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Proteins act through their interactions with other molecules. Catalytic proteins (enzymes) interact with substrate molecules; these interactions lower the activation energy of the reaction's rate limiting step, leading to an increase in the overall reaction rate. It is primarily by altering proteins (which in turn influences gene expression) that cells (and organisms) adapt to changes in their environment.
A protein's activity can be regulated in a number of ways. The first and most obvious is to control the total number of protein molecules present within the system. Let us assume that once synthesized a protein is fully active. With this simplifying assumption, the total concentration of a protein, and the total protein activity in a system [Psys] is proportional to the rate of that protein’s synthesis (dSynthesis/dt) minus the rate of that protein’s degradation (dDegradation/dt), with dt indicating synthesis or degradation per unit time. The combination of these two processes, synthesis and degradation, determines the protein’s half-life. Since both a protein’s synthesis and degradation can be regulated, its half-life can be regulated.
The degradation of proteins is mediated by a special class of enzymes (proteins) known as proteases. Proteases cleave peptide bonds via hydrolysis (adding water) reactions. Proteases that cleave a polypeptide chain internally are known as endoproteases - they generate two polypeptides. Those that hydrolyze polypeptides from one end or the other, to release one or two amino acids at a time, are known as exoproteases. Proteases can also act more specifically, recognizing and removing specific parts of a protein in order to activate or inactivate it, or to control where it is found in a cell. For example, nuclear proteins become localized to the nucleus (typically) because they contain a nuclear localization sequence or they can be excluded because they contain a nuclear exclusion sequence. For these sequences to work they have to be able to interact with the transport machinery associated with the nuclear pores; but the protein may be folded so that they are hidden. Changes in a protein’s structure can reveal or hide such NLS or NES sequences, thereby altering the protein’s distribution within the cell and therefore its activity. As an example, a transcription factor located in the cytoplasm is inactive, but it becomes active when it enters the nucleus.Similarly, many proteins are originally synthesized in a longer and inactive "pro-form". When the pro-peptide is removed, cut away by an endoprotease, the processed protein becomes active. Proteolytic processing is itself often regulated (see below).
Controlling protein levels: Clearly the amount of a protein within a cell (or organism) is a function of the number of mRNAs encoding the protein, the rate that these mRNAs are recognized and translated, and the rate at which functional protein is formed, which in turn depends upon folding rates and their efficiency. It is generally the case that once translation begins, it continues at a more or less constant rate. In the bacterium E. coli, the rate of translation at 37ºC is about 15 amino acids per second. The translation of a polypeptide of 1500 amino acids therefore takes about 100 seconds. After translation, folding and, in multisubunit proteins, assembly, the protein will function (assuming that it is active) until it is degraded.
Many proteins within the cell are necessary all of the time. Such proteins are termed “constitutive” or house-keeping proteins. Protein degradation is particularly important for controlling the levels of “regulated” proteins, whose presence or concentration within the cell may lead to unwanted effects in certain situations. The regulated degradation of a protein typically begins when the protein is specifically marked for degradation. This is an active and highly regulated process, involving ATP hydrolysis and a multi-subunit complex known as the proteosome. The proteosome degrades the polypeptide into small peptides and amino acids that can be recycled. As a mechanism for regulating protein activity, however, degradation has a serious drawback, it is irreversible.
Application of the comprehensive set of heterozygous yeast deletion mutants to elucidate the molecular basis of cellular chromium toxicity
The serious biological consequences of metal toxicity are well documented, but the key modes of action of most metals are unknown. To help unravel molecular mechanisms underlying the action of chromium, a metal of major toxicological importance, we grew over 6,000 heterozygous yeast mutants in competition in the presence of chromium. Microarray-based screens of these heterozygotes are truly genome-wide as they include both essential and non-essential genes.
