Novel reassortants detected in donor chickens co-infected with different combinations of the same A(H7N9) virus and different A(H9N2) viruses showed distinct onward transmission potential to contact chickens. Sequential transmission of novel reassortant viruses was only observed in one out of four co-infection combinations. Our results demonstrated different patterns by which influenza viruses may acquire genetic diversity through co-infection in ovo, in vivo, and under sequential transmission conditions.The segmented genome of influenza A virus has conferred significant evolutionary advantages to this virus through genetic reassortment, a mechanism that facilitates the rapid expansion of viral genetic diversity upon influenza co-infections. Therefore, co-infection of genetically diverse avian influenza viruses in poultry may pose a significant public health risk in generating novel reassortants with increased zoonotic potential. This study investigated the reassortment patterns of a Pearl River Delta-lineage avian influenza A(H7N9) virus and four genetically divergent avian influenza A(H9N2) viruses upon co-infection in embryonated chicken eggs and chickens. To characterize “within-host” and “between-host” genetic diversity, we further monitored the viral genotypes that were subsequently transmitted to contact chickens in serial transmission experiments. We observed that co-infection with A(H7N9) and A(H9N2) viruses may lead to the emergence of novel reassortant viruses  Avian influenza viruses (AIVs) are the primordial source of influenza viruses that have established themselves in humans and other animal species . Throughhaemagglutinin (HA) segments from H5N2 or H7N9 AIVs to human influenza A(H3N2) viruses [Recent studies have provided valuable insights into the mechanisms driving the genetic reassortment of influenza A viruses . Using t viruses ,9. Prote viruses . AlthougAvian influenza A(H9N2) viruses are enzootic among terrestrial poultry species in Asian, Middle Eastern, and African countries . A(H9N2)Among all known AIVs that have caused human zoonotic infections, the 2013 A(H7N9) virus is of most concern as the virus has caused 1568 confirmed human infections including 616 fatal cases . They has in vitro and in vivo have not been evaluated experimentally. Here, we aimed to characterize viral progenies generated after co-infection of A(H7N9) and A(H9N2) viruses of different genetic lineages in ovo and in White Leghorn chickens. We also monitored viral genotypes that were subsequently transmitted to the chickens in serial transmission experiments. Our results have implications for the evolution and transmission dynamics of AIV in avian hosts where co-infections might occur.A(H9N2) viruses were found co-circulating in poultry with A(H7N9) viruses in China ,31. WhilRisk assessment of co-infection protocols was conducted by the Office of Biological Safety at the University of Hong Kong (HKU). Chicken experiments were approved by the Committee on the Use of Live Animals in Teaching and Research at the HKU (#4532-17). All experiments using A(H7N9) virus were performed in a biosafety level 3 facility with restricted access following approved standard operating procedures at HKU.Madin-Darby canine kidney (MDCK) cells were maintained in modified Eagle’s medium (MEM) supplemented with 10% fetal calf serum (FCS), 1% penicillin-streptomycin (P/S), 1% vitamin, and 25 mM HEPES (Gibco). Human embryonic kidney (293T) cells were maintained in Opti-MEM supplemented with 5% FCS and 1% P/S.50) [A/silkie Chicken/Hong Kong/1772/2014(H7N9)  represents the Pearl River Delta lineage A(H7N9) viruses. The transmissibility of HK1772 in chickens had been described well previously . A/chick50) .4 PFU or 106 PFU of virus alone or a mixture of the A(H7N9) virus and one of the four A(H9N2) viruses at a 1:1 ratio with 104 PFU of each virus in a volume of 0.1 mL. After injection, eggs were incubated at 35°C and monitored routinely. Allantoic fluid was harvested at 1, 8, 16 or 48 hour post-infection (hpi) and was stored at -80°C. Viral titres were determined by plaque assay on MDCK cells. Co-infecting samples were further analysed by genotyping and NGS. Sequencing data are available in the NCBI BioProject PRJNA768344.Three 10-day old embryonated chicken eggs were infected with 10Viruses were purified using a modified plaque assay. Confluent MDCK cells in 100 mm dishes were infected with original samples and incubated at 37°C. At 70 hpi, plaques were visualized by 1% agarose containing 0.5 mg/mL MTT (Sigma-Aldrich). Individual plaques were picked randomly using 1 mL barrier tips. RNA was extracted from the agar plug using the QIAamp Viral RNA Mini Kit (Qiagen) according to the manufacturer’s instructions.H7/H9 and N2/N9 gene segments based on a multiplex probe combination on a ViiA 7 Real-Time PCR system (Thermo Fisher Scientific). Experiments were performed in 25 µL reaction mixtures with 5 µL of viral RNA and AgPath-ID One-Step RT-PCR reagents (Life Technology), in duplicate, according to the manufacturer’s instructions. High resolution melting (HRM) analysis was performed to differentiate six internal genes of the A(H7N9) virus from those of co-infected A(H9N2) viruses using LightCycler Gene Scanning Software according to the manufacturer’s instructions. Each internal gene segment was identified in 10 µL reaction mixtures containing 1 µL of the cDNA and HRM master mix (Roche) in 384-well plates, in duplicate, on the LightCycler 480 II instrument (Roche). The HRM curve was analyzed using the Light Cycler Software (Version 1.5). If any segment from a given plaque could not be determined, the plaque was not included in the reassortment analysis. Sequences of primers and TaqMan probes used in the study were listed in sTable 1. They were synthesized by Integrated DNA Technologies .A multiplex real-time RT-PCR was employed to simultaneously type 6 EID50 per virus) in 500 µL phosphate-buffered saline. At 1-day post-infection (dpi), three donors were moved and housed with three naïve contacts (designated as 1st contacts) that were separately housed in different cages. At 3 dpi, three 1st contacts were co-housed with three naïve chickens (designated as 2nd contact) in other three cages. At 5 dpi, all chickens were single-housed until the end of the experiment (17 dpi). Oropharyngeal swabs and cloacal swabs were collected from donors at 1, 2 and 3 dpi. Donors were euthanized at 3 dpi. Oropharyngeal swabs and cloacal swabs were collected from 1st contacts at 2, 3, 4, 5, 7, 9, 11 and 13 dpi and 2nd contacts at 4, 5, 7, 9, 11 and 13 dpi. All contacts were euthanized at 17 dpi. This experimental setting allowed assessing three independent primary transmission events (from donors to 1st contacts) and three secondary transmission events (from 1st contacts to 2nd contacts) under each combination of H7N9 and H9N2 co-infection. Totally, this chicken experiment contained 12 serially independent chicken transmission chains.White Leghorn chickens were hatched from SPF eggs and raised in a clean environment. Thirty-six 5-week-old chickens were randomly separated into four groups. For every group, three chickens (designated as donors) were inoculated intranasally with a mixture of the H7N9 virus and one H9N2 strain ) and 2nd contacts at 5 dpi (2 dpe) were further genotyped. Oropharyngeal swabs of donors at 3 dpi were analyzed by NGS. Sequencing data are available in the NCBI BioProject PRJNA769384. Pre- and post-sera were collected at -1 and 17 dpi from chickens for the detection of anti-HA antibody using the OIE’s haemagglutination inhibition assay (HI) [Influenza virus say (HI)  or anti-P < .05.Data were analyzed using GraphPad Prism version 8.4.1 (GraphPad Software) and R version 4.0.4 (R Development Core Team). Wilcoxon matched-pair signed-rank test was used to compare the difference of viral loads between oropharyngeal swabs and cloacal swabs. Kruskal-Wallis test was used to compare the genotype diversity index (GDI), and area under the curve (AUC) of viral loads of four groups followed by Dunn’s multiple comparisons tests for pairwise comparisons. Fisher's exact test with Bonferroni correction was performed to identify the correlation of genetic reassortment in eggs with donor chickens. Difference was considered statistically significant at in vitro. The four A(H9N2) viruses replicated well in ovo ), with a higher increment at the dose of 104 PFU than 106 PFU at 16 hpi  ). They including the A(H7N9) strain HK1772 formed visible plaques in MDCK cells, although the plaque sizes were not identical ). Co-infection was performed by co-infecting eggs in triplicate with 104 PFU/virus of H7N9 and H9N2 viruses, and allantoic fluid was harvested at 16 hpi (peak titres) for viral genotype analysis. Plaques were isolated from three eggs co-infected with HK1772+G1 (N = 161) (N = 157) (N = 217) (N = 164) (To investigate progenies generated after co-infection of A(H7N9) and A(H9N2) viruses, we first characterized the A(H7N9) virus HK1772 and four genetically divergent A(H9N2) viruses P = .0039). Specially, the GDI of HK1772+BJ and HK1772+HJ combinations differed the most, but insignificant  viruses have different capacities to generate novel reassortant genotypes with the A(H7N9) virus in ovo, with novel reassortant genotypes detected at mean frequencies of 14.6 to 50.2%.Next, we used genotype diversity index (GDI = the number of genotypes divided by the number of plaques identified) to quantify the extent of reassortment. The GDI significantly varied among four combinations  , donors co-infected with HK1772+BJ16 of which had higher viral loads compared to those co-infected with HK1772+G1   and A(H9N2) viruses , and the 2nd contacts in the HK1772+BJ16 group shed the highest number of viruses   (sTable 3), suggesting that in addition to virus-virus compatibility, differences in virus-host interactions may affect the efficiency of generating genetically diverse viral progeny after co-infections in vivo.Taken together, these results show that novel reassortants may be generated in chickens co-infected with A(H7N9) and A(H9N2) viruses, albeit at different efficiencies. Increased genetic diversity, as indicated by the higher detection frequencies for reassortant viruses, was observed in co-infected donors from 1 to 3 dpi . Interesin ovo and in chickens. When the proportion of homologous combinations falls in the centre of the graph, at 0.5 on each axis, it suggests that the two segments freely exchange. In ovo, data points of HK1772+G1, HK1772+YU335, and HK1772+HJ co-infections were shifted to the right of the X-axis, while the Y-axis data points were more evenly scattered between 0 and 1, suggesting a bias in maintaining homologous A(H7N9) gene segments in viral progenies  gene segments in viral progenies . At 3 dpSince successful serial transmission was observed from the contacts in the HK1772+YU335 and HK1772+BJ16 groups, we genotyped the oropharyngeal swabs collected from these infected contacts , demonstIn the HK1772+BJ16 group, as BJ16 was the dominant genotype in co-infected donors .Taken together, we observed different transmission dynamics in chickens following co-infections with HK1772+YU335 and HK1772+BJ16 viruses. In the HK1772+YU335 group, novel reassortants were passed onwards from co-infected donors to the 1st and 2nd contacts, demonstrating the capacity of passing on genetically diverse variants after co-infections. In contrast, a dominant genotype (BJ16) was transmitted from donors to contact chickens in the HK1772+BJ16 group.in ovo and in vivo upon co-infection with four combinations of A(H7N9) and A(H9N2) viruses. We further evaluated the onward transmission potential of novel reassortants in chickens. Despite of detecting multiple novel reassortants in donors co-infected with four combinations of A(H7N9) and A(H9N2) viruses, most of the novel reassortants were not detected in contact chickens after exposure. Onward transmission of novel reassortants from co-infected donors to the 1st and the 2nd contacts was only observed in the HK1772+YU335 group. Furthermore, among multiple novel reassortants detected in donors co-infected with HK1772+BJ16, only the parental BJ16 virus was transmitted to the 1st and 2nd contacts. Taken together, these findings demonstrate limited onward transmission potential of novel reassortants generated in chickens co-infected with A(H7N9) and A(H9N2) viruses.In this study, we compared the genetic diversity of viral progeny generated in vivo are narrower than that in ovo, possibly limiting genetic exchange of influenza viruses by decreasing the number of viruses that can attach to the cells to initiate co-infection [in ovo and the dominance of A(H9N2) viruses in donor chickens at 1 dpi. It remains to be studied if the A(H7N9) or A(H9N2) viruses possess superior replication fitness in eggs and chickens.We observed that co-infection with different A(H9N2) and the same AH7N9) viruses may lead to the emergence of reassortants at different efficiency and with dissimilar patterns. Reassortment efficiency was strongly associated with timing, dose and spatial distribution of co-infection ,7,39. InN9 virusenfection ,43. Furtnfection ,45, as win ovo and in vivo. Interestingly, most of novel reassortant genotypes detected in the co-infected donors were less likely to be transmitted onwards to contact chickens, with the exception of novel reassortant genotypes generated in donors co-infected with the H7N9 virus and YU335 virus. This result coincides with the surveillance findings that multiple H7N9 genotypes were transient and only a few genotypes have sustained more than two waves of A(H7N9) epidemics in 2013–2017 [Our results showed that co-infections of A(H7N9) and different A(H9N2) viruses could support generation of novel genotypes 013–2017 . The detth wave of H7N9 epidemic in humans by providing mammalian adaptive mutations in the PB2 and PA genes [Our study has several limitations. First, genetic analyses may be biased towards reassortants that formed visible plaques and, therefore overlook progenies that didn’t form plaques. Second, A(H7N9) viruses have two main genetic lineages in China: the Pearl River Delta lineage and the Yangtze River Delta lineage . The A virus and different A(H9N2) viruses may lead to the emergence of novel reassortants. Our results suggest that most of the reassortants exhibited limited onward transmission potential to contact chickens. These findings provide new insights into the potential mechanism by which influenza viruses may acquire genetic diversity through co-infection Click here for additional data file.


