Salmonella enterica subsp. enterica serovar Napoli (S. Napoli) is among the top serovars causing human infections in Italy, although it is relatively uncommon in other European countries; it is mainly isolated from humans and the environment, but neither the reservoir nor its route of infection are clearly defined. This serovar is characterized by high genomic diversity, and molecular evidences revealed important similarities with typhoidal serovars.S. Napoli genomes as well as 239 genomes of typhoidal and non-typhoidal serovars were analyzed in a comparative genomic study. Phylogenetic analysis and draft genome characterization in terms of Multi Locus Sequence Typing (MLST), plasmid replicons, Salmonella Pathogenicity Islands (SPIs), antimicrobial resistance genes (ARGs), phages, biocide and metal-tolerance genes confirm the high genetic variability of S. Napoli, also revealing a within-serovar phylogenetic structure more complex than previously known. Our work also confirms genomic similarity of S. Napoli to typhoidal serovars (S. Typhi and S. Paratyphi A), with S. Napoli samples clustering primarily according to ST, each being characterized by specific genomic traits. Moreover, two major subclades of S. Napoli can be clearly identified, with ST-474 being biphyletic. All STs span among isolation sources and years of isolation, highlighting the challenge this serovar poses to define its epidemiology and evolution. Altogether, S. Napoli strains carry less SPIs and less ARGs than other non-typhoidal serovars and seldom acquire plasmids. However, we here report the second case of an extended-spectrum β–lactamases (ESBLs) producing S. Napoli strain and the first cases of multidrug resistant (MDR) S. Napoli strains, all isolated from humans.179 S. Napoli, highlighting genomic similarity with typhoidal serovars and genomic features typical of non-typhoidal serovars, supporting the possibility of survival in different niches, both enteric and non-enteric. Presence of horizontally acquired ARGs and MDR profiles rises concerns regarding possible selective pressure exerted by human environment on this pathogen.Our results provide evidence of genomic plasticity of  Salmonella spp. is among the top pathogens causing infections in humans. Although more than 2600 different Salmonella serovars have been described to date ,12:i:- and derived from Huedo et al.  Mb, with a GC% content varying between 52.0 and 52.3% . A mean genome-wide corrected evolutionary rate of 8.90 × 10− 8 substitution/site/year  was estimated, indicating that divergence of the two ST-474 clones could be set around 1990 . The ST vs source association test on partitions of the contingency table . S. Napoli ST-474, however, was the most commonly found ST in our collection, irrespectively of the source of isolation.Metadata correlation analysis revealed a statistically significant association between ST and source of isolation , which resulted significantly lower compared to all other serovars, that harbored a median of 10 SPIs per sample, ranging (5-12) . Only S. Paratyphi genomes showed a comparable number of SPIs to S. Napoli ).S. Napoli SPIs profiles were characterized by the presence of SPI-5, SPI-13 and SPI-18 and by the absence of SPI-3, SPI-6, SPI-7, SPI-8, SPI-10, SPI-11 and SPI-14. S. Typhi and S. Paratyphi A genomes, which were characterized by the presence of SPI-8 in place of SPI-13, shared with S. Napoli the absence of SPI-14 and the presence of SPI-5 and SPI-18. A complete SPI-18 was found in all S. Napoli genomes but two: 16-174481_S8 and 16-174535_S1. In detail, 16-174481_S8 was missing both hlyE and taiA genes, the two genes included in SPI-18, while 16-174535_S1 was missing hlyE gene only. S. Napoli shared with all non-typhoidal Salmonella SPIs profiles the presence of SPI-13.Almost all S. Napoli serovar, no unique SPIs profile was identified, although some STs showed a prevalent SPIs profile . No link between SPIs profile and phylogenetic clustering, year of isolation or source was identified within S. Napoli genomes.Within p-value < 2.2e-16, Cramer’s V = 0.649) within Clade A. The serovar vs ARGs presence contingency table was partitioned to compare S. Napoli vs all other serovars, highlighting that ARGs were detected in fewer S. Napoli isolates than in serovar S. Typhi, S. Heidelberg, S. Newport and S. Typhimurium .A significant and strong association was found between serovar and number of genomes harboring acquired ARGs  , together with genes conferring resistance to β-lactams (bla), tetracyclines (tet) and sulphonamides (sul); the 3 genomes showing only one acquired ARG harbored a gene of the bla family, conferring resistance to β-lactams.However, 5 S. Napoli genomes, isolated from environment (N = 11), humans (N = 11), food (N = 1), livestock (N = 1), and unknown origin (N = 2) showed at least one plasmid replicon (Supplementary Table S. Napoli isolates belonged to incompatibility groups H (IncHI2A), I (IncI1), F , X (IncX), and to plasmid types ColRNAI and Col440l, with plasmid replicon IncFII being the most commonly found. All the S. Napoli genomes carrying ARGs showed also IncI, IncF or IncH plasmid replicons in the same genomic region, thus suggesting that ARGs could have been acquired by horizontal gene transfer. S. Napoli ST-1853 and ST-2101 did not show any hit in the plasmid replicon database.Twenty-six p-value < 2.2e-16, Cramer’s V = 0.56), highlighting that plasmid replicons were detected in fewer S. Napoli isolates then in serovars S. Enteritidis, S. Heidelberg, S. Newport and S. Typhimurium .Serovar and number of isolates with plasmid replicons showed a strong significant association .Moreover, The pangenome was functionally annotated and a COG (Clusters of Orthologous Groups) category was assigned to 8847 genes out of 21153 constituting the pan genome (41.82%).S. Napoli accessory genome was largely composed by genes, whose products can be assigned to the X , G (Carbohydrate transport and metabolism) and L  COG categories  S. Napoli genomes were characterized by terms suggesting mechanisms of interaction with hosts and virulence-related processes; conversely, the Clade A dataset showed enrichment in more general terms involved in DNA metabolism and processing involving mobile genetic elements.Similarly, both core and accessory genomes were annotated according to the Gene Ontology categories. At least a GO (Gene Ontology) term was assigned to 9935 genes out of 21153 constituting the pan genome (46.96%). In Additional file S. Napoli genomes only, 526 genes were found within typhoidal isolates only (S. Typhi and S. Paratyphi A), and 3181 genes were found within non-typhoidal Clade A isolates , E (Amino acid transport and metabolism) and K (Transcription).Accessory genome could also be divided into several groups: 2590 genes were found within Among all ST-unique genes, i.e. genes not shared between different STs, several belonged to G (Carbohydrate transport and metabolism) and E (Amino acid transport and metabolism) COG categories. ST-1637 and ST-2019 were characterized by genes belonging to COG category N (Cell motility), mainly fimbrial proteins, outer membrane usher proteins and chemotaxis proteins. ST-2095 and ST-474 were characterized by genes belonging to COG category X . ST-1637 only was characterized by genes belonging to COG category T , including sensor proteins and chemotaxis proteins. ST-1853 only was characterized by genes belonging to COG category V (Defense mechanisms), including restriction-modification system proteins.p-value < 2.2e-16). The number of pseudogenes detected in S. Napoli was significantly lower if compared to serovars known to be host-restricted as S. Gallinarum , S. Typhi , and S. Paratyphi A . Indeed, the number of pseudogenes detected in S. Napoli genomes was comparable to that reported for other serovars known to have a broad host range, such as S. Enteritidis and S. Typhimurium.Both the total amount of detected pseudogenes and their percentage with respect to the total annotated CDSs are reported in Supplementary Fig. S. Napoli isolates reported a significantly lower number of prophages than other serotypes . In Supplementary Fig. n = 35) were reported in a heatmap.A total of 42 lysogenic prophage sequences were detected in our dataset  it clusters with typhoidal Salmonella serovars [S. Napoli isolates do not show recombination with other serovars.Here, we compared 179 and 1,4, ,12:I, th diverse  serovar. diverse , 30, 31,S. Napoli isolates. This high genomic diversity is coherent with the difficult identification of a specific niche for this serovar [Both the genomic and the phylogenetic analyses highlighted high intra-serovar variability within  serovar , 5, 9, a serovar , 33.S. Napoli phylogeny highlighted that sequences mainly cluster by ST. Interestingly, both ML and Bayesian analysis support the hypothesis that ST-474 is biphyletic and that it could be the common ST ancestor for all the other STs identified [entified , 34.S. Napoli [Metadata integration allowed us to point out a statistically significant association between ST and source, indicating that ST-2095 was more frequently isolated from human samples, while ST-1637 and ST-2019 were more frequently isolated from the environment. Previous studies suggested that the environment, and specifically surface waters, could play an important role