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Does the magnitude of gene-expression changes decrease the more downstream a gene is from the origin of change?

Does the magnitude of gene-expression changes decrease the more downstream a gene is from the origin of change?



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If I have a decrease or increase in expression in one gene, will the decrease/increase in expression in the downstream genes always be of a magnitude lower than the previous ones, or can they be higher?

I'm thinking in terms of signal propagation in a network. If I, for example, knockdown gene $X$ to 40% of its basal level, and it influences genes $Y$ and $Z$ in the following manner:

$$X → Y → Z,$$

Will I expect genes $Y$ and $Z$ to suffer lower decreases/increases than the previous gene, for example:

$$X: 40\% ~⇒~ Y: 60\% ~⇒~ Z: 80\%$$

Or could I have a scenario where a small change in one gene's expression can lead to higher magnitude changes in downstream ones, for example:

$$X: 40\% ~⇒~ Y: 20\% ~⇒~ Z: 10\%$$

I suppose that in some networks the signal will always decrease in magnitude the farther it is from the source, but I'm wondering if, in a biological network, that will also happen and consistently in each step. I read papers with knockdowns all the time, but I never stopped to analyse if knockdowns always create lower magnitude changes in downstream genes or not. So I was wondering if this intuitive notion holds empirically or not.


Gene expression can be very well described by Hill functions, i.e.:

$$c_Y(c_X) = frac{c_X^n}{1+c_X^n}$$

when $X$ activates $Y$ and

$$c_Y(c_X) = frac{1}{1+c_X^n}$$

when $X$ represses $Y$ (omitting units and all sorts of constants for simplicity). For the common case that $n>1$, these functions look like this:

As you can see, they are far from linear, but sigmoids. For $n→∞$, these become perfect step functions.

As a result, gene regulation mostly works like a switch: As long as you are not close to the critical concentration ($1$ in the above example), changes in the regulator have a less than proportional effect. For instance in the above example, it doesn't matter whether the concentration of your activator is $2$ or $10$, the protein production is pretty much $1$.

Therefore, “signals” in gene-regulation networks get stabilised instead of diluted. There are also some other self-regulation effects contributing to this such as genes that self-regulate, the availability of amino acids, and the limits of the translation apparatus.

Note that without these sigmoidal relationships, you would also have big influences of the inevitable fluctuations of the regulator. As a result, it would be impossible to keep the metabolism in some stable state - the only stable state in a linear system is death. Simply put, you would have to keep a very precise and regular diet, lest your body stops working.


Just to expand on @Wrzlprmft's great answer with a concrete example:

Sticking to your simple gene circuit with $X$, $Y$, and $Z$, now consider the possibility that $Y$ also activates its own expression via a positive feedback loop:

$X o underset{circlearrowright}Y o Z$

Here activation of $Y$ will be only minimally dependent on $X$. Once $Y$ is activated, the following self-activation makes $Y$ practically independent of further $X$ activity, causing $Y$ to drive its own activation until saturation.

If by decreasing $X$ to 50%, its levels are still above the minimum level required for activating $Y$, this would have practically no effect on the final (steady state) levels of $Y$. Conversely, if a 50% decrease brings $X$ below the $Y$ activation level, it would lead to a complete shutdown of $Y$.


Does the magnitude of gene-expression changes decrease the more downstream a gene is from the origin of change? - Biology

Stress induces persistent, functional changes in histone code and DNA methylation.

Epigenetics mediate stress-induced changes of expression of CRH, GR, AVP.

Characteristics of epigenetic stress response depends on multitude of factors.

Epigenetic mechanisms constitute a crucial element of stress response.

Epigenetic mechanisms governing stress response are still poorly understood.


Heart Failure in the Era of Genomic Medicine

Ivor J. Benjamin , Jeetendra Patel , in Genomic and Personalized Medicine , 2009

A Case for Biologic Reclassification of Heart Failure

Gene expression profiling has significantly improved the diagnostic classification of specific conditions (e.g., breast cancer, chronic myelogenous leukemia) but remains a formidable challenge for deciphering meaningful insights about the biological mechanisms underlying disease pathogenesis ( Quackenbush, 2006 ). Among inheritable forms of cardiovascular diseases, recent advances of single-gene disorders have fundamentally altered our understanding about the cellular processes, metabolic alterations and transcriptional reprogramming of the diseased heart ( Seidman and Seidman, 2001 ). Much like the success seen for tumor classification and other improvements in cancer therapeutics ( Bell, 2004 Quackenbush, 2006 ), and beyond the availability of genetic tests for disease-causing mutations of cardiomyopathy ( Morita et al., 2005 ), the development of genomic tools that are causally linked to disease pathogenesis, termed a “molecular signature, ” will likely accelerate progress for early detection, targeted therapy and disease monitoring of inheritable heart failure ( Bell, 2004 ). We suggest that the opportunities exist for microarray-based profiling, proteomics, metabolomics, and genome-wide technologies to propel the transition from clinicopathologic to clinico-genomic classifications for heart failure.

Different gene profiles for failing and nonfailing hearts have already permitted differentiation among heart failure with different etiologies, as shown recently by Donahue and colleagues in Table 59.2 (Donahue et al., 2006). Considerable discordance, however, exists as our ability to diagnose heart failure using genomic profile lags substantially behind clinical management. Important obstacles remain, such as limitations in procuring tissue samples needed for genomic profiles and their transition from use as research tools into the realm of clinical diagnostics.

TABLE 59.2 . Discovery projects in heart failure

Upregulation of genes for atrial natriuretic peptide, sarcomeric and cytoskeletal proteins, stress proteins, and transcription/translation regulators

Down-regulation of genes regulating calcium signaling pathways

364 differentially expressed genes

Up-regulation being most prominent in genes for energy pathways, muscle contraction, electron transport, and intracellular signaling

Down-regulation was most prominent in genes for cell cycle control

95 differentially expressed genes with notable upregulation of atrial natriuretic peptide and brain natriuretic peptide

Prominent pathways up-regulated include cell signaling and muscle contraction

179 differentially expressed genes (130 up and 49 down

There was prominent up-regulation in nitric oxide pathways and down-regulation of inflammatory genes

107 differentially regulated genes (85 up and 22 down)

Prominent was the up-regulation of genes regulating vascular networks and down-regulation of genes regulating myocyte hypertrophy

DCM = dilated cardiomyopathy HCM = hypertrophic cardiomyopathy ICM = ischemic cardiomyopathy.


Contents

Understanding of epistasis has changed considerably through the history of genetics and so too has the use of the term. The term was first used by William Bateson and his collaborators Florence Durham and Muriel Wheldale Onslow. [4] In early models of natural selection devised in the early 20th century, each gene was considered to make its own characteristic contribution to fitness, against an average background of other genes. Some introductory courses still teach population genetics this way. Because of the way that the science of population genetics was developed, evolutionary geneticists have tended to think of epistasis as the exception. However, in general, the expression of any one allele depends in a complicated way on many other alleles.

In classical genetics, if genes A and B are mutated, and each mutation by itself produces a unique phenotype but the two mutations together show the same phenotype as the gene A mutation, then gene A is epistatic and gene B is hypostatic. For example, the gene for total baldness is epistatic to the gene for brown hair. In this sense, epistasis can be contrasted with genetic dominance, which is an interaction between alleles at the same gene locus. As the study of genetics developed, and with the advent of molecular biology, epistasis started to be studied in relation to quantitative trait loci (QTL) and polygenic inheritance.

The effects of genes are now commonly quantifiable by assaying the magnitude of a phenotype (e.g. height, pigmentation or growth rate) or by biochemically assaying protein activity (e.g. binding or catalysis). Increasingly sophisticated computational and evolutionary biology models aim to describe the effects of epistasis on a genome-wide scale and the consequences of this for evolution. [5] [6] [7] Since identification of epistatic pairs is challenging both computationally and statistically, some studies try to prioritize epistatic pairs. [8] [9]

Terminology about epistasis can vary between scientific fields. Geneticists often refer to wild type and mutant alleles where the mutation is implicitly deleterious and may talk in terms of genetic enhancement, synthetic lethality and genetic suppressors. Conversely, a biochemist may more frequently focus on beneficial mutations and so explicitly state the effect of a mutation and use terms such as reciprocal sign epistasis and compensatory mutation. [16] Additionally, there are differences when looking at epistasis within a single gene (biochemistry) and epistasis within a haploid or diploid genome (genetics). In general, epistasis is used to denote the departure from 'independence' of the effects of different genetic loci. Confusion often arises due to the varied interpretation of 'independence' among different branches of biology. [17] The classifications below attempt to cover the various terms and how they relate to one another.

Additivity Edit

Two mutations are considered to be purely additive if the effect of the double mutation is the sum of the effects of the single mutations. This occurs when genes do not interact with each other, for example by acting through different metabolic pathways. Simply, additive traits were studied early on in the history of genetics, however they are relatively rare, with most genes exhibiting at least some level of epistatic interaction. [18] [19]

Magnitude epistasis Edit

When the double mutation has a fitter phenotype than expected from the effects of the two single mutations, it is referred to as positive epistasis. Positive epistasis between beneficial mutations generates greater improvements in function than expected. [10] [11] Positive epistasis between deleterious mutations protects against the negative effects to cause a less severe fitness drop. [13]

Conversely, when two mutations together lead to a less fit phenotype than expected from their effects when alone, it is called negative epistasis. [20] [21] Negative epistasis between beneficial mutations causes smaller than expected fitness improvements, whereas negative epistasis between deleterious mutations causes greater-than-additive fitness drops. [12]

Independently, when the effect on fitness of two mutations is more radical than expected from their effects when alone, it is referred to as synergistic epistasis. The opposite situation, when the fitness difference of the double mutant from the wild type is smaller than expected from the effects of the two single mutations, it is called antagonistic epistasis. [15] Therefore, for deleterious mutations, negative epistasis is also synergistic, while positive epistasis is antagonistic conversely, for advantageous mutations, positive epistasis is synergistic, while negative epistasis is antagonistic.

The term genetic enhancement is sometimes used when a double (deleterious) mutant has a more severe phenotype than the additive effects of the single mutants. Strong positive epistasis is sometimes referred to by creationists as irreducible complexity (although most examples are misidentified).

Sign epistasis Edit

Sign epistasis [22] occurs when one mutation has the opposite effect when in the presence of another mutation. This occurs when a mutation that is deleterious on its own can enhance the effect of a particular beneficial mutation. [17] For example, a large and complex brain is a waste of energy without a range of sense organs, but sense organs are made more useful by a large and complex brain that can better process the information. If a fitness landscape has no sign epistasis then it is called smooth.

At its most extreme, reciprocal sign epistasis [23] occurs when two deleterious genes are beneficial when together. For example, producing a toxin alone can kill a bacterium, and producing a toxin exporter alone can waste energy, but producing both can improve fitness by killing competing organisms. If a fitness landscape has sign epistasis but no reciprocal sign epistasis then it is called semismooth. [24]

Reciprocal sign epistasis also leads to genetic suppression whereby two deleterious mutations are less harmful together than either one on its own, i.e. one compensates for the other. This term can also apply sign epistasis where the double mutant has a phenotype intermediate between those of the single mutants, in which case the more severe single mutant phenotype is suppressed by the other mutation or genetic condition. For example, in a diploid organism, a hypomorphic (or partial loss-of-function) mutant phenotype can be suppressed by knocking out one copy of a gene that acts oppositely in the same pathway. In this case, the second gene is described as a "dominant suppressor" of the hypomorphic mutant "dominant" because the effect is seen when one wild-type copy of the suppressor gene is present (i.e. even in a heterozygote). For most genes, the phenotype of the heterozygous suppressor mutation by itself would be wild type (because most genes are not haplo-insufficient), so that the double mutant (suppressed) phenotype is intermediate between those of the single mutants.

