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[EDIT] - Have just found not one but two papers that address my structure problem. However they concern RAP-1A, so I guess my question is now what is the difference in structure and function of RAP-1A and RAP-5? Does anyone know of X-ray structure analysis being used to examine RAP-5?
I'm a University Physics student writing a mock review article on what to me feels like a very 'un-physicsy' antibody - RAP-5.
Although my knowledge of Biology is pretty poor, research is going quite well. I've found a lot of papers from the 80s talking about conducting immunohisto(/cyto)chemistry experiments, most of them finding that RAP-5 can be used to determine whether a cell has the ras in it, so they are able to measure the percentage of cells that are neoplastic (I believe this means cancerous) and contain ras and the percentage of cells which are normal and contain ras. (the ras-gene being a proto-oncogene which on mutation can result in permanently switched on ras proteins (p21?), which results in proliferation of cells and therefore can cause tumors). This is all very nice but on first glance immunohistochemistry doesn't seem to involve a huge amount of physics (for my bio-physics assignment), apart from using an optical microscope.
I was hoping to be able focus a section of my article on the physical techniques involved in determining the structure of RAP-5. Although there seems to be plenty of literature on uses of RAP-5, I am struggling to find anything on the details on why it is able to be used in such experiments. In other words, I presume that its function is to bind on to an epitode specific to the ras protein (amino acids 10 -17 have popped up a few times) but I don't know if there is any imaging one can do to have a look at the structure and conclude 'yes this is why it binds to ras proteins and not to others'. Is there a technique that is likely to have been used to examine the structure of RAP-5? Is it's tertiary structure likely to be 'Y' shaped like other antibodies? Does it differ in structure from RAP1-4? (this book informs me that RAP1 and RAP2 have 60% sequence identity, but most sources seem to leave out RAP3-to-5, some evening telling me that the RAP family consists of RAP1A/B, RAP2A/B/C and no others!).
Also, if RAP-5 is an antibody, does this mean that it is produced in the body and gets involved in the ras protein signal pathway in order to reduce too much ras expression? (am I right in saying the amount of expression is the amount of protein the ras-gene is producing?) or is it only synthetically produced and used in experiments to measure the amount and location of ras proteins?
Also there seems to be little differentiation between the differences in the functions of each RAP. RAP-5 seems to be used quite a bit in experiments involving Ha-ras - but not exclusively. Do the different RAPs bind to different variants of ras? Ha-ras being the one unique to RAP-5.
The confusion that you're facing is because RAP-5 is actually known as RAB5C (GENEID). The ras superfamily (review) is divided into Ras, Rho, Rab, and Rap. But the Rap GTPases are divided only into two categories, RAP1 and RAP2. On the other hand, there are multiple Rab GTPases which include RAB5A and RAB5C.
There are a few crystal structures of both Rab5A and Rab5C in both human and murine forms.
Now, for RAP-5. RAP-5 is a monoclonal murine antibody which has no relationship to the RAP family of proteins. It's probably called RAP because Spandidos and Wilkie weren't thinking clearly when they named their antibodies RAP1-5. In this case, you're probably better off looking at the antibody Y13-259 which is probably called that since its the 259th antibody they tried.
To answer your questions, specifically, very few people have attempted to get a structure since it is an antibody and everyone knows what they look like. Secondly, the antibody is produced in a mouse and added to tissue. And yes, the RAP antibodies bind to different variants of Ras. Also, no one uses RAP-5 since anti-Ras EP1125Y seems to be more popular.
If you still want a crystal structure, I would hunt down the RAP5 patent and look up the peptide sequence. From there, you can look up similar structures in the IMGT database
Composition and Structure of the Nucleic Acids: DNA & RNA
Read this article to learn about Composition and Structure of the Nucleic Acids: DNA & RNA !
There are two major classes of nucleic acids: DNA and RNA.
Both are composed of un-branched chains of units called nucleotides, each of which contains:
(1) A nitrogenous base (either a purine or pyrimidine),
In RNA, the pentose is ribose, whereas in DNA it is 2-deoxyribose. Both DNA and RNA contain the purine nitrogenous bases adenine (abbreviated A) and guanine (G) and the pyrimidine cytosine (C), but in DNA a second pyrimidine is thymine (T), whereas in RNA it is uracil (U).
A number of other nitrogenous bases have been identified in DNA and RNA, but these occur much less frequently. The phosphoric acid component of each nucleotide is, of course, chemically identical in both nucleic acids. These relationships are summarized in Table 7-1, and the corresponding chemical formulas are shown in Figure 7-3.
The pentose of each nucleotide unit is simultaneously bonded through its number 1 carbon atom to the nitrogenous base (forming a nucleoside) and through its number 5 carbon atom to phosphoric acid. The structures of the four deoxyribonucleotides of DNA together with the specific numbering system used to identify each constituent atom are shown in Figure 7-4. Successive nucleotides of DNA and RNA are joined together by phosphodiester linkages involving the 5′-phosphate group of one nucleotide unit and the 3′-hvdroxyl group of the neighboring unit (Figure 7-5).
The “backbone” of a nucleic acid molecule is formed by the repeating sequence of pentose and phosphate groups, and this is the same in all molecules. What distinguishes one DNA (or RNA) molecule from another is the specific sequence of purine and pyrimidine bases present in the chain of nucleotides and the total number of nucleotides (i.e., the size of the molecule). Each chain of nucleotides is called a polynucleotide.
Structure of DNA:
At one time, it was believed that the four purines and pyrimidine’s of DNA occurred in approximately equal amounts in the molecule. However, the studies of E, Chargaff and others in the late 1940s showed that this was not the case. Instead, they found that the relative amounts of the nitrogenous bases varied between species but were constant within a species.
The constancy noted within a species was maintained regardless of the tissue or organ from which the DNA was isolated. Furthermore, the relative amounts of the nitrogenous bases were similar in closely related species and quite different in unrelated species.
Chargaff also made the following extremely important finding. Regardless of the species used as the source of DNA, the molar ratios of adenine and thymine were always very close to unity, and the same was true for guanine and cytosine. No such constant relationship could be demonstrated for any other combination of nitrogenous base pairs. This implied that for some reason, every molecule of DNA contained equal amounts of adenine and thymine and also equal amounts of guanine and cytosine.
Using chemical information of this sort, together with the results of X-ray crystallographic studies of DNA, J. D. Watson, F. H. C. Crick, M. H. F. Wilkins, and R. Franklin proposed a model for the structure of DNA in the early 1950s. They suggested that a molecule of DNA consists of two helical polynucleotide chains wound around a common axis to form a right- handed “double helix.”
In contrast to the arrangement of amino acid side chains in helical polypeptides (where the side chains are directed radially away from the helix axis), the purine and pyrimidine bases of each polynucleotide chain were directed inward toward the center of the double helix so that they faced each other.
On the basis of stereo-chemical studies, Watson and Crick further suggested that the only possible arrangement of the nitrogenous bases within the double helix that was consistent with its predicted dimensions was that in which a purine always faced a pyrimidine, for the diametric distance between the two polynucleotide chains is too small to accommodate two juxtaposed purines.
Which purine was matched with which pyrimidine became clear from a consideration of which pairs would be able to form the hydrogen bonds necessary to stabilize the double-helical structure. Accordingly, Watson and Crick concluded that adenine must be matched with thymine and guanine with cytosine.
This conclusion was, of course, in agreement with the chemical findings of Chargaff (see above)—in fact, Chargaff’s data may have been critical to the development of Watson and Crick’s proposals. The manner in which hydrogen bonds are formed between adenine and thymine and between guanine and cytosine is shown in Figure 7-6.
Although individually weak, the great number of these bonds contributes appreciably to the stability of the double-helical structure. In addition, the double helix is stabilized by hydrophobic bonds between neighboring nitrogenous bases of each polynucleotide chain. Certain other features of the structure of DNA should be noted.
The two polynucleotide chains that make up the molecule are antiparallel. That is, beginning at one end of the molecule and progressing toward the other, successive nucleotides of one chain are joined together by 3’→5′ phosphodiester linkages, whereas the complementary nucleotides of the other chain are joined by 5’→3′ phosphodiester linkages. This antiparallel arrangement is depicted diagrammatically in Figure 7-7.
Right-handed, double-helical DNA can exist in either of two principal forms: these are called A-DNA and B-DNA. The two forms differ primarily in the positioning of the nitrogenous bases around the axis of the double helix and in the numbers of bases per helical turn. In B-DNA, there are 10 base pairs per turn of the helix, each turn sweeping out 3.4 nm (34 A) of linear translation (see Fig. 7-8) in A- DNA, there are 11 bases per helical turn, each turn sweeping out 2.8 nm (28 A) of linear translation.
Thus in the A form, the double helix has a greater diameter. In B-DNA the complementary bases lie in a plane that is perpendicular to the axis of the helix, whereas in A- DNA, the planes of successive base pairs are tilted relative to the helical axis. It is the B form of the DNA that is believed to predominate in cells, although the A form may exist in DNA-RNA hybrids.
The two polynucleotides are twisted around one another in such a way as to produce two helical grooves in the surface of the molecule these are called the major and minor grooves (Fig. 7-8). The floor of the major groove is lined with oxygen and nitrogen atoms that could form hydrogen bonds with the amino acid side chains of proteins.
Indeed, it is the association of specific proteins with DNA that is believed to be involved in regulating gene expression. Interaction of water molecules with the atoms lining the floor of the minor groove is believed to contribute to the stability of the B form.
Replication of DNA:
One of the intrinsic properties of the genetic material is its capacity for replication. The manner in which DNA satisfies this requirement is apparent from the nitrogenous base pairing required in the model. Because the sequence of bases in one polynucleotide chain automatically determines the sequence of bases in the other, it is clear that one-half of a molecule (i.e., one of the two helices) contains all the information necessary for constructing a whole molecule.
For example, if we know that the sequence of bases along one polynucleotide chain of DNA is A T G A C, and so on, then the complementary sequence in the other chain must be T A C T G, and so on. Therefore, if the double helix were unwound, each separate polynucleotide chain could act as a template for the production of a new, complementary chain.
The result would be two identical double helices where there was only one before. Of course, one-half of each new double helix would be represented by one of the original polynucleotide chains. The basic features of this process are shown in Figures 7-9 and 7-10. A detailed description of the mechanism by which the replication of DNA occurs, together with its experimental basis.
