Monthly Archives: October 2017

Journal club: Human enterovirus 71 protein interaction network prompts antiviral drug repositioning

Viruses are small infectious agents, which possess genetic code but have no independent metabolism. They propagate by infecting host cells and hijacking their machinery, often killing the cells in the process. One of the key challenges in developing effective antiviral therapies is the high mutation rate observed in viral genomes. A way to circumvent this issue is to target host proteins involved in virion assembly (also known as essential host factors, or EHFs), rather than the virion itself.

In their recent paper, Lu Han et al. [1] consider human virus protein-protein interactions in order to explore possible host drug targets, as well as drugs which could potentially be re-purposed as antivirals. Their study focuses on enterovirus 71 (EV71), one of the leading causes of hand, foot, and mouth disease.

Human virus protein-protein interactions and target identification

EHFs are typically detected by knocking out genes in the host organism and determining which of the knockouts result in virus control. Low repeat rates and high costs make this technique unsuitable for large scale studies. Instead, the authors use an extensive yeast two-hybrid screen to identify 37 unique protein-protein interactions between 7 of the 11 virus proteins and 29 human proteins. Pathway enrichment suggests that the human proteins interacting with EV71 are involved in a wide range of functions in the cell. Despite this range in functionality, as many as 17 are also associated with other viruses, either through known physical interactions, or as EHFs (Fig 1).

Fig. 1. Interactions between viral and human proteins (denoted as EIPs), and their connection to different viruses.

One of these is ATP6V0C, a subunit of vacuole ATP-ase. It interacts with the EV71 3A protein, and is a known essential host factor for five other viruses. The authors analyse the interaction further, and show that downregulating ATP6V0C gene expression inhibits EV71 propagation, while overexpressing it enhances virus propagation. Moreover, treating cells with bafilomycin A1, a selective inhibitor for vacuole ATP-ase, inhibits EV71 infection in a dose-dependent manner. The paper suggests that therefore ATP6V0C may be a suitable drug target, not only against EV71, but also perhaps even for a broad-spectrum antiviral. While this is encouraging, bafilomycin A1 is a toxic antibiotic used in research, but not suitable for human or drug use. Rather than exploring other compounds targeting ATP6V0C, the paper shifts focus to re-purposing known drugs as antivirals.

Drug prediction using CMap

A potential antiviral will ideally disturb most or all interactions between host cell and virion. One way to do this would be to inhibit the proteins known to interact with EV71. In order to check whether any known compounds already do so, the authors apply gene set enrichment analysis (GSEA) to data from the connectivity map (CMap). CMap is a database of gene expression profiles representing cellular response to a set of 1309 different compounds.  Enrichment analysis of the database reveals 27 potential EV71 drugs, of which the authors focus on the top ranking result, tanespimycin.

Tanespimycin is an orphan cancer drug, originally designed to target tumor cells by inhibiting HSP90. Its complex effects on the cell, however, may make it an effective antiviral. Following their CMap analysis, the authors show that tanespimycin reduces viral count and virus-induced cytopathic effects in a dose-dependent manner, without evidence of cytotoxicity.

Overall, the paper presents two different ways to think about target investigation and drug choice in antiviral therapeutics — by integrating different types of known host virus protein-protein interactions, and by analysing cell response to known compounds. While extensive further study is needed to determine whether the results are directly clinically relevant to the treatment of EV71, the paper shows how  interaction data analysis can be employed in drug discovery.

References:

[1] Han, Lu, et al. “Human enterovirus 71 protein interaction network prompts antiviral drug repositioning.” Scientific Reports 7 (2017).

 

Drawing Networks in LaTeX with tikz-network

While researching on protein interaction networks it is often important to illustrate networks. For this many different tools are available, for example, Python’s NetworkX and Matlab, that allow the export of figures as pixelated images or vector graphics. Usually, these figures are then incorporated in the papers, which are commonly written in LaTeX. In this post, I want to present `tikz-network’, which is a novel tool to code and illustrate networks directly in LaTeX.

