Browsing by Author "Djordjevic, Marko (55322341500)"

Inferring transcriptional direction (orientation) of the CRISPR array is essential for many applications, including systematically investigating non-canonical CRISPR/Cas functions. The standard method, CRISPRDirection (embedded within CRISPRCasFinder), fails to predict the orientation (ND predictions) for ∼37% of the classified CRISPR arrays (>2200 loci); this goes up to >70% for the II-B subtype where non-canonical functions were first experimentally discovered. Alternatively, Potential Orientation (also embedded within CRISPRCasFinder), has a much smaller frequency of ND predictions but might have significantly lower accuracy. We propose a novel simple criterion, where the CRISPR array direction is assigned according to the direction of its associated cas genes (Cas Orientation). We systematically assess the performance of the three methods (Cas Orientation, CRISPRDirection, and Potential Orientation) across all CRISPR/Cas subtypes, by a mutual crosscheck of their predictions, and by comparing them to the experimental dataset. Interestingly, CRISPRDirection agrees much better with Cas Orientation than with Potential Orientation, despite CRISPRDirection and Potential Orientation being mutually related – Potential Orientation corresponding to one of six (heterogeneous) predictors employed by CRISPRDirection – and being unrelated to Cas Orientation. We find that Cas Orientation has much higher accuracy compared to Potential Orientation and comparable accuracy to CRISPRDirection – while accurately assigning an orientation to ∼95% of the CRISPR arrays that are non-determined by CRISPRDirection. Cas Orientation is, at the same time, simple to employ, requiring only (routine for prokaryotes) the prediction of the associated protein coding gene direction. © Copyright © 2019 Milicevic, Repac, Bozic, Djordjevic and Djordjevic.

Inferring transcriptional direction (orientation) of the CRISPR array is essential for many applications, including systematically investigating non-canonical CRISPR/Cas functions. The standard method, CRISPRDirection (embedded within CRISPRCasFinder), fails to predict the orientation (ND predictions) for ∼37% of the classified CRISPR arrays (>2200 loci); this goes up to >70% for the II-B subtype where non-canonical functions were first experimentally discovered. Alternatively, Potential Orientation (also embedded within CRISPRCasFinder), has a much smaller frequency of ND predictions but might have significantly lower accuracy. We propose a novel simple criterion, where the CRISPR array direction is assigned according to the direction of its associated cas genes (Cas Orientation). We systematically assess the performance of the three methods (Cas Orientation, CRISPRDirection, and Potential Orientation) across all CRISPR/Cas subtypes, by a mutual crosscheck of their predictions, and by comparing them to the experimental dataset. Interestingly, CRISPRDirection agrees much better with Cas Orientation than with Potential Orientation, despite CRISPRDirection and Potential Orientation being mutually related – Potential Orientation corresponding to one of six (heterogeneous) predictors employed by CRISPRDirection – and being unrelated to Cas Orientation. We find that Cas Orientation has much higher accuracy compared to Potential Orientation and comparable accuracy to CRISPRDirection – while accurately assigning an orientation to ∼95% of the CRISPR arrays that are non-determined by CRISPRDirection. Cas Orientation is, at the same time, simple to employ, requiring only (routine for prokaryotes) the prediction of the associated protein coding gene direction. © Copyright © 2019 Milicevic, Repac, Bozic, Djordjevic and Djordjevic.

Understanding variations in the severity of infectious diseases is essential for planning proper mitigation strategies. Determinants of COVID-19 clinical severity are commonly assessed by transverse or longitudinal studies of the fatality counts. However, the fatality counts depend both on disease clinical severity and transmissibility, as more infected also lead to more deaths. Instead, we use epidemiological modeling to propose a disease severity measure that accounts for the underlying disease dynamics. The measure corresponds to the ratio of population-averaged mortality and recovery rates (m/r), is independent of the disease transmission dynamics (i.e., the basic reproduction number), and has a direct mechanistic interpretation. We use this measure to assess demographic, medical, meteorological, and environmental factors associated with the disease severity. For this, we employ an ecological regression study design and analyze different US states during the first disease outbreak. Principal Component Analysis, followed by univariate, and multivariate analyses based on machine learning techniques, is used for selecting important predictors. The usefulness of the introduced severity measure and the validity of the approach are confirmed by the fact that, without using prior knowledge from clinical studies, we recover the main significant predictors known to influence disease severity, in particular age, chronic diseases, and racial factors. Additionally, we identify long-term pollution exposure and population density as not widely recognized (though for the pollution previously hypothesized) significant predictors. The proposed measure is applicable for inferring severity determinants not only of COVID-19 but also of other infectious diseases, and the obtained results may aid a better understanding of the present and future epidemics. Our holistic, systematic investigation of disease severity at the human-environment intersection by epidemiological dynamical modeling and machine learning ecological regressions is aligned with the One Health approach. The obtained results emphasize a syndemic nature of COVID-19 risks. © 2021 The Authors

Identifying the main environmental drivers of SARS-CoV-2 transmissibility in the population is crucial for understanding current and potential future outbursts of COVID-19 and other infectious diseases. To address this problem, we concentrate on the basic reproduction number R0, which is not sensitive to testing coverage and represents transmissibility in an absence of social distancing and in a completely susceptible population. While many variables may potentially influence R0, a high correlation between these variables may obscure the result interpretation. Consequently, we combine Principal Component Analysis with feature selection methods from several regression-based approaches to identify the main demographic and meteorological drivers behind R0. We robustly obtain that country's wealth/development (GDP per capita or Human Development Index) is the most important R0 predictor at the global level, probably being a good proxy for the overall contact frequency in a population. This main effect is modulated by built-up area per capita (crowdedness in indoor space), onset of infection (likely related to increased awareness of infection risks), net migration, unhealthy living lifestyle/conditions including pollution, seasonality, and possibly BCG vaccination prevalence. Also, we argue that several variables that significantly correlate with transmissibility do not directly influence R0 or affect it differently than suggested by naïve analysis. © 2021 The Authors. GeoHealth published by Wiley Periodicals LLC on behalf of American Geophysical Union.

Identifying the main environmental drivers of SARS-CoV-2 transmissibility in the population is crucial for understanding current and potential future outbursts of COVID-19 and other infectious diseases. To address this problem, we concentrate on the basic reproduction number R0, which is not sensitive to testing coverage and represents transmissibility in an absence of social distancing and in a completely susceptible population. While many variables may potentially influence R0, a high correlation between these variables may obscure the result interpretation. Consequently, we combine Principal Component Analysis with feature selection methods from several regression-based approaches to identify the main demographic and meteorological drivers behind R0. We robustly obtain that country's wealth/development (GDP per capita or Human Development Index) is the most important R0 predictor at the global level, probably being a good proxy for the overall contact frequency in a population. This main effect is modulated by built-up area per capita (crowdedness in indoor space), onset of infection (likely related to increased awareness of infection risks), net migration, unhealthy living lifestyle/conditions including pollution, seasonality, and possibly BCG vaccination prevalence. Also, we argue that several variables that significantly correlate with transmissibility do not directly influence R0 or affect it differently than suggested by naïve analysis. © 2021 The Authors. GeoHealth published by Wiley Periodicals LLC on behalf of American Geophysical Union.