Translational regulatory proteins recognize specific areas of what molecule?

Abstract

Transcription factors (TFs) are a large family of regulatory proteins that can increase or decrease the transcription of a particular gene from deoxyribonucleic acid into the corresponding ribonucleic acid. The modular structure of TFs and the presence of distinct interacting domains determine the ability of these factors to associate with each other and with co-activating/repressing proteins. In addition, DNA polymorphisms or DNA methylation status cam alter TF-DNA binding and function. TFs control the expression of many inflammatory genes and they play a key role in the pathogenesis of a large number of human respiratory diseases contributing to determine disease severity and response to treatment.

A Network reconstruction

Regulatory networks differ from metabolic networks in ways that impact the network reconstruction as well as modeling approaches (Herrgard et al. 2004). First, the components are different. Whereas metabolic networks involve metabolites, enzymes, and transport proteins, regulatory networks involve regulatory proteins and the promoter regions of target genes. Second, most of the metabolic proteins are well conserved across species. Regulatory proteins may also be conserved. However, the cis regulatory regions are generally not conserved across species, and transcription factor binding sites are extremely difficult to find in promoter regions due to their short length, although progress is being made (Beer et al. 2004). In addition, the interactions of transcription factors at one promoter region can be extremely complex (Davidson et al. 2002), and even a single nucleotide difference in a transcription factor binding site can change the specificity of cofactor binding (Leung et al. 2004).

Accordingly, the level of characterization of regulatory networks does not approach that found in metabolic networks. Currently, detailed genome-scale regulatory networks have been reconstructed only for Saccharomyces cerevisiae (Lee et al. 2002; Harbison et al. 2004) and E. coli (Shen-Orr et al. 2002; Salgado et al. 2004). These reconstructions are qualitative, including the effect of active transcription factors on target genes (whether the factor acts as an inducer, repressor, or both). More detailed reconstructions, which would include some of the dynamics of gene expression, are extremely useful but also far more difficult to obtain (Kalir et al. 2004).

Notwithstanding these challenges to those wishing to study regulation, two high-throughput technologies have made it possible to reconstruct regulatory networks at the large scale. First, microarray analysis enables the determination of the expression profile of an entire genome in one experiment (Gardner et al. 2003). Second, it is now possible to determine with some accuracy where all of the transcription factors are binding in the genome under a given set of experimental conditions (Lee et al. 2002). These two approaches, especially when used in combination with each other or with the existing literature, are a powerful way of characterizing a regulatory network (Hartemink et al. 2002; Herrgard et al. 2003).

IV.B The Lac Operon

Transcriptional repression is a key mechanism to control the activity of prokaryotic promoters. Enzymes used in a specific metabolic pathway are often organized into an operon that is transcribed into a single polycistronic mRNA. Specific repressor proteins then control the transcriptional activity of the operon by regulating RNA polymerase binding to the promoter. Repressor proteins are DNA-binding proteins that typically block RNA polymerase access to the −10 and/or −35 regions in the promoter or transcription elongation by associating with an operator sequence that is positioned downstream of the start site of transcription. Usually these regulatory proteins undergo allosteric changes in response to binding of a specific ligand. The paradigm of a prokaryotic operon regulated by a specific repressor protein is the lac operon in E. coli. In this system synthesis of proteins necessary for usage of lactose as a carbon source is repressed by the lac repressor protein if cells have the possibility to use glucose for growth. Thus, in the presence of glucose the lac repressor binds to its operator sequence, which overlaps the transcription start site in the lac operon (Fig. 6), and blocks RNA polymerase binding to the lac promoter. If cells are grown on lactose as the carbon source, lactose functions as an inducer of lac operon transcription by binding to the lac repressor and converting it to an inactive form that does not bind DNA (Fig. 6) and therefore is unable to inhibit transcription of the lac operon. The polycistronic lac mRNA encodes for the specific proteins necessary for metabolism of lactose. The lac operon represents an example of an inducible system where an inducer activates transcription. However, inducers can also have the opposite effect and repress transcription of an operon, like the trp operon in E. coli.

