Principles of Virology. Jane Flint
P(0)
The fraction of cells receiving 0, 1, and >1 virus particle in a culture of 106 cells infected with an MOI of 10 can be determined as follows.
The fraction of cells that receive 0 particles is
P(0) = e–10 = 4.5 × 10–5
and in a culture of 106 cells, this equals 45 uninfected cells.
The fraction of cells that receive 1 particle is
P(1) = 10 × 4.5 × 10–5 = 4.5 × 10–4
and in a culture of 106 cells, 450 cells receive 1 particle.
The fraction of cells that receive >1 particle is
P(>1) = 1 − e–m(m + 1) = 0.9995
and in a culture of 106 cells, 999,500 cells receive >1 particle. [The value in this equation is obtained by subtracting from 1 (the sum of all probabilities for any value of k) the probabilities P(0) and P(1).]
The fraction of cells receiving 0, 1, and >1 virus particle in a culture of 106 cells infected with an MOI of 0.001 is
1 P(0) = 99.99%P(1) = 0.0999% (for 106 cells, 104 are infected)P(>1) = 10–6The MOI required to infect 99% of the cells in a cell culture dish isP(0) = 1% = 0.01m = −1n (0.01) = 4.6 PFU per cell.
The yield of infectious virus per cell can be calculated from the data collected during a one-step growth experiment. This value varies widely among different viruses and with different virus-host cell combinations. For many viruses, increasing the multiplicity of infection above a certain point does not increase the yield: cells have a finite capacity to produce new virus particles. In fact, infecting at a very high multiplicity of infection can cause premature cell lysis and decrease virus yields.
The kinetics of the one-step infectious cycle can vary dramatically among viruses. For example, enveloped viruses that mature by budding from the plasma membrane, as discussed in Chapter 13, generally become infectious only as they leave the cell, and therefore, little intracellular infectious virus can be detected (Fig. 2.19B). The curve shown in Fig. 2.19A illustrates the pattern observed for a DNA virus with the long latent and synthetic phases typical of many DNA viruses, some retroviruses, and reovirus. For small RNA viruses, the entire growth curve is complete within 6 to 8 h, and the latent and synthetic phases are correspondingly shorter.
One-step growth curve analysis can provide quantitative information about different virus-host systems. It is frequently employed to study mutant viruses to determine what parts of the infectious cycle are affected by a particular genetic lesion. It is also valuable for studying the multiplication of a new virus or viral reproduction in a new virus-host cell combination.
When cells are infected at a low multiplicity of infection, several cycles of viral reproduction may occur (Fig. 2.18B). Growth curves established under these conditions can also provide useful information. When infection is carried out at a high multiplicity, a mutation may fail to have an obvious effect on viral reproduction. The defect may only become evident following a low-multiplicity infection. Because the effect of a mutation in each cycle is multiplied, a small effect can be amplified after several cycles. Defects in the ability of viruses to spread from cell to cell may also be revealed when multiple cycles of reproduction occur.
Global Analysis
The study of replication cycles of many viruses with one-step growth analysis has allowed a reductionist approach to understanding and defining the steps of virus attachment, entry, replication, and assembly. In contrast, new experimental and computational tools permit global analysis of viral, cellular, and host responses to infection. Global analyses apply a dizzying array of different high-throughput technologies to measure system-wide changes in DNA, RNA, proteins, and metabolites during virus infection of cells, tissues, or entire organisms. Data obtained from high-throughput measurements are integrated and analyzed using mathematical algorithms to generate models that are predictive of the system. For example, virus infections of different animals are characterized by the induction of distinct sets of cytokine genes, a property that can be correlated with different pathogenic outcomes. When a model has been developed, it can be further refined by the use of viral mutants or targeted inhibition of host genes or pathways. Global analysis is therefore a holistic, host-directed approach that complements traditional methods for studying viruses.
Figure 2.19 One-step growth curves of animal viruses. (A) Growth of a nonenveloped virus, adenovirus type 5. The inset illustrates the concept that viruses multiply by assembly of preformed components into particles. (B) Growth of an enveloped virus, Western equine encephalitis virus, a member of the Togaviridae. This virus acquires infectivity after maturation at the plasma membrane, and therefore, little intracellular virus can be detected. The small quantities observed at each time point probably represent released virus contaminating the cell extract.
Examples of global analyses include genome-wide transcriptional profiling to study the host response to infection. Introduction of the 1918 strain of influenza virus into mice leads to a rapidly fatal disease characterized by sustained induction of proinflammatory cytokine and chemokine genes. Understanding the gene expression signature that correlates with lethality is one goal of these studies. Global analysis can also predict signatures of vaccine efficacy. In one study, transcriptional profiling of peripheral blood mononuclear cells from vaccinated subjects revealed that the yellow fever virus vaccine induces the expression of genes encoding members of the complement system and stress response proteins. This pattern accurately predicts CD8+ T cell and antibody responses that are thought to mediate protection from infection with yellow fever virus. A separate signature that accurately predicts neutralizing antibody synthesis during infection was also identified.
Some of the methods used in global analysis are described below.
DNA Microarrays
An early staple of global analyses, this method enables the study of the gene expression profile of a cell in response to virus infection (Chapter 14) and can also be used to discover new viruses. In this method, millions of unique viral DNA sequences fixed to glass or silicon wafers are incubated with sequences complementary to DNAs or RNAs, which have been amplified from clinical and environmental samples by PCR. Binding is usually detected by using fluorescent molecules incorporated into amplified nucleic acids. Microarrays have been largely supplanted by high-throughput sequencing, which allows identification of transcripts and their quantification in an unbiased manner, e.g., without prior assumption of what genes are involved.
In RNAseq, RNAs extracted from cells or tissues are converted by reverse transcription to complementary DNAs, which are then subjected to high-throughput DNA sequencing. The results provide insight into sequences and quantity of RNAs in a cell at a given time under specific conditions. It allows detection and quantification of transcripts that are not represented on microarrays. Information on transcriptional activity is provided by native elongating transcript sequencing (NET-seq), in which immunoprecipitation of RNA polymerase is followed by high-throughput sequencing of the 3′ ends of the associated RNAs. A method to study the association of RNAs with ribosomes is