Co-expression of E6 and human UBE3A in wing and eye epithelia results in severe morphological abnormalities. Furthermore, E6, via its PDZ-binding motif and in cooperation with UBE3A, targets a suite of PDZ proteins that are conserved in human and Drosophila, including Magi, Dlg and Scribble. Similar to human epithelia, Drosophila Magi is a major degradation target. Magi overexpression rescues the cellular abnormalities caused by E6+UBE3A coexpression and this activity of Magi is PDZ domain-dependent. Drosophila p53 was not targeted by E6+UBE3A, and E6+UBE3A activity alone is not sufficient to induce tumorigenesis, which only occurs when E6+UBE3A are expressed in conjunction with activated/oncogenic forms of Ras or Notch. Finally, through a genetic screen we have identified the insulin receptor signaling pathway as being required for E6+UBE3A induced hyperplasia. Our results suggest a highly conserved mechanism of HPV E6 mediated cellular transformation, and establish a powerful genetic model to identify and understand the cellular mechanisms that underlie HPV E6-induced malignancy.Cervical cancer is one of the leading causes of cancer death in women worldwide. The causative agents of cervical cancers, high-risk human papillomaviruses (HPVs), cause cancer through the action of two oncoproteins, E6 and E7. The E6 oncoprotein cooperates with an E3 ubiquitin ligase (UBE3A) to target the p53 tumour suppressor and important polarity and junctional PDZ proteins for proteasomal degradation, activities that are believed to contribute towards malignancy. However, the causative link between degradation of PDZ proteins and E6-mediated malignancy is largely unknown. We have developed an  Drosophila melanogaster) to study the cellular effects of E6. The E6 protein recruits an E3 ubiquitin ligase (UBE3A) to induce the degradation of a number of cellular proteins, including members of the MAGUK family of scaffolding proteins that control the structure and polarity of epithelial cells: Dlg, Scribble and Magi. Expression of E6 and human UBE3A in the wing and eye of Drosophila disrupted these tissues. Similar to human cells we found that Drosophila Magi was a major E6 degradation target and that overexpression of Magi rescued the tissue disruption. However, Drosophila p53 was not degraded by E6/UBE3A, making our fly model potentially useful for studying the p53-independent activities of the E6+UBE3A complex. When we paired E6 expression with oncogenic proteins, including activated Ras, we observed that epithelia were transformed into mesechymal-like cells that left the epithelium and spread through the body. As a test of the potential of our system, we carried out a pilot genetic screen and identified the insulin receptor as a strong modulator of the E6-mediated disruption of Drosophila tissues. Therefore, we have developed a new system and approach to help us better understand the mechanisms that underlie how HPV infection leads to cell transformation and cancer.Human papillomaviruses (HPV) are the causative agents of cervical cancer, one of the leading causes of cancer death in women worldwide. The E6 oncoprotein encoded by HPV has been implicated in the progression of primary tumors to metastatic disease and we have developed a new model in the fruit fly ( Cervical cancer is the fourth leading cause of cancer death in women worldwide with ~500,000 new cases of cervical cancer annually and ~250,000 deaths worldwide. The main causative agents of cervical cancer are the high-risk human papillomaviruses (HPVs). HPVs can induce hyperproliferative lesions in epithelia and are responsible for >90% of cervical and anal cancer, and more than 50% of vaginal, vulvar, penile and oropharyngeal cancers as well as a significant number of head and neck squamous cell carcinomas –4. AlthoThe HPV oncogenes, E6 and E7, are key to the cell transformation that underlies HPV-mediated cancer. Multiple studies have shown that E6 and E7 work cooperatively to induce carcinogenesis ,9. E7 isThe PDZ binding motif (PBM) of E6 is almost exclusively found in the high-risk HPV types, and is essential for HPV-mediated malignancy and hyperplasia . HoweverDrosophila melanogaster has been widely and successfully used as a powerful genetic model organism to study human diseases, including cancer, owing to the strong conservation of genes and signaling pathways between Drosophila and human. Many tumor suppressor genes, including Dlg, Scribble, and Lgl, as well as oncogenic signaling pathways such as Notch, were first identified in Drosophila -Methionine . They were incubated at 30°C for the indicated times, with or without the addition of similarly translated HPV-16 and HPV-18 E6 proteins. The remaining proteins were detected by SDS-PAGE and autoradiography.These assays were performed as described previously . Brieflyin vivo has not been clearly established. As there are no other in vivo models of HPV E6 at present, and given the wide array of genetic tools and techniques available in fruit flies, we felt that developing a fly E6 model would provide the ability to further dissect the molecular pathways and identify novel partners involved in HPV E6-mediated cellular transformation. To do this, we expressed genes encoding the human HPV18 E6 tagged with Myc in two separate epithelia using the Gal4-UAS high-expression system [apterous-GAL4 and GMR-GAL4, respectively, we did not detect any abnormalities within these tissues (Drosophila has a UBE3A homologue it was possible that HPV E6 was unable to activate or interact with the Drosophila UBE3A. This suggests that the human UBE3A has a specific interaction with E6 or a function that is not conserved in the Drosophila UBE3A protein. The former is likely as the LXXLL motif necessary for E6 binding to human UBE3A [Drosophila UBE3A (this study). In support of this we found that expression of E6 together with a previously established transgene that expresses human UBE3A [Drosophila epithelia, suggesting a conservation of cellular targets downstream of the E6+UBE3A complex.While transgenic mouse models of HPV E6 have significantly contributed to the understanding of HPV-mediated tumorigenesis, the role of the HPV-E6 PDZ targets n system . When we tissues . E6 requan UBE3A  is absenan UBE3A  resultedDrosophila epithelia. E6 targets PDZ domain proteins of the polarity and junctional network including MAGI-1, hDlg1 and Scribble/Vartul for ubiquitin-mediated proteasomal degradation [apterous-GAL4 driver to drive expression in the dorsal half of the wing disc to allow for direct comparison of protein levels in the presence and absence of E6+UBE3A. We focused our attention on the Drosophila homologues of Magi, Discs-large (Dlg) and Scribble (Scrib) and monitored the protein levels on the dorsal (expressing E6+UBE3A) versus ventral (lacking E6+UBE3A) side of the wing imaginal disc  and prevents E6 binding to PDZ domain proteins [Drosophila Magi requires an intact E6 PBM.HPV E6-mediated degradation of PDZ proteins in human epithelial cells requires an intact PBM at the C-terminus of the E6 protein . We nextproteins . When coDrosophila imaginal disc are ideal for studying cellular transformation and cellular signaling pathways, and have been used as in vivo models for detailed analysis of cellular and molecular mechanisms underlying cell polarity defects and cancers [Drosophila Armadillo, Arm) and Par3 , revealed that the organization of photoreceptor cells within each ommatiduim was perturbed and that junctional as well as polarity proteins were mislocalized ..Drosophiocalized . The disTo determine whether the effects of E6+UBE3A were dosage dependent, we increased the expression of E6 and UBE3A by increasing the temperature to 29°C to increase the efficacy of Gal4 . At 29°CDrosophila JNK (bskDN) to block JNK signaling in wing discs co-expressing E6+UBE3A. Blocking JNK signaling did not suppress the E6+UBE3A-mediated cell death  is one of the main signaling pathways triggering apoptosis and MMP1 expression, we expressed a dominant negative form of ll death , suggestDrosophila Magi (Magi::Cherry) and E6+UBE3A partially rescued the adult wing phenotypes (bst) [As Magi was strongly reduced by E6+UBE3A expression, we next asked whether Magi overexpression could suppress and rescue the defects caused by E6+UBE3A in the wing epithelia. We found that co-overexpression of a Cherry-tagged form of enotypes . Specifienotypes . Consistes (bst)  resultedDrosophila Magi contains four PDZ domains and two WW domains. We expressed Magi transgenes lacking either the two WW domains (MagiΔWW) or the PDZ domains (MagiΔPDZ) [Next, we asked which domains of Magi were responsible for suppressing the E6+UBE3A mediated defects. agiΔPDZ)  in the wAs neither Magi::Cherry nor MagiΔWW were able to fully rescue the defects caused by E6+UBE3A, we tested the degree to which each could be targeted for degradation by E6. We examined the levels and localization of Magi::Cherry as well as the MagiΔWW and MagiΔPDZ mutants in wing imaginal discs co-expressing E6+UBE3A. Using apterous-GAL4 to drive expression in the dorsal half of the disc, both Magi::Cherry and MagiΔWW proteins were reduced at the plasma