Mpaniag1 Week 11

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Purpose

The purpose of this week's lab is to research and analyze the findings of 2019-nCoV in order to gain an understanding of its structure and its relationship to its function.

10 Biological Terms

  1. Epidemic-Occurring suddenly in numbers clearly in excess of normal expectancy, said especially of infectious diseases but applied also to any disease, injury or other health related event occurring in such outbreaks ("Epidemic Definition and Examples - Biology Online Dictionary", 2019)
  2. Transmit-To cause to pass over or through; to communicate by sending; to send from one person or place to another; to pass on or down as by inheritance ("Transmit Definition and Examples - Biology Online Dictionary", 2019)
  3. Lineage-term used to describe cells with common ancestry, that is developing from the same type of identifiable immature cell ("Lineage Definition and Examples - Biology Online Dictionary", 2020)
  4. Putative- the term is commonly used to describe an entity or a concept that is based on what is generally accepted or inferred even without direct proof of it, meaning it denotes something, like an inference or a supposition, that is accepted because it is deemed to be the case or what has been commonly believed even without solid evidence to back it up ("Putative Definition and Examples - Biology Online Dictionary", 2020)
  5. Residues-Any of the monomers comprising a polymer, or any of the parts that integrate to make up a larger molecule ("Residues Definition and Examples - Biology Online Dictionary", 2019)
  6. Pathogenesis- The origin and development of disease ("Pathogenesis Definition and Examples - Biology Online Dictionary", 2019)
  7. Phylogenetic-Of, or pertaining to phylogenesis; Relating to the race history of a type of organism ("Phylogenetic Definition and Examples - Biology Online Dictionary", 2019).
  8. Intermediate-lying or being in the middle place or degree, or between two extremes; coming or done between; intervening; interposed; interjacent; as, an intermediate space or time; intermediate colors ("Intermediate Definition and Examples - Biology Online Dictionary", 2019)
  9. Infectivity- The characteristic of a disease agent that embodies the capability of entering, surviving in, and multiplying in a susceptible host; The proportion of exposures in defined circumstances that result in infection.(" Infectvitiy Definition and Examples - Biology Online Dictionary", 2019)
  10. Angiotensin-A family of oligopeptides associated with increased blood pressure, mainly by causing vasoconstriction ("Angiotensin Definition and Examples - Biology Online Dictionary", 2019)

Outline

Abstract

  • 2019-nCoV has come from Wuhan, China with symptoms similar to SARS-CoV
  • Researchers used what they knew about SARS-CoV to make predictions of 2019-nCoV receptor usage
  • SARS-CoV and 2019-nCoV share similarities in sequence, leading researchers to believe they may use the same receptor
  • 2019-nCoV has residues that could interact with ACE2 well
  • 2019-nCoV has other residues that are not perfect for human ACE2, but it is still able to do human to human transmission
  • When doing phylogenetic analysis researchers discovered a bat origin, meaning that 2019-nCoV can use other animals ACE2 receptors
  • If 2019-nCoV can use other species, there is a possibility that those species could be hosts
  • The main reason for this paper is to use the knowledge found for SARS-CoV to create a model for 2019-nCoV that would give more information to use when creating safety measures

Importance

  • Researchers want to provide information that will help communities that will, later on, study and fight/create vaccines for this virus

Limitations

  • Limitations in previous studies did not use SARS-CoV structure modeling to predict 2019-nCoV virus-receptor binding
  • How did researchers overcome this limitation-Researchers used SARS-CoV modeling to make predictions of 2019-nCoV and ACE2 binding

Introduction

  • At the time this article was published 2019-nCoV had 500 cases and 17 deaths in China, more cases were present in other parts of the world like the US
  • Symptoms of 2019-nCoV and SARS-CoV are similar
  • 2019-nCoV is able to do human-human transmission
  • There are four major groups of coronaviruses
  • SARS-CoV and 2019-nCoV are in the same genus, the beta-genus
  • The virus enters the cell by using an enveloped-anchored spike protein that binds to the host's receptor and then fusing the two membranes together
  • Researchers have been able to create a crystal structure of SARS-CoV, showing that the RBD has a core and a RBM, the RBM is the thing that binds to ACE2
  • Human ACE2 has two different hot spots that are used for viral binding
  • Researchers have noticed that if certain amino acids are changed at certain positions the virus can bind more ideally to other organisms
  • The goal is to look and the structure, how it determines the function, and use those two pieces of information to improve epidemic surveillance
  • Main Result-The researchers created a model of 2019-nCoV interactions based on previous knowledge of SARS-CoV in order to prepare and predict for what the virus could do/how to stop it

Methods

  • Scientists used the predictive framework from SARS-CoV and applied it to 2019-nCoV
  • They made the comparisons using structural analysis, phylogenetic analysis, and sequence alignment, found trends, and applied those trends to previous findings of SARS-CoV to make predictions about 2019-nCoV

