12.S: Regulation of Gene Expression (Summary) - Biology

12.S: Regulation of Gene Expression (Summary) - Biology

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  • Regulation of gene expression is essential to the normal development and efficient functioning of cells
  • Gene expression may be regulated by many mechanisms, including those affecting transcript abundance, protein abundance, and post-translational modifications
  • Regulation of transcript abundance may involve controlling the rate of initiation and elongation of transcription, as well as transcript splicing, stability, and turnover
  • The rate of initiation of transcription is related to the presence of RNA polymerase and associated proteins at the promoter.
  • RNApol may be blocked from the promoter by repressors, or may be recruited or stabilized at the promoter by other proteins including transcription factors
  • The lac operon is a classic, fundamental paradigm demonstrating both positive and negative regulation through allosteric effects on trans-factors.
  • In eukaryotes, cis-elements that are usually called enhancers bind to specific trans-factors to regulate transcriptional initiation.
  • Enhancers may be modular, with each enhancer and its transcription factor regulating a distinct component of a gene’s expression pattern, as in the yellow gene.
  • Sticklebacks provide examples of recent evolutionary events in which mutation of an enhancer produced a change in morphology and a selective advantage.
  • Chromatin structure, including reversible modifications such as acetylation of histones, and methylation DNA CpG sites also regulates the initiation of transcription.
  • Chromatin modifications or DNA methylation of some genes are heritable over many mitotic, and sometimes even meiotic divisions.
  • Heritable changes in phenotype that do not result from a change in DNA sequence are called epigenetic. Many epigenetic phenomena involve regulation of gene expression by chromatin modification and/or DNA methylation.

gene expression

transcriptional regulation




lac operon







P / promoter

O / operator


CAP-binding site







cAMP binding protein


CAP binding sequence


adenylate cyclase


Oc / I- / Is

F-factor / episome

GC boxes

CAAT boxes

TATA boxes



transcription start site


transcription factors



gene families



chromatin remodeling



CpG sites


winter annual



Regulation of Gene Expression

Each of your cells has about 22,000 genes. In fact, all of your cells have the same genes. So do all of your cells make the same proteins? Do all 22,000 genes get turned into proteins in every cell? Of course not. If they did, then all your cells would do the same thing. You have cells with different functions because you have cells with different proteins. And your cells have different proteins because they “use” different genes. The regulation of gene expression, or gene regulation, includes the mechanism to turn genes “on” and transcribe the gene into RNA. Any aspect of a gene’s expression may be regulated, from the onset of transcription to the post-translational modification of a protein. It is this regulation that determines when and how much of a protein to make, giving a cell its specific structure and function.

Mechanisms of Regulation

Any step of gene expression may be modulated, from the DNA-RNA transcription step to post-translational modification of a protein. Following is a list of stages where gene expression is regulated:

• Chemical and structural modification of DNA or chromatin
• Transcription
• Translation
• Post-transcriptional modification
• RNA transport
• mRNA degradation
• Post-translational modifications

We will now focus on regulation at the level of transcription. During transcription RNA polymerase reads the DNA template to make a complementary strand of RNA. The genes to which RNA polymerase binds is a highly regulated process. When RNA polymerase binds to a gene, it binds to the promoter, a segment of DNA that allows a gene to be transcribed. The promoter helps RNA polymerase find the start of a gene.

Gene regulation at the level of transcription controls when transcription occurs as well as how much RNA is created. This regulation is controlled by cis-regulatory elements and trans-acting factors. A cis-regulatory element is a region of DNA which regulates the expression of a gene or multiple genes located on that same strand of DNA. These cisregulatory elements are often the binding sites of one or more trans-acting factors, usually a regulatory protein which interacts with RNA polymerase. A cis-regulatory element may be located in a gene’s promoter region, in an intron, or in the 3’ region.

A regulatory protein, or a transcription factor, is a protein involved in regulating gene expression. It is usually bound to a cis-regulatory element. Regulatory proteins often must be bound to a cis-regulatory element to switch a gene on (activator), or to turn a gene off (repressor).

