Lung cancer is the leading cause of cancer-related deaths with 1.8 million new cases each year and poor 5-year prognosis. Promoter hypermethylation of tumour suppressors leads to their inactivation and thereby can promote cancer development and progression.ZAR1 is expressed in normal lung but inactivated by promoter methylation in lung cancer. ZAR1 is hypermethylated in primary lung cancer samples  and 76% non-small cell lung carcinoma (NSCLC), p < 0.001) vs. normal control lung tissue (11%). In lung cancer cell lines, ZAR1 was significantly methylated in 75% of SCLC and 83% of NSCLC vs. normal tissue (p < 0.005/0.05). In matching tumours and control tissues, we observed that NSCLC primary tumour samples exhibited a tumour-specific promoter methylation of ZAR1 in comparison to the normal control lung tissue. Demethylation treatment of various lung cancer cell lines reversed ZAR1 promoter hypermethylation and subsequently re-established ZAR1 expression. In addition, we could show the growth inhibitory potential of ZAR1 in lung cancer cell lines and cancer cell lines. Exogenous expression of ZAR1 not only inhibited colony formation but also blocked cell cycle progression of cancer cell lines.In this study, we analysed ZAR1 (zygote arrest 1), which has been said to be a maternal-effect gene and its expression mostly limited to certain reproductive tissues. Our study shows that ZAR1 due to DNA methylation of its CpG island promoter. Furthermore, ZAR1 was characterised by the ability to block tumour growth through the inhibition of cell cycle progression in cancer cell lines. We propose that ZAR1 could serve as an epigenetically inactivated biomarker in lung cancer.Our study shows for the first time the lung tumour-specific epigenetic inactivation of The online version of this article (doi:10.1186/s13148-017-0360-4) contains supplementary material, which is available to authorized users. Lung cancer is currently the leading cause of cancer related deaths in men and the second leading cause of cancer death in women worldwide . Lung caZAR1 was one of the best candidates [ZAR1 or zygote arrest 1 is located on chromosome 4 (4p11) and covered by a 1.5 kb large CpG island .Our own experiments regarding lung cancer analysed epigenetic alterations in the lung cancer cell line A549 using the Infinium HumanMethylation450K BeadChip Array (Illumina). We found the maternal effect gene ndidates . Human ZZAR1 was described as a novel maternal-effect gene critical for oocyte to embryo transition in mouse and especially the C-terminus of its protein is evolutionarily highly conserved in vertebrates , 11, butZAR1 was intra-genically methylated (exon 1) and ZAR1 was overexpressed in some hypermethylated melanoma cell lines [ZAR1 was absent in hypermethylated glioma cell lines [ZAR1 was detected in hypermethylated neuroblastoma [ZAR1 intragenic methylation (first exon to intron 1) in sporadic bladder cancer was decreased in high-grade vs. low-grade tumours [ZAR1 hypermethylation of exon 1 was found [ZAR1 expression levels were inconsistent.Regarding an association of human ZAR1 and cancer only, few reports exist. In malignant melanoma, ll lines . Non-proll lines , 17. Expll lines , but ZARblastoma . ZAR1 in tumours . In hepaas found . These pZAR1 is epigenetically inactivated in lung cancer. We further found evidence that ZAR1 acts as a tumour suppressor and therefore suggest it could serve as a biomarker in lung cancer cells.We hypothesised that ZAR is a tumour suppressor inactivated in cancer by its promoter hypermethylation. In our study, we found that ZAR1 (zygote arrest 1) was amongst the most strongly deregulated genes, and up to date, no study investigated the epigenetic inactivation of the ZAR1 promoter and its consequences in lung cancer. We found that ZAR1 is expressed in normal lung , we observed altered promoter methylation of the lung cancer cell line A549 . ZAR1  and pyrosequencing for quantification , but relatively low (<10%) in breast, kidney, liver and heart. All lung cancer cell lines exhibited decreased ZAR1 levels (<5%) vs. normal lung, except for H358, which was 15%  and small cell lung cancer cells (SCLC) from cell lines and primary tumour samples  and in NSCLC, 16 out of 21 (76%)  is an inhibitor of DNA methyltransferases and demethylating agent. It was used to reverse promoter methylation and thereby verify the epigenetic inactivation of ZAR1. ZAR1 methylated cell lines HEK, NSCLC cell lines A549, A427, H1299, H358, H322 and SCLC cell line HTB171 exhibited promoter demethylation with 5 or 10 μM Aza treatment, but unmethylated HCC-15 (NSCLC) remained unaffected  compared to the unmethylated promoter or the empty control luciferease reporter construct  with on Figs.  and 4a aion Fig.  were choPCR Fig. , which wPCR Fig. . Demethyels Fig. . Coherenuct Fig. .ZAR1 is epigenetically inactivated in lung cancer cell lines and primary lung cancer tumours, we aimed to test its tumour suppressive properties. Colony formation assays were performed in A549, A427 and HTB171 lung cancer cells that are epigenetically inactivated for ZAR1 , HeLa (cervix carcinoma) and HCT116 (colon carcinoma) for reexpression of ZAR1. We performed flow cytometry analysis using propidium iodide staining to determine the nuclear DNA content. Flow cytometry in HeLa cells showed that ZAR1 overexpression increased the number of cells in S phase, but at the same time, no increase in mitosis was observed , a part of the Polycomb Repressive Complex 2, was found to bind to the ZAR1 promoter methylation but also study interacting partners, which would give insight into its network and signalling pathways. This could elucidate its precise biological function in somatic lung cells, as to whether it controls proliferation, could induce apoptosis upon cellular stress and regulates mRNA translatability via binding to 3’UTRs [We found that overexpressed ZAR1 localised throughout the cytosol. Where endogenous ZAR1 is localised will be verified with later experiments when an appropriate antibody for ZAR1 is available. With this tool on-hand, we could not only study ZAR1 expression in primary cancer samples and correlate expression with o 3’UTRs . In our ZAR1 in lung cancer and other cancer cell lines, we propose ZAR1 to be an epigenetically inactivated tumour suppressor in lung cancer. We demonstrated that ZAR1 could repress growth of cancer cell lines by inhibiting cell cycle progression. We therefore suggest that in the future, ZAR1 could serve as a hypermethylated biomarker for lung cancer detection and possibly could help differentiate between SCLC and NSCLC.Our findings give insight into the properties of ZAR1 in non-oocyte tissues and its tumour suppressive role in lung cancer. Together with the observed epigenetic inactivation of ZAR1 was analysed by CpG plot http://www.ebi.ac.uk/Tools/seqstats/emboss_cpgplot/ and shows the existence of a 1.5 kb large CpG island. Primers for bisulfite-treated DNA were designed to bind only fully converted DNA and amplify the promoter region of ZAR1. The precise promoter region was chosen for CpG content and presence of according restriction enzymes for COBRA analysis. The size of the ZAR1 COBRA PCR product is 186 bp (with TaqI site at 89). Additional file ZAR1 gene and protein product, ZAR1 CpG island, COBRA PCR product, primer positions, TaqI restriction site and pyrosequencing region.The promoter region of Lung cancer cell lines  were described previously . SCLC ca2O and incubated for 10 min with 5 μl 3 M NaOH at 37 °C. DNA was then precipitated with 100% ethanol and 7.5 M ammonium acetate and resolved in 30 μl 1 × TE buffer. Bisulfite treated DNA was subsequently used for 25 μl PCR reaction with ZAR1 COBRA primers. The PCR product was digested with 0.5 μl of TaqI (Thermo Fisher Scientific) 1 h at 65 °C and resolved on 2% TBE gel together with mock control. Pyrosequencing was performed according to manufacturer’s protocol with PyroMark Q24 System (Qiagen). In vitro methylation of genomic/plasmid DNA for positive control in COBRA assay and luciferase promoter assay was performed using CpG methyltransferase M.SssI (NEB) according to manufacturer’s protocol. Primers for COBRA analysis of the ZAR1 promoter (186 bp) were upper primer GGAGAAGGAYGAAGAGGGGTTTTT and lower primer TCCCCCAAAACCRCCATAAAC. For pyrosequencing, the lower ZAR1 primer was biotinylated. Fisher exact test was used for statistical analysis.Two microgram genomic DNA (isolation by phenol-chloroform extraction) from tumour tissue, control tissue and cancer cell lines was bisulfite treated  and incubated over night at 50 °C. DNA was purified using MSB Spin PCRapace (STRATEC Molecular), eluted in 50 μl HACTB (226 bp), upper primer AGCTGGGCAAGGAGCGGCTG and lower primer GGTGGGGCCGTTTAGGGTCCA for ZAR1 (264 bp) and upper primer TGGAGAAGGCTGGGGCTCAT and lower primer GACCTTGGCCAGGGGTGCTA for GAPDH (176 bp). Student’s t test was used for statistical analysis.RNA was isolated using Isol-RNA lysis procedure (5 Prime). RNA was DNase (Thermo Fisher Scientific) treated and then reversely transcribed by MMLV (Promega). Semi-quantitative PCR was performed in Eppendorf Mastercycler using a standard protocol. Quantitative RT–PCR was performed in triplicate with SYBR select (Thermo Fisher Scientific) using Rotor-Gene 3000 (Qiagen). Normal human RNAs were obtained from Agilent Technologies. All other RNAs were isolated from cultured cancer cell lines. Primers for RT-PCR were upper primer CCTTCCTTCCTGGGCATGGAGTC and lower primer CGGAGTACTTGCGCTCAGGAGGA for 2). For 5-Aza-2’deoxycytidine (Aza) treatment cancer cell lines were split to 10% density, Aza was added with fresh medium on four consecutive days and RNA as well as DNA were isolated. For cell line transfection, we used Turbofect (Thermo Fisher Scientific), X-tremeGENE HP (Roche) or polyethylenimin (Sigma) with either 4 μg (6-well plates) or 10 μg (10 cm dishes) of ZAR1-pEGFP, EGFP vector, ZAR1-pRLnull or pRLnull empty.Cell lines were grown in appropriate medium (DMEM or RPMI) supplemented with 10% FCS, and 1% penicillin/streptomycin under cell culture conditions . FACSDiva Software (BD Biosciences) was used for measurement/gating to distinguish transfected fluorescent cells and to determine cells in G1/G0, S and G2/M phase of the cell cycle. ZAR1-EGFP overexpression is shown in HeLa and A549 cells. Therefore, cells were seeded in 6-well plates on glass slides and transfected the following day (see above). Cells were fixed with 3.7% formaldehyde at according time points, stained with DAPI , embedded in anti-fading with Mowiol (Sigma) and analysed with Axio Observer Z1 (Zeiss) under ×63 magnification and by Volocity Software (Perkin Elmer).For colony formation, assay cells were seeded at 10% density, transfected the following day. Selection with G418 (Biochrom) with 2.5 mg/ml (A549) and 0.25 mg/ml  started the following day and continued for 21 days. After 3 weeks, when visible colonies formed, cells were dried and stained with Giemsa (Sigma). Statistical analysis was performed using paired ZAR1 coding sequence was ordered from GeneArt AG  and cloned into pEGFP-C2 (Clontech). The ZAR1 promoter  was amplified from genomic DNA and cloned into pRL-null (Promega). Primers for cloning the ZAR1 promoter were upper GGATCCCTGGGTGGTTCTCCATGCA and lower GAATTCGTAACCGTCCAGCACCTCGTCCCC (601 bp). All plasmids were controlled by sequencing, RT-PCR and immunofluorescence/Western blotting for the expression vector. For the promoter assay of ZAR1, the Dual-Luciferase Reporter Assay System (Promega) was used according to manufacturer’s protocol and HeLa cells were used. Student’s t test was used for statistical analysis.Additional file 1: Figure S1.ZAR1 gene/protein and CpG island. The ZAR1 gene is shown with its four exons (encoding an 1275 nt transcript), and its protein (424 aa) structure with a C-terminal zinc finger (ZF). The ZAR1 CpG island covers 1.5 kb of the promoter and the first exon. Black vertical lines represent single CpGs, and the restriction enzyme TaqI recognition site is marked (scissors) for COBRA methylation assay. Bent arrow indicates transcriptional start site (TSS). Horizontal arrows mark COBRA methylation analysis PCR product of 186 bp. Methylation analysis by pyrosequencing covers five CpGs (asterisk) within the COBRA analysed region. 1 kb standard is shown. (PDF 118 kb)Schematic structure of the Additional file 2: Table S1.Supplemental Table 1. (XLSX 14 kb)Additional file 3: Table S2.Supplemental Table 2. (XLSX 13 kb)Additional file 4: Figure S2.ZAR1 overexpression blocks cell cycle progression of cancer cell lines. Cell lines HeLa, A549 and HCT116 were transfected with EGFP or ZAR1-EGFP, isolated after 24 h and fixed with ethanol. DNA content was measured by FACS CantoII using propidium iodide staining to determine cell cycle arrest. S phase alteration is marked in red. A–C Flow cytometry gating is shown for HeLa, A549 and HCT116 cancer cells and D shows quantification for HCT116. (PDF 345 kb)Additional file 5: Figure S3.ZAR1. UCSC genome browser data revealed that transcription factors as EZH2 (arrowhead) bind to the promoter of ZAR1 as analysed by ChIP-seq by the ENCODE project. EZH2 binding overlaps with the ZAR1 CpG island promoter (green) and its first exon (blue). (PDF 576 kb)Genomic organisation of


