Khaini, Moist-snuff, Qiwam, and Snus). Further, the functional metagenome was speculated by PICRUSt (Phylogenetic Investigation of Communities by Reconstruction of Unobserved States) to assign the abundance of genes related to nitrogen metabolism, bacterial toxins, antibiotic drug resistance and other pro-inflammatory molecules. Highly diverse bacterial communities were observed in all moist STPs. Taxonomic analysis revealed a total of 549 genera belonging to four major phyla Proteobacteria, Firmicutes, Bacteroidetes and Actinobacteria. Overall, the core bacterial genera Acinetobacter, Bacillus, Prevotella, Acetobacter, Lactobacillus, Paracoccus, Flavobacterium, and Bacteroides were significantly abundant in moist STPs. Elevated moisture-holding products like Moist-snuff and Qiwam harbor rich bacterial species diversity and showed similar bacteriome composition. Furthermore, Qiwam products showed the highest level of genes associated with nitrogen metabolism, antibiotic resistance, toxins, and pro-inflammation (predicted by PICRUSt) which can contribute to the synthesis of TSNAs and induction of oral cancer. The present broad investigation of moist STPs-associated bacteriome prevalence and their detailed metabolic potential will provide novel insight into the oral carcinogenesis induced by STPs.Smokeless tobacco products (STPs) carry assorted microbial population that contributes to carcinogens synthesis like tobacco-specific nitrosamines (TSNAs). Extensive exploration of microbiota-harboring STPs is required to understand their full carcinogenic potential. Here, we applied 16S rRNA gene sequencing to investigate bacteriome present in moist STPs immensely consumed in India ( Non-combustible form of tobacco (smokeless tobacco) is used by >300 million people worldwide and majorly (>85%) in South Asian countries . A reporKhaini, Moist-snuff, Qiwam (Kiwam), and Snus , Bacteroidia (Prevotella and Porphyromonas), Bacilli , Chlamydiae, Clostridia (Clostridium), Fusobacteria (Fusobacterium and Streptobacillus), α-Proteobacteria (Brucella and Rickettsia), β-Proteobacteria (Neisseria and Spririllum), γ-Proteobacteria (Haemophilus and Pseudomonas), Spirochete (Borrelia and Leptospira) and Mollicutes (Mycoplasma) (The microbiota has been previously reported in cured tobacco leaves and STPs using both culture-dependent and culture-independent approaches . The micoplasma) . Furtheroplasma) . Additiooplasma) .Despite the significant potential of STP associated microbes to form TSNAs, their presence in various STPs is still not well explored and significant metagenome analysis has not been conducted on STPs, especially those available in India. Therefore in this study, we have selected 11 moist SLT products based on high consumption and elevated moisture content which can facilitate microbial growth inside these STPs. The bacterial community structure was investigated by 16S rDNA and prediction of critical functional genes was performed related to nitrogen metabolism, antibiotic drug resistance, production of toxins and pro-inflammatory molecules. Hence, comprehensive metagenome analysis was performed using the MicrobiomeAnalyst platform to unveil the structural and functional bacteriome present in moist STPs especially consume in India.Khaini , Moist-snuff , Qiwam , and Snus (S1 and S2) samples. The STPs were stored at −20°C to inhibit the further growth of microorganisms. The STPs were open in the sterilized conditions and microbial metagenomic DNA was isolated with a Power-soil DNA isolation kit as per the protocol provided by the manufacturer . The purity and quantity of isolated metagenomic DNA from the different STPs was checked by NanoDrop . Further, the quality of metagenomic DNA was confirmed agarose gel (1%) before amplification by PCR. The metagenomic DNA concentration of all tested SLT products was found to be >30 ng/μl.The domestic moist STPs were purchased from vendors in the Delhi and Uttar Pradesh state of India. The 16S rDNA sequencing techniques were used to analyze the bacterial diversity of 11 moist STPs from 4 different categories including 3–V4 region of metagenomic DNA isolated from the different moist STPs. The extracted metagenomic DNA (40 ng) was amplified with a pair of universal primers (10 pM of each) (FP: 5′-AGAGTTTGATGMTGGCTCAG-3′ and RP: 5′-TTACCGCGGCMGCSGGCAC-3′) as described earlier (2 (3.2 mM), high-fidelity DNA polymerase and PCR enzyme buffer. The PCR amplification was performed with the following condition: 95°C for 3 min chase by 25 cycles at 95°C/15 s, 60°C/15 s and 72°C for 120 s and with a final elongation at 72°C for 10 min. The amplified 16S PCR amplicons were purified and subjected to agarose gel (2%) and NanoDrop for quality check.The PCR was performed to amplify the 16S rDNA V earlier . Along wPRJNA767533.The Ampure beads  were used to purify the amplicons of each sample by eliminating the unused primers and to prepare the sequencing libraries an additional 8 cycles of PCR was executed via Illumina barcoded adapters . Further1. The QC passed samples were again analyzed by Quantitative Insights Into Microbial Ecology (QIIME version 1.9.0) workflow including fusion of paired-end reads, chimera elimination, OTU clustering and taxonomy assignment  algorithm (Snus samples (S1_dup and S2_dup) were taken.The raw data binary base call (BCL) file received from the sequencer was de-multiplexed into FASTQ format. Quality control checks of raw sequenced data were performed by FastQC (Version 0.11.9) and MultiQC (Version 1.10.1) tools. Next, removal of contaminant adapters and trimming of low-quality reads were done by TrimGalore (Version 0.6.6)signment . The QIIsignment . Furthersignment . A totallgorithm . The funlgorithm . Next, Tp-value < 0.05 was considered statistically considerable.The relative abundance percentage of each OTUs of taxonomic classification was calculated and plotted using Origin software. For data set phyla, we removed OTUs < 10 reads and for the data set genus, we removed OTUs < 100 reads. Analysis of Variance (ANOVA) was used to determine differences between products. A Moist-snuff and Qiwam exhibited higher overall species diversity than Khaini and Snus which have comparatively low moisture content  indices  of STP-associated bacteriome. The cteriome . Howevercteriome . All thecteriome .F = 2.9354, R2 = 0.49456, p < 0.004)  and one sample from Snus (S1) was also found to be close to each other and clustered together with Moist-snuff and Qiwam products and the sample K3 and S2 clustered separately from the other samples  . The 3D-profiles . Next, o samples . The intectively .Khaini, Moist-snuff, Qiwam, and Snus were indicated by yellow, green, blue, and magenta-line, respectively  were observed in all moist STPs. The other notable phyla were Acidobacteria, Chloroflexi, Cyanobacteria, Fusobacteria, Gemmatimonadetes, Spirochaetes, and Verrucomicrobia. The relative abundance of phylum Proteobacteria was found to be majorly present in all STPs except S2 . Moist-snuff , Qiwam (Q-41% and Q2–50%), and Snus (S1–39%) also showed an increased level of Proteobacteria phyla compared to other phyla. In addition, phyla Firmicutes was the predominant phyla in S2 (60%) with the lowest presence in K3 (4%) (Bacteroidetes (Khaini had a lower abundance (range 2–14%) of Bacteroidetes as compared to Moist-snuff, Qiwam, and Snus (range 15–23%). The phyla Actinobacteria was noticeably present in all moist STPs ranging from 1 to 10%  . The thiroidetes . However1 to 10% .Acinetobacter was abundantly observed in Khaini . Another genus Prevotella was found to be significantly high in one of the Snus (36% of S2). However, a moderate level of Prevotella was observed in Moist-snuff , Qiwam  and Snus (9% of S1). The other important genera observed in STPs were Faecalibacterium (17% of S2), Bacillus , Lactobacillus (10% of K2), and Ruminococcus (7% of S2)  .Acinetobacter, Bacillus, and Prevotella genera was observed in moist STPs  were observed between 247 genera , Bifidobacterium , Treponema , Mycobacterium , Rhodococcus , and Lactobacillus  while it was negatively correlated with Corynebacterium  and Capnocytophaga   and Haemophilus , whereas Fusobacterium showed positive association with Capnocytophaga  and Lautropia . Another important genera Pseudomonas displayed positive concurrence with Treponema , Mycobacterium , Haemophilus , Prevotella , Acinetobacter , Bifidobacterium , Streptomyces , and Clostridium   . Strepto 0.0099) . The cornd Qiwam .p-value < 0.1 and linear discriminant analysis (LDA) > 2.0  > 2.0 . The LDA samples . Furtherproducts .Sphingobacterium, Ruminococcus, Blautia, Lactococcus, Janthinobacterium, Streptococcus, Moraxella, Achromobacter, Veillonella, Haemophilus, Faecalibacterium, Clostridioides, Acidiphilium, Prevotella, and Megasphaera , transporters (narK) and nitrate reductases (narZHJI); (ii) periplasmic nitrate reductase nap operon  were abundant in the Q3 product and Q1, Q2, and MS2 products showed a significant number of imputed genes of the nitrate reduction pathway  and noticeably present in Q3, Q1, and MS2 products. The assimilatory nitrate reductases genes nasA and nasB were also predicted by PICRUSt and abundantly monitored in Qiwam , Moist-snuff (MS2) and Snus (S2) products. The predicted genes of nitrite reductases including nirA, nirB, nirD, nirK, and nrfA were found to be abundant in all three Qiwam products and one Moist-snuff (MS2) product, whereas nirB was also prevalent in one Snus product (S2) (2). All moist STPs contained imputed genes related to denitrification nosZ (K00376), norB (K04561), and norC (K02305) but their abundance was very low (nifD1 (K02586), nifH (K02588), and nifK (K02591) were present in all moist STPs and the abundance of nifD1 and nifK were high in Q3, Q1, MS2, Q2, and K1 whereas nifH was prevalent in Q3, K2, K3, Q1, MS2, Q2, and K1 (The two important pathways involved in the extracellular accumulation of nitrite are (i) dissimilatory nitrate reduction p operon . The dis pathway . The peruct (S2) . Anothervery low . Further, and K1 .Qiwam, one Moist-snuff product (MS2) and one Snus product (S2) as compared to Khaini products , MS1, MS3, and S1 products (marc (K05595), penicillin-binding protein 1A mrcA (K05366) and penicillin-binding protein-4 (serine-type D-Ala-D-Ala carboxypeptidase/endopeptidase) dacB (K07259) was the two most abundant antibiotic resistance genes , macrolide phosphotransferase , macrolide efflux protein , zinc D-Ala-D-Ala carboxypeptidase  and multidrug resistance protein  , aminoglycoside 3-N-acetyltransferase , penicillin-binding protein 1B , and penicillin-binding protein 5/6  . The S2 products displayed antibiotic resistance genes including vancomycin resistance associated response regulator , small multi-drug resistance pump , macrolide transport system ATP binding/permease protein , fosmidomycin resistance protein , and penicillin-binding protein activator   . The pro K07258) . The sam K07337) .lptBFG) responsible for LPS transport from the inner to the outer membrane was found in moist STPs and its abundance was high in Qiwam and MS2 products. In moist STPs several genes related to LPS biosynthesis were observed (A-disaccharide synthase (lpxB) and Kdo2-lipid IVA lauroyltransferase/acyltransferase (lpxL) which were involved in LPS synthesis significantly increased in Qiwam , MS2 and S2 products (Gram-negative bacteria lipopolysaccharide (LPS) is involved in the progression and migration of oral squamous cell carcinoma (OSCC) . The LPSobserved . The abuproducts .fliC was found in all moist STPs and their abundance was high in S2, Q3, Q2, MS2, and Q1 as compared to K2, K1, S1, MS3, K3, and MS1 (Another potent pro-inflammatory molecule is Flagellin which participates in the motility of bacteria. Flagellin coding gene  and MS1 . However and MS1 .tolC (K12340), followed by putative hemolysin gene , phospholipase-C gene , Neuraminidase1 gene , hemolysin III gene  and thiol-activated cytolysin gene   and can induce genomic damage that can lead to neoplastic transformation of epithelial cells . The STP K11031) . The moi K11031) .Next, we performed a Taxon Set Enrichment Analysis (TSEA) of all significant genera using the MicrobiomeAnalyst tool and examined them across 239 known taxon set linked with host-intrinsic factors such as age and diseases . The bacBacillus, Lactobacillus, Staphylococcus, Corynebacterium, Streptococcus, Prevotella, Rothia, Pantoea, Veillonella, Propionibacterium, Fusobacterium, Actinomyces, Lactobacillus, Sphingomonas, Marinilactibacillus, Oceanobacillus, and