Responding to and managing multijurisdictional outbreaks of COVID-19 in Canadian industrial worksite/work camp settings.

SETTING: Early in the COVID-19 pandemic, the Public Health Agency of Canada (PHAC) and provincial/territorial (P/T) public health identified the need for a coordinated response to complex multijurisdictional COVID-19 outbreaks. The first large multijurisdictional industrial worksite COVID-19 outbreak highlighted the risk of transmission within these congregate work settings, the risk of transmission to the broader community(ies), and the need to develop setting-specific outbreak response frameworks. INTERVENTION: PHAC assembled a team to provide national outbreak support for multijurisdictional COVID-19 outbreaks in May 2020. The COVID-19 Outbreak Response Unit (ORU) worked with P/T partners to develop guiding principles for outbreak response and outbreak investigation processes, guidance documents, and investigation tools (e.g., minimum data elements and questionnaires). OUTCOMES: The ORU, P/T partners, and onsite industrial worksite health and safety staff leveraged outbreak investigation guidelines, industrial worksite outbreak process documents (including minimum data elements), and enhanced case questionnaires to respond to multiple COVID-19 outbreak investigations in industrial worksites. Clear roles/responsibilities and processes, along with standardized data, allowed for more efficient outbreak investigations and earlier implementation of mitigation measures. IMPLICATIONS: Multijurisdictional COVID-19 outbreaks highlighted the importance of public health collaboration with industry partners onsite. The assembly of a national outbreak response team was important to facilitate information sharing and provide technical support. Lessons learned and recommendations on outbreak preparation, detection, management, and communication are included to enhance a response framework applicable to future emerging or re-emerging pathogens with epidemic and/or pandemic potential.

RéSUMé: CONTEXTE: Au début de la pandémie de COVID-19, l'Agence de la santé publique du Canada (ASPC) et les autorités provinciales/territoriales de santé publique ont reconnu la nécessité d'une réponse coordonnée en cas d'éclosions complexes multi-juridictionnelles de COVID-19. La première grande éclosion multi-juridictionnelle de COVID-19 dans un chantier industriel a mis en évidence le risque de transmission dans ces milieux de travail collectifs, le risque de transmission à l'ensemble de la (des) communauté(s) et la nécessité d'élaborer des cadres d'intervention en cas d'éclosion spécifiques aux types de milieux. INTERVENTION: L'ASPC a formé une équipe chargée de soutenir la réponse nationale contre les éclosions multi-juridictionnelles de COVID-19 en mai 2020. L'Unité d'intervention en cas d'éclosion (UIE) de COVID-19 a collaboré avec des partenaires provinciaux et territoriaux pour élaborer des principes de référence pour la lutte contre les éclosions de COVID-19 et des processus d'enquête sur les éclosions, des documents d'orientation et des outils d'enquête (p.ex. des éléments de données minimales et des questionnaires). RéSULTATS: L'UIE, les provinces et territoires et le personnel chargé de la santé et sécurité du travail sur le site se sont appuyés sur des principes de référence aux enquêtes sur les éclosions, les documents de processus d'enquête sur les éclosions dans les sites industriels, y compris les éléments de données minimales et le questionnaire détaillé sur les cas, pour répondre à multiples enquêtes d'éclosions de COVID-19 dans les sites industriels. Des rôles/responsabilités et des processus clairs, ainsi que des données standardisées, ont permis de mener des enquêtes plus efficaces sur les éclosions et de mettre en œuvre plus rapidement des mesures d'atténuation. IMPLICATIONS: Les éclosions multi-juridictionnelles de COVID-19 ont mis en évidence l'importance de la collaboration entre les autorités de santé publique et les partenaires industriels sur site. La constitution d'une équipe nationale d'intervention en cas d'éclosion a été importante pour faciliter le partage des informations et fournir un soutien technique. Les connaissances acquises et les recommandations sur la préparation, la détection, la gestion et la communication des éclosions sont incluses afin d'améliorer le cadre de réponse aux futurs agents pathogènes émergents ou ré-émergents ayant un potentiel épidémique et/ou pandémique.

A cross-sectional study of MRI features and the gut microbiome in pediatric-onset multiple sclerosis.

OBJECTIVE: To identify gut microbiome features associated with MRI lesion burden in persons with pediatric-onset multiple sclerosis (symptom onset <18 years). METHODS: A cross-sectional study involving the Canadian Paediatric Demyelinating Disease Network study participants. Gut microbiome features (alpha diversity, phylum- and genus-level taxa) were derived using 16S rRNA sequencing from stool samples. T1- and T2-weighted lesion volumes were measured on brain MRI obtained within 6 months of stool sample procurement. Associations between the gut microbiota and MRI metrics (cube-root-transformed) were assessed using standard and Lasso regression models. RESULTS: Thirty-four participants were included; mean ages at symptom onset and MRI were 15.1 and 19.0 years, respectively, and 79% were female. The T1- and T2-weighted lesion volumes were not significantly associated with alpha diversity (age and sex-adjusted p > 0.08). At the phylum level, high Tenericutes (relative abundance) was associated with higher T1 and T2 volumes (ß coefficient = 0.25, 0.37) and high Firmicutes, Patescibacteria or Actinobacteria with lower lesion volumes (ß coefficient = -0.30 to -0.07). At the genus level, high Ruminiclostridium, whereas low Coprococcus 3 and low Erysipelatoclostridium were associated with higher lesion volumes. INTERPRETATION: Our study characterized the gut microbiota features associated with MRI lesion burden in pediatric-onset MS, shedding light onto possible pathophysiological mechanisms.

