Giardia duodenalis genotypes (assemblages) A and B. Small wild rodents such as mice and voles are discussed as potential reservoirs for G. duodenalis but are predominantly populated by the two rodent species Giardia microti and Giardia muris. Currently, the detection of zoonotic and non-zoonotic Giardia species and genotypes in these animals relies on cumbersome PCR and sequencing approaches of genetic marker genes. This hampers the risk assessment of potential zoonotic Giardia transmissions by these animals. Here, we provide a workflow based on newly developed real-time PCR schemes targeting the small ribosomal RNA multi-copy gene locus to distinguish G. muris, G. microti and G. duodenalis infections. For the identification of potentially zoonotic G. duodenalis assemblage types A and B, an established protocol targeting the single-copy gene 4E1-HP was used. The assays were specific for the distinct Giardia species or genotypes and revealed an analytical sensitivity of approximately one or below genome equivalent for the multi-copy gene and of about 10 genome equivalents for the single-copy gene. Retesting a biobank of small rodent samples confirmed the specificity. It further identified the underlying Giardia species in four out of 11 samples that could not be typed before by PCR and sequencing. The newly developed workflow has the potential to facilitate the detection of potentially zoonotic and non-zoonotic Giardia species in wild rodents. Giardiasis in humans is a gastrointestinal disease transmitted by the potentially zoonotic  Giardia spp. are flagellated protozoan parasites of vertebrates and a frequent cause of gastrointestinal disease in wild and domestic animals and humans [Giardia duodenalis is the most widespread species in mammals and subdivided into eight genetic subgroups (assemblages A-H) [Giardia muris and Giardia microti [G. microti and, in particular, for G. muris, distinct morphological characteristics in comparison to G. duodenalis have been described [d humans . Giardiages A-H) . These sges A-H) ; assemblges A-H) . Small r microti ,3,4. Forescribed .G. duodenalis assemblages A and B are genetically very distinct, and each group can be further subclassified based on genetic information determined by multi-locus sequence typing (MLST) [The zoonotic g (MLST) . Within g (MLST) . The subg (MLST) .Giardia spp., and the potential zoonotic risk has been highlighted by several studies [Giardia-positive beavers (Castor canadensis) has been estimated at 13–30%, and zoonotic transmission has been implicated by several studies [G. duodenalis assemblages A and B [Chinchilla lanigera, where mainly G. duodenalis assemblage B-type infections have been recognized [Rodents are frequently parasitized by  studies ,11,12,13 studies ,15,16. Acognized ,17,18.G. muris, G. microti and/or G. duodenalis assemblage G, respectively [The roles of zoonotic transmission by smaller rodents commonly found in natural and human habitats, such as mice, voles and rats, are not well-resolved. The few studies providing molecular typing data revealed that these genera are mainly infected by ectively ,21,22,23ectively ,25,26,27ectively ,28,29. Giardia prevalence in wild rodents often lack species information or genotyping data, so that the zoonotic risk cannot be estimated from those studies. One likely reason for the lacking data is that Giardia species determination and assemblage typing mainly relays on a cumbersome test principle, including amplification by the (nested) PCR and sequencing of genetic marker genes, such as small subunit rRNA gene (SSU), triosephosphate isomerase (TPI), glutamate dehydrogenase (GDH) and beta giardin (BG) [Giardia species is the SSU locus.Studies reporting the din (BG) ,32,33,34G. muris, G. microti and zoonotic G. duodenalis assemblage types in rodent samples. These protocols will help in future studies to better determine the potential risk for zoonotic Giardia transmission from wild rodents.The aim of the study was to provide a new workflow based on the available and newly developed real-time PCR protocols to determine the G. duodenalis assemblages A  and B  were derived from axenic in vitro cultures using standard culture procedures [G. muris (Thompson isolate) were commercially obtained . Fecal samples of G. muris and G. duodenalis-infected mice were obtained from experimental laboratory infections done in a different context. These procedures were approved by the local authorities, the Landesamt für Gesundheit und Soziales .Trophozoites of ocedures . Cysts oBalamuthia mandrillaris, Toxoplasma gondii and Leishmania donovani were previously extracted from routine in vitro cultures and available from archived materials. DNA of Entamoeba histolytica axenic cultures was a kind gift from Prof. Iris Bruchhaus . DNA samples of wild rodents are described elsewhere . DNA of Giardia species-specific oligonucleotides: G. muris (X65063 and AF113895), G. microti (AF006676 and AF006676), G. duodenalis assemblage A (M54878 and AF199446), G. duodenalis assemblage B (AF199447 and AF3898), G. duodenalis assemblage C (AF199449), G. duodenalis assemblage D (AF199443), G. duodenalis assemblage E (AF199448), G. duodenalis assemblage F (AF199444) and G. duodenalis assemblage G (AF199450). These references were supplemented with the sequence information of G. muris and G. microti derived from a screen in wild rodents (KY114167-KY114486) [Giardia-specific nested PCR and sequencing approach, as described earlier [Giardia isolates in the public databases, including all three mammalian Giardia species and assemblage types. In addition, due to its comparably high copy number in the genome, the ribosomal gene loci has been widely used for genotyping and phylogenetic approaches, as it promises supposedly higher PCR detection sensitivity [G. muris, G. microti and G. duodenalis was done using the Geneious software tool .The following reference sequences (GenBank accession numbers) of the SSU gene locus were used to design  earlier ,36. Currsitivity . The desG. duodenalis assemblage types A and B, we used a previously described protocol that detects the single-copy target gene 4E1-HP [For determination of the e 4E1-HP .n = 3) with 95% confidence intervals. DNA extraction of samples was done with the QIAamp Fast DNA Stool Mini Kit  according to the manufacturer’s protocols. Real-time PCR assays were standardized and performed using single PCR approach as the multiplexing reduced assay performance (data not shown). Conditions were as follows using the 2X Maxima Probe/ROX qPCR Master Mix  in a 25 µL PCR reaction: 12.5 µL reaction buffer, 1 µL of each forward and reverse primer (from 3.3-µM stock), 0.5 µL of probe (from 3.3-µM stock), 1–5 µL DNA template and add water for the final volume. Reactions were started at 95 °C for 10 min, followed by 45 cycles of 95 °C denaturation for 15 s, 60 °C annealing for 30 s and 72 °C extension for 30 s. Real-time PCR and analysis was performed using a CFX Maestro PCR machine and the respective software tools . Sequences of the primers and probes used in the study are presented in SSU genome fragment (see Methods for details) was used for the primer design. At this locus, a well-established and robust real-time PCR to diagnose G. duodenalis in humans has been previously described [G. muris on the one hand and G. duodenalis and G. microti on the other hand allowed for the design of a G. muris-specific primer and probe combination  indicated a high sensitivity reporting 1 fg DNA with a cq value of 36.3 ± 0.65. This equals approximately 0.1 genome equivalents for this species (estimated genome size of G. muris is 9.8 MB [The assay designed for the detection of s 9.8 MB ). G. duodenalis assemblages A and B were equally well-detected in the G. duodenalis/G. microti (Gmi/Gd PCR) approach, with a sensitivity of 10 fg  and, in the G. duodenalis-only (Gd PCR) approach, with a sensitivity of about 1 fg DNA , which equals about 0.07 genome equivalents  or approximately 7 genome equivalents per PCR reaction were detected. The lower sensitivity was expected due to the single-copy target versus multi-copy target in the former PCRs. Sensitivity of the Giardia-positive samples from a previous project [G. muris, G. microti or G. duodenalis, respectively, as previously determined by nested PCR and the sequencing of a fragment at the SSU locus [Giardia species could not be determined in the previous study. To test the applicability of the new PCR approach, we reanalyzed 38  project   does not allow to formally exclude an additional underlying G. microti infection. However, in this case, a lower cq value in the Gd/Gmi combi-PCR compared to the Gd-only PCR would be expected, which was not the case.All nine G. microti samples, 12 were confirmed by our new PCR workflow. Unexpectedly, two samples exhibited a signal in the G. muris-specific PCR with relatively low cq values of 31, but no signal was recorded in the Gd/Gmi-PCR. We can only speculate on the reason and favor the possibility of an underlying double infection with a lower G. microti load that may have been amplified in the previous nested PCR approach. The nested PCR tends to amplify G. microti  sequences with higher sensitivity than that of G. muris .Of the 14 G. duodenalis samples were also confirmed by our assay; however, two samples were not confirmed, likely due to lower sensitivity of the real-time assay compared to the nested PCR approach used in the previous study . One sample was further confirmed as a zoonotic assemblage type, whereas the other sample  was not confirmed. Again, the reason is most likely lower sensitivity of the assemblage-specific PCR that targets single-copy genes in comparison to the PCR at the multi-copy SSU locus.Two of the four G. muris real-time PCR, which indicates a possible underestimation of the G. muris prevalence in the previous work [Gd-specific PCR; however, as the Gmi/Gd PCR was negative, we do not consider this result as reliable.Four of the 11 samples that could not be typed in our previous study displayed a positive signal in the ous work . One samG. muris, G. microti and G. duodenalis infections in small rodents, including the detection of zoonotic G. duodenalis assemblage types A and B. The workflow provided an improved time-to-results procedure for the analysis of rodent fecal samples to determine the Giardia species and for an assessment of the potential zoonotic risk.Here, we provided a real-time PCR workflow for the detection of G. duodenalis assemblages A and B, and the zoonotic transmission has been described [Giardia species. Pet chinchillas harbor zoonotic G. duodenalis (mostly assemblage B) and non-zoonotic assemblages C, D and E [Two rodent species are well-described to harbor the potential zoonotic escribed ,43. One escribed . The seq D and E ,43,44,45Giardia in their analyses were based on the microscopic detection of cysts and rarely included molecular tools to determine the Giardia species or assemblage types. The potential relevance for zoonotic transmissions was nevertheless often discussed [Giardia species and may help to assess the potential relevance of rodent species for zoonotic transmissions of Giardia infections. For ubiquitous small mammals such as mice and voles, the potential relevance of zoonotic transmission is largely unknown. Most of the larger screens for zoonotic pathogens that included iscussed ,46. HereG. microti cannot be detected directly. We based the design of the primers and probes on previously acquired sequence information at the SSU locus of 106 unique G. microti and 10 unique G. muris sequences [G. microti, the sequence analysis revealed a broad variety of genotypes even at the usually highly conserved SSU gene locus [G. microti-specific PCR and needed to rely on PCR signals obtained with primers detecting G. microti/G. duodenalis or solely G. duodenalis, respectively. Notably, a previously described real-time PCR for human diagnostic purposes [G. micoti genotypes (data not shown), hence providing little benefit to the current study. The results presented here indicated that our workflow was sensitive and robust for such purposes. However, our method also had clear limitations. The most prominent limitation was that equences . To our ne locus . Due to purposes  partiallG. microti/G. duodenalis-specific real-time PCR is less sensitive than G. duodenalis PCR in particular, because the same region is covered. Possibly, conformational hindrance at the gene locus may influence the binding affinity of the primer and probe. For the G. duodenalis assemblage-specific PCR, the lower sensitivity was likely due to the single-copy target gene used for detection. The lower sensitivity compared to real-time PCR at the SSU locus has been also described previously [The analysis revealed different sensitivities for the various PCR approaches, which was important for the interpretation of the results. However, all PCRs detected one or less genome equivalents at the SSU locus and about 10 genome equivalents at the 4E1-HP locus. This was in good concordance with previously published results using these loci ,48. It ieviously . SSU sequences derived from wild rodents revealed only five G. duodenalis-positive samples, and these samples were not typable at the single locus genes TPI, GDH and BG. Four of these samples were included in the present study and revealed either very low cq values for G. duodenalis qPCR or G. duodenalis was undetectable with the presented PCR methods. Only one sample revealed detectable amounts of G. duodenalis assemblage B DNA, confirming our previous typing results. Overall, this implied a very low zoonotic risk in the studied German rodent population. Whether the low-level detection of pathogens in real-time PCR assays, as indicated by high cq values, are indeed relevant for zoonotic transmissions should be carefully evaluated in future screening studies. For example, in our original study, an analysis of 358 Giardia species populating small rodents, including the zoonotic G. duodenalis assemblage types. This approach could be used in future studies to estimate the possible relevance for zoonotic transmissions by these animals.In conclusion, we provided a processive and robust workflow for the detection of all