A growing body of evidence indicates that marine sponge-derived microbes possess the potential ability to make prolific natural products with therapeutic effects. This review for the first time provides a comprehensive overview of new cytotoxic agents from these marine microbes over the last 62 years from 1955 to 2016, which are assorted into seven types: terpenes, alkaloids, peptides, aromatics, lactones, steroids, and miscellaneous compounds. The search for cytotoxic agents from marine resources has always attracted the attention of natural products chemists ,2. More 1) and 13′-acetyltrichoverrin B (2), were isolated from Myrothecium verrucaria associated with a Spongia sp. (Coll. No. 97103) collected from the coast of Maui, HI, USA, and were shown to have potent cytotoxicity against murine lymphocytic leukemia L1210 and human colon tumor H116 cell lines [Aspergillus ustus from the sponge Suberites domuncula (collected from the Adriatic Sea) led to isolation of two new drimane sesquiterpenoids 3 and 4 (50) values ranging from 0.6 to 5.3 µg/mL [Two new trichothecenes , 3-hydro 3 and 4 . BioassaE)-6-(40-hydroxy-20-butenoyl)-strobilactone A (5), isolated from Aspergillus insuetus (OY-207) colonizing in a Mediterranean sponge Psammocinia sp., was shown to have a cytotoxic effect on the MOLT-4 cell line by 55% at 50 mg/mL [6 and 7  values of 9.31 and 12.40 µg/mL, while 7 showed selective activity with IC50 values of 2.91 and 10.20 µg/mL, respectively [(50 mg/mL . Two new[6 and 7  were metectively .8–11) were found in the fermentation broth of Aspergillus sp. in the marine sponge Xestospongia testudinaria from the South China Sea  against human promyelocytic leukemia HL-60 and human lung carcinoma A-549 [Hansfordia sinuosae derived from the sponge Niphates sp., was shown to produce six new caryophyllene-based sesquiterpenoids, punctaporonins H–M (12–17)  . Howevers >10 µM .Aspergillus ustus isolated from a Mediterranean sponge Suberites domuncula yielded five new ophiobolin-type sesterterpenoids 18–22 [Chemical examination of the marine fungus ds 18–22  (Chart 423–26), were produced by a strain of Trichoderma harzianum OUPS-111D-4 originally derived from the marine sponge Halichondria okadai collected in Osaka Bay, Japan (23 exhibited moderate cytotoxicity against murine leukemia cell lines P388 and L1210 and human leukemia cell line HL-60. Compounds 24–26 had moderate cytotoxicity against a disease-oriented panel of 39 human cancer cell lines (HCC panel). However, these diterpenes showed slightly selective growth inhibition against the central nervous system cancer SNB-75 cell line in the HCC panel [Four novel decalin derivatives, tandyukisins  . Compound 27 exhibited mild cytotoxicity towards human leukemia MOLT-4 cells [Aspergillus strain derived from an unidentified sponge  was found to metabolize three new cytotoxic meroterpenoids, tropolactones A–C (28–30)  . The str-4 cells . Another (28–30) , which sectively .Alteromonas sp. from the sponge Halichondria okadai led to the isolation of one novel tetracyclic alkaloid: 31  and 5′-hydroxystaurosporine (33) , A549 (ATCC CCL-185), HT-29 (ATCC HTB-38), and SK-MEL-28 (ATCC HTB-72) [Chemical examination of the cultured mycelium of a bacterium loid: 31 . It exhiectively . 4′-N-meine (33)  were obt HTB-72) .34 and 35  with moderate activity against an unknown tumor cell line [40) (Psychrobacter strain isolated from the marine sponge Stelletta sp. (collected from the coast of Geoje Island) and was shown to have an inhibitory effect on five human solid tumor cell lines: A-549, SK-OV-3, SK-MEL-2, XF-498, and HCT-15 with EC50 values of 2.34, 1.57, 3.44, 2.39, and 3.13 mg/mL, respectively [41), -47 (42), and -48 (43) . Cytotoxic tests indicated that compounds 41–43 possessed weakly cytotoxic activities against human acute myelogenous leukemia HL-60 cells with IC50 values of 189, 226, and 96 μM, respectively [Aspergillus ustus, isolated from the Mediterranean sponge Suberites domuncula, yielded two new pyrrolidine alkaloids, 44 and 45   . All these natural products exhibited cytotoxic activities against the lymphocytic leukemia P388 cell line . Furthermore, compound 53 had appreciable growth inhibition against tumor cell lines BSY-1 (breast) and MKN7 (stomach) lines  [Homaxinella derived fungus, Gymnascella dankaliensis, yielded a new compound dankastatin C (57)  [One new secondary metabolite, amycolactam (46) , was isoectively . Ten nov [47–56)  were proctively) ,30,31,32n C (57) . This al8 ng/mL) .N-methylated linear octapeptides, RHM1 (58) and RHM2 (59) (Acremonium sp. cultured from a Teichaxinella sp. marine sponge (collected in Papua New Guinea) and were shown to have mild cytotoxicity against murine L1210 cells by a disk diffusion soft agar colony-forming assay [Aspergillus versicolor from a marine sponge Petrosia sp.  yielded two novel lipopeptides: fellutamide C (60) and fellutamide F (61)  cancer XF498, and human colon cancer HCT15 [Aspergillus similanensis KUFA0013, derived from the sponge Rhabdermia sp.  was found to produce a new cyclohexapeptide, similanamide (62)  afforded two novel cyclodepsipeptides: scopularides A (63) and B (64) , and HT29 (colon tumor cells) was reduced by 36% (63) and 26% (64), 42% (63) and 49% (64), and 37% (63) and 24% (64), respectively [Two highly HM2 (59) , were prng assay . The inve F (61) . Compouner HCT15 ,36. One ide (62) , which pd B (64) . Bioassaectively .Penicillium brocae, obtained from a tissue sample of a Fijian sponge Zyzyya sp., led to the isolation of three novel polyketides: brocaenols A–C (65–67)  . Compounectively .68–70)  , were chactivity .Emericella variecolor derived from the marine sponge Haliclona valliculata , a new natural product called evariquinone 71  afforded three anthraquinones (72–74) , -98 (76), and -99 (77) . By the water-soluble tetrazolium-8 (WST-8) colorimetric assay, compounds 75, 76, and 77 were shown to have cytotoxic effects on HeLa cells  and ACC-MESO-1 cells  [From a strain of the fungus inone 71  was isol (72–74)  by bioac88 µg/mL . Three n-99 (77)  were proctively) .Aspergillus niger from a Mediterranean sponge Axinella damicornis, yielded one new secondary metabolite: 3,3′-bicoumarin bicoumanigrin (78) (3H-thymidine as a marker [Fractionation of the EtOAc extract of a static culture of rin (78) . MTT assa marker .79 and 80)   were purectively .Aspergillus versicolor derived from a marine sponge Petrosia sp. , afforded a new xanthone 81 -2,4-dihydroxy-1-butyl-(4-hydroxy) benzoate (82) . This metabolite was shown to exhibit potent cytotoxic effect on tsFT210 cells with an MIC (minimum inhibitory concentration) value of 8.0 µg/mL [Streptomyces sp., SpD081030ME-02  afforded a new compound JBIR-58 (83)   and showed weaker cytotoxicity against human cervical carcinoma HeLa cells and acute myelogenous leukemia LH-60 cells with IC50 values of 120 and 210 µM, respectively [Porifera sponge-derived strain, Emericella variecolor, one new aromatic varitriol (85)  , was cha.0 µg/mL . Fractio-58 (83) , exhibitof 28 µM . A new a µM [84) , was metectively . From a iol (85)  was iden(85) (86  was isol 3.62 µM .Emericella variecolor XSA-07-2 isolated from the South China Sea sponge Cinachyrella sp. afforded four new lactones varioxiranols I–L (87–90)  was found to produce two new lactones: lobophorin C (91) and D (92)  . All these compounds exhibited weak cytotoxicity against tumor cell lines K562 and A549 [Chemical analysis of  (87–90)  with difd D (92) . Compoun0 7.5 µM . Three n [93–95) , were isand A549 .96, 97, 98, 99, and 100  and exhibited significant and marginal growth inhibition against the lymphocytic leukemia P388 cell line with ED50 values of 1.6, 2.2, 2.8, 0.9, and 2.5 µg/mL, respectively [Five structurally unusual steroids,  and 100 , were meectively ,53,54.101–103)  and shown to possess strong cytotoxicity against P388 cells [Penicillium citrinum SpI080624G1f01, derived from the Demospongiae sponge , afforded a new compound, JBIR-59 (104)  and its diastereomeric isomer ulbactin G (107) . Bioassay testing indicated that 106 and 107 had a strong inhibitory effect on epidermoid carcinoma cell line A431 at non-cytotoxic concentrations with IC50 values of 6.4 and 6.1 µM, respectively [Novel metabolites trichodenones A–C (101–103)  were det88 cells . Chemica59 (104) , which hof 71 µM . One nov µM [105  producedof 30 µM . Two new G (107) , were isectively .50 or ED50 value <1 µM has great potential for application in the discovery of new anti-tumor drugs/leads, for example, tetracyclic alkaloid 31 and dankastatin C (57). These candidates may play an important role in defeating human cancer.In summary, microorganisms associated with marine sponges are a prolific source of novel cytotoxic natural products with rich chemical structures. The utilization of natural products as sources of new drugs is still alive and well, especially in the area of cancer . General