Clinical characteristics, racial inequities, and outcomes in patients with breast cancer and COVID-19: A COVID-19 and cancer consortium (CCC19) cohort study.

Background: Limited information is available for patients with breast cancer (BC) and coronavirus disease 2019 (COVID-19), especially among underrepresented racial/ethnic populations. Methods: This is a COVID-19 and Cancer Consortium (CCC19) registry-based retrospective cohort study of females with active or history of BC and laboratory-confirmed severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) infection diagnosed between March 2020 and June 2021 in the US. Primary outcome was COVID-19 severity measured on a five-level ordinal scale, including none of the following complications, hospitalization, intensive care unit admission, mechanical ventilation, and all-cause mortality. Multivariable ordinal logistic regression model identified characteristics associated with COVID-19 severity. Results: 1383 female patient records with BC and COVID-19 were included in the analysis, the median age was 61 years, and median follow-up was 90 days. Multivariable analysis revealed higher odds of COVID-19 severity for older age (aOR per decade, 1.48 [95% CI, 1.32-1.67]); Black patients (aOR 1.74; 95 CI 1.24-2.45), Asian Americans and Pacific Islander patients (aOR 3.40; 95 CI 1.70-6.79) and Other (aOR 2.97; 95 CI 1.71-5.17) racial/ethnic groups; worse ECOG performance status (ECOG PS ≥2: aOR, 7.78 [95% CI, 4.83-12.5]); pre-existing cardiovascular (aOR, 2.26 [95% CI, 1.63-3.15])/pulmonary comorbidities (aOR, 1.65 [95% CI, 1.20-2.29]); diabetes mellitus (aOR, 2.25 [95% CI, 1.66-3.04]); and active and progressing cancer (aOR, 12.5 [95% CI, 6.89-22.6]). Hispanic ethnicity, timing, and type of anti-cancer therapy modalities were not significantly associated with worse COVID-19 outcomes. The total all-cause mortality and hospitalization rate for the entire cohort was 9% and 37%, respectively however, it varied according to the BC disease status. Conclusions: Using one of the largest registries on cancer and COVID-19, we identified patient and BC-related factors associated with worse COVID-19 outcomes. After adjusting for baseline characteristics, underrepresented racial/ethnic patients experienced worse outcomes compared to non-Hispanic White patients. Funding: This study was partly supported by National Cancer Institute grant number P30 CA068485 to Tianyi Sun, Sanjay Mishra, Benjamin French, Jeremy L Warner; P30-CA046592 to Christopher R Friese; P30 CA023100 for Rana R McKay; P30-CA054174 for Pankil K Shah and Dimpy P Shah; KL2 TR002646 for Pankil Shah and the American Cancer Society and Hope Foundation for Cancer Research (MRSG-16-152-01-CCE) and P30-CA054174 for Dimpy P Shah. REDCap is developed and supported by Vanderbilt Institute for Clinical and Translational Research grant support (UL1 TR000445 from NCATS/NIH). The funding sources had no role in the writing of the manuscript or the decision to submit it for publication. Clinical trial number: CCC19 registry is registered on ClinicalTrials.gov, NCT04354701.

Use of a taxon-specific reference database for accurate metagenomics-based pathogen detection of Listeria monocytogenes in turkey deli meat and spinach.