The screening data indicated that proteasomal (protein degradation) activity is crucial for cellular chromium (Cr) resistance. Further investigations showed that Cr causes the accumulation of insoluble and toxic protein aggregates, which predominantly arise from proteins synthesised during Cr exposure. A protein-synthesis defect provoked by Cr was identified as mRNA mistranslation, which was oxygen-dependent. Moreover, Cr exhibited synergistic toxicity with a ribosome-targeting drug (paromomycin) that is known to act via mistranslation, while manipulation of translational accuracy modulated Cr toxicity.
The datasets from the heterozygote screen represent an important public resource that may be exploited to discover the toxic mechanisms of chromium. That potential was validated here with the demonstration that mRNA mistranslation is a primary cause of cellular Cr toxicity.
One finished paper amino acid.
We have a fun paper folding activity. Remember how proteins are made of building blocks called amino acids, and have their own special shape? Not only do proteins look different, they have different jobs to do inside the cell to keep your body running smoothly.
The protein we made is a channel that sits in the outer cell surface, or membrane, and works like a door that lets certain molecules pass through. Some channels are open all the time while others can be closed depending on signals from the cell or the environment. When the channel is open, other molecules can enter the cell by passing through the hole in the middle.
As you'll discover while building your origami channel, the shape of a protein is very important. If you don't fold your origami amino acids correctly, they wouldn’t fit together to make a protein chain. Or, if you make a mistake joining amino acids together, the finished channel might not be able to open and close correctly.
In nature the same thing can happen. If a protein is the wrong shape it will not work correctly.
Materials: You will need 8 square pieces of paper of the same size.
Tips: The best way to make folds is to lay the paper down on a hard, flat surface, such as a table. It's important to pay attention to the direction of the paper and make sure not to change it's orientation when following instructions.
You can find out more about how proteins fold into unique shapes to make and do work inside your body in the Protein Science section.
You can also download and print our Origami Protein Handout (PDF) for step-by-step instructions of how to make your protein channel, or watch this step by step video.
1. Fold a single piece of paper in half diagonally
2. Fold the paper in half diagonally again
3. Your folded paper should look like this
4. Unfold the paper
5. Fold the paper in half
6. Fold the paper in half again
7. Your folded paper should look like this
8. Unfold the top layer of the square halfway
9. Open the top layer of the square and flatten it into a triangle, using the existing creases.
10. Your folded paper should look like this
11. Flip it over
12. Unfold the top layer halfway
13. Open the top layer and flatten it into a triangle, using the existing creases.
14. Your folded paper should look like this
15. Fold the edges of the top layer only into the centerline
16. Your folded paper should look like this
17. Flip it over
18. Fold the edges of the top layer only into the centerline
19. You've now completed one amino acid. Repeat these steps with another piece of paper until you've created a total of eight amino acids.
And, that's it! Once you have amino acids, you are ready to move onto Part 2 to make the protein channel.
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The importance and pervasiveness of naturally occurring regulation of RNA function in biology is increasingly being recognized. A common mechanism uses inducible protein−RNA interactions to shape diverse aspects of cellular RNA fate. Recapitulating this regulatory mode in cells using a novel set of protein−RNA interactions is appealing given the potential to subsequently modulate RNA biology in a manner decoupled from endogenous cellular physiology. Achieving this outcome, however, has previously proven challenging. Here, we describe a ligand-responsive protein−RNA interaction module, which can be used to target a specific RNA for subsequent regulation. Using the Systematic Evolution of Ligands by Exponential Enrichment (SELEX) method, RNA aptamers binding to the bacterial Tet Repressor protein (TetR) with low- to subnanomolar affinities were obtained. This interaction is reversibly controlled by tetracycline in a manner analogous to the interaction of TetR with its cognate DNA operator. Aptamer minimization and mutational analyses support a functional role for two conserved sequence motifs in TetR binding. As an initial illustration of using this system to achieve protein-based regulation of RNA function in living cells, insertion of a TetR aptamer into the 5′-UTR of a reporter mRNA confers post-transcriptionally regulated, ligand-inducible protein synthesis in E. coli. Altogether, these results define and validate an inducible protein−RNA interaction module that incorporates desirable aspects of a ubiquitous mechanism for regulating RNA function in Nature and can be used as a foundational interaction for functionally and reversibly controlling the multiple fates of RNA in cells.