Porphyromonas were recognized in different STPs (The bacteriome of moist STPs was established by high throughput sequencing of 16S rDNA (V3-V4 region) sequence present in genomic DNA isolated from the STPs and first time expansively analyzed  by MicrobiomeAnalyst tool . Severalent STPs .in vitro assay using epithelial cells (AMOL-III) from the oral leukoplakia of a Khaini user and treatment with aqueous extract of Khaini showed alteration in the expression of proteins involved in cell cycle regulation and DNA methylation, suggesting the role of Khaini in oral carcinogenesis (Snus contains high levels of carcinogenic TSNAs (23.1–61.2 μg/g)   . Qiwam p2 mg/kg) .Moist-snuff and Qiwam compared to products having low moisture levels like Khaini and Snus. In contrast, Dry snuff products exhibited elevated overall species diversity compared to Moist-snuff samples. Snus products and lowest for the Yemeni Shammah product. Likewise, Snus products have increased α–diversity as compared to less moist STPs like Chewable tobacco and Snuff. The prevalence of bacteria in moist STPs may be due to the hot and high humid ambiance of India which can facilitate the growth of microorganisms involved in increasing the fermentation rate of alkaloids present in STPs to carcinogenic TSNAs. Further, β–diversity analysis of moist STPs, based on Bray–Curtis dissimilarity metric, revealed that the products of Moist-snuff  and Qiwam  clustered together and a clear bacterial community similarity was localized between Moist-snuff and Qiwam products whereas distinct separation of Khaini and Snus products was observed. Following our study, American Moist-snuff products also clustered mutually after PCoA analysis based on Weighted UniFrac distances (The α–diversity of bacteriome present in different moist STPs was determined and we have observed that the species richness (α-diversity) was more in STPs having high moisture content such as istances .Proteobacteria followed by Firmicutes, the findings being similar to a previous study  observed several predominant genera Tetragenococcus, Aerococcus, Alloiococcus, and Staphylococcus  and by membrane-bound nitrate reductase , nitrite reductase genes (nirABC) and nitrate/nitrite transporters genes were significantly abundant in American dry snuff products. In our study, we have observed that narGHJI and nirABDK were abundant in moist STPs like Qiwam and Moist-snuff. The enhanced level of TSNAs in Qiwam may be due to a high level of the nitrogen metabolizing enzymes being able to contribute to the synthesis of TSNAs. Therefore, the identification of bacterial species performing nitrogen metabolism is crucial to decipher the carcinogenic potential of bacteria present in moist STPs.The most important pathway involved in TSNAs formation is the nitrogen metabolism pathway. Microbial fermentation forms nitrite from nitrate which reacts with numerous tobacco alkaloids to form different carcinogenic TSNAs molecules . During eductase . Expresseductase . Furthereductase . Excreteeductase . Furtherof TSNAs . A few sof TSNAs . Tyx et A serious global threat of antimicrobial resistance spread lead to the emergence of multidrug-resistance bacteria or “Superbug” . The sprShigella flexneri can inhibit apoptosis by suppressing the effector caspase activity by direct attachment of lipopolysaccharide (LPS) with caspases (lptBFG) and their dominance was elevated in Qiwam and Moist-snuff. The abundance of LPS in moist STPs can provide cancer supporting environment and help in the progression of oral cancer in SLT users.Bacterial association with mucosal lining can deliver bacterial products, like LPS (Gram-negative bacteria) that stimulate many cell types and can contribute to OSCC progression . LPS cancaspases . The preStreptococcus pneumonia can induce cell death and inflammation by pore-forming cytolytic activity or by provoking the necrosis and program cell death pathways  may contribute to the health risks of STPs . Gram-popathways . We obseFusobacterium nucleatum (a Gram-negative bacterium) was found to be associated with colorectal cancer and oral cancer  can be found below: MS: experiments, data curation, data analysis, literature review, and writing original draft. SS: literature review and manuscript preparation. AmK: data validation and review and editing. AnK and HS: review and editing. MB: conceptualization, supervision, investigation, project administration, funding acquisition, data interpretation, validation, review, and editing. All authors contributed to the article and approved the submitted version.The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.


Background: Anethole is the principle essential oil component of anise and fennel. Renal ischemia/reperfusion (RIR) is one of the utmost imperative reasons for acute kidney injury and often associated with high mortality rate. The aim of this study is to investigate the protective effect of anethole on RIR status, exploring the involved mechanisms. Methods: RIR was accomplished by bilateral renal pedicle clamping for 45 min, after which the clamps were removed to achieve the reperfusion phase. Rats were randomized into five groups; Sham, Sham + anethole, RIR, and finally RIR + anethole (125 mg/kg or 250 mg/kg) groups. Animals were given anethole (in specified groups in doses) for 14 days before RIR. Results: RIR-experienced animals developed renal injury evidenced by diminished renal function and histopathological alteration. RIR induced severe oxidative, inflammatory, and apoptotic status within renal tissue. Pre-RIR management with anethole enhanced renal morphology and improved renal function. Anethole amplified GSH content and SOD, CAT, and GPx activities and lowered MDA. Anethole reduced gene and protein expression levels of HMGB1, TLR2, TLR4, MYD88, and NFκB. Anethole distinctly dropped TNF-α, IFN-γ, and MCP-1 levels, increased IL-10, and diminished caspase 3 and 9, reflecting its anti-inflammatory and anti-apoptotic actions. Conclusion: Anethole displayed anti-inflammatory, anti-oxidant, and anti-apoptotic actions against RIR-induced injury. Anethole exhibited renal protective actions, which could be through inhibiting the HMGB1/TLR2, 4/MYD88/NFκB pathway. These results could suggest anethole as a protective agent against RIR. During certain clinical settings such as renal transplant, hemi-nephrectomy, and renal artery occlusion, renal blood supply is temporarily declined or even impaired, causing renal ischemia/reperfusion (RIR) injury . RIR is Pimpinella anisum), sweet anise (Foeniculum vulgare), and star anise (Illicium verum) essential oils [Anethole (1-methoxy-4-[(E)-prop-1-enyl] benzene; ial oils . Anetholial oils . Anetholial oils , antioxiial oils , antifunial oils , antivirial oils , antihypial oils , local aial oils , vasodilial oils  activitiial oils , protectial oils , oxygen–ial oils , and cycial oils . Additioial oils . Anetholial oils  and compial oils . The intention of the existing study was to assess the outcome of anethole, as a phenylpropanoid natural compound, on renal biochemical as well as histological features following RIR. Moreover, we tried to estimate the underlying cellular and molecular pathways involved in this shielding effect of anethole. The Experimental Animal Research Centre, King Saud University, Riyadh, KSA, was the source from which Wistar male rats  were acquired. All the animals were maintained with typical laboratory food and water ad libitum in a ventilated cage system  throughout the whole experiment. The Institutional Animal Care and Use Committee of the King Faisal University allowed and permitted the experimental protocol (KFU-REC-2022-JAN-EA000361). All the animal handling and experiments as well as tests were executed according to the appropriate guidelines and regulations of the Ethical Conduct for Use of Animals in Research at King Faisal University.g) to attain serum, which were stored at −20 °C for subsequent biological determinations. The right kidney was rapidly detached and homogenized and kept at −80 °C for biochemical investigations, and the left one was preserved in 10% formalin for histopathological and immunohistochemical examination.Fasted rats (for 8 h) were anesthetized using ketamine 75 mg/kg) and xylazine (8 mg/kg) intraperitoneally. During the whole surgeries, animals’ body temperatures were maintained at 36–38 °C via retaining the animals on a surgical heating pad. Under sterilized surroundings, a midline opening was executed to detect the kidneys. Both left and right pedicles were occulted bilaterally with two non-traumatic vascular clamps for 45 min, and then the kidney color turned pale indicating establishment of renal ischemia  mg/kg an. After 4n = 6) following the experimental design stated in Rats were randomized into five groups  and periodic acid Schiff (PAS) stain to be examined under a light microscope. Two blinded pathologists assessed the histological renal injury. Renal sections were scored using a scale intended to estimate the grade of renal tubulointerstitial damages. The scoring system used was 0 (no damage), 1 (less than 10%), 2 (11–25%), 3 (26–45%), 4 (46–75%), and 5 (more than 76%), as mentioned earlier . A minimFor the immunohistochemistry (IHC) procedure, the expression of HMGB1 and NFκB were investigated as mentioned earlier , using H®; Leica Inc., Allendale, NJ, USA). Renal function was evaluated via assessing serum levels of creatinine , uric acid (ab65344), blood urea nitrogen , lactate dehydrogenase , and kidney injury molecule-1  using the specified colorimetric or ELISA kit obtained from Abcam Inc. , agreeing with the manufacturer’s practices consuming a spectrophotometer , glutathione peroxidase , and glutathione content  ELISA kits were procured from Abcam Inc. . Superoxide dismutase  and catalase  ELISA kits were acquired from My BioSource  and performed in agreement with the manufacturer’s directions. −ΔΔCq2) and presented as a fold change. Target gene expressions were assessed and related to the reference gene (β-actin), and the results are shown in the figures as relative expression. Gene expression for the TLR pathway, HMGB-1, TLR2, TLR4, myeloid differentiation primary response gene 88 (MYD88), and NFκB were quantified via real-time PCR (qPCR) consuming the primers’ sequences, shown in Western blot was performed according to the method described previously by Younis et al. (2019)  using thhttps://pubchem.ncbi.nlm.nih.gov/, accessed on 11 November 2021) and 3D optimized by LigPrep software . The TLR4 co-receptor mechanism and the key interactions that govern the molecular recognition of ligands were investigated by molecular docking. The PDB ID 4G8A was used to investigate the potential binding pattern of anethole with the TLR4-MD-2 complex. Protein preparation was performed by the Schrodinger suite protein preparation wizard. All missing atoms and loops were corrected, the structure was optimized and energy minimized. The docking grid was built using the bound LPS ligand as a guide. Anethole 2D structure was obtained from PubChem , monocyte chemoattractant protein-1 , tumor necrosis factor alpha , and interleukin 10  ELISA kits were obtained from Abcam Inc.  and executed as stated in the manufacturers’ instructions. The actions of anethole administration on the apoptotic markers, such as caspase-3 (ab39401) and caspase 9 (ab65608), were evaluated via kits acquired from Abcam Inc. , following the producers’ directions. Data were shown as mean ± SD. Multiple comparisons was performed using one-way ANOVA followed by Tukey–Kramer as a post hoc test. The 0.05 level of probability was used as the significance level. Statistical analyses were executed using GraphPad software .Light microscopic evaluation of sham groups exposed consistent normal morphology of the renal tissue; for instance, black arrowheads point to intact brush borders, while the renal section attained from animals that underwent RIR revealed severe acute tubular damage, as demonstrated in p < 0.05). Additionally, RIR + anethole (250 mg/kg) showed significant lower levels of serum Cr, BUN, uric acid, LDL, and Kim-1 in relation to the RIR + anethole (125 mg/kg) group (p < 0.05). These outcomes specify that administration of anethole prior to RIR may efficiently avert RIR-induced renal function alterations.Renal elimination of BUN and Cr was