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;96:34-45.

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.

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.

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.

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.

The ModelSEED Biochemistry Database for the integration of metabolic annotations and the reconstruction, comparison and analysis of metabolic models for plants, fungi and microbes.

For over 10 years, ModelSEED has been a primary resource for the construction of draft genome-scale metabolic models based on annotated microbial or plant genomes. Now being released, the biochemistry database serves as the foundation of biochemical data underlying ModelSEED and KBase. The biochemistry database embodies several properties that, taken together, distinguish it from other published biochemistry resources by: (i) including compartmentalization, transport reactions, charged molecules and proton balancing on reactions; (ii) being extensible by the user community, with all data stored in GitHub; and (iii) design as a biochemical 'Rosetta Stone' to facilitate comparison and integration of annotations from many different tools and databases. The database was constructed by combining chemical data from many resources, applying standard transformations, identifying redundancies and computing thermodynamic properties. The ModelSEED biochemistry is continually tested using flux balance analysis to ensure the biochemical network is modeling-ready and capable of simulating diverse phenotypes. Ontologies can be designed to aid in comparing and reconciling metabolic reconstructions that differ in how they represent various metabolic pathways. ModelSEED now includes 33,978 compounds and 36,645 reactions, available as a set of extensible files on GitHub, and available to search at https://modelseed.org/biochem and KBase.

The role of microbiome in pancreatic cancer.

Recent studies of the human microbiome have offered new insights into how the microbiome can impact cancer development and treatment. Specifically, in pancreatic ductal adenocarcinoma (PDAC), the microbiota has been shown to modulate PDAC risk, contribute to tumorigenesis, impact the tumor microenvironment, and alter treatment response. These findings provide rationale for further investigations into leveraging the microbiome to develop new strategies to diagnose and treat PDAC patients. There is growing evidence that microbiome analyses have the potential to become easily performed, non-invasive diagnostic, prognostic, and predictive biomarkers in pancreatic cancer. More excitingly, there is now emerging interest in developing interventions based on the modulation of microbiota. Fecal microbiota transplantation, probiotics, dietary changes, and antibiotics are all potential strategies to augment the efficacy of current therapeutics and reduce toxicities. While there are still challenges to overcome, this is a rapidly growing field that holds promise for translation into clinical practice and provides a new approach to improving patient outcomes.

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.

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.

Detecting evolutionary patterns of cancers using consensus trees.

MOTIVATION: While each cancer is the result of an isolated evolutionary process, there are repeated patterns in tumorigenesis defined by recurrent driver mutations and their temporal ordering. Such repeated evolutionary trajectories hold the potential to improve stratification of cancer patients into subtypes with distinct survival and therapy response profiles. However, current cancer phylogeny methods infer large solution spaces of plausible evolutionary histories from the same sequencing data, obfuscating repeated evolutionary patterns. RESULTS: To simultaneously resolve ambiguities in sequencing data and identify cancer subtypes, we propose to leverage common patterns of evolution found in patient cohorts. We first formulate the Multiple Choice Consensus Tree problem, which seeks to select a tumor tree for each patient and assign patients into clusters in such a way that maximizes consistency within each cluster of patient trees. We prove that this problem is NP-hard and develop a heuristic algorithm, Revealing Evolutionary Consensus Across Patients (RECAP), to solve this problem in practice. Finally, on simulated data, we show RECAP outperforms existing methods that do not account for patient subtypes. We then use RECAP to resolve ambiguities in patient trees and find repeated evolutionary trajectories in lung and breast cancer cohorts. AVAILABILITY AND IMPLEMENTATION: https://github.com/elkebir-group/RECAP. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Minimizing the number of optimizations for efficient community dynamic flux balance analysis.

Dynamic flux balance analysis uses a quasi-steady state assumption to calculate an organism's metabolic activity at each time-step of a dynamic simulation, using the well-known technique of flux balance analysis. For microbial communities, this calculation is especially costly and involves solving a linear constrained optimization problem for each member of the community at each time step. However, this is unnecessary and inefficient, as prior solutions can be used to inform future time steps. Here, we show that a basis for the space of internal fluxes can be chosen for each microbe in a community and this basis can be used to simulate forward by solving a relatively inexpensive system of linear equations at most time steps. We can use this solution as long as the resulting metabolic activity remains within the optimization problem's constraints (i.e. the solution to the linear system of equations remains a feasible to the linear program). As the solution becomes infeasible, it first becomes a feasible but degenerate solution to the optimization problem, and we can solve a different but related optimization problem to choose an appropriate basis to continue forward simulation. We demonstrate the efficiency and robustness of our method by comparing with currently used methods on a four species community, and show that our method requires at least 91% fewer optimizations to be solved. For reproducibility, we prototyped the method using Python. Source code is available at https://github.com/jdbrunner/surfin_fba.