APOBEC gene family of cytidine deaminases plays important roles in DNA repair and mRNA editing. In many cancers, APOBEC3B increases the mutation load, generating clusters of closely spaced, single-strand-specific DNA substitutions with a characteristic hypermutation signature. Some studies also suggested a possible involvement of APOBEC3A, REV1, UNG, and FHIT in molecular processes affecting APOBEC mutagenesis. It is important to understand how mutagenic processes linked to the activity of these genes may affect sensitivity of cancer cells to treatment.The APOBEC3A, APOBEC3B, REV1, UNG, and FHIT and with cell line chemosensitivity to 255 antitumor drugs. Among the five genes, APOBEC3B expression levels were bimodally distributed, whereas expression of APOBEC3A, REV1, UNG, and FHIT was unimodally distributed. The majority of the cell lines had low levels of APOBEC3A expression. The strongest correlations of gene expression levels with mutational loads or with measures of prevalence of APOBEC-like motif counts and kataegis clusters were observed for REV1, UNG, and APOBEC3A. Sensitivity or resistance of cell lines to JQ1, palbociclib, bicalutamide, 17-AAG, TAE684, MEK inhibitors refametinib, PD-0325901, and trametinib and a number of other agents was correlated with candidate gene expression levels or with abundance of APOBEC-like motif clusters in specific cancers or across cancer types.We used information from the Cancer Cell Line Encyclopedia and the Genomics of Drug Sensitivity in Cancer resources to examine associations of the prevalence of APOBEC-like motifs and mutational loads with expression of We observed correlations of expression levels of the five candidate genes in cell line models with sensitivity to cancer drug treatment. We also noted suggestive correlations between measures of abundance of APOBEC-like sequence motifs with drug sensitivity in small samples of cell lines from individual cancer categories, which require further validation in larger datasets. Molecular mechanisms underlying the links between the activities of the products of each of the five genes, the resulting mutagenic processes, and sensitivity to each category of antitumor agents require further investigation. APOBEC3B gene expression has been associated with genome-wide hypermutation and with kataegis, a mutagenic process that generates clusters of closely spaced, single-strand-specific DNA substitutions, which are predominantly C to T [APOBEC3B gene expression, germline polymorphisms in the APOBEC3 genome region, and higher degree of abundance of APOBEC3B mutational signatures have been associated with increased cancer risk and patient survival [APOBEC3A and APOBEC3B  are cytosine deaminases from the AID/APOBEC family, members of which play important roles in host immunity against pathogens , 2. The y C to T , 6. Clussurvival , 7.APOBEC3B have been found in several cancer types [APOBEC3B mutagenesis has a characteristic pattern of mutational specificity. It is most commonly represented by the 5′-T(C>T)W-3′ sequence motif , where “er types , 10. Adder types , 10, 11.er types –8, 11–13APOBEC3A gene is located in proximity to APOBEC3B in the APOBEC genomic cluster in the chromosomal region 22q13.1 [APOBEC3A-APOBEC3B fusion transcript may be produced due to a germline deletion polymorphism, which results in the complete loss of the coding part of the APOBEC3B gene and abolishes APOBEC3B gene expression; this deletion polymorphism produces a fusion product of the APOBEC3A gene with the 3′-UTR of APOBEC3B gene, and it has been associated with an increased risk of several types of cancer [While APOBEC3B plays a prominent role in cancer mutagenesis, several other AID/APOBEC family members also have mutagenic roles and affect DNA integrity , 14. Mos 22q13.1 . An APOBf cancer , 17. Thef cancer , 18. Howf cancer –25. In af cancer , 26.APOBEC3B mRNA expression levels with tamoxifen resistance in estrogen receptor-positive (ER+) breast cancer [APOBEC3B gene were associated with resistance to vinblastine, topotecan, paclitaxel, mitoxantrone, mitomycin C, etoposide, and doxorubicin [APOBEC3B in the 293-A3B and 293-GFP cell lines with inactivated p53 resulted in an increase in APOBEC mutagenesis and kataegic events, which were accompanied by cell hypersensitivity to small-molecule DNA damage response inhibitors including ATR (VX-970 and AZD673), CHEK1 (SAR020106), CHEK2 (CCT241553), PARP (olaparib and BMN-673), and WEE1 (AZD1775) inhibitors, as well as by sensitivity to combinations of cisplatin/ATR inhibitor, ATR/PARP inhibitor, and PARP/WEE1 inhibitor [APOBEC3B expression in breast cell lines was also correlated with sensitivity to the CHEK1 inhibitor CCT244747 [APOBEC3B or APOBEC3A expression levels were not significantly correlated with sensitivity to any drugs in breast cancer cell lines from the Genomics of Drug Sensitivity in Cancer  dataset [Based on the strong evidence for APOBEC-associated mutagenesis in a variety of cancer types, it is important to learn whether such mutagenic processes may affect cancer response to therapy, in order to exploit potential pathways involved in sensitivity and to avoid potential mechanisms of resistance. To date, the effect of APOBEC3B-like mutagenic processes on therapeutic response has not been fully understood, with several reports of divergent directions of association. Some studies suggested a potential role of APOBEC mutagenesis in tumor resistance to therapy, with a possible resistance mechanism explained by increased tumor heterogeneity when APOBEC3B activity is elevated . Clinicat cancer . In an aorubicin . In contorubicin . Experimnhibitor . Increas dataset ; however dataset .APOBEC3B expression and the loss of FHIT protein expression [At the molecular level, APOBEC3B hypermutation activity has been reported to have a synergistic effect with the absence of the uracil-specific uracil DNA glycosylase (UNG) and to involve molecular steps that require the activity of the translesion synthesis DNA polymerase REV1 , 22, 24.pression , 9, 32.APOBEC3A, APOBEC3B, UNG, REV1, and FHIT genes in cancer cell lines, in order to identify those cancer cell lines that may have experienced kataegis events. We further examined associations between mutational patterns of APOBEC3 activity, individual cancer types, and chemosensitivity to a variety of antitumor agents. This analysis was carried out using whole-exome sequencing (WES) data, gene expression microarray data, and drug response data for 255 agents from the Cancer Cell Line Encyclopedia (CCLE) [Whereas many studies have focused on the molecular roles of APOBEC3B, and to some extent APOBEC3A, possible cumulative effects of action of APOBEC3A, APOBEC3B, UNG, REV1, and FHIT on generation of APOBEC3B-like mutation motifs and on drug sensitivity in cancer have not been clearly elucidated. To address this question, we investigated the presence of APOBEC3B-like mutational patterns and mRNA expression of the a (CCLE) , 34 and a (CCLE) , 35, 36.We downloaded unprocessed WES BAM files, which were available for 325 CCLE cell lines Fig. , from thRaw BAM files were preprocessed according to the GATK Best Practices pipeline v. 3.5 as of 15 May 2016 –41 usingWe searched the WES nucleotide changes in each cell line for the presence of the three reported APOBEC3B mutation motifs, T(C>K)W, T(C>D)R, or T(C>D)D. This motif representation includes nucleotide IUPAC symbols in three consecutive genome sequence positions, with the two symbols in parenthesis separated by the “>” symbol indicating the direction of nucleotide substitution change. For example, T(C>K)W indicates that the reference genome sequence is 5′-TCA-3′ or 5′-TCT-3′, and an either C>G or C>T substitution was found in the second nucleotide of the triplet. We refer to the three sequence motifs, T(C>K)W, T(C>D)R, and T(C>D)D which were analyzed in this study, as APOBEC-like motifs, in order to distinguish them from the APOBEC mutational signature term, which commonly refers to a matrix of mutational changes that are characteristic of APOBEC activity in the 96-trinucletide format , 44. BotBecause APOBEC activity is characterized by clusters of co-occurring APOBEC motifs with closely spaced mutations on the same genome strands, we further searched each cell line for the presence of kataegis clusters, which were defined using two different but related criteria, either as (a) the same motif occurring on the same genome strand at least five times in a 1000-bp window, to which we refer as 5/1000; or as (b) the same motif occurring on the same genome strand at least six times in a 10,000-bp window, to which we refer as 6/10000. For each cell line, four possible measures of APOBEC-like mutational activity were considered, which defined overall abundance of the APOBEC-like motifs and the abundance and the length of kataegis clusters per WES data of that cell line: (1) the total number of APOBEC-like motifs present in the WES data of each cell line, (2) the number of APOBEC motifs in distinct non-overlapping kataegis regions in WES data of that cell line, (3) the number of distinct non-overlapping kataegis regions in WES data of that cell line, and (4) the total combined length of distinct non-overlapping kataegis regions in WES data of that cell line. We also examined seven overall nucleotide substitution counts for each cell line, including the combined counts of all categories of nucleotide substitutions, and the numbers of C>G, C>T, or C>K substitutions on the reference genome strand and on both genome strands.2-transformed gene expression levels that were available for 1036 cell lines from the Cancer Cell Line Encyclopedia .Logresource , with SpAPOBEC3A and APOBEC3B with the germline APOBEC3B gene deletion, we downloaded the copy number status of the APOBEC3B gene from the CCLE web resource of the Broad Institute [2 ratios of the copy number estimates performed using the circular binary segmentation algorithm [To examine possible associations of expression levels of nstitute . The coplgorithm .10(IC50) scale, to which we further refer as log(IC50). Identities of cell lines present in both CCLE and GDSC datasets were verified using information from Cellosaurus [The IC50 measures of cell line chemosensitivity, representing the total drug inhibitor concentration that reduced cell activity by 50%, were available for 24 drug agents from the Cancer Cell Line Encyclopedia  Fig. 1)1). Theselosaurus . Drug selosaurus , we anallosaurus , 48 and losaurus . While slosaurus , 49.10(IC50)) in a combined analysis of all cancer types and within individual types of cancer. The p values were adjusted for multiple testing using the Benjamini and Hochberg method of adjustment for false discovery rate, or FDR [p < 0.05 were considered statistically significant. In this report, ρ denotes the Spearman correlation coefficient, p is a p value prior to FDR adjustment, padj is an FDR-adjusted p value, Ntests is the number of correlation tests for which the FDR adjustment of p values was made, and n is the sample size . We focused our discussion on statistically significant moderate or strong correlation results with padj < 0.05 and the absolute value of Spearman correlation coefficient |ρ| > 0.25.We examined Spearman rank-order correlation among gene expression values, mutation counts, measures of abundance of motifs and kataegis clusters, and drug sensitivity values (log10IC) in a co+ and ER− breast cancer cell lines, with their estrogen receptor status defined based on available literature reports [Analyses of candidate gene expression levels, motif and kataegis cluster abundance, and correlation analyses were performed both in a combined dataset of all cell lines from different cancer types , and also within 32 individual cancer categories Table . Many ca reports –54.TableBioinformatic and statistical analyses were performed using Python v. 2.7 and R v. 3.4.APOBEC3B expression , head and neck , chronic myelogenous leukemia , and multiple myeloma . Several other cancer types had increased levels of expression of the APOBEC3B gene, but their mean expression levels of APOBEC3A were comparable to the mean APOBEC3A expression across all cancer types. Among the cancer categories with ≥ 5 cell lines, these included acute myeloid leukemia  and melanoma .When compared to the mean APOBEC3B and APOBEC3A expression in cell lines from several cancer types presented in Table APOBEC3B and APOBEC3A in bladder cancer and of APOBEC3B in head and neck cancer patients [APOBEC3A and APOBEC3B were also reported in multiple myeloma patients, most commonly in those with the t(14:16) translocation, which