membrane although this reduction was not uniform . The Magts [In humans there is usually a period of 15–20 years from the time of HPV infection to the development of cancer. Transgenic mice also showed a latency of 16–20 months for cancer development in presence of HPV E6 . These rts ,65. A shAs HPV E6 has been implicated in the later stages of tumorigenesis, we wanted to determine whether coexpression of E6+UBE3A and Ras85DV12 could cause malignancy and metastasis. In order to do this we expressed E6+UBE3A, Ras85DV12 and a membrane-bound GFP marker (mCD8::GFP) using GMR-Gal4 in the eye, so that we could readily detect the migration of malignant cells into other regions of the body. Expression of E6+UBE3A in the presence of Ras85DV12 resulted in cellular migration, with many GFP-positive cells detected in the abdomen of these animals , arrow, Drosophila eye appeared to be an ideal model system to identify the signaling pathways involved in E6+UBE3A-mediated cell abnormalities. Therefore, we carried out a small genetic screen to identify which signaling molecules and pathways functionally interact with E6. We tested a range of signaling pathways (Buffy and Debcl) (Drosophila retinoblastoma protein (pRb) [Given the high degree of conservation of its genes and signaling pathways, the pathways  and obsed Debcl) , known tin (pRb) . In partin (pRb)  did not Drosophila Rolled) (Drosophila Basket) (Drosophila Armadillo) (Drosophila Yorkie)  in conjunction with E6+UBE3A, the resulting eyes displayed large necrotic scars , which is an indication of cell death . Co-expr Rolled)  and JNK  Basket) , had no madillo)  or Hippo Yorkie)  had no eDrosophila melanogaster, an excellent system for further investigating the molecular mechanisms underlying E6-mediated cellular transformation. In our model we found that E6 expression alone is insufficient to disrupt the host cellular function but dysfunction also requires the human E3 ubiquitin ligase, UBE3A [Drosophila UBE3A does not interact with the E6 protein, and this would be expected as Drosophila UBE3A lacks the critical interaction site which is bound by E6 on human UBE3A [In this study we have developed a new model of E6+UBE3A-mediated cell transformation in e, UBE3A ,21. Thesan UBE3A . In suppan UBE3A . Similaran UBE3A ,63. Our Drosophila with apparently different degrees of degradation. Magi is a major target of E6, showing a markedly greater susceptibility to E6-induced degradation compared with other polarity proteins such as Dlg and Scrib. Previous studies have also identified MAGI-1 as a major target of high-risk human papillomaviruses HPV 16 and 18 E6 oncoproteins in vertebrate epithelial cells [Drosophila Magi is also highly susceptible to E6-induced degradation in vitro, in a manner analogous to that of MAGI-1.These findings suggest that there is a hierarchy of E6 targets in flies, and indicates that a number of cellular pathways targeted by E6 are conserved between insects and mammals. Interestingly, we did not observe any degradation of P53 in the presence of E6+UBE3A and an expression of a dominant negative form of P53 in E6+UBE3A expressing eyes did not have any effect on the cellular defects caused by E6+UBE3A co-expression. P53 is a key target of HPV E6-mediated polyubiquitination and degradation [Drosophila are independent of P53 degradation, making our model system potentially useful for investigating the other activities of E6+UBE3A on cellular transformation.We further show that E6 targets a number of PDZ domain-containing proteins in al cells . We founradation . Our resDrosophila and are involved in important cellular processes including cell-cell junction, cell polarity and regulation of cell signaling. Hence disruption of these in combination could play a role in E6+UBE3A-mediated cellular defects. Using our model system we are in a position to determine the hierarchy and importance of targeting these PDZ proteins in HPV E6-mediated cellular transformation.The degradation of Magi and the other PDZ domain-containing proteins requires an intact E6 PBM, demonstrating that the mechanism of interaction between E6 and its PDZ containing targets is conserved between insects and mammals. The cellular defects caused by E6+UBE3A co-expression could be partly due to loss or disruption of a suite of PDZ domain proteins targeted by E6. Both Dlg and Scrib are polarity proteins and a reduction of these regulators, or a disruption in the balance between the levels of these proteins, could affect the polarity of cells as suggested by the mislocalization of polarity protein Baz (Par3) in the eye. Magi plays a role in remodeling adherens junctions in interommatidial cells in the eye, and loss of Magi disrupts the organization of the interommatidial cells . ConsistMagi gene in Drosophila has no deleterious effect, and animals deficient for Magi live to adulthood with no detectable abnormalities [Drosophila mutants of another major HPV target, p53, are also adult viable. p53 mutants (in both mouse and Drosophila) live to adulthood with no detectable abnormalities [http://cancer.sanger.ac.uk/cosmic/gene/analysis?ln=MAGI1_ENST00000402939). Mutations, or aberrations in expression, of MAGI family members, have been found in global analyses of a number of different human cancers [Identifying Magi as a major target of HPV E6 in our model is similar to what is observed in human cells, and supports the finding that Magi has a significant biological relevance to HPV infection. Expression of a form of MAGI-1 resistant to E6-mediated degradation represses cancer cell proliferation and induces apoptosis . Loss ofmalities ,70. Howemalities ,82. Howemalities . Hence,  cancers –92. HoweDrosophila model of HPV E6-mediated cell transformation is highly amenable to genetic manipulation, with a strong degree of conservation at the gene and cellular levels. As a first test of this model we have identified the insulin receptor as a potential partner in the cellular transformation mediated by expression of E6+UBE3A, using the classic approach of testing for genetic interaction in the Drosophila eye. A growing number of studies have implicated the insulin receptor pathway in cancer development and progression. Conditions associated with insulin resistance such as Type 2 diabetes mellitus (T2DM), obesity, and metabolic syndrome are now recognized as major risk factors for development and progression of cancer [As the molecular mechanism and the steps that lead to tumor formation and malignancy in HPV-positive cells are largely unknown, establishing a model of E6-mediated cell transformation that is amenable to large scale, unbiased genetic analysis is essential. Our f cancer –98. Incrf cancer ,100 and f cancer . Additiof cancer . For insf cancer . IGFBP2 f cancer . Earlierf cancer . These fDrosophila E6+UBE3A model is an important new tool to study the molecular mechanism underlying HPV-mediated cancer and malignancy. As adult flies expressing E6+UBE3A exhibit severe wing and eye abnormalities this represents a uniquely valuable model with which to conduct modifier genetic screens to identify molecules that can inhibit E6-induced cellular defects. As a proof of principle, we have confirmed Magi as an essential element in the ability of E6 to induce cell transformation, demonstrated cooperation between E6 and different cellular oncogenic signaling pathways, and identified the insulin receptor as a downstream effector of E6 induced malignancy.Altogether we believe that our S1 Fig(A-C) Co-expression of E6 and UBE3A had no effect on PDZ protein Bazooka (Baz/Par-3). (E-G) Co-expression of E6 and UBE3A had no effect on the PDZ domain protein Par-6 (I-K) Co-expression of E6 and UBE3A had no effect on the adherens junction protein Ecad. (M-O) Expression of E6 alone had no effect on the level or localization of Magi. (Q-S) Expression of E6 alone had no effect on the level or localization of Dlg. (U-W) Expression of human UBE3A alone had no effect on the levels or localization of Magi or Dlg.  Co-expression of E6V158A and UBE3A had no effect on the level of Dlg (Y) or Scrib (Z).  Graphs representing the quantification results for the levels of Baz, Ecad, Magi, Dlg, and Magi respectively. n = 5 for each experiment. ns indicates that the difference is not statistically significant. Error bars indicate SEM. Scale bars indicate 10μm.UAS-transgenes were expressed under the control of apterous-Gal4 driver in the dorsal compartment of the wing imaginal discs. In all images dorsal is to the left and ventral is to the right. Dashed lines indicate the boundary between the dorsal and ventral compartments. (TIF)Click here for additional data file.S2 Fig(A) E6+UBE3A expressing eye with a rough eye phenotype. Reduction of Buffy(B), Debcl(C) or overexpression of Debcl (D) had no effect on the E6+UBE3A mediated eye defects. (E) Expression of a dominant negative form of P53 (P53 H159N) had no effect on the E6+UBE3A eye phenotype. (F) Control eye for D in which overexpression of Debcl has a similar effect on the eye as when Debcl is co-expressed with E6+UBE3A.Transgenes were driven with eye specific GMR-Gal4 driver. (TIF)Click here for additional data file.