BACKGROUND: The reliability of culture-independent pathogen detection in foods using metagenomics is contingent on the quality and composition of the reference database. The inclusion of microbial sequences from a diverse representation of taxonomies in universal reference databases is recommended to maximize classification precision for pathogen detection. However, these sizable databases have high memory requirements that may be out of reach for some users. In this study, we aimed to assess the performance of a foodborne pathogen (FBP)-specific reference database (taxon-specific) relative to a universal reference database (taxon-agnostic). We tested our FBP-specific reference database's performance for detecting Listeria monocytogenes in two complex food matrices-ready-to-eat (RTE) turkey deli meat and prepackaged spinach-using three popular read-based DNA-to-DNA metagenomic classifiers: Centrifuge, Kraken 2 and KrakenUniq. RESULTS: In silico host sequence removal led to substantially fewer false positive (FP) classifications and higher classification precision in RTE turkey deli meat datasets using the FBP-specific reference database. No considerable improvement in classification precision was observed following host filtering for prepackaged spinach datasets and was likely a consequence of a higher microbe-to-host sequence ratio. All datasets classified with Centrifuge using the FBP-specific reference database had the lowest classification precision compared to Kraken 2 or KrakenUniq. When a confidence-scoring threshold was applied, a nearly equivalent precision to the universal reference database was achieved for Kraken 2 and KrakenUniq. Recall was high for both reference databases across all datasets and classifiers. Substantially fewer computational resources were required for metagenomics-based detection of L. monocytogenes using the FBP-specific reference database, especially when combined with Kraken 2. CONCLUSIONS: A universal (taxon-agnostic) reference database is not essential for accurate and reliable metagenomics-based pathogen detection of L. monocytogenes in complex food matrices. Equivalent classification performance can be achieved using a taxon-specific reference database when the appropriate quality control measures, classification software, and analysis parameters are applied. This approach is less computationally demanding and more attainable for the broader scientific and food safety communities.

Clinical Characteristics, Racial Inequities, and Outcomes in Patients with Breast Cancer and COVID-19: A COVID-19 and Cancer Consortium (CCC19) Cohort Study.

Background: Limited information is available for patients with breast cancer (BC) and coronavirus disease 2019 (COVID-19), especially among underrepresented racial/ethnic populations. Methods: This is a COVID-19 and Cancer Consortium (CCC19) registry-based retrospective cohort study of females with active or history of BC and laboratory-confirmed severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) infection diagnosed between March 2020 and June 2021 in the US. Primary outcome was COVID-19 severity measured on a five-level ordinal scale, including none of the following complications, hospitalization, intensive care unit admission, mechanical ventilation, and all-cause mortality. Multivariable ordinal logistic regression model identified characteristics associated with COVID-19 severity. Results: 1,383 female patient records with BC and COVID-19 were included in the analysis, the median age was 61 years, and median follow-up was 90 days. Multivariable analysis revealed higher odds of COVID-19 severity for older age (aOR per decade, 1.48 [95% CI, 1.32 - 1.67]); Black patients (aOR 1.74; 95 CI 1.24-2.45), Asian Americans and Pacific Islander patients (aOR 3.40; 95 CI 1.70 - 6.79) and Other (aOR 2.97; 95 CI 1.71-5.17) racial/ethnic groups; worse ECOG performance status (ECOG PS ≥2: aOR, 7.78 [95% CI, 4.83 - 12.5]); pre-existing cardiovascular (aOR, 2.26 [95% CI, 1.63 - 3.15])/pulmonary comorbidities (aOR, 1.65 [95% CI, 1.20 - 2.29]); diabetes mellitus (aOR, 2.25 [95% CI, 1.66 - 3.04]); and active and progressing cancer (aOR, 12.5 [95% CI, 6.89 - 22.6]). Hispanic ethnicity, timing and type of anti-cancer therapy modalities were not significantly associated with worse COVID-19 outcomes. The total all-cause mortality and hospitalization rate for the entire cohort was 9% and 37%, respectively however, it varied according to the BC disease status. Conclusions: Using one of the largest registries on cancer and COVID-19, we identified patient and BC related factors associated with worse COVID-19 outcomes. After adjusting for baseline characteristics, underrepresented racial/ethnic patients experienced worse outcomes compared to Non-Hispanic White patients.

The DataHarmonizer: a tool for faster data harmonization, validation, aggregation and analysis of pathogen genomics contextual information.

Pathogen genomics is a critical tool for public health surveillance, infection control, outbreak investigations as well as research. In order to make use of pathogen genomics data, they must be interpreted using contextual data (metadata). Contextual data include sample metadata, laboratory methods, patient demographics, clinical outcomes and epidemiological information. However, the variability in how contextual information is captured by different authorities and how it is encoded in different databases poses challenges for data interpretation, integration and their use/re-use. The DataHarmonizer is a template-driven spreadsheet application for harmonizing, validating and transforming genomics contextual data into submission-ready formats for public or private repositories. The tool's web browser-based JavaScript environment enables validation and its offline functionality and local installation increases data security. The DataHarmonizer was developed to address the data sharing needs that arose during the COVID-19 pandemic, and was used by members of the Canadian COVID Genomics Network (CanCOGeN) to harmonize SARS-CoV-2 contextual data for national surveillance and for public repository submission. In order to support coordination of international surveillance efforts, we have partnered with the Public Health Alliance for Genomic Epidemiology to also provide a template conforming to its SARS-CoV-2 contextual data specification for use worldwide. Templates are also being developed for One Health and foodborne pathogens. Overall, the DataHarmonizer tool improves the effectiveness and fidelity of contextual data capture as well as its subsequent usability. Harmonization of contextual information across authorities, platforms and systems globally improves interoperability and reusability of data for concerted public health and research initiatives to fight the current pandemic and future public health emergencies. While initially developed for the COVID-19 pandemic, its expansion to other data management applications and pathogens is already underway.

