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Dynamics of immunoglobulin sequence diversity in HIV-1 infected individuals.

Hoehn KB, Gall A, Bashford-Rogers R, Fidler SJ, Kaye S, Weber JN, McClure MO, SPARTAC Trial InvestigatorsKellam P, Pybus OG - Philos. Trans. R. Soc. Lond., B, Biol. Sci. (2015)

Bottom Line: We applied previously published methods for visualizing and quantifying B-cell sequence diversity, including the Gini index, and compared their efficacy to alternative measures.While we found significantly greater clonal structure in HIV-infected patients versus healthy controls, within HIV patients, we observed no significant relationships between statistics of B-cell clonal expansion and clinical variables such as viral load and CD4(+) count.Importantly, we find a significant association between observed Gini indices and sequencing read depth, and we conclude that more robust analytical methods and a closer integration of experimental and theoretical work is needed to further our understanding of B-cell repertoire diversity during viral infection.

View Article: PubMed Central - PubMed

Affiliation: Department of Zoology, University of Oxford, Oxford, UK.

ABSTRACT
Advances in immunoglobulin (Ig) sequencing technology are leading to new perspectives on immune system dynamics. Much research in this nascent field has focused on resolving immune responses to viral infection. However, the dynamics of B-cell diversity in early HIV infection, and in response to anti-retroviral therapy, are still poorly understood. Here, we investigate these dynamics through bulk Ig sequencing of samples collected over 2 years from a group of eight HIV-1 infected patients, five of whom received anti-retroviral therapy during the first half of the study period. We applied previously published methods for visualizing and quantifying B-cell sequence diversity, including the Gini index, and compared their efficacy to alternative measures. While we found significantly greater clonal structure in HIV-infected patients versus healthy controls, within HIV patients, we observed no significant relationships between statistics of B-cell clonal expansion and clinical variables such as viral load and CD4(+) count. Although there are many potential explanations for this, we suggest that important factors include poor sampling resolution and complex B-cell dynamics that are difficult to summarize using simple summary statistics. Importantly, we find a significant association between observed Gini indices and sequencing read depth, and we conclude that more robust analytical methods and a closer integration of experimental and theoretical work is needed to further our understanding of B-cell repertoire diversity during viral infection.

No MeSH data available.


Related in: MedlinePlus

BCR diversity statistics and viral load values for patients 1–4 (a–d) over the study period of 108 weeks. Patients 1–3 were untreated, while patient 4 received ART until week 48. Four plots are provided in each panel. The top plot shows viral load, with each point representing a clinical sample. The second plot shows vertex Gini index values of the BCR sequences obtained at each time point. The third plot shows the proportion of reads at each time point that belong to ‘large’ clones, i.e. those that occupy more than 0.1% of reads at any time point. The bottom plot in each panel shows the proportion of all reads at each time point that are occupied by the 20 largest clones observed across all time points. Each of the 20 largest clones is represented by a bar of a different colour, and lines connect bars at adjacent time points that represent the same clone.
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RSTB20140241F2: BCR diversity statistics and viral load values for patients 1–4 (a–d) over the study period of 108 weeks. Patients 1–3 were untreated, while patient 4 received ART until week 48. Four plots are provided in each panel. The top plot shows viral load, with each point representing a clinical sample. The second plot shows vertex Gini index values of the BCR sequences obtained at each time point. The third plot shows the proportion of reads at each time point that belong to ‘large’ clones, i.e. those that occupy more than 0.1% of reads at any time point. The bottom plot in each panel shows the proportion of all reads at each time point that are occupied by the 20 largest clones observed across all time points. Each of the 20 largest clones is represented by a bar of a different colour, and lines connect bars at adjacent time points that represent the same clone.

Mentions: Full results from all patients are summarized in figures 2 and 3. All patients had seroconverted recently (median = 56 days) before the start of the study (week zero). Patients 1–3 were untreated through the course of the study period (weeks 0 to 108). Patients 4–8 received an ART regimen between weeks 0 and 48, after which they were untreated. BCR sequencing was performed on PMBC samples from each time point for each patient (yielding 7.4 × 104 to 1.0 × 106 filtered BCR reads per sample). BCR network analysis was applied to these sequencing datasets to quantify the clonal architecture of these samples according to Bashford-Rogers et al. [8]. Four plots are shown for each patient, which show the change through time in (i) viral load, (ii) the vertex Gini index of BCR sequences, (iii) the proportion of BCR sequences in ‘large clones', and (iv) the size distributions of the 20 largest clones as a proportion of the total number of reads at each time point.Figure 2.