Variovorax sp. PAMC28711. Trehalose, a disaccharide, is used by the microorganism as an alternative carbon source.Metabolism including anabolism and catabolism is a prerequisite phenomenon for all living organisms. Anabolism refers to the synthesis of the entire compound needed by a species. Catabolism refers to the breakdown of molecules to obtain energy. Many metabolic pathways are undisclosed and many organism-specific enzymes involved in metabolism are misplaced. When predicting a specific metabolic pathway of a microorganism, the first and foremost steps is to explore available online databases. Among many online databases, KEGG and MetaCyc pathway databases were used to deduce trehalose metabolic network for bacteria Variovorax sp. PAMC28711, we found that all enzymes that were missing in KEGG and MetaCyc databases were involved in the trehalose metabolic pathway.While using KEGG and MetaCyc databases, we found that the KEGG pathway database had one missing enzyme . The MetaCyc pathway database also had some enzymes. However, when we used RAST to annotate the entire genome of Findings of this study shed light on bioinformatics tools and raise awareness among researchers about the importance of conducting detailed investigation before proceeding with any further work. While such comparison for databases such as KEGG and MetaCyc has been done before, it has never been done with a specific microbial pathway. Such studies are useful for future improvement of bioinformatics tools to reduce limitations. Variovorax sp. PAMC28711 selected in this study to explore trehalose metabolism is one of cold adapted lichen-associated bacteria isolated from Antarctica. Analysis of enzymes from cold-adapted microorganisms has become common in recent years because cold-adapted enzymes from organisms living in Polar regions, deep oceans, and high altitudes have various benefits [Variovorax is a cold adapted, Gram-negative, motile bacterium that comes in a variety of shapes, including flat, slightly curved, and rod shapes. Because of the presence of carotenoid pigments, Variovorax colonies are yellow, slimy, and shiny [Variovorax sp. PAMC28711. One of them is trehalose metabolic pathway. Trehalose is a naturally occurring alpha-linked disaccharide formed by two molecules of glucose. It was first isolated by French chemist Marchellin Berthelot in the mid-nineteenth century from Trehala manna, a sweet substance obtained from nests and cocoons of the Syrian coleopterous insects (Larinus maculatus and Larinus nidificans) known to feed on the foliage of a variety of thistles. Trehalose is used for biopharmaceutical preservation of labile protein drugs and cryopreservation of human cells. It is also widely used in the food industry [Metabolism refers to all biochemical processes that occur during the growth of a cell or an organism. Microbial metabolism involves a group of complex chemical compounds. It includes anabolism and catabolism for microorganisms to obtain energy and nutrients for survival and reproduction. A microbe’s metabolic properties are the foremost important factors in determining its condition. They may be accustomed to monitor biogeochemical cycles and industrial processes . Therefobenefits . Genus Vnd shiny . There aindustry . Trehaloindustry . MetaCycindustry , 11. Rapindustry . RAST anindustry –16.Variovorax sp. PAMC28711 and predict enzymes involved in this pathway, these two databases were chosen in study. This is the first study to compare cold-adapted bacteria to well-known databases and predict missing enzymes using RAST annotation software for further analysis of results obtained from KEGG and MetaCyc databases. Furthermore, this paper provides insight into how to validate computational data’s outcomes and proceed further.KEGG and MetaCyc are two well-known and popular databases for metabolic pathway prediction. To study trehalose metabolic pathway in Variovorax sp. PAMC28711 was obtained from the National Center for Biotechnology Information (NCBI) genome database (https://www.ncbi.nlm.nih.gov/) for this metabolic pathway study. The GenBank accession number of Variovorax sp. PAMC28711 is NZ_CP014517.1.A complete genome information of http://www.kegg.jp/ or http://www.genome.jp/kegg) and MetaCyc database (MetaCyc.org) were used to predict trehalose metabolic pathway in the complete genome of Variovorax sp. PAMC28711. During prediction of pathway via the annotated file, bioinformatics tools such as RAST annotation server (https://rast.nmpdr.org/rast.cgi) were used to find the missing enzyme.The KEGG pathway database  and trehalose-6-phosphate phosphatase (OtsB). The TreS reversible pathway has one enzyme, trehalose synthase.The comparison of three programs  for the prediction of enzymes involved in trehalose metabolism in As shown in Table Variovorax sp. PAMC28711 obtained from the KEGG pathway database [Variovorax sp. PAMC28711 lacks enzyme maltooligosyl-trehalose synthase (TreY: EC 5.4.99.15), which makes the TreY/TreZ pathway incomplete.Figure database –20. TrehVariovorax sp. PAMC28711 obtained from the MetaCyc database. Figure Variovorax sp. PAMC28711: trehalose biosynthesis I (OtsA: EC 2.4.1.15 and OtsB: EC 3.1.3.12), trehalose biosynthesis IV (TS: EC 5.499.16), and trehalose biosynthesis V . According to MetaCyc, trehalose biosynthesis V has three enzymes . However, Variovorax sp. PAMC28711 lacks enzyme TreY: EC 5.4.99.15, which prevents the trehalose biosynthesis V pathway from being complete. Therefore, it is assumed that the trehalose biosynthesis V pathway is absent in Variovorax sp. PAMC28711 as results suggest that only two trehalose biosynthesis pathways are involved in this strain.Figure Variovorax sp. PAMC28711 has three pathways for trehalose biosynthesis OtsA/OtsB, TS, and TreY/TreZ. Enzymes involved in these three pathways are trehalose 6-phosphate synthase (OtsA: EC 2.4.1.15), trehalose 6-phosphate phosphatase (OtsB: EC 3.1.3.12), trehalose synthase (TS: EC 5.499.16), maltooligosyl-trehalose synthase (TreY: EC 5.4.99.15), and maltooligosyl-trehalose trehalohydrolase (TreZ: EC 5.3.2.1.141). The trehalose degradation pathway (TreH) in Variovorax sp. PAMC28711 possesses one enzyme, trehalase. Figure Variovorax sp. PAMC28711. The missing enzyme (TreY: EC 5.4.99.15) was found from results of RAST annotation through SEED Viewer which started and stopped at 335612 to 3352054 coding sequence (CDS) , in KEGG results. RAST annotation is an excellent starting point for a more systematic annotation initiative since it can differentiate between two types of annotation and use reasonably accurate subsystem-based statements as the basis for a through metabolic reconstruction [Variovorax sp. PAMC28711 used all three trehalose biosynthesis pathways. In addition, we examined MetaCyc pathway database to compare our results and found that the enzyme maltooligosyl-trehalose synthase (TreY: EC 5.4.99.15) was also missing in this database (Table FIGfams) [Variovorax sp. PAMC28711. MetaCyc is widely used in a variety of fields, including genome annotation, biochemistry, enzymology, metabolomics, genome and metagenome analysis, and metabolic engineering, duet to its exclusively experimentally determined results, intensive curation, comprehensive referencing, and user-friendly and highly integrated design. Although these two databases (KEGG and MetaCyc) have distinct features, both bioinformatics tools have certain drawbacks that should be considered when conducting research validation. It is important to note that different pathway databases have different pathway boundaries. The KEGG database favors complex metabolic maps that include all known reactions related to a general topic, regardless of whether they occur within the same species or even the same kingdom. UniPathway [Trehalose metabolism is one of metabolism pathways for carbohydrates. Five distinct pathways for trehalose synthesis have been described. However, there is only one pathway for trehalose synthesis in fungi, plants, and animals . These ftruction . As a reFIGfams) . The KEGFIGfams) . A five-FIGfams) . Other mFIGfams) . It contiPathway , on the iPathway .Variovorax sp. PAMC28711 possessed the enzyme maltooligosyl-trehalose synthase (TreY: EC 5.4.99.15) in the TreY/TreZ pathway for trehalose biosynthesis. Therefore, researchers should be aware of this when conducting preliminary screening employing bioinformatics tools. Many researchers are employing bioinformatics tools to predict their hypothesis before conducting any experiments. Our exploration of the trehalose metabolic pathway using two commonly used pathway databases demonstrated that bioinformatics tools might not provide accurate results. Thus, we need to evaluate databases before drawing definite conclusions.Before performing any kind of wet laboratory work, bioinformatics methods play a crucial role in predicting pathways. Online software has been proven to be useful in predicting research projects. Although commonly used online programs have good features, they have some limitations. In this study, we compared results of predicting trehalose metabolism pathways using two common databases. We found that both databases had some limitations as both databases showed enzymes missing for specific pathways. However, RAST annotation revealed that


