Background: Pseudomonas aeruginosa is a pathogenic bacterium, causing nosocomial infections with  intrinsic and acquired resistance mechanisms to a large group of antibiotics, including β-lactams. This study aimed to determine the susceptibility pattern to selected antibiotics and to index the first reported β-lactamases genes frequency inPs. aeruginosa in Khartoum State, Sudan.Methods: 121Ps. aeruginosa clinical isolates from various clinical specimens were used in this cross sectional study conducted in Khartoum State. Eighty isolates were confirmed asPs.aeruginosa through conventional identification methods and species specific primers. The susceptibility pattern of the confirmed isolates to selected antibiotics was done following the Kirby Bauer disk diffusion method. Multiplex PCR was used for detection of seven β-lactamase genes .Results: Of the 80 confirmedPs. aeruginosa isolates, 8 (10%) were resistant to Imipenem while all isolates were resistant to Amoxicillin and Amoxyclav (100%). A total of 43 (54%)Ps. aeruginosa isolates were positive forblaTEM, blaSHV, blaCTXM-1, blaVEB and blaOXA-1 genes, while 27 (34%) were positive for class C β- Lactamases, and 20 (25%) were positive for both classes. Frequency of beta-lactamases genes was as follows: blaTEM, 19 (44.2%); blaSHV, 16 (37.2%); bla CTX-M1, 10 (23.3%); blaVEB, 14 (32.6%); blaOXA-1, 7 (16.3%). blaAmpC 22 (81.5%) and bla DHA 8 (29.6%).  In total, 3 (11.1%) isolates were positive for both bla AmpC and blaDHA genes.Conclusion: Ps. aeruginosa isolates showed a high rate of β- lactamases production, with co-resistance to other antibiotic classes. The lowest resistance rate ofPs. aeruginosa was to Imipenem followed by Gentamicin and Ciprofloxacin. No statistically significant relationship between production of β-lactamases inPs. aeruginosa and resistance to third generation cephalosporins was found. Pseudomonas aeruginosa is one of the leading causes of nosocomial infections worldwide with high mortality rates, particularly among immunocompromised patients1.P. aeruginosa infections are difficult to treat, due to its extraordinary antimicrobial resistance to all available classes of antimicrobial agents2, including β-lactams, aminoglycosides and fluoroquinolones3. Resistance to β-lactams occurs by different mechanisms, including blaAmpC overexpression due to genetic mutations, mutant gene acquisition, overproduction of efflux system, or low permeability3. β-lactamases refer to enzymes that hydrolyze the amide bond of the β-lactam ring leading to drug inactivation and therapy failure4. β-lactamases are classified molecularly into four groups: class A (extended spectrum β-lactamases (ESBLs)), class B , class C , and class D (oxacillinases)5.6 and they also confer resistance to penicillins and narrow spectrum cephalosporins3. ESBLs are inhibited by β-lactamase inhibitors, such as clavulanic acid7. β-lactamases are transformed to ESBLs usually after point mutations in the β-lactamases gene. These mutations alter the substrate specificity because of changes in the amino acid sequences near the enzyme active site5. ESBLs producingP. aeruginosa have been reported worldwide in different countries17.ESBLs are enzymes that extend their hydrolyzing ability to hydrolyze broad spectrum cephalosporins18 and usually resist the inhibition by clavulanic acid4. Normally, AmpC is a chromosomal β-lactamase gene that is regulated by ampR gene and expressed constantly. Point mutations of ampR gene inEnterobacter cloacae activate AmpC that mediate resistance to β-lactams19. InP. aeruginosa over expressed AmpC β-lactamase mediate the resistance to broad spectrum cephalosporins3. AmpC β-lactamase inP. aeruginosa has also been reported in different countries around the world27.AmpC β-lactamases are class C cephalosporinases that mediate bacterial resistance to cephalosporins and cephamycins. They also exhibit low rates of monobactam, cefepime and carbapenem hydrolysisP. aeruginosa in Khartoum State, Sudan is unknown; therefore, the aim of this study was to determine the susceptibility pattern to selected antibiotics and to determine the frequency of β-lactamases producingP. areuginosa isolates collected in Khartoum State hospitals.The exact frequency of β-lactamase producingth January 2017). Written informed consent from participants was waived by the same ethical committee as the study only used previously collected human bio-specimens with limited participant data.This is a cross-sectional study conducted between February 2017 and October 2017. Ethical approval for the study was obtained from the ethical committee of the College of Medical Laboratory Science, Sudan University of Science and Technology (SUST) . The following antimicrobial discs  were used for sensitivity testing: Amoxicillin (25 µg), Cefotaxime (30 µg), Amoxicillin-Clavulanic acid (30 µg), Gentamicin (10 µg), Ciprofloxacin (5 µg), Chloramphenicol (30 µg) and Imipenem (10 µg).P. aeruginosa ATCC 27853 was used as quality control strain to control the performance of the test and ensure that the test is properly performed.Muller Hinton medium  was prepared and sterilized as instructed by the manufacturer. Antimicrobial susceptibility testing was performed following the modified Kirby-Bauer disc diffusion methodPhenotypic identity of the isolates was confirmed through conventional bacterial identification methods such as Gram stain, oxidase test, reactions in media containing sugars such as Kligler Iron Agar, urease test and citrate test.18. The extracted DNA was used as a template for amplification of target genes using multiplex PCR using TECHNE TC-312 (UK) thermocycler. Firstly, oprI and oprL primers  and class C genes (blaAmpC and blaDHA). The oprI and oprL reaction was carried out with the following cycling conditions: denaturation at 95°C for 5 min; 33 cycles of denaturation at 95°C for 30 secs, annealing at 58°C for 30 sec and extension at 72°C for 30 secs, and final extension at 72°C for 5 min28.Genomic DNA was extracted by simple boiling methodprimers  , containing 13 µl of double distilled water (DDW), 0.3µl of each five forward and 0.3 µl of each five reverse primers (1.5 µl), 2 µl of Dimethyl sulfoxide (DMSO) and 2 µl of template DNA.β-lactamases detection was done in two batches; the first batch was used for detection of blaAmplification of the second batch was done in 20µl using Maxime PCR Premix kits , containing 14.4 µl of DDW, 0.4 µl of each two forward and 0.4 µl of each two reverse primers (0.8 µl), 2 µl of DMSO and 2 µl of template DNA. Cycling conditions for both amplification reactions were as follows: initial denaturation at 94°C for 2 minutes, then 35 cycles of denaturation at 94°C for 30 secs, annealing at 54°C for 30 secs and extension at 72°C for 50 secs, and final extension at 72° for 5 minutes.Amplified products were analyzed by electrophoreses at 80 volts for 20 minutes on 1.5% agarose gel containing ethidium bromide and then visualized using UV transilluminator (Uvitec–UK) with 50bp or 100bp molecular DNA ladder .Statistical analysis of the data was performed using chi-square test (level of significance was 0.05) with SPSS software version 20 and GraphPad prism 5 demo.P. aeruginosa through conventional methods and species-specific primers; the remaining 41 (34%) isolates were considered as other Gram-negative rod bacteria. The distribution of clinical isolates according to site of infection was as follows: urine, 34 (42%); wound swab, 24 (30%); ear swab, 8 (10%); sputum, 8 (10%); and blood, 6 (7.5%).From 121 clinical isolates collected from different hospitals in Khartoum State, only 80 (66%) were confirmed asThe results of antimicrobial susceptibility test for selected antibiotics are presented inP. aeruginosa isolates, 54 (68%) were Pyocyanin pigment producers, while 26 (32%) were not pigment producers. There was a significant association between pyocyanin pigment production and site of infection (P=0.000) (P=0.020) and Cefotaxime (P=0.000), while there was insignificant association between pigment production and resistance to other antimicrobials used in this study (Out of 80=0.000) . There ws study .P. aeruginosa was as follows: blaTEM, 19 (44.2%); blaSHV, 16 (37.2%); blaCTX-M1, 10 (23.3%); blaVEB, 14 (32.6%); and blaOXA-1, 7 (16.3%). There was a significant association between the presence ofTEM, blaSHV, blaCTXM-1, blaVEB or blaOXA-1bla genes inP. aeruginosa and site of infection (P=0.030)  of the isolates were positive for at least one β-lactamases gene, while 37 (46%) were negative for all genes . The fre=0.030) . Co-presP. aeruginosa isolates; while 53 (66%) were negative ; blaDHA, 8 (29.6%); and 3 (11.1%) isolates were positive for both genes. Association between presence of Class C β-lactamases genes and site of infection was insignificant (P=0.215) (Class C β-lactamases gene were positive in 27 (34%)egative . The fre=0.215) .P. aeruginosa isolates were positive for both β-lactamase  and class C β-lactamase genes 37, where the frequency of β-lactamases inP. aeruginosa isolates was 46%. However, the results of this study disagree with another report from India by Guptaet al. (2016)38 where the frequency of β-lactamases inP. aeruginosa isolates was 22.9%. These discrepancies could be due to differences in strains of the clinical isolates, the antibiotics used or sample size.In this study the frequency of β-lactamases genes amongTEM, blaSHV, blaCTXM-1, blaVEB, blaOXA-1bla) producingP. aeruginosa isolates detected in this study wasblaTEM gene , followed byblaSHV,blaVEB,CTXMbla-1 andblaOXA-1 (16 (37.2%), 14 (32.6%), 10 (23.3%) and 7 (16.3%), respectively). Similar results were reported by Salahet al. (2016)39 in Egypt concerning the presence ofblaTEM,blaSHV andblaOXA-1 . The frequency ofblaTEM gene is also similar to that reported by Rafieeet al. (2014)40, which was present in 39.2% of isolates. In a study in Iran by Saleset al. (2017)41, similar results were reported concerning the presence ofblaCTXM-1 (27.3%), while in India Jamaliet al. (2017)15 reported a higher frequency (57.5%) of this gene.The most abundant gene among the β-lactamases 42 whereblaVEB gene frequency was 13.3%, while the frequency ofblaSHV gene was 6.6%. The report by Jamaliet al. (2017)15 concerning the genesblaTEM (15%) andblaSHV (75%) are also different from those reported in this study, and this could be due to the variation in strains of clinical isolates and the sample size used.On the other hand, the frequency ofP. aeruginosa isolates was 27 (34%). This result is close to a report from India by Guptaet al. (2016)38 where 43% ofP. aeruginosa were AmpC producers, and disagrees with a report from Thailand by Katvoravutthichaiet al. (2016)43 where 11% ofP. aeruginosa isolates were AmpC producers. In this study, out of the class C β- lactamase producingP. aeruginosa isolates, 22 (81.5%) and 8 (29.6%) isolates were positive forblaAmpC andblaDHA genes, respectively. Qureshi and Bhatnagar (2016)37 in India reported that noP. aeruginosa isolates were positive forblaAmpC gene, while Rafieeet al. (2014)40 in Iran reported that 60.8% ofP. aeruginosa were positive forblaAmpC gene.In this study, the frequency of class C β-lactamase genes inP. aeruginosa clinical isolates tested in our study were resistant to Amoxicillin and Amoxicillin/clavulanate and this may be due to the misuse of antibiotics in Sudan44, where plenty of antimicrobial agents are sold over the counter. This rate of resistance is higher than the rate of resistance reported by Ahmadet al. (2016)45 in Pakistan where the resistance to Amoxicillin and Amoxicillin/clavulanate was 73.4% and 67.7% respectively, and this could be justified by the time difference between the