was associated with poor survival [Our findings of elevated patients , 55, 56.patients . Similarpatients , 57, 58.survival , 60.UNG expression, but not of other candidate genes, were found in the prostate adenocarcinoma  and small cell lung cancer  cell lines . Therefore, both high levels of APOBEC3B and APOBEC3A and low levels of FHIT expression may influence APOBEC mutagenesis in the head and neck cancer.Elevated levels of ma PRAD; 0.15 and APOBEC3B , 9, 32, APOBEC3B showed strong and statistically significant positive correlation with APOBEC3A expression in 21 cancer categories . These categories . Interestingly, breast cancer cell lines were among the cancer types with positive correlation between APOBEC3A and APOBEC3B expression . This negative correlation is notable because low levels of the FHIT gene expression or the loss of FHIT function have been reported to have a cooperative effect with APOBEC3B in mutagenesis, even though APOBEC3B overexpression and DNA damage induced by the replication stress caused by the loss of FHIT have been proposed to occur independently from each other [APOBEC3B and FHIT expression levels could potentially produce hypermutated clusters in those cells where APOBEC3B expression were elevated and FHIT expression were diminished. However, this did not appear to be the case because in our analysis of glioma cell lines, which included astrocytoma, lower-grade glioma, and glioblastoma multiforme cell lines, mean APOBEC3B and FHIT expression levels were comparable to those in the pan-cancer dataset  and with APOBEC3A expression in chronic lymphocytic leukemia cells . Expression levels of UNG and REV1 were significantly positively correlated in non-small cell lung cancer cell lines .Prevalence of mutation counts and single nucleotide positions in the combined analysis of all cancer categories and within individual cancer types in the 325 cell lines with available WES data is provided in Table Table Similar trends in the abundance of motifs and kataegis-like clusters were also observed among individual cancer categories, as presented in Fig. Our findings for bladder cancer, melanoma, non-small cell lung cancer, uterine corpus endometrial carcinoma, and prostate adenocarcinoma were consistent with previous reports which suggested the roles for APOBEC3 mutagenesis in those cancer types , 60, 65.APOBEC3B gene and protein expression in hepatocellular carcinoma as compared to non-tumor tissues, as well as the high rates of C>D mutation changes in the genomes of hepatocellular carcinoma tumors have been documented previously [The statistically significantly increased eviously –72, in aeviously . Other meviously , 73, 74,r| ≤ 0.161) of expression levels of candidate genes with motif counts, counts of kataegis clusters, and mutation counts in the WES data. None of these correlations were statistically significant (padj ≥ 0.08). Among the five candidate genes, the strongest correlations were observed for APOBEC3A, APOBEC3B, and REV1.Analysis of the pan-cancer dataset showed a very weak correlation (|ρ between − 0.738 and − 0.902) and statistically significant (padj < 0.05) negative correlation of the frequencies of C>T, C>G, and C>K substitutions and overall nucleotide substitution counts with REV1 expression in sarcoma and UNG expression in melanoma  with several categories of mutation counts in renal cell carcinoma. APOBEC3B expression also had the strongest correlation with mutation counts in RCC as opposed to other cancer categories; however, such correlations for APOBEC3B were somewhat weaker and less significant  than those for APOBEC3A (data not shown). These correlation results suggest a strong contribution of REV1, UNG, and possibly APOBEC3A to overall mutagenesis in sarcoma, melanoma, and renal cell carcinoma, respectively. A large proportion of C>T and C>G substitutions in melanoma cell lines were likely generated via mutagenic processes related to UV radiation exposure [Among individual cancer types, we observed a strong  of the T(C>D)D motif in melanoma, and for APOBEC3A expression with motif counts and kataegis measures in renal cell carcinoma R and T(C>D)D motifs; data not shown).Among the correlations of gene expression levels with APOBEC-like motif counts and measures of kataegis, significant or nearly significant correlations were observed for ρ| > 0.25) statistically significant (padj < 0.05) correlations between candidate gene expression levels and cell line chemosensitivity to drug treatment. Several strong correlations were observed in PAAD, PRAD, CESC, MM, SAR, RCC, NSCLC, MEL, and SCLC cell lines.Table APOBEC3A and UNG expression was significantly negatively correlated  with log(IC50) of the BET inhibitor JQ1  cell lines, both JQ1 Fig. a. JQ1 haREV1 in the non-small cell lung cancer cell lines was significantly positively correlated with log(IC50) of MEK (mitogen-activated protein kinase) inhibitors PD-0325901, RDEA119, and trametinib, as well as AKT inhibitor VIII, XIAP inhibitor embelin, PI3Kβ inhibitor AZD6482, and a cyclin-dependent kinase (CDK) 4/6 inhibitor PD-0332991, or palbociclib . A number of these agents, e.g., trametinib and its combination with palbociclib, have been used or are under investigation for treatment of NSCLC [Expression of of NSCLC , 80. PD-of NSCLC , 82. RDEof NSCLC .FHIT expression was associated with chemoresistance to the ALK inhibitor TAE684 .In melanoma cell lines, n = 5), and therefore the validity of such correlations may require confirmation in a larger dataset. Similarly, additional correlations found in MM, SAR, CESC, RCC, and SCLC cell lines reported in Table n between 5 and 6 and also require a follow-up confirmation in larger datasets.Multiple strong significant correlations between expression levels of each of the five candidate genes and sensitivity to multiple agents were found in prostate adenocarcinoma Table ; howeverAPOBEC3B expression in breast cancer cell lines and sensitivity to CHK1 inhibitors AZD7762  or Calbiochem 681,640 , and no other correlations between gene expression and log(IC50) in breast cancer cell lines were statistically significant. Although an association between APOBEC3B expression in breast cancer cells and sensitivity to another CHK1 inhibitor, CCT244747, was previously reported [In agreement with an earlier report , we did reported , that agAPOBEC3B expression was significantly negatively correlated with sensitivity to an HSP90 (molecular chaperone heat shock protein 90) inhibitor 17-AAG (tanespimycin) . Higher levels of APOBEC3B expression were associated with higher sensitivity to this agent, which may have a clinical significance. 17-AAG acts in a variety of tumor types [APOBEC3B in an earlier analysis of RNA-seq gene expression in the CCLE and GDSC cell lines by Cescon and Haibe-Kains [In the pan-cancer analysis, be-Kains .padj close to 0.05. For example, higher level of expression of APOBEC3B in glioma was correlated with increased sensitivity to an HSP90 inhibitor AUY922 . This correlation may have a clinical significance, as this agent has an antitumor effect in glioblastoma [Some other strong association results did not reach statistical significance, but they had blastoma .padj > 0.1 for comparisons). In a stratified analysis among cancer types, a number of statistically significant strong correlations  were observed in BREAST, COAD/READ, GLIOMA, OVARIAN, and PAAD cell lines (Table n = 5–7), and therefore, these correlations need future confirmation in larger collections of cell lines of their respective cancer categories. Among notable correlations, the combined length of clusters with the T(C>D)D motif had a strong correlation  with chemoresistance to bicalutamide, a nonsteroidal antiandrogen drug, in the pancreatic adenocarcinoma and breast cancer cell lines  with the overall counts of the motif T(C>K)W which is commonly attributed to APOBEC3B activity. Similarly, log(IC50) of the hedgehog signaling pathway inhibitor vismodegib [REV1 expression . Further validation of observations presented in Table Multiple other strong correlations were observed in different cancer categories. For example, in pancreatic adenocarcinoma cell lines, log(IC50) values of tipifarnib, a farnesyl transferase inhibitor of the Ras pathway , the AKTsmodegib  and of tsmodegib  were asssmodegib , whereasAPOBEC3B expression and unimodal distributions of APOBEC3A, REV1, UNG, and FHIT in the pan-cancer dataset  for correlations of both APOBEC3B and APOBEC3A expression levels with log(IC50) of tamoxifen in breast cancer cell lines. Stratified analysis of ER− and ER+ breast cell lines with available information about their estrogen receptor status showed the absence of association in the ER− cell lines with log(IC50) of tamoxifen . In the ER+ cell lines, we observed an association with sensitivity to tamoxifen for both genes  which was consistent with that of Law et al. [p = 0.157 for APOBEC3A and 0.224 for APOBEC3B), possibly due to a small number of ER+ breast cell lines in the dataset. Additionally, the study of Law et al. [APOBEC3B expression with tamoxifen resistance, included primary breast tumors from hormone therapy-naïve patients, whereas some of the cell lines in our analysis were likely obtained from patients with prior treatment. In our study, none of the correlations of chemosensitivity to tamoxifen with expression of either of the five candidate genes in any cancer category or in the pan-cancer analysis achieved statistical significance. Therefore, while our use of cell line resources was able to draw from a wealth of molecular information and the data on sensitivity to multiple tumor agents, in using the cell line-based approach, we also encountered several limitations including restricted clinical information, much smaller sample sizes than those available for patient-based clinical studies, and the absence of normal tissues from the same patients that could allow for more accurate inference of mutation calls and for tissue-specific normalization of gene expression levels.Because our study analyzed cell line data, it could examine only cell line response to chemotherapy and did not account for in vivo effects that may also influence therapy response. Several correlations of us study . Our anat cancer  due to tAPOBEC3A and APOBEC3B expression levels, that have also been reported in patient tumor samples [APOBEC3B, but also with those of APOBEC3A, REV1 UNG, and FHIT. Three of these genes, REV1, UNG, and APOBEC3A, were also associated with overall mutation activity and/or with prevalence of APOBEC-like motifs and kataegis clusters in specific cancer types. Because APOBEC3A is also involved in RNA editing [Despite these limitations, we observed a number of correlations, e.g., those between  samples . In addi editing , associa editing , its invAPOBEC3A, APOBEC3B, REV1, UNG, and FHIT also require further investigation. Nevertheless, specific correlations observed in our studies suggest that both expression levels of candidate genes and the prevalence of APOBEC-like motifs and their clusters could potentially be examined for their roles as biomarkers of drug sensitivity to several agents. Association of activity of these genes with drug response could be examined further when significantly associated agents are evaluated in experimental in vitro studies and in a clinical setting.Recent studies suggest that clustered mutations, including those attributed to APOBEC activity, more accurately represent mutagenic processes in tumors than do overall mutation rates . We obseAPOBEC3A, APOBEC3B, REV1, and UNG genes and with abundance of sequence motifs and kataegis clusters attributed to APOBEC activity. The analysis of exome sequence data suggested that expression of REV1 and UNG and to a lesser extent of APOBEC3A was correlated with mutation patterns attributed to APOBEC activity, suggesting that APOBEC-like mutagenic patterns may result from the complex interplay among multiple molecular factors. Future studies may examine the biological mechanisms that could explain how each of the five genes associated with APOBEC-like mutagenic processes may contribute to sensitivity or resistance of tumor cells to cancer drug treatment.Our analysis of cancer cell line data identified associations of drug sensitivity with expression levels of


