Finally, TCR downregulation during MDV infection in the MHC-matched model was strongest in the MD-susceptible line, and MDV reactivation downregulated TCR expression in a tumor cell line.Marek’s disease (MD) is a lymphoproliferative disease of chickens induced by Marek’s disease virus (MDV), an oncogenic α-herpesvirus. MDV has increased in virulence, prompting continued efforts in both improved vaccines and enhanced genetic resistance. Model pairs of genetically MD-resistant and MD-susceptible chickens that were either MHC-matched or MHC-congenic allowed characterization of T cell receptor (TCR) repertoires associated with MDV infection. MD-resistant chickens showed higher usage of Vβ-1 TCRs than susceptible chickens in both the CD8 and CD4 subsets in the MHC-matched model, and in the CD8 subset only in the MHC-congenic model, with a shift towards Vβ-1+ CD8 cells during MDV infection. Long and short read sequencing identified divergent TCRβ loci between MHC-matched MD-resistant and MD-susceptible chickens, with MD-resistant chickens having more TCR Vβ1 genes. TCR Vβ1 CDR1 haplotype usage in MD-resistant x MD-susceptible F Marek’s disease (MD) is a commonly diagnosed T cell lymphoproliferative disease in chickens, first identified as an infectious polyneuritis by Joseph Marek in 1907 . Since iB locus) haplotypes were found to exert major effects on resistance to the disease, with the B21 haplotype conferring particularly high resistance Which allows haplotype construction of up to 40 bp in length, limits the search to a 50-bp region spanning the CDR1 mutational hotspot, requires a minimum of 4 calls per haplotype, and treats the sample ploidy as unknown.1 Illumina RNAseq dataset were analyzed for the presence of CDR3 sequences. The trimmed fastq files were searched against a set of conserved Vβ1 primer sequences .A subset of three MDV-infected and uninfected samples each from the above line 6 × 7 FTCRβ reference sequence published in Genbank , using default SWISS-MODEL parameters [A Vβ1 protein structure was modeled for the chicken rameters ,65 with rameters . Substitrameters ,68.Whole blood samples from one healthy adult male bird each of lines 6 and 7 were extracted for DNA using a non-column method, which involved overnight lysis in SDS/TE buffer  with proteinase K (60 μg/mL), protein precipitation with Qiagen Precipitation Solution and ethanol precipitation of the soluble DNA phase to minimize shearing. DNA samples were resuspended in distilled water and submitted for PacBio Sequel sequencing by the USDA-ARS Genomics and Bioinformatics Unit . Raw sequences were submitted to the NCBI Sequence Read Archive (BioProject: PRJNA574234). Read correction, overlapping and de novo assembly was performed using Canu v. 1.8 , and asswww.jamovi.org, accessed on 13 October 2017), followed by post-hoc Tukey’s tests for significance. In vitro flow cytometry data were analyzed by Bonferonni-corrected Student’s t-testing in Prism 8.0.0 for MacOS  or Excel for Mac v. 15.41 , or by 2-way ANOVA with Sidak’s multiple comparisons tests in Prism 8.0.0 for MacOS (reactivation assays). Plots were generated in Prism 8.0.0 for MacOS) or Excel for Mac v. 15.41. TCR spectratyping data were analyzed for total divergence score [Ex vivo flow cytometry data (from day 5 post-infection to day 35) were analyzed by three-way analysis of variance in Jamovi 0.8.0.10  and MD-susceptible (line 7) chickens. We separately analyzed the response of CD4+ and CD8+ populations to ConA and PHA stimulation using CellTrace cell labeling for proliferation , and conp = 0.026 at 8 days post infection), and in both lines in replicate 1 (p < 0.05 for both lines by 21 days post infection)  TCR repertoire of splenic cells in line 6 and line 7 chickens with and without MDV infection over time, via flow cytometry . As showfection) A; howevefection) B. In repfection) A,B.p < 0.001) were similar to those in bulk T cells. However, upon infection, we observed a significant bias toward higher TCR2+ frequency in the CD8+ T cells of line 6 birds in replicate 2, beginning on day 8 (p < 0.05); and in replicate 1, this bias was observed across both lines (p < 0.001), but only day 21 in line 6 was individually statistically significant (p = 0.002) (p = 0.025) . In replicate 1, we included antibodies against both the CD8α and CD8β chains in our flow cytometry panel, which allowed us to examine the conventional CD8αβ T cell population, while in the second replicate, we only used the CD8α antibody to identify CD8+ T cells, as CD8αα cells made up only 5–10% percent of the CD3+ population in replicate 1 (we replaced the CD8β marker with CD4 in replicate 2). The between-line differences in TCR usage on CD8+ T cells (= 0.002) C. In con= 0.025) D. We exa= 0.025) E.p < 0.001) in the MD-susceptible line 7 birds. CD4+ T cells were strongly increased in both lines by day 21 with infection (p < 0.001), from 16–25% of the splenic lymphocyte population in controls to 44–74% in infected birds (p < 0.001), with no statistically significant difference between lines in either CD4+ or CD8+ T cell percentage at 21 days post-infection  in experiment 1, replicate 1; however, our fluorescent secondary streptavidin conjugate gave no specific staining in any samples from days 5–14, including single-color controls, so we removed this marker from later flow cytometry timepoints. We confirmed in single-stain analysis of cecal tonsil lymphocytes from each line and a pooled aliquot of spleen cells from both lines, at day 21, using a different fluorescent conjugate, that the TCR1 antibody itself does bind TCR1+ cells from both lines.p < 0.05) differences in MHC class II expression, but not MHC class I expression, on splenic B cells in uninfected birds, with the resistant line 6 birds expressing higher MHC class II; with infection, both lines upregulated MHC class II to a similar degree (p < 0.001), although the difference between lines was not significant in infected birds (p < 0.01) (p < 0.01) and macrophages (p < 0.001) at 7 days.To test the hypothesis that differences in MHC expression on antigen-presenting cells may account for the differences in baseline TCR usage in line 6 and line 7, despite the shared MHC haplotype of these lines, we compared MHC class I and MHC class II expression on splenic B cells and macrophages in at 7 days of age by flow cytometry ; additioed birds A. On spl < 0.01) B. ConverWe performed TCR spectratyping on splenic tissue from MD-resistant line 6 and MD-susceptible line 7 birds at multiple stages of MDV infection. We were able to identify oligoclonality in bulk TCR Vβ1 and TCR Vβ2 T cell populations in infected birds as early as 14 days post-infection A,B, whic5.B21  and 15I5.B19 , or in response to MDV infection. As in the line 6 and line 7 birds, TCR2+ T cells make up a much greater percentage of splenic T cell populations (approximately 90% of CD4+ T cells) than TCR3+ T cells (approximately 10% of CD4+ T cells). In contrast, TCR2+ and TCR3+ frequency in splenic CD8+ T cells differed mildly between lines, suggesting that intra-thymic MHC class I  has a direct effect on establishing different TCR repertoires in this model of MD resistance (p < 0.001), with line 15I5.B19 (susceptible) gradually increasing in TCR2 frequency in CD8+ T cells until day 21 post infection, at which time both lines were essentially expressing this receptor at the same frequency (p < 0.05)  but differ at non-MHC loci. We also compared TCR usage in a genetic resistance model comparing two congenic lines that differ at the MHC locus but share the same genetic background (presumably including TCR loci), in order to demonstrate whether MHC-TCR interactions might play a role in determining genetic differences in T cell immunity A–D. As ssistance A. As in requency A. We als < 0.05) C, which  < 0.05) D.We examined baseline TCR usage within the PBMCs of a panel of line 6 × line 7 recombinant congenic lines (RCS), which have been developed to allow linkage analysis of parental line phenotypes and have been characterized for lymphoid organ size , and in We compared the genomic TCR Vβ1 sequences from Illumina DNA-seq data previously generated from pooled blood collected from uninfected MD-resistant line 6 and MD-susceptible line 7 animals. While short (92-bp) read Illumina sequencing does not provide sufficient read-length to uniquely map reads to Vβ genes, we estimated diversity at the Vβ1 locus by aligning all Vβ1-mapping reads to a single model Vβ1 gene and calling variants . InteresA variant hotspot located at the hypervariable CDR1 loop allowed manual identification of longer  haplotypes in spanning reads; interestingly, seven unique Vβ1 haplotypes could be identified in line 6, while only four unique haplotypes could be identified in line 7 at this site, with one additional haplotype shared between lines. We modelled these CDR1 sequences in the context of a published TCRβ chain (GenBank ABU93634.1), using a crystallography-supported mouse TCR-pMHC complex as a modelling framework; chicken TCRβ adopted the expected two-immunoglobulin domain structure . CDR1 se1) hybrids of line 6 and line 7, at 4 days post-infection (18 days of age) [In order to estimate TCR Vβ1 gene usage at the mRNA level, we compared the usage of Vβ1 CDR1 haplotypes in a pre-existing Illumina RNA-seq dataset from MDV-infected and uninfected spleens of first-generation (F of age) . Using F1 hybrid birds either uninfected or infected with MDV at hatch, we observed approximately 69% usage of the canonical Gln-111 if the upstream codon was unmodified, while Ile-111 was used 4% of the time. In this sequence context, usage of Gln-111 was reduced during early MDV infection, while Ile-111 usage remained stable, as did usage of a codon-initial adenine at this position, which was present in about 11% of sequences  amino acid change at position 111, just upstream to the V-D junction of the CDR3 loop . Using tequences .TCRβ locus for each line (TCRβ genes in one contig) using stringent defaults for PacBio data , and contained ten Vβ1 genes and six Vβ2 genes , and contained at least twelve Vβ1 genes and only four Vβ2 genes  PacBio DNA sequencing data from both lines 6 and 7. De novo assembly in Canu allowed reconstruction of the ach line . Despiteβ2 genes . In contβ2 genes , consistβ2 genes . TCRβ loci in line 6 and 7, with the single shared CDR1 haplotype in Vβ1, SHKESVIQTMF, highlighted between the lines; coding sequences for Vβ1 and Vβ2, and full sequences for putative Vβ3, are provided in Similarly to the Illumina data, CDR1 haplotypes were identified for each Vβ1 gene in both lines shown in ; most prLevels of TCR surface expression on gated bulk TCR+ CD3+ T cells were examined by flow cytometry in experiment 1, replicate 1. In both lines, a mild reduction of TCRαβVβ1 surface expression by TCR2 antibody labeling was seen on day 8; however, this became statistically significant in line 7 on day 14, while it was not significant in line 6 until day 21 A. TCRαβVWe examined the levels of TCRαβVβ1 expression on the TCR2+ CD4+ T cell tumor line UAO4, which contains latent, reactivatable MDV and expresses gB-GFP upon MDV reactivation with bromodeoxyuridine , soluble anti-CD3 and anti-CD28 antibodies  A, phorboWe broadly characterized the T cell repertoires and responses of MD-resistant and MD-susceptible chickens using two pairs of inbred chicken lines, in order to identify immune responses that correlate with a resistant phenotype in vivo. Initially we hypothesized that intrinsic activation capacity in peripheral  T cells might differ between lines in our MHC-matched model, in accordance with an early hypothesis that T cell infection relies on activation  and, thuTCRβ locus itself, and in fact we were able to identify differences in number and sequence of genes at the TCRβ locus in these lines. While we cannot rule out a potential influence of differences in MHC II expression on shaping the TCR repertoires of these lines, MHC class I was expressed similarly on antigen-presenting cells of both lines, making it less likely that the baseline TCRβ usage differences seen in both CD4+ and CD8+ T cells of these lines are due to T cell selection. In addition, we cannot rule out an influence of MHC genes located within the non-classical Rfp-Y locus on thymic or peripheral T cell selection, as Rfp-Y haplotype differs between these lines. Rfp-Y is not linked to MD resistance in this model [Rfp-Y loci) but carry MHC haplotypes linked to different levels of MD resistance, showed mild (5–15%) differences in TCR usage in the CD8+ T cell population only, indicating that MHC class I, but not necessarily MHC class II in this model, shapes different T cell repertoires during thymic selection that are recognizable at the bulk (gene family) expression level. This may help explain the critical importance of the MHC locus as the strongest genetic determinant of host resistance against MD, in addition to differences in quantity of MDV antigen presented by differing MHC alleles [TCRβ locus appears reduced in gene number in the chicken relative to mammalian TCRβ loci, are also co-evolving along with their MHC binding partners, and selection for pathogen resistance or susceptibility may skew TCR repertoires towards or away from “best fit” receptors that recognize viral peptides as presented on MHC.We identified differences in the usage of the two TCR Vβ families in the T cell repertoire of both our MHC-matched and MHC-congenic MD-resistance models. In our MHC-matched model, in which the same MD-susceptible MHC haplotype has been maintained in inbred lines divergent for disease resistance ,82,83, wis model , and oneis model ,84, but  alleles . Genetic alleles ,41,42,43We identified significant shifts in TCR2+ and TCR3+ T cell subsets during MDV infection in our flow analyses. In both of our models, we saw little change in TCR usage within CD4+ populations prior to late infection when CD4+ T cell lymphomagenesis was likely to be occurring. However, in our MHC-matched model, TCR2+ responses visible in the splenic lymphoid population as a whole could only be attributed to CD8+ and not CD4+ T cells, indicating that the shift in CD8+ T cells toward TCR2 in the MD-resistant line 6 was large, even though the relative contribution of CD8+ T cells to the splenic T cell pool decreased significantly during the course of infection. Our measurements are based on relative cell population frequencies; therefore we cannot definitely state whether the shifts seen in TCR usage within the CD8+ population are due to expansion or recruitment of TCR2+ CD8+ cells, or loss of another TCR-bearing subset. Prior literature suggests that TCRαβVβ1+, but not TCRαβVβ2+ T cells are important for the development of anti-MDV serotype 2 (non-oncogenic MDV) vaccinal immunity , and anaEimeria [Additionally, we were able to identify clonality within the TCR Vβ1+ and TCR Vβ2+ subsets directly, using TCR spectratyping analysis. While both resistant and susceptible lines showed increased oligoclonality in MDV-infected birds by day 14 post-infection, this occurred primarily in the TCR Vβ2 subset in the resistant line, while the susceptible line showed early oligoclonality in both TCR subsets. The unusually high oligoclonality in TCR Vβ2 in the MD tumor-resistant line 6 is most likely due to the very low usage of TCRαβVβ2 in this line, magnifying the effect of T cell expansion in response to viral antigens or deletion of infected subsets; however, it is possible that we are detecting early expansion of tumor cells within the small TCRαβVβ2+ T cell population in line 6, which can subsequently be controlled by antiviral or anti-tumor immunity. By day 21 post-infection, very high clonality consistent with tumor development was visible in the spleens of susceptible line 7 birds, while resistant birds showed a high deviation from discrete-normality but no strong clones in either TCR subset, more consistent with a robust T cell response. High oligoclonality has been previously observed in the CD8+ T cell subsets in birds with (CD4+) MD tumors, within both spleen and tumor tissue , indicatEimeria . Such puEimeria ,90,91.TCRβ locus segregating across these lines in the same pattern as TCR usage within historically collected microsatellite data [We hypothesized that the relatively high usage of TCRαβVβ1 in line 6, visible both in the flow cytometry results and as reduction in clonal diversity in TCR Vβ2 in TCR spectratyping, may be due to heritable structural differences in the TCR Vβ locus in this line. Therefore, we attempted to genetically map the TCR usage trait using a panel of RCS strains between our two MHC-matched inbred lines; we were unable to uniquely identify a genomic region across the ite data , collectWe were able to draw inferences about the heritability of the TCR usage trait  and also to compare the usage patterns with known MD susceptibilities in several of the RCS lines. While most lines followed the TCR usage phenotype present in the resistant line , the several lines known to be relatively susceptible to MD showed lower percentages of conventional T cells overall within the peripheral lymphocyte pool, regardless of the distribution of TCR frequency within T cells. This could indicate that not only the TCR locus, but also factors that affect over-all T cell numbers, influence the availability of cells responsive to infection. A rigorous comparison of the immune profiles of these RCS lines in MDV infection has not been completed, to our knowledge, but would help answer questions about the relative contribution of these phenotypes to MD resistance; unfortunately these lines are no longer being maintained .TCRβ locus for lines 6 and 7, we were able to compare the gene diversity present in the TCR Vβ1 family in our MHC-matched MD-resistant model, using both standard Illumina DNA-seq and long-read PacBio DNA-seq. Our PacBio assemblies of the TCRβ locus were overall organized similarly to recently assembled Red jungle fowl assemblies [TCRβ haplotypes can contain differing numbers of gene elements within the locus, similarly to structural TCRβ locus rearrangements recently inferred in humans [Although we lacked a robust structural model of the semblies ,46, contsemblies ; howeversemblies  containen humans  and known humans ,94,95. Fn humans ; this seTCRβ RNA sequence data from line 6 × 7 F1 splenocytes, the canonical Gln-111 was maintained 69% of the time during codon end processing if the upstream codon was present (and greater than 50% of the time regardless of upstream codon modification). We modelled TCRβ-chains in the context of both the original sequence and the substituted Ile-111 in Swiss-Model  at this phase. We speculate that the line 6 repertoire may have been further shaped by inclusion of a non-canonical variant which is not outcompeted by other early responding T cells, or perhaps is less susceptible to lytic infection.We noted that in terms of single nucleotide polymorphism (SNP) density, line 6 was more divergent from the Red jungle fowl reference sequence for Vβ1, and also exhibited a larger number of multi-variant haplotypes, indicating more inter-gene diversity and potentially more variable gene blocks present within the Vβ1 family, and fewer within the Vβ2 family which was confirmed by long-read sequence analysis. Additionally, we also identified a four-base deletion in one TCR Vβ1 gene in our MD-resistant line, which is likely to affect affinity and usage of that Vβ1 gene. As this deletion occurred just upstream of the V-D junction in the CDR3 loop of this TCR, it is likely to produce an in-frame product after VDJ recombination; however, unless nucleotides are deleted upstream of the V-D junction, the canonical Gln-111 that is present in all other TCR Vβ1 genes will be substituted with Ile-111. In ss-Model  and foun1 birds. Conversely, the most commonly