Interplay of Immunosuppression and Immunotherapy Among Patients With Cancer and COVID-19.

Importance: Cytokine storm due to COVID-19 can cause high morbidity and mortality and may be more common in patients with cancer treated with immunotherapy (IO) due to immune system activation. Objective: To determine the association of baseline immunosuppression and/or IO-based therapies with COVID-19 severity and cytokine storm in patients with cancer. Design, Setting, and Participants: This registry-based retrospective cohort study included 12 046 patients reported to the COVID-19 and Cancer Consortium (CCC19) registry from March 2020 to May 2022. The CCC19 registry is a centralized international multi-institutional registry of patients with COVID-19 with a current or past diagnosis of cancer. Records analyzed included patients with active or previous cancer who had a laboratory-confirmed infection with SARS-CoV-2 by polymerase chain reaction and/or serologic findings. Exposures: Immunosuppression due to therapy; systemic anticancer therapy (IO or non-IO). Main Outcomes and Measures: The primary outcome was a 5-level ordinal scale of COVID-19 severity: no complications; hospitalized without requiring oxygen; hospitalized and required oxygen; intensive care unit admission and/or mechanical ventilation; death. The secondary outcome was the occurrence of cytokine storm. Results: The median age of the entire cohort was 65 years (interquartile range [IQR], 54-74) years and 6359 patients were female (52.8%) and 6598 (54.8%) were non-Hispanic White. A total of 599 (5.0%) patients received IO, whereas 4327 (35.9%) received non-IO systemic anticancer therapies, and 7120 (59.1%) did not receive any antineoplastic regimen within 3 months prior to COVID-19 diagnosis. Although no difference in COVID-19 severity and cytokine storm was found in the IO group compared with the untreated group in the total cohort (adjusted odds ratio [aOR], 0.80; 95% CI, 0.56-1.13, and aOR, 0.89; 95% CI, 0.41-1.93, respectively), patients with baseline immunosuppression treated with IO (vs untreated) had worse COVID-19 severity and cytokine storm (aOR, 3.33; 95% CI, 1.38-8.01, and aOR, 4.41; 95% CI, 1.71-11.38, respectively). Patients with immunosuppression receiving non-IO therapies (vs untreated) also had worse COVID-19 severity (aOR, 1.79; 95% CI, 1.36-2.35) and cytokine storm (aOR, 2.32; 95% CI, 1.42-3.79). Conclusions and Relevance: This cohort study found that in patients with cancer and COVID-19, administration of systemic anticancer therapies, especially IO, in the context of baseline immunosuppression was associated with severe clinical outcomes and the development of cytokine storm. Trial Registration: ClinicalTrials.gov Identifier: NCT04354701.

The metabolic potential of the paediatric-onset multiple sclerosis gut microbiome.

BACKGROUND: The aim of this study was to examine the gut microbiome's metabolic potential in paediatric-onset MS patients (symptom onset <18 years). METHODS: We included 17 MS participants and 20 controls similar for sex, age, race, and stool consistency from the Canadian Paediatric Demyelinating Disease Network study. Stool-derived gut metagenome gene abundances were used to estimate relative abundances and turnover scores of individual microbial metabolites and the composition and diversity of carbohydrate-active enzymes (CAZymes). MS participants and controls were compared using the Wilcoxon rank-sum test, as were the disease-modifying drug (DMD) exposed and naïve MS participants. RESULTS: The median age(s) at MS symptom onset=16.1 years (interquartile range [IQR]=1.7), and at stool sample procurement=16.9/15.8 years (IQR=2.0/1.4), for the MS participants/controls. Most MS and control participants were girls (80-82%). Five (29%) of the MS participants had never been exposed to a DMD pre-stool sample and 12 (71%) had (7 to beta-interferon and 5 glatiramer acetate). While the relative abundance of metabolites did not differ between MS participants and controls, turnover scores did. MS participants had a greater potential to metabolize lipopolysaccharides than controls (score difference=1.6E-04, p = 0.034) but lower potential to metabolize peptidoglycan molecules and starch (score differences<2.2E-02, p<0.040). Further, although CAZymes diversity did not differ (p>0.050), starch-degrading subfamilies were underrepresented in MS participants versus controls (relative abundance differences >-0.34, p<0.040) and in the DMD exposed verses DMD naïve MS participants (relative abundance differences>-0.20, p<0.049). CONCLUSION: Paediatric-onset MS participants had an altered gut microbiome-related metabolic potential compared to controls, including higher breakdown of lipopolysaccharide molecules, but lower resistant starch metabolism.

The need for linked genomic surveillance of SARS-CoV-2.

