Understanding risk factors of a new variant outburst through global analysis of Omicron transmissibility.

The emergence of a new virus variant is generally recognized by its usually sudden and rapid spread (outburst) in a certain world region. Due to the near-exponential rate of initial expansion, the new strain may not be detected at its true geographical origin but in the area with the most favorable conditions leading to the fastest exponential growth. Therefore, it is crucial to understand better the factors that promote such outbursts, which we address in the example of analyzing global Omicron transmissibility during its global emergence/outburst in November 2021-February 2022. As predictors, we assemble a number of potentially relevant factors: vaccinations (both full and boosters), different measures of population mobility (provided by Google), estimated stringency of measures, the prevalence of chronic diseases, population age, the timing of the outburst, and several other socio-demographic variables. As a proxy for natural immunity (prevalence of prior infections in population), we use cumulative numbers of COVID-19 deaths. As a response variable (transmissibility measure), we use the estimated effective reproduction number (Re) averaged in the vicinity of the outburst maxima. To select significant predictors of Re, we use machine learning regressions that employ feature selection, including methods based on ensembles of decision trees (Random Forest and Gradient Boosting). We identify the young population, earlier infection onset, higher mobility, low natural immunity, and low booster prevalence as likely direct risk factors. Interestingly, we find that all these risk factors were significantly higher for Africa, though curiously somewhat lower in Southern African countries (where the outburst emerged) compared to other African countries. Therefore, while the risk factors related to the virus transmissibility clearly promote the outburst of a new virus variant, specific regions/countries where the outburst actually happens may be related to less evident factors, possibly random in nature.

COVID-19 severity determinants inferred through ecological and epidemiological modeling.

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.

Inferring the Main Drivers of SARS-CoV-2 Global Transmissibility by Feature Selection Methods.

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 R 0, 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 R 0, 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 R 0. We robustly obtain that country's wealth/development (GDP per capita or Human Development Index) is the most important R 0 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 R 0 or affect it differently than suggested by naïve analysis.

A systems biology approach to COVID-19 progression in population.

A number of models in mathematical epidemiology have been developed to account for control measures such as vaccination or quarantine. However, COVID-19 has brought unprecedented social distancing measures, with a challenge on how to include these in a manner that can explain the data but avoid overfitting in parameter inference. We here develop a simple time-dependent model, where social distancing effects are introduced analogous to coarse-grained models of gene expression control in systems biology. We apply our approach to understand drastic differences in COVID-19 infection and fatality counts, observed between Hubei (Wuhan) and other Mainland China provinces. We find that these unintuitive data may be explained through an interplay of differences in transmissibility, effective protection, and detection efficiencies between Hubei and other provinces. More generally, our results demonstrate that regional differences may drastically shape infection outbursts. The obtained results demonstrate the applicability of our developed method to extract key infection parameters directly from publically available data so that it can be globally applied to outbreaks of COVID-19 in a number of countries. Overall, we show that applications of uncommon strategies, such as methods and approaches from molecular systems biology research to mathematical epidemiology, may significantly advance our understanding of COVID-19 and other infectious diseases.

PM2.5 as a major predictor of COVID-19 basic reproduction number in the USA.

Many studies have proposed a relationship between COVID-19 transmissibility and ambient pollution levels. However, a major limitation in establishing such associations is to adequately account for complex disease dynamics, influenced by e.g. significant differences in control measures and testing policies. Another difficulty is appropriately controlling the effects of other potentially important factors, due to both their mutual correlations and a limited dataset. To overcome these difficulties, we will here use the basic reproduction number (R0) that we estimate for USA states using non-linear dynamics methods. To account for a large number of predictors (many of which are mutually strongly correlated), combined with a limited dataset, we employ machine-learning methods. Specifically, to reduce dimensionality without complicating the variable interpretation, we employ Principal Component Analysis on subsets of mutually related (and correlated) predictors. Methods that allow feature (predictor) selection, and ranking their importance, are then used, including both linear regressions with regularization and feature selection (Lasso and Elastic Net) and non-parametric methods based on ensembles of weak-learners (Random Forest and Gradient Boost). Through these substantially different approaches, we robustly obtain that PM2.5 is a major predictor of R0 in USA states, with corrections from factors such as other pollutants, prosperity measures, population density, chronic disease levels, and possibly racial composition. As a rough magnitude estimate, we obtain that a relative change in R0, with variations in pollution levels observed in the USA, is typically ~30%, which further underscores the importance of pollution in COVID-19 transmissibility.

Understanding Infection Progression under Strong Control Measures through Universal COVID-19 Growth Signatures.