Transcription of a gene by RNA polymerase can be regulated by at least five mechanisms:

Specificity factors (proteins) alter the specificity of RNA polymerase for a promoter or set of promoters, making it more or less likely to bind to the promoter and begin transcription.
Repressors (proteins) bind to non-coding sequences on the DNA that are close to or overlap the promoter region, impeding RNA polymerase’s progress along the strand.
Basal factors, transcription factors that help position RNA polymerase at the start of a gene.
• Enhancers are sites on the DNA strand that are bound by activators in order to loop the DNA, bringing a specific promoter to the initiation complex.
An initiation complex is composed of RNA polymerase and trans-acting factors.
* (proteins) that enhance the interaction between RNA polymerase and a particular promoter.

As the organism grows more sophisticated, gene regulation becomes more complex, though prokaryotic organisms possess some highly regulated systems. Some human genes are controlled by many activators and repressors working together. Obviously, a mutation in a cis-regulatory region, such as the promoter, can greatly affect the proper expression of a gene. It may keep the gene permanently off, such that no protein can be made, or it can keep the gene permanently on, such that the corresponding protein is constantly made. Both of these can have detremental effects on the cell.

Prokaryotic Gene Regulation

In prokaryotes, a combination of activators and repressors determines whether a gene is transcribed. As you know, prokaryotic organisms are fairly simple organisms with much less DNA. Prokaryotic genes are arranged in operons, a region of DNA with a promoter, an operator (defined below), and one or more genes that encode proteins needed to perform a certain task. To maintain homeostasis (and survive), the organism must quickly adapt changing environmental conditions. The regulation of transcription plays a key role in this process.

For a bacteria, many aspects of gene regulation are due to the presence or absence of certain nutrients. In prokaryotes, repressors bind to regions called operators that are generally located immediately downstream from the promoter. Activators bind to the upstream portion of the promoter.

The Lac Operon

The lac operon (Figure 1) is an operon required for the transport and metabolism of lactose in E. coli. The lac operon is regulated by the availability of lactose. The lac operon consists of a promoter, an operator, three adjacent structural genes which code for enzymes and a terminator. The three genes are: lacZ, lacY, and lacA. All three genes are controlled by the same regulatory elements.

Figure 1: The lac operon. The lac operon contains genes for three enzymes, lac, lacY, and lac A, as well as the promoter, operator, and terminatory regulatory regions.

In bacteria, the lac repressor protein blocks the synthesis of enzymes that digest lactose when there is no lactose present (Figure 2). When lactose is present, it binds to the repressor, causing it to detach from the DNA strand.

Specific control of the lac operon depends on the availability of lactose. The enzymes needed to metabolize lactose are not produced when lactose is not present. When lactose is available, and therefore needs to be metabolized, the operon is turned on, RNA polymerase binds to the promoter, and the three genes are transcribed into a single mRNA molecule. However, if lactose is not present (and therefore does not need to be metabolized), the operon is turned off by the lac repressor protein (Figure 2).

The lacI gene, which encodes the lac repressor, lies near the lac operon and is always expressed (constitutive). Therefore, the lac repressor protein is always present in the bacteria. In the absence of lactose, the lac repressor protein will bind to the operator, just past the promoter in the lac operon. The repressor blocks the binding of RNA polymerase to the promoter, keeping the operon turned off (Figure 2).

When lactose is available, a lactose metabolite called allolactose binds to the repressor. This interaction causes a conformational change in the repressor shape and the repressor falls off the operator, allowing RNA polymerase to bind to the promoter and initiate transcription (Figure 2).

Figure 2: Regulation of the lac operon. When lactose is present, RNA polymerase (red) binds to the promoter (P) and the three genes are expressed, producing a single mRNA for the three genes. When lactose is unavailable, the lac repressor (yellow) binds to the operator (O) and inhibits the binding of RNA polymerase to the promoter. The three genes are not expressed.

