Comparative transcriptomic analysis of Staphylococcus epidermidis associated with periprosthetic joint infection under in vivo and in vitro conditions.

Staphylococcus epidermidis is part of the commensal microbiota of the skin and mucous membranes, though it can also act as a pathogen in certain scenarios, causing a range of infections, including periprosthetic joint infection (PJI). Transcriptomic profiling may provide insights into mechanisms by which S. epidermidis adapts while in a pathogenic compared to a commensal state. Here, a total RNA-sequencing approach was used to profile and compare the transcriptomes of 19 paired PJI-associated S. epidermidis samples from an in vivo clinical source and grown in in vitro laboratory culture. Genomic comparison of PJI-associated and publicly available commensal-state isolates were also compared. Of the 1919 total transcripts found, 145 were from differentially expressed genes (DEGs) when comparing in vivo or in vitro samples. Forty-two transcripts were upregulated and 103 downregulated in in vivo samples. Of note, metal sequestration-associated genes, specifically those related to staphylopine activity (cntA, cntK, cntL, and cntM), were upregulated in a subset of clinical in vivo compared to laboratory grown in vitro samples. About 70% of the total transcripts and almost 50% of the DEGs identified have not yet been annotated. There were no significant genomic differences between known commensal and PJI-associated S. epidermidis isolates, suggesting that differential genomics may not play a role in S. epidermidis pathogenicity. In conclusion, this study provides insights into phenotypic alterations employed by S epidermidis to adapt to infective and non-infected microenvironments, potentially informing future therapeutic targets for related infections.

Botulinum toxin A injection for post-stroke upper limb spasticity and rehabilitation practices from centers across Asian countries.

Purpose: Describe real-life practice and outcomes in the management of post-stroke upper limb spasticity with botulinum toxin A (BoNT-A) in Asian settings. Methods: Subgroup analysis of a prospective, observational study (NCT01020500) of adult patients (≥18 years) with post-stroke upper limb spasticity presenting for routine spasticity management, including treatment with BoNT-A. The primary outcome was goal attainment as assessed using goal-attainment scaling (GAS). Patients baseline clinical characteristics and BoNT-A injection parameters are also described. Results: Overall, 51 patients from Asia were enrolled. Rates of comorbid cognitive and emotional problems were relatively low. Patients tended to have more severe distal limb spasticity and to prioritize active over passive function goals. Most (94.1%) patients in the subgroup were treated with abobotulinumtoxinA. For these patients, the median total dose was 500 units, and the most frequently injected muscles were the biceps brachii (83.3%), flexor carpi radialis (72.9%), and flexor digitorum profundus (66.7%). Overall, 74.5% achieved their primary goal and the mean GAS T score after one treatment cycle was 56.0 ± 13.0, with a change from baseline of 20.9 ± 14.3 (p < 0.001). The majority (96.1%) of Asian patients were rated as having improved. Conclusion: In the Asian treatment setting, BoNT-A demonstrated a clinically significant effect on goal attainment for the real-life management of upper limb spasticity following stroke.

Diagnostic Utility of MOG Antibody Testing in Cerebrospinal Fluid.

OBJECTIVE: The aim of this study was to assess the diagnostic utility of cerebrospinal fluid (CSF) myelin oligodendrocyte glycoprotein antibodies (MOG-IgG) testing. METHODS: We retrospectively identified patients for CSF MOG-IgG testing from January 1, 1996, to May 1, 2023, at Mayo Clinic and other medical centers that sent CSF MOG-IgG for testing including: controls, 282; serum MOG-IgG positive MOG antibody-associated disease (MOGAD), 74; serum MOG-IgG negative high-risk phenotypes, 73; serum false positive MOG-IgG with alternative diagnoses, 18. A live cell-based assay assessed CSF MOG-IgG positivity (IgG-binding-index [IBI], ≥2.5) using multiple anti-human secondary antibodies and end-titers were calculated if sufficient sample volume. Correlation of CSF MOG-IgG IBI and titer was assessed. RESULTS: The pan-IgG Fc-specific secondary was optimal, yielding CSF MOG-IgG sensitivity of 90% and specificity of 98% (Youden's index 0.88). CSF MOG-IgG was positive in: 4/282 (1.4%) controls; 66/74 (89%) serum MOG-IgG positive MOGAD patients; and 9/73 (12%) serum MOG-IgG negative patients with high-risk phenotypes. Serum negative but CSF positive MOG-IgG accounted for 9/83 (11%) MOGAD patients, and all fulfilled 2023 MOGAD diagnostic criteria. Subgroup analysis of serum MOG-IgG low-positives revealed CSF MOG-IgG positivity more in MOGAD (13/16[81%]) than other diseases with false positive serum MOG-IgG (3/15[20%]) (p = 0.01). CSF MOG-IgG IBI and CSF MOG-IgG titer (both available in 29 samples) were correlated (Spearman's r = 0.64, p < 0.001). INTERPRETATION: CSF MOG-IgG testing has diagnostic utility in patients with a suspicious phenotype but negative serum MOG-IgG, and those with low positive serum MOG-IgG results and diagnostic uncertainty. These findings support a role for CSF MOG-IgG testing in the appropriate clinical setting. ANN NEUROL 2024.