Immunomodulatory Capacity of MSC

MSCs are potent immune suppressors and promote systemic, long-lasting control of inflammation and enhanced tissue regeneration. Increasing evidence has shown that the restorative and immunosuppressive functions exerted by MSCs are cell-contact dependent and also mediated through the secretion of immune mediators and trophic factors that allow paracrine and systemic effects. MSCs produce a broad range of soluble immunosuppressive molecules such as interleukin (IL)-6, transforming growth factor-β (TGF-β), prostaglandin E2 through COX2, hepatocyte growth factor (HGF), soluble human leukocyte antigen-G (HLA-G), tumor necrosis factor-inducible gene 6 (TSG6), programmed death-1 ligands (PD-L1 and PD-L2) and extracellular vesicles, among others. MSCs are also able to hydrolyze extracellular pro-inflammatory ATP toward antiinflammatory adenosine through CD39/CD73 action. Murine MSCs exert potent immunosuppression by locally increasing nitric oxide (NO) through inducible nitric oxide synthase (iNOS), whereas human MSCs express indoleamine 2,3-dioxygenase (IDO), the rate-limiting enzyme involved in the catabolism of the essential amino acid tryptophan, required for cell proliferation, and resulting in the accumulation of N-formyl-kynurenine, which also suppresses T cell responses. Moreover, secreted chemokines and adhesion molecules can recruit and retain immune cells in the vicinity of MSCs, where cell contact together with these paracrine mediators can downmodulate immune cell activation.

MSCs suppress the innate immune response by inhibiting neutrophil infiltration, oxidative burst and NET release, reducing NK cell activation, proliferation and cytotoxic activity, and also controlling activation of the complement system by the production of the C3b-regulatory protein Factor H.

Several studies have also shown the ability of MSCs to modulate effector cellular immune responses, as MSCs are able to inhibit mitogenic, antigenic and allogeneic T cell proliferation, reduce T cell migration and cytotoxic activity of CD8+ T cells and promote apoptosis of activated T cells. They induce the shift from inflammatory Th1 and Th17 toward a Th2 polarization of T cell response, by reducing interferon gamma (IFN-γ) and IL-17 and promoting IL-4 secretion instead. MSCs are also capable of inducing the generation of regulatory T cells (CD4+ CD25+ FoxP3+, IL-10 and TGF-β-producing Tregs), either through the direct action of IDO and TGF-β or helped by the generation of antiinflammatory “M2” monocytes, producers of antiinflammatory TGF-β, IL-10, and CCL18.

Regarding antigen presenting cells, MSCs skew monocytes and macrophages toward an “M2” antiinflammatory phenotype, impair their differentiation toward dendritic cells and restrict their maturation. MSCs can directly modulate also the humoral effector immune response, as they inhibit B cell activation and proliferation, impair B cell maturation and differentiation to plasmablasts, thus reducing IgM and IgG production, and generate IL-10-secreting regulatory B cells (Bregs).

The application of off-the-shelf MSC-based immunosuppressive and regenerative therapies would require that MSCs evade allogeneic rejection and at the same time, retain their properties in the context of inflammatory setting. A growing number of studies suggest that mismatched MSCs are able to evade immune allorecognition thanks to their intrinsic hypoimmunogenicity, due to a low expression of class I MHC and absence of costimulatory molecules. Also, they are able to suppress immune cell functions in a cognate-independent manner, which make them suitable for both allogeneic and third party cell therapies. Regarding class II MHC molecules, they have shown to be absent in resting cells, while can be upregulated under inflammatory stimuli, arguing for the use of autologous MSC in the context of transplantation. Nevertheless, at the same time, the activation with TLR agonists and/or presence of pro-inflammatory cytokines such as IFN-γ or tumor necrosis factor alpha (TNF-α) actually primes MSCs for an enhanced immunosuppressive function, which has been studied both in vitro and in vivo. In fact, an inflammatory stimulus is necessary for murine and human MSCs to express iNOS and IDO, respectively, and enhances the PGE2 production by COX2, all powerful inhibitors of the immune response. This paradox is now being studied to finally clarify whether allogeneic MSCs can be safely used in the clinical setting and will be further discussed below.

These properties make MSCs an excellent potential cell therapy for immune-related diseases such as the management of inflammation and graft rejection after solid organ transplantation (SOT).

5.3 Robustness of miRNA-Mediated Regulatory Networks

A good procedure is to disturb only particular interaction and monitor the phenotypic effects. A beneficial technique is disrupting a single miRNA–target interaction by using antisense reagents that hybridize to the target site, thus to disallow miRNA pairing. The phenotypic effects of these preserved interactions are very challenging task, especially their detection in the wet lab, although the simultaneously perturbation of all miRNA interactions by their knocking out usually does not have considerable phenotypic effects [83]. One of the more reasons of toleration of such disturbances for miRNA targets, which are gene regulatory proteins, is the regulatory network buffering. Many regulatory interactions, including many miRNA–target interactions, belong to complex regulatory networks with bifurcating pathways and feedback control enabling accurate reaction regardless of an inoperative node in the network. With this ability to buffer the effects of missing a node, such networks must be disturbed somewhere else before the missing miRNA interaction has evident phenotypic effects [83]. Perturbation of the miRNA node is expected to make the network susceptive to discover the importance of the rest of regulatory nodes.

