Corona Virus (CoVid19) Genome: Genomic and Evolutionary Analysis Point Towards Its Possible Synthetic Origin

2020 ◽  
Author(s):  
Tapan Kumar Mohanta ◽  
Yugal Kishore Mohanta ◽  
Ahmed Al-Harrasi
Keyword(s):  
Natural Selection ◽  
Genomic Sequence ◽  
Sequence Data ◽  
Sequence Similarity ◽  
The United States ◽  
Evolutionary Analysis ◽  
Corona Virus ◽  
New Genotype ◽  
Meat Market ◽  
German Isolate

The Severe acute respiratory syndrome (SARS) corona virus (CoV) 2 SARS-CoV-2 mediated epidemic is a global pandemic. The first genome sequence data of SARS-CoV-2 (CoVid19) concluded that it has a bat reservoir and bat was the immediate donor. Andersen et al., (2020) has reported that it is improbable to do laboratory manipulation of SARS CoV [1]. But, Lau et al., (2010) has already reported the generation of recombinant bat SARS CoV and they had reported three recombinant genotypes. Hence laboratory based manipulation has already completed long before[2]. A deep comparative study of bat SARS CoV with other SARS CoVs (including human SARS CoV of German isolate) revealed, human SARS CoV-2 genomes (isolates of China, India, Italy, Nepal, and the United States of America) had sequence similarity of 79-80% only with bat SARS CoV and it has sequence similarity of approximately 60% with the human SARS CoV (German isolate). The presence of large genomic dissimilarity of bat SARS CoV genome with human SARS CoV-2 cannot be considered as an immediate donor to human SARS CoV-2. However, the genomic sequence similarity within the SARS CoV-2 isolates of China, India, Italy, Nepal, and USA shared 99-100% similarity. This suggests that human SARS CoV-2 did not undergo heavy mutation to generate immediate new genotype. If the SARS CoV-2 infection happened to the human through the SARS CoV of bat from Wuhan meat market, it should have sequence similarity of more than 99% which was not found in the study. Phylogenetic analysis revealed, bat SARS CoV did not fall with the group of SARS CoV-2 of China, India, Italy, Nepal, and USA isolates. This suggests that bat SARS CoV has genomic and evolutionary dissimilarity and cannot be considered as immediate and direct donor of human SARS CoV-2. The natural selection of bat genome before transfer to the zoonotic organism is a time-consuming process and natural selection in human post zoonotic transfer is also time-consuming event. Therefore, concept mentioned by Andersen et al., (2020)[1] regarding its transfer from a bat of Wuhan meat market is irrefutably incorrect. Sequence alignment revealed the presence of inserted codons in human SARS CoV-2 and synteny analysis corroborated with the presence of extra nucleotides/codons in the human SARS CoV-2. Relative time tree analysis revealed it origin before 0.00 million year ago, suggesting its recent synthetic/modified origin.

scholarly journals Corona virus (CoVid19) genome: genomic and biochemical analysis revealed its possible synthetic origin

2020 ◽  
Vol 7 (5) ◽  
pp. 200-213
Author(s):  
Tapan Kumar Mohanta
Keyword(s):  
Sequence Data ◽  
Sequence Similarity ◽  
Proteome Analysis ◽  
The United States ◽  
Surface Glycoprotein ◽  
Steam Treatment ◽  
Origin And Evolution ◽  
Corona Virus ◽  
Global Pandemic ◽  
German Isolate

