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3.5: Induce protein and evaluate DNA - Biology

3.5: Induce protein and evaluate DNA - Biology


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Introduction

Last time you transformed your mutant DNA into BL21(DE3) cells. I won't shy away from telling you that there are many things that can go wrong at this stage! However, each one is certainly a learning experience.

As evidenced by Nagai's work, wild-type inverse pericam is not toxic to BL21(DE3) cells. Although it is unlikely for your small mutation to dramatically change this fact, in general a novel protein may turn out to be toxic. If this is the case, only very small amounts of protein are produced before the bacteria die. Keep in mind that overexpressing a single protein may come at the expense of producing proteins needed for survival, and will most likely cause cell death eventually; however, toxic proteins hasten this demise. Aberrant toxicity can sometimes be alleviated by reducing the culture temperature (e.g., to 30 °C).

Based on its fluorescence activity, wild-type inverse pericam allows proper folding of (cp) EYFP, and based on its response to calcium, it also allows calmodulin to fold. One problem you may encounter is that your mutant proteins will no longer fold correctly. Since you made mutations in the calcium sensor part of IPC, rather than the fluorescent part, it is unlikely that your protein will destroy EYFP fluorescence. However, a common problem with misfolded proteins is the formation of insoluble aggregates, due for instance to improperly exposed hydrophobic surfaces. Proteins can be purified from these aggregates – called inclusion bodies – but the process is more labour-intensive than for soluble proteins. (The proteins must be extracted under more harsh conditions than you will use next time, then purified under denaturing conditions, before finally attempting to renature the proteins.) Inclusion bodies sometimes form simply due to very high expression of the protein of interest, causing it to pass its solubility limit. This outcome can be prevented by lowering the culture temperature or time, the amount of IPTG, or the growth phase of the bacteria.

One final point to keep in mind is that not all proteins can be produced in bacteria. Eukaryotic proteins that require post-translational modifications (such as glycosylation) for activity require eukaryotic hosts (such as yeast, or the ubiquitous CHO – Chinese hamster ovary – cells). Sometimes eukaryote-derived proteins will be truncated or otherwise mistranslated by E. coli due to differential codon bias (Kane, 1995); errors in translation can be prevented by providing additional tRNAs to the culture or directly to the bacteria via plasmids (McNulty, et al., 2003). Despite all this complexity, prokaryotic hosts have been plenty good enough to produce proteins for certain therapies, notably the cytokine G-CSF for patients needing to replenish their white blood cells (e.g., after chemotherapy), sold as Neupogen® by Amgen.

After you induce your cells with IPTG, you will let the resultant protein factories do their work for 2-3 hours. During this time, you will evaluate the DNA from your two X#Z candidates (and from the M124S mutant). First, you will run your diagnostic digests from last time out on a gel. The banding patterns will allow you to determine (or diagnose) whether either of your putative X#Z mutants actually contains the new restriction site that you introduced. Of course, there is a slim possibility that the silent mutation was incorporated but the non-silent mutation wasn't. To get more direct evidence for whether the site-directed mutagenesis worked, you will analyze data from the sequencing reactions that you set up last time.

The invention of automated sequencing machines has made sequence determination a relatively fast and inexpensive endeavor. The method for sequencing DNA is not new but automation of the process is recent, developed in conjunction with the massive genome sequencing efforts of the 1990s. At the heart of sequencing reactions is chemistry worked out by Fred Sanger in the 1970s which uses dideoxynucleotides (see schematic above left). These chain-terminating bases can be added to a growing chain of DNA but cannot be further extended. Performing four reactions, each with a different chain-terminating base, generates fragments of different lengths ending at G, A, T, or C. The fragments, once separated by size, reflect the DNA's sequence. In the "old days" (all of 10 years ago!) radioactive material was incorporated into the elongating DNA fragments so they could be visualized on X-ray film (image above center). More recently fluorescent dyes, one color linked to each dideoxy-base, have been used instead. The four colored fragments can be passed through capillaries to a computer that can read the output and trace the color intensities detected (image above right). Your sample was sequenced in this way on an ABI 3730 DNA Analyzer.

Analysis of sequence data is no small task. "Sequence gazing" can swallow hours of time with little or no results. There are also many web-based programs to decipher patterns. The nucleotide or its translated protein can be examined in this way. Thanks to the genome sequence information that is now available, a new verb, "to BLAST," has been coined to describe the comparison of your own sequence to sequences from other organisms. BLAST is an acronym for Basic Local Alignment Search Tool, and can be accessed through the National Center for Biotechnology Information (NCBI) home page.