weakened; thus, they were maintained in the blood due to RIR-induced injury. Likewise, LDH was released into the bloodstream after tissue injury. Furthermore, Kim-1, which is a biomarker for kidney proximal tubule damage , was meap < 0.05), as displayed in p < 0.05). All these results together suggest that RIR-induced oxidative stress may be managed by anethole due to its efficient antioxidant ability. Reoxygenation following ischemia causes tissue oxidative stress during RIR. To appraise the oxidative stress accompanying RIR injury, numerous antioxidant enzymes activities were measured. The current study verified that in RIR rats, the activities of SOD, CAT, and GPx were considerably declined, whereas anethole administration enhanced RIR-induced renal attenuation in SOD, CAT, and GPx activities a,b,d. Adp < 0.05) lowered mRNA expression of TLR2, TLR4, and their adaptor protein MYD88  in RIR related to sham, with subsequent augmentation in TLR2, TLR4, and MYD88. At the end of the studied pathway, NFκB showed higher levels in RIR related to sham (p < 0.05). On the other hand, anethole pretreatment prior to the renal ischemia/reperfusion lowered the HMGB1, subsequently TLR2, TLR4, and their adaptor protein MYD88, and finally NFκB (p < 0.05), signifying that the protective role of anethole could occur via the inhibition of the HMGB1/TLR2,4/MYD88/NFκB pathway. As for protein expression , a similp < 0.05). HMGB1 and NFκB positive cells were lowered in animal groups pretreated with anethole compared with the RIR group (p < 0.05). These results provide evidence that reno-protective effects of anethole could be via the inhibition of the HMGB1/TLR2, 4/MYD88/NFκB pathway.As for the IHC analysis, the results revealed that NFκB a and HMGThe in vivo outcomes of the current study suggests diminished expression levels for TLR2 and 4 and subsequent downstream genes  and protIn the TLR4, MD-2 forms a β-cup fold structure comprising antiparallel beta sheets, allowing a central deep hydrophobic cavity for ligand binding. The docking investigation with TLR4 demonstrates that anethole binds deep within the MD-2 hydrophobic pocket, indicating that it is a ligand for this receptor . Anetholp < 0.05) in the RIR group, whereas anethole administration prior to renal ischemia/reperfusion distinctly prevented TNF-α, IFN-γ, and MCP-1 boost in RIR rats (p < 0.05) following RIR activity   e,f, reflAcute kidney injury (AKI), caused by renal ischemia/reperfusion RIR, is an imperative problem for nephrology consultants in hospitalized patients, during which a rapid decline in renal function evaluated via glomerular filtration rate (GFR) is observed . In the With HMGB1 intensification, TLR2, TLR4, and their adaptor protein MYD88 and subsequently NFκB gene and protein expression levels were all amplified in RIR experienced animals. HMGB1 is crucial in mediating apoptosis as well as inflammation in renal injury . Studies–/– and MyD88–/– mice exhibited protection against renal dysfunction following RIR [HMGB1 triggers cellular signaling through TLR, especially TLR2 and TLR4, leading to proinflammatory cytokine and chemokine augmentation, causing RIR damage . TLR4 exwing RIR . During wing RIR ,12,28,34In the existing study, the RIR group showed elevated levels of HMGB1, which in turn activated TLR 2, TLR 4, and NFκB, and subsequently inflammatory factors  were activated. Numerous stimuli, including ischemia/reperfusion injury, activate NFκB signaling by the degradation of IκB and release of the NFκB p65-p50 dimer, which translocates to the nucleus, binds to κB binding sites on DNA, and regulates the transcriptional activation of target genes . FurtherMedicinal plants have been the basis of conventional medicine for hundreds of years and often a foremost foundation of pharmacological active constituents. Plant essential oils and their constituents are being utilized in both foods and pharmaceuticals. Anethole is extensively used in cosmetic, perfumery, and pharmaceutical industries and is employed as a flavoring additive in food manufacturing. Various studies have disclosed several pharmacological actions of anethole, for instance anti-inflammatory ,17, antiManagement with anethole prior to RIR showed enhanced renal morphology and improved renal function, as evidenced by the lowered levels of Cr, BUN, uric acid, LDL, and Kim-1 when related to the RIR group. These results postulate that anethole averts RIR-induced renal function alterations and histological changes. Previously, trans-anethole showed the protective effects on the GFR, protein, urine production, and kidney/body weight ratio, as well as lowered tubule vascular degeneration, and glomerular and tubulointerstitial sclerosis in STZ diabetic animals . As for the antioxidant effects, anethole amplified GSH renal content and SOD, CAT, and GPx activities and prohibited the amplified levels of MDA, indicating lower lipid peroxidation. These results together suggest that RIR-induced oxidative stress might be managed by anethole due to its efficient antioxidant ability. Anethole inhibited TNF-α induced by both lipid peroxidation and ROS generation . FurtherComputational studies suggested the binding of anethole with the deep hydrophobic pocket of MD-2, which might interfere with LPS and binding of other MD-2 ligands. This suggests that anethole might modulate MD-2-TLR4 interactions and affect downstream signaling, allowing for an additive anti-inflammatory response.Renal ischemia/reperfusion (RIR)-experienced animals exhibited progressive renal injury as evidenced by lowered renal function and alterations in histology examination. RIR induced severe oxidative, inflammatory, and apoptotic status within renal tissue. Management with anethole prior to RIR showed enhanced renal morphology and improved renal function. Anethole amplified GSH renal content and SOD, CAT, and GPx activities, and lowered MDA levels, indicating inferior lipid peroxidation and superior antioxidant enzyme activities. The intake of anethole prior to RIR diminished the expression levels of HMGB1, TLR2, TLR4, MYD88, and NFκB, indicating that the nephro-protective effect of anethole could occur via the inhibition of the HMGB1/TLR2, 4/MYD88/NFκB pathway. Pretreatment with anethole administration declined TNF-α, IFN-γ, MCP-1, and caspase 3 and 9, whereas it increased IL-10, reflecting the anti-inflammatory and anti-apoptotic actions of anethole. These outcomes could support the use of anethole, or the compound-containing plant essential oils, to protect against RIR in susceptible patients.


Ovarian follicle development plays an important role in determination of poultry egg production. The follicles at the various developmental stages possess their own distinct molecular genetic characteristics and have different biological roles in chicken ovary development and function. In the each stage, several genes of follicle-specific expression and biological pathways are involved in the vary-sized follicular development and physiological events. Identification of the pivotal genes and signaling pathways that control the follicular development is helpful for understanding their exact regulatory functions and molecular mechanisms underlying egg-laying traits of laying hens.VIPR1, VIPR2, ADRB2, and HSD17B1 were identified, respectively. Moreover, NDUFAB1 and GABRA1 genes, two most promising candidates potentially associated with egg-laying performance were screened out from the 13 co-expressed DEGs in the GWF, SYF and LYF samples. We further investigated the biological effects of NDUFAB1 and GABRA1 on ovarian follicular development and found that NDUFAB1 promotes follicle development by stimulating granulosa cell (GC) proliferation and decreasing cell apoptosis, increases the expression of CCND1 and BCL-2 but attenuates the expression of caspase-3, and facilitates steroidogenesis by enhancing the expression of STAR and CYP11A1. In contrast, GABRA1 inhibits GC proliferation and stimulates cell apoptosis, decreases the expression of CCND1, BCL-2, STAR, and CYP11A1 but elevates the expression of caspase-3. Furthermore, the three crucial signaling pathways such as PPAR signaling pathway, cAMP signaling pathway and neuroactive ligand-receptor interaction were significantly enriched, which may play essential roles in ovarian follicle growth, differentiation, follicle selection, and maturation.The comparative mRNA transcriptomic analysis of ovarian follicles at three key developmental stages including slow growing white follicles (GWF), small yellow follicles (SYF) of recruitment into the hierarchy, and differentiated large yellow follicles (LYF), was accomplished in the layers with lower and higher egg production. Totally, 137, 447, and 229 of up-regulated differentially expressed genes (DEGs), and 99, 97, and 157 of down-regulated DEGs in the GWF, SYF and LYF follicles, including The current study provided new molecular data for insight into the regulatory mechanism underlying ovarian follicle development associated with egg production in chicken.The online version contains supplementary material available at 10.1186/s12864-021-08213-w. Egg production initiates from the follicle prehierarchical and hierarchical development, maturation, and finally ovulates from the hen ovary, which is governed by the hypothalamic–pituitary–ovarian axis –3. OvariFTH1, TB, EEF1A1, TXN, ANXA2, ING4, and ACADL) associated with high rates of egg production in chicken ovarian follicles were screened out by cDNA microarray data analysis [STAR, HSD3B, CYP11A1, and CYP19), paracrine signaling  and transcription  were identified by transcriptome analysis of the small ovarian follicles 0.5, 1, and 2 mm in diameter after oocyte removal in chicken, which may be important during early avian follicular development [Wnt4 takes part in the regulation of chicken follicle selection [VLDLR1, WIF1, NGFR, AMH, BMP15, GDF6 and MMP13. They might play certain roles in chicken follicle selection [SOWAHA) gene was confirmed to affect the expression of genes involved in chicken follicle selection and to inhibit the proliferation of granulosa cells [To intensively explore the molecular regulatory mechanisms underlying follicle development for better understanding egg production capacity, previous studies on transcriptome analyses of ovarian follicles, granulosa cells and theca cells of follicles as well as hypothalamus, pituitary gland and ovary, have revealed many genes involved in ovarian follicular development and follicle selection, which potentially correlate with the high capacity for egg production in chicken, goose, duck, and turkey. It has been reported that 20 differentially expressed transcripts  between the laying period and ceased period, in which the 12 validated genes, i.e.,  pathway . Ren shoelopment . Moreoveelopment . HoweverThe Jilin Black (JB) chicken, an indigenous Chinese breed , is characterized by high quality meat, but a low rate of egg production, low laying peak, and strong broodiness . In contTo find the most potential genes and signaling pathways associated with egg production, comparative transcriptome analysis of ovarian GWF, SYF and LYF follicles between the JB and LB breeds was implemented. The present study aims to identify key functional genes implicated in ovarian follicular growth and development (including follicle selection) that directly contribute to egg-laying production. These results laid a molecular foundation for further elucidating the regulatory mechanisms of the promising genes in ovary development, and the pivotal signaling pathways in formation of chicken egg-laying traits.P < 0.001). HH at 72 weeks of age was higher in LB than JB hens (P < 0.001). Moreover, egg production rates at 21, 30, 43, 57 and 66 weeks of age were also higher in LB when compared to JB hens (P < 0.001).Performance of egg-laying production related traits in JB and LB hens was evaluated, including age at first egg (AF), average body weight at 21 and 66 weeks of age (21BW and 66 BW), egg weight at 66 weeks of age (EW), hen-housed egg production (HH) at 72 weeks of age, and egg production  rate at 21, 30, 43, 57 and 66 weeks of age . As shown in Table gallus gallus and 81.97% reads were mapped uniquely. The dynamic range of the expression values was estimated and exhibited as a box plot of logarithmic transformed FPKM values for each sample separately . Among them, the most important potential genes causing development of the undifferentiated