The list of differentially expressed biomarkers is used for functional interpretation and retrieve biological meaning, however, it requires moderate computational skills. Thus, researchers with no programming expertise find difficulty in data interpretation. Several bioinformatics tools are available to analyze such data; however, they are less flexible for performing the multiple steps of visualization and functional interpretation.In the past decade, RNA sequencing and mass spectrometry based quantitative approaches are being used commonly to identify the differentially expressed biomarkers in different biological conditions. Data generated from these approaches come in different sizes  for extensive pathway enrichment analysis. OMnalysis also fetches the literature information from PubMed to provide supportive evidence to the biomarkers identified in the analysis. In a nutshell, we present the OMnalysis as a well-organized user interface, supported by peer-reviewed R packages with updated databases for quick interpretation of the differential transcriptomics and proteomics data to biological meaning.We developed an easy-to-use Shiny based web application  that provides users with a single platform to analyze and visualize the differentially expressed data. The OMnalysis accepts the data in tabular form from edgeR, DESeq2, MaxQuant Perseus, R packages, and other similar software, which typically contains the list of differentially expressed genes or proteins, log of the fold change, log of the count per million, the https://github.com/Punit201016/OMnalysis. OMnalysis can also be accessed from - http://lbmi.uvlf.sk/omnalysis.html. OMnalysis is hosted on a Shiny server at https://omnalysis.shinyapps.io/OMnalysis/. The minimum system requirements are: 4 gigabytes of RAM, i3 processor . It is compatible with any operating system . The OMnalysis is heavily tested on Chrome web browsers; thus, Chrome is the preferred browser. OMnalysis works on Firefox and Safari.The OMnalysis codes are entirely written in R language and freely available at  High-throughput techniques have emerged as powerful tools to study differential omics. RNA-sequencing (RNA-seq) and liquid chromatography coupled with mass spectrometry (LC-MS) based quantitative approaches are increasingly being used . The lowe.g., log fold change), significance value , and transcript count (log counts per million). The output usually depends on the type of statistical package and function used, for example, EdgeR   that contains columns of identified genes, the magnitude of the change against control  , IRIS   and DEBrnalysis) , which pIn omics studies, it is often difficult to narrow down 2–5 top biomarkers from thousands of differentially expressed genes (DEGs) or protein (DEPs). It requires moderate computational effort, iterative re-testing, and use of the multiple databases. Towards this end, we developed an R Shiny based web-tool—the OMnalysis, that accepts the list of transcriptomics and proteomics normalized quantitative expression data generated from bioinformatics tools like edgeR version 3.32.0  and PersOMnalysis is an interactive R Shiny based web application composed of multiple sectioned user interface (UI) in the form of tabs. It is built to perform exploration of differential expression data efficiently and iteratively. R packages used for the development of the UI and its components are as follows: R Shiny version 1.6.0 , flexdasUpload data, PCA, Plots, Statistical filtering, GO enrichment analysis, GO heatmaps, Pathway enrichment analysis, Enriched pathway visualization, Literature info and Help.To enhance the user experience and proper execution of the OMnalysis pipeline , peer-rehttp://www.ebi.ac.uk/arrayexpress). Expression analysis was performed on human brain microvascular endothelial cells (hBMEC) induced with various pathogens: Borrelia burgdorferi  from each of those supplemental files were copied to make a master file in CSV format  in OMnalysis.Transcriptomic data matrix contains Ensembl IDs, while proteomic data comes with UniProt IDs. To convert these IDs into five different ID types  we used the n 2.46.3  to fetchn 2.46.3 . We havePCA tab  and the second gseGO function on sorted genes or proteins with respect to logFC values to perform gene set enrichment analysis (GSEA) (Drosophila melanogaster (org.Dm.eg.db) and C.elegans (org.Ce.eg.db)  . To supps (GSEA)  was usede.eg.db) . P-valuemethods   were useGO heatmaps UI tab   , was useand gsub  to arranronment)  to prepaenrichpathway function of ReactomePA version 1.34.0 .A on 2.2.2  was usedEnriched pathway visualization tab of UI  were set to select the DEPs. The resultant list of the differentially expressed candidates was automatically transferred to the next level i.e., the functional and pathway analysis.Further, OMnalysis provides the user functionality to filter out the genes or proteins which are not significantly expressed or identified. The software provides an option to set the cutoff values for logFC, logCPM, and mic data , whereasq-value cutoff) and GSEA  on the differentially expressed candidates selected above  analysis were performed using ORA  and enriched them using ORA  and GSEAOMnalysis was extended to include the STRINGdb, which covers several databases  to enrich the DEGs .One of the main features of OMnalysis is the visualization of the level of expression (using different color codes) of the given gene or protein in different treatments. As shown in In the case of the proteomics study, OMnalysis processed the list of DEPs using ORA  and GSEAPCA, Plots, Statistical filtering, and GO heatmaps sections can be downloaded in TIFF, PNG, JPEG, and PDF format. In the case of the enriched pathway section, the visualization output can be downloaded in PNG image format. Furthermore, users have the option to adjust the dimension and resolution of the images generated in each tab of OMnalysis.OMnalysis was developed considering the maximum flexibility and customization of the resulting output. Users were provided with the option to download the converted accession IDs, results from GO enrichment analysis and pathway enrichment analysis in CSV file format. All the diagrams produced in e.g., inability to perform differential expression analysis using count or spectral data and lack of metadata table, it benefits the larger research community by minimizing the time to obtain the result and to understand the normalization methods. Some tools like iDEP  are developed, which accept count data to analyze differential expression, however, this approach is complicated for the biologist in terms of selecting the normalization methods. The normalization methods that available are CPM (count per million), TPM (transcripts per kilobase million), FPKM/RPKM (fragment/reads per kilobase of transcript per million mapped reads), DESeq2 (median of rations) , and Edgike iDEP , ShinyNGike iDEP  and DEBrike iDEP  require e.g., iDEP, IRIS-EDA, START app, DEBrowser, etc.), in terms of input data requirements, types of visualization, ease of use, and database used for GO and Pathway enrichment analysis. The details of this comparison are provided in We compared the OMnalysis with existing freeware used for DEGs analysis and exploration  or the number of transcripts (logCPM), the OMnalysis provides an option with customizable resolution and dimensions of the images, and enables various image format to download . The iDEP generates scatter and volcano plots only in esp format, whereas the IRIS-EDA and START applications produce plots only in png formats. For statistical filtering of non-significant genes, the IRIS-EDA uses adjusted P value and fold change, whereas in OMnalysis can filter out the non-significant genes based on fold change, P values, or adjusted P value and log counts per million in case of transcriptomic data.When it comes to the representation of data in the form of scatter and volcano plots to demonstrate the level of expression against the level of significance  and a P value  to gain more reliable information. OMnalysis also provides options to segregate the DEGs or DEPs into various GO classes . Whereas, in OMnalysis the functional interpretation and enrichment analysis is integrated and supported by ORA and its extension GSEA. ORA uses the hypergeometric test  and GSEAomponent ), which Heatmaps are one of the best representation tools to compare the level of expression among various treatments. Users often use third-party heatmapper  web applFinally, using the R Shiny platform and Bioconductor packages, we were able to integrate the several functionalities into OMnalysis. The streamlined functionalities include uploading of expression data, PCA to identify correlation and variability among treatments, plots to visualize differential expressions, statistical filtering to segregate the candidate according to the statistical significance, GO enrichment analysis, heatmaps to compare expression among treatments, pathway enrichment analysis, and pathway visualization capabilities. All together the OMnalysis provides the user with a comprehensive explanation of the transcriptomics and proteomics data. To our knowledge, no integrated web tool provides visualization of pathways based on KEGG and Reactome, and visualization of PPI network using STRING in one place. OMnalysis with higher flexibility, easy-to-use interface, multiple visualizations, and extensive coverages of curated databases outperforms many of the currently available web application available to explore and analyze the quantitative transcriptomics and proteomics data.e.g., Wiki pathways, Pathbank, etc.), and characterization of functional units of discovered biomarkers . Currently, we have added an option to download the set of codes, so that bioinformaticians can extend the functionality of the OMnalysis tool. With the existing capabilities, we are confident that OMnalysis will be a useful web application for researchers, with no or less bioinformatics experience, who want to analyze quantitative transcriptomic and proteomic data into actionable biological insight.OMnalysis has integrated an array of scattered packages and curated databases to provide a user-friendly data analysis tool. The overall functionality was tested on the four real datasets of transcriptomics and proteomics. Using these datasets, we were able to perform series of downstream analysis, starting from PCA and visualization of differentially expressed candidates in single or multiple treatments in the form of scatter or volcano plots, Venn diagram and histogram. Further, this tool was able to segregate gene sets based on any of the three gene ontology classes  with seven possible multiple hypothesis correction methods and two types of enrichment analysis (ORA and GSEA). This tool provided different view on transcriptomics and proteomics data using three enrichment methods  and network topology analysis using four different databases . Additionally, STRING gave overall picture of enrichment and interaction among molecules. Comparing with the other tools, OMnalysis provides more customizable and functional options. We envisage developing an advanced version of OMnalysis, which will include more animal species, omics types, additional pathway networks (10.7717/peerj.12415/supp-1Supplemental Information 1Click here for additional data file.10.7717/peerj.12415/supp-2Supplemental Information 2Click here for additional data file.10.7717/peerj.12415/supp-3Supplemental Information 3Click here for additional data file.10.7717/peerj.12415/supp-4Supplemental Information 4Click here for additional data file.10.7717/peerj.12415/supp-5Supplemental Information 5Click here for additional data file.10.7717/peerj.12415/supp-6Supplemental Information 6Click here for additional data file.10.7717/peerj.12415/supp-7Supplemental Information 7Click here for additional data file.10.7717/peerj.12415/supp-8Supplemental Information 8Click here for additional data file.10.7717/peerj.12415/supp-9Supplemental Information 9Click here for additional data file.10.7717/peerj.12415/supp-10Supplemental Information 10Click here for additional data file.10.7717/peerj.12415/supp-11Supplemental Information 11Click here for additional data file.10.7717/peerj.12415/supp-12Supplemental Information 12Click here for additional data file.10.7717/peerj.12415/supp-13Supplemental Information 13Click here for additional data file.10.7717/peerj.12415/supp-14Supplemental Information 14Click here for additional data file.10.7717/peerj.12415/supp-15Supplemental Information 15Click here for additional data file.10.7717/peerj.12415/supp-16Supplemental Information 16Click here for additional data file.10.7717/peerj.12415/supp-17Supplemental Information 17Click here for additional data file.10.7717/peerj.12415/supp-18Supplemental Information 18Click here for additional data file.10.7717/peerj.12415/supp-19Supplemental Information 19Click here for additional data file.10.7717/peerj.12415/supp-20Supplemental Information 20Click here for additional data file.10.7717/peerj.12415/supp-21Supplemental Information 21Click here for additional data file.10.7717/peerj.12415/supp-22Supplemental Information 22Click here for additional data file.10.7717/peerj.12415/supp-23Supplemental Information 23Click here for additional data file.10.7717/peerj.12415/supp-24Supplemental Information 24The top right color key presents the range of logFC values. The OMnalysis has divided gene boxes into four sections, each presenting one treatment with pseudo color according to the level of expression.Click here for additional data file.10.7717/peerj.12415/supp-25Supplemental Information 25The top right color key presents the range of positive and negative logFC. The Gene box in the pathway is divided into four sections, each presenting one treatment with pseudo color according to the level of expression.Click here for additional data file.10.7717/peerj.12415/supp-26Supplemental Information 26PPI network generated by OMnalysis. Each node presents a gene with the log fold change value in blue - downregulation, red - upregulation. Edges in the network provide evidence of the interactions between the genes or proteins. The color code of the edges is provided in the figure.Click here for additional data file.10.7717/peerj.12415/supp-27Supplemental Information 27Click here for additional data file.