Recent studies have uncovered that target hub genes, which carry vast number of TFBSs, are possible subject to massive regulation by many miRNAs. It means that nodes with more connections will more probably obtain new connections during time. The top genes with big number of both miRNA binding sites and TFBS are boosted in the functions related to development and differentiation of cells. Many of these target hub genes are transcription regulators, proposing a crucial pathway for miRNAs to indirectly regulate genes by repressing TFs [19].

miRNAs could be also target of hub genes. There is a class of miRNAs regulated by a large number of TFs, while the others are regulated by only a few TFs. miRNA expression profiles in embryonic developmental stages and adult tissues or cancer samples had disclosed that the miRNAs from the first class have higher expression levels in embryonic developmental stages, while the second class miRNAs are more expressed in adult tissues or cancer samples.

Regulator hub genes are more likely to have interactions with miRNAs, because they regulate large number of targets. miRNAs together with master TFs prefer to coregulate their targets. Regulator hub genes are very important constituents in the GRNs, since perturbations on them can disturb functions of numerous target genes. As miRNAs buffer stochastic perturbations, their preference to regulator hub genes could provide robustness of the regulatory network [19].

MicroRNAs as Key Regulators of Senescence

MicroRNAs (miRNA) are small non-coding single-stranded RNAs (18–22 nucleotides) that regulate post-transcriptional gene expression through inducing degradation of mRNA or inhibiting protein translation by binding to complimentary sequences on the 3′-untranslated regulatory regions of mRNAs. There is increasing evidence that miRNA play an important role in in aging through the regulation of several regulatory proteins that are involved in senescence, including p16 (miR-24), the SASP response (miR-146), p53 (miR-885-5p) and sirtuin-1 expression (miR-34a) (Munk et al., 2017).

MiR-34a inhibits sirtuin-1 and shows increased expression in peripheral lungs and epithelial cells of COPD patients, and is correlated with increased expression of senescence markers in lung cells (Baker et al., 2016a, b). MiR-34a also regulates sirtuin-6, but not other sirtuins. MiR-34a is increased by oxidative stress through activation of PI3K-mTOR signaling resulting in a parallel reduction in sirtuin-1 and sirtuin-6 (Fig. 3), whereas other sirtuins are unchanged, as in COPD lungs. An antagomir of miR-34a, which blocks its action, restores sirtuin-1 and sirtuin-6 in senescent small airway epithelial cells from COPD patients, reduces markers of cellular senescence (p16, p21, p53), reduces the SASP response (TNF-α, IL-1β, IL-6, CCL2, CXCL8, MMP9), and increases proliferation of senescent epithelial cells by reversing cell cycle arrest (Baker et al., 2016a, b). MiR-34a is also increased in COPD macrophages and may be associated with impaired phagocytosis and uptake of apoptotic cells (efferocytosis) observed in this disease (McCubbrey et al., 2016). Another miRNA, miR-570, also inhibits sirtuin-1 (but not sirtuin-6) and is activated via p38 MAP kinase and AP-1. MiR-570 is increased in COPD peripheral lung and small airway epithelial cells. An antagomir restores sirtuin-1, reduces senescence markers and reverses cell cycle arrest (Baker et al., 2019). This suggests that blocking specific miRNAs may result in rejuvenation of senescent COPD cells. MiR-126, plays an important role in endothelial cell function and maintaining vascular integrity. It is reduced in endothelial progenitor cells and airway epithelial cells from COPD patients and may regulate the DNA damage response pathway that is linked to cellular senescence (Paschalaki et al., 2018).

14.2 Molecular basis of gene regulation

Gene expression is the process of reading and interpreting a given stretch of DNA to make functioning protein. While the control of gene expression and activity can take place at several different stages, most models of gene regulation focus on the transcriptional control component and, more specifically, transcriptional control in eukaryotes.

The structure of a typical eukaryotic gene is shown in Figure 14.1. The actual gene product, the protein, is coded for by the coding region. For the gene to be expressed, the coding region is transcribed by the enzyme RNA polymerase into messenger RNA (mRNA), which in turn is translated into a string of amino acids, eventually yielding a three-dimensionally folded protein.

The initiation of transcription is dependent on a number of factors, most importantly the presence of regulatory proteins called transcription factors (TFs). As an essential component, general transcription factors must bind to a stretch always immediately preceding the coding region, called the promoter (more specifically, they bind to a short sequence within the promoter called the TATA box, which is rich in the nucleotides thymidine and adenine). Once the general transcription factors have assembled, RNA polymerase can dock to transcribe the coding region.

In addition to the promoter, the control region of a gene typically contains a number of additional regulatory sequences (also called cis-elements). These can be located before (upstream) or, less frequently, after (downstream) the actual coding region. Regulatory sequences need not be close to the gene, but can in fact be located several thousand bases away.

