The paralogous scaffold proteins axin and conductin/axin2 are key factors in the negative regulation of the Wnt pathway transcription factor β-catenin, thereby representing interesting targets for signaling regulation. Polymerization of axin proteins is essential for their activity in suppressing Wnt/β-catenin signaling. Notably, conductin shows less polymerization and lower activity than axin. By domain swapping between axin and conductin we here identify an aggregation site in the conductin RGS domain which prevents conductin polymerization. Induction of conductin polymerization by point mutations of this aggregon results in enhanced inhibition of Wnt/β-catenin signaling. Importantly, we identify a short peptide which induces conductin polymerization via masking the aggregon, thereby enhancing β-catenin degradation, inhibiting β-catenin-dependent transcription and repressing growth of colorectal cancer cells. Our study reveals a mechanism for regulating signaling pathways via the polymerization status of scaffold proteins and suggests a strategy for targeted colorectal cancer therapy. Polymerization of axin proteins is essential to suppress Wnt/β-catenin signaling. Here, the authors identify an aggregation site in the conductin/axin2 RGS domain that prevents its polymerization and show that a short peptide masking this aggregon promotes polymerization of conductin/axin2, downregulation of Wnt pathway activity and growth inhibition of colorectal cancer cells. Deregulation of the pathway is causally associated with severe diseases1. About 90% of all colorectal carcinomas, which globally represent the third leading cause for cancer-associated death2, exhibit genetic changes activating the Wnt/β-catenin signaling pathway3. Under physiologic conditions, β-catenin levels are tightly regulated by the β-catenin destruction complex. The assembly of this complex is mediated by a central scaffold protein, axin, which has binding sites for β-catenin and the other complex components adenomatous polyposis coli (APC), casein kinase 1 α (CK1α), and glycogen synthase kinase 3 (GSK3)4. Within this complex, β-catenin is phosphorylated earmarking it for subsequent ubiquitination and proteasomal degradation5. The destruction complex is physiologically inhibited by Wnt ligand–receptor interactions leading to stabilization of β-catenin and its association with T-cell factor (Tcf)/lymphoid enhancer-binding factor (Lef) transcription factors in the nucleus to activate transcription of target genes7. In colorectal cancer, mutations of APC, axin or β-catenin prevent degradation of β-catenin leading to its permanent stabilization and transcription of cancer-driving target genes such as MYC and CCND1 (refs. 10).The Wnt/β-catenin signaling pathway regulates pivotal processes from pattering of body axes in early embryonic development to tissue homeostasis and stem-cell maintenance in adults11. Moreover, AXIN2 is a β-catenin target gene14, acting in a negative feedback loop to limit and fine-tune Wnt signaling15. In colorectal cancer, conductin levels are relatively high due to the constant hyperactivation of the Wnt/β-catenin signaling pathway but cannot prevent cancer growth12.Conductin, also named axin2, is an axin paralog exhibiting similar domain architecture Fig. . Like ax16. This axin polymerization can be readily detected as it results in the formation of microscopically visible spherical structures, so-called axin puncta17. In contrast to axin, conductin shows no such indications for polymerization appearing diffusely distributed in the cytoplasm. In line with lower polymerization, conductin is less active in degrading β-catenin than axin15. We hypothesized that conductin-induced degradation of β-catenin might be enhanced by triggering its polymerization. By molecular comparison of axin and conductin, we show here that an aggregation site in the conductin RGS domain prevents DIX domain-mediated polymerization of conductin. Mutational inactivation of this aggregon enhances conductin polymerization and inhibition of Wnt/β-catenin signaling by conductin. Of note, conductin polymerization can also be induced by expression of a small peptide which interferes with RGS domain aggregation. Moreover, this peptide blocks β-catenin-dependent transcription and growth of colorectal cancer cells in a conductin-dependent manner suggesting potential of forced conductin polymerization for cancer therapy.Efficient assembly of the β-catenin destruction complex and β-catenin degradation requires homo-polymerization of axin mediated by head-to-tail polymerization of its C-terminal DIX domain15, whereas axin formed microscopically visible round polymers, so-called puncta in transfected cells  still formed puncta and conductin with the axin DIX domain (CdtAxinDIX) remained diffuse ; vice versa the axin RGS domain led to puncta formation of conductin (CdtAxinRGS) 16 led to diffuse distribution  Fig. . ExchangGS) Fig. . These eGS) Fig. , whereasGS) Fig. . Puncta ion Fig. . Togethe19. This indicates that aggregation of the RGS domain prevents DIX-mediated polymerization. We speculated that conductin might contain an aggregation domain that keeps conductin permanently in a diffuse, non-polymerized state. Scanning of the conductin RGS domain for potential aggregation sites predicted by the TANGO algorithm20 identified three sites, two of which are exclusively predicted for conductin but not axin  not only reduced the TANGO score but also activated conductin polymerization as indicated by the formation of puncta  abolished puncta formation, as shown above  did not . Vector-based transient expression of this 14-mer peptide P182–195 efficiently induced polymerization of co-expressed conductin in about 80% of the cells  previously shown to confer cell permeability21, as the peptide was designed to be applicable in cell-based experiments later on. Importantly, membrane-spotted P182–195-R9 strongly interacted with GFP-tagged proteins containing the conductin RGS domain  present in cell extracts  knockout cells showing that P182–195 induces β-catenin degradation via axin2  present in the RGS domain of conductin, but not axin, which prevents efficient DIX-mediated polymerization. Mutation of this site induced puncta formation of conductin and increased its activity. Interestingly, also axin polymerization is sensitive to RGS-mediated aggregation: transfer of the conductin RGS domain into axin, but not of the aggregon mutated conductin RGS domain, prevented axin polymerization. In line with this, axin mutations occurring in colorectal and liver cancer have been shown to induce axin RGS aggregation and thereby block polymerization19. How the aggregation of the RGS domain prevents DIX interactions is presently unclear. It is conceivable that the DIX domains become spatially separated in the conductin aggregates so that they cannot form ordered head-to-tail polymers essential for β-catenin destruction activity. Alternatively, the highly dynamic polymerization process involving rapid association and disassociation of DIX domains might be impaired by RGS aggregation-mediated reduction of mobility. In this study, polymerization into puncta was observed using ectopically expressed proteins. This puncta formation is considered a meaningful model of endogenous destruction complex assembly, since it was confirmed for endogenous axin23 and impaired polymerization correlates with reduced inhibition of β-catenin24.Axin and conductin act in a similar way as scaffolds for downregulation of β-catenin, but also differ in several aspects, including transcriptional regulation and activityAxin1 knockout mice was rescued by Axin2 knock-in suggesting functional equivalence of the two paralogs25. In contrast, several studies described differences between axin and axin2/conductin in vitro29. Here, we add to this list that axin and conductin also differ in their subcellular distribution and their capacity to degrade β-catenin due to an aggregon exclusively present in conductin. These differences might not have been revealed in the in vivo study (i) due to compensatory mechanisms, (ii) because they become important only under specific physiological/pathological conditions, or (iii) because only Axin2 was knocked in into the Axin1 locus but not vice versa25.In vivo, lethality of 182–195) centered on the aggregon induced conductin puncta formation, i.e. polymerization. P182–195 showed direct interaction with the conductin RGS domain in vitro and reduced RGS-mediated aggregation strongly suggesting that it induces conductin polymerization by saturation of the aggregon. It is conceivable that physiologic RGS interaction partners, like e.g. Gα subunits31, might also regulate conductin activity via this mechanism. Regulation of a signaling pathway through the polymerization status of involved scaffold proteins, as described here for Wnt signaling and conductin, offers an elegant mechanism to control signaling activity, and could also be deployed in other pathways.Importantly, our data suggest that the aggregon in the conductin RGS domain represents a target to regulate conductin activity by enhancing its polymerization. As a proof of principle we show that expression of free RGS domains or of a small peptide  reduces Wnt/β-catenin signaling and inhibits cell growth when added to colorectal cancer cells. In addition, expression of the peptide synergized with tankyrase inhibitors that stabilize axin222, suggesting molecular options for combined pharmaceutical interference with Wnt signaling.Activation of human conductin, i.e. axin2, by increasing its polymerization holds potential for cancer therapy. Elevated β-catenin signaling is a hallmark of colorectal cancer with more than 90% of cancers exhibiting mutations of Wnt signaling components which activate the pathway2 atmosphere at 37 °C, and subcultured according to recommendations by ATCC where the cell lines originated from. HEK293T and U2OS cells were transfected with polyethylenimine, and DLD1 and SW480 cells with Lipofectamine2000 (Invitrogen), according to the manufacturer’s instructions. Wnt3a conditioned medium was prepared as originally described33. G007-LK was obtained from AdooQ Bioscience and MG132 from Sigma-Aldrich. The 9x arginine