Bacteria-targeting nanomaterials have been widely used in the diagnosis and treatment of bacterial infectious diseases. These nanomaterials show great potential as antimicrobial agents due to their broad-spectrum antibacterial capacity and relatively low toxicity. Recently, nanomaterials have improved the accurate detection of pathogens, provided therapeutic strategies against nosocomial infections and facilitated the delivery of antigenic protein vaccines that induce humoral and cellular immunity. Biomaterial implants, which have traditionally been hindered by bacterial colonization, benefit from their ability to prevent bacteria from forming biofilms and spreading into adjacent tissues. Wound repair is improving in terms of both the function and prevention of bacterial infection, as we tailor nanomaterials to their needs, select encapsulation methods and materials, incorporate activation systems and add immune-activating adjuvants. Recent years have produced numerous advances in their antibacterial applications, but even further expansion in the diagnosis and treatment of infectious diseases is expected in the future. Antimicrobial resistance in bacteria has become a substantial threat to human health in recent years; however, few new antimicrobial agents with innovative mechanisms of action are available to effectively combat these bacteria . MoreoveNanomaterial-based therapies represent promising tools to fight against bacterial infections that are difficult to treat, for example, those characterized by biofilm formation or antimicrobial resistance . In thisBacteria-targeting nanomaterials have been widely used to treat bacterial, fungal, viral and parasitic infectious diseases in recent years ,12,13,14Staphylococcus aureus (MRSA), vancomycin-resistant Enterococcus (VRE), Pseudomonas aeruginosa, and Klebsiella pneumoniae  chains self-assembles to form micelles in aqueous solution and has antibacterial properties against both gram-positive and gram-negative bacteria without evident antibiotic resistance while being biodegradable . A . A g-indications . A polymgradable . SNAPs aIn addition to serving as treatments, nanotechnology has improved the accuracy of diagnosing bacterial infectious diseases ,45,46,47−15 m in bio-fluids [The unique 2D structure and single-atom thickness of graphene sheets enable graphene-based biosensors to be highly flexible to any tiny changes in the surrounding environmental conditions . Grapheno-fluids .S. aureus gene sequences, with detection limits as low as 12.4 pg/mL [S. enterica, in a wide linear dynamic range from 101 to 108 cfu mL−1 and with a 101 cfu mL−1 limit of detection [E. coli infection and could detect with a detection limit of 1000 CFU [E.coli O157:H7 with the sensors transduced through electric charges [2O3 shows on-time diagnosis of E. coli bacteria [E. coli cell within 50 s in a 1 µL sample volume [Vibrio parahaemolyticus was developed by merging loop-mediated isothermal amplification (LAMP) and one-use electrochemical sensors with screen-printed graphene electrodes (SPGEs) [V. parahaemolyticus toxR gene could diagnose V. parahaemolyticus within 45 min with the detection limit of 0.3 CFU per 25 g of uncooked seafood [For bacterial infection, surfaces of graphene products, graphite, graphene, graphene oxide and reduced graphene oxide have been revealed to be a good platform for antibacterial effects and improved biocompatibility in tissue engineering, biosensors or antimicrobial agent-carriage . A graph.4 pg/mL . A bioseetection . A fluor1000 CFU . A label charges . Thermalbacteria . The sene volume . A quick (SPGEs) . The LAM seafood . A novel seafood . GraphenMycoplasma pneumoniae [Salmonella using a microfluidic-based electrochemical system provides a rapid detection method with high sensitivity and specificity [P. aeruginosa, among other pathogens [H. influenzae, which has been proven to be reliable, rapid, and not complicated [S. aureus species [E. faecalis, Neisseria meningitidis and M. pneumoniae detection, which is significant for diagnosis and follow-up treatment [An assay that combines LAMP with an NP-based lateral flow biosensor (LFB), named LAMP-LFB, enables the simple, reliable, and objective detection of eumoniae . The devcificity . Multiplathogens . Au/Ag hathogens . The mulplicated . An elecplicated . Another species . The LAMreatment ,69,70.H. pylori immunosensor based on a platinum NPs/poly/reduced graphene oxide (Ptnano/PEDOT/red-GOx)-modified gold electrode (Au-ET) was fabricated in a stepwise manner for the detection of cytotoxin-associated gene A antibody [H pylori after covalent conjugation of the antibody (CagA) on the nanomaterials altered the Au electrode [P. syringae pv. lachrymans antibodies and conjugated with bovine plasma albumin was developed and sensor development was characterized by cyclic voltammetry (CV) and antigen detection by electrochemical impedance spectroscopy (EIS) measurements [Salmonella typhimurium detection based on the blocking surface principle [E.coli directly in ascitic fluid [An antibody . This noantibody . A labellectrode . An Au durements . A new principle . A photoic fluid .P. aeruginosa and S. aureus spiked in plasma [E. coli [An electropolymerized self-assembled layer of Au NPs was fabricated in a portable nanosieving microfluidic system (NS-MFS). Redox-active gold NPs (raGNPs) enhanced the electrical conductivity and provided the detection limit of the device, reaching 10 CFU/mL for n plasma . A micro[E. coli .mcr-1 [Au NPs have been widely used as biosensors for bacteria detection. Thiolation of the chimeric phages directed on various bacterial pathogens caused aggregation of AuNPs, resulting in a visible colorimetric reaction in front of at least about 100 cells of the target bacteria . The phomcr-1 .This is an electrochemical biosensor that is constructed from single walled carbon nanotubes that could detect the change in intracellular hydrogen peroxide during contact with lipopolysaccharides, an important component of gram-negative bacteria .These materials are some examples of using nanotechnology to diagnose bacterial infections. With the increasing application of nanotechnology in the detection of bacteria, the accuracy and efficacy of diagnostic tests in bacterial infections will increase substantially.K. pneumoniae (CRKP) are believed to be potential vaccines for intractable CRKP infection [Streptococcus pneumoniae, with long-term reaction mediated by T helper (Th)2 or T follicular helper (Tfh) cells [S. aureus  may serve as an active-targeting antibiotic carrier [S. aureus-infected macrophages with higher efficiency and accumulate within major organs , suggesting that they are beneficial for treating metastatic S. aureus infections [NPs can be used as vaccine vectors to deliver antigenic proteins that induce humoral and cellular immune responses ,83. NPs nfection . NPs con carrier . NPs coafections . NPs havS. aureus, E. coli or P. aeruginosa, have the capacity to self-secrete a matrix of extracellular polymeric substances (EPSs) that encapsulate bacterial societies. This matrix then progresses to form an advanced 3D structure called a biofilm that functions as a protective barrier against host immunity, environmental pressure, and the inner diffusion of antimicrobial agents [Biofilm formation is a key step in the bacterial infection of implants, catheters and naive tissues . Many bal agents ,87,88,89S. aureus, for example, recognizes host extracellular matrix (ECM) molecules and initiates colonization of the epithelium through microbial surface recognizing adhesive matrix molecules (MSCRAMMs), such as fibronectin binding protein A (FnBPA) [A common application of nanotechnology is biomaterial implants. These devices generally present a 5% failure rate, with most cases attributed to the bacterial colonization of biomaterial surfaces, formation of biofilms and spread into adjacent tissues ,91,92,93 (FnBPA) .Bacteria-sensitive NPs prepared from poly(lactic-coglycolic acid) and poly(Ɛ-caprolactone), which were decorated with chitosan and combined with dissolved microneedles of doxycycline, could serve as antimicrobial agents with improved biofilm penetration specifically in the delivery of doxycycline to an infection site . ZeoliteAcinetobacter spp. on coated surfaces [P. aeruginosa biofilms, modulating bacterial motility and other important virulence factors related to biofilm formation [P. aeruginosa and disrupting the expression of the exotoxin A (toxA) gene [E. coli (UPEC) strains [NPs have been used as one of the therapeutic strategies for nosocomial bacterial infections . A nanossurfaces . A synthormation . AnotherxA) gene . Similar strains .S. aureus antibodies were coupled with Au NPs to develop an immunochromatographic strip (ICS) [2 CFU/mL S. aureus in 15 min among patients with neonatal sepsis [2 CFU/mL S. aureus within 15 min. An optoelectronic machine by the dual array of interdigitated micro- and nanoelectrodes in parallel could detect the bacterial biofilm development by the optical and electrical monitor methods [Biofilm formation is critical in antimicrobial agent-resistance and treatment failure; however, so far there is no detection method to monitor the formation of biofilms. Anti-ip (ICS) . The invl sepsis . NP-basel sepsis . The dev methods .E. faecalis biofilms, together with the support for the dental pulp stem cells [Tissue regeneration is often retarded when biofilms are formed during bacterial infections. Injectable calcium phosphate bone cement-chitosan paste containing graphene oxide has been used for endodontic therapy, with antimicrobial effect against em cells . The fasem cells . Ag NPs em cells . Copper-em cells .Biofilm formation is a troublesome problem when treating bacterial infections, and NPs have been shown to be effective at inhibiting biofilm formation and eradicating preformed biofilms.S. aureus strains, but also to detect methicillin-resistant genes from all S. aureus species [nuc gene (S. aureus-specific gene) and mecA gene (encoding penicillin-binding protein-2′), has been shown to detect S. aureus strains and MRSA within 85 min [mcr) gene mcr-1 in Enterobacteriaceae is very important in combating bacterial infections [Enterococcus and Staphylococcus with possible antimicrobial agent resistance [mecA resistance genes in uncultured nasal, groin, axilla, and wound swabs from patients was done with 90% clinical sensitivity and 95% clinical specificity [As mentioned above the m-LAMP-LFB was applied not only to detect all  species . Multipln 85 min . Fast difections . MCDA mefections . Coupledsistance . The mulsistance . MRSA wasistance . Detecticificity . Rapid dcificity . The nanNanotechnology-based therapeutic methods have revealed an effective therapeutic result against multi-drug resistant bacterial infection. There are some examples of advances in nanotechnology in treating bacterial infectious diseases with possible antibiotic resistance.A. baumannii isolated from patients [A. baumannii [Ag NPs have shown synergic action with colistin, and imipenem against pandrug-resistant patients . The effpatients . Ag NPs@patients . The compatients . Based opatients . The IPMaumannii .Bacteriophages, or “phages” can attack bacteria, takeover their intracellular mechanism to let themselves survive, and thus kill the bacteria. Phage therapy has been proposed as good weapon in combating bacteria with multi-antimicrobial resistance . HoweverP. aeruginosa, an overwhelming cause of terminal and persistent lung infections in cystic fibrosis patients [S. Typhimurium [Surface engineering of nano-cargos has the advantages of aiming and controlling the antimicrobial-resistance mechanisms, and thus possibly overwhelms the bacterial resistance . The incpatients . Chitosahimurium . Nano-enhimurium .K. pneumoniae [Some new antimicrobial agents might be effective in combating antimicrobial agent resistant bacteria, but the clinical use might be limited because of the hydrophobicity or low oral bioavailability. A nanotechnology-based delivery method might provide a practicable way for therapy with these new antimicrobial agents. Platensimycin (PTM), a natural agent, could target bacterial fatty acid synthases and treat infections by MRSA . To enabeumoniae .S. typhimurium in macrophages [P. aeruginosa and E. coli, by inhibiting the efflux pumps [Chitosan, itself an antimicrobial polysaccharide, is produced to be readily soluble in neutral aqueous solution during systemic therapy . After mrophages . Chitosaux pumps .Recent advances in the development of NPs to treat bacterial infectious diseases are summarized in E. coli K1 infection, a chitosan-modified poly(lactic-coglycolic acid) (PLGA) nanoparticle was used as a vector for the recombinant protein OmpAVac (Vo), inducing protective immunity against