The Severe acute respiratory syndrome (SARS) corona virus 2 SARS-CoV-2 mediated epidemic is a global pandemic. It has evolved as a curse to the human civilization and at the present situation, where most of the cities in the world are on lockdown. The first genome sequence data of SARS-CoV-2 (CoVid19) and their reports that followed concluded that it was a member of the genus Betacoronavirus and has a bat reservoir. To understand its origin and evolution, we conducted a deep comparative study by comparing the genomes of bat SARS CoV and other SARS CoVs (including human SARS CoV of German isolate). Results revealed that CoVid19 genomes from isolates of China, India, Italy, Nepal, and the United States of America has sequence similarity of 79-80% only with the bat SARS CoV and it has sequence similarity of approximately 60% with the human SARS CoV of German isolate. Whereas, the sequence similarity within the CoVid19 genomes of these countries was 99-100%. If the SARS CoV infection happened to human through the SARS CoV of bat origin, it should have sequence similarity of more than 99% which was absent in this case. Phylogenetic analysis revealed, bat SARS CoV did not fall with the group of SARS CoV of China, India, Italy, Nepal, and USA isolates. The genome analysis revealed the presence of multiple microsatellite repeats sequences. Proteome analysis revealed, the melting temperature (Tm) of surface glycoprotein was less than 55oC, suggesting the steam treatment can be an ideal preventative measure to destabilize the CoVid19, and thus it’s spreading

scholarly journals Integrating structure-based machine learning and co-evolution to investigate specificity in plant sesquiterpene synthases

2020 ◽  
Author(s):  
Janani Durairaj ◽  
Elena Melillo ◽  
Harro J Bouwmeester ◽  
Jules Beekwilder ◽  
Dick de Ridder ◽  
...  
Keyword(s):  
Machine Learning ◽  
Plant Species ◽  
Sequence Data ◽  
Reaction Pathway ◽  
Sequence Similarity ◽  
Homology Modelling ◽  
Enzyme Engineering ◽  
Evolutionary Analysis ◽  
Structural Factors ◽  
Terpene Synthases

AbstractSesquiterpene synthases (STSs) catalyze the formation of a large class of plant volatiles called sesquiterpenes. While thousands of putative STS sequences from diverse plant species are available, only a small number of them have been functionally characterized. Sequence identity-based screening for desired enzymes, often used in biotechnological applications, is difficult to apply here as STS sequence similarity is strongly affected by species. This calls for more sophisticated computational methods for functionality prediction. We investigate the specificity of precursor cation formation in these elusive enzymes. By inspecting multi-product STSs, we demonstrate that STSs have a strong selectivity towards one precursor cation. We use a machine learning approach combining sequence and structure information to accurately predict precursor cation specificity for STSs across all plant species. We combine this with a co-evolutionary analysis on the wealth of uncharacterized putative STS sequences, to pinpoint residues and distant functional contacts influencing cation formation and reaction pathway selection. These structural factors can be used to predict and engineer enzymes with specific functions, as we demonstrate by predicting and characterizing two novel STSs from Citrus bergamia.Author summaryPredicting enzyme function is a popular problem in the bioinformatics field that grows more pressing with the increase in protein sequences, and more attainable with the increase in experimentally characterized enzymes. Terpenes and terpenoids form the largest classes of natural products and find use in many drugs, flavouring agents, and perfumes. Terpene synthases catalyze the biosynthesis of terpenes via multiple cyclizations and carbocation rearrangements, generating a vast array of product skeletons. In this work, we present a three-pronged computational approach to predict carbocation specificity in sesquiterpene synthases, a subset of terpene synthases with one of the highest diversities of products. Using homology modelling, machine learning and co-evolutionary analysis, our approach combines sparse structural data, large amounts of uncharacterized sequence data, and the current set of experimentally characterized enzymes to provide insight into residues and structural regions that likely play a role in determining product specifcity. Similar techniques can be repurposed for function prediction and enzyme engineering in many other classes of enzymes.

PoGB-Pred: Prediction of Antifreeze Proteins Sequences using Amino Acid Composition with Feature Selection followed by a Sequential based Ensemble Approach

2020 ◽  
Vol 15 ◽  
Author(s):  
Affan Alim ◽  
Abdul Rafay ◽  
Imran Naseem
Keyword(s):  
Amino Acid ◽  
Dimension Reduction ◽  
Protein Identification ◽  
Cold Water ◽  
Genomic Sequence ◽  
Sequence Data ◽  
Antifreeze Proteins ◽  
Building Blocks ◽  
Gradient Boosting ◽  
Proposed Model