Genomic surveillance during the coronavirus disease 2019 (COVID-19) pandemic has been key to the timely identification of virus variants with important public health consequences, such as variants that can transmit among and cause severe disease in both vaccinated or recovered individuals. The rapid emergence of the Omicron variant highlighted the speed with which the extent of a threat must be assessed. Rapid sequencing and public health institutions' openness to sharing sequence data internationally give an unprecedented opportunity to do this; however, assessing the epidemiological and clinical properties of any new variant remains challenging. Here we highlight a "band of four" key data sources that can help to detect viral variants that threaten COVID-19 management: 1) genetic (virus sequence) data; 2) epidemiological and geographic data; 3) clinical and demographic data; and 4) immunization data. We emphasize the benefits that can be achieved by linking data from these sources and by combining data from these sources with virus sequence data. The considerable challenges of making genomic data available and linked with virus and patient attributes must be balanced against major consequences of not doing so, especially if new variants of concern emerge and spread without timely detection and action.

Stability of the gut microbiota in persons with paediatric-onset multiple sclerosis and related demyelinating diseases.

OBJECTIVE: Examine if the gut microbiota composition changes across repeated samples in paediatric-onset multiple sclerosis (MS) or monophasic-acquired demyelinating syndromes (monoADS). METHODS: A total of 36 individuals (18 MS/18 monoADS) with ⩾2 stool samples were included. Stool sample-derived DNA was sequenced. Alpha/beta diversities and genus-level taxa were analysed. RESULTS: Mean ages at first sample procurement (MS/monoADS) = 18.0/13.8 years. Median time (months) between first/second samples = 11.2 and second/third = 10.3. Alpha/beta diversities did not differ between stool samples (p > 0.09), while one genus - Solobacterium did (p = 0.001). CONCLUSIONS: The gut microbiota composition in paediatric-onset MS and monoADS exhibited stability, suggesting that single stool sample procurement is a reasonable first approach.

Clinical Metagenomics Is Increasingly Accurate and Affordable to Detect Enteric Bacterial Pathogens in Stool.

Stool culture is the gold standard method to diagnose enteric bacterial infections; however, many clinical laboratories are transitioning to syndromic multiplex PCR panels. PCR is rapid, accurate, and affordable, yet does not yield subtyping information critical for foodborne disease surveillance. A metagenomics-based stool testing approach could simultaneously provide diagnostic and public health information. Here, we evaluated shotgun metagenomics to assess the detection of common enteric bacterial pathogens in stool. We sequenced 304 stool specimens from 285 patients alongside routine diagnostic testing for Salmonella spp., Campylobacter spp., Shigella spp., and shiga-toxin producing Escherichia coli. Five analytical approaches were assessed for pathogen detection: microbiome profiling, Kraken2, MetaPhlAn, SRST2, and KAT-SECT. Among analysis tools and databases compared, KAT-SECT analysis provided the best sensitivity and specificity for all pathogens tested compared to culture (91.2% and 96.2%, respectively). Where metagenomics detected a pathogen in culture-negative specimens, standard PCR was positive 85% of the time. The cost of metagenomics is approaching the current combined cost of PCR, reflex culture, and whole genome sequencing for pathogen detection and subtyping. As cost, speed, and analytics for single-approach metagenomics improve, it may be more routinely applied in clinical and public health laboratories.

Coinfections in Patients With Cancer and COVID-19: A COVID-19 and Cancer Consortium (CCC19) Study.

BACKGROUND: The frequency of coinfections and their association with outcomes have not been adequately studied among patients with cancer and coronavirus disease 2019 (COVID-19), a high-risk group for coinfection. METHODS: We included adult (≥18 years) patients with active or prior hematologic or invasive solid malignancies and laboratory-confirmed severe acute respiratory syndrome coronavirus 2 (SARS-COV-2) infection, using data from the COVID-19 and Cancer Consortium (CCC19, NCT04354701). We captured coinfections within ±2 weeks from diagnosis of COVID-19, identified factors cross-sectionally associated with risk of coinfection, and quantified the association of coinfections with 30-day mortality. RESULTS: Among 8765 patients (hospitalized or not; median age, 65 years; 47.4% male), 16.6% developed coinfections: 12.1% bacterial, 2.1% viral, 0.9% fungal. An additional 6.4% only had clinical diagnosis of a coinfection. The adjusted risk of any coinfection was positively associated with age >50 years, male sex, cardiovascular, pulmonary, and renal comorbidities, diabetes, hematologic malignancy, multiple malignancies, Eastern Cooperative Oncology Group Performance Status, progressing cancer, recent cytotoxic chemotherapy, and baseline corticosteroids; the adjusted risk of superinfection was positively associated with tocilizumab administration. Among hospitalized patients, high neutrophil count and C-reactive protein were positively associated with bacterial coinfection risk, and high or low neutrophil count with fungal coinfection risk. Adjusted mortality rates were significantly higher among patients with bacterial (odds ratio [OR], 1.61; 95% CI, 1.33-1.95) and fungal (OR, 2.20; 95% CI, 1.28-3.76) coinfections. CONCLUSIONS: Viral and fungal coinfections are infrequent among patients with cancer and COVID-19, with the latter associated with very high mortality rates. Clinical and laboratory parameters can be used to guide early empiric antimicrobial therapy, which may improve clinical outcomes.

