Additional file 1: of Text messages to increase attendance to follow-up cervical cancer screening appointments among HPV-positive Tanzanian women (Connected2Care): study protocol for a randomised controlled trial

SPIRIT Checklist. Complete SPIRIT Checklist. (PDF 887 kb

RNA-Seq of Kaposi’s sarcoma reveals alterations in glucose and lipid metabolism

<div><p>Kaposi’s sarcoma-associated herpesvirus (KSHV) is the etiologic agent of Kaposi’s sarcoma (KS). It is endemic in a number of sub-Saharan African countries with infection rate of >50%. The high prevalence of HIV-1 coupled with late presentation of advanced cancer staging make KS the leading cancer in the region with poor prognosis and high mortality. Disease markers and cellular functions associated with KS tumorigenesis remain ill-defined. Several studies have attempted to investigate changes of the gene profile with in vitro infection of monoculture models, which are not likely to reflect the cellular complexity of the in vivo lesion environment. Our approach is to characterize and compare the gene expression profile in KS lesions versus non-cancer tissues from the same individual. Such comparisons could identify pathways critical for KS formation and maintenance. This is the first study that utilized high throughput RNA-seq to characterize the viral and cellular transcriptome in tumor and non-cancer biopsies of African epidemic KS patients. These patients were treated anti-retroviral therapy with undetectable HIV-1 plasma viral load. We found remarkable variability in the viral transcriptome among these patients, with viral latency and immune modulation genes most abundantly expressed. The presence of KSHV also significantly affected the cellular transcriptome profile. Specifically, genes involved in lipid and glucose metabolism disorder pathways were substantially affected. Moreover, infiltration of immune cells into the tumor did not prevent KS formation, suggesting some functional deficits of these cells. Lastly, we found only minimal overlaps between our in vivo cellular transcriptome dataset with those from in vitro studies, reflecting the limitation of in vitro models in representing tumor lesions. These findings could lead to the identification of diagnostic and therapeutic markers for KS, and will provide bases for further mechanistic studies on the functions of both viral and cellular genes that are involved.</p></div

Heat map showing the relative expression of cellular genes with the highest fold changes between the lesion and controls.

<p>Top 30 most differentially expressed cellular genes that were also significantly correlated with the KSHV RNA load in the lesions. Green circle indicates genes selected for real time-PCR validation.</p

Predicted disease and biological functions associated with cellular gene expression.

<p>(A) Enrichment analysis for functions significantly affected in the lesions based on ≥5 fold of gene expression changes. Top functions with significantly predicted activation state (p<10⁻⁵, |Z|>2) are shown. “pv” = p-value, “n” = number of known target genes changed in lesions, “Z” = z-score for the predicted change of activity, “ns” = unassigned. (B) Significantly changed genes involved in overlapping lipid-related functions and/or glucose metabolism disorder in lesions are shown. Small squares indicate significantly enriched sub-functions that share >90% of genes with major functions (big squares). Top portion of the figure (above the grey line) represent overlap between various lipid functions, while the bottom portion (below the grey line) illustrates cross-talk with glucose metabolism disorder. The hexagons beside major functions indicate the number of genes contributing to that function’s activation (red), inhibition (blue) or unspecified effect (gray). The brown circles beside major functions indicate the number of genes which belong exclusively to that major function. Circles indicate genes shared between at least 2 functions that are up-regulated (red) or down-regulated (blue). Gene involvements are shown by solid and dotted lines (red for increased activity, blue for decreased activity, black for unspecific activity) and by small circles attached to a given gene with color matched to the color of square representing the corresponding function. <i>Illustrative example</i>: Concentration of lipid (green big square) in the center of the figure is repressed in lesion (blue font), as evidenced by 44 genes suppressing that function in lesions (blue hexagon), while only 21 genes activate the function (red hexagon) and 17 genes affect it in an unspecified direction (gray hexagon). Out of these 82 genes, there are 23 unique genes (brown circle) specific to this function and the rest are shared with at least one other function. Green line link with the upper left group of genes that are shared with fatty acid metabolism. Green line link with the upper right group of genes that are shared with synthesis of lipid. Green line in the middle link to the group of genes that are shared between the three major lipid functions (Fatty acid metabolism, concentration of lipid and synthesis of lipid). ADIPOQ (locates at the bottom of the middle group of genes) is a gene shared between these three major lipid functions. It is also involved in the metabolism of acylglycerol (attached small yellow circle), storage of lipid (attached small dark green circle) and glucose metabolism disorder (center small cyan circle). Given its involvement in glucose metabolism disorder, ADIPOQ is also shown in the bottom portion of the figure where the attached small circles illustrate its involvement in the three major lipid functions. Its location in the red group of genes at the lower left is indicative of genes whose changes result in activation of the function (red arrow). Given that ADIPOQ is represented by a blue circle (down-regulated genes in lesions), it means that the downregulation of ADIPOQ in lesion results in activation of glucose metabolism disorder. Finally, ADIPOQ is among the genes that suppress one lipid function (blue dotted line with ┬) and activate another (red dotted line with arrow).</p

Estimated differences in the blood cell populations between the lesion and control tissues.

<p>Blood cell abundance was estimated by CIBERSORT from the gene expression and compared between the lesion and control tissues. Significant p-values are highlighted in red for the increased and in blue for the decreased blood cell abundance in the lesion. * denote statistical significant in comparison to the controls.</p

Cellular gene expression.

<p>(A) Relationship between the samples based on cellular gene expression using the Principal Component (PC) analysis. Combination of the first and the second PC coincided with the KSHV RNA load, indicating that KSHV genes expression affected the cellular gene expression. (B) Number of cellular genes significantly (FDR<5%) differentially upregulated (red) or downregulated (blue) in the lesion vs control samples, expressed in fold changes. (C) Significantly differentially expressed cellular genes and their raw expression (maximum raw read count across samples). All cellular genes with ≥20 fold change are highlighted with yellow dots and genes selected for real time-PCR validation are highlighted with green dots.</p

Validation of selected cellular gene expression by real time-PCR.

<p>(A) Upregulated genes, ITGA9, ADAM19, PALD1 and TIE1 in the lesion relative to the control tissues. (B) Downregulated genes, CRYAB and ADIPOQ in the lesion relative to the control tissues.</p

Expression and functional classification of KSHV genes.

<p>(A) KSHV genes significantly upregulated in the lesions (p<0.05). (B) Other KSHV genes detected in the lesions. (C) Clustering of genes and patients based on KSHV genes with ≥10 reads in at least one sample. Each cluster with ≥5 genes was then annotated with the most enriched function.</p

Upstream regulators significantly affected in the lesions.

<p>(A) List of known regulators that demonstrated significant expression changes and affected the expression of its known downstream target genes. “pv” = p-value, “n” = number of known target genes changed in lesions, “fold” = expression changes for the regulator, “Z” = z-score for the predicted change of the regulator activity. (B) Top regulator network discovered by Ingenuity Pathway analysis with IFNA predicted to be activated, resulting in expression changes of the CXCL9, 10 and 11 that mainly activate multiple blood-related functions.</p

RNA-seq raw reads and alignment statistics.

<p>RNA-seq raw reads and alignment statistics.</p