This work was supported by the Senior and Junior Technological Innovation Team (20210509055RQ), Guizhou Provincial Science and Technology Projects (ZK2023-297), the Science and Technology Foundation of Health Commission of Guizhou Province (gzwkj2023-565), Science and Technology Project of Education Department of Jilin Province (JJKH20220245KJ and JJKH20220226SK), the National Natural Science Foundation of China (U19A2061), the Jilin Provincial Key Laboratory of Big Data Intelligent Computing (20180622002JC), and the Fundamental Research Funds for the Central Universities, JLU. We extend our sincerest appreciation to the review editor and the three anonymous reviewers for their constructive critiques, which have been instrumental in substantially enhancing the rigor and clarity of this protocol.

NOTE: The following protocol describes the details of the informatics analytic procedure and Python commands of the major modules. Figure 2 illustrates the three major steps with example commands utilized in this protocol and refer to previously published works26,38 for more technical details. Do the following protocol under a normal user account in a computer system and avoid using the administrator or root account. This is a computational protocol and has no biomedical hazardous factors.
1. Prepare Python environment
<ol>
	<li>Create a virtual environment.
	<ol>
		<li>This study used the Python programming language and a Python virtual environment (VE) with Python 3.7. Follow these steps (Figure 3A): 
		conda create -n healthmodel python=3.7 
		conda create is the command to create a new VE. The parameter -n specifies the name of the new environment, in this case, healthmodel. And python=3.7 specifies the Python version to be installed. Choose any preferred name and Python version supporting the above command.</li>
		<li>After running the command, the output is similar to Figure 3B. Enter y and wait for the process to complete.</li>
	</ol>
	</li>
	<li>Activate the virtual environment
	<ol>
		<li>In most cases, activate the created VE with the following command (Figure 3C): 
		conda activate healthmodel</li>
		<li>Follow the platform-specific instructions for the VE activation, if some platforms require the user to upload the platform-specific configuration files for activation.</li>
	</ol>
	</li>
	<li>Install PyTorch 1.13.1
	<ol>
		<li>PyTorch is a popular Python package for artificial intelligence (AI) algorithms. Use PyTorch 1.13.1, based on the CUDA 11.7 GPU programming platform, as an example. Find other versions at &#160;https://pytorch.org/get-started/previous-versions/. Use the following command (Figure 3D): 
		pip3 install torch torchvision torchaudio 
		NOTE: Using PyTorch version 1.12 or newer is strongly recommended. Otherwise, installing the required package torch_geometric may be challenging, as noted on the official torch_geometric website:&#160;https://pytorch-geometric.readthedocs.io/en/latest/install/installation.html.</li>
	</ol>
	</li>
	<li>Install additional packages for torch-geometric
	<ol>
		<li>Following the guidelines at https://pytorch-geometric.readthedocs.io/en/latest/install/installation.html, install the following packages: torch_scatter, torch_sparse, torch_cluster, and torch_spline_conv using the command (Figure 3E): 
		pip install pyg_lib torch_scatter torch_sparse torch_cluster torch_spline_conv -f&#160;https://data.pyg.org/whl/torch-1.13.0+cu117.html</li>
	</ol>
	</li>
	<li>Install torch-geometric package.
	<ol>
		<li>This study requires a specific version, 2.2.0, of the torch-geometric package. Run the command (Figure 3F): 
		pip install torch_geometric==2.2.0</li>
	</ol>
	</li>
	<li>Install other packages.
	<ol>
		<li>Packages like pandas are usually available by default. If not, install them using the pip command. For instance, to install pandas and xgboost, run: 
		pip install pandas 
		&#8203;pip install xgboost</li>
	</ol>
	</li>
</ol>
2. Using the pre-trained HealthModel to generate the mqTrans features
<ol>
	<li>Download the code and the pre-trained model.
	<ol>
		<li>Download the code and the pre-trained HealthModel from the website: http://www.healthinformaticslab.org/supp/resources.php,&#160;which is named HealthModel-mqTrans-v1-00.tar.gz (Figure 4A). The downloaded file can be decompressed to a user-specified path. The detailed formulation and the supporting data of the implemented protocol can be found in26.</li>
	</ol>
	</li>
	<li>Introduce the parameters to run HealthModel.
	<ol>
		<li>Firstly, change the working directory to the HealthModel-mqTrans folder in the command line. Use the following syntax for running the code: 
		python main.py &#60;data folder&#62; &#60;model folder&#62; &#60;output folder&#62; 
		The details regarding each parameter and the data, model and output folders are as follows: 
		data folder: This is the source data folder, and each data file is in the csv format. This data folder has two files (see detailed descriptions in steps 2.3 and 2.4). These files need to be replaced with personal data. 