Denaturation and Renaturation of DNA:
DNA is readily denatured by extremes of temperature and pH. The denaturation takes the form of an unwinding of the double helix as hydrogen bonds between complementary bases are disrupted. This form of DNA denaturation is referred to as melting and produces separate DNA strands. Solutions of DNA absorb ultraviolet light (UVL) having a wavelength of 260 nm. When the temperature of a native solution of DNA is elevated, the resulting melting is accompanied by an increase in UVL absorption.
This hyperchromic effect occurs because the purines and pyrimidines of separated strands can absorb more light energy than when they are part of a double helix. Some viruses contain a single-stranded form of DNA (see later), and because this form does not exhibit the hyperchromic effect, it is readily distinguished from double- stranded forms.
When DNA is melted thermally, denaturation begins in regions of the double helix that are rich in A-T base pairs and progressively shifts to regions of greater and greater G-C content. This is because the two hydrogen bonds holding each A-T pair together can be broken more easily (hence, at a lower temperature) than the three hydrogen bonds holding each G-C pair together. A quantitative measure of the change in UVL absorbance that takes place as the temperature of a DNA solution is slowly elevated is called a melting curve (Fig. 7-11).
The point in the melting curve at which the change from double-stranded to single- stranded DNA is half complete is called the Tm value and is characteristic of a particular source of DNA. The species specificity that is characteristic of DNA melting curves reflects differences in the G-C and A-T compositions of different kinds of DNA (Fig. 7-12).
Thermally denatured DNA can be re-natured by lowering the temperature of the solution, whereby separated strands recombine to form double helices as hydrogen bonds between complementary bases are reformed. This re-annealing can be monitored as a decrease in UVL absorption by the DNA solution. The capacity for denatured DNA to re-anneal can be used to assess the size of an organism’s genome and the complexity of the DNA that is present.
When reannealing studies are to be performed, the isolated DNA is first broken by shearing force into lengths of several hundred to several thousand nucleotide pairs. The double-helical DNA is then thermally denatured yielding single strands. A known concentration of the single-stranded DNA is then incubated at the reannealing temperature (usually about 25° below the Tm) and the reannealing rate is determined from the rate of change in UVL absorbance.
A large genome reanneals more slowly than a small genome because there is a greater number and variety of DNA fragments. Thus, each fragment takes a longer time to “seek out” and anneal with its complementary partner. The kinetics of DNA renaturation (Fig. 7-12) is represented by a curve relating the percentage of reassociated fragments to the “C0t number” (i.e., the concentration of DNA in moles of nucleotides per liter (C0) times the reaction time (t) in seconds).
As seen in Figure 7-12, viral DNA (curve c) reanneals more rapidly than prokaryote DNA (curve d), and the latter reanneals more quickly than eukaryote DNA (curve e). The discovery of a eukaryotic DNA fraction in mammalian cells that reanneals unexpectedly rapidly (curve b) revealed for the first time the existence in the genomes of higher organisms of repetitive DNA sequences.
In Figure 7-12, curve shows the reannealing rate of a solution containing a mixture of synthetically produced polyuridylic acid (a nucleotide chain with only uracil bases) and poly- adenylic acid strands. Even though uracil is not usually found in DNA, like thymine it can form hydrogen bonds with adenine and does so in many RNA molecules (see below).
For about 25 years following the original establishment of the Watson – Crick Model of DNA structure, it was presumed that all naturally occurring double- helical DNA was right-handed. However, in 1979 A. Rich confirmed earlier observations reported by F. M. Pohl and T. Jovin that a left-handed form of DNA also exists. As in right-handed DNA, the two helices are held together by complementary base pairing and the strands are antiparallel. Because the sugar- phosphate backbones of the two polynucleotides trace a zigzag course around the axis of the helices (Fig. 7- 13), this left-handed DNA has been called Z-DNA.
Though the structure of Z-DNA originally proposed by Rich was based on studies of DNA crystals produced in the laboratory, Z-DNA has since been identified in the chromosomes of a number of eukaryotic cells. A variety of indirect evidence also implies that Z-DNA is a normal constituent of animal cells, plant cells, and bacteria. Unlike B-DNA,’Z-DNA is highly immunogenic, making it possible to readily produce antibodies against Z-DNA. The reaction of these antibodies with DNA isolated from a variety of cell types implies the presence of the Z form. Naturally occurring Z-DNA-binding proteins have also been isolated from a number of cells.
The Z form of DNA appears to coexist with the B form in the same DNA molecules. Indeed it is believed that DNA can “flip” between the B and Z forms in those regions of a double helix that are rich in sequences having alternating purines and pyrimidines. These sequences appear to occur in selective regions of a cell’s DNA, lending credence to the idea that switches in DNA helicity between right-handed and left-handed forms may be involved in selective gene expression.
Although in nearly every case so far studied, DNA consists of two polynucleotide chains twisted about one another to form a double helix, it is now apparent that in a few bacterial viruses (i.e., the φ X 174 and S13 E. coli phages) DNA exists as a single polynucleotide chain. This was initially suspected when chemical analyses of the nitrogenous base contents of these viral DNAs revealed that the amounts of adenine and thymine, as well as guanine and cytosine, were not equal.
During reproduction of these viruses, the single-stranded DNA (referred to as the ” + strand”) is injected into the host bacterial cell, where it acts as a template for the reproduction of a complementary polynucleotide chain (called the “- strand”) these two polynucleotides combine to form a conventional double helix, which then serves as the template for the production of additional + strands. The newly produced + strands are then enclosed in the viral protein coats to form new virus particles.
Structure of RNA:
RNA and DNA differ chemically in two notable ways: in RNA, ribose is the pentose (not deoxyribose as in DNA) and the pyrimidine uracil occurs in place of thymine (Fig. 7-14). Early chemical analyses of the nitrogenous base contents of RNAs from various sources revealed that the A:U and G:C molar ratios were quite different from the A:T and G:C molar ratios of DNA. On this basis, it was concluded that RNA occurs as a single polynucleotide chain.
This contention is also supported by physico- chemical studies, but it should be noted that there are some viral RNAs that are double stranded. Although only one polynucleotide chain is usually present, RNA does possess regions of double-helical coiling where the single chain loops back upon itself.
These regions are stabilized by the formation of hydrophobic bonds between neighboring bases (as in DNA) and also by the formation of hydrogen bonds between guanine and cytosine and between adenine and uracil.
In RNA, A and U can form two hydrogen bonds similar to the two bonds formed between A and T in DNA. As in DNA, the portions of the RNA strand that are twisted around each other to form a double helix are antiparallel. In double-helical RNA, the helices and the complementary base pairs are arranged in much the same manner as in A-DNA.
Synthesis of RNA:
Except perhaps in the case of the reproduction of certain RNA viruses, the synthesis of RNA appears ‘to be directed by DNA and is called transcription. The formation of the RNA polynucleotide takes place using the base sequence along only one of the two deoxyribonucleotide helices of DNA (producing a temporary RNA-DNA hybrid) as a template and results in the release of a single, complementary polyribonucleotide chain in which the base uracil occurs in place of thymine (Figure 7-14).
Replication of DNA and RNA Viruses:
The viruses may be divided into two classes: viruses whose genetic complement consists of DNA and viruses whose genetic complement consists of RNA. In cells infected with DNA viruses, the infecting viral DNA is replicated, forming new viral DNA that is then transcribed into RNA this RNA is then translated into viral protein (Fig. 7-15a).
The newly produced viral DNA and viral proteins combine in the assembly of new, complete virus particles that are released upon lysis of the host cell. A latent state can also be established in which the viral DNA is incorporated into the host cell’s genome, being replicated and distributed along with the host cell’s native DNA, until it is transcribed once again into additional viral RNA and then into viral proteins.
For most RNA viruses (e.g., poliomyelitis, influenza, common cold, etc.), DNA involvement is essentially bypassed. For example, during the infection of a cell with polio virus, the single-stranded RNA (called a + strand) enters the host cell, where it acts as a template for the synthesis of complementary – strands. The latter are then employed in the proliferation of new + strands and these are translated into viral proteins (Fig. 7-15b).
The mechanism described above is varied in several other viruses in which the RNA is either double stranded (e.g., reoviruses, in which only one of the two RNA strands produced during replication is transcribed) or the infecting single RNA strand is complementary (rather than identical) to the newly produced viral RNAs that are to be translated into viral proteins (e.g., Sendai virus, Newcastle disease virus, etc.)
Yet another mechanism exists in the case of the RNA tumor viruses (e.g., Rous sarcoma virus). These viruses do not transfer information from RNA to RNA, but rather from RNA to DNA and then to new RNA. The viral RNA is employed as a template for the synthesis of DNA by the infected cell (a phenomenon that is called “reverse transcription”). Some of the resulting “viral DNA” may be incorporated into the genome of the host cell, establishing what is called the provirus state.
The provirus state has been suggested as the basis of a number of different RNA virus-induced and DNA virus-induced cancers. According to this view, one or more of the provirus genes—which are normally repressed by the host cell—may become derepressed and cause the production of an oncogenic (i.e., cancer- causing) substance that alters the cell’s normal properties or behavior. Such a change may be delayed for a number of generations, depending on the period of latency.
During the 1960s and early 1970s, the so-called “central dogma” of molecular biology was the orderly and unidirectional flow of information encoded in the base sequences of a cell’s DNA to RNA and then to protein, that is,
The discovery of reverse transcription by certain RNA viruses in which the information of RNA is passed on to DNA has necessitated a reexamination of that dogma and raised the question of whether a similar interaction between RNA and DNA might normally occur in cells (i.e., cells not infected by viruses) under specific conditions (e.g., during cellular differentiation). The central dogma might more appropriately be represented as,
Types of Cellular RNA:
Cells contain three major functional types of RNA: ribosomal RNA (abbreviated rRNA), messenger RNA (mRNA), and transfer RNA (tRNA). All these are transcribed from DNA and are engaged in mediating the expression of the genetic message of DNA by participating in the synthesis of the cell’s proteins. It has already been noted that RNA occasionally serves as the genetic material of viruses.
Of the cellular RNAs, rRNA is the most abundant, accounting for up to 85% of the total RNA of the cell. Only three “or four- different kinds of rRNA are present in cells, and these are confined for the most part to the cell’s ribosomes. mRNA accounts for about 5 to 10% of the cell’s RNA and is much more heterogeneous with respect to size and nitrogenous base content than the rRNAs.
This results from the relationship (see below) between the chain lengths and base sequences of mRNAs and the variable sizes and primary structures of polypeptides synthesized in a cell. Most mRNA occurs in the cytoplasm, where it transiently combines with ribosomes during protein synthesis.
