Estimating disease risk using the microbiome: the Microbial Risk Score (preprint)

Our microbiome has profound impacts on our health, and technological advances have allowed for ever-growing pools of data from microbiome-wide studies. This means that our microbiome could be used to estimate disease risk, but the nature of the data makes the endeavor difficult. Similar issues with using genetics to predict disease risk led to the development of the polygenic risk score. Motivated by the success of that framework, a team of researchers recently developed the microbial risk score (MRS). MRS summarizes the complex microbial profile by first identifying a sub- community consisting of disease-associated microbial taxa and then integrating those microbial taxa into a continuous score based on the alpha diversity of the identified sub-community. MRS can be easily integrated with the other risk scores built upon metatranscriptomics, host genetics, or host transcriptomics, making it useful for 'multi-omics' approaches as well. The researchers validated this new algorithm on three cohort datasets that included data on COVID-19, several gastrointestinal diseases, and type 1 diabetes. While MRS needs to be tested in more populations, it showed promise in this study as a tool to predict disease and could also be applied to microbial ecology research and exploring the microbiome’s clinical potential. 

Effect of E-beam irradiation on the qualitative attributes of shrimp (Penaeus vannamei)

As a non-thermal food processing technology, Electron beam (E-beam) irradiation has been used to enhance microbial safety by deactivating unwanted spoilage and pathogenic microorganisms in food industry. This study evaluated the effects of E-beam irradiation at doses killing SARS-COV-2 on qualities and sensory attributes. The results showed that irradiation caused little effect on the proximate composition, amino acid content, texture, and sensory attributes (P > 0.05). However, E-beam increased TBARS (Thiobarbituric acid reactive substances) and lowered vitamin E content in dose-dependently. Irradiation up to 10 kGy significantly decreased unsaturated fatty acid (UFA) content and inhibited the increase in TVB-N (The total volatile basic nitrogen) while reducing cohesiveness and chewiness (P < 0.05). E-beam irradiation with 7–10 kGy caused greater ΔE values (ΔE > 5) via the significant increase of b*, accompanied by big visual difference in shrimp (P < 0.05). A dose of 4 kGy E-beam irradiation was recommended without altering its physicochemical properties and sensory attributes.

Microbial Risk Score for Capturing Microbial Characteristics, Integrating Multi-omics Data, and Predicting Disease Risk (preprint)

Background With the rapid accumulation of microbiome-wide association studies, a great amount of microbiome data are available to study the microbiome’s role in human disease and advance the microbiome’s potential use for disease prediction. However, the unique features of microbiome data hinder its utility for disease prediction. Methods Motivated from the polygenic risk score framework, we propose a microbial risk score (MRS) framework to aggregate the complicated microbial profile into a summarized risk score that can be used to measure and predict disease susceptibility. Specifically, the MRS algorithm involves two steps: 1) identifying a sub-community consisting of the signature microbial taxa associated with disease, and 2) integrating the identified microbial taxa into a continuous score. The first step is carried out using the existing sophisticated microbial association tests and pruning and thresholding method in the discovery samples. The second step constructs a community-based MRS by calculating alpha diversity on the identified sub-community in the validation samples. Moreover, we propose a multi-omics data integration method by jointly modeling the proposed MRS and other risk scores constructed from other omics data in disease prediction. Results Through three comprehensive real data analyses using the NYU Langone Health COVID-19 cohort, the gut microbiome health index (GMHI) multi-study cohort, and a large type 1 diabetes cohort separately, we exhibit and evaluate the utility of the proposed MRS framework for disease prediction and multi-omics data integration. In addition, the disease-specific MRSs for colorectal adenoma, colorectal cancer, Crohn’s disease, and rheumatoid arthritis based on the relative abundances of 5, 6, 12, and 6 microbial taxa respectively are created and validated using the GMHI multi-study cohort. Especially, Crohn’s disease MRS achieves AUCs of 0.88 ([0.85-0.91]) and 0.86 ([0.78-0.95]) in the discovery and validation cohorts, respectively. Conclusions The proposed MRS framework sheds light on the utility of the microbiome data for disease prediction and multi-omics integration, and provides great potential in understanding the microbiome’s role in disease diagnosis and prognosis.

