Limits...
Lassa and Marburg viruses elicit distinct host transcriptional responses early after infection.

Caballero IS, Yen JY, Hensley LE, Honko AN, Goff AJ, Connor JH - BMC Genomics (2014)

Bottom Line: Lassa virus and Marburg virus are two causative agents of viral hemorrhagic fever.We have validated these infection-specific expression differences by using microarrays on a larger set of samples, and by quantifying the expression of individual genes using RT-PCR.These results suggest that host transcriptional signatures are correlated with specific viral infections, and that they can be used to identify highly pathogenic viruses during the early stages of disease, before standard detection methods become effective.

View Article: PubMed Central - PubMed

Affiliation: Bioinformatics Graduate Program, Boston University, 24 Cummington St, Boston, MA 02215, USA. nacho@bu.edu.

ABSTRACT

Background: Lassa virus and Marburg virus are two causative agents of viral hemorrhagic fever. Their diagnosis is difficult because patients infected with either pathogen present similar nonspecific symptoms early after infection. Current diagnostic tests are based on detecting viral proteins or nucleic acids in the blood, but these cannot be found during the early stages of disease, before the virus starts replicating in the blood. Using the transcriptional response of the host during infection can lead to earlier diagnoses compared to those of traditional methods.

Results: In this study, we use RNA sequencing to obtain a high-resolution view of the in vivo transcriptional dynamics of peripheral blood mononuclear cells (PBMCs) throughout both types of infection. We report a subset of host mRNAs, including heat-shock proteins like HSPA1B, immunoglobulins like IGJ, and cell adhesion molecules like SIGLEC1, whose differences in expression are strong enough to distinguish Lassa infection from Marburg infection in non-human primates. We have validated these infection-specific expression differences by using microarrays on a larger set of samples, and by quantifying the expression of individual genes using RT-PCR.

Conclusions: These results suggest that host transcriptional signatures are correlated with specific viral infections, and that they can be used to identify highly pathogenic viruses during the early stages of disease, before standard detection methods become effective.

Show MeSH

Related in: MedlinePlus

Biomarker genes quantified using RNA sequencing and microarrays. For each gene, the y-axis represents its amount of expression in each RNA sample, and the x-axis represents these samples ordered by time of infection, colored in increasingly darker shades of blue for Lassa, and red for Marburg. The samples in (A) were quantified using RNA sequencing and the fold change represents the log2 difference between the average infected and uninfected normalized read counts. The samples in (B) were measured using two-color microarrays and the fold change represents the log2 ratio between the intensity of the red channel and the green channel (see Methods).
© Copyright Policy - open-access
Related In: Results  -  Collection

License 1 - License 2
getmorefigures.php?uid=PMC4232721&req=5

Fig5: Biomarker genes quantified using RNA sequencing and microarrays. For each gene, the y-axis represents its amount of expression in each RNA sample, and the x-axis represents these samples ordered by time of infection, colored in increasingly darker shades of blue for Lassa, and red for Marburg. The samples in (A) were quantified using RNA sequencing and the fold change represents the log2 difference between the average infected and uninfected normalized read counts. The samples in (B) were measured using two-color microarrays and the fold change represents the log2 ratio between the intensity of the red channel and the green channel (see Methods).

