dataesr/scientific-paragraphs-categorization huggingface dataset:
MGEassociated CDSs were found in each genome, ranging from 18 to 113 per genome (Fig. 6a , Table  S8 ). Additionally, the minimal backbone of functional ICEs, with CDS5 and CDS17 encoding for TraE and TraG conjugation factors and CDS22 encoding for the ICE DDE-transposase  [59] , were retrieved in four strains (5632, 4025, 13377, and 14668; Fig. 6a ). Vestigial ICE forms were detected in nearly all strains, except 4055. Despite the paucity of prophage sequences in mycoplasmas, this study also retrieved a prophage in two strains (13377 and 14668). Thus far, M. agalactiae prophages have only been detected in strains isolated from ibex  [60]. These data were used to evaluate the correlation between the presence of RM systems, the number of methylated sites detected (methylome), and the presence of MGE-related CDSs in the genomes of M.
Occurrence of M. agalactiae MTases outside the species Whether MTases identified above were specific to M. agalactiae was addressed by BLASTP analyses against bacterial genomes available in current databases (see Methods). Furthermore, the five following MTase genes had no homolog outside of the Mollicutes class, which includes Mycoplasma spp. , as well as Ureaplasma, Spiroplasma, and Acholeplasma spp. : the CpG methyltransferase, as previously described (see above), the Type III related MTases (MAGa1570, MAGa1580, and MAG1530), and the Type II MTase of 13377, J7894_02115 (Fig. 3 , Table  S9 ). Of the 11 Type II and III MTases identified in M. agalactiae, all were found in at least one other Mollicute, of a different species or genus and of a different phylogenetic group (Fig. 7 , Table  S9 ).
M. agalactiae strain 5632 can uptake and integrate chromosomal DNA from other strains via a conjugative, distributive mechanism designated MCT, which remains to be fully understood. This phenomenon was rarely observed with other strains, and when it occurred, it was at low frequency and in strains that were genetically similar to 5632. One hypothesis was that 5632 had the capacity to incorporate the incoming chromosomal DNA only once digested. In support of this hypothesis, our data indicated that cloning MAGa4250 into PG2 almost abolished the incorporation of PG2 chromosomal DNA into 5632, most likely by protecting the PG2 chromosomal DNA from being digested by the MAGa4250 MTase cognate REase. A search across the mycoplasma database (data not shown) retrieved a MAGa4250 homolog only in a few strains of M.
Acknowledgements This work was performed in collaboration with the  GeT core facility, Toulouse, France  (http://get. genotoul. fr/),supported by  France Génomique National infrastructure , and funded as part of the ' Investissement d'avenir ' programme managed by  Agence Nationale pour la Recherche  (contract  ANR-10-INBS-09 ) and by the  GET-PACBIO programme (« Programme operationnel FEDER-FSE MIDI-PYRENEES ET GARONNE 2014-2020  »). We are grateful to the  Genotoul bioinformatics platform Toulouse Midi-Pyrenees, France , and the  Sigenae group  for providing support and storage resources. This manuscript was proofread by a professional service.
All supporting data, code and protocols have been provided within the article or through supplementary data files. Four supplementary figures and ten supplementary tables are available with the online version of this article. 000829 data files (Figs S1-S4, Tables  S1-S10 ) available in the online version of this article. Raw data and assembled genomes generated for this study are available from the NCBI Sequence Read Archive (SRA) and GenBank (Bioproject PRJNA717738). The complete.
Multigene assays can provide insight into key biological processes and prognostic information to guide development and selection of adjuvant cancer therapy. We report a comprehensive genomic and transcriptomic analysis of tumor samples from 171 patients at high risk for recurrent renal cell carcinoma post nephrectomy from the S-TRAC trial (NCT00375674). We identify gene expression signatures, including STRAC11 (derived from the sunitinib-treated population). The overlap in key elements captured in these gene expression signatures, which include genes representative of the tumor stroma microenvironment, regulatory T cell, and myeloid cells, suggests they are likely to be both prognostic and predictive of the anti-angiogenic effect in the adjuvant setting. These signatures also point to the identification of potential therapeutic targets for development in adjuvant renal cell carcinoma, such as MERTK and TDO2.
Classic ccRCC mutations do not predict outcome following tumor resection As part of a retrospective hypothesis-generating analysis, we submitted 171 tumor tissues from nephrectomy or biopsy of patients enrolled in the S-TRAC trial (NCT00375674) to whole exome sequencing (WES) to identify and, when possible, compare genetic correlates of clinical outcome with the results reported elsewhere in the metastatic setting. Molecular characterization of ccRCC has led to the identification of commonly mutated genes, including PBRM1, SETD2, and BAP1. After VHL, PBRM1 is the most commonly mutated gene in ccRCC, which, depending on context, can act as a tumor suppressor or oncogene  16. A meta-analysis of seven studies in localized RCC found that PBRM1 loss of function was associated with reduced overall survival (OS) and progression-free survival (PFS), as well as advanced clinicopathologic features  17.