Various types of stem cell lines have been derived from preimplantation or postimplantation mouse embryos: embryonic stem cell lines, epiblast stem cell lines, and trophoblast stem cell lines. It is not known if extraembryonic endoderm stem (XEN) cell lines can be derived from postimplantation mouse embryos. Here, we report the derivation of 77 XEN cell lines from 85 postimplantation embryos at embryonic day E5.5 or E6.5, in parallel to the derivation of 41 XEN lines from 69 preimplantation embryos at the blastocyst stage. We attain a success rate of 100% of XEN cell line derivation with our E5.5 whole-embryo and E6.5 disaggregated-embryo methods. Immunofluorescence and NanoString gene expression analyses indicate that the XEN cell lines that we derived from postimplantation embryos (post-XEN) are very similar to the XEN cell lines that we derived from preimplantation embryos (pre-XEN) using a conventional method. After injection into blastocysts, post-XEN cells contribute to extraembryonic endoderm in chimeras at E6.5 and E7.5. Mouse preimplantation embryonic development culminates in the blastocyst stage. A blastocyst consists of three cell lineages: epiblast, trophectoderm, and primitive endoderm (PrE). The epiblast develops into most of the embryo proper, the amnion, and the extraembryonic mesoderm of the yolk sac; the trophectoderm gives rise ultimately to the fetal portion of the placenta; and the primitive endoderm forms the two extraembryonic endoderm lineages – the visceral endoderm (VE) and the parietal endoderm (PE) of the yolk sac134in vitro model to identify patterning activities of the extraembryonic endoderm such as factors involved in cardiac induction17Stem cell lines have been derived from these three cell lineages91114Gata4 or Gata6 . We find that this and other pre-XEN cell lines are immunoreactive for XEN cell markers GATA4, GATA6, SOX7, SOX17, and DAB2, but negative for ES cell markers OCT4 and NANOG, and negative for TS cell marker CDX2.Xist locus on the X-chromosome; Sox17 and Gata6 are XEN-cell markers; and Cdx2 is a marker for trophoblast stem cells. We removed the ectoplacental cone of the embryos as much as possible, and transferred each embryo separately into a well of 4-well dish coated with 0.1% gelatin and covered with MEF in TS cell medium including 25 ng/ml FGF4 and 1 μg/ml heparin (referred to as F4H). One day later, the embryos had attached to the surface and started to form an outgrowth. The embryos had formed a large outgrowth after 5 days. We used TrypLE Express to disaggregate the outgrowths and passaged cells into a well of a 4-well dish. After cells reached 70–80% confluency, they were passaged into a well of a 12-well dish. After they reached 70–80% confluency again, cells were passaged into a well of a 6-well dish, and we then obtained stable post-XEN cell lines. The intrinsic red fluorescence of mTomato produced from the Gata6 promoter in the transgene was sufficiently high to detect it in the whole embryo and outgrowth, but not in the established post-XEN cell line at day 60  by immunofluorescence for GATA4 and counterstaining with DAPI. Sets of fluorescence images were captured for each line visualizing the intrinsic fluorescence of GFP, DAPI, and GATA4 immunoreactivity . The imaWe asked if post-XEN cells can differentiate into a VE identity by incubation with BMP438Dab2, Gata4, Gata6, Pdgfra, Sox7, and Sox17, versus low or no expression of ES cell-specific genes such as Nanog, Pou5f1/Oct4, Sox2, and Nr0b1. There is no expression of EpiSC-specific genes such as Cer1 and Fgf5 (data not shown). Thus, the NanoString gene expression analysis confirms and extends the immunofluorescence profiles.We applied the NanoString multiplex platform for gene expression4041in vivo. We injected into blastocysts  cells of two R26-tauGFP41+ pre-XEN cell lines (X-ICM-4 and X-ICM-5) and one PDGFRa-GFP+ pre-XEN cell line (X47) that we had derived from isolated ICMs or blastocysts, and cells from four R26-tauGFP41+ post-XEN cell lines  and one PDGFRa-GFP+ post-XEN cell line (X-E6.5-Z0663-9) that we had derived from E5.5 embryos or disaggregated E6.5 embryos  that are at the origin of the post-XEN cell lines.Sox17 locus, we cannot detect GFP expression in embryos and cell lines derived from them.) The Xist1loxGFP strainThe PDGFRa-GFP strainAdvanced RPMI-1640  was supplemented with 20% (vol/vol) FBS , 2 mM GlutaMAX Supplement (Gibco #35050), 1% penicillin/streptomycin , 0.1 mM β-mercaptoethanol (Gibco #21985-023), 1 mM sodium pyruvate (Gibco #11360-039), supplemented with 25 ng/ml FGF4 (Peprotech #100-31) and 1 μg/ml heparin (Sigma #H3149).ES cell lines were maintained on MEF-coated, pregelatinized tissue culture dishes  in DMEM  supplemented with 15% FBS (HyClone #SH30071.03), 2 mM GlutaMAX Supplement, 1% penicillin/streptomycin, 1% β-mercaptoethanol , 0.1 mM nonessential amino acids (Gibco #11140-035), 1 mM sodium pyruvate, and 1000 IU/ml leukemia inhibitory factor (LIF) (Millipore #ESG1107).Embryos were collected at the 2–8 cell stage by flushing oviducts using M2 medium (Sigma #M7167), and cultured in KSOM medium (Millipore #MR-106-D) to the blastocyst stage. The zona pellucida of blastocysts was then removed using acid Tyrode solution (Sigma #T1788). Blastocysts were transferred separately into a well of a 96-well dish  coated with 0.1% gelatin  and covered with MEF in ES medium supplemented with LIF and 1 μM PD0325901 (Axon #1408) and 3 μM CHIR99021 (Axon #1386), a combination of chemicals that is typically referred to as “2i”. After 4 days TrypLE Express (Gibco #12604-013) was used to disaggregate the embryonic outgrowths, and cells were passaged to a well of 24-well dish to derive ES cell lines.Embryos were collected at the 2–8 cell stage embryos and cultured in in KSOM medium to the blastocyst stage. The zona pellucida of blastocysts was removed using acid Tyrode solution. Blastocysts were transferred separately into a well of a 4 well-dish (Nunc #176740) coated with 0.1% gelatin and covered with MEF in ES medium supplemented with LIF. The XEN lines were derived as describedThe ectoplacental cone was removed with forceps or needles. A whole embryo was placed in a well of a 4-well dish (Nunc #176740) coated with 0.1% gelatin and covered with MEF in TS medium including F4H. After the embryos formed a large outgrowth, TrypLE Express was used to disaggregate the outgrowths and passage cells into a 4-well dish. When cells reached 70–80% confluency, they were passaged into a well of a 12-well dish until XEN cell lines were obtained, which were then passaged into a well of a 6-well dish. If the outgrowth of an E5.5 embryo grew well and XEN cells thrived, we continued to culture cells in TS medium including F4H. But if the outgrowth grew slowly and XEN cells were surrounded by trophoblast-derived cells, we cultured cells in ES medium supplemented with LIF, culture conditions that would inhibit trophoblast-derived cells; when XEN cells started to become abundant, we switched to TS medium including F4H. To prepare XEN cells for RNA extraction, cells were cultured in dishes coated with 0.1% gelatin but without MEF in TS medium including F4H.The ectoplacental cone was removed with forceps or needles. A whole embryo was treated with 0.1 mg/ml collagenase (Gibco #17104-019) and 0.01 mg/ml deoxyribonuclease (Gibco #D5025) for 20–30 min at room temperature, followed by 0.2 mg/ml TrypLE for 5 min at room temperature. The embryo was disaggregated into single cells using a glass pipette with a diameter of 50–60 μm. Cells were transferred into a well of a 4-well dish (Nunc #176740) coated with 0.1% gelatin and covered with MEF in TS medium including F4H. Three days later XEN colonies appeared. We picked these colonies, disaggregated them with a glass pipette or by TrypLE Express for 5 min at 37 °C, and passaged them into a well of a 4-well dish. When cells reached 70–80% confluency, they were passaged into a well of a 12-well dish until XEN cell lines were obtained, which were then passaged into a well of a 6-well dish.http://cellprofiler.org/ . The DAPI signal was used to segment individual cells by thresholding, de-clumping, and applying size and roundness filter in an effort to evaluated single cells that are not MEFs. After filtering, the GFP and GATA4 signals were evaluated in each cell, and the population characteristics for these two markers were quantified.Images representing Fields of View of post-XEN cell lines were imaged for intrinsic fluorescence of GFP, fluorescence of DAPI, and GATA4 immunoreactivity. Automated cell population characteristics were determined with CellProfiler Gelatin-coated plates were prepared by coating with 0.1% gelatin overnight at room temperature. XEN cells were cultured in TS medium with F4H and with or without 10 ng/mL BMP4  on gelatin-coated plates for four days.Cell lines X42, X44, X47 (PDGFRa-GFP × CAG::mRFP1); X35, X36 (B2D6F1 × D4/EGFP); X97, X107 (D4/EGFP × DBA2/N); X-E6.5-81346-8 (ROSA-STOP-taulacZ x Sox17-Cre); X-E6.5-Z0617-2, X-E6.5-Z0617-5, X-E6.5-Z0617-8 (R26-tauGFP41 x Sox17-Cre); X-E6.5-78097-4 (Xist1loxGFP × DBA/2 N); X-E6.5-82278-4 (Gata6-mTomato × Cdx2-GFP); X-E6.5-Z0663-12 (CD1 × PDGFRa-GFP); and X-E5.5-6, X-E5.5-8, X-E5.5-9, X-E5.5-10, X-E5.5-13 (R26-tauGFP41 × Sox17-Cre) were cultured in 4-well or 24-well dishes. Cells were fixed in 4% paraformaldehyde at 4 °C overnight or room temperature for 30 min, permeabilized with 0.1% Triton X-100 in 1× PBS (1× PBST) for 30 min and blocked with 5% normal donkey serum  diluted in 1× PBST (blocking solution) for 1 hr. Primary antibodies were diluted at 1:50–1:200 in blocking solution and samples incubated at 4 °C rotating overnight. After three 10-min washes in 1× PBST 10 min, samples were incubated for 1–1.5 hr at room temperature in a 1:500 dilution of secondary antibody in blocking solution, then washed and covered with 1× PBST containing DAPI. Images were taken with an AMG EVOS (Life Technologies), Zeiss LSM 710 confocal microscope, and Nikon SMZ25 stereofluorescence microscope.Primary antibodies from Santa Cruz Biotechnology were against GATA4 (#SC-1237), DAB2 (#SC-13982), OCT3-4 (#SC-5279), NANOG (#SC-376915), and CDX2 (#SC-166830). Primary antibodies from R&D Systems were against GATA6 (#AF1700), SOX7 (#AF2766), SOX17 (#AF1924), and PDGFRa (#AF1062). Primary antibodies against E-cadherin (ECCD2) were from Invitrogen (#13-1900). Secondary antibodies from Jackson ImmunoResearch Laboratories were Cy5™ AffiniPure Donkey anti-goat IgG (H+L) (#705-175-147), and Cy5™ AffiniPure Donkey anti-rabbit IgG (H+L) (#711-175-152). Secondary antibodies from Invitrogen were Donkey anti-rabbit IgG (H+L) with Alexa Fluor 546 (#A10040), Goat anti-rat IgG (H+L) with Alexa Fluor 546 (#A11081), Donkey anti-mouse IgG with Alexa Fluor 546 (#A10036), Donkey anti-mouse IgG with Alexa Fluor 488 (#A21202), and Goat anti-chicken IgY (H+L) with Alexa Fluor 488 (#A11039).Actb and Gapdh. Normalized counts are displayed in a heatmap generated by the nSolver Analysis Software v2.5, using agglomerative clustering.Cells were cultured in 12-well plates with TS medium including F4H or ES medium supplemented with LIF. Dissociated cells were collected by trypsinization and centrifugation. Cell pellets were dispensed directly in RNAlater Stabilization Solution (Qiagen) and stored in −80 °C for later use. Cell pellets were lysed in RLT Lysis Plus Buffer using a TissueLyser LT (Qiagen) at 40 Hz for 2 min. Total RNA extraction was performed using RNeasy Plus Micro kit (Qiagen) according to manufacturer’s protocol. The custom NanoString CodeSet “Extra” was used. 100 ng of total RNA samples were hybridized at 65 °C for 18 hr and processed with the nCounter Analysis System GEN1 (NanoString Technologies). The reporter counts were processed using nSolver Analysis Software v2.5 (NanoString). Two normalizations were performed to the counts, the first normalization to the generic positive controls, followed by normalization to the reference genes, Below are the nucleotide sequences of the NanoString probes; first line is the capture probe, second line the reporter probe.ActbACGATGGAGGGGCCGGACTCATCGTACTCCTGCTTGCGGGTGTAAAACGCAGCTCAGTAACAGTCCGCCTAGAAGCACTTGCGGTGCDab2GTCTCCTCGAGCATCAGGCACATCATCAATACCGATTAGCTTGGCCCAGCTGCTGCCATTCCCTTGAGTTTCATCATAGAATCCTGACTCATTTTDnmt3lGAGGCAGCGCATACTGCAGGATCCGGTGGAACTGGAACATGCCATGAATATCCAGAAGAAGGGCCGCTGACTCTCCTGGCDppa4CAAGTCTTTACAGTTGACTGCTGAACTGGTTATGACGCCCGTTGTGCTGGCACTACAACCCAGGGAAGAGGACATGCATGCGGAGGCTACAGGTATAAGCDppa5aCGCACGGCCCACAGCTCCAGGTTCAGGAAGTTTTAGTACCCTGCCAAGGAACCAGACTTCAGGGAAGACGAGATCAAGCTTATCCACCAEsrrbTTTCCAGAATGAACCGCTTCATCTTTAGGACATCCTGTCAACCCAAACCCGAGCAGGTAAAGCCGGAGGACTTGTCATGAAAGTGGCGTGTCCATFgf4ATTCTGGTAACAAAATTCCAAAGATACAGTCTTGTCCCTGGGCGCAGGAAACAGACCGACTCGGTAACAGTGGCAGATACAGAGCAGAAACATCAAACCCFoxa2CACAGACAGGTGAGACTGCTCCCTTGAGGCCTGAAGTCCCTTCCCTATTTAGAATGACAGATCACTGTGGCCCATCTATTTAGGGAFoxq1GCTGTCCTTACTCCGAGGTTTAGAGACTTTGAGCGGAAGACAAGCGTTTTGATTGTTGGGTGAAGTGAGGAGTGGAGTGATAGAAGTTGGTGCAGTFstAGGACTTTGTGATACACTTTCCCTCATAGGCTAATCCAATGGATCTGCCCCTTGGAATCCCATAGGCATTTTTTCCCGCCGCCACACTGGATATCTTCACFxyd3TGAGCCCGCCGACTCGGAGGCTGTACCAATCATAGTAGAAAGGATCATTTGCCACTCATAAGGACTATAATGCCCAGGGCACAGAGAATCCCTGCACAAAGapdhATACTTGGCAGGTTTCTCCAGGCGGCACGTCAGATCCACGACGCCTCAGATGCCTGCTTCACCACCTTCTTGATGTCATCGata4AGAGCCAGGTAACTGTCTGACTTAAGAGGGCTTGGCTTGGGCAAACAGTCTGTATTTTCTAGACAAAGGATCTGTGCTGGAGAAAGTCCCGata6ATCTGGACTGCTGGACAATATCAGACACAAGTGGTATGAGGCCTTCAGAGGGCACAGAAATCACGCATCGAAGGAATGTTATGTCTGCATTTTTGCTGCCKrt8TTCCCATCTCGGGTTTCAATCTTCTTCACAACCACAGCCTTGCAGTGGCCATTCACTTGGACACGACATCAGAAGACTCGGACACCAGCLama1CATTGGCTAAATCGGCATGGCGGTCATCCTTGATACAGACAGAACTCAGACATAACCTTTCCTACATGGACACTGACCTGGCCACTTTCLamb1CCAGGAAGGAATGCGGTCCTGAATGTACTGCCTTTCCACCACAATGACAAAAACTCCAAATAAGCCCCTTCAGGCACCCGGACGAACCCAGGTCCTGTNanogCATATTTCACCTGGTGGAGTCACAGAGTAGTTCAGGAATAATTCCAAGGCGAAGGAACCTGGCTTTGCCCTGACTTTAAGCCCAGATGTTGCGTAAGTCTNr0b1CACTTGAAAAAGAAACTCTTGATGGCCTGGACCGCAGCAGCTGGGAGCAACCTTTCAGATAGGCATACTCTTTGGTGTCAATGTTCAGACTCCAGPdgfraTATGGAGTAAGTCGCTCTCACACACTTACCACACCACCATGTTGGGAACAAAACATGAACAGGGGTATCTGGAAGCCATCTTGTATTGGAAGACCCTTCCPou5f1ACATGGTCTCCAGACTCCACCTCACACGGTTCTCAATGAGTGATCTGCTGTAGGGAGGGCTTCGGGCACTTCAGAAPth1rCCTGGGCACGGTGCAGCAGGAAAATCTGTTCCTCTTTGGTAAAGACATCGTGCTGTGTGCAGAACTTCCTTGAGCAGCTTGTCACATTGCGSox2CCCCGCCGCCCTCAGGTTTTCTCTGTACAAAAATAGTCCCCCAAAAAGAATGCGTAGTTTTTTTCCTCCAGATCTATACATGGTCCGATTCCCCCGCCCTSox7AGAAATCAGCACACCCCAACACTTTTGTGGACAGACGTTTTAGGTTTCTATCATCTTTTGCATCTGTAGATAAGAGTATGCTACAGCTCTGCTCTSox17GGCAGATACTGTTCGAATTCCGTGCGGTCCACCTCTGTCCCTGGTAGGGAAGACCCATCTCGGGCTTATACACAAAGTet3TTTTCAAAGAGCTGAATGAATGCACCAGGATTTTAGGATGGGCGTGTTTCGTCAGGCCCAACCTCTCAATGTCACAGAAATTAAAGCACCAACGTTCTCAAll animal studies were carried out in accordance with the German Animal Welfare Act, European Communities Council Directive 2010/63/EU, and institutional ethical and animal welfare guidelines of the Max Planck Institute of Biophysics and the Max Planck Research Unit for Neurogenetics. All experimental protocols were approved by the Regierungspräsidium Darmstadt and the Veterinäramt of the City of Frankfurt.How to cite this article: Lin, J. et al. Efficient derivation of extraembryonic endoderm stem cell lines from mouse postimplantation embryos. Sci. Rep.6, 39457; doi: 10.1038/srep39457 (2016).Publisher's note: Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.


Gemcitabine is used as a standard drug treatment for non-small cell lung cancer (NSCLC), but treatment responses vary among patients. Our previous studies demonstrated that POLA2 + 1747 GG/GA single nucleotide polymorphism (SNP) improves differential survivability and mortality in NSCLC patients. Here, we determined the association between POLA2 and gemcitabine treatment in human lung cancer cells.Human PC9, H1299 and H1650 lung cancer cell lines were treated with 0.01-100 μM gemcitabine for 72 h. Although all 3 cell lines showed decreased cell viability upon gemcitabine treatment, H1299 was found to be the most sensitive to gemcitabine treatment. Next, sequencing was performed to determine if POLA2 + 1747 SNP might be involved in gemcitabine sensitivity. Data revealed that all 3 cell lines harbored the wild-type POLA2 + 1747 GG SNP, indicating that the POLA2 + 1747 SNP might not be responsible for gemcitabine sensitivity in the cell lines studied. Silencing of POLA2 gene in H1299 was then carried out by siRNA transfection, followed by gemcitabine treatment to determine the effect of POLA2 knockdown on chemosensitivity to gemcitabine. Results showed that H1299 exhibited increased resistance to gemcitabine after POLA2 knockdown, suggesting that POLA2 does not act alone and may cooperate with other interacting partners to cause gemcitabine resistance.Collectively, our findings showed that knockdown of POLA2 increases gemcitabine resistance in human lung cancer cells. We propose that POLA2 may play a role in gemcitabine sensitivity and can be used as a prognostic biomarker of patient outcome in NSCLC pathogenesis. Lung cancer is a leading cause of cancer mortality worldwide, accounting for approximately 28% of all cancer deaths annually. The prognosis for lung cancer is poor. Most lung cancer patients are diagnosed at advanced stage and only 16% remained surviving 5 years after initial diagnosis. Out of all lung cancer cases, about 85–90% of diagnosis are non-small cell lung cancer (NSCLC) [Gemcitabine, a cytidine analogue, is well-known to have a significant therapeutic effect in NSCLC cases . It is aThe eukaryotic DNA polymerase α, one of the main polymerases involved in nuclear DNA replication, is a four-subunit  enzyme which possesses DNA polymerase and primase activities. Earlier biochemical studies have reported that subunit A displays catalytic activity, while subunits C and D exhibit primase activity , 8. SubuWe have previously demonstrated that POLA2 + 1747 GG/GA improves differential survivability and mortality in NSCLC patients, and proposed that this novel single nucleotide polymorphism (SNP) may be used as a prognostic biomarker of patient outcome in NSCLC pathogenesis . In the https://genome.ucsc.edu/). POLA2 is located on chromosome 11q13.1 and contains 21 exons. Assessment of the DNA sequence using data retrieved from the Encyclopedia of DNA Elements (ENCODE) indicated the presence of several integrated transcriptional regulatory elements. Comparative genomics analysis using multiple alignments of vertebrate species revealed conservation of the POLA2 gene. We further explored the presence of SNP sites within the POLA2 genomic region. Figure To understand POLA2 genomic sequence, we first examined the sequence of POLA2 using the human Dec. 2013 (GRCh38/hg38) assembly on the UCSC Genome Browser  and Gene Ontology (GO) framework databases, we uncovered 44 unique interactants for POLA2 Table . These i50 among the 3 cell lines studied.To examine the effect of gemcitabine on human lung cancer cell lines, we treated PC9, H1299 and H1650 cell lines with 0.01–100 μM gemcitabine for 72 h. As shown in Fig. The POLA + 1747 GG/GA SNP and SLC28A2 + 65 CC SNP are two of the 21 SNPs of the 9 genes involved in gemcitabine transport, metabolism and activity. From our in silico prediction analysis reported earlier , these tBoth H1299 and H1650 cell lines were transfected for 48–72 h with the respective small interfering RNAs (siRNAs) to silence POLA2 and SLC28A2 genes. However, H1650 cell line could not be successfully transfected (data not shown), hence only the H1299 cell line was used for further studies. Western blot analysis showed that POLA2 protein expression was significantly attenuated after 72 h of transfection Fig. . A chemoPreviously, we have demonstrated that the POLA2 + 1747 GG/GA SNP is associated with improved survivability and mortality in NSCLC patients . In thisNSCLC is one of the most chemoresistant cancer types. Clinical treatment using traditional chemotherapy agents have reached a plateau in NSCLC patients due to resistance to chemotherapy drugs. Molecular targeted therapy through interruption of the epithelial growth factor receptor was one of the earlier treatment strategies used . HoweverThe DNA polymerase α subunit B, which is involved in the initiation of chromosomal DNA replication, is encoded by the POLA2 gene. We have previously reported that different variants of the POLA2 gene may improve the prognosis of NSCLC patients . We haveGemcitabine is a potent DNA synthesis inhibitor, which is a deoxycytidine analogue with anti-tumor activity against various solid tumours such as NSCLC, pancreatic cancer, breast cancer and ovarian cancer . Gemcita. [One important aspect of genetic studies is the interactions between SNPs. In a study of SNP-SNP interactions, a small number of SNPs is genotyped and then tested if any interactions are present. Such SNP-SNP interaction studies may help to identify genomic hotspots in human diseases. The possibility of using statistical modeling by many types of regression techniques with straightforward implementation of interaction analysis is also an additional advantage of SNP-SNP interaction studies. We have applied biostatistics to calculate the probability between different SNP pairs and their overall association to the survival of lung cancer patients . From in. , who sho. [. [. [Earlier studies have reported other genes that caused resistance to gemcitabine treatment. Davidson et al.  proposed. [.  reported. [. [.  