A new strategy for identifying potent RNase H-dependent antisense oligonucleotides (ASOs) is presented. Our analysis of the human transcriptome revealed that a significant proportion of genes contain unique repeated sequences of 16 or more nucleotides in length. Activities of ASOs targeting these repeated sites in several representative genes were compared to those of ASOs targeting unique single sites in the same transcript. Antisense activity at repeated sites was also evaluated in a highly controlled minigene system. Targeting both native and minigene repeat sites resulted in significant increases in potency as compared to targeting of non-repeated sites. The increased potency at these sites is a result of increased frequency of ASO/RNA interactions which, in turn, increases the probability of a productive interaction between the ASO/RNA heteroduplex and human RNase H1 in the cell. These results suggest a new, highly efficient strategy for rapid identification of highly potent ASOs. Antisense oligonucleotide (ASO) mediated reduction of targeted RNAs has been broadly exploited as both a research tool and in development of human therapeutics Despite the fact that ASOs of the same chemical class and length have very similar properties, ASOs targeting the same gene vary widely in potency In this manuscript, we consider another potential factor that might contribute to ASO activity and specificity: the presence of multiple cognate or nearly perfect sites in the target RNA. Receptor theory applies to ASOs hybridizing to RNAs just as it does to small molecules interacting with proteins, but there are subtle differences. For example, with most small molecules, scanning or sampling the environment to find the receptor site is extremely rapid. In contrast, scanning of nucleic acid space by ASOs is slower and correlates with affinity. Another potentially important difference is the possibility that RNA might contain multiple quasi-binding sites for an ASO. Since human RNase H1 is rate-limiting with respect to ASO activity in cells In the current study, we demonstrate that a significant fraction of the transcriptome contains repeated sequences present in only one target RNA. Our data clearly demonstrate that ASOs complementary to these repeat sequences are more potent than ASOs targeting single sites in the same mRNA. These repeats appear more frequently in introns, but occur throughout transcripts. Further, the mechanism underlying the increase in potency is simply that repeated target sites increase the probability of a productive interaction between ASO/RNA and human RNase H1. These results suggest that targeting ASOs to repeat sites should reduce the time and cost necessary to identify potent ASOs and, ultimately, will result in increased specificity of ASO-based therapeutics.http://www.r-project.org/).Human primary and processed transcript sequences from the Reference GRCh37.p13 Primary Assembly were obtained from Entrez. A multi-threaded and paralleled processing Java program was written to extract unique 16-mer sequences from the primary and processed transcripts. The locations of each 16-mer on primary and processed transcripts were then determined using Bowtie, an ultrafast and memory-efficient program for aligning short DNA sequences to the human genome Synthesis and purification of phosphorothioate oligonucleotides was performed using an Applied Biosystems 380B automated DNA synthesizer as described previously Electroporation of ASOs into HepG2 cells was carried out using the HT-200 BTX electroporator with the ElectroSquare Porator (ECM 830) voltage source in 96-well electroporation plates . Cells were harvested 16 hours after electroporation. Cells were electroporated in the presence of ASOs at the indicated concentrations and plated. For lipid transfection of HeLa, T-REx-293, and HepG2 cells, cells were seeded in 96 well plates at ∼50% confluency then treated the following day with the indicated concentrations of ASO in Opti-MEM media (Invitrogen) containing 5 µg/ml Lipofectamine 2000 (Invitrogen) for 4 hours, as described previously Following ASO treatment, total RNA was purified using an RNeasy 3000 BioRobot (Qiagen). Target mRNA levels were assessed by qRT/PCR performed with 10 µl (∼10 ng) of total RNA and Express One-Step SuperScript qRT-PCR SuperMix reagents (Life Technologies) on an ABI StepOne Real Time PCR System (Applied Biosystems). The reverse transcription step was performed for 30 minutes at 48°C followed with 40 thermal cycles of 30 s at 94°C and 1 minute at 60°C. To avoid artifacts based upon well-to-well variation in cell number, mRNA levels were normalized to the total amount of RNA present in each reaction as determined by Ribogreen assay (Invitrogen) 50 of the dose response curves were analyzed by one-way ANOVA and compared using Bonferroni’s or or Newman-Keuls multiple comparison test. IC50 values for multiple repeat vs. single site screens were compared by Mann-Whitney U Test.Dose-response curves were generated by nonlinear regression analysis using GraphPad PRISM software. Best-fit values for the logICAGAGC to GCTAGC (mutated bases underlined). Synthetic DNA oligonucleotides, CTAG GGATCC GAGCTC GGTACC TGGGCACCTCGGGAACC and CTAG GGTTCCCGAGGTGCCCA GGTACC GAGCTC GGATCC, were annealed to create a double stranded adapter containing Kpn I, Sac I, and Bam H1 restriction sites, followed by a single GCGR site and flanked by Nhe I sticky ends, which was then ligated into the minigene at each Nhe I site to give pSOD/E4GR1X (exon 4), pSOD/E5GR1X (exon 5), or pSOD/I4GR1X (intron 4). To add multiple repeat sites, these plasmids were digested with Kpn I and NheI, then dsDNA oligonucleotides with complimentary ends were ligated directionally to give plasmids for expression of transcripts containing two tandem repeats  and four tandem repeats . Non-tandem repeats were inserted by SDM QuikChange Lightning Site-Directed Mutagenesis Kit. Primers were designed using the QuikChange Primer Design Program  such that the sequence TGGGCACCTCGGGAACC GGT (GCGR 449884 site plus Age I restriction site) would be inserted at positions 19, 334, 472, and 523 relative to the first SOD1 base of the minigene. Sequences of primers are given in The preparation of the SOD-1 minigene construct has been previously described E. coli RNase H were generated as described previously T-REx-293 cells were purchased from Invitrogen and cultured in DMEM supplemented with 10% fetal calf serum, streptomycin (0.1 µg/ml), penicillin (100 units/ml), and blasticidin (5 µg/ml). SOD/GCGR minigene plasmids were transfected into T-REx-293 cells using Effectene transfection reagent according to the manufacturer’s protocol (Qiagen). Cells containing the stably integrated minigene were selected in DMEM media containing 250 µg/ml Zeocin. Zeocin-resistant colonies were expanded and then tested for tetracycline-inducible expression by qRT/PCR using minigene specific primers and probes GCTGTTTTGACCTCCATAGAA) and downstream of the GCGR repeat site in exon 4 (GAATGATGCAATGGTCTCCTG). pSOD/E4GR1X and pSOD/E4GR4X were used as templates to produce PCR fragments corresponding to the GCGR repeat sequence. T7 transcribed mRNA was generated from these DNA templates using a MEGAscript Kit according to the manufacturer’s protocol . Following a 5-hour incubation at 37°C, 5 U of DNase I (Life Technologies) was added for 30 minutes at 37°C to remove template DNA. After the DNase treatment, the reaction was adjusted to 300 mM sodium acetate and extracted once with phenol/chloroform and once with chloroform. The RNA was then precipitated with 2 volumes of 100% ethanol. The RNA was 5′-end labeled with 32P by first dephosphorylating the transcript using 50 µg of RNA and 10 µL of Antarctic Phosphatase (New England Biolabs) in 100 µL 1X Antarctic Phosphatase buffer and incubating at 37°C for 60 minutes, followed by heat inactivation at 65°C for 5 minutes. The dephosphorylated RNA was purified using an RNeasy Mini Kit as per manufacturer’s instructions . The RNAs were 5′-end labeled using 40 pmol of RNA, 20 U of T4 polynucleotide kinase , 120 pmol [γ-32P] ATP (6000 ci/mmol) , 70 mM Tris, pH 7.6, 10 mM MgCl2 and 50 mM dithiothreitol. The kinase reaction was incubated at 37°C for 30 minutes and quenched using Gel Loading Buffer II . The labeled transcript and oligoribonucleotides were purified by gel electrophoresis on a 12% denaturing polyacrylamide gel.The SOD/GCGR minigene is preceded by T7 RNA polymerase promoter 2, and 0.1 mM TCEP, pH 7.5); 10 µl aliquots of the cleavage reaction were removed at time points ranging from 1.5 to 120 minutes and quenched by adding 5 µl of stop solution (8 M urea and 500 mM EDTA). The aliquots were heated at 90°C for 2 minutes, resolved on a 12% denaturing polyacrylamide gel, and the substrate and product bands were quantitated on an Amersham Biosciences PhosphorImager.For cleavage assays the heteroduplex substrate was prepared as previously described glucagon receptor (GCGR) transcript reduce its expression in liver improving hyperglycemia and hyperlipidemia 5′-ATTGGGCACCTCGGGAACCC-3′, is repeated eight times  were more active than any of the ASOs targeting single copy sites with intron 1 , suggesting that by targeting repeated sites, a significant increase in potency can be achieved relative to even the best single-site targeting ASOs evaluated.It has previously been demonstrated that ASOs specific to the ht times . Howeverle sites  were evale sites . All ASOintron 1 . To expaly ∼4 nM . In contGCGR repeat sequence as well as single sites within the intron. HepG2 cells were lipid-transfected with ASOs at concentrations between 0.5 nM and 150 nM and levels of GCGR mRNA were determined. ASOs targeting the repeat site (449881-5) had IC50’s ranging between 3 and 19 nM, with an average IC50 of 10.6±5.8 nM , most likely as a result of the low levels of GCGR mRNA expression in HepG2 cells, the data suggest that targeting multiply repeated sites may result in increased ASO potency.To optimize ASO activity, we designed 17-nucleotide MOE gapmer ASOs  to the G6±5.8 nM , red. In50s for reduction of nucleoporin mRNA by these ASOs . For single sites the average IC50 was determined to be ∼21 nM, while for ASOs targeting repeat sites, the average IC50 of 4.5 nM was significantly less (P<0.001). Interestingly, the most potent ASOs were not necessarily those complementary to the sequences repeated most often. However, many of the ASOs perfectly complementary to sites repeated eight or nine times have single mismatches to many more. It could also be the case that the maximum effect on potency plateaus after a certain number of repeats and that additional repeats give no advantage.Within a 696-nucleotide span of intron 6 of STAT3, there are 36 16-nucleotide sequences repeated between eight and 36 times . 