control peptide (R9), P182–195-R9 and P182–195-R9 QV-PS were obtained from Thermo Scientific.DLD1, HEK293T, SW480, and U2OS cells were grown in Dulbecco's Modified Eagle Medium (DMEM) supplemented with 10% fetal calf serum and 1% penicillin/streptomycin in a 10% COAXIN2 knockout cells and AXIN2 Q188P, M189S cells were generated by using the CRISPR/Cas9 system, as described elsewhere34. In short, a top scoring guide RNA according to the CRISPR Design tool provided by the Zhang Lab (crispr.mit.edu) targeting exon 2 of human AXIN2  was cloned in the PX458 Cas9/GFP/guideRNA-expression vector purchased from Addgene (#48138). The generated PX458 plasmid was transfected in SW480 cells together with a single-strand template for homology directed repair (HDR). The HDR repair template contained two homology arms, and was designed to introduce the desired QM-PS mutation and a silent SpeI restriction site for screening. Twenty-four hours post transfection, GFP-positive cells were sorted and seeded as one cell per well in 96-well plates. From the outgrowing colonies, DNA was isolated, amplified by PCR , and test digested with SpeI. SpeI digestion-positive clones were expanded and the homozygote mutation of QM to PS was verified by sequencing. When generating AXIN2 knockout cells, outgrowing cell colonies were initially screened for loss of axin2 expression by western blot, and AXIN2 knockout clones were verified by sequencing.SW480 35, APC36, Flag-Axin, Flag-Cdt, GFP, GFP-β-catenin, GFP-Axin (human), GFP-Axin (rat), GFP-AxinM3, GFP-Axin2, GFP-Cdt, GFP-AxinCdtDIX, and GFP-CdtAxinDIX, and the siRNAs against GFP and human conductin/axin2 are published37. The other plasmids were generated by classical molecular biology methods . The exchanges to clone expression plasmids for chimeric axin conductin (AC) proteins  are based on Clustal Omega alignments38, and the remaining conductin amino acids are indicated for all chimeric proteins in Supplementary Fig. AxinRGS, GFPCdtΔRGSM3, GFP-Cdt Y93S, GFP-Cdt F112R, GFP-Cdt Q188P V189S, GFP-Cdt Q188P V189S M3, GFP-Cdt Q188P, GFP-Cdt V189S, GFP-Cdt L99R, GFP-Cdt L99R Q188P V189S, GFP-AxinCdtRGS QV-PS, GFP-Axin2 Q188P M189S, GFP-Cdt 2–345 QV-PS, GFP-Cdt 2–345 Y93S, GFP-Cdt 2–345 F112R, and peptide P182–195 QV-PS). All generated plasmids were verified by sequencing.Expression plasmids for mScarlet-tubulinCells were fixed either in −20 °C cold methanol for 5 min Figs.  and 4b oCells were lysed 24 h or, in case of siRNA experiments, 48 h after transfection in Triton X-100-based lysis buffer  in hypotonic lysis buffer  when analyzing β-catenin levels Fig. , or in 22O or 8 nmol of the indicated synthetic peptides, air-dried, and blocked with 5% skimmed milk in phosphate-buffered saline (PBS) to reduce unspecific protein binding. Afterwards, membrane pieces were incubated individually with indicated cell lysates or in vitro-translated proteins (see below), and membrane-bound GFP proteins were visualized using primary and HRP-conjugated secondary antibodies, as described above for the western blot. GFP intensities were quantified with AIDA 2D densitometry.For dot blotting, nitrocellulose membrane pieces (VWR) were placed individually in wells of a 96-well plate, spotted with H39 before overlaying with the sample. Centrifugation was performed using a MLS-50 rotor in an Optima MAX Ultracentrifuge (Beckman Coulter) at 50,000 r.p.m. for 16 h at 25 °C. Afterwards, 20 fractions of equal volumes were collected from top to bottom, and indicated fractions were analyzed by western blotting (see above).For sucrose density gradient preparation, 2 ml of a 50% (w/v) sucrose solution was overlaid with 2 ml of a 12.5% (w/v) sucrose solution in a 13 × 51 mm centrifuge tube (Beckman Coulter), and placed horizontally for 3 h at RT to allow equilibration of a linear density gradientSequences encoding amino acids 74–219 of rat axin, the respective amino acids 67–208 of mouse conductin, and the point mutated versions  were cloned into the pGEX-6P-1 expression vector. Expression of GST-tagged proteins was induced by IPTG in transformed Rosetta (DE3) bacteria within their logarithmic growth phase at 37 °C for 3 h. Then, bacteria were pelleted and lysed in STE buffer  supplemented with lysozyme (100 µg/ml), protease inhibitors, and either 1% SDS or 1% Triton X-100, as indicated in Fig. Escherichia coli translation, following the manufacturer’s guidelines.For in vitro preparation of GFP-RGS, the coding sequence was cloned in a T7 expression vector via standard molecular biology methods. Transcription and translation was performed using PURExpress (NEB), a cell-free transcription/translation system reconstituted from the purified components necessary for DLD1, HEK293T, or SW480 cells were transfected with a luciferase expression plasmid either under the control of a β-catenin-dependent promoter  or with a β-catenin-independent promoter , together with a β-galactosidase expression plasmid. Co-transfection of other plasmids or siRNA, and treatment with Wnt3a, G007-LK, or synthetic peptides for 24–48 h is indicated in the respective figure legends and figures. Cells were lysed , and the luciferase and β-galactosidase activities were measured as emission of light upon luciferin decarboxylation in a Centro LB 960 Microplate Luminometer (Berthold technologies) and as release of yellow ortho-Nitrophenol upon ortho-nitrophenyl-β-galactoside hydrolysis with a Spectra MAX 190 (Molecular Devices), respectively. TOP and FOP luciferase activities were normalized to the respective β-galactosidase activity to correct for small variations in transfection efficiency between samples, before calculating TOP/FOP ratios. TOP/FOP assays were performed in technical duplicates.DLD1 and SW480 cells were transfected with GFP-Axin, GFP-Conductin, or GFP-Conductin QV-PS, and 24 h post transfection low GFP-positive cells were sorted , and visualized in a UV gel documentation system (Herolab), where images were acquired at constant settings. Using the MetaMorph analysis software (Carl Zeiss), numbers of colonies were quantified from these images in an automated fashion by applying a threshold for light objects. The assay was performed in technical duplicates.In case of transient peptide expression, SW480 cells were transfected with GFP either together with a control plasmid, a plasmid encoding the peptide PAXIN2, LGR5, and MYC was normalized to the housekeeping gene GAPDH40. AXIN2 expression was not analyzed in AXIN2 knockout cells since the premature stop codon could affect mRNA stability.Following cell treatment with indicated concentrations of synthetic peptides for 48 h, whole cellular RNA was isolated , cDNA was synthesized  and quantitative PCR was performed in a CFX96 Real-Time System (Bio-Rad) in technical triplicates. mRNA expression of the Wnt/β-catenin target genes AXIN2: for: CCTCAGAGCGATGGATTTCGGG; rev: CCAGTTCCTCTCAGCAATCGGCGAPDH: for: GTCAAGGCTGAGAACGGGAAGC; rev: GGACTCCACGACGTACTCAGCGLGR5: for: CTTCCAACCTCAGCGTCTTCACC; rev: GTCAGAGCGTTTCCCGCAAGACMYC: for: CTTCTCTGAAAGGCTCTCCTTGC; rev: CGAGGTCATAGTTCCTGTTGGTGt-tests were performed. Depending on the experimental setup, tests were performed in a non-paired , as indicated in figure legends.To probe data sets for statistical significant differences two-tailed Student’s ed Figs.  or paireFurther information on research design is available in the Supplementary InformationPeer Review FileReporting SummarySource Data


Rhynchophorus ferrugineus Olivier, is a destructive pest for palm trees worldwide. Recent studies have shown that RPW gut is colonized by microbes and alterations in gut microbiota can significantly modify its hemolymph nutrition content. However, the exact effects of gut microbiota on RPW phenotype and the underlying mechanisms remain elusive. Here germ-free (GF) RPW larvae were generated from dechorionated eggs which were reared on sterilized artificial food under axenic conditions. Compared with controls, the larval development of GF RPW individuals was markedly depressed and their body mass was reduced as well. Furthermore, the content of hemolymph protein, glucose and triglyceride were dropped significantly in GF RPW larvae. Interestingly, introducing gut microbiota into GF individuals could significantly increase the levels of the three nutrition indices. Additionally, it has also been demonstrated that RPW larvae monoassociated with Lactococcus lactis exhibited the same level of protein content with the CR  insects while feeding Enterobacter cloacae to GF larvae increased their hemolymph triglyceride and glucose content markedly. Consequently, our findings suggest that gut microbiota profoundly affect the development of this pest by regulating its nutrition metabolism and different gut bacterial species show distinct impact on host physiology. Taken together, the establishment of GF and gnotobiotic RPW larvae will advance the elucidation of molecular mechanisms behind the interactions between RPW and its gut microbiota.Red palm weevil (RPW),  Drosophila melanogaster have indicated that intestinal microbes can impact many host physiological traits such as development, immunity, maturation, longevity and nutrition , orldwide . This peorldwide . Since il system . RPW larl system . Currentl system . Furtherl system . Becauseand mice .Enterobacter cloacae can produce various glycolytic enzymes, containing cellulases, trehalases and other glucosidases, which participate in food digestion (Lactococcus lactis is a lactic acid bacterium which is involved in digesting and fermenting some plant polymers to improve insect nutrition acquisition (E. cloacae RPWL3 (access no. MF185375) and L. lactis RPWL8 (access no. MF185378) are present in RPW gut with higher abundance  and Pingtan District  of Fujian Province. The adults were fed with sugarcane stems in the incubator  at 27 ± 1°C, 75% relative humidity (RH) under the photoperiod of 12 h light/12 h dark, while larvae were reared under the following conditions: 27 ± 1°C, 75% RH and a photoperiod of 24 h dark. The fresh eggs, within 12 h after oviposition, were collected for dechorionation to generate GF insects. These collected eggs were divided into the following two groups: the dechorionated eggs which were processed to sequential