CDKN1B and its C-terminal truncated form, namely p27*, which is translated from CDKN1B pre-mRNA, is one of the causes of G1 phase arrest caused by SSA treatment. However, the detailed molecular mechanism underlying G1 phase arrest caused by SSA treatment remains to be elucidated. In this study, we found that SSA treatment caused the downregulation of cell cycle regulators, including CCNE1, CCNE2, and E2F1, at both the mRNA and protein levels. We also found that transcription elongation of the genes was deficient in SSA-treated cells. The overexpression of CCNE1 and E2F1 in combination with CDKN1B knockout partially suppressed G1 phase arrest caused by SSA treatment. These results suggest that the downregulation of CCNE1 and E2F1 contribute to the G1 phase arrest induced by SSA treatment, although they do not exclude the involvement of other factors in SSA-induced G1 phase arrest.The potent splicing inhibitor spliceostatin A (SSA) inhibits cell cycle progression at the G1 and G2/M phases. We previously reported that upregulation of the p27 cyclin-dependent kinase inhibitor encoded by  Cell cycle progression is closely controlled by cell cycle regulators. Among these regulators, cyclin family proteins and cyclin-dependent kinases (CDKs) promote cell cycle progression ,2. In G1In higher eukaryotes, most precursor mRNAs (pre-mRNAs) consist of exon sequences and intervening sequences (introns) ,20. DuriWe previously reported that the upregulation of p27 and p27* expression is a cause of G1 phase arrest in SSA-treated cells ,23. HoweCCNE1, CCNE2, and E2F1 to determine whether they also decreased following SSA treatment. The levels of these mRNAs in SSA-treated cells were significantly lower than those in control cells at all time points  were almost identical to those of downstream exons , which suggests that SSA did not cause a transcription elongation defect between CCNE2 Ex5 and Ex11 or between E2F1 Ex3 and Ex6. We speculate that the transcription elongation defect occurs at positions further upstream of CCNE2 exon 5 and E2F1 exon 3.Another possibility is that SSA inhibits the transcription elongation of the genes. We previously reported that transcription elongation of at least ~20% of genes is defective in SSA-treated cells ,27,28. Tof CCNE1 C. HoweveCCNE2 and E2F1 were significantly higher than those of the downstream exons , we designed primer sets for upstream exons; however, these primers did not work well because some upstream exons are GC-rich, whereas others are too short to design both forward and reverse primers for one exon. Therefore, to measure the levels of upstream exons, we reanalyzed the exon array data that we had previously published, in which we analyzed only newly synthesized mRNA to detect the effect of SSA on transcription . We founam exons D, which gulation . TogetheRRP4, a component of the nuclear exosome, and confirmed successful knockdown using immunoblotting  ,32. To iCCNE1 and E2F1 suppresses G1 phase arrest, the cells should be arrested at the next G2/M phase. Following SSA treatment, vector-transfected cells were arrested in both the G1 and G2/M phases, and overexpression of CCNE1 and E2F1 decreased the proportion of cells in the G1 phase and increased the proportion of cells in the G2/M phase  cells. We established p27KO cells using the CRISPR/Cas9 system and confirmed successful knockout using immunoblotting and sequencing . The pronificant . Furthern groups . These rCDK2 is expressed throughout the cell cycle  was higher than those of exons 2–6 . This may be a technical bias of the exon array. To clarify this result, we will perform similar experiments using other methods such as next-generation sequencing. How transcription elongation is inhibited in splicing-deficient cells remains unclear. We previously demonstrated that some RNA-binding proteins suppress transcription elongation defects caused by SSA treatment [Our results showed that the mRNA and protein levels of cyclin E1, cyclin E2, and E2F1 were downregulated in SSA-treated cells. We investigated the reasons underlying the downregulation and found that transcription elongation of these three genes was defective following SSA treatment. We previously reported that the transcription elongation of ~20% of genes is inhibited in SSA-treated cells and that  defects ,27. In aefective D. Howevexons 2–6 D. Similareatment . These RCCNE1 and E2F1 combined with p27/p27* knockout did not completely suppress G1 arrest. We speculate that other G1 regulators may be downregulated in SSA-treated cells, leading to the SSA-induced G1 phase arrest. Numerous factors in addition to p27, cyclin E1, and E2F1 are involved in regulating the G1/S phase transition [E2F1-overexpressing cells, transcription initiation of genes important for the initiation of the S phase should be activated, but transcription elongation and splicing of the genes might be disrupted by SSA treatment. To understand the molecular mechanism underlying the G1 arrest caused by splicing inhibition comprehensively, future studies should investigate and identify the additional factors that contribute to G1 phase arrest.We also found that co-overexpression of ansition ,9,24. E2CCNE1, CCNE2, and E2F1 are downregulated in SSA-treated cells and that overexpression of CCNE1 and E2F1 suppresses SSA-induced G1 phase arrest. Splicing inhibitors such as SSA are potent antitumor reagents [In conclusion, we found that the gene expressions of reagents ,34,35,362. For cell cycle synchronization, the cells were treated with 2 mM thymidine  for 18 h. The treated cells were washed twice with medium to induce release from the first thymidine block and then cultured in fresh medium for 8 h. The cells were then treated with 2 mM thymidine for another 16 h and washed twice with culture medium to induce release from the double thymidine block. Cycloheximide was purchased from Sigma-Aldrich .HeLa S3 cells were cultured in Dulbecco’s modified Eagle’s medium  containing 10% heat-inactivated fetal bovine serum  at 37 °C with 5% COMouse monoclonal anti-α-tubulin (B-5-1-2) antibody was purchased from Sigma-Aldrich . Mouse monoclonal anti-cyclin E1 (HE12), rabbit polyclonal anti-cyclin E2, rabbit polyclonal anti-E2F1, and rabbit monoclonal anti-p27 Kip1 (D69C12) antibodies were purchased from Cell Signaling Technology . Mouse monoclonal anti-CDK2 antibody (D-12) was purchased from Santa Cruz Biotechnology . Rabbit polyclonal anti-RRP4 antibody (ab156698) was purchased from Abcam . Mouse monoclonal anti-Myc antibody was purchased from MBL . HRP-conjugated anti-mouse IgG and anti-rabbit IgG secondary antibodies were purchased from GE Healthcare .For immunoblotting, the cells were directly lysed on plates with 1× SDS-PAGE sample buffer. The cell lysates were then separated by SDS-PAGE and then transferred onto a PVDF membrane by electroblotting. Following incubation of the membrane with primary and secondary antibodies using standard techniques, protein bands were detected using the NOVEX ECL Chemiluminescent Substrate Reagent Kit  on an ImageQuant LAS 4000mini .The cells were fixed in 70% ethanol, rinsed with phosphate-buffered saline, and stained with a solution containing 20 µg/mL propidium iodide (Thermo Fisher Scientific), 0.05% Triton X-100, and 0.1 mg/mL RNase A (Thermo Fisher Scientific). Cell cycle progression was monitored using the image-based cytometer Tali .RRP4 knockdown were purchased from Horizon Discovery . siRNA transfection was performed using Lipofectamine RNAiMAX  in accordance with the manufacturer’s instructions.siGENOME Control Pool Non-Targeting #2 and siGENOME Human EXOSC2 siRNA for Total RNA was extracted from the cells using TRIzol reagent  in accordance with the manufacturer’s instructions. cDNA was prepared using Primescript II RTase  and random primers. Nascent RNA was purified using the Click-iT Nascent RNA Capture Kit . Briefly, the cells were treated with 200 µM 5-EU for 1 h to label nascent RNAs. Total RNA was then extracted from the cells using TRIzol reagent. Labeled RNA was biotinylated via the click reaction and then purified using streptavidin beads. Subsequently, cDNA was synthesized using the SuperScript VILO cDNA Synthesis Kit . qRT-PCR was performed using the MX3000P system  with SYBR Green dye chemistry. The amount of 18S rRNA was measured as an internal control. All primers are listed in CCNE1, CCNE2, and E2F1 promoter regions were amplified by PCR from HeLa S3 genomic DNA using CCNE1 pro cloning for MluI and CCNE1 pro cloning rev HdIII primers, CCNE2 pro cloning for MluI and CCNE2 pro cloning rev HdIII primers, and E2F1 pro cloning for MluI and E2F1 pro cloning rev HdIII primers, respectively. The PCR products were digested with MluI and HindIII and subcloned into pcDNA3.1-GFP to construct pcDNA3.1-CCNE1 promoter–GFP plasmid, pcDNA3.1-CCNE2 promoter–GFP plasmid, and pcDNA3.1-E2F1 promoter–GFP plasmid. To construct the CCNE1-Myc plasmid, the DNA fragment of CCNE1 ORF was amplified by PCR from HeLa S3 cDNA using CCNE1 cloning for RI and CCNE1 cloning rev Xho primers. The PCR product was digested with EcoRI and XhoI and then subcloned into pcDNA3.1/Myc-HIS A . To construct the CCNE2-Myc plasmid, the DNA fragment of CCNE2 ORF was amplified by PCR from HeLa S3 cDNA using CCNE2 cloning for RI and CCNE2 cloning rev Xho primers. The PCR product was digested with EcoRI and XhoI and then subcloned into pcDNA3.1/Myc-HIS A. To construct the E2F1-Myc plasmid, the DNA fragment of E2F1 ORF was amplified by PCR from HeLa S3 cDNA using E2F1 cloning for RI and E2F1 cloning rev Xba primers. The PCR product was digested with EcoRI and XbaI and then subcloned into pcDNA3.1/Myc-HIS A. All primers are listed in To construct the pcDNA3.1-GFP plasmid, the GFP gene was amplified by PCR from pcDNA6.2-enGFP  using GFP F-Hind III and GFP ATTTA-R-KpnI primers. The PCR product was digested with HindIII and KpnI and then subcloned into pcDNA3.1 . The DNA fragments of the ® CRISPR-Cas9 crRNA against CDKN1B (Design ID: Hs.Cas9.CDKN1B.1.AA), Alt-R® CRISPR-Cas9 tracrRNA, and Alt-R® S.p. HiFi Cas9 Nuclease V3 were purchased from Integrated DNA Technologies . Formation of guide RNA (crRNA:tracrRNA duplex), formation of the Cas9–guide RNA complex, and electroporation using the Neon transfection system  were each performed in accordance with the manufacturer’s instructions. Clone selection and validation via immunoblotting and sequencing were then performed. All primers are listed in Alt-Rhttps://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE45379) (accessed on 26 October 2021) [Probeset intensities of the core probe sets, as defined by Affymetrix, were calculated from the CEL files, which were previously deposited in the GEO database (er 2021) , using Pt-test (p < 0.05 was considered statistically significant.Statistical analysis was performed using R Commander. A two-tailed t-test C and onet-test B or Dunnt-test B were pe


