Streptococcus (GBS), is a leading cause of neonatal death and an emerging pathogen in adults. Additionally, GBS is a bovine pathogen causing intramammary infections. The likelihood of GBS interspecies transmission is largely unknown. We explored the potential transmission of GBS between cattle and people on dairy farms in Colombia and compared the antimicrobial resistance (AMR) profiles of isolates from both host species. Across 33 farms, throat swabs and rectal swabs were collected from 191 people, and rectal swabs and composite milk samples from 2092 cattle, yielding 60 human isolates and 301 bovine isolates. The majority (64%) of isolates belonged to shared sequence types (ST). Sequence type (ST) 1 was the most common strain in both host species, suggesting that interspecies transmission may be possible. Two members of the bovine-specific clonal complex 61/67 were detected in human samples (ST718 and ST1175), providing evidence for the lack of genuine species barriers. Apparent prevalence of penicillin resistance was surprisingly high in human and bovine isolates. Further investigation of this phenomenon is needed and could lead to modification of standard testing and treatment recommendations in human and veterinary medicine.Group B  Streptococcus agalactiae or Group B Streptococcus (GBS) is an important etiologic agent in a wide variety of human infections. GBS is often carried asymptomatically by healthy adults, ranging from 20 to 40% in developed countries4. In South America, the reported prevalence of GBS colonization in pregnant women ranges from 6% to 26%, depending on country and culture methods8. In pregnant women, GBS may reach the amniotic fluid and fetal membranes, and cause fetal deaths9. Furthermore, GBS is a severe neonatal infections such as sepsis, meningitis and pneumonia10. Despite the fact that GBS colonization may lead to neonatal infection, the predominant strains associated with carriage, notably sequence type (ST)1, ST19 and ST23, differ from the major neonatal clade, ST1711.12. Surveillance in the United States of America (USA) suggests that GBS infection is an emerging disease in nonpregnant adults, where ST1 and ST196 have been recognized as emerging clades11.In adults, GBS can cause bacteremia, respiratory infections, urinary tract infections, joint and bone infections, endocarditis or meningitis, and skin and soft tissues infections, particularly in elderly and immunocompromised patientsStreptococcus is also a bovine pathogen causing intramammary infections leading to mastitis13. The prevalence of GBS in dairy herds varies from less than 10% in Canada15 and northern European countries18, to approximately 50% in South America20, and more than 90% in China21. Based on studies in epidemiologically unrelated populations, it has been suggested that bovine and human strains are largely distinct populations and interspecies transmission is unlikely23. There are no studies on this topic from emerging economies where the epidemiology of GBS may be different from high income countries with advanced animal health infrastructure25. To elucidate the molecular epidemiology of GBS, typing methods must have high typeability and discriminatory power. Serotyping of GBS, which is widely used in human medicine, only identifies 10 serotypes so it has very limited discriminatory ability. Moreover, many bovine isolates are not typeable by means of serotyping26, which is possibly due to pseudogenization of the capsular operon in bovine isolates27. Even among human isolates, more than 12% may be non-typeable based on serotyping8. For those reasons, multilocus sequence typing (MLST) is a more suitable typing method for epidemiological studies, particularly those involving cattle. In a European study of contemporaneous sympatric isolates from humans and cattle, five shared STs comprised more than half of the GBS isolates in the study28.Group B 29. These findings support the possibility of on-farm interspecies transmission. This phenomenon has not been studied in countries with high GBS prevalence in the cattle population.Comparison of isolates that were collected in the same countries in the same years was an improvement over comparisons based on epidemiologically unrelated isolates, but there was no known contact between people and cattle. In a study of 68 farming families and their cattle in the United States, a shared ST was detected in a cow’s feces and in stool of a couple living on the farm30. The drugs of second choice, particularly in people with penicillin allergies, members of the Macrolide-Lincosamide-Streptogramin group such as the macrolide erythromycin and the lincosamide clindamycin31. Decreased susceptibility of human GBS strains has been reported for penicillin32, erythromycin and clindamycin34. A study in Colombia, showed that 20% of cows treated with beta-lactam antibiotics remained GBS positive after use in accordance with the pharmaceutical company’s recommendations35. One potential explanation for this observation is the emergence of antimicrobial resistance (AMR) in bovine GBS. This has been described in China and may be the evolutionary consequence of routine use of antimicrobials for infection control36. Interspecies transmission of resistant GBS is thought to occur based on tetracycline resistance profiles of human and bovine GBS isolates26. The emergence of penicillin resistance in South American dairy cattle would aggravate concerns about interspecies transmission of GBS.In human and bovine medicine, the treatment of choice for GBS infection consists of beta-lactam antibiotics, specifically penicillinWe investigated the potential for on-farm transmission of GBS between cows and people on dairy farms using MLST and AMR testing to understand possible public health risks of dairy farming in areas with high GBS prevalence in cattle, and to explore the need to update current GBS mastitis control recommendations.Of 189 people from 33 farms tested by rectal and throat swab, 15 (8%) were positive by both swabs, 20 (11%) by rectal swab only and 10 (5%) by throat swab only. Human carriers were detected on 25 (76%) of 33 farms. The frequency of GBS isolation was significantly different between throat and rectum (P < 0.001) Table .Table 1FAmong a grand total of 366 GBS isolates from human (n = 60), bovine n = 301) and environmental (n = 5) samples, 18 STs belonging to eight CCs were detected  belonged to shared STs, including 23 (38%) human isolates and 196 (65%) bovine isolates. Shared STs were detected on 7 of 22 (31.8%) farms where both host species tested positive for GBS. ST1 was the shared type on 6 farms and ST718 on one farm. On two farms, ST1 was found in the cows’ environment and a throat swab  were viable after prolonged storage and available for phenotypic and genotypic antimicrobial susceptibility testing. This included 33 (55%) of 60 human isolates representing 12 of 14 STs identified among human GBS, and 236 (78.4%) of 301 bovine isolates, including all STs identified among bovine GBS in this study (Supplementary Table tet(M) (P = 0.001) and tetO (P = 0.020), but not tet(K), which was detected in only 5 isolates. Tetracycline resistance encoded by tet(M) was significantly more common among bovine isolates than among human isolates  but not ermA (P = 0.27). The efflux pump gene mefA was only detected in two human isolates. The prevalence of ermA or ermB was not significantly different between human and bovine isolates (Table ermA was overrepresented in ST1 compared to non-ST1 (P = 0.02) whilst ermB was overrepresented in ST1 compared to non-ST1, in ST356 compared to non-ST356 and in ST718 compared to non-ST718  was significantly associated with detection of es Table . It was es Table . Resista28. All human participants in our study lived on farms with dairy cattle, and about the half of them participated directly in the milking process. Those that did not participate in milking were likely to be in contact with cattle, cattle manure, farm run-off, or milk, which is often consumed raw in Colombia, particularly on dairy farms37, in contrast to the situation in high-income countries were most work on GBS epidemiology has been conducted. Based on our MLST results and potential transmission mechanisms28, interspecies transmission may occur in both directions. For example, consumption of raw milk may explain occurrence of shared STs between bovine milk samples and human throat swabs, whereby GBS in milk may originate from intramammary infection  that are shared between farmers and cattle within dairy farms, including the highly prevalent ST1. Previous studies have suggested the possibility of zoonotic or anthroponotic transmission based on characterization of contemporaneous and sympatric isolates from humans and bovines without known epidemiological connections29. ST1 is emerging as a leading cause of invasive disease in non-pregnant adults, which is attributed host adaptation through a series of small and cumulative genetic events40. The ability of bacteria to adapt to different host species and to acquire additional virulence genes or resistance genes in different functional environments poses a major threat to public health and food safety41. It may lead to repeated host switching, a phenomenon that has been demonstrated for Staphylococcus aureus. Like GBS, this S. aureus colonizes and infects humans cattle and other host species, and is a major cause of bovine mastitis41. It is conceivable that GBS could also evolve to switch between human and animal hosts, acquiring accessory genome content from different niches in the process.Sequence type 1 was detected in every sample type and it was the dominant strain in both host species, in agreement with previous studies42. Adaptation to cattle involves acquisition of bovine-specific virulence factors, e.g. the lactose operon43 as well as loss of human-specific virulence factors e.g. capsule27. In our study, members of CC61/67 were also isolated from human. We repeated GBS isolation and MLST analysis to rule out laboratory errors and confirm this unexpected finding. To our knowledge, we report CC61/67 carriage in humans for the first time here. Meanwhile, CC61/67 has also been isolated from human clinical samples from patients with vaginitis and urethritis in Asia44. Their emergence as human pathogen was associated acquisition of the CC17-specific type C gbs2018 gene44. It underscores the risk of ongoing evolution and expansion of the host range of GBS clades. Likewise, CC103, is a common cause of mastitis in dairy cattle in places as far apart as northern Europe45, China46, and Colombia25, whereas it has only rarely been reported in humans47. In the current study, CC103 member ST248 was the second most common strain shared by people and cattle. It has also been reported as a human strain in China44. The examples given here show the lack of a genuine species barriers, at least at ST level. Simple interventions aimed at improved hygiene during milking harvesting and consumption could reduce the risk of transmission of GBS between cattle and people. For instance, avoid raw milk consumption could interrupt the transmission from cattle to people. Improved sanitation and hand hygiene or use of gloves during milking could prevent transmission from people to cattle.In contrast to ST1, members of clonal complex (CC) 61/67 are considered to represent a host-adapted clade of GBS. This was the first CC of GBS to be described in cattle, and it is still the predominant cattle clade in bovines in parts of Europe48. However, penicillin resistance in GBS has been reported from Asia, including among hospital isolates from humans in Japan49, and isolates from tilapia50 and dairy cattle