washing by 10% sodium hypochlorite solution, 75% ethanol and sterilized water, others served as the control which was only treated with sterilized water. These treated eggs were moved to a sterilized Petri dish with autoclaved wet cottons for hatching and their hatching status were observed every 24 h. After dechorionation, the germfree individuals were always maintained in the laminar hood  which can prevent the bacterial contamination from outside environment. The neonatal larvae were fed on sterile artificial diet  with or without antibiotic cocktail . AntibioE. cloacae RPWL3 and L. lactis RPWL8 were cultivated at 37°C overnight in nutrient broth . Then 50 μl bacterial suspensions (OD600 = 0.15) of two species were spread on sterilized food to feed the fifth instar GF RPW larvae. During the generation of gnotobiotic insects, their food was refreshed every 2 day. For the control groups, the same volume of NB was introduced on the food to feed insects. Seven day later, four insects were randomly selected from each group to verify the load of two introduced bacterial species. Gut homogenates of these insects were prepared, poured on nutrient agar  and incubated as above. Bacterial species was determined by 16S rRNA-based PCR diagnosis and the number of colony-forming units (CFUs) was counted 24 h after incubation.The fifth instar GF larvae were employed to generate the gnotobiotic insects and they were transferred to sterilized food without antibiotics. -1 to 10-4) and incubated aerobically at 37°C for 24 h. Finally, the number of bacterial colony forming units (CFUs) was counted for further analysis.To verify if there were some commensal bacteria in the gut of RPW larvae, three Fifth-instar larvae were randomly dissected to collect guts. Before dissection, each individual surface was cleaned with 75% ethanol, followed by three rinses in the sterilized water. The whole gut of each insect was pulled out in a clean Petri dish with sterilized forceps. Each gut was put in 1 ml sterile PBS as a replicate and homogenized by the Scientz-48 tissue lyser . Each treatment comprises at least three replicates. In the culture-dependent assays, 100 μl gut homogenate was poured on the nutrient agar media in triplicate after serial dilution (10By using a DNeasy Blood & Tissue Kit (Qiagen), total gut bacterial DNA was extracted from RPW larvae in the four treatments according to the procedures as described by The instar and survival of each larva were observed daily and its body mass was also measured by an electric microbalance (Mettler Toledo AL104) to the accuracy of 0.01 μg. The seventh instar larvae were used to collect the hemolymph for the quantification of four nutritional indices, including protein, glucose, triglyceride (TAG) and trehalose concentration. Before hemolymph collection, each larva was washed with running water to remove excrement and food particles, and then anesthetized for 3–5 min on ice for immobilization. Subsequently, its epidermis was pierced by a fine sharp needle for collecting 50 μl of hemolymph per larva into a labeled 1.5 ml clean micro-centrifuge tube with 2 μl of 0.2% phenylthiourea (PTU) to inhibit the hemolymph coagulation. The protein content of each sample was analyzed with the BCA protein assay kit . Glucose was measured using Glucose Measurement Kit . TAG content was determined with Triglyceride assay kit . The concentration of trehalose was quantified with trehalose assay kit , following the manufacturer’s instructions.t-test. The variations of nutrition indices across different groups were assessed by analysis of variance (ANOVA) and multiple comparisons were conducted with Tukey’s HSD post hoc analysis. Before ANOVA or t-test, Kolmogorov-Smirnov test and test of homogeneity of variances was run for determining if our data has normal distribution and equal variances, respectively. If not, the data were transformed to meet the prerequisite of parametric tests. All analyses were performed using IBM SPSS Statistics (22.0). Log rank test procedure was used to determine effects of microbiota on survival test. The significance level to threshold was set at P < 0.05.The differences in body mass, development duration and eggs hatching success between GF and conventionally reared (CR) group was detected by independent The culture-dependent validations revealed that the greatest quantity of bacterial colonies was found in guts of CR group, followed by DNA and NDA, while no bacterial colony was detected in GF group . Moreovet-test: t = 0.310, P = 0.91) and the time to hatching of dechorionated eggs and controls . When compared to CR larvae, body mass of RPW GF individuals were decreased by 17.59% (No significant difference was detected in hatching success (y 17.59% .F2,20 = 38.02, P < 0.001, F4,20 = 615.31, P < 0.001, F4,20 = 527.84, P < 0.001, No significant differences were found in our assayed nutrient indices in RPW larvae from NDA and CR group , revealiF4,20 = 3.26, P = 0.033, The concentrations of protein, glucose and TAG in GF+B larvae, with introduced gut microbiota, were still markedly lower than those of CR insects. But their contents were significantly increased in contrast with GF larvae . TherefoE. cloacae and L. lactis, being harvested from gut homogenate of gnotobiotic larvae was 30,317.66 ± 1913.98 CFUs/ml and 24150.00 ± 4084.42 CFUs/ml (E. cloacae and L. lactis were successfully established. Furthermore, we also found that gnotobiotic RPW larvae with L. lactis exhibited markedly higher protein content as compared to that of GF insects (ANOVA: F3,12 = 8.459, P = 0.003, L. lactis could restore the protein level to that of CR larvae. In contrast, the level of TAG (ANOVA: F3,12 = 12.357, P = 0.001, F3,12 = 13.215, P < 0.001, E. cloacae were significantly improved in contrast to the GF individuals. But no significant difference was detected in trehalose content between gnotobiotic and GF groups (ANOVA: F3,12 = 1.985, P = 0.304, E. cloacae and L. lactis, could play different roles in regulating the nutrition status of RPW larvae.The number of  CFUs/ml , respectD. melanogaster must consume bacteria from its food to establish and maintain its microbiome (Recent evidence demonstrated that RPW harbor a complex gut microbiota with profound effects on food digestion and nutrition assimilation of this pest . Howevercrobiome . In the Acetobacter and Lactobacillus species, and are known to reduce the digestibility of starch and other carbohydrates by mammals (L. plantarum in D. melanogaster can enhance its dietary protein digestion and amino acid intake by upregulating the expression of intestinal peptidases (D. melanogaster (A. mellifera (We found body mass of RPW GF larvae was less than that of CR insects and the development rate from eggs to GF prepupae was prolonged significantly as well, suggesting the promoting effect of gut microbiota on the growth and development of this pest. Additionally, our data uncovered that the content of hemolymph protein, glucose and triglyceride of GF larvae were significantly lower than that of CR insects. Candidate bacterial products are acetic acid and lactic acid, which are derived from  mammals . L. planptidases . Therefonogaster , 2018 anellifera . To noteellifera . Unfortuellifera . More reL. lactis to RPW GF larvae could elevate the protein content significantly. L. lactis, being involved in the production of fermented dairy products containing lactic acid (Lactobacillus has been used as model lactic acid bacteria because they are widely recognized as the potential health beneficial microbes in human gastrointestinal tract (L. plantarum can promote the growth of Drosophila under chronic undernutrition via enhancing the expression of intestinal peptidases and then increasing host dietary protein digestion and amino acid intake (L. plantarum-derived D-alanylated teichoic acids can ensure the optimal intestinal peptidase expression (L. lactis on the nitrogen nutrition assimilation of this pest remain elusive. Because L. lactis is a lactic acid bacterium as well, we postulate that this commensal bacterium possibly enhance host physiology via the similar actions.In this study, we uncovered that feeding tic acid , is prestic acid . Lactobaal tract . More ind intake . Furtherpression . HoweverEnterobacteriaceae has been determined as the dominant populations in the RPW gut (Enterobacteriaceae are known as nitrogen fixers (E. cloacae exhibited the dramatically recapitulated level of hemolymph glucose and triglyceride, being consistent with the observed effects of E. cloacae B29 in gnotobiotic mice (E. cloacae, producing various carbohydrate modifying and glycolytic enzymes like cellulases, trehalases and other glucosidase to facilitate insect nutrition acquisition, was found to be the most abundant species of gut bacteria in P. xylostella larva (E. cloacae can assist in trehalose hydrolysis by secreting trehalase to modulate energy metabolism in P. xylostella (E. cloacae might release some carbohydrate hydrolytic enzymes into RPW larva gut to facilitate the digestion of food polymers to regulate its energy metabolism. Collectively, the mechanisms behind how E. cloacae and L. lactis affect nutrition metabolism of RPW need be defined further with these gnotobiotic models. RPW gut . Membersn fixers . Here, wtic mice . E. cloala larva . Recentllostella . In thisL. lactis and E. cloacae, could restore the content of some nutrition in contrast to GF RPW larvae. Given the importance of gut microbiota on RPW physiology, it is a very promising alternative way to control this pest by disrupting the interactions between RPW and its gut microbiota.In summary, we determined that RPW has established facultative mutualistic interplays with its gut microbiota. Gut bacteria promote the development and growth of RPW via modulating nutrition metabolism of this pest. Additionally, the monoassociation of two dominant gut bacteria, Rhynchophorus ferrugineus is exempted from above mentioned requirements.ZS conceived and designed this project. PH, AM, TJ, RX, and XY performed the experiments. ZS, PH, AM and YH performed the analysis and wrote this manuscript. All authors have approved the final version of the manuscript.The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.