showed t. [. [. . Similar. [. [. , 25, 26.. [Our findings showed that POLA2 may be a novel gene that causes resistance to gemcitabine. Similar to the RRM1 gene, POLA2 is involved in DNA synthesis and hence, a decrease in its expression might cause the up-regulation of genes that play a role in DNA synthesis and repair. These genes and their polymorphisms should be studied to determine whether there are any SNP-SNP interactions present among them, as well as with the POLA2 gene, that could have led to gemcitabine resistance. Van de Wiel et al.  reportedTaken together, our findings suggested that POLA2 may play a role in gemcitabine resistance. This has important implications as patients with certain variants of the POLA2 gene may be resistant to gemcitabine and may not survive well on gemcitabine treatment. We propose that POLA2 may be used as a prognostic biomarker of patient outcome in NSCLC pathogenesis. More work needs to be done to characterize the correlation between POLA2 and gemcitabine resistance. Functions of the POLA2 + 1747 GG/GA SNP should also be further studied to determine its role in drug resistance and sensitivity. Our earlier report suggested that the presence of SLC28A2 + 65 CC/CT SNP may increase the sensitivity to gemcitabine and thus giving NSCLC patients a better prognosis . Howeverhttps://genome.ucsc.edu/). Human POLA2 sequence was aligned with genome assemblies of other vertebrates and compared using Multiz Alignments Configuration. SNP sites within the POLA2 genomic region were investigated following the Single Nucleotide Polymorphism database . Only SNPs having a minor allele frequency of at least 1% and mapped to a single location in the human reference genome assembly were flagged and included in subsequent analyses.The genome sequence of POLA2 was examined using the human Dec. 2013 (GRCh38/hg38) assembly and Encyclopedia of DNA Elements (ENCODE) on the UCSC Genome Browser  and Gene Ontology (GO) framework databases. Interactants were inferred from physical interactions, sequence or structural similarities, as well as direct experimental assays. The Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway database was used to further mine for major pathways involving POLA2. Only experimentally validated interactants were chosen to generate a network of interactions using the Biological General Repository for Interaction Datasets (BioGRID), in which POLA2 plays a central role.Human lung cancer H1650 (NCI-H1650) and H1299 (NCI-H1299) cell lines were purchased from American Type Culture Collection . Human lung adenocarcinoma PC-9 cell line was purchased from RIKEN BioResource Center . All cell lines were cultured in Roswell Park Memorial Institute (RPMI) 1640 medium  containing 10% fetal bovine serum  as recommended by the respective suppliers.4 cells per well in 100 μl culture medium and incubated at 37 °C overnight. Cells were treated with various concentrations of gemcitabine  on the following day to determine the IC50 values. Final concentrations of gemcitabine treatment were 0.01, 0.1, 1, 10 and 100 μM. Cell viability was determined using the CellTiter 96 AQueous One Solution Cell Proliferation Assay  following manufacturer’s instructions.Cells were seeded into 96-well plates  at a density of 2 x 1050 values to assess the SNP association previously reported [Cell lines were sequenced to confirm the presence of the POLA2 + 1747 and SLC28A2 + 65 SNPs . Sequencing results were then correlated with the ICreported .Silencing of the POLA2 gene was done by siRNA transfection using DharmaFECT transfection reagents  following manufacturer’s instructions. The H1299 cell line was used, as this cell line had been tested and validated by the manufacturer. Transfection durations were 24, 48 and 72 h. Western blotting was then performed to check for protein expression of the silenced gene, as described previously [


These groups may represent putative heterotic gene pools in pearl millet.Pearl millet is a climate resilient crop and one of the most widely grown millets worldwide. Heterotic hybrid development is one of the principal breeding objectives in pearl millet. In a maiden attempt to identify heterotic groups for grain yield, a total of 343 hybrid parental [maintainer (B-) and restorer (R-)] lines were genotyped with 88 polymorphic SSR markers. The SSRs generated a total of 532 alleles with a mean value of 6.05 alleles per locus, mean gene diversity of 0.55, and an average PIC of 0.50. Out of 532 alleles, 443 (83.27%) alleles were contributed by B-lines with a mean of 5.03 alleles per locus. R-lines contributed 476 alleles (89.47%) with a mean of 5.41, while 441 (82.89%) alleles were shared commonly between B- and R-lines. The gene diversity was higher among R-lines (0.55) compared to B-lines (0.49). The unweighted neighbor-joining tree based on simple matching dissimilarity matrix obtained from SSR data clearly differentiated B- lines into 10 sub-clusters (B1 through B10), and R- lines into 11 sub-clusters (R1 through R11). A total of 99 hybrids (generated by crossing representative 9 B- and 11 R- lines) along with checks were evaluated in the hybrid trial. The 20 parents were evaluated in the line trial. Both the trials were evaluated in three environments. Based on  Pennisetum glaucum (L.) R. Br.] known by several names, such as bulrush millet, spiked millet, cattail millet, candle millet, bajra, is a climate resilient nutritious cereal  as control (repeated five times). These are International Crops Research Institute for the Semi-Arid Tropics (ICRISAT)-bred lines representing genetic diversity of mainly Asia and Africa. The world reference line, Tift 23D2B1-P1-P5 is a single plant selection done at ICRISAT from Tift 23D2B1 line which was bred at the Coastal Plain Experiment Station, Tifton, Georgia, USA. Tift 23D2B1-P1-P5 has recently been sequenced  and 182 R- (restorer) lines along with Tift 23DOut of 124 markers used in this study, 88 SSR markers were selected for final analysis. These included 72 EST-SSRs (69 IPES and 3 ICMP) and 16 genomic SSRs. These SSRs produced clear, scorable and polymorphic profile upon PCR amplification. Number of repeats in the SSR motifs were 2, 3, 4, 5, and 6 for 30, 28, 10, 15, and 5 markers, respectively. Of the 88 SSR markers, 81 were mapped on the 7 linkage groups of pearl millet, with 14, 12, 10, 7, 6, 19, and 14 markers located on LG1, LG2, LG3, LG4, LG5, LG6, and LG7, respectively  and 5 μl of distilled water. The pooled PCR amplicons were denatured for 5 min at 95°C and cooled immediately on ice. These amplicons were size separated based on the principle of capillary electrophoresis using an ABI Prism 3730 DNA analyzer (Applied Biosystems Inc.). Raw data obtained from ABI 3730 × l Genetic Analyser was subjected to analysis using the software Genemapper® version 4.0 . Based on the relative migration of internal size standard, product sizes were scored in base pairs (bp). Further analysis was done using Allelobin 2.0 program  and one R- (from R1) line were excluded from crossing plan due to a poor plant stand. Therefore, 99 crosses made from 9 B- (lines) and 11 R- (testers) lines were taken forward. The details of the selected parental lines are given in Table The crossing program was undertaken at ICRISAT, Patancheru during 1 hybrids were evaluated in two contiguous, but separate trials at three locations. In the hybrid trial, a total of 123 F1 hybrids along with seven checks were evaluated, while in the inbred line trial a total of 60 inbred lines (54 inbreds + 6 checks) were evaluated during rainy season, 2015 over two locations viz., ICRISAT, Patancheru and Agricultural Research Station, Vizianagaram, Acharya N. G. Ranga Agricultural University (ANGRAU); and at one location during post-rainy season, 2015 at Agricultural College Farm, Naira, ANGRAU. Both hybrid and line trials were laid out in a two-replication Alpha lattice design, where each entry was sown in 2 rows of 2 m length, spaced at 15 cm between plants and 75 cm between rows. However, only 99 hybrids and three checks viz., HHB 67 Improved, ICMH 356 and HHB 146 Improved were considered for analysis in the hybrid trial, and 20 lines (11R- lines and 9 B- lines involved in the cross combinations) were considered for the line trial.The parental lines and F−1 and 46 of P ha−1; irrigations were given soon after sowing, subsequently as and when required. Seedlings were thinned at 15 days after sowing to maintain one healthy seedling per hill at a spacing of ~15 cm. The other cultural practices like weeding, protection against insects, pests, diseases and birds were done throughout the growing period as and when required. The data on grain yield was recorded on plot basis in all the experimental trials at all locations.Standard agronomic management practices were followed in each of the trials viz., basal dose of 100 kg of DAP  was applied at the time of field preparation and 100 kg of urea (46% N) was applied as top dressing to meet the recommended dose of 64 kg of N ha1 hybrid in relation to the check(s). It was calculated by the formula, [(F1 – check)/check] × 100. Analysis of variance (ANOVA)  of the FST index which represented the distribution of allelic diversity across multiple levels of population subdivisions. Statistical significance for FST was computed by random permutation of all the population samples. PhiST was calculated after every reshuffling step for generation of a distribution of PhiST values. “Codom-Allelic” randomization method was selected where all alleles at a single locus were randomly shuffled among individuals. Comparison of the observed FST values to the distribution of 999 permutations provided P-values for the B- and R- lines.Analysis of molecular variance (AMOVA)  are given in Table 2B1-P1-P5 repeated five times along with experimental material had shown identical allele size for each of the markers indicating the robustness of the results.The SSR markers used in the present study had allele size within a range of 108–122 bp (Xipes0066) to 409–414 bp (Xipes0205). Moreover, all the markers used in the study had shown band sizes in correspondence with the expected band sizes. The check, Tift 23DA total of 532 alleles were found among 342 parental lines and check with a mean value of 6.05 alleles per locus. The number of alleles ranged from 2  to 28 (Xpsmp2070) alleles per locus, followed by Xipes0233 (21), Xipes0027 (17) and Xipes0098 (16). Seventy-two out of 88 markers detected alleles within the range of 3–10 with a mean value of 5.14 alleles per locus, whereas 5 markers identified alleles within the range of 11–15 with an average of 13.20. The 72 EST-SSRs used in study resulted an average of 5.89 alleles, ranged from 2  to 21 (Xipes0233), whereas 16 genomic SSRs identified number of alleles which varied from 2 (Xpsmp2235 and Xpsmp2253) to 28 (Xpsmp2070), with a mean value of 6.75. Out of 532 alleles, 443 (83.27%) alleles were contributed by maintainer (B-) lines with a mean of 5.03 alleles per locus, whereas restorer (R-) lines contributed 476 alleles (89.47%) with a mean of 5.41. A total of 441 (82.89%) alleles out of 532 alleles were shared commonly between B- and R- lines. All the markers were polymorphic across R-lines, while one marker Xipes0147 was found to be monomorphic among B-lines.Gene diversity is defined as the probability that two randomly chosen alleles from the population are different. The average gene diversity in this study was 0.55, varied from 0.02 (Xipes0147) to 0.90 (Xpsmp2070). Out of 88 SSR markers, 60 loci showed gene diversity of equal to or more than 0.50, with a mean value of 0.66, whereas 28 markers resulted in gene diversity < 0.50 with a mean value of 0.29. The EST-SSRs and genomic SSRs recorded mean gene diversity values of 0.56 and 0.48, respectively. Based on individual analysis among B- and R- lines, pollen parents (0.55) had high average gene diversity than seed parents (0.49).Seventy-one of the 88 SSR markers revealed heterozygosity, of which marker Xipes0226 detected maximum (0.12) heterozygotes followed by Xipes0027 (0.07) and Xipes0206 (0.07), while 21 markers could not find any heterozygotes. The mean heterozygosity was 0.02. Of 71 markers, 56 SSRs showed < 0.05 heterozygosity with an average of 0.02 and 11 SSRs had more than 0.05 heterozygosity with a mean of 0.06. The average heterozygosity was greater among R- lines (0.03) than B- lines (0.01).The PIC ranged from 0.02 (Xipes0147) to 0.90 (Xpsmp2070) with an average of 0.50. Out of 88 markers, 50 markers showed PIC > 0.50 with a mean of 0.65, whereas 21 markers resulted in PIC values that ranged from 0.30 to 0.50, and rest of the 17 markers had PIC < 0.30. The PIC value ranged from 0.00 (Xipes0147) to 0.79 (Xipes0098) among B-lines