used CDR1 variant (SHKESVIQTMF) in line 6 × 7 F1 birds, which is overrepresented at 25% usage, is a line 7 haplotype; the corresponding line 6 haplotype is not similarly overrepresented and also contains a downstream substitution in the Vα-Vβ interface. While the presence of two genes in line 7 containing that haplotype could explain its over-usage in F1 birds, two other duplicated genes in line 7 were not similarly overrepresented. These data suggest that the line 6 TCR Vβ1 repertoire has been selected away from a “best-fit” relationship between the SHKESVIQTMF motif and at least one of the B2-encoded MHC molecules; this could provide increased protection from MD either by maximizing the use of different TCRs that better recognize MD antigens, especially on MHC class I, or by reducing the availability of activated CD4+ target cells for MDV infection.Additionally, we noted nearly twice as many variants present within the CDR1 hypervariable region of Vβ1 in line 6 as line 7, and noted changes in both the structure of haplotypes and their selection in the peripheral repertoire, suggesting that there are functional differences between the CDR1 regions in each line. The CDR1 region is thought to primarily bind to MHC, although it may also interact with bound peptide . NotablyLastly, we studied the influence of MDV on TCR expression. MDV has been shown to down-regulate TCR signaling pathways in MD lymphoblastic cell lines using transcriptome sequencing . SeveralTCRα, TCRβ, and MHC loci, as well as self and environmental antigens. We have examined the most tractable of these, TCRβ, and found indications that selection for resistance and susceptibility to MDV in an MHC-matched model has modified the repertoire of MHC ligands, i.e., the TCR repertoire. Intuitively, rational breeding strategies to take advantage of MHC-linked resistance to MDV could also incorporate TCR repertoire optimization; further work will be required to develop these kinds of strategies for creating highly disease-resistant poultry stock.While the MHC is the only single locus of large effect on MDV resistance found to date, we have examined an MHC-matched resistance model to identify other potentially interacting factors that may have been selected for in the development of highly MD-resistant chicken lines. The TCR repertoire is a complex trait that is shaped by


Sorafenib is a potent inducer of ferroptosis used to manage hepatocellular carcinoma (HCC). The ferroptosis induced by sorafenib activates the p62–Keap1–Nrf2 pathway. Abnormal activation of Nrf2 reduces sorafenib’s efficiency and ferroptosis action and induces sorafenib’s resistance. Consequently, our study tried to study the effect of a novel combination of sorafenib and Camptothecin  to improve sorafenib’s ferroptosis action and reduce sorafenib resistance in the treatment of HCC. We evaluated the efficacy of sorafenib and/or CPT using HepG2 and Huh7 cell lines. MTT assay evaluated the anti-proliferation effects. The combination index (CI) and dose reduction index (DRI) were calculated using Isobologram analysis. Malondialdehyde (MDA), total antioxidant capacity (TAC), iron concentration, glutathione peroxidase (GPX4), and glutathione reductase (GR) activity assays were used to determine the ferroptosis action of drugs. Western blot was used to investigate the expression of the implicated proteins. Bioinformatics tools were used to determine the correlation between these proteins. Finally, the HPLC technique is used to measure cellular drug uptake. Our results revealed a strong synergism between sorafenib and CPT. The synergetic combination significantly increases lipid peroxidation and iron concentration, decreases TAC, GPX4 and GR activity, and reduces the expression of both Nrf2 and SLC7A11. The downregulation of Nrf2 expression has a vital role in the reduction of resistance mediators to sorafenib against HCC cells like  and improves the cellular uptake of sorafenib. The current study provided evidence that Nrf2 inhibition by CPT improves sorafenib’s sensitivity and reduces sorafenib’s resistance via the augmentation of sorafenib’s ferroptosis action. Hepatocellular carcinoma (HCC) is the world's sixth most prevalent cancer and the third leading cause of cancer mortality . 80% of In patients with advanced HCC, sorafenib has been found to increase survival. Because of this, the Food and Drug Administration (FDA) has authorized it as a first-line therapy for advanced HCC . Yet, seUnder ferroptosis's oxidative stress, cancer cells stimulate p62  which promotes autophagic degradation of Kelch-like ECH-associated protein 1 (Keap1), resulting in a positive feedback loop of continuous nuclear factor E2-related factor 2 (Nrf2) activation to maintain oxidative homeostasis , 9. The Additionally, sorafenib activates directly the p62-Keap1-Nrf2 antioxidative signaling pathway, accordingly, sorafenib's ability to induce ferroptosis was consequently reduced –14. As aCamptothecin (CPT), a repurposing topoisomerase inhibitor, is one of the most potent Nrf2 inhibitors through reducing transcription, translation, and/or increasing mRNA degradation of Nrf2 , 16, 17.® Germany respectively). Dulbecco's Modified Eagle's Medium (DMEM), penicillin, streptomycin, fetal bovine serum, and phosphate buffer saline (PBS) were obtained from (Gibco-USA). RIPA buffer (50 mM Tris–HCl (pH 7.4), 150 mM NaCl, 5 mM EDTA, 1% Triton X-100, 0.5% Sodium deoxycholate, and 0.1% SDS), HEPES buffer (4-(2-hydroxyethyl)-1-piperazineethanesulfonic acid) were purchased from . Antibodies against Nrf2, p62, SLC7A11, MT-1G, and ABCG2 were provided by .Sorafenib, Camptothecin, and Dimethyl fumarate  were purchased from (Tocris Biosciences). Ferrostatin-1 and ZVAD-FMK were obtained from Sigma-Aldrich . Dimethyl sulfoxide (DMSO) and 3--2,5-diphenyltetrazolium bromide (MTT) were provided from . Cell lines were grown in Dulbecco's Modified Eagle's Medium (DMEM) supplemented with penicillin (100 U/ml), streptomycin (100 mg/ml), and 10% fetal bovine serum (Gibco-USA).Hepatocellular carcinoma (HepG2 and Huh7) cell lines were purchased from (Nawah Scientifico C with 5% CO2). Drug stock solutions were prepared in DMSO. All experiments were repeated three times.Cell lines were maintained in the Center of Excellence "Helwan Structural Biology Research, HSBR" under standard laboratory conditions  and 1.0% penicillin–streptomycin and kept at 37 50 was interpolated using the 4-parameter logistic model (4PL).The viability of studied cell lines after treatment with (sorafenib or CPT) and different combinations of two drugs with a constant concentration ratio (1:1) were measured using a colorimetric MTT assay as described by van Meerloo et al. . BrieflyIn addition, we used MTT assay to investigate the mechanism of cell death induced by the used combination (sorafenib and CPT). So, we treated both cell lines with another two compounds. The first, the cells treated with 1 μM of ferrostatin-1 (a ferroptosis inhibitor) to the combination of sorafenib and CPT, and the second treated with 10 μM of ZVAD-FMK (an apoptosis inhibitor) to the combination of sorafenib and CPT .Based on the isobologram technique established by Chou and Talalay . The mulThe MTT results were used to calculate the combination index (CI) where CI < 1 indicates synergism, CI = 1 indicates additive or CI > 1 indicates antagonism. Furthermore, the software determines the dose reduction index (DRI) which estimates a fold-decrease in the dose of each drug when compared to their combined dose . Dependi6 cells per flask (15 flasks for each cell line) and were allowed to adhere overnight. Then, flasks were categorized into five groups; the first group was a control (1% DMSO), the second group was treated with IC50 of sorafenib, the third group was treated with IC50 of CPT, and the fourth group was treated with a mixture of CPT and sorafenib at their lowered synergistic doses determined by DRI values, and the last group was treated with IC50 of sorafenib and DMF  [Cells were seeded at a density of 1 × 10 100 μM) .All groups were incubated for 72 hrs. Subsequently, cells were lysed in pre-cold RIPA lysis buffer supplemented with protease and phosphatase inhibitors cocktail (Sigma-Aldrich) as described by El-Hanbosy et al.  and incuA. Total Antioxidant Capacity® kit, Cairo, Egypt) according to the manufacturer’s protocol. Then, TAC in each sample was calculated using the following equation.B. Lipid Peroxide )The total antioxidant capacity (TAC) in the cell lysate of different HCC cell lines was evaluated calorimetrically according to Koracevic et al.  using  according to the manufacturer’s protocol. The MDA concentration in each sample was calculated using the following equation.C. Glutathione Reductase (GR) ActivityThe malondialdehyde (MDA) content in cell lysate was measured using (BiodiagnosticsD. Glutathione Peroxidase (GPX4)The Glutathione reductase (GR) activity was reported by the kinetic method according to Illingworth J using  according to the manufacturer’s protocol . Then, t® kit, Cairo, Egypt). Then, the absorbance was measured simultaneously every minute for 3 min, and the change in absorbance per min was measured. Finally, the glutathione peroxidase (GPX4) activity in each sample was calculated using the following equation.E. Iron AssayThe Glutathione Peroxidase (GPX4) activity was measured by kinetic method according to the manufacturer’s protocol  according to the manufacturer’s protocol. The iron concentration in each sample was calculated using the following equation.The relative iron concentration in cell lysates was measured using (Biodiagnostics5 cells/mL was seeded per well into six-well plastic plate for each cell line. The cells were then rinsed with 200 µl of HBSS (uptake buffer) and allowed to equilibrate for 1 h in an incubator after reaching 80 to 90% confluence. Upon the removal of the HBSS, for each cell line, plates were categorized into three groups: The first group treated with IC50 of sorafenib, a second group treated with the IC50 of CPT and the third one treated with mixture of CPT and sorafenib at their lowered synergistic doses determined by DRI values and were incubated for 3 hrs. Then, the cells were harvested with a spatula. The cell lysate was transferred to an Eppendorf tube and centrifuged at 15,000 rpm for 10 min after being washed four times with 4 °C PBS. High-performance liquid chromatography (HPLC)  equipped with an SPD-20 A UV/VIS detector was used to quantitatively evaluate the amounts of sorafenib and CPT in the cells. The mobile phase was 0.1% Trifluoro acetic acid: Acetonitrile gradient elution with a flow rate: 1 ml/min; column temperature, 25 °C; UV detection wavelength 260 nm; and injection volume, 50 µl. The samples were extracted using acetonitrile [To form cell monolayers, 2 × 10onitrile . The datThe expression of Nrf2, p62, SLC7A11, MT-1G, and ABCG2 proteins in Huh7 and HepG2 cells treated with sorafenib alone, CPT alone and a combination of CPT and sorafenib at their reduced synergistic doses were determined using western blot analysis according to the method described by Burnette WN .