Estimation of SARS-CoV-2 fitness gains from genomic surveillance data without prior lineage classification.

The emergence of SARS-CoV-2 variants with increased fitness has had a strong impact on the epidemiology of COVID-19, with the higher effective reproduction number of the viral variants leading to new epidemic waves. Tracking such variants and their genetic signatures, using data collected through genomic surveillance, is therefore crucial for forecasting likely surges in incidence. Current methods of estimating fitness advantages of variants rely on tracking the changing proportion of a particular lineage over time, but describing successful lineages in a rapidly evolving viral population is a difficult task. We propose a method of estimating fitness gains directly from nucleotide information generated by genomic surveillance, without a priori assigning isolates to lineages from phylogenies, based solely on the abundance of single nucleotide polymorphisms (SNPs). The method is based on mapping changes in the genetic population structure over time. Changes in the abundance of SNPs associated with periods of increasing fitness allow for the unbiased discovery of new variants, thereby obviating a deliberate lineage assignment and phylogenetic inference. We conclude that the method provides a fast and reliable way to estimate fitness advantages of variants without the need for a priori assigning isolates to lineages.

Forecasting local hospital bed demand for COVID-19 using on-request simulations.

Accurate forecasting of hospital bed demand is crucial during infectious disease epidemics to avoid overwhelming healthcare facilities. To address this, we developed an intuitive online tool for individual hospitals to forecast COVID-19 bed demand. The tool utilizes local data, including incidence, vaccination, and bed occupancy data, at customizable geographical resolutions. Users can specify their hospital's catchment area and adjust the initial number of COVID-19 occupied beds. We assessed the model's performance by forecasting ICU bed occupancy for several university hospitals and regions in Germany. The model achieves optimal results when the selected catchment area aligns with the hospital's local catchment. While expanding the catchment area reduces accuracy, it improves precision. However, forecasting performance diminishes during epidemic turning points. Incorporating variants of concern slightly decreases precision around turning points but does not significantly impact overall bed occupancy results. Our study highlights the significance of using local data for epidemic forecasts. Forecasts based on the hospital's specific catchment area outperform those relying on national or state-level data, striking a better balance between accuracy and precision. These hospital-specific bed demand forecasts offer valuable insights for hospital planning, such as adjusting elective surgeries to create additional bed capacity promptly.

Multiscale Molecular Dynamics Studies Reveal Different Modes of Receptor Clustering by Gb3-Binding Lectins.

The recognition of carbohydrate receptors on host cell membranes by pathogenic lectins is a crucial step in the microbial invasion. Two bacterial lectins, the B-subunit of Shiga toxin from Shigella dysenteria (StxB) and lectin I from Pseudomonas aeruginosa (LecA), are specific to the same galactolipid-globotriaosylceramide (Gb3). In this study we present a coarse-grained (cg) model of Gb3, which we further apply to unravel the molecular details of glycolipid binding by two lectins on the surface of a DOPC/cholesterol/Gb3 bilayer. In cg molecular dynamics simulations with time scales of dozens of microseconds, Gb3 was randomly distributed. The binding of both StxB or LecA is accompanied by Gb3 clustering in a cholesterol environment and with exclusion of DOPC in protein vicinity. StxB being bound by all 15 binding sites induced membrane bending, while LecA interacted with two out of four binding sites for most of the time causing a smaller inward curvature of the model membrane. Stable interactions occurred preferably when LecA was normal to the membrane surface. Furthermore, all-atom simulations revealed that LecA bound Gb3's headgroup at only one out of two possible conformations of the carbohydrate moiety observed at protein-free conditions. The results shed light on the mechanism of interactions between two lectins and Gb3 on the membrane surface and offer a coarse-grained model to study more complex systems at large spatiotemporal scales.

Binding of SV40's Viral Capsid Protein VP1 to Its Glycosphingolipid Receptor GM1 Induces Negative Membrane Curvature: A Molecular Dynamics Study.

The binding of the pentameric capsid protein VP1 of simian virus 40 to its glycosphingolipid receptor GM1 is a key step for the entry of the virus into the host cell. Recent experimental studies have shown that the interaction of variants of soluble VP1 pentamers with giant unilamellar vesicles composed of GM1, DOPC, and cholesterol leads to the formation of tubular membrane invaginations to the inside of the vesicles, mimicking the initial steps of endocytosis. We have used coarse-grained and atomistic molecular dynamics (MD) simulations to study the interaction of VP1 with GM1/DOPC/cholesterol bilayers. In the presence of one VP1 protein, we monitor the formation of small local negative curvature and membrane thinning at the protein binding site as well as reduction of area per lipid. These membrane deformations are also observed under cholesterol-free conditions. However, here, the number of GM1 molecules attached to the VP1 binding pockets increases. The membrane curvature is slightly increased for asymmetric GM1 distribution that mimics conditions in vivo, compared to symmetric GM1 distributions which are often applied in experiments. Slightly smaller inward curvature was observed in atomistic control simulations. Binding of four VP1 proteins leads to an increase of the average intrinsic area per lipid in the protein binding leaflet. Membrane fluctuations appear to be the driving force of VP1 aggregation, as was previously shown for membrane-adhering particles because no VP1 aggregation is observed in the absence of a lipid membrane.

Phase Transition of Glycolipid Membranes Studied by Coarse-Grained Simulations.

Glycolipids are important components of biological membranes. High concentrations of glycolipids are particularly found in lipid rafts, which take part in many physiological phenomena. This different partitioning and interaction pattern of glycolipids in the membrane as compared to those of phospholipids are likely due to their different chemical structures: the polar regions of glycosphingolipids can be even larger than for their hydrophobic moieties, giving rise to a rich conformational landscape. Here we study the influence of glycosphingolipids galactosylceramide (GCER) and monosialotetrahexosylganglioside (GM1) on the structural and thermodynamic properties of a phospholipid (DPPC) bilayer. Using the method of coarse-grained molecular dynamics simulation we show that both glycolipids increase the phase-transition temperature of phospholipid membranes and that the extent of this increase depends on the headgroup size and structure. GM1 shows a strong tendency to form mixed clusters with phospholipids, thereby stabilizing the membrane. In contrast, GCER is dispersed in the membrane. By occupying the interstitial space between phospholipids it causes a tighter packing of the lipids in the membrane.