Mobilisation and analyses of publicly available SARS-CoV-2 data for pandemic responses.

The COVID-19 pandemic has seen large-scale pathogen genomic sequencing efforts, becoming part of the toolbox for surveillance and epidemic research. This resulted in an unprecedented level of data sharing to open repositories, which has actively supported the identification of SARS-CoV-2 structure, molecular interactions, mutations and variants, and facilitated vaccine development and drug reuse studies and design. The European COVID-19 Data Platform was launched to support this data sharing, and has resulted in the deposition of several million SARS-CoV-2 raw reads. In this paper we describe (1) open data sharing, (2) tools for submission, analysis, visualisation and data claiming (e.g. ORCiD), (3) the systematic analysis of these datasets, at scale via the SARS-CoV-2 Data Hubs as well as (4) lessons learnt. This paper describes a component of the Platform, the SARS-CoV-2 Data Hubs, which enable the extension and set up of infrastructure that we intend to use more widely in the future for pathogen surveillance and pandemic preparedness.

Systematic detection of co-infection and intra-host recombination in more than 2 million global SARS-CoV-2 samples.

Systematic monitoring of SARS-CoV-2 co-infections between different lineages and assessing the risk of intra-host recombinant emergence are crucial for forecasting viral evolution. Here we present a comprehensive analysis of more than 2 million SARS-CoV-2 raw read datasets submitted to the European COVID-19 Data Portal to identify co-infections and intra-host recombination. Co-infection was observed in 0.35% of the investigated cases. Two independent procedures were implemented to detect intra-host recombination. We show that sensitivity is predominantly determined by the density of lineage-defining mutations along the genome, thus we used an expanded list of mutually exclusive defining mutations of specific variant combinations to increase statistical power. We call attention to multiple challenges rendering recombinant detection difficult and provide guidelines for the reduction of false positives arising from chimeric sequences produced during PCR amplification. Additionally, we identify three recombination hotspots of Delta - Omicron BA.1 intra-host recombinants.

Identification of mutations in SARS-CoV-2 PCR primer regions.

Due to the constantly increasing number of mutations in the SARS-CoV-2 genome, concerns have emerged over the possibility of decreased diagnostic accuracy of reverse transcription-polymerase chain reaction (RT-PCR), the gold standard diagnostic test for SARS-CoV-2. We propose an analysis pipeline to discover genomic variations overlapping the target regions of commonly used PCR primer sets. We provide the list of these mutations in a publicly available format based on a dataset of more than 1.2 million SARS-CoV-2 samples. Our approach distinguishes among mutations possibly having a damaging impact on PCR efficiency and ones anticipated to be neutral in this sense. Samples are categorized as "prone to misclassification" vs. "likely to be correctly detected" by a given PCR primer set based on the estimated effect of mutations present. Samples susceptible to misclassification are generally present at a daily rate of 2% or lower, although particular primer sets seem to have compromised performance when detecting Omicron samples. As different variant strains may temporarily gain dominance in the worldwide SARS-CoV-2 viral population, the efficiency of a particular PCR primer set may change over time, therefore constant monitoring of variations in primer target regions is highly recommended.

The COMPARE Data Hubs.

Data sharing enables research communities to exchange findings and build upon the knowledge that arises from their discoveries. Areas of public and animal health as well as food safety would benefit from rapid data sharing when it comes to emergencies. However, ethical, regulatory and institutional challenges, as well as lack of suitable platforms which provide an infrastructure for data sharing in structured formats, often lead to data not being shared or at most shared in form of supplementary materials in journal publications. Here, we describe an informatics platform that includes workflows for structured data storage, managing and pre-publication sharing of pathogen sequencing data and its analysis interpretations with relevant stakeholders.

Worldwide human mitochondrial haplogroup distribution from urban sewage.

Community level genetic information can be essential to direct health measures and study demographic tendencies but is subject to considerable ethical and legal challenges. These concerns become less pronounced when analyzing urban sewage samples, which are ab ovo anonymous by their pooled nature. We were able to detect traces of the human mitochondrial DNA (mtDNA) in urban sewage samples and to estimate the distribution of human mtDNA haplogroups. An expectation maximization approach was used to determine mtDNA haplogroup mixture proportions for samples collected at each different geographic location. Our results show reasonable agreement with both previous studies of ancient evolution or migration and current US census data; and are also readily reproducible and highly robust. Our approach presents a promising alternative for sample collection in studies focusing on the ethnic and genetic composition of populations or diseases associated with different mtDNA haplogroups and genotypes.

Uniaxial strain induced topological phase transition in bismuth-tellurohalide-graphene heterostructures.