		data.csv: The transcriptomic matrix file. The first row lists the feature (or gene) IDs, and the first column gives the sample IDs. The list of genes includes the regulatory factors (TFs and lincRNAs), and the regulated mRNA genes. 
		label.csv: The sample label file. The first column lists the sample IDs, and the column with the name &#34;label&#34; gives the sample label. 
		model folder: The folder to save information about the model: 
		HealthModel.pth: The pre-trained HealthModel. 
		regulatory_geneIDs.csv: The regulatory gene IDs used in this study. 
		target_geneIDs.csv: The target genes used in this study. 
		adjacent_matrix.csv: The adjacent matrix of regulatory genes. 
		output folder: The output files are written to this folder, created by the code. 
		test_target.csv: The gene expression value of target genes after Z-Normalization and imputation. 
		pred_target.csv: The predicted gene expression value of target genes. 
		mq_target.csv: The predicted gene expression value of target genes.</li>
	</ol>
	</li>
	<li>Prepare the transcriptomic matrix file in the csv format.
	<ol>
		<li>Each row represents a sample, and each column represents a gene (Figure 4B). Name the transcriptomic data matrix file as data.csv in the data folder. 
		NOTE: This file may be generated by manually saving a data matrix in the .csv format from software such as Microsoft Excel. The transcriptomic matrix may also be generated by computer programming.</li>
	</ol>
	</li>
	<li>Prepare the label file in the csv format.
	<ol>
		<li>Similar to the transcriptomic matrix file, name the label file as label.csv in the data folder (Figure 4C). 
		NOTE: The first column gives the sample names, and the class label of each sample is given in the column titled label. The 0 value in the label column means this sample is negative, 1 means a positive sample.</li>
	</ol>
	</li>
	<li>Generate the mqTrans features.
	<ol>
		<li>Run the following command to generate the mqTrans features and get the outputs shown in Figure 4D. The mqTrans features are generated as the file ./output/mq_targets.csv, and the label file is resaved as the file ./output/label.csv. For the convenience of further analysis, the original expression values of the mRNA genes are also extracted as the file ./output/ test_target.csv. 
		python ./Get_mqTrans/code/main.py ./data ./Get_mqTrans/model ./output</li>
	</ol>
	</li>
</ol>
3. Select mqTrans Features
<ol>
	<li>Syntax of the feature selection code
	<ol>
		<li>Firstly, change the working directory to the HealthModel-mqTrans folder. Use the following syntax: 
		python ./FS_classification/testMain.py &#60;in-data-file.csv&#62; &#60;in-label-file.csv&#62; &#60;output folder&#62; &#60;select_feature_number&#62; &#60;test_size&#62; &#60;combine&#62; &#60;combine file&#62; 
		The details of each parameter are as follows: 
		in-data-file: The input data file 
		in-label-file: The label of the input data file 
		output folder: Two output files are saved in this folder, including Output-score.xlsx (the feature selection method and the accuracy of the corresponding classifier), and Output-SelectedFeatures.xlsx (the selected feature names for each feature selection algorithm).
		<ol>
			<li>select_feature_number: select the number of features, ranging from 1 to the number of the features of the data file.</li>
			<li>test_size: Set the ratio of the test sample to split. For example, 0.2 means that the input dataset is randomly split into the train: test subsets by the ratio of 0.8:0.2.</li>
			<li>combine: If true, combine two data files together for feature selection, i.e., the original expression values and the mqTrans features. If false, just use one data file for feature selection, i.e., the original expression values or the mqTrans features.</li>
			<li>combine file: If combine is true, provide this file name to save the combined data matrix. 
			NOTE: This pipeline aims to demonstrate how the generated mqTrans features perform on classification tasks, and it directly uses the file generated by section 2 for the following operations.</li>
		</ol>
		</li>
	</ol>
	</li>
	<li>Run feature selection algorithm for mqTrans feature selection.
	<ol>
		<li>Turn combine =False if the user selects mqTrans features or original features.</li>
		<li>Firstly, select 800 original features and split the dataset into train: test=0.8:0.2: 
		python ./FS_classification/testMain.py ./output/test_target.csv ./output/label.csv ./result 800 0.2 False</li>
		<li>Turn combine =True, if the user wants to combine the mqTrans features with the original expression values to select features. Here, the demonstrative example is to select 800 features and split the dataset into train: test=0.8:0.2: 
		python ./FS_classification/testMain.py ./output/mq_targets.csv ./output/label.csv ./result_combine 800 0.2 True ./output/test_target.csv 
		NOTE: Figure 5 shows the output information. The supplementary files required for this protocol are in HealthModel-mqTrans-v1-00.tar folder (Supplementary Coding File 1).</li>
	</ol>
	</li>
</ol>

GAT Framework for Identifying Dark Biomarkers in Transcriptomes

The authors have nothing to disclose.