follicles, which may be associated with egg production performance, were uncovered such as GABRA1, ADGRD1, VIPR2, APOD, BRINP1, PAK5, GPR65, RYR2, ZPBP, PIK3R1, EDIL3, TP53I3, BLK, POSTN, PERP1, HACD4, ZP1, WNT2B, CYP4F22, ADRB2, STARD4, CREB3L3, ACSBG2, GPER1, NDUFAB1, NCAM2, WISP1, MC2R, LOC424014, and RXRG  in the follicles of GWF, SYF and LYF between JB and LB hens were identified (The relevant datasets have been published by our group and are available at PRJNA669967 and PRJNA670553 in NCBI), respectively. In GWF follicles, 137 up-regulated and 99 down-regulated DEGs were revealed by comparison of JB with LB chickens , cAMP signaling pathway (ko 04024), and neuroactive ligand-receptor interaction (ko 04080) were significantly enriched. Moreover, NDUFAB1 gene as a member of oxidative phosphorylation pathway . Simultaneously, cell proliferation ratio of the GCs was notably decreased as compared to the negative control . It was suggested that NDUFAB1 promotes follicle development by stimulating GC proliferation and decreasing cell apoptosis, increases the expression of CCND1 and BCL-2 but attenuates the expression of caspase-3, and facilitates steroidogenesis by enhancing the expression of STAR and CYP11A1.To further confirm the exact roles of GABRA1 silence of GCs, the expression levels of STAR, CYP11A1, CCND1, and BCL-2 transcripts and their proteins were notably elevated . Furthermore, cell proliferation ratio of the GCs was remarkably enhanced as compared to the negative control . It was proposed that GABRA1 inhibits GC proliferation and increases cell apoptosis, decreases the expression of CCND1, BCL-2, STAR, and CYP11A1 but elevates the expression of caspase-3.For Ovary is an important reproductive organ in adult poultry, which contains many various-sized follicles corresponding to the different follicular developmental stages, including the small white follicle resting (months or years) stage < 2 mm), the slow growing undifferentiated prehierarchical stage , the stage of follicle selection , and the final differentiated hierarchy  stage , 4, 5. T mm, the VIPR2, GABRA1, PERP1, and ZP1, and lower mRNA levels of WISP1, MC2R, STARD4, and NDUFAB1 genes, in which the most representative gene VIPR2 is also named pituitary adenylate cyclase-activating polypeptide (PACAP) receptor (VPAC2), encoding the VPAC2/vasoactive intestinal peptide (VIP) receptor (VIPR2) belonging to the VIP/PACAP type II receptors, including VPAC1/VIPR1 and VPAC2/VIPR2 [VIPR2 transcript in GWF developmental stage may be relevant to broodiness and lower egg-laying trait of JB breed by blocking follicle growth and maturation in ovarian follicle development.To mine the key genes implicated in follicular development at the stages of follicle selection, before and after the selection, in this study, transcriptome analysis of the GWF, SYF and LYF follicles was implemented between the JB and LB chicken breeds. To our knowledge, this is one of the few studies to reveal potential pivotal genes of involvement in hen ovarian follicles at the developmental stages immediately before and after follicle selection, which may be associated with high and low rates of egg production. Firstly, in the GWF follicles of this study, the JB hens with low egg production showed higher mRNA levels of C2/VIPR2 , 31. PreC2/VIPR2 –34. MoreC2/VIPR2 . The parC2/VIPR2 , 37. TheBCL2L14, LOC424014, ADRB2, and GABRA1, and lower mRNA levels of PRLL, HSD17B1, NCAM2, and NDUFAB1 genes in the JB hens as compared with LB hens, in which the most representative genes, ADRB2 and HSD17B1, have been intensively studied in ovarian functions [ADRB2 gene was mainly expressed in the GCs and Theca cells (TCs) of ovarian follicles and regulated the levels of cAMP and steroid production through activation of ADRB2/cAMP/protein kinase A (PKA) signaling pathway and/or ADRB2/cAMP/protein, phospholipase C (PLC)/protein kinase C (PKC)/cAMP response element-binding protein (CREB) signaling cascade [ADRB2 gene may induce layer broodiness by activation of adenylate cyclase through the action of G proteins and stimulate anovulation [In this study, transcriptomic analysis of SYF follicles revealed higher mRNA levels of unctions , 39. The cascade , 43, 44.vulation , 43.HSD17B1 gene, to efficiently catalyze reversible interconversion of a low-active precursor estrogen estrone (E1) to the highly active E2 that is necessary for normal ovary development [HSD17B1 transcript in SYF follicles of JB hens may affect ovarian dominant follicle selection and follicle growth and function by repressing 17β-estradiol production and follicle cell proliferation, and finally lead to a low egg production.The hydroxysteroid (17beta) dehydrogenase type 1 (HSD17B1) is a steroidogenic enzyme encoded by elopment , 45. It elopment –48. Neveelopment , 49, whielopment . It was CYP2D6, CRH, GABRA1, and GHRHR-LR, and lower mRNA levels of ID4, SSTR2, CDKN1A and NDUFAB1 genes in the JB than in the LB layers. Among them, the most representative gene GHRHR-LR, also named VIPR1, its encoding product VIPR1 was mainly expressed in granulosa cells and residual ovarian tissue [VIP and VIPR1 genes were associated with chicken broodiness and egg production [VIPR1 transcript in LYF follicles of JB hens may inhibit ovarian follicle growth, differentiation and maturation, and contribute to the lower egg production.Transcriptomic analysis of LYF follicles revealed higher mRNA levels of n tissue . PACAP mn tissue . Moreoveoduction , 53. It GABRA1 mRNA and down-regulated NDUFAB1 mRNA of the GWF, SYF and LYF follicles were co-expressed differentially in JB hen ovaries when compared with LB hen. Previous studies have reported that GABRA1 gene encodes γ-aminobutyric acid type A receptor α1 subunit (GABRA1) is one subtype of the neurotransmitter γ-aminobutyric acid (GABA) activating GABAA receptors that act as the components of the gamma-aminobutyric acid signaling pathway [A receptors are vital for controlling neuronal excitability and can display significant levels of constitutive activity that contributes to tonic inhibition [A receptor, is sedative in high concentrations but may precipitate paradoxical adverse effects on mood at levels corresponding to luteal phase concentrations in susceptible women [A receptors may be loosely linked to reproductive performance in chicken. Furthermore, And the configuration of GABRA1 proves the receptor’s vital role in apoptosis regulations, mitochondrial function, inhibitory effects of different neurotransmitters, and synaptic plasticity by interaction with neuroactive steroids such as androstane and progesterone derivatives [NDUFAB1 gene demonstrated that the NDUFAB1 gene encodes the acyl carrier protein (ACP), NDUFAB1 subunit of mitochondrial complex I (NADH: ubiquinone oxidoreductase), which is involved in cytosolic and mitochondrial fatty acid synthesis pathways [GABRA1 and the NDUFAB1 in ovarian follicular function remain unclear in chicken.Interestingly, the significantly up-regulated  pathway , 55. The pathway –58. The le women . It indiivatives , 60. Morpathways . ACP molpathways , exists pathways , 64. HowGABRA1 and NDUFAB1 genes in ovarian follicle development, silencing GABRA1 and NDUFAB1 genes were conducted in the GCs of SYF, respectively. The result showed that expression levels of the steroidogenic-related genes, both STAR and CYP11A1, were remarkably enhanced when silencing GABRA1 was implemented in the GCs. As known, the STAR and P450scc were two key enzymes in regulation of P4 synthesis and secretion within the GCs. In which, STAR functions to transport cholesterol to the inner membrane of mitochondria from the outer membrane [STAR and CYP11A1 expression level. But the much lower P4 level or P4 absence also suppressed folliculogenesis and ovulation [BCL-2 and CCND1 genes were significantly elevated in the GCs, whereas the expression of CASP3 gene was notably decreased. Furthermore, the cell proliferation ratio of the GCs was remarkably enhanced as compared to the negative control; conversely, cell apoptosis rate was sharply decreased as GABRA1 expression was silenced. Although the exact roles and regulatory mechanism of GABRA1 and the GABAR in ovarian follicular function remain unclear in chicken, a study reported the inhibitory effect of xanthohumol on evoked glutamate release was antagonized by suppressing the GABAR/Ca2+-calmodulin/AC/cAMP/PKA cascade in rat hippocampus [GABRA1 gene plays an inhibitory role in ovarian follicle development, follicle selection and differentiation by suppressing steroidogenesis, GC proliferation and stimulating cell apoptosis of SYF.To further confirm the roles of membrane , then P4membrane . It is wmembrane , 68. P4 membrane . Howevermembrane , 70. In vulation , 72. Conpocampus . Moreovepocampus , 75. OurNDUFAB1 mRNA in the ovarian follicles of three developmental stages was down-regulated in the JB hens. Previous studies corroborated that NDUFAB1 is linked directly to mitochondrial ATP production, indirectly to potential reactive oxygen species generation [NDUFAB1 expression was silenced. Simultaneously, it was accompanied by the significant downregulation of STAR, CYP11A1, CCND1, and BCL-2 expression, and remarkably upregulated Caspase-3 expression. It confirmed that NDUFAB1 has a positive regulation in ovary follicular growth and differentiation at the developmental stage of follicle selection by enhancing steroidogenesis, GC proliferation and decreasing cell apoptosis. Furthermore, this study has shown the expression levels of NDUFAB1 mRNA across the three developmental stages of GWF, SYF and LYF in ovary development keeps lower in JB hens than in LB layers, but the higher expression of GABRA1 transcripts. Accordingly, it was concluded both NDUFAB1 and GABRA1 genes were strongly associated with egg-laying production of hens.As aforementioned, it was found that the expression neration , and redneration . Moreoveneration . It was 2+-calmodulin/AC/cAMP/PKA cascade of the GABAergic synapse pathway [NDUFAB1, MC2R, VIPR2, GHRHR-LR, MC2R and SSTR2 that were identified currently may be implicated in granulosa cell proliferation and apoptosis, oocyte meiosis and maturation, follicular differentiation and atresia, and secretion of gonadotrophin-release hormone via crosstalk or intracellular interactions with the PPAR pathway [Additionally, in this study three crucial signaling pathways including PPAR signaling pathway, cAMP signaling pathway, and neuroactive ligand-receptor interaction were screened out. Among them, the cAMP signaling pathway was not only involved in the neuroactive ligand-receptor interaction such as the GABAR/Ca pathway , 73, it  pathway , 79. As  pathway , 81. FSH pathway , 82. The pathway . Accordi pathway –86.Collectively, the transcriptome comparative analysis of ovarian follicles at the GWF, SYF and LYF developmental stages reveals the key genes and signaling pathways involved in ovarian follicular follicle growth (including follicle selection), differentiation, and maturation, which has provided molecular evidences for new insight into the regulatory mechanism underlying ovarian follicle development associated with egg production in chicken.After hatching, LB and JB hens were raised in layered batteries under the same rearing conditions, including free access to water and feed in accordance with the recommendations for nutrient levels of in Lohmann breeds , 87. AppIn this study, ten layers of every JB and LB breeds were obtained and euthanized at 21 weeks of age, and the GWF, SYF and LWF follicles were harvested. After the surrounding vascular and connective tissues of the follicles were removed with fine forceps and a scalpel , follicl2 in humidified chambers following our published method [The GCs from the SYFs were isolated and cultured in Medium199  supplemented with 10% (v/v) fetal calf serum (Gibco) at 37 °C with 5% COd method , 89. Thed method , 90.Total RNA was isolated from follicles of each hen using Trizol Reagent  according to the recommended manufacturer’s protocol, and concentration, quality, and integrity were determined by a Thermo NanoDrop 8000 Spectrophotometer . Three micrograms of RNA were used as input material for RNA sample preparations. The other parts of the samples were stored at − 80 °C for validation experiments.ftp://ftp.ensembl.org/pub/release-86/fasta/gallusgallus/dna/Gallusgallus.Gallusgallus-5.0.dna.toplevel.fa.gz) by using sequence alignment program HISAT 2.1.0 [The 18 