PhyDOSE: Design of follow-up single-cell sequencing experiments of tumors.

The combination of bulk and single-cell DNA sequencing data of the same tumor enables the inference of high-fidelity phylogenies that form the input to many important downstream analyses in cancer genomics. While many studies simultaneously perform bulk and single-cell sequencing, some studies have analyzed initial bulk data to identify which mutations to target in a follow-up single-cell sequencing experiment, thereby decreasing cost. Bulk data provide an additional untapped source of valuable information, composed of candidate phylogenies and associated clonal prevalence. Here, we introduce PhyDOSE, a method that uses this information to strategically optimize the design of follow-up single cell experiments. Underpinning our method is the observation that only a small number of clones uniquely distinguish one candidate tree from all other trees. We incorporate distinguishing features into a probabilistic model that infers the number of cells to sequence so as to confidently reconstruct the phylogeny of the tumor. We validate PhyDOSE using simulations and a retrospective analysis of a leukemia patient, concluding that PhyDOSE's computed number of cells resolves tree ambiguity even in the presence of typical single-cell sequencing errors. We also conduct a retrospective analysis on an acute myeloid leukemia cohort, demonstrating the potential to achieve similar results with a significant reduction in the number of cells sequenced. In a prospective analysis, we demonstrate the advantage of selecting cells to sequence across multiple biopsies and that only a small number of cells suffice to disambiguate the solution space of trees in a recent lung cancer cohort. In summary, PhyDOSE proposes cost-efficient single-cell sequencing experiments that yield high-fidelity phylogenies, which will improve downstream analyses aimed at deepening our understanding of cancer biology.

Comparison of Three Commercial Tools for Metagenomic Shotgun Sequencing Analysis.

Metagenomic shotgun sequencing for the identification of pathogens is being increasingly utilized as a diagnostic method. Interpretation of large and complicated data sets is a significant challenge, for which multiple commercial tools have been developed. Three commercial metagenomic shotgun sequencing tools, CosmosID, One Codex, and IDbyDNA, were compared to determine whether they result in similar interpretations of the same sequencing data. We selected 24 diverse samples from a previously characterized data set derived from DNA extracted from biofilms dislodged from the surfaces of resected arthroplasties (sonicate fluid). Sequencing data sets were analyzed using the three commercial tools and compared to culture results and prior metagenomic analysis interpretation. Identical interpretations from all three tools occurred for 6 samples. The total number of species identified included 28 by CosmosID, 59 by One Codex, and 41 by IDbyDNA. All of the tools performed similarly in detecting those microorganisms identified by culture, including polymicrobial mixes. These data show that while all of the tools performed well overall, there were some differences, particularly in their predilection for identifying low-abundance or contaminant organisms as present.

Evaluation of the CosmosID Bioinformatics Platform for Prosthetic Joint-Associated Sonicate Fluid Shotgun Metagenomic Data Analysis.

We previously demonstrated that shotgun metagenomic sequencing can detect bacteria in sonicate fluid, providing a diagnosis of prosthetic joint infection (PJI). A limitation of the approach that we used is that data analysis was time-consuming and specialized bioinformatics expertise was required, both of which are barriers to routine clinical use. Fortunately, automated commercial analytic platforms that can interpret shotgun metagenomic data are emerging. In this study, we evaluated the CosmosID bioinformatics platform using shotgun metagenomic sequencing data derived from 408 sonicate fluid samples from our prior study with the goal of evaluating the platform vis-à-vis bacterial detection and antibiotic resistance gene detection for predicting staphylococcal antibacterial susceptibility. Samples were divided into a derivation set and a validation set, each consisting of 204 samples; results from the derivation set were used to establish cutoffs, which were then tested in the validation set for identifying pathogens and predicting staphylococcal antibacterial resistance. Metagenomic analysis detected bacteria in 94.8% (109/115) of sonicate fluid culture-positive PJIs and 37.8% (37/98) of sonicate fluid culture-negative PJIs. Metagenomic analysis showed sensitivities ranging from 65.7 to 85.0% for predicting staphylococcal antibacterial resistance. In conclusion, the CosmosID platform has the potential to provide fast, reliable bacterial detection and identification from metagenomic shotgun sequencing data derived from sonicate fluid for the diagnosis of PJI. Strategies for metagenomic detection of antibiotic resistance genes for predicting staphylococcal antibacterial resistance need further development.