Section 2 (Use the pre-trained HealthModel to generate the mqTrans features) of the protocol is the most critical step within this protocol. After preparing the computational working environment in section 1, section 2 generates the mqTrans view of a transcriptomic dataset based on the pre-trained large reference model. Section 3 is a demonstrative example of selecting the generated mqTrans features for biomarker detections and prediction tasks. The users can conduct other transcriptomic analyses on this mqTrans dataset ...

Transcriptome consists of the expressions of all the genes in a sample and may be profiled by high-throughput technologies like microarray and RNA-seq1. The expression levels of one gene in a dataset are called a transcriptomic feature, and the differential representation of a transcriptomic feature between the phenotype and control groups defines this gene as a biomarker of this phenotype2,3. Transcriptomic biomarkers have been extensively utilized in the investigations of disease diagnosis4, biological mechanism5, and survival analysis6,7, etc.
Gene activity patterns in the healthy tissues carry crucial information about the lives8,9. These patterns offer invaluable insights and act as ideal references for understanding the complex developmental trajectories of benign disorders10,11 and lethal diseases12. Genes interact with each other, and transcriptomes represent the final expression levels after their complicated interactions. Such patterns are formulated as transcriptional regulation network13 and metabolism network14, etc. The expressions of messenger RNAs (mRNAs) can be transcriptionally regulated by transcription factors (TFs) and long intergenic non-coding RNAs (lincRNAs)15,16,17. Conventional differential expression analysis ignored such complex gene interactions with the assumption of inter-feature independence18,19.
Recent advancements in graph neural networks (GNNs) demonstrate extraordinary potential in extracting important information from OMIC-based data for cancer studies20, e.g., identifying co-expression modules21. The innate capacity of GNNs renders them ideal for modeling the intricate relationships and dependencies among genes22,23.
Biomedical studies often focus on accurately predicting a phenotype against the control group. Such tasks are commonly formulated as binary classifications24,25,26. Here, the two class labels are typically encoded as 1 and 0, true and false, or even positive and negative27.
This study aimed to provide an easy-to-use protocol for generating the transcriptional regulation (mqTrans) view of a transcriptome dataset based on the pre-trained graph-attention network (GAT) reference model. The multitask GAT framework from a previously published work26 was used to transform transcriptomic features to the mqTrans features. A large dataset of healthy transcriptomes from the University of California, Santa Cruz (UCSC) Xena platform28 was used to pre-train the reference model (HealthModel), which quantitatively measured the transcription regulations from the regulatory factors (TFs and lincRNAs) to the target mRNAs. The generated mqTrans view could be used to build prediction models and detect dark biomarkers. This protocol utilizes the colon adenocarcinoma (COAD) patient dataset from The Cancer Genome Atlas (TCGA) database29 as an illustrative example. In this context, patients in stages I or II are categorized as negative samples, while those in stages III or IV are considered positive samples. The distributions of dark and traditional biomarkers across the 26 TCGA cancer types are also compared.
Description of the HealthModel pipeline 
The methodology employed in this protocol is based on the previously published framework26, as outlined in Figure 1. To commence, users are required to prepare the input dataset, feed it into the proposed HealthModel pipeline, and obtain mqTrans features. Detailed data preparation instructions are provided in section 2 of the protocol section. Subsequently, users have the option to combine mqTrans features with the original transcriptomic features or proceed with the generated mqTrans features only. The produced dataset is then subjected to a feature selection process, with users having the flexibility to choose their preferred value for k in k-fold cross-validation for classification. The primary evaluation metric utilized in this protocol is accuracy.
HealthModel26 categorizes the transcriptomic features into three distinct groups: TF (Transcription Factor), lincRNA (long intergenic non-coding RNA), and mRNA (messenger RNA). The TF features are defined based on the annotations available in the Human Protein Atlas30,31. This work utilizes the annotations of lincRNAs from the GTEx dataset32. Genes belonging to the third-level pathways in the KEGG database33 are considered as mRNA features. It is worth noting that if an mRNA feature exhibits regulatory roles for a target gene as documented in the TRRUST database34, it is reclassified into the TF class.
This protocol also manually generates the two example files for the gene IDs of regulatory factors (regulatory_geneIDs.csv) and target mRNA (target_geneIDs.csv). The pairwise distance matrix among the regulatory features (TFs and lincRNAs) is calculated by the Pearson correlation coefficients and clustered by the popular tool weighted gene co-expression network analysis (WGCNA)36 (adjacent_matrix.csv). Users can directly utilize the HealthModel pipeline together with these example configuration files to generate the mqTrans view of a transcriptomic dataset.
Technical details of HealthModel 
HealthModel represents the intricate relationships among TFs and lincRNAs as a graph, with the input features serving as the vertices denoted by V and an inter-vertex edge matrix designated as E. Each sample is characterized by K regulatory features, symbolized as VK&#215;1. Specifically, the dataset encompassed 425 TFs and 375 lincRNAs, resulting in a sample dimensionality of K = 425 + 375 = 800. To establish the edge matrix E, this work employed the popular tool WGCNA35. The pairwise weight linking two vertices represented as <img alt="Equation 1" src="/files/ftp_upload/66030/66030eq01.jpg" style="margin: auto;" /> and <img alt="Equation 2" src="/files/ftp_upload/66030/66030eq02.jpg" style="margin: auto;" />, is determined by the Pearson correlation coefficient. The gene regulatory network exhibits a scale-free topology36, characterized by the presence of hub genes with pivotal functional roles. We compute the correlation between two features or vertices, <img alt="Equation 1" src="/files/ftp_upload/66030/66030eq01.jpg" style="margin: auto;" /> and <img alt="Equation 2" src="/files/ftp_upload/66030/66030eq02.jpg" style="margin: auto;" />, using the topological overlap measure (TOM) as follows:
<img alt="Equation 3" src="/files/ftp_upload/66030/66030eq03.jpg" style="margin: auto;" />&#160; &#160; &#160;(1)
<img alt="Equation 4" src="/files/ftp_upload/66030/66030eq04.jpg" style="margin: auto;" />&#160; &#160; &#160;(2)
The soft threshold&#160;&#946; is calculated using the &#39;pickSoft Threshold&#39; function from the WGCNA package. The power exponential function aij is applied, where <img alt="Equation 5" src="/files/ftp_upload/66030/66030eq05.jpg" style="margin: auto;" />&#160;represents a gene excluding i&#160;and j, and <img alt="Equation 6" src="/files/ftp_upload/66030/66030eq06.jpg" style="margin: auto;" /> represents the vertex connectivity. WGCNA clusters the expression profiles of the transcriptomic features into multiple modules using a commonly employed dissimilarity measure (<img alt="Equation 7" src="/files/ftp_upload/66030/66030eq07.jpg" style="margin: auto;" />37.
The HealthModel framework was originally designed as a multitask learning architecture26. This protocol only utilizes the model pre-training task for the construction of the transcriptomic mqTrans view. The user may choose to further refine the pre-trained HealthModel under the multitask graph attention network with additional task-specific transcriptomic samples.
Technical details of feature selection and classification 
The feature selection pool implements eleven feature selection (FS) algorithms. Among them, three are filter-based FS algorithms: selecting K best features using the Maximal Information Coefficient (SK_mic), selecting K features based on the FPR of MIC (SK_fpr), and selecting K features with the highest false discovery rate of MIC (SK_fdr). Additionally, three tree-based FS algorithms assess individual features using a decision tree with the Gini index (DT_gini), adaptive boosted decision trees (AdaBoost), and random forest (RF_fs). The pool also incorporates two wrapper methods: Recursive feature elimination with the linear support vector classifier (RFE_SVC) and recursive feature elimination with the logistic regression classifier (RFE_LR). Finally, two embedding algorithms are included: linear SVC classifier with the top-ranked L1 feature importance values (lSVC_L1) and logistic regression classifier with the top-ranked L1 feature importance values (LR_L1).
The classifier pool employs seven different classifiers to build classification models. These classifiers comprise linear support vector machine (SVC), Gaussian Na&#239;ve Bayes (GNB), logistic regression classifier (LR), k-nearest neighbor, with k set to 5 by default (KNN), XGBoost, random forest (RF), and decision tree (DT).
The random split of the dataset into the train: test subsets can be set in the command line. The demonstrated example uses the ratio of train: test = 8: 2.

