Bovine tuberculosis (bTB), a chronic zoonotic disease in cattle, has a substantial socio-economic and public health impact. This study was conducted to estimate the prevalence and geographical distribution of bTB in the Sylhet district of Bangladesh. A cross-sectional study was conducted at all 12 upazilas of Sylhet district, which included 512 randomly selected cattle from 48 farms. Selected animals were tested with the Caudal Fold Tuberculin (CFT) test to identify bTB-positive cattle. Out of 512 cattle, only one animal was identified as a reactor, providing an estimated prevalence of 0.19% . The only positive reactor was found in Zakiganj upazila. As the prevalence of bTB in cattle in Sylhet appears to be low, it indicates that most of the upazilas of Sylhet district are free of the bTB infection. This prevalence is lower than the reported prevalence in other parts of Bangladesh. Thus, attempts should be made to maintain the current situation of bTB infection in cattle of Sylhet district. The disntreated . Due to ntreated . The trantreated ,7. Like ntreated ,8,9. Thentreated . Studiesntreated ,14. Withntreated ,15.The study area Sylhet district, situated in the northeastern part of Bangladesh, has around 7 million cattle population comprising both indigenous and crossbreed mostly reared under a semi-intensive system . Despite22.1We conducted a cross-sectional study from 01 September 2014 through 28 February 2015 to determine the prevalence of bTB from selected farms of twelve upazilas (sub-districts) in the Sylhet district of Bangladesh . Sylhet 2.2The target population of this study was cattle of any age belonging to the twelve upazilas of the Sylhet district. We collected a list of farms from the local Upazila Livestock Offices. Farms were selected from the list using a simple random sampling method. The inclusion criterion for selecting cattle farms was that the farms consisted of at least seven animals. Though we considered including five farms from each upazila, however, in some upazilas it was difficult to select five farms that fulfill the inclusion criterion. Only 48 farms from twelve upazilas fulfilled the requirement of inclusion criteria and hence were included in the study based on the consent of the owners for the participation, animal testing, as well as data collection . These 42.3The calculated sample size was 214 using the formula described by Humphry et al.  consider2.4All these selected cattle were tested with the CFT test using the standard procedure . A hypod2.5After diagnosing tuberculosis with the CFT test, we noted the numbers of positive and negative animals in the record book. The individual animal information was collected from the farmer's record books and also from a survey using a well-designed pre-tested questionnaire (Supplementary File 2). Owners of the selected farms were interviewed face to face for recording farm and cattle information using the same questionnaire. This questionnaire was especially focused on the information of individual animals as well as farm-level information to determine prevalence and determinants. We captured the geographical information as geographical coordinates by a global positioning (GPS) reader during sample collection. Collected cattle and farm information and test results were initially entered into a Microsoft Excel spreadsheet and coded for analysis (Supplementary File 3). We used Statistical Analysis System (SAS) version 9.4 to perform all statistical analyses. After initial descriptive analysis, some of the continuous variables like age of cattle and herd size were categorized due to their skewed distribution. The age was characterized as young (≤2 years) and older cattle (>2 years) arbitrarily, whereas the farm size was categorized as large (>25 cattle) and small farms (≤25 cattle) taking the mean of size as a cut-off. We created a few weighted variables by combining some information from the questionnaire. For example, the variable ‘hygienic condition’ was created by merging some variables related to farm hygiene practice. For each positive response to the question related to farm hygiene practice, the score was ‘1’ and for a negative response the score was ‘0’. The total score was calculated for each respondent by adding the score of each variable. The mean of the total scores of all respondents was calculated and used as a cut-off to create the new categorical variable ‘hygienic condition’, that is, if the combined score of a respondent was above the mean, then the farm was considered to have good hygiene. We also followed the same procedure was also to create the variable ‘management practice’. Information regarding all the variables under ‘hygienic condition’ and ‘management practice’ were available in Supplementary File 4.2.6We estimated the prevalence of positive results with a 95% confidence interval using the Proc SURVEYFREQ command in SAS. The difference in prevalence between individual animal traits and farm-level traits was tested with Fisher's exact test. We constructed prevalence and dot maps of bTB in ArcMap 10.1 and ArcCatalog 10.1 using geospatial data (latitude and longitude coordinates of selected farms) along with farm ID, upazilas, and test results collected from study sites.3From 1 September 2014 through 28 February 2015, a total of 512 cattle were randomly selected from twelve upazilas in the Sylhet district. Among these 512 cattle, 36.32% were crossbred and 63.67% were indigenous cattle. In terms of gender, 351 (68.55%) were female and 163 (31.45%) were male . The meaOnly one animal tested positive as a reactor among the 512 animals tested. Thus, the prevalence of bTB estimated in this study was 0.19% . The single reactor was a local-breed female aged 4.5 years.In univariable analysis, none of the determinants were statistically significant (p > 0.05) . The dot4M. bovis in cattle of the Sylhet district of Bangladesh. It implies that if bTB is present, despite the results of this study, the prevalence in cattle is at any rate low. In Sylhet, the farmed ruminant species cannot be considered epidemiologically separate populations as holdings having combined production with several species and sharing of pasture between various ruminant species are common practices. The presence of tuberculosis in beef cattle farms, goat farms, or sheep farms would presumably have led to infected dairy cattle as well, indicating that if the infection is present, the prevalence is probably low also in these populations.bTB is endemic in many countries  and is cHossain et al.  conducteIn contrast, Rahman et al.  reportedIt is a well-known fact for many infectious diseases including bTB that the risk of disease transmission increases with increasing population density . Pabna aThe present study used the CFT test to screen out the positive reactor cattle in the study population. Picasso-Risso et al.  used a B5This study indicates that the prevalence of bTB is low in the tested cohort. The local zebu cattle have better resistance or adopted tolerance to bTB, which may lead to less bTb infection resulting in low prevalence. Though the present study explores the very low prevalence of bTB in the local breed, further epidemiological investigation on disease burden, transmission dynamics, ecological and environmental factors, and the economic analysis of the effect of the disease could be undertaken by incorporating crossbred cattle to explore the complete epidemiology and ecology of bTB in Sylhet, Bangladesh.This research did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors.Data is included in supplementary material in the article.Pradeep Kumar Mandal: Data curation, Formal analysis, Investigation, Writing – original draft. Md Irtija Ahsan: Data curation, Formal analysis, Investigation, Writing – original draft, Writing – review & editing. Hrithik Deb Apu: Data curation, Formal analysis, Investigation. Sharmin Akter: Data curation, Formal analysis, Investigation. Syed Sayeem Uddin Ahmed: Conceptualization, Formal analysis, Supervision. Suman Paul: Conceptualization, Formal analysis, Methodology, Supervision.The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.


Our results show that UltraSEQ successfully detected all expected species across the tree of life in the in silico sample and detected all 10 bacterial and fungal species in the mock microbial community data set. For clinical data sets, even without requiring data set-specific configuration setting changes, background sample subtraction, or prior sample information, UltraSEQ achieved an overall accuracy of 91%. Furthermore, as an initial demonstration with a limited patient sample set, we show UltraSEQ’s ability to provide antibiotic resistance and virulence factor genotypes that are consistent with phenotypic results. Taken together, the above-described results demonstrate that the UltraSEQ platform offers a transformative approach for microbial and metagenomic sample characterization, employing a biologically informed detection logic, deep metadata, and a flexible system architecture for the classification and characterization of taxonomic origin, gene function, and user-defined functions, including disease-causing infections.Applied metagenomics is a powerful emerging capability enabling the untargeted detection of pathogens, and its application in clinical diagnostics promises to alleviate the limitations of current targeted assays. While metagenomics offers a hypothesis-free approach to identify any pathogen, including unculturable and potentially novel pathogens, its application in clinical diagnostics has so far been limited by workflow-specific requirements, computational constraints, and lengthy expert review requirements. To address these challenges, we developed UltraSEQ, a first-of-its-kind accurate and scalable metagenomic bioinformatic tool for potential clinical diagnostics and biosurveillance utility. Here, we present the results of the evaluation of our novel UltraSEQ pipeline using an IMPORTANCE Traditional clinical microbiology-based diagnostic tests rely on targeted methods that can detect only one to a few preselected organisms or slow, culture-based methods. Although widely used today, these methods have several limitations, resulting in rates of cases of an unknown etiology of infection of >50% for several disease types. Massive developments in sequencing technologies have made it possible to apply metagenomic methods to clinical diagnostics, but current offerings are limited to a specific disease type or sequencer workflow and/or require laboratory-specific controls. The limitations associated with current clinical metagenomic offerings result from the fact that the backend bioinformatic pipelines are optimized for the specific parameters described above, resulting in an excess of unmaintained, redundant, and niche tools that lack standardization and explainable outputs. In this paper, we demonstrate that UltraSEQ uses a novel, information-based approach that enables accurate, evidence-based predictions for diagnosis as well as the functional characterization of a sample. Although widely used today, these methods have several limitations, especially for infections caused by more than one etiological agent, novel pathogens, and unculturable organisms. Furthermore, these tests are often singleplex, requiring multiple tests to be run prior to diagnosis. Due to these limitations, the rate of unknown etiology of infection has been reported to be >50% for diseases such as pneumonia and encephalitis , 2. ThisMassive developments in sequencing technologies have made it possible to apply metagenomic methods to clinical diagnostics, which may alleviate the issues described above. Metagenomics offers a hypothesis-free approach to identify any pathogen, including unculturable and potentially novel pathogens, in a massively multiplexed assay that requires little to no wet-laboratory assay development for new etiological agents. The approach is largely unbiased, can work well with very-low-biomass samples, can often replace invasive sampling regimes, and, ultimately, can lead to better patient outcomes and reduce