3-10-3 to 70 nM  black. AMAPT). ASOs were designed to both single sites and repeat sites in each of these introns . These data indicate that even when targeting only two repeats, there is a significant improvement in ASO potency relative to single target sites (P<0.001).We next wanted to determine whether ASO activity was enhanced even when there were only two copies of a target sequence in a gene. Several two-copy repeats were identified in introns 2, 7, and 10 of the pre-mRNA encoding microtubule-associated protein tau ( introns . Signifi introns  at conceopioid growth factor receptor (OGFR) gene contains a polymorphic region of six imperfect 60-nucleotide tandem repeats (OGFR mRNA was determined by qRT/PCR. ASOs targeting single sites had a broad range of IC50s (from 0.9 to 42.9 nM) and an average IC50 of 9.7 nM (50 of 2.2 nM and a range from 0.5 to 11.5 nM (red). Because the OGFR repeats are imperfect, the ASOs screened were perfectly matched to between three and six sites. It is interesting to note that the two least active ASOs targeting repeats, 696878 and 696879, had only three perfect matches within the exon, whereas the rest had five or six matches. If only these ASOs with five or six perfect matches are evaluated, the average IC50 improves to 0.7 nM, making these multi-site targeting ASOs on average almost 14-fold more potent than those targeting single sites.We next evaluated ASO targeting of repeated sequences present in exons. Although unique repeats are rarer in exons than introns, activity of ASOs in exonic repeats should be less dependent on pre-mRNA processing kinetics than activity in introns. The coding sequence of the human  repeats . HeLa ce repeats  at concef 9.7 nM 5B, blackBcl-2-related ovarian killer (BOK), contains a 41-nucleotide sequence that is imperfectly repeated 15 times in the 3′ UTR. Several ASOs were designed to target portions of the repeat sequence as well as single sites on the mRNA and were evaluated for activity at concentrations ranging from 0.1 to 100 nM . In contrast to our observations for OGFR, for BOK there appeared to be little correlation between the number of repeats complementary to an ASO and potency. For example, the three most potent ASOs tested targeting BOK were complementary to nine, five, and 10 repeats, whereas the three least potent multi-site ASOs were complementary to 14 or 15 repeats. These data are more similar to those obtained in our screen of ASOs targeting STAT3 intron 7 and suggest that after a certain number of repeats, little advantage is gained with respect to potency.Another gene, o 100 nM . As obseGCGR repeat sequence into the intron of a SOD1 minigene construct MAPT screen  or four repeat sequences (4X RNA). End-labeled RNA (0.1 pmol) was hybridized with 40 pmoles of ASO 449884. The reaction was incubated with human RNase H at 37°C for the indicated times. Cleavage fragments were resolved on denaturing acrylamide gels . The ratGCGR repeat at four sites dispersed throughout the SOD1 minigene . Stable cell lines harboring minigenes with four tandem repeats in exon 5 or four non-tandem sites were treated with ASO 449884  was similar to those targeting STAT3 despite the presence of only duplicate repeats in MAPT  The location of GCGR sites screened is represented relative to position on GCGR intron 1 (pink) for multiple repeat targeting ASOs (red) or single site ASOs (black). Bordering exons are shaded yellow. Alignments of each multiple repeat region screened were performed against the 20 fully resequenced individuals from the 1000 genomes project to verify that the repeat structures were fully conserved. B) Multiple repeat binding site structure prediction. The optimal secondary structure the first two repeats depicted in (PDF)Click here for additional data file.Figure S2Identification of repeated sequences unique to a gene. Transcripts from 39787 genes were analyzed for 16-mer repeat sequences as detailed in (PDF)Click here for additional data file.Figure S3STAT3 intron 6 ASO screen. A) Location of STAT3 ASO target sites. The location of sites screened is represented relative to position on STAT3 intron 6 (pink) for multiple repeat targeting ASOs (red) or single site ASOs (black). Bordering exons are shaded yellow. B) IC50 curves for STAT3 ASO screen using Exon 8/9 primer/probe set. Significant differences in IC50 values between all ASOs targeting single sites (black) and those targeting multiple sites (red) calculated using the Mann–Whitney U test is shown. C) IC50 curves for STAT3 ASO screen using Exon 3 primer/probe set. D) Comparison of IC50 values obtained using Exon 8/9 (solid bars) or exon 3 (hatched bars).(PDF)Click here for additional data file.Figure S4MAPT intron 7 ASO screen. A) Location of MAPT ASO target sites. The location of sites screened is represented relative to position on MAPT intron 7 (pink) for multiple repeat targeting ASOs (red) or single site ASOs (black). Bordering exons are shaded yellow. B) IC50 curves for MAPT ASO screen using Exon 13/14 primer/probe set. Significant differences in IC50 values between all ASOs targeting single sites (black) and those targeting multiple sites (red) calculated using the Mann–Whitney U test is shown.(PDF)Click here for additional data file.Figure S5OGFR ASO screen. A) Location of OGFR ASO target sites. The location of sites screened is represented relative to position on OGFR mRNA (NM_007346) for multiple repeat targeting ASOs (red) or single site ASOs (black). The location of the primer/probe set is shown in green with CDS in grey and exons in yellow. B) IC50 curves for OGFR ASO screen using Exon 6/7 primer/probe set. Significant differences in IC50 values between all ASOs targeting single sites (black) and those targeting multiple sites (red) calculated using the Mann–Whitney U test is shown.(PDF)Click here for additional data file.Figure S6BOK ASO screen. A) Location of BOK ASO target sites. The location of sites screened is represented relative to position on BOK mRNA (NM_032515) for multiple repeat targeting ASOs (red) or single site ASOs (black). The location of the primer/probe set is shown in green with CDS in grey and exons in yellow. B) IC50 curves for BOK ASO screen using Exon 5 primer/probe set. Significant differences in IC50 values between all ASOs targeting single sites (black) and those targeting multiple sites (red) calculated using the Mann–Whitney U test is shown.(PDF)Click here for additional data file.Figure S7Effect of snRNA reduction on SOD1 minigene processing. A) Sequence of ASOs trageting snRNAs. ASOs are phosphorothioate at each position with MOE-substituted bases underlined. B) Northern analysis of U4/U6 snRNA reduction was carried out as previously described (PDF)Click here for additional data file.Table S1Sequences of primers/probes used for qRT/PCR. For primers complementary to the minigene, vector sequence is in lower case.(PDF)Click here for additional data file.Table S2Sequences of primers used for insertion of GCGR site at non-tandem positions in the SOD1 minigene.(PDF)Click here for additional data file.Table S3Sequences of ASOs complementary to GCGR. All ASOs are phosphorothioate at each position with MOE-substituted bases underlined. The number of sites is equal to the number of times the ASO is perfectly matched to the target sequence. Tm is calculated for RNA/DNA heteroduplexes (PDF)Click here for additional data file.Table S4Sequences of ASOs complementary to STAT3. All ASOs are phosphorothioate at each position with cEt-substituted bases underlined. The number of sites is equal to the number of times the ASO is perfectly matched to the target sequence.(PDF)Click here for additional data file.Table S5Sequences of ASOs complementary to MAPT. All ASOs are phosphorothioate at each position with MOE-substituted bases underlined. The number of sites is equal to the number of times the ASO is perfectly matched to the target sequence.(PDF)Click here for additional data file.Table S6Sequences of ASOs complementary to OGFR. All ASOs are phosphorothioate at each position with MOE-substituted bases underlined.(PDF)Click here for additional data file.Table S7Sequences of ASOs complementary to BOK. All ASOs are phosphorothioate at each position with MOE-substituted bases underlined.(PDF)Click here for additional data file.