<table><tbody><tr><td>Anaconda</td><td>Anaconda</td><td>version 2020.11</td><td>Python programming platform</td></tr><tr><td>Computer</td><td>N/A</td><td>N/A</td><td>Any general-purpose computers satisfy the requirement</td></tr><tr><td>GPU card</td><td>N/A</td><td>N/A</td><td>Any general-purpose GPU cards with the CUDA computing library</td></tr><tr><td>pytorch</td><td>Pytorch</td><td>version 1.13.1</td><td>Software</td></tr><tr><td>torch-geometric</td><td>Pytorch</td><td>version 2.2.0</td><td>Software</td></tr></tbody></table>

generating the transcriptional regulation view of transcriptomic features for prediction task and dark biomarker detection on small datasets

Transcriptome represents the expression levels of many genes in a sample and has been widely used in biological research and clinical practice. Researchers usually focused on transcriptomic biomarkers with differential representations between a phenotype group and a control group of samples. This study presented a multitask graph-attention network (GAT) learning framework to learn the complex inter-genic interactions of the reference samples. A demonstrative reference model was pre-trained on the healthy samples (HealthModel), which could be directly used to generate the model-based quantitative transcriptional regulation (mqTrans) view of the independent test transcriptomes. The generated mqTrans view of transcriptomes was demonstrated by prediction tasks and dark biomarker detection. The coined term &#34;dark biomarker&#34; stemmed from its definition that a dark biomarker showed differential representation in the mqTrans view but no differential expression in its original expression level. A dark biomarker was always overlooked in traditional biomarker detection studies due to the absence of differential expression. The source code and the manual of the pipeline HealthModelPipe can be downloaded from&#160;http://www.healthinformaticslab.org/supp/resources.php.