Metabolic model-based ecological modeling for probiotic design.

The microbial community composition in the human gut has a profound effect on human health. This observation has lead to extensive use of microbiome therapies, including over-the-counter 'probiotic' treatments intended to alter the composition of the microbiome. Despite so much promise and commercial interest, the factors that contribute to the success or failure of microbiome-targeted treatments remain unclear. We investigate the biotic interactions that lead to successful engraftment of a novel bacterial strain introduced to the microbiome as in probiotic treatments. We use pairwise genome-scale metabolic modeling with a generalized resource allocation constraint to build a network of interactions between taxa that appear in an experimental engraftment study. We create induced sub-graphs using the taxa present in individual samples and assess the likelihood of invader engraftment based on network structure. To do so, we use a generalized Lotka-Volterra model, which we show has strong ability to predict if a particular invader or probiotic will successfully engraft into an individual's microbiome. Furthermore, we show that the mechanistic nature of the model is useful for revealing which microbe-microbe interactions potentially drive engraftment.

Characterization and Optimization of Multiomic Single-Cell Epigenomic Profiling.

The snATAC + snRNA platform allows epigenomic profiling of open chromatin and gene expression with single-cell resolution. The most critical assay step is to isolate high-quality nuclei to proceed with droplet-base single nuclei isolation and barcoding. With the increasing popularity of multiomic profiling in various fields, there is a need for optimized and reliable nuclei isolation methods, mainly for human tissue samples. Herein we compared different nuclei isolation methods for cell suspensions, such as peripheral blood mononuclear cells (PBMC, n = 18) and a solid tumor type, ovarian cancer (OC, n = 18), derived from debulking surgery. Nuclei morphology and sequencing output parameters were used to evaluate the quality of preparation. Our results show that NP-40 detergent-based nuclei isolation yields better sequencing results than collagenase tissue dissociation for OC, significantly impacting cell type identification and analysis. Given the utility of applying such techniques to frozen samples, we also tested frozen preparation and digestion (n = 6). A paired comparison between frozen and fresh samples validated the quality of both specimens. Finally, we demonstrate the reproducibility of scRNA and snATAC + snRNA platform, by comparing the gene expression profiling of PBMC. Our results highlight how the choice of nuclei isolation methods is critical for obtaining quality data in multiomic assays. It also shows that the measurement of expression between scRNA and snRNA is comparable and effective for cell type identification.

Seizure characteristics and outcomes in patients with neurological conditions related to high-risk paraneoplastic antibodies.

OBJECTIVE: Seizures are a common manifestation of paraneoplastic neurologic syndromes. The objective of this study was to describe the seizure characteristics and outcomes in patients with high-risk paraneoplastic autoantibodies (>70% cancer association) and to determine factors associated with ongoing seizures. METHODS: Patients from 2000 to 2020 with seizures and high-risk paraneoplastic autoantibodies were retrospectively identified. Factors associated with ongoing seizures at last follow-up were evaluated. RESULTS: Sixty patients were identified (34 males, median age at presentation = 52 years). ANNA1-IgG (Hu; n = 24, 39%), Ma2-IgG (n = 14, 23%), and CRMP5-IgG (CV2; n = 11, 18%) were the most common underlying antibodies. Seizures were the initial presenting symptom in 26 (43%), and malignancy was present in 38 (63%). Seizures persisted for >1 month in 83%, and 60% had ongoing seizures, with almost all patients (55/60, 92%) still being on antiseizure medications at last follow-up a median of 25 months after seizure onset. Ongoing seizures at last follow-up were associated with Ma2-IgG or ANNA1-IgG compared to other antibodies (p = .04), highest seizure frequency being at least daily (p = .0002), seizures on electroencephalogram (EEG; p = .03), and imaging evidence of limbic encephalitis (LE; p = .03). Death occurred in 48% throughout the course of follow-up, with a higher mortality in patients with LE than in those without LE (p = .04). Of 31 surviving patients at last follow-up, 55% continued to have intermittent seizures. SIGNIFICANCE: Seizures in the setting of high-risk paraneoplastic antibodies are frequently resistant to treatment. Ongoing seizures are associated with ANNA1-IgG and Ma2-IgG, high seizure frequency, and EEG and imaging abnormalities. Although a subset of patients may respond to immunotherapy and achieve seizure freedom, poor outcomes are frequently encountered. Death was more common among patients with LE.