antibiotic misuse by the quick, accurate determination of the cause of the disease . ClinicaThe limitations associated with current clinical metagenomic offerings result from the fact that the backend bioinformatic pipelines are optimized for the specific parameters described above, resulting in an excess of unmaintained, redundant, and niche tools that lack standardization and explainable outputs. For pathogen identification, several different classifiers exist, including those that leverage alignment-based (both nucleic acid and protein) and k-mer-based approaches , 5. ClasTo address all of these challenges, we developed UltraSEQ, a first-of-its-kind metagenomics-based tool with diagnostic potential that is accurate and scalable and provides an information-based approach for clinical research applications. As described above, many bioinformatic routines for pathogen identification suffer from high false-positivity rates, the inability to identify organisms not included in their reference database, and either complexity associated with the interpretation of results or nonexplainable “black box” answers. In contrast, UltraSEQ uses a novel, information-based approach that leverages a fast aligner that can handle both DNA and protein databases to make sample-level predictions (including taxonomic profiling) at the most specific taxonomic levels possible given the information for the sample and the database(s) used. UltraSEQ was built from the ground up to make predictions for regions of sequences (including taxonomic binning), full sequences, and collections of sequences  without complicated user settings and the necessity for background data set subtraction. This novel approach enables accurate, evidence-based predictions for diagnosis as well as the functional characterization of a sample, including virulence factor and antibiotic resistance (AbR) profiles. Predictions are backed by our curated database that provides end users with additional contextual metadata of predictions, such as whether the identified pathogen typically or rarely causes disease or is a potential normal flora contaminant as well as other user-defined characteristics. In our previous work, we described the development of this database , which uThe overall schema of the UltraSEQ pipeline is presented in in silico-synthesized metagenome consisting of genomic DNAs from 22 different microbial species, including bacteria, fungi, viruses, and humans. In step 2, we evaluated UltraSEQ against a mixed microbial community data set to measure real-world performance. Finally, in step 3, we evaluated UltraSEQ using publicly available clinical metagenomic data sets to compare our results with clinical microbiology test results as well as those from other metagenomic pipelines. The results for each of these comparisons are presented below.To comprehensively evaluate UltraSEQ’s ability to accurately taxonomically profile metagenomic samples with the goal of data-set-agnostic diagnostic application, we applied a 3-step evaluation approach. In step 1, we evaluated the specificity and sensitivity of UltraSEQ using an in silico-synthesized metagenomic sample, we extensively tested the different parameters and found that the tuning of UltraSEQ’s metagenomics service module (metagenomic module) had the most consequential effect on taxonomic assignment reporting. Within the metagenomic module, this tuning is achieved by altering the user-settable k-means clustering centroid distance tolerance threshold, referred to here as the metagenomic clustering threshold (MCT) . Our results show that a minimum MCT of 0.5 results in the lowest FN counts for the data set tested, with a very marginal increase in the FP count  species Aspergillus niger, Lactobacillus casei, Prevotella denticola, and Streptococcus mutans, respectively.The information-theory-based approach combined with machine-learning algorithms in UltraSEQ allows a highly tunable final report, ranging from very conservative reporting, where false positives are minimized, to more permissive settings, which allow more false positives while ensuring minimal false negatives (FNs). Using our FP count . Using ahttps://benlangmead.github.io/aws-indexes/k2)  . Bar ploin silico data set, these samples were also processed with an MCT of 0.5. UltraSEQ correctly predicted the presence of all 10 species for both the long- and short-read data sets with relative abundance values close to the expected values , disease types , sequence types (RNA and DNA), and sequence generators . For each data set, we evaluated UltraSEQ’s diagnostic capability by comparing the results from metagenomic data sets of clinical samples to microbiological results for the same samples. For this analysis, we calculated the positive percent agreement (PPA), negative percent agreement (NPA), and accuracy (ACC) of UltraSEQ with the publicly available sequence data sets or in-house data sets. Overall, UltraSEQ demonstrated 86% PPA, 97% NPA, and 91% accuracy across all 10 sets of data, demonstrating UltraSEQ’s utility across the wide range of data sets tested. For each data set, we further compared the performance of UltraSEQ to those of other informatic tools with the above-mentioned metrics as well as additional metrics , as detailed below. In contrast to other pipelines, no background data sets were used to generate UltraSEQ results, which is the most direct comparison to clinical microbiological results. All results are shown in File S3, with each study in a separate tab.PRJNA516289) investigated the use of both RNA sequencing (RNAseq)-based and DNA sequencing (DNAseq)-based metagenomics to diagnose encephalitis/meningitis, and the SURPI pipeline was used to make organism calls. For this study, the 90 data sets derived from 76 samples without a high host background as reported by those authors were run for comparison. The results for these 90 data sets were compared to the results of 173 clinical and/or confirmatory tests (positive or negative). Overall, UltraSEQ performed the same in terms of accuracy (95%) compared to Miller et al.’s pipeline across all 173 clinical tests (Table SF1). UltraSEQ performed better than Miller et al.’s pipeline in terms of NPA (100% for UltraSEQ) but not PPA . Importantly, UltraSEQ reported zero false positives even without the use of a background data set subtraction method. Across the various categories tested, UltraSEQ performed better for fungi and bacteria, worse for DNA viruses and RNA viruses, and the same for parasites (Table SF1). For fungi, Miller et al.’s one false negative was due to the lack of identification of Sporothrix schenckii, which UltraSEQ identified as a pathogen that can cause encephalitis/meningitis  also investigated the use of metagenomic RNA sequencing for the diagnosis of encephalitis/meningitis. Overall, UltraSEQ performed slightly better than those authors’ pipeline for the PPA and ACC metrics, with one fewer false-negative result (Table FS2). Not surprisingly, a general correlation between qPCR cycle threshold (TC) values and positive results was observed. For the seven cases where qPCR values were relatively high and both UltraSEQ and Saha et al. reported FNs, UltraSEQ detected Streptococcus pneumoniae in 6/7 (86% of the cases). However, all were below the 1% threshold used to make a positive call. If the criteria for S. pneumoniae were loosened from these default criteria to require only a 0.3% relative abundance, 3 additional FNs would become TPs . UltraSEQ reported no false positives for all samples (100% NPA), but a direct comparison of NPA values to those of Saha et al. could not be performed since those authors did not report false positives.A study by Saha et al.  (BioProjStreptococcus parasanguinis). To further demonstrate the differences between UltraSEQ and CZ ID, results from the analysis of sample CW322 by UltraSEQ (Table SF3) and CZ ID  were compared. UltraSEQ identified only two species and automatically filtered out other species with low confidence, without requiring user input. In contrast, CZ ID identified many bacterial and viral genera , and within each genus, several species were identified. For example, Fig. SF2 shows partial results for Neisseria in which 22 species were identified.The encephalitis/meningitis data set of Hasan et al.  was selePRJEB7888), Illumina RNAseq reads from 24 different respiratory disease patients were evaluated in comparison to qPCR influenza clinical data. The UltraSEQ results were compared to the results of Fischer et al.’s in-house BLAST pipeline and Explify. For this comparison, positive detection was defined as the identification of influenza virus, and negative detection was considered a lack of influenza virus detection. All pipelines demonstrated 100% NPA but varied in their PPAs, with UltraSEQ failing to identify influenza virus in one more and two more cases than with Fischer et al.’s and Explify’s pipelines, respectively (Table SF4 and File S3).In a previous study by Fischer et al.  (BioProjPRJNA554856) included samples from two patients with ventilator-associated pneumonia (VAP), collected on day 1 and day 3, for a total of 4 samples sequenced using the Thermo Fisher Scientific IonTorrent platform. For patient 1, UltraSEQ identified Staphylococcus aureus only on day 3, which is in good agreement with the culture results and Watts et al.’s results (File S3). For patient 2, UltraSEQ identified S. aureus and Klebsiella aerogenes on day 1, which is in good agreement with the culture results and those authors’ results. Other positive results were identified as well, including Prevotella melaninogenica, Pseudomonas aeruginosa, and human alphaherpesvirus for patient 1.A study by Watts et al.   profiles based on the presence of genes that are known to cause resistance to various antibiotics, as described in Materials and Methods. The results of AbR profiling for esFinder . UltraSEPRJNA554461) investigated the use of clinical metagenomics using an Oxford Nanopore MinION sequencer for patients with VAP. Here, the UltraSEQ results were compared to the results of Oxford Nanopore’s What’s in My Pot (WIMP) workflow, as reported by Yang et al. As shown in Table SF6, UltraSEQ’s results demonstrated a much higher NPA due to 4 fewer false positives but a slightly lower PPA due to one more false negative. For the 4 culture-negative pneumonia cases (cases 10 and 12 to 14) and the 8 control cases (cases 15 to 22), the UltraSEQ results were consistent with those authors’ (WIMP) results, with some exceptions, as detailed in the scores tab of File S3. For the 10 culture-positive pneumonia cases (cases 1 to 9 and 11), UltraSEQ identified at least one concordant pathogen in 8/10 cases (discordant for cases 7 and 9); in comparison, those authors identified the concordant pathogen in 9/10 cases. However, those authors identified 2 false positives for both case 1 and case 4, which UltraSEQ correctly did not report. For case 1, UltraSEQ identified the FP organisms, but both fell below the abundance threshold described in Materials and Methods. For case 4, UltraSEQ did not identify the 2 FPs at any abundance.A previous study by Yang et al.   investigated the use of Illumina RNA sequencing of 24 upper respiratory tract samples for the diagnosis of diseases caused by respiratory viruses. No negative controls were included in these studies, and all results were compared to the results of PCR tests. For these 24 samples, Flygare et al. reported an average of 95% sensitivity (positive predictive agreement) for their Protonomer module of Taxonomer software  investigated Illumina RNA sequencing data sets from nasopharyngeal (NP) swabs for the diagnosis of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection. Pathogen detections were compared to the results for SARS-CoV-2 reverse transcription-PCR (RT-PCR)-positive patients (n = 45) and RT-PCR-negative subjects (n = 30), including one viral coinfection confirmed by RT-PCR. We compared UltraSEQ’s results to those of Babiker et al., who used KrakenUniq . These results were compared to gold-standard PCR tests (File S3). For 6 of the 8 samples, we correctly