Evaluation of the mqTrans view of the transcriptomic dataset 
The test code uses eleven feature selection (FS) algorithms and seven classifiers to evaluate how the generated mqTrans view of the transcriptomic dataset contributes to the classification task (Figure 6). The test dataset consists of 317 colon adenocarcinoma (COAD) from The Cancer Genome Atlas (TCGA) database29. The COAD patients at stages I or II are regarded as the negative samples,...

Watch this Scientific Journal Video about Author Spotlight: Impact of Intergenic Interactions on Disease-Identifying Dark Biomarkers at JoVE.com

Author Spotlight: Impact of Intergenic Interactions on Disease-Identifying Dark Biomarkers

Here, we introduce a protocol for converting transcriptomic data into a mqTrans view, enabling the identification of dark biomarkers. While not differentially expressed in conventional transcriptomic analyses, these biomarkers exhibit differential expression in the mqTrans view. The approach serves as a complementary technique to traditional methods, unveiling previously overlooked biomarkers.

Generating the Transcriptional Regulation View of Transcriptomic Features for Prediction Task and Dark Biomarker Detection on Small Datasets

Transcriptome represents the expression levels of many genes in a sample and has been widely used in biological research and clinical practice. ...

generating-transcriptional-regulation-view-transcriptomic-features

Research

JoVE Journal

Biology

628 Views.  Jilin University. Ons onderzoek richt zich op de intergenetische interacties op ziekte. We ontdekten dat veel genen complexe, met elkaar verweven relaties met elkaar hebben. Door deze relaties te onderzoeken, willen we een nauwkeurigere diagnose stellen bij de behandeling en het beheer van ziekten.We ontdekten dat veel donkere biomarkers die traditionele, conventionele methoden negeerden, niet worden ondersteund door veel medische literatuur. Dit geeft aan dat de aanpak biologen verder kan helpen bij he...

Video: Auteur in de schijnwerpers: impact van intergene interacties op ziekte-identificerende donkere biomarkers

Determining Genome-wide Transcript Decay Rates in Proliferating and Quiescent Human Fibroblasts

Quiescence is a temporary, reversible state in which cells have ceased cell division, but retain the capacity to proliferate. Multiple studies, including ours, have demonstrated that quiescence is associated with widespread changes in gene expression. Some of these changes occur through changes in the level or activity of proliferation-associated transcription factors, such as E2F and MYC. We have demonstrated that mRNA decay can also contribute to changes in gene expression between proliferating and quiescent cells. In this protocol, we describe the procedure for establishing proliferating and quiescent cultures of human dermal foreskin fibroblasts. We then describe the procedures for inhibiting new transcription in proliferating and quiescent cells with Actinomycin D (ActD). ActD treatment represents a straightforward and reproducible approach to dissociating new transcription from transcript decay. A disadvantage of ActD treatment is that the time course must be limited to a short time frame because ActD affects cell viability. Transcript levels are monitored over time to determine transcript decay rates. This procedure allows for the identification of genes and isoforms that exhibit differential decay in proliferating versus quiescent fibroblasts.

We describe a protocol for generating proliferating and quiescent primary human dermal fibroblasts, monitoring transcript decay rates, and identifying differentially decaying genes.

Quiescence is a temporary, reversible state in which cells have ceased cell division, but retain the capacity to proliferate. Multiple studies, including ...

Genetics

Real-time Analysis of Transcription Factor Binding, Transcription, Translation, and Turnover to Display Global Events During Cellular Activation

Upon activation, cells rapidly change their functional programs and, thereby, their gene expression profile. Massive changes in gene expression occur, for example, during cellular differentiation, morphogenesis, and functional stimulation (such as activation of immune cells), or after exposure to drugs and other factors from the local environment. Depending on the stimulus and cell type, these changes occur rapidly and at any possible level of gene regulation. Displaying all molecular processes of a responding cell to a certain type of stimulus/drug is one of the hardest tasks in molecular biology. Here, we describe a protocol that enables the simultaneous analysis of multiple layers of gene regulation. We compare, in particular, transcription factor binding (Chromatin-immunoprecipitation-sequencing (ChIP-seq)), de novo transcription (4-thiouridine-sequencing (4sU-seq)), mRNA processing, and turnover as well as translation (ribosome profiling). By combining these methods, it is possible to display a detailed and genome-wide course of action.
Sequencing newly transcribed RNA is especially recommended when analyzing rapidly adapting or changing systems, since this depicts the transcriptional activity of all genes during the time of 4sU exposure (irrespective of whether they are up- or downregulated). The combinatorial use of total RNA-seq and ribosome profiling additionally allows the calculation of RNA turnover and translation rates. Bioinformatic analysis of high-throughput sequencing results allows for many means for analysis and interpretation of the data. The generated data also enables tracking co-transcriptional and alternative splicing, just to mention a few possible outcomes.
The combined approach described here can be applied for different model organisms or cell types, including primary cells. Furthermore, we provide detailed protocols for each method used, including quality controls, and discuss potential problems and pitfalls.