in the diffusion of . Napoli .stimuli. Moreover, ST-1853 was characterized by showing an unshared set of defense mechanism proteins, including restriction-modification system proteins, which are known to be employed by bacteria in natural genetic transformation by recombination [Interestingly, ST-1637 and ST-2019, found to be associated to environmental sources, were the only STs showing a high number of genes coding for cell motility proteins, as well as sensor proteins. These features might be favoring the ability of these STs to sense the surrounding environment and eventually activated motility mechanisms in response to different bination . NoteworS. Napoli serovar is characterized by the frequent identification of P88 phage, which is reported to be phylogenetically divergent from other P2-like prophages [S. Napoli genomes are characterized by the absence of many prophage sequences that are commonly identified in S. enterica such as Gifsy-1 and Gifsy-2 [S. enterica, since they provide sources of virulence determinants and can be acquired by horizontal gene transfer [S. Napoli serovar, although we cannot exclude prophage underestimation due to fragmentation of de novo assembly.According to our data, rophages . Moreove Gifsy-2 . Previoutransfer . HoweverS. Typhi [S. Napoli genome in our collection. This pathogenicity island includes two ORFs, corresponding to hlyE gene encoding for a pore-forming hemolysin, and taiA gene, which encodes for a S. Typhi-associated invasin [hlyE gene has been found also in the human specific typhoidal serovars S. Paratyphi A [hlyE gene to the ability of S. Typhi to enter human epithelial cells in vitro. Interestingly, SPI-18 has been detected also in other different serovars, namely S. Brandenburg [S. Montevideo [S. Panama and S. Schwarzengrund [S. Napoli genomes supports its affinity to typhoidal serovars, as already suggested by Huedo et al. [Concerning SPIs, they appeared highly heterogeneous and not related to ST, source or year of isolation. SPI-18, firstly described in S. Typhi , was ide invasin . hlyE geatyphi A  and, desatyphi A . Moreoveatyphi A  demonstrndenburg , S. Montntevideo , S. Panazengrund . The preo et al. .S. Napoli to non-typhoidal serovars; this SPI, indeed, is present in different S. enterica serovars, including S. Enteritidis, Typhimurium, Cholerasuis and Gallinarum, but it is absent in S. Typhi and S. Paratyphi A, which instead carry SPI-8 in the same genomic location [S. Enteritidis in murine macrophages, but not human macrophages due to a lower amount of itaconate produced by human macrophages in response to bacterial infections.Conversely, the presence of SPI-13 suggests some persisting affinity of location –48. Espilocation  showed tS. Napoli genomes strengthens our speculation that this serovar could have developed a peculiar genomic asset, that allows it to invade a wider range of hosts and environments. This was confirmed by metadata analyses and pseudogene content that was found comparable to S. Enteritidis and S. Typhimurium, which are known to have a wide host range [Since the presence of different SPIs ensures the accumulation of different virulence mechanisms , the evist range .S. Napoli, S. Typhi and S. Paratyphi A are characterized by a complex deletion pattern, including the mer, oqx, pco, sil and ter operons. Although many different gene sets may concur to antibacterial biocide- and metal-tolerance [S. Napoli may be less adapted to survive in biocide- or metal- enriched environments, such as farming environment. Such observation is coherent with our sampling frequencies as well as previous studies that rarely found S. Napoli in domestic or farmed animals [S. Napoli showed a diverse set of profiles, highlighting once again that this serovars displays high intra-serovar genomic variability for non-core functions.Concerning the presence of biocide- and metal-tolerance genes, olerance , 52, suc animals , 29, 53.S. Napoli genomes. Over 179 sequences, only five isolates showed ARGs, conferring resistance to β-lactams, tetracyclines, sulphonamides and aminoglycosides, thus confirming a general level of pansusceptibility within S. Napoli serovar [aadA25 gene [blaCTX-M-1 [blaCTX-M-15 genes  were reported. To the best of our knowledge, this is the first time that S. Napoli resistant isolates not belonging to ST-474 (i.e. ST-2008 and ST-2095) are identified. Additionally, for the first time as well, in the present work MDR profiles in serovar Napoli are described. In addition to that, four out of five resistant isolates in our collection are of human origin, and with ARGs location of conceivably plasmidic origin, given the contextual presence of plasmid replicons. Altogether, these evidences led us to speculate that only few S. Napoli clones, circulating in human hosts, might be subjected to selective pressure leading to horizontal acquisition of ARGs.Due to the importance of horizontal gene transfer in the spread of drug resistance and virulence factors , 55, we A25 gene , and resaCTX-M-1 , 12 and S. Napoli serovar hardly acquires genes by means of horizontal gene transfer (unless the existence of a selective pressure favors it) or that maintaining plasmids is not convenient for the fitness of S. Napoli. Indeed, blaCTX-M-15 determinants have extensively been associated with F-type plasmids [As for plasmid replicons, only 26 samples showed at least one replicon, therefore we speculate that plasmids , and witplasmids , where tplasmids . Interesplasmids –61.S. Napoli serovar supports its high adaptability to different selective pressure conditions: from wild environments, where sensing and mobility are enforced but isolates are still pansusceptible, to human environment, where the acquisition of plasmids and antimicrobial resistances may be advantageous. Although not conclusive on investigating S. Napoli genomic diversity, these evidences should testify the need of attention for this serovar from healthcare providers, also outside Italian borders.Possibly, the high diversity found in S. Napoli from different sources and countries, has to be ascribed to the absence of an EU target control plan for S. Napoli, evenly spanning both years/sources of isolation.We cannot exclude, however, that our results might be hindered by our data collection protocol. Our dataset suffers indeed from a collection bias due to voluntary submission of data. This bias, which may have impacted the proper estimation of the frequency of isolation of S. Napoli, our study aimed at shedding light on the genomic asset of this successful serovar. This study performed comparative genomic analysis of an unprecedently met number of S. Napoli genomes, and investigated its possible relationship with both typhoidal and non-typhoidal Salmonella serovars by phylogenetic and pangenome analyses. Results definitively clarified that this iNTS shares genome similarity with typhoidal serovars, although preserving several mechanisms common to non-typhoidal serovars. These features, together with the evidence of its high genomic plasticity, support its ability to colonize different niches, from humans, to animals and the environment.The availability of large whole genome sequences datasets greatly contributes to improve the knowledge on the genomic content of pathogens of interest for public health. In the specific case of http://www.engage-europe.eu/), a total number of 140 Italian S. Napoli isolates were collected and sequenced. The Italian Reference Laboratory for Salmonellosis at Istituto Zooprofilattico Sperimentale delle Venezie (IZSVe) provided 32 isolates and acted as a hub for the Italian S. Napoli isolates, with 40 strains contributed from Istituto Superiore di Sanità (ISS) and 68 contributed from Istituto Zooprofilattico Sperimentale del Mezzogiorno. Moreover, 16 S. Napoli sequences from other European countries, all spanning years 2005–2017, were obtained: the German Federal Institute for Risk Assessment (BfR) provided 11 sequenced genomes, and Technical University of Denmark (DTU) provided 5 sequenced genomes  plates and incubated at 37 °C for 24 h. Genomic DNA (gDNA) was extracted using a commercial column-based protocol , and purified gDNA was quantified with a Qubit 3.0 Fluorometer (Life Technologies). Libraries for whole genome sequencing were prepared using a Nextera XT DNA sample preparation kit (Illumina). High-throughput sequencing was performed with MiSeq Reagent kit v3, resulting in 251-bp-long paired-ends reads. Sample quality was assessed via FastQC v0.11.2 [Pure colonies of  v0.11.2  and samp v0.11.2  with the v0.11.2  and the  v0.11.2 .in-silico serotyping, 14 S. Napoli genomes were excluded because serotype was not confirmed (data not shown). Table After Salmonella serovars Typhi, Paratyphi A, Choleraesuis, Newport, Enteritidis, Dublin, Heidelberg, Typhimurium and 1,4, [GenBank  and RefSand 1,4, ,12:i:- aand 1,4, ; 77 Cladand 1,4,  and Worland 1,4, ; 1 Cladeand 1,4, . For eacS. Napoli sequence data available on Enterobase [N= 47). Eleven of them were excluded because they were corresponding to internally sequenced genomes.All terobase   [MOST 1.0  and SeqSMOST 1.0  on raw dMOST 1.0  on assem03/2018)  for BLAS03/2018) ).S. Napoli confirms to be part of Clade A, we performed a core SNPs phylogenetic analysis including a number of 78 genomes from serovars in Clade B (S. Schwarzengrund (n=3), S. Montevideo (N=21), S. Javiana (N=2), S. Panama (N=1), S. Brandenburg (N=3), S. Miami (N=6), S. Poona (N=1), S. Gallinarum (N=4), S. Pomona (N=1), S. Eastborne (N=6), S. Nottingham (N=25), S. Bredney (N=2), S. Decatur (N=2)) and Clade C (S. Weslaco (N=1)). A concatenation of SNPs was obtained using kSNP3 3.0 [In order to elucidate whether or not SNP3 3.0 , with a SNP3 3.0 . The phySNP3 3.0 , with deS. Napoli genomes only and within the Clade A genomes collection.For further comparison, all Clade A genomes (both the downloaded reference genomes and the assembled ones) were automatically annotated using Prokka  and subS. Napoli population, a core-, nonhomoplastic- SNP matrix was generated using kSNP3 3.0 [S. Napoli STs testing two molecular clocks  and two demographic models  using BEAST v1.8 [S. Napoli genome .For deeper investigating of SNP3 3.0  with a kSNP3 3.0 . The corAST v1.8  under 50AST v1.8  was usedAST v1.8 . Only vaAST v1.8 . The treSalmonella enterica gene prediction model file, was used as input for STRUCTURE 2.3.3 [A core-genome MLST (cgMLST) matrix, obtained using chewBBACA  with defRE 2.3.3 . The cgMOverall genome relatedness index, measured as the Orthologous Average Nucleotide Identity (OrthoANI) was determined by using the OrthoANI tool .corr.test function in R  were analyzed for correlation/independence using the  package ). Moreov package ), respec package ). ResultSalmonella Pathogenicity Islands (SPIs) against SPIFinder 1.0 database ; for acquired antimicrobial resistance genes (ARGs) against ResFinder 3.0 database (downloaded on 05/03/2018) [S. Typhi str. CT18 reference genome  [S. Napoli as well as typhoidal serovars [Contigs were searched for plasmid replicons using blastn  against03/2018) . Finally03/2018) ) against03/2018) . E-value456,801) , as it wBacMet hits were used to create a profile according to the presence/absence of the genes included in the database. Such presence/absence profiles where grouped by hierarchical clustering based on Jaccard distance  and WardGene presence/absence within the entire sample set was exported; firstly singletons (i.e. genes found in one sample only) were removed, then data were divided into core genome (i.e. genes found in 99 to 100% of samples) and accessory genome .Core- and accessory genome genes were characterized in terms of COG (Clusters of Orthologous Groups of Proteins) . The funSimilarly, both core and accessory genome were annotated for the GO (Gene Ontology)  terms inS. Napoli isolates, genes found within typhoidal isolates (S. Typhi and S. Paratyphi A) and genes found within non-typhoidal Clade A isolates , and each of these groups of genes were characterized in terms of most frequent COG.Accessory genome was also explored to extract genes found within and 1,4, ,12:i:-).Pseudogenes were detected using DFAST v1.2.4  with defin-house python script to convert them in tabular format; only “intact” prophages were considered for downstream analysis.Prophages sequences were detected in both assemblies and downloaded sequences using PHASTER , throughAdditional file 1: Tabular data. Metadata of included genomes. Additional file 1 reports the sample name, organism name, source of isolation, isolation year, country of isolation, sequence type (ST) for each of the included genomes. Origin details whether the genome was derived from the ENGAGE consortium, from Enterobase/GenBank/RefSeq database. Summary metrics  are also reported. Supplementary Table 1 (“S_Napoli genomes” sheet) details info for S. Napoli collected isolates, Supplementary Table 2 (“Other serovar genomes” sheet) details info for the downloaded reference genomes belonging to Clade A.Additional file 2: Tabular data. Pivot table of sample metadata. Number of S. Napoli isolates per source of isolation, isolation year, country of isolation and ST, respectively.Additional file 3: Figure. S1. MLST minimum-spanning tree of all S. Napoli genomes. Core genome MLST profiles of all S. Napoli isolates was used to build a minimum spanning tree with pie charts describing sources of isolation found for each ST. S. Napoli shows a high variability of sources contributing to each ST, with the exception of ST-1853, related to a single outbreak occurred in Italy in 2012 and associated to kennel dogs [Homo sapiens, EN: Environment, FO: Food, CA: Companion Animal, LI: Livestock, PO: poultry, WA: Wild Animal, LA: laboratory.nel dogs , and ST-Additional file 4: Figure S2. Core genome alignment-based ML phylogeny of Salmonella serovars belonging to Clade A, B, and C. Core genome alignment was used for phylogenetic reconstruction kSNP3 3.0 [S. Napoli clusters within Clade A, in the typhoidal subclade.SNP3 3.0 , with a SNP3 3.0 , with deAdditional file 5: Figure S3. Population recombination analysis of serovars belonging to Clade A, B, and C. Core-genome MLST (cgMLST) matrix, including 3065 alleles, trained with Salmonella enterica gene prediction model file, was used as input for STRUCTURE 2.3.3 [S. Napoli isolates always group together in a single population, thus highlighting that S. Napoli isolates do not show recombination with other serovars.RE 2.3.3 . A modelAdditional file 6: Figure S4. Core genome alignement-based ML phylogeny of all genomes belonging to Clade A. core genome alignment was used for phylogenetic reconstruction using RAxML  with bon 7.2.8, . S. NapoAdditional file 7 Figure S5. Core genome alignement-based ML phylogeny of S. Napoli genomes. Core genome alignment was used for phylogenetic reconstruction using RAxML  with bon 7.2.8, . Within Additional file 8 Figure S6. Bayesian phylogenetic analysis. S. Napoli STs divergence investigation was performed using BEAST [ng BEAST . The comng BEAST  and indiAdditional file 9: Tabular data. Genomic characterization summary table. Supplementary Table 3, Additional file S. Napoli genomes only), plasmid replicons, antimicrobial resistance genes (AMR), SPI profiles and BacMet profiles. Additionally, it also reports basic metadata as collection year, country of isolation, source of isolation and serotype to ease data investigation.Additional file 10 Tabular data. BacMet hits description. Additional file S. Napoli, S. Typhi and S. Paratyphi A subgroup, presence in the core of the non-typhoidal Salmonella subgroup, presence in at least two samples of S. Napoli, S. Typhi and S. Paratyphi A subgroup, presence in at least two samples of the non-typhoidal Salmonella subgroup.Additional file 11: Figure S7. BacMet hierarchical tree. BacMet genes presence/absence profiles in the investigated genomes where grouped by hierarchical clustering based on Jaccard distance [S. Napoli, S. Typhi and S. Paratyphi A genomes; the other one, including 53 different BacMet profiles and grouping together all the non-typhoidal genomes. The two main subgroups differ mainly by the absence of gesAB and golTS in the S. Napoli, S. Typhi and S. Paratyphi A genomes.distance  and Warddistance , 87. BacAdditional file 12: Tabular data. Top GO hits. Additional file S. Napoli genomes only vs the whole dataset.Additional file 13: Tabular data. COG characterization of unshared/shared genes between typhoidal/non typhoidal genomes and Napoli genomes. Additional file S. Napoli genomes only, in typhoidal serovar genomes only, in non-typhoidal serovars genome only as well as genes shared by S. Napoli genomes and typhoidal serovar genomes or genes shared by S. Napoli genomes and non-typhoidal serovar genomes. Each gene is characterized by the gene name, the description of its annotation, the number of genomes it was found in, the COG ID number, the COG category, its name and description, as well as the protein ID identified by Prokka, the gene length in aa, the Uniprot Best match and the associated GO terms. Genes annotated as “Hypotetical protein” are filtered from the default view.Additional file 14: Fig.ure S8. Boxplot of detected pseudogenes distribution. Pseudogene content of S. Napoli genome was compared to two well known host-generalist serovars (S. Enteritidis and S. Typhimurium) and three host-restricted serovars . Both total number of identified pseudogenes (Panel A) and percentage of pseudogenes over total amount of annotated genes (Panel B) are reported. Kruskal Wallis rank sum test confirmed that a significant difference could be found in pseudogene number distribution among different serovars . The number of pseudogenes detected in S. Napoli is lower if compared to serovars known to be host-restricted as S. Gallinarum , S. Typhi , and S. Paratyphi A .Additional file 15 Tabular data. Prophage hit description. Number of intact/incomplete/questionable prophages and their name, for each sample. In addition, serotype, ST and subclade (for S. Napoli genomes only) metadata are reported to ease data investigation.Additional file 16: Figure S9. Heatmap of intact prophage presence/absence in all Clade A investigated genomes. Prophage presence (green) and absence (black) in the investigated genomes is represented in a heatmap. The y axis reports the names of the identified intact prophage sequences; the x axis reports Salmonella genomes, grouped by serovar for ease of interpretation. Globally, S. Napoli isolates reported a significantly lower number of prophages than other serotypes . P88 prophage was found in 45 S. Napoli genomes, thus resulting the most frequently prophage detected in this serovar.