identified SARS-CoV-2 (75% true-positivity rate). No other pathogens  were identified. For the two false negatives, no reads were identified that aligned to SARS-CoV-2, and the lack of detection did not correlate with the qPCR results, suggesting that the lack of detection was due to a lack of a signal in the data set and not due to UltraSEQ.To further test UltraSEQ’s ability to diagnose respiratory disease with a different sample type , we generated and analyzed 8 RNAseq metagenomic data sets from coronavirus disease 2019 (COVID)-19-positive patients , can beWhile database coverage is important for pipeline performance, curation of the data contained in the database is perhaps even more important. All but two tools leverage curated lists of pathogenic organisms to enable the specific reporting of pathogens identified in a sample. For example, SURPI is backed by annotated lists of pathogens that cause encephalitis/meningitis, Karius is backed by lists of sepsis-causing pathogens, and Explify is backed by lists of respiratory-disease-causing pathogens, including granularity on phenotypic groups . UltraSEQ provides these curated lists as well but provides additional granularity, including the likelihood of the pathogen causing disease , contaminant type , and specific disease subtypes , etc.Stenotrophomonas maltophilia, Staphylococcus epidermidis, Pseudomonas aeruginosa, and Klebsiella pneumoniae for case 1, but only S. maltophilia was the likely disease-causing organism based on culture results. UltraSEQ identified S. maltophilia and S. epidermidis, but S. epidermidis could be eliminated because of its metadata (lack of association with VAP and known skin contaminant), and the reference for this annotation is provided to the end user.In addition, providing end users with human-readable text and primary references related to organisms, gene functions, and other information can minimize the burdens (such as review time) associated with bioinformatic predictions. Based on our research, UltraSEQ and at least two additional tools provide such text and citations. For example, CZ ID provides background information on organisms and provides Wikipedia citations, and WIMP provides background information on organisms at the genus level . HoweverLike some other tools, UltraSEQ provides the functionality to both preprocess and remove host-derived reads. While most other tools perform read preprocessing, at least five do not include a host read removal step, which can increase the computation time. Furthermore, UltraSEQ and four other tools can process both short-read  and long-read  sequencing data. Furthermore, UltraSEQ provides the additional flexibility of being deployed in different formats, including cloud deployment.a priori knowledge.Most importantly, unlike the implementation of other tools described in this paper across the clinical case studies discussed, we have demonstrated that the potential implementation of UltraSEQ for clinical metagenomics for different sample types does not rely on parameters that are specific to read depth and background data set subtraction. For example, in their implementation of CZ ID, Saha et al. used specific filters that were dependent on read depth  . For bacEscherichia coli was a pervasive contaminant in many of these data sets, including the data sets of Hasan et al. and Miller et al. For these data sets, UltraSEQ also identified E. coli in most samples. Unlike the authors who used several custom user-defined filters and background samples to remove this contaminant, UltraSEQ correctly identifies E. coli as being present in the sample but provides the metadata label of biological contaminant. This context enables the end user, with minimal manual interpretation, to identify E. coli as a false positive and therefore eliminate it from the result without requiring background data. Overall, UltraSEQ made all of its predictions without a background sample, maintaining an accuracy comparable to or better than those in the studies that utilized a background sample subtraction analysis step. As for qPCR and other molecular assays, we suggest that background samples should be included as separate controls (and not used for background subtraction). Therefore, we believe that this feature of UltraSEQ will be a major benefit in its clinical metagenomics application since UltraSEQ does not require specific empirical testing to remove background signals and apply specific thresholds, thus expanding its utility across any sample type.To further illustrate UltraSEQ’s lack of reliance on arbitrary depth thresholds and a background sample, we ran several data sets at various read depths and saw no degradation (or improvement) of UltraSEQ’s performance (data not shown). Furthermore, we noticed that ERR690513). For this sample, human alphaherpesvirus dominated the relative viral abundance maps ; thus, UltraSEQ likely did not report influenza virus in this case due to a low number of influenza virus reads relative to the number of human alphaherpesvirus reads. Another area of future development would be toward expanding and optimizing UltraSEQ’s ability to detect and classify emerging novel pathogens. Recent work in our laboratory analyzing novel SARS-CoV-2 demonstrated that UltraSEQ could identify SARS-CoV-2 as a SARS-CoV-1-related virus (data not shown). Furthermore, analytical models built from hazardous function signals . For example, in the data set of Fischer et al. , UltraSElication ) demonstThe overall UltraSEQ pipeline is illustrated in (i) Preprocessing service. UltraSEQ’s preprocessing routine includes steps to trim low-quality sequence regions, remove adapter sequences, remove duplicate sequences,  merge paired-end reads, and  remove host sequences (–equences –34. Diff(ii) Aligner service. Each sequence is rapidly aligned using LAMBDA2 version 1.9.5 (https://www.uniprot.org/help/downloads]). UniRef100 provides coverage across the tree of life (pathogens and nonpathogens) and provides the foundation for taxonomic calls in UltraSEQ. In addition, we used Battelle’s Sequence of Concern (SoC) protein database, described previously (on 1.9.5  against eviously . This da(iii) Query mapper service. The query mapper service maps regions within query sequences  to identify high-quality-alignment regions as well as chimeric reads/out-of-context DNA sequences. For each query sequence, the positions of alignment starts and stops from high-quality alignments were compiled. A k-means clustering approach was used to identify positions in the query sequence with a high abundance of alignment starts/stops, which were subsequently used to identify regions of the query sequence.(iv) Context services and subservices. Context services and subservices generate contextual information and pass information to downstream services. Information from the context services is passed to the prediction services and flagging system (rules engine), as described below.(v) Region-based prediction subservices. For each region identified from the query mapper service, UltraSEQ predicts the taxonomy, function (gene ontology terms), and threat associated with the region. For this study, UltraSEQ’s sample-level taxonomy predictions (metagenomics module described below) were used for sample-level taxonomy calls, and UltraSEQ’s region-based function and threat prediction were not used. These calculations are provided in File S1B for the sake of completeness as they are useful for other use cases.(vi) Metagenomics service. The metagenomics service provides the sample-level taxonomic composition based on the regions identified from reads processed by the query mapper service in 3 steps: (i) filtering out low-quality reads, (ii) scoring the remaining reads based on the information content of the reads, and (iii) predicting the taxonomic composition based on the scores. Sequence-region-level taxonomy predictions are associated with confidence scores that are based on the alignment quality. For each unique taxonomy identified in the sample, the confidence scores from the alignments from all query regions that are associated with it are assigned. A k-means clustering approach was used to identify taxonomies with high sample-level scores independently by taxonomy domain . Detailed calculations are provided in File S1A.(vii) Rules engine service. The rules engine service combines all of the above-described context and prediction services for regions, sequences, and samples using user-defined logic rules for rapid sequence triage. UltraSEQ currently has 4 default rules engines, but only the metagenomics diagnostics rules engine (MDRE) service was leveraged for this study (is study . The MDR(viii) Reporting services. UltraSEQ provides several reports, as described below.(a) Top-alignment report. The top-alignment report is a simple report that provides the top alignments from each database (UniRef100 and SoC databases for this study) for each query sequence from the aligner and query mapper services. This report is used for downstream reports, as described below.(b) Taxonomy report. The taxonomy report provides the alignments, annotated with the reference accession’s taxonomy identifier (TaxID), used by the metagenomics service and is used to determine the taxonomic composition of the sample. This report is used for downstream reports, as described below.(c) Default report. The default report provides UltraSEQ’s region-level predictions for each sequence, including predictions for coarse threat functions (described above), taxonomy, gene ontology, and the sequence-level results of a rules engine(s) if applicable.(d) Sample report. File S2 in the supplemental material provides a detailed description of the sample report. The “main-report” tab provides a list of all organisms identified from the above-described metagenomics service, the results associated with the identified organisms , and the metadata associated with the organism from Battelle’s SoC database . These results and metadata are used in the MDRE service (described above) for each disease type studied here , which are summarized in the “trigger-summary” tabs. Virulence and antimicrobial resistance (AMR) factors (both sample wide and agent specific) are described in downstream tabs, as detailed in File S1A.Several data sets were used during this study, as described below.In silico data set.