This protocol describes the combinatorial use of ChIP-seq, 4sU-seq, total RNA-seq, and ribosome profiling for cell lines and primary cells. It enables tracking changes in transcription-factor binding, de novo transcription, RNA processing, turnover and translation over time, and displaying the overall course of events in activated and/or rapidly changing cells.

Upon activation, cells rapidly change their functional programs and, thereby, their gene expression profile. Massive changes in gene expression occur, for ...

Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances

Biomarker detection is one of the more important biomedical questions for high-throughput &#39;omics&#39; researchers, and almost all existing biomarker detection algorithms generate one biomarker subset with the optimized performance measurement for a given dataset. However, a recent study demonstrated the existence of multiple biomarker subsets with similarly effective or even identical classification performances. This protocol presents a simple and straightforward methodology for detecting biomarker subsets with binary classification performances, better than a user-defined cutoff. The protocol consists of data preparation and loading, baseline information summarization, parameter tuning, biomarker screening, result visualization and interpretation, biomarker gene annotations, and result and visualization exportation at publication quality. The proposed biomarker screening strategy is intuitive and demonstrates a general rule for developing biomarker detection algorithms. A user-friendly graphical user interface (GUI) was developed using the programming language Python, allowing biomedical researchers to have direct access to their results. The source code and manual of kSolutionVis can be downloaded from http://www.healthinformaticslab.org/supp/resources.php.

Existing algorithms generate one solution for a biomarker detection dataset. This protocol demonstrates the existence of multiple similarly effective solutions and presents a user-friendly software to help biomedical researchers investigate their datasets for the proposed challenge. Computer scientists may also provide this feature in their biomarker detection algorithms.

Biomarker detection is one of the more important biomedical questions for high-throughput &#39;omics&#39; researchers, and almost all existing biomarker ...

Cancer Research

Identification of Transcription Factor Regulators using Medium-Throughput Screening of Arrayed Libraries and a Dual-Luciferase-Based Reporter

Transcription factors can alter the expression of numerous target genes that influence a variety of downstream processes making them good targets for anti-cancer therapies. However, directly targeting transcription factors is often difficult and can cause adverse side effects if the transcription factor is necessary in one or more adult tissues. Identifying upstream regulators that aberrantly activate transcription factors in cancer cells offers a more feasible alternative, particularly if these proteins are easy to drug. Here, we describe a protocol that can be used to combine arrayed medium-scale lentiviral libraries and a dual-luciferase-based transcriptional reporter assay to identify novel regulators of transcription factors in cancer cells. Our approach offers a quick, easy, and inexpensive way to test hundreds of genes in a single experiment. To demonstrate the use of this approach, we performed a screen of an arrayed lentiviral RNAi library containing several regulators of Yes-associated protein (YAP) and transcriptional co-activator with PDZ-binding motif (TAZ), two transcriptional co-activators that are the downstream effectors of the Hippo pathway. However, this approach could be modified to screen for regulators of virtually any transcription factor or co-factor and could also be used to screen CRISPR/CAS9, cDNA, or ORF libraries.

To identify novel regulators of transcription factors, we developed an approach to screen arrayed lentiviral or retroviral RNAi libraries using a dual-luciferase-based transcriptional reporter assay. This approach offers a quick and relatively inexpensive way to screen hundreds of candidates in a single experiment.

Transcription factors can alter the expression of numerous target genes that influence a variety of downstream processes making them good targets for ...

Describing a Transcription Factor Dependent Regulation of the MicroRNA Transcriptome

While the transcription regulation of protein coding genes was extensively studied, little is known on how transcription factors are involved in transcription of non-coding RNAs, specifically of microRNAs. Here, we propose a strategy to study the potential role of transcription factor in regulating transcription of microRNAs using publically available data, computational resources and high throughput data. We use the H3K4me3 epigenetic signature to identify microRNA promoters and chromatin immunoprecipitation (ChIP)-sequencing data from the ENCODE project to identify microRNA promoters that are enriched with transcription factor binding sites. By transfecting cells of interest with shRNA targeting a transcription factor of interest and subjecting the cells to microRNA array, we study the effect of this transcription factor on the microRNA transcriptome. As an illustrative example we use our study on the effect of STAT3 on the microRNA transcriptome of chronic lymphocytic leukemia (CLL) cells.