You might be wondering why you would ever go through the trouble of designing and performing diagnostic digests, when sequencing is relatively simple and yields more information. Here, the idea of scale becomes important. Sequencing costs $8 per reaction, which can add up if you need to examine, say, 10 or more candidates. Agarose gel electrophoresis, by comparison, costs perhaps $1 per candidate. Since both methods require DNA isolation, one is not dramatically more labour intensive than the other. (A method called colony PCR avoids this labour. Can you guess what it might entail?) Finally, banding patterns can give a quick readout of many candidate colonies compared to the time it takes for the individual sequencing analyses you will perform today. Of course, there's no reason one couldn't automate the analysis process with a bit of (computer, not DNA) code!

Protocols

Part 1: Cell Measurement and IPTG Induction

  1. For each mutant protein (X#Z candidates 1 and 2, M124S), you will be given a 6 mL aliquot of BL21(DE3) cells carrying the mutant plasmid; you will also receive a tube of BL21(DE3) carrying wild-type inverse pericam. These cells should be in or close to the mid-log phase of growth for good induction, just as they were for transformation. Like last time, check the OD600 values of your cells until they fall between 04.and 06. (Better to overshoot a little than undershoot.)
  2. Once your cells have reached the appropriate growth phase, set aside - on ice - 1.5 mL of cells from each tube as a control (no IPTG) sample. Then take an aliquot of cold IPTG (0.1 M), and add to your remaining cells at a final concentration of 1 mM. You should prepare three mutant and one wild-type tube.
  3. Return your tubes to the rotary shaker in the 37 °C incubator, and note down the time.
  4. While your IPTG-stimulated cells are producing protein, you will analyze the sequence data and digests of the plasmids they are carrying. At the end of the day, you will choose only one of your X#Z candidates to save, and give the other sample to the teaching faculty to be bleached and thrown away.

Part 2: Run Diagnostic Gel

The scheme below assumes that both Digest 1 (D1, used to analyze the M124S mutant) and Digest 2 (D2, used to analyze your X#Z mutant candidates) use only one enzyme. If you are doing double-digests and need to run single enzyme controls, hopefully you spoke to the teaching faculty about this last time. Load your samples on a 1% agarose gel in the following order, using 10 μL per ladder and 20 μL per plasmid:

LANESAMPLEDIGEST
1IPCuncut
2IPCD1
3M124SD1
41 Kb marker
5IPCD2
6X#Z-1D2
7X#Z-2D2
8100 bp marker(if relevant for your band sizes)
9X#Z-1uncut
10X#Z-2uncut

Once all the samples are loaded, the power will be applied (100 V for 45 minutes) and the gel will be photographed. When the gel is ready, you will compare the band sizes in the photograph with the expected band sizes that you previously calculated. In the meantime, you can analyze your sequence data.

Part 3: Analyze Sequence Data

Your goal today is to analyze the sequencing data for three samples - two independent colonies from your X#Z mutant, and one M124S clone for practice - and then decide which colony to proceed with for the X#Z mutant. You will want to have this Day 4 sequence document handy (DOC), and to mark the expected location of your mutation with bold text before proceeding. (Just compare to your annotation of the Day 1 IPC sequence document (DOC) using Word Count or the Find tool.) The new file contains the inverse pericam sequence as before, but also the flanking sequences (from the pRSET vector) before and after IPC, which are separated from IPC by a blank line. The second page of the document contains the reverse complement of IPC/pRSET, which was generated using this website tool "Reverse and/or complement DNA sequences." Be sure to bold your mutant codon in the reverse complement sequence as well.

Next we will use some data from the MIT Biopolymers Facility. [Only available to enrolled students in the class, not accessible for OCW users.] From this link, we can see sequencing traces and sequence text.

Rather than look through the sequence to magically find the relevant portion, you can align the data you just got with the standard inverse pericam sequence and the differences will be quickly identified. There are several web-based programs for aligning sequences and still more programs that can be purchased. The steps for using one web-based tool are sketched below.