05 and the log2(HR) was clustered in a heatmap. Non-significant cell types for both arms and overall cohort are not shown. For significant and nonsignificant cell types for both arms and overall cohort, xCell scores are provided in the file Figure4c_celltypes_DFS/xCell. complete. tsv CI confidence interval, DFS disease-free survival, HR hazard ratio, NE not estimable, TCGA The Cancer based on their clinicopathologic characteristics and could potentially benefit from additional combination therapy strategies for this setting. First, we focused on the sunitinib treatment arm of the S-TRAC study to identify molecular characteristic that might associate with the resistance to anti-angiogenesis adjuvant treatment and could be predictive in nature. Using the sunitinib treatment arm dataset only, we identified a GES signature that consists of 11 genes (STRAC11): APO-BEC3A, AMT, PRKAB1, MROH8, SLPI, TDO2, SNX29, SLC16A1, MERTK, ARRDC1, and STEAP1 (Fig.
5a ). In the placebo arm, low and high STRAC11 GES expression groups had similar DFS (HR 0. 8 [95% CI: 0. 36-1. 82]; Fig. 5b ). In contrast, in sunitinib-treated patients, low versus high expression of the STRAC11 GES was associated with longer DFS (HR 0. 05 [95% CI: 0. 02-0. 14]) (Fig. 5c ). Following the coexpression and network analyses, this signature was found to be enriched for genes involved in the regulation of the stroma component of the tumor (TDO2, STEAP1) as well as Treg cells (SLC16A1, PRKAB1) and myeloid cell (APOBEC3A, MERTK, SNX29) subsets (Table  1 ). To verify its strength, the STRAC11 GES signature was applied to two independent datasets in the advanced RCC setting: the TCGA KIRC dataset (Cancer Genome Atlas Research, 2013) (Fig. 5d, e ) and JAVELIN Renal 101 metastatic RCC population (Fig.
5f, g ). In the TCGA KIRC dataset, expression was particularly high in m3, and to a lesser extent, in m4 TCGA KIRC subtypes. As anticipated, low versus high expression of STRAC11 GES was associated with longer PFS and longer OS (Fig. 5d ,    5g ). We also investigated the placebo arm of the S-TRAC study to identify the molecular characteristics that might associate with a higher risk of recurrence, independent of anti-angiogenesis adjuvant therapy with sunitinib. Using the same approach, we identified a GES that consists of 14 genes (STRAC14) and that is set to be prognostic in nature, beyond the UISS clinic-pathological classification (Fig. S2A ). In the overall study population, low versus high expression of STRAC14 GES was associated with longer DFS (HR 0. 36 [95% CI: 0. 21-0. 64]) (Fig.
S2B ). In the placebo arm, low versus high expression of the STRAC14 GES was associated with longer DFS (HR 0. 07 [95% CI: 0. 02-0. 24]) (Fig. S2C ). Similar to STRAC11, the STRAC14 signature was also verified in the TCGA KIRC dataset and JAVELIN Renal 101 metastatic RCC population (Figs. S2D-G ). Following the co-expression and network analyses, the STRAC14 signature was also found to be enriched for genes involved in the regulation of the stroma component of the tumor, as well as Treg cells and myeloid cell subsets (Table  1 ). Finally, we expanded this exploration with the overall sample set, looking for the group of patients at the highest risk of recurrence based on molecular criteria. Using the overall sample set, we identified a GES signature that consisted of 13 genes (STRAC13) (Fig.
S3A ). In the overall study population, low versus high expression of STRAC13 GES was associated with longer DFS in the overall study population (Fig. S3B ) and in the sunitinib and placebo arms (Fig. S3C ). The STRAC13 signature was also verified in the TCGA KIRC dataset and JAVELIN Renal 101 metastatic RCC population (Fig. S3D-G ), and it was found to be enriched for genes involved in the regulation of the stroma component of the tumor as well as Treg cells, general T cells, and myeloid cell subsets (Table  1 ).
