Diversity of KIR genes and their HLA-C ligands in Ugandan populations with historically varied malaria transmission intensity.

BACKGROUND: Malaria is one of the most serious infectious diseases in the world. The malaria burden is greatly affected by human immunity, and immune responses vary between populations. Genetic diversity in KIR and HLA-C genes, which are important in immunity to infectious diseases, is likely to play a role in this heterogeneity. Several studies have shown that KIR and HLA-C genes influence the immune response to viral infections, but few studies have examined the role of KIR and HLA-C in malaria infection, and these have used low-resolution genotyping. The aim of this study was to determine whether genetic variation in KIR and their HLA-C ligands differ in Ugandan populations with historically varied malaria transmission intensity using more comprehensive genotyping approaches. METHODS: High throughput multiplex quantitative real-time PCR method was used to genotype KIR genetic variants and copy number variation and a high-throughput real-time PCR method was developed to genotype HLA-C1 and C2 allotypes for 1344 participants, aged 6 months to 10 years, enrolled from Ugandan populations with historically high (Tororo District), medium (Jinja District) and low (Kanungu District) malaria transmission intensity. RESULTS: The prevalence of KIR3DS1, KIR2DL5, KIR2DS5, and KIR2DS1 genes was significantly lower in populations from Kanungu compared to Tororo (7.6 vs 13.2%: p = 0.006, 57.2 vs 66.4%: p = 0.005, 33.2 vs 46.6%: p < 0.001, and 19.7 vs 26.7%: p = 0.014, respectively) or Jinja (7.6 vs 18.1%: p < 0.001, 57.2 vs 63.8%: p = 0.048, 33.2 vs 43.5%: p = 0.002, and 19.7 vs 30.4%: p < 0.001, respectively). The prevalence of homozygous HLA-C2 was significantly higher in populations from Kanungu (31.6%) compared to Jinja (21.4%), p = 0.043, with no significant difference between Kanungu and Tororo (26.7%), p = 0.296. CONCLUSIONS: The KIR3DS1, KIR2DL5, KIR2DS5 and KIR2DS1 genes may partly explain differences in transmission intensity of malaria since these genes have been positively selected for in places with historically high malaria transmission intensity. The high-throughput, multiplex, real-time HLA-C genotyping PCR method developed will be useful in disease-association studies involving large cohorts

Diversity of KIR genes and their HLA-C ligands in Ugandan populations with historically varied malaria transmission intensity

Abstract: Background: Malaria is one of the most serious infectious diseases in the world. The malaria burden is greatly affected by human immunity, and immune responses vary between populations. Genetic diversity in KIR and HLA-C genes, which are important in immunity to infectious diseases, is likely to play a role in this heterogeneity. Several studies have shown that KIR and HLA-C genes influence the immune response to viral infections, but few studies have examined the role of KIR and HLA-C in malaria infection, and these have used low-resolution genotyping. The aim of this study was to determine whether genetic variation in KIR and their HLA-C ligands differ in Ugandan populations with historically varied malaria transmission intensity using more comprehensive genotyping approaches. Methods: High throughput multiplex quantitative real-time PCR method was used to genotype KIR genetic variants and copy number variation and a high-throughput real-time PCR method was developed to genotype HLA-C1 and C2 allotypes for 1344 participants, aged 6 months to 10 years, enrolled from Ugandan populations with historically high (Tororo District), medium (Jinja District) and low (Kanungu District) malaria transmission intensity. Results: The prevalence of KIR3DS1, KIR2DL5, KIR2DS5, and KIR2DS1 genes was significantly lower in populations from Kanungu compared to Tororo (7.6 vs 13.2%: p = 0.006, 57.2 vs 66.4%: p = 0.005, 33.2 vs 46.6%: p < 0.001, and 19.7 vs 26.7%: p = 0.014, respectively) or Jinja (7.6 vs 18.1%: p < 0.001, 57.2 vs 63.8%: p = 0.048, 33.2 vs 43.5%: p = 0.002, and 19.7 vs 30.4%: p < 0.001, respectively). The prevalence of homozygous HLA-C2 was significantly higher in populations from Kanungu (31.6%) compared to Jinja (21.4%), p = 0.043, with no significant difference between Kanungu and Tororo (26.7%), p = 0.296. Conclusions: The KIR3DS1, KIR2DL5, KIR2DS5 and KIR2DS1 genes may partly explain differences in transmission intensity of malaria since these genes have been positively selected for in places with historically high malaria transmission intensity. The high-throughput, multiplex, real-time HLA-C genotyping PCR method developed will be useful in disease-association studies involving large cohorts

