ORCID Profile
0000-0001-9096-7634
Current Organisation
Hong Kong University of Science and Technology
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Publisher: Elsevier BV
Date: 04-2021
Publisher: Cold Spring Harbor Laboratory
Date: 23-07-2019
DOI: 10.1101/711861
Abstract: Identifying the genetic drivers of adaptation is a necessary step in understanding the dynamics of rapidly evolving pathogens and cancer. However, signals of selection are obscured by the complex, stochastic nature of evolution. Pervasive effects of genetic linkage, including genetic hitchhiking and clonal interference between beneficial mutants, challenge our ability to distinguish the selective effect of in idual mutations. Here we describe a method to infer selection from genetic time series data that systematically resolves the confounding effects of genetic linkage. We applied our method to investigate patterns of selection in intrahost human immunodeficiency virus (HIV)-1 evolution, including a case in an in idual who develops broadly neutralizing antibodies (bnAbs). Most variants that arise are observed to have negligible effects on inferred selection at other sites, but a small minority of highly influential variants have strong and far-reaching effects. In particular, we found that accounting for linkage is crucial for estimating selection due to clonal interference between escape mutants and other variants that sweep rapidly through the population. We observed only modest selection for antibody escape, in contrast with strong selection for escape from CD8+ T cell responses. Weak selection for escape from antibody responses may facilitate bnAb development by ersifying the viral population. Our results provide a quantitative description of the evolution of HIV-1 in response to host immunity, including selection on the viral population that accompanies bnAb development. More broadly, our analysis argues for the importance of resolving linkage effects in studies of natural selection.
Publisher: MDPI AG
Date: 03-09-2022
DOI: 10.3390/V14091960
Abstract: Beginning in May 2022, a novel cluster of monkeypox virus infections was detected in humans. This virus has spread rapidly to non-endemic countries, sparking global concern. Specific vaccines based on the vaccinia virus (VACV) have demonstrated high efficacy against monkeypox viruses in the past and are considered an important outbreak control measure. Viruses observed in the current outbreak carry distinct genetic variations that have the potential to affect vaccine-induced immune recognition. Here, by investigating genetic variation with respect to orthologous immunogenic vaccinia-virus proteins, we report data that anticipates immune responses induced by VACV-based vaccines, including the currently available MVA-BN and ACAM2000 vaccines, to remain highly cross-reactive against the newly observed monkeypox viruses.
Publisher: Cold Spring Harbor Laboratory
Date: 23-06-2022
DOI: 10.1101/2022.06.23.497143
Abstract: Starting May 2022, a novel cluster of monkeypox virus infections was detected in humans. This has spread rapidly to non-endemic countries and sparked global concern. Vaccinia virus vaccines have demonstrated high efficacy against monkeypox viruses in the past and are considered an important outbreak control measure. Viruses observed in the current outbreak carry distinct genetic variation that have the potential to affect vaccine-induced immune recognition. Here, by investigating genetic variation with respect to orthologous immunogenic vaccinia-virus proteins, we report data that anticipates vaccine-induced immune responses to remain highly cross-reactive against the newly observed monkeypox viruses.
Publisher: Springer Science and Business Media LLC
Date: 30-11-2020
Publisher: Cold Spring Harbor Laboratory
Date: 2022
DOI: 10.1101/2021.12.31.21268591
Abstract: New and more transmissible variants of SARS-CoV-2 have arisen multiple times over the course of the pandemic. Rapidly identifying mutations that affect transmission could facilitate outbreak control efforts and highlight new variants that warrant further study. Here we develop an analytical epidemiological model that infers the transmission effects of mutations from genomic surveillance data. Applying our model to SARS-CoV-2 data across many regions, we find multiple mutations that strongly affect the transmission rate, both within and outside the Spike protein. We also quantify the effects of travel and competition between different lineages on the inferred transmission effects of mutations. Importantly, our model detects lineages with increased transmission as they arise. We infer significant transmission advantages for the Alpha and Delta variants within a week of their appearances in regional data, when their regional frequencies were only around 1%. Our model thus enables the rapid identification of variants and mutations that affect transmission from genomic surveillance data.