N-methyl-d-aspartic acid (NMDA) or TWS119 (an activator of the β-catenin pathway). After the corresponding treatment with EBR-84, RGC death and the protein expression levels of β-catenin, cyclooxygenase-2 (COX-2), and vascular endothelial growth factor (VEGF) in rat retinas were examined. β-Catenin accumulated in the retinal ganglion cell layer (GCL) of NMDA-treated rats. EBR-84  significantly attenuated the NMDA-induced RGC loss accompanying the reduction of β-catenin expression. Moreover, the expression levels of COX-2 and VEGF were decreased by EBR-84 in a dose-dependent manner. For the TWS119-treated rats, EBR-84 also ameliorated RGC loss and lowered the expression levels of β-catenin, COX-2, and VEGF. In vitro, EBR-84 increased the viability of NMDA-treated RGC-5 while decreased β-catenin expression. In conclusion, EBR-84 retarded ratretinopathy, and the β-catenin signaling pathway played an important role during this protective process.Extract of the Blood Circulation-Promoting Recipe (EBR-84) from the Chinese Herbal medicine “Blood Circulation Promoting Recipe” could retard retinopathy development. This study investigated whether EBR-84 protects retinas by inhibiting the β-catenin pathway using a rat model of retinopathy and a retinal ganglion cell 5 (RGC-5) cell death model. RGC death was induced by either  N-methyl-d-aspartic acid (NMDA) by intravitreal injection cause the loss of retinal ganglion cells (RGCs) and vascular degeneration in rats [N-methyl-d-aspartic acid receptor (NMDAR) activation leads to Wingless/Integrated (Wnt) release and nuclear accumulation of β-catenin in primary cortical cultures [High concentrations of  in rats ,2,3. Sev in rats ; (2) α-a in rats ,6; (3) t in rats ; (4) cyc in rats ; and (5)cultures .The Wnt/β-catenin signaling pathway was found to play an important role in retinopathy . In the Panax notoginseng, Salvia miltiorrhiza Bunge, Herba Epimedii, and Chrysanthemum morifolium Ramatc, was based on the known functions of each herb used in traditional Chinese medicine for a variety of medical purposes, including retinopathy [In our previous study, we found that EBR-84, which was extracted from the traditional Chinese compositus “Huo-Xue-Qu-Yu-Fang” (Blood Circulation-Promoting Recipe), could alleviate the NMDA-induced RGC loss . This miinopathy .Despite the various effects of EBR-84 on retinopathy, knowledge of its effect and mechanism on retinopathy is limited. We hypothesized that EBR-84 is protective against NMDA-induced retinopathy. The present study investigated whether EBR-84 could protect NMDA-induced retinopathy by regulating the Wnt/β-catenin signaling pathway. Retina-injured rat models were generated by the intravitreal injection of NMDA (40 nM). By counting the RGC number and measuring the expression levels of β-catenin, COX-2, and VEGF present in the retinas of rats treated with EBR-84, we examined the neural protective effect of EBR-84 on retinal damage in rats. Additionally, the β-catenin signaling pathway was mimicked by intravitreal injection of TWS119 (a GSK-3β inhibitor) to the rats. This could further prove whether EBR-84 protects retinas by regulating the β-catenin signaling pathway.O-beta-glucoside (3), Rutin (4), Isorhamnetin-3-O-beta-glucoside (5), Luteolin (6), Epimedium glucoside (7), and Tanshinone 2A (8) by their retention time and UV absorbance of purified standards. To determine the quantity of these eight components in the herbal medicinal compositus prescribe, calibration curves were calculated based on the peak areas obtained from the chromatograms of eight different concentrations of the standard solutions. According to the plot of the peak–area ratio (y) vs. concentration , the regression equations of the eight constituents and their correlation coefficients (r) were determined as follows: Salvianic acid A sodium, y = 8976x − 9488 (r2 = 0.9995); Salvianolic acid, y = 40990x − 41287 (r2 = 0.9996); Quercetin-7-O-beta-glucoside, y = 61725x − 59699 (r2 = 0.9995); Rutin, y = 36799x − 38193 (r2 = 0.9993); Isorhamnetin-3-O-beta-glucoside, y = 59873x − 60198 (r2 = 0.9992); Luteolin, y = 7548.6x − 6919 (r2 = 0.9999); Epimedium glucoside, y = 13039x − 14678 (r2 = 0.9998); Tanshinone 2A, y = 10128x − 11134 (r2 = 0.9997). The high correlation coefficients of the calibration curves of each concentration indicated a good linearity within the range under investigation. Quantification was performed on the basis of linear calibration plots of the peak areas versus the concentration. The relative contents of components from 1–8 in the herbal medicinal compositus prescribed were 0.67%, 5.87%, 3.79%, 0.76%, 0.89%, 0.95%, 1.69%, and 1.21%, respectively.HPLC chromatograms showed eight marker component peaks present in herbal medicinal compositus prescribed samples and the analyzed peaks had appropriate baseline separations with adjacent peaks. As shown in p < 0.001). MK801 is a selective NMDA-receptor antagonist [p < 0.001). Rats were pre-treated for 7 days with EBR-84  before the injection with NMDA, and experiments were continued for another 7 days with persistent EBR-84 treatment. In comparison to the NMDA group, EBR-84  increased the RGC number by approximately 10%, 27%, and 34% (p < 0.001), respectively, indicating that pretreatment with EBR-84 could retard RGC loss in a dose-dependent manner. EBR-84 (7.8 g/kg) exhibited a similar effect to MK801 (1 μM).As shown in tagonist ,18 that The immunofluorescence images show more intensive β-catenin signals in RGCL of NMDA-treated rats compared to normal rats. MK801 and EBR-84 prevented the NMDA-induced RGCL β-catenin expression a. p < 0.001). As shown in p < 0.001). These results suggested that the protective effect of EBR-84 on the NMDA-damaged retinas might have been due to inhibition of the β-catenin pathway. A Western blot result revealed a similar increase in β-catenin expression in the NMDA-treated retina b. Pretrep < 0.001). There was also a statistically significant difference in the expression of VEGF between the NMDA group and the control group (p< 0.001). In addition, the VEGF expression in the RGCL of the rat retinas was dose-dependently reduced by EBR-84  .To further prove the effect of EBR-84 on both COX-2 and VEGF expression, we analyzed their content levels by Western blot assay. As seen in The results demonstrated that EBR-84 inhibited the NMDA-induced overexpression of COX-2 and VEGF in the RGCL of rats, accompanied by a reduced RGCL β-catenin level, which indicated that ERB-84 could recover the RGCL neuron number by inhibiting the β-catenin pathway.p < 0.001).To further elucidate whether EBR-84 protects NMDA-damaged retinasby inhibiting β-catenin signaling activation, we injected the rats with 1 μM TWS119, which mimics the canonical β-catenin signaling pathway by inhibiting GSK-3β . As showp < 0.001). EBR-84 (7.8 and 15.6 g/kg) decreased the COX-2 expression level by 22% and 44%, respectively (p < 0.001).COX-2 and VEGF were inspected by immunohistochemical assays, and the expression levels were quantified using a multispectral image analysis a. TWS119ectively b. In FigThe effect of EBR-84 on the RGCL neuron number of the TWS119-treated rats was then studied. The H&E staining result indicated that EBR-84  remarkably ameliorated the TWS119-caused RGC loss. The cell counting measurement shown in p < 0.001). As we increased the concentration of EBR-84 , the viability of the RGC-5 cells incubated with NMDA gradually increased. MK801, which is a selective inhibitor of NMDA, showed a similar effect to EBR-84 (3.7 μg/mL).The RGC-5 cell line is a transformed proliferating cell line that expresses RGC-specific markers similar to RGCs . First, p < 0.001). EBR-84 (37 μg/mL) and DKK-1 (50 ng/mL) attenuated the NMDA-induced high expression of β-catenin (p < 0.001). The depressive effect of 1 μM MK801 on the β-catenin expression was lower than that of the EBR-84 (37 μg/mL) and DKK-1 (50 ng/mL), but still notable (p = 0.039). These findings once again demonstrated that EBR-84 blocked the β-catenin pathway, and thereby protected the NMDA-damaged retinas.We next