studies, as well as the difference in the strains and antibiotics used.AllP. aeruginosa to Imipenem was 10% (n=8). This may be due to the infrequent use of Imipenem antibiotics. This percentage agrees with a study reported in Pakistan by Ahmadet al. (2016)45 where 11.1% ofP. aeruginosa isolates were resistant to Imipenem, while in Sudan Altom and Ahmed (2015)46 reported that 5.7%P. aeruginosa isolates were resistant to Imipenem. This finding may indicate that carbapenem resistance is on the rise inP. aeruginosa isolates from Sudan.The resistance rate ofP. aeruginosa isolates resistant to Cefotaxime, Chloramphenicol and Ceftazidime was 71 (88.8%), 71 (88.8%) and 46 (57.5%), respectively. The rate of resistance to Cefotaxime in this study is different from that reported in Pakistan by Ahmadet al. (2016)45 who found that 20.3% ofP. aeruginosa isolates were resistant to Ceftazidime. The resistance rate in this study also disagrees with that reported by Albadawi (2010)47 in Sudan who found that resistance ofP. aeruginosa to Ceftazidime and Cefotaxime were 31% and 42%, respectively. These findings also indicate the rapidly increasing rates ofP. aeruginosa resistance to antimicrobial agents in Sudan perhaps due to antibiotic misuse.In this study, the number ofP. aeruginosa clinical isolates were resistant to Ciprofloxacin and Gentamicin, respectively. Altom and Ahmed (2015)46 in Sudan also reported that 18.6% ofP. aeruginosa were resistant to Gentamicin. Different results were reported by Ahmadet al. (2016)45, where the percentage of resistance to Gentamicin and Ciprofloxacin were 74.3% and 44%, respectively. These percentages are much higher than those reported in this study probably due to their different geographical location, study time difference and the antibiotic-use rates.In this study, 23 (28.8%) and 19 (23.8%) ofP=0.030).P. aeruginosa isolated from blood showed the highest production ofTEM, blaSHV, blaCTXM-1, blaVEB and blaOXA-1bla genes followed by ear swab, urine, sputum and wound swab. This result agrees with a study in India reported by Basaket al. (2012)48 where the highest β-lactamases producingP. aeruginosa isolates were from blood, but disagrees with Aziziet al. (2015)49 in Iran who found that highest production was inP. aeruginosa isolated from wound followed by urine, sputum and blood. There was insignificant association between the presence of class C β-lactamase genes and site of infection (P=0.215) found in the present study. The highest frequency of class C β-lactamases genes inP. aeruginosa were isolated from blood followed by urine, ear swab, wound swab and sputum. These results agree with the study in India reported by Basaket al. (2012)48 in that the highest percentage of class C β-lactamase genes inP. aeruginosa were found in blood.In this study, there was significant association between β-lactamases production and site of infection 50 where out of 38 phenotypically β-lactamases-negative isolates, 20 isolates were positive for β-lactamases genes.In the present study, there were four clinical isolates phenotypically sensitive to third generation cephalosporins (Ceftazidime and Cefotaxime) and genotypically positive for β-lactamases genes. This result indicates thatP. aeruginosa isolates was 67.5%. In a study in India a higher percentage was reported where 82.5% ofP. aeruginosa were pigment producers 51. The present study revealed that there was no relationship between pigment production and pattern of antimicrobial resistance, except in Chloramphenicol (P=0.02) and Cefotaxime (P=0.00).In this study, the frequency of pigment producingP=0.000).P. aeruginosa isolated from wound infections were the highest pigment producing isolates followed by isolates from blood, urine and sputum. There was no pigment production inP. aeruginosa isolated from ear infections in this study.In this study, the relationship between pigment production and site of infection was significant Data Dictionary. docxThis project contains the following underlying data:Creative Commons Attribution 4.0 International license (CC-BY 4.0).Data are available under the terms of the P. aeruginosa in Khartoum State, Sudan. The author claimed the study to be the first one to describe the exact frequency of ESBL producingP. aeruginosa in Khartoum, Sudan.The manuscript “Extended spectrum β-lactamases and class C β-lactamases gene frequency in  Pseudomonas aeruginosa isolated from various clinical specimens in Khartoum State, Sudan: a cross sectional study” describe important findings on drug resistance (ESBL and Class C Beta lactamases) ofP.aeruginosa so there is no need mention about other bacterial species.In the abstract section, the author has mentioned that 40 isolates were other bacterial species. I think the objective of the study is the molecular detection of beta lactamases inPs. aeruginosa through conventional identification methods and species specific primers” is really confusing. If 80P. aeruginosa isolates were finally analyzed for drug resistance then there no need to state 121P. aeruginosa clinical isolates.The statement “121 Ps. aeruginosa clinical isolates from various clinical specimens were used in this cross-sectional study conducted in Khartoum State. A total of 80 isolates were confirmed asP. aeruginosa, so the irrelevant information such as Pyocyanin pigment production should be deleted.As mentioned above the focus of manuscript is drug resistance inThough in methodology section, the author has claimed statistical tests , but there is no information on statistical tests in figures. I think statistical values should be included in figures.In all tables, please proper define the abbreviations, number and percentage (n(%)) in foot note.Figures should be properly labeled (Frequency (n)).The percentage should be added in all tables, as it will help the readers to understand and interpret the tables in better way.Some of the references are inconsistent/incomplete which should be arranged as per journal format. On the whole the research is scientifically carried out and written. Following are few minor suggestionsIs the work clearly and accurately presented and does it cite the current literature?YesIf applicable, is the statistical analysis and its interpretation appropriate?PartlyAre all the source data underlying the results available to ensure full reproducibility?YesIs the study design appropriate and is the work technically sound?YesAre the conclusions drawn adequately supported by the results?YesAre sufficient details of methods and analysis provided to allow replication by others?YesReviewer Expertise:Medical MicrobiologyI confirm that I have read this submission and believe that I have an appropriate level of expertise to confirm that it is of an acceptable scientific standard. Thank you for these comments, minor changes have been done following the reviewer’s comments. Changes include specifying the final included sample size, addition of results percentages to the tables with minor edits in the figures titles and in the abstract the Beta-lactamases genes naming changed according to the title. The revised version is described appropriately using correct scientific terms.Is the work clearly and accurately presented and does it cite the current literature?PartlyIf applicable, is the statistical analysis and its interpretation appropriate?PartlyAre all the source data underlying the results available to ensure full reproducibility?PartlyIs the study design appropriate and is the work technically sound?PartlyAre the conclusions drawn adequately supported by the results?PartlyAre sufficient details of methods and analysis provided to allow replication by others?YesReviewer Expertise:microbiology, public healthI confirm that I have read this submission and believe that I have an appropriate level of expertise to confirm that it is of an acceptable scientific standard.  P. aeruginosa should be written as "80". "121" should not appear here, because 41 isolates were notP. aeruginosa.Abstract: Number ofPs. aeruginosa" should be corrected as "P. aeruginosa", as formal abbreviation."Amoxyclav may be a commercial name of the drug. It should be written as general name. Maybe it is "amoxicillin/clavulanate". Authors may have serious misunderstanding regarding the term of ESBL. NOT all of TEM, SHV, OXA type beta-lactamases are ESBL. TEM-1, 2, SHV-1, OXA-1 are not ESBL. Other subtypes of TEM, SHV, and OXA are called as "ESBL". Those subtypes can be identified only by sequencing of full-length ORF of these genes. In this manuscript, only CTX-M type gene can be called as "ESBL". However, it is not sure that TEM, SHV, OXA type genes are ESBL genes or not, because sequence was not determined. In this regard, this manuscript must be revised substantially. Authors can mention CTX-M as ESBL, others should not be referred as ESBL, but just individual enzyme nams, e.g., "TEM gene". According to this alteration, whole manuscript should be rewitten.CTX-M-1 should be written as "CTX-M-1 group". CTX-M types cannot be determined by only PCR. Authors should read and study literature on ESBL and betalactamases of bacteria, to exactly understand their nomenclature and definition.Table 3 cannot be understood. Authors should make a table to show combination pattern of beta-lactamases and their frequency.Figure 5: "Class AmpC genes" is wrong. It should be "Class C beta-lactamase" or "AmpC gene". In this case, "AmpC gene" is better.This is a kind of primitive study that provides only limited information. Only importance is that this study was done in Sudan. However, still number of isolates were low, and errors in descriptions of technical terms are found in this manuscript.Is the work clearly and accurately presented and does it cite the current literature?PartlyIf applicable, is the statistical analysis and its interpretation appropriate?PartlyAre all the source data underlying the results available to ensure full reproducibility?PartlyIs the study design appropriate and is the work technically sound?PartlyAre the conclusions drawn adequately supported by the results?PartlyAre sufficient details of methods and analysis provided to allow replication by others?YesReviewer Expertise:microbiology, public healthI confirm that I have read this submission and believe that I have an appropriate level of expertise to state that I do not consider it to be of an acceptable scientific standard, for reasons outlined above. TEM, blaSHV, blaCTXM-1, blaVEBblaandOXA-1bla changed from ESBLs to β-lactamases enzymes generally, they named in the draft as individual genes and classified asβ-lactamases group. Classification of This change applied in the whole draft including tables and figures, even the title changed to“β-lactamases TEM, blaSHV, blaCTXM-1, blaVEB, blaOXA-1(bla)Pseudomonas aeruginosa isolated from various clinical specimens in Khartoum State, Sudan: a cross sectional study” and class C β-lactamases gene frequency in80P. aeruginosa isolates, the remaining41 isolate other thanP. aeruginosa deleted.In abstract: The sample size mentioned as the confirmedPs. aeruginosa” in the text changed to “P. aeruginosa” as formal abbreviation.All “Amoxyclav” in the text changed to “amoxicillin/clavulanate”All “Class AmpC genes" in figure 5 changed to “Class C β -lactamase” "ESBLs” changed to “β- lactamases” in figure 3 and figure 5.“