Widespread growth signatures in COVID-19 confirmed case counts are reported, with sharp transitions between three distinct dynamical regimes (exponential, superlinear, and sublinear). Through analytical and numerical analysis, a novel framework is developed that exploits information in these signatures. An approach well known to physics is applied, where one looks for common dynamical features, independently from differences in other factors. These features and associated scaling laws are used as a powerful tool to pinpoint regions where analytical derivations are effective, get an insight into qualitative changes of the disease progression, and infer the key infection parameters. The developed framework for joint analytical and numerical analysis of empirically observed COVID-19 growth patterns can lead to a fundamental understanding of infection progression under strong control measures, applicable to outbursts of both COVID-19 and other infectious diseases.

A Simple Criterion for Inferring CRISPR Array Direction.

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.

Effects of Population Dynamics on Establishment of a Restriction-Modification System in a Bacterial Host.

In vivo dynamics of protein levels in bacterial cells depend on both intracellular regulation and relevant population dynamics. Such population dynamics effects, e.g., interplay between cell and plasmid division rates, are, however, often neglected in modeling gene expression regulation. Including them in a model introduces additional parameters shared by the dynamical equations, which can significantly increase dimensionality of the parameter inference. We here analyse the importance of these effects, on a case of bacterial restriction-modification (R-M) system. We redevelop our earlier minimal model of this system gene expression regulation, based on a thermodynamic and dynamic system modeling framework, to include the population dynamics effects. To resolve the problem of effective coupling of the dynamical equations, we propose a "mean-field-like" procedure, which allows determining only part of the parameters at a time, by separately fitting them to expression dynamics data of individual molecular species. We show that including the interplay between kinetics of cell division and plasmid replication is necessary to explain the experimental measurements. Moreover, neglecting population dynamics effects can lead to falsely identifying non-existent regulatory mechanisms. Our results call for advanced methods to reverse-engineer intracellular regulation from dynamical data, which would also take into account the population dynamics effects.

In silico Analysis Suggests Common Appearance of scaRNAs in Type II Systems and Their Association With Bacterial Virulence.

In addition to its well-established defense function, CRISPR/Cas can also exhibit crucial non-canonical activity through endogenous gene expression regulation, which was found to mainly affect bacterial virulence. These non-canonical functions depend on scaRNA, which is a small RNA encoded outside of CRISPR array, that is typically flanked by a transcription start site (TSS) and a terminator, and is in part complementary to another small CRISPR/Cas-associated RNA (tracrRNAs). Identification of scaRNAs is however largely complicated by the scarcity of RNA-Seq data across different bacteria, so that they were identified only in a relatively rare CRISPR/Cas subtype (IIB), and the possibility of finding them in other Type II systems is currently unclear. This study presents the first effort toward systematic detection of small CRISPR/Cas-associated regulatory RNAs, where obtained predictions can guide future experiments. The core of our approach is ab initio detection of small RNAs from bacterial genome, which is based on jointly predicting transcription signals - TSS and terminators - and homology to CRISPR array repeat. Particularly, we employ our improved approach for detecting bacterial TSS, since accurate TSS detection is the main limiting factor for accurate small RNA prediction. We also explore how our predictions match to available RNA-Seq data and analyze their conservation across related bacterial species. In Type IIB systems, our predictions are consistent with experimental data, and we systematically identify scaRNAs throughout this subtype. Furthermore, we identify scaRNA:tracrRNA pairs in a number of IIA/IIC systems, where the appearance of scaRNAs co-occurs with the strains being pathogenic. RNA-Seq and conservation analysis show that our method is well suited for predicting CRISPR/Cas-associated small RNAs. We also find possible existence of a modified mechanism of CRISPR-associated small RNA action, which, interestingly, closely resembles the setup employed in biotechnological applications. Overall, our findings indicate that scaRNA:tracrRNA pairs are present in all subtypes of Type II systems, and point to an underlying connection with bacterial virulence. In addition to formulating these hypotheses, careful manual curation that we performed, makes an important first step toward fully automated predictor of CRISPR/Cas-associated small RNAs, which will allow their large scale analysis across diverse bacterial genomes.

Scoring Targets of Transcription in Bacteria Rather than Focusing on Individual Binding Sites.

Reliable identification of targets of bacterial regulators is necessary to understand bacterial gene expression regulation. These targets are commonly predicted by searching for high-scoring binding sites in the upstream genomic regions, which typically leads to a large number of false positives. In contrast to the common approach, here we propose a novel concept, where overrepresentation of the scoring distribution that corresponds to the entire searched region is assessed, as opposed to predicting individual binding sites. We explore two implementations of this concept, based on Kolmogorov-Smirnov (KS) and Anderson-Darling (AD) tests, which both provide straightforward P-value estimates for predicted targets. This approach is implemented for pleiotropic bacterial regulators, including σ70 (bacterial housekeeping σ factor) target predictions, which is a classical bioinformatics problem characterized by low specificity. We show that KS based approach is both faster and more accurate, departing from the current paradigm of AD being slower, but more accurate. Moreover, KS approach leads to a significant increase in the search accuracy compared to the standard approach, while at the same time straightforwardly assigning well established P-values to each potential target. Consequently, the new KS based method proposed here, which assigns P-values to fixed length upstream regions, provides a fast and accurate approach for predicting bacterial transcription targets.