In non reciprocal sign epistasis, fitness of the mutant lies in the middle of that of the extreme effects seen in reciprocal sign epistasis.

When two mutations are viable alone but lethal in combination, it is called Synthetic lethality or unlinked non-complementation. [25]

Haploid organisms Edit

In a haploid organism with genotypes (at two loci) ab, Ab, aB or AB, we can think of different forms of epistasis as affecting the magnitude of a phenotype upon mutation individually (Ab and aB) or in combination (AB).

Interaction type ab Ab aB AB
No epistasis (additive) 0 1 1 2 AB = Ab + aB + ab
Positive (synergistic) epistasis 0 1 1 3 AB > Ab + aB + ab
Negative (antagonistic) epistasis 0 1 1 1 AB < Ab + aB + ab
Sign epistasis 0 1 -1 2 AB has opposite sign to Ab or aB
Reciprocal sign epistasis 0 -1 -1 2 AB has opposite sign to Ab and aB

Diploid organisms Edit

Epistasis in diploid organisms is further complicated by the presence of two copies of each gene. Epistasis can occur between loci, but additionally, interactions can occur between the two copies of each locus in heterozygotes. For a two locus, two allele system, there are eight independent types of gene interaction. [26]

Additive A locus Additive B locus Dominance A locus Dominance B locus
aa aA AA aa aA AA aa aA AA aa aA AA
bb 1 0 –1 bb 1 1 1 bb –1 1 –1 bb –1 –1 –1
bB 1 0 –1 bB 0 0 0 bB –1 1 –1 bB 1 1 1
BB 1 0 –1 BB –1 –1 –1 BB –1 1 –1 BB –1 –1 –1
Additive by Additive Epistasis Additive by Dominance Epistasis Dominance by Additive Epistasis Dominance by Dominance Epistasis
aa aA AA aa aA AA aa aA AA aa aA AA
bb 1 0 –1 bb 1 0 –1 bb 1 –1 1 bb –1 1 –1
bB 0 0 0 bB –1 0 1 bB 0 0 0 bB 1 –1 1
BB –1 0 1 BB 1 0 –1 BB –1 1 –1 BB –1 1 –1

Additivity Edit

This can be the case when multiple genes act in parallel to achieve the same effect. For example, when an organism is in need of phosphorus, multiple enzymes that break down different phosphorylated components from the environment may act additively to increase the amount of phosphorus available to the organism. However, there inevitably comes a point where phosphorus is no longer the limiting factor for growth and reproduction and so further improvements in phosphorus metabolism have smaller or no effect (negative epistasis). Some sets of mutations within genes have also been specifically found to be additive. [27] It is now considered that strict additivity is the exception, rather than the rule, since most genes interact with hundreds or thousands of other genes. [18] [19]

Epistasis between genes Edit

Epistasis within the genomes of organisms occurs due to interactions between the genes within the genome. This interaction may be direct if the genes encode proteins that, for example, are separate components of a multi-component protein (such as the ribosome), inhibit each other's activity, or if the protein encoded by one gene modifies the other (such as by phosphorylation). Alternatively the interaction may be indirect, where the genes encode components of a metabolic pathway or network, developmental pathway, signalling pathway or transcription factor network. For example, the gene encoding the enzyme that synthesizes penicillin is of no use to a fungus without the enzymes that synthesize the necessary precursors in the metabolic pathway.

Epistasis within genes Edit

Just as mutations in two separate genes can be non-additive if those genes interact, mutations in two codons within a gene can be non-additive. In genetics this is sometimes called intragenic complementation when one deleterious mutation can be compensated for by a second mutation within that gene. This occurs when the amino acids within a protein interact. Due to the complexity of protein folding and activity, additive mutations are rare.

Proteins are held in their tertiary structure by a distributed, internal network of cooperative interactions (hydrophobic, polar and covalent). [28] Epistatic interactions occur whenever one mutation alters the local environment of another residue (either by directly contacting it, or by inducing changes in the protein structure). [29] For example, in a disulphide bridge, a single cysteine has no effect on protein stability until a second is present at the correct location at which point the two cysteines form a chemical bond which enhances the stability of the protein. [30] This would be observed as positive epistasis where the double-cysteine variant had a much higher stability than either of the single-cysteine variants. Conversely, when deleterious mutations are introduced, proteins often exhibit mutational robustness whereby as stabilising interactions are destroyed the protein still functions until it reaches some stability threshold at which point further destabilising mutations have large, detrimental effects as the protein can no longer fold. This leads to negative epistasis whereby mutations that have little effect alone have a large, deleterious effect together. [31] [32]

In enzymes, the protein structure orients a few, key amino acids into precise geometries to form an active site to perform chemistry. [33] Since these active site networks frequently require the cooperation of multiple components, mutating any one of these components massively compromises activity, and so mutating a second component has a relatively minor effect on the already inactivated enzyme. For example, removing any member of the catalytic triad of many enzymes will reduce activity to levels low enough that the organism is no longer viable. [34] [35] [36]

Heterozygotic epistasis Edit

Diploid organisms contain two copies of each gene. If these are different (heterozygous / heteroallelic), the two different copies of the allele may interact with each other to cause epistasis. This is sometimes called allelic complementation, or interallelic complementation. It may be caused by several mechanisms, for example transvection, where an enhancer from one allele acts in trans to activate transcription from the promoter of the second allele. Alternately, trans-splicing of two non-functional RNA molecules may produce a single, functional RNA. Similarly, at the protein level, proteins that function as dimers may form a heterodimer composed of one protein from each alternate gene and may display different properties to the homodimer of one or both variants.

Fitness landscapes and evolvability Edit

In evolutionary genetics, the sign of epistasis is usually more significant than the magnitude of epistasis. This is because magnitude epistasis (positive and negative) simply affects how beneficial mutations are together, however sign epistasis affects whether mutation combinations are beneficial or deleterious. [10]

A fitness landscape is a representation of the fitness where all genotypes are arranged in 2D space and the fitness of each genotype is represented by height on a surface. It is frequently used as a visual metaphor for understanding evolution as the process of moving uphill from one genotype to the next, nearby, fitter genotype. [18]

If all mutations are additive, they can be acquired in any order and still give a continuous uphill trajectory. The landscape is perfectly smooth, with only one peak (global maximum) and all sequences can evolve uphill to it by the accumulation of beneficial mutations in any order. Conversely, if mutations interact with one another by epistasis, the fitness landscape becomes rugged as the effect of a mutation depends on the genetic background of other mutations. [37] At its most extreme, interactions are so complex that the fitness is ‘uncorrelated’ with gene sequence and the topology of the landscape is random. This is referred to as a rugged fitness landscape and has profound implications for the evolutionary optimisation of organisms. If mutations are deleterious in one combination but beneficial in another, the fittest genotypes can only be accessed by accumulating mutations in one specific order. This makes it more likely that organisms will get stuck at local maxima in the fitness landscape having acquired mutations in the 'wrong' order. [32] [38] For example, a variant of TEM1 β-lactamase with 5 mutations is able to cleave cefotaxime (a third generation antibiotic). [39] However, of the 120 possible pathways to this 5-mutant variant, only 7% are accessible to evolution as the remainder passed through fitness valleys where the combination of mutations reduces activity. In contrast, changes in environment (and therefore the shape of the fitness landscape) have been shown to provide escape from local maxima. [32] In this example, selection in changing antibiotic environments resulted in a "gateway mutation" which epistatically interacted in a positive manner with other mutations along an evolutionary pathway, effectively crossing a fitness valley. This gateway mutation alleviated the negative epistatic interactions of other individually beneficial mutations, allowing them to better function in concert. Complex environments or selections may therefore bypass local maxima found in models assuming simple positive selection.

High epistasis is usually considered a constraining factor on evolution, and improvements in a highly epistatic trait are considered to have lower evolvability. This is because, in any given genetic background, very few mutations will be beneficial, even though many mutations may need to occur to eventually improve the trait. The lack of a smooth landscape makes it harder for evolution to access fitness peaks. In highly rugged landscapes, fitness valleys block access to some genes, and even if ridges exist that allow access, these may be rare or prohibitively long. [40] Moreover, adaptation can move proteins into more precarious or rugged regions of the fitness landscape. [41] These shifting "fitness territories" may act to decelerate evolution and could represent tradeoffs for adaptive traits.

The frustration of adaptive evolution by rugged fitness landscapes was recognized as a potential force for the evolution of evolvability. Michael Conrad in 1972 was the first to propose a mechanism for the evolution of evolvability by noting that a mutation which smoothed the fitness landscape at other loci could facilitate the production of advantageous mutations and hitchhike along with them. [42] [43] Rupert Riedl in 1975 proposed that new genes which produced the same phenotypic effects with a single mutation as other loci with reciprocal sign epistasis would be a new means to attain a phenotype otherwise too unlikely to occur by mutation. [44] [45]

Rugged, epistatic fitness landscapes also affect the trajectories of evolution. When a mutation has a large number of epistatic effects, each accumulated mutation drastically changes the set of available beneficial mutations. Therefore, the evolutionary trajectory followed depends highly on which early mutations were accepted. Thus, repeats of evolution from the same starting point tend to diverge to different local maxima rather than converge on a single global maximum as they would in a smooth, additive landscape. [46] [47]

Evolution of sex Edit

Negative epistasis and sex are thought to be intimately correlated. Experimentally, this idea has been tested in using digital simulations of asexual and sexual populations. Over time, sexual populations move towards more negative epistasis, or the lowering of fitness by two interacting alleles. It is thought that negative epistasis allows individuals carrying the interacting deleterious mutations to be removed from the populations efficiently. This removes those alleles from the population, resulting in an overall more fit population. This hypothesis was proposed by Alexey Kondrashov, and is sometimes known as the deterministic mutation hypothesis [48] and has also been tested using artificial gene networks. [20]

However, the evidence for this hypothesis has not always been straightforward and the model proposed by Kondrashov has been criticized for assuming mutation parameters far from real world observations. [49] In addition, in those tests which used artificial gene networks, negative epistasis is only found in more densely connected networks, [20] whereas empirical evidence indicates that natural gene networks are sparsely connected, [50] and theory shows that selection for robustness will favor more sparsely connected and minimally complex networks. [50]

Regression analysis Edit

Quantitative genetics focuses on genetic variance due to genetic interactions. Any two locus interactions at a particular gene frequency can be decomposed into eight independent genetic effects using a weighted regression. In this regression, the observed two locus genetic effects are treated as dependent variables and the "pure" genetic effects are used as the independent variables. Because the regression is weighted, the partitioning among the variance components will change as a function of gene frequency. By analogy it is possible to expand this system to three or more loci, or to cytonuclear interactions [51]

Double mutant cycles Edit

When assaying epistasis within a gene, site-directed mutagenesis can be used to generate the different genes, and their protein products can be assayed (e.g. for stability or catalytic activity). This is sometimes called a double mutant cycle and involves producing and assaying the wild type protein, the two single mutants and the double mutant. Epistasis is measured as the difference between the effects of the mutations together versus the sum of their individual effects. [52] This can be expressed as a free energy of interaction. The same methodology can be used to investigate the interactions between larger sets of mutations but all combinations have to be produced and assayed. For example, there are 120 different combinations of 5 mutations, some or all of which may show epistasis.