cDNA libraries were generated using the TruSeq RNA Sample Preparation Kit  according to the method published by our group . Among tAT 2.1.0 , 92.https://sourceforge.net/projects/mev-tm4/files/latest/download). The resulting p-values were adjusted using the Benjamini and Hochberg’s approach for controlling the false discovery rate and an absolute value of the |log2FoldChange| (Log2FC) was served as the threshold for judging significance of the gene expression. Candidate genes with an adjusted p-value (padj) < 0.05 and Log2FC > 1 were identified as differentially expressed. MA plots to visualize up- and down-regulated genes of each sample were generated by using the R package “ggplot2” (version 3.5.0), according to the values of padj and Log2FC [In accordance with our earlier reported method , the HTSd Log2FC .http://geneontology.org/), was utilized to annotate and analyze the molecular and functional characteristics of the DEGs [p-value was Bonferroni corrected, taking the corrected p-value < 0.05 as a threshold for GO annotation. Another online biological tool, Kyoto Encyclopedia of Genes and Genomes , provided the comprehensive database resources for the KEGG pathway enrichment of the DEGs. In this step, four databases were utilized to reveal high-level functions and biological systems of the DEGs, including Reactome (http://www.reactome.org), KEGG pathway (http://www.genome.jp/kegg/). Results with P < 0.05 were considered significantly enriched by DEG.An online biological tool, Gene Ontology  as described previously . The priNDUFAB1 or GABRA1 gene were designed using an InvivoGen siRNA Wizard v3.1 and the most effective siRNA was screened out as we previously reported [NDUFAB1 or -GABRA1 carrying the specific siRNA was constructed, GCs were then transfected with the NDUFAB1 shRNA or GABRA1 shRNA lentivirus in 24-well plates (2 × 105 cells/well), respectively; and incubated at 37 °C with 5% CO2. After 24 h of culture, the GCs were collected for EdU cell proliferation and cell apoptosis assay, and lysed for Western blotting and RT-qPCR analysis. The sequence information of NDUFAB1 shRNA, GABRA1 shRNA, shRNA negative control and the frame of lentiviral vectors was shown in Table NDUFAB1 siRNA 5′-CCACAAGAGAUAGUAGAUUTT-3′; GABRA1 siRNA 5′- GCAGAAUGUCCAAUGCAUUTT-3′. Scrambled siRNA was used as the negative control siRNA: 5′- UUCUCCGAACGUGUCACGUTT-3′, which was synthesized by Genepharma .Three small interfering (siRNA) sequences targeting reported , 89. AftWestern blot analysis for STAR, CYP11A1, CCND1, BCL-2, and Caspaes-3 was performed using total cellular extracts according to our previously described , 89. TheNDUFAB1 or GABRA1 silence, a variation in cell proliferation was determined by EdU (5′-Ethynyl-2′- deoxyuridine) incorporation assay using the Cell-Light EdU imaging kit  according to the manufacturer’s protocol. Each experiment was performed in triplicate and repeated five times. The number of EdU-positive cells was expressed as a percentage and calculated relative to the total number of cells  as previously described [Following the shRNA transfection of the GCs with escribed . The ratThe apoptosis of GCs was determined by flow cytometry  using the Annexin V- Allophycocyanin(APC)/7-amino-actinomycin D (AAD) Apoptosis Detection Kit , in accordance with the manufacturer’s protocols. The apoptosis rate of GCs was estimated, i.e., total number of apoptotic cells comprises the number of cells in the right upper quadrant (Q1-UR) and number of cells in the right lower quadrant (Q1-LR), and analyzed using FlowJo v7.6 software . Each experiment was carried out for at least three times.t-test for comparisons between the RNA-Sequencing and RT-qPCR determination after confirmation of normal distributions for non-parametric analysis. Values were presented as mean ± SEM and bars with superscript symbols that indicate the significant difference compared with control groups at *** p < 0.001, ** p < 0.01, * p < 0.05.Statistical analysis was fulfilled using the SPSS12.0 software package . Data weAdditional file 1: Supplementary Table S1. Results of the cleaned data of reads filtered and the reads mapped. Supplementary Table S2. Construction frame of lentiviral vectors against NDUFAB1 and GABRA1 genes. Supplementary Table S3. Antibodies used for western blot analysis. Supplementary Figure S1. The oxidative phosphorylation pathway (gga: 00190) and its components. Supplementary Figure S2. The neuroactive ligand-receptor interaction pathway (gga: 04080) and its components. Supplementary Figure S3. The images show the original gels of western blot in the Fig. Supplementary Figure S4. The images show the original gels of western blot in the Fig.  gga: 0400 and its gga: 0400 and its


Whole genome sequencing (WGS) of neoplasms not only offers a comprehensive genome-wide detection of gene fusions/other structural variants (SVs), gene mutations, and copy number abnormalities (CNAs) but also provides information on acquired mutational profiles, including localized hypermutations (kataegis) and single base substitution (SBS) mutational signatures. The latter are recurring trinucleotide patterns of somatic single nucleotide variants (SNVs) and their flanking nucleotides that are, in some instances, associated with the etiology or pathogenesis of neoplastic disorders, for example C>T transitions in UV-associated melanoma, C>A transversions in smoking-induced lung cancer, and C>T transitions and C>G transversions caused by overactive APOBEC enzymes , 2. In aPLEKHA5-ADAMTS20, RAB11FIP2-NEURL4, and TCF3-HOXB9  and lymph nodes (LNs) . The PLEKHA5-ADAMTS20 fusion involves two genes that are often fused to other genes in, mainly, epithelial malignancies but that are also normally expressed in BM and LNs. ; this increases the likelihood that they are pathogenetically important. Second, the fact that the fusion genes were identified by WGS clearly shows that they existed on the DNA level and, hence, were not transcription-induced [Apart from confirming all gene fusions detected in clinical routine, the SV analysis revealed three novel in-frame fusions: F3-HOXB9 . The TCFe of ALL , was alsP = 0.3). Of the 10 920 somatic variants identified  and two uniparental isodisomies were detected; none of the cases displayed chromothripsis . The frehttps://cancer.sanger.ac.uk/cosmic/signatures/SBS/SBS18.tt). The SBS39, SBS37, and SBS32 signatures were detected in 18, 15, and 13 of the cases, with median values of relative contributions of 4.0%, 3.9%, and 3.2%, respectively. The etiologies of SBS37 and SBS39 are unknown, whereas SBS32 has been associated with prior treatment with azathioprine (https://cancer.sanger.ac.uk/cosmic/signatures/SBS). In the hierarchical cluster analysis of the above-mentioned signatures, the RUNX1-RUNXT1-positive cases clustered together in a separate branch, most likely because of the high proportions of SBS18 signatures/C>A transversions in these cases  was the most common one, detected in all 20 AMLs with relative contributions of 15–34% per case  and 33% (case 2) of the SNVs/indels at relapse were identical to those detected at diagnosis; the other SNVs/indels were either novel or lost at relapse , ATPB5 , and ASCC3, MACF1, USF2, ZFAT, and ZNF251 .Of the 10,920 SNVs/indels, 123 (1.1%) occurred within coding genes  of them occurred in REs, mainly enhancers (41%) and promoters (26%) . The potPLEKHA5-ADAMTS20, RAB11FIP2-NEURL4, and TCF3-HOXB9) and revealed relatively few CNAs and SNAs/indels as compared with other malignancies. Furthermore, regions with kataegis were rare and there were no signs of chromothripsis. Thus, childhood AML is characterized by a low degree of genomic complexity and mutational burden. Of the acquired variants detected, only a minority targeted REs (~1%) and coding genes (~1%). However, a large proportion (39%) of the coding genes had previously not been implicated in AML. Although fusion genes are considered strong driver mutations, requiring few additional hits for generating overt leukemia [RUNX1-RUNXT1-positive cases were for the first time associated with a higher prevalence of the ROS-associated SBS18 signature, likely due to the high frequency of C>A transversions in this AML subtype. Although the correlation between SBS18 and t should be considered preliminary—additional studies are needed to confirm or refute this—the present finding indicates that DNA damage caused by ROS [RUNX1-RUNXT1-positive AML.In conclusion, the present WGS analysis of pediatric AML identified both known and novel fusion genes (leukemia , this wad by ROS , 15 may Supplemental MaterialSupplemental Information


Mitochondrial electron transport chain complex I (ETCC1) is the essential core of cancer metabolism, yet potent ETCC1 inhibitors capable of safely suppressing tumor growth and metastasis in vivo are limited. From a plant extract screening, we identified petasin (PT) as a highly potent ETCC1 inhibitor with a chemical structure distinct from conventional inhibitors. PT had at least 1700 times higher activity than that of metformin or phenformin and induced cytotoxicity against a broad spectrum of tumor types. PT administration also induced prominent growth inhibition in multiple syngeneic and xenograft mouse models in vivo. Despite its higher potency, it showed no apparent toxicity toward nontumor cells and normal organs. Also, treatment with PT attenuated cellular motility and focal adhesion in vitro as well as lung metastasis in vivo. Metabolome and proteome analyses revealed that PT severely depleted the level of aspartate, disrupted tumor-associated metabolism of nucleotide synthesis and glycosylation, and downregulated major oncoproteins associated with proliferation and metastasis. These findings indicate the promising potential of PT as a potent ETCC1 inhibitor to target the metabolic vulnerability of tumor cells.  Cancer cells exhibit addiction to a specific metabolism , 2. WellThe essential core of these 2 metabolisms resides in mitochondrial electron transport chain complex I ETCC1), an NADH ubiquinone oxidoreductase. ETCC1 provides the NAD that allows cancer cells to drive the action of NAD-dependent enzymes necessary for the rapid synthesis of various glucose- or glutamine-derived intermediates in the glycolytic pathway and TCA cycle , an NADH, 7. IndeAlthough ETCC1 is a crucial target to annihilate cancer-specific metabolism, currently available inhibitors of ETCC1 have major limitations for use in cancer treatment because of their lack of adequate potency, e.g., biguanides, such as metformin and phenformin , 12, or Petasites japonicus as a highly potent and specific inhibitor of ETCC1 with 1700 times higher activity than that of metformin or phenformin. We also uncovered its potential mechanism underlying inhibition of tumor cell growth and metastasis, one involving cancer-specific metabolic disruption and subsequent inhibition of oncoprotein expression.Here, we report the identification of petasin (PT) from Petasites japonicus, a plant native to Japan, showed the most potent cytotoxicity, having an IC50 of 3.21 μg/mL . We then proceeded to undertake a more detailed investigation to clarify the characteristics of PT by mainly using B16F10 cells, a well-established model for assessing both tumor growth and metastasis  is an NAD/NADH oxidoreductase with a different structure from that of the mammalian ETCC1; therefore, enforced expression of NDI1 allows mammalian cells to recover the NAD/NADH oxidoreductase activity even under inhibition of endogenous ETCC1 activity . As expected, PT treatment prominently decreased the levels of TCA cycle–associated and glycolysis-associated metabolites in B16F10 cells . AspartaAnother pathway primarily affected by PT treatment was glycolysis and its related pathways . The fir+; Among these altered pathways, aspartate metabolism, PPP, and 1-carbon metabolism eventually flow into nucleotide synthesis; hence, their inhibition could severely hinder cell replication. In fact, the nucleotides  and their precursors ; however, the levels of their downstream metabolites were not apparently changed, suggesting that these upregulated metabolites were not well utilized in tumor cells under PT treatment . In spitNext, we sought to examine the difference between PT and biguanides regarding their effects on metabolism. PT has a completely different chemical structure from biguanides ; howeverPSPH, PSAT1), asparagine (ASNS), and arginine (ASS1) metabolism. These