Discussion We report a comprehensive genomic and transcriptomic analysis of tumor samples from patients lacking overt metastases but at high risk for recurrent RCC post nephrectomy. These analyses were performed using data from patients treated in S-TRAC, a phase III trial comparing adjuvant sunitinib to placebo and meeting its primary endpoint of prolonging DFS. We confirmed the importance of the angiogenic context as a prognostic and predictive marker of the outcome. We identified three GES based on the sample set considered for the discovery. Each signature consists of genes representative of the tumor stroma microenvironment, including fibroblasts, Treg cells, and myeloid cells that, when highly expressed, were associated with poor outcomes and short DFS. The overlap in key elements captured in these three GES suggests that they are likely to be prognostic and also predictive of the anti-angiogenic effect in the adjuvant setting.
TDO2 (also part of STRAC11) is potentially another target for combination with anti-angiogenic therapy. Higher expression of TD02 was associated with poor survival outcomes in breast cancer  46, 47. Targeting indolamine-2,3-dioxygenase-1 (IDO1) expression enhanced response to IFN treatment in RCC cell lines  48. Furthermore, IDO1 is known to promote tumor neovascularization by modulating the expression of IFN-γ and interleukin (IL)-6  49. The immunomodulating activities of IDO1 and TDO2 led to the development of an inhibitor of these two enzymes, which is currently under investigation in a phase I trial (ClinicalTrials. gov, NCT03641794). Other STRAC11 genes that have been implicated in kidney injury or cancer progression include SLPI 50,51 , SLC16A1 52 , and ARRDC1  53. Downregulation of STEAP1 increased proliferation and clonogenicity, and promoted cell migration, invasion, and the progress of mesenchymal transition in endometrial carcinomas  54  and breast cancer  55.
Whole exome and transcriptome sequencing Archival de-identified FFPE tumor tissue blocks from nephrectomy or tumor biopsy were obtained from patients who provided informed consent for genomic analyses. Sample were processed as per below and the same sections were used for both DNA and RNA extractions. For WES (Accuracy and Content Enhanced [ACE] version 3; Illumina NovaSeq), resulting sequences were processed by the Personalis ACE Cancer Exome pipeline (Personalis, Inc, Menlo Park, CA), which uses BWA, GATK, MuTect, VarDict, and Picard to generate variant calls. Variant calls were further filtered using Personalis proxy-normal database (which consists of in part the use of normal variant database MuTect, the Genome Aggregation Database (gnomAD; https:// gnomad. broadinstitute. org)  59 and custom filters to remove many germline variants found in normal tissue as well as sequencing platform bias.
Mutations with a minimum of five mutant reads (that is, found on at least five separate DNA molecules in an individual tumor sample) that were not annotated as synonymous variants and annotated as resulting in a change in protein coding sequence were included in the analysis. The clinical significance of TMB was assessed through WES analysis. The presence of non-synonymous single nucleotide variants per megabase (NSSNV/Mb) defined the TMB high and low status relative to the median value. Whole-transcriptome profiles were generated using RNA-seq (ACE version 3) on FFPE tumor tissue. Transcript levels were quantified by the Personalis ACE Cancer Transcriptome Analysis pipeline (Personalis, Inc, Menlo Park, CA), which uses STAR version 2. 4. 2a-p1 to align reads to the NCBI hs37d5 annotation 105 reference genome and produces transcripts per million (TPM) values for each gene.
TPM values were log2 transformed for further analysis of individual genes or standardized gene pathway signature scores. Briefly, for each gene, we calculated the mean expression and s. d. across samples. Then, we subtracted the mean and divided by the s. d. to standardize the gene score to be centered at zero with units of s. d. (z-score). The pathway score for each sample was calculated as the average of the standardized values for the set of genes within the pathway. To assess sample purity and rule out potential bias, tumor purity was calculated from the corresponding RNA-seq expression data using the ESTIMATE method  60. Using the RNA ESTIMATE Tumor Purity score, the tumor purity ranged from 28% to 92% with a median of 55%. Overall, tumor purity did not bias the tumor mutation burden; the mutant status of individual genes was not associated with increased tumor purity; and it did not confound gene expression.
Gene expression signature analyses GES analyses included published signatures from the IMmotion 150 study "[Teff, angiogenesis, myeloid inflammation (Minf)]", and the JAVELIN Renal 101 study [Immune, angiogenesis]  18, 20. TCGA subtype classification labels by unsupervised gene expression clustering  27  were used to define centroids from the z-scored scaled expression level of the top 20% variable genes from the TCGA KIRC dataset. Expression data from the trial were z-scored and mapped to the nearest centroid to assign the subtype label. The method used was implemented by the Data4Cure molecular affinity app. RNA-seq data was processed using xCell to generate scores for 64 cell type signatures  38. Elastic net analysis. Multi-feature signatures were derived using samples with complete data from the sunitinib arm for STRAC11, the placebo arm for STRAC14, and from both arms for STRAC13.