® 7 . Isobologram analysis was performed by CompuSyn software . Western blot bands were analyzed by Total lab analysis software (Ver.1.0.1). Empower software (Version 3) was used for HPLC analysis. STRING database tool was used to predict protein–protein interactions and identify the correlation between proteins [All data were expressed as the mean ± SEM. The differences between the groups were analyzed using unpaired student t- test and one-way analysis of variance (ANOVA) followed by post hoc Tukey's multiple comparison test and graphical data were represented using Graph Pad Prismproteins .Statisti50 values  after 48 hrs. of incubation. Additionally, the combination of sorafenib and CPT with ratio (1:1) inhibited cell viability with an IC50 equal to 0.05 ± 0.01 µM as shown in Fig. 50 equal to 3.87 ± 0.21 µM but CPT’s IC50 was 0.091 ± 0.01 µM. While the IC50 for the combination of sorafenib and CPT was 0.044 ± 0.01 µM as shown in Fig.In HepG2 cell line, sorafenib and CPT had IC50 (5.47 ± 0.31 µM), while CPT showed an IC50 (1.04 ± 0.93 µM). The combination showed an IC50 (0.33 ± 0.12 µM) as shown in Fig. In Huh7 cell line after 48 hrs. of incubation, sorafenib showed an IC50 values . While a combination of sorafenib and CPT showed an IC50 equal to 0.152 ± 0.03 µM as shown in Fig. But after 72 hrs. of incubation, sorafenib and CPT had ICThe results demonstrated that the combination of sorafenib and CPT significantly augmented cell growth inhibition more than each drug alone. The combination is more potent after 72 hrs. than 48 hrs. of incubation.50 dose obtained from MTT assay were calculated using CompuSyn software to determine the type of drug interaction and the results expressed in Table Combination index (CI) and DRI at the IC50 after 48 hrs. of incubation, CI value was (0.398) in HepG2 and (0.377) in Huh7 cell lines. These CI values, which are markedly lower than 1, indicate a synergism in sorafenib and CPT combination in both Huh7 and HepG2 cell lines.At ICFurthermore, the same scenario occurred after 72 hrs. of incubation, CI was (0.494) in HepG2 and (0.435) in Huh7cells revealing a synergistic effect for sorafenib and CPT combination in both Huh7 and HepG2 cell lines.50 of sorafenib decreased by  after 48 hrs. of incubation. While after 72 hrs. of incubation, the IC50 of sorafenib decreased by . The DRIs results clearly indicated that addition of CPT to sorafenib potentiates its efficacy.In addition, the DRIs exhibited remarkable dose reduction for sorafenib and CPT where, the ICThe results of the MTT, CI, and DRI clearly showed that the synergetic combination of sorafenib and CPT after 72 hrs. of incubation is more effective than 48 hrs. of incubation.CI < 1, CI = 1, or CI > 1, indicate synergistic effect, additive effect, or antagonistic effect, respectively. DRI was calculated by comparing doses when used as a single treatment or in combination. CI stands for combination index, and DRI stands for dose reduction index., and decreased significantly compared to control group (p < 0.0001) as shown in Fig. In addition, in both cell lines, the cells treated with a combination of  showed a significant increase in cell viability compared to both cells treated with a combination of  and cells treated with a combination of sorafenib and CPT alone Furthermore, cells treated with a combination of  did not show any significant difference in cell viability compared to cells treated with a combination of sorafenib and CPT alone. So, cell viability was restored by ferrostatin-1 while not by ZVAD-FMK in both cell lines Fig. a, b. Thep < 0.0001), CPT group , control group  and mix 2  . While mix 2 showed statistically significant increase in TAC value when compared to mix 1, sorafenib, CPT and control  as shown in Fig. Total antioxidant capacity (TAC) was determined calorimetrically in HepG2 and Huh7 cell lysates in 72 hrs. The results showed that in HepG2 cell line, TAC was decreased significantly by using mix 1 (combination of CPT and sorafenib) (0.7914 ± 0.05 mM/L) compared to both sorafenib group , CPT group  as well as control group  but it did not show significant differences compared to mix 2 group. While mix 2 showed statistically significant increase in TAC value when compared to mix1, CPT and control  as shown in Fig. Furthermore, the same scenario occurred in Huh7 cell line, mix 1 showed a significant decrease in TAC value (1.228 ± 0.02 mM/L) compared to both sorafenib group , hence increasing ROS level, and inducing ferroptosis action in both cell lines.p < 0.0001), CPT  mix2  and control group . Furthermore, MDA value was significantly decreased by using Mix2 compared to control, mix1 and CPT  as shown in Fig. In HepG2 cell line, mix 1 showed a significant increase in MDA level (2.982 ± 0.2 nmol/ml) compared to sorafenib , CPT group , mix2  and control group  as shown in Fig. p < 0.001). This may indicate that the combination of CPT with sorafenib causes an increase in lipid peroxide in both cell lines and accelerates cell death via ferroptosis.While in Huh7 cell lysate, MDA level increased significantly in the mix1 (3.17 ± 0.07 nmol/ml) compared to the sorafenib group , CPT , mix 2  and control groups  as shown in Fig. p < 0.0001, p < 0.0001, p < 0.0001, p < 0.05 respectively).In HepG2 cells, GR activity showed a significant decrease in cells treated with mix 1 (2.503 ± 0.3 U/ml) compared to both sorafenib (4.349 ± 0.07 U/mlp < 0.0001), CPT , mix 2 , and control groups  as shown in Fig. p < 0.0001, p < 0.0001, p < 0.0001, p < 0.0001 respectively).Also, the same results occurred in Huh7 cell lysate, by using mix 1, the GR activity decreased significantly (1.973 ± 0.05 U/ml) compared to sorafenib , CPT , mix 2  and control groups  as shown in Fig. p < 0.0001, p < 0.0001, p < 0.0001, p < 0.0001, respectively).In HepG2 cells, GPX4 activity showed a significant decrease in cells treated with mix 1 (0.8056 ± 0.02 U/ml) compared to both sorafenib (8.169 ± 0.1 U/mlp < 0.0001), CPT , mix 2 , and control groups  as shown in Fig. Also, the same results occurred in Huh7 cell lysate, by using mix 1, the GPX4 activity decreased significantly (0.2196 ± 0.008 U/ml) compared to sorafenib .Moreover, mix 2 showed statistically significant increase in GPX4 activity when compared to mix 1, CPT, control and sorafenib , CPT  mix2  and control group . Furthermore, the iron concentration was significantly decreased by using mix2 compared to control, mix1 and CPT  as shown in Fig. In HepG2 cell line, mix 1 showed a significant increase in iron concentration (32.74 ± 0.2 µmol/L) compared to sorafenib , CPT group , mix2  and control group  as shown in Fig. p < 0.0001, p < 0.0001, p < 0.0001, p < 0.0001, respectively). This may indicate that the combination of CPT with sorafenib causes an increase in iron concentration in both cell lines and accelerates cell death via ferroptosis.While in Huh7 cell lysate, iron concentration increased significantly in the mix1 (34.6 ± 0.46 µmol/ml) compared to the sorafenib group  and ferroptosis transporter protein (SLC7A11) in both cell lines compared to the control, sorafenib and CPT groups as shown in Fig. To determine the effect of CPT on the uptake of sorafenib by HepG2 and Huh7 cells, the experiment was conducted on two separate plates one for sorafenib alone and the other for the mixture (sorafenib and CPT). The tests showed that the percentage of sorafenib uptake on HepG2 cells was significantly increased from 86.64% in cells treated with sorafenib alone to 98.95% (p < 0.01) in cells treated with combination of sorafenib and CPT.p < 0.001) in cells treated with a combination of sorafenib and CPT as shown in Fig. While the uptake of sorafenib by Huh7 cells was significantly increased from 65.94% in cells treated with sorafenib alone to 91.03% (p < 0.01) and from 63.3% to 90.5% (p < 0.001) in HepG2 and Huh7 cell line, respectively Fig. Furthermore, the uptake of CPT was significantly increased from 79.6% to 90.1%  and ferroptosis proteins  as shown in Fig. In the past few decades, although a number of studies have been carried out to improve the efficacy of sorafenib by overcoming its resistance and reducing its side effects , it is s50 in HepG2 and Huh7 cell lines compared to IC50 of sorafenib alone after 48 and 72 hrs. of incubation. This results strongly recommended that down-regulation of Nrf2 by CPT favored the inhibition of HCC growth by sorafenib.Our study demonstrated that cell viability, CI and DRI indicated strong synergism between sorafenib, and CPT. CPT potentiates sorafenib efficacy by reducing its ICIt is well evidenced that p62-Keap1-Nrf2 axis is one of the most critical pathways implicated in HCC carcinogenesis . Also, tAbnormal activation of Nrf2 enhances transcription