and from 0.03 (Xpsmp2253) to 0.91 (Xpsmp2070) among R-lines with an average of 0.44 and 0.50 in B- and R- lines, respectively. The mean PIC value of EST-SSRs (0.51) was greater than that of genomic SSRs (0.45).The dendrogram generated from the cluster analysis using simple matching dissimilarity matrix obtained from SSR data depicted in Figure Among the clusters of B- lines, the cluster B10 was largest with 28 lines. It comprised of 20 seed parents and 8 pollen parents, while smallest cluster was B8 with 7 maintainer lines. The clusters B1, B3, B4, B6, and B9 consisted of 26, 14, 22, 15, and 27 B-lines, respectively. The remaining clusters B2 comprised of 9 B-lines, B5 grouped 7 B- and 1 R-lines and B7 had 4 B- and 5 R-lines in their clusters. Out of 10 clusters of B-lines, 6 had more than 13 lines, whereas 4 clusters possessed < 10 lines in their groups. Based on individual cluster analysis, the lines in B1, B2, B3, B4, B5, B6, B7, B8, B9, and B10 were grouped at 0.30, 0.36, 0.37, 0.39, 0.31, 0.39, 0.39, 0.39, 0.41, and 0.47 genetic dissimilarity values. The mean representative entries in each cluster on the basis of dissimilarity values were 74 (0.30), 41 (0.36), 48 (0.37), 162 (0.39), 105 (0.33), 84 (0.42), 137 (0.40), 75 (0.38), 25 (0.41), and 132 (0.47) in B1, B2, B3, B4, B5, B6, B7, B8, B9, and B10, respectively.Amongst the clusters of R-lines, R10 was the largest with 28 lines with 27 R- lines and one B-line and R4 was the smallest cluster with 5 R-lines. The remaining clusters of R- lines were R1, R2, R3, R5, R6, R7, R8, R9, and R11 with 25 (22 R- and 3 B-lines), 7, 8, 16, 15 (12R- and 3 B-lines), 22 (18 R- and 4 B-lines), 22, 13 (12 R-lines and 1B-line), and 12 (11 R-lines and 1 B-line) lines, respectively. On the basis of individual cluster analysis, the grouping of clusters R1, R2, R3, R4, R5, R6, R7, R8, R9, R10, and R11 was done at genetic dissimilarity values of 0.41, 0.35, 0.35, 0.38, 0.41, 0.50, 0.47, 0.50, 0.46, 0.47, and 0.44, respectively. The mean representative entries in each cluster on the basis of dissimilarity values were 298 (0.40), 222 (0.35), 336 (0.35), 201 (0.37), 270 (0.41), 333 (0.50), 199 (0.47), 192 (0.50), 185 (0.46), 172 (0.47), and 174 (0.44) in R1, R2, R3, R4, R5, R6, R7, R8, R9, R10, and R11, respectively. Nine lines were included in the undetermined cluster at dissimilarity value of 0.43 and the representative entry of this cluster was 233 (0.43).P = 0.001) differences between the clusters for different cross combinations.The combined analysis of variance for grain yield of the testcross hybrids generated by crossing 9 representative B- lines with 10 representative R- lines and one representative R- line from the undetermined cluster is presented in Table ST values to the distribution of 999 permutations provided highly significant (P = 0.001) differences between the 10 B- line clusters; and between 10 R- line clusters, and an undetermined group. Genetic variations among individual for B- and R- lines  was significantly higher compared to within individual variance for B- and R- lines  (Table AMOVA was generated using genotyping data from 88 microsatellite loci for 160 B- lines and 182 R-lines. The comparison of the observed Fy) Table .P ≤ 0.01) differences among parents, hybrids, hybrids vs. parents, parents × environment interaction, hybrid × environment interaction for grain yield per plant.Analysis of variance for combining ability for different grain yield per plant based on line × tester analysis is presented in Table per se performance of parents and hybrids, general combining ability and specific combining ability effects for grain yield per plant based on pooled data of three environments are presented in Tables 1s generated from cross between a representative of each B- line group with a representative of each R- line group, cluster combination B1R3 (42.66 g), B2R4 (42.24 g), B3R5 (57.23 g), B4UD (47.73 g), B5R11 (43.88 g), B6R3 (44.25 g), B8R4 (39.87 g), B9R7 (38.26 g) and B10R5 (70.88 g) recorded high grain yield per plant than their counterparts.The details on gca effects for grain yield per plant varied from −3.03** (B2) to 4.44** (B10) among B- line groups and among R- line groups the range varied from −7.34** (R9) to 7.15** (R5). Only one B- line, B10 (4.44**) and three R- lines, R5 (7.15**), R3 (2.95*) and R4 (2.38*) exhibited positive and significant gca effects whereas two B- lines, B2 (−3.03**) and B9 (−2.71**) and three R-lines, R9 (−7.34**), R8 (−3.51**), and R2 (−3.06**) showed negative and significant gca effects.The sca effects varied from −14.29** (B10UD) to 25.35** (B10R5). Nine and seven hybrids possessed significant negative and positive sca effects, respectively. Of seven hybrids (mean ranged from 31.89 to 70.88 g) with specific combining ability in a desirable direction, three crosses had at least one best general combiner for this trait as their parent. The cross combination, B10R5 (25.35**) with H+gca x H+gca parental combination, showed highest significant sca effect followed by B3R5 (15.72**) (L+gca x H+gca), B3R6 (15.66**) (L+gca x L+gca), B4UD (13.43**) (L−gca x L+gca), B5R11 (12.36**) (L−gca x L−gca), B2R4 (8.91*) (H−gca x H+gca), and B9R9 (8.00*) (H−gca x H−gca).Among 99 hybrids, 1s is presented in the Table * (B3R9) to 74.12** (B10R5), for the check, ICMH 356, it ranged from −49.50* (B3R9) to 71.93** (B10R5), and for the check, HHB 146 Improved, the standard heterosis ranged from −42.67 (B3R9) to 95.21** (B10R5). Positive heterosis is a desirable feature for this trait. Two of 99 hybrids, B10R5 (95.21**), and B3R5 (57.10*) recorded significant positive standard heterosis over the superior check, HHB 146 Improved.The estimates of standard heterosis for yield over standard checks  of 99 F1 hybrid relative to the mid-parent value (mid-parent/average heterosis), or to the better parent (better-parent heterosis or heterobeltiosis), or over a suitable check cultivar (standard heterosis).Heterosis has been an area of intense research in many cross-pollinated and a few self- and often cross-pollinated crops for over a century. It has been defined as the superior (or inferior) performance of the F2B1-P1-P5 as a check in our study. The identical allele size of the check for each marker indicated the accuracy of protocol and reproducibility of the allelic data for the set of markers used in this study. The number of alleles obtained in the present investigation was higher than the earlier reports in pearl millet  in their parentage. In addition, B10 comprised of eight R-lines, of which six had [((MC 94 S1-34-1-B × HHVBC)-16-2-1) × (IP 19626-4-2-3)] in their pedigrees.The lines with similar a pedigree in their parentage are grouped together in the same cluster with minor deviations indicated the precision of marker-based genetic distance in grouping of the diverse parental lines. For example, among B- line clusters, in B1, 21 lines had 843B as a common parent in their pedigree, while remaining lines possessed mixed parentage. The check Tift 23DLikewise, amongst the R- line clusters, one B- line grouped with R-lines in R1 cluster, which might be due to its common parentage with most of the lines in the corresponding cluster. The lines with ICMR 312 in their parentage grouped in R2, while lines with AIMP 92901 clustered in R3. The cluster R5 is dominated by lines derived from crosses involving ICMR 312 S1-3-2-1-2-4 in their parentage. Majority of the lines possessed (SRC II C3 S1-19-3-2 × HHVBC) commonly in their parentages in R7, where B- lines were grouped in this cluster which could be due to involvement of cross combination (SRC II C3 S1-19-3-2 × HHVBC) in their parentages. In the cluster R10, out of 28, 16 lines possessed MRC series in their pedigree. A similar result of coincidence of clustering patterns based on marker distance with pedigree data was given by Satyavathi et al. .The presence of significant phenotypic differences for grain yield between the 11 R- line groups involved in a total of 99 crosses, and significant molecular differences among B- and R- line clusters Tables ,B suggesgca effects, one B-line 132 (B10) and three R- lines 336 (R3), 201 (R4), and 270 (R5) were found as good general combiners for the trait, grain yield per plant. Therefore the lines of groups (represented by mean representative entries) with trait of interest can be utilized in breeding program straightaway as parents for production of hybrids by crossing with other divergent lines or may be used in the line development programs.Based on the +gca × H+gca), (L+gca × H+gca), (L+gca × L+gca), (L−gca × L+gca), (L−gca × L−gca), (H−gca × H+gca), (H−gca × H−gca), respectively. The cross between two high general combiners revealed additive and additive × additive genetic components of variance. The cross between high × low general combiners that resulted in superior cross combination might be due to complimentary action arising out of both additive and non-additive genetic components. The superiority of the crosses having low gca parents may be due to high nicking ability and high sca effects for the parents. It will, therefore, be rewarding to design hybrid breeding programs which precisely estimate not just gca effects, but also sca effects of the hybrid parental lines by making factorial crosses with the right set of testers. Also, since every B- and R- line cluster was different and distinct in terms of genetic distance, the presence of heterotic combinations in just a few groups over others, suggests a more complex interaction of genetic distance with sca effects. This result is similar to Pucher et al. , the best heterotic cross combinations identified for grain yield per plant were obtained from F1s generated from mean representatives of groups B3 and B10 with representative of group R5. Other high yielding cross combinations were obtained between groups B1 and R3, B2 and R4, B3 and R5, B4 and undetermined cluster, B5 and 11R, B6 and R3, B8 and R4, B9 and R7 and B10 and R5. This clearly suggests that the crosses between the given B × R combinations from the specific clusters resulted in higher grain yield compared to the other groups. These may be due to a high degree of gene complementation and dispersion of favorable alleles between the groups for the manifestation of a higher degree of heterosis. These groups may represent putative heterotic gene pools in pearl millet.Based on overall performance (The current study is a step closer toward defining heterotic gene pools in pearl millet. A relatively large number of B- and R- lines were grouped using SSR-assisted genetic distances. Their representative testcross hybrids were evaluated in three environments in this study, which shed light on the existence of putative heterotic gene pools in B- and R- lines for the first time. However, these heterotic groups need to be further refined and broadened by selecting more appropriate set of testers for maximizing combining ability, and by evaluating the testcross hybrids in more number of representative environments.Apart from further study on the genetic aspects, it might be interesting to integrate epigenomics, metabolomics, proteomics, and systems biology approaches for gaining better insights into the heterotic gene pools of pearl millet.RS planned and coordinated this study. ARR, PK, AGBR generated lab and field data. AR helped in data analysis. RS, CS, RG, RY, LA provided technical guidance during the conduct of research work. RS, ARR, RG, CS, SK, RY, MM drafted the manuscript. RS critically revised the paper for final publication.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.