Features of CRISPR-Cas Regulation Key to Highly Efficient and Temporally-Specific crRNA Production.

Bacterial immune systems, such as CRISPR-Cas or restriction-modification (R-M) systems, affect bacterial pathogenicity and antibiotic resistance by modulating horizontal gene flow. A model system for CRISPR-Cas regulation, the Type I-E system from Escherichia coli, is silent under standard laboratory conditions and experimentally observing the dynamics of CRISPR-Cas activation is challenging. Two characteristic features of CRISPR-Cas regulation in E. coli are cooperative transcription repression of cas gene and CRISPR array promoters, and fast non-specific degradation of full length CRISPR transcripts (pre-crRNA). In this work, we use computational modeling to understand how these features affect the system expression dynamics. Signaling which leads to CRISPR-Cas activation is currently unknown, so to bypass this step, we here propose a conceptual setup for cas expression activation, where cas genes are put under transcription control typical for a restriction-modification (R-M) system and then introduced into a cell. Known transcription regulation of an R-M system is used as a proxy for currently unknown CRISPR-Cas transcription control, as both systems are characterized by high cooperativity, which is likely related to similar dynamical constraints of their function. We find that the two characteristic CRISPR-Cas control features are responsible for its temporally-specific dynamical response, so that the system makes a steep (switch-like) transition from OFF to ON state with a time-delay controlled by pre-crRNA degradation rate. We furthermore find that cooperative transcription regulation qualitatively leads to a cross-over to a regime where, at higher pre-crRNA processing rates, crRNA generation approaches the limit of an infinitely abrupt system induction. We propose that these dynamical properties are associated with rapid expression of CRISPR-Cas components and efficient protection of bacterial cells against foreign DNA. In terms of synthetic applications, the setup proposed here should allow highly efficient expression of small RNAs in a narrow time interval, with a specified time-delay with respect to the signal onset.

Understanding key features of bacterial restriction-modification systems through quantitative modeling.

BACKGROUND: Restriction-modification (R-M) systems are rudimentary bacterial immune systems. The main components include restriction enzyme (R), which cuts specific unmethylated DNA sequences, and the methyltransferase (M), which protects the same DNA sequences. The expression of R-M system components is considered to be tightly regulated, to ensure successful establishment in a naïve bacterial host. R-M systems are organized in different architectures (convergent or divergent) and are characterized by different features, i.e. binding cooperativities, dissociation constants of dimerization, translation rates, which ensure this tight regulation. It has been proposed that R-M systems should exhibit certain dynamical properties during the system establishment, such as: i) a delayed expression of R with respect to M, ii) fast transition of R from "OFF" to "ON" state, iii) increased stability of the toxic molecule (R) steady-state levels. It is however unclear how different R-M system features and architectures ensure these dynamical properties, particularly since it is hard to address this question experimentally. RESULTS: To understand design of different R-M systems, we computationally analyze two R-M systems, representative of the subset controlled by small regulators called 'C proteins', and differing in having convergent or divergent promoter architecture. We show that, in the convergent system, abolishing any of the characteristic system features adversely affects the dynamical properties outlined above. Moreover, an extreme binding cooperativity, accompanied by a very high dissociation constant of dimerization, observed in the convergent system, but absent from other R-M systems, can be explained in terms of the same properties. Furthermore, we develop the first theoretical model for dynamics of a divergent R-M system, which does not share any of the convergent system features, but has overlapping promoters. We show that i) the system dynamics exhibits the same three dynamical properties, ii) introducing any of the convergent system features to the divergent system actually diminishes these properties. CONCLUSIONS: Our results suggest that different R-M architectures and features may be understood in terms of constraints imposed by few simple dynamical properties of the system, providing a unifying framework for understanding these seemingly diverse systems. We also provided predictions for the perturbed R-M systems dynamics, which may in future be tested through increasingly available experimental techniques, such as re-engineering R-M systems and single-cell experiments.

Heavy flavor puzzle at LHC: a serendipitous interplay of jet suppression and fragmentation.