of anti-ferroptosis downstream genes as solute carrier family 7 membrane 11 (SLC7A11/xCT), NAD(P)H quinone oxidoreductase 1 and detoxifying enzymes , reducing the sensitivity of ferroptosis in the end  and Nrf2In our study, CPT inhibited Nrf2 and SLC7A11 expression effectively compared to control (Untreated group). While sorafenib could increase ROS level and induce high Nrf2 expression compared to control. Additionally, we found that the combination of sorafenib and CPT significantly increased lipid peroxidation by (218.8% and 152.1%), iron concentration by , decreased TAC by (54.1% and 68.08%), decreased GR (63.4% and 57.9%), decreased GPX4 activity  and reduced the expression of SLC7A11 compared to sorafenib alone in HepG2 and Huh7, respectively.For more confirmation, Nrf2 activator compound DMF was used . It was CPT alone or sorafenib alone can activate autophagy to maintain cell survival by decreasing the ROS (ferroptosis) and reducing the drug’s apoptosis action , 43. BesIn addition, CPT alone or sorafenib alone can also activate the apoptosis mechanism to induce cell death , 47. IndBy addition of ZVAD-FMK (an apoptosis inhibitor) to the combination of sorafenib and CPT, there was no change in cell viability compared to cells treated with a combination of sorafenib and CPT alone. So, our results showed that combination treatment did not suppress HCC cells proliferation through apoptosis. Moreover, the concentration of the combination used in our experiment is considerably lower than the concentration of sorafenib and CPT that other researchers have shown is necessary to induce apoptosis in HCC cell lines –50.On the other hand, cells treated with ferrostatin-1 (a ferroptosis inhibitor) to the combination of sorafenib and CPT, showed a significant increase in cell viability compared to our combination . These findings indicate that the combination of sorafenib and CPT suppressed HCC cell proliferation via ferroptosis.Nrf2 supposedly induces sorafenib’s resistance through controlling the transcriptional production of some resistance genes such as Metallothionein-1G (MT-1G)  and p62 In our study, Nrf2 inhibition by CPT reduces the expression of MT-1G and p62 compared to sorafenib alone. This is the first research that has been done, as far as we are aware, to examine the role of CPT in lowering sorafenib resistance markers.These findings were similar to those in several studies suggesting that using Nrf2 pharmacologic suppressor reduces chemotherapeutic resistance , 41, 51.Furthermore, sorafenib’s resistance can occur by overexpression of drug efflux transporters, such as ATP-binding cassette (ABC), which is upregulated by Nrf2 . SeveralThe present investigation may provide evidence on a potent synergism between sorafenib and CPT in the treatment of HCC. Nrf2 inhibition by CPT improves sorafenib’s efficacy and reduces its resistance through augmenting sorafenib’s ferroptosis action. Furthermore, down regulation of ABCG2 by this combination induces sorafenib’s uptake by HCC cells. The following graphical abstract illustrates our study's concluding remarks Fig.


Striga resistant, provitamin A enriched, and quality protein maize inbred lines. In our breeding program, line TZMi501 belonging to a flint heterotic group (HGA), and TZMi407-S and TZMi214, representing the dent heterotic group (HGB), were identified as the most useful founder inbred lines. The 623 inbred lines were consistently separated into four clusters based on Ward’s hierarchical clustering, structure, and principal component analyses, with the 20 founder inbred lines spread into all clusters. The founder inbred lines were more genetically related to the productive inbred lines but showed genetic divergence from the provitamin A enriched inbred lines. These results provide a better understanding of the breeding history of the sub-tropical maize inbred lines to facilitate parental selection aligned to existing heterotic groups for use in breeding programs targeting the improvement of essential traits in maize.Understanding the genetic relationships between the key founder inbred lines and derived inbred lines could provide insight into the breeding history and the structure of genetic diversity of the available elite inbred lines with desirable target traits. The maize improvement program at the International Institute of Tropical Agriculture (IITA) analyzed the pedigree information of 623 sub-tropical maize inbred lines generated at the IITA maize breeding program to identify the key founder inbred lines. We also used 5032 SNP markers to assess the genetic similarities of the founder inbred lines with their progenies subsequently developed for specific target traits. The results of pedigree analysis and SNP markers-based similarity scores identified 20 key founder inbred lines with significant contributions to the development of drought tolerant, early maturing, productive,  The maize breeding program at the International Institute of Tropical Agriculture2 has thus made a modest investment in the development of germplasm required for optimal response to sub-tropical production environments in Nigeria, Cameroon, and other countries in WCA2. The breeding program has focused on improving key traits that affect production, meet consumer needs, and confer consistent performance across environments3.Maize has emerged as a major staple food crop for millions of people in West and Central Africa (WCA). The crop is cultivated in the lowland tropics and sub-tropics that differ in seasonal rainfall, temperatures, elevation, and length of the growing period that affect the presence of different types of diseases, pests, and other adaptive traits2. Additional sub-tropical populations were formed in Cameroon by crossing TZMSR to hybrids from Kenya, Zimbabwe, DEKALB Seed Company, and diverse germplasm from CIMMYT, Tanzania, Kenya, Costa Rica, Guatemala, Brazil, and Thailand. The diverse sub-tropical populations developed in Nigeria and Cameroon had been sources of elite maize inbred lines for developing high-yielding hybrids with resistance to turcicum leaf blight, common rust, grey leaf spot, and the maize streak virus disease for commercialization in the two countries7. The most diverse elite maize inbred lines developed in Nigeria and Cameroon prior to 1995 were crossed with each other as well as with introduced and tropical inbred lines as donors of novel alleles to generate source populations for the continual development of elite maize inbred lines with desirable agronomic features, tolerance to drought, resistance to Striga hermonthica, earliness, high lysine, tryptophan, and provitamin A content for two decades. Understanding the contributions of pre-1995 elite inbred lines to the subsequent development of elite inbred lines with different target improved traits can thus help in identifying vital inbred lines with significant impact in our breeding program. A thorough understanding of the founder inbred lines may also provide insights into the consequences of the breeding strategy used in managing the genetic diversity of the sub-tropical maize germplasm in IITA8.The breeding programs in Nigeria developed a sub-tropical source population known as TZMSR from crosses of maize varieties and hybrids from Eastern, Southern, and Central Africa, as well as germplasm from the International Maize and Wheat Improvement Center (CIMMYT) and the temperate zone with tropical maize streak virus (MSV) resistant maize germplasm9. Many studies in maize used pedigree-based relationships to identify key founder inbred lines of germplasm adapted to central Europe and the United States and provided insights into the industry germplasm structure and relationships between pre-commercial and commercial maize inbred lines13. Single nucleotide polymorphism (SNP) markers have successfully confirmed the importance of the founder inbred lines in ex-PVP germplasm and identified common heterotic groups in temperate maize. Further high-density haplotype analysis using SNPs revealed significant haplotype sharing between historical founders and ex-PVP inbred lines14. Additionally, Minamikawa et al.15, using SNPs, traced founder haplotypes of Japanese apple varieties for use in genetic improvement of fruit quality traits and understanding the selection history of germplasm in their breeding program.Effective use of existing genetic diversity is a critical prerequisite for achieving sustained genetic gain in productivity, stress resilience, and improved nutritional quality. A comprehensive analysis of pre-1995 elite sub-tropical maize inbred linesin the continual development of inbred lines over the years is vital in identifying key maize inbred lines contributing the most to the breeding program. Likewise, such analyses can help select underutilized inbred lines to broaden and diversify the genetic base of adapted sub-tropical germplasm and provide the basis for monitoring genetic diversityThe present study, examined a comprehensive list of 88 elite maize parental inbred lines and their 535 elite inbred progenies adapted to sub-tropical environments. We run both pedigree and high-density SNP data-based analyses to define the genetic structure of these inbred lines and examine the contribution of the pre-1995 parental inbred lines to subsequent line development. The objectives of this study were then to (i) identify vital founder inbred lines with significant contributions to the development of sub-tropical inbred lines, (ii) assess the relationship of the founder inbred lines with the sub-tropical inbred lines, and (iii) evaluate the genetic structure and diversity of the sub-tropical inbred lines.The sub-tropical elite maize inbred lines used in the present study consisted of (i) 89 drought tolerant inbred lines (DT), (ii) 11 early inbred lines (EARLY), (iii) 88 key parental inbred lines (Founder), (iv) 124 productive inbred lines (PT), (v) 86 provitamin