Chinese medicine Toujie Quwen granule (TJQW) has proven to be effective in the treatment of mild coronavirus disease 2019 (COVID-19) cases by relieving symptoms, slowing the progression of the disease, and boosting the recovery of patients. But the bioactive compounds and potential mechanisms of TJQW for COVID-19 prevention and treatment are unclear. This study aimed to explore the potential therapeutic mechanism of TJQW in coronavirus disease 2019 (COVID-19) based on an integrated network pharmacology approach.TCMSP were used to search and screen the active ingredients in TJQW. The Swiss TargetPrediction was used to predict the potential targets of active ingredients. Genes co-expressed with ACE2 were considered potential therapeutic targets on COVID-19. Venn diagram was created to show correlative targets of TJQW against COVID-19. Cytoscape was used to construct a “drug-active ingredient-potential target” network, STRING were used to construct protein-protein interaction network, and cytoHubba performed network topology analysis. Enrichment of biological functions and signaling pathways of core targets was performed by using the clusterProfiler package in R software and ClueGO with CluePedia plugins in Cytoscape.A total of 156 active ingredients were obtained through oral bioavailability and drug-likeness screenings. Two hundred twenty-seven potential targets of TJQW were related to COVID-19. The top ten core targets are EGFR, CASP3, STAT3, ESR1, FPR2, F2, BCL2L1, BDKRB2, MPO, and ACE. Based on that, we obtained 19 key active ingredients: umbelliprenin, quercetin, kaempferol, luteolin, praeruptorin E, stigmasterol, and oroxylin A. And the enrichment analysis obtained multiple related gene ontology functions and signaling pathways. Lastly, we constructed a key network of “drug-component-target-biological process-signaling pathway”. Our findings suggested that TJQW treatment for COVID-19 was associated with elevation of immunity and suppression of inflammatory stress, including regulation of inflammatory response, viral process, neutrophil mediated immunity, PI3K-Akt signaling pathway, MAPK signaling pathway, Jak-STAT signaling pathway, Complement and coagulation cascades, and HIF-1 signaling pathway.Our study uncovered the pharmacological mechanism underlying TJQW treatment for COVID-19. These results should benefit efforts for people around the world to gain more knowledge about Chinese medicine TJQW in the treatment of this vicious epidemic COVID-19, and help to address this pressing problem currently facing the world. As an acute respiratory infectious disease, COVID-19 is transmitted through respiratory droplets and close contact, and the general population is susceptible to the infection. No specific drug but symptomatic supporting treatment is currently available for COVID-19. According to the traditional Chinese medicine (TCM) theory, COVID-19 belongs to the category of pestilence. Since the outbreak of COVID-19, the Chinese government has explicitly requested adherence to the integration of TCM and western medicine, enhancement of the research efforts, and acceleration of the development of drugs with good clinical efficacy for COVID-19. The Diagnosis and Treatment Program of Novel Coronavirus Pneumonia  jointly issued by the National Health Commission of China and the National Administration of TCM of the People’s Republic also included a TCM treatment section [Since December 2019, a novel coronavirus pneumonia, namely coronavirus disease 2019 (COVID-19) has rapidly spread from Wuhan City to various provinces across China and other countries around the world [http://www.gz8h.com.cn/, Guangdong Province, China) introduced Toujie Quwen granules  based on the febrile disease theory of TCM and climate characteristics of Lingnan region, TJWQ formulation has achieved good therapeutic results. TJQW have been mainly used to treat mild to moderate COVID-19 cases to effectively reduce fever, cough, and expectoration in the patients. On February 8, 2020, Guangdong Medical Products Administration officially recommended TJQW as COVID-19 treatment to 30 designated hospitals for COVID-19 in the Guangdong Province [Forsythiae Fructus (lian-qiao), edible tulip (shan-ci-gu), Lonicera japonica , Radix Scutellariae baicalensis (huang-qin), Folium Isatidis (da-qing-ye), Bupleurum root (chai-hu), Artemisia apiacea (qing-hao), Periostracum Cicadae (chan-tui), Radix Peucedani (qian-hu), Fritillaria cirrhosa (chuan-bei-mu), Fritillaria thunbergii (zhe-bei-mu), Poria cocos (fu-ling), Fructus Mume (wu-mei), radix Scrophulariae (xuan-shen), Astragalus propinquus (huang-qi), and radix Pseudostellariae (tai-zi-shen), which were mainly used for heat-clearing, detoxifying, relieving symptoms, expelling pathogenic factors, reinforcing qi, and nourishing yin according to the TCM theory [Among all recommended TCM formulations for COVID-19 treatment, Guangzhou Eighth People’s Hospital  [TJQW had a total of 16 TCM components, and the active ingredients in the related TCM components were searched through the TCM systems pharmacology database and the analysis platform  . LSP capmsp.php) , 13. Thehttps://pubchem.ncbi.nlm.nih.gov/) using their CAS number or InChIKey to acquire the corresponding molecular structures which were stored in canonical simplified molecular-input line-entry system (SMILES) format.The active ingredients obtained from screening were entered into the PubChem  [Homo sapiens) as the study species, to obtain the potential effector targets.The Swiss TargetPrediction (on 2019)  was thenOur study was based on the results of single-cell sequencing of colonic epithelial cells in a previous study  to selechttps://cytoscape.org/, version 3.7.0) [The components and the obtained active ingredients of TJQW and the target correspondence were imported into the Cytoscape (n 3.7.0)  to consthttps://string-db.org/, version:11.0) [Homo sapiens”; the lowest mutual action threshold was set to “medium confidence” (> 0.4), and other parameters were the default settings. And the PPI results were imported into cytoHubba  [The Search Tool for the Retrieval of Interacting Genes/Proteins (STRING) (on:11.0) , was useion 0.1)  for netwhttps://www.bioconductor.org/, a package for the genome wide annotation of Human, primarily based on mapping using Entrez Gene identifiers, version 3.1.1) [https://www.bioconductor.org, a package implements methods to analyze and visualize functional profiles of gene and gene clusters, version 3.1.1) [The org. Hs.eg.db (n 3.1.1)  and the n 3.1.1)  on the Rn 3.1.1)  and the n 3.1.1) . In our Homo sapiens (9606) and Symbol ID was input, then we selected “WikiPathways-503 terms/pathways with 6558 available unique genes-27.02.2019”. Regarding statistical options, we employed two-sided hypergeometric test and bonferroni step down for p value correction. With P < 0.05 were selected. The results of p value≤0.05 were used to graph the GO and KEGG pathways, illustrating the roles of targets and signaling pathways in COVID-19 treatment with TJQW.ClueGO (version 2.5.4)  and CluePeriostracum Cicadae did not have any relevant retrieval results, which ingredients did not match the parameters with ≥30% OB and ≥ 0.18 DL) to obtain 238 active ingredients. Details of 15 components and its 238 active ingredients (without removing duplication) including herbal name, molecule ID, molecule name, OB information, DL information, and their CAS/InChIKey codes were shown in Supplementary material The TCMSP database was used to search for the active ingredients of TJQW, followed by using the parameters with ≥30% OB and ≥ 0.18 DL as the criteria to screen the 15 components , GO-biological process (BP), and GO-cell component (CC), followed by the selection of the top 10 enrichment results. In Fig. KEGG pathways enrichment analysis was shown in Fig. P < 0.05) were involved in ACE Inhibitor pathway, prostaglandin synthesis and regulation, nuclear receptors meta-pathway, ethanol effects on histone modifications, nuclear receptors in lipid metabolism and toxicity, apoptosis modulation and signaling, TNF related weak inducer of apoptosis (TWEAK) signaling pathway, TNF alpha signaling pathway, and AGE/RAGE pathway. Furthermore, based on the above comprehensive analysis, the key network of “drug-component-target-biological process- signaling pathway” was presented  , UMB indRadix Scutellariae baicalensis inhibits the apoptosis induced by influenza virus infection through regulating the expression of apoptosis-related genes in the CASP8-mediated exogenous pathway and the pathways of endoplasmic reticulum [In terms of potential therapeutic targets, based on this study, all potential biotargets of TJQW action against COVID-19 were methodically obtained, and the potential core pharmacological targets were identified as EGFR, CASP3, STAT3, ESR1, FPR2, F2, BCL2L1, BDKRB2, MPO, and ACE, majority of them have been confirmed to be involved in pulmonary inflammation. EGFR-mediated signaling pathways are important for cell proliferation, differentiation, and survival in many tissues and cell types. Suppression of the EGFR pathway via the reduction of eIF2α phosphorylation increases susceptibility to cellular oxidative stress . The pareticulum . ACE2, aeticulum . In the eticulum . STAT3 aeticulum . A critieticulum .Estrogen receptor decreases NF-kappa-B DNA-binding activity and inhibits NF-kappa-B-mediated transcription from the IL6 promoter . The humThe bioinformatics findings of this study suggested that the specific inactivation of signaling pathways of inflammatory stress and targeted modulation of intrapulmonary EGFR, CASP3, STAT3, ESR1, FPR2, F2, BCL2L1, BDKRB2, MPO, and ACE expressions, maybe potential pathways of TJQW action against COVID-19. These are the potential therapeutic mechanisms of active ingredients in TJQW and targets in the treatment of COVID-19. This study provided new predictions on the molecular-based therapeutic mechanism of TJQW in COVID-19 treatment and it offered