E. coli K1 infection [S. pneumoniae, H. influenzae, and N. meningitides [When treating patients with central nervous system (CNS) infection, the most important challenge for drug delivery is the ability of antimicrobial agents to penetrate the BBB . The BBBnfection . Transcrnfection . For thenfection ; therefongitides . The effP. aeruginosa [P. aeruginosa, resulting in an obvious reduction in the bacterial load and improved survival in a mouse model [Respiratory tract infections, especially pneumonia, are often caused by some multidrug-resistant bacterial strains. MDR may be overcome through the nanoparticle-mediated delivery of high-concentration antibacterial agents at the site of infection ,122,123.ruginosa . AntibacA. baumannii, a critical pathogen causing nosocomial respiratory tract infections. Ag NPs decreased the number of intracellular bacteria and bacterial adherence without any cytotoxic effects on human pulmonary cells [Chemically engineered polyvinylpyrrolidone (PVP)-capped Ag NPs showed antibacterial activity against carbapenem-resistant strains of ry cells .Burkholderia mallei is associated with weaponization by inducing aerosol inhalation, which results in glanders [B. thailandensis, protect against lethal inhalations of B. mallei [Multidrug resistant, high mortality glanders . Au NPs . mallei .M. pneumoniae, one of the most common pathogens causing respiratory tract infection. This method has been particularly effective in the diagnosis of community-acquired pneumonia in school-age children [NPs have also been utilized for the accurate diagnosis of respiratory infectious disease . LAMP-LFchildren .Nanomaterials have been widely used to detect or treat bacterial infections in the respiratory system. In an era of respiratory tract infection with multidrug resistant pathogens, the high concentration of the drug delivered by NPs is expected to have more potent antibacterial activity. However, the main nanotechnologies used in the diagnosis or treatment of the respiratory system target only one or a few specific pathogens. In the real world, some of the pathogens in the respiratory system are multidrug resistant and polymicrobial, indicating that infection is caused by many bacteria. The treatment of multidrug-resistant and polymicrobial pathogens in the respiratory system will be the aim of future investigations.Helicobacter pylori is the most well-known pathogen associated with gastric ulcers and gastric cancer, but the acidity of the stomach limits the therapeutic efficacy of antibiotics that eradicate H. pylori at the infection site [H. pylori. These pH-sensitive amoxicillin-loaded AMX-PLGA/UCCs-2 NPs protected antimicrobial drugs from the acidic environment of the stomach, safely delivering them to the H. pylori infection site [The acidic environment of the stomach is an important defense mechanism of the human body, which eradicates ingested pathogens, but this acidity is also a threat to the therapeutic efficacy of antibiotics or probiotics ,127. TheBifidobacterium, are vulnerable to gastric juices or gastrointestinal enzymes, and thus encapsulation is crucial to bypassing erosion and reaching target environments in the intestine or colon [B. breve protected against simulated gastric acid and antimicrobial agents (tetracycline), thus successfully eradicating E. coli adhering to intestinal epithelial layers and maintaining surface coverage of Transwell membranes and their barrier integrity [Probiotics, such as or colon . After entegrity .V. cholerae, which induces diarrhea by secreting cholera toxins [Apis mellifera) venom and was capable of promoting the clearance of S. enterica serovar Typhimurium infection through the upregulation of T helper type 1-specific immune responses [One of the most widespread toxin-mediated gastrointestinal pathogens is a toxins . The keya toxins . Anotheresponses .S. typhi, a common intestinal pathogen. These NPs were characterized as noncytotoxic to eukaryotic cells but exhibited strong bacteriolytic activity against intracellular S. typhi [Shigella flexneri (Shigellosis) was fortunately prevented by some outer membrane vesicles (OMVs) [S. flexneri when delivered through the nasal route [An antimicrobial peptide, VG16KRKP (VARGWKRKCPLFGKGG), delivered by gold nanoparticles (Au-VG16KRKP) was designed to tag against S. typhi . Anothers (OMVs) . OMVs enal route .NPs have been widely used as delivery vectors for therapeutic agents to bypass the acidic environment of the stomach as treatments for bacterial infections in the gastrointestinal system. The gastrointestinal system is a very complex environment composed of the microbiome and metabolome. The effects of NPs on the microbiota and its metabolites in the gut requires further evaluation.E. coli and gram-positive S. aureus species, with additional osteogenic properties when coated on bone tissue [S. aureus infections [S. aureus infection [S. aureus in bone cells, decreasing systemic toxicity and forming the basis of targeting specific markers expressed in bone infections [Many NPs with bactericidal effects have been used to treat bone infection (such as osteomyelitis) or as part of the components of implants in the skeletal system ,130,131.e tissue . Calciumnfection . Silver-S. aureus is the most noticeable threat contributing to skin and soft tissue infection worldwide, with the capacity to form biofilms during infection [S. aureus biofilms and effectively eliminating them through the collaborative therapeutic effect of DNase I and CO gas-potentiated photothermal treatment [S. aureus soft tissue (thigh) infection in a mouse model [nfection ,17,85. Nreatment . A dextrse model .Biocompatible enzyme-responsive Ag NP assemblies (ANAs) are high-efficiency targeted antimicrobial treatments for MRSA and have been utilized as effective wound dressings to accelerate the healing of wounds with MRSA infections . A biomiStreptococcus (GAS), along with its infamous pore-forming streptolysin O (SLO) toxin, is a major pathogen causing severely invasive skin infections, including necrotizing fasciitis [Group A asciitis . The phaasciitis .V. vulnificus could cause quick and severe tissue damage, with the mortality rate as high as 50% [V. vulnificus infection by damaging membrane integrity, and decreasing intracellular V. vulnificus numbers up to 90% [h as 50% ,142. Thep to 90% .S. aureus in vivo. However, evidence of the effect of NPs on eradicating bacteria hidden in biofilms or implants in clinical patients is still limited. More clinical studies are needed to confirm the effect of NPs on eradicating bacteria in biofilms or implants.NPs have the advantages of targeting bacteria and their biofilms during the treatment of skin and soft tissue infections, especially tissues infected with S. pneumoniae, S. aureus, P. aeruginosa, and E. coli [Several ocular drug delivery systems, such as hydrogels, microparticles, nanoemulsions, microemulsions, and liposomes, have been analyzed for drug release in the eye and sustain therapeutic levels for a prolonged period . Nanomat E. coli . In addi E. coli . AlthougS. aureus and P. aeruginosa, as well as anti-inflammatory effects achieved by reducing the expression and release of inflammatory cytokines, including IL-6 and TNF-α, and inducing the osteogenic differentiation of mesenchymal stem cells that guide bone regeneration [The oral cavity and oropharynx are complex locations that face physical, chemical, and microbiological damage and have pathological and cancerous variations . Alveolaneration . Micro/nneration . NPs canThe incidence of reproductive system infections , especially vaginal infections, has recently increased, especially among women of reproductive age, and requires an effective concentration of topical antimicrobial treatment for its direct therapeutic action, reduced drug doses and side effects, and self-insertion . HoweverNeisseria gonorrhoeae, the causative agent of gonorrhea, is an important target for the treatment of sexually transmitted disease. MtrE, the outer membrane channel of the highly conserved gonococcal MtrCDE active efflux pump, is a surrogate for vaccine targets because of its significance in defending N. gonorrhoeae from host innate effectors [ffectors . Thus, fffectors .Although NPs have not been used as often as treatments for the reproductive system thus far, the use of nanomedicine to increase the retention time and decrease discomfort is expected in the future. However, the effects of these NPs on the fertility of clinical patients should be evaluated.The reticuloendothelial system (RES) is also called the macrophage system because of the common property of phagocytosis, where the cells engulf and destroy bacteria, viruses, and other foreign substances, as well as ingest worn-out or abnormal cells in the body. Since intravenously administered NPs are recognized and quickly cleared from systemic circulation by the RES , the useB. breve against simulated gastric fluid and antimicrobial agents (tetracycline) than protamine-assisted SiO2 NPs, yolk−shell packing (weak interactions across a void), and ZIF-8 mineralization  [Many methods have been shown to increase the function of NPs in preventing bacterial infection or improving wound repair, including encapsulation methods and material design , the incbonding) . The selS. aureus hiding inside biofilms [The hydrophilicity and roughness of NPs modulate their ability to prevent biomaterial-associated infections . Au NPs biofilms . A dextrbiofilms . The modS. aureus, E. coli and Candida albicans [Combinations of biomaterials encapsulated in NPs might provide additive therapeutic effects on bacterial infectious diseases . Combinialbicans . Liposomalbicans . Antibioalbicans . An enzyalbicans . The strS. pneumoniae αGC-embedded NPs was used to trigger murine iNKT cells and B cells while avoiding iNKT anergy [S. pneumoniae polysaccharides provoked vigorous IgM and IgG responses and defended mice against lethal S. pneumoniae infections [S. aureus biofilms, inducing increased uptake of antibiotics to the bacterial cells and promoting the bactericidal activity of macrophages against intracellular bacteria via the generation of reactive oxygen species (ROS) [The addition of immune-activating adjuvants represents another method for enhancing the effect of NPs . InvariaT anergy . NPs confections . Magneties (ROS) . The incModification of the hydrophilicity and roughness of the NPs, combinations of biomaterials, or incorporation of immune-activating adjuvants are predicted to increase the therapeutic effect of NPs without increasing the adverse effects. However, the selection of the appropriate methods to increase the function of nanotechnology depends on the aims of the diagnosis or treatment of the bacterial infectious disease.In an era of bacterial infections characterized by increasing antimicrobial resistance, medicine is running out of weapons to combat these pathogens. Although some new antimicrobial agents have been developed in recent years, the emergence of bacterial resistance against these new drugs was reported a few years later. The development of NPs against these troublesome bacteria might be an alternative therapeutic choice with more bactericidal effects and less cytotoxicity. In recent years, numerous advances in the application of organic, inorganic and hybrid NPs to combat bacterial infectious diseases have been reported. SNAPs have excellent antibacterial activity and low toxicity; expanded use of star-shaped polymers for both the diagnosis and treatment of infectious diseases is expected in the future. To date, most developed NPs have been applied superficially on the body, such as in wound infections, skin and soft tissue infections, dental infections, or ophthalmological infections. Thus, the use of NPs inside the body to combat internal organ infections still has some challenges to overcome, including methods to guide the particle to the target organ or area, confirm the biosafety of the particle when administered inside the body, protect the particle against the damage of gastric juices or intestinal enzymes  and confirm that encapsulated agents had higher efficacy than unencapsulated agents. Fortunately, many methods to increase the function of NPs in preventing bacterial infection or reducing cytotoxicity have been developed, including changing encapsulation methods and materials, incorporating activation systems, or adding immune-activating adjuvants. The construction of star-shaped polymers, for example, has shown the potential for increased antibacterial effects and decreased toxicity to mammalian cells. The development of appropriate NPs specific for the target organ using an effective delivery method will be a challenge to overcome when developing NPs as treatments for bacterial infections in the future.