Dynamics of immunoglobulin sequence diversity in HIV-1 infected individuals.

Hoehn KB, Gall A, Bashford-Rogers R, Fidler SJ, Kaye S, Weber JN, McClure MO, SPARTAC Trial InvestigatorsKellam P, Pybus OG - Philos. Trans. R. Soc. Lond., B, Biol. Sci. (2015)

BCR diversity statistics and viral load values for patients 1–4 (a–d) over the study period of 108 weeks. Patients 1–3 were untreated, while patient 4 received ART until week 48. Four plots are provided in each panel. The top plot shows viral load, with each point representing a clinical sample. The second plot shows vertex Gini index values of the BCR sequences obtained at each time point. The third plot shows the proportion of reads at each time point that belong to ‘large’ clones, i.e. those that occupy more than 0.1% of reads at any time point. The bottom plot in each panel shows the proportion of all reads at each time point that are occupied by the 20 largest clones observed across all time points. Each of the 20 largest clones is represented by a bar of a different colour, and lines connect bars at adjacent time points that represent the same clone.
© Copyright Policy - open-access
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC4528418&req=5

RSTB20140241F2: BCR diversity statistics and viral load values for patients 1–4 (a–d) over the study period of 108 weeks. Patients 1–3 were untreated, while patient 4 received ART until week 48. Four plots are provided in each panel. The top plot shows viral load, with each point representing a clinical sample. The second plot shows vertex Gini index values of the BCR sequences obtained at each time point. The third plot shows the proportion of reads at each time point that belong to ‘large’ clones, i.e. those that occupy more than 0.1% of reads at any time point. The bottom plot in each panel shows the proportion of all reads at each time point that are occupied by the 20 largest clones observed across all time points. Each of the 20 largest clones is represented by a bar of a different colour, and lines connect bars at adjacent time points that represent the same clone.
Mentions: Full results from all patients are summarized in figures 2 and 3. All patients had seroconverted recently (median = 56 days) before the start of the study (week zero). Patients 1–3 were untreated through the course of the study period (weeks 0 to 108). Patients 4–8 received an ART regimen between weeks 0 and 48, after which they were untreated. BCR sequencing was performed on PMBC samples from each time point for each patient (yielding 7.4 × 104 to 1.0 × 106 filtered BCR reads per sample). BCR network analysis was applied to these sequencing datasets to quantify the clonal architecture of these samples according to Bashford-Rogers et al. [8]. Four plots are shown for each patient, which show the change through time in (i) viral load, (ii) the vertex Gini index of BCR sequences, (iii) the proportion of BCR sequences in ‘large clones', and (iv) the size distributions of the 20 largest clones as a proportion of the total number of reads at each time point.Figure 2.

Bottom Line: We applied previously published methods for visualizing and quantifying B-cell sequence diversity, including the Gini index, and compared their efficacy to alternative measures.While we found significantly greater clonal structure in HIV-infected patients versus healthy controls, within HIV patients, we observed no significant relationships between statistics of B-cell clonal expansion and clinical variables such as viral load and CD4(+) count.Importantly, we find a significant association between observed Gini indices and sequencing read depth, and we conclude that more robust analytical methods and a closer integration of experimental and theoretical work is needed to further our understanding of B-cell repertoire diversity during viral infection.

View Article: PubMed Central - PubMed

Affiliation: Department of Zoology, University of Oxford, Oxford, UK.

ABSTRACT
Advances in immunoglobulin (Ig) sequencing technology are leading to new perspectives on immune system dynamics. Much research in this nascent field has focused on resolving immune responses to viral infection. However, the dynamics of B-cell diversity in early HIV infection, and in response to anti-retroviral therapy, are still poorly understood. Here, we investigate these dynamics through bulk Ig sequencing of samples collected over 2 years from a group of eight HIV-1 infected patients, five of whom received anti-retroviral therapy during the first half of the study period. We applied previously published methods for visualizing and quantifying B-cell sequence diversity, including the Gini index, and compared their efficacy to alternative measures. While we found significantly greater clonal structure in HIV-infected patients versus healthy controls, within HIV patients, we observed no significant relationships between statistics of B-cell clonal expansion and clinical variables such as viral load and CD4(+) count. Although there are many potential explanations for this, we suggest that important factors include poor sampling resolution and complex B-cell dynamics that are difficult to summarize using simple summary statistics. Importantly, we find a significant association between observed Gini indices and sequencing read depth, and we conclude that more robust analytical methods and a closer integration of experimental and theoretical work is needed to further our understanding of B-cell repertoire diversity during viral infection.

No MeSH data available.


Related in: MedlinePlus