Scientific Reports10.1038/s41598-019-49759-w, published online 16 September 2019Correction to: This Article contains errors.In the Results section, the subheading,E. coli and yeast cells to salt and heat-”.“Enhancement of the tolerance of recombinant should read:E. coli and yeast cells to salt and heat-stress”.“Enhancement of the tolerance of recombinant TaLEA genes under ABA and abiotic stress treatments’,Furthermore, in the Results section, under the subheading ‘Determining the endogenous wheat ABA content and expression patterns of TaLEA genes belonging to LEA_1, LEA_2, LEA_3, LEA_4, LEA_5, LEA_6, SMP and three DHN subfamilies to investigate the expression patterns of TaLEA genes in wheat seedlings subjected to ABA and abiotic stress treatments by real-time PCR .”“To confirm the results of the microarray data, we selected 29 should read:TaLEA genes belonging to LEA_1, LEA_2, LEA_4, LEA_5, SMP and three DHN subfamilies to investigate the expression patterns of TaLEA genes in wheat seedlings subjected to ABA and abiotic stress treatments by real-time PCR .”“To confirm the results of the microarray data, we selected 22 E. coli’,In the Methods section, under the subheading ‘In vivo assay of the stress tolerance of transformed 48.”“Heat and salt tolerance assays were performed as described previouslyshould read:26.”“Heat and salt tolerance assays were performed as described previouslyFinally, this Article contains errors in Figure 5 and Supplementary Table S2, where the primers of TaLEA_3-5, TaLEA_6-1, TaLEA_6-2, TaLEA_6-3, TaSMP8 and TaSMP9 are incorrectly included. In addition, Figure 5 incorrectly includes the primer of TaLEA_3-3, and Supplementary Table S2 incorrectly includes the primer of TaLEA_3-4. The correct Figure 5 and Supplementary Table S2 appear below as Figure


Mycobacterium smegmatis or Rhodococcus sp. However, no improvements in their antibacterial activity were observed. All of the 12 bacteria tested were identified as Streptomyces spp. LC-MS analysis of EtOAc extracts from the most active strains RS3 and RC4 revealed the presence of a number of dactinomycin analogues and potentially new secondary metabolites. Symbiotic Streptomyces spp. from sponges and corals of the Indonesian marine environments have great potential as a source of broad-spectrum antibacterial agents.Indonesian marine environments are known to house diverse organisms. However, the potential for bacteria from these environments as a source of antibacterial agents has not been widely studied. This study aims to explore the antibacterial potential of secondary metabolites produced by bacterial symbionts from sponges and corals collected in the Indonesian waters. Extracts of 12 bacterial isolates from sponges or corals were prepared by cultivating the bacteria under a number of different media conditions and using agar well diffusion assays to test for antibacterial activity. In addition, the morphology, physiology, and biochemical characteristics and 16S rRNA sequence of each isolate were used to determine their taxonomic classification. All tested bacterial isolates were able to produce secondary metabolites with various levels of antibacterial activity depending on medium composition and culture conditions. Two of the bacteria (RS3 and RC4) showed strong antibacterial activities against both Gram-negative and Gram-positive bacteria. A number of isolates  were co-cultured with mycolic acid-containing bacteria,  The widespread emergence of antibiotic-resistant pathogens has become a major healthcare problem worldwide. This problem is being compounded by the decline in the number of new antibiotics being discovered and developed in recent years , 2. ThisStreptomyces, Nocardiopsis, and Micromonospora, members of which can be terrestrial or aquatic, including living symbiotically in various marine organisms such as sponges, corals, and seaweeds [Actinobacteria have been known to be a rich source of bioactive compounds. Among the most productive genera of the Actinomycetales are seaweeds . Bacteriseaweeds , 5. In cseaweeds . Howeverseaweeds , 8. Neveseaweeds , 9.The Indonesian archipelago is among the places on the earth that house highly diverse marine organisms, including sponges and corals. However, their potential as a source of antibacterial agents has not been widely studied. Here, we report the antibacterial potential of secondary metabolites from a number of sponge- and coral-associated bacterial symbionts that were collected from Indonesian waters around West Kalimantan.Corals and sponges were collected in 2017 from waters around Baru Island, Randayan Island, and Lemukutan Island, Bengkayang District, West Kalimantan, Indonesia . All samμg/mL) and nalidixic acid (100 μ/mL) at room temperature (27°C–32°C). Spore-forming colonies growing on the agar were repeatedly transferred to new plates until pure isolates were obtained.The sponges and corals were rinsed with sterilized seawater, homogenized with a sterilized hand blender, and inoculated on agar plates containing ISP1  or ISP2  supplemented with nystatin 6Mo7O24•4H2O 25 mg. The amylostatin medium (AM) consists of maltose 5%, soy flour 2.5%, wheat germ 1%, NaCl 0.25%, and pH adjusted to 7. The trace element consists of ZnCl2 40 mg/L, FeCl3 6H2O 200 mg/L, CaCl2•2H2O 10 mg/L, MnCl2•4 H2O 10 mg/L, NaB4O7•10H2O 10 mg/L, and (NH4)6Mo7O24•4H2O 10 mg/L. A seed culture (5 mL) was inoculated into a 500 mL Erlenmeyer flask containing 100 mL of the medium and grown at 200 rpm, 30°C. After 7 days, the culture was harvested and centrifuged at 3,000 × g for 10 min, and the supernatant was extracted successively with ethyl acetate (EtOAc) and n-butanol (n-BuOH). A rotary evaporator was used to evaporate organic solvents to obtain EtOAc and n-BuOH extracts.Seed cultures were prepared by inoculating bacterial spores into the ISP1 medium (50 mL) and grown at 30°C for 2–3 days with shaking at 200 RPM. The seed cultures were used for mono and co-culture experiments. Monoculture production was carried out in different media, e.g., modified Bennet with artificial seawater (Himedia) (MB + ASW), ISP1 with artificial seawater and trace element (ISP1 + ASW + TE), and amylostatin media (AM) –12. The Mycobacterium smegmatis and Rhodococcus sp. The media used for these experiments were AM, BTT  [M. smegmatis and Rhodococcus sp. were prepared by inoculating the bacteria into ISP2 and Luria-Bertani (LB) media , respectively, and grown at 200 rpm and 30°C for 3 days. Each seed culture (1 mL) was inoculated into the production media (50 mL) and grown at 200 rpm and 30°C for 7 days. The cultures were harvested and centrifuged, and the supernatants were extracted successively with EtOAc and n-BuOH. A rotary evaporator was used to evaporate organic solvents to obtain EtOAc and n-BuOH extracts.Co-culture experiments were carried out using isolates RS1, RS3, and RC2 and two counterpart bacteria,  to 7.4) , and A3M to 7.4) . Seed cu4)2SO4 2.64 g/L, KH2PO4 anhydrous 2.38 g/L, K2HPO4•3H2O 5.65 g/L, MgSO4•7H2O 1 g/L, CuSO4•5H2O 0.0064 g/L, FeSO4•7H2O 0.0011 g/L, MnCl2•4H2O 0.0079 g/L, and ZnSO4•7H2O 0.0015 g/L and pH adjusted to 7.2–7.4 [The physiological and biochemical characteristics of each isolate were examined based on spore color, diffusible pigment, melanoid pigmentation, salt tolerance, pH tolerance, hydrolysis of protein and starch, and utilization of carbohydrates . The bac 7.2–7.4 . Each isA large fragment of the 16S rRNA gene was sequenced to determine the taxonomic classifications for the isolates. The 16S rRNA gene was amplified using a pair of universal primers 27F (5′-AGAGTTTGGATCMTGGCTCAG-3′) and 1492R (5′-CGGTTACCTTGTTACGACTT-3′) and Takara Taq™ DNA polymerase. PCR was carried out according to the manufacturer's guidelines. The annealing temperature was set at 63°C. The PCR products were cleaned up using the EZNA purification kit and sequenced on an ABI3730 capillary sequencing machine at the Oregon State University Center for Genome Research and Biocomputing.The EBI Clustal Omega webserver was used to calculate pairwise percent identity between all 16S rRNA gene sequences , 18. BLAStaphylococcus aureus ATCC 12600, Bacillus subtilis ATCC 6051, Salmonella enterica ATCC 14028, Escherichia coli ATCC 11775, Pseudomonas aeruginosa ATCC 9721, and Mycobacterium smegmatis ATCC 14468 were used as test bacteria. The test bacteria, except for M. smegmatis, were inoculated into the LB medium and grown overnight at 200 rpm, 37°C. M. smegmatis was inoculated into the ISP2 medium and grown at 200 RPM, 30°C for 3 days. Antibacterial assays were carried out using the agar well diffusion technique. Fifteen milliliters of warm LB agar was mixed with 300 μL test bacterial culture and plated out in a petri dish with a diameter of 9 cm. After solidification, a sterilized puncher was used to punch a hole into the agar. The extract  was then loaded into the well. Once the extract dried, the plate was then incubated at 37°C (30°C for M. smegmatis). Antibacterial activity was determined based on the formation of an inhibition zone and the diameter of the zone.μm) at a flow rate of 0.4 mL/min and the following gradient. Line A was water with 0.1% (v/v) formic acid, and line B was acetonitrile with 0.1% (v/v) formic acid. The column was preequilibrated with 90% A/10% B. Upon injection, the mobile phase composition was maintained for 1 minute upon when the mobile phase was changed using a linear gradient to 0% A/100% B over the following 20 minutes. This concentration was held for 6 minutes followed by changing the mobile phase to 90% A/10% B over 1 min. The Q-ToF mass spectrometer was operated in the Auto MS/MS mode.High-resolution mass spectrometry (HR-MS) was obtained using an Agilent 1260 HPLC upstream of an Agilent 6545 Q-ToF. The separation was achieved using InfinityLab Poroshell EC-C18 column  analysis using the “Compounds Discovery” workflow with the “Auto-Select Compound Mining” settings . MS/MS pStreptomyces were selected for further study  were randomly selected and cultivated in the ISP1 medium with or without artificial salt water (ASW) to determine if ASW could influence antibacterial production. The culture broths were then extracted successively with EtOAc and P1 alone . HoweverNext, the effects of trace element (TE) supplementation on antibiotic production were evaluated. In this study, isolates RS3, RS4, and RS5 were cultivated in ISP1 + ASW + TE and the products were tested for their antibacterial activity. The results showed that the addition of TE negatively impacted the production of antibiotics in the RS3 and RS5 cultures, as extracts from RS3 and RS5 cultivated in ISP1 + ASW + TE had lower antibacterial activities than those in ISP1 + ASW. However, extracts from the ISP1 + ASW + TE culture of RS5 were more active than those without TE .S. aureus, B. subtilis, S. enterica, E. coli, P. aeruginosa, and M. smegmatis, using an agar diffusion assay  showed growth inhibition zones equal to or better than the positive control tetracycline (2 μg). Of particular interest was extracts from isolates RC3, RC4, RS3, and RS4, which were active against the highly difficult to treat Gram-negative pathogen P. aeruginosa.Based on the above results, three different media  with very different carbon and nitrogen source compositions were selected for further studies. The primary carbon sources in MB and AM media are glucose and maltose, respectively, whereas in the ISP1 medium, the carbon sources are yeast extract and peptone. All of the media contain nitrogen sources such as tryptone and yeast extract (in the ISP1 medium), soy flour and wheat germ (in the AM medium), or yeast extract, beef extract, and soytone (in the MB medium). All of the isolates were cultivated in these media, and the extracts were tested against on assay . The resGordonia sp., Tsukamurella sp., Rhodococcus sp., and Mycobacterium sp., have been shown to be good inducers of biosynthetic gene expression [Mycobacterium smegmatis and/or Rhodococcus sp. as the counterpart. Isolates RS1, RS2, and RC2 were cultivated alone or together with M. smegmatis in AM, BTT, and/or A3M media. Isolate RS2 was also cultivated with Rhodococcus sp. The EtOAc and/or n-BuOH extracts of the culture broths were tested against S. aureus, B. subtilis, E. coli, P. aeruginosa, and M. smegmatis. However, no significant improvement of the antibacterial activity of the co-culture extracts was observed compared to those of the monocultures   or with an oxidized variant (“+O”), respectively. We note that the compound generating the protonated ion at 1277.6178 is a prominent peak in both extracts and reasonably explains the observed antibacterial activity of the extracts. We also note other clusters of compounds unannotated by GNPS  . In bothStreptomyces spp. All 12 of the strains showed the ability to produce potent antibacterial compounds, and some of them were active against the opportunistic Gram-negative bacterial pathogen P. aeruginosa. The ability of the strains to produce antibacterial compounds is greatly dependent on the production medium and culture condition. Interestingly, co-cultures of selected strains with mycolic acid-containing bacteria did not improve the production of antibacterial compounds in those strains. Metabolic profiling of extracts from the most active strains indicates the presence of known antibacterial agents (the actinomycins and a cyclo-(L-Phe-D-Pro)), as well as a number of unidentified natural products. The results underscore the great potential of marine bacterial symbionts as a highly promising source of new antibiotics.From 205 bacterial isolates obtained from unidentified sponges and corals collected off the coast of West Kalimantan, Indonesia, 12 strains have been identified to be members of the