Similar to the TATA box, regulatory sequences act as binding sites for transcription factors (also called trans-elements). The probability of a binding event between a given sequence/TF pair is determined by the 3-dimensional fit between the DNA and the protein structure. Hence, TFs tend to be sequence specific.

Once bound, transcription factors can influence the expression of specific genes (typically by physically interacting with the promoter complex of the gene or with other transcription factors). DNA folding enables interactions even over several thousand bases. Broadly, two types of regulators are distinguished: enhancers increase the probability that a given gene is expressed, inhibitors decrease it.

Transcription factors regulate the presence of structural proteins needed to build and maintain an organism. However, transcription factors themselves, as proteins, are of course subject to the same gene regulatory processes as all other proteins. In other words, transcription factors regulate the expression of transcription factors. It follows that gene regulatory systems typically take the shape of complex dynamic networks of interacting transcription factors, whose output is the switching on and off of structural genes.

Histology and Structure

Muscle fibers- (Fig. 6)

Skeletal muscle fibers are multinucleate cells containing striated, thread-like myofibrils, which run the entire length of the muscle fiber and can be seen by light microscopy. They also contain mitochondria, and have an extensive sarcoplasmic reticulum (SR), which stores calcium.

Fig. 6. (A) Light micrograph of a single human diaphragm muscle fiber (× 40 magnification). (B) Electron micrograph of human diaphragm muscle (× 10000 magnification). Sarcomeres, the functional units of skeletal muscle, extend from Z line to Z line. (C) Schematic diagram of the main contractile elements of a single sarcomere. The striated appearance seen in Fig. 6A is created by a pattern of alternating dark A bands (thick filaments containing myosin), and light I bands (containing actin, tropomyosin and troponin) which are bisected by dense Z lines. Z lines anchor the sarcomeres, which extend from Z line to Z line. The H zone is the portion of the A band where the thick and thin bands do not overlap.

Light and electron micrographs courtesy of Alistair Moore & Alison Stubbings, Imperial College, London.

The outer cell membrane (sarcolemma) has extensions that project deep into the fiber to the SR, which surrounds the myofibrils. These are the transverse (t)-tubules. Voltage-sensitive calcium release channels (dihydropyridine receptor DHPR) in t-tubular membranes, and isoforms of the calcium-sensitive calcium release channels, or ryanodine receptors, (RyR1 and RyR3), mediate calcium release from the SR during excitation-contraction coupling in skeletal muscle.

2 Stochastic models

Simulating differential equations to model reaction-diffusion processes will accurately predict the average behavior of (1) large numbers of molecules within cells and (2) the average outcome of a cell process over a large number of cells. However, in many cases deterministic and continuous approaches cannot accurately simulate biological phenomena that arise from stochastic effects. For example, in the case of cancer random molecular and cellular effects with low individual probability accumulate, eventually causing dramatic physiological effects.

Biological systems, particularly those involved with genetic regulation, are very noisy—and distinct phenotypic outcomes directly result from that noise (McAdams and Arkin 1997, 1999; Elowitz and Leibler 2000). The problem of noise is exacerbated by the low cellular concentrations typical of many key regulatory proteins. If one speaks of nanomolar concentrations of a protein, that corresponds to just a few to tens of individual protein molecules. For example, in gene regulation there are only a few sites on DNA (which can be thought of as individual “molecules” or reaction sites) where transcription factors can bind and mRNA be produced. Therefore, stochastic and discrete simulations may be necessary to develop accurate reaction-diffusion models for such processes. Recently, an extensive review focusing on simulation in bacterial cells was conducted by McAdams and Arkin (1998).

Because biological processes involve a large number of molecules and protein species, the state space is too large for an exact solution of stochastic differential equations describing a reaction. Gillespie (1976, 1977) proposed a Monte Carlo method to exactly simulate the stochastic time evolution of a reaction system. The probability of each reaction occurring is a function of its rate constant (measured experimentally) and the number of available reactants in the simulation. At each point in time, there exists a joint probability distribution function for both the reaction and the time at which it can occur.

This generates a random trajectory through the state space that converges in the mean to the solution of the continuum model. Similarly, an average over an appropriate set of repeated experiments is expected to lead to the solution from a continuum model, and in this context one may view deterministic spatiotemporal models as the expected solutions from an appropriate ensemble average of experiments. This is convenient in that these ensemble averages are the simplest experimentally reproducible observables.

What molecule is being regulated in translational regulation?

Which molecule is specific to the process of translation?

Which molecules are involved in the translation of proteins?

How does a regulatory protein identify its binding site?