(i)  The complete reference genomes of 21 different organisms across the tree of life were downloaded from the NCBI RefSeq database using the open-source bioinformatic tool ncbi-genome-download v0.3.1 (https://github.com/kblin/ncbi-genome-download) with the commands described in its user manual. The downloaded genomes were combined, and a few sequences from the human genome were added to it to test the host removal process. The combined genomes were used to computationally generate a metagenomic sample using the open-source tool InSilicoSeq v1.5.4 (q v1.5.4  based on(ii) Mock microbial community data sets. To further test UltraSEQ’s taxonomic profiling capability, we analyzed data sets derived from mixed microbial communities as reported previously by Nicholls et al. (ERR3152364 (Nanopore data set) and ERR2984773 (Illumina data set) were run.s et al. . Specifi(iii) Clinical data sets. Clinical data sets are summarized in Approximately 1- to 5-mL specimens were self-collected in RNase-free 50-mL Falcon tubes. Briefly, subjects were instructed to swallow a couple of times prior to the collection of normal saliva that naturally pools in the mouth without coughing or sniffing. After collection, the sample tubes were screwed shut and sterilized with a disinfecting alcohol wipe. Samples were submitted to a collection site within 30 min of collection, where they were immediately placed on ice. Samples were shipped to the laboratory and stored at 2°C to 8°C until diagnostic testing could be conducted. All samples were tested within 72 h of collection, and leftover saliva was stored at −70°C or lower for subjects who enrolled in the Battelle Biorepository.TC value of <37 and the RNase P gene had any value; it was a positive reportable result; if the RNase P gene had a TC value of <35 and the SARS-CoV-2 N1 gene had a TC value of ≥40, it was a reportable negative result; and if the RNase P gene had a TC value of ≥35 and the SARS-CoV-2 N1 gene had a TC value of ≥40, the test was considered invalid, and the test was repeated for that sample.Self-collected saliva samples were analyzed the same day using SalivaDirect DualPlex RT-qPCR targeting the N1 gene of SARS-CoV-2 and human RNase P. Briefly, samples were vortexed until they were homogeneous, and 50 μL of saliva was then mixed with 2.5 μL of MagMAX viral/pathogen proteinase K (Thermo Fisher) and vortexed at room temperature at 3,000 to 5,000 rpm for 1 min. Proteinase K was then heat inactivated for 5 min at 95°C prior to RT-PCR. RT-PCR was conducted using TaqPath one-step RT-qPCR master mix according to the SalivaDirect protocol on 5 μL of extraction-free saliva lysate. For each 96-well sample plate tested, a negative template control (NTC) and two positive controls (Twist synthetic SARS-CoV-2 RNA controls at 100 copies/μL) were assessed. Test results were valid only when the NTC returned a negative result and the positive controls returned the expected positive results for SARS-CoV-2 N1 and negative results for the RNase P gene. The detection criteria for clinical samples were as follows: if the SARS-CoV-2 N1 gene had a Saliva samples from each enrolled subject that returned a positive SARS-CoV-2 qPCR result underwent RNA extraction using the QIAamp viral RNA kit for QIAcube (Qiagen) according to the manufacturer’s instructions. RNA extracts were then treated using Kapa Hyper prep with RiboErase according to the manufacturer’s instructions. Library quantification was conducted using a Kapa library quant kit (Illumina) with universal qPCR mix , and the libraries were normalized to 4 nM and pooled for sequencing with the Illumina NextSeq 500/550 high-output kit v2.5 . FASTQ data were processed through UltraSEQ as described above.The agreement of the metagenomic results with the pipeline was compared to reported molecular results  in terms of positive predictive agreement (PPA) [PPA = TP/(TP + FN)], negative predictive agreement (NPA) [NPA = TN/(TN + FP)], and accuracy (ACC) [ACC = (TP + TN)/(TP + FN + FP + FN)]. In cases where negative samples were not available , an addPRJNA554461, PRJNA516289, and PRJNA516582, additional filters were applied due to obvious E. coli contamination. Specifically, for the data set under BioProject accession number PRJNA554461, E. coli was considered a positive detection only if it was identified with a >5% relative abundance and >7 SoCs were identified; for the data set under BioProject accession number PRJNA516582, filters of a >50% relative abundance and >21 SoCs were applied; and for the data set under BioProject accession number PRJNA516289, E. coli-positive results were not reported under any condition.For each published data set and corresponding pipeline, positive and negative calls were documented as reported in the publications, and the detailed results are described in File S3. In addition to the pipelines described in the studies in https://www.ncbi.nlm.nih.gov/bioproject) under accession number PRJNA856680. The UltraSEQ platform is available for trial account use by contacting the authors.Metagenomic reads for the saliva samples from this study have been submitted to the NCBI BioProject database (


Klinefelter syndrome (KS) is associated with an increased risk of lower socioeconomic status and a higher risk for morbidity and mortality, which may have a significant impact on quality of life (QOL). The objective of this study is to investigate QOL in a large European cohort of men with KS.Cross-sectional multicentre study.Two-hundred-eighteen men with KS were recruited from 14 clinical study centres in 6 European countries which participated in the European dsd-LIFE study. Male normative data from a healthy and a psychiatric reference population were used for comparison. The validated World Health Organization (WHO) QOL (WHOQOL)-BREF questionnaire was used to investigate five main domains of quality of life (WHOQOL): global, physical, psychological, environment, and social.s.d. 19.4, n = 193; healthy reference population: 76.5; s.d. 16.2, n = 1324, P < 0.001) but higher compared to the psychiatric reference population . The WHOQOL-psychological domain score was lower for men with KS compared to the healthy reference population  but higher compared to the psychiatric reference population , n = 77, P < 0.001). The social domain score on the WHOQOL questionnaire was found to be lower in men with Klinefelter syndrome (KS) compared to the healthy reference population . However, this score was similar to that of the psychiatric reference population . The WHO environment domain score of men with KS  was similar to the healthy reference population  but higher compared to the psychiatric reference population . Experienced discrimination, less social activities, and the presence of chronic health problems were associated with significantly decreased QOL in men with KS.The QOL physical domain score was lower for men with KS compared to the healthy reference population (KS: 66.9; Overall QOL in European men with KS is significantly worse compared to a healthy European reference population. Especially, the presence of discrimination, less social activities, and chronic health problems is associated with lower physical, psychological, and social QOL. Further studies are necessary to investigate if a multidisciplinary approach may help to provide adequate counselling and psychosocial support to improve QOL. With a prevalence of 1 in 660 males, Klinefelter syndrome KS, 47,XXY) is one of the most common sex chromosome disorders , 3. A la7,XXY is However, little is known about the quality of life (QOL) in men with KS. Therefore, the objective of this study is to investigate QOL in a large European cohort of men with KS and to associate QOL with social activities, age at diagnosis, hormonal substitution, presence of chronic health problems, and experienced discrimination.https://www.dsd-life.eu/), a non-interventional, clinical, cross-sectional study . The 14 centres approached former and current patients by mail, e-mail, phone, or direct contact of the physician and promoted participation in patient support groups from February 2014 to September 2015. Participants had to be at least 16 years old with a medically confirmed clinical and/or genetic diagnosis. Details on the theoretical and methodological framework of the dsd-LIFE study have been published earlier .Men with KS were asked to fill out a digital patient-reported outcome (PRO) form that comprised validated and self-constructed questionnaires on health status, mental health, QOL, psychological well-being, psychosexual outcome, testosterone treatment, fertility, experiences with care, and sexuality. To ensure confidentiality, the participants were asked to fill out the PRO with a secure password, either in the clinic or at home. Data were entered anonymously into a database.n = 1324) reference population as well as a psychiatric control group (n = 77) reported by Skevington et al., 2012, for comparison of the mean scores for the World Health Organization QOL–BREF (WHOQOL-BREF) domain scores .We used a healthy  were excluded from analysis. Factors possibly associated with quality of life (QOL) that have been examined include BMI, engagement in social activities, the presence of chronic health issues, encounters with discrimination related to one's condition, encounters with discrimination for various reasons, the use of testosterone treatment, and the age at which the diagnosis was made. Further elaboration on these factors can be found in s.d.) or frequencies and percentages.Characteristics of the men with KS are described using means and standard deviations  using the statistical software R version 4.2.1 (https://www.r-project.org/). For all other analyses, SPSS software version 22.0 was used .Linear regression analysis was done within the KS study population to investigate possible associations between QOL and the above-described possibe associated factors, such as hormone therapy with testosterone, age at diagnosis, and presence of chronic health problems amongst men with KS. There was no correction for multiple comparisons because of the exploratory nature of this study and the primary concern about type II error. Domain scores of the WHOQOL-BREF for men with KS, the healthy, and the psychiatric reference population have been compared by unpaired A total of 218 men with KS were included in the study, but thirteen men with KS were excluded from the analysis due to mosaicism or more than one additional X-chromosome. A total of 205 men with KS had a 47,XXY karyotype and were included in the analysis. The baseline characteristics of men with KS are listed in s.d. 21.7) (s.d. 23.4) compared to participants without the presence of chronic health problems . Especially, mental chronic health problems were associated with a lower QOL , compared to the presence of physical chronic health problems for men with KS . Furthermore, less participation in social activities was associated with a lower global group score for QOL (P = 0.45), current testosterone therapy (P = 0.46), and age at diagnosis (P = 0.94). Our network plot shows strong positive associations between the QOL global domain and all other QOL domains  . The sco for QOL . There w domains .Figure n = 193) was 66.9 (n = 1324) who achieved a mean score of 76.5  scored significantly lower (mean score 54.6) than men with KS (P < 0.001) and the healthy reference population , BMI (P = 0.22), or age at diagnosis (P = 0.77).Our univariate analysis shows that the mean QOL physical domain score was significantly lower amongst men with KS who reported to have experienced discrimination based on their KS condition (mean score 57.1) compared to men who did not experience discrimination (mean score 69.0). Furthermore, men with the patient-reported presence of chronic health problems had a significantly lower QOL physical domain score (mean score 60.5) compared to men without the presence of chronic health problems (mean score 75.3). Less participation in social activities was also associated with lower physical domain scores. There was no significant association between the QOL physical domain score and current testosterone therapy (n = 193) was 63.6 (s.d. 17.8). This was significantly lower compared to the healthy reference population (n = 1324) who had a mean score of 67.8 (P < 0.001 n = 77) scored significantly lower than the men with KS and the healthy reference population with a mean score of 45.9 , BMI (P = 0.10), or age at diagnosis (P = 0.37)  .n = 193) was 60.0. This was significantly lower compared to the healthy reference population (n = 1324) who achieved a mean score of 68.2  who had a mean score of 61.0 . Less participation in social activities was associated with lower social domain scores (P < 0.001). There were no statistically significant associations with self-reported presence of chronic health problems (P = 0.26), BMI (P = 0.75), testosterone therapy at present (P = 0.15), or age at diagnosis (P = 0.35).The mean WHOQOL-social domain score of men with KS  was 70.0. This was comparable to the healthy reference population (n = 1324) who achieved a mean score of 70.5  scored significantly lower than the men with KS (P = 0.002) and the healthy reference population (P < 0.001); P < 0.047; P = 0.53), BMI (P = 0.64), or age at diagnosis (P = 0.41).The WHOQOL-environment domain score of men with KS  under grant agreement n°305373.German Clinical Trials Register: Registration identification number: DRKS00006072, date of registration April 17th, 2014. DRKS00006072 .The datasets analysed during the current study are not publicly available as long as primary analyses for other outcomes of dsd-LIFE are not completed. Afterwards, scientific public use files are planned. The data will be made available by the principal investigator upon request to researchers after publication of the primary outcomes described in the grant by the consortium.