A enriched inbred lines (PVA), (vi) 31 quality protein maize (QPM) inbred lines and (vii) 194 Striga tolerant inbred lines (STR). All of the sub-tropical inbred lines in this study have been derived from broad-based populations, varieties, biparental crosses, backcrosses, and multi-parent synthetics through repeated self-pollination with selection for desirable agronomic traits and resistance to the major diseases and other target traits. The inbred lines have undergone seven to nine generations of selfing and represent the present elite sub-tropical inbred lines in the IITA maize breeding program. A detailed summary of the genetic composition of these inbred lines and their heterotic affinities are presented in Table 18. From the set of 635 maize inbred lines, a total of 38,052 SNP-based DArTseq markers were generated. Molecular markers were subjected to a filtering process using TASSEL 5.2.48 ; all those SNPs with a missing rate higher than 10%, minor allele frequency (MAF) < 0.05, and maximum allele frequency > 0.95 excluded from the genotypic dataset. Finally, 5032 high-quality SNPs distributed across the ten chromosomes of the maize genome were retained for subsequent analysis  based technology at Diversity Arrays Technology Pty Ltd  as described earlier20 to identify the founder inbred lines with significant contributions to the development of the sub-tropical inbred lines. The similarity of the pre-1995 founder inbred lines to subsequent sub-tropical elite maize inbred lines development was also assessed based on the SNP markers using Flapjack software version 1.21.02.0421. The essential parameters of diversity, including gene diversity, heterozygosity, major allele frequency (MAF), and polymorphic information content (PIC), were estimated using PowerMarker version 3.2522. Analysis of molecular variance (AMOVA) among groups, among inbred lines within groups, and among all inbred lines was conducted using GenAlEx version 6.50323. A Fixation index24 was also calculated to determine the genetic differentiation between groups25.The pedigree relationships between the inbred lines were analysed and visualized using Helium software version 1.19.09.0326. The simulation was performed without prior information on the individuals with the number of expected sub-populations or clusters (K) ranging from 1 to 10 with ten replications. Each replication was run with iterations and burn-ins set to 10,000. The Evanno transformation27 was used to determine the most appropriate K-value within the set of inbred lines using Structure Harvester . A threshold value of ≥ 60% was used to assign each inbred line to a specific group, while the inbred lines presenting values below the threshold limit were assigned to the mixed group.To determine the inherent population structure of the 623 inbred lines, a model-based clustering method was implemented using Structure software version 2.3.428 was used to calculate the pairwise genetic distance  among the inbred lines for hierarchical clustering. The IBS matrix was then used to generate Ward’s minimum variance hierarchical cluster dendrogram and a neighbor-joining tree using the Analyses of Phylogenetics and Evolution (ape) package29 implemented in R version 4.2.030. Principal Component Analysis (PCA) was also carried out in Tassel version 5.2.8319 to visualize the pattern of genetic dissimilarities within and between sub-groups.PLINKThe use of plant material complies with relevant institutional, national, and international guidelines and legislation.Striga (STR). The founder line, TZMi501, representing our breeding program's flint heterotic group (HGA), was utilized in developing 101 elite DISTR inbred lines , earliness, PVA content, QPM, productivity, and resistance to The SNP markers-based similarity matrix between the 20 key founder inbred lines, and the sub-tropical inbred lines is presented in Supplementary Table The 5032 SNPs data set recorded for the 623 elite sub-tropical maize inbred lines had a MAF ranging from 0.50 to 0.95 with an average of 0.77 Table , indicatP = 0.001) among trait-based groups, accounting for only 6% of the molecular variance. The respective variations among inbred lines within trait-based groups and among all inbred lines represent 22.72% of the total molecular variance . Also, the PVA inbred lines had high genetic differentiation (0.10 to 0.12), with inbred lines belonging to other trait-based groups. In contrast, the productive inbred lines had the lowest fixation index values with other trait-based groups.An analysis of molecular variance (AMOVA) was conducted to assess the relatedness among trait-based line subgroups as well as the founder line group to determine the level of genetic differentiation among subgroups. The AMOVA showed significant differentiation  among clusters accounting for only 14% of the molecular variance  confirmed Ward’s hierarchical cluster analyses and the genetic structure by separating the sub-tropical maize inbred lines into four clusters Fig. A. The cl31. Therefore, there is a pressing need to optimize the use of currently available genetic resources to improve grain yield and other agronomic and adaptive traits of the major crops, including maize. Over two decades, the maize improvement program at IITA has made significant progress in developing maize inbred lines with desirable agronomic features and adaptation to sub-tropical environments and shared with partners32. Different breeding methods were deployed to generate the inbred lines, including the introgression of desirable genes from exotic tropical and temperate inbred lines, followed by selection for improvements in target traits. Amongst the 88 elite sub-tropical inbred lines developed prior to 1995, 20 founder inbred lines were sources of adaptive traits in the subsequent development of the sub-tropical inbred lines with high grain yields, earliness, drought tolerance, Striga resistance, high lysine, tryptophan, and provitamin A content. In the present study, pedigree information was used to identify the founder inbred lines with significant contributions to the development of the different sets of inbred lines with improved target traits12. Such information is useful for breeders in IITA and partner institutions in their search for useful germplasm to further improve maize in sub-tropical environments. Molecular markers were also used to assess the genetic relationships between the founder inbred lines and the resultant trait-based groups of sub-tropical inbred lines33. Indeed, the genetic differentiation of trait-based groups of inbred lines from the founder inbred lines could provide insights for the optimal exploitation of elite inbred lines to develop superior hybrids to meet the current demand for higher yields, better nutrient content, and resistance to pests and diseases.The alarming population growth and climate change are grave challenges facing agricultural production globally13.In the present study, founder inbred lines with significant contributions to the development of the sub-tropical maize inbred lines were identified based on pedigree information. The founder line, TZMi501 belonging to the flint heterotic group (HGA) in our breeding program, was the most valuable line contributing to the development of the highest number of inbred lines. This inbred line was used to develop mainly STR, DT, and productive inbred lines and thus could be used in other breeding programs for the improvement of these target traits. The founder line, TZMi4017-S, belonging to the dent heterotic group (HGB) in our program, was involved in developing many STR inbred lines, whereas TZMi214, which also belongs to the dent heterotic group (HGB), was involved in the development of many PVA inbred lines. TZMi214 could benefit breeding programs aiming at increasing PVA content in maize. Previous studies have used a similar approach to identify founder inbred lines with significant contributions to developing various inbred lines with United States plant variety protection certificates34 who reported that pedigree information could be incomplete and may not be useful in some cases. The founder inbred lines TZMi407-S, and TZMi214 had high genetic similarities with the STR (71%) and PVA (68%) inbred lines, respectively, consistent with the results from pedigree relationships. It should be noted that the 20 key founder inbred lines identified in the present study were separated into different clusters. The founder inbred lines belonging to the same clusters could be aligned to existing heterotic groups and used for broadening the genetic base of the breeding materials in other breeding programs targeting the improvement of the same set of traits.Line TZMi501, identified as the top founder line based on pedigree information, was also the top founder line identified using the 5032 SNP-based genetic relationships with the sub-tropical inbred lines. As expected, TZMi501 had high genetic similarity with the DT (72%), STR (71%), and productive inbred lines (72%). Even though the pedigree analysis did not show any relationships between TZMi501 and the QPM inbred lines, this line had a high genetic similarity (72%) with QPM inbred lines. Such observation confirms the findings of Messmer et al.35 for 527 inbred lines genotyped with Illumina GoldenGate assays. The PIC value of 0.26 obtained from this study was also slightly lower than the 0.28 value for 362 maize inbred lines genotyped with Illumina MaizeSNP50 SNP markers and 0.29 value for 103 inbred lines genotyped with LGC genomics SNP markers, reported by Zhang et al.36 and Abu et al.37. However, this value is higher than the 0.19 PIC value for 94 tropical maize inbred lines genotyped with Diversity Arrays Technology (DArT) SNP markers38.The 5032 SNP markers found a significant level of genetic diversity among the sub-tropical inbred lines with an average GD of 0.32, which was comparable to the 0.39 GD value