Boosting Functional Response Models for Location, Scale and Shape with an Application to Bacterial Competition

We extend Generalized Additive Models for Location, Scale, and Shape (GAMLSS) to regression with functional response. This allows us to simultaneously model point-wise mean curves, variances and other distributional parameters of the response in dependence of various scalar and functional covariate effects. In addition, the scope of distributions is extended beyond exponential families. The model is fitted via gradient boosting, which offers inherent model selection and is shown to be suitable for both complex model structures and highly auto-correlated response curves. This enables us to analyze bacterial growth in \textit{Escherichia coli} in a complex interaction scenario, fruitfully extending usual growth models.Comment: bootstrap confidence interval type uncertainty bounds added; minor changes in formulation

Effects of stochasticity and division of labor in toxin production on two-strain bacterial competition in Escherichia coli

In phenotypically heterogeneous microbial populations, the decision to adopt one or another phenotype is often stochastically regulated. However, how this stochasticity affects interactions between competing microbes in mixed communities is difficult to assess. One example of such an interaction system is the competition of an Escherichia coli strain C, which performs division of labor between reproducers and self-sacrificing toxin producers, with a toxin-sensitive strain S. The decision between reproduction or toxin production within a single C cell is inherently stochastic. Here, combining experimental and theoretical approaches, we demonstrate that this stochasticity in the initial phase of colony formation is the crucial determinant for the competition outcome. In the initial phase (t < 12h), stochasticity influences the formation of viable C clusters at the colony edge. In the subsequent phase, the effective fitness differences (set primarily by the degree of division of labor in the C strain population) dictate the deterministic population dynamics and consequently competition outcome. In particular, we observe that competitive success of the C strain is only found if (i) a C edge cluster has formed at the end of the initial competition phase and (ii) the beneficial and detrimental effects of toxin production are balanced, which is the case at intermediate toxin producer fractions. Our findings highlight the importance of stochastic processes during the initial phase of colony formation, which might be highly relevant for other microbial community interactions in which the random choice between phenotypes can have long-lasting consequences for community fate

Microbe-host interplay in atopic dermatitis and psoriasis

Despite recent advances in understanding microbial diversity in skin homeostasis, the relevance of microbial dysbiosis in inflammatory disease is poorly understood. Here we perform a comparative analysis of skin microbial communities coupled to global patterns of cutaneous gene expression in patients with atopic dermatitis or psoriasis. The skin microbiota is analysed by 16S amplicon or whole genome sequencing and the skin transcriptome by microarrays, followed by integration of the data layers. We find that atopic dermatitis and psoriasis can be classified by distinct microbes, which differ from healthy volunteers microbiome composition. Atopic dermatitis is dominated by a single microbe (Staphylococcus aureus), and associated with a disease relevant host transcriptomic signature enriched for skin barrier function, tryptophan metabolism and immune activation. In contrast, psoriasis is characterized by co-occurring communities of microbes with weak associations with disease related gene expression. Our work provides a basis for biomarker discovery and targeted therapies in skin dysbiosis.Peer reviewe

Mortality and pulmonary complications in patients undergoing surgery with perioperative SARS-CoV-2 infection: an international cohort study

Background: The impact of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) on postoperative recovery needs to be understood to inform clinical decision making during and after the COVID-19 pandemic. This study reports 30-day mortality and pulmonary complication rates in patients with perioperative SARS-CoV-2 infection. Methods: This international, multicentre, cohort study at 235 hospitals in 24 countries included all patients undergoing surgery who had SARS-CoV-2 infection confirmed within 7 days before or 30 days after surgery. The primary outcome measure was 30-day postoperative mortality and was assessed in all enrolled patients. The main secondary outcome measure was pulmonary complications, defined as pneumonia, acute respiratory distress syndrome, or unexpected postoperative ventilation. Findings: This analysis includes 1128 patients who had surgery between Jan 1 and March 31, 2020, of whom 835 (74·0%) had emergency surgery and 280 (24·8%) had elective surgery. SARS-CoV-2 infection was confirmed preoperatively in 294 (26·1%) patients. 30-day mortality was 23·8% (268 of 1128). Pulmonary complications occurred in 577 (51·2%) of 1128 patients; 30-day mortality in these patients was 38·0% (219 of 577), accounting for 81·7% (219 of 268) of all deaths. In adjusted analyses, 30-day mortality was associated with male sex (odds ratio 1·75 [95% CI 1·28–2·40], p\textless0·0001), age 70 years or older versus younger than 70 years (2·30 [1·65–3·22], p\textless0·0001), American Society of Anesthesiologists grades 3–5 versus grades 1–2 (2·35 [1·57–3·53], p\textless0·0001), malignant versus benign or obstetric diagnosis (1·55 [1·01–2·39], p=0·046), emergency versus elective surgery (1·67 [1·06–2·63], p=0·026), and major versus minor surgery (1·52 [1·01–2·31], p=0·047). Interpretation: Postoperative pulmonary complications occur in half of patients with perioperative SARS-CoV-2 infection and are associated with high mortality. Thresholds for surgery during the COVID-19 pandemic should be higher than during normal practice, particularly in men aged 70 years and older. Consideration should be given for postponing non-urgent procedures and promoting non-operative treatment to delay or avoid the need for surgery. Funding: National Institute for Health Research (NIHR), Association of Coloproctology of Great Britain and Ireland, Bowel and Cancer Research, Bowel Disease Research Foundation, Association of Upper Gastrointestinal Surgeons, British Association of Surgical Oncology, British Gynaecological Cancer Society, European Society of Coloproctology, NIHR Academy, Sarcoma UK, Vascular Society for Great Britain and Ireland, and Yorkshire Cancer Research