in China36. Results from our study suggest that penicillin resistance may also be emerging in South America. Interpretation of our data is somewhat hampered by the fact that we used standard methods for veterinary microbiology rather than the CLSI or EUCAST protocols. In addition, there are no guidelines for the interpretation of penicillin MIC data for cattle. Despite those caveats, our results give reason for concern. Further investigation of this phenomenon is needed, especially considering that penicillin is currently the treatment of choice in both host species. No evidence was found of penicillin resistance in human carriage isolates in Japan51, suggesting that antimicrobial use in clinical settings, agriculture and aquaculture may exert the selective pressure needed to provide penicillin resistant GBS with a survival advantage. This emphasizes the need to use antimicrobials judiciously, and to focus on prevention of GBS transmission in dairy herds to stop the ongoing need for treatment of individual animals. Eradication from dairy herds can be achieved with “Blitz therapy”, whereby all infected animals are detected and treated simultaneously53. However, this approach would need to be combined with good external and internal biosecurity to prevent re-introduction and onward transmission within dairy herds, a scenario that appears to be quite common in Colombia based on the within-farm strain heterogeneity that we observed. When considering treatment of the whole herd to eradicate GBS, susceptibility testing should be undertaken to inform drug selection.The Centers for Disease Control and prevention (CDC) in the USA describe GBS as completely susceptible to beta lactams antibiotics and CLSI reports that GBS isolates that are non-susceptible to beta lactams are extremely rare26. The difference may be related to the fact that in the USA, GBS screening in all pregnant women and antimicrobial treatment in all positive cases has been implemented since the 1990s31, whereas GBS is not that common on dairy farms due to better implementation of the 5-point plan compared to Colombia. Conversely, in Colombia, GBS screening and treatment of GBS positive pregnant women was recommended by health authorities only in 201354, GBS prevalence in dairy farms is high and antimicrobial treatment of cows is common. The prevalence of tet(M) among human isolates was low in comparison with the “global” study conducted by Da Cunha and colleagues55. That study did not include a single isolate from South America. In addition, their study preferentially included invasive isolates, specifically ST17, whereas our study focused on carriage isolates, with only a few isolates belonging to ST17. Differences in clinical and geographical origin of the isolates may explain the observed difference in tetracycline resistance and serve as further warning that extrapolation and comparison of study results must be conducted with caution and epidemiological insight.Regardless of the choice of interpretation criteria, the prevalence of apparent phenotypic macrolide and tetracycline resistance was only 10 to 14 percentage points higher in human isolates than in bovine isolates. This contrasts with results reported from New York State in the USA, where macrolide and tetracycline resistance were significantly more prevalent in human isolates than in bovine isolatestet(M) or tet(O)56. In our study, tet(M) was the most common resistance gene in human and bovine isolates. These results contrast to those from the USA, where bovine GBS isolates predominantly carried tetO and human GBS isolates predominantly carried tetM26. This comparison demonstrates the importance of local surveillance of AMR to detect and address regional differences in prevalence and drivers.In GBS, tetracycline resistance is primarily due to ribosomal protection and encoded by in vitro and in silico methods is needed and could lead to modification of standard treatment recommendations as well as greater emphasis on biosecurity within and between dairy herds.In conclusion, the results of this study suggest that on-farm interspecies transmission of GBS between people and cattle is possible, particularly for ST1, which is a strain capable of colonizing different hosts and environments, including the human throat and rectum, the bovine udder and rectum, and the farm environment. Other strains, which are currently mainly found in cattle, such as members of CC61/67 and CC103, could emerge from the bovine reservoir as a commensal or pathogen of humans. In addition, the apparent prevalence of penicillin resistance was surprisingly high in human and bovine isolates. Further investigation of this phenomenon using This study was approved by the bioethics councils for human and animal experimentation of the Universidad de Caldas, Manizales, Colombia (documents CBCS023-15 and 130705B-15), and all research was conducted in accordance with the relevant guidelines and regulations. Written informed consent was obtained from all participants or legal guardians for those younger than 18 years of age. Owners of the farms selected for conducting this work gave their permission to collect bovine and environmental samples.This study was conducted between July 2016 and October 2017. To be included in the study, farms had to be located in the province of Caldas, Colombia; to have been GBS positive by bacteriological culture (with species identity of isolates confirmed by PCR) in two out of three bulk tank milk samples collected at weekly intervals one to three months prior the start of this study. In addition, consent had to be given by the owner (for collection of samples from lactating dairy cows) and by at least one milker (for participation in human sampling). Farms were located at 5.3 to 36.1 km from the laboratory, with transport times ranging from 0.2 to 3.5 hours.In total, 191 people living or working on 33 farms participated in the study. Most participants were men , and more than a half of the participants were directly involved in the milking process . With one exception, all men lived on the farm. Median age of participants was 36 years (range: 16–59 years).Throat (n = 191) and rectal (n = 189) swabs were collected by trained nurses. Swabs were collected and transported using Cary Blair medium . Swabs were transported to the laboratory of milk quality at Universidad de Caldas and immediately placed in 2 ml of Todd Hewitt Broth (THB)  with 0.01 mg/ml colistin sulphate and 5 µg/ml oxoline acid . Samples were incubated for 24 hours at 37 °C.Milk samples were taken from all 2092 lactating cows  in the 33 herds. After udder preparation for milking, teat ends were disinfected with 70% ethanol and the first streaks of milk were discarded. Composite samples, i.e. approximately equal volumes of milk from each functional mammary quarter were collected into a 30 mL sterile vial. Samples were immediately transported to the laboratory under refrigeration.Rectal swabs were collected from 1922 of the cows  included in milk sampling. Swabs were collected using sterile cotton wool swabs, and immediately placed in 2 ml of THB, with streptococcus selective supplement COBA™ . Inoculated THB were transported to the laboratory under refrigeration. Enrichments were incubated for 24-hour at 37 °C.Streptococcus selective supplement COBA™ . From the 33 herds, 128 swabs were collected (2 to 11 per herd). Swabs from the internal walls of 47 drinking water containers were collected in 24 herds (Range: 1 to 4 per herd) using sterile cotton swabs. All swabs (n = 175) were processed as described for bovine rectal swabs.After milking, areas in feeders where cows had been eating/licking, as evidenced by wet spots, were rubbed with cotton wool swabs that had been moistened with THB with 57, using the primers STRA-AgI (AAGGAAACCTGCCATTTG) and STRA-AgII (TTAACCTAGTTTCTTTAAAACTAGAA), which target the 16S–23S intergenic spacer region, with amplicons of 270 bp58. The remaining 10 µL of the colony suspension were subcultured in Todd Hewitt Broth and incubated for 24 h at 37 °C. Overnight cultures were archived at −80 °C with 20% glycerol (v/v). This procedure ensured that species confirmation and subsequent MLST and susceptibility testing were conducted on the same isolate. An isolate is defined here as “a population of microbial cells in pure culture derived from a single colony on an isolation plate and characterized by identification to the species level”, in accordance with European and American guidelines59. In accordance with those same guidelines, the term “strain” is reserved for “An isolate or group of isolates exhibiting phenotypic and/or genotypic traits which are distinctive from those of other isolates of the same species”, such as the antimicrobial resistance (AMR) profile or sequence type (ST).Milk samples and enrichments (for swabs) were plated onto a chromogenic agar  and incubated for 24–48 hours at 37 °C. Potential GBS isolates (light pink to dark red colonies) were subcultured onto blood-esculin agar, using one colony per morphotype. Plates were aerobically incubated overnight at 37 °C. Esculin-negative colonies were diluted in 20 µL of distilled water. Ten microliters were used as template for GBS confirmation by PCR60) was used to characterize a single GBS isolate for each sample. Sequencing of standard MLST loci was conducted using an Illumina MiniSeq™  and allelic profiles and STs were assigned using the publicly available on-line Streptococcus agalactiae database in PubMLST61.High-throughput multi-locus sequence typing  or their common use on Colombian dairy farms . After one to two years of storage, 269 of the stored isolates from farmers and bovines could be recovered for MIC determination. After revival on blood-esculin agar  one colony per plate was diluted in ultra-pure water to 0.5 McFarland. Fifty microliters were then added to 5.5 mL of cation-adjusted Mueller Hinton Broth (MHB) and agitated, with 50 µL of MHB subsequently used as inoculum for the commercial plate. After 24 hours of incubation at 37 °C, plates were read using the Sensititre Vizion™ visualizer . A Streptococcus pneumoniae strain (ATCC49619) with known MIC was processed every work session. MIC breakpoints have not been defined for bovine GBS. Instead, data were interpreted based on breakpoints for human GBS as set by the Clinical Laboratory Standards Institute48 and the European Committee for Antimicrobial Susceptibility Testing62 (Supplementary Table Minimum inhibitory concentrations (MIC) of ampicillin, penicillin, erythromycin and tetracycline were determined by microdilution method, using Sensititre Mastitis Plate™ . Compounds were selected based on their relevance as drug of 1ermA, ermB and mefA)63 and tetracycline resistance genes 64. Primers are listed in Supplementary Table DNA was extracted from all GBS isolates used for resistance phenotyping with a Bacteria DNA Preparation Kit™ . DNA was used for PCR to detect macrolides resistance genes  was used for a comparative electronic analysis based upon related sequence types, eBURST (http://eburst.mLst.net/)65, and to create a population snapshot. Closely related STs were assigned to clusters or clonal complexes (CC), using sharing of five of seven alleles to define CCs. Antimicrobial resistance patterns were mapped onto CCs using the goeBURST algorithm in PHYLOViZ (http://www.phyloviz.net/). The association between STs and AMR phenotype or genotype was analyzed for STs with at least 10 isolates.Statistical analysis was conducted using Stata 15 . Descriptive statistics and frequency tables were generated. Unconditional associations between categorical variables were explored using Pearson χSupplementary Information.