new ideas for further study.According to the results of GO enrichment analysis, the potential therapeutic targets of TJQW were mainly distributed in the regulation of inflammatory response, viral process, neutrophil mediated immunity, response to lipid, regulation of vasoconstriction, and were also involved in promotion/suppression of inflammation, cell division, abnormal cell proliferation, oxidation/reduction reactions, ion-channel regulation, and immune regulation. From the results of KEGG enrichment analysis, the pathways mainly involved in the treatment process, PI3K-Akt signaling pathway, MAPK signaling pathway, Jak-STAT signaling pathway, Complement and coagulation cascades, and HIF-1 signaling pathway, were important inflammatory or metabolism pathways.This study implicated MAPK signaling as part of the potential mechanisms underlying TJQW antiviral function. MAPK cascades are crucial signaling pathways in the regulation of host immune response to infection . MAPK phBesides, the arachidonic acid metabolic pathway is mainly used for the synthesis of inflammatory mediators, which mediate the production of various inflammatory cytokines, such as monocyte chemotactic-protein-1, TNF, IL, and interferon, which are related to the development, progression, and regression of inflammation . In addiThese findings suggested that TJQW may exert antioxidative, antiviral, and anti-inflammatory effects, along with immune system activation effects through the aforementioned targets, thereby playing an early protective role in the lungs. While in the TCM treatment program for COVID-19 in Guangdong Province, TJQW was mainly used for the treatment of COVID-19 in the early stage.In summary, this study used network pharmacology techniques and approach to retrieve the active ingredients and potential targets of TCM in TJQW, by screening the common targets for TCM active ingredients and diseases and performing GO and KEGG enrichment analyses of the targets. As shown in the key network of “drug-component-target-biological process- signaling pathway”, we speculated that the active ingredients in the TJQW treatment of COVID-19 may be umbelliprenin, quercetin, luteolin, praeruptorin E, stigmasterol. The potential effector targets were EGFR, CASP3, STAT3, ESR1, FPR2, F2, BCL2L1, BDKRB2, MPO, and ACE, through regulating signaling pathways, such as PI3K-Akt signaling pathway, MAPK signaling pathway, Jak-STAT signaling pathway, to suppress the inflammatory response, regulate the immune function, and reduce lung injury, thereby achieving the purpose of COVID-19 treatment.From the perspective of research limitations. Since the gene co-expressed with ACE2 was selected as a potential therapeutic target for COVID-19 in this study, it is not a specific therapeutic target and needs further study.Our study uncovered the pharmacological mechanism underlying TJQW treatment for COVID-19. These results should benefit efforts for people around the world to gain more knowledge about Chinese medicine TJQW in the treatment of this vicious epidemic COVID-19, and help to address this pressing problem currently facing the world.Additional file 1.


The widespread use of glass fibre reinforced polymer (GFRP) bars in reinforced concrete (RC) elements has yet been limited due to the anisotropic and non-homogeneous material behaviour of GFRP. The material characteristics of GFRP bars from different manufacturers vary as a function of several factors. Several standards have developed various procedures to investigate the mechanical characteristics of GFRP bars, but universal methods to test different types and diameters of GFRP bars in tension have not been fully developed. Due to the lack of such a standardized test procedure, there are some doubts and gaps in terms of the behaviour of GFRP bars in tension, which has led to lack of reliable information on their tensile properties. The determination of tensile characteristics of GFRP bars, including the tensile strength, modulus of elasticity, and ultimate strain, according to various test standards, is the main subject of the paper. This paper reports test results for tensile characterization obtained on four types of GFRP bars from four manufacturers with six various diameters. Moreover, the study compares various test procedures according to seven standards to characterize the tensile properties of GFRP bars, to examine the proposed test procedures, and to reveal main differences. Glass fibre reinforced polymer (GFRP) reinforcement has emerged as an attractive alternative of steel rebars due to the higher ultimate tensile strength to weight ratio, resistance to corrosion and chemical attack, electromagnetic neutrality, and long-term strength and durability of GFRP bars in harsh and corrosive environments ,2,3,4. GHowever, the widespread use of GFRP bars in reinforced concrete (RC) elements has been limited due to the anisotropic and non-homogeneous material behaviour of GFRP. Material characteristics of GFRP bars from different manufacturers vary as a function of several factors, including the type and proportions of fibre and resin and the quality control of the final products. Moreover, the difference in geometric dimensions and reinforcement ratio over the cross-section of the bars entails different resistance to applied loads. GFRP rebars may also differ in both raw components and surface texture (projection), which governs their mechanical behaviour. These factors might constitute an effective parameter in the diversity of the reported values for the strength and modulus of elasticity of GFRP bars.There have been multiple studies on the characterization of GFRP bars in tension ,17,18,19The determination of tensile characteristics of GFRP bars, including the tensile strength, modulus of elasticity, and ultimate strain, according to various test standards, is the main subject of the current paper. This paper reports test results for tensile characterization obtained on four types of GFRP bars from four manufacturers, with six various diameters and two manufacturing factors. At least three specimens of each type/diameter/factor were tested to yield a statistically valid mean and distribution of tensile strength. 55 bar specimens were investigated. The data obtained through the tensile tests were analysed using a statistical data analysis software program, Matlab. Moreover, an investigation of load–strain behaviour and failure modes, and their correlation to the tensile characteristics are discussed.There is a fast-growing number of GFRP bars used in construction, and many variables determine the material characteristics of these products. Because of this, a comprehensive investigation of the most recent GFRP reinforcing bars was believed to be an essential step toward designing GFRP reinforced concrete structures, demonstrating satisfactory performance. Various mechanical characteristics have been reported for GFRP bars in tension by researchers using different test procedures. Due to the lack of such a standardized test procedure, there are some doubts and gaps in terms of the behaviour of GFRP bars in tension which led to a lack of reliable information on their tensile properties, i.e., tensile strength, modulus of elasticity, and ultimate strain. Moreover, the notable scatter in the gathered results occurred because of the various bars’ diameters, types of resin used in manufacturing, as well as fibre volume content of the tested FRP bars. Therefore, this study compares various test procedures according to standards ,25,26,27The GFRP bars were produced by the pultrusion process and comprised continuous glass fibres with different fibre contents bonded together with epoxy or vinyl ester resin. In the current research GFRP bars from four different manufacturers  with sizTo calculate the tensile properties, the diameter of the bar must be known. Most of the standards ,25,26,27In the current research, both methods of determining bars’ diameter were used: underwater weighing (3 measurements) and measuring by micrometre with appropriate accuracy (6 measurements). No attempt was made to determine the diameter of each bar. Instead, the average diameter was determined from a randomly chosen bar of each series. The average value obtained by underwater weighting is designated as “effective diameter”. In this study, due to the assumption that any ribs and sand coating had not taken part in carrying tension stress, only solid bar cores were taken into further analysis and designated as “core diameter”. Therefore, in the case of the sand-coated bars, the sand layer was carefully removed before measurement. In the case of series of D type bars, to determine the core diameter, twice the hight of the ribs, measured with a micrometer, is subtracted from the exterior diameter. The average diameter values of each series are presented in The use of conventional grips for testing unidirectional pultruded bar having high axial tensile strength and low transverse compressive strength can cause premature failure of the bar due to high compressive force applied by the grips. The special anchorages should be used to reduce the compressive forces exerted on the bar. The anchorages shall be suited to the geometry of the test rebar and must allow the full strength of the bar to be obtained. Furthermore, anchors shall ensure concentric and torsion-free loading and shall ensure that specimens rupture occurs outside the anchorage zone. Most standards recommend anchorages of steel tubes filled with expansive cement grout, polymer resin, or 1:1 mixture by weight of resin and sand. However, anchorages are recommended but not required, and alternative methods for attaching specimens to the testing machine are acceptable. For example, steel tabs adapted to the shape of the bar are recommended by standards ,24. The total length of the specimen shall be taken to be the sum of the length of the test section and the length of the anchoring section or greater. The requirements