Microbial signatures in the lower airways of mechanically ventilated COVID19 patients associated with poor clinical outcome (preprint)

Mortality among patients with COVID-19 and respiratory failure is high and there are no known lower airway biomarkers that predict clinical outcome. We investigated whether bacterial respiratory infections and viral load were associated with poor clinical outcome and host immune tone. We obtained bacterial and fungal culture data from 589 critically ill subjects with COVID-19 requiring mechanical ventilation. On a subset of the subjects that underwent bronchoscopy, we also quantified SARS-CoV-2 viral load, analyzed the microbiome of the lower airways by metagenome and metatranscriptome analyses and profiled the host immune response. We found that isolation of a hospital-acquired respiratory pathogen was not associated with fatal outcome. However, poor clinical outcome was associated with enrichment of the lower airway microbiota with an oral commensal ( Mycoplasma salivarium ), while high SARS-CoV-2 viral burden, poor anti-SARS-CoV-2 antibody response, together with a unique host transcriptome profile of the lower airways were most predictive of mortality. Collectively, these data support the hypothesis that 1) the extent of viral infectivity drives mortality in severe COVID-19, and therefore 2) clinical management strategies targeting viral replication and host responses to SARS-CoV-2 should be prioritized.

Microbial signatures in the lower airways of mechanically ventilated COVID19 patients associated with poor clinical outcome (preprint)

Mortality among patients with COVID-19 and respiratory failure is high and there are no known lower airway biomarkers that predict clinical outcome. We investigated whether bacterial respiratory infections and viral load were associated with poor clinical outcome and host immune tone. We obtained bacterial and fungal culture data from 589 critically ill subjects with COVID-19 requiring mechanical ventilation. On a subset of the subjects that underwent bronchoscopy, we also quantified SARS-CoV-2 viral load, analyzed the microbiome of the lower airways by metagenome and metatranscriptome analyses and profiled the host immune response. We found that isolation of a hospital-acquired respiratory pathogen was not associated with fatal outcome. However, poor clinical outcome was associated with enrichment of the lower airway microbiota with an oral commensal (Mycoplasma salivarium), while high SARS-CoV-2 viral burden, poor anti-SARS-CoV-2 antibody response, together with a unique host transcriptome profile of the lower airways were most predictive of mortality. Collectively, these data support the hypothesis that 1) the extent of viral infectivity drives mortality in severe COVID-19, and therefore 2) clinical management strategies targeting viral replication and host responses to SARS-CoV-2 should be prioritized.

Autoimmune anti-DNA antibodies predict disease severity in COVID-19 patients (preprint)

COVID-19 can lead to severe disease and death, however the mechanisms of pathogenesis in these patients remain poorly understood. High levels of autoimmune antibodies have been observed frequently in COVID-19 patients but their specific contribution to disease severity and clinical manifestations remain unknown. We performed a retrospective study of 115 COVID-19 hospitalized patients with different degrees of severity to analyze the generation of autoimmune antibodies to common antigens: a lysate of erythrocytes, the lipid phosphatidylserine (PS) and DNA. High levels of IgG autoantibodies against erythrocyte lysates were observed in a large percentage (up to 41%) of patients. Anti-DNA antibodies determined upon hospital admission correlated strongly with later development of severe disease, showing a positive predictive value of 89.5% and accounting for 22% of total severe cases. Statistical analysis identified strong correlations between anti-DNA antibodies and markers of cell injury, coagulation, neutrophil levels and erythrocyte size. Anti-DNA autoantibodies may play an important role in the pathogenesis of COVID-19 and could be developed as a predictive biomarker for disease severity and specific clinical manifestations.