About 10 to 20% of the cell’s RNA is tRNA. All tRNA molecules are similar in size and typically contain 75 nucleotide units. In spite of these similarities, a single cell may contain about 60 species of tRNA differing in their base sequences. Because most of the tRNA is recovered in the cytoplasmic (i.e., soluble) phase of disrupted cells following centrifugation, tRNA is also called soluble RNA (i.e., sRNA).
In view of its small size and relative ease of isolation, tRNA has been more extensively studied than the other two ribonucleic acids, and the specific primary, secondary, and tertiary structures of many tRNAs have already been determined. tRNAs contain moderate amounts of unusual nucleotides such as ribothymidine, dihydrouridine, pseudouridine, and methylguanosine. These are formed by modification of the four common RNA bases after the tRNA has been synthesized from the four unmodified ribonucleotides. The modified bases play a crucial role in establishing the unique spatial organization of these molecules.
Although the mechanisms of DNA replication and protein synthesis are considered in depth, it is appropriate that a brief accounting of the functional relationships among the nucleic acids and between nucleic acids and proteins be made at this time. Inheritable information is encoded in the various nitrogenous base sequences possessed by the cell’s DNA, and by the process of transcription and translation these base sequences are employed to specify the primary structures of all proteins, produced by the cell.
Most important among these proteins are the enzymes that catalyze and regulate the myriad of chemical reactions characterizing the cell’s metabolism. Therefore, the information of DNA confined essentially to the cell nucleus manifests itself primarily in the cytoplasm as the synthesis of a unique assemblage of proteins.
The replication of DNA that precedes mitotic cell division and the equal distribution of the duplicated DNA among the progeny cells provides for the passage of complete sets of information from one generation of cells to another. In addition to serving as templates for their own replication, the nucleotide sequences of DNA are used during transcription to produce complementary base sequences of RNA.
The resulting RNAs then serve as intermediaries in translating the original message into protein. Of paramount importance in this process are the mRNA molecules whose base sequences directly determine the primary structures of the polypeptides. These mRNA molecules leave the nucleus of the cell following their synthesis and attach in the cytoplasm to one (or several) ribosome.
The rRNA of each ribosome is believed to play a role in this attachment. tRNA molecules also produced in the nucleus enter the cytoplasm, where they combine with specially activated amino acids (distinct tRNAs exist for each species of amino acid). The resulting complexes, directed by the base sequence of mRNA attached to the ribosomes, sequentially deposit their amino acids in the growing polypeptide chains.
The Ras superfamily of monomeric GTPases controls a wide range of cellular processes. The prototypical member of this class of regulatory molecules, Ras, plays a role in 㸰% of human cancers. Rap1 is a member of the subset of monomeric GTPases that are most closely related to Ras itself. Whereas the biological functions of Ras, particularly its roles in cellular growth and differentiation, are well established, the functions of Rap1 are poorly understood. Originally described as a suppressor of Ras-mediated oncogenic transformation (Kitayama et al., 1989), one model held that Rap1 functions by competing for Ras effectors, a view that was supported by the ability of Rap1 to bind to Raf-1 but to not activate the MAPK cascade (Bos, 1998). This view lost some credence when it was reported that Rap1 can stimulate MAPK through B-Raf (Vossler et al., 1997) and that overexpressed Rap1 was capable of inducing oncogenic transformation in Swiss 3T3 fibroblasts (Altschuler and Ribeiro-Neto, 1998). Other Ras effectors that promote cellular growth (e.g., RalGDS) are also activated by interaction with Rap1 (Kishida et al., 1997).
Growth control is but one of a variety of processes in which Rap1 has been implicated. The Rap1 guanine nucleotide exchange factor (GEF) Epac2 has been linked to cAMP-regulated exocytosis, implicating Rap in the control of vesicular trafficking (Ozaki et al., 2000). Overexpression of Rap1 stimulated integrin-dependent adhesion of human T cells, and adhesion of T cells was blocked by expression of dominant-negative Rap1 (Reedquist et al., 2000). Particularly illustrative of the distinct functions of Ras and Rap is the recent finding that these GTPases have opposing effects on AMPA receptor trafficking (Zhu et al., 2002).
The search for functions of Rap1 has included analyses in lower eukaryotes. Bud1, a Saccharomyces cerevisiae orthologue of Rap1, is critical for the establishment of yeast polarity through the assembly of the actin cytoskeleton during bud formation (Park et al., 1999). In Dictyostelium discoideum, membrane ruffling and lamellipodia formation, both actin-based morphological changes, were regulated by Rap1 (Rebstein et al., 1997). In Drosophila melanogaster, the distribution of adherens junctions in epithelium is controlled by an orthologue of Rap1 (Knox and Brown, 2002).
The distinct functions of Ras and Rap1 suggest that despite 70% sequence identity within the effector binding region, including complete identity among residues shown to make contact with the Ras binding domain (RBD) of effectors (Bos et al., 2001), these GTPases are differentially regulated. Indeed, although some GEFs are shared between Ras and Rap1 (e.g., Ras-GRP2), others are Rap1 specific (e.g., C3G Bos et al., 2001). Once loaded with GTP, modulation of the nucleotide binding state differs between Ras and Rap because, unlike Ras and most Ras-related GTPases that have a glutamine residue at position 61, Rap1 has a threonine and therefore very low intrinsic GTPase activity. In addition to this intrinsic difference, Rap1-specific GTPase activating proteins (GAPs) have been described previously (Polakis et al., 1991). Despite the similarities of the effector domains of Ras and Rap1, particularly in the switch 1 domain, the relative affinities for effectors differs considerably, perhaps due to differences in the switch 2 domain. For example, whereas the RBD of Raf-1 binds to Ras with a 50-fold higher affinity than to Rap1, the opposite is true for the RBD of RalGDS (Herrmann et al., 1996).
Ras and Rap1 also differ in their COOH-terminal hypervariable regions that direct posttranslational modification and membrane targeting. Whereas Ras proteins are modified with a farnesyl isoprenoid, Rap1 is modified with a geranylgeranyl lipid. Consistent with their distinct membrane-targeting motifs, the subcellular localizations of Ras and Rap1 have been reported to differ, a feature that could explain, in part, differential function. In primary myeloid cells, Rap1, but not Ras, is associated with specialized vesicular compartments that serve as pools of membrane that can be rapidly mobilized to the cell surface during degranulation (Maridonneau-Parini and de Gunzburg, 1992 Mollinedo et al., 1993 Berger et al., 1994). Indirect immunofluorescence analysis of cultured fibroblasts and epithelial cells has revealed Rap1 in the Golgi region (Beranger et al., 1991) and on endosomes (Pizon et al., 1994) but not on the plasma membrane (PM). In contrast, in the same cells, Ras proteins are expressed at steady state on both the PM and the Golgi apparatus (Choy et al., 1999). Rap1, like Ras, has been shown to undergo GTP/GDP exchange in response to various growth factors (Zwartkruis et al., 1998). Using a fluorescent probe, we have recently determined that both the PM and Golgi pool of Ras is activated as a consequence of growth factor signaling (Chiu et al., 2002). Recent works have examined the subcellular location of Rap signaling by using a chimeric FRET-based GTPase sensor, Raichu-Rap1 (Mochizuki et al., 2001 Ohba et al., 2003). Despite its targeting to the PM with the hypervariable region of K-Ras4B, this probe reported activation of Rap1 on endomembranes in living cells stimulated with EGF.
By analyzing GFP-tagged Rap1 proteins in living cells and by localizing endogenous Rap1 using subcellular fractionation, we found that the steady-state localization of Rap1 includes endosomes and the PM but not the Golgi apparatus, that PM localization was dependent on GTP binding, and that growth factors stimulated a rapid increase in Rap1 expression on the PM that was dependent on exocytosis. By fusing GFP to the RBD of RalGDS (GFP-RBDRalGDS), we have developed a Rap1-specific probe that has allowed us to determine where and when Rap1 is activated in living cells. We found that, in contrast to Ras (Chiu et al., 2002), only the pool of Rap1 associated with the PM became activated in response to growth factors. Similar results were obtained in T cells in which Rap1-mediated adhesion was blocked by inhibition of exocytosis.
Although many biochemical and structural studies have demonstrated that DNA sequences containing runs of adjacent guanines spontaneously fold into G-quadruplex DNA structures in vitro, only recently has evidence started to accumulate for their presence and function in vivo. Genome-wide analyses have revealed that functional genomic regions from highly divergent organisms are enriched in DNA sequences with G-quadruplex-forming potential, suggesting that G-quadruplexes could provide a nucleic-acid-based mechanism for regulating telomere maintenance, as well as transcription, replication and translation. Here, we review recent studies aimed at uncovering the in vivo presence and function of G-quadruplexes in genomes and RNA, with a particular focus on telomeric G-quadruplexes and how their formation and resolution is regulated to permit telomere synthesis.
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The Critical Factor of cAMP Signaling pathway
cAMP, also known as cyclic adenosine 3,5-monophosphate, regulates pivotal physiologic processes including metabolism, secretion, calcium homeostasis, muscle contraction, cell fate, and gene transcription. cAMP is a cyclic nucleotide that serves as a vital second messenger in several signaling pathways.
The intracellular levels of cAMP are regulated by the balance between the activities of two enzymes: adenylyl cyclase (AC) and cyclic nucleotide phosphodiesterase (PDE). Different isoforms of these enzymes are encoded by a large number of genes, which differ in their expression patterns and mechanisms of regulation, generating cell-type and stimulus-specific responses.
V6.5 mouse ES cells were cultured on 0.2% gelatin-coated plates at 37 °C with mES media: 500 ml Knockout DMEM (Gibco), 90 ml FBS, 6 ml non-essential amino acid (NEAA, 100×, Gibco), 6 ml glutamine or glutamax (200 mM stock solution), 6 ml Pen/Strep, 1 ml BME, and 60 μlLIF (Millipore, ESG1106). Mouse embryonic fibroblast (MEF) cells were cultured at 37 °C and 5% CO2 in 450 ml DMEM, 50 ml FBS, 5 ml Pen/Strep, 5 ml NEAA, 5 ml pyruvate, and 4 μl beta-mercaptoethanol. Mouse Neural Precursor cells (NPCs) were cultured in N2B27 medium (DMEM/F12 (Invitrogen, 11320-033), Neurobasal (Gibco, 21103-049), NDiff Neuro-2 Medium Supplement (Millipore, SCM012), B27 Supplement (Gibco, 17504-044)) supplemented with EGF and FGF (10 ng/ml, each) (315-09 and 100-18B, Peprotech). Cells were passaged using Accutase (SCR005, Millipore) and cultured on 0.2% gelatin-coated plates. H9 human embryonic stem cells were seeded in a feeder-free system using Matrigel hESC-Qualified Matrix (354277, Corning) and were maintained in Essential 8 media (A1517001, Thermo Fisher Scientific) as described previously . Cells were passaged every 3 days as clumps with 0.5 mM EDTA. Human Female Fibroblasts (HFF) were cultured at 37 °C and 5% CO2 in DMEM supplemented with 1% pen/strep and 10% FBS.