Colorectal Cancer Screening in Inflammatory Bowel Diseases-Can Characterization of GI Microbiome Signatures Enhance Neoplasia Detection?

Current noninvasive methods for colorectal cancer (CRC) screening are not optimized for persons with inflammatory bowel diseases (IBDs), requiring patients to undergo frequent interval screening via colonoscopy. Although colonoscopy-based screening reduces CRC incidence in IBD patients, rates of interval CRC remain relatively high, highlighting the need for more targeted approaches. In recent years, the discovery of disease-specific microbiome signatures for both IBD and CRC has begun to emerge, suggesting that stool-based biomarker detection using metagenomics and other culture-independent technologies may be useful for personalized, early, noninvasive CRC screening in IBD patients. Here we discuss the utility of the stool microbiome as a noninvasive CRC screening tool. Comparing the performance of multiple microbiome-based CRC classifiers, including several multi-cohort meta-analyses, we find that noninvasive detection of colorectal adenomas and carcinomas from microbial biomarkers is an active area of study with promising early results.

Metagenomic Analysis of the Pediatric-Onset Multiple Sclerosis Gut Microbiome.

BACKGROUND AND OBJECTIVES: Little is known of the functional potential of the gut microbiome in pediatric-onset multiple sclerosis (MS). We performed metagenomic analyses using stool samples from individuals with pediatric-onset MS and unaffected controls. METHODS: Persons ≤21 years old enrolled in the Canadian Pediatric Demyelinating Disease Network providing a stool sample were eligible. Twenty patients with MS (McDonald criteria) with symptom onset <18 years were matched to 20 controls by sex, age (±3 years), stool consistency, and race. Microbial taxonomy and functional potentials were estimated from stool sample-derived metagenomic reads and compared by disease status (MS vs controls) and disease-modifying drug (DMD) exposure using alpha diversity, relative abundance, and prevalence using Wilcoxon rank sum, ALDEx2, and Fisher exact tests, respectively. RESULTS: Individuals with MS were aged 13.6 years (mean) at symptom onset and 8 were DMD-naive. Mean ages at stool sample were 16.1 and 15.4 years for MS and control participants, respectively; 80% were girls. Alpha diversity of enzymes and proteins did not differ by disease or DMD status (p > 0.20), but metabolic pathways, gene annotations, and microbial taxonomy did. Individuals with MS (vs controls) exhibited higher methanogenesis prevalence (odds ratio 10, p = 0.044) and Methanobrevibacter abundance (log2 fold change [LFC] 1.7, p = 0.0014), but lower homolactic fermentation abundance (LFC -0.48, p = 0.039). Differences by DMD status included lower phosphate butyryl transferase for DMD-naive vs exposed patients with MS (LFC -1.0, p = 0.033). DISCUSSION: The gut microbiome's functional potential and taxonomy differed between individuals with pediatric-onset MS vs controls, including higher prevalence of a methane-producing pathway from Archaea and depletion of the lactate fermentation pathway. DMD exposure was associated with butyrate-producing enzyme enrichment. Together these findings indicate that the gut microbiome of individuals with MS may have a disturbed functional potential.

The gut microbiota in pediatric multiple sclerosis and demyelinating syndromes.

OBJECTIVE: To examine the gut microbiota in individuals with and without pediatric-onset multiple sclerosis (MS). METHODS: We compared stool-derived microbiota of Canadian Pediatric Demyelinating Disease Network study participants ≤21 years old, with MS (disease-modifying drug [DMD] exposed and naïve) or monophasic acquired demyelinating syndrome [monoADS] (symptom onset <18 years), and unaffected controls. All were ≥30 days without antibiotics or corticosteroids. V4 region 16S RNA gene-derived amplicon sequence variants (Illumina MiSeq) were assessed using negative binomial regression and network analyses; rate ratios were age- and sex-adjusted (aRR). RESULTS: Thirty-two MS, 41 monoADS (symptom onset [mean] = 14.0 and 6.9 years) and 36 control participants were included; 75%/56%/58% were female, with mean ages at stool sample = 16.5/13.8/15.1 years, respectively. Nine MS cases (28%) were DMD-naïve. Although microbiota diversity (alpha, beta) did not differ between participants (p > 0.1), taxa-level and gut community networks did. MS (vs. monoADS) exhibited > fourfold higher relative abundance of the superphylum Patescibacteria (aRR = 4.2;95%CI:1.6-11.2, p = 0.004, Q = 0.01), and lower abundances of short-chain fatty acid (SCFA)-producing Lachnospiraceae (Anaerosporobacter) and Ruminococcaceae (p, Q < 0.05). DMD-naïve MS cases were depleted for Clostridiales vadin-BB60 (unnamed species) versus either DMD-exposed, controls (p, Q < 0.01), or monoADS (p = 0.001, Q = 0.06) and exhibited altered community connectedness (p < 10-9 Kruskal-Wallis), with SCFA-producing taxa underrepresented. Consistent taxa-level findings from an independent US Network of Pediatric MS Centers case/control (n = 51/42) cohort included >eightfold higher abundance for Candidatus Stoquefichus and Tyzzerella (aRR = 8.8-12.8, p < 0.05) in MS cases and 72%-80% lower abundance of SCFA-producing Ruminococcaceae-NK4A214 (aRR = 0.38-0.2, p ≤ 0.01). INTERPRETATION: Gut microbiota community structure, function and connectivity, and not just individual taxa, are of likely importance in MS.

