can be used to perform descriptive statistics, PCA, and replicate correlation as quality control measures. In the final section, LIMMA is used to identify differentially altered lipid species. T

Sharing/Access information

Links to other publicly accessible locations of the data:

• https://github.com/danaezarf/TF_Screen_Glycerol
• Genomics data related to this publication are deposited in NCBI's Gene Expression Omnibus with the accession number GSE247818
• Proteomic 1172 data are deposited in the PRIDE database with the accession number PXD046964