Mycobacterial cellular variations in growth and division increase heterogeneity in cell length, possibly contributing to cell-to-cell variation in host and antibiotic stress tolerance. This may be one of the factors influencing Mycobacterium tuberculosis persistence to antibiotics. Tuberculosis (TB) is a major public health problem in developing countries, antibiotic persistence, and emergence of antibiotic resistance further complicates this problem. We wanted to investigate the factors influencing cell-length distribution in clinical M. tuberculosis strains. In parallel we examined M. tuberculosis cell-length distribution in a large set of clinical strains (n = 158) from ex vivo sputum samples, in vitro macrophage models, and in vitro cultures. Our aim was to understand the influence of clinically relevant factors such as host stresses, M. tuberculosis lineages, antibiotic resistance, antibiotic concentrations, and disease severity on the cell size distribution in clinical M. tuberculosis strains. Increased cell size and cell-to-cell variation in cell length were associated with bacteria in sputum and infected macrophages rather than liquid culture. Multidrug-resistant (MDR) strains displayed increased cell length heterogeneity compared to sensitive strains in infected macrophages and also during growth under rifampicin (RIF) treatment. Importantly, increased cell length was also associated with pulmonary TB disease severity. Supporting these findings, individual host stresses, such as oxidative stress and iron deficiency, increased cell-length heterogeneity of M. tuberculosis strains. In addition we also observed synergism between host stress and RIF treatment in increasing cell length in MDR-TB strains. This study has identified some clinical factors contributing to cell-length heterogeneity in clinical M. tuberculosis strains. The role of these cellular adaptations to host and antibiotic tolerance needs further investigation.

In tuberculous meningitis (TBM), a higher pretreatment CSF bacterial load was associated with increased disease severity and host inflammation. The bacterial load can be used to predict new neurological events but not death. Owing to the divergent pathogenesis of TBM-associated neurological complications and deaths, strategies to reduce them may need reassessment.

The relationship between age and seroprevalence can be used to estimate the annual attack rate of an infectious disease. For pathogens with multiple serologically distinct strains, there is a need to describe composite exposure to an antigenically variable group of pathogens. In this study, we assay 24,402 general-population serum samples, collected in Vietnam between 2009 to 2015, for antibodies to eleven human influenza A strains. We report that a principal components decomposition of antibody titer data gives the first principal component as an appropriate surrogate for seroprevalence; this results in annual attack rate estimates of 25.6% (95% CI: 24.1% – 27.1%) for subtype H3 and 16.0% (95% CI: 14.7% – 17.3%) for subtype H1. The remaining principal components separate the strains by serological similarity and associate birth cohorts with their particular influenza histories. Our work shows that dimensionality reduction can be used on human antibody profiles to construct an age-seroprevalence relationship for antigenically variable pathogens.

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BackgroundIn temperate and subtropical climates, respiratory diseases exhibit seasonal peaks in winter. In the tropics, with no winter, peak timings are irregular.MethodsTo obtain a detailed picture of influenza‐like illness (ILI) patterns in the tropics, we established an mHealth study in community clinics in Ho Chi Minh City (HCMC). During 2009‐2015, clinics reported daily case numbers via SMS, with a subset performing molecular diagnostics for influenza virus. This real‐time epidemiology network absorbs 6000 ILI reports annually, one or two orders of magnitude more than typical surveillance systems. A real‐time online ILI indicator was developed to inform clinicians of the daily ILI activity in HCMC.ResultsFrom August 2009 to December 2015, 63 clinics were enrolled and 36 920 SMS reports were received, covering approximately 1.7M outpatient visits. Approximately 10.6% of outpatients met the ILI case definition. ILI activity in HCMC exhibited strong nonannual dynamics with a dominant periodicity of 206 days. This was confirmed by time series decomposition, stepwise regression, and a forecasting exercise showing that median forecasting errors are 30%‐40% lower when using a 206‐day cycle. In ILI patients from whom nasopharyngeal swabs were taken, 31.2% were positive for influenza. There was no correlation between the ILI time series and the time series of influenza, influenza A, or influenza B (all P > 0.15).ConclusionThis suggests, for the first time, that a nonannual cycle may be an essential driver of respiratory disease dynamics in the tropics. An immunological interference hypothesis is discussed as a potential underlying mechanism.

HighlightsWe investigate whether disease dynamics can be inferred by repeated serum collections.Measuring antibody waning is critical for inference in serological time series.Collecting 200 samples every 2 months allows for inference of transmission parameters.Low-level seasonality is difficult to detect statistically.

Seroepidemiological studies aim to understand population-level exposure and immunity to infectious diseases. Their results are normally presented as binary outcomes describing the presence or absence of pathogen-specific antibody, despite the fact that many assays measure continuous quantities. A population's natural distribution of antibody titers to an endemic infectious disease may include information on multiple serological states -naiveté, recent infection, non-recent infection, childhood infection -depending on the disease in question and the acquisition and waning patterns of immunity. In this study, we investigate 20,152 general-population serum samples from southern Vietnam collected between 2009 and 2013 from which we report antibody titers to the influenza virus HA1 protein using a continuous titer measurement from a protein microarray assay. We describe the distributions of antibody titers to subtypes 2009 H1N1 and H3N2. Using a model selection approach to fit mixture distributions, we show that 2009 H1N1 antibody titers fall into four titer subgroups and that H3N2 titers fall into three subgroups. For H1N1, our interpretation is that the two highest-titer subgroups correspond to recent and historical infection, which is consistent with 2009 pandemic attack rates. Similar interpretations are available for H3N2, but right-censoring of titers makes these interpretations difficult to validate.The distribution of antibodies in a human population is a fossil imprint of the population's past exposure to infectious disease. If individuals' antibody concentrations can be measured accurately, they can be used to infer both the size and timing of past epidemics. The two key post-epidemic processes that need to be measured to make this inference possible are the rate of antibody acquisition and the rate of antibody waning. The rate of antibody acquisition post-infection is rapid (weeks) for most viral pathogens, but more difficult to measure for more complex pathogens that present the immune system with a diverse set of antigens. The rate of antibody waning, however, is rarely measured even for viral pathogens. To correctly translate a population's antibody titer distribution to its epidemic history, accurate measures of both these rates are necessary. To validate that this reconstruction has been done correctly, a large cohort with long-term follow-up and precise antibody measurements would be required. Studies like these are difficult to run and difficult to find in the scientific literature -both in methodological

Seroepidemiological studies aim to understand population-level exposure and immunity to infectious diseases. Their results are normally presented as binary outcomes describing the presence or absence of pathogen-specific antibody, despite the fact that many assays measure continuous quantities. A population’s natural distribution of antibody titers to an endemic infectious disease may include information on multiple serological states – naiveté, recent infection, non-recent infection, childhood infection – depending on the disease in question and the acquisition and waning patterns of immunity. In this study, we investigate 20,152 general-population serum samples from southern Vietnam collected between 2009 and 2013 from which we report antibody titers to the influenza virus HA1 protein using a continuous titer measurement from a protein microarray assay. We describe the distributions of antibody titers to subtypes 2009 H1N1 and H3N2. Using a model selection approach to fit mixture distributions, we show that 2009 H1N1 antibody titers fall into four titer subgroups and that H3N2 titers fall into three subgroups. For H1N1, our interpretation is that the two highest-titer subgroups correspond to recent and historical infection, which is consistent with 2009 pandemic attack rates. Similar interpretations are available for H3N2, but right-censoring of titers makes these interpretations difficult to validate.