T. maritima enzyme, has been determined at 1.9 Å resolution by molecular replacement after extensive molecular remodelling of the template.The crystal structure of a human dihydrouridine synthase, an enzyme associated with lung cancer, with 18% sequence identity to a  phenix.mr_rosetta starting from a bacterial Dus enzyme with only 18% sequence identity and a significantly divergent structure. The overall fold of the human Dus2 is similar to that of bacterial enzymes, but has a larger recognition domain and a unique three-stranded antiparallel β-sheet insertion into the catalytic domain that packs next to the recognition domain, contributing to domain–domain interactions. The structure may inform the development of novel therapeutic approaches in the fight against lung cancer.The reduction of uridine to dihydrouridine at specific positions in tRNA is catalysed by dihydrouridine synthase (Dus) enzymes. Increased expression of human dihydrouridine synthase 2 (hDus2) has been linked to pulmonary carcinogenesis, while its knockdown decreased cancer cell line viability, suggesting that it may serve as a valuable target for therapeutic intervention. Here, the X-ray crystal structure of a construct of hDus2 encompassing the catalytic and tRNA-recognition domains (residues 1–340) determined at 1.9 Å resolution is presented. It is shown that the structure can be determined automatically by  The nonplanar base of dihydrouridine is unable to form stacking interactions with bases of other nucleosides, increasing flexibility . There is growing evidence that overexpression of hDus2 potentiates the growth of non-small cell lung carcinoma  Fig. 1a. The nThermotoga maritima , we investigated whether any of these could be used to solve the hDus2 structure by automated molecular replacement and rebuilding using phenix.mr_rosetta. In order to compare the phasing methods, the phases were also determined experimentally using SAD techniques.The rapid growth of the PDB has led to an increasingly large pool of search models for use in molecular replacement. In many cases search models with low identity produce solutions that are correctly placed, but the noisy electron-density maps calculated using phases from the model contain insufficient information to guide improvement of the model. In such cases, recent developments using molecular-modelling tools to rebuild the placed model have produced spectacular results  cells were used for expression and were grown in minimal medium supplemented with 40 µg ml−1l-selenomethionine  was subjected to crystallization screening using the PACT screen  PEG 1500 , Didcot, England using an ADSC Q315r CCD detector. Indexing and integration was performed with XDS  defined by the TLS Motion Determination server  using selenomethionyl protein. Selenium sites were identified using 2.3.phenix.mr_rosetta , TmDus (PDB entry 1vhn) and EcDusC (PDB entry 4bfa) was generated using HHpred , the native data set and the sequence of hDus2 residues 1–340 were used as input for phenix.mr_rosetta executed on a single computer. 100 Rosetta models were built for each MR solution, taking between 50 and 75 CPU hours. As the best solution from the final phenix.autobuild step of the first round is (by default) rebuilt again with Rosetta, a run of phenix.mr_rosetta took around 100–150 CPU hours to generate a final model from each molecular-replacement solution. The density-modified phases and FOMs from the highest scoring cycle of phenix.autobuild in the final stage of phenix.mr_rosetta for each of the three templates were used for model building with Buccaneer  methods with that produced by experimental phasing, the Buccaneer model built into the experimentally phased map (as described previously) was used as a MR search model with the (higher resolution) native data. The placed model was used to generate density-modified phases with RESOLVE via phenix.autobuild , TmDus (PDB entry 1vhn) and EcDusC (PDB entry 4bfa) were aligned by HHpred  was performed using the Expresso server  and hDus2 were aligned by secondary-structure matching using Superpose . Cofactor-coordination figures were generated using LigPlot+  failed to produce solutions with an Rfree of less than 50%. As the sequence alignment generated with HHpred contains residues 1 to 340 of hDus2, the search models used in phenix.mr_rosetta contain parts of the recognition domains of TtDus, TmDus and EcDusC that align with the recognition domain of hDus2. The Phaser translation-function Z-score (TFZ) and LLG for the top solutions were relatively poor: TtDus, TFZ 4.7, LLG 39.7; TmDus, TFZ 5.2, LLG 26.0; EcDusC, TFZ 4.8, LLG 25.6. Correlation between the 2mFo − DFc maps calculated from these solutions and the map calculated from the final refined structure of hDus2 was ∼0.35 for TtDus and TmDus and ∼0.30 for EcDusC (Supplementary Table S1). It is therefore unsurprising that autobuilding from these MR solutions was not possible. However, despite the low sequence identity to TtDus, TmDus and EcDusC , phenix.mr_rosetta was able to generate excellent maps for hDus2 using TtDus and TmDus as search models. Autobuilding into these maps with Buccaneer produced almost complete models .Previous attempts at rebuilding potential MR solutions obtained with the EcDusC proved to be the poorest search model, as demonstrated by the lowest correlation between maps calculated from this model and the final refined structure, and phenix.mr_rosetta failed to generate a map suitable for autobuilding. However, during the revision stage of this manuscript we investigated whether the latest versions of PHENIX and Rosetta would be able to rebuild from the MR solution obtained for the EcDusC model, which was clearly correctly placed in the unit cell. It transpired that the current versions are able to improve this model and generate maps suitable for autobuilding and refinement using the same versions of Buccaneer/REFMAC5 as previously .Despite having the highest sequence identity to hDus2, phenix.mr_rosetta, we investigated whether it was possible to solve the structure of hDus2 by more conventional approaches without remodelling by Rosetta. Superposition of TtDus, TmDus and EcDusC revealed a highly similar catalytic domain but variation in the conformation and the relative position of the C-terminal recognition domain . Sequence alignments with hDus2 indicated homology in the catalytic domain . Hence, search models were generated using Sculptor based on the HHpred alignments used as input for phenix.mr_rosetta but encompassing only the catalytic domain. These minimal catalytic domain structures were used as search models with Phaser. A clear solution  was found for the TtDus catalytic domain model, with a 2mFo − DFc map correlation of 0.37 to the map calculated from the final refined structure of hDus2 (Supplementary Table S3). 100 cycles of REFMAC jelly-body refinement resulted in an R and Rfree of 46.4 and 48.3%, respectively, and a map correlation of 0.56. Iterative rebuilding and refinement of this model with Buccaneer and REFMAC5 generated a near-complete model of hDus2 with an Rfree of <30%. Attempts to repeat this approach with the TmDus and EcDusC catalytic domains as search models produced reasonable map correlation after jelly-body refinement only for TmDus (Supplementary Table S3). Whilst rebuilding the TmDus solution to a complete model of hDus2 was possible, it required extensive iteration of jelly-body refinement and Buccaneer rebuilding/REFMAC refinement. In agreement, superposition of the catalytic domains shows that hDus2 is most similar to TtDus (Cα r.m.s.d. of 1.5 Å for residues 3 to 240). In contrast, TmDus (Cα r.m.s.d. of 2.0 Å for residues 5–228) and EcDusC (Cα r.m.s.d. of 1.9 Å for residues 1–240) were more divergent .Given the success of 3.3.b and 2c). The catalytic domain, containing the bound FMN cofactor , comprises residues 7–258 of the protein, being roughly two times larger than the recognition domain (residues 259–339). Superposition of hDus2 with TtDus illustrates that the enzymes are broadly similar . There are two major differences between hDus2 and its bacterial enzyme homologues. Firstly, the catalytic domain contains a three-stranded antiparallel β-sheet insertion . Secondly, the recognition domain is larger, comprising a five-helix bundle with a considerably longer C-terminal helix which is positioned differently with respect to the catalytic domain .Like other Dus enzymes, hDus2 folds into catalytic and tRNA-recognition domains  reveals differences at the interface between the catalytic and recognition domains . The different orientation of the recognition domain in hDus2 results in helices α10 and α11 extending into the area occupied by tRNA in TtDus . The position of the recognition domain is stabilized by the three-stranded β-sheet insertion in the catalytic domain, which packs next to the recognition domain. The β4–β5 loop (residues 58–60) of the β-sheet interacts with residues 327–334 of the recognition domain . In addition to mediating interactions between the two domains, the β-sheet insertion extends into the tRNA-occupied area of TtDus , suggesting a role in protein–tRNA interactions. It is interesting to note that the three-stranded β-sheet insertion appears to be unique to the eukaryotic Dus2 subfamily .Superposition of hDus2 and ns Fig. 3b. The dus Fig. 3c. The pin Fig. 3b. In ad3.4.TtDus makes contacts with the tRNA T-loop through helix α6 (residues 96–101). The corresponding segment of hDus2 (residues 118–128) is not defined in the electron-density maps. TtDus also forms hydrogen bonds to the target residue U20 and to G44 in the variable-loop region via Lys175 and Arg178 of the 310-helix (residues 174–180). This helix is not present in hDus2, nor is there any significant sequence conservation in this segment , indicating there may be differences in the manner that hDus2 binds its target tRNA substrates.d) which appears to be most similar to the positively charged area of TtDus, but its size is smaller than in bacterial enzymes . Proximal to the catalytic Cys116 is a loop which was not visible in the electron density (residues 118–128). Some decrease in the positively charged area may be due to two lysine residues present in this loop. Significantly, the positively charged area shows high sequence conservation , indicating functional importance. Part of the positively charged and conserved area of hDus2 corresponds to the active site containing the bound FMN molecule. The residues coordinated to the FMN are highly conserved in TtDus, TmDus and EcDusC , indicating a similar enzymatic mechanism. In the structures of TtDus and the lower resolution structure of EcDusC (PDB entry 3w9z; Chen et al., 2013TmDus model. Notably, no equivalent density is observed in the difference electron-density maps of hDus2.hDus2 has a positively charged surface area Fig. 3d which on Fig. 3e, indic4.et al., 2005We have determined the structure of the human tRNA-modification enzyme hDus2 that has been implicated in lung cancer (Kato phenix.mr_rosetta were successfully used post hoc to determine the structure of hDus2, demonstrating that the structures of functional homologues, despite having sequence identities below 20%, can be used for structure determination by molecular replacement. Whilst it was in fact possible to solve the structure of hDus2 without the use of phenix.mr_rosetta. this required extensive manual intervention that included refinement and model rebuilding. Counterintuitively, the highest identity search model proved to be the worst template, complicating structure determination. In contrast, the use of phenix.mr_rosetta, while computationally expensive, required no intervention from the user to achieve a near-complete model. Retrospective analaysis suggested that it was not necessary to build as many as 100 Rosetta models for each solution. Indeed, by taking only the top MR solution for each of the three templates and building 20 Rosetta models for each solution a complete model can be produced in ∼25 h on a modest four-core desktop computer. Such an automated approach can liberate time for other ‘bottlenecks’ of biological crystallography associated with protein production and obtaining diffraction-quality crystals.In spite of the low sequence identity to available search models, automatic approaches using human dihydrouridine synthase, 4xp7PDB reference: Click here for additional data file.10.1107/S1399004715009220/rr5097sup4.pptxSupplementary Figures. DOI: 10.1107/S1399004715009220/rr5097sup1.pdfSupplementary Tables.. DOI:


Antibodies have been increasingly used as pharmaceuticals in clinical treatment. Thermal stability and unfolding process are important properties that must be considered in antibody design. In this paper, the structure-encoded dynamical properties and the unfolding process of the Fab fragment of the phosphocholine-binding antibody McPC603 are investigated by use of the normal mode analysis of Gaussian network model (GNM). Firstly, the temperature factors for the residues of the protein were calculated with GNM and then compared with the experimental measurements. A good result was obtained, which provides the validity for the use of GNM to study the dynamical properties of the protein. Then, with this approach, the mean-square fluctuation (MSF) of the residues, as well as the MSF in the internal distance (MSFID) between all pairwise residues, was calculated to investigate the mobility and flexibility of the protein, respectively. It is found that the mobility and flexibility of the constant regions are higher than those of the variable regions, and the six complementarity-determining regions (CDRs) in the variable regions also exhibit relative large mobility and flexibility. The large amplitude motions of the CDRs are considered to be associated with the immune function of the antibody. In addition, the unfolding process of the protein was simulated by iterative use of the GNM. In our method, only the topology of protein native structure is taken into account, and the protein unfolding process is simulated through breaking the native contacts one by one according to the MSFID values between the residues. It is found that the flexible regions tend to unfold earlier. The sequence of the unfolding events obtained by our method is consistent with the hydrogen-deuterium exchange experimental results. Our studies imply that the unfolding behavior of the Fab fragment of antibody McPc603 is largely determined by the intrinsic dynamics of the protein. Ti.e., two heavy chains and two light chains linked together by disulfide bonds [VH) domain and several constant (CH) domains, and each light chain has one variable (VL) domain and one constant (CL) domain. According to the function of the antibody, the Ig molecular can be divided into three functionally distinct fragments: two identical antigen-binding fragments (Fabs) and one crystallizable fragment (Fc), as shown in H domains, which is responsible for the interactions with effector molecules and cells. The Fab fragments correspond to the two arms of the Y-shaped structure of the antibody, which are composed of the VL and CL domains of the light chains paired respectively with the VH domain and one CH domain of the heavy chains, as shown in VH and three CDRs in VL  monomer, which is a large Y-shaped protein produced by B-cells. The structure of Ig monomer is symmetrical, which consists of four polypeptide chains, de bonds ,11,12,13n VL see c, which Gaussian network model (GNM), which is one kind of ENM, is a simple yet effective method to investigate the dynamical properties encoded in protein structure ,16. GNM In this work, the structure-encoded dynamics and unfolding process of this protein were investigated by using GNM. Firstly, the temperature factors for the residues of the protein were calculated with GNM and then compared with the experimental measurements. Then, the mean-square fluctuation (MSF) of the residues in the protein, as well as the MSF in the internal distance (MSFID) between all pairwise residues, was calculated to evaluate the mobility and flexibility for different parts of the protein. Finally, the unfolding process of this protein was also investigated by using a GNM-based iterative method proposed by our group . In our In the GNM, the spring coefficient γ is an adjustable parameter, which can be determined by normalizing the theoretical B-factor against the experimental one. The theoretical B-factor for all the residues in the protein was computed according to Equation (5), and compared with the data from X-ray crystallographic experiments. Then, the value of the spring coefficient was determined to be i.e., CH and CL, exhibit distinctly larger MSF values than the variable regions, i.e., VH and VL, which indicates that the constant regions are more mobile than the variable regions. The X-ray crystallographic experiments have shown that the value of B-factors of the constant regions is obviously larger than that of the variable regions , L2 (56–61) and L3 (97–102)) and three CDRs in VH (represented by H1 (28–32), H2 (50–58) and H3 (102–109)) exhibit relatively large MSF values. These six CDRs form the antigen binding site. The large amplitude motions of the CDRs may be responsible for the binding of antigens with high specificity.In the variable regions i.e., VL, VH, CL and CH, were respectively calculated based on Equation (7). The smaller value of MSFID indicates more rigid motion of the corresponding residues. On the contrary, the higher MSFID value means more flexible residue motions. The calculation results are shown in i.e., VL, CL, VH and CH. It is found that the MSFID values in the constant regions CL and CH are relatively larger than those in the variable regions VL and VH, which implies that the constant regions are more flexible than the variable regions. The unfolding process of this protein discussed in the following section also showed that the constant regions unfold earlier than the variable regions due to their relative higher flexibility. The MSF value of residues only reflects the mobility of different parts in the studied protein. In order to investigate the flexibility for different substructures of the protein, the MSF in the internal distance (MSFID) between all pairwise residues in the substructures of the protein, VL and CL are respectively similar to those of VH and CH, as shown in VH and VL have been studied by hydrogen-deuterium exchange experiments. Both of these two domains contain an inner β-sheet and an outer β-sheet composed of several strands. In the pulsed exchange and folding competition experiments, the inner β-sheet of the VL domain is formed early, but the VH domain fails to form an extended nucleus in the early state of the folding reaction. It implies that the VL domain folds before the VH domain [VH domain as compared to the VL domain [The X-ray crystallographic experiments showed that the structure of the Fab fragment of antibody McPC603 is almost symmetrical. The structures of H domain ,32. It iL domain .CL domain, as indicated by the change of the contact map from (a) to (b). Then, most of the native contacts in CH domain are completely disrupted, as shown in panel (c). At this stage, the VL and VH domains still maintain their native structures. This result indicates that the constant regions CL and CH unfold earlier than the variable regions VL and VH. This is consistent with the intrinsic dynamics of these domains discussed in the previous sections, in which the mobility and flexibility of the constant regions are larger than those of the variable regions. Roccatano et al. have also shown that the collective dynamics encoded in protein native structure largely determine the unfolding process of proteins [VH domain are lost, whereas the native contacts in VL domain are maintained. The VH domain is less stable and thus unfolds faster than VL domain, which agree well with the hydrogen-deuterium exchange experiments. In our method, only the topology of native structure is taken into account. Therefore, the small topological difference between them may be the reason why these two structural similar domains show different stability during folding/unfolding processes of this protein. From VL domain are those formed by β-strand 4 (residues Leu39-Gln44), β-strand 5 (residues Lys51-Tyr55), and β-strand 9 (residues Ala90-Asn96) in the inner β-sheet, as displayed in In the present work, the unfolding behavior of the antibody McPC603 Fab fragment is studied with the iterative method proposed by our group. The unfolding process of this protein was simulated by our method and the contact maps of several representative snapshots during unfolding process are shown in proteins . From FiCL and CH, and the jump from states (c) to (d) represents the cooperative rapture of the variable regions VL and VH. The experimental studies have also shown that both the constant regions and variable regions unfold in a cooperative, two-states manner [i.e., 300 K, then the melt temperatures for the constant regions and variable regions can be estimated to be 442 and 464 K, respectively. The calculated melt temperatures are higher than the experimental results, which may be due to the neglect of the sophisticated interactions between residues in this coarse-grained model.Similar to the analysis of Srivastava and Granek , the coos manner ,35. Our The Gaussian network model (GNM) is originally proposed by Flory and coworkers to study the fluctuation of elastic polymers . Then, iHere, The cross-correlation between the fluctuations of two residues in the protein can be calculated byAccording to Debye-Waller theory, the B-factor of the The inverse of the Kirchhoff matrix can be calculated by eigen-decomposition method given byHere, In this work, in order to investigate the flexibility for different substructures of the protein, the mean-square fluctuation in the internal distance (MSFID) between all pairwise residues of the protein was calculated. The MSFID between the puted by ,39(7)〈, the values of MSFID for all residue pairs in the protein are calculated based on the topology of native structure.II. The residue pair with the largest MSFID value is disrupted, and then a new structural topology, corresponding to a newIII. Based on the new IV. The above steps (III) and (IV) are iterated until all the non-bonded contacts in the protein are disrupted.V. All the structural topologies obtained by the above method are collected to reproduce the unfolding process of the protein.i.e., Gaussian network model (GNM).Antibodies play important roles in the immune system of living organisms. Currently, antibodies have been growingly used as pharmaceuticals in the clinical treatment of various diseases. In antibody design, the thermal stability as well as unfolding and aggregation are important issues that must be considered. The investigation of antibody unfolding process can provide valuable information for antibody design. Many studies have indicated that the native structural topology largely determines the folding/unfolding process of proteins. In the present work, the intrinsic dynamical properties and the unfolding process for the Fab fragment of antibody McPC603 were investigated by use of a topology-based model, In order to reveal the intrinsic dynamics encoded in protein structure, the mean-square fluctuation (MSF) of the residues in the protein was calculated by the sum of first six low-frequency modes. It is found that the constant regions exhibit larger MSF values than the variable regions, implying more mobile motions for the constant regions compared to the variable regions. In the variable regions, the six complementarity-determining regions (CDRs) also have relative large MSF