Microbiome-Mediated Immune Signaling in Inflammatory Bowel Disease and Colorectal Cancer: Support From Meta-omics Data.

Chronic intestinal inflammation and microbial dysbiosis are hallmarks of colorectal cancer (CRC) and inflammatory bowel diseases (IBD), such as Crohn's disease and ulcerative colitis. However, the mechanistic relationship between gut dysbiosis and disease has not yet been fully characterized. Although the "trigger" of intestinal inflammation remains unknown, a wealth of evidence supports the role of the gut microbiome as a mutualistic pseudo-organ that significantly influences intestinal homeostasis and is capable of regulating host immunity. In recent years, culture-independent methods for assessing microbial communities as a whole (termed meta-omics) have grown beyond taxonomic identification and genome characterization (metagenomics) into new fields of research that collectively expand our knowledge of microbiomes. Metatranscriptomics, metaproteomics, and metabolomics are meta-omics techniques that aim to describe and quantify the functional activity of the gut microbiome. Uncovering microbial metabolic contributions in the context of IBD and CRC using these approaches provides insight into how the metabolic microenvironment of the GI tract shapes microbial community structure and how the microbiome, in turn, influences the surrounding ecosystem. Immunological studies in germ-free and wild-type mice have described several host-microbiome interactions that may play a role in autoinflammation. Chronic colitis is a precursor to CRC, and changes in the gut microbiome may be an important link triggering the neoplastic process in chronic colitis. In this review, we describe several microbiome-mediated mechanisms of host immune signaling, such as short-chain fatty acid (SCFA) and bile acid metabolism, inflammasome activation, and cytokine regulation in the context of IBD and CRC, and discuss the supporting role for these mechanisms by meta-omics data.

Rapid and accurate SNP genotyping of clonal bacterial pathogens with BioHansel.

Hierarchical genotyping approaches can provide insights into the source, geography and temporal distribution of bacterial pathogens. Multiple hierarchical SNP genotyping schemes have previously been developed so that new isolates can rapidly be placed within pre-computed population structures, without the need to rebuild phylogenetic trees for the entire dataset. This classification approach has, however, seen limited uptake in routine public health settings due to analytical complexity and the lack of standardized tools that provide clear and easy ways to interpret results. The BioHansel tool was developed to provide an organism-agnostic tool for hierarchical SNP-based genotyping. The tool identifies split k-mers that distinguish predefined lineages in whole genome sequencing (WGS) data using SNP-based genotyping schemes. BioHansel uses the Aho-Corasick algorithm to type isolates from assembled genomes or raw read sequence data in a matter of seconds, with limited computational resources. This makes BioHansel ideal for use by public health agencies that rely on WGS methods for surveillance of bacterial pathogens. Genotyping results are evaluated using a quality assurance module which identifies problematic samples, such as low-quality or contaminated datasets. Using existing hierarchical SNP schemes for Mycobacterium tuberculosis and Salmonella Typhi, we compare the genotyping results obtained with the k-mer-based tools BioHansel and SKA, with those of the organism-specific tools TBProfiler and genotyphi, which use gold-standard reference-mapping approaches. We show that the genotyping results are fully concordant across these different methods, and that the k-mer-based tools are significantly faster. We also test the ability of the BioHansel quality assurance module to detect intra-lineage contamination and demonstrate that it is effective, even in populations with low genetic diversity. We demonstrate the scalability of the tool using a dataset of ~8100 S. Typhi public genomes and provide the aggregated results of geographical distributions as part of the tool's output. BioHansel is an open source Python 3 application available on PyPI and Conda repositories and as a Galaxy tool from the public Galaxy Toolshed. In a public health context, BioHansel enables rapid and high-resolution classification of bacterial pathogens with low genetic diversity.

Global population structure of Haemophilus influenzae serotype a (Hia) and emergence of invasive Hia disease: capsule switching or capsule replacement?