Statistical coupling analysis Edit

Computational prediction Edit

Numerous computational methods have been developed for the detection and characterization of epistasis. Many of these rely on machine learning to detect non-additive effects that might be missed by statistical approaches such as linear regression. For example, multifactor dimensionality reduction (MDR) was designed specifically for nonparametric and model-free detection of combinations of genetic variants that are predictive of a phenotype such as disease status in human populations. [53] [54] Several of these approaches have been broadly reviewed in the literature. [55] Even more recently, methods that utilize insights from theoretical computer science (the Hadamard transform [56] and compressed sensing [57] [58] ) or maximum-likelihood inference [59] were shown to distinguish epistatic effects from overall non-linearity in genotype-phenotype map structure, [60] while others used patient survival analysis to identify non-linearity. [61]


Results

Host RNA-seq sample preprocessing and quality assessment

We first examined gene expression in colonic biopsies from 18 CF and 15 healthy individuals. Overall, CF and healthy samples had comparable number of reads (28,250,473 and 30,041,827 reads on average, respectively) with the average quality greater than 30 phred score across all samples (Additional file 2: Figure S2). The sequences were annotated to generate estimated read counts and transcripts per kilobase million (TPM) using kallisto [19], resulting in 173,259 total transcripts, of which 56,283 passed the filter of mean TPM greater than 1 (TPM > 1). While the principal component analysis (PCA) plots showed an overlap between the expression profile of most samples from CF and healthy individuals, it identified two possible outliers (samples 1096 and 1117) (Additional file 2: Figure S3). In addition, the top five transcripts driving the PC were of mitochondrial origin (Additional file 2: Figure S4). Hence, to reduce any bias in identifying differentially expressed genes, we filtered out all the mitochondrial transcripts from the data. We further investigated the outliers using the remaining transcripts by calculating Cook’s distance between the samples and found that the two samples (1096 and 1117) were still outliers (Additional file 2: Figure S5). This was further evident by the heatmap of the top 20 most highly expressed genes (Additional file 2: Figure S6), where we found an alternate expression pattern for the two samples, compared to the rest. Therefore, the two outlier CF samples (1096 and 1117) were eliminated from further analysis.

Differentially expressed host genes between CF and healthy mucosal samples

To examine gene expression differences we used read counts from the remaining 16 CF and 15 healthy samples. Using DESeq2, we identified 1543 differentially expressed genes at q value < 0.05 (Benjamini-Hochberg correction see Additional file 2: Figure S8 for a volcano plot). Of the 1543 differentially expressed genes, 919 (59%) were upregulated and 624 (41%) were downregulated in CF patients. Including sex as a covariate in the model did not substantially alter the results (only 43 additional differentially expressed genes were identified) therefore, we did not include sex in downstream analyses. The full list of differentially expressed genes significant at q value < 0.05 is available in Additional file 3.

We visualized the expression pattern of five (three upregulated and two downregulated) randomly selected differentially expressed representative genes and CFTR, from genes included in the colorectal cancer disease pathway (Fig. 1a). Consistent with the expectation of changes in mucosal immunity that could compensate for a diminished protective mucus function, we noted LCN2 to be one of the top differentially expressed genes (q value = 2.54E−08, Wald’s test). LCN2 encodes for lipocalin 2, which limits bacterial growth by sequestering iron-laden bacterial siderophore [41]. However, a number of other top genes are involved in major cellular biology processes and were previously related to cancer pathogenesis and colon cancer. Examples include RRS1 (q value = 6.16E−09), which encodes for the ribosomal biogenesis protein homolog that promotes angiogenesis and cellular proliferation, but suppresses apoptosis [42] KRTAP5-5 (q value = 4.89E−08), which encodes for keratin-associated protein 5-5, a protein that plays important roles in cytoskeletal function and facilitates various malignant behaviors that include cellular motility and vascular invasion [43] and ALDOB (q value = 2.64E−07), which encodes for aldolase B, an enzyme that promotes metastatic cancer-associated metabolic reprogramming [44]. Additional examples of differentially expressed genes (log-fold change > 0.5 and q value < 0.05), such as CDH3, TP53INP2, E2F1, CCND2, and SERPINE1, were also previously shown to have direct roles in colorectal and digestive cancers [45,46,47]. While some of these genes participate in basic cancer-related cellular functions such as proliferation and invasion [45, 47,48,49,50], others, e.g., BEST2, play important roles in gut barrier function and anion transport [51]. To test signatures of inflammation in our data, we intersected our DEGs (q value < 0.05) with data from Hong et al. [52], who compared gene regulation in Crohn’s disease (CD) patients (with and without inflammation) and healthy controls. Of the 43 genes enriched in CD patients with inflammation in their study [52], we only found 2 genes, SERPINE1 and APOB that overlapped with our DEGs (Fisher’s exact test, p value = 1). In addition to the genes visualized in Fig. 1a, additional randomly selected differentially expressed genes are visualized in Additional file 2: Figure S12), showing expression pattern differences between the CF and healthy samples.

Differentially expressed (DE) genes in the host. a Box plot of six genes that are a part of the gastrointestinal cancer pathway (one of the key disease pathways influenced by DE gene at q value < 0.05 cutoff), showing differential expression between healthy and CF samples. b Disease and functional pathways that are most significantly enriched with DE genes (q value < 0.05), sorted by the p value (cut off − log10(p value) < 5). The dark gray bars represent cancer-related pathways. c Gastrointestinal cancer pathway gene network with upregulated genes represented in green and downregulated genes represented in red. The intensity of the color is indicative of higher (brighter) or lower (duller) difference in expression. The shapes represent each protein’s role (see legend) and the figure also illustrates the part of the cell they are most active in

We next performed an enrichment analysis to categorize functional and disease pathways among differentially expressed genes (q value < 0.05) in IPA. The top canonical pathways (Additional file 2: Figure S13) are mostly responsible for signaling and regulatory functions, such as EIF2 signaling (p value = 3.32E−35), mTOR signaling (p value = 3.83E−08) and regulation of chromosomal replication (p value = 1.60E−06). Of the 39 significantly enriched disease and functional pathways (p value < 1.00E−05 Fig. 1b), 14 are related to cancer, including gastrointestinal cancer (p value = 2.61E−06), abdominal cancer (p value = 9.23E−03), large intestine cancer (p value = 7.00E−05), and colorectal cancer (p value = 8.63E−03). In addition, using the list of differentially expressed genes, we found that the promoter sequences are enriched with binding sites of 96 potential transcription regulators (p value < 0.01 see “Methods”). Among these transcription factors, many have been previously shown to control cancer-related pathways. For example, MYCN and KRAS are prominently involved in neuroblastoma and colorectal cancer, respectively [53, 54]. NHF4A is involved in transcriptional regulation of many aspects of epithelial cell morphogenesis and function, which has been linked to colorectal cancer [55]. CST5, which encodes cytostatin D, is a direct target of p53 and vitamin D receptor and promotes mesenchymal-epithelial transition to suppress tumor progression and metastasis [56]. E2F3 is a potent regulator of the cell cycle and apoptosis that is commonly deregulated in oncogenesis [57].

A metabolic network for the gastrointestinal (GI) cancer-related differentially expressed genes is shown in Fig. 1c, illustrating the interactions between genes that are upregulated in CF (e.g., TP53INP1, SERPINE1, NCOR1, and CAPN2) and downregulated in CF (E2F1, MED1, ECND2, and AS3MT), highlighting the cellular location of these genes’ product. Additional gene network for colorectal cancer can be found in Additional file 2: Figure S14), where the genes are also positioned in the region of the cell where they are most active. We found that genes such as BEST2 (involved in ion transport) and RUVBL1 (involved in cell cycle, cell division, and cell damage) are downregulated, while genes such as TP53INP2 (involved in transcription regulation) and CDH3 (involved in sensory transduction) are upregulated. Given the predicted role of gene regulation in colorectal cancer and the dysregulation of CRC-related pathways, these results may help understand mechanisms controlling early onset of colon cancer in cystic fibrosis.

Difference in microbiome composition between CF and healthy gut mucosa

To further understand the potential of altered microbiota-host interaction in the CF colon, we next investigated differences in the composition of the mucosal microbiome between CF and healthy individuals. We used negative sequenced controls to verify that our downstream results were not affected by any potential contaminants (see “Methods”). We found a significant difference between beta-diversity of gut mucosal microbiome in CF patients compared to healthy individuals with respect to unweighted UniFrac and non-phylogenetic Bray-Curtis metrics (Adonis p value = 0.001). As observed in the PCoA plot (Fig. 2a), the samples were clustered based on their disease condition (CF or healthy). The overall biodiversity of mucosal microbiome was depleted in CF compared to healthy samples, which was depicted by a significant decrease in alpha diversity measured by Chao1 (p value = 0.015, Wilcoxon rank-sum test, Fig. 2a) and observed OTUs (p value = 0.024, Wilcoxon rank-sum test, in Additional file 2: Figure S15)) metrics in CF relative to healthy controls.

Differences between cystic fibrosis (CF) and healthy gut mucosal microbiota. a (left) Principal coordinate analysis plot based on Bray-Curtis distance indicating difference in beta-diversity between CF and healthy gut mucosal microbiome. The axes represent the percentage variance along the first two principal components and the color of samples indicates their mutation status, i.e., Healthy, CF (other), and CF (df508) (right) Boxplot depicting difference in alpha diversity (Chao1 metric) between CF and healthy gut microbiome. b Dotplot showing significantly differentially abundant OTUs (q value < 0.1), where OTUs are grouped by genera along the y-axis and colored by phylum. The x-axis indicates the log2 fold-change in CF compared to healthy as baseline. c Boxplots indicating the percentage relative abundance of taxa showing differential abundance between CF and healthy gut microbiome (q value < 0.1). d Boxplot depicting gradient-like trend in abundance for Actinobacteria for three genotypes—Healthy, CF (other), and CF (df508)

We assessed the changes in abundance of microbes at various taxonomic levels between CF and healthy gut mucosal microbiome using phyloseq. We found 51 OTUs that were significantly differentially abundant between CF and healthy individuals (q value < 0.1, Additional file 4). At different taxonomic ranks, we found 7 genera, 10 families, 4 orders, 4 classes, and 5 phyla differentially abundant between CF and healthy samples (q value < 0.1 by Wald’s test Additional file 4). Overall, an increased abundance in taxa, predominantly belonging to Firmicutes (specifically Clostridium) and Fusobacteria, was observed in CF individuals compared to healthy controls, while taxa belonging to Bacteroidetes, Verrucomicrobia, and Proteobacteria phyla showed a marked decrease in patients with CF relative to healthy controls (Fig. 2b). In particular, there was an increase in abundance of class Actinobacteria in individuals with CF compared to healthy controls (q value = 0.079), while Butyricimonas (q value = 0.009), Ruminococcaceae (q value = 0.081), and Sutterella (q value = 0.040) were found depleted in CF samples (Fig. 2c). Additional examples of differentially abundant taxa between CF and healthy samples can be found in the Additional file 2: Figure S16).

Next, we tested whether CFTR genotype, which affects disease severity, is associated with variation in the microbiome. Specifically, we hypothesized that variation in the microbiome is correlated with the number of alleles of the DF508 mutation, a deletion of an entire codon within CFTR that is the most common cause for CF. To test this, we performed a likelihood ratio test to identify differentially abundant taxa between three genotype classes: CF-DF508 (homozygous for the DF508 mutation), CF-other (either one or zero copies of the DF508 mutation), and healthy (no known mutations in CFTR). We found a gradient-like trend in abundance for Actinobacteria (q value = 0.081), showing increase in abundance with increasing severity of mutation status (Fig. 2d).