Eukaryotic Gene Regulation

All your cells have the same DNA (and therefore the same genes), yet they have different proteins because they express different genes. In eukaryotic cells, the start of transcription is one of the most complex aspects of gene regulation. Transcriptional regulation involves the formation of an initiation complex involving interactions between a number of transcription factors, cis-regulatory elements, and enhancers, distant regions of DNA that can loop back to interact with a gene’s promoter. These regulatory elements occur in unique combinations within a given cell type, resulting in only necessary genes being transcribed in certain cells. Transcription factors bind to a DNA strand, allowing RNA polymerase to bind and start transcription.

Each gene has unique cis-regulatory sequences, only allowing specific transcription factors to bind. However, there are common regulatory sequences found in most genes. The TATA box is a cis-regulatory element found in the promoter of most eukaryotic genes. It has the DNA sequence 5’-TATAAA-3’ or a slight variant, and has been highly conserved throughout evolution. When the appropriate cellular signals are present, RNA polymerase binds to the TATA box, completing the initiation complex. A number of transcription factors first bind to the TATA box while other transcription factors bind to the previously attached factors, forming a multi-protein complex. It is only when all the appropriate factors are bound that RNA polymerase will recognize the complex and bind to the DNA, initiating transcription.

One of the more complex eukaryotic gene regulation processes is during development. What genes must be turned on during development so that tissues and organs form from simple cells?

Regulation of Gene Expression During Development

What makes the heart form during development? What makes the skin form? What makes a structure become an arm instead of a leg? These processes occur during development because of a highly specific pattern of gene expression. This intensely regulated pattern of gene expression turns genes on in the right cell at the right time, such that the resulting proteins can perform their necessary functions to ensure proper development. Transcription factors play an extremely important role during development. Many of these proteins can be considered master regulatory proteins, in the sense that they either activate or deactivate the transcription of other genes and, in turn, these secondary gene products can regulate the expression of still other genes in a regulatory cascade. Homeobox and gap genes are important transcription factors during development.

Homeobox Genes

Homeobox genes contain a highly conserved DNA sequence known as a homeobox and are involved in the regulation of genes important to development. A homeobox is about 180 base pairs long it encodes a 60 amino acid domain within the protein (known as the homeodomain), which can bind DNA. Proteins with a homeodomain are therefore transcription factors. These factors typically switch on series of other genes, for instance, the genes needed to encode the proteins to make a leg.

A particular subgroup of homeobox genes are the Hox genes. Protein products of Hox genes function in patterning the body, providing the placement of certain body parts during development. In other words, Hox genes determine where limbs and other body segments will grow in a developing fetus or larva. Mutations in any one of these genes can lead to the growth of extra, typically non-functional body parts in invertebrates. The Antennapedia mutation in Drosophila results in a leg growing from the head in place of an antenna. A mutation in a vertebrate Hox genes usually results in miscarriage.

A gap gene controls the shape of a developing zygote early in its development. The products of these genes produce gaps in a rather uniform arrangement of cells (Figure 3). One example of this is the Kruppel gene, which regulates the activity of a number of other genes. Gap genes encode transcription factors, and the Kruppel gene is a zinc-finger protein. A zinc finger is a DNA binding region within the protein. A zinc finger consists of two antiparallel sheets and an helix with a zinc ion, which is important for the stability of this region. Gap genes control the expression of other genes within specific regions of cells in the developing organism. This allows specific genes to be expressed in certain cells at the appropriate stage of development.

Figure 3: Gap gene expression. Shown is the expression pattern of four gap genes, Kruppel, Giant, Knirps, and Tailless, in a developing Drosophilia embryo. Note how the expression of these genes creates an unique pattern resulting in gaps in what was a rather uniform arrangement of cells.

Regulation of Gene Expression in Cancer

Carcinogenesis depends on both the activation of oncogenes and deactivation of tumor suppressor genes. At least two separate mutations are necessary to develop cancer. For example, a mutation in a proto-oncogene would not necessarily lead to cancer, as normally functioning tumor suppressor genes would counteract the effects of the oncogene. It is the second mutation in the tumor suppressor gene that could lead to uncontrolled cell growth and possibly cancer. Both oncogenes and tumor suppressor genes play an important role in gene regulation and cell proliferation (Figure 4).