Align with "bl2seq" from NCBI

  1. The alignment program can be accessed through the NCBI BLAST page or directly from this link.
  2. To allow for gaps in the sequence alignment, uncheck the "filter" box. All the other default settings should be fine.
  3. Paste the sequence text from your sequencing run into the "Sequence 1" box. This will now be the "query." If there were ambiguous areas of your sequencing results, these will be listed as "N" rather than "A" "T" "G" or "C" and it's fine to include Ns in the query.
  4. Paste the inverse pericam sequence into the "Sequence 2" box. For samples probed with the forward primer, use the regular IPC sequence; for those using a reverse primer, you should put in the reverse complement. Which alignment will be more useful depends on the location of your mutation.
  5. Click on Align. Matches will be shown by vertical lines between the aligned sequences. You should see a long stream of matches, followed by lots of errors in the last ~200bp of the sequence – ignore the error-ridden part of the data, as it may not accurately reflect your mutant plasmid. In this stream of matches, the 1-3 missing lines indicating your mutant codon should stand out. If they don't, use the numbering or Find tool to locate the appropriate codon.
  6. You should print a screenshot of each alignment to PDF (and to paper if you desire). These will be used to prepare a figure showing what you found today.

If both colonies for your mutant have the correct sequence, flip a coin and proceed with one or the other; ditto if both are inconclusive. If one appears right and the other doesn't, of course proceed with the former. Finally, if both are clearly wrong, talk to a member of the teaching faculty.

For your reference, another popular sequence alignment program is "CLUSTAL-W" from EMBL-EBI.

Part 4: Observe Mutant Colonies

Last time you transformed BL21(DE3) cells with three different plasmids (two candidates for the X#Z mutant, and one M124S clone). Compare the relative colony formation of cells carrying the different plasmids. If all the plates have dense cell growth, there is no need for you to get an exact colony count; just do your best to get a relative estimate, and describe any findings in your notebook.

Part 5: Cell Observation and Collection

  1. After ~2-3 hours, you will pour 1.5 mL from each tube (from Part 1) into a labeled eppendorf, then spin for 1 min. at maximum speed. Save the other 3 mL!
  2. Aspirate the supernatant from each eppendorf, using a fresh yellow pipet tip on the end of the glass pipet each time.
  3. Observe the colour of each of your pellets, and compare to the above example. If the wild-type and both mutant pellets all appear yellow-greenish to the eye, proceed as follows:
    • Do NOT toss the rest of the liquid cultures. First, measure their OD600 values, according to part 6 of today's protocol.
    • Next, pour 1.5 mL more of the relevant liquid culture on top of each pellet, spin again, and aspirate the supernatant.
    • The last 1.5 mL of culture may be aspirated in your vacuum flask, to be later bleached and discarded.
  4. If one or more of your pellets are white or only dimly coloured, please ask one of the teaching staff to show you the room temperature rotary shaker. You will continue to grow your bacteria overnight. Tomorrow morning, the teaching staff will collect your pellets for you and freeze them. As you can see above, the +IPTG pellets are from 3 mL of culture, while the -IPTG pellets come from 1.5 mL of culture.

Part 6: Preparation for Next Time

Next time, you will lyse your bacterial samples to release their proteins, and run these out on a protein gel. In order to compare the amount of protein in the -IPTG versus +IPTG samples, you would like to normalize by the number of cells. At this point, you may have only three samples ready (-IPTG only), or you may have all six. In either case, measure the OD600 of a 1:10 dilution of cells for each finished sample (for -IPTG you have done so already), and write this number down in your notebook. Then spin down the cells and aspirate the supernatant. Give the cell pellets to the teaching faculty; they will be stored frozen. (Be sure to make a 2X pellet for the +IPTG samples.)

For Next Time

  1. Prepare a figure depicting the results of your diagnostic digest. Keep in mind the best practices for figures that we have discussed, from content to presentation. For example, the content should include the expected band sizes, and the presentation should include labeling of lanes as well as a few reference bands.
  2. Write a few sentences of results section text to accompany your diagnostic digest figure.
  3. Day 6 of this module will be an intense one, and has the potential to run long. You will do yourself a great service if you carefully read the text in advance, then consider what preparations you will need to do on that day, and organize your thoughts in your notebook and/or with your partner. This part will not be collected or evaluated.
  4. Your Module 1 report revision is due next time by 11 AM.