To assess the potential functional changes in the microbiome, we predicted abundance of metabolic pathways and enzymes using the PICRUSt pipeline [31] and KEGG database and compared them for differences between CF and healthy individuals. Seven predicted pathways (as defined by KEGG level 3) were found to be differentially abundant between CF and healthy: bacterial toxins were enriched in CF compared to healthy, while propanoate metabolism, restriction enzyme, pantothenate and CoA biosynthesis, thiamine metabolism, amino acid-related enzymes, and aminoacyl-tRNA biosynthesis were depleted in CF compared to healthy (q value < 0.2 using Wilcoxon rank-sum test in Additional file 2: Figure S17).

Interactions between gastrointestinal cancer-related host genes and gut microbes

In order to investigate the relationship between host genes and microbes in the colonic mucosa and their potential role in the pathogenesis of gastrointestinal cancers in CF patients, we considered correlations between 250 differentially expressed genes enriched for GI cancers and 35 microbial taxa (collapsed at the genus or last characterized level and filtered at 0.1% relative abundance, see “Methods”). Using Spearman correlations, we found 50 significant unique gene-microbe correlations in the gut (q value < 0.1), where the magnitude of correlation (Spearman rho) ranged between − 0.77 and 0.79 (Additional file 5). Interestingly, most of the taxa that significantly correlated with the genes also differed significantly in abundance between CF and healthy individuals. We visualized all the correlations between taxa abundance and host gene expression in Fig. 3a. In particular, we found some significant positive gene-taxa correlations (q value < 0.05), between Butyricimonas and ZNHIT6 (Spearman rho = 0.76), Christensenellaceae and MDN1 (Spearman rho = 0.78), and Oscillospira and NUDT14 (Spearman rho = 0.79). A few significant negative correlations (q value < 0.05), such as between Christensenellaceae and TBX10 (Spearman rho = − 0.78), and Ruminococcaceae and LCN2 (Spearman rho = − 0.77) were also found.

Interactions between genes associated with colorectal cancer and gut mucosal microbes. a Correlation plot depicting gene-microbe correlations. Color and size of the squares indicate the magnitude of the correlation, asterisks indicate significance of correlation (** indicates q value < 0.05 and * indicates q value < 0.1). b Network visualizing significant gene-microbe correlations (solid edges, q value < 0.1) and significant microbe-microbe correlations (dashed edges, SparCC |R| > =0.1 and p value < 0.05). Blue edges indicate positive correlation and red edges indicate negative correlation. Edge thickness represents the strength of the correlation. c Scatterplots depicting pattern of grouping by cystic fibrosis (red) and healthy (blue) samples in a few representative gene-microbe correlations, where the strength of correlation (Spearman rho) and significance (q) is indicated at the top of each plot

To characterize potential microbe-microbe interactions in our dataset, we computed correlations between the microbes significantly correlated (q value < 0.1) with the genes using SparCC (see “Methods” and Additional file 5) [35]. The notable aspects of the significant gene-microbe correlations (q value < 0.1) and significant microbe-microbe correlations (SparCC |R| > =0.1 and pseudo-p value < 0.05) are graphically represented in Fig. 3b, where solid edges denote gene-microbe correlations and dashed edges represent microbe-microbe correlations. This subnetwork of microbe-microbe correlations depicts correlated abundance changes in the microbiome as a function of their presence (Fig. 3b, dashed edges). For instance, Bilophila and Butyricimonas are both depleted in CF (q value < 0.05), and the abundance of the two genera is also correlated across individuals (SparCC R = 0.5, pseudo-p value = 0.04). On the other hand, Ruminococcaceae was found depleted in CF (q value = 0.081), while Clostridium was enriched in CF (q value = 0.0004), and this inverse co-occurrence pattern leads to a negative correlation between the two taxa across study participants (SparCC R = − 0.66, pseudo-p value = 0). Furthermore, in the gene-microbe subnetwork (Fig. 3b, solid edges), microbial nodes have more edges on average compared to genes, where Christensenellaceae and Clostridium formed distinct hubs in the network. This potentially implies that these microbes and their pathways are shared across multiple GI cancer-associated genes. Of note, Bilophila, Clostridium, and Pseudomonas are mostly negatively correlated with GI cancer genes, while Haemophilus, Oscillospira, Veillonella, Fusobacterium, and Acidaminococcus are only positively correlated with GI cancer genes (q value < 0.1).

In addition to the overall network, Fig. 3c depicts pairwise correlations between host gene expression and microbial taxa where both have been previously linked to CRC and thus may be of interest. For example, LCN2, known to be overexpressed in human CRC and other cancers [58], is negatively correlated with Ruminococcaceae (Spearman rho = − 0.77, q value = 0.040), which is found depleted in CRC [59, 60]. Both DUOX2 and DUOXA2 are found to be negatively correlated with Christensenellaceae (Spearman rho < − 0.65, q value < 0.1), while DUOXA2 is positively correlated with Veillonella (Spearman rho = 0.70, q value = 0.082). DUOX2 and its maturation factor DUOXA2 are responsible for H2O2 production in human colon and are known to be upregulated in gastrointestinal inflammation [61, 62]. Christensenellaceae, a heritable taxon [63], has been shown to decrease in abundance in conventional adenoma [60], a precursor of CRC, whereas Veillonella, which is known to be proinflammatory, is found to be represented in human CRC [64]. Thus, the pattern of grouping by CF and healthy samples in these representative correlations are found to be similar to known associations in CRC and other gastrointestinal malignancies.


Contents

Transcription factors are essential for the regulation of gene expression and are, as a consequence, found in all living organisms. The number of transcription factors found within an organism increases with genome size, and larger genomes tend to have more transcription factors per gene. [12]

There are approximately 2800 proteins in the human genome that contain DNA-binding domains, and 1600 of these are presumed to function as transcription factors, [3] though other studies indicate it to be a smaller number. [13] Therefore, approximately 10% of genes in the genome code for transcription factors, which makes this family the single largest family of human proteins. Furthermore, genes are often flanked by several binding sites for distinct transcription factors, and efficient expression of each of these genes requires the cooperative action of several different transcription factors (see, for example, hepatocyte nuclear factors). Hence, the combinatorial use of a subset of the approximately 2000 human transcription factors easily accounts for the unique regulation of each gene in the human genome during development. [11]

Transcription factors bind to either enhancer or promoter regions of DNA adjacent to the genes that they regulate. Depending on the transcription factor, the transcription of the adjacent gene is either up- or down-regulated. Transcription factors use a variety of mechanisms for the regulation of gene expression. [14] These mechanisms include:

  • stabilize or block the binding of RNA polymerase to DNA
  • catalyze the acetylation or deacetylation of histone proteins. The transcription factor can either do this directly or recruit other proteins with this catalytic activity. Many transcription factors use one or the other of two opposing mechanisms to regulate transcription: [15]
      (HAT) activity – acetylates histone proteins, which weakens the association of DNA with histones, which make the DNA more accessible to transcription, thereby up-regulating transcription (HDAC) activity – deacetylates histone proteins, which strengthens the association of DNA with histones, which make the DNA less accessible to transcription, thereby down-regulating transcription
  • Transcription factors are one of the groups of proteins that read and interpret the genetic "blueprint" in the DNA. They bind to the DNA and help initiate a program of increased or decreased gene transcription. As such, they are vital for many important cellular processes. Below are some of the important functions and biological roles transcription factors are involved in:

    Basal transcription regulation Edit

    In eukaryotes, an important class of transcription factors called general transcription factors (GTFs) are necessary for transcription to occur. [17] [18] [19] Many of these GTFs do not actually bind DNA, but rather are part of the large transcription preinitiation complex that interacts with RNA polymerase directly. The most common GTFs are TFIIA, TFIIB, TFIID (see also TATA binding protein), TFIIE, TFIIF, and TFIIH. [20] The preinitiation complex binds to promoter regions of DNA upstream to the gene that they regulate.

    Differential enhancement of transcription Edit

    Other transcription factors differentially regulate the expression of various genes by binding to enhancer regions of DNA adjacent to regulated genes. These transcription factors are critical to making sure that genes are expressed in the right cell at the right time and in the right amount, depending on the changing requirements of the organism.

    Development Edit

    Many transcription factors in multicellular organisms are involved in development. [21] Responding to stimuli, these transcription factors turn on/off the transcription of the appropriate genes, which, in turn, allows for changes in cell morphology or activities needed for cell fate determination and cellular differentiation. The Hox transcription factor family, for example, is important for proper body pattern formation in organisms as diverse as fruit flies to humans. [22] [23] Another example is the transcription factor encoded by the sex-determining region Y (SRY) gene, which plays a major role in determining sex in humans. [24]

    Response to intercellular signals Edit

    Cells can communicate with each other by releasing molecules that produce signaling cascades within another receptive cell. If the signal requires upregulation or downregulation of genes in the recipient cell, often transcription factors will be downstream in the signaling cascade. [25] Estrogen signaling is an example of a fairly short signaling cascade that involves the estrogen receptor transcription factor: Estrogen is secreted by tissues such as the ovaries and placenta, crosses the cell membrane of the recipient cell, and is bound by the estrogen receptor in the cell's cytoplasm. The estrogen receptor then goes to the cell's nucleus and binds to its DNA-binding sites, changing the transcriptional regulation of the associated genes. [26]

    Response to environment Edit

    Not only do transcription factors act downstream of signaling cascades related to biological stimuli but they can also be downstream of signaling cascades involved in environmental stimuli. Examples include heat shock factor (HSF), which upregulates genes necessary for survival at higher temperatures, [27] hypoxia inducible factor (HIF), which upregulates genes necessary for cell survival in low-oxygen environments, [28] and sterol regulatory element binding protein (SREBP), which helps maintain proper lipid levels in the cell. [29]

    Cell cycle control Edit

    Many transcription factors, especially some that are proto-oncogenes or tumor suppressors, help regulate the cell cycle and as such determine how large a cell will get and when it can divide into two daughter cells. [30] [31] One example is the Myc oncogene, which has important roles in cell growth and apoptosis. [32]

    Pathogenesis Edit

    Transcription factors can also be used to alter gene expression in a host cell to promote pathogenesis. A well studied example of this are the transcription-activator like effectors (TAL effectors) secreted by Xanthomonas bacteria. When injected into plants, these proteins can enter the nucleus of the plant cell, bind plant promoter sequences, and activate transcription of plant genes that aid in bacterial infection. [33] TAL effectors contain a central repeat region in which there is a simple relationship between the identity of two critical residues in sequential repeats and sequential DNA bases in the TAL effector's target site. [34] [35] This property likely makes it easier for these proteins to evolve in order to better compete with the defense mechanisms of the host cell. [36]

    It is common in biology for important processes to have multiple layers of regulation and control. This is also true with transcription factors: Not only do transcription factors control the rates of transcription to regulate the amounts of gene products (RNA and protein) available to the cell but transcription factors themselves are regulated (often by other transcription factors). Below is a brief synopsis of some of the ways that the activity of transcription factors can be regulated:

    Synthesis Edit

    Transcription factors (like all proteins) are transcribed from a gene on a chromosome into RNA, and then the RNA is translated into protein. Any of these steps can be regulated to affect the production (and thus activity) of a transcription factor. An implication of this is that transcription factors can regulate themselves. For example, in a negative feedback loop, the transcription factor acts as its own repressor: If the transcription factor protein binds the DNA of its own gene, it down-regulates the production of more of itself. This is one mechanism to maintain low levels of a transcription factor in a cell. [37]

    Nuclear localization Edit

    In eukaryotes, transcription factors (like most proteins) are transcribed in the nucleus but are then translated in the cell's cytoplasm. Many proteins that are active in the nucleus contain nuclear localization signals that direct them to the nucleus. But, for many transcription factors, this is a key point in their regulation. [38] Important classes of transcription factors such as some nuclear receptors must first bind a ligand while in the cytoplasm before they can relocate to the nucleus. [38]

    Activation Edit

    Transcription factors may be activated (or deactivated) through their signal-sensing domain by a number of mechanisms including:

      binding – Not only is ligand binding able to influence where a transcription factor is located within a cell but ligand binding can also affect whether the transcription factor is in an active state and capable of binding DNA or other cofactors (see, for example, nuclear receptors). [39][40] – Many transcription factors such as STAT proteins must be phosphorylated before they can bind DNA.
    • interaction with other transcription factors (e.g., homo- or hetero-dimerization) or coregulatory proteins

    Accessibility of DNA-binding site Edit

    In eukaryotes, DNA is organized with the help of histones into compact particles called nucleosomes, where sequences of about 147 DNA base pairs make

    1.65 turns around histone protein octamers. DNA within nucleosomes is inaccessible to many transcription factors. Some transcription factors, so-called pioneer factors are still able to bind their DNA binding sites on the nucleosomal DNA. For most other transcription factors, the nucleosome should be actively unwound by molecular motors such as chromatin remodelers. [41] Alternatively, the nucleosome can be partially unwrapped by thermal fluctuations, allowing temporary access to the transcription factor binding site. In many cases, a transcription factor needs to compete for binding to its DNA binding site with other transcription factors and histones or non-histone chromatin proteins. [42] Pairs of transcription factors and other proteins can play antagonistic roles (activator versus repressor) in the regulation of the same gene.