values. The large amplitude motions of the CDRs are considered to be associated with the immune function of the antibody.Then, the mean-square fluctuation in the internal distance (MSFID) between all pairwise residues was calculated to investigate the flexibility of different substructures in the protein. The calculation results show that the MSFID values in the constant regions are relatively larger than those in the variable regions, and the six CDRs in the variable regions also exhibit larger MSFID values. These flexible regions tend to unfold earlier during the unfolding process of the protein.CL domain unfolds first, which is followed by the unfolding of CH domain. At this stage, the VL and VH domains still maintain their native structures. Then, the native structure of VH domain is disrupted. The most resistant part is the substructure formed by β-strands 4, 5 and 9 in the inner β-sheet of VL domain. Our results show that the flexible regions tend to unfold earlier during the unfolding process. The sequence of the unfolding events obtained by our method is consistent with the pulsed hydrogen-deuterium exchange and folding competition experiments results. Our studies indicate that the unfolding behavior of the antibody McPc603 Fab fragment is largely determined by the intrinsic dynamics encoded in the native structure of the protein.At last, the unfolding process of the antibody McPC603 Fab fragment was simulated by the iterative method proposed by our group. Our simulation results show that The Fab fragment of antibody McPC603 is an all-β protein. Our method can successfully reproduce the unfolding process of this kind of protein structures. Besides that, our previous study has indicated that this method is also suited to the proteins with mixed α/β structures . Certain


Saccharomyces cerevisiae, they have been shown to sustain parental identity through asymmetric segregation of ageing factors during closed mitosis. Even though barriers have been extensively studied in the plasma membrane, their identity and organization within the nucleus remains poorly understood. Based on different lines of experimental evidence, we present a model of the composition and structural organization of a nuclear diffusion barrier during anaphase. By means of spatial stochastic simulations, we propose how specialised lipid domains, protein rings, and morphological changes of the nucleus may coordinate to restrict protein exchange between mother and daughter nuclear lobes. We explore distinct, plausible configurations of these diffusion barriers and offer testable predictions regarding their protein exclusion properties and the diffusion regimes they generate. Our model predicts that, while a specialised lipid domain and an immobile protein ring at the bud neck can compartmentalize the nucleus during early anaphase; a specialised lipid domain spanning the elongated bridge between lobes would be entirely sufficient during late anaphase. Our work shows how complex nuclear diffusion barriers in closed mitosis may arise from simple nanoscale biophysical interactions.Diffusion barriers are effective means for constraining protein lateral exchange in cellular membranes. In  Spatial segregation of molecular contents is often necessary for an accurate, timely accomplishment of cellular functions, such as signal transduction and cell-fate decisions. For instance, budding yeast division requires the asymmetric segregation of proteins to distinguish a newborn cell from its parent. However, the strategies to achieve this parental identity are poorly understood. This holds especially true for key proteins and molecular complexes involved in mitosis that diffuse within the nuclear envelope. In fact, segregation within the nuclear envelope has been experimentally verified, but both the nature and configuration of any plausible diffusion barrier remain unknown. In this work, we built virtual models of the nucleus and carried out simulations testing the plausibility of specialised lipid domains and protein rings constituting the diffusion barrier. Moreover, we explored distinct barrier configurations in early and late stages of cell division, and verified our simulation results match experimental observations. Our work shows that the biophysical properties of these molecules, coordinated with morphological changes in the nucleus, make them suitable components of the nuclear diffusion barrier. Importantly, our research approach offers a novel avenue to study diffusion barriers in other biological membranes. Asymmetric segregation of ageing factors during cell division is essential to maintain parental identity between mother and daughter cells. This is an intense area of research – not only due to its applicability in disease models, but also due to the key role played by asymmetric cell division in the generation of eukaryotic diversity. Cell division is a highly dynamic process, starting at the establishment of polarity between the future mother and daughter cells, continuing via spatiotemporal coordination of lipids and structural proteins involved in membrane remodelling, and ending at cytokinesis.S. cerevisiae undergoes closed mitosis. That is, its nucleus remains intact at all times, and only breaks down right before cytokinesis. During anaphase, complex changes in the nuclear envelope (NE) result in a dramatic re-shaping of the nucleus: first, the mother lobe buds into a nascent daughter lobe, resembling joined ellipsoids; then, a dumbbell shape emerges, where both lobes remain connected by a long, narrow bridge. As anaphase progresses, cell fate factors become laterally compartmentalized along the cell division axis. Both the rapidly changing nuclear morphology and NE constitution are likely contributors to the compartmentalization of nuclear proteins. As a consequence, discerning their respective contributions has been the focus of much recent research.In contrast to most other unicellular eukaryotes, the yeast By using photobleaching techniques and computational simulations, it was recently shown that geometry changes may account for compartmentalization in the nucleoplasm, while lateral diffusion barriers located between nuclear halves could be responsible for protein segregation at the inner and outer nuclear membranes  S. cerevisiae nucleus during anaphase are compatible with the hypothesis of sphingolipid domains being present at the NE between lobes Sphingolipids are characterized by long, saturated hydrocarbon chains that favour their assembly into tightly-packed, thick bilayers. Sphingolipid-enriched domains are typically more viscous than other lipid phases of the membrane Separately, septins are a family of filament-forming, membrane-interacting cytoskeletal GTPases involved in many cellular membrane-remodelling events Lastly, recent experiments suggest that Bud6 S. cerevisiae during anaphase. We postulate sphingolipids to form such specialised lipid domains, aligning to experimental evidence in silico models and simulated specialised lipid domains and protein rings using realistic nuclear geometries based on experimental measurements. Our results show that, in LA, a specialised lipid domain at the nuclear bridge is enough to compartmentalize the nucleus into different diffusion regimes. Moreover, we explored three different specialised lipid domain configurations in LA and found an optimum agreement with experiments when the domain spans the entire bridge In this work, we explore the roles of specialised lipid domains and structural proteins, organized as rings, in establishing nuclear lateral diffusion barriers in The compartmentalization effects of nuclear diffusion barriers are known to increase alongside anaphase, and are specific to the nucleoplasm, ONM and INM Subsequently, we placed a virtual plane at the neck in EA, and midpoint of the bridge in LA, simulating a hypothetical barrier as in effective diffusion rates for the reporter proteins used in this study. These rates already account for crowding exerted by the highly inhomogeneous media where proteins diffuse. In addition, previous mathematical models suggest that factors other than excluded volume, such as protein-protein interactions, are contributors to the concentration dependence of lateral mobility For the particular case of the NPC, which constitutes the largest diffusing complex in the NE, we further tested whether volume exclusion could generate a crowding effect that hindered its exchange between nuclear lobes. In fact, this effect could easily arise given the narrow thickness of the perinuclear space at the neck and at the bridge in EA and LA nuclei, respectively . MoreoveFurthermore, considering time-varying diffusion coefficients didn't improve the fit of our simulations to FLIP data. Specifically, we could not fit our model to experimental observations in EA and LA by assuming a continuous range of varying diffusion coefficients. A proper fit was only possible when assuming independent ranges for EA and LA that would not make biological sense. Hence, the discrepancy found during the first 50 s  may be rOverall, our spatial-stochastic simulations based on realistic 3D models of a heterogeneous sample of nuclear morphologies supported previous findings, and confirmed that missing variation bounds can be fully accounted for by considering distinct cell volumes away from an idealized average. Moreover, our simulations offer a suitable stage for testing diverse barrier compositions and configurations.o) rigid phase with decreased solubility for membrane proteins. Accordingly, sphingolipid domains are good candidates for constituting diffusion barriers by the direct contribution of two effects: an increased viscous drag that slows down protein diffusion within the Lo phase of the domain, and the exclusion of proteins coming from the liquid disordered phase (Ld) outside the domain. The latter originates from the hydrophobic matching between the protein amphipathic domains and the membrane where it diffuses. Moreover, in EA, partial depletion of NPCs has been observed at the bud neck; while in LA, loss of fluorescence at the bridge was markedly different from the lobes suggesting different diffusion dynamics  did not account for compartmentalization  or at both the ONM and INM (Pin = 1.5%).For the domain in EA, we followed observations of reporter proteins delocalization at the bud neck lization . The estreporter  are listin values to those previously found for EA and estimated the ring's width that would better fit the experimental data , and correlated them with the position of the bleaching spot along the bridge relative to the normalized length of the entire nucleus. These experiments are aimed at identifying the position of putative diffusion barriers by observing the intersection of the °CP and °CP−1 curves. In particular, curves intersecting in a single point would suggest a narrow barrier, whereas curves intersecting at many points (or overlapping) would indicate a distributed barrier. Results from our simulations were compared against FLIP experiments performed in similar conditions −1 vs. the bleaching spot position are plotted.To determine which domain configuration in LA better reproduces the biological reality, we simulated FLIP experiments as in From Overall, our results support specialised lipid domains to be plausible constituents of diffusion barriers, showing a spatial configuration compatible with nuclear morphological changes during anaphase.in values were much lower than those of other domain configurations in LA , which is ten times higher than its corresponding value in LA , which suggests a polymeric protein would be a good candidate for constituting the ring. Conversely, NPC compartmentalization at the NE requires much less proteins at the ring (∼80). Notably, the latter was estimated when we assumed the ring to be exclusively located at