To create an illustration you define the network’s nodes with their positions and edges between these nodes. An example of a simple network is

\begin{tikzpicture}
   \Vertex[color = blue]{A}
   \Vertex[x=3,y=1,color=red]{B}
   \Vertex[x=0,y=2,color=orange]{C}
   \Edge[lw=5pt](A)(B)
   \Edge[lw=3pt,bend=15,Direct](A)(C)
\end{tikzpicture}

The illustrations can be much more complex and allow dashed lines, opacity, and many other features. Importantly, the properties do not need to be specified in the LaTeX file itself but can also be saved in an external file and imported with the  \Vertices{data/vertices.csv}command. This allows the representation of more complex networks, for example the multilayer network below is created from the two files, the first representing the nodes

id, x, y ,size, color,opacity,label,layer 
A, 0, 0, .4 , green, .9 , a , 1
B, 1, .7, .6 , , .5 , b , 1
C, 2, 1, .8 ,orange, .3 , c , 1
D, 2, 0, .5 , red, .7 , d , 2
E,.2,1.5, .5 , gray, , e , 1
F,.1, .5, .7 , blue, .3 , f , 2
G, 2, 1, .4 , cyan, .7 , g , 2
H, 1, 1, .4 ,yellow, .7 , h , 2

and the second having the edge information:

u,v,label,lw,color ,opacity,bend,Direct
A,B, ab  ,.5,red   ,   1   ,  30,false
B,C, bc  ,.7,blue  ,   1   , -60,false
A,E, ae  , 1,green ,   1   ,  45,true
C,E, ce  , 2,orange,   1   ,   0,false
A,A, aa  ,.3,black ,  .5   ,  75,false
C,G, cg  , 1,blue  ,  .5   ,   0,false
E,H, eh  , 1,gray  ,  .5   ,   0,false
F,A, fa  ,.7,red   ,  .7   ,   0,true
D,F, df  ,.7,cyan  ,   1   ,   30,true
F,H, fh  ,.7,purple,   1   ,   60,false
D,G, dg  ,.7,blue  ,  .7   ,   60,false

For details, please see the extensive manual on the GitHub page of the project. It is a very new project and I only started using it but I like it so far for a couple of reasons:

  • it is easy to use, especially for small example graphs
  • the multilayer functionality is very convenient
  • included texts are automatically in the correct size and font with the rest of the LaTeX document
  • it can be combined with regular tikz commands to create more complex illustrations

Comparing naive and immunised antibody repertoire

Hi! This is my first post on Blopig as I joined OPIG in July 2017 for my second rotation project and DPhil.

During immune reactions to foreign molecules known as antigens, surface receptors of activated B-cells undergo somatic hypermutation to attain its high binding affinity and specificity to the target antigen. To discover how somatic hypermutation occurs to adapt the antibody from its germline conformation, we can compare the naive and antigen-experienced antibody repertoires. In this paper, the authors developed a protocol to carry out such comparison, detected, synthesised, expressed and validated the observed antibody genes against their target antigen.

What they have done:

  1. Mice immunisation: Naive (no antigens), CGG (a large protein), NP-CGG (hapten attached to a large protein).
  2. Sequencing: Total RNA was extracted from each spleen, cDNA was synthesised according to standard procedures, and amplified with the universal 5’-RACE primer (as oppose to the degenerate 5’-Vh primers) and the 3’-CH1 primer to distinguish between immunoglobulin-classes (IgG1, IgG2c and IgM). High throughput pyrosequencing was then used to recover the heavy chain sequences only.
  3. VDJ recombination analysis: V, D and J segments were assigned and the frequency of the VDJ combinations were plotted in a 3D graph.
  4. Commonality of the VDJ combination: For each VDJ combination, the “commonality” was counted from the average occurrence if n mice have the combination: if n=1, it’s the average occurrence if any 1 mouse has the combination; if n=5, the combination must be observed in all mice to generate a degree of commonality – otherwise it’s 0.
    • The effect of increasing n on commonality scores in IgG1 class: As we tighten the requirement for the commonality calculation, it becomes clear that IGHV9-3 is likely to target the CGG carrier, while IGHV1-72 is against the NP hapten.
    • IGHV9-3 can accommodate a wider range of D gene when targeting CGG alone. IGHV1-72 only uses IGHD1-1.
  5. Clustering V gene usage: Sequences were aligned to the longest sequence in the set (of VDJ combination), and the pairwise distance between sequences in the set were used to cluster the sequences using the UPGMA method.
    • A number of sequences were commonly found in different individuals. Among these sequences, one was randomly selected to proceed to the next step.
  6. Synthesis and validation of the detected antibody against the NP hapten: by comparing the antibody repertoires against the CGG and NP-CGG, the gene of the antibody against NP can be recovered. The authors in this paper chose to pair 3 different light chains to the chosen heavy chain, and assess the binding of the 3 antibodies.
    • NP-CGG bind well to both IGHV1-72 and IGHV9-3 antibodies; NP-BSA to IGHV1-72 only; and CGG to IGHV9-3 only.
    • The binding capabilities are affected by the light chain in the pair.

Key takeaway:

This work presented a metric of defining the “commonality” between individuals’ antibody repertoire and validated the identified antibody against its target antigen by combining with different light chains.