Mentions: To understand in more detail the differences in expression reported by RNA sequencing and microarrays, we classified a subset of genes into four categories: 1) Housekeeping genes, those that showed high levels of expression across all samples. 2) Common response genes, those where infected samples show higher levels of expression than uninfected samples, and where these levels of expression were similar in both types of infection. 3) Marburg-specific response genes, those where Marburg-infected samples showed significantly higher (or lower) patterns of expression when compared to uninfected or Lassa-infected samples. 4) Lassa-specific response genes, those where Lassa-infected samples showed significantly higher patterns of expression when compared to uninfected or Marburg-infected samples.To determine if we could use the genes showing unique regulation at early times of infection to distinguish between different types of infected samples, we applied Multidimensional Scaling (MDS)—a dimensionality reduction technique similar to Primary Component Analysis—to the microarray samples. Instead of applying MDS on the expression of every gene, we chose a subset of common response genes, Marburg-specific response genes and Lassa-specific response genes (see Figure 4). Reducing the dimensionality of all samples using these genes resulted in three clear clusters: uninfected samples, infected with Lassa virus, and infected with Marburg virus. Each of the infected clusters contained not only the early-infected samples, but also those samples taken at later stages of infection. This indicates that the expression patterns of these genes are useful indicators throughout both early and late stages of infection.We then looked at the reported expression levels for these genes in both platforms (Figure 5). We only used samples that were quantified in both sequencing and arrays to ensure that differences in variability would only be related to the platform, not to the number of samples. The majority of genes showed similar levels of expression in both platforms. The biggest differences came from genes like HSPA1L and IGJ, which showed much smaller fold changes in arrays than in sequencing. On average, both highly upregulated and downregulated genes showed fold changes with a 1.5-2 times smaller magnitude in arrays when compared to sequencing.This comparison confirmed that both RNA sequencing and microarrays show unique changes in mRNA levels early after infection. This led us to test the hypothesis that a more direct RT-PCR analysis of a small number of these sentinel genes would show similar patterns of unique regulation, with the advantage that the format of this assay could be applied more quickly and cheaply in the field. We chose to analyze the expression of two genes, SIGLEC1 and HSPA1B, which showed unique upregulation at early stages of Lassa or Marburg virus infection, respectively. RT-PCR assays carried out on uninfected samples, and on samples collected 3 days post-infection, showed that there was no statistically significant change in SIGLEC1 expression between these two time points in Marburg-infected animals, but that there was a 70-fold change in expression in Lassa-infected animals. Similarly, HSPA1B showed a minor change in Lassa-infected animals, but a 36-fold change in Marburg-infected animals. This confirmed that SIGLEC1 becomes upregulated during the early stages of Lassa infection but not Marburg infection, while HSPA1B is only upregulated during the early stages of Marburg infection (Figure 6).Figure 4


Lassa and Marburg viruses elicit distinct host transcriptional responses early after infection.

Caballero IS, Yen JY, Hensley LE, Honko AN, Goff AJ, Connor JH - BMC Genomics (2014)

Biomarker genes quantified using RNA sequencing and microarrays. For each gene, the y-axis represents its amount of expression in each RNA sample, and the x-axis represents these samples ordered by time of infection, colored in increasingly darker shades of blue for Lassa, and red for Marburg. The samples in (A) were quantified using RNA sequencing and the fold change represents the log2 difference between the average infected and uninfected normalized read counts. The samples in (B) were measured using two-color microarrays and the fold change represents the log2 ratio between the intensity of the red channel and the green channel (see Methods).
© Copyright Policy - open-access
Related In: Results  -  Collection