Frequency of HIV serodifferent couples within TB-affected households in a setting with a high burden of HIV-associated TB

Abstract Introduction Strong epidemiological links between human immunodeficiency virus (HIV) and tuberculosis (TB) may make household TB contact investigation an efficient strategy for HIV screening and finding individuals in serodifferent partnerships at risk of HIV and linking them to HIV prevention services. We aimed to compare the proportions of HIV serodifferent couples in TB-affected households and in the general population of Kampala, Uganda. Methods We included data from a cross-sectional trial of HIV counselling and testing (HCT) in the context of home-based TB evaluation in Kampala, Uganda in 2016–2017. After obtaining consent, community health workers visited the homes of participants with TB to screen contacts for TB and offer HCT to household members ≥ 15 years. We defined index participants and their spouses or parents as couples. Couples were classified as serodifferent if confirmed by self-reported HIV status or by HIV testing results. We used a two-sample test of proportions to compare the frequency of HIV serodifference among couples in the study to its prevalence among couples in Kampala in the 2011 Uganda AIDS Indicator Survey (UAIS). Results We included 323 index TB participants and 507 household contacts aged ≥ 18 years. Most index participants (55%) were male, while most (68%) adult contacts were female. There was ≥ 1 couple in 115/323 (35.6%) households, with most couples (98/115, 85.2%) including the index participant and spouse. The proportion of households with HIV-serodifferent couples was 18/323 (5.6%), giving a number-needed-to-screen of 18 households. The proportion of HIV serodifference among couples identified in the trial was significantly higher than among couples in the UAIS (15.7% vs. 8%, p = 0.039). The 18 serodifferent couples included 14 (77.8%) where the index participant was living with HIV and the spouse was HIV-negative, and 4 (22.2%) where the index partner was HIV-negative, while the spouse was living with HIV. Conclusions The frequency of HIV serodifference among couples identified in TB-affected households was higher than in the general population. TB household contact investigation may be an efficient strategy for identifying people with substantial exposure to HIV and linking them to HIV prevention services

Data sources for prevalence and incidence modeling scenarios.

Data sources for prevalence and incidence modeling scenarios.</p

Flow diagrams of mathematical models used in this study.

(A) Flow-chart depicting the process of estimating the number of individuals living in households affected by tuberculosis (TB) and the frequency of individuals living with human immunodeficiency virus (HIV) in these households. (B) Flow-chart depicting the process of generating the proportion of HIV-serodifferent couples (SDCs) using outputs from (A). Global Burden of Disease, Injuries, and Risk Factors Study (GBD); Demographic and Health Surveys (DHS); Households (HHs); Household contacts (HHC); Population-based HIV Impact Assessment (PHIA); the proportion of serodifferent couples among people living with HIV who are in stable partnerships (PSDC); Relative risk (RR); Serodifferent couples (SDCs).</p

Prevalence of HIV infection and bacteriologically confirmed tuberculosis among individuals found at bars in Kampala slums, Uganda