Medulloblastoma is the most common malignant pediatric brain tumor and is classified into four molecular subgroups: Wnt, Shh, Group 3, and Group 4. Of these subgroups, patients with Myc+ Group 3 MB have the worst prognosis. Using an RNAi functional genomic screen, we identified the lysine methyltransferase SMYD3 as a crucial epigenetic regulator responsible for promoting Group 3 MB cell growth. We demonstrated that SMYD3 drives MB cell cycle progression by inducing cyclin D3 transcription and preventing cyclin D1 ubiquitination. Using in vitro and ex vivo studies, we showed that SMYD3 suppression by shRNA and BCI-121 significantly impaired proliferation, resulting in the downregulation of cyclin D3, cyclin D1, and pRBSer795. Moreover, we are the first to show that SMYD3 methylates the cyclin D1 protein, indicating that the SMYD3 stabilizes cyclin D1 through post-translational modification. Collectively, our studies position SMYD3 as a promising treatment option for Group 3 Myc+ MB patients.Medulloblastoma (MB) is the most common malignant pediatric brain tumor. Maximum safe resection, postoperative craniospinal irradiation, and chemotherapy are the standard of care for MB patients. MB is classified into four subgroups: Shh, Wnt, Group 3, and Group 4. Of these subgroups, patients with Myc+ Group 3 MB have the worst prognosis, necessitating alternative therapies. There is increasing interest in targeting epigenetic modifiers for treating pediatric cancers, including MB. Using an RNAi functional genomic screen, we identified the lysine methyltransferase SMYD3, as a crucial epigenetic regulator that drives the growth of Group 3 Myc+ MB cells. We demonstrated that SMYD3 directly binds to the cyclin D3 promoter to activate its transcription. Further, SMYD3 depletion significantly reduced MB cell proliferation and led to the downregulation of cyclin D3, cyclin D1, pRBSer795, with concomitant upregulations in RB in vitro. Similar results were obtained following pharmacological inhibition of SMYD3 using BCI-121 ex vivo. SMYD3 knockdown also promoted cyclin D1 ubiquitination, indicating that SMYD3 plays a vital role in stabilizing the cyclin D1 protein. Collectively, our studies demonstrate that SMYD3 drives cell cycle progression in Group 3 Myc+ MB cells and that targeting SMYD3 has the potential to improve clinical outcomes for high-risk patients. Medulloblastoma (MB) is an aggressive embryonal brain tumor affecting the posterior fossa and is classified into four molecular subgroups: Shh, Wnt, Group 3, and Group 4 , with eaEpigenetic abnormalities resulting from defects in chromatin modifiers and remodelers often promote malignant transformation ,7 and arSMYD3 is a member of the SET and MYND-domain (SMYD) lysine methyltransferase family and plays an essential role in epigenetic regulation by acting as a transcription factor and targeting histones and non-histone molecules for methylation . In thisThe medulloblastoma cell line D283 MED was obtained from American Type Culture Collection (ATCC) and maintained in serum-containing advanced minimum essential medium (MEM). Normal human astrocytes (NHA), UW228, ONS-76, D425, D458, and MB002 cells  were grown and transfected for 48 h using standard conditions ,14. All The four unique GFP constructs of shSMYD3 were purchased from Origene  and pXPG plasmid encoding a firefly luciferase reporter gene was purchased from Addgene . CRISPR/Cas9 knockout all-in-one plasmids for specific deletion of three non-overlapping regions of SMYD3 (KO_SMYD3 1/2/3) were purchased from GeneCopoeia, MD (HCP260717-CG04-3-B). SMYD3 overexpression plasmids were purchased from Sino Biologicals . Anti-SMYD3 antibody was purchased from Abcam , cyclin D3 and p-RB antibodies were purchased from AB clonal . HRP and Alexa-fluor conjugated secondary antibodies were purchased from Novus Biologicals  and Invitrogen , respectively. All the other antibodies were purchased from Santa Cruz Biotechnology . Human recombinant Cyclin D1/cdk4 and Cyclin D3/cdk4 protein complex was purchased from Thermo Fisher .http://www.oncomine.org (accessed on 2 July 2020)). The Kaplan–Meier survival curve was plotted using the Cavalli dataset within the R2 program (Available online: http://r2.amc.nl (25 March 2021)). Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway annotations were used to identify gene sets significantly enriched for SMYD3 binding. The ‘Upstream Regulatory Analysis’ and ‘Pathway Analysis’ tools available within IPA software (Available online: http://www.ingenuity.com (28 January 2019)) were used to analyze the RNA-seq data.SMYD3 transcripts in MB tissues were assessed using the Oncomine database  were incubated with UbiCapture-Q Matrix  by gentle agitation at 4 °C overnight to pull down all ubiquitinated proteins according to the manufacturer’s instructions. After washing three times, captured proteins were eluted with 2× SDS-PAGE loading buffer and analyzed by western blotting using anti–cyclinD1 antibody as per the standard protocol ,18.ChIP assay was performed with the ChIP-IT Express Kit  using standard protocol . The anthttps://epd.epfl.ch//index.php (accessed on 24 June 2020)) and SMYD3_ChIP-seq data. The SMYD3 binding sites containing RI (ChIP identified region −260 to 300 bp) and RII (SMYD3 binding region within RI −277 to −7 bp) along with the RIII (SMYD3 negative control −800 to −400 bp) regions were amplified and cloned in pXPG luciferase reporter plasmid . The cloned plasmids were purified using GeneJET miniprep kit (Thermo Scientific) and subjected to restriction digestion and sequencing analysis . For the SMYD3 binding reporter assay, MB cells were transiently transfected with 1 μg/mL of the cloned cyclin D3 promoter  plasmids. After 48 h, cells were lysed to determine luciferase activity using the luciferase reporter gene assay kit .The Cyclin D3 gene (CCND3) promoter region was analyzed for SMYD3 binding sites using the EPD database  following the manufacturer’s instructions. TUNEL assay was done using an in situ cell death detection kit . The colony-forming assay was performed using control and shSMYD3 treated cells using an established protocol [For cell cycle analysis, MB cells were washed with PBS, centrifuged at 870 g for 3 min at room temperature prior to fixation with 70% ethanol and treatment with RNAase (50 µg/mL stock). Cells were subsequently placed in propidium iodide  at 50 µg/mL concentration and incubated for 15 min at 4 °C. The proportion of cells in each phase of the cell cycle was assessed using Cell Quest software (Becton Dickinson Bioscience). To exclude the doublet population, the cells were sequentially gated then followed up with either an area-height or an area-width gate using forward scatter. The Annexin V assay was performed using the RayBioprotocol   for at least 72 h. To account for tumor-heterogeneity, cultivation was performed in triplets (n = 3) for each drug concentration and for the untreated control group. The culture media were changed every 24 h and slices were monitored for any signs of apoptosis. Media was collected on treatment days 0, 1, 2, and 3 for LDH analysis .MED-411 FH, a patient-derived xenograft orthotropic mice tumor samples  . Image J software was used for all the densitometry and fluorescence intensity analyses. Statistical significance was calculated using one-way analysis of variance (ANOVA), and data were expressed as means ± S.E. In all Figures, statistical significance is labeled the following way: * n = 94) compared with patients with low SMYD3 expression (n = 19)  C. These In addition to epigenetic regulation, SMYD3 can also influence signaling pathways outside of the nucleus . ImmunobTo investigate the transcriptional impact of SMYD3, we performed ChIP-seq analysis to identify SMYD3-binding sites across the D458 genome. The heatmap of ChIP-seq vs. Input signals + 5 kb from the transcription start site (TSS) shows the 5′ ends of the start-seq reads are centered on the gene TSS, sorted by chromosome and position A. The saTo determine whether SMYD3 affects the transcription of cyclin D3, we assessed cyclin D3 transcript levels by RT-PCR in the control and shSMYD3 treated Group 3 MB cells. SMYD3 knockdown was performed by transfecting MB cells with shRNA plasmids targeting SMYD3 . We founTo study the downstream effects of SMYD3 depletion in Group 3 Myc+ MB, we performed whole transcriptome analyses of shSMYD3 treated cells using RNA sequencing. In agreement with our previous data, the heatmap generated from the RNA-seq analysis revealed downregulation of CCND3 and CCNE1 and upregulation of p27, caspase 7, and caspase 9 A. To ide® Agarose beads tagged with GFP Nanobody . The IPed proteins were then immunoblotted for GFP, SMYD3, and cyclin D1 proteins   A,B. To i< 0.001) . However< 0.001) , suggest< 0.001) C,D. Like< 0.001) E.SMYD3 knockdown is associated with reduced Group 3 Myc+ MB cell survival and downregulation of cyclin D3/D1 proteins. To determine whether the addition of recombinant cyclin D1+CDK4 or cyclin D3+CDK4 complex proteins could antagonize the anti-proliferative effects of SMYD3 silencing, we performed an MTT assay. D458 cells were transduced with CRISPR plasmids to completely silence SMYD3 (KO-SMYD3) and the viability was compared with or without the addition of cyclin D1+CDK4 or cyclin D3+CDK4 complex. Predictably, MB cell viability was enhanced following the addition of cyclin D1+CDK4 or cyclin D3+CDK4 A. HoweveIt has been reported that cyclin D1 and D3 are critical for the growth of breast cancer cells in vitro and in vivo, with loss of cyclin D3 resulting in compensatory upregulation of cyclin D1 . Therefo50 of ~80 μM, whereas the drug had no effect on normal human astrocytes (NHA)  A. In addes (NHA) . Althouges (NHA) B. We choes (NHA) C. To cones (NHA) D, mirrorPediatric cancers, including MB, display a high degree of epigenetic dysregulation . HoweverOur studies demonstrate that SMYD3 inhibition attenuates the proliferative ability of Group 3 Myc+ MB cells and induces cell cycle arrest followed by apoptosis in vitro. Mechanistically, SMYD3 transcriptionally upregulates cyclin D3, resulting in RB phosphorylation and G1 phase progression E. FurtheThe oncogenic properties of SMYD3 have been reported in numerous cancer types ,46. For Both cyclin D1 and cyclin D3 are frequently upregulated in various cancers  and theiAn important consideration is the effect of BCI-121 on Group 3 Myc+ MB cells. BCI-121 directly targets SMYD3 to inhibit proliferation in cancer cells with high SMYD3 expression . StudiesCurrent treatment options for MB patients are relatively uniform, centered on craniospinal irradiation, chemotherapy, and palliative care . As neceUsing an RNAi functional genomic screen, we identified SMYD3 as an important epigenetic regulator of Myc+ Group 3 MB cell growth. We showed that SMYD3 drives MB cell cycle progression by activating cyclin D3 transcription, resulting in the phosphorylation of RB. We showed that SMYD3 inhibition significantly impaired MB cell proliferation while augmenting apoptosis. Mass-spectrometry analysis revealed that SMYD3 targets the cyclin D1 protein for methylation. As SMYD3 knockdown enhanced the ubiquitination of cyclin D1, our studies indicate that SMYD3 stabilizes cyclin D1 in the nucleus via lysine methylation. Ex vivo studies revealed that SMYD3 inhibition using BCI-121 was cytotoxic, further supporting the clinical significance of targeting SMYD3 in Group 3 Myc+ MBs.