Comparison of MRI T2-lesion evolution in pediatric MOGAD, NMOSD, and MS.

BACKGROUND: Magnetic resonance imaging (MRI) T2-lesions resolve more often in myelin oligodendrocyte glycoprotein (MOG) antibody-associated disease (MOGAD) than aquaporin-4 IgG-positive neuromyelitis optica spectrum disorder (AQP4 + NMOSD) and multiple sclerosis (MS) in adults but few studies analyzed children. OBJECTIVE: The main objective of this study is to investigate MRI T2-lesion evolution in pediatric MOGAD, AQP4 + NMOSD, and MS. METHODS: Inclusion criteria were as follows: (1) first clinical attack; (2) abnormal MRI (⩽6 weeks); (3) follow-up MRI beyond 6 months without relapses in that region; and (4) age < 18 years. An index T2-lesion (symptomatic/largest) was identified, and T2-lesion resolution or persistence on follow-up MRI was determined. RESULTS: We included 56 patients (MOGAD, 21; AQP4 + NMOSD, 8; MS, 27) with 69 attacks. Index T2-lesion resolution was more frequent in MOGAD (brain 9 of 15 [60%]; spine 8 of 12 [67%]) than AQP4 + NMOSD (brain 1 of 4 [25%]; spine 0 of 7 [0%]) and MS (brain 0 of 18 [0%]; spine 1 of 13 [8%]), p < 0.01. Resolution of all T2-lesions occurred more often in MOGAD (brain 6 of 15 [40%]; spine 7 of 12 [58%]) than AQP4 + NMOSD (brain 1 of 4 [25%]; spine 0 of 7 [0%]), and MS (brain 0 of 18 [0%]; spine 1 of 13 [8%]), p < 0.01. Reductions in median index T2-lesion area were greater in MOGAD (brain, 305 mm; spine, 23 mm) than MS (brain, 42 mm [p<0.001]; spine, 10 mm [p<0.001]) without differing from AQP4 + NMOSD (brain, 133 mm [p=0.42]; spine, 19.5 mm [p=0.69]). CONCLUSION: In children, MRI T2-lesions resolved more often in MOGAD than AQP4 + NMOSD and MS which is similar to adults suggesting these differences are related to pathogenesis rather than age.

Stiff person spectrum disorder diagnosis, misdiagnosis, and suggested diagnostic criteria.

BACKGROUND: Stiff person spectrum disorder (SPSD) is heterogeneous, and accurate diagnosis can be challenging. METHODS: Patients referred for diagnosis/suspicion of SPSD at the Mayo Autoimmune Neurology Clinic from July 01, 2016, to June 30, 2021, were retrospectively identified. SPSD diagnosis was defined as clinical SPSD manifestations confirmed by an autoimmune neurologist and seropositivity for high-titer GAD65-IgG (>20.0 nmol/L), glycine-receptor-IgG or amphiphysin-IgG, and/or confirmatory electrodiagnostic studies (essential if seronegative). Clinical presentation, examination, and ancillary testing were compared to differentiate SPSD from non-SPSD. RESULTS: Of 173 cases, 48 (28%) were diagnosed with SPSD and 125 (72%) with non-SPSD. Most SPSD were seropositive (41/48: GAD65-IgG 28/41, glycine-receptor-IgG 12/41, amphiphysin-IgG 2/41). Pain syndromes or functional neurologic disorder were the most common non-SPSD diagnoses (81/125, 65%). SPSD patients more commonly reported exaggerated startle (81% vs. 56%, p = 0.02), unexplained falls (76% vs. 46%, p = 0.001), and other associated autoimmunity (50% vs. 27%, p = 0.005). SPSD more often had hypertonia (60% vs. 24%, p < 0.001), hyperreflexia (71% vs. 43%, p = 0.001), and lumbar hyperlordosis (67% vs. 9%, p < 0.001) and less likely functional neurologic signs (6% vs. 33%, p = 0.001). SPSD patients more frequently had electrodiagnostic abnormalities (74% vs. 17%, p < 0.001), and at least moderate symptomatic improvement with benzodiazepines (51% vs. 16%, p < 0.001) or immunotherapy (45% vs. 13% p < 0.001). Only 4/78 non-SPSD patients who received immunotherapy had alternative neurologic autoimmunity. INTERPRETATION: Misdiagnosis was threefold more common than confirmed SPSD. Functional or non-neurologic disorders accounted for most misdiagnoses. Clinical and ancillary testing factors can reduce misdiagnosis and exposure to unnecessary treatments. SPSD diagnostic criteria are suggested.