    Availability of other cofactors/transcription factors Edit

    Most transcription factors do not work alone. Many large TF families form complex homotypic or heterotypic interactions through dimerization. [43] For gene transcription to occur, a number of transcription factors must bind to DNA regulatory sequences. This collection of transcription factors, in turn, recruit intermediary proteins such as cofactors that allow efficient recruitment of the preinitiation complex and RNA polymerase. Thus, for a single transcription factor to initiate transcription, all of these other proteins must also be present, and the transcription factor must be in a state where it can bind to them if necessary. Cofactors are proteins that modulate the effects of transcription factors. Cofactors are interchangeable between specific gene promoters the protein complex that occupies the promoter DNA and the amino acid sequence of the cofactor determine its spatial conformation. For example, certain steroid receptors can exchange cofactors with NF-κB, which is a switch between inflammation and cellular differentiation thereby steroids can affect the inflammatory response and function of certain tissues. [44]

    Interaction with methylated cytosine Edit

    Transcription factors and methylated cytosines in DNA both have major roles in regulating gene expression. (Methylation of cytosine in DNA primarily occurs where cytosine is followed by guanine in the 5’ to 3’ DNA sequence, a CpG site.) Methylation of CpG sites in a promoter region of a gene usually represses gene transcription, [45] while methylation of CpGs in the body of a gene increases expression. [46] TET enzymes play a central role in demethylation of methylated cytosines. Demethylation of CpGs in a gene promoter by TET enzyme activity increases transcription of the gene. [47]

    The DNA binding sites of 519 transcription factors were evaluated. [48] Of these, 169 transcription factors (33%) did not have CpG dinucleotides in their binding sites, and 33 transcription factors (6%) could bind to a CpG-containing motif but did not display a preference for a binding site with either a methylated or unmethylated CpG. There were 117 transcription factors (23%) that were inhibited from binding to their binding sequence if it contained a methylated CpG site, 175 transcription factors (34%) that had enhanced binding if their binding sequence had a methylated CpG site, and 25 transcription factors (5%) were either inhibited or had enhanced binding depending on where in the binding sequence the methylated CpG was located.

    TET enzymes do not specifically bind to methylcytosine except when recruited (see DNA demethylation). Multiple transcription factors important in cell differentiation and lineage specification, including NANOG, SALL4A, WT1, EBF1, PU.1, and E2A, have been shown to recruit TET enzymes to specific genomic loci (primarily enhancers) to act on methylcytosine (mC) and convert it to hydroxymethylcytosine hmC (and in most cases marking them for subsequent complete demethylation to cytosine). [49] TET-mediated conversion of mC to hmC appears to disrupt the binding of 5mC-binding proteins including MECP2 and MBD (Methyl-CpG-binding domain) proteins, facilitating nucleosome remodeling and the binding of transcription factors, thereby activating transcription of those genes. EGR1 is an important transcription factor in memory formation. It has an essential role in brain neuron epigenetic reprogramming. The transcription factor EGR1 recruits the TET1 protein that initiates a pathway of DNA demethylation. [50] EGR1, together with TET1, is employed in programming the distribution of methylation sites on brain DNA during brain development and in learning (see Epigenetics in learning and memory).

    Transcription factors are modular in structure and contain the following domains: [1]

    • DNA-binding domain (DBD), which attaches to specific sequences of DNA (enhancer or promoter. Necessary component for all vectors. Used to drive transcription of the vector's transgene promoter sequences) adjacent to regulated genes. DNA sequences that bind transcription factors are often referred to as response elements.
    • Activation domain (AD), which contains binding sites for other proteins such as transcription coregulators. These binding sites are frequently referred to as activation functions (AFs), Transactivation domain (TAD) or Trans-activating domainTAD but not mix with topologically associating domain TAD. [51]
    • An optional signal-sensing domain (SSD) (e.g., a ligand binding domain), which senses external signals and, in response, transmits these signals to the rest of the transcription complex, resulting in up- or down-regulation of gene expression. Also, the DBD and signal-sensing domains may reside on separate proteins that associate within the transcription complex to regulate gene expression.

    DNA-binding domain Edit

    The portion (domain) of the transcription factor that binds DNA is called its DNA-binding domain. Below is a partial list of some of the major families of DNA-binding domains/transcription factors:

    Family InterPro Pfam SCOP
    basic helix-loop-helix [52] InterPro: IPR001092 Pfam PF00010 SCOP 47460
    basic-leucine zipper (bZIP) [53] InterPro: IPR004827 Pfam PF00170 SCOP 57959
    C-terminal effector domain of the bipartite response regulators InterPro: IPR001789 Pfam PF00072 SCOP 46894
    AP2/ERF/GCC box InterPro: IPR001471 Pfam PF00847 SCOP 54176
    helix-turn-helix [54]
    homeodomain proteins, which are encoded by homeobox genes, are transcription factors. Homeodomain proteins play critical roles in the regulation of development. [55] [56] InterPro: IPR009057 Pfam PF00046 SCOP 46689
    lambda repressor-like InterPro: IPR010982 SCOP 47413
    srf-like (serum response factor) InterPro: IPR002100 Pfam PF00319 SCOP 55455
    paired box [57]
    winged helix InterPro: IPR013196 Pfam PF08279 SCOP 46785
    zinc fingers [58]
    * multi-domain Cys2His2 zinc fingers [59] InterPro: IPR007087 Pfam PF00096 SCOP 57667
    * Zn2/Cys6 SCOP 57701
    * Zn2/Cys8 nuclear receptor zinc finger InterPro: IPR001628 Pfam PF00105 SCOP 57716

    Response elements Edit

    The DNA sequence that a transcription factor binds to is called a transcription factor-binding site or response element. [60]

    Transcription factors interact with their binding sites using a combination of electrostatic (of which hydrogen bonds are a special case) and Van der Waals forces. Due to the nature of these chemical interactions, most transcription factors bind DNA in a sequence specific manner. However, not all bases in the transcription factor-binding site may actually interact with the transcription factor. In addition, some of these interactions may be weaker than others. Thus, transcription factors do not bind just one sequence but are capable of binding a subset of closely related sequences, each with a different strength of interaction.

    For example, although the consensus binding site for the TATA-binding protein (TBP) is TATAAAA, the TBP transcription factor can also bind similar sequences such as TATATAT or TATATAA.

    Because transcription factors can bind a set of related sequences and these sequences tend to be short, potential transcription factor binding sites can occur by chance if the DNA sequence is long enough. It is unlikely, however, that a transcription factor will bind all compatible sequences in the genome of the cell. Other constraints, such as DNA accessibility in the cell or availability of cofactors may also help dictate where a transcription factor will actually bind. Thus, given the genome sequence it is still difficult to predict where a transcription factor will actually bind in a living cell.

    Additional recognition specificity, however, may be obtained through the use of more than one DNA-binding domain (for example tandem DBDs in the same transcription factor or through dimerization of two transcription factors) that bind to two or more adjacent sequences of DNA.

    Transcription factors are of clinical significance for at least two reasons: (1) mutations can be associated with specific diseases, and (2) they can be targets of medications.

    Disorders Edit

    Due to their important roles in development, intercellular signaling, and cell cycle, some human diseases have been associated with mutations in transcription factors. [61]

    Many transcription factors are either tumor suppressors or oncogenes, and, thus, mutations or aberrant regulation of them is associated with cancer. Three groups of transcription factors are known to be important in human cancer: (1) the NF-kappaB and AP-1 families, (2) the STAT family and (3) the steroid receptors. [62]

    Below are a few of the better-studied examples:

    Condition Description Locus
    Rett syndrome Mutations in the MECP2 transcription factor are associated with Rett syndrome, a neurodevelopmental disorder. [63] [64] Xq28
    Diabetes A rare form of diabetes called MODY (Maturity onset diabetes of the young) can be caused by mutations in hepatocyte nuclear factors (HNFs) [65] or insulin promoter factor-1 (IPF1/Pdx1). [66] multiple
    Developmental verbal dyspraxia Mutations in the FOXP2 transcription factor are associated with developmental verbal dyspraxia, a disease in which individuals are unable to produce the finely coordinated movements required for speech. [67] 7q31
    Autoimmune diseases Mutations in the FOXP3 transcription factor cause a rare form of autoimmune disease called IPEX. [68] Xp11.23-q13.3
    Li-Fraumeni syndrome Caused by mutations in the tumor suppressor p53. [69] 17p13.1
    Breast cancer The STAT family is relevant to breast cancer. [70] multiple
    Multiple cancers The HOX family are involved in a variety of cancers. [71] multiple
    Osteoarthritis Mutation or reduced activity of SOX9 [72]

    Potential drug targets Edit

    Approximately 10% of currently prescribed drugs directly target the nuclear receptor class of transcription factors. [73] Examples include tamoxifen and bicalutamide for the treatment of breast and prostate cancer, respectively, and various types of anti-inflammatory and anabolic steroids. [74] In addition, transcription factors are often indirectly modulated by drugs through signaling cascades. It might be possible to directly target other less-explored transcription factors such as NF-κB with drugs. [75] [76] [77] [78] Transcription factors outside the nuclear receptor family are thought to be more difficult to target with small molecule therapeutics since it is not clear that they are "drugable" but progress has been made on Pax2 [79] [80] and the notch pathway. [81]

    Gene duplications have played a crucial role in the evolution of species. This applies particularly to transcription factors. Once they occur as duplicates, accumulated mutations encoding for one copy can take place without negatively affecting the regulation of downstream targets. However, changes of the DNA binding specificities of the single-copy LEAFY transcription factor, which occurs in most land plants, have recently been elucidated. In that respect, a single-copy transcription factor can undergo a change of specificity through a promiscuous intermediate without losing function. Similar mechanisms have been proposed in the context of all alternative phylogenetic hypotheses, and the role of transcription factors in the evolution of all species. [82] [83]

    There are different technologies available to analyze transcription factors. On the genomic level, DNA-sequencing [84] and database research are commonly used [85] The protein version of the transcription factor is detectable by using specific antibodies. The sample is detected on a western blot. By using electrophoretic mobility shift assay (EMSA), [86] the activation profile of transcription factors can be detected. A multiplex approach for activation profiling is a TF chip system where several different transcription factors can be detected in parallel.