Background: Proteins contribute significantly in every task of cellular life. Their functions encompass the building and repairing of tissues in human bodies and other organisms. Hence they are the building blocks of bones, muscles, cartilage, skin, and blood. Similarly, antifreeze proteins are of prime significance for organisms that live in very cold areas. With the help of these proteins, the cold water organisms can survive below zero temperature and resist the water crystallization process which may cause the rupture in the internal cells and tissues. AFP’s have attracted attention and interest in food industries and cryopreservation. Objective: With the increase in the availability of genomic sequence data of protein, an automated and sophisticated tool for AFP recognition and identification is in dire need. The sequence and structures of AFP are highly distinct, therefore, most of the proposed methods fail to show promising results on different structures. A consolidated method is proposed to produce the competitive performance on highly distinct AFP structure. Methods: In this study, we propose to use machine learning-based algorithms Principal Component Analysis (PCA) followed by Gradient Boosting (GB) for antifreeze protein identification. To analyze the performance and validation of the proposed model, various combinations of two segments composition of amino acid and dipeptide are used. PCA, in particular, is proposed to dimension reduction and high variance retaining of data which is followed by an ensemble method named gradient boosting for modelling and classification. Results: The proposed method obtained the superfluous performance on PDB, Pfam and Uniprot dataset as compared with the RAFP-Pred method. In experiment-3, by utilizing only 150 PCA components a high accuracy of 89.63 was achieved which is superior to the 87.41 utilizing 300 significant features reported for the RAFP-Pred method. Experiment-2 is conducted using two different dataset such that non-AFP from the PISCES server and AFPs from Protein data bank. In this experiment-2, our proposed method attained high sensitivity of 79.16 which is 12.50 better than state-of-the-art the RAFP-pred method. Conclusion: AFPs have a common function with distinct structure. Therefore, the development of a single model for different sequences often fails to AFPs. A robust results have been shown by our proposed model on the diversity of training and testing dataset. The results of the proposed model outperformed compared to the previous AFPs prediction method such as RAFP-Pred. Our model consists of PCA for dimension reduction followed by gradient boosting for classification. Due to simplicity, scalability properties and high performance result our model can be easily extended for analyzing the proteomic and genomic dataset.

scholarly journals Challenges in evaluating the use of viral sequence data to identify HIV transmission networks for public health

2020 ◽  
Vol 12 (s1) ◽  
Author(s):  
Rami Kantor ◽  
John P. Fulton ◽  
Jon Steingrimsson ◽  
Vladimir Novitsky ◽  
Mark Howison ◽  
...  
Keyword(s):  
Public Health ◽  
United States ◽  
Hiv Transmission ◽  
Sequence Data ◽  
The United States ◽  
Viral Sequence ◽  
Transmission Networks ◽  
New Methods ◽  
Hiv Epidemic ◽  
The World

AbstractGreat efforts are devoted to end the HIV epidemic as it continues to have profound public health consequences in the United States and throughout the world, and new interventions and strategies are continuously needed. The use of HIV sequence data to infer transmission networks holds much promise to direct public heath interventions where they are most needed. As these new methods are being implemented, evaluating their benefits is essential. In this paper, we recognize challenges associated with such evaluation, and make the case that overcoming these challenges is key to the use of HIV sequence data in routine public health actions to disrupt HIV transmission networks.

scholarly journals Geographic Distribution and Phylogeny of Soricine Shrew-Borne Seewis Virus and Altai Virus in Russia

Viruses ◽  
2021 ◽  
Vol 13 (7) ◽  
pp. 1286
Author(s):  
Liudmila N. Yashina ◽  
Sergey A. Abramov ◽  
Alexander V. Zhigalin ◽  
Natalia A. Smetannikova ◽  
Tamara A. Dupal ◽  
...  
Keyword(s):  
Geographic Distribution ◽  
Genomic Sequence ◽  
Common Shrew ◽  
The United States ◽  
Eastern Siberia ◽  
Geographic Ranges ◽  
Lena River ◽  
Far Eastern Russia ◽  
Far Eastern ◽  
Eastern Russia