Corpus callosum involvement in MOG antibody-associated disease in comparison to AQP4-IgG-seropositive neuromyelitis optica spectrum disorder and multiple sclerosis.

BACKGROUND: Data on corpus callosum involvement in myelin oligodendrocyte glycoprotein antibody-associated disease (MOGAD) are limited. OBJECTIVE: The objective of the study was to compare callosal lesions in MOGAD, multiple sclerosis (MS), and aquaporin-4-IgG positive neuromyelitis optica spectrum disorder (AQP4+NMOSD). RESULTS: Callosal lesion frequency was similar in MOGAD (38/171 (22%)), MS (24/72 (33%)), and AQP4+NMOSD (18/63 (29%)). Clinical phenotypes included encephalopathy (47%) and focal supratentorial (21%) or infratentorial (45%) deficits. None had callosal-disconnection syndromes. Maximal callosal-T2-lesion diameter (median (range)) in millimeter was similar in MOGAD (21 (4-77)) and AQP4+NMOSD (22 (5-49); p = 0.93) but greater than in MS (10.5 (2-64)). Extracallosal extension (21/38 (55%)) and T2-lesion resolution (19/34 (56%)) favored MOGAD. CONCLUSIONS: Despite similar frequency and imaging overlap, larger lesions, sagittal midline involvement, and lesion resolution favored MOGAD.

Diagnostic and prognostic potential of the microbiome in ovarian cancer treatment response.

Ovarian cancer (OC) is the second most common gynecological malignancy and the fifth leading cause of death due to cancer in women in the United States mainly due to the late-stage diagnosis of this cancer. It is, therefore, critical to identify potential indicators to aid in early detection and diagnosis of this disease. We investigated the microbiome associated with OC and its potential role in detection, progression as well as prognosis of the disease. We identified a distinct OC microbiome with general enrichment of several microbial taxa, including Dialister, Corynebacterium, Prevotella, and Peptoniphilus in the OC cohort in all body sites excluding stool and omentum which were not sampled from the benign cohort. These taxa were, however, depleted in the advanced-stage and high-grade OC patients compared to early-stage and low-grade OC patients suggestive of decrease accumulation in advanced disease and could serve as potential indicators for early detection of OC. Similarly, we also observed the accumulation of these mainly pathogenic taxa in OC patients with adverse treatment outcomes compared to those without events and could also serve as potential indicators for predicting patients' responses to treatment. These findings provide important insights into the potential use of the microbiome as indicators in (1) early detection of and screening for OC and (2) predicting patients' response to treatment. Given the limited number of patients enrolled in the study, these results would need to be further investigated and confirmed in a larger study.

Acinetobacter baumannii Genomic Sequence-Based Core Genome Multilocus Sequence Typing Using Ridom SeqSphere+ and Antimicrobial Susceptibility Prediction in ARESdb.

Whole-genome sequencing (WGS) is rapidly replacing traditional typing methods for the investigation of infectious disease outbreaks. Additionally, WGS data are being used to predict phenotypic antimicrobial susceptibility. Acinetobacter baumannii, which is often multidrug-resistant, is a significant culprit in outbreaks in health care settings. A well-characterized collection of A. baumannii was studied using core genome multilocus sequence typing (cgMLST). Seventy-two isolates previously typed by PCR-electrospray ionization mass spectrometry (PCR/ESI-MS) provided by the Antimicrobial Resistance Leadership Group (ARLG) were analyzed using a clinical microbiology laboratory developed workflow for cgMLST with genomic susceptibility prediction performed using the ARESdb platform. Previously performed PCR/ESI-MS correlated with cgMLST using relatedness thresholds of allelic differences of ≤9 and ≤200 allelic differences in 78 and 94% of isolates, respectively. Categorical agreement between genotypic and phenotypic antimicrobial susceptibility across a panel of 11 commonly used drugs was 89%, with minor, major, and very major error rates of 8%, 11%, and 1%, respectively.

