Open Access Highly Accessed Research article

CCL3L1 copy number, HIV load, and immune reconstitution in sub-Saharan Africans

Eleni Aklillu1, Linda Odenthal-Hesse2, Jennifer Bowdrey2, Abiy Habtewold13, Eliford Ngaimisi14, Getnet Yimer13, Wondwossen Amogne56, Sabina Mugusi7, Omary Minzi4, Eyasu Makonnen3, Mohammed Janabi8, Ferdinand Mugusi8, Getachew Aderaye5, Robert Hardwick2, Beiyuan Fu9, Maria Viskaduraki10, Fengtang Yang9 and Edward J Hollox2*

Author Affiliations

1 Department of Clinical Pharmacology, Karolinska Institutet, Stockholm, Sweden

2 Department of Genetics, University of Leicester, University Road, Leicester, LE1 7RH, UK

3 Department of Pharmacology, Addis Ababa University, Addis Ababa, Ethiopia

4 Unit of Pharmacology, School of Pharmacy, Muhimbili University of Health and Allied Sciences, Dar es Salaam, Tanzania

5 Internal Medicine, Addis Ababa University, Addis Ababa, Ethiopia

6 Institution of Medicine, Unit of Infectious Diseases, Karolinska Institutet, Karolinska University Hospital, Huddinge, Sweden

7 Department of Internal Medicine, Muhimbili National Hospital, Dar es Salaam, Tanzania

8 Department of Internal Medicine, Muhimbili University of Health and Allied Sciences, Dar es Salaam, Tanzania

9 Wellcome Trust Sanger Institute, Hinxton, Cambridge, UK

10 College of Medicine, Biological Sciences and Psychology, University of Leicester, Leicester, UK

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BMC Infectious Diseases 2013, 13:536  doi:10.1186/1471-2334-13-536

Published: 12 November 2013

Additional files

Additional file 1: Table S1:

Sample sizes used in the study. Arm 3 was recruited with CD4 > 200 and TB, had CCL3L1 copy number for 96 patients called but was not matched to clinical data for this study.

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Additional file 2: Table S2:

Baseline characteristics of patients analysed.

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Additional file 3: Figure S1:

Analysis of PRT measurement noise in control samples. Individual unrounded PRT values are plotted on the y-axis, according to the different copy numbers of the four controls (x-axis). Each point is coloured according which of the three different PRT assays generated it, all three assays measuring CCL3L1 copy number.

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Additional file 4: Figure S2:

Clustering of PRT raw data between different assays. For the complete dataset (n = 1133), density scatterplots were draw comparing each of the three different assays with each other. Axis labels indicate raw PRT values, and the colour bar on the left indicates the density of individual datapoints. One extreme point has been omitted.

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Additional file 5: Figure S3:

Analysis of the distribution of PRT values about a single copy number. a). The density of raw unrounded PRT values of the control samples, shown in supplementary Figure 1, is plotted, with values normalised to centre on a mean of zero. The red dotted line represents a Gaussian distribution with a mean and standard deviation taken from the PRT data. The blue dashed line represents a Gaussian distribution fitted to the PRT data. b). Gaussian quantile-quantile plot of raw unrounded PRT values of the control samples. Each value is plotted according the copy number of the control sample, as shown in the legend. The straight line is plotted through the first and third quantiles.

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Additional file 6: Figure S4:

Confidence of integer copy number calls from raw PRT data.Raw PRT calls of the entire dataset (average of three PRT assays) are plotted on the x-axis with posterior probability of the resulting integer copy number call on the y-axis. Points plotted as red triangles are those where P < 0.8 with a repeat measurement which gave a different estimate of integer copy number (±1). Points plotted as green crosses are those where P < 0.8 with a repeat measurement which gave the same estimate of integer copy number.

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Additional file 7: Figure S5:

Examples of assay heterogeneity. Six Ethiopian samples are highlighted, together with the raw PRT ratios, coloured by PRT assay, after several repeat tests.

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