Candida auris, a yeast pathogen of humans. C. auris has since grown into a global problem in intensive care settings, where it causes systemic infections in patients with underlying health issues. Recent whole-genome sequencing has discerned five C. auris clades with distinct phenotypic features which display genomic divergence on a DNA sequence and a chromosome structure level. In the absence of sexual reproduction in C. auris, the mechanism(s) behind the rapid genomic evolution of this emerging killer yeast has remained obscure. Yet, one important bit of information about chromosome organization was missing, the identification of the centromeres. In a recent study, Sanyal and coworkers  filled this knowledge gap by mapping the centromeres in C. auris and its close relatives. This represents a major advance in the chromosome biology of the Candida/Clavispora clade.2009 saw the first description of  Saccharomyces, which represent the pinnacle of genome streamlining, to the large epigenetically defined regional centromeres flanked by repetitive DNA in Schizosaccharomyces  defined by unique nonrepetitive sequences  lusitaniae has somewhat similarly sized (∼4.0 to 4.5 kb) centromeres with unique sequences . Com. ComSaccaromyces , 2. Thusaromyces –7. Fungaromyces –. A notabequences  3, 8). , 8. Sacc present . Clavispequences , but theCandida auris, Candida haemulonii, Candida pseudohaemulonii, and Candida duobushaemulonii make up the Candida haemulonii complex which, together with C. lusitaniae, belongs to the Clavispora clade of the Metschnikowiaceae . To check the correct localization of Cse4 and the functionality of the tagged version, the authors performed indirect immunofluorescence in mitotic C. auris cells carrying the Cse4-protein A construct. As expected, Cse4-protein A formed one discrete focus per nucleus in nondividing cells and a focus each at the leading edge of dividing daughter nuclei (C. auris (C. lusitaniae (see above)  content, gene synteny, and GC content. Within C. auris, clade II differs most from the other three clades due to major chromosomal rearrangements (C. auris.The centromere sizes and positions were corroborated by ChIP-qPCR (ChIP followed by quantitative PCR) in the clade I strain (B8441) harboring the protein A-tagged Cse4 . This stngements , 18. Intngements , 18; tecngements . The karngements , 18. ThiC. haemulonii complex species displayed the same centromere properties as C. auris and C. lusitaniae  had 8 full-length centromeres like C. lusitaniae.Strains of the other sitaniae , 7. Thes species  but is iClavispora raises several intriguing questions about their chromosome biology. It is generally accepted that centromeric DNA repeats are a source of genome instability via ectopic recombination, but it has also been suggested that recombination between centromeric repeats is important for their maintenance (Clavispora species, how do they contribute to karyotype diversification? Sanyal and coworkers suggest that due to the proximity of centromeric DNAs in the centromere clusters (C. albicans, centromeres seem to be specified epigenetically as unchromatinized centromeric DNA sequence does not establish a functional centromere (C. albicans centromeres also display identifying, partially heterochromatic, histone modifications (de novo presumably enables the formation of neocentromeres (C. lusitaniae centromeres also have specific modifications, albeit none which are typical for heterochromatin (Clavispora yeasts, including C. auris, harbor the same histone modifications as C. lusitaniae? Are these histone modifications indeed defining centromere identity? And finally, if the underlying DNA sequence is not important, why is centromeric DNA attrition observed after chromosome fusions, as is the case with the centromere of C. lusitaniae chromosome 8 in C. haemulonii complex species (The nonrepetitive nature of centromeric DNA in ntenance . When ceclusters , 20, chrntromere . But theications ; mechanitromeres . Histonehromatin . Do cent species ? Answeri


Ormosia is a particular genus in the Fabaceae family with its striking seeds. The genus Ormosia boluoensis is a newly reported and critically endangered species, and field investigations have indicated that there are only hundreds of it. For the effective conservation, we report its complete mitochondrial genome. The length of the O. boluoensis mitochondrial genome is 248,619 bp, including 28 protein-coding genes, 14 transfer RNA genes, 3 ribosomal RNA genes, and 45 simple sequence repeats. Phylogenetic analysis revealed that O. boluoensis was a sister to the clade including Sophora flavescens, Ammopiptanthus nanus, and Ammopiptanthus mongolicus. Ormosia is a monophyletic but small genus in the Fabaceae family. It comprises approximately 130 species . The Ormosia species are striking for their seeds, black or red, used as decorations, such as jewelry, beads and other trinkets. Ormosia boluoensis Y. Q. Wang & P. Y. Chen is a newly reported species . A voucher specimen was deposited at the Herbarium of South China Botanical Garden  with No. IBSC0000922. The genomic DNA of O. boluoensis was extracted using a modified CTAB method. The extracted DNA was stored in the Ecological Genetics Laboratory in South China Botanical Garden. The isolated DNA were constructed as a long- and a short-read library. The two libraries were then sequenced using the Nanopore promethION and Illumia HiSeq X Ten platforms. Before O. boluoensis mitochondrial genome assembly, minimap2 v2.17-r974-dirty . To perform phylogenetic analysis for O. boluoensis, the mitochondrial genomes of the other 18 species including 16 Fabaceae species and two outgroup species (Nicotiana attenuate and Nicotiana tabacum) were downloaded from GenBank. 20 shared protein coding genes in their mitochondrion genomes were then extracted, translated into amino acid sequence, aligned and concatenated using PhyloSuite 1.2.2 , in which mononucleotide-repeat SSRs were the most abundant with 25 SSRs, followed by pentanucleotide-repeat SSRs with 11 SSRs. Phylogenetic analysis supported a monophyletic relationship of O. boluoensis to the clade of Sophora flavescens, Ammopiptanthus nanus, and Ammopiptanthus mongolicus (The mitochondrial genome of ngolicus .


