Computational MHC-I epitope predictor identifies 95% of experimentally mapped HIV-1 clade A and D epitopes in a Ugandan cohort.

BACKGROUND: Identifying immunogens that induce HIV-1-specific immune responses is a lengthy process that can benefit from computational methods, which predict T-cell epitopes for various HLA types. METHODS: We tested the performance of the NetMHCpan4.0 computational neural network in re-identifying 93 T-cell epitopes that had been previously independently mapped using the whole proteome IFN-γ ELISPOT assays in 6 HLA class I typed Ugandan individuals infected with HIV-1 subtypes A1 and D. To provide a benchmark we compared the predictions for NetMHCpan4.0 to MHCflurry1.2.0 and NetCTL1.2. RESULTS: NetMHCpan4.0 performed best correctly predicting 88 of the 93 experimentally mapped epitopes for a set length of 9-mer and matched HLA class I alleles. Receiver Operator Characteristic (ROC) analysis gave an area under the curve (AUC) of 0.928. Setting NetMHCpan4.0 to predict 11-14mer length did not improve the prediction (37-79 of 93 peptides) with an inverse correlation between the number of predictions and length set. Late time point peptides were significantly stronger binders than early peptides (Wilcoxon signed rank test: p = 0.0000005). MHCflurry1.2.0 similarly predicted all but 2 of the peptides that NetMHCpan4.0 predicted and NetCTL1.2 predicted only 14 of the 93 experimental peptides. CONCLUSION: NetMHCpan4.0 class I epitope predictions covered 95% of the epitope responses identified in six HIV-1 infected individuals, and would have reduced the number of experimental confirmatory tests by > 80%. Algorithmic epitope prediction in conjunction with HLA allele frequency information can cost-effectively assist immunogen design through minimizing the experimental effort

The utility of integrating nanopore sequencing into routine HIV-1 drug resistance surveillance.

HIV continues to be a significant global public health concern. In 2022, an estimated 29.8 million people living with HIV received antiretroviral treatment (ART). From this, an estimated 10-15% of individuals living with HIV have drug-resistant strains of the virus. Testing for resistance to antiretroviral drugs is recommended before initiating ART. However, such services are often inaccessible due to costs and the need for complex laboratory infrastructure. The assessment of HIV drug resistance (HIVDR) relies on genotyping sequencing and algorithms to interpret genotypic resistance test results. Genotypic assays involve Sanger sequencing of the reverse transcriptase (RT), protease (PR) and integrase (IN) genes of circulating RNA in plasma to detect mutations that are known to confer drug resistance. While state-of-the-art sequencing technologies have swept the globe and enhanced our global pandemic response capabilities, they are still sparingly used for HIVDR surveillance. The scale-up of ART, especially in low- and middle-income countries, necessitates the establishment of cheap, expeditious and decentralized methods for HIVDR monitoring. Here, we outline how one low-capital next-generation sequencing platform, namely, nanopore sequencing, could augment efforts in expanding HIVDR surveillance efforts, especially in resource-limited settings. We discuss that because of its versatility, nanopore sequencing can accelerate HIVDR surveillance in conjunction with scaling up ART efforts and outline some of the challenges that need to be considered before its widespread and routine adaptation to detect drug resistance rapidly

Emergence and spread of a SARS-CoV-2 lineage A variant (A.23.1) with altered spike protein in Uganda

Here, we report SARS-CoV-2 genomic surveillance from March 2020 until January 2021 in Uganda, a landlocked East African country with a population of approximately 40 million people. We report 322 full SARS-CoV-2 genomes from 39,424 reported SARS-CoV-2 infections, thus representing 0.8% of the reported cases. Phylogenetic analyses of these sequences revealed the emergence of lineage A.23.1 from lineage A.23. Lineage A.23.1 represented 88% of the genomes observed in December 2020, then 100% of the genomes observed in January 2021. The A.23.1 lineage was also reported in 26 other countries. Although the precise changes in A.23.1 differ from those reported in the first three SARS-CoV-2 variants of concern (VOCs), the A.23.1 spike-protein-coding region has changes similar to VOCs including a change at position 613, a change in the furin cleavage site that extends the basic amino acid motif and multiple changes in the immunogenic N-terminal domain. In addition, the A.23.1 lineage has changes in non-spike proteins including nsp6, ORF8 and ORF9 that are also altered in other VOCs. The clinical impact of the A.23.1 variant is not yet clear and it has not been designated as a VOC. However, our findings of emergence and spread of this variant indicate that careful monitoring of this variant, together with assessment of the consequences of the spike protein changes for COVID-19 vaccine performance, are advisable