CONCLUSION AND FUTURE PROSPECTS
Recent studies have revealed that the rapid, dynamic remodeling of the cytoskeleton in response to diverse internal and external signals relies on regulation by MAPs and ABPs. Over the past several years, it has become clear that the activities and protein levels of MAPs and ABPs are regulated in response to signal-mediated changes in the cellular microenvironment. Research in this field has seen significant recent progress as several signals involved in fine-tuning MAP and ABP activities or levels have been identified. Based on accumulating evidence and some speculation, we summarize how MAPs and ABPs are regulated by these signals and contribute to the organization and dynamics of MTs and F-actin in plants in response to the microenvironment. Although MAP and ABP levels and activities are regulated in various ways, how these mechanisms are triggered to control cytoskeleton dynamics via MAPs and ABPs in diverse cellular processes remains to be determined.
Unlike animal cells, plant cells have a rigid cell wall surrounding the plasma membrane ( Liu et al., 2015 ). This structure helps plant cells withstand different stresses based on their different architectures. Although many MAPs and ABPs have been proposed to link various signals to the patterns of cytoskeleton dynamics in plant cells, other MAPs and ABPs whose activities are regulated in specific environments remain to be identified. Future work should explore how these proteins modulate microtubule and actin dynamics in a coordinated manner via changes in their activities, thereby functioning in plant responses to particular signals. It would also be of interest to examine how changes in the cytoskeleton via modulation of the activities of MAPs and ABPs affect cell wall formation and vice versa, particularly in response to environmental and endogenous stimuli. Multidisciplinary approaches, including genetics, biochemistry, and advanced imaging techniques, should be used to construct a spatial and temporal network that integrates different regulatory factors and the activities or levels of cytoskeleton-associated proteins. Such a network would shed light on the highly sophisticated mechanisms regulating the cytoskeleton.
Exosomes are small nano-sized (50–150 nm) membrane vesicles secreted by most cell types including hematopoietic, neuronal, fibroblastic and various tumor cells . Discovered 25 years ago , they were thought to be involved in just discarding unwanted cellular debris. Later research, however, uncovered their role as essential cell-to-cell communication vehicles that function via addressed delivery of specific sets of proteins and bioactive lipids . These vesicles have recently attracted a great interest after the discovery that they contain mRNA [3, 4], microRNA [3–5] and DNA . Interestingly, the RNA patterns of exosomes were found to be substantially different from their host cells. Many of the mRNAs and miRNAs were highly enriched or even exclusively present in exosomes suggesting an existence of a dedicated mechanism for selective targeting of the RNAs into these vesicles [3, 4]. We recently identified several linear motifs highly enriched in secreted RNAs and proposed that their combination within a given RNA defines a zipcode recognized by trans-acting factors targeting RNAs to exosomes . Exosomes are present in various body fluids and expression profiling of their RNA in blood plasma, for example, could differentiate between healthy controls and patients with certain types of cancer  demonstrating their potential value as biomarkers. Exosomal mRNAs could be transferred to other cells in culture dish experiments [3, 4]. Moreover, in one report  host cell-derived exosomal mRNA was functional as it could be translated into proteins in target cells. The ability of exosomes to deliver mRNA to cells at a distance suggests their potential role in altering the recipient cell protein production . Intact mammalian mRNAs vary in length from 400 nt to 12,000 nt with the average size of transcripts 2,100 nt . However, the majority of investigated normal and cancer cells secrete exosomal RNAs with a size distributed between 25 and 700 nt. For example, RNA of a small size (<700 nt) was present in human plasma , saliva and breast milk exosomes [10, 11]. Human mesenchymal stem cells  and human tracheobronchial epithelial cells  were found to secrete even smaller RNA species (< 500 nt in length).