Gut microbiota contributes to colorectal cancer (CRC) pathogenesis through microbes and their metabolites. The importance of microbiota-associated metabolites in colorectal carcinogenesis highlights the need to investigate the gut metabolome along the adenoma-carcinoma sequence to determine their mechanistic implications in the pathogenesis of CRC. To date, how and which microbes and metabolites interactively promote early events of CRC development are still largely unclear. We aim to determine gut microbiota-associated metabolites and their linkage to colorectal carcinogenesis.We performed metabolomics and metagenomics profiling on fecal samples from 386 subjects including 118 CRC patients, 140 colorectal adenomas (CRA) patients and 128 healthy subjects as normal controls (NC). We identified differences in the gut metabolite profiles among NC, CRA and CRC groups by partial least squares-discriminant and principal component analyses. Among the altered metabolites, norvaline and myristic acid showed increasing trends from NC, through CRA, to CRC. CRC-associated metabolites were enriched in branched-chain amino acids, aromatic amino acids and aminoacyl-tRNA biosynthesis pathways. Moreover, metabolites marker signature (twenty metabolites) classified CRC from NC subjects with an area under the curve (AUC) of 0.80, and CRC from CRA with an AUC of 0.79. Integrative analyses of metabolomics and metagenomics profiles demonstrated that the relationships among CRC-associated metabolites and bacteria were altered across CRC stages; certain associations exhibited increasing or decreasing strengths while some were reversed from negative to positive or vice versa. Combinations of gut bacteria with the metabolite markers improved their diagnostic performances; CRC vs NC, AUC: 0.94; CRC vs CRA, AUC 0.92; and CRA vs NC, AUC: 0.86, indicating a potential for early diagnosis of colorectal neoplasia.This study underscores potential early-driver metabolites in stages of colorectal tumorigenesis. The Integrated metabolite and microbiome analysis demonstrates that gut metabolites and their association with gut microbiota are perturbed along colorectal carcinogenesis. Fecal metabolites can be utilized, in addition to bacteria, for non-invasive diagnosis of colorectal neoplasia.Video AbstractThe online version contains supplementary material available at 10.1186/s40168-021-01208-5. Fusobacterium nucleatum is a commonly reported CRC-enriched microbe that increases gene methylation and induces microsatellite instability [Escherichia coli, induces DNA methylation [Bilophila wadsworthia and Pyramidobacter spp are other examples of CRC-enriched microbes, which reportedly enhanced carcinogenesis by producing genotoxic hydrogen sulphide in the gut [Faecalibacterium, Roseburia, Bifidobacterium, Eubacterium and Lactobacillus, can ferment dietary fibers to short-chain fatty acids (SCFA), which are gut-protective and negatively associated with CRC. SCFAs including butyrate, propionate and acetate protect against CRC through mechanisms such as regulation of gut inflammation and immune system [Colorectal cancer (CRC) remains a significant global health burden, with the gut microbiota identified as a key player in its development. Recent studies have shown that gut microbiota alteration can drive carcinogenesis by promoting hyperproliferation of colonic cells. During their colonization and propagation, gut bacteria produce an array of metabolites, which have both direct and indirect influence on host metabolism and immune responses. It has also been proposed that perturbation of the gut microbiota can enhance the production of carcinogenic products from damaging bacteria . In parttability , 5. Trimhylation  that is hylation . Bilophi the gut –11. On te system –14. Butye system .The importance of microbiota-associated metabolites in colorectal carcinogenesis highlights the need to investigate the gut metabolome along the adenoma-carcinoma sequence to determine their mechanistic implications in CRC pathogenesis. To date, only few studies have simultaneously performed gut metagenomics and metabolomics from same subjects in order to resolve the interplay between gut microbiota and metabolites in colorectal tumorigenesis –19. It iHere, we integrated the gut metabolome and microbiota profiles of patients with CRC and colorectal adenomas (CRA) and compared them with those from healthy subjects. Our metabolite pathway enrichment and integrative analysis show that the gut metabolites and their association with gut microbiota were perturbed along colorectal carcinogenesis and that fecal metabolites can be utilized, in addition to bacteria, for non-invasive diagnosis of both CRA and CRC.All 386 subjects underwent standard colonoscopy examinations at Prince of Wales Hospital, the Chinese University of Hong Kong, including 118 patients with CRC, 140 patients with CRA and 128 normal control participants. The average age of NC group was 64.03 years, 65.84 years for CRA group and 73.21 years for CRC group  [P values in both PCA and PLS-DA plot were calculated by permutational multivariate analysis of variance (PERMANOVA) using distance matrices through the R package vegan [p values < 0.05 were considered significant. Interactions among disease associated metabolites were estimated by Spearman’s rank correlation. Metabolite set enrichment analysis (MSEA) was performed using the online tool MetaboAnalyst. All heatmaps were drawn using the R package Complex Heatmap [The metabolomics data analysis was conducted with R and online versions of MetaboAnalyst (lyst.ca) . Partiallyst.ca) . P valuege vegan . Differege vegan . The sigge vegan  (FDR) wa Heatmap  The workp-values < 0.05 as the cut-off. The workflow for the metagenomics analysis is shown in Fig. SWhole-genome shotgun sequencing of all samples was carried out on an Illumina HiSeq 2000  platform. Trimmomatic v_0.36 was used to remove low quality sequences. Human sequences were removed after alignment with a reference genome (hg38 database) using Bowtie2 v_2.2.9, with default settings. Bacteria taxonomic profiles were obtained using MetaPhlAn 2.0 . The avep-value)*sign (Beta) from the results of ZINB regressions, where Beta is the regression of the metabolite.Zero-inflated negative binomial (ZINB) regression (R package pscl), developed for modeling over-dispersed count outcome variables with excessive zeros, as found in microbial read counts data, was used to estimate the associations among metabolites and bacterial species. The read counts of bacterial species were treated as dependent variables in the ZINB regressions, while the concentrations of metabolites were considered as independent variables. The strengths of associations were measured by -log10. Multiple group comparisons were conducted using Kruskal-Wallis test. Fisher’s exact test was performed on categorical variables. The dissimilarity tests among groups (PERMANOVA) were conducted on Euclidean distance for metabolites and Bray-Curtis distance for bacteria, with 10,000 permutations in the R package, vegan. All statistical analyses were performed using R version 3.6.1.p = 0.001), indicating a gut-metabolite shift in colon carcinogenesis.Our study included 386 subjects, namely 118 patients with CRC, 140 patients with CRA and 128 healthy subjects as normal control (NC). A total of 97 metabolites were quantified from stool samples using GC-TOFMS. PLS-DA  in stages leading to CRC. The top 4 enriched pathways in CRC compared with NC Fig. E and CRAWe further explored the potential use of gut microbiome-associated metabolites for non-invasive diagnosis of CRC. Using the identified significantly altered metabolites Fig. C and D, p = 0.001; Fig. Peptostreptococcus stomatis, Fusobacterium nucleatum, Parvimonas micra, Peptostreptococcus anaerobius and Bacteroides fragilis, were enriched in CRC compared to NC  achieved a higher AUC of 0.9417  and 6 bacterial species  among CRC-enriched metabolites, such as L-proline, L-aspartic acid, L-methionine, oxoglutaric acid, L-leucine, L-valine, gamma-aminobutyric acid, L-isoleucine, L-phenylalanine and L-tyrosine supporting their potential role in CRC Fig. S. Taken tAccumulating evidence reveals that the gut microbiota and their metabolites play important roles in colorectal tumorigenesis. Here, we profiled the fecal metabolites and microbiome of CRC patients and compared them with those of precancerous CRA patients and healthy subjects. We demonstrated that key metabolic pathways were disrupted along CRC pathogenesis. Integrated metabolomic and microbiome analysis showed that interactions among CRC associated bacteria and metabolites are altered along the development of CRC. Importantly, we demonstrate a promising potential of fecal metabolites, in addition to bacteria, for non-invasive diagnosis of CRC.Metabolomics has the potential for diagnosis of cancer including CRC , 33. OurPathway enrichment analysis showed that aminoacyl-tRNA biosynthesis, aromatic amino acids biosynthesis and BCAAs metabolisms were altered in CRC patients and adenoma patients compared with healthy subjects. Aminoacyl-tRNA biosynthesis needs aminoacyl-tRNA synthetases, an important class of enzymes with an evolutionarily conserved mechanism for protein synthesis, some of which show positive associations with colorectal tumor development . BCAAs iF. nucleatum, P. anaerobius, P. micra, R. inulinivorans, E. corrodens and X. perforans. Interestingly, we observed that a combination of 4 metabolites namely alpha linoleic acid, L-homoserine, phenyl lactic acid and phenyl acetic acid, and bacteria markers including F. nucleatum, P. anaerobius, P. micra, R. inulinivorans, E. corrodens and X. perforans classified CRA from NC, with an AUC of 0.7499 demonstrating the potential for early diagnosis of colorectal adenoma from healthy patients.We further explored the potential use of gut microbiome associated metabolites in non-invasive diagnosis of CRC. CRC was classified from NC and from CRA with 20 and 13 metabolite markers, respectively, each with an AUC of about 0.80. Six bacterial species distinguished CRC from NC and CRA with an AUC of 0.91 and 0.89 respectively. With the combination of metabolites and bacterial markers, a higher discriminating power demonstrated by an AUC of 0.94 was achieved with 11 metabolites and 6 bacterial species including P. anaerobius that drives CRC via the PI3K-Akt-NF-κB signaling pathway [P. anaerobius in the development of CRC. In addition, Clostridium symbiosum, which was found to increase in abundance from the colon tissues of healthy subjects to adenoma patients and finally to colonic cancer patients [Moreover, our association analysis revealed that the relationships among metabolites and bacteria were significantly different in CRC patients compared with NC and CRA subjects. While some associations followed increasing or decreasing trends along CRC progression, some were reversed from negative to positive and vice versa. Notable is the increased correlations between glycine, reported to support cancer cell growth , and P.  pathway , suggestpatients , was sigThe microbial related metabolites reported in this study were based on metabolomics data and chemical properties of human microbiome associated metabolites , 51. AutIn conclusion, our integrated metabolites and microbiome study demonstrates that gut metabolites along with the microbiome are altered along stages of colorectal carcinogenesis and that the combination of metabolites and bacterial taxa can increase the chance of non-invasive diagnosis of colorectal cancer and adenoma. This study underscores potential early-driver metabolites in CRC tumorigenesis and informs further experiments towards the development of better CRC diagnosis and prevention strategies.Additional file 1: Table S1. Demographic and clinical details of samples.Additional file 2: Table S2. Differential test results for CRC vs NC.Additional file 3: Table S3. Differential test results for CRC vs CRA.Additional file 4: Table S4. The list of significant associations among metabolites and bacteria following direct trends.Additional file 5. Supplementary Methods.Additional file 6: Figure S1. The workflow for metabolomics data analysis.Additional file 7: Figure S2. The workflow for metagenomics data analysis.Additional file 8: Figure S3. Volcano plots of significantly altered metabolites between groups.Additional file 9: Figure S4. Significantly altered metabolites show direct trends along CRC progression. Pairwise comparisons were performed using Wilcoxon rank-sum test. CRC; colorectal cancer, CRA; colorectal adenoma, NC; normal control.Additional file 10: Figure S5. Metabolites markers for pairwise discriminations of CRC, CRA and NC groups with adjustment of age, gender and obesity.Additional file 11: Figure S6. Validation of metabolites markers for pairwise discriminations of CRC, CRA and NC groups by random forest model with 10-fold cross validation.Additional file 12: Figure S7. Differentially abundant bacterial species show direct trends along CRC progression.Additional file 13: Figure S8. Bacterial species markers for pairwise discriminations of CRC, CRA and NC groups.Additional file 14: Figure S9. Bacterial species markers for pairwise discriminations of CRC, CRA and NC groups with adjustment of age, gender and obesity.Additional file 15: Figure S10. Validation of bacterial species markers for pairwise discriminations of t CRC, CRA and NC groups by random forest model with 10-fold cross validation.Additional file 16: Figure S11. Combination of bacteria and metabolites markers for pairwise discriminations of CRC, CRA and NC groups.Additional file 17: Figure S12. Distributions of association between significantly altered metabolites and bacterial species for CRC, CRA and NC groups.Additional file 18: Figure S13. Interactions among metabolites and bacteria are altered in CRC.Additional file 19: Figure S14. Heatmap of correlations between disease associated metabolites. The correlation strengths were measured by Spearman’s rank correlation coefficient. Only correlation coefficients with p > 0.05 were shown on the heatmap. The size of the circles are proportional to the correlation strength.