Figure 4: Signal transduction pathways. Ras (upper middle section) activates a number of pathways but an especially important one seems to be the mitogen-activated protein kinases (MAPK). MAPK transmit signals downstream to other protein kinases and gene regulatory proteins. Note that many of these pathways are initiated when a signal binds to its receptor outside the cell. Most pathways end with altered gene regulation and cell proliferation. The p53 tumor suppressor protein is shown at the lower section of the figure stimulating p21. The complexity of the pathways demonstrate the significant role these play in the cell.

The products of proto-oncogenes are required for normal growth, repair and homeostasis. However, when these genes are mutated, they turn into oncogenes and play a role in the development of cancer. Proto-oncogenes may be growth factors, transcription factors, or other proteins involved in regulation. A very common oncogene, ras, is normally a regulatory GTPase that switches a signal transduction chain on and off. Ras and Ras-related proteins are products of oncogenes found in 20% to 30% of human tumors.

Ras is a G protein, a regulatory GTP hydrolase that cycles between an activated and inactivated form. When a growth factor binds to its receptor on the outside of the cell, a signal is relayed to RAS. As a G protein, Ras is activated when GTP is bound to it. The active Ras then passes the signal to a series of protein kinases, regulatory proteins that eventually activate transcription factors to alter gene expression and produce proteins that stimulate the cell cycle (Figure 4). Many of the genes and proteins involved in signal transduction pathways are interconnected to ras. Any mutation that makes ras more active or otherwise interrupts the normal signal transduction pathways (Figure 4) may result in excessive cell division and cancer.

Tumor Suppressor Genes

An example of a tumor suppressor gene is p53, which encodes a 53,000 dalton protein, The p53 gene is activated by DNA damage. DNA may be damaged by ultraviolet light, and any damaged DNA may be harmful to the cell. Mutations causing problems with any of the components of Figure 4 may lead to the development of cancer. So that damaged DNA is not replicated, the cell cycle must be temporarily stopped so that the DNA can be repaired. The p53 tumor suppressor gene encodes a transcription factor that regulates the synthesis of cell cycle inhibiting proteins (Figure 4). p53 often activates a gene named p21, whose protein product temporarily stops the cell cycle. If the DNA can not be repaired, p53 activates other genes that lead to cell death, or apoptosis. This prevents the cell from passing on damaged DNA. If the p53 tumor suppressor gene is defective, as by mutation, DNA damage in the cell may accumulate and the cell may survive to replicate the damaged DNA. The damaged DNA would then be passed to other cells through many cell divisions, and cancer could develop.


This study is designed for use as an independent group assignment to engage students in application of the concepts that are being discussed in lecture. Teams of six students can be randomly assigned and the case study distributed to each team. The following six lecture periods cover material in the form of Power Points that highlight key concepts from Alberts et al., Molecular Biology of the Cell, 5th edition. The lectures and their objectives are posted online for student access. Chapters 6 (transcription and translation), 7 (control of gene expression), and 8 (basic techniques for manipulating proteins, DNA, and RNA) serve as an extensive resource for the student. The completed case study from each team is due at the beginning of the next (seventh) period with the accurately completed assignment receiving a 50-point value (within a 1,000-point course total). During this period the professor can lead a discussion, having the student teams provide answers with work on the chalkboard provided by the professor to clarify confusing issues. The following period (period eight) is a 100-point written exam with 50% of the exam from the case study objectives and 50% from the lecture objectives that are unique: ∼25% of the lecture objectives are covered by the case study objectives.

12.S: Regulation of Gene Expression (Summary) - Biology

Graph generated 30 August 2019 using data from PubMed using criteria.

Literature Analysis

Mouse over the terms for more detail many indicate links which you can click for dedicated pages about the topic.