    The most commonly used method for identifying transcription factor binding sites is chromatin immunoprecipitation (ChIP). [87] This technique relies on chemical fixation of chromatin with formaldehyde, followed by co-precipitation of DNA and the transcription factor of interest using an antibody that specifically targets that protein. The DNA sequences can then be identified by microarray or high-throughput sequencing (ChIP-seq) to determine transcription factor binding sites. If no antibody is available for the protein of interest, DamID may be a convenient alternative. [88]

    As described in more detail below, transcription factors may be classified by their (1) mechanism of action, (2) regulatory function, or (3) sequence homology (and hence structural similarity) in their DNA-binding domains.

    Mechanistic Edit

    There are two mechanistic classes of transcription factors:

      are involved in the formation of a preinitiation complex. The most common are abbreviated as TFIIA, TFIIB, TFIID, TFIIE, TFIIF, and TFIIH. They are ubiquitous and interact with the core promoter region surrounding the transcription start site(s) of all class II genes. [89]
    • Upstream transcription factors are proteins that bind somewhere upstream of the initiation site to stimulate or repress transcription. These are roughly synonymous with specific transcription factors, because they vary considerably depending on what recognition sequences are present in the proximity of the gene. [90]

    Functional Edit

    Transcription factors have been classified according to their regulatory function: [11]

    • I. constitutively active – present in all cells at all times – general transcription factors, Sp1, NF1, CCAAT
    • II. conditionally active – requires activation
      • II.A developmental (cell specific) – expression is tightly controlled, but, once expressed, require no additional activation – GATA, HNF, PIT-1, MyoD, Myf5, Hox, Winged Helix
      • II.B signal-dependent – requires external signal for activation
        • II.B.1 extracellular ligand (endocrine or paracrine)-dependent – nuclear receptors
        • II.B.2 intracellular ligand (autocrine)-dependent - activated by small intracellular molecules – SREBP, p53, orphan nuclear receptors
        • II.B.3 cell membrane receptor-dependent – second messenger signaling cascades resulting in the phosphorylation of the transcription factor
          • II.B.3.a resident nuclear factors – reside in the nucleus regardless of activation state – CREB, AP-1, Mef2
          • II.B.3.b latent cytoplasmic factors – inactive form reside in the cytoplasm, but, when activated, are translocated into the nucleus – STAT, R-SMAD, NF-κB, Notch, TUBBY, NFAT

          Structural Edit

          Transcription factors are often classified based on the sequence similarity and hence the tertiary structure of their DNA-binding domains: [91] [10] [92] [9]


          Contents

          Much of the early understanding of transcription came from bacteria, [2] although the extent and complexity of transcriptional regulation is greater in eukaryotes. Bacterial transcription is governed by three main sequence elements:

            are elements of DNA that may bind RNA polymerase and other proteins for the successful initiation of transcription directly upstream of the gene. recognize repressor proteins that bind to a stretch of DNA and inhibit the transcription of the gene.
        • Positive control elements that bind to DNA and incite higher levels of transcription. [3]
        • While these means of transcriptional regulation also exist in eukaryotes, the transcriptional landscape is significantly more complicated both by the number of proteins involved as well as by the presence of introns and the packaging of DNA into histones.

          The transcription of a basic bacterial gene is dependent on the strength of its promoter and the presence of activators or repressors. In the absence of other regulatory elements, a promoter's sequence-based affinity for RNA polymerases varies, which results in the production of different amounts of transcript. The variable affinity of RNA polymerase for different promoter sequences is related to regions of consensus sequence upstream of the transcription start site. The more nucleotides of a promoter that agree with the consensus sequence, the stronger the affinity of the promoter for RNA Polymerase likely is. [4]

          In the absence of other regulatory elements, the default state of a bacterial transcript is to be in the “on” configuration, resulting in the production of some amount of transcript. This means that transcriptional regulation in the form of protein repressors and positive control elements can either increase or decrease transcription. Repressors often physically occupy the promoter location, occluding RNA polymerase from binding. Alternatively a repressor and polymerase may bind to the DNA at the same time with a physical interaction between the repressor preventing the opening of the DNA for access to the minus strand for transcription. This strategy of control is distinct from eukaryotic transcription, whose basal state is to be off and where co-factors required for transcription initiation are highly gene dependent. [8]

          Sigma factors are specialized bacterial proteins that bind to RNA polymerases and orchestrate transcription initiation. Sigma factors act as mediators of sequence-specific transcription, such that a single sigma factor can be used for transcription of all housekeeping genes or a suite of genes the cell wishes to express in response to some external stimuli such as stress. [9]

          In addition to processes that regulate transcription at the stage of initiation, mRNA synthesis is also controlled by the rate of transcription elongation. [10] RNA polymerase pauses occur frequently and are regulated by transcription factors, such as NusG and NusA, transcription-translation coupling, and mRNA secondary structure. [11] [12]

          The added complexity of generating a eukaryotic cell carries with it an increase in the complexity of transcriptional regulation. Eukaryotes have three RNA polymerases, known as Pol I, Pol II, and Pol III. Each polymerase has specific targets and activities, and is regulated by independent mechanisms. There are a number of additional mechanisms through which polymerase activity can be controlled. These mechanisms can be generally grouped into three main areas:

          • Control over polymerase access to the gene. This is perhaps the broadest of the three control mechanisms. This includes the functions of histone remodeling enzymes, transcription factors, enhancers and repressors, and many other complexes
          • Productive elongation of the RNA transcript. Once polymerase is bound to a promoter, it requires another set of factors to allow it to escape the promoter complex and begin successfully transcribing RNA.
          • Termination of the polymerase. A number of factors which have been found to control how and when termination occurs, which will dictate the fate of the RNA transcript.

          All three of these systems work in concert to integrate signals from the cell and change the transcriptional program accordingly.

          While in prokaryotic systems the basal transcription state can be thought of as nonrestrictive (that is, “on” in the absence of modifying factors), eukaryotes have a restrictive basal state which requires the recruitment of other factors in order to generate RNA transcripts. This difference is largely due to the compaction of the eukaryotic genome by winding DNA around histones to form higher order structures. This compaction makes the gene promoter inaccessible without the assistance of other factors in the nucleus, and thus chromatin structure is a common site of regulation. Similar to the sigma factors in prokaryotes, the general transcription factors (GTFs) are a set of factors in eukaryotes that are required for all transcription events. These factors are responsible for stabilizing binding interactions and opening the DNA helix to allow the RNA polymerase to access the template, but generally lack specificity for different promoter sites. [13] A large part of gene regulation occurs through transcription factors that either recruit or inhibit the binding of the general transcription machinery and/or the polymerase. This can be accomplished through close interactions with core promoter elements, or through the long distance enhancer elements.

          Once a polymerase is successfully bound to a DNA template, it often requires the assistance of other proteins in order to leave the stable promoter complex and begin elongating the nascent RNA strand. This process is called promoter escape, and is another step at which regulatory elements can act to accelerate or slow the transcription process. Similarly, protein and nucleic acid factors can associate with the elongation complex and modulate the rate at which the polymerase moves along the DNA template.

          At the level of chromatin state Edit

          In eukaryotes, genomic DNA is highly compacted in order to be able to fit it into the nucleus. This is accomplished by winding the DNA around protein octamers called histones, which has consequences for the physical accessibility of parts of the genome at any given time. Significant portions are silenced through histone modifications, and thus are inaccessible to the polymerases or their cofactors. The highest level of transcription regulation occurs through the rearrangement of histones in order to expose or sequester genes, because these processes have the ability to render entire regions of a chromosome inaccessible such as what occurs in imprinting.

          Histone rearrangement is facilitated by post-translational modifications to the tails of the core histones. A wide variety of modifications can be made by enzymes such as the histone acetyltransferases (HATs), histone methyltransferases (HMTs), and histone deacetylases (HDACs), among others. These enzymes can add or remove covalent modifications such as methyl groups, acetyl groups, phosphates, and ubiquitin. Histone modifications serve to recruit other proteins which can either increase the compaction of the chromatin and sequester promoter elements, or to increase the spacing between histones and allow the association of transcription factors or polymerase on open DNA. [14] For example, H3K27 trimethylation by the polycomb complex PRC2 causes chromosomal compaction and gene silencing. [15] These histone modifications may be created by the cell, or inherited in an epigenetic fashion from a parent.

          At the level of cytosine methylation Edit

          Transcription regulation at about 60% of promoters is controlled by methylation of cytosines within CpG dinucleotides (where 5’ cytosine is followed by 3’ guanine or CpG sites). 5-methylcytosine (5-mC) is a methylated form of the DNA base cytosine (see Figure). 5-mC is an epigenetic marker found predominantly within CpG sites. About 28 million CpG dinucleotides occur in the human genome. [16] In most tissues of mammals, on average, 70% to 80% of CpG cytosines are methylated (forming 5-methylCpG or 5-mCpG). [17] Methylated cytosines within 5’cytosine-guanine 3’ sequences often occur in groups, called CpG islands. About 60% of promoter sequences have a CpG island while only about 6% of enhancer sequences have a CpG island. [18] CpG islands constitute regulatory sequences, since if CpG islands are methylated in the promoter of a gene this can reduce or silence gene transcription. [19]

          DNA methylation regulates gene transcription through interaction with methyl binding domain (MBD) proteins, such as MeCP2, MBD1 and MBD2. These MBD proteins bind most strongly to highly methylated CpG islands. [20] These MBD proteins have both a methyl-CpG-binding domain as well as a transcription repression domain. [20] They bind to methylated DNA and guide or direct protein complexes with chromatin remodeling and/or histone modifying activity to methylated CpG islands. MBD proteins generally repress local chromatin such as by catalyzing the introduction of repressive histone marks, or creating an overall repressive chromatin environment through nucleosome remodeling and chromatin reorganization. [20]

          Transcription factors are proteins that bind to specific DNA sequences in order to regulate the expression of a gene. The binding sequence for a transcription factor in DNA is usually about 10 or 11 nucleotides long. As summarized in 2009, Vaquerizas et al. indicated there are approximately 1,400 different transcription factors encoded in the human genome by genes that constitute about 6% of all human protein encoding genes. [21] About 94% of transcription factor binding sites (TFBSs) that are associated with signal-responsive genes occur in enhancers while only about 6% of such TFBSs occur in promoters. [22]

          EGR1 protein is a particular transcription factor that is important for regulation of methylation of CpG islands. An EGR1 transcription factor binding site is frequently located in enhancer or promoter sequences. [23] There are about 12,000 binding sites for EGR1 in the mammalian genome and about half of EGR1 binding sites are located in promoters and half in enhancers. [23] The binding of EGR1 to its target DNA binding site is insensitive to cytosine methylation in the DNA. [23]

          While only small amounts of EGR1 transcription factor protein are detectable in cells that are un-stimulated, translation of the EGR1 gene into protein at one hour after stimulation is drastically elevated. [24] Expression of EGR1 transcription factor proteins, in various types of cells, can be stimulated by growth factors, neurotransmitters, hormones, stress and injury. [24] In the brain, when neurons are activated, EGR1 proteins are up-regulated and they bind to (recruit) the pre-existing TET1 enzymes which are highly expressed in neurons. TET enzymes can catalyse demethylation of 5-methylcytosine. When EGR1 transcription factors bring TET1 enzymes to EGR1 binding sites in promoters, the TET enzymes can demethylate the methylated CpG islands at those promoters. Upon demethylation, these promoters can then initiate transcription of their target genes. Hundreds of genes in neurons are differentially expressed after neuron activation through EGR1 recruitment of TET1 to methylated regulatory sequences in their promoters. [23]