investigated the effects of EBR-84 on β-catenin expression in the RGC-5 cell line. RGC-5 cells were cultured in Dulbecco’s modified Eagle’s medium (DMEM) with 40 nM NMDA. As shown in Retinal ganglion cells (RGCs) are responsible for the transmission of visual signals to the brain, which plays an important role in retinopathy. Progressive death of RGCs occurring in glaucoma and several other retinal diseases can lead to visual impairment and blindness . IntraviMany signaling pathways are involved in this NMDA-induced RGC damage, including the TGF-β, bone morphogenetic protein , and AMPCOX-2 and VEGF are targets of the β-catenin signaling pathway in different tissues ,14. COX-Moreover, we mimicked the β-catenin signaling pathway by intraperitoneal injection of TWS119 (1 μM). Activation of β-catenin signaling triggers a series of molecular events that involves inhibition of the kinase GSK-3β (encoded by GSK-3β), thereby inhibiting the phosphorylation of β-catenin, inhibiting proteasomal degradation, and enhancing accumulation of β-catenin in the cytosol and nucleus. As a result, GSK-3β inhibitors have been used experimentally as pharmacologic activators of Wnt/β-catenin signaling . TWS119,Panax notoginseng, Salvia miltiorrhiza Bunge, Herba Epimedii, and Chrysanthemum morifolium Ramatc, etc., which is clinically used to treat retinopathy by cleansing the liver, improving blood circulation, and promoting healing [Salvia miltiorrhiza Bunge could promote neural stem cell proliferation and differentiation, which may contribute to protecting retinal neurons [Salvia miltiorrhiza Bunge could ameliorate microvascular disturbance in rat [EBR-84 is an extract of a Chinese herbal blood circulation-promoting recipe, composed of  healing ,16. A pr neurons . Moreovee in rat ,36, whicIn our study, EBR-84 alleviated the NMDA-induced RGC loss. We found that EBR-84 could reduce the overexpression of β-catenin, COX-2 and VEGF in rat retinas. This suggested that EBR-84 protected the retina by regulating the β-catenin signaling pathway. To further elucidate whether EBR-84 protected against retina damaged through inhibiting β-catenin signal activation, we injected the rats with TWS119 (1 μM) to activate the β-catenin signaling pathway. EBR-84 depressed β-catenin expression, downregulated COX-2 and VEGF expression and reversed the neuron loss caused by TWS119. MK801 could inhibit the NMDA-induced retina damage to some extent, because it is a selective NMDA receptor inhibitor, but nevertheless, it could not block the TWS119-induced RGCL neuron loss. Combined with the preceding results, EBR-84 could protect NMDA-damaged retinas by blocking the β-catenin pathway.In the retina, RGC cells are particularly sensitive to excitotoxicity, and excess glutamate has been proposed to underlie common neurodegenerative disorders of the eye . We alsoIn summary, our data show that the expression of β-catenin, COX-2, and VEGF were increased during the RGC loss induced by NMDA or TWS119, while EBR-84 could alleviate the RGC loss by inhibiting the β-catenin signaling pathway. Our findings also suggest that EBR-84 could potentially be developed as effective agents for retinal diseases.Panax notoginseng, Chrysanthemum morifolium Ramat, Herba Epimedii, and Salvia miltiorrhiza Bunge were collected from Yunnan, Zhejiang, Sichuan, and Anhui provinces, respectively, in China in 2010 and identified by botanist Hong Zhang . All voucher specimens were deposited at the herbarium of the Department of Pharmacy, South-Central University for Nationalities . These medicinal herbs, which constitute EBR-84, were prepared as followed.Panax notoginseng and Chrysanthemum morifolium Ramat were extracted with 10 volumes of 70% ethanol for 90 min at 60 °C. The procedure was repeated twice. The extracts were filtered and then concentrated under vacuum into residues. Herba Epimedii and Salvia miltiorrhiza Bunge were extracted twice with 15 volumes of hot water (60 °C) for 90 min. After filtration, the supernatant was separated and concentrated. All residues were lyophilized into powder. The EBR-84 was obtained and used for animal administration. The weight ratio of the dried Panax notoginseng, Chrysanthemum morifolium Ramat, Herba Epimedii, and Salvia miltiorrhiza Bunge was 1:2:2:2. The respective yields of the extracts were 28%, 31%, 16%, and 19% (w/w).3COOH solution (solvent A) and methanol (solvent B) at a flow rate of 1.0 mL/min. The gradient elution program was performed as follows: 0–20 min, 10–30% B; 20–40 min, 30–60% B; 40–60 min, 60–85% B; 60–80 min, 85–100% B, and then back to the stable initial conditions for another 10 min. The column temperature was maintained at 25 °C and the detection wavelength was set at 210 nm for all of the mixtures. The identification of the investigated mixtures was confirmed by comparison of their retention times with those of standards under the same conditions. All chromatographic measurements were performed on an Agilent 1200 liquid chromatography system  equipped with avacuum degasser, a quaternary, low-pressure mixing pump, a thermostated column compartment, and an Agilent 1200 photodiode array detector. Chromatographic separation was performed on a Thermo Syncronis RP-C18 column . The sample solution was filtered through a syringe filter (0.45 μm), and the injection volume was 10 μL. The mobile phase consisted of a 0.1% CFExperimental male Sprague-Dawley rats (weighing 180–200 g) were provided by Hubei Centers for Disease Control and Prevention . Animals were acclimatized to the laboratories for one week prior to manipulation and were group housed in a controlled environment  under a 12-h light–dark cycle. The rats were allowed free access to food and water. The care and use of animals and experimental protocols for this study were performed according to the Guide for Animal Experimentation, South-Central University for Nationalities and the Committee of Research Facilities for Laboratory Animal Sciences, South-Central University for Nationalities, China. The protocols were approved by the Committee on the Ethics of Animal Experiments of the South-Central University for Nationalities, China .The protocol of animal grouping and drug treatment is shown in n = 5 per eye) [The slides containing the rat eyes were stained with H&E. The RGC number was randomly counted in six areas of the RGC layer (100 μm each) of each section (per eye) . The immper eye) ,39.The multispectral image analysis of slides was referred to our previous study . BrieflyWestern blot analysis was performed as previously described . Anti-β-2 filter-capped cell culture flasks and incubated under 5% CO2 at 37 °C. Cells were passaged every 2–3 days. RGC-5 cells of passages 7–15 were used in the present study.RGC-5 cells were purchased from the American Type Culture Collection . They were cultured in DMEM containing 10% fetal bovine serum, 25 mM glucose, 100 U/mL penicillin, and 100 μg/mL streptomycin . The cel2 at 37 °C. Then, the cells were divided into nine groups for a further 24 h culture: control group, NMDA (40 nM), five NMDA+EBR-84  groups, an NMDA+MK801 (1 μΜ) group, and an NMDA+DKK-1 (50 ng/mL) group. After the treatments with various agents, cells were incubated with MTT  as described previously [Cell viability of RGCs was assessed using the colorimetric reagent, 3--2,5-diphenyltetrazolium bromide . Briefly, cells were seeded in 96-well plates, with 5000 cells in 100 μL medium per well, and cultured for 24 h for cell stabilization under 5% COeviously . The iniImmunocytochemical staining for β-catenin in cultured RGC-5 cells on coverslips was performed as described previously . Brieflyp < 0.05 was considered to indicate a significant difference.The neuron numbers and the expression levels of β-catenin, COX-2, and VEGF in each group were normalized to that of the control group. Data are presented as the means ± Standard error of the mean (SEM). All statistical analyses were performed using the SPSS 19.0 software package . The statistical significance of differences was determined by a one-way ANOVA with Tukey’s post hoc test.