Vitis vinifera L.) in the winemaking process present a diverse phytochemical profile of (poly)phenols, essentially represented by phenolic acids, flavonoids, and stilbenes, which have health benefits. In winemaking, solid (grape stems and pomace) and semisolid (wine lees) by-products are generated, negatively impacting the sustainability of the agro-food activity and the local environment. Although information on the phytochemical profile of grape stems and pomace has been reported, especially information concerning (poly)phenols, research on wine lees is necessary to take advantage of the compositional traits of this residue. So, in the present work, an updated, in-depth comparison of the (poly)phenolic profiles of these three resulting matrices in the agro-food industry has been carried out to provide new knowledge and interesting data on the action of yeast and lactic acid bacteria (LAB) metabolism in the diversification of phenolic composition; additionally, we extract complementarities for the possible joint application of the three residues. The phytochemical analysis of the extracts was carried out using HPLC-PDA-ESI-MSn. The (poly)phenolic profiles of the residues showed significant discrepancies. The results obtained showed that the greatest diversity of (poly)phenols was found in the stems of the grapes, followed closely by the lees. Through technological insights, it has been suggested that yeasts and LAB, responsible for the fermentation of must, might play a key role in the transformation of phenolic compounds. This would provide new molecules with specific bioavailability and bioactivity features, which might interact with different molecular targets and, consequently, improve the biological potential of these underexploited residues.The by-products of grapes ( Vitis vinifera L.) is one of the most extensive crops worldwide, producing more than 78.0 million tonnes (Mt) globally in 2020. This crop is of special relevance in the countries of the Mediterranean Basin, especially Italy, Spain, France, and Turkey  , where traditional wine consumption is strongly tied to regional and cultural factors − pseudo molecular ion at m/z 865 arbitrary mass units (amu), corresponding to the molecular formula C45H38O18. The fragment ion was at m/z 695 amu [M − H − 170]−  or m/z 577 amu [M − H − 288]−  in the grape pomace and wine lees could be due to the metabolic activity of the yeast burden characterizing the latter’s matrices − which included ions at m/z 729, 677, 577, and 407 amu, linking the major fragments (m/z 729 and 577amu) to the loss of the (epi)catechin moiety [M − H − 288]− and (epi)catechin gallate moieties [M − H − 441]− [m/z 729 amu produced in MS3 [M − H]− ions at 577 was linked to the loss of a galloyl group [M − H − 152] − . In turn− pseudo molecular ions at m/z 593 amu (molecular formula C30H26O13), m/z 577 amu (C30H26O12), m/z 881 amu (peak P14), and m/z 729 amu . The compounds P3, P4, P6, and P7 with pseudo molecular ions at m/z 593 amu displayed MS2 spectra, including product ions with m/z 441 amu [M − H − 152]−  and 423 amu [M − H − 152 − 18]−  − pseudo molecular ion at m/z 577 amu, indicating they are B-type proanthocyanidins formed by two (epi)catechins. The MS2 spectra showed the presence of product ions with m/z 425 [M − H − 152]−  and 407 [M − H − 152 − 18]−  −  and 559 [M − H − 152 + 18]−  [The LC-ESI-MSn analyses allowed us to identify four dimeric proanthocyanidins with [M − H]15H14O7), catechin (C15H14O6), epicatechin glucoside (C21H24O11), and epicatechin (C15H14O6)  were also identified on the basis of their [M − H]− pseudo molecular ions, recorded at m/z 305, 289, 449, and 289, respectively. Gallocatechin generated fragments in MS3 at m/z 167 and 137 amu − pseudo molecular ion at m/z 331 amu, corresponding to the molecular formula C13H16O10. The major fragment obtained at m/z 169 amu [M − H − 162]− (loss of hexose) is characteristic of the unesterified form of gallic acid − pseudo molecular ion at m/z 169 amu, corresponding to the molecular formula C7H6O5. The gallic acid isomers generated fragments in MS2 at m/z 125 amu [M − H − 44]− , which is characteristic of this compound − (loss of glucose) [A [M − H]glucose) . Isomers7H6O4), caftaric acid (C13H12O9), caftaric acid-glucuronide (C15H16O10), caftaric acid derivative, p-coumaric acid pentoside (C14H17O7), p-coumaric acid (C9H8O3), ferulic acid pentoside (C15H19O8), and ethyl gallate  were also identified on the basis of their [M − H]− pseudo molecular ions, recorded at m/z 153, 311, 487, 623, 295, 162, 325 and 197, respectively, and molecular losses at MS2, according to previous descriptions in the literature −  and MS3 fragment at m/z 125 amu  − pseudo molecular ions at m/z 405 and 243 amu which allowed them to be detected in the wine lees, while they were absent in the grape stems and pomace (m/z 243 amu [M − H − 162]− (loss of a glucose molecule) and 224 [M − H − 19]− amu , corresponding to the molecular formula C20H22O8, was detected and featured a [M – H]− pseudo molecular ion at m/z 389 amu that fragmented in MS2 to yield a single ion at m/z 227 [M − H − 162]− (loss of a glucose molecule) [St5 and St7 corresponded to isomeric forms of Σ-viniferin according to the [M − H]− pseudo molecular ion at m/z 453 amu which after fragmentation generated in MS2 the product ion m/z 359 amu corresponding to [M − H − 94]− (loss of a single phenol group). Both isomers were present in the three matrices , and thus appear to be the most ubiquitous stilbenes of the winery by-products − pseudo molecular ion at m/z 905 amu. In turn, the pseudo molecular ion at m/z 717 amu produced in MS2 is the result of the loss of two phenol groups (94 + 94) [M − H − 188]− −  and has  − 188]− .When assessing the flavonol profile of the grape stems and pomace, as well as wine lees, 13 compounds were found in concentrations higher than the LOD . In the Fl1 and Fl2), myricetin hexoside (peak Fl3), kaempferol 3-glucoside (peak Fl4), quercetin 3-glucuronide (peak Fl5), quercetin 3-glucoside isomers (peaks Fl6 and Fl7), myricetin (peak Fl8), kaempferol-glucoside (peak Fl9), isorhamnetin hexoside (peak Fl10), quercetin (peak Fl11), kaempferol (peak Fl12), and isorhamnetin (peak Fl13)  .Fl1 and Fl2 were two isomers of a flavonol identified as 3’,5’-di-methyltricetin derivative according to its [M − H]− pseudo molecular ion (m/z 509 amu). The MS2 fragmentation of these two isomers generated product ions at m/z 329 amu [M − H − 162 − 18]− (loss of hexoside plus water) [The flavonols represented by the peaks s water) . Isomer Fl6 and Fl7 corresponded to two isomeric forms of quercetin 3-glucoside (molecular formula C21H19O12). Both isomers presented a [M − H]− pseudo molecular ion at m/z 463 amu and exhibited a fragmentation pattern towards the MS2 product ion at m/z 301 amu [M − H − 162]−, which is indicative of a loss of a glucose molecule − (loss of a glucose molecule) [Fl3 and Fl10 were identified as myricetin hexoside (C21H20O13) and isorhamnetin hexoside (C22H22O12) due to the [M − H]− pseudo molecular ions at m/z 479 and 477 amu, respectively, which correspond to the major deprotonated fragments in MS2 at m/z 317 and 315 amu [M − H − 162]− (loss of glucose) − pseudo molecular ion at m/z 477amu that by fragmentation gave rise to a major MS2 fragment at m/z 301 corresponding to unesterified quercetin after the loss of glucuronide moiety [M − H − 176] [An additional esterified flavonol found in all three winery by-products was quercetin 3-glucuronide (H − 176] .15H10O8), quercetin (C15H10O7), kaempferol (C15H10O6), and isorhamnetin (C16H12O7)  were detected. These flavonols presented [M − H]− pseudo molecular ions at m/z 317, 301, 285, and 315 amu, respectively. The most abundant product ions in the MS2 spectra for these unesterified flavonols were m/z 179, 179, 214, and 301 amu, respectively + pseudo molecular ions at m/z 465, 449, 479, 463, and 493 amu, respectively. The MS2 fragmentation pattern evidenced in all cases the loss of a glucose molecule [M − 162]+ to yield protonated ions at m/z 303, 387, 317, 301, and 331 amu, respectively, which is in good agreement with previous descriptions in the literature of the anthocyanin profile of grape and grape by-products +) pseudo parent molecular ions at m/z 507, 521, 505, and 535 amu, respectively, and MS2 major fragments at m/z 303, 317, 301, and 331 amu due to the loss of the acetylglucoside moiety [M + H − 204]+ + pseudo molecular ion at m/z 665 amu that generated a final MS3 fragment at m/z 315 amu [M − 350]+ corresponding to the loss of the glucose and caffeic acid moieties  + pseudo molecular ions, recorded at m/z 611, 595, 625, 609, and 639 amu, respectively, and corresponding MS2 fragments at m/z 303, 287, 317, 301, and 331 amu [M − 326 + 18]+ (loss of the coumaroylglucose moiety and acquisition of a water molecule) [Finally, delphinidin 3-olecule) .The (poly)phenolic profiles of the grape stems, grape pomace, and wine lees revealed to a great extent a complementary distribution of compounds belonging to the different families of (poly)phenols . However, despite the common phenolic profile among the three oenology by-products , importaRegarding flavan-3-ol derivatives and proanthocyanidins, although twenty-nine individual compounds were found at a level higher than the LOD allowing for their proper identification, only eight compounds presented concentrations higher than the limit of quantification (LOQ). These were found in the following decreasing order of concentration : catechin (96.15 mg/kg dw) > proanthocyanidin dimer (B-type) (84.60 mg/kg dw) > proanthocyanidin trimer (B-type) (67.67 mg/kg dw) > proanthocyanidin dimer monogallate (45.04 mg/kg dw) > epicatechin (42.02 mg/kg dw) > proanthocyanidin dimer digallate (8.61 mg/kg dw) > gallocatechin (7.99 mg/kg dw) > catechin-gallocatechin (7.83 mg/kg dw) . In geneOn the other hand, proanthocyanidin trimer (B-type), catechin-gallocatechin, and catechin were found at the highest concentration in the grape pomace , being mainly provided by the grape skin and seeds as previously reported . AdditioLactiplantibacillus plantarum) and also colonic microbiota have been shown to provide bioavailable compounds with proven biological benefits in humans [When viewing the quantitative flavan-3-ols derivative’s profile as a whole, it is noticed that, interestingly, the grape pomace and wine lees presented high concentrations of monomers of flavan-3-ols in comparison with the grape stems. This fact could be due to the catabolization capacity of yeasts and LAB present in the grape must during the fermentation process. In regards to this matter, to the best of our knowledge, the hydrolysis of proanthocyanidins by yeasts has not been described yet, though LAB species . To understand the different phenolic compositions of the grape pomace and wine lees relative to the grape stems, it should be noted that both the pomace and lees during the winemaking process are in contact with the yeasts and LAB. In this sense, Teixeira et al. (2014) reported that yeasts possess enzymes responsible for the hydrolysis and transformation of complex (poly)phenolic substrates into phenolic compounds with a high added value [Regarding the phenolic acids present in wine by-products in quantifiable concentrations , almost ed value , such asL. plantarum, a malolactic starter culture in winemaking [These molecular transformations are relevant because they provide those matrices with specific (poly)phenolic profiles featuring high concentrations of strongly antioxidant compounds. Moreover, the grape pomace was the only plant material that obtained quantifiable concentrations of galloyl-hexoside, protocatechuic acid, and caftaric acid, and had a protocatechuic mean concentration of 58.13 mg/kg dw. Again, as for the wine lees, the grape pomace is involved in the winemaking process, in contrast to grape stems, so it could be also subject to the fermentation process and the consequent transformation of the phenolic burden by yeast and LAB. As a result of such metabolization, phenolic diversity is obtained with a positive impact on biology (bioavailability and bioactivity) [nemaking . NonetheFinally, in regards to phenolic acids, ferulic acid pentoside, although at a low concentration, is mainly present in a significant way in the wine lees. The possible transformation