PIRCh-seq library preparation
To harvest the cells for PIRCh-seq, approximately 4 × 10 7 cells were trypsinized and pooled into a 50-ml falcon tube, after washing with 40 ml of cold PBS once. Fresh 1% glutaraldehyde in room temperature PBS was created from 25% stock, and the remaining stock was discarded. The cell pellet was resuspended in 1 ml of glutaraldehyde solution, and a p1000 pipette was used to resuspend cells and to top up to 40 ml (1 ml 1% glutaraldehyde/1 million cells). After inverting several times, the tube was gently shaken for 10 min, and then quenched with 1/10 volume of 1.25 M glycine. The tube was inverted several times, shaken gently for 5 min, and spun down 2000g for 4 min. The pellet was then washed once with 40 ml cold PBS. The pellet was responded in 1 ml/20million cells of cold PBS. Cells were aliquoted at 1 ml each to a fresh Eppendorf tube and spun down 2000g for 4 min. After supernatant was carefully aspirated, cell pellets were flash frozen and stored at − 80 °C if necessary.
For sonication, prepared cell pellets were spun down at 2000g for 4 min and any remaining PBS was removed. Lysis per 20 million cells was performed with 1 ml of lysis buffer (1% SDS, 50 mM Tris 7.0, 10 mM EDTA, 1 mM PMSF, 0.1 U/μl Superase-in (Ambion), 1× Proteinase inhibitor (Roche)). Lysate was then sonicated till the chromatin size was
300–2000 bp and the lysate was clear. We used the Covaris E220 equipment with the following settings: fill level 10, duty cycle 15, PIP 140, and cycles/burst 200. In terms of time, we sonicated 20-min pulses to test the optimal time to generate chromatin size
300–2000 bp. The lysates were spun down at 16,000g for 10 min. Supernatants were flash frozen and stored at − 80 °C if necessary.
For PIRCh-seq library construction, chromatin was thawed and 10 μl was taken as input. Two hundred microliters was aliquoted per reaction, and 400 μl dilution buffer was added to each reaction. H3 or a specific histone modification antibody was then added (dilution buffer: 0.01% SDS, 1.1% Triton X 100, 1.2 mM EDTA, 16.7 mM Tris 7.0, 167 mM NaCl, 1 mM PMSF, 0.1 U/μl Superase-in (Ambion), 1× Proteinase inhibitor (Roche)). The reaction was shaken end to end at 4 °C overnight. Fifty microliters of Protein A dynabeads was used per 5 μg antibody IP. Beads were washed with 5 times the original volume of dilution buffer 4 times. Notice that it is important not to exceed 200 μl original volume of beads per tube. During the last wash, beads were aliquoted to 1 tube per reaction. The buffer was aspirated, and 200 μl of the IP sample was used to resuspend and transfer beads to the IP sample. The reaction was shaken end to end at room temperature for 2 h. The beads were then washed with 1 ml wash buffer 4 times and resuspended in 50 μl IP elution buffer (1% SDS, 50 mM NaHCO3). The reaction was then vortexed at setting 1 for 15 min. The supernatant was then transferred to a fresh tube, and the bead elution was repeated. The supernatant was combined for a total of 100 μl. Five microliters of 3 M NaOAc was immediately added to neutralize pH. Ten microliter TurboDnase buffer and 1 μl TurboDnase (Ambion) were added, and the reaction was incubated at 37 °C for 30 min. Three microliters of 500 mM EDTA was added to eliminate divalent ions. Five microliters of Proteinase K (Ambion) was added, and the reaction was incubated at 50 °C for 45 min.
To make our sequencing libraries, we extracted RNA using Trizol/chloroform and precipitated the RNA with an equal volume of isopropanol. RNA pellet was washed in 1 ml 70% EtOH, and pellets were resuspended in 10 μl H2O. One microliter of TurboDnase buffer was added, followed by 1 μl TurboDnase, and the reaction at 37 °C for 30 min. 1.2 μl of TurboDnase inactivating reagents were added. The reaction was vortexed for 3 min and spun down. The 10-μl supernatant was heated at 75 °C for 10 min to kill DNase. The reaction was purified using a Nugen Ovation v2 kit and eluted in 5 μl for library preparation.
ChIRP-seq library preparation
To determine the genome-wide localization of lnc-Nr2f11, we followed protocols previously described . ChIRP was performed using biotinylated probes designed against mouse lnc-Nr2f1 using the ChIRP probes designer (Biosearch Technologies). Independent even and odd probe pools were used to ensure lncRNA-specific retrieval as protocols previously described . “Even” and “odd” sets of probes shared no overlapping sequences, as we performed two independent ChIRP-seq experiments with these two sets of probes separately. Two sets of data were then combined for downstream analysis (see below). Mouse NPC samples are crosslinked in 3% formaldehyde. RNase pre-treated samples are served as negative controls for probe-DNA hybridization. ChIRP libraries are constructed using the NEBNext DNA library preparation kit (New England Biolabs). Sequencing libraries were barcoded using TruSeq adapters and sequenced on HiSeq or NextSeq instruments (Illumina).
Experimental validation of antibody specificity after glutaraldehyde crosslinking using modified mononucleosomes with barcodes
To ensure that chemical crosslinking with glutaraldehyde did not affect antibody specificity, we followed previous study to test antibody specificity using SNAP-ChIP . During IP pulldown, 15 μl of recombinant nucleosomes (SNAP-ChIP, EpiCypher, 19-1001) was fixed with fresh 1% glutaraldehyde. One percent glutaraldehyde was prepared on the same day in room temperature PBS from 25% stock. Fixation was performed for 10 min at room temperature with gentle shaking. The reaction was then quenched with 1/10 of the original reaction volume of 2.5 M glycine. Tubes were then inverted several times and incubated for 5 min at room temperature with gentle shaking.
Five hundred microliters of fixed chromatin was then added to each tube and pipetted up and down several times to mix well. Ten microliters of nucleosomes mixed with chromatin was taken out of each tube to be used as input during the qPCR. One tablet of Roche complete protease inhibitor was dissolved (Roche, 11697498001) in 50 ml of DI water to obtain a working solution of 50× protease inhibitor cocktail. Sixty microliters of 50× protease inhibitor was added to 3 ml of blank dilution buffer (0.01% SDS, 1.1% Triton X100, 1.2 mM EDTA, 16.7 mM Tris pH 7.0, 167 mM NaCl). One milliliter of dilution buffer with protease inhibitor was then added to each reaction. Five micrograms of appropriate detection antibody for IP pulldown was added to 300 μl of chromatin mixed with crosslinked nucleosomes for each condition. Samples were then incubated at 4 °C overnight with end-to-end shaking.
IP product was eluted as specified during PIRCH library construction. DNA of interest was purified using a Zymo DNA Clean and Concentrator-5 kit (Zymo Research, D4013). The qPCR reaction was performed using Roche’s LightCycler and Brilliant II SYBR® Green QRT-PCR Master Mix (Agilent). We analyzed enrichment for target histone modifications by amplifying unique DNA barcodes at the 3′ end, using primer sequences provided by EpiCypher.
For qRT-PCR analysis, we used Roche’s LightCycler and Brilliant II SYBR® Green QRT-PCR Master Mix (Agilent).
PIRCh-seq data alignment
Raw reads were uniquely mapped to mm9/hg19 using Tophat with default parameters . Samtools and BedTools were used to transform the mapped bam file into bedGraph and bigwig files for visualization on the UCSC genome browser [71, 72]. RPKM and raw read count for each gene were calculated by self-designed scripts with ensemble annotation, Homo_sapiens.GRCh37.75.gtf for human and Mus_musculus.NCBIM37.67.gtf, and a number of previous publications for mouse samples, respectively . The PIRCh-seq read counts in each sample were then normalized as if the total sequencing depth was 10 million.
Calculate exon/intron ratio to estimate nascent transcripts
To compare the exon/intron ratios between the PIRCh-seq profiles and other chromatin-associated RNA detection technologies, we aligned raw reads to the same hg19 genome index with Tophat and calculated the reads mapped to intron/exon with ensemble annotation gtf file as described above . For the average read counts around introns, three steps were taken: (1) scaled every intron based on its length, and extended 1 exon length up- and downstream of the selected intron (2) divided the entire region to 300 windows, and calculate the average number of read counts mapped in each window and then take log2 to scale down the values to avoid interferences from the outliers and (3) take average for all the windows among all introns. To estimate the correlation between the histone modification-specific PIRCh-seq profile with its corresponding ChIP-seq signals, we obtained ChIP-seq profiles of each histone modification in mESC from ENCODE. And then, for each expressed gene in mESC, the histone modification ChIP-seq signal over input on the gene exon were calculated as the ChIP signal for that gene, and were compared with the corresponding PRICh-seq enrichment score with the same histone modification, and our results indicated that there was no significant correlation with these two sets of signals.
Gene set enrichment analysis
GSEA software was downloaded from (http://software.broadinstitute.org/gsea/index.jsp) at the Broad Institute website and was utilized to perform the significant differential chromatin enrichment from PIRCh-seq against ncRNA versus coding genes . The ncRNA set consisted of the annotated snoRNA, snRNA, rRNA, lncRNA, miRNA, and miscRNA.