          The methylation of promoters is also altered in response to signals. The three mammalian DNA methyltransferasess (DNMT1, DNMT3A, and DNMT3B) catalyze the addition of methyl groups to cytosines in DNA. While DNMT1 is a “maintenance” methyltransferase, DNMT3A and DNMT3B can carry out new methylations. There are also two splice protein isoforms produced from the DNMT3A gene: DNA methyltransferase proteins DNMT3A1 and DNMT3A2. [25]

          The splice isoform DNMT3A2 behaves like the product of a classical immediate-early gene and, for instance, it is robustly and transiently produced after neuronal activation. [26] Where the DNA methyltransferase isoform DNMT3A2 binds and adds methyl groups to cytosines appears to be determined by histone post translational modifications. [27] [28] [29]

          On the other hand, neural activation causes degradation of DNMT3A1 accompanied by reduced methylation of at least one evaluated targeted promoter. [30]

          Through transcription factors and enhancers Edit

          Transcription factors Edit

          Transcription factors are proteins that bind to specific DNA sequences in order to regulate the expression of a given gene. There are approximately 1,400 transcription factors in the human genome and they constitute about 6% of all human protein coding genes. [21] The power of transcription factors resides in their ability to activate and/or repress wide repertoires of downstream target genes. The fact that these transcription factors work in a combinatorial fashion means that only a small subset of an organism's genome encodes transcription factors. Transcription factors function through a wide variety of mechanisms. In one mechanism, CpG methylation influences binding of most transcription factors to DNA—in some cases negatively and in others positively. [31] In addition, often they are at the end of a signal transduction pathway that functions to change something about the factor, like its subcellular localization or its activity. Post-translational modifications to transcription factors located in the cytosol can cause them to translocate to the nucleus where they can interact with their corresponding enhancers. Other transcription factors are already in the nucleus, and are modified to enable the interaction with partner transcription factors. Some post-translational modifications known to regulate the functional state of transcription factors are phosphorylation, acetylation, SUMOylation and ubiquitylation. Transcription factors can be divided in two main categories: activators and repressors. While activators can interact directly or indirectly with the core machinery of transcription through enhancer binding, repressors predominantly recruit co-repressor complexes leading to transcriptional repression by chromatin condensation of enhancer regions. It may also happen that a repressor may function by allosteric competition against a determined activator to repress gene expression: overlapping DNA-binding motifs for both activators and repressors induce a physical competition to occupy the site of binding. If the repressor has a higher affinity for its motif than the activator, transcription would be effectively blocked in the presence of the repressor. Tight regulatory control is achieved by the highly dynamic nature of transcription factors. Again, many different mechanisms exist to control whether a transcription factor is active. These mechanisms include control over protein localization or control over whether the protein can bind DNA. [32] An example of this is the protein HSF1, which remains bound to Hsp70 in the cytosol and is only translocated into the nucleus upon cellular stress such as heat shock. Thus the genes under the control of this transcription factor will remain untranscribed unless the cell is subjected to stress. [33]

          Enhancers Edit

          Enhancers or cis-regulatory modules/elements (CRM/CRE) are non-coding DNA sequences containing multiple activator and repressor binding sites. Enhancers range from 200 bp to 1 kb in length and can be either proximal, 5’ upstream to the promoter or within the first intron of the regulated gene, or distal, in introns of neighboring genes or intergenic regions far away from the locus. Through DNA looping, active enhancers contact the promoter dependently of the core DNA binding motif promoter specificity. [34] Promoter-enhancer dichotomy provides the basis for the functional interaction between transcription factors and transcriptional core machinery to trigger RNA Pol II escape from the promoter. Whereas one could think that there is a 1:1 enhancer-promoter ratio, studies of the human genome predict that an active promoter interacts with 4 to 5 enhancers. Similarly, enhancers can regulate more than one gene without linkage restriction and are said to “skip” neighboring genes to regulate more distant ones. Even though infrequent, transcriptional regulation can involve elements located in a chromosome different to one where the promoter resides. Proximal enhancers or promoters of neighboring genes can serve as platforms to recruit more distal elements. [35]

          Enhancer activation and implementation Edit

          Up-regulated expression of genes in mammals can be initiated when signals are transmitted to the promoters associated with the genes. Cis-regulatory DNA sequences that are located in DNA regions distant from the promoters of genes can have very large effects on gene expression, with some genes undergoing up to 100-fold increased expression due to such a cis-regulatory sequence. [36] These cis-regulatory sequences include enhancers, silencers, insulators and tethering elements. [37] Among this constellation of sequences, enhancers and their associated transcription factor proteins have a leading role in the regulation of gene expression. [38]

          Enhancers are sequences of the genome that are major gene-regulatory elements. Enhancers control cell-type-specific gene expression programs, most often by looping through long distances to come in physical proximity with the promoters of their target genes. [39] In a study of brain cortical neurons, 24,937 loops were found, bringing enhancers to promoters. [36] Multiple enhancers, each often at tens or hundred of thousands of nucleotides distant from their target genes, loop to their target gene promoters and coordinate with each other to control expression of their common target gene. [39]

          The schematic illustration in this section shows an enhancer looping around to come into close physical proximity with the promoter of a target gene. The loop is stabilized by a dimer of a connector protein (e.g. dimer of CTCF or YY1), with one member of the dimer anchored to its binding motif on the enhancer and the other member anchored to its binding motif on the promoter (represented by the red zigzags in the illustration). [40] Several cell function specific transcription factor proteins (in 2018 Lambert et al. indicated there were about 1,600 transcription factors in a human cell [41] ) generally bind to specific motifs on an enhancer [22] and a small combination of these enhancer-bound transcription factors, when brought close to a promoter by a DNA loop, govern the level of transcription of the target gene. Mediator (coactivator) (a complex usually consisting of about 26 proteins in an interacting structure) communicates regulatory signals from enhancer DNA-bound transcription factors directly to the RNA polymerase II (RNAP II) enzyme bound to the promoter. [42]

          Enhancers, when active, are generally transcribed from both strands of DNA with RNA polymerases acting in two different directions, producing two eRNAs as illustrated in the Figure. [43] An inactive enhancer may be bound by an inactive transcription factor. Phosphorylation of the transcription factor may activate it and that activated transcription factor may then activate the enhancer to which it is bound (see small red star representing phosphorylation of a transcription factor bound to an enhancer in the illustration). [44] An activated enhancer begins transcription of its RNA before activating a promoter to initiate transcription of messenger RNA from its target gene. [45]

          Regulatory landscape Edit

          Transcriptional initiation, termination and regulation are mediated by “DNA looping” which brings together promoters, enhancers, transcription factors and RNA processing factors to accurately regulate gene expression. [46] Chromosome conformation capture (3C) and more recently Hi-C techniques provided evidence that active chromatin regions are “compacted” in nuclear domains or bodies where transcriptional regulation is enhanced. [47] The configuration of the genome is essential for enhancer-promoter proximity. Cell-fate decisions are mediated upon highly dynamic genomic reorganizations at interphase to modularly switch on or off entire gene regulatory networks through short to long range chromatin rearrangements. [48] Related studies demonstrate that metazoan genomes are partitioned in structural and functional units around a megabase long called Topological association domains (TADs) containing dozens of genes regulated by hundreds of enhancers distributed within large genomic regions containing only non-coding sequences. The function of TADs is to regroup enhancers and promoters interacting together within a single large functional domain instead of having them spread in different TADs. [49] However, studies of mouse development point out that two adjacent TADs may regulate the same gene cluster. The most relevant study on limb evolution shows that the TAD at the 5’ of the HoxD gene cluster in tetrapod genomes drives its expression in the distal limb bud embryos, giving rise to the hand, while the one located at 3’ side does it in the proximal limb bud, giving rise to the arm. [50] Still, it is not known whether TADs are an adaptive strategy to enhance regulatory interactions or an effect of the constrains on these same interactions. TAD boundaries are often composed by housekeeping genes, tRNAs, other highly expressed sequences and Short Interspersed Elements (SINE). While these genes may take advantage of their border position to be ubiquitously expressed, they are not directly linked with TAD edge formation. The specific molecules identified at boundaries of TADs are called insulators or architectural proteins because they not only block enhancer leaky expression but also ensure an accurate compartmentalization of cis-regulatory inputs to the targeted promoter. These insulators are DNA-binding proteins like CTCF and TFIIIC that help recruiting structural partners such as cohesins and condensins. The localization and binding of architectural proteins to their corresponding binding sites is regulated by post-translational modifications. [51] DNA binding motifs recognized by architectural proteins are either of high occupancy and at around a megabase of each other or of low occupancy and inside TADs. High occupancy sites are usually conserved and static while intra-TADs sites are dynamic according to the state of the cell therefore TADs themselves are compartmentalized in subdomains that can be called subTADs from few kb up to a TAD long (19). When architectural binding sites are at less than 100 kb from each other, Mediator proteins are the architectural proteins cooperate with cohesin. For subTADs larger than 100 kb and TAD boundaries, CTCF is the typical insulator found to interact with cohesion. [52]

          Of the pre-initiation complex and promoter escape Edit

          In eukaryotes, ribosomal rRNA and the tRNAs involved in translation are controlled by RNA polymerase I (Pol I) and RNA polymerase III (Pol III) . RNA Polymerase II (Pol II) is responsible for the production of messenger RNA (mRNA) within the cell. Particularly for Pol II, much of the regulatory checkpoints in the transcription process occur in the assembly and escape of the pre-initiation complex. A gene-specific combination of transcription factors will recruit TFIID and/or TFIIA to the core promoter, followed by the association of TFIIB, creating a stable complex onto which the rest of the General Transcription Factors (GTFs) can assemble. [53] This complex is relatively stable, and can undergo multiple rounds of transcription initiation. [54] After the binding of TFIIB and TFIID, Pol II the rest of the GTFs can assemble. This assembly is marked by the post-translational modification (typically phosphorylation) of the C-terminal domain (CTD) of Pol II through a number of kinases. [55] The CTD is a large, unstructured domain extending from the RbpI subunit of Pol II, and consists of many repeats of the heptad sequence YSPTSPS. TFIIH, the helicase that remains associated with Pol II throughout transcription, also contains a subunit with kinase activity which will phosphorylate the serines 5 in the heptad sequence. Similarly, both CDK8 (a subunit of the massive multiprotein Mediator complex) and CDK9 (a subunit of the p-TEFb elongation factor), have kinase activity towards other residues on the CTD. [56] These phosphorylation events promote the transcription process and serve as sites of recruitment for mRNA processing machinery. All three of these kinases respond to upstream signals, and failure to phosphorylate the CTD can lead to a stalled polymerase at the promoter.

          In vertebrates, the majority of gene promoters contain a CpG island with numerous CpG sites. [57] When many of a gene's promoter CpG sites are methylated the gene becomes silenced. [58] Colorectal cancers typically have 3 to 6 driver mutations and 33 to 66 hitchhiker or passenger mutations. [59] However, transcriptional silencing may be of more importance than mutation in causing progression to cancer. For example, in colorectal cancers about 600 to 800 genes are transcriptionally silenced by CpG island methylation (see regulation of transcription in cancer). Transcriptional repression in cancer can also occur by other epigenetic mechanisms, such as altered expression of microRNAs. [60] In breast cancer, transcriptional repression of BRCA1 may occur more frequently by over-expressed microRNA-182 than by hypermethylation of the BRCA1 promoter (see Low expression of BRCA1 in breast and ovarian cancers).