that takes place in the wine lees could also be observed in the data for ferulic acid pentoside in which it appears to be the majority.trans piceid acid were found only in the wine lees in quantifiable concentrations . Thereby, these phenolics could be considered bioactive compounds characteristic of wine lees, resulting from the metabolism of grape (poly)phenols during the fermentation process. In this regard, it would be important to mention that, currently, it is known that the elicitation of in vitro plant cultures with yeasts improves the biosynthesis of stilbenes (resveratrol and oxyresveratrol); thus, the main results obtained in the present work are an additional demonstration of the synthesis of stilbenoids in wine and winemaking residues by yeasts and LAB [In regards to stilbenes , oxyresv and LAB .trans resveratrol dimer was found in all three residues analyzed in the following decreasing order: wine lees (15.79 mg/kg dw) > grape pomace (9.47 mg/kg dw) > grape stems (1.96 mg/kg dw). Again, the highest concentration recorded for E-Viniferin in the wine lees is possibly a result of hydrolysis reactions of more complex stilbenes by yeast and LAB [These findings would support the contribution of these microorganisms to the diversification of the bioactive compounds present in winery by-products, as well as to the formation of the derivatives of said molecules. This would entail an enhancement of our biological scope because of the possible interaction of the new phenolics formed with molecular targets responsible for complementary forms of modulating cell biology. In the case of oxyresveratrol , low con and LAB . The for and LAB . This mo and LAB .Regarding flavonols, which are mainly located in the skin of grapes, as well as, to a lesser extent, in the seeds of some varieties , seven op < 0.05). These results were very similar to the recently reported results by Souza da Costa, et al. 2022, in which the authors found 261.41 and 116.34 mg/kg dw of quercetin glucoside in grape stems and pomace, respectively [Regarding quercetin glucoside, the concentrations recorded  were also noticeable in the wine lees, grape stems, and grape pomace, respectively, appearing to only have statistically significant differences between the wine lees and grape pomace . Thereby, they should be considered a valuable foodstuff for the generation of new functional ingredients, foods, or feeds, providing a diversity of flavonols with beneficial effects for health  ,64,65,66The autolysis of the yeasts in the wine lees , in addiAlong with flavonols, the presence of compounds belonging to the anthocyanin phenolic class has been well-documented in wine and winery by-products ,56. So, The results retrieved evidenced that the 3-glucoside derivatives of delphinidin, petunidin, peonidin, and malvidin were found at the highest concentration in the wine lees, which exhibited concentrations ranging between 89.29 and 670.47 mg/kg dw . The secRegarding anthocyanins’ acetylglucoside derivatives, the highest amounts corresponded again to the wine lees with values ranging between 6.71 and 13.23 mg/kg dw, except for malvidin-3-acetylglucoside, which was only found in quantifiable concentrations in the grape stems (2.01 mg/kg dw), and delphinidin 3-acetylglucoside, which had no significant differences between the grape stems and wine lees , while it was under the LOQ in the grape pomace .p-coumaroylglucoside, malvidin 3,6-caffeoylglucoside, cyanidin 3-p-oumaroylglucoside, petunidin 3-coumaroylglucoside, peonidin 3-(6-trans-p-coumaroyl)-glucoside, and malvidin 3-p-coumaroylglucoside were found at the highest concentrations in the wine lees   .Oenococcus oeni and Lactiplantibacillus plantarum) strains can absorb anthocyanin glucosides through the cell wall, through the production of β-glycosidase enzymes that cleave the anthocyanin glucoside glycosidic bonds and can further degrade the aglycons into phloroglucinol aldehyde and corresponding phenolic acids [Although the mechanisms responsible for such diversity are poorly understood (even more so in regards to winery by-products), the fermentation process’s impact on the diverse composition of phytochemicals is well known  and thisic acids ,70. In tIn summary, regarding the quantitative profile of wine by-products , proanthocyanidins (dimer (B-type)) were the more abundant phenolic compounds in the grape stems, phenolic acids (protocatechuic and caftaric acids) in the grape pomace, and flavonols  and anthocyanins  in the wine lees. These findings agree with those of a previous report . Thus, t• scavenging capacity of the hydro-methanolic extracts of phenolic compounds of the grape stems, grape pomace, and wine lees, the significantly highest values corresponded to the grape stems , which surpassed the anti-radical power of the grape pomace and wine lees by 47.3% and 98.6%, respectively (Because of the broad information included in the article on the (poly)phenolic composition of the grape stems, grape pomace, and wine lees, the radical scavenging capacity of these materials was assessed by a single method. Because of the accepted requirement to measure antioxidant activity by eight or more methods, we selected the DPPH test, according to Baliyan et al. (2022), since this method is the quickest, easiest, and most affordable approach for the measurement of antioxidant power . When evectively . Recentl2 coefficient, proanthocyanidin dimer monogallate  > oxyresveratrol .The analysis of correlation pointed out significant positive associations of individual phenolic compounds in the following order of decreasing regression strength according to the RAdditionally, since the present work was focused on the characterization of hydro-methanolic extracts, the results obtained can be considered in designing winery by-products as sources of bioactive (poly)phenols for applications other than as a dietary antioxidant supplement . However, additional determinations of the bioaccessibility of phenolic compounds from the separate matrices and the antioxidant activity of the digested fractions should be developed to identify the best source of dietary antioxidants .•) as well as the standard catechin, 1,4-chlorogenic acid, resveratrol, quercetin-3-O-glucoside, and cyanidin-3-O-glucoside were obtained from Sigma-Aldrich . The 6-hydroxy-2,5,7,8-tetramethylchroman-2-carboxylic acid (Trolox) was purchased from Fluka Chemika . Formic acid was purchased from Panreac . All LC-MS grade solvents were obtained from JT Baker . Ultrapure water was produced using a Millipore water purification system.The compounds 2,2-diphenyl-1-picrylhydrazyl radical (DPPHVitis vinifera L. var. ‘Monastrell’) stems and pomace) and semisolid (wine lees) winery by-products were obtained from the winery Industry Bodegas Viña Elena S.L.  during the 2020 season. For analytical purposes, these by-products were freeze-dried up to constant weight using a CHRIST vacuum concentrator 2-4D . The dry materials were ground to a fine powder, stored, and protected from light for further phytochemical and radical scavenging analyses.Solid (grape (v/v/v), vortexed, and sonicated in an ultrasonic bath for 60 min. Afterwards, the samples were kept at 4 °C overnight and sonicated again for 60 min. Then, a centrifugation step at 10,000× g for 5 min was used to separate the extract from the solid residue, and the supernatants were filtered through a 0.22 µm PVDF filter .Dehydrated samples (100 mg) were mixed with 1 mL of methanol/formic acid/water  (solvent A) and acetonitrile/formic acid  (solvent B). Spectral data from all peaks were detected in the 200–600 nm range and chromatograms were recorded at 280 nm for proanthocyanidins, 330 nm for phenolic acids and stilbenes, 360 nm flavonols, and 520 nm for anthocyanins. The HPLC-PDA-ESI/MSn analyses were carried out by the ChemStation software . Mass spectrometry data were acquired in the negative  and positive (anthocyanins) ionization modes. The identification of the phenolic compounds was performed by examining the retention time (min), parent ions, and the MS2 and MS3 fragmentation patterns in comparison with those of databases and descriptions available in the literature. Phenolic compounds were characterized and quantified by PDA chromatograms recorded at the different wavelengths described above for the distinct phenolic classes, using daily prepared calibration curves, with catechin (proanthocyanidins), chlorogenic acid (phenolic acids), resveratrol (stilbenes), quercetin-3-O-glucoside (flavonols), and cyanidin-3-O-glucoside (anthocyanins).The chromatographic separation of the phenolic compounds present in the analytical extracts was performed on a Luna C18 column  using an Agilent HPLC 1100 series equipped with a diode array detector and mass detector in series . The HPLC consisted of a binary pump (model G1312A), an autosampler (model G1313A), a degasser (model G1322A), a photodiode array (PDA) detector (model G1315B), and an ion trap spectrometer (model G2445A) equipped with an electrospray ionization interface and controlled by LCMSD software , operated according to the chromatographic and ionization specifications described by Barros et al. . The mob● method adapted to microscale according to Domínguez-Perles et al. [● solution at 515 nm after 50 min of reaction. The results were expressed as mM of Trolox equivalents per g of dry weight (mM TE/g dw).The free radical scavenging activity was determined by the DPPHs et al. , measurin = 6) for each plant material . Results are presented in bar plots with an indication of the mean ± SD. Data were analyzed using the Statistical Package for the Social Sciences (SPSS) 24.0 software package . All data were subjected to a one-way analysis of variance (ANOVA). The fulfilment of the one-way ANOVA requirements, specifically the normal distribution of the residuals and the homogeneity of variance, was tested using the Kolmogorov–Smirnov (with Lilliefors correction) and Levene tests, respectively. When statistical differences were identified, the variables were compared using Tukey’s multiple range test. Pearson’s correlation analysis was performed between the content of phenolic compounds and the radical scavenging capacity to corroborate the relationships between parameters. All statistical tests were performed at a 5.0% significance level.The design of the experiment was completely randomized with six replications phenols and their radical scavenging capacity. Thus, it combines wine lees, grape pomace, and grape stems in a detailed test. By extracting the relevance of each one of the by-products in terms of the molecules that it could contribute and its richness, as well as by examining the complementarity of the three matrices (given that they have numerous compounds in common), the present diversification is remarkable. We can greatly expand the range of phytochemicals that can be provided for bioavailability purposes and thereby take advantage of their bioactivity traits. According to the demonstrated benefits of grapes and wine, it would be reasonable to reuse these by-products, as they have similar profiles and potential. As it is pertinent to identify new uses for winery by-products, this would allow us to avoid management costs and enable their valorization as new ingredients, or sources of valuable bioactive nutrients and non-nutrients. Thus, we can provide a market opportunity to develop a more efficient industry, or to generate resources from discarded waste. From this research, we can conclude that in addition to the different compounds associated with the diverse nature of each by-product and its processing specificities, the microorganisms responsible for the fermentation processes associated with wine production are key for the diversification of the phytochemicals present in these materials  and, therefore, the design of the valorization processes. These are materials containing molecules that can be used in applications based on their valuable technological properties and as health-promoting agents for humans. Furthermore, the different profiles displayed by the separate winery by-products assessed in the present work allow us to envisage their combination to obtain a mixture with the optimal (poly)phenolic burden and thus, take advantage of their application as a technological ingredient or dietary supplement.