reported byThe AMOVA showed some level of differentiation among the founder inbred lines and other trait-based groups. However, most of the diversity resided among inbred lines within trait-based groups and among inbred lines. The founder inbred lines were found to be more closely related to the group of productive inbred lines, possibly due to their extensive use for increasing grain yields and other adaptive traits. In contrast, the founder inbred lines showed greater genetic divergence from the PVA inbred lines because only a few founder inbred lines were used for developing the inbred lines. Moreover, desirable genes associated with PVA accumulation in maize were introgressed into the tropical inbred lines using temperate donor parents during the development of the PVA inbred lines, which were used as parents for generating the sub-tropical PVA inbred lines.Striga resistant, whereas others combine high yield potential with drought tolerance. Only the PVA inbred lines containing temperate germplasm in their genetic backgrounds were grouped into a major cluster because they originated from crosses of tropical PVA inbred lines with a few elite sub-tropical inbred lines. The lack of correspondence between the existing heterotic groups and the clusters generated using SNP markers could arise due to the origin of the parents of the sub-tropical inbred lines from broad-based populations and the germplasm of unknown heterotic patterns. The resulting cluster analyses and testcross evaluation could thus be used as the bases to refine further the heterotic patterns of the diverse sup-tropical maize inbred lines.Ward’s hierarchical clustering, structure, and PCA analysis demonstrated the presence of similar genetically distinct groups of sub-tropical maize inbred lines used in our study. The productive, STR, DT, and founder lines assigned to existing HGA and HGB by breeders were scattered across different clusters, indicating that these inbred lines originated from crosses of diverse parents during their development. Some inbred lines are productive and This study elucidated the genetic relationships between key founder inbred lines and all elite sub-tropical maize inbred lines developed from them in the Maize Improvement program at IITA for two decades. The study has provided insight into the structure of the genetic diversity for the 623 sub-tropical maize inbred lines. Twenty key founder inbred lines with significant contributions to the development of DT, EARLY, Productive, STR, PVA, and QPM inbred lines were identified based on both pedigree data and SNP markers-based similarity estimates. TZMi501, TZMi407-S, and TZMi214 were identified as the most widely used founder inbred lines. TZMi501 was mainly used to develop DT, STR, and Productive inbred lines, whereas TZMi407-S and TZMi214 were used to develop the STR and the PVA inbred lines, respectively. Considerable genetic diversity was found among the sub-tropical inbred lines included in the present study. All the 623 inbred lines were consistently separated into four clusters based on Ward’s hierarchical clustering, structure, and PCA analyses. The productive, STR, DT, and founder inbred lines were scattered across different clusters, whereas the PVA inbred lines were grouped into the same cluster. The results of the analysis of AMOVA highlighted the presence of significant differences among trait-based groups and clusters and among inbred lines within the trait-based groups and clusters. These results will facilitate the selection of diverse inbred lines belonging to the different trait-based groups for use in breeding programs targeting the improvement of desirable traits in specific heterotic groups. The clusters also provide a useful breeding structure to generate inbred lines aligned to specific heterotic groups to exploit higher expression of heterosis in hybrids in IITA and partner breeding programs.Supplementary Figures.Supplementary Table S1.Supplementary Table S2.Supplementary Table S3.Supplementary Table S4.Supplementary Table S5.Supplementary Table S6.


Normal human cells have 22 pairs of chromosomes as well as 2 sex chromosomes for a total of 46 chromosomes; this normal karyotype is called diploidy (euploidy). On the other hand, aberrant numbers of chromosomes, i.e., gains and/or losses of chromosomes, have been found in most human cancer cells. This condition is called aneuploidy. Within in a clinical context, aneuploidy has been shown to be a marker of poor prognosis and drug resistance. Importantly, the deadliest stage of a cancer occurs when the cancer has been found to have spread from a primary tumor site to other organ sites, which is called metastasis. Controlled comprehensive clinical studies of metastatic cancer, which require an interrogation of the affected site(s), such as lungs, or liver, brain, or bone, with the goal of developing better treatment are very challenging. Therefore, repeatable controlled studies of complex human metastatic disease are simulated in animal systems using human cancer cells in special mouse strains. We used such a model system to better understand the chromosomal changes and the processes that bring them about, along with a study of gene variants, chromosomal amplifications, gains, and losses in metastatic cancer cells. We compared these differences to their primary tumor cell counterparts. This information aids us in suggesting possible new therapeutic treatments that may have a potential to limit the growth of metastatic cancer.Aneuploidy, a deviation in chromosome numbers from the normal diploid set, is now recognized as a fundamental characteristic of all cancer types and is found in 70–90% of all solid tumors. The majority of aneuploidies are generated by chromosomal instability (CIN). CIN/aneuploidy is an independent prognostic marker of cancer survival and is a cause of drug resistance. Hence, ongoing research has been directed towards the development of therapeutics aimed at targeting CIN/aneuploidy. However, there are relatively limited reports on the evolution of CIN/aneuploidies within or across metastatic lesions. In this work, we built on our previous studies using a human xenograft model system of metastatic disease in mice that is based on isogenic cell lines derived from the primary tumor and specific metastatic organs . As such, these studies were aimed at exploring distinctions and commonalities between the karyotypes; biological processes that have been implicated in CIN; single-nucleotide polymorphisms (SNPs); losses, gains, and amplifications of chromosomal regions; and gene mutation variants across these cell lines. Substantial amounts of inter- and intra-heterogeneity were found across karyotypes, along with distinctions between SNP frequencies across each chromosome of each metastatic cell line relative the primary tumor cell line. There were disconnects between chromosomal gains or amplifications and protein levels of the genes in those regions. However, commonalities across all cell lines provide opportunities to select biological processes as druggable targets that could have efficacy against the primary tumor, as well as metastases. Aneuploidy, a deviation in chromosome numbers from the normal diploid set, has a long history and was first described 130 years ago from observations in fresh human carcinoma specimens . It is nIn this work, we built on our previous omics studies on a human xenograft model system of metastatic disease in mice ,41,42. T6) cells into the second thoracic mammary fat pad of 5 female NOD-SCID mice. After 13–15 weeks of tumor growth, the mice were sacrificed and, 1° tumor, brain, liver, lungs, and spine were immediately excised, dissected away from fat and muscle, and placed into sterile phosphate-buffered saline on ice. All organs/bones were inspected using fluorescence microscopy for any signs of metastatic burden, which was easily discerned as bright tdT red fluorescence. Areas of fluorescence, along with adjacent tissue, were cut away and placed into 100 mm cell culture plates in 10 mL sterile medium and then immediately minced within a sterile hood. Generation and characterization of the parental MDA-MB-435-tdTomato (435-tdT) fluorescent cell line and subsequent isogenic primary (1°) tumor and metastatic cell lines have been previously described ,43. Brie2.All tissue explants were initially cultured in Roswell Park Memorial Institute (RPMI)-10% fetal bovine serum (FBS) medium supplemented with antibiotics (100 I.U./mL penicillin (pen), 100 μg/mL streptomycin (strep), 100 μg/mL ampicillin, and 100 μg/mL kanamycin) and, as necessary, Fungizone. Medium was refreshed every 2–3 days, and after 2 weeks of culture, the medium was changed to RPMI-10% FBS supplemented with pen/strep. Further studies resulted in optimal media selections: Dulbecco’s modified Eagle medium (DMEM-10% FBS) for the parental cell line and DMEM:Ham’s F12 (50:50)-5% FBS for the 1° tumor and all metastatic cell lines. Cells were cultured in standard humidified incubators at 37 °C and 5% COProteomics were performed from a single sampling of each cell line’s proteins in the Mass Spectroscopy and Proteomics Facility at the Johns Hopkins University Medical School using tandem mass tags (TMTs) for direct comparisons of all 10 samples in a single tandem MS experiment, as previously described . RNA-seq was performed from a single sampling of each cell line’s RNA at a commercial facility , as previously described ; the exov:v), mounted on grease-free chilled 4 °C slides, and air-dried. Giemsa–trypsin banding was performed for chromosome examination. One hundred mitotic cells per sample were analyzed. The abnormal karyotypes were described using the International System for Human Cytogenomic Nomenclature (ISCN 2020). Conventional G-banded chromosome studies were performed using standard techniques. Cells in the exponential phase of growth were incubated with colchicine  