Helicobacter pylori (H. pylori) alters metabolism during the gastric carcinogenesis process. This study aimed to determine the metabolites in the gastric mucosa according to the status of the H. pylori infection. Patients who visited the outpatient clinic for a gastroscopy and H. pylori tests were included. Gas chromatography–time-of-flight mass spectrometry (GC-TOF-MS) analysis was performed using gastric biopsied specimens from the corpus. Twenty-eight discriminative metabolites were found in the gastric mucosa of 10 patients with current H. pylori infection, in 15 with past infection, and in five with no infection history. The relative abundances (RAs) of amino acids and sugars/sugar alcohols were higher in patients with no infection history than in patients with current or past infection. The current infection group showed higher RAs of organic acids and lower RAs of fatty acids and lipids compared with the other groups. The RA of inosine was highest in the past infection group. Based on GC-TOF-MS analysis findings, metabolites differed not only between the infected and non-infected patients, but also between those with and without infection history. Amino acid and sugars/sugar alcohol metabolites decreased in patients with current or past infection, whereas fatty acid and lipid metabolites decreased only during current infection. Helicobacter pylori (H. pylori) infection induces the carcinogenesis process in the human stomach. Some precancerous lesions including gastric atrophy and intestinal metaplasia (IM) may persist even after the regression of H. pylori; therefore, the risk of gastric cancer is higher in stomachs with infection history than in H. pylori-naive stomachs [stomachs ,2. Furthstomachs ,4.H. pylori alters metabolism after infection and changes metabolic dynamics when eradicated [H. pylori infection leads to the loss of carboxylic acids and amino acids in the corpus and antrum [H. pylori-infected, non-infected, and gastric cancer patients [adicated . H. pylod antrum . In the patients . Althougpatients . MoreoveH. pylori infection status. We hypothesized that metabolites observed in the corpus would differ not only between the infected and non-infected patients, but also between patients with and without an infection history. Furthermore, we aimed to determine the different metabolites according to the degree of atrophy and IM.In this study, we attempted to determine the metabolites found in the gastric mucosa according to the H. pylori tests between March and April 2021 were included. Patients were included if they agreed to undergo a gastric biopsy during endoscopy. Patients were excluded if they had significant comorbidities  or if certain drugs  had been administered within 30 days.Patients who visited one outpatient clinic (Dr. Lee S.-Y.) for an upper gastrointestinal endoscopic examination and H. pylori tests. This study was approved by the Institutional Review Board (IRB) of the Konkuk University Medical Center (IRB no. 1010691). The procedures performed in this study were in accordance with the ethical standards of the IRB and Helsinki Declaration.All included patients provided written informed consent before the gastroscopy and H. pylori serology and serum pepsinogen (PG) assays were performed as described in our previous study [The gastroscopy was performed by a gastroenterologist (Dr. Lee S.-Y.) using GIF-H290 . A rapid urease test  was performed using gastric biopsy specimens taken from the greater curvature side of the upper body. Pathology findings were reported according to the updated Sydney classification as previously described [us study .Endoscopic findings were scored from 0 to 8 based on the Kyoto classification scoring system for gastritis . GastricH. pylori test findings. The remaining patients without a history of eradication were classified as patients with no infection history. If there was a discrepancy between the H. pylori serology and invasive test findings , the 13C-urea breath test  was performed for confirmation.Current infection was diagnosed if the rapid urease test or Giemsa staining findings were positive. Past infection was defined as a successful history of eradication in patients with negative ®, Haan, Germany) at a frequency of 30 Hz for 10 min with sonication for another 10 min. Subsequently, the extracted samples were incubated for 1 h at 4 °C and centrifuged at 13,000 rpm for 10 min at 4 °C. Then, the supernatants were filtered using 0.2 μm polytetrafluorethylene (PTFE) filters . The filtered samples were completely evaporated in a speed vacuum concentrator . The completely dried extracts were reconstituted with 50 μL of 100% methanol.The extraction of gastric mucosa metabolites was performed using the methods described by Park et al., with a few modifications . GastricN-methyl-N-trimethylsilyl trifluoroacetamide (MSTFA), being used as a silylating agent, was incubated for 30 min at 37 °C. One microliter of each derivatized samples was injected into an Agilent 7890A GC system  equipped with an L-PAL3 autosampler and Pegasus® HT TOF-MS system . Metabolites were separated using an RTX-5MS column  with a constant flow of helium (1.5 mL/min) as the carrier gas. The analytical parameters and program were adopted from our previous study [For gas chromatography–time-of-flight mass spectrometry (GC-TOF-MS) analysis, the reconstituted samples (50 μL) were dried using a speed vacuum concentrator. The dried samples were derivatized using the following protocol. First, 25 μL of methoxyamine hydrochloride (20 mg/mL in pyridine) was added to the dried samples and placed on a thermomixer for 90 min at 30 °C to protect the ketone and aldehyde groups. Then, 25 μL of us study . All samm/z), and retention time (min). Multivariate statistical analysis was performed using SIMCA-P+ software . Principal component analysis (PCA) and orthogonal partial least squares discriminant analysis (OPLS-DA) were performed to compare significantly different metabolites among the three groups. The significance of the OPLS-DA models was determined through the analysis of variance testing of cross-validated predictive residuals (CV-ANOVA) derived from the SIMCA-P+ program. Discriminative variables were selected based on the variable importance in the projection (VIP) scores derived from the OPLS-DA model. The discriminative metabolites obtained from GC-TOF-MS were tentatively identified by comparing their retention time, mass spectrum, and mass fragment pattern with available databases such as the National Institute of Standards (NIST) and Technology database , Wiley 9, the Human Metabolome Database , and in-house libraries and standard compounds analyzed under identical GC-TOF-MS analysis conditions. Significant differences were evaluated through ANOVA or the Student’s t-test using PASW Statistics 18 software .MS data processing and multivariate statistical analyses were performed as described in our previous study . The rawr) values.For continuous variables, differences between the three groups were analyzed using ANOVA with Bonferroni correction, and findings were presented as mean ± standard deviation. For continuous variables with asymmetrical distribution, the Kruskal–Wallis test was used, and findings were presented as medians and ranges. Categorical variables were analyzed using the Chi-squared test with Bonferroni correction, and findings were presented as percentage. Furthermore, correlation analysis was performed to verify the link between the RA of each metabolite and the degree of gastric atrophy and IM. The findings were presented as Pearson’s correlation coefficient  = 0.316, R2Y (cum) = 0.560, Q2 (cum) = 0.240, and p < 0.05, which indicated the model validation, prediction accuracy, fitness, and cross-validation analysis, respectively. Nonetheless, the unsupervised PCA score plots were not clearly separated from the three groups (p < 0.05).To determine significantly distinguished metabolites among the three groups, non-targeted metabolite profiling of the gastric mucosa was conducted using GC-TOF-MS followed by multivariate statistical analysis, including unsupervised PCA and supervised OPLS-DA. The OPLS-DA score plot obtained from the GC-TOF-MS dataset showed a clearly distinguished pattern among the CI, PI, and NI groups according to OPLS1 (4.96%) A. The ste groups . DistingA total of 28 metabolites including four organic acids, nine amino acids, four sugars and sugar alcohols, six fatty acids and lipids, four others, and one unknown compound were tentatively identified . The relr = −0.481, p = 0.011), leucine , and isoleucine  were inversely correlated with the degree of gastric atrophy. With regard to the degree of IM, only the RA of valine exhibited inverse correlation . The RAs of salicylic acid  and sucrose  showed positive correlations with the degree of IM.Among the 28 significantly different metabolites, the RAs of valine  were relatively lower in the patients with a history of gastric neoplasm than those in their counterparts A. ConverH. pylori infection, whereas only seven (46.7%) patients showed mononuclear cell or neutrophil infiltration among the 15 patients with past infection. Among the five H. pylori-naive patients, two (40%) showed a mild degree of inflammatory cell infiltration. Therefore, nine patients were considered to have H. pylori-negative gastritis. The RAs of fatty acids/lipids, amino acids (except GABA and cystine), and sugars/sugar alcohols (except sorbitol) were relatively higher in the H. pylori-negative gastritis group than those in the H. pylori gastritis group  and non-exposed patients (no infection history group). From a metabolic perspective, amino acids and sugars/sugar alcohols were decreased in patients with an exposure history. Fatty acid and lipids were decreased only during current infection and seemed to be restored after the regression of H. pylori. To the best of our knowledge, this is the first study to show differences in gastric corpus metabolites between the patients with current infection, past infection, and no infection history.In this study, we found that metabolites of the gastric corpus differed not only between H. pylori infection, the gastric mucosal barrier becomes fragile due to accelerated fatty acid metabolism in gastric mucosal cells [H. pylori infection altered fatty acid metabolism by increasing the concentrations of arachidonic acid and prostaglandin E2 in their study. These changes diminished with the regression of H. pylori; hence, decreased fatty acid and lipid metabolites were no longer observed in the absence of infection. Our findings are supported by a previous study showing that lipid peroxide increased during H. pylori infection due to gastric mucosal oxidative inflammation [H. pylori infection, with significant changes in lipid metabolites.Fatty acid and lipid metabolites decreased in patients with current infection. During al cells . H. pyloammation . From thH. pylori infection, and histological findings [H. pylori infection. Recent Chinese studies using gastric cancer tissues also reported a significant decrease in lipid metabolites [In a study using plasma metabolites, the loss of linoleic acid and palmitic acid was correlated with the progression of IM after adjusting for age, sex, findings . Anotherfindings . These tfindings ,19. Becafindings , these Kabolites ,22. TherH. pylori infection [H. pylori infection [Notably, metabolites on sugars and sugar alcohols were decreased in patients with current or past infection. In an in vivo study, 11 metabolites of glycolysis, tricarboxylic acid cycle, and amino acid metabolism were altered by nfection . In humanfection ,25. Siminfection .Gastric cancers are more common in the severe atrophy group than in the moderate, mild, and none atrophy groups . In theiH. pylori-negative gastritis did not show loss of fatty acids, lipids, amino acids, sugars, and sugar alcohols in our study. This finding can be supported by our previous study, which showed that H. pylori-negative gastritis was not associated with atrophy and IM [H. pylori strains than those infected with duodenal ulcer-derived strains [H. pylori strains was stronger than that induced by duodenal ulcer-derived strains. Altogether, patients with H. pylori-negative gastritis or duodenal ulcer history rarely show gastric carcinogenesis, in which valine, leucine, isoleucine synthesis and glycolysis are disturbed.Interestingly, patients with y and IM . Only H. strains . They deH. pylori infection, there is a risk of unintended eradication in patients without a history of infection. Older patients may have been infected in the past. Second, our findings may not be observed outside the East Asia, because 94.4–96.2% of Koreans with H. pylori infection show positive East Asian type CagA findings [This study had several limitations. First, in an endemic area of findings ,32. Thirfindings ,34. DespH. pylori, because it is only observed during current infection. Collectively, gastric mucosa tissue derived from H. pylori infection or exposure history affected metabolite variation. Our multi-omics data would be useful for not only determining the status of H. pylori infection but also discriminating significant gastritis related to gastric cancer. New insights into delaying atrophy and IM might be provided by understanding the downregulation of valine, leucine, and isoleucine metabolites. Furthermore, the efficacy of novel targeting agents for gastric cancer prevention should be evaluated by measuring the downregulation of the H. pylori-related metabolome. Comprehensive multi-omics approaches are needed to understand the meaning of gastric mucosa-related metabolic changes observed during H. pylori-induced gastric carcinogenesis.Non-targeted metabolite profiling of gastric mucosa tissues reveals that the loss of amino acids and sugar/sugar alcohols is observed in patients with current or past infection. Loss of fatty acids and lipids seems to recover after regression of