Data normalization and identification of the chromatin-enriched RNAs
The chromatin-enriched ncRNAs were identified through the limma algorism in R . First, a data matrix was obtained, where each raw read was a gene and each column a sample, and the element of the matrix represented the number of raw reads from PIRCh-seq experiments and inputs. To filter low-express gene, “filterByExpr” method in edgeR  was applied as limma algorism recommend. The filtered values in this matrix were then normalized by the limma-voom method in R. After that, differential analysis was performed using the limma gene-wise linear model for each pair of PIRCh replicates over inputs. Non-coding RNAs with P value< 0.05 and log2 fold change over inputs > 0 were defined as chromatin enriched. We obtained 258 chromatin-enriched ncRNAs in mouse V6.5 cell line, 200 in MEF, and 110 in NPC. Variation score of each gene was defined as the standard deviations of the fold change among all histone modification-specific PIRCh-seq profiles. The Pearson correlation coefficients between each two PIRCh-seq experiments were calculated, and unsupervised clustering of the correlation matrix was performed in cluster.
Computational validation of the PIRCh-seq-enriched ncRNAs
In order to validate the PIRCh-enriched candidates by similar methods, we examined 96 published chromatin-association datasets from ChIRP/CHART/RAP/GRID-seq experiments collected by the LnChrom database . We found a total of 23 expressed lncRNAs in the LnChrom database, including Xist, Firre, Rmrp, and Tug1, and all of them were positively enriched in our PIRCh experiment and 14 of which were significant with P value< 0.05, suggesting the high sensitivity of the PIRCh approach in identifying chromatin-associated lncRNAs. Furthermore, we obtained the genomic binding sites (peaks) of 23 lncRNAs from the aforementioned experiments, and overlapped them with the histone ChIP-seq peaks  and got a ratio of the overlap for each lncRNA. We then calculated the Spearman correlation coefficients of these ratios with their corresponding lncRNA’s PIRCh-seq enrichment scores in the same cell line (normalized by the total number of different ChIP-seq peaks), and found that these correlations were significantly higher than random permutations. Peak calling was performed by MACS2  with FDR < 0.05.
The chromatin association states of the enriched ncRNAs
To cluster chromatin-enriched ncRNAs in distinct groups for functional prediction, we performed t-SNE and K-means clustering on the PIRCh enrichment score matrix with the chromatin-associated ncRNAs. The proper K number (K = 6) was determined by silhouette score .
Nearby coding gene expression comparison
To further evaluate the functional prediction for chromatin-enriched ncRNAs, we first grouped chromatin-enriched ncRNAs by functional classification and then obtained lists of the nearby (± 100 Kb) coding genes. We then calculated the gene expressions of these coding genes and represented them in box plots. Similarly, we obtained a different list of nearby coding genes if the chromatin-enriched ncRNAs were classified based on their chromatin enrichment scores on each histone modification. The significance between each group was estimated by two-tailed Welch’s T test.
Lnc-Nr2f1 ChIPR-seq analysis
To further validate the PIRCh-seq candidates, we performed ChIRP-seq on one of the H3K4me3-modified PIRCh-seq-enriched lncRNAs named lnc-Nr2f1. Experimental methods were mentioned above, where independent “even” and “odd” probe sets were applied. LncRNA lnc-Nr2f1 ChIRP-seq data were then analyzed by applying a previously published pipeline , where the read alignment was performed in bowtie2 and peak calling in MACS2. Signals from even and odd ChIRP-seq profiles were then merged to reduce false positive caused by probes. We confirmed that lnc-Nr2f1-associated genomic regions were indeed enriched with H3K4me3 but no other modifications in NPCs, where the NPC ChIP-seq data was obtained from GSE117289, indicating the high specificity of our PIRCh-seq approach.
Allelic-specific enrichment analysis in NPC
We first built the CAST/EiJ and 129S1/SvImJ reference genome. The vcf files containing the SNPs in the CAST and 129S1 strains were downloaded from the dbSNP database with the mm9 assembly . Their corresponding genome fasta file was made by GATK toolkit FastaAlternateReferenceMaker and SelectVariants tools . After that, the inputs and PIRCh-seq data in were re-aligned against the CAST and 129S1 indexes by TopHat2 with 0 mismatch (parameter -N 0) to improve the specificity . The allele-specific alignment files were then converted to the bedGraph and bigWigs format using BEDtools. For each gene, its allele-specific expression and enrichment analysis was performed for every SNP on the list, and estimated the significance between CAST and 129S1 through the Mann-Whitney-Wilcoxon test, and P value < 0.05 was defined as significant.
Enriched peak calling from PIRCh-seq profiles
To further investigate the underlying mechanism of RNA-chromatin association, we performed peak calling on PIRCh-seq profiles to identify the bases on each enriched RNA that were mostly affiliated with histone proteins. We first merged data from two replicates of each gene to minimize the experimental deviation bias, and smoothed the normalized read counts on each base through a 5-bp sliding window, along with a 2-bp step size. Peak calling was performed on the smoothed signal with a homemade script. We defined a peak in the local maximum that is fivefold or more amplified relative to the median read counts of the transcript. Next, we applied a bootstrap method by randomly sampling 1000 times with reads from the transcripts, and then estimated the P value of each peak as the percentage of cases that were more enriched than observed. Finally, we calculated the relative fold change of each peak with respect to the input control. Significant peaks were filtered based on fold change and P value. Finally, RNA structural and modification information was integrated with PIRCh-seq peaks for downstream analysis.
IcSHAPE analysis and structural prediction using RNAfold
To estimate the structure information around PIRCh peak, we integrate mouse V6.5 icSHAPE data from previous paper [61, 65]. Each transcript’s icSHAPE score was calculated by the original icSHAPE pipeline with default parameter. We used homemade script to count icSHAPE score around PIRCh peak (± 200 bp) among all transcripts, and the significance between histone-modification PIRCh peak and random background region was estimated by two-tailed Welch’s T test. In terms of Gas5 in NPC, the structure information of 129S1 allele was represented by V6.5 icSHAPE data, since they have the same sequence. Structure prediction of 129S1 allele and CAST allele was performed by RNAfold web server with default parameter . For 129S1 allele, the higher icSHAPE score at peak region indicates single strand structure, which is similar to the structure prediction from RNAfold. Furthermore, structure prediction of CAST allele of Gas5 in NPC shows that riboSnitches around PIRCh peak might be the cause of the allele-specific enrichment of Gas5s in NPC.
For data presented in Fig. 1b (RT-PCR), P values were calculated via the Mann-Whitney-Wilcoxon test in Python. For data presented in Fig. 2d and Additional file 1: Figure S6B (GSEA), enrichment score, P values, and FDR were calculated in GSEA. For data presented in Additional file 1: Figure S2G, binomial P values were calculated by GREAT. For all T test presented in this paper, including Fig. 2e, f, i Fig. 3f Additional file 1: Figure S5C and Fig. 5d–g, P values were calculated via two-tailed Welch’s T test in Python. For data presented in Fig. 4f, P values were calculated via the chi-square test.
Research Program Areas
Positron emission tomography (PET) is a highly sensitive whole-body imaging technique based on the detection of radiation from the decay of a radioactive probe ("tracer") administered to the patient. Unlike other types of imaging which primarily image the structure of the body, PET can sensitively measure the rate of particular biochemical processes including metabolic activity, cell signaling, DNA replication, cell proliferation, gene expression, etc., depending on the particular tracer administered. In the clinic, PET is used to diagnose, locate, and stage cancer, monitor the effectiveness of chemotherapy, and perform diagnostic bone scans. It can also be used to diagnose heart and mental health disorders, track the progression of drugs or toxins through the body, and monitor infectious disease processes. PET is also used extensively in disease research and drug development.
Despite the usefulness and potential of PET, research is hindered by the inability to acquire most tracers at reasonable cost. With current technologies, cost of production soars due to the need for specialized radiation safety infrastructure (e.g., hot cells), automated synthesis equipment, analytical testing equipment, and personnel trained in radiochemistry. A couple of tracers are available at an affordable price from commercial radiopharmacies, who divide the production costs over many customers, but thousands of other tracers remain largely unavailable.
The Chemical Synthesis Platform Technology program is focused on eliminating this roadblock, by developing systems to simplify, miniaturize and lower the cost of production of diverse PET tracers. Ultimately we aim to develop a standalone, user-friendly benchtop system that can synthesize a variety of radiotracers on demand. Systems in development span the entire process of PET tracer production including dispensing of the radioisotope, synthesis, purification and formulation of the tracer, and analytical testing of the final batch. Most of these systems incorporate microfluidic components to take advantage of the many scientific and practical benefits of working at small scales. We are also interested in developing tools that enable radiochemists to more easily and rapidly discover new tracers and optimize their synthesis.
Recent and current projects include: (i) a radioisotope and probe dispenser, (ii) a new &ldquounit operations&rdquo based paradigm for developing automated synthesis programs, (iii) a 3-reactor high-pressure compatible synthesizer (ELIXYS) for synthesis of complex and diverse tracers, (iv) a chip-based microfluidic synthesizer, (v) a microfluidic system for optimizing reaction conditions for immunoPET, (vi) a microfluidic PET probe concentrator, and (vii) a Cerenkov imaging system to monitor processes within microfluidic chips.
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Figure 1: Envisioned concept of benchtop production of PET tracers to eliminate bottlenecks and high costs of current production methods. The benchtop synthesizer could potentially be placed in any biology research lab or clinical imaging center. The user would install a microfluidic kit designed for the desired tracer. The computer would automatically dispense the desired amount of radioisotope, deliver it to the radiosynthesizer, and synthesize the tracer on chip. From a single supply of radioisotope, the user could make sequential batches of several different tracers on demand, depending on the needs of the clinic or research lab.
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Figure 2: ELIXYS radiosynthesizer for automated synthesis of diverse PET tracers. This synthesizer was developed at UCLA and then commercialized by Sofie Biosciences, Inc. The schematic at the right shows the main elements of the synthesizer: three reaction vessels, up to three disposable reagent cassettes, a reagent-handling robot, and an interface to an HPLC purification system. Due to the unique design principle, this system can support fully-sealed reactions at much higher pressures and temperatures than other systems. Furthermore, the use of robotics rather than valves eliminates the need for reconfiguration/replumbing of the system, thus enabling the synthesis of diverse tracers on a single system. In addition to the system itself, our group developed a novel &ldquounit operation&rdquo approach for programming new synthesis protocols. This approach greatly reduces the complexity of programs and reduces programming time.
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Figure 3: Digital microfluidic radiosynthesizer. Using electrowetting-on-dielectric (EWOD) technology, droplets of reagents can be manipulated electronically to carry out the basic operations (droplet loading, moving, merging, mixing, heating) needed for microscale chemical synthesis. This figure depicts the synthesis of the most commonly used PET tracer, [18F]FDG. In addition to small physical size, which dramatically reduces the amount of radiation shielding needed, there are fundamental advantages of radiochemistry in tiny volumes.