Development of a multiomics model for identification of predictive biomarkers for COVID-19 severity: a retrospective cohort study.

BACKGROUND: COVID-19 is a multi-system disorder with high variability in clinical outcomes among patients who are admitted to hospital. Although some cytokines such as interleukin (IL)-6 are believed to be associated with severity, there are no early biomarkers that can reliably predict patients who are more likely to have adverse outcomes. Thus, it is crucial to discover predictive markers of serious complications. METHODS: In this retrospective cohort study, we analysed samples from 455 participants with COVID-19 who had had a positive SARS-CoV-2 RT-PCR result between April 14, 2020, and Dec 1, 2020 and who had visited one of three Mayo Clinic sites in the USA (Minnesota, Arizona, or Florida) in the same period. These participants were assigned to three subgroups depending on disease severity as defined by the WHO ordinal scale of clinical improvement (outpatient, severe, or critical). Our control cohort comprised of 182 anonymised age-matched and sex-matched plasma samples that were available from the Mayo Clinic Biorepository and banked before the COVID-19 pandemic. We did a deep profiling of circulatory cytokines and other proteins, lipids, and metabolites from both cohorts. Most patient samples were collected before, or around the time of, hospital admission, representing ideal samples for predictive biomarker discovery. We used proximity extension assays to quantify cytokines and circulatory proteins and tandem mass spectrometry to measure lipids and metabolites. Biomarker discovery was done by applying an AutoGluon-tabular classifier to a multiomics dataset, producing a stacked ensemble of cutting-edge machine learning algorithms. Global proteomics and glycoproteomics on a subset of patient samples with matched pre-COVID-19 plasma samples was also done. FINDINGS: We quantified 1463 cytokines and circulatory proteins, along with 902 lipids and 1018 metabolites. By developing a machine-learning-based prediction model, a set of 102 biomarkers, which predicted severe and clinical COVID-19 outcomes better than the traditional set of cytokines, were discovered. These predictive biomarkers included several novel cytokines and other proteins, lipids, and metabolites. For example, altered amounts of C-type lectin domain family 6 member A (CLEC6A), ether phosphatidylethanolamine (P-18:1/18:1), and 2-hydroxydecanoate, as reported here, have not previously been associated with severity in COVID-19. Patient samples with matched pre-COVID-19 plasma samples showed similar trends in muti-omics signatures along with differences in glycoproteomics profile. INTERPRETATION: A multiomic molecular signature in the plasma of patients with COVID-19 before being admitted to hospital can be exploited to predict a more severe course of disease. Machine learning approaches can be applied to highly complex and multidimensional profiling data to reveal novel signatures of clinical use. The absence of validation in an independent cohort remains a major limitation of the study. FUNDING: Eric and Wendy Schmidt.

Potential Role of Inflammation-Promoting Biliary Microbiome in Primary Sclerosing Cholangitis and Cholangiocarcinoma.

BACKGROUND: Primary sclerosing cholangitis (PSC) is a major risk factor for cholangiocarcinoma (CCA). We investigated biliary and fecal microbiota to determine whether specific microbes in the bile or stool are associated with PSC or CCA. METHODS: Bile was obtained from 32 patients with PSC, 23 with CCA with PSC, 26 with CCA without PSC, and 17 controls. Over 90% of bile samples were from patients with perihilar CCA. Stool was obtained from 31 patients with PSC (11 were matched to bile), 16 with CCA with PSC (10 matched to bile), and 11 with CCA without PSC (6 matched to bile). Microbiota composition was assessed using 16SrRNA-marker-based sequencing and was compared between groups. RESULTS: Bile has a unique microbiota distinguished from negative DNA controls and stool. Increased species richness and abundance of Fusobacteria correlated with duration of PSC and characterized the biliary microbiota in CCA. Stool microbiota composition showed no significant differences between groups. CONCLUSIONS: We identified a unique microbial signature in the bile of patients with increased duration of PSC or with CCA, suggesting a role for microbiota-driven inflammation in the pathogenesis and or progression to perihilar CCA. Further studies are needed to test this hypothesis.