Herein we propose a strategy to study the effect of a transcription factor of interest on the microRNA transcriptome using publically available data, computational resources and high throughput data from microRNA arrays after transfecting cells with small hairpin (sh)RNA targeting a transcription factor of interest.

While the transcription regulation of protein coding genes was extensively studied, little is known on how transcription factors are involved in ...

Bioengineering

Metabolic Labeling of Newly Transcribed RNA for High Resolution Gene Expression Profiling of RNA Synthesis, Processing and Decay in Cell Culture

The development of whole-transcriptome microarrays and next-generation sequencing has revolutionized our understanding of the complexity of cellular gene expression. Along with a better understanding of the involved molecular mechanisms, precise measurements of the underlying kinetics have become increasingly important. Here, these powerful methodologies face major limitations due to intrinsic properties of the template samples they study, i.e. total cellular RNA. In many cases changes in total cellular RNA occur either too slowly or too quickly to represent the underlying molecular events and their kinetics with sufficient resolution. In addition, the contribution of alterations in RNA synthesis, processing, and decay are not readily differentiated.
We recently developed high-resolution gene expression profiling to overcome these limitations. Our approach is based on metabolic labeling of newly transcribed RNA with 4-thiouridine (thus also referred to as 4sU-tagging) followed by rigorous purification of newly transcribed RNA using thiol-specific biotinylation and streptavidin-coated magnetic beads. It is applicable to a broad range of organisms including vertebrates, Drosophila, and yeast. We successfully applied 4sU-tagging to study real-time kinetics of transcription factor activities, provide precise measurements of RNA half-lives, and obtain novel insights into the kinetics of RNA processing. Finally, computational modeling can be employed to generate an integrated, comprehensive analysis of the underlying molecular mechanisms.

Total cellular RNA provides a poor template for studying short-term changes in RNA synthesis and decay as well as the kinetics of RNA processing. Here, we describe metabolic labeling of newly transcribed RNA with 4-thiouridine followed by thiol-specific biotinylation and purification of newly transcribed RNA allowing to overcome these limitations.

The development of whole-transcriptome microarrays and next-generation sequencing has revolutionized our understanding of the complexity of cellular gene ...

Identification of Key Factors Regulating Self-renewal and Differentiation in EML Hematopoietic Precursor Cells by RNA-sequencing Analysis

Hematopoietic stem cells (HSCs) are used clinically for transplantation treatment to rebuild a patient&#39;s hematopoietic system in many diseases such as leukemia and lymphoma. Elucidating the mechanisms controlling HSCs self-renewal and differentiation is important for application of HSCs for research and clinical uses. However, it is not possible to obtain large quantity of HSCs due to their inability to proliferate in vitro. To overcome this hurdle, we used a mouse bone marrow derived cell line, the EML (Erythroid, Myeloid, and Lymphocytic) cell line, as a model system for this study.
RNA-sequencing (RNA-Seq) has been increasingly used to replace microarray for gene expression studies. We report here a detailed method of using RNA-Seq technology to investigate the potential key factors in regulation of EML cell self-renewal and differentiation. The protocol provided in this paper is divided into three parts. The first part explains how to culture EML cells and separate Lin-CD34+ and Lin-CD34- cells. The second part of the protocol offers detailed procedures for total RNA preparation and the subsequent library construction for high-throughput sequencing. The last part describes the method for RNA-Seq data analysis and explains how to use the data to identify differentially expressed transcription factors between Lin-CD34+ and Lin-CD34- cells. The most significantly differentially expressed transcription factors were identified to be the potential key regulators controlling EML cell self-renewal and differentiation. In the discussion section of this paper, we highlight the key steps for successful performance of this experiment.
In summary, this paper offers a method of using RNA-Seq technology to identify potential regulators of self-renewal and differentiation in EML cells. The key factors identified are subjected to downstream functional analysis in vitro and in vivo.

RNA-sequencing and bioinformatics analyses were used to identify significantly and differentially expressed transcription factors in Lin-CD34+ and Lin-CD34- subpopulations of mouse EMLcells. These transcription factors might play important roles in determining the switch between self-renewing Lin-CD34+ and partially differentiated Lin-CD34- cells.

Hematopoietic stem cells (HSCs) are used clinically for transplantation treatment to rebuild a patient&#39;s hematopoietic system in many diseases such as ...