For each dataset, we performed 1000 bootstrap runs of fitting a Cox proportional hazards model for DFS with regularization by an elastic net penalty  61  and a fivefold cross-validation. Features were ranked by the frequency observed in the bootstraps, and the number of top features was selected using a local maximum concordance index. A composite signature score was computed by a weighted sum of the top features, and each feature was weighted by its average coefficient across bootstrap models. The STRAC14, STRAC13, and STRAC11 GES were further verified using two independent datasets, the JAVELIN Renal 101 study in patients with metastatic advanced RCC (n = 720) and the KIRC dataset of TCGA (n = 488).
Acknowledgements The authors thank the participating patients and their families, as well as the investigators, sub-investigators, research nurses, study coordinators, and operations staff. The authors also thank  Amber Donahue, PhD , for support with the biomarker analyses for this study. This study was sponsored by  Pfizer. Patients treated at Memorial Sloan Kettering Cancer Center were supported in part by  Memorial Sloan Kettering Cancer Center   Support Grant/Core Grant  ( P30 CA008748 ). Medical writing support was provided by  Vardit Dror, PhD , and  Leon Adams, PhD , of  Engage Scientific Solution  and was funded by  Pfizer.
Data availability Relevant summarized data and supplemental materials cited in the manuscript are publicly available  62. Source data are available with this manuscript. The tumor tissue sequencing raw data that support the findings of this study have been submitted to the European Genome-Phenome Archive (EGA) with accession numbers EGAS00001006528 (transcriptome sequencing) and EGAS00001006529 (exome sequencing); due to informed consent limitations on patient confidentiality and secondary use of data, restrictions apply to the availability of the data. Anonymized data can be made available from the authors under a Data Access Agreement upon reasonable request and with permission of the Pfizer Data Access Committee. Additional datasets used in this analysis included subsets of TCGA databases https://xenabrowser.
net/datapages/?dataset=tcga_RSEM_gene_tpm&host=https://toil. xenahubs. net; https://portal. gdc. cancer. gov/projects/TCGA-KIRC) and a subset of data from the JAVELIN Renal 101 Study B9991003 (https:// www. nature. com/articles/s41591-020-1044-8#Sec33; https://staticcontent. springer. com/esm/art%3A10. 1038%2Fs41591-020-1044-8/ MediaObjects/41591_2020_1044_MOESM3_ESM. xlsx; Clinical Table  S11  and Expression Table  S13 ). Source data are provided with this paper.
Code availability The R scripts used for elastic net analyses are available  62. R statistical software environment version 3. 5 was utilized (https://www. r-project. org/). Data4Cure was utilized for analysis and visualization (https:// www. data4cure. com). SAS version 9. 4 (TS1M5) was utilized for data analysis (https://www. sas. com/). Processing of Whole Exome and Whole Transcriptome raw sequencing data into summarized data was performed at Personalis (https://www. personalis. com/biomarkerdiscovery/).
Antimicrobial peptides (AMPs) are peptides that kill microbes or inhibit their growth  [1] [2] [3]. AMPs represent an important part of defense strategies against pathogenic microbes, and are produced by both eukaryotic and prokaryotic life forms, although the majority of identified AMPs are from animals  4. Due to the increasing prevalence of multidrug-resistant bacteria, AMPs have received considerable interest as an alternative to currently used antibiotics  5, 6. In addition to their potential use to treat infectious diseases in humans, AMPs are also considered as alternatives to antibiotics as growth promoters in animal husbandry  7. AMPs are generally short polypeptides. The average length of all AMPs in the Antimicrobial Peptide Database (APD) is 33. 19 amino acids  4  (database accessed in February 2022).
To this end, two of the constructs used for supertransformation of the RT7RP recipient line, were also used to transform wild-type plants. The resulting transplastomic lines were named PT7::fAMP, as opposed to the RAmpER::fAMP lines containing the RT7RP (Fig. 3 ). 1 , Supplementary Data 1 and 2). Plants incapable of photoautotrophic growth on soil are shown in tissue culture boxes. Photos were taken three months, or 4. 5 months (6-MW-15) or six months (3-W-5) after transfer of (apical) shoot cuttings to fresh medium. 3-N-5 was the only fAMP line capable of growing on soil. The photo was taken five months after transfer from tissue culture. b Phenotypes of transplastomic plants expressing single AMPs. Shown are (from left to right) a CXCL9 plant three months after transfer of a shoot into a new box with synthetic medium, HA-WAM1, HA-Novispirin and a wild-type plant one month after sowing on soil, and a wild-type plant after one month of growth on synthetic medium.