Boosting functional response models for location, scale and shape with an application to bacterial competition

We extend generalized additive models for location, scale and shape (GAMLSS) to regression with functional response. This allows us to simultaneously model point-wise mean curves, variances and other distributional parameters of the response in dependence of various scalar and functional covariate effects. In addition, the scope of distributions is extended beyond exponential families. The model is fitted via gradient boosting, which offers inherent model selection and is shown to be suitable for both complex model structures and highly auto-correlated response curves. This enables us to analyse bacterial growth inEscherichia coliin a complex interaction scenario, fruitfully extending usual growth models

Boosting functional response models for location, scale and shape with an application to bacterial competition

We extend generalized additive models for location, scale and shape (GAMLSS) to regression with functional response. This allows us to simultaneously model point-wise mean curves, variances and other distributional parameters of the response in dependence of various scalar and functional covariate effects. In addition, the scope of distributions is extended beyond exponential families. The model is fitted via gradient boosting, which offers inherent model selection and is shown to be suitable for both complex model structures and highly auto-correlated response curves. This enables us to analyse bacterial growth in Escherichia coli in a complex interaction scenario, fruitfully extending usual growth models.Peer Reviewe

Theoretical analysis emphasizes the importance of intermediate toxin producer fractions for ColicinE2 producer strain (C strain) success.

<p>(A) The four different competition outcomes in simulations (bright magenta: living S cells, dark magenta: growth-inhibited S cells, green: reproducing C cells, white: toxin-producing C cells). Scale bar = 1 mm. Values of the switching rate s_<b>C</b> from left to right: 0.001/0.02/0.001622/synchronicity scenario I. (B) The interaction scheme underlying the theoretical model (<a href="http://www.plosbiology.org/article/info:doi/10.1371/journal.pbio.2001457#sec004" target="_blank">Methods</a>). (C) C dominance phase diagrams for toxin effectivity <b>s</b>_<b>S</b> and toxin producer fraction. (D) The final fraction of the C strain (dot plot) and classified outcomes (pie and bar plots) after simulated competition at fixed <b>s</b>_<b>S</b> = 1,500 and in dependence on toxin producer fractions are in accordance with experimental results (<a href="http://www.plosbiology.org/article/info:doi/10.1371/journal.pbio.2001457#pbio.2001457.g001" target="_blank">Fig 1C</a>). The color code represents competition outcome and is identical to that in <a href="http://www.plosbiology.org/article/info:doi/10.1371/journal.pbio.2001457#pbio.2001457.g001" target="_blank">Fig 1C</a>.</p

Characterization of interaction dynamics.

<p>The increase of the toxin producer fraction and not the ColicinE2 producer strain (C strain) growth rate determines the change in outcome distributions. Unless otherwise stated, colors indicate competition outcome (magenta: sensitive strain, green: producer strain, grey/black: coexistence), error bars represent mean ± standard deviation. (A) Sample image time series and corresponding area and relative fraction curves show C domination outcome (0.01 μg/ml MitC). Scale bar represents 400 μm. (B) Average expansion rates of single-strain colonies obtained in control experiments (no interaction). (C) Effective expansion rates of the entire S–C colony during competition (interaction present). Colors indicate the outcome of the competition: e.g., magenta indicates that >90% of S are present and responsible for colony expansion. (D) Temporal evolution of the producer fraction in the C population in high-resolution control experiments (<a href="http://www.plosbiology.org/article/info:doi/10.1371/journal.pbio.2001457#sec004" target="_blank">Methods</a>). (E), The dependence of transition time (time taken for C to capture more than 50% on the colonized area) on inducer concentration. (B, C, E: asterisks indicate significant differences as obtained by pairwise <i>t</i>-tests; not significant (ns): <i>p</i> > 0.05, ***: <i>p</i> < 0.001, for details see <a href="http://www.plosbiology.org/article/info:doi/10.1371/journal.pbio.2001457#pbio.2001457.s015" target="_blank">S1 Data</a>).</p