Further analyses exhibited that berberine treatment caused G0/G1 phase arrest at 48 h due to high cyclin D1 (CCND1) and low cyclin-dependent kinase 4 (CDK4) protein and mRNA levels, simultaneous downregulation of human telomerase reverse transcriptase (TERT) mRNA and human telomerase RNA component (TERC) levels, as well as a decrease in the TERT protein level and telomerase activity. The effect of berberine treatment on the cell cycle was time dependent as it resulted in a delayed cell cycle and doubling time by 2.18-fold. Telomerase activity and level was significantly decreased, and telomere erosion followed suit. In summary, our findings suggested that berberine could decrease telomerase activity and level of HCT 116, which in turn inhibits the proliferative ability of the cells.Colorectal cancer (CRC) is the most common cancer among males and females, which is associated with the increment of telomerase level and activity. Some plant-derived compounds are telomerase inhibitors that have the potential to decrease telomerase activity and/or level in various cancer cell lines. Unfortunately, a deeper understanding of the effects of telomerase inhibitor compound(s) on CRC cells is still lacking. Therefore, in this study, the aspects of telomerase inhibitors on a CRC cell line (HCT 116) were investigated. Screening on HCT 116 at 48 h showed that berberine (10.30 ± 0.89 µg/mL) is the most effective (lowest IC Colorectal cancer (CRC) (10.2% incidence) is the third most common cancer among males and females worldwide after lung (11.6% incidence) and breast cancers (11.6% incidence) . TelomerhTR or TERC), telomerase-associated protein 1 (TEP1), heat shock protein 90 (HSP90), prostaglandin E synthase 3 (P23), and dyskerin (DKC) [TERC is a functional RNA sequence that is responsible for providing a template for 3′TTAGGG5′ addition at telomere ends [TERC are enough for telomerase to function [TERC serves as the prime target for telomerase level and activity inhibition. The most recent publication on the telomerase structure of the colorectal cancer cell line, HCT 116, surprisingly revealed that there were no TEP1, P23, and HSP90 subunits present [TERC are required for telomerase to function. It might also suggest that telomerase could adopt different structures.It has been previously suggested that human telomerase consists of six subunits, namely the human telomerase reverse transcriptase (hTERT or TERT), human telomerase RNA component (in (DKC) . Howeverin (DKC) . It has in (DKC) . This coin (DKC) . TERC isere ends . It was function . Due to Peumus boldus, which has been used as a traditional medicine in Chile [Silybum marianum (milk thistle) [Berberis spp, such as B. aristata, B. aquafolium, and B. vulgaris [Tinospora spp, such as T. sinensis and T. cordifolia [Since telomerase is the hallmark of CRC, targeting telomerase could be a potential treatment for telomerase-positive cancers, specifically CRC in this case. Many natural products have been listed as telomerase-targeting compounds or, in other words, telomerase inhibitors . Boldinein Chile . Silymarin Chile ,13, whicthistle) . On the vulgaris . Besidesrdifolia . These crdifolia .d), telomerase activity, and level as well as on the telomere length of a colorectal cancer cell line, using HCT 116 as a model.Telomerase is highly expressed in proliferative cells, such as cancer, stem, and gamete cells, but not in normal cells . However0/G1 phase (35.13 ± 0.25%) compared to 24 h and 48 h . Based on the results, the percentage of S phase was significantly highest at 48 h (50.10 ± 1.15%), followed by 24 h (43.43 ± 1.59%) and 72 h (36.70 ± 0.75%). The RTA of HCT 116 indeed peaked at 48 h (114.26 ± 1.26%) followed by 24 h (98.69 ± 0.42%) and 72 h (90.24 ± 0.91%). All three time points gave an insignificant percentage of G2/M phase. Thus, 48 h was chosen for the treatment duration for subsequent experiments since it showed the highest percentage of S phase and RTA.It has been hypothesized that telomerase activity peaked during S phase ,20, thus50 (compound concentration required to inhibit HCT 116 by 50%). Berberine was found to be the most effective, as it showed the lowest IC50 compared to boldine and silymarin  of HCT 116 to be delayed. The green fluorescence intensity observed under the UV channel peaked at 24 h, then decreased at 72 h , moderately cytotoxic (between 2 to 89 µg/mL), and not cytotoxic  based on their IC0 values . In the nstitute .0/G1 phase arrest in MCF-7 (breast cancer) after 48 h [0/G1 phase arrest in HCT 116. Since the binding of CCND1 to CDK4 is a rate-limiting event in G1 to S phase transition [0/G1 arrest would depend mainly on these two proteins instead of other cyclins and CDKs. Besides that, telomerase is mostly active during S phase, hence the reason the study was focused more on CCND1 and CDK4 instead of other cyclins and CDKs. Due to this reason, the experiments were designed in such flow, so that the study was more focused to the factors that are associated within the telomerase aspect.In several telomerase-positive cancer cell lines, berberine treatment has been shown to cause Gter 48 h , Huh-7 ater 48 h , and A27ter 48 h . Either ansition , theoret0/G1 arrest in U87 and LN229 (brain cancer) was caused by the downregulation of miR-21, which caused a decrement of STAT3 phosphorylation and protein level, thus causing the TERT protein level to decrease. In a separate study, berberine showed the ability to repress the miR-21 level in oral squamous cell carcinoma cancer stem cell lines (OECM-1 and SAS), which decreased aldehyde dehydrogenase 1 (ALDH1) activity, cell migration, invasion capability, and self-renewal [0/G1 phase arrest in HL-60 (leukemia) is also associated with a decrement of telomerase activity as well as TERT and TERC levels [0/G1 phase arrest in HCT 116 in a dose-dependent manner [0/G1 phase arrest in HCT 116 induced by berberine treatment were not exclusively caused by the downregulation of TERT.Wang, et al.  reportedC levels . It was t manner . Based od) for untreated HCT 116 was 16.39 ± 2.36 h, similar to the average td of previous findings. The average td of berberine-treated HCT 116 was 35.65 ± 6.50 h, significantly higher than the average td of untreated HCT 116. Data analysis revealed that the td of berberine-treated HCT 116 was overdue by approximately two-fold (2.18-fold).Based on various references, the average duration of untreated HCT 116 cell cycle was about 17 h (16.88 ± 2.56 h) ,34,35,36The localization of berberine in nuclei after 48 h further strengthens our justification of choosing this time point for further investigation. This is due to the fact that the transcription, synthesis, and activity of telomerase mainly takes place in the nucleus ,37. SincTERT mRNA [TERC level.It was previously established that berberine treatment on HCT 116 inhibits cell proliferation in a dose- and time-dependent manner ,40, and ERT mRNA  and viceERT mRNA . BerberiERT mRNA , and cerERT mRNA . PreviouBerberine has been shown to decrease the telomerase level and activity, which is significantly followed by telomere erosion. Interestingly, by conducting telomere restriction fragment (TRF) analysis, Xiong et al.  revealedIn this study, cells from four flasks  were pooled to obtain sufficient material for several different analyses. All extractions and preparations were also performed from the same set of samples for the sake of uniformity. Although pooling of samples may rightfully raise some concerns, it is still widely viewed as a valid method, saves cost, and reduces biological variance, while not seriously affecting the results when compared to un-pooled samples ,57,58. B® water and sterilized by filtration through a Minisart® 0.22 µm polyethersulfone filter syringe . Boldine and silymarin was dissolved in dimethylsulfoxide (DMSO) and filtered as well. All primers were synthesized by Integrated DNA Technologies . Monoclonal antibodies of CCND1 (A-12), CDK4 (DCS-31), TERT (A-6), and β-actin (ACTB) (AC-15) as well as mouse IgG kappa binding protein conjugated to horseradish peroxidase (m-IgGκ BP-HRP) were obtained from Santa Cruz Biotechnology . Molecular-grade absolute ethanol  was diluted with autoclaved Milli-Q® water. All reagents were from Sigma-Aldrich unless mentioned otherwise.Berberine was diluted in autoclaved Milli-Q2-enriched atmosphere . Cells were rinsed by using 1× phosphate-buffered saline (PBS) , repeated three times, and cells were detached by using trypsin-EDTA , followed by RPMI addition to stop the trypsinization; cells were pelleted by centrifugation at 1000 rpm, resuspended in RPMI for sub-culturing or seeding purpose, or 1× PBS was used to resuspend for harvesting as a washing step, followed by second-time pelleting of the cells. During harvesting, after observation under the microscope and cell count was conducted, cells from four flasks were pooled and pelleted in the amount as required for further analyses. The microcentrifuge tubes containing a pelleted known number of cells were immediately flash frozen in liquid nitrogen before storage in −80 °C until further usage.Colorectal cancer cell line, HCT 116, was cultured in Roswell Memorial Institute (RPMI) medium 1640  supplemented with 10% fetal bovine serum , 1% Antibiotic-Antimycotic solution , maintained at 37 °C in a humidified 5% CO6 pelleted cells in 2 mL microcentrifuge tube were washed with 1 mL of sample buffer (1× PBS containing 1 g/L glucose) once by vortexing briefly, followed by centrifugation at 17,000× g for 5 min. The supernatant was removed and about 0.1 mL was left in the tube. Then, 1 mL of 70% ethanol (−20 °C) was slowly added, 100 µL at a time, drop by drop while vortexing. The suspension was stored at 4 °C for 24 h. After fixation, 1 mL of sample buffer was added while vortexing, followed by centrifugation at 17,000× g for 10 min. Staining solution made up of 0.5 µg/mL 4′,6 -diamidino-2-phenylindole (DAPI) , 5 µg/mL RNase A, and 0.1% Tween 20  