transcriptional changes were highly correlated with the increased levels of serine, asparagine, and putrescine in PT-treated tumor cells  in the tumor tissues . We demonstrated that PT showed prominent cytotoxicity toward a broad spectrum of tumor cell lines. PT-treated tumor cells showed significantly attenuated proliferation, motility, and invasion activities, eventually resulting in necrotic cell death with ATP depletion. Such prominent cytotoxicity was due to the exceptionally high inhibitory potency against ETCC1. The PT-mediated ETCC1 inhibition disrupted the TCA cycle–associated and glycolysis-associated metabolism, leading to severe impairment of nucleotide synthesis and glycosylation. Furthermore, oncoproteins associated with aggressive proliferation and metastasis were drastically downregulated in the PT-treated cells. Such antigrowth/antimetastatic activities were also evident in multiple human xenograft and mouse syngeneic models. Despite the prominent tumor inhibitory activities, PT had only minor effects on the nontumor cells and normal organs. These findings suggest that PT has promising potential as a potent ETCC1 inhibitor for cancer treatment through disruption of cancer cell metabolism.Although PT exerted prominent cytotoxicity and metabolic disruption in the tumor cells, it induced only minor changes in the nontumor cells. The tumor specificity may be explained by the high dependency of tumor cells on NAD metabolism. NAD is an essential cofactor to drive glycolysis and the TCA cycle , and tumSeveral acute toxicities have been reported for other potent ETCC1 inhibitors. The well-established potent ETCC1 inhibitor rotenone has nonspecific interaction with microtubes and induces off-target toxicity, such as severe bone marrow suppression , 30. AlsCellular ATP and NAD levels are cooperatively maintained by oxidative phosphorylation (OXPHOS) and anaerobic glycolysis. ETCC1 and subsequent OXPHOS are major sources of NAD and ATP; thus, inhibition of ETCC1 could cause shortages of ATP and NAD. Indeed, PT treatment increased glucose consumption and lactate production in tumor cells, suggesting that tumor cells upregulated anaerobic glycolysis to compensate the energy shortage. However, the anaerobic glycolysis is much less efficient for producing ATP than OXPHOS ; therefore, such metabolic changes force tumor cells to consume an extensive amount of glucose as compensation. Also, NAD is regenerated by lactate dehydrogenase in anaerobic glycolysis; however, this reaction does not increase the net amount of NAD since GAPDH consumes the same amount of NAD in the process. Therefore, the upregulation of anaerobic glycolysis would be insufficient to fully compensate the loss of ATP and NAD, and such changes could cause a shortage of glucose-derived metabolites.Indeed, PT treatment caused downregulation of glucose-derived metabolites in 2 major branch pathways from glycolysis, the hexosamine biosynthetic pathway (HBP) and PPP. These 2 pathways are upregulated in tumor cells and contribute to the synthesis of more macromolecules for tumor growth and metastasis . ParticuThe loss of NAD also caused severe depletion of aspartate, an indispensable metabolite for the synthesis of purine and pyrimidine nucleotides . The aspPT treatment significantly inhibited the formation of lung metastatic colonies in both spontaneous metastatic and i.v. injection models. This effect was likely due to ETCC1 inhibition and its subsequent events, including energy depletion, cytoskeletal remodeling, focal adhesion inhibition, oncoprotein downregulation, and cell-cycle arrest. Particularly, PT treatment induced depletion of ATP, an energy source for all metastatic steps, including invasion, extravasation, and colonization. Tumor cells flexibly utilize both glycolysis and OXPHOS for ATP production; however, PT treatment defunctionalized OXPHOS-mediated ATP production and made tumor cells highly dependent on glycolysis for their ATP production. Such tumor cells easily demonstrated ATP depletion under the low-glucose condition. Given that glucose is typically less available in the tumor microenvironment , the losPT treatment induced necrotic cell death, which could result in immunological reactions. Such immunogenicity may promote tumor pathology under some circumstances; however, immunogenic cell death also works for protecting against cancer by inducing long-lasting protective antitumor immunity . FurtherIn conclusion, we identified PT as a highly potent ETCC1 inhibitor. Given that PT-mediated ETCC1 inhibition simultaneously disrupted multiple metabolism essential for tumor growth and metastasis, such a strategy could provide a better chance to conquer metastatic tumors that have heterogeneous molecular backgrounds and thus are resistant to conventional single molecule–targeted therapy. Also, since PT has a unique chemical structure and high potency, it may serve as a lead compound to develop a novel family of potent and less toxic ETCC1 inhibitors for treating metastatic tumors.NDI1 overexpression; generation of Rho-0 cells; NAD/NADH ratios; metabolome analysis; cDNA microarray analysis; proteome analysis; animal experiments; scratch wound healing assay; Matrigel invasion assay; cell attachment assay; and Rac1 pulldown assay.Please see 2 at 37°C. Amino acid or glucose was supplemented depending on the experiment . The cells were treated with compounds dissolved in DMSO (PT) or water (metformin and phenformin). The DMSO concentration used in cell culture was 0.1% at the final concentration.MDA-MB-231, WiDr, B16F10, and CMeC1 cells were cultured in DMEM (Wako) supplemented with 10% FBS (Sigma-Aldrich); NB-1 cells in high-glucose DMEM (Wako) supplemented with 20% FBS; MCF-7, Jyg-MCB, HeLa, A549, Panc-1, MiaPaCa2, KK47, A2058, G-361, RD, ASF 4-1, TIG-3-20, SVts-8, and H9c2 cells in EMEM (Wako) containing 10% FBS; BJMC3879, DLD-1, HT-29, Colo 201, HCT116, SW480, SW620, SW837, BxPC3, MKN1, MKN7, MKN45, T24, 253JBV, DU145, LNCaP, PC3, U-251MG, Rh30, Rh41, K562, Ba/F3 T315I, RPMI8226, KCL-22, and NOMO-1 cells in RPMI1640 (Wako) supplemented with 10% FBS; and human mammary epithelial cells (HMECs) in mammary epithelial cell basal medium  supplemented with mammary epithelial cell growth medium BulletKit . MCF-7, Jyg-MCB, DLD-1, WiDr, Colo 201, SW480, SW837, Panc-1, MiaPaCa2, MKN1, MKN7, MKN45, T24, PC3, U-251MG, NB-1, A2058, G-361, RD, K562, RPMI8226, KCL-22, NOMO-1, ASF 4-1, TIG-3-20, and SVts-8 cells were obtained from the Japanese Cancer Research Resources Bank; HCT116, DU145, LNCaP, and B16F10 cells from the Riken Bioresource Research Center; and KK47 from the Cell Resource Center for Biomedical Research of Tohoku University. MDA-MB-231, HeLa, A549, HT-29, SW620, and H9c2 cells were procured from the American Type Culture Collection. BxPC3 cells were obtained from the European Collection of Authenticated Cell Cultures. BJMC3879 and 253JBV cells were provided by the Osaka Medical College; Rh30 and Rh41 cells, by the Kyoto Prefectural University of Medicine; CMeC1 cells, by the University of Tokyo; and Ba/F3 T315I, by the National Cancer Center Japan. HMECs were purchased from Lonza. Cell lines used were confirmed as being negative by mycoplasma testing . All cells were cultured in a humidified incubator with 5% COCell and tissue lysates were prepared with 1% SDS buffer. The protein concentrations were measured by using a DC Protein Assay Kit (Bio-Rad). Protein samples (1–5 μg/lane) were resolved in 10%–12.5% SDS-PAGE gells and transferred to 0.45 μm PVDF membranes . After having been blocked with PVDF Blocking Reagent for Can Get Signal (TOYOBO) for 1 hour, the membranes were incubated overnight at 4°C with primary antibodies. The primary antibodies used for immunoblotting are summarized in B2m. The gene used as an internal control was selected from Tbp, B2m, Gapdh, Actb, Hprt by using the NormFinder algorithm (https://moma.dk/normfinder-software).Total RNA from cells was purified by using NucleoSpin miRNA (MACHEREY-NAGEL). The concentration of total RNA was assessed by use of UV spectrophotometry. RNA integrity was checked with an Agilent 2100 Bioanalyzer (Agilent). cDNA was reverse transcribed from 0.5 μg of the total RNA by using a PrimeScript RT Reagent Kit (Takara). The cDNA was amplified with Universal SYBR Select Master Mix (Applied Biosystems), and signals were recorded with a Takara Thermal Cycler Dice Real Time System II. Relative expression levels were quantified by the ΔCt method and normalized to Tissues for H&E staining and immunohistochemistry were fixed in 4% neutral buffered paraformaldehyde, embedded in paraffin, sectioned at 5 μm, and mounted on microscope glass slides . The slides were deparaffinized in lemosol and rehydrated by passage through graded alcohols. H&E staining was performed with Tissue-Tek Hematoxylin 3G  and Tissue-Tek Eosin . For immunohistochemistry, the sections were incubated in 0.3% hydrogen peroxide in methanol for 20 minutes to inhibit endogenous peroxidase activity. For antigen retrieval, the sections were then autoclaved for 1 minute at 120°C in antigen retrieval buffers  and cooled down to room temperature. After 3 washes with TBS-T, the sections were blocked with 2.5% normal horse serum  and incubated with primary antibodies overnight at 4°C. The primary antibodies used are listed in 5 cells/mL. After treatment with PT (0.3 μM) or DMSO and incubation for 72 hours, the cells were washed with ice-cold PBS and fixed with 4% paraformaldehyde in PBS for 30 minutes at 4°C. After 3 washes with PBS, the cells were made permeable with 0.1% Triton X-100 in PBS for 5 minutes and then blocked with 10% goat serum in PBS for 1 hour. Subsequently, the coverslips were incubated overnight at 4°C with anti–p-FAKY397  as the primary antibody. After 4 washes with PBS, the coverslips were incubated for 45 minutes at room temperature with anti-rabbit antibody conjugated with Alexa Fluor 488 (Life Technologies) as the secondary antibody and phalloidin conjugated with Alexa Fluor 568 (Life Technologies). The coverslips were finally incubated with DAPI (200 nM) for 5 minutes and mounted on slides with Lab Vision PermaFluor (Thermo Fisher Scientific). Confocal images were obtained with a laser scanning confocal microscope . The images obtained were analyzed by using ImageJ .B16F10 cells were seeded onto coverslips at the concentration of 0.25 × 10P values, and statistical tests is given in the figure legends. P values of less than 0.05 were considered significant. If not specified, biological replicates  were employed for performing the experiments, and the measurements were taken from distinct samples in each experiment. All experiments were performed at least twice, and the technical and biological replicates were reliably reproduced.GraphPad Prism 8 (version 8.4.0), and JMP  were used for statistical analysis and illustrating heatmaps. The detailed information on error bars, cDNA microarray data have been deposited in the Gene Expression Omnibus (GEO) under accession code GSE150348.Guide for the Care and Use of Laboratory Animals .All mouse experiments were approved by the IACUC of Gifu University and performed according to the NIH NDI1 overexpression was performed by KH and RH. Histology and immunohistochemistry were performed by KH and HS. Immunocytochemistry, confocal microscopy, and Rac1 pulldown assay were performed by MN and HU. Transmission electron microscopy analysis was done by YI and KH. KH conducted data analysis. KH wrote the manuscript with contributions from YA. All authors read and approved the final manuscript.KH and YA conceived the study. Development and screening of a phytochemical library, HPLC, and purification of PT were performed by RI. Viable cell counts for various cancer cell lines were carried out by KH, NS, YK, and TN. Spheroid formation assay; cell-cycle analysis; immunoblotting; Matrigel invasion assay; senescence-associated β-galactosidase staining; MitoTracker orange/green staining; mitochondrial ETC activity assays; determination of NAD/NADH ratios; cDNA microarray analysis; generation of Rho-0 cells; measurement for glucose, lactate, and pH in the culture medium; measurement of ATP/ADP ratio; cDNA microarray analysis; proteome analysis; scratch wound healing assay; cell attachment assay; and statistical analysis were performed by KH. RT-qPCR and animal experiments were done by KH and NS. Metabolome analysis was carried out by KH and TS.