to a final concentration of 0.8 μg/mL for 4 h and harvested. Cells were then treated with a hypotonic solution of potassium chloride (0.075 mol/L) and incubated at 37 °C for 30 min, fixed in acetic acid:methanol  are tightly linked, with the former being generated during a loss of high-fidelity cell division, which is a function of the latter ,44. Therhttps://cnvkit.readthedocs.io/en/stable, last accessed 30 June 2022) were used to generate variants  and genome-wide copy number discovery from the targeted NGS data. Only specimens with more than a 1000× unique sequencing read depth were processed through gene variant/mutation and CNV analysis pipelines. Copy number variants were determined using autosomal log2 ratio thresholds set at 1.3, 0.6–1.0, and −1.0 for the detection of amplification, gain, and loss, respectively. Analysis was performed using human reference sequence genome assembly hg19 .The targeted next-generation sequencing (NGS) assay has been previously described ,47. DNA Conventional cytogenomic analyses revealed complex karyotypes with multiple structural and numerical chromosome abnormalities across all cell lines. All cell lines were hyperdiploid with similar modal numbers of chromosomes of 56, 56, 56, 55, and 56 for the 1° tumor, brain, liver, lung, and spine metastatic cell lines, respectively. Nevertheless, comprehensive karyotyping analyses provided evidence of a vast amount of intra- and intercell line karyotype heterogeneities, with the overall numbers of chromosomes ranging from 54 to 58 . As seenCompared to the modal 1° tumor karyotype, the modal brain karyotype had a gain of two abnormal number 6 chromosomes characterized by additional material added to the 6q13 arm, i.e., a gain of 6q11-6q13, a loss of five marker chromosomes , and a gain of two novel marker chromosomes . Inter- and intrakaryotype heterogeneities between the modal karyotype of the 1° tumor cell line and the five brain cell line karyotypes, as well as between the model brain cell line karyotype and four (panels: Br-2–Br-5) additional representative brain cell line karyotypes, are indicated by blue and orange arrows, respectively, and it can be noted that the five brain cell lines are distinctly different. The five right-hand-side karyotypes in To gain a better understanding of factors that could compromise cellular processes of cell division (S/G2-mitosis) and, consequently, be involved in driving/maintaining chromosomal instability (CIN) and, subsequently, aneuploidy, we evaluated changes in the expression levels of the proteins (in our proteomic dataset) of these processes. The analysis of these S/G2 mitosis-associated processes and proteins was extended to a comparison of these proteins in the metastatic cell lines relative to the 1° tumor cell line . As in TAlong the lines of the analysis described above for To determine whether there were gene-level instability differences between the 1° tumor and parental cell lines, as well as between the 1° tumor cell line and each metastatic cell line and between metastatic cell lines, we analyzed the linear fold change in SNP frequencies in each chromosome in each cell line. A compilation of the linear F.C. of SNP frequencies for each chromosome in the 1° tumor cell line relative to the parental cell line and metastatic cell lines relative to the 1° tumor cell line is shown in MNX, XRCC2, KMT2C, CHPF2, and EZH2; to five copies of EPHB6, PRSS1, MGAM, BRAF, MET, RINT1, and EPHB4; and to four copies for the other 21 genes  have not yet been associated with this cancer. GNAS, CD40, PTPRT, and MAFB and to 4 copies for the remaining 13 genes. Only MAFB has not been reported in breast cancer. PTPRO and CDKN1B have been reported to be tumor suppressor genes [ETNK1, ABCC9, RECQL, and ETV6) can be upregulated in breast cancer [MNX1 , PRSS1 (germline-associated gene), CCT6P1 (pseudogene), and GRM3, were not represented in our transcriptome dataset and therefore not recorded in XRCC2, KEL, MGAM, SMO, GRM8, PIK3CG, RELN, CD36, MAGI2, SEMA3A, RTEL1, PTPRT, MAFB, and ASXL1, were absent from the proteome dataset, regardless of transcript level. The interstitial loss of genes in chromosome arm 12p  were shared across all cell lines in this study, while 18 variants (13%) were presented either only in one cell line or shared in two to five cell lines . The parll lines . GRIN2A  variant . Both livariants . The 1° ell line . Among tell line .The focus of our previous multiomics-based studies was to characterize the transcriptomic, proteomic, and metabolomic distinctions of the isogenic cell lines that were generated from a human xenograft model system of metastatic disease in mice ,41,42. OCytogenomic studies in clinical settings have consistently demonstrated that copy number variations, ploidy, chromosomal aberrations, and heterogeneity are very often independent prognostic markers of survival and resistance to chemotherapies ,30,55,56The scope of this complexity increased when found that the processes involved in the progression of metastasis to brain, liver, lung, and spine caused further evolution, which resulted in a variety of organ-specific karyotypes that differed from the 1° tumor , as wellTo better understand possible causes of this vast inter- and intrakaryotype heterogeneity, we studied CIN. CIN, the loss of the absolute fidelity of chromosomal replication and segregation during cell division, has been established as the principal cause of aneuploidy ,10,15. SOn the other hand, all the metastatic cell lines showed predominant decreases in F.C.s of proteins relative to the 1° tumor cell line across these biological processes . This coTo gain further insights into the stability of the genomes of the 1° tumor and metastatic cell lines, we analyzed the fold change in SNP frequencies across all chromosomes and the cumulative changes for each cell line’s genome. In the case of the 1° tumor cell line, when considering cumulative changes across all chromosomes, these results indicate a significant average increase in SNP frequencies in the 1° tumor cell line relative to the parental cell line, which demonstrates that controlling factors/processes that modulate SNPs are decreased or compromised in the 1° tumor cell line relative to the parental cell line. Similarly, the brain and liver cell lines exhibited increased instabilities with respect to repairing SNP, causing events such as significant cumulative SNP frequencies exceeding those of the 1° tumor cell line and, by extension, the parental cell line as well. Cumulatively, the frequencies of SNPs in the lung and spine cell line did not change relative to the 1° tumor cell line. In addition, SNP data analyses provided evidence that individual chromosomes have varying degrees of stability toward SNP formation, with the numbers chromosomes and specific chromosomes involved, as well as the amount of change in stability, being a function of tissue type. Thus, we found higher numbers of chromosomes with SNP instability in the 1° tumor, brain, and liver cell lines, while in general, fewer chromosomes with SNP instabilities were found in the lung and spine cell lines. Notably, an increase in SNP stability was infrequently found, i.e., occurring in only one, five, and four chromosomes of the 1° tumor, lung, and spine cell lines, respectively. Our findings of differential tissue-specific distinctions in biological processes implicated in CIN and exome-specific SNP frequencies across cell lines are further indications that tissue-specific biochemical conditions modulate cancer cell evolution during their adaptations to each tissue’s microenvironment. Finally, DNA-based NGS revealed different gene variants among the parental cell line, i.e., the 1° tumor cell line, and metastatic cell lines with various gene-variant allele frequencies, which further supports the idea that selection pressures contribute to various organ-specific alterations to the genome populations of the metastases. Karyotyping revealed that our cell lines are not isogenic, i.e., they are instead populations of a variety of related yet distinctly different aberrant karyotypes. The inter- and intrakaryotype heterogeneities that we observed here starkly reflect the well known histological and genetic profiling descriptions of the complex heterogeneity of solid tumors and metastatic lesions . These Overall, our analyses underscore the complexity of tissue-specific differential distinctions between all our cell lines from the level of the genome  and gene (differences in SNP signatures) to the transcript and protein levels. This is important to note from the perspective of recent clinical practices aimed at developing targeted treatment regimens. This concept is generally aimed at finding a single or a few druggable targets in a patient’s primary tumor, as it is difficult to find targets that are common to a primary tumor and its metastases due to the divergence of the metastatic cells from their primary tumor, as emphasized here. Notably, our results indicate that even a comprehensive search for such dual lesion targets will miss rare clones, and a proportion of these may be resistant to treatment. Consequently, although it cannot completely solve this problem, our biological process results allow us to suggest some possible pan-metastatic therapeutic targets, i.e., the biological processes that were common to all four or at least three metastatic cell lines: CNTROB (centrosome regulation); NCAPG2 (DNA condensation); TRIP13 and ZW10 (spindle checkpoint); and PRIM1, CDC45, and RFC3 (DNA replication). Moreover, in the case of the brain metastatic cell line, the DNA damage process could be added to this list. Furthermore, the findings of our study of the interstitial amplification within chromosome arm 7q, which was retained across all cell lines (including the 1° tumor cell line), CUX1 and its associated pathways emerged as important therapeutic targets ,74. Fina