for the free length between the anchorages depend on the test standard. Most of them recommend that the length of the test section shall be not less than 40 times the effective bar diameter. The detailed requirements of the standards are presented in In this research, the ends of GFRP bar specimens were embedded into steel tubes filled with expansive cement grout. The PCV cups with a concentric through–hole of appropriate diameter were used for plugging steel tubes and centring a bar inside the tube . This faSpecimens shall be instrumented with longitudinal strain gauges or extensometers to measure the elongation of the test piece under loading. The gauge length of the extensometer shall be met requirements according to the appropriate standard presented in The tensile load shall be applied at a constant rate until the failure of the specimen. Most of the standards ,24,25,26All tensile tests were carried out using a 1200 kN—capacity universal testing machine with video non-contacting extensometer . The tesThe observed tensile Load-strain behaviour of tested GFRP bars is presented in All specimens failed suddenly as expected. The observed tensile failure mode of a representative specimen of each type of tested GFRP bars are shown in 2).Tensile strength varies with the cross–sectional dimensions of the bar due to shear lag effect and therefore tensile strength should be referenced based on the bar size. In case of all standards ,25,26,27However, the cross-sectional area of each bar depends on the bar diameter, which is determined as “core” and “effective” one . In FiguStatistical data analysis of tensile strength test results is reported in Modulus of elasticity is the slope of the load–strain curve obtained from extensometer or strain gauge readings. Equation (2) shall be used to calculate the value of the modulus of elasticity:The start and end points of the load–strain curve chosen to modulus calculation depend on the standards used. Some standards specify strain interval ,19 and oThe calculation of modulus of elasticity was made according to all presented standards. If data for the exact stress/strain point were not available, the closest available data were applied. In The average modulus of elasticity ranges from 41 to 68 GPa when based on core diameter and from 38 to 61 GPa when based on effective diameter . The lowest values of moduli were obtained for A–D8a and B–D5a series and the highest value for C–D12 series, regardless of the standard used for determination. Modulus of elasticity is affected by the level of fibre content  is aboutStatistical data analysis of modulus of elasticity test results is reported in The analysis of test results carried out in this study reveals the high dependence of modulus of elasticity on the test standard used to determine its value. Two main requirements which distinguish these standards are: primarily, the location of start and end points of the load–strain curve taken into account in the modulus calculation and, secondly, the method of bar diameter determination (core or effective), which is used for 2), and The most of standards ,24,26,27Method II is also acceptable by most of the standards ,25,26,27In Statistical data analysis of ultimate strain test results is reported in To compare the tensile properties of GFRP bars produced by four different manufacturers, the series with the same diameter (12 mm) were taken into consideration, i.e., A–D12, B–D11, C–D12, and D–D12. This size of GFRP bars is also the most commonly used in construction . As far as the tensile strength and modulus of elasticity are concerned, the highest values for both were determined for C type bars, while the lowest for B type bars. The difference between these two types of bars for both parameters is about 20%. It is mainly due to fibre content; lowest for B series, much greater for C series . HoweverInfluence of bar diameter on tensile properties was evaluated for two bar types  for which bars with at least three various diameters were tested. There are A–D8a, A–D10, A–D12 series for A type and B–D5a, B–D8, and B–D11 series for B type. Tensile properties considered in this analysis were determined in the same manner as previously . The valNo clear influence of bar diameter on the tensile properties of GFRP bare was determined in the current research. There were only three diameters taken into consideration and it seems to be not sufficient to evaluate the objective influence of bar diameter on its tensile properties. Therefore only slight relationships based on the limited number of tests can be discussed as follows. In case of both type of bars, the increase of bar diameter up to 8 mm increased tensile strength and modulus of elasticity, but further increasing of diameter showed the opposite effect. However, the trend lines show the proportional increase of both tensile properties for A type and constant values for B type. This is a bit surprising because the tensile strength of GFRP bars generally tends to reduce with an increasing diameter ,2,3,4. TThe influence of two manufacturing factors: modification of resin and uniformity of batch on tensile properties was also investigated. To check if the tensile properties remain invariable regardless of a production batch, the A–D8a and A–D8b series were taken into account for the analysis, while the series of B–D5a, B–D5b, and B–D5c were compared to determine the impact of resin modification. The bars of B–D5a series were manufactured with the standard resin used by the company (reference series), while the B–D5b and B–D5c series had the modified resin in search of better technological parameters. The tensile properties of the above-mentioned series of bars are presented in The comparison of batch homogeneity for A type bars shows very good manufacturing quality. The tensile strength of A-D8a and A-D8b series shows very good compliance with the difference of less than 1%. The modulus of elasticity and ultimate strain of both series have also good agreement, however, the difference in these cases is bigger, about 4%–5%. Therefore, it can be said that the tensile properties of A type bars remain invariable between two manufacturing batches (means different manufacturing time). However, attention should be paid to the fact the A-D8a series has more than two times greater variability of test results. The modification of resin for B type bars had no significant influence on tensile strength and modulus of elasticity; the difference is in the range of 4%–9%. However, in the case of B-D5c series, the modification caused a significant impact on the ultimate strain, resulted in a 17% reduction. The resin modification had a positive influence on the homogeneity of test results. For all tensile properties, the coefficient of variation for B-D5b and B-D5c series was smaller than for the reference series even by nine times in the case of tensile strength.Unfortunately, there is no worldwide unified test standard for determination of tensile properties of GFRP rebars. It hinders to compare the products of various manufacturers and differentiates the design requirements (limit states) in various countries, where the national codes are mostly obliged. To evaluate the scale of this problem quantitatively seven test standards ,20,21,22To establish the tensile properties of the bar, the diameter and subsequently the cross–sectional area should be first determined. In the current research two methods of diameter measurement were investigated: based on underwater weighting (“effective” diameter) and using a micrometer (“core” diameter). The difference between “core” and “effective” diameters and relevant cross–sectional areas are in the range of 1.8%–7.2% and 3.6%–13.9%, respectively. This brings in considerable differences to the calculation of tensile strength and modulus of elasticity through bar cross-sectional area. Only the European standard  requiresThere is no difference between standards for tensile strength calculation. However, the above–mentioned difference in diameter determination affects tensile strength values, which are proportionally greater for the “core” diameter. Thus the tensile strength determined according to standard  is aboutIn the case of modulus of elasticity determination, another difficulty precludes the direct standard comparison: the specification of how the start/end points at the load–strain curve should be considered for modulus calculation. The current test reveals that calculation of modulus of elasticity based on measurement in the range from 20% to 60% of ultimate tensile load ,25,26,27Two methods of ultimate strain determination were considered according to various standards: I, based on direct measurement, and II, based on calculation with previously determined tensile strength and modulus of elasticity. Since Method II shows less scattered values, it seems to be more appropriate for determination of ultimate strain. The advantage of this method is also no necessity to use extensometer up to the failure of the tested bar. Method II is also acceptable by most of the standards ,25,26,27Summing up, the current research reveals that for determination of GFRP bars’ tensile properties the following conditions should be taken into account: cross-sectional properties based on core diameter, the start/end points at the Load-strain curve in the range from 20% to 60% of the ultimate tensile load for modulus of elasticity determination, and Method II for ultimate strain determination. However, it does not exist such a test standard, including all these conditions. Hence, the paper reveals the main differences in test procedures according to various standards which can lead to adverse differentiation of the tensile properties of the same GFRP bars.The paper reports the comprehensive tensile testing of four types of GFRP rebars (designated as A to D) to determine their tensile properties according to various test standards and to evaluate the type, size (diameter) and