Main Routes of Entry and Genomic Diversity of SARS-CoV-2, Uganda.

We established rapid local viral sequencing to document the genomic diversity of severe acute respiratory syndrome coronavirus 2 entering Uganda. Virus lineages closely followed the travel origins of infected persons. Our sequence data provide an important baseline for tracking any further transmission of the virus throughout the country and region

SARS-CoV-2 Variants, South Sudan, January-March 2021.

As the coronavirus pandemic continues, severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) sequence data are required to inform vaccine efforts. We provide SARS-CoV-2 sequence data from South Sudan and document the dominance of SARS-CoV-2 lineage B.1.525 (Eta variant) during the country's second wave of infection

Uncovering the viral aetiology of undiagnosed acute febrile illness in Uganda using metagenomic sequencing.

Viruses associated with acute febrile illness in Africa cause a spectrum of clinical disease from mild to life-threatening. Routine diagnostic methods are insufficient to identify all viral pathogens in this region. In this study, 1281 febrile Ugandan patients were prospectively recruited as part of the CDC-UVRI Acute Febrile Illness Study and pre-screened for common pathogens. 210/1281 undiagnosed samples, and 20 additional samples from viral outbreaks were subjected to metagenomic sequencing. Viral pathogens were identified in 44/230 (19%), including respiratory, hepatitis, blood-borne, gastrointestinal and vector-borne viruses. Importantly, one case of Crimean-Congo haemorrhagic fever and two cases each of Rift Valley fever, dengue and yellow fever were detected in 7/230 (3%) of cases. Le Dantec virus, last reported in 1969, was also identified in one patient. The presence of high-consequence and (re-)emerging viruses of public health concern highlights the need for enhanced population-based diagnostic surveillance in the African region

Computational MHC-I epitope predictor identifies 95% of experimentally mapped HIV-1 clade A and D epitopes in a Ugandan cohort

Abstract Background Identifying immunogens that induce HIV-1-specific immune responses is a lengthy process that can benefit from computational methods, which predict T-cell epitopes for various HLA types. Methods We tested the performance of the NetMHCpan4.0 computational neural network in re-identifying 93 T-cell epitopes that had been previously independently mapped using the whole proteome IFN-γ ELISPOT assays in 6 HLA class I typed Ugandan individuals infected with HIV-1 subtypes A1 and D. To provide a benchmark we compared the predictions for NetMHCpan4.0 to MHCflurry1.2.0 and NetCTL1.2. Results NetMHCpan4.0 performed best correctly predicting 88 of the 93 experimentally mapped epitopes for a set length of 9-mer and matched HLA class I alleles. Receiver Operator Characteristic (ROC) analysis gave an area under the curve (AUC) of 0.928. Setting NetMHCpan4.0 to predict 11-14mer length did not improve the prediction (37–79 of 93 peptides) with an inverse correlation between the number of predictions and length set. Late time point peptides were significantly stronger binders than early peptides (Wilcoxon signed rank test: p = 0.0000005). MHCflurry1.2.0 similarly predicted all but 2 of the peptides that NetMHCpan4.0 predicted and NetCTL1.2 predicted only 14 of the 93 experimental peptides. Conclusion NetMHCpan4.0 class I epitope predictions covered 95% of the epitope responses identified in six HIV-1 infected individuals, and would have reduced the number of experimental confirmatory tests by &gt; 80%. Algorithmic epitope prediction in conjunction with HLA allele frequency information can cost-effectively assist immunogen design through minimizing the experimental effort. </jats:sec

Computational MHC-I epitope predictor identifies 95% of experimentally mapped HIV-1 clade A and D epitopes in a Ugandan cohort.