According to this study, both compounds showed potent cytotoxicity towards ovarian adenocarcinoma cells (SKOV3) with IC50 1.2 and 0.6 μg/mL, respectively. With respect to metastatic prostate cells (PC-3), the two compounds 1 and 2 showed a significantly promising cytotoxicity effect with IC50 of 7.4 and 4.5 μg/mL, respectively. The tested fungal metabolites showed higher rates of early and late apoptosis with little or no necrotic apoptotic pathway in all treated prostate adenocarcinoma (PC-3) and ovary adenocarcinoma (SKOV3) human cell lines, respectively. The results reported in this study confirmed the promising biological properties of terretonin N (1) and butyrolactone I (2) as anticancer agents via the induction of cellular apoptosis. However, further studies are needed to elucidate the molecular mechanism by which cellular apoptosis is induced in cancer cells.The anticancer activity of terretonin N ( Despite the progress made in the field of cancer research, there is still a need to discover and develop anticancer therapeutic agents, representing the main target of numerous research groups worldwide, especially as cancer is responsible for millions of deaths across the whole world. The latter is responsible for approximately 7.6 million deaths worldwide and this is expected to increase to 13.1 million by 2030 [Structural diversity and complex molecular architectures represent key features of the natural products from fungi. They exhibit a remarkably wide range of biological activities, including anticancer properties ,5,6,7,8.Aspergillus terreus TM8 [Aspergillus terreus TM8 previously [In the course of this work, two interesting compounds, terretonin N (1)  and butyreus TM8 , were exreus TM8 ,12,13. Ieviously ,11,12,13eviously ,11,12,131) and butyrolactone I (2) obtained from the thermophilic fungus Aspergillus terreus TM8 have been reported recently by us [Production and structure assignments of terretonin N  and butyrolactone I (2) was evaluated by sulforhodamine B (SRB) assay towards PC-3 and SKOV3 cancer cell lines using serial concentrations (0.001 to 1000 μg/mL). The tested compounds  indicated analogous cytotoxicity profile against both tumor cells, where they showed the most potent cell-killing effect towards ovarian adenocarcinoma cells (SKOV3) at IC50 1.2 and 0.6 μg/mL, respectively. With respect to metastatic prostate cells (PC-3), the two compounds  displayed a significantly promising cytotoxicity effect with IC50 7.4 and 4.5 μg/mL, respectively (The cytotoxicity of terretonin N (ectively .1) and butyrolactone I (2) were tested as apoptotic agents against the cancer cells of PC-3 and SKOV3. Both PC-3 and SKOV3 were treated with metabolites  for 48 h and stained with Acridine Orange/Ethidium Bromide (AO/EtBr), respectively. A higher percentage of early apoptosis was observed in human metastatic prostate cells (PC-3) compared to ovarian adenocarcinoma cells (SKOV3) which showed cells with late-phase apoptosis following treatment with terretonin N (1) and butyrolactone I (2), as shown in 1) and butyrolactone I (2) induced higher nuclear DNA fragmentation in SKOV3 than PC-3 cells  and butyrolactone I (2) was evaluated by SRB assay towards PC-3 and SKOV3 cancer cell lines using serial concentrations (0.001 to 1000 μg/mL). The tested compounds  indicated analogous cytotoxicity profiles against both tumor cells, where they showed the most potent cell killing effect towards ovarian adenocarcinoma cells (SKOV3) with IC50 1.2 and 0.6 μg/mL, respectively. With respect to metastatic prostate cells (PC-3), compounds 1 and 2 displayed a significantly promising cytotoxicity effect with IC50 7.4 and 4.5 μg/mL, respectively.The cytotoxicity of terretonin N (1) announced no significantin vitro cytotoxicity towards human cervical cancer cells KB-3-1. Interestingly, in thisstudy, it has been shownthat terretonin (1) has a selective cytotoxic potency against PC-3 and SKOV3 cell lines. Alternatively, butyrolactone I (2) is a cyclin-dependent kinase (CDK) inhibitor [50 values being 32.3 and 20.2 μM, respectively [In a previous study conducted by us , terretonhibitor , and Cdcnhibitor . Butyrolectively ,17.1, 2) were tested as apoptotic agents against PC-3 and SKOV3 cancer cells using AO/EtBr staining for 48 h. A higher percentage of early apoptosis was observed in human metastatic prostate cells (PC-3) compared to ovarian adenocarcinoma cells (SKOV3), showing cells with late-phase apoptosis after therapy with terretonin N and butyrolactone I. In addition, the results demonstrated that both terretonin N and butyrolactone I induced higher nuclear DNA fragmentation in SKOV3 cells than in PC-3 cells. In this investigation, the results showed that the activity of the metabolites terretonin N and butyrolactone I against SKOV3 cells, more than PC-3 cancer cells, only proceeded via an apoptotic pathway.The isolated metabolites terretonin N and butyrolactone I  and butyrolactone I (2) isolated from Aspergillus terreusTM8, could induce pro-apoptosis and late-phase apoptosis without the necrotic cell death of tumor cells. Conversely, other fungal metabolites activate various cell death pathways, apoptosis, autophagic cell death, and programmed necrosis (necroptosis) [Talaromyces sp., promoted autophagy and caspase-dependent apoptosis in cancer cells [In our study, it was reported that the two secondary metabolites, terretonin N (optosis) ,24. In aer cells .The results reported herein confirm the promising biological properties of terretonin N and butyrolactone I as anticancer agents against prostate (PC-3) and ovarian adenocarcinoma (SKOV3) cell lines by inducing cellular apoptosis. However, further studies are needed to illuminate the molecular mechanism, by which cellular apoptosis is induced in these cancer cells.Aspergillus terreus TM8 was previously isolated in Egypt from a sub-surface soil sample and its growth optimized at different temperatures (15–55 °C). The optimum growth of the fungus appeared at 45–50 °C and it was concluded that the strain has thermophilic properties [The fungus operties ,13. In aoperties ,11.Aspergillus terreus TM8 have been reported in our recently published article [Details of isolation and characterization of the extremophilic  article .1, 300 mg) and butyrolactone I  as the main products with colorless/crystalline appearance [Fermentation, working up of the obtained fungal extract followed by a series of chromatographic purifications including silica gel column, Sephadex LH-20, and preparative thin-layer chromatography afforded the desired two compounds, terretonin N  was identified by NMR (1D and 2D) and MS spectral data (see The structure of terretonin (data see  and by c2) has been identified by NMR (1D and 2D) and MS spectral data (see The structure of butyrolactone I (data see  and by cv/v) and incubated at 37 °C with 5% CO2 [Prostate adenocarcinoma (PC-3) and ovary adenocarcinoma (SKOV3) human cell lines were purchased from the American Type Culture Collection (ATCC). Cells were cultured at the Roswell Park Memorial Institute (RPMI), 1640 Medium and 10% fetal bovine serum was added (FBS) . Cells were of healthy growth, cultured in a 96-well tissue culture plate for 24 h. The spent media were replaced with a new treated medium containing serial concentrations of each compound (0.01 to 1000 µg/mL), and cell control was included. Triplicate wells were then incubated for 72 h and later fixed with TCA (10% w/v) for one h at 4 °C. After washing three times in a dark environment, SRB dye 20 μL0.4% (w/v) was added for 10 min. Excess stain was removed using 1% glacial acetic acid (v/v), and the plates left to dry for 24h. SRB-stained cells were thawed with Tris-HCl and the color intensity was detected using a microplate reader at 540 nm. The IC50 values  were analyzed using SigmaPlot 12.0 software [Cell viability of (PC-3 and SKOV3) human tumor cells after treatment with compounds software .50s calculated for the chosen compounds and incubated for 48 h. Cells were washed with a cool PBS twice. Cells were marked with a combination of acridine orange 100μg/mL and ethidium bromide (AO/EB) 100μg/mL in PBS 1× on each well and then left for 5 min at RT. Cover slides were placed with cultured stained cells on slides and were observed immediately via a fluorescence microscope [DNA dyes acridine orange (AO) and ethidium bromide (EtBr) were used to test the morphological changes in cells treated . Both AO and EtBr engaged with dead and alive cells that emitted green fluorescence when interpolated and hooked on DNA. EtBr was engaged by dead cells only, whereas it was omitted by alive cells and produced red, bloodshot fluorescence when intercalated into DNA. Healthy cells were plated on a sterile cover slide into a six-well plate. After 24 h incubation, the cells were treated with pre ICcroscope ,29.p < 0.05. All statistical analyses were performed using GraphPad InStat software, version 3.05 . Graphs were plotted using GraphPad Prism software, version 6.00 .Data are presented as mean SD unless otherwise indicated. Statistical significance was acceptable to a level of 1) and butyrolactone I (2) isolated from Aspergillus terreusTM8 have potential cytotoxicity towards both human metastatic prostate cells (PC-3) and ovarian adenocarcinoma cells (SKOV3) lines. The cytotoxic activity of the two fungal metabolites against cancer cells proceeded via the apoptotic pathway with little or no necrotic pathway. The data of the biological anticancer activity presented here, previous full characterization of these fungal metabolites, and future work in this direction will provide an important clue for potential drug development from fungal secondary metabolites in the treatment of human prostate and ovarian cancers.The results revealed that secondary metabolites terretonin N (


Aging and neurodegenerative diseases are typical metabolic-related processes. As a metabolism-related long non-coding RNA, EPB41L4A-AS has been reported to be potentially involved in the development of brain aging and neurodegenerative diseases. In this study, we sought to reveal the mechanisms of EPB41L4A-AS in aging and neurodegenerative diseases.+ levels, nicotinamide riboside treatment, Chromatin Immunoprecipitation) in neurons and glial-derived cells.Human hippocampal gene expression profiles downloaded from the Genotype-Tissue Expression database were analyzed to obtain age-stratified differentially expressed genes; a weighted correlation network analysis algorithm was then used to construct a gene co-expression network of these differentially expressed genes to obtain gene clustering modules. Gene Ontology, Kyoto Encyclopedia of Genes and Genomes, protein–protein interaction network, and correlation analysis were used to reveal the role of EPB41L4A-AS1. The mechanism was verified using Gene Expression Omnibus dataset GSE5281 and biological experiments (construction of cell lines, Real-time quantitative PCR, Western blot, measurement of ATP and NAD+ synthesis pathway. Furthermore, these genes were highly associated with neurodegenerative diseases and positively correlated with EPB41L4A-AS1. In addition, biological experiments proved that the downregulation of EPB41L4A-AS1 could reduce the expression of these genes via histone H3 lysine 27 acetylation, resulting in decreased NAD+ and ATP levels, while EPB41L4A-AS1 overexpression and nicotinamide riboside treatment could restore the NAD+ and ATP levels.EPB41L4A-AS1 was downregulated in aging and Alzheimer's disease. EPB41L4A-AS1 related genes were found to be enriched in the electron transport chain and NAD+ biosynthesis but also affects ATP synthesis. As a result, the high demand for NAD+ and ATP in the brain cannot be met, promoting the development of brain aging and neurodegenerative diseases. However, overexpression of EPB41L4A-AS1 and nicotinamide riboside, a substrate of NAD+ synthesis, can reduce EPB41L4A-AS1 downregulation-mediated decrease of NAD+ and ATP synthesis. Our results provide new perspectives on the mechanisms underlying brain aging and neurodegenerative diseases.Downregulation of EPB41L4A-AS1 not only disturbs NADThe online version contains supplementary material available at 10.1186/s13578-021-00705-2. Long non-coding RNAs (lncRNAs) are a class of non-coding RNAs usually longer than 200 nucleotides in length . For a lEPB41L4A-AS1 was first reported as a protein-coding gene named TIGA1 related to cell proliferation ; later s+ depletion contributes to mitochondrial dysfunction, reducing ATP production, the main currency of brain energy metabolism, leading to brain dysfunction [+ is not only a key coenzyme in energy metabolism but also a cofactor or substrate of hundreds of enzymes, playing multiple roles in DNA damage repair, gene expression, and stress response [+ during aging influences many key cellular functions and induces many aging-associated diseases, including neurodegenerative diseases [+ depletion in the aging brain and neurodegenerative diseases and the role of lncRNAs remain unclear.Intriguingly, the decline of brain energy metabolism is