Clostridium spp. and Staphylococcus spp. We also monitored the microbiome of different locations across three ambulances over time, and examined the dynamic community of microorganisms found in emergency medical service vehicles. Observed differences identified hot spots, which may require heightened monitoring and extensive cleaning. Through metagenomics analysis it is also possible to identify how microorganisms spread between patients and colonize an ambulance over time. The sequencing results aid in the development of practices to mitigate disease spread, while also providing a useful tool for outbreak prediction through ongoing analysis of the ambulance microbiome to identify new and emerging pathogens. Overall, this pipeline allows for the tracking and monitoring of pathogenic microorganisms of epidemiological interest, including those related to health-care associated infections.Pathogen monitoring, detection and removal are essential to public health and outbreak management. Systems are in place for monitoring the microbial load of hospitals and public health facilities with strategies to mitigate pathogen spread. However, no such strategies are in place for ambulances, which are tasked with transporting at-risk individuals in immunocompromised states. As standard culturing techniques require a laboratory setting, and are time consuming and labour intensive, our approach was designed to be portable, inexpensive and easy to use based on the MinION third-generation sequencing platform from Oxford Nanopore Technologies. We developed a transferable sampling-to-analysis pipeline to characterize the microbial community in emergency medical service vehicles. Our approach identified over sixty-eight organisms in ambulances to the genera level, with a proportion of these being connected with health-care associated infections, such as  Clostridium difficile and Staphylococcus aureus, highlight the importance of pathogen identification and monitoring of antibiotic resistance genes to reduce their impact on disease spread and to combat the rise of antibiotic resistance [Staphylococcus aureus (MRSA) died [The epidemic potential of disease causing microorganisms (pathogens) and the spread of antibiotic resistance continues to pose a serious threat to global health security . Examplesistance , 5. Withsistance . For exaSA) died . The cosIn response to the threat of HAIs and pathogen-associated disease, most hospitals have implemented pathogen monitoring and decontamination procedures to help prevent the spread and occurrence of HAIs, including standardized guidelines for hand hygiene and surface cleaning –11. In cet al. (2017) provides a more detailed summary of the literature on pathogen prevalence in EMS vehicles [Little is known about the epidemiological importance of EMS-patient contact on pathogen transmission between the community and hospitals, as well as the types of pathogens contained within ambulances. A limited number of studies from 1986 to 2016 have reported on the types of bacteria found in ground ambulances, but have not addressed the complete microbial environment or transmission of pathogens between the EMS, patients and community –25. In tvehicles .As an alternative to previous cell-culturing methods, a study by O’Hara and colleagues (2017) used a shotgun sequencing approach to profile the microbial community of ambulances within the United States . Their rde novo whole genome sequencing of bacteria [Salmonella outbreaks in hospitals [In this study, we aim to develop a detection and monitoring platform with the potential for on-site and rapid sequencing utilizing the MinION Nanopore sequencing device from Oxford Nanopore Technologies (ONT). This device offers several key advantages to previous DNA sequencing platforms; it is relatively inexpensive, small, and portable providing the potential to perform DNA sequencing in remote locations –32. The bacteria –36, virubacteria –39 and ebacteria –42. Whilbacteria –45. Addibacteria , 46, 47,bacteria , monitorospitals , identifospitals –52, and ospitals .We utilized the MinION sequencing technology to develop and demonstrate a complete workflow for characterizing microbial communities in ambulance vehicles. DNA samples recovered from surface swabs of three high-risk areas were obtained from three ambulances over a three-week period and subjected to partial 16S ribosomal RNA (rRNA) gene amplification and MinION DNA sequencing of amplicons for microbial identification. The 16S rRNA gene has been shown previously to be a useful molecular marker for bacterial identification, including for pathogens with clinical relevance . This seTo assess the variation of the microbial community over time and between EMS vehicles, three locations were sampled from three vehicles  once a week over a three-week period. A sampling control was included to mimic the sampling procedure without physically touching any surface, providing a baseline measurement of bacteria present on the swab or introduced during processing. The sampling workflow  was desi2 finger monitor, (ii) rear cab interior door handle and (iii) bottom of the soft kits. These locations were identified by taking into consideration concerns expressed by EMS workers, locations sampled in previous work [2 finger monitor is frequently used and comes into direct skin contact with patients, providing specific information on patient contamination. The rear cab interior door handle is used by emergency personnel when transferring patients from the field into the vehicle and out of the vehicle to the hospital, providing information on EMS worker contamination. Lastly, the soft kits are transported on-site to each emergency call and are exposed to diverse environments, therefore it is representative of environmental contamination.Three high-risk contamination areas were chosen to be sampled: (i) SPOous work , 21, 24,2 area of each location was swiped using Grade I 15 mm diameter Whatman filter paper  soaked in 70% ethanol. Samples were stored in 500 μL of 70% ethanol at -20°C until further processing. Sampling kits were prepared under sterile conditions prior to sampling. Nitrile gloves and sterile tweezers were used when handling filter paper to minimize external sample contamination. Each week an environmental control was included to identify bacteria introduced after sampling or during subsequent processing, where the filter paper was handled to mimic the sampling procedure but did not physically swipe a location.Samples were acquired from each location under sterile conditions. A 4 cmTo extract DNA from the filter paper, a protocol from  was adapU341F (5' - CCTACGGGRSGCAGCAG - 3') and U1053R (5' - CTGACGRCRGCCATGC – 3’) [in silico as reliable and sensitive biomarkers for taxonomic classification and phylogenetic studies [Specific universal primers, GC – 3’) , were us studies . ~10 ng Pseudomonas aeruginosa genomic DNA was included as a second internal control to validate amplification accuracy.For each week of samples, barcoding kit 1  and sequencing kit SQK-NSK007 (Oxford Nanopore Technologies) were used to generate the DNA library of pooled samples for sequencing. Less than 1,000 ng of purified amplicon DNA was end repaired and dA-tailed using the NEBNext Ultra II End-Repair/dA-tailing Module  incubating at 20°C for 5 min and 65°C for 5 min, followed by AMPure XP bead  cleanup per the manufacturer’s instructions. Approximately 10–30 ng/μL was recovered. 20 μL of Adaptor Mix  was added to 30 μL of the dA-tailed DNA. From the dA-tailed/Adaptor Mix, 5 μL was removed, added to 5 μL of Blunt/TA ligase master mix (NEB) and incubated at room temperature for 10 min, followed by EZ-10 spin column purification (BioBasic). For a 50 μL PCR barcoding reaction, 1 μL of barcode from 1 to 12  was added to 24 μL of ligated product and amplified using Phusion High Fidelity polymerase (Thermo Scientific). PCR barcoding conditions were as follows: 98°C for 2 min; 25 cycles of 98°C for 15s, 64°C for 15s and 72°C for 1 min; followed by 72°C for 2 min. For each week of samples, the barcoded DNA was pooled together in the appropriate ratios to prepare 1 μg of DNA in 45 μL of nuclease-free water. The pooled barcoded DNA was end-repaired and dA-tailed using the NEBNext Ultra II End-Repair/dA-tailing Module (NEB) at 20°C for 5 minutes and 65°C for 5 min, followed by AMPure XP bead (Beckman Coulter Inc.) cleanup following the aforementioned protocol. 8 μL of nuclease free water, 10 μL of Adaptor mix, 2 μL of Hairpin HP Adaptor (HPA), and 50 μL of Blunt/TA ligase master mix was added to the dA-tailed pooled DNA and incubated at room temperature for 10 min. Then 1 μL of hairpin tether (HPT) was added and incubated at room temperature for 10 min. The adapted, tethered DNA was purified using MyOne C1 beads (Thermo Scientific) and eluted in 25 μL of elution buffer, while incubating at 37°C for 10 min. 5 μL of internal control DNA (DNA CS) was added to the sampling control for each sequencing preparation prior to barcoding, and to the pooled samples after barcoding to assess sequencing accuracy. An additional sample containing An R9.4 SpotON MinION flowcell was used and primed with Nanopore Priming Buffer in a stepwise fashion, loading 800μL, and after 10 min, loading an additional 200μL. Platform quality control (QC) was performed prior to loading of the priming buffer to ensure proper functioning of the MinION device. The library was then loaded onto the flowcell in a dropwise fashion, allowing each drop to enter into the flowcell before loading the next. The MinION was connected to the analysis computer and run using MinKNOW Version 1.4.2 using the SQK-LSK208 protocol for 2D barcoded reads. The sequencing reaction proceeded for between 4 and 24 hours at which point the flowcell was washed using the provided Wash Buffer. A subsequent library was loaded onto the flowcell thereafter; a maximum of three libraries were sequenced on each flowcell. Raw reads were obtained as fast5 files and subsequently analyzed. An identical procedure was followed for an R9 MinION flowcell prior to the R9.4 SpotON iteration becoming available.poretools [porechop [Nanostat and NanoPlot [The bioinformatics pipeline is summarized in oretools . Best quporechop  with a lporechop . The reaNanoPlot .Assigning taxonomy to each individual reads was done using QIIME 1.9.2  using thLambda phage internal controls reads to a Lambda phage genome reference sequence and calculating percentage identity and coverage. BWA MEM v.189 [x ont2d option. The outputs were converted to sam files and processed with samtools [Pseudomonas aeruginosa internal control reads. Taxonomic assignment was done using the RDP classifier [Quality of the read was assessed by aligning EM v.189  was usedsamtools . A similassifier  against assifier .Phylogenetic trees were obtained using assigned taxa and the PhyloT online program to generate trees in Newick format. The Newick trees were then uploaded to iTOL (Interactive Tree of Life) and phylogenies were generated using phylogenetic information and molecular data from NCBI taxonomic databases for organisms obtained in sequencing runs over 3 sampling weeks.A total of five Nanopore DNA sequencing runs were performed. Run 1 and Run 2 included Week 1 samples taken from Ambulance B; the same set of prepared samples was used for both sequencing runs to assess reproducibility. Run 3 included the remaining samples from Week 1 (Ambulance A and C), while Run 4 and 5 included all samples  from Week 2 and Week 3, respectively. The raw reads generated for the five runs ranged from 1,369 to 79,044 reads and consisted of one dimensional (1D) forward, 1D reverse and 2D reads . The obsLambda phage sequence using BWA MEM v.189 [Pseudomonas aeruginosa genomic DNA was amplified, barcoded and sequenced for each run  mapped to the reference EM v.189  S2 Fig)Lambda phEM v.189 , 68–70. each run . GreaterFor Week 1, approximately 38% of the filtered reads were assigned to the order and family classification, 5% to genus and 3% to species. For