some manufacturing factors on tensile properties. More than 50 specimens of different type and size were tested and the results were evaluated according to seven standards.Due to the existence of two standard methods for bar diameter determination, the difference between “core” and “effective” diameters and relevant cross-sectional areas are in the range of 1.8%–7.2% and 3.6%–13.9%, respectively. This brings in considerable differences to the calculation of tensile strength and modulus of elasticity through bar cross-sectional area.The rebars from different manufactures vary in the extent of tensile properties. The highest values of tensile strength and modulus of elasticity were determined for C type bars, while the lowest for B type bars. The difference between these two types of bars for both parameters is about 20%. It is mainly due to fibre content.No clear influence of bar diameter on the tensile properties of GFRP bare was determined. However, the trend lines show the proportional increase of tensile strength and modulus of elasticity for A type bars and constant values for B type, while the decreasing impact on ultimate strain was observed.Tensile properties of A type bars stay invariable in a different batch of manufacturing. The type of resin for B type bars had no significant impact on tensile properties but influenced data variation.For the determination of GFRP bars’ tensile properties, the following conditions should be taken into account: cross-sectional properties based on core diameter, the start/end points at the load–strain curve in the range of 20%–60% of the ultimate tensile load for modulus of elasticity determination, and Method II for ultimate strain determination.The following conclusions can be drawn based on the test results of this research:Due to the irregular cross-sectional dimensions of the GFRP rebars, one should consider adopting standard limits for ultimate bar force instead of ultimate stress. Such an approach would eliminate the need to measure the effective bar diameter to demonstrate compliance with strength specifications. A core bar diameter could be used for the consideration of cover depth, development length, and bar spacing.This study also established the variability of the tensile properties of GFRP bars produced by different manufacturers. It provides a better understanding of the inherent variability of GFRP reinforcement. Such information is critical for determining the number of required tests to demonstrate compliance with standards and to establish the characteristic (or minimum) properties for design purposes. The research results can help the construction industry to manage GFRP reinforcement implementation and thus to widespread their use for construction.


Pathway analysis is widely applied in transcriptome analysis. Given certain transcriptomic changes, current pathway analysis tools tend to search for the most impacted pathways, which provides insight into underlying biological mechanisms. Further refining of the enriched pathways and extracting functional modules by “crosstalk” analysis have been proposed. However, the upstream/downstream relationships between the modules, which may provide extra biological insights such as the coordination of different functional modules and the signal transduction flow have been ignored.To quantitatively analyse the upstream/downstream relationships between functional modules, we developed a novel GEne Set Topological Impact Analysis (GESTIA), which could be used to assemble the enriched pathways and functional modules into a super-module with a topological structure. We showed the advantages of this analysis in the exploration of extra biological insight in addition to the individual enriched pathways and functional modules.GESTIA can be applied to a broad range of pathway/module analysis result. We hope that GESTIA may help researchers to get one additional step closer to understanding the molecular mechanism from the pathway/module analysis results.Supplementary information accompanies this paper at 10.1186/s12864-020-07148-y. Pathway analysis is a routine process in transcriptome analysis used to gain biological insights. As reviewed in several recent works –3, such Despite the wide application of these tools in transcriptome analysis, there are biases and false positives in these pathway analysis results. Donato et al. 2013  showed tTo explore the biological insights beyond individual enriched pathways and functional modules and generate extra clues to help inferring the molecular mechanisms, we propose a new strategy that quantitatively assesses the upstream/downstream relationships between pathways/modules. Our new algorithm uses topology, but also utilizes gene-gene interaction at supra-pathway level. This is based on the idea that the pathways/modules of a biological process are coordinately regulated, hence they may have interactions, especially upstream/downstream relationships. To our knowledge, there is currently no such algorithm/software to identify the upstream/downstream relationships between pathways/modules. The existing pathway crosstalk analysis software  , 12 invehttp://github.com/yanshen2953/GESTIA).In this study, we developed a novel algorithm called GEne Set Topological Impact Analysis (GESTIA). GESTIA is based on a global network merged from KEGG pathways , 16. BasTo assess the relationships between gene sets or pathways, the significance of the enrichment of the overlapping genes is often used as an indicator. However, the number of overlapping genes between pathways does not reflect the upstream/downstream interactions of the pathways. As observed in the example signaling pathways  of the two calculable influence scores, pathway/module However, with any given topological structure of two pathways/modules, there is a chance for random interactions between genes that belong to different pathways/modules. The influence scores of these random interactions, therefore, form the null distribution. Because of the variability of the topological structure of pathways, it is difficult to construct a universal null distribution for all the combinations of different pathways. Therefore, we permutated the interactions between the two pathways/modules being studied to calculate an empirical null distribution of the influence score, while maintaining the internal topological structure of each of the two pathways/modules.For pathways with overlapping genes, the interactions between the shared genes and other genes can be considered as both intra-pathway/module and inter-pathway/module interactions, which impede the permutations, because if we perturb these inter-pathway/module interactions, we will have to change the pathways’/modules’ topological structure, since these interactions are also intra-pathway/module. To overcome this dilemma, we create pseudo-vertices in the network that duplicate the shared genes Fig. d. In thip-value of 0.49 for Fig. p-value of 1.0. For Fig. p-value of 0.001 is as expected. In general, GESTIA score and its p-value can correctly indicate upstream/downstream relationships between two pathways.We applied GESTIA first on the example pathway pairs in Fig. To compare GESTIA with commonly used pathway analysis tools, we applied GESTIA on the DNA repair pathways and oncogenic pathways, most of which were selected from Chi et al. 2019 . We firsThe heatmap of GESTIA score matrix, however, shows a distinct structure of relationships between these pathways Fig. . Since thigh Vs. TERTlow hepatocytes from Lin et al. 2018 [High population, and enriched gene sets associated with ribosome components, mitochondrial proteins, the electron transport chain and hepatocyte metabolic activities in the TERTLow population. However, the paper did not further explore the relationships between these enriched pathways.Next, we demonstrate the ability of GESTIA to analyze the upstream/downstream relationships of enriched pathways in real datasets. We first analyzed the up/down-regulated pathways enriched by GSEA analysis of the RNA-seq data of TERTal. 2018 . In thisp-value 0.049 and 0.095), increasing the activation of Prakaca and Prakacb, which would consequently inhibit the Nduf genes, consistent with the up-regulation of GO_CELL_CYCLE and the down-regulation of GO_MITOCHONDRIAL_MEMBRANE_ PART.After conversion of the GESTIA matrix  hallmark gene sets comparing remission BCR-ABL− stem cells against remission BCR-ABL+ stem cells from another published work [− stem cells. Taken together, GESTIA analysis identified the upstream/downstream relationships of the enriched pathways and revealed the orchestrated regulation of pathways.We then calculated the GESTIA matrix for the significantly enriched , or gene sets constructed from co-expression analysis, or gene sets from module analysis from protein-protein interaction network, or any other methods that yield biological meaningful gene sets. This feature makes GESTIA highly flexible with input data. The output of GESTIA algorithm is a score estimating the upstream/downstream relationship between two pathways. The GESTIA scores can be arranged into a matrix, which can then be assembled into a directed graph. We call this directed graph “super-module” since it uses pathways as nodes. To distinguish the single GESTIA algorithm and the whole analysis starting from single GESTIA score calculation, matrix construction and super-module assembly, we call the later “GESTIA analysis”. The fundamental goal of GESTIA analysis is to elucidate the static upstream/downstream relationship between pathways, so that we can construct a weighted, directed pathway interaction network which is similar to gene interaction network despite that the nodes in the network are pathways.p-value will ensure that those strong GESTIA scores are generated from true upstream/downstream relationships, not by certain topological structures of the pathways .The design of GESTIA score makes it favor the direct upstream/downstream