Publisher: MDPI AG
Date: 31-03-2022
Abstract: Memory SARS-CoV-2-specific CD8+ T cell responses induced upon infection or COVID-19 vaccination have been important for protecting against severe COVID-19 disease while being largely robust against variants of concern (VOCs) observed so far. However, T cell immunity may be weakened by genetic mutations in future SARS-CoV-2 variants that lead to widespread T cell escape. The capacity for SARS-CoV-2 mutations to escape memory T cell responses requires comprehensive experimental investigation, though this is prohibited by the large number of SARS-CoV-2 mutations that have been observed. To guide targeted experimental studies, here we provide a screened list of potential SARS-CoV-2 T cell escape mutants. These mutants are identified as candidates for T cell escape as they lie within CD8+ T cell epitopes that are commonly targeted in in iduals and are predicted to abrogate HLA–peptide binding.
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 09-03-2020
DOI: 10.36227/TECHRXIV.11920419
Abstract: Vaccines have saved more lives than any other medical intervention throughout human history by preventing the spread of infectious diseases. However, despite several decades of research, there is no effective vaccine against fast evolving viruses such as the human immunodeficiency virus (HIV) and the hepatitis C virus (HCV). A confounding factor in the development of a HIV or HCV vaccine is that these viruses have a unique ability to make a lot of mutations in their genetic code. This enables them to escape the human immune system while retaining their ability to propagate infection. For developing a vaccine against such viruses, scientists are developing novel strategies which seek to target specific parts of the virus that are most vulnerable (i.e., where it is difficult for the virus to survive mutations) in order to induce a focused and potentially effective immune response. To determine the existence and location of such parts of HIV and HCV, initial studies have leveraged recently-available sequence data for these viruses, and looked for those positions in the genome for which the frequency of mutation was lowest. Unfortunately, vaccines based on such first-order statistics have not enjoyed much success, and there is increasing evidence suggesting that interactions between mutations is also important and must be considered when designing an effective vaccine against HIV and HCV. It is almost impossible to determine effects of interactions between all mutations experimentally as it requires performing billions of experiments. In this article, we explain how by leveraging virus sequence data, mutational interactions can be estimated using statistical techniques and incorporated in designing novel and potentially effective vaccine strategies against such fast-evolving viruses. br
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 09-03-2020
DOI: 10.36227/TECHRXIV.11920419.V1
Abstract: Vaccines have saved more lives than any other medical intervention throughout human history by preventing the spread of infectious diseases. However, despite several decades of research, there is no effective vaccine against fast evolving viruses such as the human immunodeficiency virus (HIV) and the hepatitis C virus (HCV). A confounding factor in the development of a HIV or HCV vaccine is that these viruses have a unique ability to make a lot of mutations in their genetic code. This enables them to escape the human immune system while retaining their ability to propagate infection. For developing a vaccine against such viruses, scientists are developing novel strategies which seek to target specific parts of the virus that are most vulnerable (i.e., where it is difficult for the virus to survive mutations) in order to induce a focused and potentially effective immune response. To determine the existence and location of such parts of HIV and HCV, initial studies have leveraged recently-available sequence data for these viruses, and looked for those positions in the genome for which the frequency of mutation was lowest. Unfortunately, vaccines based on such first-order statistics have not enjoyed much success, and there is increasing evidence suggesting that interactions between mutations is also important and must be considered when designing an effective vaccine against HIV and HCV. It is almost impossible to determine effects of interactions between all mutations experimentally as it requires performing billions of experiments. In this article, we explain how by leveraging virus sequence data, mutational interactions can be estimated using statistical techniques and incorporated in designing novel and potentially effective vaccine strategies against such fast-evolving viruses. br
No related grants have been discovered for Muhammad Saqib Sohail.