in 1× PBS was prepared immediately before incubation. The supernatant was removed, and 1 mL of staining solution was added followed by vortexing and incubation at room temperature for 40 min. Flow cytometry was done by using a MACSQuant® Analyzer  following the protocol suggested by Miltenyi Biotec with optimizations. The fluorescence channel was set to V1 and measured by linear acquisition. The V1 voltage was set to 334 V and the trigger was set to 128.00. The number of events was set to 10,000 events for each replicate. The data was analyzed by using FlowJo version 10.In total, 1 × 102-enriched incubator for 24 h before compound treatments. The spent medium was pipetted out and replaced with 150 µL of RPMI containing either boldine, silymarin, or berberine at various concentrations  achieved by serial dilutions as well as RPMI without any of the named compounds as untreated (control). The treatments were done for 48 h. After 48 h, each plate was subjected to Sulforhodamine B (SRB) assay [untreated = Absorbance of untreated cells at 492 nm; and Abstreated = Absorbance of berberine treated cells at 492 nm.In total, 5000 cells/100 µL per well were seeded in 96-well plates, incubated at 37 °C in a humidified 5% COB) assay . Total p50 values was determined by using the software. The cells were also cultured in T75 flasks to scale-up the production of HCT116 cells for subsequent analysis and were also used to determine a new IC50 value (for berberine).Graphs of the percentage of inhibition against the concentration were plotted by using GraphPad Prism 7.00 by performing the 4-parameter logistic model (4PL)  and the y = Final number of cells; y0 = Initial number of cells; e = Euler’s number; λ = Growth rate; t = Time that has passed; and the td was calculated using the following equation:Cell count was done by using Trypan blue (TB) exclusion assay. In total, 100 µL of the cell suspension were pipetted into a microcentrifuge tube and 100 µL of Trypan blue  were gently mixed by pipetting in and out. The Trypan blue-dyed cells were then pipetted to fill up both chambers of a hemocytometer , followed by observation and cell counting under an inverted microscope. Dead cells were stained blue while living cells were not stained. The average number of cells at each time point was used to plot the exponential growth curve following this equation:cell = Integrated density of selected cell; Acell = Area of selected cell; and background = Average background fluorescence reading.Cells were observed by using a Leica DMI6000 B inverted microscope  under the phase contrast channel. Without changing the position, the channel was changed to UV to observe berberine fluorescence. To avoid cellular damage by UV radiation, the exposure was kept as minimal as possible, whereby the channel was shut off within 30 s (or less) after capturing the image by using a Digital Color Camera Leica DFC310 FX . The corrected total cellular fluorescence (CTCF) was determined by using a free software, ImageJ version 1.52a , by usinTELOTAGGG Telomerase PCR ELISA kit  with minor adjustments, whereby 5 × 105 cells were used. The protein content was assessed by Bradford Assay  as suggested by the Bio-Rad protocol. The Telomeric Repeat Amplification Protocol (TRAP) reaction was done using a thermal cycler, StepOnePlus™ Real Time-Polymerase Chain Reaction (RT-PCR) Systems . In total, 2 µg of lysate were used in this reaction.Protein extraction was done based on the protocol of the 6 cells harvested were lysed and DNase I treatment was done for 1 h 30 min at 30 °C. RNA extracted was quantified by using a NanoDrop™ 2000 . The purity was also assessed using the same instrument based on the A260/280 and A260/230 ratios. RNA integrity was evaluated using 1% native agarose gel electrophoresis containing 5 µL of RedSafe™  conducted in 1× Tris-Borate-EDTA (TBE)  buffer at 70 V until the tracking dye travelled three-quarters of the gel. Gel picture was visualized, and the image was captured using Gel Documentation System Fusion FX7-7026 .RNA extraction was conducted using the protocol of the ReliaPrep™ RNA Cell Miniprep System  with minor adjustments, whereby 1 × 10® 2-Step RT-qPCR System  with minor adjustments, whereby 2500 ng of RNA were transcribed in 20 µL of total reaction mixture. In total, 250 ng of RNA equivalent cDNA were used in RT-PCR reaction mixture and 50 nM of primers  and ACTB.The RNA is said to be intact if the band intensity (28S:18S ratio) is 2:1. Complementary DNA (cDNA) was synthesized using the protocol of the GoTaq primers  were use6 harvested cells were used for the gDNA extraction. A NanoDrop™ 2000 was used for gDNA quantification and quality assessment. The quantification of the relative telomere length was done according to Vasilishina et al. [® 2-Step RT-qPCR System were used. The amplification was done for 40 cycles and conducted using StepOnePlus™ RT-PCR Systems. Single copy gene (SCG), interferon beta 1 (IFNB1) was used for normalization.Genomic DNA (gDNA) extraction was conducted using the protocol of the Quick-DNA™ Miniprep Plus Kit . In total, 1 × 10a et al.  with min6 harvested cells were lysed in 60 µL Radio Immunoprecipitation Assay (RIPA) lysis buffer containing 1% protease inhibitor cocktail, sonicated on ice bath for 1 min, incubated for 30 min on ice, followed by centrifugation at 14,000× g for 15 min to pellet the cell debris, and subjected to protein estimation by employing Bradford Assay. Then, 20 µg of total protein denatured at 70 °C were separated on 4% and 12% SDS-polyacrylamide stacking and resolving gels, respectively. Electrophoresis was done at 80 V for 30 min, followed by 100 V for 1 h 40 min using pre-chilled (4 °C) 1× SDS running buffer, followed by protein transferring onto 0.22-µm nitrocellulose membrane . Electroblotting was conducted using a Mini-Protean® Tetra System  with pre-chilled (-20 °C) 1× SDS transfer buffer and ice pack (−80 °C) for 1 h 40 min on ice. The membrane was removed and incubated in Blocking One  for 1 h at room temperature, on a shaker at 100 rpm. The membrane was incubated for at least 13 h in a cold room (5–10 °C) and was constantly shaken at 90 rpm in monoclonal primary antibody at 1:1000 dilution for ACTB and 1:100 dilution for CCND1, CDK4, and TERT, respectively, in Blocking One. The membrane was then washed 3 times with 1× Tris-buffered saline containing 0.1% Tween 20 (TBST), 5 min each time, and on a shaker at 120 rpm. The membrane was incubated on a shaker at 90 rpm with m-IgGκ BP-HRP at 1:10,000 dilution in Blocking One for 2 h at room temperature. The 1× TBST washing step was repeated as previously stated. The membranes were incubated in chemiluminescence solution of Western Bright Sirius  following the manufacturer’s protocol before the membrane was visualized, and the image was captured using Gel Documentation System Fusion FX7-7026 . ACTB was used as the loading control. Protein signal intensities were analyzed by using ImageJ version 1.52a. CCND1, CDK4, and TERT protein signal intensities were normalized to the loading control.In total, 1 × 10t-test or one-way ANOVA. Statistical values were expressed as mean ± standard error. p-values < 0.05, < 0.01, and < 0.001 were considered as statistically significant, very statistically significant, and highly statistically significant, respectively. Mean values were compared using Duncan’s Multiple Range Test (DMRT). Most statistical analyses were conducted using either Microsoft® Office Excel version 365 or IBM SPSS Statistics version 23.All data obtained in this study were subjected to either TERT and TERC levels, which resulted in a decrease of the TERT protein level. Berberine induced G0/G1 arrest by the upregulation of CCND1 and downregulation of CDK4, whereby the effect of berberine on the cell cycle was time dependent. Telomerase activity of HCT 116 was decreased by berberine treatment and the telomere length was also significantly eroded. Elucidation of berberine’s potential as a telomerase inhibitor in CRC would require further extensive and elaborative efforts in the future.In summary, our study demonstrated that berberine decreased the telomerase activity in HCT 116 by simultaneous downregulation of


Biochar is a rich-carbon charcoal obtained by pyrolysis of biomasses, which was used since antiquity as soil amendant. Its storage in soils was demonstrated contributing to abate the effects of climate changes by sequestering carbon, also providing bioenergy, and improving soil characteristics and crop yields. Despite interest in this amendant, there is still poor information on its effects on soil fertility and plant growth. Considerable variation in the plant response has been reported, depending on biomass source, pyrolysis conditions, crop species, and cultivation practices. Due to these conflicting evidences, this work was aimed at studying the effects of biochar from pyrolyzed wood at 550°C, containing 81.1% carbon and 0.91% nitrogen, on growth and yield of tomato plants experiencing low-input farming conditions. San Marzano ecotype from Southern Italy was investigated, due to its renowned quality and adaptability to sustainable farming practices. Biochar administration improved vegetative growth and berry yield, while affecting gene expression and protein repertoire in berries. Different enzymes of carbon metabolism and photosynthesis were over-represented, whereas various stress-responsive and defense proteins were down-represented. Molecular results are here discussed in relation to estimated agronomic parameters to provide a rationale justifying the growth-promoting effect of this soil amendant. Marzano ecotype cultivated in an acidic soil, under low-input conditions. The San Marzano ecotype was chosen for this study because it is a traditional cultivar from Campania, South Italy, which has become a top quality variety owing to its peculiar organoleptic traits ; further2 following seeds removal. Fruits were stored at −80°C until their use.Verfofood biochar was purchased from Green Biochar, Torino, Italy. This fine grain coal is produced from wood at a maximum pyrolysis temperature of 550°C. It contains 81.1% of carbon and 0.91% nitrogen; it also has a pH value of 8.21 and an ash content of 7.74%. The soil matrix used for the growth of tomato plants was a typical Mediterranean agriculturally managed soil, classified as Eutric Cambisol collected in Campania, Italy. After sampling, the soil was dried at 40°C for 24 h, and sieved (<2 mm). The soil pH in water was 5.6 and its Water Holding Capacity (WHC) and ash content was 35.85% and 88%, respectively. Seeds of the 204-San Marzano 2 accession  were sowed in plastic pots (10 l) containing soil or soil with 5% of biochar. Ten seeds each pot were sowed and five pots containing soil plus five pots containing soil and 5% biochar were prepared; all pots were irrigated with the same volume of water two times a week. Fifteen days after sowing, a single plant for each pot was selected to continue the experiment, and pots were placed in greenhouse, under controlled conditions, with 14 h of light/day. After 50 days of growth, agricultural parameters, like height and number of flowers buds, were estimated every 15 days. At the end of the experiment (177 days after sowing), tomato fruits from each pot were collected and their number and weight registered. Immediately after collection, fruits were cut longitudinally into four parts and frozen in liquid N2, and suspended in 7.5 ml of extraction buffer  for 15 min, on ice. After the addition of an equal volume of Tris-saturated phenol , the mixture was vortexed for 10 min and then centrifuged at 10,000 × g, for 15 min, at 4°C. The upper phenol phase