relationships, especially the downstream genes in one pathway that directly affect the upstream genes in the other. The pathways that have intense interactions but do not exhibit such “tail to head” interactions will get low GESTIA scores. On the other hand, filtering by We introduced pseudo vertices during the calculation of the null distribution, which may lack real biological meanings. However, the newly created pseudo vertices preserve the topological structures of both of the pathways, and at the same time, maintains the number of inter-pathway gene interactions (those arrows that start from one pathway and end in the other). We tested the raw influence score calculation on multiple pathway pairs with different topologies and with overlapping genes, with/without creating pseudo vertices. The raw influence scores remain the same with/without pseudo vertices, indicating that although the pseudo vertices did not serve as nodes with biological meanings, they did preserve the topological features of the two pathways. For the convenience of calculation, we decided to use this way of splitting the intersected gene sets in null distribution calculation.https://github.com/yanshen2953/GESTIA/tree/master/PathwaysSetResult .Since the GESTIA score assesses the static pathway interactions, in theory we could apply GESTIA once and for all for every gene set of pathways, however, this would be too time consuming, although calculation of 10 to 20 input gene sets by GESTIA only takes a short period of time, which is sufficient for users to test GESTIA on their own interested pathways. Nevertheless, calculation of the GESTIA matrix of pathways from popular databases is still ongoing; the result for 179 KEGG pathways can be found at: Since the GESTIA score calculation is based on a global network delineating all the possible gene interactions, the accuracy of this network may influence the result of the GESTIA score. With the accumulation and curation of pathway databases, this global network will be rapidly improved and, consequently, GESTIA will provide better estimation of the pathway relationships.To generate a concise and accurate assembly of pathways/modules, another important factor is the accuracy of the pathway/module analysis itself. Take the crosstalk analysis result as an example, before filtering by crosstalk analysis, there are obvious false positive pathways in the list. These false positive pathways usually carry functional modules which grant the false positives upstream/downstream relationships with other genuine pathways with high possibility, resulting in inaccurate and redundant assembly of the pathways/modules. To date, there are numerous algorithms and tools to analyze pathway enrichment and generate functional modules, including construction of functional modules based upon protein-protein interaction networks or co-expression networks. GESTIA can be applied on top of these tools’ results and organize the pathways/modules into directed networks, which is helpful for the validation of the pathways/modules and the understanding of the molecular mechanisms.For the weakness of GESTIA, firstly, the relationships between pathways are far more complex than just upstream/downstream relationships, but GESTIA can only assess a small sub-set of the pathway relationships (which is the upstream/downstream relationships). However, unlike the cooperative relationships  or functional relations , the upstream/downstream relationships between pathways are far less quantitively studied (at least to our knowledge). Secondly, GESTIA analysis does not optimize the gene members of the pathway gene sets, like the crosstalk analysis proposed by Donato et al. 2013 do, nor does it construct new gene sets de novo from co-expression data or interactome data. Therefore, the quality of the super-modules produced by GESTIA analysis greatly relies on the quality of the input gene sets, i.e. whether the pathway gene set represents the real functional module in a certain study. Refinement of the pathway gene set can improve the quality of output of GESTIA analysis, as shown in Fig. GESTIA can be applied to a broad range of pathway/module analysis result, not just from transcriptome analysis, since the current GESTIA algorithm does not incorporate gene expression changes. It can assemble the candidate pathways/modules to form a topological structure, which reflects the internal biological relationships based on gene interactions. Additional tools to visualize such gene interactions between the pathways are also available in the R package that implements GESTIA. We hope that GESTIA may help researchers to get one additional step closer to understanding the molecular mechanism from the pathway/module analysis results.Due to the fact that genes often interact with other genes beyond a specific pathway, it is necessary to build a global network before assessing the interactions between pathways, otherwise the interactions of pathways will be limited to only the shared genes. To build a comprehensive gene interaction network, we combined 285 KEGG pathways. Currently, KEGG includes metabolic and non-metabolic pathways, which are totally different in expressing the correlations between genes. In metabolic pathways, the proteins (encoded by certain genes) often act as enzymes in biochemical reactions, so the substrates of the proteins are often metabolites. In contrast, in non-metabolic pathways, the proteins may directly modify the target proteins , or bind to the target genes’ promoter/enhancer (as a transcription factor), then influence the expression of the target gene, or act as a epigenetic modifier that activates/suppresses the expression of certain genes. Due to these differences, some of the existing tools which built their own gene interaction networks excluded the metabolic pathways . In our study, we extracted the gene interactions of solely signalling pathways from KEGG by a R function, “KGML2igraph,” picked from the package “NetPathMiner”. We then converted the gene IDs into gene symbols and removed the isolated genes. The loops defined as “edges for which the two endpoints are the same vertex” within the igraph object was then removed using the function “simplify” from the R package “igraph”. Then, the igraph object was used as the global network in GESTIA calculation.A on the genes of pathway B is more than the genes of B on A, we will conclude that A is upstream of B. Hence, we define the GESTIA score to be the normalized relative impact of pathway A on pathway B, so that if the normalized impact of A on B is higher than B on A, then GESTIA score will be positive, and vice versa. The input of GESTIA algorithm are two gene sets representing two pathways.The purpose of the GESTIA score is to assess the upstream/downstream relationships of two pathways, which is essential to interrogate which of the two pathways has more of an impact on the other, and to what degree. Normally, if one gene can impact another gene , we would conclude that the former is upstream of the later. Here, we extend this upstream/downstream notion from genes to pathways. For pathways, if the impact of the genes in pathway A  on the whole pathway B is defined as the adjusted sum of its direct impact on B. For the same reason mentioned in the previous paragraph, we wanted to favour the “tail upstream of head” situation; therefore, the weights of genes in B (wBi) were used to adjust each individual influence score before taking the sum, so that the genes impacted by GeneAi in B would be given higher weights if they were the “head” genes. Taken together, the influence score of GeneAi on B is given by the following formula:Currently, we do not have a comprehensive estimation of the strength of each gene’s impact; therefore, all of the impacts  by randomly select the starting genes and the ending genes while fixing the direction of the permutated edges to be the same with original one. For example, in a case wherein one edge originally starts from A and ends in B, the permutation was done by randomly selecting the starting gene from A and the ending gene from B , which forms the empirical null distribution. The relative influence scores are then normalized by subtracting the mean of the permutated relative influence scores:Although the raw GESTIA score describes the relative impact of one pathway on the other, it is still possible that this impact is no more than the result of random effects. To remove the random effects, we first calculated the null distribution of the relative influence score of the two pathways, m B Fig. d. Howevem B Fig. d. Then, p-value was estimated by the chance of permInfluenceScoreA – permInfluenceScoreB to be more significant than raw GESTIAA-B. For pathways that do not interact at all, the GESTIA score will be 0 and p-value will be 1.The We subsequently define the normalized GESTIA score to be:permInfluenceScoreA does not equal to permInfluenceScoreB. This subtraction of two influence scores means that normalized GESTIA score is an estimation of the differences of the impact of pathway A on B and B on A.Note that Additional file 1: Figure S1. Visualization of the gene interactions between PID_ERBB2_ERBB3_PATHWAY and REACTOME_SIGNALING_BY_ERBB2. (DOCX 258 kb)Additional file 2: Figure S2. Visualization of the gene interactions between REACTOME_PI3K_EVENTS_IN_ERBB2_SIGNALING and REACTOME_SIGNALING_BY_ERBB2. (DOCX 394 kb)Additional file 3. R code for constructing global network. (TXT 2 kb)Additional file 4: Matrix Data 1. JI matrix for Fig. Additional file 5: Matrix Data 2. GESTIA score matrix for Fig. Additional file 6: Matrix Data 3. GESTIA score matrix for Fig. Additional file 7: Matrix Data 4. GESTIA score matrix for Fig. Additional file 8: Figure S3. Visualization of the gene interactions between KEGG_DILATED_CARDIOMYOPATHY and KEGG_LEUKOCYTE_TRANSENDOTHELIAL_MIGRATION. (DOCX 741 kb)Additional file 9. Additional discussion of the related issues. (DOCX 15 kb)