Week 2 and 3, a greater number of filtered reads were assigned to each taxonomy with 65% of the reads from Week 2 and 76% of the reads from Week 3 assigned to the order level, and 55% of the reads from Week 2 and 72% of the reads from Week 3 assigned to the family level. Approximately 25% of the reads from Week 2 and 3 were assigned to the genus level and 20% assigned to the species level. Of the total filtered reads, 90.1% were classified as bacteria, 0.7% as archaea and 9.2% as unclassified, with the latter referring to sequences that were not mapped to any reference genome.Clostridium spp. and Staphylococcus spp. were detected, which are the leading cause of HAIs [Campylobacter, the leading cause of bacterial gastroenteritis in the developed world [Listeria, which results in a fatal bacterial illness [Shigella, a highly contagious bacteria that causes the infectious disease shigellosis [A total of thirteen bacterial genera containing important pathogenic species were detected in ambulances . Of part of HAIs . Other ned world ; Listeri illness , 74; andgellosis , 76.Actinobacillus, Campylobacter, Capnocytophaga, Clostridium, Haemophilus, Listeria, Pasteurella, Prevotella, Roseomonas, Shigella, Staphylococcus, Stenotrophomonas, and Streptococcus (indicated by the red star). Approximately 4% of the reads assigned to the genera level are bacteria with pathogenic species. Although representing a relatively minor component of the sequencing reads, the identification of any bacteria with pathogenic species is an important consideration when transporting and treating immunocompromised patients.A phylogenetic tree was generated using the online program PhyloT . Sixty-eThe corresponding bar graph shows the variation in relative abundance of bacterial genera with pathogenic species detected at each sampling location  and in eCandidatus Carsonella”. This organism is given the “Candidatus” designation since it has not been cultured in the laboratory, and thus would not have been detected using traditional cell-culturing methods [Lactobacillus, common to the human microbiome, as well as Bacillus, common to the urban built environment [Our analysis identified a “yet-to-be-cultured” genera: “ methods . We deteironment . Several2-fold change in each taxonomic unit from Week 2 and 3 compared to Week 1 samples. A z-score was calculated by taking the difference between the total normalized reads assigned to a given taxon at Week 2 or 3 and Week 1 and dividing it by the square root of the sum of the reads assigned to a given taxon for the same weeks. A p value is then calculated from the assigned z-score, and if <0.05, the difference is considered significant [Burkholderia, which had a fold change increase of 9.72 and 9.87 in Week 2 and 3 relative to Week 1. For other microorganisms, their abundance decreased over time, such as members of the family Caulobacteraceae, which had a fold change decrease of 2.36 and 1.13 in Week 2 and 3 relative to Week 1. This technique provides a tool for monitoring the dynamics of a microbiome over time, allowing for trends to be monitored and recorded. Ultimately, this enables a better understanding of pathogen transmission, cleaning effectiveness and identifying how the microbiome composition contributes to microbial spread.The temporal change in relative abundance of the microbial composition in relation to Week 1 is shown using heat maps at each taxonomic level . The heanificant . This alFor rapid identification of the microbial community, the MinION was used, which is advantageous due to its small size, portability, and rapidly developing sequencing technology. Sample preparation and sequencing using the MinION is accelerated with each iteration of the platform. For example, recently developed kits and devices, such as the ONT VolTRAX and SmidgION, simplify and speed up sample preparation, barcoding, and analysis, further highlighting the potential for on-site sequencing of EMS vehicles. Recently commercialized flow cells can now process up to 450 bases of DNA per second and per channel, allowing for fast data acquisition. Additionally, the sequencing platform is scalable with use of the PromethION benchtop sequencer which allows for many flow cells to be run in parallel yielding an increasingly more comprehensive picture of the microbial community. Studies have also shown the use of ONT’s sequencing devices to identify antibiotic resistance genes, as well as specific viruses, by using alternative primers during amplification showing the diverse types of organisms that can be identified using this platform , 49.While the sequencing approach used is indeed streamlined and portable, there are elements of the pipeline that could be improved. The first of these concerns is the number of very long  reads that were obtained from our sequencing runs, which were filtered out. These are likely a result of improper adapter ligation and creation of chimeric species of the amplified PCR products. This type of ligation artifact has been improved in current iterations of ONT sequencing kits using ligase free adapter addition (16S Barcoding Kit SQk-RAB204). Second, although our approach was reproducible, as results obtained in our first run were consistent with the results of our second run using newly prepared samples, subsequent runs using the same flow cell exhibited a reduced read number. This indicates a limitation to how many times a flow cell can be re-used. The rapid development of the ONT system also necessitates flexibility in sample preparation and sequencing. As the sequencing runs are often carried out on separate flow cells that may not behave in the same way, further optimization of the reproducibility and quality of reads between flow cells should be considered. The Flongle, a new device from ONT ideal for small-scale, rapid tests, provides real-time sequencing information and can be used to assess the quality of a run before performing a large-scale experiment. This device provides a promising approach for improving read quality and for real-time, on-site assessment.Candidatus Carsonella”, which has not been cultured further demonstrating our ability to detect novel bacteria. 16S rRNA genes were chosen for bacterial identification due to their essentiality in prokaryotes and common use in taxonomic identification [Haemophilis, Staphylococcus, and Stenotrophomonas [Clostridium and Streptococcus, two genera commonly associated with HAIs, that were not reported in the US based study.Analysis of the sequencing data resulted in the identification of sixty-eight unique organisms at the genus level. Our approach provided a new and more comprehensive picture of the microbial community in ambulances in comparison to cell-culturing techniques used in previous studies, which focused on the identification of specific pathogens of interest, such as MRSA , 21, 22.fication , 95. Thefication . Regardification , we detephomonas . AdditioThe bacterial community was shown to vary both between locations and over time. Potential pathogenic bacteria were located primarily on the finger monitor in direct contact with patients, secondarily on the door handle in direct contact with the EMS workers, and also identified on the soft kits in contact with the environment. Differences were also observed when comparing between ambulances and, in comparison to Week 1, select organisms were decreased or enriched, suggesting that the cleaning products and cycle used were effective for some bacteria but not all, and that more stringent disinfection might be required. The observed differences in the microbial community may also be impacted by the frequency each vehicle and piece of equipment is used, as well as the type of emergency responded to, which would vary from week to week.While it is likely not possible to ensure a workspace is sterile for the duration of the workday, our work shows that methods to monitor vehicle cleanliness can maintain proper hygiene. Building on the described approach, emergency medical personnel will be able to rapidly analyze the microbial load in less than 24 hours and modify their cleaning practices as necessary. This will reduce unnecessary cleaning and costs, while in general making targeted cleaning much easier. Additionally, identification of select species within a few minutes can be introduced using paper-based tests designed to detect pathogens of interest previously identified in the sequencing runs . EmerginThis proof of principle study successfully identified variations in the EMS microbiome and the presence of bacterial genera that contain important pathogenic species. As a result of re-using flow cells, leading to a low sequencing read count, the identification of bacteria was limited to the genus level. Sequencing is expected to improve with the availability of new flow cell chemistry continually being upgraded to provide better basecalling accuracy. Maximizing the amount of DNA extracted will further improve the accuracy of identifying and detecting bacteria present. Previous work using a mock microbial community achieved species-level resolution with the MinION , suggestBy amplification of the 16S rRNA gene, we restricted identification in this study to bacteria excluding the identification of viruses or fungi. To broaden the detection, less biased methods such as Multiple Displacement Amplification (MDA)  to ampliOther efforts to streamline our pipeline include implementation of new kits and devices available from ONT (such as “The Rapid Sequencing Kit” and the VolTRAX device), which would allow for simplified sample acquisition, preparation and sequencing. On this background, the described MinION sequencing pipeline provides a tool to detect variations in the EMS microbiome, which has great utility for assessing disease transmission and maintenance within EMS. Improvements addressing the respective design limitations will further extend the utility of the reported approach.2 finger monitor. For the three locations sampled, we also provided our local EMS with the necessary data to identify cleaning products that effectively eliminate the pathogens detected. Since many of the potentially pathogenic bacteria were found on the finger monitor, an area of high contact by patients during transport and a difficult area to clean, there is a high likelihood that ambulances are indeed vehicles for pathogens into hospitals and vice versa. We recommend that a more detailed analysis and tracking of pathogen spread between these environments should be implemented to mitigate this potential health hazard. Large-scale analysis of the sequencing data generated over a longer period will also enable epidemiological studies and the identification of new emerging pathogens, improving our ability to predict and mitigate pathogen outbreaks.Our pipeline provides a detection and monitoring platform giving EMS personnel a means of rapid feedback and evaluation of best cleaning practices within ambulances, and is a step towards understanding the ambulance microbiome. Through our initial study, we demonstrate the ability to identify potential hot spots that require more attention and monitoring throughout cleaning cycles, such as the SPOS1 Fig(A) Run 1. (B) Run 2. (C) Run 3. (D) Run 4. (E) Run 5.(PDF)Click here for additional data file.S2 Fig(A) Run 1 barcoded. (B) Run 2 barcoded. (C) Run 4 barcoded. (D) Run 1 unbarcoded. (E) Run 2 unbarcoded. (F) Run 4 unbarcoded.(PDF)Click here for additional data file.S3 Fig(A) Run 1 barcoded. (B) Run 2 barcoded. (C) Run 4 barcoded. (D) Run 1 unbarcoded. (E) Run 2 unbarcoded. (F) Run 4 unbarcoded.(PDF)Click here for additional data file.S4 FigRepresented as the average normalized sequencing reads for bacterial genera detected over a three-week period from (A) all ambulances for each sampling location and (B) from all sampling locations for each ambulance. (C) Average normalized sequencing reads for bacterial genera detected in all three ambulances and sampling locations for each week.(PDF)Click here for additional data file.