was removed and extracted twice with the extraction buffer. Proteins were precipitated from the phenol phase by the addition of five volumes of saturated ammonium acetate in methanol, overnight at −20°C. Precipitated proteins were centrifuged at 10,000 × g, for 30 min (Fruit samples (2.5 g of frozen tomato fruits) were powdered in a mortar using liquid Nr 30 min .Protein pellets were washed once with ice-cold methanol and three times with ice-cold acetone, dried and solved in IEF buffer  . Protein concentration was quantified using the BioRad protein assay, using BSA as a standard. IPG strips  were rehydrated with 300 µl of IEF buffer containing 400 µg of total proteins, overnight. Proteins were focused using a Protean IEF Cell  at 12°C, applying 250 V (90 min), 500 V (90 min), 1,000 V (180 min), and 8,000 V, for a total of 53 KVh. After focusing, proteins were reduced by incubating the IPG strips with 1% w/v DTT in 10 ml of 50 mM Tris-HCl (pH 8.8), 6 M urea, 30% w/v glycerol, 2% w/v SDS, and a dash of bromophenol blue, for 15 min, and alkylated with 2.5% w/v iodoacetamide in 10 ml of the same buffer, for 15 min. Electrophoresis in the second dimension was carried out using a Protean apparatus  and 12% polyacrylamide gels in 25 mM Tris (pH 8.3), 1.92 M glycine and 1% w/v SDS, with 120 V applied for 12 h. Gels were stained with colloidal Coomassie G-250. Analyzes were done on two technical replicates for three biological samples.2-DE gel images were acquired using a GS-800 calibrated densitometer . Image analysis was performed using the PD Quest software . Spot detection and matching between gels were performed automatically, followed by manual verification. Protein spots were annotated only if detectable in all gels. After normalization of the spot densities against the whole gel densities, the percentage volume of each spot was averaged for six different (two replicates of three samples) gels; Student’s t-test analysis (p < 0.05) was performed to find out statistically significant spot volume fold changes (>1.5 or <0.66) associated with biochar presence in soil.in-gel reduced with dithiothreitol, S-alkylated with iodoacetamide, and then digested with trypsin. Resulting peptide mixtures were subjected to a desalting/concentration step on μZip-TipC18 devices  before MS analysis. Recovered peptides were then analyzed for protein identification by nanoLC-ESI-LIT-MS/MS, using an LTQ XL mass spectrometer  equipped with a Proxeon nanospray source connected to an Easy-nanoLC . Peptides were resolved on an Easy C18 column  (Proxeon)  . Mobile Viridiplantae) to identify protein(s) present within each gel spot. Database searching was performed by using Cys carbamidomethylation and Met oxidation as fixed and variable protein modifications, respectively, a mass tolerance value of 1.8 Da for precursor ion and 0.8 Da for MS/MS fragments, trypsin as proteolytic enzyme, and a missed cleavage maximum value of 2. Other MASCOT parameters were kept as default. Protein candidates assigned on the basis of at least two sequenced peptides with an individual peptide expectation value <0.05 (corresponding to a confidence level for peptide identification >95%) were considered confidently identified. Definitive peptide assignment was always associated with manual spectra visualization and verification.Raw nanoLC-ESI-LIT-MS/MS data were searched with v.2.2.06 MASCOT software  against an updated (07/2017), non-redundant UniProtKB database  into a pre-cooled tube containing 750 μl of kit lysis solution adding 1% of v/v β-mercaptoethanol. After 5-min incubation at 55°C, samples were centrifuged at 14,000 × g, and the supernatant loaded onto a filtration column included in the kit. The filtrate was recovered by centrifugation at 14,000 × g, for 1 min, which was added with 1.5 vol of binding solution from the same kit. The mixture was loaded onto the binding column, and the corresponding filtrate removed by centrifugation at 14,000 × g, for 1 min. To remove any traces of phospholipids, proteins, and carbohydrates, the column was washed with 700 μl of pure ethanol, 500 μl of binding solution and 500 μl of pure ethanol. In order to elute nucleic acids, 30 μl of nuclease-free H2O were added to the column and the eluate was recovered by centrifugation at 14,000 × g, for 1 min. To degrade genomic DNA and obtain pure RNA, we used the RNeasy/QIamp columns and RNase-free DNase set , following the manufacturer’s instructions. Extracted RNA was retrotranscribed to cDNA by using the ImProm-II Reverse Transcription System kit  and the mini thermal cycler BioRad . To 10 μl of purified RNA, 1 μl of Primer Oligo (dt) s and 1 μl of dNTP were added; the mixture was incubated in the thermal cycler at 70°C, for 5 min. After the addition of 8 μl of master mix , the mixture was incubated again in the thermal cycler at 25°C, for 5 min, at 50°C for 60 min, and at 70°C, for 15 min. After retrotranscription, the samples were stored at −20°C.The MirPremier microRNA isolation kit  was used to extract RNA from tomato berry samples. The samples (0.1-g fresh weight) were homogenized in liquid NGene primers were selected according to  and desiAgronomic parameter data are reported as mean ± SD of three independent experiments. Statistical analysis was performed by ANOVA followed by the Student-Newman-Keulus test, with the minimum level of significance being p < 0.05. Statistical significance of differences in protein spot densities from densitometric analysis of 2D-electrophoretic gels was assessed automatically by PD Quest software (Bio-Rad), using the Student’s t test (p < 0.05).Analysis of soil with and without the addition of biochar for corresponding pH, and carbon (C), phosphorus (P), potassium (K), magnesium (Mg), and total nitrogen (N) content values is reported in In order to evaluate the effect of a 5% biochar administration on the aboveground growth and productivity of San Marzano tomato plants, the corresponding height and the number of flower buds were measured every 14 days, starting from the 50th day after sowing  and the mass range 10–150 kDa (second dimension), followed by gel staining with colloidal Coomassie blue . This analysis allowed a comparison of the protein repertoire of biochar-amended and control samples to determine statistically significant quantitative variations due to biochar addition to soil. In particular, average proteomic maps for berries from biochar-treated and control plants showed 875 and 805 reproducible spots, respectively, with a similarity degree of 68%. The master gel of this proteomic analysis is shown in Most of the differentially represented proteins in the fruits of from tomato plants grown in soil amended with 5% biochar belong to the carbon/energy metabolism functional group, which included 17 unique protein entries (present in 26 spots). All resulted over-represented in the berries of biochar-treated plants. In tomato berries, glycolysis and respiration represent the main carbon and energetic fluxes that fuel substrates to sustain the respiratory burst as well as biosynthesis of cofactors, pigments, and metabolites during the maturation process . Interes2 balance. These data suggest that different energetic processes are activated in San Marzano as result of biochar addition to the soil, ultimately determining improved growth and yield of mature berries.In biochar-challenged plants, above-mentioned proteomic changes corresponded to a parallel over-representation of enzymes involved in photosynthetic carbon assimilation, namely, ribulose bisphosphate carboxylase large (spot 40) and small (spot 1) chain, and ribulose biphosphate carboxylase/oxygenase activase , and in (photosynthetic) energy production, e.g., ATP synthase β-subunit (spots 44 and 45), ATP synthase CF1 beta subunit (spots 46 and 47), and oxygen-evolving enhancer protein 1-2 (spot 24), which directly contributed in stabilizing/synthesizing molecules essential to provide the fruit energetic supply and to maintain the berry endogenous O2O2 and reactive oxigen species (ROS) as well as of membrane lipid peroxidation . In particular, seven components of the cellular antioxidant machinery showed down-represented levels in biochar-treated plants, namely, (Cu-Zn) superoxide dismutase (spot 3), two isoforms of ascorbate peroxidase , glutathione-S-transferase L3 and φ class (spot 15 and 19), thioredoxin peroxidase 1 (spot 10), and lactoylglutathione lyase (spot 27). In mature berries, antioxidant enzymes are abundant proteins since fruit ripening is generally paralleled by an increase of oxidative metabolism, with accumulation of Hxidation . This ROant cell . Besidesant cell . On the tabolism , while ltabolism . On the A number of other components involved in the plant response to stress, which belong to the heat shock protein (HSP) or chaperonin protein families, were also detected as down-represented in tomato plants challenged with biochar. They were 17.6 (spot 4) and 17.8 (spot 7) kDa class I sHSPs, chloroplast sHSP (spot 9 and 13), two HSP70 isoforms , mitochondrial HSO70 2 (spot 56), HSP-interacting protein 1 (spot 48), HSP 60 (spot 51), chaperonin 21 (spot 14), chaperone protein ClpB3 (spot 55), and chaperonin 60 (spot 49) and β (spot 52) subunits. While functional characterization of chaperonins in plants is quite limited, more information is available on HSPs. The latter are a family of ubiquitous molecular chaperones whose main function is to prevent protein aggregation during stress, which group into five classes according to their molecular mass . sHSPs aFinally, other proteins involved in the plant response to different stress conditions were also ascertained with decreased concentration levels in tomato plants treated with biochar, namely, fruit-ripening protein (spot 2), TSI-1 protein (spot 5), embrio-abundant EMB (spot 25), dehydrin ERD10 (spot 33), stress-induced protein sti1-like protein (spot 50), glycine-rich RNA-binding protein (spot 6), and metacaspase 1 (spot 11). Fruit-ripening protein, embrio-abundant EMB, dehydrin ERD10, and stress-induced protein STI-1-like protein are molecules generally related to plant response to various environmental stresses. In particular, fruit-ripening protein shares 90% sequence identity with abscisic acid stress ripening protein 1, a nuclear protein that interacts with chromatin and accumulates in leaves in response to water stress and in fruits during maturation . AnalogoArabidopsis and lettuce treated with biochar  biotic stresses. Even though conflicting data exist as far as the biochar-mediated modulation of stress and defense response in plants, our proteomic results are in line with gene expression studies on  biochar , where aArabidopsis and lettuce plants.Recently, microarray investigations have suggested that genes related to the signaling pathway of the defense response hormone jasmonic acid (JA) are down-regulated in biochar-treated plants . Since wArabidopsis and lettuce, which were performed by a gene expression platform on plants grown in a soil treated or not with biochar (Nowadays, agronomic data on the effects of biochar administration on tomato growth, yield, and fruit quality are scarce and contrasting , whereas biochar . These i biochar  as key dThe raw data supporting the conclusions of this article will be made available by the authors, without undue reservation, to any qualified researcher.MT and MR were responsible for design of and planning the experiments, analyzed data, and drafted manuscript. MT and SA performed experiments. MM, AS, and MR interpreted results of experiments, edited, and revised manuscript. MR approved final version of manuscript.The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.