Myocardial infarction (MI) is one of the most serious health threats, resulting in huge physical and economic burdens worldwide. Wnt signaling pathways play an important role in developmental processes such as tissue patterning, cell differentiation and cell division. Appropriate regulation of the activities of Wnt signaling pathways is also important for heart development and healing in post-MI heart. Moreover, Wnt pathway inhibitors have been identified as novel antitumor drugs and applied in ongoing clinical trials. This research progress has generated increasing interests for investigating the effects of Wnt pathway inhibitors on MI healing. In this short review, we summarize the roles of Wnt signaling pathways in post-MI heart and the therapeutic effects of Wnt pathway inhibitors on MI, and discuss the underlying mechanisms of Wnt pathway inhibitors in cardiac repairing. Myocardial infarction (MI) is one of the leading causes of morbidity and mortality and threatens human health worldwide . In spitWnt signaling pathways are essential in heart development and active in post-MI adult heart , 5. ReceInt-1, and then it was found to be a homologous gene of the wingless gene in Drosophila [β-catenin pathway, non-canonical Wnt/planar cell polarity (PCP) pathway and Wnt/Ca2+ dependent pathway  protein inside the cell [LRP) is also found in the Wnt/Frizzled complex [β-catenin is phosphorylated and degraded by a destruction complex, including Axin, glycogen synthase kinase 3β (GSK3β), adenomatous polyposis coli (APC), and casein kinase 1α (CK1α). Wnt stimulation phosphorylates Dvl protein, dissociates β-catenin and induces the nuclear translocation of β-catenin, leading to its interaction with TCF/LEF transcription factors and gene transcription [Dvl phosphorylation results in RhoA/ROCK and Rac/Jnk/NFAT pathway activation [2+ pathway, phospholipase C (PLC) is activated and induces the accumulation of intracellular Ca2+, leading to the activation of calmodulin-dependent kinase II (CamKII), calcineurin and protein kinase C (PKC) [Wnt was first discovered as osophila . Wnt sigthe cell . The secthe cell . Another complex . In the cription . In the tivation . In the  C (PKC) .Fig. 1WnWnt-3 and Wnt-8a regulate mesoderm induction through canonical signaling, whereas Wnt-2, Wnt-5a/b, and Wnt-11 regulate cardiomyocyte differentiation via non-canonical signaling [β-catenin is the central player of the canonical Wnt pathway, and its nuclear accumulation is a hallmark of Wnt signaling activation [β-catenin-mediated transcription is linked with atrial and ventricular growth in zebrafish, indicating its role in cardiomyocyte proliferation [The mammalian heart is developed from the lateral plate mesoderm, which fuses together to form the heart tube and subsequently organizes the ventricle and atria . Wnt sigignaling . Dissocitivation . The Wnttivation , 25. Morferation .The pathological process of MI injury includes three major phases: inflammatory reaction, the formation of granulation tissue and fibrosis . The infWnt-2, Wnt-4, Wnt-10b, and Wnt-11 upregulation 5 days after MI [Wnt-1 was upregulated from 1 to 14 days after MI, and upregulated Wnt-4 was observed from 7 to 14 days after MI in a subsequent study [Axin2 and LEF1 are both Wnt target genes and serve as readouts of Wnt signaling intensity. Using a fate-mapping technique with the Axin2 promoter and LacZ labeling, Wnt signaling is proved to be activated in cardiomyocytes located at infarct border zone [TCF/LEF1, increased Wnt signaling activity has been detected 4 days after MI [There is increasing evidence showing that Wnt signaling is triggered during the pathological process of MI injury. Quantitative analysis for the expression of Wnt proteins has shown strong after MI . Wnt-1 wnt study . Axin2 ader zone . The intder zone . Using Tafter MI . AccordiWnt-5a, which is exclusively expressed in cardiomyocytes [IL-1, IL-6 and IL-8 from mononuclear cells, indicating the pro-inflammatory effect of Wnt signaling [SFRP1 are transplanted into the infarcted border zone by blocking leukocyte activation and cytokine production [SFRP5 is able to inhibit inflammatory cytokine and chemokine gene expression in ischemic heart [) leads to more inflammatory monocytes and severe adverse remodeling, whereas cardiomyocyte-specific WIF1 overexpression attenuates monocyte response and improves cardiac function [CamKII /NFAT pathway [SFRP5 functions to mitigate inflammation through the non-canonical Wnt-5a/JNK signaling pathway [The inflammatory reaction is first activated in response to MI, which aims at removing necrotic debris and healing the infarct but also leads to cardiac remodeling and dysfunction . It has myocytes , promoteignaling . Moreoveoduction . Similaric heart . Furtherfunction . Increas pathway . In addi pathway .β-catenin [β-catenin in endothelial cells shows progressive cardiac dysfunction via ErbB signaling, indicating Wnt inhibition as a therapeutic strategy for heart failure [SFRP1 can increase capillary density in the scar of MI by inhibiting the accumulation of cytosolic β-catenin [DKK1) has shown a diminished role in regulating angiogenesis [β-catenin via inhibiting GSK3β, is able to promote angiogenesis and improve ventricular function following MI by activating the Wnt signaling pathway [WISP-1), which is a responsive gene of Wnt activation [Angiogenesis is reflected as newly formed vessels by endothelial cells, which contribute to cardiac repair and functional recovery after MI. A previous study showed that the Wnt signaling pathway was activated in endothelial cells of the infarct area, which were identified by the accumulation of -catenin . Conditi failure . Indeed,-catenin . Dickkopogenesis . However pathway . Moreovetivation , can protivation .β-catenin signaling pathway plays a dominant role in the regulation of cardiac fibrosis following MI. There is evidence showing that the down-regulation of β-catenin by aldehyde dehydrogenase-2 (ALDH2) activity leads to reduced cardiac fibrosis, which may be mediated by phosphorylated GSK3β, Wnt-1, and WISP-1 [β-catenin and myofibroblast proliferation via directly binding with DKK2 [Wnt-3a can promote myofibroblast differentiation and TGFβ expression by triggering the canonical Wnt signaling pathway [TGFβ in the β-catenin-dependent pathway using a mouse model of autoimmune myocarditis [β-catenin function in cardiac fibroblasts results in improved cardiac function and suppressed interstitial fibroblasts in a mouse model of pressure overload [β-catenin interruption in cardiac fibroblasts, impaired wound healing and decreased cardiac performance have been observed [β-catenin in epicardial cells leads to compromised cardiac function after MI [Cardiac fibrosis is a common process in remodeling hearts after MI that is formed by myofibroblast invasion and collagen secretion . The cand WISP-1 . In the ith DKK2 . Furtherith DKK2 . Wnt-3a  pathway . The Wntcarditis , and losoverload . Howeverobserved . Conversafter MI . These dAxin degradation and stabilizing β-catenin, which can increase Ki67+ cardiomyocytes in the peri-infarct area and alleviate cardiac remodeling in a mouse model of MI [Wnt-3a/5a, and acts as an antagonist for Frizzled proteins to inhibit Wnt signaling pathway transduction. UM206 could reduce infarct size, increase the numbers of capillaries, decrease myofibroblasts in infarct area of post-MI heart, and ultimately suppress the development of heart failure [β-catenin and CBP in the Wnt canonical signaling pathway, can promote the differentiation of epicardial progenitors, thereby contributing to myocardial regeneration and improving cardiac function in a rat model of MI [Wnt-3, a pro-fibrotic agonist, from cardiac fibroblasts and its signaling to cardiomyocytes [DVL protein is decreased in both the canonical and non-canonical Wnt signaling pathways by WNT-974 administration [Sca1+ cardiac progenitors and reducing the apoptosis of cardiomyocytes [Hippo/YAP-independent manner [Pyrvinium is a drug approved by the FDA and identified as a Wnt inhibitor by inhibiting el of MI . UM206 i failure . ICG-001el of MI . Small mmyocytes . The phostration . GNF-623t manner . These rBoth the canonical and non-canonical Wnt signaling pathways are essential for heart development and play an important role in adult heart with MI injury. The inhibition of Wnt signaling pathways has been demonstrated to be beneficial in MI via improving cardiac remodeling. These Wnt inhibitory compounds have been proven to be safe in clinical trials and can be potential drugs for treating MI. However, novel Wnt pathway inhibitors with minimized toxicity and a more exclusive effect on the heart are still needed.


Network connectivity problems are abundant in computational biology research, where graphs are used to represent a range of phenomena: from physical interactions between molecules to more abstract relationships such as gene co-expression. One common challenge in studying biological networks is the need to extract meaningful, small subgraphs out of large databases of potential interactions. A useful abstraction for this task turned out to be the Steiner Network problems: given a reference “database” graph, find a parsimonious subgraph that satisfies a given set of connectivity demands. While this formulation proved useful in a number of instances, the next challenge is to account for the fact that the reference graph may not be static. This can happen for instance, when studying protein measurements in single cells or at different time points, whereby different subsets of conditions can have different protein milieu.condition Steiner Network problem in which we concomitantly consider a set of distinct biological conditions. Each condition is associated with a set of connectivity demands, as well as a set of edges that are assumed to be present in that condition. The goal of this problem is to find a minimal subgraph that satisfies all the demands through paths that are present in the respective condition. We show that introducing multiple conditions as an additional factor makes this problem much harder to approximate. Specifically, we prove that for C conditions, this new problem is NP-hard to approximate to a factor of monotonically between conditions, and show that this problem admits substantially improved approximation algorithms. We also developed an integer linear programming solver for the general problem and demonstrate its ability to reach optimality with instances from the human protein interaction network.We introduce the Our results demonstrate that in contrast to most connectivity problems studied in computational biology, accounting for multiplicity of biological conditions adds considerable complexity, which we propose to address with a new solver. Importantly, our results extend to several network connectivity problems that are commonly used in computational biology, such as Prize-Collecting Steiner Tree, and provide insight into the theoretical guarantees for their applications in a multiple condition setting. The body of knowledge encapsulated within the human PPI network  networks, edges represent physical contact between proteins, either within stable multi-subunit complexes or through transient causal interactions  is rout studies –6. The bument}vi . In anote active , 9.S) is assumed to be known  for each protein (node) in the network . The goal in this case is to find a probable subtree which contains the majority of the high cost proteins in G, while accounting for penalties paid by both edge usage and missing proteins, in order to capture the biological activity represented in such a network [In most applications, the identity of the so called terminal nodes , ChIP-seq . Tuncbagcondition can refer to different points in time [While these studies contributed to our understanding of signal transduction pathways in living