To address the complex challenge of global antimicrobial resistance (AMR), a pandemic treaty should include mechanisms that 1) equitably address the access gap for antimicrobials, diagnostic technologies, and alternative therapies; 2) equitably conserve antimicrobials to sustain effectiveness and access across time and space; 3) equitably finance the investment, discovery, development, and distribution of new technologies; and 4) equitably finance and establish greater upstream and midstream infection prevention measures globally. Biodiversity, climate, and nuclear governance offer lessons for addressing these challenges. Literature on antimicrobial resistance (AMR) has grown enormously in the last 10 years. While this growth is welcomed, the sudden rise in AMR literature presents a challenge for time-pressured policymakers. Specifically, it makes it difficult to gather and grasp all the necessary information, concepts, and controversies relevant for devising policy solutions for AMR. This plight is increasingly cumbersome in the context of global pandemic instrument negotiations, where negotiators must quickly identify the sum of issues that fit within a pandemic instrument and design strategies to effectively resolve these issues. In this paper, we present two tools from social science that treaty negotiators can leverage to identify the relevant governance challenges associated with AMR and design a pandemic instrument that incorporates effective solutions to address this urgent threat. The first is a problem synthesis framework that outlines the range of global governance problems around antimicrobial resistance. The second includes select examples where collective action theory has been concretely applied to address other global common pool resources similar to AMR, namely, biodiversity, climate, and nuclear governance. These examples can help signal toward potential global solutions for AMR.1.In this paper, we present two tools from social science that treaty negotiators can leverage to identify the relevant governance challenges associated with AMR and design a pandemic instrument that incorporates effective solutions to address this urgent threat. The first is a problem synthesis framework that outlines the range of global governance problems around antimicrobial resistance. The second includes select examples where collective action theory has been concretely applied to address other global common pool resources similar to AMR, namely, biodiversity, climate, and nuclear governance. These examples can help signal toward potential global solutions for AMR.Social science research reveals a long catalogue of social challenges associated with the rise of global antimicrobial resistance. With the help of this first tool, we translate these social problems into tangible and discrete governance challenges that must be addressed at the global level . These sWe find that AMR governance challenges fall into four broad areas: (1) improving access to antimicrobials, diagnostic technologies, and alternative therapy; (2) conserving the effectiveness of existing antimicrobials; (3) spurring innovation for antimicrobials, diagnostic technologies, and alternative therapies, as well as new behavioral, policy, and economic techniques; and (4) bolstering global infection prevention measures.These areas accentuate existing inequities that must be overcome by any policy intervention, making a fifth governance area, the challenge of achieving equity, a transversal issue that cuts across the first four areas. For each of the four areas of access, conservation, innovation, and prevention, we describe the social aspects of the problem, emphasize the global governance issues that persist within them, examine the equity considerations that each area presents, and, finally, prescribe an AMR specific principle to guide how the pandemic instrument should addresses AMR .1.1For many, the most urgent challenge remains the fact that millions of people around the world, mostly in low-income settings, still do not have access to effective antimicrobial medicines.First, there are few measures available for those who face the greatest access challenges to improve their situation due to ongoing legacies of colonialism, commercial determinants of health, and current global systems of exploitation.Second, actors with access to effective antimicrobials  are unlikely to act and help those without access unless they recognize a compelling reason or obligation to do so. A sufficiently strong motivation or obligation might arise from a sense of justice, whether directly  or indirectly , international law, or, alternatively, an enlightened self-interest. The possible connection with domestic interests is that global health security from infectious disease can only be achieved when all have basic minimum access to antimicrobials.The governance challenge for access is two-fold. 1.2Conserving antimicrobial effectiveness for current and future generations remains a major problem.This involves big challenges, however. Some highly entrenched uses of antimicrobials — in multiple sectors — would need to change for conservation policies to be successful. Another obstacle for enacting conservation measures is that effects will not be noticeable unless many countries implement them at the same time, which means that tackling overuse in a country may make little difference overall if other countries are not doing the same. Three further factors compound this governance challenge. First, antimicrobial use is driven by deeply entrenched structures and incentives meaning that it is costly and difficult to transform the relevant social practices.1.3Innovation for new antimicrobials is plagued with several systemic market failures. Pharmaceutical firms feel poorly incentivized to develop new drugs because the market for last resort antimicrobials is by definition small . That means that new antimicrobials are not being developed fast enough to replace those to which key pathogens are becoming resistant.The current lack of investment in antimicrobial research and development is driven by a mismatch between their market and social value. One could also argue that despite the lack of economic incentives to innovate, there is a moral imperative for pharmaceutical companies to develop new drugs and expand access, given their position to do so, the high social value of antimicrobials, and the history of uneven use across countries of different income levels.A range of market considerations further exacerbate the innovation challenge including the high costs and failure rates for drug development, the possibility that new antimicrobials may reduce markets for existing antimicrobials, the fact that new drugs are only needed in case of current antimicrobial failure, and the fact that only one course of antimicrobials is typically needed per treatment.1.4Antimicrobials are so widely used across sectors that they are essentially like invisible infrastructure that cover up shortcomings in infection prevention measures.Another challenge for IP is AMR surveillance.The governance challenges for improving IP in this context are similar to those for improving access. It is not fair to expect, those without the current capacity to improve their IP to do so without external help. Perhaps, a better way to formulate this challenge is that high-income countries and pharmaceutical firms could, in earnest, do more to improve global IP including through global wealth and medicine redistribution, but they lack (or fail to see) the incentive to do so. To reiterate, achieving a more equitable approach to global investments in IP would either require strong moral compasses in places with the capacity to intervene, or the recognition of shared global interdependence and vulnerability to infectious disease.1.5Equity is a major challenge in all matters of global health and represents a significant AMR governance issue. AMR simultaneously acts as a prism that reveals many existing global inequities and a multiplier that deepens and exacerbates them. Fundamentally, equity has to do with the distribution of things of value  and in this way, all AMR policies must recognize how interventions may reproduce or worsen global health inequities.Considerations for improving IP must recognize that lower- and middle-income countries face disproportionately high levels of infectious disease, but low levels of investment in health system capacity due to enduring legacies of colonialism and current economic structures shaped by capitalism and globalization.Additionally, while no one is immune to resistant pathogens, different sectors of the population face different risk levels and distributions of infectious disease prevalence and burden. Critical race, class, and gender dimensions are significant here as women and other marginalized groups are more likely to live in poverty, be frontline and informal workers, and face increased risks of infection exposure during pregnancy, abortion, and childbirth.2.Theory-driven insights can link governance challenges with potential policy solutions. Here we draw on collective action theory and select examples of how collective action is achieved in biodiversity, climate, and nuclear governance to make potential solutions for AMR more tractable. We focus on the one specific type of collective action problem for AMR most frequently identified in the literature: managing the global common pool of antimicrobial effectiveness. In doing so, we must heed a word of caution when using collective action theory, or any other theory, narrowly like this to conceptualize policy problems and solutions. Primarily, when the dominant framing is one of a common pool resource problem, there is a risk of reducing the multifaceted challenge of AMR to only one dimension of the issue . However, as Considering this limitation, a direct application of collective action theory to AMR can still tease out policy insights to help address the common-pool resource problem that AMR presents, including which design, implementation, or enforcement tools might provide more effective and ethical solutions. By way of background, collective action problems are an especially difficult social problem where the best collective outcome is threatened when individuals follow their immediate self-interests. Common-pool resource challenges are one specific type of collective action problem and existing research has observed that antimicrobial effectiveness resembles a non-excludable but rivalrous common pool resource challenge.provision and use of the common pool. In alignment with the governance challenges for AMR described in part 1, the dimension of provision translates into questions about innovation, while the dimension of use translates into questions about who has access to the resource and who must make a greater conservation effort to preserve it.Techniques used in climate, biodiversity, and nuclear governance offer lessons for managing the global common pool of antimicrobial effectiveness. More specifically, they indicate that a suite of at least 5 design elements can help the pandemic instrument distribute responsibilities and benefits across the governance areas of access, conservation, innovation, and prevention to equitably manage antimicrobial effectiveness as a global common pool resource.Collective action theory indicates that global common pool resources require governance mechanisms that can change individual self-seeking behaviors — or change the social structures that shape those behaviors — to achieve a collectively beneficial outcome. Specifically, common-pool resource challenges require these kinds of behavior-changing mechanisms across two dimensions: Following the implications of collective action theory, the act of governing the global common pool of antimicrobial effectiveness must inevitably involve decisions about whose interests are counted