High-throughput Screening for Chemical Modulators of Post-transcriptionally Regulated Genes

Both transcriptional and post-transcriptional regulation have a profound impact on genes expression. However, commonly adopted cell-based screening assays focus on transcriptional regulation, being essentially aimed at the identification of promoter-targeting molecules. As a result, post-transcriptional mechanisms are largely uncovered by gene expression targeted drug development. Here we describe a cell-based assay aimed at investigating the role of the 3&#39; untranslated region (3&#8217; UTR) in the modulation of the fate of its mRNA, and at identifying compounds able to modify it. The assay is based on the use of a luciferase reporter construct containing the 3&#8217; UTR of a gene of interest stably integrated into a disease-relevant cell line. The protocol is divided into two parts, with the initial focus on the primary screening aimed at the identification of molecules affecting luciferase activity after 24 hr of treatment. The second part of the protocol describes the counter-screening necessary to discriminate compounds modulating luciferase activity specifically through the 3&#8217; UTR. In addition to the detailed protocol and representative results, we provide important considerations about the assay development and the validation of the hit(s) on the endogenous target. The described cell-based reporter gene assay will allow scientists to identify molecules modulating protein levels via post-transcriptional mechanisms dependent on a 3&#8217; UTR.

Here we describe a cell-based reporter gene assay as a valuable tool to screen chemical libraries for compounds modulating post-transcriptional control mechanisms exerted through 3&#8217; UTR.

Both transcriptional and post-transcriptional regulation have a profound impact on genes expression. However, commonly adopted cell-based screening assays ...

Transcriptome Profiling for High-Throughput Drug Screening

Cost-Efficient Transcriptomic-Based Drug Screening

Transcriptomics allows&#160;to obtain comprehensive insights into cellular programs and their responses to perturbations. Despite a significant decrease in the costs of library production and sequencing in the last decade, applying these technologies at the scale necessary for drug screening remains prohibitively expensive, obstructing the immense potential of these methods. Our study presents a cost-effective system for transcriptome-based drug screening, combining miniaturized perturbation cultures with mini-bulk transcriptomics. The optimized mini-bulk protocol provides informative biological signals at cost-effective sequencing depth, enabling extensive screening of known drugs and new molecules. Depending on the chosen treatment and incubation time, this protocol will result in sequencing libraries within approximately 2 days. Due to several stopping points within this protocol, the library preparation, as well as the sequencing, can be performed time-independently. Processing simultaneously a high number of samples is possible; measurement of up to 384 samples was tested without loss of data quality. There are also no known limitations to the number of conditions and/or drugs, despite considering variability in optimal drug incubation times.

Author Spotlight: Cost-Effective Transcriptomic Drug Screening - Unlocking New Targets 

This protocol describes a workflow from ex vivo or in vitro cell cultures to transcriptomic data pre-processing for cost-effective transcriptome-based drug screening.

Transcriptomics allows&#160;to obtain comprehensive insights into cellular programs and their responses to perturbations. Despite a significant decrease ...

Reporter Gene Repression Assay to Study Translational Regulation of a Target Gene

Transform the prepared plasmids into chemically competent bacterial cells already containing an RBP-mCerulean plasmid. To save time, plate the cells using an 8-channel pipettor on 8-lane plates containing Luria-Bertani agar with relevant antibiotics. Colonies should appear in 16 hours at 37 degrees Celsius.
Select a single colony for each double transformant and transfer to 48-well plates containing 1.5 milliliters of LB with appropriate antibiotics. Grow the cells over a period of 18 hours at 37 degrees Celsius shaking at 250 RPM. Store the overnights&#39; glycerol stocks at -80 degrees Celsius in 96-well plates.
In the morning, program the robot to warm 180 microliters of semi-poor medium, or SPM, in 96-well plates. Program the robot to prepare the inducer plate.
In a clean 96-well plate, prepare wells with SPM consisting of 95% BA and 5% LB. The number of wells corresponds to the desired number of inducer concentrations. Add C4-HSL to the wells in the inducer plate that will contain the highest inducer concentration.
Next, program the robot to serially dilute medium from each of the highest concentration wells into 23 lower concentrations ranging from 0 to 218 nanomolar. The volume of each inducer dilution should be sufficient for all strains.
To dilute the overnight strains, program the robot to dilute by a factor of 10 by mixing 20 microliters of bacteria with 180 microliters of SPM in 48-well plates. Then, dilute again by a factor of 10 by taking 20 microliters from the diluted solution into the pre-prepared strain plate suitable for fluorescent measurements.
Now, program the robot to add the diluted inducer from the inducer plate to the 96-well plates with the diluted strains according to the final concentrations. Shake the 96-well plates at 37 degrees Celsius for 6 hours. During that time, take measurements of the optical density or OD at 595 nanometers, as well as mCherry and mCerulean fluorescence via a plate reader every 30 minutes.

Generating the Transcriptional Regulation View of Transcriptomic Features for Prediction Task and Dark Biomarker Detection on Small Datasets

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