One possible explanation for this observation could be that exosomes are enriched in mRNAs encoding very short proteins. However, Frith with colleagues  analyzed RNA sizes for different ranges of proteins and found that the center of the RNA length distribution is almost same (around 2,100 nt) for large (>300 amino acids) and short (<100 amino acids) proteins. Thus, the size distribution of exosomal RNA suggests that the most of the RNA molecules present in these vesicles consist of species intermediary in a length between mature miRNAs (22 nt), pre-miRNAs (70 nt) and full-length mRNAs. The simplest explanation for this size distribution would be that exosomes are enriched in truncated mRNAs. Recent studies established that RNA transcripts may undergo a widespread post-transcriptional cleavage producing a range of smaller coding and noncoding RNAs . Post-transcriptional RNA cleavage appears to be a tightly controlled process as it is highly tissue-specific and developmentally regulated .
Next generation sequencing-based method RNA-Seq allows accurate determination of transcript boundaries and thus could be used to verify the hypothesis that exosomes carry RNA fragments. However, in case of exosome microvesicles, this approach was applied only for the analysis of small RNAs [16, 17]. Interestingly, these studies uncovered that exosomes contain a large number of transfer-, vault- and Y-RNA fragments [16, 17].
To detect possible presence of mRNA fragments in exosomes we utilized a microarray dataset from the published study  that analyzed mRNA content of exosomes released by cultured glioblastoma primary cells. The microarray analysis of mRNA was performed using the Agilent whole genome microarray whose 60-mer oligonucleotide probes are designed in a way that allows interrogation of expression levels of various RNA regions. We analyzed gene expression in cells and their secreted exosomes probe-wise. RefSeq transcripts were classified by the presence of signals from their microarray probes in exosomes and within the cells into four classes: i) 511 transcripts for which all the probes targeting each transcript were secreted from cells via exosomes, ii) 687 transcripts for which exactly half of the probes were secreted and half retained in the cell (including 656, 27 and 4 with 1, 2 and 3 secreted/retained probes, respectively), iii) 279 transcripts for which more than a half of the probes was secreted, iv) 145 transcripts for which less than a half of the probes was secreted (Figure 1A).
Detection of mRNA fragments in exosomes secreted by human cells. (A) Distribution of the transcripts by exosomal secretion of their fragments. Secreted transcripts were identified with ECER ≥ 3. Transcripts with all secreted probes were considered as secreted unfragmented. Fragmented transcripts were classified into three classes with i) majority, ii) minority and iii) exactly half of the probes secreted. (B) Cumulative fraction of fragmented transcripts in the total RNA measured in exosomes versus secretion magnitude (ECER cutoff). (C) Distribution of individual probe expression in exosomes by the magnitude of their secretion (ECER) and their location. The probes measure exosomal expression of specific fragments of the transcripts. Expression level is depicted with color ranging from red (low expression) to yellow (high expression). Relative location of the probes within their transcripts is represented by a number ranging from 0 (5’-end) to 1 (3’-end) with precision step 0.02 of the total relative length (1.0) of a transcript. (D) Dependence of probe localization relative to the 3’- and the 5’-ends of each individual transcript on the magnitude of its secretion (ECER). Only strongly secreted transcripts (ECER ≥ 10) are shown. Each dot represents a representative probe pair for an individual transcript (see details in Methods section). (E) Genomic view of CNDP2, RHO, and PPFIBP1 genes, along with the qPCR results for the SF295 intracellular and exosomal samples. The position of Agilent probes and the amplicons generated by PCR are shown in green and red. Expression was quantified by ΔCT between the genes of interest and that of a firefly luciferase cDNA spike-in control (See Additional file 3: Table S2 and Additional file 5). The potential post-transcriptional cleavage sites are designated by long dashed arrows.
Classes ii-iv, representing putative transcripts secreted in fragments, constituted 68.5% of all secreted transcripts. Moreover, we observed that with increase of the enrichment in exosomes the fraction of partially secreted transcripts of both classes increased (Figure 1B). The fraction of transcripts with exactly half of the probes secreted demonstrated almost two-fold increase (from 37% to 66% ) and positively correlated with the secretion efficiency (τ = 0.90). In contrast, the fraction of secreted intact transcripts decreased more than seven-fold (from 36% to 5% ) and negatively correlated with the secretion (τ = −0.91). These results suggest that transcript fragmentation and secretion are inter-related.