Systems biology is fundamental to the Crump Institute's goals to develop new in vitro and in vivo (imaging) molecular diagnostic technologies. By studying cells and organisms from a systemwide perspective, we learn what are the most informative events to detect and image to assess the healthy or diseased state of the patient's tissue. The Systems Biology program employs an integrated experimental and theoretical approach to study cancer and immune diseases, and to understand the interactions of microorganisms with the human host. Microbes form an intricate symbiotic system with the host. By studying the genomes and the transcriptomes of the microbes living inside humans, we aim to understand how microorganisms interact with and influence the human immune system and play a role in human health and disease. Model systems, such as normal and cancer cells in culture, and small animal models of disease, provide controlled environments where the genetic composition and surrounding conditions can be defined and analyzed in a systematic fashion. Equally important is the ability to obtain serum and tissue from patients, that directly reflect the biology of human disease. From these biological samples, large-scale systemwide measurements can be made, of gene and protein expression that produce cell communication systems and metabolic activity to carry out normal functions of our organ systems and altered ones of disease. Finally, theoretical and computational approaches are used to untangle the complex network of interactions and feedback loops within cells and tissues. Our fundamental goal is to decipher the integrated circuits within and between cells - the networks that form the computational logic, or &ldquothinking&rdquo of the cell that determines how it will react under normal conditions, or mis-react in disease.
The Systems Biology program works collaboratively with physicians and biologists studying diseases like cancer and immune disorders to help define what molecular events need to be detected or imaged based on the biology of the affected cells and tissues, and with the technology programs of the Crump to develop new methods to make experimental and diagnostic measurements.
In VitroMolecular Diagnostics (IVMD)
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Circulating tumor cells (CTCs) are cancer cells that break away from either a primary tumor or metastatic site, and circulate in the peripheral blood as the cellular origin of metastasis. With their role as &ldquotumor liquid biopsy&rdquo, CTCs provide convenient access to all disease sites, including that of the primary tumor and the site of fatal metastases. It is conceivable that detecting and analyzing CTCs will provide insightful information in assessing the disease status without the flaws and limitations encountered in performing conventional tumor biopsies. However, identifying CTCs in patient blood samples is technically challenging due to the extremely low abundance of CTCs among a large number of hematologic cells. To address this unmet need, there have been significant research endeavors devoted to developing CTC detection, isolation, and characterization technologies.
Inspired by the nanoscale interactions observed in the tissue microenvironment, our research team at UCLA pioneered a unique concept of &ldquoNanoVelcro&rdquo cell-affinity substrates, in which CTC capture agent-coated nanostructured substrates were utilized to immobilize CTCs with high efficiency. The working mechanism of NanoVelcro cell-affinity substrates mimics that of Velcro TM &ndash when the two fabric strips of a Velcro fastener are pressed together, tangling between the hairy surfaces on two strips leads to strong binding. Through continuous evolution, 3 generations (gens) of NanoVelcro CTC Chips have been established to achieve different clinical utilities. The 1st-gen NanoVelcro Chip, composed of a silicon nanowire substrate (SiNS) and an overlaid microfluidic chaotic mixer, was created for CTC enumeration. Side-by-side analytical validation studies using clinical blood samples suggested that the sensitivity of 1st-gen NanoVelcro Chip outperforms that of FDA-approved CellSearch TM . In conjunction with the use of laser microdissection (LMD) technique, 2nd-gen NanoVelcro Chips (i.e., NanoVelcro-LMD), based on polymer nanosubstrates, were developed for single-CTC isolation. The individually isolated CTCs can be subjected to single-CTC genotyping (e.g., Sanger sequencing and next-generation sequencing, NGS) to verify CTC&rsquos role as tumor liquid biopsy. By grafting thermoresponsive polymer brushes onto SiNS, 3rd-gen NanoVelcro Chips (i.e., Thermoresponsive NanoVelcro) have demonstrated the capture and release of CTCs at 37 and 4°C, respectively. The temperature-dependent conformational changes of polymer brushes can effectively alter the accessibility of the capture agent on SiNS, allowing for rapid CTC purification with desired viability and molecular integrity. We envision that NanoVelcro CTC Assays will lead the way for powerful and cost-efficient diagnostic platforms for researchers to better understand underlying disease mechanisms and for physicians to monitor real-time disease progression.
Molecular Imaging Using ImmunoPET
As part of the Crump Institute's broad goal of providing platform imaging strategies for detection of any molecular biomarker of choice, we are developing engineered antibodies as a means for imaging cell surface proteins in vivo. Approximately 20% of a cell's proteins are expressed on its surface, making them accessible to antibody-based molecular imaging probes. These cell surface biomarkers include important classes of proteins such as growth factor receptors, adhesion molecules, enzymes and proteases, tissue-specific markers, differentiation and activation markers &mdash making the cell surface highly rich in informative targets that can reveal the biological state of the cell, both in normal states and in their transitions to disease.
Antibodies of high specificity for a selected protein target can be readily isolated by methods such as phage display. The ImmunoPET group then genetically engineers these antibodies for optimal function as imaging agents - rapid, high-level targeting to specific protein and low non-specific background in surrounding tissue. The ImmunoPET team relies on collaborations with the Systems Biology and Molecular Diagnostics programs to identify informative cell-surface proteins as biomarkers in disease, particularly in cancer and immune disorders. Implementation of these strategies requires close collaboration with the Chemical Synthesis and Preclinical Imaging programs to rapidly radiolabel and evaluate these novel tracers in animal models of disease.
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Figure 1: Engineered antibody fragments for in vivo PET imaging of cell surface markers. The top panel shows a native, intact antibody with the variable regions that form the binding site shown in green, and the constant domains in blue. Engineered fragments depicted include single-chain variable fragments (scFv), the diabody (a dimer of scFv fragments), and minibody (fusion of scFv and CH3 domain). The lower panel shows coronal slices of co-registered microPET/microCT images with arrows indicating the tumor, all acquired at 20 h following intravenous administration of a radiolabeled engineered antibody fragment in athymic mice carrying different subcutaneous human tumor xenografts. A. Imaging of an LS174T colon cancer tumor using a carcinoembryonic antigen (CEA)-specific diabody labeled with the positron emitter 124 I. B. Imaging of a B-cell lymphoma using CD20-specific minibody labeled with 124 I. C. Detection of an LAPC-9 prostate cancer xenograft using a minibody that recognizes prostate stem cell antigen (PSCA), labeled with 124 I.
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Figure 2: Immuno-PET imaging of 64 Cu-NOTA-2.43 Mb 4 h p.i. is shown. Immuno-PET/CT images were acquired 4 h after i.v. injection in B/6 mice. The white arrows (2-mm transverse MIPs) are used to highlight uptake in various lymph nodes (Right) and the spleen seen in the whole-body 20-mm coronal MIPs (Left). A.LN, axillary lymph nodes B, bone C.LN, cervical lymph nodes I.LN, inguinal lymph nodes Li, liver MIPs, maximum intensity projections P.LN, popliteal lymph nodes Sp, Spleen. Proc Natl Acad Sci USA. 2014 Jan 21 111(3): 1108&ndash1113.
Preclinical Imaging Systems
Preclinical imaging at the Crump Institute spans domains from in vitro diagnostics and imaging cell cultures, through in vivo imaging of mouse models of disease, using microPET, microCT, and optical imaging modalities. Overall, the focus of the Preclinical Imaging Systems program is to develop innovative technologies that enable the visualization and measurement of biological processes to facilitate research on the molecular mechanisms of cells in health and disease. To further these goals, highly sensitive silicon-based solid state imaging sensors (Position Sensitive Avalanche Photodiode Detectors, or PSAPDs), are being developed that can detect radiolabeled probes at amounts as low as a few picocuries. These devices are integrated with microfluidic chips in collaborations with the in vitro Molecular Diagnostics and Chemical Synthesis Platform program areas to produce novel imaging devices for in vitro assays in arrays of cell cultures in chips. For in vivo imaging, multimodality instrumentation is being designed and optimized for combining PET with anatomical imaging (such as CT) and optical imaging of mouse models of disease. These state-of-the-art technologies are made available to Crump investigators in coordination with the Preclinical Imaging Technology Center.
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(a) Cross-section of a microfluidic chip coupled with a Si based Position Sensitive Avalanche Photodiode Detector (PSAPD) camera (b) Linearity and dynamic range of PSAPD camera to the minimum detection of activity (MDA) (c) Cell cultures incubated with [F-18] 2-fluorodeoxyglucose (FDG) for 30 minutes. The cell culture wells in the integrated microfluidic chip have progressively decreasing numbers of cells in individual wells. The two wells in the lowest row only have one cell (arrows) (d) Quantitative analysis indicates that the average FDG activity retained in each cell is the same for all wells.
Molecular Diagnostics in Cancer and Immune Disorders
Cancer and immunology are intertwined at many levels. For example, cancer cells as well as immune cells (B and T lymphocytes, macrophages, and other cells that coordinately participate in immune responses), both undergo similar programs of activation, altered gene expression, enhanced proliferation and migration. Elucidating the molecular signatures associated with the transition from a normal tissue to a cancer lesion and with the activation of the immune system to fight pathogens and tumors could lead to the identification of novel biomarkers that can precisely distinguish health and disease states at molecular and cellular levels. Furthermore, while it is widely accepted that abnormal immune function is a hallmark of many diseases, including cancer, autoimmune disorders, cardiovascular diseases, diabetes, and neurological disorders, there is an increasing recognition of the immune system as an internal sensory organ capable of real-time monitoring of cells and tissues throughout the body. Deviations from normal physiology due to infection, activation of oncogenes and other types of cellular injuries can be detected by the immune system leading to programmed cellular and humoral responses. In turn, these responses result in dynamic changes in the composition of essential compartments of the immune system, including the T cell repertoire.
The Molecular Diagnostics program has developed new technologies for high-throughput analysis of the T cell repertoire in animal models and in humans. The increased sensitivity and multiplexing capability of this approach will significantly enhance our ability to monitor immune responses using novel in vitro diagnostic platforms. Furthermore, a novel PET probe discovery platform based on differential screening to select molecular probes that target proteins specific to a biological process or disease has already led to development of a series of imaging probes for monitoring immune activation and can be used to predict responses of cancer to common chemotherapy drugs. These tracers have been extensively evaluated in preclinical models and have been brought into the clinic for studies in patients in less than 18 months.