  • Transcription Factor RelA
  • Messenger RNA
  • Cell Survival
  • Promoter Regions
  • siRNA
  • Cancer Gene Expression Regulation
  • Cell Line
  • NF-kappa B p52 Subunit
  • Neoplasm Proteins
  • Gene Expression Regulation
  • Cell Proliferation
  • Virus Replication
  • Proto-Oncogene Proteins c-rel
  • Proto-Oncogene Proteins
  • DNA-Binding Proteins
  • Vertebrates
  • I-kappa B Kinase
  • Cell Nucleus
  • Transcription Factors
  • NF-kappa B
  • Protein Binding
  • Vascular Cell Adhesion Molecule-1
  • Cell Movement
  • Gene Expression Profiling
  • RNA Interference
  • Drug Resistance
  • Transcription
  • Transcription Factor RelB
  • Proto-Oncogenes
  • rac1 GTP-Binding Protein

Specific Cancers (3)

Data table showing topics related to specific cancers and associated disorders. Scope includes mutations and abnormal protein expression.

Note: list is not exhaustive. Number of papers are based on searches of PubMed (click on topic title for arbitrary criteria used).

Useful Links

OMIM, Johns Hopkin University
Referenced article focusing on the relationship between phenotype and genotype.

International Cancer Genome Consortium.
Summary of gene and mutations by cancer type from ICGC

Cancer Genome Anatomy Project, NCI
Gene Summary

COSMIC, Sanger Institute
Somatic mutation information and related details

GEO Profiles, NCBI
Search the gene expression profiles from curated DataSets in the Gene Expression Omnibus (GEO) repository.

Latest Publications: RELB (cancer-related)

33, 27, and 26% respectively when LTβR was silenced via short hairpin RNA. Activation of LTβR had no effect on 5,637 cell growth, despite CyclinD1 and Survivin mRNA levels increasing by

2.7 and 1.3‑fold, respectively, compared with unstimulated cells. In conclusion, activation of LTβR induced the expression of RelA mRNA levels. LTβR activation might be an important mediator in promoting an inflammatory microenvironment in bladder cancer, via the upregulation of TNFα and IL𔂫β mRNA levels. LTβR may be a potential therapeutic target for bladder cancer.

Disclaimer: This site is for educational purposes only it can not be used in diagnosis or treatment.

Cite this page: Cotterill SJ. RELB, Cancer Genetics Web: Accessed:

This page in Cancer Genetics Web by Simon Cotterill is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.
Note: content of abstracts copyright of respective publishers - seek permission where appropriate.

[Home] Page last revised: 30 August, 2019 Cancer Genetics Web, Established 1999

Author Summary

High-throughput genotypic and expression data for individuals in a segregating population can provide important information regarding causal regulatory events. However, it has proven difficult to predict these regulatory relations, largely because of statistical power limitations. The use of additional available resources may increase the accuracy of predictions and suggest possible mechanisms through which the target genes are regulated. In this study, we combine genotypic and expression data across the segregating population with complementary regulatory information to identify modules of genes that are jointly affected by changes in activity of regulatory proteins, as well as by genotypic changes. We develop a novel approach called ReL analysis, which automatically learns such modules. A unique feature of our approach is that all three components of the module—the genes, the underlying polymorphism, and the regulatory proteins—are predicted simultaneously. The integrated analysis makes it possible to capture weaker linkage signals and suggests possible mechanisms underlying expression changes. We demonstrate the power of the method on data from yeast segregants, by identifying the roles of new as well as known polymorphisms.

Citation: Gat-Viks I, Meller R, Kupiec M, Shamir R (2010) Understanding Gene Sequence Variation in the Context of Transcription Regulation in Yeast. PLoS Genet 6(1): e1000800.

Editor: Trudy F. C. Mackay, North Carolina State University, United States of America

Received: July 10, 2009 Accepted: December 7, 2009 Published: January 8, 2010

Copyright: © 2010 Gat-Viks et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

Funding: RS was supported by the Israel Science Foundation (grant 802/08) and by the European Community's Seventh Framework Programme (grant agreement no. HEALTH-F4-2009-223575 for the TRIREME project). MK was supported by grants from the Israel Science Foundation and the Israel Cancer Research Fund. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.