Results

  • 2019-nCoV is in the beta-genus like SARS-CoV
  • Ancestral to both the human and rat forms of SARS-CoV, which both use ACE2
  • SARS-CoV and 2019-nCoV share sequence similarities
  • When looking at the ACE2-contacting residues, 9 are conserved and 4 are semi-conserved when comparing SARS-CoV and 2019-nCoV sequences
  • SARS-CoV and 2019-nCoV have 77% sequence similarities when looking at the whole protein
  • MERS and HKU4 share even fewer similarities, yet they both use the same receptor
  • The high sequence similarities between SARS-CoV and 2019-nCoV, suggest that they could also use the same receptor
  • Researchers used the knowledge they had from SAR-CoV and created models of 2019-nCoV
  • Found critical residues that when mutated affect binding
  • Found that 2019-nCoV does not bind ideally to that of a civet ACE2, suggesting that it has not yet adapted to that certain host's receptor

Residues

  • Researchers looked at five important residues
  • 493-Gln493 works with hot spot 31--> 2019-nCoV is able to recognize ACE2 of humans
  • 501-Asn501 supports hot spot 353-->when comparing it to SARS-CoV, 2019-nCoV recognizes ACE2 better than the 2003 SARS virus
  • 455, 486, 494-these residues provide favorable interactions and support, supplying further evidence that 2019-nCoV can recognize ACE2 of humans

Other Species

  • Based on the residues, researchers found that most likely 2019-nCoV can still use the ACE2 receptor of a civet but it is not ideal.
  • Most likely does not bind to mice and rats receptor
  • Maybe can recognize receptors for pigs, ferrets, cats, orangutans, and monkeys in addition to humans

Figures

  • Figure 1 a-Human ACE2 and SAR-CoV (from 2002) complex created previously
  • Figure 1 b-Five critical residues for ACE2 binding, SARS-CoV and the corresponding 2019-nCoV, mutations in these residues cause changes in binding affinity
  • Figure 1 c-Optimized SARS-CoV for ACE2 (human) binding complex created previously
  • Figure 1 d-Possible model for human ACE2 and 2019-nCoV complex based on what they knew from Figure 1c
  • Figure 2-Phylogenetic tree that shows that 2019-nCoV is in the beta-genus lineage, all viruses in tree use ACE2
  • Figure 3 a-Sequence alignment of SARS-CoV and 2019-nCoV, pink shows specific ACE2 contacting residues, suggest the possibility of having ACE2 has a receptor
  • Figure 3 b-Comparison of the different part of the viruses (2019-nCoV and SARS-CoV, sequence similarities percents)
  • Figure 3 c-Comparison of the different part of the viruses (MERS-Human and HKU4-bat, sequence similarities percents)
  • Figure 3b and 3c-Show that viruses with fewer similarities (MERS and HKU4) can share receptors, likely that sequences with more similarities (SARS-CoV and 2019-nCoV) have the same receptor, ACE2
  • Figure 4 a-Changes in certain residues present in the different ACE2 host species
  • Figure 4 b-Structure of optimized civet SARS-CoV and civet ACE2
  • Figure 4 c-Researchers created a model of 2019n-CoV and civet ACE 2 based on what they knew from Figure 4b

Discussion

  • Important implications are that if Researchers know how 2019-nCoV works then they will be able to try and create vaccines to prevent it
  • Knowledge of SARS-CoV was used to make predictions for 2019-nCoV
  • 2019n-CoV can bind better to human ACE2 than SARS-CoV from 2003
  • Researchers make the prediction based on these structures that an individual mutation can significantly affect the binding affinity and must be watched
  • When looking at the phylogenetic tree you can see that it is most likely the 2019-nCoV came from bats
  • Unlike SARS-CoV, 2019-nCoV does not have a lot of evidence that it mutated to ideally bind to civets
  • Other animals, such as pigs, cats, and ferrets have ACE2 receptors that 2019-nCoV has the possibility to bind to, and must be screened
  • This study's main purpose was to make predictions that would better equip members of scientific communities to fight this virus
  • Future directions these researchers could make a move toward testing if the predictions made are true, and editing mice and rats to have human ACE2 proteins to see how certain vaccines would work

Evaluation

  • I think the authors did well in regard to making the connections between SAR-CoV and 2019-nCoV but I think the order of the paper could have changed so that the authors start with the sequence similarities and build off of that to limit confusion

Presentation

Wan_Journal_Club_Presentation_CDDCMP_S2020.pdf

Conclusion

  • In conclusion, Wan et al. (2020) provided a model for ACE2 and 2019-nCoV binding, using previous knowledge they had gained when studying SARS-CoV. This model can be used to make predictions to help fight and create vaccines for this virus.

Acknowledgemtns

  • All the information above can from the paper Receptor recognition by the novel coronavirus from Wuhan: an analysis based on decade-long structural studies of SARS coronavirus. by Wan et al.
  • I copied and modified the protocol for Week 11 assignment
  • I worked with my homework partners Drew J. Cartmel and Christina L. Dominguez on the journal club Presentation, we met on Tuesday, April 14th to review it and texted questions back and forth throughout the rest of the week
  • I asked Kam D. Dahlquist, Ph.D. to go through the journal club presentation with me for 40 minutes on Wed, April 15, 2020 over Zoom Call
  • Except for what is noted above, this individual journal entry was completed by me and not copied from another source.

Mpaniag1 (talk) 13:52, 15 April 2020 (PDT)

References

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