and prioritized over others, who must change their behavior, what that new behavior is , and who makes these decisions around access to, conservation of, and innovation for antimicrobials.In this context, techniques used in climate, biodiversity, and nuclear governance offer lessons for managing the global common pool of antimicrobial effectiveness. More specifically, they indicate that a suite of at least 5 design elements can help the pandemic instrument distribute responsibilities and benefits across the governance areas of access, conservation, and innovation to equitably manage antimicrobial effectiveness as a global common pool resource.a multi-stakeholder forum can help inclusively set priorities for access, conservation, and innovation.First, a science-policy-evidence interface can help equitably determine best practices for achieving established priorities, so long as it acknowledges that different ways of knowing constitute the global knowledge base. The science and policy interfaces established to curate knowledge on biodiversity and climate issues, the Intergovernmental Science-Policy Platform on Biodiversity and Ecosystem Services (IPBES) and the Intergovernmental Panel on Climate Change (IPCC) respectively, still struggle with representing different understandings of the problems and how best to address them. Including AMR in a pandemic instrumentcould deliver a similar mechanism that can help curate knowledge and confront existing uncertainties for AMR. But learning from these other challenges means recognizing the political challenges associated with this task head on.Second, a combination of common and differentiated responsibilities can help equitably distribute burdens and benefits for AMR.Third, enforcement mechanisms because there is an inherent risk that countries may free ride off the efforts of others. Enforcement mechanisms are also one of the few design elements proven to affect treaty outcomes in international law.Fourth, a pandemic instrument should include an international legal agreement can unify these elements under a common and coordinating framework. Efforts to manage these other common pool resources benefit from legally enshrined frameworks that foster cooperation, craft incentives, and distribute responsibilities and benefits across countries. AMR currently lacks an equivalent vehicle, but a pandemic instrument could potentially fill this gap if designed to also include AMR.Finally, 3.AMR is among the leading causes of mortality worldwide, and the governance challenges identified in part 1 offer several potential sites for action that a pandemic instrument could address to mitigate AMR’s global impact. Specifically, we argue that a pandemic instrument must include mechanisms that: (1) equitably address the access gap for antimicrobials, diagnostic technologies, and alternative therapies including other medical countermeasures; (2) equitably conserve antimicrobials to sustain effectiveness and access across time and space; (3) equitably finance the investment, discovery, development, and distribution of new technologies including new antimicrobials and other medical countermeasures; and (4) equitably finance and establish greater upstream and midstream infection prevention and control measures globally.Existing theoretical work elaborated in part 2 can also be leveraged to generate policy insights for managing the global common pool of antimicrobial effectiveness. Applying collective action theory to the common pool resource problem of AMR indicates that negotiators must carefully consider questions about how burdens and benefits are ethically distributed, including who accesses the common pool resource, whose access gets restricted, and who innovates to replenish it. Past examples from climate, biodiversity, and nuclear governance offer concrete examples for addressing the political considerations associated with these political decisions.While we still need to generate theory-driven solutions for the full range of collective action problems for AMR the pandemic treaty offers a much-needed opportunity to swiftly deliver global regulations on AMR. With the pandemic instrument, we can start turning “collective action problems” into “collective action solutions” for AMR. Given the urgency of the situation, there is no more time to wait.


Circular Health is a novel approach to address complex health issues that is based on the expansion of the One Health Paradigm. Circular health recognizes the need for a multidisciplinary convergence effort to complement the biomedical dimension of health. Antimicrobial resistance (AMR) is one of the greatest global concerns for public health that is likely on the rise, given the extensive use of antibiotics during the early Covid-19 years. Prior to the Covid-19 pandemic, an expert group chaired by Jim O’Neill published “The Review on Antimicrobial Resistance”, which contains a final report and recommendations on how to tackle AMR. The report, for the first time, considers AMR from a multi-perspective viewpoint highlighting how it cannot be successfully addressed unless there is a converging approach encompassing many dimensions of the problem. In this perspective, we propose to include the recommendations from that seminal report and other more recent reviews which include the lessons learnt from the Covid-19 pandemic, into the operational framework of the sustainable development goals (SDGs). AMR represents a perfect case study to explore how the SDG roadmap has the potential of becoming the driving force and implementation tool to address complex health issues by pursuing the optimization of resources and actions via a convergent and multi-stakeholder approach. The implementation of health-related policies through the whole spectrum of the SDGs could be both a novel and a well-established framework to inform multi-dimensional policies for more sustainable health in the future. Antimicrobial resistance (AMR) is recognized as a leading cause of death around the world, one of the top ten global health priorities, and a threat to development . It requOur starting point was a seminal report that analyzed the magnitude and potential impact of AMR and the numerous steps necessary to address it published as the “Review on Antimicrobial Resistance” . HoweverHence, AMR is growing fast, and novel roadmaps should be developed to comprehensively address AMR by using existing consensus documents and frameworks to empower such activities. The 2030 Agenda for Sustainable Development is an existing framework that may be used to seek a broader convergence of AMR related efforts. Here we propose an example of how to implement a series of recommendations taken from the O’Neill report and selected others from more recent studies by including them in selected targets pertaining to each SDG, excluding, for obvious reasons, SDG 3 - Health and Well-being.The aim of this paper is to exemplify how countries can contribute to fight AMR by implementing, expanding, or focusing on actions that are already part of the SDG roadmap.Poverty can be a lack of money, but also a lack of access . Both types of poverty contribute to AMR. Poorer people are more susceptible to infectious diseases treated with antimicrobials  and so iThe intensification of agriculture driven by increasing demand, especially for animal-source foods, is increasing antimicrobial use in livestock . The genLack of awareness and poor education are the main drivers of improper use and disposal of antimicrobials, in turn, one of the main causes of AMR. While certain categories of health care workers have inadequate or incorrect knowledge about how to appropriately manage antimicrobials and on the potential consequences of AMR  most medWomen represent 80% of caregivers worldwide , thus plUnsafe drinking water is one of the main sources of bacterial infections  and it iReliable access to electricity is fundamental to provide the highest level of care . Indeed,Wealthier countries, especially when experiencing rapid economic growth, tend to have a higher consumption of antimicrobials . Since AHealth care is also one of the high value-added and labor-intensive sectors described in target 8.2. Investing in AMR mitigation strategies is necessary to increase the sector’s productivity and quality of services. At the same time, promoting better jobs and innovative enterprises in health care (target 8.5 and 8.3) is essential to address AMR. This positive feedback effect could be promoted by appropriately amending targets 8.2, 8.3, and 8.5. Moreover, managing the impact of AMR through the lens of economic policy could have beneficial effects given the number of proven tools already available to policymakers .Scientific and technological innovation together with infrastructure development are the foundations of many policies to address AMR. The O’Neill report  highlighThe global volume of antibiotic consumption (40.2 billion DDD -defined daily dose- in 2018) increased 46% since 2000 . HoweverProper access to antibiotics for refugees and migrants is significantly influenced by the health systems of the host countries as reported by the WHO in 2022 . ResistaPromoting better public health practices to prevent infections, especially in poorer countries, will reduce antimicrobial consumption overall, including their misuse . SimilarA growing share of humanity, about 55% in 2020, are urbanite, highlighting the importance of building sustainable cities (United Nations Human Settlements Programme (UN-Habitat) 2020). Sustainability should include monitoring and promoting a healthy microbiome of the built environment  because,Consumption behaviors and production practices of antimicrobials are among the main causes of AMR. From a consumption perspective, target 12.8 should be expanded to clearly promote increasing public awareness on how to properly use antimicrobials, how to dispose of them, and the risks associated with their misuse . This shThere is a positive association between global warming and AMR . Higher Many human activities are drivers of AMR in marine life. Medicated feeds, a common practice in aquaculture, are an important source of AMR , and oceProtection of natural ecosystems can be significant in addressing AMR. On one hand, natural habitats can accumulate drugs discharged from hospitals, health care settings and agriculture, resulting in antimicrobial agents exerting selective pressure on environmental microorganisms . On the Antibiotic misuse is one of the most important components of AMR and dysfunctional public institutions and corruption seem to account for some of the remarkable between-region variation in antibiotic consumption. Poor governance and corruption correlate better than antibiotic usage volumes with resistance rates . A stronAMR is a highly complex problem that in a globalized world is not bound to specific geographic areas. Therefore, fighting AMR requires an interdisciplinary and multi-stakeholder response in which every country participates . This meIn this paper we propose a novel approach based on a convergence model to fight AMR, which exploits the existing SDG roadmap. Such a model is based on a circular approach to the problem that seeks to highlight win-win scenarios by enforcing existing objectives and targets to fortify the effort to combat AMR in a multidisciplinary synergistic effort Figure 1The original contributions presented in the study are included in the article/supplementary material. Further inquiries can be directed to the corresponding author.IC conceived the contents and outline of the manuscript. All authors were assigned specific sections of the manuscript and identified the literature relevant for their sections. LM wrote the first draft of the manuscript assembling and synthetizing the materials provided by all authors. AP curated the bibliography. All authors contributed to the article and approved the submitted version.