Competing interests: The authors have declared that no competing interests exist.


Genome-wide association studies (GWASs) have successfully identified thousands of associations between single-nucleotide polymorphisms (SNPs) and complex human phenotypes. Yet, the interpretation of these identified associations remains challenging, and several lines of evidence suggest that many additional associated loci remain to be identified [1,2]. A recently proposed approach transcriptome-wide association study (TWAS) [3,4] identifies genetic associations by combining GWAS data with expression quantitative trait locus (eQTL) data. TWAS can be used both to identify new associations and prioritize candidate causal genes in GWAS-identified regions [5]. TWAS integrates gene expression with GWAS data using only genotype expression imputation from a gene expression model built from eQTLs and then tests for the association between imputed gene expression level and a phenotype of interest. The main strength of TWAS is that it can infer the association of imputed gene expression with the phenotype using only GWAS summary statistics data [3,4]. TWAS can increase the statistical power by combining single-SNP association tests in a biologically motivated fashion and reducing the number of tests performed. The applications of TWAS have led to novel insights into the genetic basis for several phenotype and diseases [6].

Despite the successes of TWAS, the approach has multiple limitations [7]. First, the most relevant tissue for many human diseases and phenotypes remains unclear, and the eQTL data for these relevant tissues are usually challenging to access in large samples. The choice of the most relevant tissue-specific eQTL sample for building gene expression prediction model in TWAS remains largely ad-hoc. Two commonly adopted approaches are: (1) using the largest eQTL sample accessible (usually whole blood [3]), or (2) using the most relevant tissue based on previous knowledge and experience [6,8]. Second, the power of TWAS is mainly bounded by the sample size of eQTL data power of TWAS increases dramatically with the eQTL sample size, approaching an empirical maximum when eQTL sample size is close to 1,000 [3]. However, most available eQTL data sets have a sample size substantially smaller than 1,000. For example, Genotype-Tissue Expression(GTEx) project [9,10] have generated matched genotype and expression data for 44 human tissues, but with sample size for each tissue varying from only 70 to 361. Researchers do not always know which tissue to use, and sometimes the sample size for the tissue that they prefer to use is too small to have enough power.

Recent work in gene regulation patterns across tissues suggests that local gene expression regulation is often shared across tissues [9–11]. Thus, combining eQTL data across multiple tissues can improve the power of TWAS, by increasing the effective eQTL sample size or increasing the likelihood that the causal tissue (or a close proxy) is included in the eQTL training data. Two previously proposed approaches, UTMOST [12] and S-MultiXcan [13], have shown the advantage of a multi-tissue TWAS approach. However, these two approaches still conduct the TWAS test with single-tissue TWAS weights first, and then combine multiple single-tissue associations into a single powerful metric to quantify. UTMOST uses a generalized Berk-Jones (GBJ) test, which is a set-based method [12]. S-MultiXcan proposes a combined chi-square test that uses principal components from the tissue-specific genetically predicted expression values to integrate univariate S-PrediXcan results [13]. We refer to these two approaches as single-tissue based cross-tissue TWAS approach. We propose to leverage the correlated gene expression pattern across tissues in the eQTL dataset directly to build more stable and representative cross-tissue gene expression features using sparse canonical correlation analysis (sCCA) [14], and thus improve the gene expression prediction model for TWAS. The potential advantage of sCCA is that it can capture any genetic contribution to gene expression that is shared across multiple tissues. Because sCCA maximizes the correlation between a linear combination of tissue-specific expression values and linear combination of cis-genotypes, sCCA features are more likely to be detectably heritable than cross-tissue features constructed using principal components analysis (PCA), which constructs linear combinations to capture total (genetic plus non-genetic) expression variance [14]. In addition, we also propose an omnibus test that combines the single tissue TWAS test results with the sCCA-TWAS test results using the aggregate Cauchy association test (ACAT). ACAT is a computationally efficient P-value combination method for boosting the power in sequencing study, and has proved to be powerful for detecting a sparse signal [15].