We explore the electronic structure and topological phase diagram of heterostructures formed of graphene and ternary bismuth tellurohalide layers. We show that mechanical strain inherently present in fabricated samples could induce a topological phase transition in single-sided heterostructures, turning the sample into a novel experimental realisation of a time reversal invariant topological insulator. We construct an effective tight binding description for low energy excitations and fit the model's parameters to ab initio band structures. We propose a simple approach for predicting phase boundaries as a function of mechanical distortions and hence gain a deeper understanding on how the topological phase in the considered system may be engineered.

Formation of Two-Dimensional Micelles on Graphene: Multi-Scale Theoretical and Experimental Study.

Graphene and related two-dimensional (2D) materials possess outstanding electronic and mechanical properties, chemical stability, and high surface area. However, to realize graphene's potential for a range of applications in materials science and nanotechnology there is a need to understand and control the interaction of graphene with tailored high-performance surfactants designed to facilitate the preparation, manipulation, and functionalization of new graphene systems. Here we report a combined experimental and theoretical study of the surface structure and dynamics on graphene of pyrene-oligoethylene glycol (OEG) -based surfactants, which have previously been shown to disperse carbon nanotubes in water. Molecular self-assembly of the surfactants on graphitic surfaces is experimentally monitored and optimized using a graphene coated quartz crystal microbalance in ambient and vacuum environments. Real-space nanoscale resolution nanomechanical and topographical mapping of submonolayer surfactant coverage, using ultrasonic and atomic force microscopies both in ambient and ultrahigh vacuum, reveals complex, multilength-scale self-assembled structures. Molecular dynamics simulations show that at the nanoscale these structures, on atomically flat graphitic surfaces, are dependent upon the surfactant OEG chain length and are predicted to display a previously unseen class of 2D self-arranged "starfish" micelles (2DSMs). While three-dimensional micelles are well-known for their widespread uses ranging from microreactors to drug-delivery vehicles, these 2DSMs possess the highly desirable and tunable characteristics of high surface affinity coupled with unimpeded mobility, opening up strategies for processing and functionalizing 2D materials.

Reversible thermal switching of aqueous dispersibility of multiwalled carbon nanotubes.

Easily reversible aqueous dispersion/precipitation of multiwalled carbon nanotubes (MWNTs) has been demonstrated using small-molecule non-ionic pyrene-based surfactants, which exhibit lower critical solution temperature (LCST) phase behaviour. The MWNTs are dispersed by means of non-covalent interactions. The dispersibility can be switched "off" (i.e., MWNTs precipitated) upon heating and switched "on" (i.e., MWNTs re-dispersed) upon cooling and merely swirling the sample at room temperature, that is, under very mild conditions. This effect is also observed under high ionic strength conditions with NaCl in the aqueous phase.

Precursor configurations and post-rupture evolution of Ag-CO-Ag single-molecule junctions.

Experimental correlation analysis and first-principles theory are used to probe the structure and evolution of Ag-CO-Ag single-molecule junctions both before the formation and after the rupture of the junctions. Two dimensional correlation histograms and conditional histograms demonstrate that prior to the single-molecule bridge configuration the CO molecule is already bound parallel to the Ag single-atom contact. This molecular precursor configuration is accompanied by the opening of additional conductance channels compared to the single-channel transport in pure Ag monoatomic junctions. To investigate the post-rupture evolution of the junction we introduce a cross-correlation analysis between the opening and the subsequent closing conductance traces. This analysis implies that the molecule is bound rigidly to the apex of one electrode, and so the same single-molecule configuration is re-established as the junction is closed. The experimental results are confirmed by ab initio simulations of the evolution of contact geometries, transmission eigenvalues and scattering wavefunctions.

A study of planar anchor groups for graphene-based single-molecule electronics.

To identify families of stable planar anchor groups for use in single molecule electronics, we report detailed results for the binding energies of two families of anthracene and pyrene derivatives adsorbed onto graphene. We find that all the selected derivatives functionalized with either electron donating or electron accepting substituents bind more strongly to graphene than the parent non-functionalized anthracene or pyrene. The binding energy is sensitive to the detailed atomic alignment of substituent groups over the graphene substrate leading to larger than expected binding energies for -OH and -CN derivatives. Furthermore, the ordering of the binding energies within the anthracene and pyrene series does not simply follow the electron affinities of the substituents. Energy barriers to rotation or displacement on the graphene surface are much lower than binding energies for adsorption and therefore at room temperature, although the molecules are bound to the graphene, they are almost free to move along the graphene surface. Binding energies can be increased by incorporating electrically inert side chains and are sensitive to the conformation of such chains.