Lack of correlation of virulence gene profiles of Staphylococcus aureus bacteremia isolates with mortality.

PURPOSE: Whole genome sequencing (WGS) analysis of Staphylococcus aureus is increasingly used in clinical practice. Although bioinformatics tools used in WGS analysis readily define the S. aureus virulome, the clinical value of this type of analysis is unclear. Here, virulence genes in S. aureus bacteremia (SAB) isolates were evaluated by WGS, with superantigens (SAgs) further evaluated by conventional PCR and functional assays, and results correlated with mortality. METHODS: 152 SAB isolates collected throughout 2015 at a large Minnesota medical center were studied and associated clinical data analyzed. Virulence genes were identified from previously-reported WGS data (https://doi.org/10.1371/journal.pone.0179003). SAg genes sea, seb, sec, sed, see, seg, seh, sei, sej, and tst were also assessed by individual PCR assays. Mitogenicity of SAgs was assessed using an in vitro proliferation assay with splenocytes from HLA-DR3 transgenic mice. RESULTS: Of the 152 SAB isolates studied, 106 (69%) were methicillin-susceptible S. aureus (MSSA). The number of deaths attributed and not attributed to SAB, and 30-day survivors were 24 (16%), 2 (1%), and 128 (83%), respectively. From WGS data, both MSSA and MRSA had high proportions of adhesion (>80%) and immune-evasion (>70%) genes. There was no difference in virulomes between survivor- and non-survivor-associated isolates. Although over 60% of SAB isolates produced functional SAgs, there were no differences in the distribution or prevalence of SAg genes between survivor- and non-survivor-associated isolates. CONCLUSION: In this study of one year of SAB isolates from a large medical center, the S. aureus virulome, as assessed by WGS, and also for SAgs using individual PCRs and phenotypic characterization, did not impact mortality.

Synthesis of multi-omic data and community metabolic models reveals insights into the role of hydrogen sulfide in colon cancer.

Multi-omic data and genome-scale microbial metabolic models have allowed us to examine microbial communities, community function, and interactions in ways that were not available to us historically. Now, one of our biggest challenges is determining how to integrate data and maximize data potential. Our study demonstrates one way in which to test a hypothesis by combining multi-omic data and community metabolic models. Specifically, we assess hydrogen sulfide production in colorectal cancer based on stool, mucosa, and tissue samples collected on and off the tumor site within the same individuals. 16S rRNA microbial community and abundance data were used to select and inform the metabolic models. We then used MICOM, an open source platform, to track the metabolic flux of hydrogen sulfide through a defined microbial community that either represented on-tumor or off-tumor sample communities. We also performed targeted and untargeted metabolomics, and used the former to quantitatively evaluate our model predictions. A deeper look at the models identified several unexpected but feasible reactions, microbes, and microbial interactions involved in hydrogen sulfide production for which our 16S and metabolomic data could not account. These results will guide future in vitro, in vivo, and in silico tests to establish why hydrogen sulfide production is increased in tumor tissue.

HGT-ID: an efficient and sensitive workflow to detect human-viral insertion sites using next-generation sequencing data.

BACKGROUND: Transfer of genetic material from microbes or viruses into the host genome is known as horizontal gene transfer (HGT). The integration of viruses into the human genome is associated with multiple cancers, and these can now be detected using next-generation sequencing methods such as whole genome sequencing and RNA-sequencing. RESULTS: We designed a novel computational workflow, HGT-ID, to identify the integration of viruses into the human genome using the sequencing data. The HGT-ID workflow primarily follows a four-step procedure: i) pre-processing of unaligned reads, ii) virus detection using subtraction approach, iii) identification of virus integration site using discordant and soft-clipped reads and iv) HGT candidates prioritization through a scoring function. Annotation and visualization of the events, as well as primer design for experimental validation, are also provided in the final report. We evaluated the tool performance with the well-understood cervical cancer samples. The HGT-ID workflow accurately detected known human papillomavirus (HPV) integration sites with high sensitivity and specificity compared to previous HGT methods. We applied HGT-ID to The Cancer Genome Atlas (TCGA) whole-genome sequencing data (WGS) from liver tumor-normal pairs. Multiple hepatitis B virus (HBV) integration sites were identified in TCGA liver samples and confirmed by HGT-ID using the RNA-Seq data from the matched liver pairs. This shows the applicability of the method in both the data types and cross-validation of the HGT events in liver samples. We also processed 220 breast tumor WGS data through the workflow; however, there were no HGT events detected in those samples. CONCLUSIONS: HGT-ID is a novel computational workflow to detect the integration of viruses in the human genome using the sequencing data. It is fast and accurate with functions such as prioritization, annotation, visualization and primer design for future validation of HGTs. The HGT-ID workflow is released under the MIT License and available at http://kalarikrlab.org/Software/HGT-ID.html .

