Enhancers are important noncoding elements, but they have been traditionally hard to characterize experimentally. The development of massively parallel assays allows the characterization of large numbers of enhancers for the first time. Here, we developed a framework using Drosophila STARR-seq to create shape-matching filters based on meta-profiles of epigenetic features. We integrated these features with supervised machine-learning algorithms to predict enhancers. We further demonstrated our model could be transferred to predict enhancers in mammals. We comprehensively validated the predictions using a combination of in vivo and in vitro approaches, involving transgenic assays in mouse and transduction-based reporter assays in human cell lines (153 enhancers in total). The results confirmed our model can accurately predict enhancers in different species without re-parameterization. Finally, we examined the transcription-factor binding patterns at predicted enhancers versus promoters. We demonstrated that these patterns enable the construction of a secondary model effectively discriminating between enhancers and promoters.

Enhancers are important noncoding elements, but they have been traditionally hard to characterize experimentally. Only a few mammalian enhancers have been validated, making it difficult to train statistical models for their identification properly. Instead, postulated patterns of genomic features have been used heuristically for identification. The development of massively parallel assays allows for the characterization of large numbers of enhancers for the first time. Here, we developed a framework that uses Drosophila STARR-seq data to create shape-matching filters based on enhancerassociated meta-profiles of epigenetic features. We combined these features with supervised machine learning algorithms (e.g., support vector machines) to predict enhancers. We demonstrated that our model could be applied to predict enhancers in mammalian species (i.e., mouse and human). We comprehensively validated the predictions using a combination of in vivo and in vitro approaches, involving transgenic assays in mouse and transduction-based reporter assays in human cell lines. Overall, the validations involved 153 enhancers in 6 mouse tissues and 4 human cell lines. The results confirmed that our model can accurately predict enhancers in different species without re-parameterization. Finally, we examined the transcription-factor binding patterns at predicted enhancers and promoters in human cell lines. We demonstrated that these patterns enable the construction of a secondary model effectively discriminating between enhancers and promoters.

Background:Throughout the world, there are antiretroviral therapy–naive HIV+ individuals who maintain elevated peripheral CD4+ T-cell counts, historically referred to as long-term nonprogressors (LTNPs). With recent improvements in viral load (VL) detection methods to levels as low as 20 copies per milliliter, 2 subsets of LTNPs have been defined: elite controllers (ECs), with undetectable VLs for at least 6–12 months, and viremic controllers (VCs), with VLs between 200 and 2000 copies per milliliter. ECs and VCs have been extensively studied in the developed world to determine underlying mechanisms responsible for virologic control. In sub-Saharan Africa, most studies have characterized LTNPs based on immunologic criteria making it difficult to compare findings with the Western cohorts, which use virologic criteria. Here, we describe a cohort of Uganda ECs and VCs attending a large HIV ambulatory center in Kampala, Uganda, based initially on CD4 counts and confirmed by repeated VL measurements.Methods:A cross-sectional study was conducted among 14,492 HIV-infected, antiretroviral therapy–naive individuals aged 18 years and older under care for at least 5 years with serial peripheral CD4 counts ≥500 cells/μL. Among those, we determined the frequency of individuals with VLs <2000 copies per milliliter for at least 6 months.Results:We report a prevalence of 0.26% (38/14,492) of HIV controllers in the clinic. We identified 36 ECs and 2 VCs. These individuals were middle-aged with an average CD4 count of 858 ± 172 (mean ± SD, 95% confidence interval: 795 to 921). Their average duration in HIV care was 7.4 ± 2.1 years (mean ± SD, 95% confidence interval: 6.6 to 8.1). The majority of EC/VCs were women (87%, 33/38), reflecting the demographics of the urban clinic.Conclusions:For the first time, this study demonstrates the frequency of EC/VCs in a large urban clinic in Uganda. Further study of these East African subjects may provide insights into how some individuals are able to control HIV in the absence of medications.