A systemic review of the role of enterotoxic Bacteroides fragilis in colorectal cancer.

Enterotoxigenic Bacteroides fragilis (ETBF) has received significant attention for a possible association with, or causal role in, colorectal cancer (CRC). The goal of this review was to assess the status of the published evidence supporting (i) the association between ETBF and CRC and (ii) the causal role of ETBF in CRC. PubMed and Scopus searches were performed in August 2021 to identify human, animal, and cell studies pertaining to the role of ETBF in CRC. Inclusion criteria included the use of cell lines, mice, exposure to BFT or ETBF, and detection of bft. Review studies were excluded, and studies were limited to the English language. Quality of study design and risk of bias analysis was performed on the cell, animal, and human studies using ToxRTools, SYRCLE, and NOS, respectively. Ninety-five eligible studies were identified, this included 22 human studies, 24 animal studies, 43 cell studies, and 6 studies that included both cells and mice studies. We found that a large majority of studies supported an association or causal role of ETBF in CRC, as well as high levels of study bias was detected in the in vitro and in vivo studies. The high-level heterogeneity in study design and reporting made it difficult to synthesize these findings into a unified conclusion, suggesting that the need for future studies that include improved mechanistic models, longitudinal in vitro and in vivo evidence, and appropriate control of confounding factors will be required to confirm whether ETBF has a direct role in CRC etiopathogenesis.

The breast tissue microbiome, stroma, immune cells and breast cancer.

BACKGROUND: Stromal and immune cell composition alterations in benign breast tissue associate with future cancer risk. Pilot data suggest the innate microbiome of normal breast tissue differs between women with and without breast cancer. Microbiome alterations might explain tissue microenvironment variations associated with disease status. METHODS: Prospectively-collected sterile normal breast tissues from women with benign (n=16) or malignant (n=17) disease underwent 16SrRNA sequencing with Illumina MiSeq and Hybrid-denovo pipeline processing. Breast tissue was scored for fibrosis and fat percentages and immune cell infiltrates (lobulitis) classified as absent/mild/moderate/severe. Alpha and beta diversity were calculated on rarefied OTU data and associations analyzed with multiple linear regression and PERMANOVA. RESULTS: Breast tissue stromal fat% was lower and fibrosis% higher in benign disease versus cancer (median 30% versus 60%, p=0.01, 70% versus 30%, p=0.002, respectively). The microbiome varied with stromal composition. Alpha diversity (Chao1) correlated with fat% (r=0.38, p=0.02) and fibrosis% (r=-0.32, p=0.05) and associated with different microbial populations as indicated by beta diversity metrics (weighted UniFrac, p=0.08, fat%, p=0.07, fibrosis%). Permutation testing with FDR control revealed taxa differences for fat% in Firmicutes, Bacilli, Bacillales, Staphylococcaceae and genus Staphylococcus, and fibrosis% in Firmicutes, Spirochaetes, Bacilli, Bacillales, Spirochaetales, Proteobacteria RF32, Sphingomonadales, Staphylococcaceae, and genera Clostridium, Staphylococcus, Spirochaetes, Actinobacteria Adlercreutzia. Moderate/severe lobulitis was more common in cancer (73%) than benign disease (13%), p=0.003, but no significant microbial associations were seen. CONCLUSION: These data suggest a link between breast tissue stromal alterations and its microbiome, further supporting a connection between the breast tissue microenvironment and breast cancer.

Optimizing Nanopore Sequencing for Rapid Detection of Microbial Species and Antimicrobial Resistance in Patients at Risk of Surgical Site Infections.