The discovery of genetically distinct hantaviruses (family Hantaviridae) in multiple species of shrews, moles and bats has revealed a complex evolutionary history involving cross-species transmission. Seewis virus (SWSV) is widely distributed throughout the geographic ranges of its soricid hosts, including the Eurasian common shrew (Sorex araneus), tundra shrew (Sorex tundrensis) and Siberian large-toothed shrew (Sorex daphaenodon), suggesting host sharing. In addition, genetic variants of SWSV, previously named Artybash virus (ARTV) and Amga virus, have been detected in the Laxmann’s shrew (Sorex caecutiens). Here, we describe the geographic distribution and phylogeny of SWSV and Altai virus (ALTV) in Asian Russia. The complete genomic sequence analysis showed that ALTV, also harbored by the Eurasian common shrew, is a new hantavirus species, distantly related to SWSV. Moreover, Lena River virus (LENV) appears to be a distinct hantavirus species, harbored by Laxmann’s shrews and flat-skulled shrews (Sorex roboratus) in Eastern Siberia and far-eastern Russia. Another ALTV-related virus, which is more closely related to Camp Ripley virus from the United States, has been identified in the Eurasian least shrew (Sorex minutissimus) from far-eastern Russia. Two highly divergent viruses, ALTV and SWSV co-circulate among common shrews in Western Siberia, while LENV and the ARTV variant of SWSV co-circulate among Laxmann’s shrews in Eastern Siberia and far-eastern Russia. ALTV and ALTV-related viruses appear to belong to the Mobatvirus genus, while SWSV is a member of the Orthohantavirus genus. These findings suggest that ALTV and ALTV-related hantaviruses might have emerged from ancient cross-species transmission with subsequent diversification within Sorex shrews in Eurasia.

scholarly journals Special Issue: Genetic Basis of Phenotypic Variation in Drosophila and Other Insects

Genes ◽  
2021 ◽  
Vol 12 (8) ◽  
pp. 1212
Author(s):  
J. Spencer Johnston ◽  
Carl E. Hjelmen
Keyword(s):  
Next Generation Sequencing ◽  
Genetic Basis ◽  
Genomic Sequence ◽  
Sequence Data ◽  
Complete Genomic Sequence ◽  
Special Issue ◽  
Model Species ◽  
Road Map ◽  
Generation Sequencing ◽  
Complete Genomic

Next-generation sequencing provides a nearly complete genomic sequence for model and non-model species alike; however, this wealth of sequence data includes no road map [...]

scholarly journals 130. increase in Multidrug Resistance (2011–2018) and the Emergence of Extensive Drug Resistance (2020) in shigella Sonnei in the United States

2020 ◽  
Vol 7 (Supplement_1) ◽  
pp. S195-S195
Author(s):  
Naeemah Z Logan ◽  
Beth E Karp ◽  
Kaitlin A Tagg ◽  
Claire Burns-Lynch ◽  
Jessica Chen ◽  
...  
Keyword(s):  
United States ◽  
Multidrug Resistance ◽  
Disease Surveillance ◽  
Sequence Data ◽  
The United States ◽  
Multidrug Resistant ◽  
Shigella Sonnei ◽  
Short Read ◽  
Men 2 ◽  
The Us