Identification of Prosthetic Joint Infection Pathogens Using a Shotgun Metagenomics Approach.

Background: Metagenomic shotgun sequencing has the potential to change how many infections, particularly those caused by difficult-to-culture organisms, are diagnosed. Metagenomics was used to investigate prosthetic joint infections (PJIs), where pathogen detection can be challenging. Methods: Four hundred eight sonicate fluid samples generated from resected hip and knee arthroplasties were tested, including 213 from subjects with infections and 195 from subjects without infection. Samples were enriched for microbial DNA using the MolYsis basic kit, whole-genome amplified, and sequenced using Illumina HiSeq 2500 instruments. A pipeline was designed to screen out human reads and analyze remaining sequences for microbial content using the Livermore Metagenomics Analysis Toolkit and MetaPhlAn2 tools. Results: When compared to sonicate fluid culture, metagenomics was able to identify known pathogens in 94.8% (109/115) of culture-positive PJIs, with additional potential pathogens detected in 9.6% (11/115). New potential pathogens were detected in 43.9% (43/98) of culture-negative PJIs, 21 of which had no other positive culture sources from which these microorganisms had been detected. Detection of microorganisms in samples from uninfected aseptic failure cases was conversely rare (7/195 [3.6%] cases). The presence of human and contaminant microbial DNA from reagents was a challenge, as previously reported. Conclusions: Metagenomic shotgun sequencing is a powerful tool to identify a wide range of PJI pathogens, including difficult-to-detect pathogens in culture-negative infections.

Direct Detection and Identification of Prosthetic Joint Infection Pathogens in Synovial Fluid by Metagenomic Shotgun Sequencing.

Metagenomic shotgun sequencing has the potential to transform how serious infections are diagnosed by offering universal, culture-free pathogen detection. This may be especially advantageous for microbial diagnosis of prosthetic joint infection (PJI) by synovial fluid analysis since synovial fluid cultures are not universally positive and since synovial fluid is easily obtained preoperatively. We applied a metagenomics-based approach to synovial fluid in an attempt to detect microorganisms in 168 failed total knee arthroplasties. Genus- and species-level analyses of metagenomic sequencing yielded the known pathogen in 74 (90%) and 68 (83%) of the 82 culture-positive PJIs analyzed, respectively, with testing of two (2%) and three (4%) samples, respectively, yielding additional pathogens not detected by culture. For the 25 culture-negative PJIs tested, genus- and species-level analyses yielded 19 (76%) and 21 (84%) samples with insignificant findings, respectively, and 6 (24%) and 4 (16%) with potential pathogens detected, respectively. Genus- and species-level analyses of the 60 culture-negative aseptic failure cases yielded 53 (88%) and 56 (93%) cases with insignificant findings and 7 (12%) and 4 (7%) with potential clinically significant organisms detected, respectively. There was one case of aseptic failure with synovial fluid culture growth; metagenomic analysis showed insignificant findings, suggesting possible synovial fluid culture contamination. Metagenomic shotgun sequencing can detect pathogens involved in PJI when applied to synovial fluid and may be particularly useful for culture-negative cases.

Mackinac: a bridge between ModelSEED and COBRApy to generate and analyze genome-scale metabolic models.

SUMMARY: Reconstructing and analyzing a large number of genome-scale metabolic models is a fundamental part of the integrated study of microbial communities; however, two of the most widely used frameworks for building and analyzing models use different metabolic network representations. Here we describe Mackinac, a Python package that combines ModelSEED's ability to automatically reconstruct metabolic models with COBRApy's advanced analysis capabilities to bridge the differences between the two frameworks and facilitate the study of the metabolic potential of microorganisms. AVAILABILITY AND IMPLEMENTATION: This package works with Python 2.7, 3.4, and 3.5 on MacOS, Linux and Windows. The source code is available from https://github.com/mmundy42/mackinac . CONTACT: mundy.michael@mayo.edu or soares.maria@mayo.edu.