RESUMEN
Outbreaks of SARS-CoV-2 can be attributed to expanding small-scale localized infection subclusters that eventually propagate into regional and global outspread. These infections are driven by spatial as well as temporal mutational dynamics wherein virions diverge genetically as transmission occurs. Mutational similarity or dissimilarity of viral strains, stemming from shared spatiotemporal fields, thence serves as a gauge of relatedness. In our clinical laboratory, molecular epidemiological analyses of strain association are performed qualitatively from genomic sequencing data. These methods however carry a degree of uncertainty when the samples are not qualitatively, with reasonable confidence, deemed identical or dissimilar. We propose a theoretical mathematical model for probability derivation of outbreak-sample similarity as a function of spatial dynamics, shared and different mutations, and total number of samples involved. This Similarity Index utilizes an Essen-Möller ratio of similar and dissimilar mutations between the strains in question. The indices are compared to each strain within an outbreak, and then the final Similarity Index of the outbreak group is calculated to determine quantitative confidence of group relatedness. We anticipate that this model will be useful in evaluating strain associations in SARS-CoV-2 and other viral outbreaks utilizing molecular data.
RESUMEN
The recent rapid expansion of targeted viral sequencing approaches in conjunction with available bioinformatics have provided an effective platform for studying severe acute respiratory syndrome coronavirus-2 (CoV-2) virions at the molecular level. These means can be adapted to the field of viral molecular epidemiology, wherein localized outbreak clusters can be evaluated and linked. To this end, we have integrated publicly available algorithms in conjunction with targeted RNASeq data in order to qualitatively evaluate similarity or dissimilarity between suspect outbreak strains from hospitals, or assisted living facilities. These tools include phylogenetic clustering and mutational analysis utilizing Nextclade and Ultrafast Sample placement on Existing tRee (UShER). We herein present these outbreak screening tools utilizing three case examples in the context of molecular epidemiology, along with limitations and potential future developments. We anticipate that these methods can be performed in clinical molecular laboratories equipped with CoV-2-sequencing technology.
RESUMEN
Molecular diagnostics is a rapidly growing branch of the clinical laboratory and has accelerated the advance of personalized medicine in the fields of pharmacogenomics, pharmacogenetics, and nutrigenomics. The versatility of molecular biology allows it to be effective in several medical fields that include reproduction, immunogenetics, and virology. Implementation of molecular and sequencing technology in reproductive medicine can add another layer of understanding to better define the causes behind infertility and recurrent reproductive loss. In the following, we examine current molecular methods for probing factors behind reproductive pregnancy loss including reverse transcription polymerase chain reaction and next generation sequencing (NGS). We review several current and potential genetic (DNA) and transcriptional (RNA)-based parameters in women with infertility that can be significant in diagnosis and treatment. These molecular factors can be inferred either from genomic DNA or RNA locally within the endometrium. Furthermore, we consider infection-based abnormalities such as human herpesvirus-6 and severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2). Finally, we present future directions as well as data demonstrating the potential role of human endogenous retroviruses in pregnancy loss. We hope these discussions will assist the clinician in delineating some of the intricate molecular factors that can contribute to infertility and recurrent reproductive failures.