Abstract Background Multidrug-resistant (MDR) Shigella sonnei infections are a serious public health threat, and outbreaks are common among men who have sex with men (MSM). In February 2020, Australia’s Department of Health notified CDC of extensively drug-resistant (XDR) S. sonnei in 2 Australian residents linked to a cruise that departed from Florida. We describe an international outbreak of XDR S. sonnei and report on trends in MDR among S. sonnei in the United States. Methods Health departments (HDs) submit every 20th Shigella isolate to CDC’s National Antimicrobial Resistance Monitoring System (NARMS) laboratory for susceptibility testing. We defined MDR as decreased susceptibility to azithromycin (MIC ≥32 µg/mL) with resistance to ampicillin, ciprofloxacin, and cotrimoxazole, and XDR as MDR with additional resistance to ceftriaxone. We used PulseNet, the national subtyping network for enteric disease surveillance, to identify US isolates related to the Australian XDR isolates by short-read whole genome sequencing. We screened these isolates for resistance determinants (ResFinder v3.0) and plasmid replicons (PlasmidFinder) and obtained patient histories from HDs. We used long-read sequencing to generate closed plasmid sequences for 2 XDR isolates. Results NARMS tested 2,781 S. sonnei surveillance isolates during 2011–2018; 80 (2.9%) were MDR, including 1 (0.04%) that was XDR. MDR isolates were from men (87%), women (9%), and children (4%). MDR increased from 0% in 2011 to 15.3% in 2018 (Figure). In 2020, we identified XDR isolates from 3 US residents on the same cruise as the Australians. The US residents were 41–42 year-old men; 2 with available information were MSM. The US and Australian isolates were highly related (0–1 alleles). Short-read sequence data from all 3 US isolates mapped to the blaCTX-M-27 harboring IncFII plasmids from the 2 Australian isolates with >99% nucleotide identity. blaCTX-M-27 genes confer ceftriaxone resistance. Increase in Percentage of Shigella sonnei Isolates with Multidrug Resistance* in the United States, 2011–2018† Conclusion MDR S. sonnei is increasing and is most often identified among men. XDR S. sonnei infections are emerging and are resistant to all recommended antibiotics, making them difficult to treat without IV antibiotics. This outbreak illustrates the alarming capacity for XDR S. sonnei to disseminate globally among at-risk populations, such as MSM. Disclosures All Authors: No reported disclosures

scholarly journals Predicting bacteriophage hosts based on sequences of annotated receptor-binding proteins

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Dimitri Boeckaerts ◽  
Michiel Stock ◽  
Bjorn Criel ◽  
Hans Gerstmans ◽  
Bernard De Baets ◽  
...  
Keyword(s):  
Machine Learning ◽  
Predictive Model ◽  
Receptor Binding ◽  
Bacterial Infections ◽  
Sequence Data ◽  
Sequence Similarity ◽  
Area Under The Curve ◽  
Local Alignment ◽  
Search Tool ◽  
Different Levels

AbstractNowadays, bacteriophages are increasingly considered as an alternative treatment for a variety of bacterial infections in cases where classical antibiotics have become ineffective. However, characterizing the host specificity of phages remains a labor- and time-intensive process. In order to alleviate this burden, we have developed a new machine-learning-based pipeline to predict bacteriophage hosts based on annotated receptor-binding protein (RBP) sequence data. We focus on predicting bacterial hosts from the ESKAPE group, Escherichia coli, Salmonella enterica and Clostridium difficile. We compare the performance of our predictive model with that of the widely used Basic Local Alignment Search Tool (BLAST). Our best-performing predictive model reaches Precision-Recall Area Under the Curve (PR-AUC) scores between 73.6 and 93.8% for different levels of sequence similarity in the collected data. Our model reaches a performance comparable to that of BLASTp when sequence similarity in the data is high and starts outperforming BLASTp when sequence similarity drops below 75%. Therefore, our machine learning methods can be especially useful in settings in which sequence similarity to other known sequences is low. Predicting the hosts of novel metagenomic RBP sequences could extend our toolbox to tune the host spectrum of phages or phage tail-like bacteriocins by swapping RBPs.

scholarly journals Meat Demand in South Korea: An Application of the Restricted Source-Differentiated Almost Ideal Demand System Model

2007 ◽  
Vol 39 (1) ◽  
pp. 47-60 ◽  
Author(s):  
Shida Rastegari Henneberry ◽  
Seong-huyk Hwang
Keyword(s):  
United States ◽  
South Korea ◽  
The United States ◽  
System Model ◽  
Demand System ◽  
Almost Ideal Demand System ◽  
The South ◽  
Meat Demand ◽  
South Korean ◽  
Meat Market

The first difference version of the restricted source-differentiated almost ideal demand system is used to estimate South Korean meat demand. The results of this study indicate that the United States has the most to gain from an increase in the size of the South Korean imported meat market in terms of its beef exports, while South Korea has the most to gain from this expansion in the pork market. Moreover, the results indicate that the United States has a competitive advantage to Australia in the South Korean beef market. Results of this study have implications for U.S. meat exports in this ever-changing policy environment.

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