Detection of Mpox Virus Using Microbial Cell-Free DNA: The Potential of Pathogen-Agnostic Sequencing for Rapid Identification of Emerging Pathogens

BACKGROUND

The 2022 global outbreak of Monkeypox virus (MPXV) highlighted challenges with polymerase chain reaction detection as divergent strains emerged and atypical presentations limited the applicability of swab sampling. Recommended testing in the United States requires a swab of lesions, which arise late in infection and may be unrecognized. We present MPXV detections using plasma microbial cell-free DNA (mcfDNA) sequencing.

METHODS

Fifteen plasma samples from 12 case-patients were characterized through mcfDNA sequencing. Assay performance was confirmed through in silico inclusivity and exclusivity assessments. MPXV isolates were genotyped using mcfDNA, and phylodynamic information was imputed using publicly available sequences.

RESULTS

MPXV mcfDNA was detected in 12 case-patients. Mpox was not suspected in 5, with 1 having documented resolution of mpox >6 months previously. Six had moderate to severe mpox, supported by high MPXV mcfDNA concentrations; 4 died. In 7 case-patients, mcfDNA sequencing detected coinfections. Genotyping by mcfDNA sequencing identified 22 MPXV mutations at 10 genomic loci in 9 case-patients. Consistent with variation observed in the 2022 outbreak, 21 of 22 variants were G > A/C > T. Phylogenetic analyses imputed isolates to sublineages arising at different time points and from different geographic locations.

CONCLUSIONS

We demonstrate the potential of plasma mcfDNA sequencing to detect, quantify, and, for acute infections with high sequencing coverage, subtype MPXV using a single noninvasive test. Sequencing plasma mcfDNA may augment existing mpox testing in vulnerable patient populations or in patients with atypical symptoms or unrecognized mpox. Strain type information may supplement disease surveillance and facilitate tracking emerging pathogens.

PanGraph: scalable bacterial pan-genome graph construction

The genomic diversity of microbes is commonly parameterized as SNPs relative to a reference genome of a well-characterized, but arbitrary, isolate. However, any reference genome contains only a fraction of the microbial pangenome, the total set of genes observed in a given species. Reference-based approaches are thus blind to the dynamics of the accessory genome, as well as variation within gene order and copy number. With the widespread usage of long-read sequencing, the number of high-quality, complete genome assemblies has increased dramatically. In addition to pangenomic approaches that focus on the variation in the sets of genes present in different genomes, complete assemblies allow investigations of the evolution of genome structure and gene order. This latter problem, however, is computationally demanding with few tools available that shed light on these dynamics. Here, we present PanGraph, a Julia-based library and command line interface for aligning whole genomes into a graph. Each genome is represented as a path along vertices, which in turn encapsulate homologous multiple sequence alignments. The resultant data structure succinctly summarizes population-level nucleotide and structural polymorphisms and can be exported into several common formats for either downstream analysis or immediate visualization.

A variational method for image-based inference of internal stress in epithelial tissues

Cellular mechanics drives epithelial morphogenesis, the process wherein cells collectively rearrange to produce tissue-scale deformations that determine organismal shape. However, quantitative understanding of tissue mechanics is impaired by the difficulty of direct measurement of stress in-vivo. This difficulty has spurred the development of image-based inference algorithms that estimate stress from snapshots of epithelial geometry. Such methods are challenged by sensitivity to measurement error and thus require accurate geometric segmentation for practical use. We overcome this difficulty by introducing a novel approach - the Variational Method of Stress Inference (VMSI) - which exploits the fundamental duality between stress and geometry at equilibrium of discrete mechanical networks that model confluent cellular layers. We approximate the apical geometry of an epithelial tissue by a 2D tiling with Circular Arc Polygons (CAP) in which arcs represent intercellular interfaces defined by the balance of local line tension and pressure differentials between adjacent cells. The mechanical equilibrium of such networks imposes extensive local constraints on CAP geometry. These constraints provide the foundation of VMSI which, starting with images of epithelial monolayers, simultaneously approximates both tissue geometry and internal forces, subject to the constraint of equilibrium. We find VMSI to be more robust than previous methods. Specifically, the VMSI performance is validated by the comparison of the predicted cellular and mesoscopic scale stress with the measured myosin II patterns during early Drosophila embryogenesis. VMSI prediction of mesoscopic stress tensor correlates at the 80% level with the measured myosin distribution and reveals that most of the myosin activity in that case is involved in a static internal force balance within the epithelial layer. In addition to insight into cell mechanics, this study provides a practical method for non-destructive estimation of stress in live epithelial tissue.

Global morphogenetic flow is accurately predicted by the spatial distribution of myosin motors

During embryogenesis tissue layers undergo morphogenetic flow rearranging and folding into specific shapes. While developmental biology has identified key genes and local cellular processes, global coordination of tissue remodeling at the organ scale remains unclear. Here, we combine in toto light-sheet microscopy of the Drosophila embryo with quantitative analysis and physical modeling to relate cellular flow with the patterns of force generation during the gastrulation process. We find that the complex spatio-temporal flow pattern can be predicted from the measured meso-scale myosin density and anisotropy using a simple, effective viscous model of the tissue, achieving close to 90% accuracy with one time dependent and two constant parameters. Our analysis uncovers the importance of a) spatial modulation of myosin distribution on the scale of the embryo and b) the non-locality of its effect due to mechanical interaction of cells, demonstrating the need for the global perspective in the study of morphogenetic flow.

Active Tension Network model suggests an exotic mechanical state realized in epithelial tissues

Mechanical interactions play a crucial role in epithelial morphogenesis, yet understanding the complex mechanisms through which stress and deformation affect cell behavior remains an open problem. Here we formulate and analyze the Active Tension Network (ATN) model, which assumes that the mechanical balance of cells within a tissue is dominated by cortical tension and introduces tension-dependent active remodeling of the cortex. We find that ATNs exhibit unusual mechanical properties. Specifically, an ATN behaves as a fluid at short times, but at long times supports external tension like a solid. Furthermore, an ATN has an extensively degenerate equilibrium mechanical state associated with a discrete conformal - "isogonal" - deformation of cells. The ATN model predicts a constraint on equilibrium cell geometries, which we demonstrate to approximately hold in certain epithelial tissues. We further show that isogonal modes are observed in the fruit y embryo, accounting for the striking variability of apical areas of ventral cells and helping understand the early phase of gastrulation. Living matter realizes new and exotic mechanical states, the study of which helps to understand biological phenomena.