Surgical site infections (SSI) are a significant burden to patients and health care systems. We evaluated the use of Nanopore sequencing (NS) to rapidly detect microbial species and antimicrobial resistance (AMR) genes present in intraoperative bile aspirates. Bile aspirates from 42 patients undergoing pancreatic head resection were included. Three methods of DNA extraction using mechanical cell lysis or protease cell lysis were compared to determine the optimum method of DNA extraction. The impact of host DNA depletion, sequence run duration, and use of different AMR gene databases was also assessed. To determine clinical value, NS results were compared to standard culture (SC) results. NS identified microbial species in all culture positive samples. Mechanical lysis improved NS detection of cultured species from 60% to 76%, enabled detection of fungal species, and increased AMR predictions. Host DNA depletion improved detection of streptococcal species and AMR correlation with SC. Selection of AMR database influenced the number of AMR hits and resistance profile of 13 antibiotics. AMR prediction using CARD and ResFinder 4.1 correctly predicted 79% and 81% of the bile antibiogram, respectively. Sequence run duration positively correlated with detection of AMR genes. A minimum of 6 h was required to characterize the biliary microbes, resulting in a turnaround time of 14 h. Rapid identification of microbial species and AMR genes can be achieved by NS. NS results correlated with SC, suggesting that NS may be useful in guiding early antimicrobial therapy postsurgery. IMPORTANCE Surgical site infections (SSI) are a significant burden to patients and health care systems. They increase mortality rates, length of hospital stays, and associated health care costs. To reduce the risk of SSI, surgical patients are administered broad-spectrum antibiotics that are later adapted to target microbial species detected at the site of surgical incision. Use of broad-spectrum antibiotics can be harmful to the patient. We wanted to develop a rapid method of detecting microbial species and their antimicrobial resistance phenotypes. We developed a method of detecting microbial species and predicting resistance phenotypes using Nanopore sequencing. Results generated using Nanopore sequencing were similar to current methods of detection but were obtained in a significantly shorter amount of time. This suggests that Nanopore sequencing could be used to tailor antibiotics in surgical patients and reduce use of broad-spectrum antibiotics.

"Answers in hours": A prospective clinical study using nanopore sequencing for bile duct cultures.

BACKGROUND: Surgical site infection is a major source of morbidity in patients undergoing pancreatic head resection and is often from organisms in intraoperative bile duct cultures. As such, many institutions use prolonged prophylactic antibiotics and tailor based on bile duct cultures. However, standard cultures take days, leaving many patients unnecessarily on prolonged antibiotics. Nanopore sequencing can provide data in hours and, thus, has the potential to improve antibiotic stewardship. The present study investigates the feasibility of nanopore sequencing in intraoperative bile samples. METHODS: Patients undergoing pancreatic head resection were included. Intra-operative bile microbial profiles were determined with standard cultures and nanopore sequencing. Antibiotic recommendations were generated, and time-to-results determined for both methods. Organism yields, resistance patterns, antibiotic recommendations, and costs were compared. RESULTS: Out of 42 patients, 22 (52%) had samples resulting in positive standard cultures. All positive standard cultures had microbes detected using nanopore sequencing. All 20 patients with negative standard cultures had negative nanopore sequencing. Nanopore sequencing detected more bacterial species compared to standard cultures (10.5 vs 4.4, p < 0.05) and more resistance genotypes (10.3 vs 2.7, p < 0.05). Antimicrobial recommendations based on nanopore sequencing provided coverage for standard cultures in 27 out of 44 (61%) samples, with broader coverage recommended by nanopore sequencing in 13 out of 27 (48%) of these samples. Nanopore sequencing results were faster (8 vs 98 hours) than standard cultures but had higher associated costs ($165 vs $38.49). CONCLUSION: Rapid microbial profiling with nanopore sequencing is feasible with broader organism and resistance profiling compared to standard cultures. Nanopore sequencing has perfect negative predictive value and can potentially improve antibiotic stewardship; thus, a randomized control trial is under development.

Resource-allocation constraint governs structure and function of microbial communities in metabolic modeling.

Predictive modeling tools for assessing microbial communities are important for realizing transformative capabilities of microbiomes in agriculture, ecology, and medicine. Constraint-based community-scale metabolic modeling is unique in its potential for making mechanistic predictions regarding both the structure and function of microbial communities. However, accessing this potential requires an understanding of key physicochemical constraints, which are typically considered on a per-species basis. What is needed is a means of incorporating global constraints relevant to microbial ecology into community models. Resource-allocation constraint, which describes how limited resources should be distributed to different cellular processes, sets limits on the efficiency of metabolic and ecological processes. In this study, we investigate the implications of resource-allocation constraints in community-scale metabolic modeling through a simple mechanism-agnostic implementation of resource-allocation constraints directly at the flux level. By systematically performing single-, two-, and multi-species growth simulations, we show that resource-allocation constraints are indispensable for predicting the structure and function of microbial communities. Our findings call for a scalable workflow for implementing a mechanistic version of resource-allocation constraints to ultimately harness the full potential of community-scale metabolic modeling tools.