Biliopancreatic diversion is a surgical procedure that causes weight loss viavolume restriction and malabsorption. It is now rarely performed due to the riskof severe nutritional deficiencies including vitamin A. We report a case ofsevere vitamin A deficiency due to malabsorption from a biliopancreaticdiversion procedure for weight loss. By the time the patient presented to ourdepartment, she had developed blindness refractory to parenteral vitamin Atreatment. A unique feature of her case is the development of a rash withvitamin A injections. This reaction has only been reported in one case series of3 patients in the published literature. Her case highlights the importance ofvitamin deficiency screening in patients after bariatric surgery, and her skinreaction to the injections is a unique side effect that is not frequentlyobserved. It is unique in that it can potentially cure numerous diseases includingdiabetes, fatty liver disease, hypertension, sleep apnea, and many more. The variousprocedures involve volume restriction and/or nutrient malabsorption to achieve andsustain weight loss. One of the operations known as the biliopancreatic diversion(BPD) or the Scopinaro procedure  is now r2 The earliest and most common symptom of vitamin A deficiency is night blindness.3 Vitamin A deficiency can also cause other ophthalmologic disorders includingxeropthalmia, keratomalacia, retinopathy, and blindness.4 Early stages of vitamin Adeficiency may be reversed by oral or parenteral supplementation,5 but later stages can havevarying response to vitamin A supplementation.7A study of 376 patients who had undergone BPD with or without duodenal switch foundthat 1 year after surgery vitamin A levels were low in 52% of patients and after 4years this increased to 69% of patients, despite compliance to vitamin supplementation.A 68-year-old female with a history of BPD surgery in 1987 was referred for treatmentof vitamin A deficiency. Her symptoms began with poor vision at nighttime buteventually progressed to blindness in her left eye. At the time of her diagnosis in2013, her vitamin A level was 4 µg/dL , and she already hadfindings of retinal degeneration. She was initially treated with oral vitamin Asupplementation of 100,000 IU daily; however, levels did not normalize consistently,and her retinopathy progressed. On referral to our division, she was started onintramuscular vitamin A injections in June 2018. Prior to starting intramuscularvitamin A, her vitamin A level was 20 µg/dL .After receiving the first injection, 12 to 15 hours later she developed redness andscaling at site of injection. The second injection caused a similar reaction. Rashesseen at the injection sites are shown in 1, B12, D, and folate as well as zinc, copper, and iron studies.8 Rash in the setting of vitamin A injections has only been reported in onecase series of 3 patients.9 In this case series, patients underwent skin testing and only reacted topolysorbate 80, an emulsifier found in injectable vitamin A palmitate, but not toretinol palmitate or other vitamin A injection constituents .9 Polysorbate 80 is also used in other parental medications and some vaccines.9 Rash following vitamin A palmitate is an uncommon side effect, but it shouldbe closely monitored for progressive allergic reaction and patients should be awareof potential reactions to vaccines and other medications that contain similarcomponents.Although less frequently performed today, BPD can lead to severe nutrientdeficiencies. In our patient’s case, treatment for vitamin A deficiency was notinitiated until after the patient had irreversible retinopathy. Therefore, it isimportant to routinely screen patients for nutrient deficiencies before theydevelop. The American Society for Metabolic and Bariatric Surgery nutritionalguidelines recommends screening for vitamin and mineral deficiencies every 3 to 6months during the first 2 years and annually thereafter. For patients who have had aRoux-en-Y gastric bypass or BPD, this includes measuring serum levels of vitamins A,B


Diggings were created at a rate of around 3113 diggings ha−1 yr−1, disturbing 6.5% of the forest floor and displacing 7.1 m3 ha−1 of soil annually. Most diggings were made by eastern bettongs (Bettongia gaimardi) and short-beaked echidnas (Tachyglossus aculeatus). Many (approx. 30%) fresh diggings consisted of re-excavations of old diggings. Novel diggings displaced 5 m3 ha yr−1 of soil. Diggings acted as traps for organic matter and sites for the formation of new soil, which had higher fertility and moisture content and lower hardness than undisturbed topsoil. These effects on soil fertility and structure were strongest in habitats with dry and poor soil. Creation of fine-scaled heterogeneity by mammals, and amelioration of dry and infertile soil, is a valuable ecosystem service that could be restored by reintroduction of digging mammals to habitats from which they have declined or gone extinct.Many small- and medium-sized mammals dig for their food. This activity potentially affects soil condition and fertility. Digging is well developed especially in Australian mammals, many of which have recently become rare or extinct. We measured the effects of digging by mammals on soil in a Tasmanian temperate dry sclerophyll forest with an intact mammal community. The density of diggings was 5812 ha Bioturb of soil .Sus scrofa) may disturb 27–54% of the forest floor [Taxidea taxus) displace soil at 5.1 t ha−1 yr−1 [Hystrix africaeaustralis make up to 3463 diggings ha−1 yr−1 and displace 1.6 m3 ha−1 yr−1 [Many mammals forage by digging, creating excavations that are smaller and more ephemeral than burrows, with effects that are likely to be subtle compared with burrowing. Nonetheless, diggings can occur at high density –13 and tst floor ; Americaa−1 yr−1 ; Cape poa−1 yr−1 . These aTachyglossus aculeatus) and several species of rat-kangaroos (Potoroidae) and bandicoots (Peramelidae), which dig for patchily distributed resources such as subterranean invertebrates or fungi. Foraging by these animals creates many intermediate-sized pits, which gradually refill with soil and leaf litter. This cycle of pit excavation and soil re-formation may change soil structure, microtopography, and the structure and biomass of the litter layer; it generates fine-scaled habitat heterogeneity [Vulpes vulpes [In Australia, many medium-sized mammals forage by digging . These mogeneity . Howevers vulpes ,17. Invas vulpes ,19; thisBettongia gaimardi) and long-nosed potoroo (Potorous tridactylus), which feed mainly on subterranean fungi [Perameles gunnii) and southern brown bandicoot (Isoodon obesulus), which dig for invertebrates and fungi [Tasmania provides an opportunity to establish the ecological effects of digging mammals because, in the absence of the red fox, the community of medium-sized mammals is largely intact, and most species remain common throughout their original ranges. The mammalian fauna of Tasmania includes five species that weigh between 1 and 5 kg and feed almost entirely by digging: the eastern bettong  near the town of Colebrook in southeast Tasmania . The topography of the area is characterized by parallel ridges divided by often steep-sided gullies, over an elevation range from 200 to 400 m. The eastern part of the area is underlain by Permian silty sandstone (with thin sandy loam and yellowish clayey soils), with some quartz siltstone  and occasional dolerite deposits and pebble and mud conglomerates. The western part is underlain by Triassic quartz sandstone (thin sandy loam and yellow/orange clayey soils), shale  with occasional siltstone, mudstone and sandstone deposits. Average rainfall is 630 mm, distributed evenly throughout the year. Dominant tree species are (1)Eucalyptus amygdalina grassy forest: canopy dominated by E. amygdalina with understorey dominated by the tussock-forming Lomandra longifolia and grasses;(2)Eucalyptus amygdalina shrubby forest: canopy dominated by E. amygdalina with understorey of tall shrubs and sedges;(3)Eucalyptus obliqua shrubby forest: canopy dominated by E. obliqua with some E. amygdalina, and understorey dominated by tall wet and dry sclerophyll shrubs with a mid-storey of Exocarpos cupressiformis and Acacia species;(4)Eucalyptus tenuiramis heathy forest: canopy dominated by E. tenuiramis with some E. obliqua and an understorey of low shrubs with patches of bracken (Pteridium esculentum) and rare taller shrubs.The two E. amygdalina habitat types were rare in the study area, so we combined them into a single category of E. amygdalina forest. This forest type occurred on the more fertile soils of the area (see Results). Both E. obliqua forest and E. tenuiramis forest occurred on less fertile soils, and otherwise represented a gradient from wetter (E. obliqua) to drier (E. tenuiramis) habitats. Medium-sized mammals occurring in the area included the short-beaked echidna, southern brown bandicoot, eastern barred bandicoot, eastern bettong and long-nosed potoroo. Estimating population densities for these species was beyond the scope of this study, but a study of the eastern bettong population of the area in 2016 and 2017 obtained a density estimate of 11.5 individuals per km2 .We distinguished four habitat types using a habitat classification developed for the larger region by the Tasmanian Forest Practices Authority , as foll2.2.E. amygdalina forest, 19 in E. obliqua forest and 14 in E. tenuiramus forest. The first 20 plots were 20 × 5 m. Two further plots were 10 × 5 m and the remaining 18 were 5 × 5 m. Resampling within large plots showed that plot area did not affect estimates of digging density ; therefore, all plots were treated in the same way in the analysis. In each plot, all diggings were marked using a short wire peg to which a length of flagging tape was tied, and identified by a number code written on the tape, and locations of diggings within the plot were mapped. Each digging was assigned a value for relative age on a ten-point scale: zero value was given for freshly dug diggings  and nine was given for diggings that could no longer be distinguished from the surrounding soil surface but had been identified and marked earlier in the study. Precise identification, mapping and age-indexing of diggings allowed us to identify the appearance of new diggings and to distinguish cases of re-excavation of old diggings when plots were re-surveyed. The species responsible for excavating the digging was identified where possible by careful examination of digging direction and style, associated tracks and signs [We located sample plots using a map grid and a randomly generated set of coordinates. Twenty sample plots were in the western section of the Conservation Area, spread over 200 ha, and 20 in the eastern section spread over 600 ha. The number of plots in the three habitat types reflected the area of each: seven plots in nd signs , and by Each digging consisted of an excavated pit and associated spoil heap. We measured the long axis of each digging and the short axis perpendicular to it at its widest point and used these dimensions to calculate the surface area of an imaginary rectangle surrounding the disturbance. The percentage of this area that was disturbed was then estimated, to derive a value for the surface area of the disturbance. The dimensions of the pit and spoil heap were measured individually using the same method. Pit depth was also measured, and minimum pit volume was calculated by assuming that the space was an inverted elliptical cone, such thatMeasurements were repeated four times over one year (once every three months) in each study site; October 2012–November 2013 in the west and February 2013–March 2014 in the east.2 area. The chemical characteristics (https://www.csbp-fertilisers.com.au/agronomy/lab) using standard methods. Soil penetration resistance was measured using a penetrometer top to determine penetrometer force (Kgf cm3). Thirty random measurements within each plot of neighbouring (within 2 m) triplets of (i) soil in pits, (ii) spoil heaps and (iii) undisturbed topsoil, were made. The same process was repeated using a moisture probe to estimate soil moisture (% by weight). All measurements of soil moisture were taken within a single 4 day period and the number of days elapsed since last rainfall was recorded for each measurement day.Soil samples were collected just outside (within 5 m) all plots so as not to disturb other sampling. Samples were taken from (i) soil formed in the pit of older diggings