Reagent List

  • IPTG (isopropyl β-D-1-thiogalactoside), 0.1M
  • Loading Dye
    • 0.25% xylene cyanol
    • 30% glycerol
    • RNase
  • 1% and 1.2% agarose gels with 0.3 μg/mL ethidium bromide
  • Gels made and run in 1X TAE buffer
    • 40 mM Tris
    • 20 mM Acetic Acid
    • 1 mM EDTA, pH 8.3

Mechanisms of action of acetaldehyde in the up-regulation of the human α2(I) collagen gene in hepatic stellate cells: key roles of Ski, SMAD3, SMAD4, and SMAD7

Alcohol-induced liver fibrosis and eventually cirrhosis is a leading cause of death. Acetaldehyde, the first metabolite of ethanol, up-regulates expression of the human α2(I) collagen gene (COL1A2). Early acetaldehyde-mediated effects involve phosphorylation and nuclear translocation of SMAD3/4-containing complexes that bind to COL1A2 promoter to induce fibrogenesis. We used human and mouse hepatic stellate cells to elucidate the mechanisms whereby acetaldehyde up-regulates COL1A2 by modulating the role of Ski and the expression of SMADs 3, 4, and 7. Acetaldehyde induced up-regulation of COL1A2 by 3.5-fold, with concomitant increases in the mRNA (threefold) and protein (4.2- and 3.5-fold) levels of SMAD3 and SMAD4, respectively. It also caused a 60% decrease in SMAD7 expression. Ski, a member of the Ski/Sno oncogene family, is colocalized in the nucleus with SMAD4. Acetaldehyde induces translocation of Ski and SMAD4 to the cytoplasm, where Ski undergoes proteasomal degradation, as confirmed by the ability of the proteasomal inhibitor lactacystin to blunt up-regulation of acetaldehyde-dependent COL1A2, but not of the nonspecific fibronectin gene (FN1). We conclude that acetaldehyde up-regulates COL1A2 by enhancing expression of the transactivators SMAD3 and SMAD4 while inhibiting the repressor SMAD7, along with promoting Ski translocation from the nucleus to cytoplasm. We speculate that drugs that prevent proteasomal degradation of repressors targeting COL1A2 may have antifibrogenic properties.

Copyright © 2014 American Society for Investigative Pathology. Published by Elsevier Inc. All rights reserved.

Figures

Effect of acetaldehyde, TGF-β1, or…

Effect of acetaldehyde, TGF-β1, or the two in combination on the expression of…

TGF-β1 is involved in late…

TGF-β1 is involved in late acetaldehyde-dependent up-regulation of COL1A2 . Run-on transcription assays…

Increase in SMAD3 and SMAD4…

Increase in SMAD3 and SMAD4 mRNA and protein is delayed after acetaldehyde treatment.…

SMAD3 and SMAD4 are limiting…

SMAD3 and SMAD4 are limiting factors for acetaldehyde-mediated COL1A2 up-regulation in HSCs. Effect…

Acetaldehyde modulates SMAD7 expression. A:…

Acetaldehyde modulates SMAD7 expression. A: Total RNA was obtained from HHSCs cultured in…

Acetaldehyde modifies cellular distribution of…

Acetaldehyde modifies cellular distribution of Ski. Immunofluorescence microscopy of mouse HSCs cultured in…

Acetaldehyde and TGF-β1 down-regulate the…

Acetaldehyde and TGF-β1 down-regulate the expression of Ski in HSCs. A–D: Western analysis…

Ski and SMAD4 colocalize in…

Ski and SMAD4 colocalize in the nuclei of HSCs and translocate to the…

Lactacystin, an inhibitor of proteasomal…

Lactacystin, an inhibitor of proteasomal degradation, inhibits acetaldehyde-mediated expression of COL1A2 promoter-driven reporter…

Lactacystin blocks acetaldehyde-mediated COL1A2 gene…

Lactacystin blocks acetaldehyde-mediated COL1A2 gene transcription, but not acetaldehyde-mediated fibronectin gene transcription. A…