Tracer and Pharmacokinetic Modeling
The ultimate goal of the Crump Institute for Molecular Imaging is to develop technologies and methods for measurement of biological functions in living subjects. These efforts enable the quantitation of precise molecular events in cells with spatial information regarding their location within specific tissues and organ systems and temporal information (changes over time) to determine the rates of biological processes and biochemical reactions. In order to facilitate interpretation of results, image reconstruction, archiving, and user-friendly display are essential.
Our goals, however, extend beyond providing visualization of experimental data to converting images into assays of biological processes through the quantitative measurement capability of PET. The experimental setting in this program area ranges from the development of assays under the controlled in vitro conditions of kinetic measurement with radiolabeled probes in arrays of cell cultures on an integrated microfluidic chip to a living subject. The Tracer and Pharmacokinetic Modeling program centralizes the analysis and interpretation of kinetic in vitro measurements on a chip and in vivo measurements with microPET, by performing partial volume correction to overcome the finite spatial resolution of the PET images, developing kinetic models for new tracers, and simplifying and automating procedures to render them accessible and transparent to investigators with varied, non-mathematical backgrounds. Where needed, new technologies and methods are developed and introduced, such as a PVC method without the availability of anatomical information. As a result, accurate measurements can be made of the biochemistry, metabolism, and signaling in cells and tissues, within a living subject. Importantly, these measurements can be made over time, such as during the course of development of disease, or before and after a targeted treatment is administered, in order to more precisely measure the biology of disease and to assess the effectiveness of various therapeutic interventions.
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Figure 1: Sketch of the multiple steps contained in a software system being developed to automate the analysis of mouse microPET image data post image acquisition. The system when completed is expected to replace the current manual image analysis procedures that are time-consuming, tedious, and labor-intensive. Furthermore, the extracted kinetic and biological information will be archived and linked to a meta database system that contains a fast search engine to allow investigators to perform data search and mining conveniently. The kinetic analysis employs a previously developed software package, Kinetic Imaging System (KIS), developed by our group.
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Figure 2: Simulation results of a new PVC method that does not require knowledge of anatomical information of the object. The method is expected to be useful for improving the quantification of tumor uptake of tracers in PET images. In a simulation study (with the true distribution shown in upper left panel), the performance of the new method in processing a simulated measured image (upper right panel) gave a sharp tumor delineation and with only a moderate noise enhancement (shown in lower left panel), compared to that from a general de-convolution method (result shown in lower right).
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Figure 3: A tumor study in a mouse using the sequential images with microPET of the F-18 labeled probes FLT for imaging DNA replication and cell proliferation that was IV injected first and then 60 minutes later FDG was IV injected to image glycolysis. Along with continuous imaging, blood samples were obtained by a microfluidic blood sampler: (A) images - tumor shown with arrows (B) blood curves (C) tumor tissue curves and (D) kinetic data for FLT and FDG in the tumor after separation of the individual kinetic curves from the composite curves in B and C. These data were then used to calculate the rate constants for transport (k1 and k2) and phosphorylation (k3) and dephosphorylation (k4).
PI3K Is Essential for GbpD/Rap1-mediated Cell Protrusions and Adhesion
Previously the strong GbpD OE phenotype was used to screen for downstream targets of GbpD and Rap1 in Dictyostelium (Kortholt et al., 2006). GbpD was overexpressed in mutants defected in adhesion or cell polarity, and Phg2, a serine/threonine-specific kinase, was identified as a Rap1 effector necessary for adhesion but not for cell polarity and chemotaxis (Kortholt et al., 2006).
PIP3 is known to be a strong inducer of pseudopod formation (Parent et al., 1998 Funamoto et al., 2002 Huang et al., 2003). To investigate the possible role of PIP3 in the increased extension of pseudopodia by GbpD OE cells, we overexpressed GbpD in cells lacking PI3K1/PI3K2 (pi3k1/2- null). Disruption of PI3K1/PI3K2, results in cells that have a strongly reduced amount of PIP3, a 90% reduced AKT/PKB activation in response to cAMP, defects in polarity, and a reduced chemotaxis speed (Zhou et al., 1998 Funamoto et al., 2001, 2002). First, the morphology of the mutants was analyzed microscopically. Wild-type cells overexpressing GbpD (AX3/GbpD OE ) extended more pseudopodia and were very large and flat compared with wild-type cells (AX3 Figure 1A.). Confocal fluorescence microscopy revealed that GFP-tagged GbpD was expressed in pi3k1/2- null cells at levels approximately equal to that of wild-type cells (data not shown). Although expressing similar amounts of GbpD, pi3k1/2- null cells overexpressing GbpD (pi3k1/2 − /GbpD OE) did not show an increased amount of pseudopodia or a flattened cell morphology (Figure 1, A and B). Furthermore, starved pi3k1/2-null and pi3k1/2-null/GbpD OE cells, although defective in cell polarity, are able to aggregate and showed normal development, whereas AX3/GbpD OE cells were unable to aggregate (Figure 1C).
Figure 1. Cell morphology and adhesion of pi3k1/2-null cells overexpressing GbpD. To investigate the role of PI3K in the GbpD pathway, GbpD was expressed in wild-type (AX3) and pi3k1/2-null cells and also in cells overexpressing the PIP3-degrading enzyme PTEN. (A) Phase-contrast images of wild-type, pi3k1/2-null, and PTEN-GFP–expressing cells, grown in HG-5 medium. Scale bar, 10 μm. (B) Quantification of contact area (gray) and number of protrusions (black) in AX3, pi3k-null and AX3/PTEN-GFP cells without (control) or with overexpression of GbpD was performed as described (Bosgraaf et al., 2005). Data are presented as percentage of GbpD OE cells relative to its control values are means error bars, SDs n = 10 cells. (C) Aggregation of wild-type (AX3) and mutant cells on agar after 24 h of starvation. (D and E) Cell attachment was determined by measuring the percentage of nonadherent wild-type and mutant vegetative cells after 1 h of rotation. Results are the means error bars, SDs n = 3 independent experiments. The effect of GbpD OE is significant **p < 0.001 *p < 0.05 NS, not significant.
Second, the adhesive capacities of the cell lines were tested via an adhesion assay. As shown in Figure 1D, 77% of the pi3k1/2-null cells were detached from the substratum after 1 h of shaking, a slightly higher percentage than that for wild-type cells (68%). AX3/GbpD OE cells showed a large increase in cell-substrate adhesion, because only 18% of the cells were detached after 1 h of shaking. In contrast, cell attachment in pi3k1/2 − /GbpD OE cells is similar to that for pi3k1/2-null cells (76% detached). These results suggest that PI3K is essential for GbpD-mediated adhesion. To investigate the role of Rap1 in a more direct way, we expressed constitutive active (Rap1G12V) and dominant negative (Rap1S17N) mutants in wild-type cells and pi3k1/2-null cells. Cell attachment is substantially enhanced in AX3/Rap1G12V cells and is reduced in AX3/Rap1S17N cells as previously described (Figure 1E Jeon et al., 2007b). Consistent with an essential role for PI3K in GbpD/Rap1-mediated adhesion, expression of Rap1G12V or Rap1S17N does not alter cell-substratum attachment of pi3k1/2-null cells.
Thirdly we analyzed the chemotaxis properties of the mutants. When a pipette filled with cAMP is placed in the surrounding of starved wild-type cells, cells rapidly respond and chemotax persistently toward the source of cAMP, as depicted in Figure 2. pi3k1/2-null cells exhibit good chemotaxis toward the pipette (Funamoto et al., 2002). Overexpression of GbpD in wild-type cells strongly inhibits chemotaxis as these cells showed little movement toward the pipette. In contrast pi3k1/2 − /GbpD OE cells exhibit a chemotaxis response that is nearly identical to the response of the parent pi3k1/2 − cell line (Figure 2).
Figure 2. Chemotaxis assay. To analyze the chemotaxis behavior of wild-type (AX3), pi3k1/2 − , AX3/GbpD OE and pi3k − /GbpD OE strains, cells were starved and pulsed for 6–8 h, resuspended in PB, and monitored by phase-contrast microscopy. Cells were stimulated with a micropipette containing 10 −4 M cAMP, from the right. The contours of the cells are shown at 1-min interval for AX3, pi3k1/2 − , and pi3k1/2 − /GbpD OE and at 5-min intervals for AX3/GbpD OE for a total of 15 min.
Overexpression of GbpD in wild type affects cell morphology, adhesion, and chemotaxis. The results presented here show that none of these effects are observed upon overexpression of GbpD in pi3k1/2-null cells or addition of the PI3K-inhibitor LY294002 (data not shown), indicating that PI3K is essential for all these GbpD-mediated signaling pathways.
Enhanced PIP3 Levels in GbpD OE Cells
To investigate if GbpD is able to regulate PI3K in vivo, the effect of GbpD overexpression on PIP3 levels was measured by expressing the PIP3 detector PHcracGFP in GbpD OE cells (Parent et al., 1998). Nonstimulated, differentiated AX3/PHcracGFP-expressing cells moved in random directions and showed an evenly distributed PHcrac localization in the cytosol (Figure 3A, Movie 1). On the contrary, nonstimulated GbpD OE /PHcracGFP cells made multiple and broad PIP3 patches along the entire plasma membrane, from which multiple pseudopodia are extended (Figure 3A, Movie 2).
Figure 3. Effect of GbpD expression on PI3K activity. (A) To investigate the effect of GbpD OE on PIP3 levels, the PIP3 detector PHcracGFP was expressed in wild-type, GbpD OE , RapG12V OE and RapS17N OE cells. Confocal images are shown for unstimulated cells (left) and for cells stimulated with cAMP from a micropipette on the right (right). Movies 1 and 2 for wild-type and GbpD OE cells, respectively, are available as Supplementary Information. (B) PKB phosphorylation in wild-type and GbpD OE cells. Cells were cAMP pulsed and then stimulated with 1 μM cAMP. Samples were removed at the times indicated and lysed directly into SDS loading buffer. Samples were subjected to SDS-PAGE and analyzed by Western blotting by probing with a phospho-threonine–specific antibody. The indicated ∼51-kDa protein corresponds to the phosphorylation of PKB/Akt. The blot shown is representative of three independent experiments. (C) Quantification of PKB phosphorylation for AX3 (○) and GbpD OE (•) cells. Data are presented as fold increase relative to AX3 cells before stimulation.