Abstract Individuals found at bars in slums have several risk factors for HIV and tuberculosis (TB). To determine the prevalence of HIV and TB among individuals found at bars in slums of Kampala, Uganda, we enrolled adults found at bars that provided written informed consent. Individuals with alcohol intoxication were excluded. We performed HIV testing using immunochromatographic antibody tests (Alere Determine HIV-1/2 and Chembio HIV 1/2 STAT-PAK). TB was confirmed using the Xpert MTB/RIF Ultra assay, performed on single spot sputum samples. We enrolled 272 participants from 42 bars in 5 slums. The prevalence of HIV and TB was 11.4% (95% CI 8.1–15.8) and 15 (95% CI 6–39) per 1,000 population respectively. Predictors of HIV were female sex (aOR 5.87, 95% CI 2.05–16.83), current cigarette smoking (aOR 3.23, 95% CI 1.02–10.26), history of TB treatment (aOR 10.19, 95% CI 3.17–32.82) and CAGE scores of 2–3 (aOR 3.90, 95% CI 1.11–13.70) and 4 (aOR 4.77, 95% CI 1.07–21.35). The prevalence of HIV and TB was twice and four times the national averages respectively. These findings highlight the need for concurrent programmatic screening for both HIV and TB among high risk populations in slums

Fig 2 -

Forest plot of the relationship between HIV prevalence among adult household TB contacts (HHCs) vs. HIV prevalence in the general adult population in studies conducted in Kenya (A), Uganda (B), and South Africa (C).</p

The HIV prevalence and the SDCs in TB affected households (incidence modeling scenario).

The HIV prevalence and the SDCs in TB affected households (incidence modeling scenario).</p

Diversity of KIR genes and their HLA-C ligands in Ugandan populations with historically varied malaria transmission intensity.

BACKGROUND: Malaria is one of the most serious infectious diseases in the world. The malaria burden is greatly affected by human immunity, and immune responses vary between populations. Genetic diversity in KIR and HLA-C genes, which are important in immunity to infectious diseases, is likely to play a role in this heterogeneity. Several studies have shown that KIR and HLA-C genes influence the immune response to viral infections, but few studies have examined the role of KIR and HLA-C in malaria infection, and these have used low-resolution genotyping. The aim of this study was to determine whether genetic variation in KIR and their HLA-C ligands differ in Ugandan populations with historically varied malaria transmission intensity using more comprehensive genotyping approaches. METHODS: High throughput multiplex quantitative real-time PCR method was used to genotype KIR genetic variants and copy number variation and a high-throughput real-time PCR method was developed to genotype HLA-C1 and C2 allotypes for 1344 participants, aged 6 months to 10 years, enrolled from Ugandan populations with historically high (Tororo District), medium (Jinja District) and low (Kanungu District) malaria transmission intensity. RESULTS: The prevalence of KIR3DS1, KIR2DL5, KIR2DS5, and KIR2DS1 genes was significantly lower in populations from Kanungu compared to Tororo (7.6 vs 13.2%: p = 0.006, 57.2 vs 66.4%: p = 0.005, 33.2 vs 46.6%: p < 0.001, and 19.7 vs 26.7%: p = 0.014, respectively) or Jinja (7.6 vs 18.1%: p < 0.001, 57.2 vs 63.8%: p = 0.048, 33.2 vs 43.5%: p = 0.002, and 19.7 vs 30.4%: p < 0.001, respectively). The prevalence of homozygous HLA-C2 was significantly higher in populations from Kanungu (31.6%) compared to Jinja (21.4%), p = 0.043, with no significant difference between Kanungu and Tororo (26.7%), p = 0.296. CONCLUSIONS: The KIR3DS1, KIR2DL5, KIR2DS5 and KIR2DS1 genes may partly explain differences in transmission intensity of malaria since these genes have been positively selected for in places with historically high malaria transmission intensity. The high-throughput, multiplex, real-time HLA-C genotyping PCR method developed will be useful in disease-association studies involving large cohorts

Simulated relationship between the proportion of SDC among all people living with HIV who are in stable partnerships (<i>P</i>_<i>SDC</i>) and HIV prevalence using α between 0.7 and 0.9.

Simulated relationship between the proportion of SDC among all people living with HIV who are in stable partnerships (PSDC) and HIV prevalence using α between 0.7 and 0.9.</p