Abstract

Site-directed mutagenesis and time-resolved fluorescence spectroscopy were used to evaluate the contributions of individual amino acid side chains to the binding of DNA primer−templates to the 3‘−5‘ exonuclease site of the large proteolytic fragment (Klenow fragment) of DNA polymerase I. Mutations were introduced into side chains that have been shown crystallographically to be in close proximity to a DNA 3‘ terminus bound at the 3‘−5‘ exonuclease site. The wild-type residues were replaced by alanine in each case. To assess the effects of the mutations on DNA binding, time-resolved fluorescence anisotropy measurements were performed on dansyl-labeled primer−templates bound to the mutant enzymes. In contrast to techniques that simply monitor the overall binding of proteins to DNA, the time-resolved fluorescence anisotropy technique was used to determine the fractional occupancies of the polymerase and 3‘−5‘ exonuclease active sites of Klenow fragment. Equilibrium constants describing the partitioning of DNA between the two active sites were obtained for nine different mutant enzymes bound to both matched and mismatched DNA sequences. Mutations of Leu361 and Phe473 caused the largest effects, significantly destabilizing the binding of mismatched DNA substrates to the 3‘−5‘ exonuclease site relative to DNA bound at the polymerase site, consistent with structural data showing that the side chains of these residues are involved in intimate hydrophobic interactions with the 3‘ terminal and penultimate bases of the primer strand [Beese, L., and Steitz, T. A. (1991) EMBO J. 10, 25−33]. Mutations of the His660 and Glu357 side chains also resulted in significant effects on the binding of mismatched DNA to the 3‘−5‘ exonuclease site. Surprisingly, mutation of Tyr497 increased the partitioning of mismatched DNA into the 3‘−5‘ exonuclease site, suggesting that the tyrosine side chain in the wild-type enzyme destabilizes substrate binding, despite crystallographic data showing that Tyr497 is H-bonded to the DNA substrate. The effects of mutating the amino acid side chains that serve as ligands to two divalent metal ions bound at the 3‘−5‘ exonuclease site, designated A and B, indicated that metal A also helps to bind DNA to the 3‘−5‘ exonuclease site. These results demonstrate that the time-resolved fluorescence anisotropy technique can be used to quantify the energetic contributions associated with each of the crystallographically defined DNA−protein contacts at the 3‘−5‘ exonuclease site.


Chaotropic Agents

A chaotropic agent is a substance which denatures proteins, DNA, or RNA by disrupting the three dimensional structure of the molecule. Chaotropic agents interfere with stabilizing intra-molecular interactions that are mediated by noncovalent forces such as hydrogen bonds, van der Waals forces and hydrophobic effects. Commonly used chaotropic agents include urea (6–8 M), guanidine HCL (6 M) and lithium perchlorate (4.5 M). Guanidine thiocyanate is a potent protein denaturant that is stronger than guanidine HCl and is often used during the isolation of intact ribonucleic acid to eliminate RNase activity. Many RNA isolation protocols include the addition of mercaptoethanol, in addition to a chaotrope, to provide a reducing environment that denatures the four disulfide bridges formed between the eight cysteine residues of Ribonuclease A (RNase A). The combination of a chaotropic agent and a reducing agent such as mercaptoethanol causes the ribonuclease to unfold and completely lose its activity.


<p>This section provides information on the location and the topology of the mature protein in the cell.<p><a href='/help/subcellular_location_section' target='_top'>More. </a></p> Subcellular location i

Cytoplasm and Cytosol

Manual assertion based on experiment in i

Nucleus

Manual assertion based on experiment in i

Manual assertion inferred from sequence similarity to i

Manual assertion based on experiment in i

Nucleus
Other locations

Keywords - Cellular component i


Results

Induction of DNA damage by hydrogen peroxide

DNA damage and repair in FA cells treated with H2O2 were evaluated by measuring both 8-OHdG in DNA (HPLC-EC analysis) and DNA breaks in intact nuclei (SCGE assay).

The analysis of the 8-OHdG content in the DNA of untreated cells showed an increased basal level of DNA oxidative damage in the cells from the complementation groups FA-C and FA-E (Table I ). H2O2 treatment resulted in an increase of the 8-OHdG level in the DNA from all cell lines. All FA cell lines were more susceptible to this agent than normal lymphoblasts. The integration of the kinetic data for the repair of 8-OHdG showed that the ability to remove this adduct was reduced for all FA cell lines examined this difference, however, was statistically significant only for FA-A and FA-E (Figure 1 ). It is noteworthy that the two corrected cell lines behaved quite similarly to controls, in both the induction and repair phases of the process.

DNA breaks in FA and normal cells treated with H2O2, were evaluated by the SCGE assay (Figure 2 ). The tail length of comets was broadly similar both immediately after the treatment and at two post-treatment times, indicating that damage was induced to a similar extent and repaired at a similar rate in all cell lines.

To investigate if the removal of a specific H2O2-induced damage could be defective in FA cells, a modification of the comet assay was used. Sites sensitive to endonuclease III, an enzyme that recognizes damaged pyrimidines, were measured at different times after treatment (Figure 3 ). Linear regression analysis showed that all cell lines were able to efficiently remove the damaged pyrimidines. The rate of removal was somewhat lower in FA-E compared with the other cell lines, but this difference was not statistically significant.