We performed the analysis of gene ontologies (GOs) associated with each class of secreted transcripts (See Additional file 1: Table S1). The protein products of the fragmented mRNAs were found to be significantly enriched in enzyme modulation (P = 0.0013) and in proteins participating in extracellular transport (P = 0.028). On the other hand, the proteins encoded by the full-length secreted mRNAs are specialized at cell surface receptor linked signal transduction (P = 2.04∙10 -4 ), cell communication (P = 8.06∙10 -4 ) and system development (P = 0.029). Interestingly, the products of 17 of these transcripts are localized in the extracellular matrix (P = 0.0046). Thus it can be concluded that secreted transcript fragments might have specific functions.
We observed that secreted RNAs are characterized with a specific segmentation pattern. The larger was the enrichment of transcripts in exosomes (ECER), the stronger was the tendency of the probes to be localized in the 3′-end of the transcripts (Figure 1C). The proximity of the probes to their transcript’s 3′-end strongly positively correlated with their secretion (τ = 0.458, P < 0.001, Figure 1D). When the transcripts with most fragments secreted were studied separately, a strong positive correlation with secretion was observed only for the probes located at their untranslated regions (UTRs) (τ = 0.73, P = 6.86∙10 -9 ) (See Additional file 2: Figure S1A). For probes in the translated regions, such correlation was weak (τ = 0.27, P = 0.03) (See Additional file 2: Figure S1B). Even larger difference was observed in the fraction of strongly secreted transcripts (ECER ≥ 10), with the probe localization in the UTRs correlating with secretion positively (τ = 0.51,P = 1.04 ⋅ 10 −3 ) (See Additional file 2: Figure S1C) and localization in the translated regions correlating negatively (τ = −0.3, P = 0.047) (See Additional file 2: Figure S1D).
To validate the accuracy of the results obtained with the microarrays, we examined the presence of various transcript parts in exosomes using quantitative real-time PCR (qPCR). RNA was isolated from exosomes secreted by human glioblastoma cells SF295. Three exemplary mRNA targets were selected for qPCR analysis, for which we observed unequal distribution of probe intensities on the microarray between cellular and exosomal RNA - CNDP2, RHO, PPFIBP1 mRNAs (See Additional file 3: Table S2). The analysis revealed that the ratio of the amount of qPCR products specific for the 3′-ends to that for the 5′-ends was significantly higher in exosomal fraction suggesting predominat secretion of the 3′-end derived fragments of these thranscripts (Figure 1E, Table S2).
The fact that exosomes carry the 3′-UTRs of mRNAs may have important implications for the regulation of gene expression and protein translation in recipient cells. The 3′-UTRs of mRNAs are rich in regulatory sequences. They serve as binding sites for numerous RNA-binding proteins that modulate stability and translational efficiency of mRNAs. They also contain miRNA target sites that guide the RNA-induced silencing complex (RISC) to microRNA response elements on target transcripts resulting in their degradation or translational reppression. A single 3′-UTR contains many miRNA binding sites. We can imagine that the 3′-UTR derived mRNA fragments carried by exosomes could directly compete for binding of miRNA or specific RNA-binding proteins to the recipient cell mRNA and lead to deregulation in protein production. Lee et al. reported that expression of versican 3′-UTR induces organ adhesion in transgenic mice through binding miR-199a* and freeing mRNAs of versican from being repressed by miR-199a* . The 3′-UTRs could affect not only mRNAs from which they are derived but also mRNAs that share with them miRNA-binding sites. For example, computational analysis indicated that miRNAs that interact with the CD44 3′-UTR also have binding sites in other matrix encoding mRNA 3′-UTRs, including collagen type 1α1 (Col1α1) repressed by miR-328 and fibronectin type 1 (FN1) repressed by miR-512-3p, miR-491 and miR-671 . Transfection of the CD44 3′-UTR led to synergestic up-regulation of CD44, Col1α1, and FN1 proteins and as result enhanced cell motility, invasion and cell adhesion . Pandolfi and colleagues proposed that RNAs ability to compete with each other for miRNAs generates a large-scale trans-regulatory crosstalk across the transcriptome as a whole. They named this