Bioinformatic analysis indicates that SARS-CoV-2 is unrelated to known artificial coronaviruses.

OBJECTIVE: SARS-CoV-2 is responsible for the present coronavirus pandemic and some suggestions were made about its possible artificial origin. We, therefore, compared SARS-CoV-2 with such known viruses that were prepared in the laboratory and other relevant natural strains to estimate their genetic relatedness. MATERIALS AND METHODS: BLAST and clustalW were used to identify and align viral sequences of SARS-CoV-2 to other animal coronaviruses (human, bat, mouse, pangolin) and related artificial constructs. Phylogenetics trees were then prepared using iTOL. RESULTS: Our study supports the notion that known artificial coronaviruses, including the chimeric SL-SHC014-MA15 synthesized in 2015, differ too much from SARS-CoV-2 to hypothesize an artificial origin of the latter. On the contrary, our data support the natural origin of the COVID-19 virus, likely derived from bats, possibly transferred to pangolins, before spreading to man. CONCLUSIONS: Speculations about the artificial origin of SARS-CoV-2 are most likely unfounded. On the contrary, when carefully handled, engineered organisms provide a unique opportunity to study biological systems in a controlled fashion. Biotechnology is a powerful tool to advance medical research and should not be abandoned because of irrational fears.

Analysis of machine learning algorithms as integrative tools for validation of next generation sequencing data.

OBJECTIVE: While next generation sequencing (NGS) has become the technology of choice for clinical diagnostics, most genetic laboratories still use Sanger sequencing for orthogonal confirmation of NGS results. Previous studies have shown that when the quality of NGS data is high, most calls are indicated by Sanger sequencing, making confirmation redundant. We aimed at establishing a set of criteria that make it possible to distinguish NGS calls that need orthogonal confirmation from those that do not would significantly decrease the amount of work necessary to reach a diagnosis. MATERIALS AND METHODS: A data set of 7976 NGS calls confirmed as true or false positive by Sanger sequencing was used to train and test different machine learning (ML) approaches. By varying the size and class balance of the training dataset, we measured the performance of the different algorithms to determine the conditions under which ML is a valid approach for confirming NGS calls in a diagnostic environment. RESULTS: Our results indicate that machine learning is a valid approach to find variant calls that need more investigation, but in order to reach the high accuracy required in a clinical environment, the training data set must include enough observations and these observations must be well-balanced between true/false positive NGS calls. CONCLUSIONS: Our results show that it is possible to integrate the diagnostic NGS validation workflow with a machine learning approach to reduce the number of Sanger confirmations of high- quality NGS calls, reducing the time and costs of diagnosis.

PipeMAGI: an integrated and validated workflow for analysis of NGS data for clinical diagnostics.

OBJECTIVE: We describe how to set up a custom workflow for the analysis of next generation sequencing (NGS) data suitable for the diagnosis of genetic disorders and that meets the strictest standards of quality and accuracy. Our method goes from DNA extraction to data analysis with a computational in-house pipeline. The system was extensively validated using three publicly available Coriell samples, estimating accuracy, sensitivity and specificity. Multiple runs were also made to assess repeatability and reproducibility. MATERIALS AND METHODS: Three different Coriell samples were analyzed in a single run to perform coverage, sensitivity, specificity, accuracy, reproducibility and repeatability analysis. The three samples were analyzed with a custom-made oligonucleotide probe library using Nextera Rapid Capture enrichment technique and subsequently quantified using the Qubit method. Sample quality was verified using a 4200 TapeStation and sequenced on a MiSeq personal sequencer. Analysis of NGS data was then performed with a custom pipeline. RESULTS: The workflow enabled an accurate and precise analysis of NGS data that meets all the requirements of quality and accuracy required by international standards such as ISO15189 and the Association of Molecular Pathology. CONCLUSIONS: The proposed analysis/validation workflow has high assay accuracy, precision and robustness and can, therefore, be used for clinical diagnostic applications.

Mendelian obesity, molecular pathways and pharmacological therapies: a review.

OBJECTIVE: In this qualitative review we analyze the major pathways and mechanisms involved in the onset of genetically-determined obesity (Mendelian obesity), identifying possible pharmacological treatments and trials. MATERIALS AND METHODS: We searched PubMed with the keywords (obesity[Title/Abstract]) AND mutation[Title/Abstract], and OMIM with the keyword "obesity". In both cases, we selected non-syndromic Mendelian obesity. We then searched ClinicalTrials.gov with the following criteria: "recruitment status: active, not recruiting and completed"; "study type: interventional (clinical trial)"; "study results: with results"; type of intervention: "drug or dietary supplement". RESULTS: From the PubMed and OMIM searches we obtained a total of 15 genes associated with monogenic Mendelian obesity. From ClinicalTrials.gov we retrieved 46 completed or active trials of pharmacological treatments. CONCLUSIONS: We summarized the molecular bases of Mendelian obesity and searched for any clinical trials completed or underway for the treatment of severe forms of obesity. Most Mendelian obesities are linked to dysfunctions in the leptin/melanocortin signaling pathway, and most of the possible drugs target this pathway in order to improve energy expenditure and reduce food intake.