Micronucleus assay

The micronucleus assay was used to evaluate chromosomal damage induced by H2O2. The results, reported in Table II , showed that the basal micronucleus level is about one order of magnitude higher in FA cell lines than in normal lymphoblasts. This finding has been previously reported in fibroblasts from FA patients (Raj et al., 1980) and is in agreement with the increased level of chromosomal breaks usually found in FA cells. In FA-C cells corrected for the defective gene, a normal micronucleus level is restored.

After H2O2 treatment, micronucleus frequencies were 1.5-fold higher in FA than in normal or FA-C corrected cells, but no difference was found in the net increase (controls subtracted) of micronuclei among all cell lines examined. Mitotic index did not vary noticeably among the studied cell lines, nor was it influenced by the H2O2 treatment.

Micronuclei can be generated by either chromosomal fragments or missegregating whole chromosomes. To investigate if differences in the origin of micronuclei might exist between FA and normal lymphoblasts, samples were examined for the presence of α-centromeric sequences in the micronuclei, which were then distinguished as C– micronuclei, chromosomal fragments, and C+ micronuclei, containing chromosomal centromeric regions. Results reported in Table III show that normal and FA-corrected cells contain an equal proportion of C– and C+ micronuclei. However, most of the micronuclei in untreated FA cell lines and in H2O2-treated normal cells originated from chromosomal fragments.

Cell cycle and apoptosis

The action of H2O2 on cell cycle and its capacity to induce apoptosis were determined by FACS analysis and by DNA ladder formation, respectively.

In the FACS analysis, the difference in the percentage of cells in the G2 phase of the cell cycle between control and treated cells was evaluated. MMC was used as a positive control. As expected (Table IV ), FA cell lines were affected by MMC to a greater degree if compared with normal cell lines. The reversion of mutant genes restored the normal phenotype. A different situation was found with H2O2. First, the differences between normal and FA cell lines were not as sharp. Moreover, the induced G2 delay was more marked in the corrected FA than in the corresponding FA cell lines.

When cells were evaluated for DNA ladder formation, no ladder was seen in FA-C, FA-E and corrected FA-C cell lines, while normal, FA-A and corrected FA-A cell lines showed the typical DNA ladder, indicative of fragmentation (Figure 4 ).

To evaluate if the behavior of FA-C cells was due to a defect in the apoptotic mechanism or was specific for H2O2 treatment, the effect of a different apoptotic agent, VP16, was tested: the effect on FA-C cells was not specific for H2O2, since no ladder formation was induced even by VP16 (data not shown).


FKBPs

Endogenous Regulation

Protein Expression

Protein expression of FKBP52 was shown to be growth-regulated Doucet-Brutin et al (1995) . FK506 binding proteins were successfully expressed as GST-fusion proteins e.g., FKBP12 Chambraud et al (1996) and FKBP13 Walensky et al (1998) .

Transcription / Translation

FKBP13 mRNA is induced under conditions that misfold proteins in the endoplasmic reticulum Bush et al (1994) .

Developmental Expression

FKBP65 is regulated developmentally Patterson et al (2000) .

Protein Partners

Some FK506 binding proteins interact specifically with receptors like the ryanodine calcium channels or the unactivated steroid receptor complexes (reviewed by Schiene-Fischer and Yu (2001) ). Other specific protein partners for FKBPs were described: e.g., FKBP13 interacts with a homologue of the erythrocyte membrane cytoskeletal protein 4.1 Walensky et al (1998) the nuclear FKBP25 functionally associates with histone deacetylases and with the transcription factor YY1 Yang et al (2001) FKBP52 and other FKBPs interact with a newly identified FKBP associated protein, FAP48 Chambraud et al (1996) tropoelastin is a ligand for FKBP65 Davis et al (1998) .


Abstract

The binding affinities of protein-nucleic acid interactions could be altered due to missense mutations occurring in DNA- or RNA-binding proteins, therefore resulting in various diseases. Unfortunately, a systematic comparison and prediction of the effects of mutations on protein-DNA and protein-RNA interactions (these two mutation classes are termed MPDs and MPRs, respectively) is still lacking. Here, we demonstrated that these two classes of mutations could generate similar or different tendencies for binding free energy changes in terms of the properties of mutated residues. We then developed regression algorithms separately for MPDs and MPRs by introducing novel geometric partition-based energy features and interface-based structural features. Through feature selection and ensemble learning, similar computational frameworks that integrated energy- and nonenergy-based models were established to estimate the binding affinity changes resulting from MPDs and MPRs, but the selected features for the final models were different and therefore reflected the specificity of these two mutation classes. Furthermore, the proposed methodology was extended to the identification of mutations that significantly decreased the binding affinities. Extensive validations indicated that our algorithm generally performed better than the state-of-the-art methods on both the regression and classification tasks. The webserver and software are freely available at http://liulab.hzau.edu.cn/PEMPNI and https://github.com/hzau-liulab/PEMPNI.