Transmembrane transport of metformin is highly controlled by transporters including organic cation transporters (OCTs), plasma membrane monoamine transporter (PMAT), and multidrug/toxin extrusions (MATEs). Hepatic OCT1, intestinal OCT3, renal OCT2 on tubule basolateral membrane, and MATE1/2-K on tubule apical membrane coordinately work to control metformin disposition. Drug–drug interactions (DDIs) of metformin occur when co-administrated with perpetrators via inhibiting OCTs or MATEs. We aimed to develop a whole-body physiologically based pharmacokinetic (PBPK) model characterizing interplay of OCTs and MATEs in the intestine, liver, and kidney to predict metformin DDIs with cimetidine, pyrimethamine, trimethoprim, ondansetron, rabeprazole, and verapamil. Simulations showed that co-administration of perpetrators increased plasma exposures to metformin, which were consistent with clinic observations. Sensitivity analysis demonstrated that contributions of the tested factors to metformin DDI with cimetidine are gastrointestinal transit rate > inhibition of renal OCT2 ≈ inhibition of renal MATEs > inhibition of intestinal OCT3 > intestinal pH > inhibition of hepatic OCT1. Individual contributions of transporters to metformin disposition are renal OCT2 ≈ renal MATEs > intestinal OCT3 > hepatic OCT1 > intestinal PMAT. In conclusion, DDIs of metformin with perpetrators are attributed to integrated effects of inhibitions of these transporters. Similarly, volunteers with the heterozygote SLC22A3 rs2292334 variant also have significantly higher metformin Cmax and AUC values than the wild-type SLC22A3 genotype [Metformin is widely used for the treatment of type 2 diabetes. Under physiological pH, metformin is highly ionized, and its transmembrane transport is mainly mediated by transporters, which mainly include organic cation transporter 1–3 (OCT1-3), multidrug/toxin extrusions (MATE1 and MATE2-K) and plasma membrane monoamine transporter (PMAT) , inferrigenotype . These rgenotype , indicatgenotype . OCT1 exgenotype . MATE1 ei values for MATE1 and MATE2-K are 55 and 81 folds lower than those for OCT2 inhibition, respectively. Trimethoprim Ki values for MATE1 and MATE2-K are 52 and 391 folds lower than those for OCT2 inhibition [Metformin is commonly co-administrated with other drugs. Potential drug-drug interactions (DDIs) of metformin with inhibitors of these transporters have been demonstrated when co-administrated with some drugs including cimetidine, pyrimethamine, trimethoprim, dolutegravir, vandetanib, ondansetron, and rabeprazole ,18,19,20hibition . These r® label). It’s necessary to assess DDIs of metformin with perpetrators to avoid serious clinical consequences. The physiologically based pharmacokinetic (PBPK) model is considered to a powerful tool to explore and quantitatively predict the pharmacokinetics of drugs and the magnitude of DDIs. It is widely applied at increasingly early stages during drug development and is recommended by the US Food and Drug Administration [DDIs of metformin with perpetrators are of clinical concern as elevated plasma concentrations of metformin, which are often associated with an increased risk of lactic acidosis  and area under curve (AUC) of metformin were compared with clinic reports. Individual contributions of liver OCT1, intestinal OCT3 (as well as PMAT), renal OCT2 and renal MATEs to metformin disposition and their integrated effects were investigated, respectively. Gastrointestinal transit rate, intestinal pH values and Ki values for OCT2, OCT3 and MATEs were selected for sensitivity analysis.The aim of the study was to develop a whole-body PBPK model characterizing interplay of OCTs and MATE1/2-K in intestine, liver and kidney to predict DDIs of metformin with six perpetrators including cimetidine, pyrimethamine, trimethoprim, ondansetron, rabeprazole and verapamil. The Michaelis–Menten (M-M) model was used to illustrate non-linear intestinal absorption of metformin. The concentrations of metformin in liver were simultaneously simulated. The predicted plasma concentration profiles, peak concentration  DDI data may come from different reports; (5) perpetrators are inhibitors of OCTs or MATEs, whose Ki or IC50 values for these transporters are shown.DDIs of metformin with perpetrators were collected from publications on PubMed based on the following criterions. (1) Data may come from healthy subjects following single dose or multidose of metformin when co-administrated with perpetrators; (2) metformin and perpetrators were orally administrated to healthy subjects in immediate release formulation; (3) pharmacokinetic profiles or pharmacokinetic parameters such as Ci values of some perpetrators often showed large variations. In order to fully investigate risks of DDIs, the smallest Ki values (strongest inhibition) were selected. The selected model parameters are listed in Model parameters for illustrating pharmacokinetics of metformin and perpetrators as well as DDIs of metformin with perpetrators used in the PBPK model were selected according to following criterions. (1) The optimal parameters of metformin, cimetidine, ondansetron, trimethoprim and verapamil used in the PBPK model were previously reported ,32,33,34A whole-body PBPK model involving interplay of OCTs and MATEs in intestine, liver and kidney  was deveIn the gastrointestinal tract:0), drug amount in intestinal lumen , and in the enterocytes (Agwi) are illustrated as follows.t0 is constants of gastric emptying rate for stomach. Kti and ka,i are constants of gastric emptying rate and drug absorption rate. Kg:b is ratio of drug concentration in gut to blood. The ka,i values were cited from literatures or estimated using human intestinal effective permeability from apical to basolateral side  through equation ka,i = 2i values are intestinal radius of duodenum, jejunum and ileum, which are 2.0, 1.63 and 1.45 cm [gwi and Vgwi are blood flow and volume of the corresponding enterocytes, respectively. Aart and Vart are amount of drug in artery blood and volume of artery blood, respectively. Vmax and Km are the maximum velocity and Michaelis–Menten constant for metformin. The values of Km and Vmax for metformin absorption by OCT3 and PMAT were reported to be 2.46 mM, 12.08 nmol/mg/min and 1.68 mM, 15.28 nmol/mg/min, respectively [sf,ex,i). The expressions of OCT3 in jejunum was assumed to be 1. According to previous report [sf,ex,i values of OCT3 in duodenum, jejunum and ileum were calculated to be 1.19:1:1.155. Function of intestinal OCT3 is dependent on pH. According to a previous report [sf,pH,i was defined as relative values of OCT3 and PMAT function with different pH. The estimated Tsf,pH,i values in duodenum, jejunum and ileum for OCT3 and PMAT were 0.35, 0.74, 1 and 1, 0.48, 0.11, respectively.The gut lumen and gut wall (enterocytes) are divided into duodenum, jejunum, ileum, caecum and colon according to their physiological and anatomical characteristics. It is assumed that absorption and metabolism of drug only occur in duodenum, jejunum and ileum. M-M model is used to illustrate non-linear intestinal absorption of metformin. Contribution of intestinal OCT3 and intestinal PMAT to apical to basolateral side of metformin were assumed to be 50% and 20% based on previous reports ,7 and reectively ,52. PBSFectively ,54. The ectively , thus, ts report , the Tsfs report ,51, Tsf,max for OCT3 ,In hepatic blood :h,b and Vh,c are volume of hepatic blood and hepatocytes, which were assumed to be 608.3 mL and 1081.7 mL, respectively. Kh:b and Qh are ratio of drug concentration in liver to blood and blood flow in hepatic artery, separately. CLint,up,OCT1, CLint,pd and CLint,met are OCT1-mediated intrinsic clearance of uptake, efflux clearance from hepatocytes to blood and metabolic clearance, respectively. fub is unbound fraction of drug in blood. Ki, OCT1 is inhibition constants of perpetrators on OCT1-mediated uptake. Subscript “sp” indicates spleen.In hepatocytes  using the equations.system and CLrenal are system clearance and renal clearance. QL is total hepatic blood flow, which equaled to Qsp + Qh + Qgwi.CLh) is illustrated by a well-stirred model, i.e.,The amount of other perpetrators in liver  of drug is controlled by OCT2 and MATE1/MATE2-K in series.For metformin and cimetidine, renal excretion of drug mainly occurs via glomerular filtration and renal secretion. It was assumed that reabsorption does not occur. Renal compartment consists of blood compartment and tubule. The renal secrete clearance  and in tubule are illustrated as follows.r,b and Vr,e are renal blood volume and tubule volume, respectively. Vr,b was reported to be 33.8 mL [r,e was estimated to be 246.2 mL. Kr:b and Qr are ratio of drug concentration in renal to blood and blood flow in renal, respectively. OCT2-mediated uptake clearance  and MATE-mediated efflux clearance  of metformin were reported to be 14.2 μL/min/106 tubules and 16.6 μL/min/106 tubules [6 tubules [6 tubules, separately [i,OCT2 and Ki,MATE is inhibition constants of perpetrators on OCT2-mediated uptake and MATE-mediated efflux.Amounts of drugs in renal blood compartment , i.e.,Well-stirred model was also used to illustrate disposition of other perpetrators in kidney  using Equation (15).CLrt) and venous blood (Aven).In artery blood (Art) and venous blood (Aven) are illustrated bytotal, Vlu, Vven, Alu and Klu:b are cardiac output, lung volume, venous volume, amount of drug in lung and ratio of drug concentration in lung to blood, respectively. Subscript “j” indicates other tissues in human body, such as heart, brain, muscle, adipose, skin, and rest tissues.Amounts of drug in artery blood (Amax and AUC).Phoenix WinNonlin 8.1  was used for coding and solving of the PBPK model as well as estimating corresponding kinetic parameters (Cmax) of metformin and 6 perpetrators following oral administration to human were first simulated using the developed PBPK model and the developed PBPK model was validated by visual predictive checks (VPCs). The 5th, 50th, and 95th percentiles of the simulations and their 90% confident intervals were plotted along with the observed data. Following validation, the developed PBPK model was further used to predict DDIs of metformin with perpetrators. The predictions were compared with clinic observations. Extent of DDI was assessed as ratio of AUC (AUCR) or of Cmax (CmaxR) with perpetrators and without perpetrators. Fold errors, ratios of prediction to observation, were often used to assess prediction. The predictions were considered successful if the ratio of predication to observation fell within 0.5 and 2.0 [i, Obsi, and n is the number of predictions.Plasma concentration profiles and the plasma exposure parameters i, OCTs, Ki, MATE, intestinal pH and constant of gastrointestinal transit rate.Renal secretion of the metformin is attributed to interplay of renal OCT2 and MATEs. Intestinal absorption of metformin is mediated by intestinal OCT3, PMAT and other transporters. The function of intestinal OCT3 is dependent on pH. Gastrointestinal transit also affects intestinal absorption of metformin. Sensitivity analysis was operated on Kmax,OCT3), intestinal PMAT , hepatic OCT1 , renal OCT2  and renal MATEs  to metformin disposition were investigated.Metformin is substrates of OCT1, OCT2, OCT3, PMAT, MATE1 and MATE2-K. These transporters are differently expressed in intestine, liver and kidney. The individual contributions of intestinal OCT3 (Vi)  altered metformin disposition