Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) virus, which occurred in Wuhan, China in late 2019, caused a pandemic of ongoing coronavirus disease 2019 (COVID-19), 39.8 million worldwide. It affects more than one person’s life and has killed more than 1.11 million people to date. Although extensive sequencing efforts are underway to understand the evolution of this virus, some studies using SARS-CoV-2 sequencing data have shown that different allelic frequencies of the virus in the same patient, Heteroplus. A phenomenon called me is shown.
The most likely explanation for heterologous virus readings within this patient is the presence of multiple virus strains. Recombination is an unlikely explanation, as the virus is much less likely to function after being degraded in the host cell and reconstituted into virions with different sequences. Evidence of multiple strains of SARS-CoV-2 virus in the same COVID-19 patient is available, but evidence of substrains that recombine in the same patient is not currently available.
Clinical significance of heteroplasmy
Multiple strains of the virus that infect the same patient have significant clinical consequences for epidemiology, treatment, and pandemic control. Fluctuations in viral strains show different levels of transmission, different drug resistance mechanisms, different responses to treatment, and can explain different symptoms. Given the importance of this in treatment and vaccine development, it is imperative that more research focus on the heteroplasmy of SARS-CoV-2.
Researchers from IBM Research, TJ Watson Research Center, New York, USA recently published a general methodological framework for interpreting phylogeny from genomic data of multiple diseases, including COVID-19 and cancer.Their work is published on the preprint server bioRxiv*.
In the case of cancer, the patient’s tumor heterogeneity indicates a heterologous cytoplasm within the patient, and the absence of recombination of tumor cells is an accepted assumption. Researchers have shown multiple tumor clones with different frequencies of tumor genomic variants and co-infected multiple tumor clones with different frequency of viral genome readings, just as they provide a handle for computational inference. Suppose you provide a means to calculate the sublineage.
Schematic diagram of the Concerti framework. Given a set of multi-patient (COVID-19) or multi-site, multi-time (cancer) genomic samples, the algorithm analyzes the underlying frequency distribution as input and (1) appears by performing a negative selection. Filter the changes you make. (2) Multidimensional clustering is performed to identify pseudoclone / lineage and is enhanced by (3) merging the first negatively selected changes (3) single sample clustering. (5) All potential phylogeny is generated and compatibility is evaluated. Finally, a set of integrated phylogenetic structures over time or sites is printed with a likelihood score.
Algorithms for understanding the evolutionary lineage
This study describes a computational framework called Concerti for inferring phylogeny in both of the above scenarios. To demonstrate the accuracy of this algorithm, researchers have reproduced some known results in both scenarios. They also identified new potential parallel mutations in the SARS-CoV-2 virus and discovered new clones with treatment-resistant mutations in the context of cancer.
According to researchers, Concerti’s ability to extract and integrate information from multiple points, sites, times, or samples makes it possible to discover phylogenetic trees that capture spatial and temporal inhomogeneities. I will. These phylogenetic models are functionally related in the “birth” of clones that may contain a mechanism of resistance to treatment, the “death” of subclones with drug targets, and in clones that appear to be clinically unrelated. It can have a direct impact on treatment because it can emphasize the acquisition of mutations.
Patient GI1 Concerto Tumor Evolution Tree T. Tumor evolution tree T of GI1 multisite data in patients with colon cancer. The edges of the T are labeled with known oncogenes and the color indicates a distinct pseudoclone estimated by Concerti. The leaf nodes represent each of the different lesion sites. The single-site tree T appears at the bottom as stacked discs, and its size is proportional to its prevalence value.
The team is specific to how Concerti can be applied to genomic sequence datasets with different allelic frequencies, whether cancer or SARS-CoV-2, and how the results provided by the algorithm are important disease-specific. Showed if it has clinical significance.
“This paper shows how Concerti can be applied to genomic sequence datasets with different allelic frequencies, whether it is cancer or the new SARS-CoV-2 virus causing the COVID-19 pandemic. The results may be disease-specific. Clinical significance. “
Certain integrations of multipoint data may improve therapeutic response
Identifying the presence of many virus strains in a single host can have a significant impact on therapeutic approaches, vaccine development efforts, and infection mitigation strategies. Concerto data from COVID-19 patients show the ability to identify virus strains and discover the presence of new homoplasmies based on various allelic frequencies. Researchers believe that the results provided by Concerti effectively address the critical challenges facing research in the development of therapeutics and vaccines.
In the case of cancer, accurate monitoring of tumor growth throughout the course of the disease helps identify new drug targets and treatments that can stabilize the disease and control treatment exposure pressures and changes in the tumor environment. .. Research results highlight how the specific integration of multipoint data with Concerti can facilitate more optimized and locally targeted treatment planning for better treatment responsiveness. ..
“Concerti results may help address the overwhelming challenges research faces in developing 396 therapeutics and promote the key to effective vaccine development.”
bioRxiv Publish preliminary scientific reports that should not be considered definitive as they are not peer-reviewed, guide clinical / health-related behaviors, and should not be treated as established information.