RNA network activity “competing endogenous RNA” language . It is tempting to speculate that exosomes may utilize this RNA language as a means of communication between cells to integrate a complex network of information in multicellular organisms. By gaining a more detailed knowledge of the intercellular RNA language it will be possible to make useful predictions on the regulatory roles of RNA species carried by exosomes. It is unclear at present what mechanism might be responsible for generation of the exosomal 3′-UTR containing fragments. Mercer et al.  provided an evidence for the existence of a large number of intracellular 3′-UTR-bearing RNA fragments in human and mouse that are expressed separately from the associated protein-coding sequences to which they are normally linked. The post-transcriptional cleavage of mRNAs, rather than new transcription initiation, was proposed to be a major mechanism for the 3′-UTRs production . Regarding the site of mRNA fragmentation, we cannot exclude a possibility that the fragments are generated after secretion by RNases originating from donor cells and incorporated into exosome vesicles. We, however, believe that fragments are produced inside the cells. We noted, for example, that three transcripts selected for RT-PCR validation experiment, CNDP2, RHO, PPFIBP1 are present in various cDNA libraries not only in their full length forms but also as smaller transcript isoforms truncated at 3′-UTR, as well as, fragments derived entirely from 3′-UTR (Figure 1E).
In addition to controlling translation efficiency of mRNAs, the 3′-UTRs are also critical for the subcellular localization of mRNAs . The 3′-UTR fragments transported by exosomes might thus act as decoys to titrate trans-acting proteins recognizing localization elements and thereby affect recipient cell mRNA localization. This might serve as a mechanism of relocating proteins synthesis to different subcellular compartments.
In summary, our results provide evidence that exosomes secreted by human cells transport largely mRNA fragments derived from the 3′-ends of mRNA. This finding suggests the need to reassess the assumption that RNA messages delivered by exosomes are mainly translated into proteins by the recipient cells. Instead, we propose that RNA delivered by exosomes play largely regulatory roles. The secreted mRNA may act as competing RNAs to regulate stability, localization and translational activity of mRNAs in target cells, because 3′-UTRs contain elements that confer subcellular localization of mRNAs and are rich in miRNA-binding sites.
Gene organization and evolutionary history
Proteins of the reticulon family are present in all eukaryotic organisms examined and range in size from 200 to 1,200 amino acids. The vertebrate proteins of this family are called reticulons (RTNs) and those found in other eukaryotes are called reticulon-like proteins (RTNLs). All family members contain the reticulon homology domain (RHD), a conserved region at the carboxy-terminal end of the molecule consisting of two hydrophobic regions flanking a hydrophilic loop. Reticulons have been identified in the genomes of Homo sapiens, Mus musculus, Danio rerio, Xenopus laevis, Drosophila melanogaster, Caenorhabditis elegans, Arabidopsis thaliana, Saccharomyces cerevisiae and many other eukaryotes, but not in archaea or bacteria [1–6]. The ubiquity of reticulons in the eukaryotic kingdom is consistent with a highly conserved function and/or a diversity of functions.
Nearly all reticulon genes contain multiple introns and exons, and most are alternatively spliced into multiple isoforms . Intron losses and gains over the course of evolution have given rise to the large, diverse reticulon family. The presence of reticulons in eukaryotic but not prokaryotic organisms and their close association with the endoplasmic reticulum (ER) suggest that reticulons evolved along with the eukaryotic endomembrane system.
Across phyla, the second hydrophobic region of the RHD is the most highly conserved, followed by the first hydrophobic region, with the carboxyl terminus the least conserved . In mammals, there are four reticulon genes encoding reticulon proteins RTN1-4. The RHDs of RTN1, 3 and 4 share the highest sequence identity at the amino-acid level (average 73%), whereas RTN2 has only 52% identity with human RTN4 (Figure 1). The amino-acid sequence identity between RHDs of C. elegans and S. cerevisiae drops to 15-50%.