an important cause of the age-associated functional decline and brain disease and can subtly appear even before the diagnosis of neurodegenerative diseases . Mitochofunction –15. NAD+response , 16, 17.diseases . However+ and ATP synthesis. The downregulation of EPB41L4A-AS1 mediated the development of brain aging and neurodegenerative diseases, while EPB41L4A-AS1 overexpression could reduce the clinical symptoms. Importantly, our results provide new insights into the mechanisms of brain aging and neurodegenerative diseases.In the present study, we found that EPB41L4A-AS1 expression gradually decreased with aging. Given that EPB41L4A-AS1 plays an important role in metabolism, we hypothesized that EPB41L4A-AS1 downregulation exerted an important effect on brain metabolism and may be related to the occurrence of aging and neurodegenerative diseases. Our biological experiments showed that dysregulated expression of EPB41L4A-AS1 induced metabolic damage, presenting as a decrease of NADTwo datasets were used during the bioinformatics analysis. The GTEx (Genotype-Tissue Expression) database includes the gene expression data from 54 normal tissues. The gene expression data of 13 brain regions and corresponding information including age, gender, tissue location were extracted. GEO dataset GSE5281 was used to validate our proposed theory in Alzheimer's disease.Microarray analysis was used to identify differentially expressed genes (DEGs), which often have important biological functions. R packages edgeR  and limmWGCNA  was used to perform cluster analysis and classify genes with similar expression patterns into the same module. Besides, the R package WGCNA was used to select the most relevant modules for the target trait (age) by analyzing sample traits of modules .GO (Gene Ontology)  and KEGGCorrelation analysis was conducted to assess the strength of the correlation between EPB41L4A-AS1 and the target genes using the Pearson correlation analysis, and differences in the expression of EPB41L4A-AS1 in different age groups and between normal and AD groups were assessed using the Mann–Whitney U.https://www.string-db.org/) was used to generate a protein–protein interaction (PPI) network to exhibit the interactions between proteins, including interactions from text mining, experiments, databases, co-expression, neighborhood, gene fusion, and co-occurrence [STRING  and U251  cells were cultured in DMEM/F 12 medium (Thermo Fisher Scientific) or DMEM medium (Thermo Fisher Scientific) with 10% FBS (Thermo Fisher Scientific) in a humidified incubator with 5% CO2 at 37 ℃. SiRNAs for EPB41L4A-AS1, negative control siRNA vectors, lentivirus carrying EPB41L4A-AS1 RNAi, negative control shRNA vectors, EPB41L4A-AS1 overexpression plasmids, and empty plasmids used as negative control were purchased from GenePharma . For transient transfection, siRNAs for EPB41L4A-AS1 and EPB41L4A-AS1 overexpression plasmids were transfected using Lipofectamine™ 2000 Transfection Reagent (Thermo Fisher Scientific), according to the manufacturer's protocol. U251 and SH-SY5Y cells were transfected with shEPB41L4A-AS1 plasmid to obtain stable knockdown cell lines, followed by 1 µg/mL final concentration of puromycin (Thermo Fisher Scientific) selection.RNAiso Plus (Takara) was used to extract total RNA. Reverse transcription of the total RNA was performed by One-Step RT-PCR SuperMix (TransGen Biotech). PerfectStartTM Green qPCR SuperMix (TransGen Biotech) was used to perform Real-time qRT-PCR. All mRNA levels were measured from three independent experiments and normalized to ACTB. Primers used for Real-time quantitative PCR are shown in Additional file 4: Table S1.Proteins were extracted from cultured cells by cell extract buffer  with protease inhibitor mixture . Cell lysates were resolved by SDS-PAGE and then analyzed by western blotting. The following antibodies were used: ACTB , NMNAT2 , NDUFS4 , NDUFS6 .ATP and NADH levels were measured using Enhanced ATP Assay Kit (Beyotime Biotechnology) and Amplite™ Colorimetric NAD/NADH Ratio Assay Kit (AAT Bioquset), respectively, according to the manufacturer's protocol.NR was purchased from MCE (#HY-123033). Cells were cultured in a medium containing 100 mM NR after adhesion; follow-up experiments were carried out 24 h later. Cells cultured in the medium without NR were used as controls.The ChIP assay was conducted as described previously in . In brieGiven that EPB41L4A-AS1 has been reported to be a metabolism-related lncRNA and altered brain metabolic patterns have been established in aging and neurodegenerative diseases, especially in AD, we hypothesized that the expression of EPB41L4A-AS1 could be dysregulated under these conditions. The Mann–Whitney U test compared EPB41L4A-AS1 expression in different age groups and between normal and AD groups. As shown in Fig. It is widely acknowledged that the hippocampus is the main lesion site in aging and neurodegenerative diseases; accordingly, the gene expression profiles (32972 genes) of 197 samples from the hippocampus  were extracted for differential gene expression analysis (age-stratified). The expression profiles of two samples  were used as an expression matrix to obtain better results.A total of 6538 DEGs (3246 upregulated and 3292 down-regulated) were screened between the elderly (60–80 years) and the young (20–50 years) groups with a threshold p-value < 0.05. EPB41L4A-AS1 downregulation was also confirmed in a volcano plot of DEGs , Preiss–Handler, and salvage pathways [+ biosynthesis pathways in aging and neurodegenerative diseases and found that NMNAT2, previously documented to be enriched in the cytoplasm [+, and NMNAT2 is the predominant form of NMNATs in the brain , thus playing an important role in brain aging and neurodegenerative diseases. The detailed mechanism is shown in Additional file In short, EPB41L4A-AS1 could regulate NADway NMNAT, thus plIn the present study, the role and mechanism of EPB41L4A-AS1 in brain aging and neurodegenerative diseases were assessed in SH-SY5Y and U251 cells. For each mitochondrial respiratory chain complex, two PCGs with the highest correlation coefficient with EPB41L4A-AS1 were selected for further analysis. As a result, a total of 11 PCGs  were selected to validate if EPB41L4A-AS1 could regulate their expression.First, siRNAs were used to knock down EPB41L4A-AS1 in SH-SY5Y cells and observe the changes in the 11 PCGs. We found that two siRNAs  were successfully transiently transfected, and the relative mRNA expressions of the 11 target genes were downregulated  to assess whether NR could alleviate decreased NAD+ synthesis induced by EPB41L4A-AS1 downregulation, and further alleviate the decrease in ATP synthesis caused by decreased NAD+ synthesis. As shown in Fig. + and ATP levels was observed after NR treatment in SH-SY5Y and U251 cells. Importantly, our results showed that NR and EPB41L4A-AS1 could both increase ATP and NAD+ levels.Given that overexpression of EPB41L4A-AS1 has been proved to restore ATP and NADIt has been reported that EPB41L4A-AS1, functioning as a lncRNA, could regulate gene expression at the epigenetic level by mainly affecting the acetylation of histones . A chromIn the present study, we first performed a bioinformatics analysis to reveal the mechanisms of lncRNA EPB41L4A-AS1 in brain aging. Analysis of GTEx and GEO datasets showed that EPB41L4A-AS1 expression was downregulated in the aging brain and AD group, compared with normal people. Then, we compared the hippocampal gene expression between the elderly and the young group and constructed co-expression modules. DEGs with similar expression patterns were clustered into four distinct gene co-expression modules. EPB4L4A-AS1 was located in the turquoise module, and genes in the turquoise module were associated with aging and neurodegenerative diseases. Previous studies have shown that the occurrence of neurodegenerative diseases was closely related to changes in oxidative phosphorylation , 38, whi+. Given that NAD+ is widely acknowledged to play in mitochondrial respiration, we analyzed the NAD+ synthesis pathway genes and found that NMNAT2 was down-regulated DEG in aging and Alzheimer's disease. Moreover, NMNAT2 was highly correlated with EPB41L4A-AS1 both in aging and Alzheimer's disease. Accordingly, we substantiated that EPB41L4A-AS1 downregulation mediated brain aging and the development of neurodegenerative diseases via decreasing NAD+ and ATP synthesis.In the turquoise module, 55 genes were obtained after intersecting five datasets of different neurodegenerative diseases. Correlation analysis of EPB41L4A-AS1 and 55 PCGs in 13 brain regions found that EPB41L4A-AS1 exerted a strong positive regulatory effect on these genes. Subsequently, we validated the regulatory role of EPB41L4A-AS1 using the AD dataset GSE5281, which yielded consistent results. Furthermore, functional analysis of these 55 genes showed that these genes were highly related to the electron transport chain and ATP synthesis in the cell respiration process. As seen in the PPI network, these genes were mainly involved in forming mitochondrial respiratory complexes I-V. All five complexes could affect ATP synthesis, and among them, complex I comprised the largest number of genes, while complex I was mainly involved in the conversion of NADH to NAD+ and ATP levels. Importantly, opposite findings were observed once EPB41L4A-AS1 was overexpressed. Furthermore, the NR treatment experiment shows the positive significance of EPB41L4A-AS1 in treatment, while experiments at the epigenetic level showed that the regulatory mechanism of EPB41L4A-AS1 was based on mediating acetylated histone H3 Lys24 (H3K24ac) levels.Finally, we validated the regulatory mechanisms by cell experiments on U251 and SH-SY5Y cells. As predicted by our in silico analysis, genes regulated by EPB41L4A-AS1 were downregulated when EPB41L4A-AS1 was knocked down, with decreased NAD+ is a critical metabolite and coenzyme that is always in high demand. Sustaining its stable intracellular level is important for the normal function of multiple metabolic pathways and cellular processes. NAD+ level is maintained by three independent biosynthetic pathways, including the kynurenine, the Preiss–Handler, and the NAM salvage pathways [+ between synthesis and degradation can be disrupted during aging; however, the underlying mechanism remains unclear [+ levels in the aging brain. It has been reported that NMNAT2 depletion was related to SARM1-dependent axon degeneration in Parkinson's disease and other axonal disorders [NADpathways , 39, 40. unclear . This stisorders .+ levels play a central role in maintaining energy homeostasis and ATP production [+ biosynthesis but also affected ATP production. It has been established that NAD+ accepts hydride equivalents, forming NADH, providing electrons to the electron transport chain to generate ATP [+ could disrupt the process and impact ATP synthesis. Importantly, EPB41L4A-AS1 downregulation could inhibit the expression of genes related to the electron transport chain, causing a decrease in ATP synthesis. Under such conditions, the high demand of the brain for NAD+ and ATP cannot be met, promoting the development of brain aging and neurodegenerative diseases.An increasing body of evidence suggests that brain NADoduction . In thisrate ATP . In this+ biosynthetic pathways. EPB41L4A-AS1 downregulation mediated by aging could lead to decreased expression of these genes and decreased NAD+ and ATP levels. Accordingly, many normal physiological functions could be inhibited, impacting normal metabolism and resulting in aging and the onset of neurodegenerative diseases. Importantly, these symptoms were relieved when EPB41L4A-AS1 was overexpressed, providing a new approach for anti-aging and treating neurodegenerative diseases.In conclusion, our study reported that EPB41L4A-AS1 exerted a strong positive regulatory effect on genes that form the respiratory electron transport chain complexes I-V and genes in NADAdditional file 1: Fig. S1. (A) Correlation analysis of EPB41L4A-AS1 and 55 PCGs in 13 brains regions. (B) Fold changes in gene expression of EPB41L4A-AS1 and 55 PCGs in 13 brain regions, the fold changes are calculated by Mean  / Mean.Additional file 2: Fig. S2. (A) Gene expression for NMNAT2 in 54 normal tissues. (B) Gene expression of NMNATs in 13 brain regions. (C) The mechanism of lncRNA EPB41L4A-AS1 in regulating brain metabolism, lncRNA EPB41L4A-AS1 regulates NAD+ and ATP levels by affecting the complex I-V genes NAD+ synthesis pathway gene NMNAT2.Additional file 3: Fig. S3. EPB41L4A-AS1 regulates the expression of 11 PCGs in U251 cells. (A) Relative RNA expression of genes in si-EPB41L4A-AS1 and siNC cells. (B) Relative RNA expression of genes in sh-EPB41L4A-AS1 and shNC cells, OENC: cells transfected with empty plasmids, OE: cells transfected with EPB41L4A-AS1 overexpression plasmids. Data are shown as mean ± SD. *p<0.05, **p<0.01, ***p<0.001, ****p<0.0001, student’s t-test.Additional file 4: Table S1. List of the primer sequences for real-time quantitative PCR.