The population structure of Haemophilus influenzae serotype a (Hia) was examined by interrogation of the H. influenzae MLST website. There were 196 entries of Hia with 55 sequence types (STs) identified (as of 3 September 2020). BURST analysis clustered related STs into four complexes with ST-23, ST-4, ST-21, and ST-62 identified as their ancestral STs. The majority of Hia entries (73.4%) and STs (65.5%) were identified as clonal division I (ST-23 and ST-4 complexes). Only 43 (21.9%) entries and 14 STs (25.5%) were identified as clonal division II (ST-62 and ST-21 complexes). Current data suggest that most invasive Hia belonged to clonal division I and the ST-23 complex, while most clonal division II Hia were respiratory isolates, with the exception of ST-62 which was common among invasive Hia in the US southwest. Comparison of the capsule bexABCD genes from clonal divisions I and II strains showed sequence diversity with variations following the pattern of clonal divisions. Evidence from the literature and the current study suggests that the recent emergence of invasive Hia might be related to capsule replacement subsequent to the implementation of the Hib conjugate vaccine and possibly exacerbated by other conjugate vaccines that may have altered the microbial flora of the human respiratory tract.

The Atherogenic Index of Plasma is Independently Associated with Symptomatic Carotid Artery Stenosis.

INTRODUCTION: Small, dense low-density lipoprotein (sdLDL) is strongly associated with symptomatic carotid artery stenosis, but is not routinely evaluated in ischemic stroke patients. A method using the logarithmic transformation of the ratio of the plasma concentration of triglycerides (TGY) to HDL-cholesterol (HDL-C)[(Log[TGY/HDL-C])] has been described as a surrogate marker for sdLDL termed the atherogenic index of plasma (AIP). OBJECTIVE: To determine if the AIP is independently associated with symptomatic carotid artery stenosis. METHODS: We conducted a single center case-controlled study using a sample of ischemic stroke patients and compared risk factors of patients with and without symptomatic carotid artery stenosis. A multivariate logistic regression model was used to determine if the AIP divided into four quartiles was independently associated with symptomatic carotid artery stenosis. This model was compared to three other lipid models. Associations between non-lipid variables and the AIP were also identified. RESULTS: 31 cases of ischemic stroke due to symptomatic carotid artery stenosis and 236 controls of ischemic stroke not due to carotid artery stenosis were identified. Of the four lipid models assessed, only the model including the AIP (model 4) was found to be significantly associated with symptomatic carotid artery stenosis. The odd's ratio (OR) for quartile 3 was 3.82 (95% CI 1.03-14.17) and the OR for quartile 4 was 4.13 (95% CI 1.09-15.54) using quartile 1 as a reference. Metabolic syndrome was the only variable associated with the AIP (OR 5.06 95% CI 2.6-9.7). CONCLUSION: At our single center, the AIP was the only lipid parameter independently associated with symptomatic carotid artery stenosis; and metabolic syndrome was independently associated with the AIP. The AIP may serve as a useful surrogate of sdLDL in patients with symptomatic carotid artery stenosis.

Assessment of Inter-Laboratory Variation in the Characterization and Analysis of the Mucosal Microbiota in Crohn's Disease and Ulcerative Colitis.

BACKGROUND: In studies evaluating the microbiome, numerous factors can contribute to technical variability. These factors include DNA extraction methodology, sequencing protocols, and data analysis strategies. We sought to evaluate the impact these factors have on the results obtained when the sequence data are independently generated and analyzed by different laboratories. METHODS: To evaluate the effect of technical variability, we used human intestinal biopsy samples resected from individuals diagnosed with an inflammatory bowel disease (IBD), including Crohn's disease (n = 12) and ulcerative colitis (n = 10), and those without IBD (n = 10). Matched samples from each participant were sent to three laboratories and studied using independent protocols for DNA extraction, library preparation, targeted-amplicon sequencing of a 16S rRNA gene hypervariable region, and processing of sequence data. We looked at two measures of interest - Bray-Curtis PERMANOVA R 2 values and log2 fold-change estimates of the 25 most-abundant taxa - to assess variation in the results produced by each laboratory, as well the relative contribution to variation from the different extraction, sequencing, and analysis steps used to generate these measures. RESULTS: The R 2 values and estimated differential abundance associated with diagnosis were consistent across datasets that used different DNA extraction and sequencing protocols, and within datasets that pooled samples from multiple protocols; however, variability in bioinformatic processing of sequence data led to changes in R 2 values and inconsistencies in taxonomic assignment and abundance estimates. CONCLUSION: Although the contribution of DNA extraction and sequencing methods to variability were observable, we find that results can be robust to the various extraction and sequencing approaches used in our study. Differences in data processing methods have a larger impact on results, making comparison among studies less reliable and the combined analysis of bioinformatically processed samples nearly impossible. Our results highlight the importance of making raw sequence data available to facilitate combined and comparative analyses of published studies using common data processing protocols. Study methodologies should provide detailed data processing methods for validation, interpretability, reproducibility, and comparability.