License 1 - License 2
Show All Figures
getmorefigures.php?uid=PMC4232721&req=5

Fig5: Biomarker genes quantified using RNA sequencing and microarrays. For each gene, the y-axis represents its amount of expression in each RNA sample, and the x-axis represents these samples ordered by time of infection, colored in increasingly darker shades of blue for Lassa, and red for Marburg. The samples in (A) were quantified using RNA sequencing and the fold change represents the log2 difference between the average infected and uninfected normalized read counts. The samples in (B) were measured using two-color microarrays and the fold change represents the log2 ratio between the intensity of the red channel and the green channel (see Methods).
Mentions: To understand in more detail the differences in expression reported by RNA sequencing and microarrays, we classified a subset of genes into four categories: 1) Housekeeping genes, those that showed high levels of expression across all samples. 2) Common response genes, those where infected samples show higher levels of expression than uninfected samples, and where these levels of expression were similar in both types of infection. 3) Marburg-specific response genes, those where Marburg-infected samples showed significantly higher (or lower) patterns of expression when compared to uninfected or Lassa-infected samples. 4) Lassa-specific response genes, those where Lassa-infected samples showed significantly higher patterns of expression when compared to uninfected or Marburg-infected samples.To determine if we could use the genes showing unique regulation at early times of infection to distinguish between different types of infected samples, we applied Multidimensional Scaling (MDS)—a dimensionality reduction technique similar to Primary Component Analysis—to the microarray samples. Instead of applying MDS on the expression of every gene, we chose a subset of common response genes, Marburg-specific response genes and Lassa-specific response genes (see Figure 4). Reducing the dimensionality of all samples using these genes resulted in three clear clusters: uninfected samples, infected with Lassa virus, and infected with Marburg virus. Each of the infected clusters contained not only the early-infected samples, but also those samples taken at later stages of infection. This indicates that the expression patterns of these genes are useful indicators throughout both early and late stages of infection.We then looked at the reported expression levels for these genes in both platforms (Figure 5). We only used samples that were quantified in both sequencing and arrays to ensure that differences in variability would only be related to the platform, not to the number of samples. The majority of genes showed similar levels of expression in both platforms. The biggest differences came from genes like HSPA1L and IGJ, which showed much smaller fold changes in arrays than in sequencing. On average, both highly upregulated and downregulated genes showed fold changes with a 1.5-2 times smaller magnitude in arrays when compared to sequencing.This comparison confirmed that both RNA sequencing and microarrays show unique changes in mRNA levels early after infection. This led us to test the hypothesis that a more direct RT-PCR analysis of a small number of these sentinel genes would show similar patterns of unique regulation, with the advantage that the format of this assay could be applied more quickly and cheaply in the field. We chose to analyze the expression of two genes, SIGLEC1 and HSPA1B, which showed unique upregulation at early stages of Lassa or Marburg virus infection, respectively. RT-PCR assays carried out on uninfected samples, and on samples collected 3 days post-infection, showed that there was no statistically significant change in SIGLEC1 expression between these two time points in Marburg-infected animals, but that there was a 70-fold change in expression in Lassa-infected animals. Similarly, HSPA1B showed a minor change in Lassa-infected animals, but a 36-fold change in Marburg-infected animals. This confirmed that SIGLEC1 becomes upregulated during the early stages of Lassa infection but not Marburg infection, while HSPA1B is only upregulated during the early stages of Marburg infection (Figure 6).Figure 4

Bottom Line: Lassa virus and Marburg virus are two causative agents of viral hemorrhagic fever.We have validated these infection-specific expression differences by using microarrays on a larger set of samples, and by quantifying the expression of individual genes using RT-PCR.These results suggest that host transcriptional signatures are correlated with specific viral infections, and that they can be used to identify highly pathogenic viruses during the early stages of disease, before standard detection methods become effective.

View Article: PubMed Central - PubMed

Affiliation: Bioinformatics Graduate Program, Boston University, 24 Cummington St, Boston, MA 02215, USA. nacho@bu.edu.

ABSTRACT

Background: Lassa virus and Marburg virus are two causative agents of viral hemorrhagic fever. Their diagnosis is difficult because patients infected with either pathogen present similar nonspecific symptoms early after infection. Current diagnostic tests are based on detecting viral proteins or nucleic acids in the blood, but these cannot be found during the early stages of disease, before the virus starts replicating in the blood. Using the transcriptional response of the host during infection can lead to earlier diagnoses compared to those of traditional methods.

Results: In this study, we use RNA sequencing to obtain a high-resolution view of the in vivo transcriptional dynamics of peripheral blood mononuclear cells (PBMCs) throughout both types of infection. We report a subset of host mRNAs, including heat-shock proteins like HSPA1B, immunoglobulins like IGJ, and cell adhesion molecules like SIGLEC1, whose differences in expression are strong enough to distinguish Lassa infection from Marburg infection in non-human primates. We have validated these infection-specific expression differences by using microarrays on a larger set of samples, and by quantifying the expression of individual genes using RT-PCR.

Conclusions: These results suggest that host transcriptional signatures are correlated with specific viral infections, and that they can be used to identify highly pathogenic viruses during the early stages of disease, before standard detection methods become effective.

Show MeSH
Related in: MedlinePlus