Specifically, we propose a novel four-step pipeline to perform multi-tissue TWAS: 1. generate sparse canonical correlation analysis (sCCA) [14] -based cross-tissue features (sCCA-features) integrating eQTL data across multiple tissues 2. fit TWAS weights for these sCCA-features as well as single tissue-specific gene expression [3,4] 3. perform TWAS with weights built from sCCA-features and singe tissue gene expression [3,4] 4. combine the test results of sCCA TWAS results and single tissue TWAS results using the aggregated Cauchy association test (ACAT) [15]. We use extensive simulations to compare this approach with four other cross-tissue approaches, including: 1. performing TWAS on single most relevant tissue, 2. performing TWAS on all single tissues available and combining the test results via Bonferroni or generalized Berk-Jones (GBJ) test [16] 3. using Principal Components Analysis (PCA) to create cross-tissue features and 4. the recently proposed S-MultiXcan and UTMOST approach [12,13].

Through simulations we show that sCCA-features identify a larger number of cis-heritable transcripts than single tissue and PCA-features, and the combined test substantially improves statistical power. Importantly, all approaches successfully control the type I error rate. We also show by simulations that the power of our combined test compares favorably to other approaches despite using incomplete gene expression matrix for all individuals and all tissues thus requiring imputation, as is often the case for multi-tissue gene expression dataset like GTEx [9,10].

We applied our four-step approach to eQTL data from GTEx and 10 sets of publicly available GWAS summary statistics data. We built sCCA-features on an expression matrix including 134 individuals with data in 22 tissues. The sCCA-TWAS results were then compared with the single-tissue based TWAS results available on TWAS HUB ( sCCA+ACAT TWAS was able to increase the number of testable genes by 81% and almost double the number of identified gene-phenotype associations (75% more genes identified).

The sCCA cross-tissue weights on GTEx version 6 and 8 are available on TWAS-HUB[17] and the Rscript to perform ACAT is also available at Github repository ( The sCCA-TWAS could be easily performed with sCCA cross-tissue TWAS weights as traditional single-tissue TWAS and the test combination with ACAT is also easy to conduct. Sample code to compute sCCA cross-tissue weights and conduct sCCA+ACAT TWAS can be found at the Github repository (

This archive contains the Matlab scripts for the simulations of the IL-4 stochastic model.

Table of Contents- Mathematical appendix 1: Post-transcriptional memory time- Mathematical appendix 2: Transcription factor activity and occupancy of the binding site- Mathematical appendix 3: Fraction and mean expression value of IL-4 positive cells- Supplementary figures legends- Supplementary information references- Figure S1- Figure S2- Figure S3- Figure S4- Figure S5- Figure S6- Figure S7- Figure S8- Figure S9- Figure S10- Figure S11- Figure S12- Figure S13- Figure S14- Figure S15

(Source data for figure 2A) Facs data for figure 2A

(Source data for figure 3A) Facs histograms for figure 3A

(Source data for figure 3B) kinetics of relative signals for figure 3B (presented already in figure 1B)

(Source data for figure 4A) Facs histograms for figure 4A

(Source data for figure 4B) kinetics of IL-4-producing fraction from Facs Data for figure 4B

(Source data for figure 4C) Facs data and kinetics of IL-4-producing fraction from Facs Data for figure 4C

(Source data for figure 1A) Facs histograms for figure 1A

(Source data for figure 1B) RT-PCR, Facs and ELISA data and derived kinetics of relative signals for figure 1B

(Source data for figure 1C) Facs histograms for bottom panel of figure 1C

(Source data for figure 5B) Facs histograms for figure 5B

(Source data for figure 5D) Dose-response curves upon CSA titration for figure 5D

(Source data for figure 6A) kinetics of IL-4 expression (fraction, positive mean and varibaility) from Facs Data for figure 6A

Watch the video: Genregulation: Enhancer und Silencer (May 2022).