in which litter trapped by the pit had at least partially decomposed into soil, (ii) the freshly excavated spoil heaps of newer diggings and (iii) undisturbed topsoil. Each soil sample  from each plot comprised a subsample of 30 homogenized soil samples. The maximum diameter for pits selected for soil sampling was 15–20 cm. The samples were taken using a 2.5 inch soil corer. Triplets of samples  were taken within a 1 meristics  of each 2.3.We simplified our data on soil fertility by using a principal components analysis to identify major trends in variation among the different elemental concentrations as well as conductivity and pH, and to extract a single variable that represented correlated variation in many of these variables and was interpretable as a measure of soil fertility. We then used a mixed-effects regression model to test for effects of digging on this measure of soil fertility, by contrasting measurements on soil samples from three digging conditions: soil taken from the foraging pits themselves, from spoil heaps beside foraging pits and from adjacent areas undisturbed by digging mammals. Because these samples were collected as triplets at each plot, we entered plot ID as a random effect to control for non-independence of soil samples among the three conditions. The model also included the effects of the three main habitat types to investigate the additive and interactive effects of habitat as well as digging treatment on soil fertility. Thus, the model took the form: Soil Fertility PC1 ∼ Habitat Type + Digging Treatment + Habitat Type × Digging Treatment + (1|Plot ID).p-values profiled using Markov chain Monte Carlo (MCMC) simulations. Because of the non-independence of the soil samples in relation to digging condition, we additionally evaluated the amount of variation explained by plot ID in the model by dividing the variance explained by the random effect (plot ID) with the sum of the variance from the random and fixed effects.We used the same modelling approach to investigate the relationships of digging and habitat to soil hardness measured by penetrometer force and to soil moisture, except that for these variables we included number of days since most recent rainfall as an additional predictor covariate. This was necessary because while all samples were collected at the same time of year, they were collected on different days, and differences in time since rain could affect soil hardness and moisture content. Thus, these models took the form: Penetrometer Force or Soil Moisture Content ∼ Days Since Rainfall + Habitat Type + Digging Treatment + Habitat Type × Digging Treatment + (1|Plot ID). All models were built using a Bayesian framework in R  using th3.3.1.−1, ranging from 875 to 12 450 ha−1 at individual plots. Of all diggings, 41% were made by eastern bettongs, 42% by short-beaked echidnas, 3% by bandicoots  and 14% were unidentified. Density of eastern bettong diggings varied among habitat types, being generally low (but highly variable among plots) in E. amygdalina forest, and consistently high in the other two habitat types, especially E. tenuiramis forest. Density of echidna diggings was similar among the three major habitat types (The mean standing density of diggings from four surveys of 40 plots was 5812 (±909) diggings haat types .Figure 2, of which approximately 40% was the excavated pit and 60% the spoil heap. The average pit was roughly an elliptical cone or semi-ellipsoid with width 18.29 (±0.44) cm, length 15.45 (±0.37) cm, depth 14.40 (±0.50) cm and volume 0.0021 (±0.0007) m3. Diggings made by bettongs were generally larger than those of echidnas, disturbing nearly twice the surface area (on average 0.26 m2 compared to 0.14 m2) and with proportionally larger spoil heaps (63% of the disturbance compared to 55%) and pits . Bandicoot diggings were 0.00075 m3 and unknown diggings averaged 0.0016 m3.The mean area disturbed by each digging was 0.19 (±0.02) m3 of excavated soil per hectare. Diggings disturbed 6.5% (±2.1) of the forest floor annually, displacing 7.1 (3.7–11.6) m3 ha−1 yr−1 of soil. New diggings were created at a rate of 2377 (±499) ha−1 yr−1, and diggings decayed to non-detectability at 3113 (±798) ha−1 yr−1, except that some old diggings were re-excavated (1018 ± 256 ha−1 yr−1). Excluding re-excavations, new digging disturbed between 3.2 and 6.0% (mean 4.5%) of the forest floor annually, excavating 5.0 (2.6–8.1) m3 ha−1 yr−1 of the previously undisturbed soil. Of 196 re-excavations identified to species, 131 were by echidnas and 65 by bettongs; 137 were originally dug by bettongs and 59 by echidnas. Re-excavated diggings had average length (21.27 cm), width (19.31 cm), depth (15.83 cm) and volume (0.0036 m3), close to the average size of bettong diggings, and the largest echidna diggings.On average, diggings for all species affected 11% (±3) of the forest floor, consisting of pits 4.4% (±1.2) and spoil heaps 6.6% (±1.8), and represented 12.2 (±4.06) m3.2.The first PC axis accounted for 46.98% of soil-composition data. Scores on PC1 were strongly correlated with conductivity and organic carbon concentration, as well as with concentrations of elements such as phosphorus, potassium, sulfur, copper, zinc, calcium, magnesium, sodium and boron, and were positively but less strongly correlated with nitrogen . We intea). Eucalyptus tenuiramis and E. obliqua forest had lower soil fertility than E. amygdalina forest. Fertility was higher in the soil samples taken from foraging pits than in the samples of undisturbed soil, and lower in the samples from spoil heaps, but these differences were affected by interactions with habitat. Fertility of soil from foraging pits was elevated with respect to undisturbed soil in the E. obliqua and E. tenuiramis forest habitats, which were both of generally low fertility, while the fertility of soil from spoil heaps in those habitats did not differ from that of undisturbed soil (a). Overall, plot ID explained 54.2% (CI 35.4–72.9) of variation in soil fertility.Values of PC1 varied among habitats and digging treatments a. Eucalybed soil a. Overalb). Plot ID explained a negligible amount of variation in penetration resistance (less than 0.01%). Days since rain tended to have a negative effect on soil moisture content (c), and soil moisture was also lower in E. tenuiramis than E. amygdalina forest. Soil moisture was strongly affected by digging treatment, being high in soil from foraging pits and low in soil from spoil heaps, relative to undug soil in all habitats. Overall, there was a strong effect of plot ID on soil moisture of 77.2% (CI 60.9–86.5).Soil penetration resistance (representing hardness) was not significantly related to habitat or days since rainfall but was strongly affected by the digging treatment: soil from pits and spoil heaps was much less resistant than undisturbed soil b. Plot I content c, and so4.Digging by mammals had significant effects on soil. The digging of foraging pits, followed by the passive infilling of those pits with mixed soil and litter, created patches of loose and nutrient-rich soil that retained higher moisture content than surrounding soil. Spoil heaps thrown out of foraging pits formed patches of bare ground. Digging therefore created a fine-scaled patchwork of differing ground cover and edaphic conditions. Given the high rate at which new diggings were excavated and old diggings disappeared as a result of infilling, this patchwork was dynamic and produced a shifting pattern of fine-scaled disturbance across the forest floor. The effects of digging ameliorated other environmental stresses, because the localized increase in soil fertility due to digging was greatest in habitats of lower fertility and increases in soil moisture were greatest in habitats where soil moisture was otherwise low.Bettongia penicillata excavated 5000–16 000 diggings, and displaced an estimated 1.6–4.0 tonnes of soil, per ha per year [Macrotis lagotis and burrowing bettongs Bettongia lesueur in a large predator-free exclosure in an arid environment produced around 1100 diggings ha−1 representing 4.29 tonnes ha−1 of displaced soil [−1, depending on habitat [3 of soil each year [This was the first study of the effects of bioturbation by native mammals in a temperate dry sclerophyll forest environment. It was also the first such study in Tasmania, where all of the original native digging mammals are extant. The few measurements of diggings by medium-sized native mammals that have been made in other parts of Australia suggest that digging activity can be comparable to values recorded here and may generally be higher in woodland environments than in arid shrublands and grasslands. However, these high values are found only where populations of critical-weight-range marsupials are wholly or partially protected by control of invasive predators. In dry woodland in Western Australia, where red foxes are controlled by extensive poison baiting, a relict population of the critically endangered woylie per year . These dced soil . In a se habitat , consideach year , suggestEucalyptus tenuiramis and Acacia dealbata [The eastern bettong and short-beaked echidna were responsible for most of the digging activity in our study area. Digging by bettongs was variable in space, being greatest in drier forests with open heath understorey vegetation. This result is consistent with previous research that found positive relationships between density of bettong diggings and characteristics of dry heathy forest, especially extent of bare ground and stem densities of dealbata . In wettdealbata ,30. AlsoEchidna digging activity was similar across different soil and vegetation types in our study area, suggesting that echidnas did not favour particular environments for foraging. This may be because their food  is abundant and widespread in Tasmanian forests and woodlands. Elsewhere, variation in densities of diggings by echidnas has been linked only to the availability of shelters .Our results support other studies that have found strong effects of digging by medium-sized Australian mammals on soil characteristics –34. ThesForaging pits act as traps that accumulate mobile debris as well as moisture. Our data suggest that pits were quite efficient traps, given that they were typically infilled over periods of one or two years. We did not study the process of accumulation of material in foraging pits, but two distinct mechanisms appear to be involved. First, material that is deposited directly into pits as part of the general fall of leaves, twigs and bark from the shrub and tree company is protected from wind and water and so is unlikely to be secondarily displaced. Second, material that is moved laterally across the surrounding soil surface by wind or water may come to rest in pits, preventing further displacement; such material would include fine soil particles and microfauna as well as litter. More subtly, spoil heaps probably also impede lateral movement of these soil and organic fractions, which accumulate in drifts where their directional movement encounters spoil heaps. The effect of these processes is to cause a redistribution of such material, and to concentrate it in microsites disturbed by mammal diggings, especially in the pits themselves. Perhaps more importantly, diggings probably reduce the total loss of such material from areas—on sloping ground, for example—that would otherwise be susceptible to mass erosion of mobile soil and organic fractions by wind and water. Consequently, mammalian diggings not only cause fine-scaled redistribution and concentration of soil particles and organic matter, but may also provide insurance against the loss of such material at broader scales.A general result of the processes described above is that soil nutrients are retained and concentrated in micro-sites disturbed by mammal diggings. Soil that formed in foraging pits had higher nutrient availability than undisturbed soil. The difference was large, of a magnitude that approached differences in soil fertility across major habitat contrasts in our study area. Other studies have also reported elevated soil nutrient status in foraging pits created by Australian mammals –34,38,39Castor canadensis [We found that existing diggings were often re-excavated. This could be because foraging mammals dig in the sites that are most productive for the foods they seek , and they are therefore more likely to re-dig in the same sites. However, the high frequency of cases of previous diggings being precisely re-dug suggests another possibility: that initial disturbance by mammals promotes replenishment of the same resources, so that sites become more productive once they have been disturbed, and the animals responsible for the initial disturbance preferentially return to harvest those replenishing resources. If this speculation is correct, re-excavation of diggings could represent a form of niche construction by digging mammals, in which their activities cause a shift in resource productivity and hence an increase in carrying capacity for those same animals. Positive resource utilization–production feedback is a characteristic of some other ecosystem engineers, such as the North American beaver nadensis . An alteThe eastern bettong is extinct on mainland Australia, and persists only in Tasmania, except for a recently established mainland population in a fenced reserve . The two