Department of Biochemistry

Name: Manuel Ascano, Jr.
Title: Assistant Professor of Biochemistry, Assistant Professor of Pathology, Microbiology and Immunology
Department: Biochemistry
Office Address: 632 RRB
Phone Number: 615-875-8714
E-mail: [email protected]
Lab URL: https://medschool.vanderbilt.edu/ascano-lab
PubMed: http://www.ncbi.nlm.nih.gov/pubmed?cmd=search&term=ascano+manuel+OR+ascano+M+jr

Research Keywords: RNA, RNA binding proteins, RNA-protein interactions, PAR-CLIP, RNA-protein crosslinking, post-transcriptional gene regulation, PTGR, innate immunity, DNA-sensing, cellular stress

Research Specialty: Post-transcriptional gene regulation in innate immunity and cellular stress

Research Description: The flow of genetic information from DNA to RNA to protein requires the orchestration of specialized proteins that bind to and regulate RNAs. The human genome encodes for over 1,400 RNA-binding proteins, most of whose functions are unknown despite their importance during development and in specific human diseases. The processes by which RNA-binding proteins regulate RNA, collectively known as post-transcriptional gene regulation, permit cells to adapt rapidly to changing environmental cues such that gene expression programs, and thus cellular function, are fine-tuned. For example, post-transcriptional gene regulatory processes serve to mitigate the effects of deleterious stresses that can potentially usurp or disrupt gene expression in cases of bacterial or viral infection, when foreign nucleic acids are introduced into cells. The post-transcriptional mechanisms that prevent aberrant or pathogen gene expression, while still coordinating the expression of host-specific innate immune- and stress-activated genes, remains poorly understood.

My laboratory is currently focused on two research areas:

1) To identify and functionally characterize the critical RNA-binding proteins utilized by cells during periods of nucleic acid-induced stress and innate immune gene activation.

A key prerequisite for characterizing the function of RNA-binding proteins is to identify its stress-regulated binding targets. My laboratory utilizes RNA biochemistry and molecular biology, in conjunction with modern high-throughput technologies such as PAR-CLIP, to elucidate RNA-protein interactions and gene regulatory function at a transcriptome-wide level. As a complementary approach, we investigate the composition and function of RNA-binding protein complexes (ribonucleoprotein particles) that assemble on specific stress-induced host- or pathogen-encoded transcripts, to understand how such relationships are important for host-cell response and survival.

2) To characterize the components and signaling mechanism of a major cytosolic DNA-sensing pathway.

Detection of cytosolic DNA potently activates multiple signaling pathways leading to the transcriptional activation of interferon and stress-activated genes. Thus, the cellular response and gene expression changes associated with DNA-sensing is an ideal model for investigating stress-induced post-transcriptional gene regulatory processes. My laboratory focuses on the innate immune signaling pathway initiated by the DNA sensor and nucleotidyl transferase superfamily member cyclic GMP-AMP synthase, cGAS. Binding of cytosolic DNA to cGAS leads to its production of cyclic GMP-AMP (cGAMP), which I discovered to be the founding member of metazoan cyclical second messenger molecules containing mixed phosphodiester bonds (2’,5’ and 3’,5’). We examine the mechanism of cGAS activation by structure-function and cell biological studies. My laboratory also investigates how natural and synthetic cyclic dinucleotides and related analogs promote innate immune gene activation through the only known cGAMP receptor, Stimulator of interferon genes (STING).


A form of gene expression maintenance in which the heritable state of gene activity neither requires the continuous presence of the initiating signal nor involves changes in the DNA sequence.

(HOX genes). A family of genes that encode transcription factors which are essential for patterning along the anterior–posterior body axis.

The consequences of mutations that lead to the transformation of the identity of one body segment into the identity of another.

(Su(var)3-9, Enhancer of Zeste, Trithorax). A motif ∼ 130 amino acids in length that provides histone methyltransferase activity. It is found in many chromatin-associated proteins, including some Trithorax group and Polycomb group proteins.