It has been revealed that B7H4 is negatively correlated with PDL1 and identifies immuno-cold tumors in glioma. However, the application of the B7H4-PDL1 classifier in cancers has not been well testified.A pan-cancer analysis was conducted to evaluate the immunological role of B7H4 using the RNA-sequencing data downloaded from the Cancer Genome Atlas (TCGA). Immunohistochemistry (IHC) and multiplexed quantitative immunofluorescence (QIF) were performed to validate the primary results revealed by bioinformatics analysis.The pan-cancer analysis revealed that B7H4 was negatively correlated with PDL1 expression and immune cell infiltration in CeCa. In addition, patients with high B7H4 exhibited the shortest overall survival (OS) and relapse-free survival (RFS) while those with high PDL1 exhibited a better prognosis. Multiplexed QIF showed that B7H4 was mutually exclusive with PDL1 expression and the B7H4-high group exhibited the lowest CD8 + T cell infiltration. Besides, B7H4-high predicted highly proliferative subtypes, which expressed the highest Ki67 antigen. Moreover, B7H4-high also indicated a lower response to multiple therapies.Totally, the B7H4-PDL1 classifier identifies the immunogenicity and predicts proliferative subtypes and limited therapeutic options in CeCa, which may be a convenient and feasible biomarker in clinical practice.The online version contains supplementary material available at 10.1186/s12935-021-02423-8. With the development of treatment strategies for malignant cancers, immune checkpoint blockade (ICB) is emerging as inspiring immunotherapy transforming the standard of treatment . RepreseIn addition to PDL1, B7H4 is highly expressed in tumor tissues, making them attractive candidate immunotherapeutic targets and biomarkers . B7H4 waIn the current research, we conducted a pan-cancer analysis of the correlation between B7H4 and immunological features. We found that B7H4 was negatively correlated with immunological features in several cancers, including cervical cancer (CeCa), glioma and melanoma. We selected CeCa for further analysis and found that B7H4 was negatively correlated with PDL1 and immune cells infiltration. Briefly, B7H4 high expression identifies immuno-cold subtype in CeCa. It was also suggested that B7H4 predicted the hyperproliferative subtype in CeCa. Overall, the application of the B7H4-PDL1 classifier in CeCa may be a better choice.https://xenabrowser.net/datapages/). The abbreviations for various cancer types were given in Additional file http://www.cgga.org.cn/index.jsp).The pan-cancer normalized RNA-sequencing (RNA-seq) datasets and corresponding clinical information from the Cancer Genome Atlas (TCGA) dataset were downloaded from Xena  was obtained from Outdo BioTech . The TMA contained 126 CeCa tissues and 42 paired adjacent tissues. Detailed clinico-pathological characteristics follow-up data of the cohorts were provided by Outdo BioTech as well and shown in original data. Besides, we also 30 CeCa tissues submitted for multiplexed quantitative immunofluorescence (QIF) in this research and all the patients were recruited by the Wuxi Maternal and Child Health Hospital Affiliated to Nanjing Medical University. Ethical approval for the use of the TMA was granted by the Clinical Research Ethics Committee in Wuxi Maternal and Child Health Hospital Affiliated to Nanjing Medical University.Immunohistochemistry (IHC) staining was conducted on the TMA. The primary antibodies used were as follows: anti-B7H4 , anti-PD-L1 , anti-KI67  and anti-EGFR . Antibody staining was visualized with DAB and hematoxylin counterstain, and stained sections were scanned using Aperio Digital Pathology Slide Scanners. A total of 118 TMA points were retained for further analysis after the exfoliated points were removed. All stained sections were independently evaluated by two independent pathologists using the immunoreactivity score (IRS) criterion .To identify the immune subtypes in CeCa based on the B7H4-PDL1 classifier, we first defined the positive staining with the threshold of IRS ≥ 3 in the IHC cohort. Thus, PDL1 was 36.44% positive in the cohort and B7H4 was 19.49%. For the patients in the TCGA dataset, we defined PDL1 as positive expression with the threshold of top 35% and B7H4 as positive expression with the threshold of top 20% by referring to positive rate in the IHC cohort.To measure the levels of PD-L1, B7-H4 and CD8 in the CeCa samples, the multiplexed QIF was directly performed on the tissue section using a previously described protocol with simultaneous detection of DAPI . The priAll statistical analyses were applied by R version 4.0.0. Wilcoxon rank-sum test was used to measure the difference between groups with continuous values, while Fisher exact method for evaluating the difference among grouping variables. For all analyses, a two-paired P-value < 0.05 was regarded as statistical significance if not noted.., the positive rates of B7H4 and PDL1 were approximately 20% revealed by IHC analysis [. declared that the B7H4-PDL1 classifier distinguished different immunogenicity in glioma [According to the report of Chen et alanalysis . We defin glioma . Based on glioma –19. HoweTo explore the appropriate cancer types in which the B7H4-PDL1 classifier could be applied, we analyzed the immunological correlation of B7H4 in different types of cancers using RNA-seq datasets from the TCGA project. We found that B7H4 expression was negatively correlated with immunomodulators, immune checkpoints and TIICs infiltration in testicular germ cell tumor and CeCa Fig. A–C. ThusNext, IHC analysis was performed on the CeCa TMA using the antibodies against B7H4 and PDL1. The results showed that B7H4 and PDL1 were highly expressed in the tumor tissues compared with adjacent tumors, and the positive rates for B7H4 and PDL1 were 19.49% and 36.44%, respectively  The correlation between PDL1 and T cell inflamed score in CeCa. (A) The correlation between B7H4 and T cell inflamed score in CeCa. (C) Correlations between B7H4 and common inhibitory immune checkpoints in CeCa. The color reveals the Pearson correlation coefficient.Additional file 3: Figure S2. Correlations between B7H4-PDL1 classifier and immunological features in CeCa (supplement). (A) The proportion of B7H4-high, PDL1-high, co-low and co-high subgroups in CeCa. (B) Differences in the gene markers of the common TIICs in B7H4-high, PDL1-high and co-low subgroups. (C) Differences in T cell inflamed score in B7H4-high, PDL1-high and co-low subgroups.