Ginsenoside CK (GCK) serves as the potential anti-colorectal cancer (CRC) protopanaxadiol (PPD)-type saponin, which could be mainly bio-converted to yield PPD by gut microbiota. Meanwhile, the anti-CRC effects of GCK could be altered by gut microbiota due to their different diversity in CRC patients. We aimed to investigate the bioconversion variation of GCK mediated by gut microbiota from CRC patients by comparing with healthy subjects.Gut microbiota profiled by 16S rRNA gene sequencing were collected from healthy volunteers and CRC patients. GCK was incubated with gut microbiota in vitro. A LC-MS/MS method was validated to quantify GCK and PPD after incubation at different time points.Bifidobacterium, Roseburia, Bacteroides and Collinsella. Spearman’s correlation analysis showed bacteria enriched in healthy subjects group were positively associated with the biotransformation of GCK, while bacteria enriched in CRC group displayed non correlation character. Among them, Roseburia which could secrete β-glycosidase showed the strongest positive association with the bioconversion of GCK.The bioconversion of GCK in healthy subjects group was much faster than CRC group, as well as the yield of PPD. Moreover, significant differences of PPD concentration between healthy subjects group and CRC group could be observed at 12 h, 48 h and 72 h check points. According to 16S rRNA sequencing, the profiles of gut microbiota derived from healthy volunteers and CRC patients significantly varied, in which 12 differentially abundant taxon were found, such as The bioconversion of GCK in healthy subjects was much faster than CRC patients mediated by gut microbiota, which might alter the anti-CRC effects of GCK. The online version contains supplementary material available at 10.1186/s13020-021-00436-z. GCK c and Rd , 6. Meanc and Rd , 8. The c and Rd , while tn beings . The pern beings . Moreoven beings –14. The Bifidobacterium and Lactobacillus are significantly higher in healthy volunteers than CRC patients, while healthy volunteers possess a relative lower abundance of Bacteroides. Studies have verified that Bifidobacterium and Lactobacillus could secret some enzymes to hydrolyze the glycosidic bond, such as β-glycosidase [Gut microbiota could be modulated by many factors such as physiologic health and disease including CRC , 16. Thecosidase , 20. HerIn this study, GCK and PPD were quantified by a validated LC-MS/MS method after in vitro incubation with gut microbiota at different time points. Gut microbiota collected from CRC patients and healthy volunteers were profiled by 16S rRNA gene sequencing. The results indicated that the bioconversion of GCK mediated by gut microbiota derived from healthy subjects was much faster than CRC patients.S)-GCK and 20(S)-protopanaxatriol (PPT), were purchased from Chengdu Push Bio-technology Co., Ltd , and 20(S)-PPD was supplied by Baoji Herbest Bio-Tech Co., Ltd . Their chemical structures were presented in Fig. The standards, 20(The stool samples were collected from 11 CRC patients (CRC group) and 11 healthy volunteers , respectively, who had not taken any probiotics or antibiotics in the last 30 days and abused alcohol or tobacco. CRC patients were screened and diagnosed through physical examination, who had similar body-mass index value with healthy volunteers  were suspended in 15 mL of cold sterile physiological saline by following the preparation process described in our previous work . The sto2, 10% H2 and 10% CO2) for 1 h, 2 h, 4 h, 8 h, 12 h, 24 h, 48 h and 72 h. Samples were prepared in triplicate for each time check point.The biotransformation of GCK mediated by gut microbiota in vitro was performed in 0.5 mL of incubation system, which contained 2.5 µL of GCK solution , 100 µL of work solution of gut microbiota and 398 µL of GAM broth. The mixture was incubated at 37 ℃ in the anaerobic chamber . The chromatographic separation was achieved on an ACQUITY BEH Shield RP18 column  with a gradient elution of 2 mM ammonium acetate in water (A) and ACN (B) at a flow rate of 0.3 mL/min. The gradient profile was optimized as following, 20% (B) for 0.01–1 min, 20−65% (B) for 1–2 min, 65–75% (B) for 2–3 min, 75–90% (B) for 3–4 min, 90–100% (B) for 4–5 min and 100% (B) for 5–6 min. The mass spectrometer parameters were optimized as following, ion source gas 1 and 2, 45 psi (nitrogen); curtain gas, 25 psi (nitrogen); temperature, 450 ℃ and ion spray voltage, − 4500 V. The optimized MRM parameters of each compound were listed in Additional file The primary stock solutions of GCK (0.408 mg/mL), PPD (0.402 mg/mL) and PPT  were prepared by dissolving each compound in methanol, respectively. Working solutions were prepared by diluting the stock solutions appropriately with methanol-water (V:V = 1:1) and stored at 4 ℃. The standard calibration curves samples and QC samples were prepared by spiking 2.5 µL of working solution with 100 µL of gut microbiota work solution and 398 µL of GAM broth. The solution of PPT (IS) was diluted to 1.96 µg/mL using methanol-water.2) linear least squares regression model. The intra- and inter-day precision and accuracy were determined by analyzing six replicate samples at each QC level within one day and three consecutive days. The QC samples were set at 2.55 ng/mL, 7.65 ng/mL, 51 ng/mL and 306 ng/mL for GCK, and 2.51 ng/mL, 7.54 ng/mL, 50.25 ng/mL and 301.5 ng/mL for PPD. The recovery and matrix effect were investigated accordingly.The method was validated by following respect to selectivity, sensitivity, linearity, precision, accuracy, recovery and matrix effect according to the United States Food and Drug Administration (FDA) guidelines. The selectivity of this method was evaluated by comparing chromatograms of extracted blank gut microbiota solution obtained from six different human feces samples with those spiked with GCK (0.51 µg/mL), PPD (0.5025 µg/mL) and PPT (1.96 µg/mL) work solutions. The seven-point standard curves, ranging from 2.55 to 408 ng/mL for GCK and 2.51 to 402 ng/mL for PPD, were plotted on the peak area ratio of target ions to the IS versus the corresponding concentrations by a weighted  according to the standard protocols.Microbial genomic DNA was extracted from fecal samples by using Qiagen QIAamp DNA Stool Mini Kit , and the V3-V4 region of the bacteria 16S rRNA gene was amplified by PCR using primers 341F 5′-CCTAYGGGRBGCASCAG-3′ and 806R 5′-GGACTACNNGGGTATCTAAT-3′. Amplicons were extracted and purified by using Gene JET Gel Extraction Kit . Sequencing libraries were constructed by using Ion Plus Fragment Library Kit 48 rxns . Purified amplicons were sequenced on Ion S5https://qiime2.org). Raw sequence data were developed into clean data by cut off the barcode and primer sequences (Cutadapt plugin), de-multiplexed (demux plugin), and denoised with DADA2 (Dada2 plugin) [http://greengenes.lbl.gov/cgi-bin/nph-index.cgi) was applied for the taxonomic classification. Alpha diversity and beta diversity were analyzed by Diversity plugin.Bioinformatics analysis of bacteria 16S rRNA sequencing data was conducted by using QIIME 2 software , 22 (ver plugin) . OperatiTo analyze the alpha diversity, Shannon index, Observed OTUs, Faith’s index and Pielou’s index were calculated to assess community diversity, richness, phylogenetic diversity and evenness, respectively. The data were visualized by using GraphPad Prism (version 7.00). Beta diversity was assessed by using principal co-ordinates analysis (PCoA). Bray Curtis distance and Jaccard distance were calculated according to phylogenetic measures, while Unweighted Unifrac distance and Weighted Unifrac distance were employed for the sequence measures. Moreover, the linear discriminative analysis effect size  analysis was conducted to discover the differentially abundant taxon by using LEfSe software (LDA score > 4). Furthermore, Phylogenetic Investigation of Communities by Reconstruction of Unobserved States (PICRUSt) analysis was used to predict the metabolic function of bacteria, of which the different function was expressed as LDA scores (LDA scores > 2). Lastly, a heat map of Spearman’s correlations was constructed by using gplots and RColorBrewer packages of R software (version 4.0.0).t test was used to evaluate the biotransformation and alpha diversity. Kruskal-Wallis test and Wilcoxon test were employed to assess LEfSe and the relative abundance of bacteria. Significant differences were expressed as *p < 0.05, **p < 0.01 and #p < 0.001.Statistical analyses was performed on SPSS software (version 17.0), and student’s The developed method displayed a good selectivity for GCK and PPD  showed that all samples could be unambiguously clustered into two groups. The 16S rRNA sequencing data provided that the alpha and beta diversity of gut microbiota were significantly different between health group and CRC group.Firmicutes was much higher in health group, while Bacteroidetes significantly decreased . Compared with CRC group, the relative abundance of sed Fig. a. Top tesed Fig. b were Bap < 0.05, LDA scores > 4) were found, which could be mainly categorized into Actinobacteria, Bacteroidetes, Firmicutes and Proteobacteria (Phylum) . The data  between its relative abundance and PPD yield at 4 h. The data implied that Roseburia might be a main contributor for the bioconversion variation of GCK between health group and CRC group.For better understanding the relationship of gut microbiota and bioconversion of GCK, Spearman’s correlation index was calculated between the bioconversion rate of GCK and the relative abundance of 12 differentially abundant taxon Fig. . Bacteriin vivo effects after oral administration with GCK might also vary. Moreover, PPD also showed different pharmaceutical activities, such as anti-inflammatory and anti-CRC effects [In our study, the bioconversion rate of GCK in the healthy volunteers was higher than CRC patients mediated by gut microbiota, which implied that GCK and PPD absorbed into human plasma would be different between healthy subjects and CRC patients after oral administration with equivalent dosage of GCK. Similarly,  effects . HoweverFirmicutes phyla to Bacteroidetes phyla was lower in CRC subjects, which was consistent with the reported literatures, as well as some top ten gut microbials in genus level, such as Bacteroides and Roseburia [Bifidobacterium and Roseburia were enriched in health volunteers, while Bacteroides and Collinsella were enriched in CRC patients [In addition, besides genetic impacts, gut microbiota are also re-shaped by many environmental factors, such as gastro-intestinal diseases . Hence, oseburia . The respatients , 26. Theβ-glycosidase, the ability of gut microbiota to secret these key enzymes should be predominantly considered. Studies verifies that more than half of the low G+ C% Gram-positive Firmicutes harbored β-glycosidase, as well as Bifidobacterium spp. and Lactobacillus [Bifidobacterium, Rumnococcus and Roseburia could secret β-glycosidase [Bacteroides ovatus. Therefore, Roseburia, which belongs to butyrate-producing bacteria and alleviates experimental colitis pathology by inducing anti-inflammatory responses  [In addition, the results of Spearman’s correlation analysis found that 4 genus bacteria were positively associated with the bioconversion of GCK. As the metabolism of GCK was catalyzed by bacillus , 20. Meacosidase . Importatinalis) , 29, migAs gut microbiota provide crucial signals to host immune system, more and more studies have been reporting that drugs have interaction with gut microbiota , 30. In Roseburia might be a main contributor for bioconversion variation of GCK.A LC-MS/MS method was validated to quantify GCK and PPD in the gut microbiota incubation system. The bioconversion rate of GCK mediated by gut microbiota derived from healthy volunteers was much higher than CRC patients. The profiles of gut microbiota between health subjects and CRC patients were significantly different through 16S rRNA sequencing. Additional file 1: Table S1. Population Characteristics of recruited volunteers. Table S2. MRM parameters of GCK, PPD and PPT. Table S3. Linearity range, correlation coefficients (r), calibration curves and LLOQ of GCK and PPD. Table S4. Precision and accuracy of GCK and PPD. Table S5. Recovery and matrix effect of GCK and PPD. Table S6. Taxonomical information of the differentially abundance bacteria by LEfSe analysis. Additional file 2: Figure S1. Representative MRM chromatograms of blank gut microbiota solution, GCK, PPD and PPT. a1,b1,c1 were the corresponding chromatograms of blank gut microbiota solution; a2: GCK; b2: PPD; c2: PPT. Figure S2. Relative abundance of differentially abundant bacteria in each sample. (a) Bifidobacterium; (b) Blautia; (c) Corynebacterium; (d) Enhydrobacter; (e) Rothia; (f) SMB53; (g) Bacteroides; (h) Collinsella; (i) Coprobacillus; (j) Enterobacteriaceae; significant difference of relative abundance were analyzed using Kruskal-Wallis test and Wilcoxon test; * p < 0.05, ** p < 0.01 and # p < 0.001. Figure S3. Significant functional pathways between health and CRC group by PICRUSt analysis .