cells, they do not account for a critical aspect of the underlying biological complexity. In reality, proteins (nodes) can become activated or inactivated at different conditions, thereby giving rise to a different set of potential PPIs that might take place . Here, t in time , differe in time , or, mor in time . Indeed, in time . A compl in time .k-Labeling (MKL)” problem. In this setting, each connectivity demand comes with a label, which represents a certain experimental condition or time point. The task is to label edges in the PPI network so as to satisfy each demand using its respective label, while minimizing the number of edges in the resulting sub-graph and the number of labels used to annotate these edges. While MKL was an important first step, namely introducing the notion of different demands for each condition, the more difficult challenge still remains that of considering variability in the reference graph, namely different sets of proteins that may be active and available for use in each condition. To this effect, we note the existence of multi-layer networks in the data-mining space. In this context, studies have focused on networks which have edges that span across specified dimensions, or conditions [The next challenge is therefore to study connectivity problems that take into account not only the endpoints of each demand, but also the condition in which these demands should be satisfied. This added complication was tackled by Mazza et al. , who intnditions , 16. HowG, a set of C conditions and a set of u to node v through a path of nodes that are present in condition c”. The goal is to find a minimum-weight subgraph of G that satisfies all the demands  problem. In this setting, we are given a weighted undirected graph mentclass2pt{minimp(v) for each vertex), it is NP-hard to solve it concomitantly in C conditions, such that the weight of the obtained solution is less than C times that of the optimal solution. Interestingly, a theoretical guarantee of We first show that it is NP-hard to find a solution that achieves a nontrivial approximation factor . This result extends to several types of connectivity problems and provides a theoretical lower bounds to the best-possible approximation guarantee that can be achieved in a multiple condition setting Table . For insmentclass2pt{minimWhile these results provide a somewhat pessimistic view, they rely on the assumption that the network frames The Steiner Tree problem, along with its many variants and generalizations, form a core family of NP-hard combinatorial optimization problems. Traditionally, the input to one of these problems is a single  graph, along with requirements about which nodes need to be connected in some way; the goal is to pick a minimum-weight subgraph satisfying the connectivity demands.multi-condition perspective; in our setting, multiple graphs over the same vertex set , are all given as input, and the goal is to pick a subgraph satisfying condition-sensitive connectivity requirements. Our study of this problem draws motivation and techniques from several lines of research, which we briefly summarize.In this paper, we offer a shortest path between two nodes; this problem is efficiently solved using, for example, Dijkstra’s algorithm.A basic problem in graph theory is finding the T. A further generalization is Steiner Forest: given D. Currently the best known approximation algorithms give a ratio of 1.39 for Steiner Tree [A natural extension of this is the Steiner Tree problem: given a weighted undirected graph ner Tree  and 2 foner Tree . These pner Tree .k demands k is fixed, DSN admits a polynomial-time exact algorithm [k, the best known approximation algorithms have ratio r. This problem has an For directed graphs, we have the Directed Steiner Network (DSN) problem, in which we are given a weighted directed graph lgorithm . For genment}ϵ>0 , 22. On ment}ϵ>0  ruled oun scheme  and a lolog2-ϵn) .Finally, a Steiner variant that has found extensive use in computational biology is the Prize-Collecting Steiner Tree problem, in which the input contains a weighted undirected graph is known .conditionsIn this paper, we generalize the Shortest Path, Steiner Tree, Steiner Forest, Directed Steiner Network, and Prize-Collecting Steiner Tree problems to the multi-condition setting. In this setting, we have a set of Our main object of study is the natural generalization of Steiner Forest (in the undirected case) and Directed Steiner Network (in the directed case), which we call Condition Steiner Network:Condition Steiner Network (CSN)) We are given the following inputs:conditione in the underlying edge set A sequence of undirected graphs kconnectivity demandsA set of We call underlying graph. We say a subgraph satisfies demand H contains an a-b path P along which all edges exist in (Directed Condition Steiner Network (DCSN)) This is the same as CSN except that all the edges are directed, and a demand  must be satisfied by a directed path from a to b in  Steiner Tree, and Prize-Collecting Steiner Tree. We give hardness results and algorithms for these problems by demonstrating reductions to and from CSN and DCSN.Condition Shortest Path (CSP), Directed Condition Shortest Path (DCSP)) These are the special cases of CSN and DCSN in which the demands are precisely (Condition Steiner Tree (CST)) We are given a sequence of undirected graphs terminal nodessatisfies the terminal set H that exist at condition c. The goal is to find a minimum-weight subgraph H that satisfies (Condition Prize-Collecting Steiner Tree (CPCST)) We are given a sequence of undirected graph T that minimizes  This is the special case of DCSN in which the demands are precisely root vary across the conditions, or in which both nodes and edges vary. In Problem variants, we show that all three variants are in fact equivalent; thus we focus on the edge-based formulations.In this work, we perform a systematic study of the condition Steiner problems defined above, from the standpoint of approximation algorithms—that is, algorithms that return subgraphs whose total weights are not much greater than that of the optimal subgraph—as well as integer linear programming (ILP). Since all of the condition Steiner problems listed in the previous section turn out to be NP-hard  we cannot hope for algorithms that find optimal solutions and run in polynomial time.First, in Hardness of condition Steiner problems, we show a series of strong negative results, starting with (directed and undirected) Condition Steiner Network:CSN and DCSN are NP-hard to approximate to a factor ofas well asfor every fixedand every constantFor DCSN, this holds even when theunderlying graph is acyclic.(Main Theorem) C or k; the latter upper bound is easily achieved by the trivial “union of shortest paths” algorithm: for each demand , compute the shortest a-b path at condition c; then take the union of these k paths. This contrasts with the classic Steiner Network problems, which have nontrivial approximation algorithms and efficient fixed-parameter algorithms.Thus the best approximation ratio one can hope for is Next, we show similar hardness results for the other three condition Steiner problems. This is achieved by a series of simple reductions from CSN and DCSN.Condition Shortest Path, Directed Condition Shortest Path, Condition Steiner Tree, and Condition Prize-Collecting Steiner Tree are all NP-hard to approximate to a factor offor every fixedandO(C)-approximation algorithm. Our result shows that in the worst case, one cannot do much better.Note that each of these condition Steiner problems can be naively approximated by applying the best known algorithm for the classic version of that problem in each graph in the input, then taking the union of all those subgraphs. If the corresponding classic Steiner problem can be approximated to a factor of entclass1pt{minimamonotonic: if an edge e exists in some graph While these results provide a somewhat pessimistic view, the proofs rely on the assumption that the edge sets in the input networks  for a common root node r), we show the following:In the directed case, for monotonic DCSN with a single source . In experiments on real-world inputs derived from the human PPI network, we find that these ILPs are capable of reaching optimal solutions in a reasonable amount of time.Table Note that the formulations of CSN and DCSN in the introduction involved a fixed vertex set; only the edges change over the conditions. It is also natural to formulate the Condition Steiner Network problem with nodes changing over condition, or both nodes and edges. However by the following proposition, it is no loss of generality to discuss only the edge-condition variant.The edge, node, and node-and-edge variants of CSN are mutually polynomial-time reducible via strict reductions (i.e. preserving the approximation ratio exactly). Similarly all three variants of DCSN are mutually strictly reducible.We defer the precise definitions of the other two variants, as well as the proof of this proposition, to Problem variants.In this edge-condition setting, it makes sense to define certain set operations on graphs, which will be of use in our proofs. To that end, let Next we state the Label Cover problem, which is the starting point of one of our reductions to CSN.Label Cover (LC)) An instance of this problem consists of a bipartite graph projection functionsC is a common set of colors; G is a function satisfies an edge u, v) is consistent under (This problem was first defined in . It has For everythere is a constantsuch that the following promise problem is NP-hard: Given a Label Cover instancedistinguish between the following cases:(YES instance) There exists a total labeling ofG; i.e. a labeling that satisfies every edge.(NO instance) There does not exist a labeling ofGthat satisfies more thanedges.k-Partite Hypergraph Label Cover. Out of space considerations we defer the statement of this problem and its gap hardness to Proof of inapproximability for general C and k, where the C and demands k.In Hardness of condition Steiner problems, we use Label Cover to show k-Partite Hypergraph Label Cover.Here we outline our strategy for reducing Label Cover to the condition Steiner problems. First, we reduce to the CSN problem restricted to having only Consider the nodes The last piece of the construction is to ensure that the Steiner instance has a low-cost solution if and only if the Label Cover instance has a consistent labeling. This is accomplished by setting all the simple strand is a directed path of the form A contact edge. Contact edges have weight 1; all other edges in our construction have zero weight.In a simple strand, we say that bundle is a graph gadget consisting of a source node strands from A chain of bundles is a sequence of bundles, with the sink of one bundle serving as the source of another.A strand can be made more complicated, by replacing a contact edge with another bundle (or even a chain of them). In this way, bundles can be nested, as shown in Fig. More generally, a merge two or more simple strands from different bundles by setting their contact edges to be the same edge, and making that edge existent at the union of all conditions when the original edges existed  whose labels map to the same color are merged.u gets either label 1 or 2, v gets label 2) correspond to 2-CSN solutions of cost 1 -path, and both contain the -path). If this were a NO instance and edge e could not be satisfied, then the resulting 2-CSN sub-graphs The input is a YES instance of Label Cover whose optimal labelings  k-Partite Hypergraph Label Cover, a generalization of Label Cover to hypergraphs, to CSN, or DCSN with an acyclic graph. Using the same ideas as in the k demands composed of parallel paths corresponding to labelings, and merge edges so that a good global labeling corresponds to a large overlap between those