Mast cells, widely residing in connective tissues and on mucosal surfaces, play significant roles in battling against influenza A viruses. To gain further insights into the host cellular responses of mouse mast cells with influenza A virus infection, such as the highly pathogenic avian influenza A virus H5N1 and the human pandemic influenza A H1N1, we employed high-throughput RNA sequencing to identify differentially expressed genes (DEGs) and related signaling pathways. Our data revealed that H1N1-infected mouse mast P815 cells presented more up- and down-regulated genes compared with H5N1-infected cells. Gene ontology analysis showed that the up-regulated genes in H1N1 infection were enriched for more degranulation-related cellular component terms and immune recognition-related molecular functions terms, while the up-regulated genes in H5N1 infection were enriched for more immune-response-related biological processes. Network enrichment of the KEGG pathway analysis showed that DEGs in H1N1 infection were specifically enriched for the FoxO and autophagy pathways. In contrast, DEGs in H5N1 infection were specifically enriched for the NF-κB and necroptosis pathways. Interestingly, we found that Nbeal2 could be preferentially activated in H5N1-infected P815 cells, where the level of Nbeal2 increased dramatically but decreased in HIN1-infected P815 cells. Nbeal2 knockdown facilitated inflammatory cytokine release in both H1N1- and H5N1-infected P815 cells and aggravated the apoptosis of pulmonary epithelial cells. In summary, our data described a transcriptomic profile and bioinformatic characterization of H1N-1 or H5N1-infected mast cells and, for the first time, established the crucial role of Nbeal2 during influenza A virus infection. Influenza A viruses (IAVs) are a class of aggressive pathogens that lead to acute respiratory infections in humans and animals and result in periodic pandemics around the world, thus, posing a great threat to human and animal health due to their potential rapid spread and high morbidity and mortality ,2. IAVs Mast cells (MCs) are an important effector and regulatory immune cells in the body, widely distributed in the mucosa and submucosal connective tissues of the skin, uterus, bladder, respiratory tract and gastrointestinal tract . MCs arePrevious studies on MCs mainly focused on allergic diseases. In recent years, immune regulation of MCs in the process of neoplastic disease, parasite, bacterial and virus infection has attracted much attention ,8,9. OurHigh-throughput RNA sequencing (RNA-seq) technology coupled with bioinformatic analysis has revolutionized the study of biomedical research in recent years. Cellular signaling mechanisms born out of transcriptomic analysis can uncover cellular and metabolic alterations and reveal the pathogenesis of diseases. For instance, the PI3K–AKT /mTOR pathway was reported to regulate adipocyte differentiation as determined by KEGG enrichment analysis . The corNot surprisingly, transcriptomic analysis has been widely adopted to study defense mechanisms against virus infection with the hope to identify interactions between the host and influenza viruses . Over thThe differences in the pathogenesis observed between different IAV strains are complicated with characteristics specific to virus strains and the host immune responses . In ordeGO annotation revealed specific GO terms of molecular function (MF), biological process (BP) and cellular component (CC). KEGG pathway analysis searched for related molecular interactions and relationship networks among metabolism, biochemical signaling, regulation and diseases. In particular, we focused on how the Neurobeachin-like 2 Protein  was induced by the two subtypes of IAV infection in order to provide novel insights into its potential mechanism mediating pathogenesis.To the best of our knowledge, our research established, for the first time, that Nbeal2 of mouse MCs may play a significant role in the inflammatory response upon influenza A virus infection.The H1N1 (A/WSN/33) and H5N1 (A/Chicken/Henan/1/04) viruses used were described in our previous publication, and virus titers were determined as described previously ,20. The 6 cells/well) plates and incubated at a multiplicity of infection (MOI) of 1 for 1 h at 37 °C. Cell monolayers were washed with phosphate buffer saline (PBS) and then incubated with DMEM supplemented with 1% FBS for 12 h.P815 cells were grown in 6-well  according to the manufacturer’s instructions.The RNA concentration was determined using a NanoDrop 2000 , and RNA quality was assessed with an Agilent 2100 Bioanalyzer . Viral RNA treated with DNase was reversely transcribed into cDNA. RNA-seq was performed by the Illumina Novaseq6000 to generate 150-bp paired-end reads. Biological triplicates of each group were used and analyzed in this study. The raw reads were cleaned by removing the adaptor sequence and low-quality reads. https://ftp.ncbi.nlm.nih.gov/genomes/all/GCF/000/001/635/GCF_000001635.26_GRCm38.p6/GCF_000001635.26_GRCm38.p6_genomic.fna.gz, accessed on 20 December 2021) and reference genome annotation file: GCF_000001635.26_GRCm38.p6_genomic.gff.gz  were used as references. The cDNA libraries were prepared according to the standard Illumina protocol. To annotate and evaluate the transcript abundances from the sequenced reads, the reference genome file: GCF_000001635.26_GRCm38.p6_genomic.fna.gz  < 0.05. After the mouse genome index file was established with clean reads using HISAT2 software, mapping the sequencing reads to the reference genome efficiently according to the gene location information specified in the genome annotation file *. gtf was carried out. DESeq2 software was used for screening DEGs, and the screening criteria were set as |log2FoldChange| > 1 and FDR-adjusted https://biit.cs.ut.ee/gprofiler/gost, accessed on 20 December 2021, was used for the functional enrichment analysis of DEGs. The 30 subsets of enriched KEGG pathway terms with the significant p-values were selected and rendered as a network plot. The network was visualized using Cytoscape, where each node represents an enriched term and is labeled by its cluster ID and p-value with color mark.The online website g: Profiler, which is an open-access resource available at To validate the differential expression of selected DEGs obtained by RNA-seq analysis and their significant roles in inflammatory responses, qRT-PCR was performed. The procedures were as previous described .Nbeal2 gene were designed and provided by GenePharma . We seeded 1 × 106 P815 cells in 6-well plates, and transfection was carried out when cultures were 70% confluent. We diluted 5 µL Lipofectamine 2000 reagent  in 245 µL DMEM and incubated at room temperature for 5 min. Three independent siRNAs for the mouse Additionally, 100 pmol siRNA duplex or negative control siRNA was diluted in 250 µL DMEM, mixed with pre-diluted Lipofectamine 2000 for 20 min at room temperature and then was added into the matching well. The transfection efficiency was measured by qRT-PCR. The sequences were listed in Cells were harvested at the 24 h after infection and lysed with RIPA lysis buffer containing protease inhibitor cocktail . Protein concentrations were determined using a BCA protein assay kit (Beyotime Institute of Biotechnology). Equal amounts of protein were separated on 10% SDS-PAGE gel and transferred to a polyvinylidene difluoride (PVDF) membrane . The membranes were blocked using 5% non-fat dry milk  at room temperature for 2 h, washed and probed using the specified antibodies. Nbeal2 rabbit mAb  and β-actin mouse mAb  were used, and corresponding horseradish-peroxide-conjugated secondary antibodies were obtained from Beyotime . Protein bands were visualized using Western Lightning Plus-ECL . β-actin served as a loading control.P815 cells were infected with H1N1 and H5N1 viruses (1 pfu/cell). The supernatants were collected at 24 h post-infection and prepared by ultracentrifugation for 3 h at 30,000 rpm to remove viral particles. There was no viral RNA or infectious viral particle detected in the supernatants by qRT-PCR or haemagglutination assays. A549 cells (a human lung cell line) were incubated with the supernatant, and apoptosis was measured 24 h later by flow cytometry using ANNEXIN V-FITC/PI  as previously described .GraphPad Prism V8.0 software was used to analyze the statistical graphs. Data are shown as the mean ± SD. Statistical analysis was performed by two-way analysis of variance (ANOVA) contained in the prism software suite. The results are expressed as the means and standard deviations.Samples collected from H1N1-infected, H5N1-infected and control mouse MC line P815 were subject to RNA-seq. Pearson correlation analysis among the samples revealed a high degree of correlation between biological replicates of the same condition A. Both HH1N1 and H5N1 infection significantly activated the gene expression of cytokines and chemokines, such as Cxcl10, Il13, Ccl3, Ccl4 and Isg15. Interestingly, H1N1 infection down-regulated certain genes, such as Itgb3, Irf9, Hvcn1 and Nbeal2, which was not observed during H5N1 infection. To analyze the host immune responses that were common and specific to H1N1 and H5N1 virus infection, we compared the DEGs (including both up- and down-regulated genes) under each infection condition. Among all the DEGs identified in H1N1-infected P815 cells, 1792 were up-regulated, while 450 were down-regulated C. On the contrary, H5N1 infection changed the host transcriptome to a much lesser degree, with only 868 up-regulated genes and 217 down-regulated genes. There were 824 up-regulated genes and 136 down-regulated genes shared between H1N1- and H5N1-infected P815 cells D. In addThe heatmap of DEGs showed that H1N1 and H5N1 infection also triggered the expression of marker genes related to different biological events E. For exThe transcripts of both HA gene and examined marker genes exhibited significant increases at different time points post-infection. Taken together, these results suggest that H1N1 infection led to a greater global change to the transcriptome of P815 cells than H5N1 infection. The shared DEGs between the two IAV strains largely overlapped with the DEGs identified from H5N1 infection, and their infection led to the expression of marker genes characterized to different pathways.To dive into the possible biological processes related to different viral infections, the above DEGs were analyzed using online tools. The DEGs in each group were first annotated and then subject to enrichment analysis. The enriched GO terms are shown in MF GO term analysis showed that the up-regulated DEGs under H1N1 infection were more enriched in scavenger receptor activity, cytokine receptor binding, TNF receptor binding, NF-κB binding and CCR chemokine receptor binding compared with H5N1_vs_Mock B. BP GO We also examined network enrichment in the DEGs to identify the statistically enriched KEGG pathways of MCs infected by the two IAV strains. The up-regulated genes in the H1N1-infected group were significantly enriched in the FoxO signaling pathway and autophagy, whereas the up-regulated genes in the H5N1-infected group were enriched in the NF-κB signaling pathway and necroptosis. On the contrary, the down-regulated genes in H5N1-infected group were enriched in osteoclast differentiation while the down-regulated genes in the H1N1-infected group were enriched in antigen processing and presentation. These results indicated the two IAV strains induced different signal transduction pathways and cell death pathways.To further investigate the difference between H5N1 and H1N1 infection, we directly compared the transcriptomes of H5N1- and H1N1-infected cells and identified 36 up-regulated and 112 down-regulated DEGs A,B. The Consistent with the result of RNA-seq analysis, the Nbeal2 expression showed significant difference between H5N1- and H1N1-infected P815 cells at 12 h post-infection. We next followed Nbeal2 transcription in H5N1- and H1N1-infected cells every 6 h upon infection in details. Between 6 and 12 h post-infection, the Nbeal2 mRNA levels decreased in P815 cells infected with H1N1 virus, while they significantly increased in those infected with H5N1. From 18 to 24 h post-infection, the mRNA levels of Nbeal2 increased in P815 cells infected with either H1N1 or H5N1 virus. Meanwhile, H5N1-infected P815 cells showed significantly higher Nbeal2 mRNA levels compared with H1N1-infected P815 cells. The Nbeal2 mRNA expression in P815 cells infected with H1N1 or H5N1 virus peaked at 24 h post-infection before decreasing E. ConsisTo further identify the functional relevance of Nbeal2 in the cell response, P815 cells were transfected with three mixed small interfering RNA sequences to knockdown Nbeal2 expression before they were infected with H1N1 and H5N1 influenza viruses H. To furRemarkably, the knockdown of Nbeal2 significantly promoted the production of IL-1α, IP-10, IL-6, CCL-4 and TNF-α at 24 h post-infection in P815 cells infected with H1N1 virus and H5N1 virus. The knockdown of Nbeal2 did not affect the viral replication of P815 cells infected with H1N1 virus and H5N1 virus. . These rThe supernatant collected from the infected, Nbeal2-knockdown P815 cells  is a state-of-the-art approach that offers nearly unlimited possibilities in modern research ,25. Our The present study aimed to further create comprehensive and comparative transcriptional profiles of MCs responding to IAV infection. Using RNA-seq, we first analyzed differences between H5N1 virus and H1N1 virus. We successfully identified 2242 and 1085 DEGs in mouse MCs (P815 cells) infected with either virus, while our previous study identified 216 and 101 DEGs in MCs (P815 cells) infected with H1N1 virus and H5N1 virus, respectively . MoreoveHere, the top network-enriched KEGG pathways showed distinct differences between H1N1 virus and H5N1 virus infection in mouse mast cells. H1N1 virus infection more intensely activated pathways, such as the foxO signaling pathway and autophagy pathway, compared with H5N1 virus infection, suggesting that the mechanisms in these signaling pathways might be associated with varying subtypes of IAVs. Therein, consistent with our previous analysis that the foxO pathway plays a significant role in IAV infection through mediating the anti-apoptosis and anti-inflammatory responses , this maH5N1 virus infection dramatically enriched NF-κB, which is known as a major regulator of inflammatory response and necroptosis pathway. Our results were consistent with the previous research that low pathogenic influenza strains revealed a much weaker and less NF-κB-dependent host cell response compared with H5N1 virus . HoweverThe results showed that H5N1 infection appeared to activate MCs more intensely than H1N1 infection. We next analyzed the transcriptomes pf H5N1- and H1N1-infected MCs in parallel and found that many specific genes were differentially expressed among which Nbeal2 stood out. We prioritized Nbeal2, as it has been previously associated with MC degranulation .Nbeal2 belongs to a gene family involved in granule development . MutatioFurthermore, Nbeal2 has also been reported to mediate granule formation and the effector function of MCs . These dNext, we explored the role of Nbeal2 in the immune response. MCs have been reported to play a key role during IAV infection ,35. MCs Some studies have suggested that Nbeal2 controls the release of cytokines by regulating the granule formation . We therExpectedly, Nbeal2 knockdown did not directly affect the viral replication of IAV-infected P815 cells, mainly due to mast cells acting primarily as inflammatory cells triggering a pro-inflammatory response during IAV infection rather than as target cells for viral replication ,32,33. AIn summary, for the first time, we established the roles of Nbeal2 in two IAV strain infections and the crosstalk between Nbeal2 regulation by IAV infection as well as its effect on innate immunity in MCs, which suggested that Nbeal2 may be a promising target for future therapeutic interventions.