          DEcode software and pre-trained models for tissue- and person-specific transcriptomes are available at www.differentialexpression.org, https://github.com/stasaki/DEcode and from our Code Ocean capsule (https://doi.org/10.24433/CO.0084803.v1). DEcode is licensed under a BSD 3-Clause license.

          Lee, T. & Young, R. Transcriptional regulation and its misregulation in disease. Cell 152, 1237–1251 (2013).

          Lambert, S. A. et al. The human transcription factors. Cell 172, 650–665 (2018).

          Glisovic, T., Bachorik, J. L., Yong, J. & Dreyfuss, G. RNA-binding proteins and post-transcriptional gene regulation. FEBS Lett. 582, 1977–1986 (2008).

          Bartel, D. P. MicroRNAs: target recognition and regulatory functions. Cell 136, 215–233 (2009).

          Schoenfelder, S. & Fraser, P. Long-range enhancer–promoter contacts in gene expression control. Nat. Rev. Genet. 20, 437–455 (2019).

          Smith, Z. D. & Meissner, A. DNA methylation: roles in mammalian development. Nat. Rev. Genet. 14, 204–220 (2013).

          Roundtree, I. A., Evans, M. E., Pan, T. & He, C. Dynamic RNA modifications in gene expression regulation. Cell 169, 1187–1200 (2017).

          Avsec, Ž. et al. The Kipoi repository accelerates community exchange and reuse of predictive models for genomics. Nat. Biotechnol. 37, 592–600 (2019).

          Libbrecht, M. W. & Noble, W. S. Machine learning applications in genetics and genomics. Nat. Rev. Genet. 16, 321–332 (2015).

          Jaganathan, K. et al. Predicting splicing from primary sequence with deep learning. Cell 176, 535–548 (2019).

          Zhou, J. et al. Deep learning sequence-based ab initio prediction of variant effects on expression and disease risk. Nat. Genet. 50, 1171–1179 (2018).

          Alipanahi, B., Delong, A., Weirauch, M. T. & Frey, B. J. Predicting the sequence specificities of DNA- and RNA-binding proteins by deep learning. Nat. Biotechnol. 33, 831–838 (2015).

          Yevshin, I., Sharipov, R., Valeev, T., Kel, A. & Kolpakov, F. GTRD: a database of transcription factor binding sites identified by ChIP-seq experiments. Nucleic Acids Res. 45, D61–D67 (2017).

          Zhu, Y. et al. POSTAR2: deciphering the post-transcriptional regulatory logics. Nucleic Acids Res. 47, D203–D211 (2019).

          Agarwal, V., Bell, G. W., Nam, J. & Bartel, D. P. Predicting effective microRNA target sites in mammalian mRNAs. eLife 4, e05005 (2015).

          Melé, M. et al. Human genomics. The human transcriptome across tissues and individuals. Science 348, 660–665 (2015).

          Shrikumar, A., Greenside, P. & Kundaje, A. Learning important features through propagating activation differences. In Proceedings of the 34th International Conference on Machine Learning, Proceedings of Machine Learning Research Vol. 70 (eds Precup, D. & Teh, Y. W.) 3145–3153 (ICML, 2017).

          Lundberg, S. M. & Lee, S. A unified approach to interpreting model predictions. Adv. Neural Inf. Process. Syst. 30, 4765–4774 (2017).

          Chong, J. A. et al. REST: a mammalian silencer protein that restricts sodium channel gene expression to neurons. Cell 80, 949–957 (1995).

          Imperato, M. R., Cauchy, P., Obier, N. & Bonifer, C. The RUNX1-PU.1 axis in the control of hematopoiesis. Int. J. Hematol. 101, 319–329 (2015).

          Soares, E. & Zhou, H. Master regulatory role of p63 in epidermal development and disease. Cell. Mol. Life Sci. 75, 1179–1190 (2018).

          Watt, A. J., Garrison, W. D. & Duncan, S. A. HNF4: a central regulator of hepatocyte differentiation and function. Hepatology 37, 1249–1253 (2003).

          Lefterova, M. I., Haakonsson, A. K., Lazar, M. A. & Mandrup, S. PPARγ and the global map of adipogenesis and beyond. Trends Endocrinol. Metab. 25, 293–302 (2014).

          Ge, Z., Quek, B. L., Beemon, K. L. & Hogg, J. R. Polypyrimidine tract binding protein 1 protects mRNAs from recognition by the nonsense-mediated mRNA decay pathway. eLife 5, e11155 (2016).

          Wang, Y. et al. N 6 -methyladenosine modification destabilizes developmental regulators in embryonic stem cells. Nat. Cell Biol. 16, 191–198 (2014).

          Lek, M. et al. Analysis of protein-coding genetic variation in 60,706 humans. Nature 536, 285–291 (2016).

          Goh, K. et al. The human disease network. Proc. Natl Acad. Sci. USA 104, 8685–8690 (2007).

          Ardlie, K. G. et al. The Genotype-Tissue Expression (GTEx) pilot analysis: multitissue gene regulation in humans. Science 348, 648–660 (2015).

          Gamazon, E. R. et al. A gene-based association method for mapping traits using reference transcriptome data. Nat. Genet. 47, 1091–1098 (2015).

          Gerstberger, S., Hafner, M. & Tuschl, T. A census of human RNA-binding proteins. Nat. Rev. Genet. 15, 829–845 (2014).

          Gaiteri, C., Ding, Y., French, B., Tseng, G. C. & Sibille, E. Beyond modules and hubs: the potential of gene coexpression networks for investigating molecular mechanisms of complex brain disorders. Genes Brain Behav. 13, 13–24 (2014).

          Crow, M., Lim, N., Ballouz, S., Pavlidis, P. & Gillis, J. Predictability of human differential gene expression. Proc. Natl Acad. Sci. USA 116, 6491–6500 (2019).

          Bergstra, J., Komer, B., Eliasmith, C., Yamins, D. & Cox, D. D. Hyperopt: a Python library for model selection and hyperparameter optimization. Comput. Sci. Discov. 8, 014008 (2015).

          Korotkevich, G., Sukhov, V. & Sergushichev, A. Fast gene set enrichment analysis. Preprint at bioRxiv https://www.biorxiv.org/content/10.1101/060012v2 (2019).

          Liberzon, A. et al. The Molecular Signatures Database (MSigDB) hallmark gene set collection. Cell Syst. 1, 417–425 (2015).

          Merico, D., Isserlin, R., Stueker, O., Emili, A. & Bader, G. D. Enrichment map: a network-based method for gene-set enrichment visualization and interpretation. PLoS ONE 5, e13984 (2010).

          Dawes, R., Lek, M. & Cooper, S. T. Gene discovery informatics toolkit defines candidate genes for unexplained infertility and prenatal or infantile mortality. NPJ Genom. Med. 4, 8–11 (2019).

          Smith, C. L., Blake, J. A., Kadin, J. A., Richardson, J. E. & Bult, C. J. Mouse Genome Database (MGD)-2018: knowledgebase for the laboratory mouse. Nucleic Acids Res. 46, D836–D842 (2018).

          Koscielny, G. et al. The International Mouse Phenotyping Consortium web portal, a unified point of access for knockout mice and related phenotyping data. Nucleic Acids Res. 42, D802–D809 (2014).

          Tsherniak, A. et al. Defining a cancer dependency map. Cell 170, 564–576 (2017).

          Kuleshov, M. V. et al. Enrichr: a comprehensive gene set enrichment analysis web server 2016 update. Nucleic Acids Res. 44, W90–W97 (2016).

          Chen, E. Y. et al. Enrichr: interactive and collaborative HTML5 gene list enrichment analysis tool. BMC Bioinformatics 14, 128 (2013).

          Clarke, D. J. B. et al. eXpression2Kinases (X2K) Web: linking expression signatures to upstream cell signaling networks. Nucleic Acids Res. 46, W171–W179 (2018).

          Ritchie, M. E. et al. Limma powers differential expression analyses for RNA-sequencing and microarray studies. Nucleic Acids Res. 43, e47 (2015).

          Robin, X. et al. pROC: an open-source package for R and S+ to analyze and compare ROC curves. BMC Bioinformatics 12, 77 (2011).

          Kanehisa, M. & Goto, S. KEGG: Kyoto Encyclopedia of Genes and Genomes. Nucleic Acids Res. 28, 27–30 (2000).


          Introduction

          hnRNPH and F control alternatively spliced events (ASE) by binding to G tracts positioned in close proximity to the 5′ or 3′ splice sites (ss), with G triplets being the basic recognition motif [1], [2], [3], [4], [5]. hnRNPH and F can either enhance or inhibit the alternatively spliced exon and the magnitude of the effect is dependent on the length of the G tracts, the intronic vs. exonic position and the strength of the 5′ ss [6], [7], [8], [9], [10]. We have shown that hnRNPH/F regulate the major myelin proteolipid protein (PLP)/DM20 ratio predominantly by enhancing the selection of the DM20 5′ splice site through long G tracts positioned in exon 3B immediately downstream of the DM20 5′ss [11], [12], [13]. Unlike other ASEs, hnRNPH and F exert a novel synergistic regulation of the PLP alternatively spliced event and their function is not redundant [12].

          The alternative splicing of PLP is a differentiation dependent event in the oligodendrocytes (OL), the myelin producing cells of the central nervous system (CNS). Endogenous hnRNPH and F expression is high in oligodendrocyte progenitor cells (OPC) and decreases in differentiated OL in vitro at the time when the PLP/DM20 ratio increases [12]. Furthermore, siRNA-mediated knock down of hnRNPH/F increases the PLP/DM20 ratio in the oligodendrocyte cell line, Oli-neu cells [12]. The down regulation of hnRNPH/F is temporally related to the transition of oligodendrocyte progenitor cells to differentiated OL, suggesting that hnRNPH/F may contribute broadly to differentiation-induced changes in gene splicing and expression that occur as part of the OL differentiation program.

          Many excellent genomewide studies have characterized the role of G tracts in splicing [6], [7], [14]. A global analysis of genome wide hnRNPH/F mediated regulation of alternative splicing has been conducted in human 293 T cells [15] and, for a relatively small number of genes related predominantly to apoptosis and cancer, in cancer cells [16]. In this study, we sought to investigate the global impact of hnRNPH/F-mediated regulation of splicing events in oligodendrocytes and to determine whether genes involved in OL lineage progression are regulated by hnRNPH/F.

          To this end, we have performed a genome-wide transcriptomic analysis at the gene and exon levels in Oli-neu cells treated with siRNA that target hnRNPH/F vs. untreated cells using Affymetrix exon array platforms. Gene expression levels and regulated exons were identified with the EASANA algorithm [17], [18]. Bioinformatics analyses were performed to determine the structural properties of G tracts, such as length, distance and position that correlate with the enhancing vs. silencing effect of hnRNPH/F. The expression of genes involved in signaling pathways was regulated by hnRNPH/F. Genes that regulate the transition of OPC to OL are differentially expressed in hnRNPH/F silenced Oli-neu cells. These changes were confirmed in developing OL in vivo.

          This is the first genome wide analysis of splicing events and genes differentially regulated by hnRNPH/F in OL and the first report that hnRNPH/F regulate genes involved in the transition from OPC to OL.


          For more information about the types of gene variants:

          The National Human Genome Research Institute offers a Talking Glossary of Genetic Terms. This resource includes definitions, diagrams, and detailed audio descriptions of several of the gene variants listed above.

          A brief explanation of different variants types is available from yourgenome.org, a service of the Wellcome Trust.


          Watch the video: Βίντεο 340 Πώς Μειώνουμε Το Μέγεθος Ενός Βίντεο (August 2022).