paths. The full proof is left to Proof of inapproximability for general C and k. We perform a reduction from k-approximation algorithm is to simply choose k alone. In contrast, most classic Steiner problems have good approximation algorithms [k [Note that a mentclass2pt{minimgorithms , 24, 25,ithms [k .We take advantage of our previous hardness of approximation results in Theorem Condition Shortest Path, Directed Condition Shortest Path, ConditionSteiner Tree, and Condition Prize-Collecting Steiner Tree are all NP-hard to approximate to a factor offor every fixedandWe first reduce from CSN to CSP (and DCSN to DCSP). Suppose we are given an instance of CSN with graph sequence G. Add to a and b, which exist at all conditions e exist in Create new nodes Let Lastly, the demands are Initialize a, b, Given a solution Observe that essentially the same procedure shows that DCSN reduces to DCSP; simply ensure that the edges added by the reduction are directed rather than undirected.G. Take the set of terminals in each condition to be Next, we reduce CSP to CST. Suppose we are given an instance of CSP with graph sequence p. Suppose we are given an instance of CST with graph sequence G. Set p as follows:H spans the terminals H remains the same cost in the constructed CPCST instance. Conversely, suppose we are given a solution Finally, we reduce CST to CPCST. We do this by making an appropriate assignment of the penalties k, in the CSN instance is the same as the number of the conditions, C, in the CSP instance; we conclude that CSP is NP-hard to approximate to a factor of C remains the same in the two subsequent reductions, we also have that CST and CPCST are NP-hard to approximate to a factor of To summarize: in the first reduction from CSN to CSP, the number of demands, monotonic:In light of the strong lower bounds in the previous theorems, in this section we consider more tractable special cases of the condition Steiner problems. A natural restriction is that the changes over conditions are Monotonic {CSN, DCSN, CSP, DCSP, CST, CPCST}) In this special case (of any of the condition Steiner problems), we have that for each  for each k demands e has priority (ner Tree ) The inpPriority Steiner Tree was introduced by Charikar, Naor, and Schieber , who gavloglogn) . We now Fix any functionIf either Priority Steiner Tree or Monotonic CSN can be approximated to a factor off(k) in polynomial time, then so can the other.e has priority p(e), it now exists at all conditions We transform an instance of Priority Steiner Tree into an instance of Monotonic CSN as follows: the set of priorities becomes the set of conditions; if an edge Furthermore, the If Monotonic CSN can be approximated to a factor off(k) for some functionfin polynomial time, then Monotonic CST can also be approximated to withinf(k) in polynomial time.a, b, c) for each k, then the number of constructed demands is f(k)-approximation for CSN implies an We now show a reduction from CST to CSN. Suppose we are given a CST instance on graphs In the directed case, we give an approximation-preserving reduction from a single-source special case of DCSN to the Directed Steiner Tree (DST) problem , then apply a known algorithm for DST. Recall the definition of Single-Source DCSN:Single-Source DCSN) This is the special case of DCSN in which the demands are precisely root(Fix any functionIf either Monotonic Single-Source DCSN or Directed Steiner Tree can be approximated to a factor off(k) in polynomial time, then so can the other.w for each Now consider the DST instance For the remainder of this section, we refer to Monotonic Single-Source DCSN as simply DCSN. Towards proving the theorem, we now describe a reduction from DCSN to DST. Given a DCSN instance If the DCSN instancehas a solution of costthen the constructed DST instancehas a solution of cost at mostearliest necessary condition of  to be the minimum u, v) would cause Let There exists a solution that is a directed tree and has cost at most Moreover for every path infrom the root a to some target as we traversefrom a to, the earliest necessary conditions of the edges are non-decreasing.Consider a partition of e also does not cause If there is a directed cycle or parallel paths in the first sub-graph v has another in-edge in the sub-graph. However by monotonicity,  does not help satisfy any new demands, as v is already reached by some other path from the root. Hence by removing all such redundant edges, we have Now suppose by induction that for some We conclude that c is the earliest necessary condition of  in Now let mentclass2pt{minimc and To see that If the constructed DST instancehas a solution of costthen the original DCSN instancehas a solution of cost at mostC. For each i, then taking the free edges u, v) to First note that any DST solution ought to be a tree; let Lemma Monotonic Single-Source DCSN has a polynomial-timeapproximation algorithm for everyIt has noapproximation algorithm unlessThe upper bound follows by composing the reduction (from Monotonic Single-Source DCSN to Directed Steiner Tree) with the algorithm of Charikar et al.  for Direuthgamer , who shoIn Methods such as Directed Condition Steiner Network can be key in identifying underlying structure in biological processes. As a result, it is important to assess the runtime feasibility of solving for an optimal solution. We show via simulation on human protein–protein interaction networks, that our algorithm on single-source instances is able to quickly and accurately infer maximum likelihood subgraphs for a certain biological process.We represent the human PPI network as a weighted directed graph, where proteins serve as nodes, and interactions serve as edges. The network was formed by aggregating information from four sources of interaction data, including Netpath , PhosphoSingle-Source DCSN) This is the special case of DCSN in which the demands are precisely root at condition c, if it exists; each variable u, v) is ultimately in the chosen solution sub-graph; Each variable We note that DCSN easily reduces DCSP, as outlined in Theorem G, we sample an instance of the node-variant Single-Source DCSN as soa.Instantiate a source node a, for each of the C conditions, giving us Independently sample v lies on the shortest path from a to one of For each node c, include p.For all other nodes Using a workstation running an Intel Xeon E5-2690 processor and 250 GB of RAM, optimal solutions to instances of modest size (generated using the procedure just described) were within reach  problem and its directed variant, in which the goal is to find a minimal subgraph satisfying a set of O(C). At the same time, by relating the various condition Steiner problems to one another, we also obtained some positive results: the condition versions of Shortest Path and Steiner Tree admit good approximations when the conditions are monotonic. Moreover, all of the condition problems (with the exception of Prize-Collecting Steiner Tree) can be solved using a natural integer programming framework that works well in practice.Importantly, along the way we showed implications of these results for CSN on other network connectivity problems that are commonly used in PPI analysis—such as Shortest Path, Steiner Tree, Prize-Collecting Steiner Tree—when conditions are added. We showed that for each of these problems, we cannot guarantee  a solution with a value below There are several natural ways to formulate the condition Steiner Network problem, depending on whether the edges are changing over condition, or the nodes, or both.Condition Steiner Network (edge variant)) This is the formulation described in the introduction: the inputs are (Condition Steiner Network (node variant)) Let the underlying graph be t if all edges along that path exist in (Condition Steiner Network (node and edge variant)) The inputs are precisely  must be satisfied by a directed Similarly, define the corresponding directed problem Directed Condition Steiner Network (DCSN) with the same three variants. The only difference is that the edges are directed, and a demand . Similarly all three variants of DCSN are mutually strictly reducible.The following statements shall hold for both undirected and directed versions. Clearly the node-and-edge variant generalizes the other two. It suffices to show two more directions:u, v) be an edge existent at a set of conditions w, and an edge W in the node-and-edge instance corresponds to a node-condition solution of cost W, and vice-versa.(Node-and-edge reduces to node) Let  be an edge whose endpoints exist at conditions u, v) exist at conditions W in the node-condition instance corresponds to an edge-condition solution of cost W, and vice-versa. (Node reduces to edge) Let ) An instance of this problem consists of a k-partite, k-regular hypergraph k parts) and a set of possible labels projection functionG is a function strongly satisfies a hyperedge weakly satisfies a hyperedge (The following gap hardness for this problem was shown by Feige :For everyand every fixed integerthere is a constantsuch that the following promise problem ishard: Given ak-Partite Hypergraph Label Cover instancedistinguish between the following cases:(YES instance) There exists a labeling ofGthat strongly satisfies every edge.(NO instance) Every labeling ofGweakly satisfies at mostedges.The proof ofhardness andhardness follows the same outline as thecase  k-PHLC instance in the form i-th node in k demands) as follows. For every bundle from each k-tuple e is the shared edge. If there are no such combinations, then the e-bundle is a single simple strand.Given the For k-PHLC, with optimal labeling c and for each The analysis follows the E|, one for each contact edge. The sub-graphs of In tallying the total edge cost, k-PHLC, so that for any labeling e, the projection functions of all nodes in e disagree. By definition, any solution to the constructed DCSN instance contains a simple c. As before, E|. However, at least G cannot be weakly satisfied; for these hyperedges e, for every pair of neighbors e-bundle in e-bundle in e in the k-PHLC instance). Therefore paths Conversely suppose we have a NO instance of It follows from the gap between the YES and NO cases that DCSN is NP-hard to approximate to within a factor of We provide a modified version of the approximation algorithm presented in Charikar et al.  for Direv with distance C from the source, and with distance 0 to each target. In such a case, this trivial approximation algorithm will achieve only an v, which was connected to all the targets via shortest path. In the case of the above example, this would find us the optimal sub-graph. The algorithm below generalizes this process, by progressively finding optimal substructures with good cost relative to the number of targets connected. We show that this algorithm provides a good approximation ratio.We provide a similar explanation as of that presented in Charikar et al. Consider a trivial approximation algorithm, where we take the shortest path from the source to each individual target. Consider the example where there are edges of cost Metric closure of a condition graph) For a directed condition graph metric closure to be w, (V(T)) Let T be a tree with root r. We say a demand of the form  is satisfied by T if there is a path in T from r to b at condition c. V(T) is then the set of demands satisfied by T.(D(T)) The density of a tree T is cost(T) is the sum of edge weights of T..In the input, rather than keeping track of the current root as some vertex v to x at condition j, i.e. The distance from some Instead of looping through all vertices in the form Therefore this algorithm guarantees the same approximation ratio for Monotonic Single Source DCSN as the original algorithm achieved for DST. In particular for all To see this, note that in our reduced instance, we see a collection of vertices, O(nik2i) , 394. \d