Detailed analysis of the metabolome revealed several candidate metabolites and perturbed phenylalanine, arginine, proline and tryptophan metabolisms as putative biomarkers. A pilot retrospective analysis of longitudinal trajectories of a BC metabolic biomarkers during post TURBT surveillance was carried out and the results give strong support for the clinical use of metabolomic profiling in assessing NMIBC recurrence.Non Muscle Invasive Bladder Cancer (NMIBC) is among the most frequent malignant cancers worldwide. NMIBC is treated by transurethral resection of the bladder tumor (TURBT) and intravesical therapies, and has the highest recurrence rate among solid tumors. It requires a lifelong patient monitoring based on repeated cystoscopy and urinary cytology, both having drawbacks that include lack of sensitivity and specificity, invasiveness and care costs. We conducted an investigative clinical study to examine changes in the urinary metabolome of NMBIC patients before and after TURBT, as well during the subsequent surveillance period. Adjusting by prior probability of recurrence per risk, discriminant analysis of UPLC-MS metabolic profiles, displayed negative predictive values for  BC is associated with a high mortality rate (between 30 and 70% according to the disease stage) and the number of BC cases and deaths are expected to almost double in the near future because of smoking prevalence and the increase in life expectancy over time1. BC comprises two diseases entities with distinct clinical outcomes and very different molecular profiles: non-muscle invasive (NMIBC) and muscle invasive bladder cancer (MIBC)2. Despite its high prevalence and incidence, treatment of BC has not changed much over the past 25 years. MIBC is treated by radical cystectomy, adjuvant chemotherapy, or (chemo)-radiotherapy. On the other hand, NMIBC is treated by transurethral resection of the bladder tumor (TURBT) unspecific stimulation of the immune system  or intravesical chemotherapy 3.Bladder cancer (BC) is the ninth most common cancer worldwide1, tumor invades subepithelial connective tissue) represents 70–80% of the diagnosed cases and, although it is not life-threatening, it has the highest recurrence rate within tumors (up to 70% in five years4), requiring a lifelong monitoring of the patient after TURBT, by means of cystoscopy and urinary cytology. Active surveillance programs rely on the BC stage and grade at the time of detection. For high-grade tumors, cystoscopy and voided urine cytology are recommended every 3 months for 2 years, then every 6 months for the following 5 years and yearly thereafter. As each recurrence re-starts the monitorization schedule, patients are subjected to a very large number of cystoscopic examinations, making NMIBC one of the most health care demanding cancers. As an example, in the year 2010 bladder cancer was the 9th most expensive cancer to treat and monitor in the USA, with cumulative costs of 4 billion US dollars, which represents 3.2% of all cancer-related care5.NMIBC in the two early stages . Urine cytology is the most frequent non-invasive method used for the detection of BC. It has demonstrated its clinical utility when combined with cystoscopy, or when high-grade malignancy or CIS is present. However, cytological interpretation, although standardized by scales, relies on the ability of the examiner and affords a median sensitivity of 35% and median specificity of 94%7; it can only detect 50% of early stage/low-grade BC’s, just when they are most curable. The present limitations of cytology and cystoscopy have fostered the research on alternative, minimally invasive, approaches for BC diagnosis and surveillance.BC diagnosis and therapy surveillance are largely based on cystoscopy and urinary cytology but both tests have important drawbacks, including lack of sensitivity and specificity, invasiveness and care costsomic’s technologies in the past recent years combined with improved computing resources and mathematical models for data analysis, has expanded our capabilities for searching new biomarkers of disease. Precise genomic analysis, SNP’s epigenome analysis, transcriptomics, proteomics and more recently miRNA and metabolomic analysis have opened a vast field to search for new specific disease biomarkers. Nowadays, six urinary diagnostic tests for BC have been approved by the Food and Drug Administration (USA) for clinical use, namely, BTA stat, BTA TRAK, NMP22 BC, NMP22 BladderChek, uCyt+ and UroVysion. Despite initial promising results, these markers display higher sensitivity but lower specificity than urinary cytology, and unfortunately a low sensitivity for the early stages of BC recurrence compared to primary BC detection. As a matter of fact, no single urinary biomarker can replace cystoscopy – for the time being –as a non-invasive BC surveillance test.The astonishing development of 4. Differences in metabolism between cancer and normal cells are recognized as hallmarks of cancer4. Tumors reprogram pathways of nutrient acquisition and metabolism to meet bioenergetic, biosynthetic and redox demands8 as well as metabolic control of inflammation and immunity that differentiate benign from cancerous tissues. Metabolomic studies in BC have shown altered energy, cell membranes formation, nucleic acid synthesis and oxidative stress pathways biomarkers10. Consistent with these findings, preliminary studies point at using metabolic profiles with discriminatory capabilities for BC, as novel urinary biomarkers18.The metabolome of a cell/tissue is the result of the interaction of the genome, epigenome, transcriptome, proteome and the set of external interventions. Metabolites are involved in almost every biochemical reaction in the human body including signaling pathways and hence the metabolome is considered to provide a direct meaningful readout of the dynamic biochemical status of a biological system. Because of that, metabolomics is now considered as a highly relevant approach to explore individual phenotypes in systems biology of cancerhealthy baseline profile. Thus, a study aiming at the identification of new diagnostic biomarkers of primary BC should include patients undergoing investigation for suspected bladder cancer and with other non-cancerous urothelium affectations. Conversely, the occurrence of disease biomarkers in the course of disease recurrence should include patients undergoing surveillance after TURBT. Previous studies aiming at the identification of metabolomic biomarkers for BC diagnosis were limited by the use of heterogeneous cohorts of NMIBC and MIBC patients, not fully representative of the target population19 and that difficulted the interpretation of the outcomes as multiple distinct molecular subtypes and/or individual phenotypes of MIBC and NMIBC2 were jointly analyzed.BC is a pan-urothelial disease and the urinary metabolic profile should be considered as the expression of the tumor as well the entire urothelium. It is then conceivable that after TURBT, the metabolic profile may undergo changes approaching a In order to circumvent these problems, we conducted, to the best of our knowledge, the first clinical investigative study for the analysis of urinary metabolome changes in NMIBC patients before and after TURBT, as well in cancer recurrence, using ultraperformance liquid chromatograhy combined with time of flight mass spectrometry (UPLC-TOFMS). As results of our work, it has been possible to identify metabolites capable of discriminating BC patients with a high sensitivity (87.9%) and specificity (100%), and a negative likelihood value of 0.1, as well high negative predictive values for low, low-intermediate and high-intermediate and high-risk patients. The metabolomic analysis revealed altered phenylalanine, arginine, proline, and tryptophan intermediate metabolism associated to NMIBC. Results from analysis of longitudinal trajectories of the metabolic profile during surveillance after TURBT gave support to the idea of using a metabolomic approach to monitor early NMIBC recurrence in patients.The present study was approved by the Ethics Committee for Biomedical Research of the Instituto de Investigación Sanitaria Hospital Universitario y Politécnico La Fe   and all methods were performed in accordance with the relevant guidelines and regulations. Urine samples were prospectively collected from patients that had given written informed consent to participate in the study. Patients with diagnosed bladder tumor undergoing planned transurethral resection of the bladder tumor (TURBT) were invited to participate in the study. Inclusion criteria were: 20–90 years old males, NMIBC diagnosed, single or multiple tumors, tumor size greater than 0.5 cm, primary or recurrent tumors. No therapy was used at the time of sample collection. Exclusion criteria were: urinary catheter carrier, re-staging TURBT, rescue TURBT because of incomplete first TURBT, bladder randomized biopsy. Also, patients with infiltrative tumor diagnosed (pT2-4), no tumor diagnosed (pT0), papilloma or pTis pathological anatomy diagnosis, were excluded. After recurrent risk group classification according to the European Organisation for Research and Treatment of Cancer score, several patients were included in a monthly monitoring group to collect serial urine samples until recurrence. In this study, we analyzed the metabolomic profiles of 316 urine samples collected from 31 patients between March 2012 and December 2016. Table g . Then, 100 µL of supernatant was withdrawn and 200 µL of HCOOH 0.1% v/v in H2O was added and the solution was vortexed 10 s and centrifuged at 10000 × g . No molecular weight cutoff (MWCO) filters were used during sample preparation. Thereafter, 100 µL of the supernatant was transferred to a 96 well plates where each sample was spiked with 5 µL of an internal standard solution containing Phenylalanine-D5 , caffeine-D9 , leukine enkephalin  and reserpine (Sigma-Aldrich Química) in H2O:CH3OH , at a final concentration of 1 µM each. Control blanks were prepared by replacing urine by H2O. A quality control (QC) sample was prepared by mixing 5 µL of each prepared sample. All solvents were of LC-MS grade and were purchased from Scharlau . Ultra-pure water was generated with a Milli-Q water purification system . Formic acid (≥95%) was obtained from Sigma-Aldrich Química.Urine samples, once collected were kept at −80 °C until analysis. Samples were thaw at room temperature on ice, vortexed for 10 s and centrifuged at 10000 × 18  column. Autosampler and column temperatures were set to 4 °C and 55 °C, respectively and the injection volume was 4 µL. A gradient elution was performed at a flow rate of 400 µL min−1 as follows: initial conditions of 98% of mobile phase A  were kept for 0.5 min, followed by a linear gradient from 2% to 20% of mobile phase B (0.1% v/v HCOOH in CH3CN) for 3.5 min and from 20% to 95% B in 4 min. 95% B was held for 1 min and then, a 0.25 min gradient was used to return to the initial conditions. Between runs, the initial conditions were held for 2.75 min for column re-equilibration. Full scan MS data from 70 to 1700 m/z Da, with a scan frequency of 6 Hz (1274 transients/spectrum) was collected on a quadrupole time of flight (QTOF) Agilent 6550 spectrometer  in the TOF MS mode. The following positive electrospray ionization (ESI) parameters were selected: gas T, 200 °C; drying gas, 14 l/min; nebulizer, 37 psig; sheath gas T, 350 °C; sheath gas flow, 11 l/min. Automatic MS spectra recalibration during analysis was carried out introducing a mass reference standard into the source via a reference sprayer valve using the 149.02332 (background contaminant), 121.050873 (Purine) and 922.009798 (HP-0921) m/z Da as references. Sample acquisition was randomized and the QC sample was analyzed every 5 injections to monitor and correct changes in the instrument response. Eight replicates of the QC were injected at the beginning of each batch for column conditioning. Data acquired during conditioning was excluded from the analysis. The sample set included 315 urine samples, 56 QCs, and 4 blanks. Sample analysis was carried out in two batches to reduce the time that samples are kept in the autosampler during analysis. Batch 1 included 224 injections of 187 urine samples , 4 Blanks and 33 QCs. Batch 2 included 152 injections of 129 urine samples  and 23 QCs. All samples from the same patient were analyzed in the same batch. Patient distribution between batches was randomized.Chromatographic analysis was performed on an Agilent 1290 Infinity UPLC chromatograph using a UPLC BEH Chttp://proteowizard.sourceforge.net/) before generating peak tables using XCMS software20. The centWave method was used for peak detection with the following parameters: ppm: 15, peakwidth: , signal to noise threshold: 6. A minimum difference in m/z of 5 mDa was selected for peaks with overlapping retention times (RTs). Intensity weighted m/z values of each feature were calculated using the wMean function. Peak limits used for integration were found through descent on the Mexican hat filtered data. Peak grouping was carried out using the ‘nearest’ method using mzVsRT = 1 and RT and m/z tolerances of 6 s and 5 mDa, respectively. After peak grouping, the fillPeaks method with the default parameters was applied to fill missing peak data. RT and m/z tolerances used for peak table generation and alignment of features across batches was based on the observed variation in five selected metabolites  and spiked internal standards (ISs) ; see for example Fig. 21. The ε-insensitive loss parameter, the error penalty C and the kernel parameter γ used for the fitting of the SVR functions were selected using the 10-fold root mean squared cross validation error (RMSECV) as estimates of the expected generalization error. The ε-insensitive loss parameter for each metabolic feature was selected as the expected instrumental precision . The error penalty C was calculated as the median value of the responses in QCs21. The kernel parameter γ providing the lowest RMSECV for each variable in the  range was selected. Between-batch effects were eliminated by scaling the intensity of each metabolic feature in each sample using a factor defined as the ratio between the median intensity in QCs in the corresponding batch and the median intensity across batches. Finally, metabolic features showing RSD% >15 in QCs were considered unreliable and removed, leaving 2006 features for data analysis. Batch effects affecting the number of missing values due to e.g. instability of the chromatographic separation or wrong feature alignments were not considered in this work.Centroid raw UPLC-TOF-MS data was converted into mzXML format using ProteoWizard  as well as the sensitivity, selectivity and negative and positive likelihood ratios were employed as PLS-DA figures of merit.The data set was initially split into two subsets for train and validation. The train set was used for partial least squares – discriminant analysis (PLS-DA) model development and feature selection. The validation set was exclusively used for the evaluation of the model predictive performance. Data scaling included multiplicative scatter correction with the median QC as reference followed by pareto scaling. The selection of the optimal number of PLS-DA latent variables (LVs) was carried out using the root mean square error of cross validation (RMSECV) and a leave-http://www.hmdb.ca) and METLIN databases (http://metlin.scripps.edu/) with 5 ppm accuracy. Molecular formulae were estimated by MassHunter Workstation Software-Qualitative Analysis (Agilent). Data acquisition and manual integration of peaks of IS and selected metabolites were carried out using MassHunter workstation (Agilent). PLS-DA was carried out using PLS Toolbox 8.0  and in-house written MATLAB  scripts. Support Vector Regression was carried out in MATLAB using the LIBSVM library22. Pathway analysis was carried out with MetaboAnalyst 3.023. MATLAB scripts used in this work are available from the authors. The datasets generated during and/or analyzed during the current study are available from the corresponding author on reasonable request.Identification of metabolites was carried out by matching m/z (Da) values against the Human Metabolome Database  in QCs as a function of the injection order showed trends both within and between batches, as well as heteroscedastic variance across batches  in raw data depicted in Fig. QC) in batch 2. Figure QC <15% increased from 652 up to 2006, and the median RSDQC decreased from 20.2% down to 8.7%. PCA scores after batch effect correction depicted in Fig. The replicate analysis of a QC sample throughout the batch enables a straightforward evaluation of the instrument performancesee Fig. . Likewisvs CTRL, BC vs MONITOR and CTRL vs MONITOR). Train and tests sets selected for the three models are summarized in Table vs CTRL model provided an accurate sample classification of 27/33 BC and 10/11 CTRL (sensitivity: 82% and specificity: 91%). The second model, build for the discrimination between BC vs MONITOR samples, performed worse in classifying BC samples (sensitivity: 70% and specificity: 75%). Finally, the analysis of the differences between CTRL vs MONITOR groups provided non-significant predictive performances, lower sensitivity (45%) and specificity (76%) values, in agreement with the higher overlapping of CTRL and MONITOR samples, as depicted in Fig. To disclose the differences in the metabolic profiles among BC, CTRL and MONITOR samples, three independent PLS-DA models were considered in which the groups were pairwise compared , which correctly classified 29/33 BC and 11/11 CTRL samples of the validation set, providing an AUROC = 0.96 and slightly improved sensitivity (87.9 (71.8–96.6)%), specificity (100 (71.5–100)%) and the negative likelihood ratio (NLR) (0.1 (0.05–0.3)). Adjusting by the prior probability of recurrence per risk grouping at 15%, 24%, 28% and 61%, the negative predictive values for low, low-intermediate, high-intermediate and high-risk groups were 96.5%, 94.0%, 92.9% and 76.1% respectively. Figure vs CTRL model summarized in Table p-value and pathway impact value, respectively. Phenylalanine, arginine, proline and tryptophan pathways were found significantly altered . This observation was in agreement with recent results26 reporting increased levels of four tryptophan metabolites  in serum samples of BC patients compared to healthy controls, and previous studies in BC tissue9 and urine12 that suggested the potential role of kynurenine in the malignancy BC associated to IDO and IDO2, two tryptophan-metabolizing enzymes that control the tryptophan catabolism-signaling pathway. The generation of kynurenine and other tryptophan metabolites can modulate T-cell immunity via activation of suppressive regulatory T-cells and activation of aryl hydrocarbon receptor, thus promoting cancer cell survival27. Higher levels of pyroglutamic acid and lower levels of hippuric acid before TURBT were in also agreement with previous studies reporting results from the analysis of urine samples collected from BC patients and reference healthy groups30. Phenylacetylglutamine is synthesized in the liver from glutamine and phenylacetyl-CoA and is a dosing biomarker for patients with urea cycle disorders; it is also a known microbial metabolite31. Altered levels of phenylacetylglutamine might indicate a deregulation of the phenylalanine or glutamine metabolism as well, as observed in a previous urinary metabolomic study involving BC patients and healthy controls that attributed this deregulation to the increased energy demands of cancer cells for growing and proliferation26. Urinary citrate is a normal component in urine and the major inhibitor of kidney stone formation. Citrate acid was found at lower concentrations before TURBT. This metabolite, key intermediate in the TCA cycle, has been repeatedly associated with an increased conversion into fatty acids required for ß-oxidation to support cancer cell proliferation. Carnitine and several carnitine metabolites were also among the most discriminant metabolites. Carnitine is an essential metabolite for the transport of long-chain fatty acids into the mitochondria and for the regulation of the intramitochondrial ratio of Acetyl-CoA to free CoA. Hence, results may support higher levels of fatty acids ß-oxidation deregulation associated to the BC tumor33. Altered pyrimidine and purine metabolism have been previously attributed to enhanced cancer cells cycle activity34. Taken together, the results of the present study give support to the hypothesis of the existence of a urinary metabolic profile associated with the occurrence of NMIBC tumor. Besides, the observed metabolic shift after TURBT is well aligned with such hypothesis supporting the idea of using this metabolic shift for the surveillance of cancer recurrence after TURBT in NMIBC patients.The relative importance of each metabolic feature in the projection used in PLS-DA models was evaluated using the Variable Importance in Projection (VIP) scoresy PLS-DA values in 6 patients with multiple episodes of BC recurrence. Results show CTRL and BC samples were accurately classified  and, for some patients (see patients #123 and #143), the longitudinal trajectory during surveillance indicated a gradual shift of the metabolic profile towards a BC profile, that was consistent with the confirmatory results of cystoscopy and, especially in the case of #143, seemed to anticipate results obtained by cystoscopy. Figure The analysis of the longitudinal trajectories of the metabolic biomarkers discriminating between BC and CTRL samples, allowed a preliminary evaluation of its potential utility to monitor NMIBC relapse in patients undergoing surveillance for tumor recurrence. Figure a priori advantageous features for the development of a non-invasive metabolomic analysis. However, urine analysis is challenging due to the variation in chemical composition and concentrations across and within individuals. A wide range of potential confounding factors such as individual genotype, diet, water consumption, environmental exposure or drug intake may affect the urine metabolome. The effect of uncontrolled sources of variation may justify the abovementioned few anomalous observations in the test set. We minimized such effects by comparing urine samples before and after TURBT, and after tumor relapse. However, further research is needed to assess the sources of variability in urine and increase the robustness of metabolic tests in exploratory studies to facilitate the validation and translation of biomarker discoveries into clinical practice.Nevertheless, in few patients, the follow up of biomarkers showed trajectories inconsistent with a gradual progression of the disease after TURBT (see e.g. MONITOR samples from patients #62 and 66 marked by an * in Fig. Results from this exploratory clinical study disclosed a statistically significant shift in the urinary metabolic profile in NMIBC patients before and after TURBT and give support to the hypothesis of a specific urinary metabolic profile associated to the occurrence of an NMIBC tumor. Consistent with this view, follow up of the urine metabolome in the course of cancer recurrence surveillance revealed a gradual shift in the metabolic profile towards the BC profile when the tumor reappeared, as confirmed by cystoscopy. Taken together, these results provide a strong basis for the use of a metabolomic-based biomarker analysis as a non-invasive monitoring system to detect NMIBC recurrence at an early stage and eventually adjust therapies according to NMIBC risk. A larger sample size of representative samples towards the size of a population should, theoretically, lead to more generalizable results and increase the ability to discriminate patients pre- and post-resection. Larger population sizes need to be studied during the clinical validation of the proposed biomarkers. Moreover, a better characterization of the differences between pre- and post-resection urinary metabolic profiles may result in improved models with larger effect sizes.Supplementary Information