Abstract Background: Identifying immunogens that induce HIV-1-specific immune responses is a lengthy process that can benefit from computational methods, which predict T-cell epitopes for various HLA types. Methods: We tested the performance of the NetMHCpan4.0 computational neural network in re-identifying 93 T-cell epitopes that had been previously independently mapped using the whole proteome IFN-g ELISPOT assays in 6 HLA class I typed Ugandan individuals infected with HIV-1 subtypes A1 and D. To provide a benchmark we compared the predictions for NetMHCpan4.0 to MHCflurry1.2.0 and NetCTL1.2. Results: NetMHCpan4.0 performed best correctly predicting 88 of the 93 experimentally mapped epitopes for a set length of 9-mer and matched HLA class I alleles. Receiver Operator Characteristic (ROC) analysis gave an area under the curve (AUC) of 0.928. Setting NetMHCpan4.0 to predict 11-14mer length did not improve the prediction (37-79 of 93 peptides) with an inverse correlation between the number of predictions and length set. Late time point peptides were significantly stronger binders than early peptides (Wilcoxon signed rank test: p =0.0000005). MHCflurry1.2.0 similarly predicted all but 2 of the peptides that NetMHCpan4.0 predicted and NetCTL1.2 predicted only 14 of the 93 experimental peptides. Conclusion: NetMHCpan4.0 class I epitope predictions covered 95% of the epitope responses identified in six HIV-1 infected individuals, and would have reduced the number of experimental confirmatory tests by &gt;80%. Algorithmic epitope prediction in conjunction with HLA allele frequency information can cost-effectively assist immunogen design through minimizing the experimental effort. Keywords: HIV-1, epitope mapping, T-cell, artificial neural network, in-silico , NetMHCpan4.0., MHCflurry1.2.0 and NetCTL1.2.</jats:p

Computational MHC-I epitope predictor identifies 95% of experimentally mapped HIV-1 clade A and D epitopes in a Ugandan cohort.

Abstract Background: Identifying immunogens that induce HIV-specific immune responses is a lengthy process that can benefit from computational methods, which predict T-cell epitopes for various HLA types. Methods: We tested the performance of the NetMHCpan4.0 computational neural network in re-identifying 93 T-cell epitopes that had been previously independently mapped using the whole proteome IFN-g ELISPOT assays in 6 HLA class I typed Ugandan individuals infected with HIV-1 subtypes A1 and D. Results: NetMHCpan4.0 correctly predicted 88 of the 93 experimentally mapped epitopes for a set length of 9-mer and matched HLA class I alleles. Receiver Operator Characteristic (ROC) analysis gave an area under the curve (AUC) of 0.928. Setting NetMHCpan4.0 to predict 11-14mer length did not improve the prediction (37-79 of 93 peptides) with an inverse correlation between the number of predictions and length set. Late time point peptides were significantly stronger binders than early peptides (Wilcoxon signed rank test: p =0.0000005). Conclusion: NetMHCpan4.0 class I epitope predictions covered 95% of the epitope landscape recognised by HIV-1 infected individuals, and would have reduced the number of experimental confirmatory tests by &gt;80%. Algorithmic epitope prediction in conjunction with HLA allele frequency information can cost-effectively assist immunogen design. Keywords: HIV-1, epitope mapping, T-cell, artificial neural network, in-silico and NetMHCpan4.0.</jats:p

Recent HIV-1 infection in a high-risk Ugandan cohort: implications for Phase IIB test-of-concept HIV vaccine trials.

Assessment of vaccine efficacy on end points used in Phase IIB test-of-concept trials will require taking into consideration the effect of variables correlated with the end points and distribution of the variables within subgroups of the trial population. Here we report that evaluation of sexual activity in vaccinees and longitudinal collection of plasma viral load data from putative transmitters prior to transmission will contribute to the plausible assessment of efficacy against acquisition of infection. Data also suggest that efficacy on post-infection end points may depend on whether transmission pairs are matched or mismatched for HLA class I alleles