Both AX3/PHcracGFP and GbpD OE /PHcracGFP cells were stimulated with cAMP. Similar to previous investigations (Parent et al., 1998 Funamoto et al., 2002 Huang et al., 2003), introduction of a pipette filled with cAMP in the surroundings of wild-type cells induced a strong translocation of PHcracGFP from the cytosol to the leading edge (Figure 3A, Movie 1). Pseudopodia are extended from PHcracGFP-containing areas of the plasma membrane, and cells move persistently toward the pipette. On the contrary, GbpD OE /PHcracGFP cells make multiple and broad PIP3 patches along the entire plasma membrane. Cells extend multiple, functional pseudopodia at the front and at lateral sides and showed little movement toward the pipette (Figure 3A, Movie 2). In Dictyostelium the activity of Akt/PKB is transiently stimulated in response to cAMP, in a PIP3-dependent manner (Meili et al., 1999 Lim et al., 2005). Akt/PKB is activated by phosphorylation at a conserved threonine residue, which can be detected by using a phospho-threonine specific antibody (Lim et al., 2005 Kortholt et al., 2007). Wild-type and GbpD OE cells were stimulated with cAM, and cell lysates were analyzed by Western blotting. In lysates from wild-type cells, a 51-kDa protein was transiently phosphorylated, peaking at 10 s after stimulation (Figure 3, B and C). GbpDOE cells show about twofold higher basal level of active PKB the response to cAMP is similar as in wild type, but it recovers to the elevated basal activity.
These experiments show that GbpD OE cells have enhanced PIP3 formation. Consistently, cells expressing dominant active RapG12V also make broad PIP3 patches and show poor chemotaxis (Figure 3A). These data suggest a stimulatory role for GbpD and Rap1 in the regulation of PI3K activity.
Subcellular Distribution of Activated Rap1 Coincides with PI3K Activation and PIP3 Formation
To examine the localization of Rap1 activation, a GFP-fused RalGDS-RBD construct (GFP-RBDRalGDS) was expressed in AX3 and AX3/GbpD OE cells. Previously it has been shown that GFP-RBDRalGDS only interacts with the GTP-bound form of Rap and can be used as a reporter to study dynamic changes in Rap activation in human cells (Bivona et al., 2004). Like PHcracGFP, GFP-RBDRalGDS is mainly localized in the cytosol of nonstimulated wild-type cells (Figure 4A, Movie 3), whereas in GbpD OE cells multiple and broad patches of GFP-RBDRalGDS along the entire plasma membrane were observed (Figure 4B). Because GbpD does not activate RasC or RasG, these results indicate that the GFP-RBDRalGDS probe can be used to visualize Rap activation, although it may not be completely specific. As shown in the right panel of Figure 4A, introduction of a pipette filled with cAMP in the surroundings of wild-type cells resulted in the rapid translocation of cytosolic GFP-RBDRalGDS to the membrane, similar to the translocation of the PIP3 detector PHcracGFP (Parent et al., 1998 Funamoto et al., 2002 Huang et al., 2003). Jeon et al. (2007) previously presented a similar localization of activated Rap in wild-type cells to that presented here. On the contrary, GbpD OE cells make multiple and broad GFP-RBDRalGDS patches along the entire plasma membrane (Figure 4B), similar to the distribution of PHcracGFP. These data show that the localization of Rap1 activation coincides with the localization of PI3K activation and PIP3 formation.
Figure 4. Subcellular localization of Rap1 activation. To analyze the subcellular localization of Rap1 activation, the GFP-RBDRalGDS reporter was expressed in AX3 (A) and AX3/GbpD OE (B). Confocal images are shown for unstimulated cells (left) and for cells stimulated with cAMP from a micropipette on the right (right).
Rap1 and RasG Bind to the RBD Domain of PI3K
PI3K consist of an N-terminal membrane-targeting region, an RBD, a C2 lipid-binding domain, a PI3K catalytic association domain (PI3Ka), and a PI3K catalytic domain (PI3Kc Figure 5A). RBD domains have an ubiquitin fold and interact tightly only with the GTP-bound but not with the GDP-bound conformation of Ras-like proteins (Herrmann, 2003). Previous studies have shown that the RBD of PI3K is essential for its activation and has been shown to interact weakly with RasD and RasC and strongly with RasG (Funamoto et al., 2002 Kae et al., 2004). To further investigate the binding specificity, pulldown experiments with the RBD domain of PI3K1–3 were performed. As shown in Figure 5B, all three PI3K RBD domains are able to bind activated Rap1 and RasG with similar properties. Jeon et al. (2007b) showed that Rap1 is rapidly activated upon cAMP stimulation. Consistent with these results, more active Rap1 was detected in cell lysates stimulated with cAMP than in lysates from unstimulated cells (Figure 5B). To further characterize the PI3K/Rap1 and PI3K/RasG interaction, in vitro GDI assays were performed. The interaction of the GTP-bound G-protein with an effector protein stabilizes the interaction between the G-protein and the nucleotide. This stabilization results in decreased dissociation of the nucleotide from the G-protein/nucleotide/effector complex (Herrmann et al., 1996). Incubating G-protein loaded with mGppNHp, a hydrolysis-resistant fluorescent GTP analogue, with an excess of unlabeled GppNHp results in the exchange of mGppNHp for GppNHp. This exchange is monitored as decay in fluorescence, from which the rate constant kobs is calculated. The addition of increasing concentrations of the effector results in a concentration dependent decrease of kobs. The affinity (Kd) between the G-protein and the effector is determined from this dependency (Herrmann et al., 1996). For the PI3K/Rap1 and PI3K/RasG interaction we determined a Kd of 40 and 24 μM, respectively (Figure 5, C and D). This affinity is similar to that described for the interaction between Rap1 and Phg2 (Kortholt et al., 2006) and the interaction of human Rap with several effectors (Wohlgemuth et al., 2005). Together our data show that both in vivo and vitro Rap1 and RasG bind with approximately the same affinity and specificity to PI3K.
Figure 5. Interaction between the RBD domain of PI3K and Rap1. (A) Schematic showing the domain composition of PI3K. The bracket indicates the isolated fragment. RBD, Ras-binding domain PI3Ka, PI3K catalytic association domain PI3Kc, PI3K catalytic domain. (B) To determine the ability of Rap1 and RasG to bind to the RBD domains of PI3K1, PI3K2, and PI3K3, pulldown experiments were performed. At the indicated time points after cAMP stimulation (s), the amounts of activated Rap1 (top) and RasG (bottom) that bound to the indicated GST-RBD constructs were determined, as described in Materials and Methods. In lane 1 the total amount of G-protein in the lysate is shown (L). The previously described RBD domain of Schizosaccharomyces pombe Byr2 was used as a control for activity of the G-proteins (lanes 2–4). The dissociation rate of mGpp(NH)p from Rap1 (C) and RasG (D) was measured in the presence of varying concentration of PI3K2-RBD. These data were used to calculate the observed rate constants, kobs. The kobs values were plotted against the indicated effector concentration of PI3K-RBD. The addition of increasing concentrations of the effector results in a concentration dependent decrease of kobs. These data were used to calculate the dissociation constant of the Rap1-GTP/PI3K-RBD and RasG-GTP/PI3K-RBD complex, yielding a Kd of 40 and 24 μM, respectively.
The GbpD OE Phenotype Is Lost upon Overexpression of the PIP3-degrading Enzyme PTEN
The data presented in the previous sections suggest that GbpD/Rap regulated PIP3 formation is important for cell morphology, adhesion, and chemotaxis. If PIP3 formation is essential for GbpD mediated signaling, expression of the PIP3 degrading enzyme PTEN should interfere with GbpD-mediated processes. To investigate this hypothesis, PTEN was expressed in wild-type (AX3) and GbpD OE cells. Expression of PTEN in wild-type cells did not influence cell morphology (Figure 1A) or adhesive capacity (Figure 1D). In contrast, the strong phenotype of GbpD OE cells was lost upon expression of PTEN. Overexpression of GbpD in AX3 cells induced a ∼125% increase of the surface contact area and the number of protrusions (Figure 1B). Overexpression of PTEN-GFP in these cells (AX3/GbpD OE /PTEN-GFP) resulted in a pronounced reduction of the GbpD OE -induced increase of the surface contact area and the number of protrusions (Figure 1B). Furthermore, the strong substrate attachment of GbpD OE cells (only 18% of cells detached from the substratum after 1-h shaking) is almost completely lost upon expression of PTEN (64% detached cells Figure 4D). Finally, although AX3/GbpD OE cells fail to aggregate upon starvation, overexpression PTEN in these cells allows cell aggregation (Figure 1C). These data reveal that the PIP3-degrading enzyme PTEN largely reverses all the phenotypic defects induced by overexpression of GbpD.
<p>This section provides any useful information about the protein, mostly biological knowledge.<p><a href='/help/function_section' target='_top'>More. </a></p> Function i
Functions as a guanine nucleotide exchange factor (GEF), which activates Rap and Ras family of small GTPases by exchanging bound GDP for free GTP in a cAMP-dependent manner. Serves as a link between cell surface receptors and Rap/Ras GTPases in intracellular signaling cascades. Acts also as an effector for Rap1 by direct association with Rap1-GTP thereby leading to the amplification of Rap1-mediated signaling. Shows weak activity on HRAS. It is controversial whether RAPGEF2 binds cAMP and cGMP or not. Its binding to ligand-activated beta-1 adrenergic receptor ADRB1 leads to the Ras activation through the G(s)-alpha signaling pathway. Involved in the cAMP-induced Ras and Erk1/2 signaling pathway that leads to sustained inhibition of long term melanogenesis by reducing dendrite extension and melanin synthesis. Provides also inhibitory signals for cell proliferation of melanoma cells and promotes their apoptosis in a cAMP-independent nanner. Regulates cAMP-induced neuritogenesis by mediating the Rap1/B-Raf/ERK signaling through a pathway that is independent on both PKA and RAPGEF3/RAPGEF4. Involved in neuron migration and in the formation of the major forebrain fiber connections forming the corpus callosum, the anterior commissure and the hippocampal commissure during brain development. Involved in neuronal growth factor (NGF)-induced sustained activation of Rap1 at late endosomes and in brain-derived neurotrophic factor (BDNF)-induced axon outgrowth of hippocampal neurons. Plays a role in the regulation of embryonic blood vessel formation and in the establishment of basal junction integrity and endothelial barrier function. May be involved in the regulation of the vascular endothelial growth factor receptor KDR and cadherin CDH5 expression at allantois endothelial cell-cell junctions.
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