via affecting physiological parameters of body and expressions/function of drug transporters. For example, diabetes was reported to alter pharmacokinetics of metformin via affecting gastrointestinal transit and renal function . To betti)  were citMetformin is substrate of OCT1-3 and MATE1/2-K, which are individually expressed in intestine, liver and kidney. Several evidences have demonstrated that ranks for gene expressions of transporters in human intestine ,64,65 armax and AUC (max and AUC) also fall within 0.5–2.0 folds of clinic data. RSE for Cmax and AUC were calculated to be 1.1% and 9.6%, respectively. The estimated GMFE values for Cmax and AUC were 1.004 and 0.890, near to 1.0. All these results demonstrate that both the developed PBPK model and the selected corresponding parameters are appropriate for describing pharmacokinetics of metformin and perpetrators.The developed PBPK model considering alliance of OCTs and MATE1/2-K in intestine, liver and kidney was applied to predict plasma concentrations  and thei and AUC  of metfo and AUC . The resM-M model is successfully used to illustrate OCT3-and PMAT-mediated intestinal absorption of metformin absorption following 50–1000 mg of oral metformin to human . The resmax,OCT3), liver OCT1 , renal OCT2 , renal MATEs , and gastrointestinal transit rate (Kti) of metformin were investigated as factors for inter-individual variability. Exponential model and multiplicative residual error model were used to simulate the inter-individual variability and intra-individual variability of these parameters. The parameter estimation method was the first order conditional estimation of Lindstrom-Bates (FOCE L-B) [max,OCT3, CLint,up,OCT1, CLint,up,OCT2, CLint,efflux,MATEs, and Kti, which were regarded as random effect parameters were simulated with six sets of the observation data. Subsequently, visual predictive checks were performed on Phoenix NLME module  based on 1000 times of simulations. VPCs were indicated as comparison between 5, 50, 95 percentiles of the observations and the corresponding simulations  were made for validating the model in human populations and assessing the accuracy of the predictions following oral single dose (500 mg) administration of metformin to human. Intestinal OCT3 (VOCE L-B) . Togetheulations c. The viThe selected perpetrators are all strong inhibitors of OCTs, some of which  are stronger inhibitors of MATE1/2-K , indicatmax and AUC) for DDI also fall within 0.5–2.0 folds of observations, inferring that the developed PBPK model may be used to successfully predict the DDIs of metformin and its possible mechanisms.It was found that most of predicted concentrations are within 0.5–2.0 folds of observed concentrations. In line, 86.4% (57/66) predicted plasma exposure parameters , indicating that inhibition of intestinal OCT3 also contributes to DDIs of metformin, although the contribution of intestinal OCT3 inhibition is contrast to that of renal OCT2 and MATE1/2-K inhibitions. No evidence demonstrates that verapamil inhibits functions of MATEs, Ki values of verapamil for OCT2 and OCT3 are similar. Following oral 180 mg verapamil, concentration of verapamil in intestinal lumen was estimated to be 1.58 mM, which is higher than Ki values for OCT3 (3.6 μM). The inhibition of intestinal OCT3 by verapamil partly abolishes the increased plasma exposure to metformin by inhibitions of renal OCT2, which is in line with little alterations in plasma concentrations of metformin. Simulation shows that rabeprazole, a weak inhibitor of OCTs and MATE1, does not affect plasma concentrations of metformin, which is consistent with clinical observations [Among the tested perpetrators, cimetidine, pyrimethamine, trimethoprim and ondansetron show stronger inhibitions on MATE1/2-K than those on OCTs, inferring that the four perpetrators increase plasma exposure to metformin mainly via inhibiting renal secretion mediated by MATE1/2-K. However, intestinal concentrations of perpetrators may be higher than their KLiver is a main targeted organ for metformin. Roles of hepatic OCT1 in plasma exposure to metformin is minor, but distribution of metformin in liver is highly controlled by hepatic OCT1, in turn, affecting antihyperglycemic activity of metformin. Concentrations of metformin in liver were simultaneously simulated following coadministration of these perpetrators. The simulation data shows that coadministration of trimethoprim, rabeprazole and verapamil may increase plasma concentrations of metformin, while obviously decreased concentrations of metformin. Although the decreases in concentration of metformin in liver by the three perpetrators needed to be supported by clinical data, the simulations partly explained the curious phenomenon that why co-administration of trimethoprim, rabeprazole and verapamil little affect or attenuate antihyperglycemic activity of metformin in clinical ,99,103.i values for OCTs and MATEs, intestinal pH values and gastrointestinal transit rate on DDI of metformin were investigated using cimetidine as a representative for sensitive analysis. The variabilities of these parameters were based on the reality and reports. Ki values of cimetidine for OCT2, OCT3, MATE1 and MATE2-K [i values were set to be 0.1, 1 and 10, respectively. Variabilities in gastrointestinal transit time were set to be 0.5, 1, and 2. Moreover, function of intestinal OCT3 and PMAT is dependent on pH values. Based on physiological structural characteristics, three pH conditions were taken into account. Condition 1 (control): pH values in duodenum, jejunum and ileum were set to be 5.5, 6.5, and 7.5, respectively. Condition 2: pH values in duodenum, jejunum and ileum were set to be 6.5, 7.5, and 7.5, respectively. Conditions 3: pH values in duodenum, jejunum and ileum were set to be 7.5, 7.5, and 7.5, respectively. It was found that the tested factors remarkably alter DDIs of metformin with cimetidine  and 10 × Ki of MATEs were estimated to be 1.23, 1.54 and 1.98, respectively. OCT3 mainly affected metformin absorption, altering Cmax of metformin. The estimated CmaxR values at 0.1 × Ki, 1 × Ki (45.7 μM) and 10 × Ki of OCT3 were 1.47, 1.28 and 1.03, respectively. Alterations in Ki values for OCT1 little affected plasma exposure to metformin, consistent with minor roles of OCT1 in metformin disposition.Metformin mainly eliminates via renal secretion, which is highly controlled by renal OCT2 and MATEs. Intestinal absorption of metformin is mainly mediated by intestinal OCT3 and PMAT. Effects of K MATE2-K  are repometidine . The cons report , a decreFunctions of OCT3 and PAMT are also dependent on pH ,51 and imax,OCT3) and PMAT , liver OCT1 , renal OCT2  and MATEs , and the integrated effects  to metformin disposition were also investigated (non transporter − AUCcontrol)/AUCcontrol) × 100%, where AUCnon-transporter is AUC of metformin without considering the transporter. The results  and renal MATEs (12.48 μg·h/mL). The renal secretion of metformin is attributed to sequential work of uptake transporter OCT2 and efflux transporter MATEs , which mThese simulations have demonstrated importance of OCT1, OCT2, OCT3 and MATE1/2-K in metformin disposition and metformin antihyperglycemic activity. Diabetes was reported to increase plasma exposure to metformin following oral dose, accompanied by decrease in renal and system clearance . CreatinHowever, the developed PBPK model also has some limitations. Here, we assumed that 70% of intestinal metformin absorption was attributed to intestinal OCT3 and PMAT expressed on apical membrane of enterocytes, and the rested 30% was due to other transporters , whether are suitable also needs further investigation. Moreover, some diseases such as diabetes affected DDIs of metformin with perpetrators via affecting physiological parameters of body and expressions/function of drug transporters, which was reported before .max and AUC than the younger subjects (23 ± 3 years) [max and AUC of metformin than the wild SLC22A3 [Several different ways  have been tried to close the gap between prediction and observation, but the discrepancies between prediction and observation still exist . The dis3 years) . Subject3 years) ,122,123,3 years) . However SLC22A3 . AnotherIn conclusion, the PBPK model characterizing the interplay of OCTs and MATEs is successfully applied to predict the pharmacokinetics of metformin and its DDIs with perpetrators. Perpetrator-induced DDIs of metformin should be attributed to integrated effects of intestinal OCT3, renal OCT2/MATE1/2-K, and hepatic OCT1 inhibitions.


We report the growth, structural and magnetic properties of the less studied Eu-oxide phase, Eu Therefore, they exhibit unique magnetic, thermal and electrical properties [+ valence. However, in trieuropium tetroxide  similar to CaFe2O4 with the lattice parameters abcEuN), at a critical field of 2.4 kOe [JJThe EuGraphene is a potential material for many technological applications due to its attractive properties, such as its long spin-diffusion length, negligible spin-orbit coupling and high electron mobility ,8,9. So tructure ,15,16,17ications ,19,20. Aications ,24,25,26compound ,29,30,31TC) of In this study, 20 nm EuDepth-profile X-ray photon electron spectroscopy (XPS) scans were performed to prove the mixed valence of Eu cations in EuFirstly, 20 nm EuAn in-house built quartz crystal microbalance system was used during the deposition to monitor the growth rate and thus the thicknesses of the layers. RT XRD scans were used to study the crystallinity of the grown films. The XRR measurements were performed to confirm the results of the microbalance readings and deduce the density and roughness between the layers. These acquisitions were carried out using a Bruker D8 Discover HRXRD  with a Cu KDepth-profile XPS scans were performed on the Si/SiOThe RT XRD scans (from 10TC of ∼5.5 M/dT vs. T (insets), which agrees with the values reported in the literature [T T<20 K ,33.M vs. T measurements  due to the strong unfilled 4fd hole interaction, whereas the 3d states have a weaker multiplet splitting and broader photoexcitation cross-section. Therefore, the latter is usually used to analyse the Eu XPS spectra and better estimate the Eu initial valence [The existence of mixed-valence Eu cations was investigated by performing depth-profile XPS scans while measuring the Eu 3 valence ,35,36,37d XPS spectra after subtracting an optimised Shirley background, measured at t =210 s, 360 s, 450 s and 510 s. The peaks were deconvoluted using Gaussian–Lorentzian fitting, while the d and 4d peaks.ddfd hole in the final state [d photoelectrons create plasmon excitation structures observed as broad peaks at BE ∼1146 eV and ∼1170 eV [ddd peaks, their FWHM, their corresponding multiplet satellites, the ratio of EuAll spectra in d values ,36,38. Tal state . Further∼1170 eV . The XPSand Eu3+ ,40,41. Moped ZnO  and Eu-doped ZnO . Table 1G and 2D peaks at ∼1582 cmD peak at ∼1350 cmD peak to the intensity of the G peak (IIRaman spectroscopy is a versatile and nondestructive technique widely used to study the graphene’s structural and electronic properties ,45. A go350 cm−1 ,47,48. Tderlayer .ive lens b shows tan scans .IIIIFour random Raman scans taking on two different areas of the sample’s surface, bare graphene and the graphene/Euensities b compareensities a indicatr values b, comparr values a. This igraphene ,50. HoweTC of In summary, we discussed the experimental work carried out to study the growth of Eu