Integrative analysis of multimodal patient data identifies personalized predictors of tuberculosis treatment prognosis

Tuberculosis (TB) afflicted 10.6 million people in 2021, and its global burden is increasing due to multidrug-resistant TB (MDR-TB) and extensively resistant TB (XDR-TB). Here, we analyze multi-domain information from 5,060 TB patients spanning 10 countries with high burden of MDR-TB from the NIAID TB Portals database to determine predictors of TB treatment outcome. Our analysis revealed significant associations between radiological, microbiological, therapeutic, and demographic data modalities. Our machine learning model, built with 203 features across modalities outperforms models built using each modality alone in predicting treatment outcomes, with an accuracy of 83% and area under the curve of 0.84. Notably, our analysis revealed that the drug regimens Bedaquiline-Clofazimine-Cycloserine-Levofloxacin-Linezolid and Bedaquiline-Clofazimine-Linezolid-Moxifloxacin were associated with treatment success and failure, respectively, for MDR non-XDR-TB. Drug combinations predicted to be synergistic by the INDIGO algorithm performed better than antagonistic combinations. Our prioritized set of features predictive of treatment outcomes can ultimately guide the personalized clinical management of TB.

The sound of silent RNA in tuberculosis and the lncRNA role on infection

Tuberculosis (TB) is one of the leading causes of death worldwide, and Diabetes Mellitus is one of the major comorbidities (TB/DM) associated with the disease. A total of 103 differentially expressed ncRNAs have been identified in the TB and TB/DM comparisons. A machine learning algorithm was employed to identify the most informative lncRNAs: ADM-DT, LINC02009, LINC02471, SOX2-OT, and GK-AS1. These lncRNAs presented substantial accuracy in classifying TB from HC (AUCs >0.85) and TB/DM from HC (AUCs >0.90) in the other three countries. Genes with significant correlations with the five lncRNAs enriched common pathways in Brazil and India for both TB and TB/DM. This suggests that lncRNAs play an important role in the regulation of genes related to the TB immune response.

Mathematical models to study the biology of pathogens and the infectious diseases they cause

Mathematical models have many applications in infectious diseases: epidemiologists use them to forecast outbreaks and design containment strategies; systems biologists use them to study complex processes sustaining pathogens, from the metabolic networks empowering microbial cells to ecological networks in the microbiome that protects its host. Here, we (1) review important models relevant to infectious diseases, (2) draw parallels among models ranging widely in scale. We end by discussing a minimal set of information for a model to promote its use by others and to enable predictions that help us better fight pathogens and the diseases they cause.

Non-propagative human parainfluenza virus type 2 nasal vaccine robustly protects the upper and lower airways against SARS-CoV-2

We developed an intranasal vaccine against severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) using the replication-incompetent human parainfluenza virus type 2 (hPIV2) vector BC-PIV, which can deliver ectopic gene as stable RNA and ectopic protein on the envelope. BC-PIV expressing the full-length prefusion-stabilized spike gene (K986P/V987P) of SARS-CoV-2, S-2PM, possessed a corona-like viral envelope. Intranasal vaccination of mice with BC-PIV/S-2PM induced high levels of neutralizing immunoglobulin G (IgG) and mucosal IgA antibodies against the spike protein. Although BC-PIV showed hemagglutinating activity, BC-PIV/S-2PM lacked such activity, in accordance with the presence of the massive spike protein on the viral surface. Furthermore, single-dose intranasal vaccination of hamsters with BC-PIV/S-2PM completely protected the lungs from SARS-CoV-2 at 11-week post-immunization, and boost vaccination two weeks before the challenge conferred virtually complete protection of the nasal turbinates against SARS-CoV-2. Thus, this chimeric hPIV2/spike intranasal vaccine is a promising vaccine candidate for SARS-CoV-2 to curtail virus transmission.

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Non-replicating viral vector against spike induces mucosal immunity to block infection

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The viral vector carries spike protein on its envelope with corona-like structure

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One-shot nasal vaccination of hamsters completely protects lungs against severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2)

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Two-shot nasal vaccination of hamsters nearly completely protects the upper airway

SARS-CoV-2 seroprevalence in the urban population of Qatar: An analysis of antibody testing on a sample of 112,941 individuals

The study objective was to the assess level of detectable severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) antibodies in the urban population of Qatar. Antibody testing was performed on residual blood specimens for 112,941 individuals (∼10% of Qatar's urban population) attending for routine/other clinical care between May 12 and September 9, 2020. Seropositivity was 13.3% (95% confidence interval [CI] = 13.1-13.6%) and was independently associated with sex, age, nationality, clinical care encounter type, and testing date. Median optical density (antibody titer) among antibody-positive persons was 27.0 (range = 1.0-150.0), with higher values associated with age, nationality, clinical care encounter type, and testing date. Seropositivity by nationality was positively correlated with the likelihood of having higher antibody titers (Pearson correlation coefficient = 0.85; 95% CI = 0.47-0.96). Less than two in every 10 individuals in Qatar's urban population had detectable antibodies against SARS-CoV-2, suggesting this population is still far from herd immunity and at risk of subsequent infection waves. Higher antibody titer appears to be a biomarker of repeated exposures to the infection.

Comparative analysis of antibodies to SARS-CoV-2 between asymptomatic and convalescent patients

The SARS-CoV-2 viral pandemic has induced a global health crisis, which requires more in-depth investigation into immunological responses to develop effective treatments and vaccines. To understand protective immunity against COVID-19, we screened over 60,000 asymptomatic individuals in the Southeastern United States for IgG antibody positivity against the viral Spike protein, and approximately 3% were positive. Of these 3%, individuals with the highest anti-S or anti-RBD IgG level showed a strong correlation with inhibition of ACE2 binding and cross-reactivity against non-SARS-CoV-2 coronavirus S-proteins. We also analyzed samples from 94 SARS-CoV-2 patients and compared them with those of asymptomatic individuals. SARS-CoV-2 symptomatic patients had decreased antibody responses, ACE2 binding inhibition, and antibody cross-reactivity. Our study shows that healthy individuals can mount robust immune responses against SARS-CoV-2 without symptoms. Furthermore, IgG antibody responses against S and RBD may correlate with high inhibition of ACE2 binding in individuals tested for SARS-CoV-2 infection or post vaccination.