C: CXCL9, N: HA-Novispirin, W: HA-WAM1, 3M: 3-Molluxubin, 3W: 3-Wamdeposin, 3N: 3-Novescandin, 6MW: 3M + 3W, 9MNW: 3M + 3N + 3W. Source data are provided as a Source Data file. The homoplasmic state for the transplastomic RAmpER::fAMP and PT7::fAMP plants (Fig. 3a ) was confirmed by RFLP analysis (Supplementary Fig. 2 ) and seed test (Fig. 3c ) as described before.
Accumulation of SUMO fusion proteins The improved growth of RAmpER::SUMO-3-Wamdeposin-5 compared to RAmpER::3-Wamdeposin-5 plants could be due to reduced fAMP accumulation upon fusion to SUMO. However, immunoblot analysis revealed the opposite, as a much stronger signal was seen for RAm-pER::SUMO-3-Wamdeposin-5 than for RAmpER::3-Wamdeposin-5 (Fig. 6c ). For all fAMPs tested, the SUMO fusion proteins accumulated to higher levels than the unfused versions (Fig. 6c ). No consistent difference was observed in the steady-state abundance of the transgenic transcripts between the versions with and without SUMO, suggesting that the observed increase in protein accumulation is caused by improved stability of SUMO fusion proteins (Fig. 6b, c ).
The Coomassie-stained membrane is shown below the immunoblot as a loading control. This blot was performed once, but results were confirmed for RAmpER::3-W-5 by an independent blot. Note that SUMO-GFP (SG) accumulates to similarly high levels as the large subunit of RuBisCO (R) in both Prrn::SUMO-GFP (P::S-GFP) and RAmpER::SUMO-GFP (R::S-GFP) plants. Note that the SUMO-GFP protein is not detected in the western blot, due to absence of an HA-tag. S25: 25 kDa band of the Spectra™ Multicolor Low Range Protein Ladder (1. 7 to 40 kDa; ThermoFisher); P25: 25 kDa band of the PageRuler™ Plus Prestained Protein Ladder (10 to 250 kDa; ThermoFisher). RNA and protein was extracted from plants grown on synthetic medium to enable inclusion of lines that did not survive on soil. Source data are provided as a Source Data file.
0 buffer) were incubated with or without 2. 5 units of SUMO protease, and applied in an antimicrobial activity radial diffusion assay. Antimicrobial activity is observed as a clear zone without bacterial growth around the well. The well in the center of the plate was loaded with the buffer control. Three independent assays were performed with similar results. For abbreviations, see (b). Source data are provided as a Source Data file. Data 2). This RT7RP recipient line will greatly simplify construct assembly in future studies using the RAmpER system for transgene expression in plastids. In our work, yields of up to 56 µg SUMO-AMP per g leaf material were obtained, corresponding to 18 µg of HA-WAM1 and 12 µg of HA-Novispirin. Previous studies on the production of cationic AMPs from the plastid genome reported production of AMP fusions to GFP with yields of 5 µg per g fresh leafy biomass (chromatography purification) or 53 µg per g (organic extraction) for a Retrocyclin-GFP fusion and 8 µg per g for a Protegrin-1-GFP fusion  26.
Total protein extraction To extract total leaf protein from tobacco plants, leaf pieces were snap frozen in liquid nitrogen in 2 mL Eppendorf tubes and pulverized with 5 mm steel balls for one minute and 15 s at 20 Hz in a Retsch mill. Total protein was isolated by a phenol-based extraction protocol  89. Protein pellets were dissolved in 1% sodium dodecyl sulfate (SDS). Protein concentrations were quantified with the Pierce BCA Protein Assay Kit (Thermo Fisher Scientific) and measured with a CLARIOstar plate reader (BMG Labtech) using a bovine serum albumin (BSA) dilution series as standard. Calculations of protein concentrations based on spectrophotometric data were done with Microsoft Excel (Microsoft 365 version 2202).
Eluted fractions (3. 5 mL of 4 mL) were loaded on a PD-10 Desalting Column (GE Healthcare), starting with the most concentrated fractions, buffer-exchanged into 20 mM Tris/HCl pH 8. 0, and concentrated by ultrafiltration (Amicon® Ultra-4 centrifugal filters; Merck) with a 3000 Nominal Molecular Weight Limit (NMWL) cut-off. Protein concentrations were determined with the Pierce BCA Protein Assay Kit (Thermo Fisher Scientific) and measured with a CLARIOstar plate reader (BMG Labtech) using a dilution series of BSA as standard. Protein concentrations based on spectrophotometric data were calculated with Microsoft Excel (Microsoft 365 version 2202).