the ONM, which follows from evidence suggesting the diffusion barrier is stronger at the ONM than at the INM 3 proteins at the ring showed a 30% to 35% deviation of the simulations with respect to experimental data. However, a lower number of proteins , compared to Nsg1-GFP (ONM), may be related to the former being a larger protein than the latter  determines its lateral exchange. On the other hand, compartmentalization of the INM has been shown to markedly occur during LA, and its unlikely that it is caused by INM proteins interacting with scaffolding proteins On the other hand, the estimated PS. cerevisiae nucleus during closed mitosis. The model is based on the biophysical properties of two molecular complexes, sphingolipid domains and protein rings, which are known to be involved in diffusion barriers in other cellular membranes S. cerevisiae may achieve asymmetrical segregation of ageing factors during closed mitosis. Moreover, our model suggests novel experiments and provides quantitative predictions that may be further tested to better understand diffusion regimes in the nucleus. Additionally, it offers a suitable theoretical framework to explore diffusion barriers in other cellular membranes.In summary, we propose a plausible model for how diffusion barriers may be constituted and organized in the Nuclear geometries were developed based on data obtained from fluorescence microscopy . Accordingly, the same bleaching rate was used for all of our FLIP stochastic simulations.To estimate the rate of the bleaching reaction occurring at the bleaching spot, we relied upon the TetR-GFP diffusion rate already estimated in −1) values obtained previously in For simulating the FLIP experiments shown in S. cerevisiae.Simulating protein rings at the neck of EA nuclei required a few considerations. Namely, that each protein was defined as a hard sphere, thus allowing for volume exclusion among them and within the ring. However, to reflect their polymerization properties in a more realistic way, we allowed a slight overlapping between them, which amounted to ∼15% of their total diameter. The size of the proteins constituting the ring was fixed to 11 nm, comparable to the thickness of septin and acting filaments (∼10 nm) Smoldyn, a computer program for simulating off-lattice, spatial stochastic chemical kinetics, on a microscopic size scale Smoldyn algorithms are an implementation of Smoluchowski diffusion theory, we are required to provide suitable simulation parameters to achieve accuracy within reasonable spatiotemporal resolution. To that end, we set a time step of 40 µs, which corresponds to a spatial resolution of ∼7 nm. This time step was calculated according to Stochastic simulations were carried out using The Saffman-Delbrück model a priori the value of −3≤ε≤105) with a relative error below 0.015% with respect to the exact solution Thus, the Saffman-Delbrück model in GFP-Src1 , the SafFrom our TEM image analysis , we esti−4 Pa s. Thus, the bulk viscosity of cytoplasm is −4 Pa s. Given that we know the effective diffusion coefficients and sizes of the membrane-bound proteins diffusing in the membrane  may be radically different than those of the nuclear membrane of yeast cells. Moreover, in our case of interest, yeast cells are dividing. This is particularly important given that during anaphase cytoskeleton undergoes a dramatic rearrangement that may prevent membrane-bound proteins to diffuse normally.The viscous drag measurements by Pralle et al. As the viscous drag is calculated from an “effective” diffusion rate and this rate is estimated from FLIP experiments, it turns out we're actually looking at the sum of many processes affecting diffusion. In other words, not only the presence of sphingolipid domains, but also of membrane proteins constituting physical obstacles at the barrier may be the cause of an over-estimated viscous drag.The permeability of a diffusion barrier with respect to a diffusing molecular species The partition coefficient Importantly, in in silico experiments where a specialised lipid domain was the sole component of the barrier. Taking into account the diffusion rates within the domain  Figure S1Defining nuclear geometries for simulations. Spatial dimensions used for developing realistic in silico 3D models of yeast nuclei in (A) early and (B) late anaphase.(EPS)Click here for additional data file.Figure S2Diffusion barriers compartmentalize nuclear membranes, but not the nucleoplasm in early and late anaphase. Mean ± SD fluorescence versus time of nuclei in early (21 cells) and late anaphase (34 cells). FLIP experiments (black) are compared with simulations (red for mother and green for daughter lobe). A hypothetical diffusion barrier permeability P was estimated for TetR-GFP (nucleoplasm), Nsg1-GFP (ONM), Nup49-GFP (NE) and GFP-Src1 (INM).(EPS)Click here for additional data file.Figure S3TEM images of yeast nuclei during anaphase. Each row, from top to bottom, shows cells in  early anaphase and  late anaphase. Zoomed areas from left column are shown in centre and right columns: early anaphase: (B) bud neck and (C) daughter nuclear lobe; late anaphase: (E) whole bridge and (F) bridge at neck. The average thickness of perinuclear space ((TIF)Click here for additional data file.Figure S4Crowding effects occasioned by volume exclusion of the NPC do not affect its compartmentalization. Nup49-GFP mean ± SD fluorescence versus time of nuclei in early (21 cells) and late anaphase (34 cells). NPCs were considered as diffusing non-overlapping spheres. Given the number and size of NPCs Click here for additional data file.Figure S5The specialised lipid domain requires protein exclusion together with a decreased diffusion rate. Deviations (in percentages) of stochastic simulations from the experimental mean, for each experimental time step. Mother and daughter lobe deviations were calculated as the absolute value of the difference between simulations and FLIP experiments. The average deviation is the mean of both nuclear lobes' deviations over time. Data from Nsg1-GFP (ONM) FLIP profiles in EA was used (21 cells). Given the diffusion rate at the lobes was estimated to be 2/s at the lobes, we explored decreasing that value to a half, a tenth, and a hundredth at the specialised lipid domain; while fixing Pin = Pout = 100%.(EPS)Click here for additional data file.Figure S6in values for the specialised lipid ring domain at the neck of EA nuclei.Estimated P Deviations (in percentages) of stochastic simulations from the experimental mean, for each experimental time step. Mother and daughter lobe deviations were calculated as the absolute value of the difference between simulations and FLIP experiments. The average deviation is the mean of both nuclear lobes' deviations over time. The best fit  is shown as a continuous line. Data from Nsg1-GFP, GFP-Src1 and Nup49-GFP FLIP profiles in EA was used (21 cells). For the NPC data, we considered both scenarios where the specialised lipid domain lies only at the ONM or at the whole NE (ONM+INM). Effective diffusion coefficients were fixed as in out = 100% in all cases. The estimated Pin values were Pin = 3.5% for Nsg1-GFP (ONM), Pin = 7% for GFP-Src1 (INM), Pin = 1.6% for NPCs (domain at ONM) and Pin = 1.5% for NPCs (domain at ONM and INM).(EPS)Click here for additional data file.Figure S7Estimated width of a single specialised lipid ring domain centred at the bridge of LA nuclei. Deviations (in percentages) of stochastic simulations from the experimental mean, for each experimental time step. Mother and daughter lobe deviations were calculated as the absolute value of the difference between simulations and FLIP experiments. The average deviation is the mean of both nuclear lobes' deviations over time. The best fit  is shown as a continuous line. Data from Nsg1-GFP, GFP-Src1 and Nup49-GFP FLIP profiles in LA was used (34 cells). For the NPC data, we considered both scenarios where the domain lies only at the ONM or at the whole NE (ONM+INM). Effective diffusion coefficients were fixed as in in values were also fixed as in out = 100% in all cases. The estimated width of the specialised single ring domain was of 300 nm for Nsg1-GFP (ONM) and GFP-Src1 (INM), while a width of 100 nm fitted best for NPCs, independently of whether the domain was assumed only at the ONM or at the whole NE (ONM+INM).(EPS)Click here for additional data file.Figure S8in values for two different specialised lipid domain configurations at the bridge of LA nuclei.Estimated P Average deviations (in percentages) of stochastic simulations from the experimental mean for each experimental time step. Mother and daughter lobe deviations are calculated as the absolute value of the difference between simulations and FLIP experiments. The average deviation is the mean of both nuclear lobes' deviations over time. The best fit  is shown as a continuous line. For the NPC, we considered both scenarios where the specialised lipid domain lies only at the ONM or at both ONM and INM. Effective diffusion coefficients were fixed as in out = 100% in all cases. The estimated Pin values are shown in (EPS)Click here for additional data file.Table S1Diffusing protein parameters for spatial-stochastic simulations. Protein numbers were taken from the Yeast GFP Fusion Localization Database (PDF)Click here for additional data file.Table S2Estimated parameters of sphingolipid domains. Nuclear membranes estimated thickness, viscosity, viscous drag, diffusion coefficient and permeability of the sphingolipid domain. Values outside and inside of the sphingolipid domain are specified where applies. All values were estimated using the Petrov-Schwille model.(PDF)Click here for additional data file.Movie S1Simulation of GFP-Src1 diffusion in an EA nucleus. A protein ring (n = 200) and a specialised lipid ring domain at the neck (width = 300 nm) constrain lateral diffusion. The bleaching reaction begins at time t = 0 s, and occurs only at the bleach spot. The whole model is rotated 180° around the spindle axis and the mitotic axis to ease visualization of its 3D shape. Colour code: bleached (purple), non-bleached (green), bleached and non-bleached at domain (orange), proteins at ring (red). All molecular sizes have been doubled to ease visualization.(GIF)Click here for additional data file.Movie S2Simulation of Nsg1-GFP diffusion in an LA nucleus. A specialised lipid domain spanning the entire bridge length constrains lateral diffusion. The bleaching reaction begins at time t = 0 s, and occurs only at the bleach spot. The whole model is rotated 180° around the spindle axis and the mitotic axis to ease visualization of its 3D shape. Colour code: bleached (purple), non-bleached (green), bleached and non-bleached at domain (orange). All molecular sizes have been doubled to ease visualization.(GIF)Click here for additional data file.Movie S3Simulation of GFP-Src1 FLIP experiments in an EA nucleus. A protein ring (n = 200) and a specialised lipid ring domain at the neck (width = 300 nm) constrain lateral diffusion. The whole model is slightly tilted to ease visualization of the protein ring. Colour code: bleached (dark green), non-bleached (light green), bleached and non-bleached at domain (orange), proteins at ring (red). All molecular sizes have been doubled to ease visualization.(GIF)Click here for additional data file.Movie S4Simulation of NPC FLIP experiments in an EA nucleus. A protein ring (n = 200) and a specialised lipid ring domain at the neck (width = 300 nm) constrain lateral diffusion. The whole model is slightly tilted to ease visualization of the protein ring. Colour code: bleached (dark green), non-bleached (light green), bleached and non-bleached at domain (orange), proteins at ring (red). Both the ONM and INM are shown, with all NPCs diffusing within the perinuclear space between them.(GIF)Click here for additional data file.