The pivotal cell involved in the pathogenesis of liver fibrosis, i.e., the activated hepatic stellate cell (HSC), has a wide range of activities during the initiation, progression and even regression of the disease. These HSC-related activities encompass cellular activation, matrix synthesis and degradation, proliferation, contraction, chemotaxis and inflammatory signaling. When determining the in vitro and in vivo effectivity of novel antifibrotic therapies, the readout is currently mainly based on gene and protein levels of α-smooth muscle actin (α-SMA) and the fibrillar collagens (type I and III). We advocate for a more comprehensive approach in addition to these markers when screening potential antifibrotic drugs that interfere with HSCs. Therefore, we aimed to develop a gene panel for human in vitro and ex vivo drug screening models, addressing each of the HSC-activities with at least one gene, comprising, in total, 16 genes. We determined the gene expression in various human stellate cells, ranging from primary cells to cell lines with an HSC-origin, and human liver slices and stimulated them with two key profibrotic factors, i.e., transforming growth factor β (TGFβ) or platelet-derived growth factor BB (PDGF-BB). We demonstrated that freshly isolated HSCs showed the strongest and highest variety of responses to these profibrotic stimuli, in particular following PDGF-BB stimulation, while cell lines were limited in their responses. Moreover, we verified these gene expression profiles in human precision-cut liver slices and showed similarities with the TGFβ- and PDGF-BB-related fibrotic responses, as observed in the primary HSCs. With this study, we encourage researchers to get off the beaten track when testing antifibrotic compounds by including more HSC-related markers in their future work. This way, potential compounds will be screened more extensively, which might increase the likelihood of developing effective antifibrotic drugs. In liver fibrosis research, when screening the efficacy of a promising new antifibrotic compound, the current gold standards to assess the in vivo antifibrotic efficacy are α-smooth muscle actin (α-SMA) and collagens type I and III. α-SMA is highly expressed by the pathogenic-activated hepatic stellate cells (HSCs) that transform into myofibroblasts in the fibrotic liver . In addiUpon liver injury, HSCs are activated to develop into myofibroblasts following a response that is associated with the release of a variety of profibrogenic mediators, of which TGFβ and platelet-derived growth factor BB (PDGF-BB) are established as the central players . AlthougIt is nowadays commonly accepted that fibrosis, even in advanced stages, is reversible upon eradication of the inciting stimulus or can be induced by (antifibrotic) drugs . The exaCurrently, patients suffering from end-stage liver disease can only be cured by organ transplantation, of which the availability is limited . TherefoSince the activated HSCs are an important target for future drug intervention of hepatic fibrosis, in our current study we focused on this pivotal cell type with in vitro studies in primary human cells and commonly used human HSC lines, and with ex vivo studies using precision-cut liver slices (PCLS). PCLS, freshly prepared from residual human livers, represent an ex vivo tissue culture technique that retains the multicellular characteristics of the hepatic (fibrotic) environment in vivo . In our l-glutamine, 1 mM sodium pyruvate, 1× Minimal Essential Medium (MEM) non-essential amino acids solution and 250 ng/mL amphotericin B. The Human Hepatic Stellate Cells (HHSteC) were obtained from ScienCell Research Laboratories , and cultured in poly-l-lysine -coated flasks and plates in complete stellate cell medium containing 2% FBS, 100 U/mL penicillin, 100 μg/mL streptomycin and 1% stellate cell growth supplement . HHSteCs and pHSCs were sub-cultured up to 5 times, ensuring minimal variation in the myofibroblast activation state. LX-2 hepatic stellate cells were kindly provided by prof. Scott Friedman  [Human livers were obtained under the University College London—Royal Free BioBank Ethical Review Committee, reference number: NC2015.020 (B-ERC-RF) after informed consent was obtained for each donor. Primary human hepatic stellate cells (pHSC) were freshly isolated from these human livers as described before  and cultNY, USA) . Cells wNY, USA) .Cells were plated in 12-well plates  and either not stimulated (control), or stimulated for 24 h with 5 ng/mL TGFβ1 or 50 ng/mL PDGF-BB , and harvested for RNA isolation and cDNA synthesis. Materials were purchased at Thermo Fisher Scientific  unless stated otherwise. Photomicrographs of the different cell types were captured at 40× and 100× magnification.n = 4) were sliced in enhanced ice-cold carbogen-saturated Krebs–Henseleit buffer and cut with a Krumdieck tissue slicer  at 250–300 μm thickness and 4–5 mg wet weight. Slices were stored in University of Wisconsin (UW) until incubation for 48 h in 12-well plates filled with 1.3 mL William’s E Medium (Thermo Fisher Scientific) enriched with 25 mM glucose  and 50 μg/mL gentamycin (Thermo Fisher Scientific), either not supplemented (control) or supplemented with 5 ng/mL TGFβ1 or 50 ng/mL PDGF-BB (both PeproTech). Culture medium was refreshed every 24 h. Wells of slices incubated with TGFβ1 were precoated with 10% bovine serum albumin (BSA) solution (Sigma-Aldrich). Per condition, 3 slices were pooled and snap-frozen for RNA isolation and cDNA synthesis.Healthy human liver tissue was obtained from donor organs unsuitable for transplantation, resized donor organs and unaffected tissue of patients undergoing surgical excision of carcinoma. The experimental protocols were approved by the Medical Ethical Committee of the University Medical Center Groningen. Precision-cut liver slices were prepared according to standard procedures, as described previously ,20. In s® LEV simply RNA Cells/Tissue kit  according to the manufacturer’s instructions. RNA concentrations were determined using a NanoDrop One spectrophotometer (Thermo Fisher Scientific). Conversion of RNA to cDNA was performed using MLV (murine leukemia virus) reverse transcriptase (Promega) in an Eppendorf Mastercyler gradient device, with the gradient at 20 °C for 10 min, 42 °C for 30 min, 20 °C for 12 min, 99 °C for 5 min and 20 °C for 5 min. The transcription levels were measured in 10 ng cDNA by quantitative real-time PCR  using a QuantStudio 7 Flex Real-Time PCR system . Data was analyzed using QuantStudio Real-Time PCR software (Thermo Fisher Scientific). For each model, mRNA expression was normalized to housekeeping genes , and expressed as 2−ΔΔCt (fold induction). Differences between treatment groups and untreated controls are in the main text referred to as percentage increase or decrease. The used primer sequences are listed in Total RNA was isolated from cells or precision-cut liver slices using a Maxwell−ΔΔCt values by the Kruskal–Wallis test followed by Dunn’s multiple comparison test.At least 3 individual experiments were done, and these data are represented as mean ± standard error of the mean (SEM). The graphs and statistical analyses were performed in GraphPad Prism version 8.0 . Statistical differences were assessed on 2We evaluated the gene panel in human HSCs with diverse backgrounds. In addition to primary HSCs, both freshly isolated from human livers (primary HSC) and obtained from a commercial source (HHSteC), we also include the immortalized HSC-lines LX-2 and TWNT-4 in our studies. First, these HSCs were examined for their morphology. All cultures displayed more or less the characteristic stellate star shape . The freACTA2 71.8% ± 12.8%, CCN2 125.2% ± 12.8%, COL1A1 46.4% ± 7.7%, FN1 49.2% ± 15.5%), as well as in HHSteCs   (MYLK (62.7% ± 2.6%) and ROCK2 (34.2% ± 3.9%) in primary HSCs  and PPARG (68.1% ± 12.9% in HHSteCs) was significantly lower compared to cells not stimulated with TGFβ  A,B. In t ± 8.5%) C. Intereary HSCs E was marith TGFβ H, indicaCCND1 78.6% ± 15.1%, MKI67 151.2% ± 49.8%) and the HHSteCs   (TIMP1 and CCL2 expression were clearly induced by PDGF-BB in the primary HSC cultures (40.6% ± 12.6% and 79.8% ± 20.7%)  and in HHSteCs (PDGFRB 43.9% ± 9.31%) after incubation with PDGF-BB  D. The inctively) G. Moreov± 20.7%) C,F. Rema PDGF-BB A. More gACTA2 62.4% ± 12.3%, PDGFRB 86.0% ± 10.7%, CCN2 150.6% ± 18.0%, COL1A1 165.3% ± 6.2%, FN1 139.2% ± 15.1%), whereas the gene expression of COL1A1 was increased in TWNT-4 cells (91.3% ± 28.7%) (Both primary HSC types showed stronger responses to either TGFβ- or PDGF-BB-stimulation as compared to the HSC-lines LX-2 and TWNT-4. LX-2 cells did show a clear increase in activation and fibrogenesis genes upon TGFβ-stimulation (± 28.7%) . Of note± 28.7%) , and the± 28.7%) . Cycle tTIMP1 (137.4% ± 60.8%) and showed a clear increasing trend in expression of activation and fibrogenesis markers  (PPARG seemed to be reduced (23.1% ± 14.9%), which was again comparable to the in vitro results  A–C. Inte results H.CCND1 and MKI67 (86.4% ± 37.7% and 165.0% ± 47.3%), TIMP1 (335.6% ± 118.0%) and COL1A1 (180.6% ± 73.4%) (CCL2 is expressed higher compared to the untreated control (99.9% ± 49.7%)  B–D. Addi± 49.7%) F.The increasing incidence of patients with liver cirrhosis, combined with the lack of effective antifibrotic drugs to stop or reverse the disease, urgently demands drug development ,21. CleaCCND1, MKI67 and CCL2 in primary HSCs and HHSteCs. Of note, in the HSC-lines that we studied as well (Following the activation of HSCs, the perpetuation phase is induced as the injury persists, which involves the proliferation, contraction, fibrogenesis, altered matrix degradation, chemotaxis and inflammatory signaling of the activated phenotype . All the as well , we coul as well D–F delinPPARG and CYGB as markers for the quiescence of HSCs [Removal of the fibrotic stimulus or treatment with an effective antifibrotic therapy would result in amelioration of hepatic fibrosis, associated with the resolution of the inflammatory response and the reduction and reversion of activated HSCs ,28. Stud of HSCs ,30, and As previously mentioned, there is no effective therapy for fibrosis available on the market yet, despite several compounds that were shown to effectively reduce fibrotic parameters both in vitro and in animal models . An impoUsing monocultures of hepatic stellate cells of human origin, the role of the key pathogenic cell type involved in liver fibrogenesis and the effectivity of drugs can be studied in detail. In addition, this can be studied in PCLS as well, in which the HSCs are present in their original microenvironment . We obseIn this study, we used genetic markers to screen for the distinct functions of HSCs in hepatic fibrosis, as selected from the large database of fibrosis markers available in literature. Evidently, the choice for the markers per HSC function is open for interpretation and should be adapted at one’s own discretion, depending on the mechanism of action of the potential antifibrotic compound. We intended to create a framework for preclinical antifibrotic drug testing at the gene level. In addition to the genetic markers, the complementary protein levels are interesting as well. Currently, a wide variety of markers to be used in future clinical trials is under investigation. Methods include, for example, immunohistochemical staining for collagens, α-SMA or PDGFβ-receptor to study liver histology, elastography to determine liver stiffness and serum fibrosis markers .In conclusion, model test systems to screen the antifibrotic potential of novel therapeutics should include all aspects of HSC activities in fibrosis, and thus look beyond the standard markers such as α-SMA and fibrillary collagens. The used models should be human-based, like primary HSCs or precision-cut liver slices freshly isolated or prepared from human livers, as these models will better predict therapeutic effects in patients. This way, the outcomes of in vitro studies with antifibrotic compounds can be broadened, thereby boosting drug development in the fibrosis field.