Both charged hadrons and D mesons are considered to be excellent probes of QCD matter created in ultrarelativistic heavy ion collisions. Surprisingly, recent experimental observations at LHC show the same jet suppression for these two probes, which--contrary to pQCD expectations--may suggest similar energy losses for light quarks and gluons in the QCD medium. We here use our recently developed energy loss formalism in a finite-size dynamical QCD medium to analyze this phenomenon that we denote as the "heavy flavor puzzle at LHC." We show that this puzzle is a consequence of an unusual combination of the suppression and fragmentation patterns and, in fact, does not require invoking the same energy loss for light partons. Furthermore, we show that this combination leads to a simple relationship between the suppressions of charged hadrons and D mesons and the corresponding bare quark suppressions. Consequently, a coincidental matching of jet suppression and fragmentation allows considerably simplifying the interpretation of the corresponding experimental data.

CRISPR transcript processing: a mechanism for generating a large number of small interfering RNAs.

BACKGROUND: CRISPR/Cas (Clustered Regularly Interspaced Short Palindromic Repeats/CRISPR associated sequences) is a recently discovered prokaryotic defense system against foreign DNA, including viruses and plasmids. CRISPR cassette is transcribed as a continuous transcript (pre-crRNA), which is processed by Cas proteins into small RNA molecules (crRNAs) that are responsible for defense against invading viruses. Experiments in E. coli report that overexpression of cas genes generates a large number of crRNAs, from only few pre-crRNAs. RESULTS: We here develop a minimal model of CRISPR processing, which we parameterize based on available experimental data. From the model, we show that the system can generate a large amount of crRNAs, based on only a small decrease in the amount of pre-crRNAs. The relationship between the decrease of pre-crRNAs and the increase of crRNAs corresponds to strong linear amplification. Interestingly, this strong amplification crucially depends on fast non-specific degradation of pre-crRNA by an unidentified nuclease. We show that overexpression of cas genes above a certain level does not result in further increase of crRNA, but that this saturation can be relieved if the rate of CRISPR transcription is increased. We furthermore show that a small increase of CRISPR transcription rate can substantially decrease the extent of cas gene activation necessary to achieve a desired amount of crRNA. CONCLUSIONS: The simple mathematical model developed here is able to explain existing experimental observations on CRISPR transcript processing in Escherichia coli. The model shows that a competition between specific pre-crRNA processing and non-specific degradation determines the steady-state levels of crRNA and is responsible for strong linear amplification of crRNAs when cas genes are overexpressed. The model further shows how disappearance of only a few pre-crRNA molecules normally present in the cell can lead to a large (two orders of magnitude) increase of crRNAs upon cas overexpression. A crucial ingredient of this large increase is fast non-specific degradation by an unspecified nuclease, which suggests that a yet unidentified nuclease(s) is a major control element of CRISPR response. Transcriptional regulation may be another important control mechanism, as it can either increase the amount of generated pre-crRNA, or alter the level of cas gene activity.

A simple biosynthetic pathway for large product generation from small substrate amounts.

A recently emerging discipline of synthetic biology has the aim of constructing new biosynthetic pathways with useful biological functions. A major application of these pathways is generating a large amount of the desired product. However, toxicity due to the possible presence of toxic precursors is one of the main problems for such production. We consider here the problem of generating a large amount of product from a potentially toxic substrate. To address this, we propose a simple biosynthetic pathway, which can be induced in order to produce a large number of the product molecules, by keeping the substrate amount at low levels. Surprisingly, we show that the large product generation crucially depends on fast non-specific degradation of the substrate molecules. We derive an optimal induction strategy, which allows as much as three orders of magnitude increase in the product amount through biologically realistic parameter values. We point to a recently discovered bacterial immune system (CRISPR/Cas in E. coli) as a putative example of the pathway analysed here. We also argue that the scheme proposed here can be used not only as a stand-alone pathway, but also as a strategy to produce a large amount of the desired molecules with small perturbations of endogenous biosynthetic pathways.

Radiative energy loss in a finite dynamical QCD medium.

The radiative energy loss of a quark jet traversing a finite size QCD medium with dynamical constituents is calculated to first order in opacity. Although finite size corrections reduce the energy loss relative to an infinite dynamical QCD medium, under realistic conditions it remains significantly larger than in a static medium. Quantitative predictions of jet suppression in relativistic heavy ion collisions must therefore account for the dynamics of the medium's constituents. Finite size effects are shown to induce a nonlinear path length dependence of the energy loss. Our results suggest a simple general mapping between energy loss expressions for static and dynamical QCD media.

Open charm and beauty at ultrarelativistic heavy ion colliders.

Important goals of BNL RHIC and CERN LHC experiments with ion beams include the creation and study of new forms of matter, such as the quark gluon plasma. Heavy quark production and attenuation provide unique tomographic probes of that matter. We predict the suppression pattern of open charm and beauty in Au+Au collisions at RHIC and LHC energies based on the DGLV formalism of radiative energy loss. A cancellation between effects due to the sqrt[s] energy dependence of the high p(T) slope and heavy quark energy loss is predicted to lead to surprising similarity of heavy quark suppression at RHIC and LHC.