Phylogenetic analysis of the reticulon homology domains (RHDs) of selected RTNs and RTNLPs. Alignments were created using the ClustalW2 program  and the tree was generated with Phylo_win software . Bootstrap numbers are shown the number of repetitions was 1,000. The tree was generated using the maximum likelihood method. GenBank accession numbers are as follows: H. sapiens RTN1A, NP_066959 H. sapiens RTN2A, NP_005610 H. sapiens RTN3A, NP_006045 H. sapiens RTN4A, NP_065393 M. musculus RTN1A, NP_703187 M. musculus RTN2B, NP_038676 M. musculus RTN3A, NP_001003934 M. musculus RTN4A, NP_918943 G. gallus RTN4, NP_989697 X. laevis RTN2A, NP_001089014 X. laevis RTN4, NP_001083238 D. rerio RTN4, NP_001018620 D. melanogaster Rtnl1A, NP_787987 C. elegans RET-1, NP_506656 S. cerevisiae RTNLA, NP_010077 A. thaliana RTNLB3, NP_176592.
In contrast to the highly conserved carboxy-terminal RHD, the amino-terminal regions of reticulons display no sequence similarity at all, even among paralogs within the same species . Furthermore, the expression patterns of different reticulons and their splice isoforms can be variable, even within the same organism [9–11]. This divergence in sequence and expression is consistent with evolution of species- and cell-type-specific roles for reticulons . This is particularly clear in the mammalian RTN family, in which the longest isoform of RTN4, RTN4A, also known as Nogo-A, has been shown to inhibit neurite outgrowth and axon regeneration in models of injury [8, 13–18]. Interestingly, RTN4A was found to be absent in fishes, organisms in which there is extensive regeneration of the CNS after injury . Divergent results for genetic knockouts of different regions and isoforms of RTN4 suggest that the amino-terminal domain might contribute to the inhibition of nerve regeneration after injury . Thus, the divergent reticulon amino-terminal domains appear to carry out species- and cell-specific roles, whereas the RHD may carry out more basic cellular functions.
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How is it that cells manage to regulate their shape and organization during embryonic development in order to form the various tissues and diverse body plans seen in adult organisms? In most circumstances, cells utilize the coordinated efforts of signaling pathways, effector proteins, cytoskeletal networks, and contractile myosins to elicit the changes in cell morphology needed toform tissues and structures with beautiful and elaborate architecture. Understanding the regulation and integration of these cellular components, pathways, and networks in these fascinating processes is the main objective of the work in the Hildebrand lab. We use a variety of genetic, cellular, biochemical, molecular, and structural approaches to understand how cell and tissue morphology is regulated. We are currently studying the function and regulation of the Shroom family of proteins as a model to understand these processes.
Using numerous in vivo and in vitro model systems and approaches, we have shown that Shroom proteins are a family of actin-associated scaffolding molecules that control cellular architecture and tissue morphology during processes such as neural tube closure (Figure 1), kidney formation (Figure 2), and Drosophila embryogenesis. To date we have characterized the functions of vertebrate Shroom2, 3, and 4 and the ortholog of Shroom, from Drosophila melanogaster. It appears that all Shroom proteins control cell and tissue architecture by regulating the distribution of contractile actomyosin networks. This activity is dependent on the ability of Shroom proteins to bind and recruit Rho-kinase, an activator of non-muscle myosin II, to specific regions of the cell. Once recruited, we hypothesize that Rock locally activates myosin II and subsequently changes or regulates cell contractility or shape. The current work in our lab endeavors to understand the molecular, biochemical, and cellular basis for how Shroom proteins control actomyosin networks. In addition, we are trying to elucidate how different types of contractile networks are assembled in a cell and what outcomes these different types of networks may have on cell behavior and tissue morphology. Finally, we are using cellular and genetic analysis to define other players in the Shroom network and identify other pathways that cooperate with Shroom to control cellular behaviors.