Background: Evidences suggest that single nucleotide polymorphisms (SNPs) can be considered as potential biomarkers for disease progression and therapeutic response in cervical cancer. The present study investigated the association of CYP1A1 T>C (rs4646903), CYP1A1 A>G (rs1048943), CYP2E1 T>A (rs6413432), RAD51 G>C (rs1801320), XRCC1 G>A (rs25487), XRCC2 G>A (rs3218536) and XRCC3 C>T (rs861539) polymorphisms with treatment outcome of cisplatin based chemoradiation (CRT).Methods: Total 227 cervical cancer cases, treated with the same chemoradiotherapy regimen were selected for the study. Genotyping analysis was performed by PCR-restriction fragment length polymorphisms (PCR-RFLP). Treatment response was evaluated by Response Evaluation Criteria in Solid Tumors (RECIST). Association of all clinical data  and single nucleotide polymorphisms (SNPs) was analysed by using SPSS (version 21.0).Results: Patients with TA/AA genotype of CYP2E1 T>A polymorphism showed significantly poor response while those with GC/CC genotype of RAD51 G>C showed better response . Death was significantly higher in patients with GG genotypes of RAD51 G>C and XRCC1 G>A . Women with GC+CC genotype of RAD51 G>C and AG+GG of XRCC1 showed better survival and also reduced risk of death .Conclusion: Results suggested that CYP2E1 T>A (rs6413432), RAD51 G>C (rs1801320), and XRCC1 G>A (rs25487) polymorphisms may be used as predictive markers for clinical outcomes in cervical cancer patients undergoing cisplatin based concomitant chemoradiotherapy. Cervical cancer is the most common cancer among women all over the world and majority of cases are diagnosed at advanced stage , 2. In eCYP and DNA repair genes are associated with differential repair activities and may explain inter-individual differences in treatment response influencing clinical outcome   receiving chemoradiation  . Higher adiation . DNA repotherapy . The XRCpatients .viz. CYP1A1 T>C (rs4646903), CYP1A1 A>G (rs1048943), CYP2E1 T>A (rs6413432), RAD51 G>C (rs1801320), XRCC1 G>A (rs25487), XRCC2 G>A (rs3218536) and XRCC3 C>T (rs861539) in the treatment outcome of cisplatin based chemoradiation in cervical cancer patients.A clear understanding of the molecular genetics of cervical cancer is necessary to deduce new therapeutic strategies that will benefit patients suffering from the disease. In this study, we have investigated the role of different genetic polymorphisms The patients prescribed for cisplatin based concomitant chemoradiation (CRT) treatment were recruited for this study from the Departments of Radiotherapy and Obstetrics and Gynecology, King George’s Medical University (KGMU), Lucknow, India. The recruited women were with no associated co-morbid conditions and had received no previous radiation or chemotherapy. Demographic and clinical characteristics of patients were obtained from medical records while staging and clinical diagnosis of patients were performed by expert clinicians as per guidelines of International Federation of Gynecology and Obstetrics (FIGO) 2009. Samples were collected after informed consent of all study subjects and approval of Institutional Ethics Committee of KGMU, Lucknow (No.274/R.Cell-10). Five milliliters venous blood samples were obtained from 244 subjects for genotyping study at the start of treatment regimen.2 concomitant cisplatin followed by three applications of high dose rate (HDR) intracavitory brachytherapy . The patients who violated the treatment protocol or did not complete the planned chemoradiation dose were excluded from the study. The patient response to treatment was measured by response evaluation criteria in solid tumors (RECIST) version 1.0 after 1 month of treatment. Patients were followed-up after treatment and checked for survival. The primary endpoint was overall survival (OS) observed from the date of diagnosis to the date of death from any cause. Women who were alive at the end of the study were censored.Doses of both therapies (chemotherapy and radiation) were same for all patients. All patients received a total dose of 50 Gy in 25 fractions of pelvic external beam radiotherapy with weekly 40 mg/mCYP1A1 T>C (rs4646903), CYP1A1 A>G (rs1048943), CYP2E1 T>A (rs6413432), RAD51 G>C (rs1801320), XRCC1 G>A (rs25487), XRCC2 G>A (rs3218536) and XRCC3 C>T (rs861539) polymorphisms were genotyped by Restriction Fragment Length Polymorphism (PCR-RFLP) by using specific primers  and specific restriction enzymes .Genomic DNA was extracted from venous blood by salting out method with slight modification . DNA samχ2 analysis and Fisher’s exact test (for categoric variables) and one-way analysis of variance (for continuous variables). Genotype and overall survivals were evaluated by Kaplan-Meier function and Cox proportional hazards model. Log-rank test was used to detect differences in overall survival across different genotypes. 95% Confidence Intervals (CIs) and Hazard ratios (HRs) were estimated using multivariate Cox proportional hazards model/Cox regression analysis. The regression data was adjusted for age, stage and histopathology. All differences in p values were considered statistically significant for p < .05. All statistical analyses were performed by SPSS (Version 21.0).Demographic and clinical information was correlated with genotypes using Out of 244 cervical cancer patients, 227 were included in the study. Seventeen cases were excluded due to protocol violations. Mean age of patients was 49.0 ± 8.68 years. Histopathologically, 216 patients (95.2%) had squamous cell carcinoma and remaining 11 (4.8%) had adenocarcinoma. Squamous cell carcinoma was distributed into three categories: 96 well (44.4%), 79 moderate (36.6%) and 21 poor (9.7%) while no differentiation was reported in 20 cases (9.3%). The staging of tumor according to FIGO was 117 cases (51.5%) with stage IIB and 110 (48.5%) with stage IIIA+IIIB . ResponsCYP1A1 T>C rs4646903 genotypes showed 33.5% cases with TT and 66.5% with TC/CC while distribution of CYP1A1 A>G rs1048943 showed 54.6% cases with AA and 45.4% with AG/GG. The distribution of CYP2E1 T>A rs6413432 genotypes showed 81.1% cases with TT, 18.9% with TA/AA. The distribution of RAD51 G>C rs1801320 genotypes showed 58.6% cases with GG and 41.4% with GC/CC while distribution of XRCC1 G>A rs3218536 showed 42.7% cases with GG and 57.3% with GA/AA. The XRCC2 G>A rs3218536 genotypes showed 78.9% cases with GG, 21.1% with GA/AA. However, distribution of XRCC3 C>T rs861539 showed 59.5% cases with CC genotypes and 40.5% with CT/TT genotypes. The significant differences in treatment response were found for CYP2E1 T>A rs6413432, but not for CYP1A1 T>C rs4646903 and A>G rs1048943 polymorphisms. The cases with TA genotype of CYP2E1 T>A rs6413432 polymorphism showed significant decrease in treatment response when compared to those with TT genotype . In case of RAD51 G>C rs1801320, homozygous GG genotype had a significantly bad response when compared with GC genotype  and same results were found with CC genotype but no statistical significance . In case of XRCC1 G>A rs25487, XRCC2 G>A rs3218536 and XRCC3 C>T rs861539, we did not observe any significant association  but CYP1A1 T>C rs4646903 and A>G rs1048943 polymorphisms did not show any association  while none with XRCC2 G>A rs3218536 and XRCC3 C>T rs861539 genotypes exhibited similar response. The risk of recurrence was significantly reduced in GA/AA genotypes of XRCC1 G>A rs25487 (p = .025) (RAD51 G>C rs1801320 when compared with women having GG genotype and HR = 0.484 with GA/AA genotypes of XRCC1 G>A rs25487 when compared with women having AA genotype (p = .008 and p = .003 respectively)  (XRCC2 G>A rs3218536 and XRCC3 C>T rs861539 polymorphisms.Disease recurrence was significantly decreased in women with TA/AA genotypes of ociation . Most of = .025) . The medctively) . The Kapctively) . There wCisplatin based concomitant chemoradiation is the standard treatment of locally advanced cervical cancer . Howeveretc, which demonstrate their anti-neoplastic effects by producing reactive oxygen species (ROS) whose cytotoxic effects cause tumor cell death and are likely to impact the treatment efficacy as well as survival after treatment (CYP1A1 m2 (rs1048943) polymorphism with platinum drugs (e.g. Cisplatin) was observed, which represent an important class of anticancer agents and are frequently used in treatment of various types of solid cancers including cervical cancer  polymorphisms was found with survival of cervical cancer patients treated with cisplatin based chemoradiation.Members of Xenobiotic metabolizing enzymes (XMEs) are CYP1A1, CYP1B1, CYP2D6, CYP2E1, mEH, NAT1 reatment , 21. CYPl cancer , 23. In RAD172 G>T (rs1801321) polymorphism has been associated with altered gene transcription (RAD51 expression is an independent predictor for tumor progression as well as tumor recurrence (p < .05) in individuals with “C” allele of RAD51 G>C rs1801320 polymorphism   (XRCC1 R399Q polymorphism is a well-studied single nucleotide polymorphism (SNP) located in the BRCT1 domain and have been found to influence treatment response (XRCC1 +399 G>A (rs25487) polymorphism . Most of the cases with GA/AA genotype of XRCC1 +399G>A were found to be alive at the end of the study period as compared to cases with AA genotype (p = .003)  cases treated with definitive radiotherapy  ; Table 3 = .006) . X-Ray rresponse , 28. Twomorphism , 30. Accmorphism , individ>T rs18011 polymor = .003) . XRCC2 i = .003) . The G>Aotherapy . In pancgenotype . High exmorphism . In presIn this study, we have focused on the effect of genetics on inter-individual differences in response to DNA damaging agents. The differential activity of cytochrome P-450 and DNA repair enzymes have an impact on treatment outcome of individual patients. Therefore, this kind of study may help clinicians to alter treatment strategies for cervical cancer patients on a personalized basis.• Cisplatin based concomitant chemoradiation is the standard treatment for locally advanced cervical cancer. Poor response and high inter-individual variations in treatment response occurs due to differences in genetic makeup.• The differential activity of metabolic  and DNA repair enzymes have an impact on treatment outcome.• Genetic polymorphisms in metabolic and DNA-repair enzymes contribute to inter-patient variability in treatment response.• Genetic variants in drug metabolizing and DNA repair genes with treatment outcome of CRT in cervical cancer patients.CYP2E1rs6413432, RAD51rs1801320, and XRCC1rs25487 were associated with inter-individual variations in response to chemoradiotion.• • These polymorphisms may act as prognostic biomarkers for prediction of clinical outcome of CRT in cervical cancer patients.