A family of histone acetyltransferases that is defined by the founding members Moz, Ybf2 (Sas3), Sas2 and Tip60.

An intracellular signal transduction pathway involving RAS. RAS activates many signalling cascades involved in multiple developmental events controlling cell proliferation, migration and survival.

(Switch/sucrose nonfermentable). A chromatin-remodelling complex family that was first identified genetically in yeast as a group of genes required for mating type switching and growth on alternative sugar sources to sucrose. This complex is required for the transcriptional activation of ∼ 7% of the genome.

A conserved protein module, which was first identified in the Drosophila melanogaster protein Brahma and has subsequently been found in many chromatin-associated proteins. This domain can recognize acetyl-Lys motifs.

A conserved histone-binding domain that takes its name from the proteins in which it was initially identified: Swi3, ADA2, N-CoR and TFIIB.

(NURF). A chromatin-remodelling complex identified in Drosophila melanogaster and belonging to the imitation switch subfamily.

A motif of ∼ 60 amino acids that is found in many chromatin-associated proteins and forms a binding pocket for methylated histone residues.

Bivalent chromatin domains

Domains that are characterized by the juxtaposition of active and inactive epigenetic histone marks.

(PHD finger). A PHD-linked zinc-finger that chelates double zinc ions. This protein motif is found in many chromatin regulators and binds histones in a methylation-dependent or -independent manner.

This term describes the fact that post-translational modifications on one histone tail can influence those on another, even when they are located on different histones, resulting in a specific gene expression output.

(Cyclin-dependent kinase inhibitor). Members of the CIP and KIP family of CDKIs (p21, p27 and p57) inhibit CDK2- and CDK1-containing complexes, and members of the INK4 family (p15, p16, p18 and p19) inhibit cyclin D-containing complexes. Expression of CDKIs generally causes growth arrest and, when CDKIs are acting as tumour suppressors, may cause cell cycle arrest and apoptosis.

(Skp–cullin–F box and anaphase-promoting complex (also known as the cyclosome)). Multiprotein E3 ubiquitin ligase complexes that are involved in the recognition and ubiquitylation of specific cell cycle target proteins for proteasomal degradation.

Enzymes that target specific proteins for degradation by the proteasome by causing the attachment of ubiquitin to Lys residues on their substrates.

A change undergone by animal cells, caused by escape from control mechanisms (for example, upon infection by a cancer-causing virus). Transformed cells have increased growth potential, alterations in cell surface, karyotypic abnormalities and the ability to invade and metastasize.

(Ataxia-telangiectasia- and RAD3-related). A caffeine-sensitive, DNA-activated protein kinase that is involved in DNA damage checkpoints.

Radioresistant DNA synthesis

(RDS). When mutant cells fail to repress the firing of DNA replication origins in the presence of ionizing radiation-induced DNA damage.

This pathway is a highly conserved intercellular signalling mechanism that is essential not only for cell proliferation but also for numerous cell fate-specification events.

Extracellular signal-regulated kinase

(ERK). A protein involved in a mitogen-activated protein kinase signal transduction pathway that functions in cellular proliferation, differentiation and survival. Its inappropriate activation is a common occurrence in the human cancers.

The rapid phosphorylation of histone H3 that occurs concomitantly with the induction of immediate early genes, which is mediated through alternative mitogen-activated protein kinase cascades.

A signalling pathway involving widely conserved secreted signalling molecules of the Wingless family, which regulate many processes during animal development.

(Janus kinase–signal transducer and activator of transcription). A rapid signal transduction pathway used by a range of cytokines and growth factors. Binding of a cytokine or growth factor to its receptor activates cytoplasmic JAK, which then phosphorylates STAT and triggers its translocation into the nucleus, where it induces the transcription of specific genes.

(NPCs). A stem cell type found in adult neural tissue that can give rise to neuron and supporting cells (glia). During development, NPCs produce the enormous diversity of neurons and glia in the developing central nervous system, and they have been also shown to engage in the replacement of dying neurons.

(LT-HSCs). Haematopoietic stem cells that have long-term regeneration capacities and can restore the haematopoietic system of an irradiated mouse over months.

(ST-HSCs). Haematopoietic stem cells that, under normal circumstances, cannot renew themselves over a long term. They are also referred to as progenitor or precursor cells, as they are relatively immature cells that are precursors to a fully differentiated cell of the same tissue type.


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