Biology subjects

Sakuntabhai, A.

Publications and source records attributed to Sakuntabhai, A..

8 recordsLinked to original sources

bioRxiv2026

Comparative Population Genomics across Three Continents Reveals Immune Adaptation and Implications for Vaccine Development

Human populations have evolved distinct immune responses to local pathogens, and investigating signatures of natural selection across populations can reveal both shared and population-specific immune response mechanisms. We compared genomic signatures of natural selection in Senegalese, Thai, and Peruvian populations using two complementary methods, hypothesising that this tri-continental comparison would reveal population-specific adaptive variants in genes regulating immune pathways. Our findings confirm that genetic diversity among these populations is reflected in their immune responses to regionally endemic pathogens. Furthermore, candidate immune-related genomic regions identified through selection scanning provide insights for ancestry-informed vaccine development by directing antigen design for region-specific pathogens, incorporating population-specific adjuvants, and tailoring administration strategies.

genomics↗

bioRxiv2023

Role of two modules controlling the interaction between SKAP1 and SRC kinasesComparison with SKAP2 architecture and consequences for evolution

AbstractSRC kinase associated phosphoprotein 1 (SKAP1), an adaptor for protein assembly, plays an important role in the immune system such as stabilizing immune synapses. Understanding how these functions are controlled at the level of the protein-protein interactions is necessary to describe these processes and to develop therapeutics. Here, we dissected the SKAP1 modular organization to recognize SRC kinases and compared it to that of its paralog SRC kinase associated phosphoprotein 2 (SKAP2). Different conserved motifs common to either both proteins or specific to SKAP2 were found using this comparison. Two modules harboring different binding properties between SKAP1 and SKAP2 were identified: one composed of two conserved motifs located in the second interdomain interacting at least with the SH2 domain of SRC kinases and a second one composed of the DIM domain modulated by the SH3 domain and the activation of SRC kinases. This work suggests a convergent evolution of the binding properties of some SRC kinases interacting specifically with either SKAP1 or SKAP2.

molecular biology↗

bioRxiv2021

Differential transcriptomic response of Anopheles arabiensis to Plasmodium vivax and Plasmodium falciparum infection

Plasmodium vivax malaria is now recognized as the second most dangerous parasitic threat to human health with the regular decrease of Plasmodium falciparum worldwide over recent decades. A very limited numbers of studies address the interaction of P. vivax with its Anopheles mosquito vectors. Those studies were conducted in P. vivax endemic countries with P.vivax local major vectors for which limited genomic and genetic tools are available. Despite the presence of P. vivax in several African countries and increasing reports on its occurrence in many others, there is virtually no data on the molecular responses of Anopheles arabiensis, a major African mosquito vector, to P. vivax, which limits the development of further "mosquito-targeted" interventions aimed at reducing P. vivax transmission. Taking advantage of the situation of Madagascar where P. falciparum, P. vivax and An. arabiensis are present, we explore the molecular responses of An. arabiensis towards these two human malaria parasites. RNA sequencing on RNAs isolated from mosquito midguts dissected at the early stage of infection (24 hours) was performed using mosquitoes fed on the blood of P. vivax and P. falciparum gametocyte carriers in a field station. From a de novo assembly of An. arabiensis midgut total RNA transcriptome, the comparative analysis revealed that a greater number of genes were differentially expressed in the mosquito midgut in response to P. vivax (209) than to P. falciparum (81). Among these, 15 common genes were identified to be significantly expressed in mosquito midgut 24 hours after ingesting P. vivax and P. falciparum gametocytes, including immune responsive genes and genes involved in amino-acid detoxification pathways. Importantly, working with both wild mosquitoes and field circulating parasites, our analysis revealed a strong mosquito genotype by parasite genotype interaction. Our study also identified 51 putative long non-coding RNAs differentially expressed in An. arabiensis mosquito infected midgut. Among these, several mapped to the published An. arabiensis genome at genes coding immune responsive genes such as gambicin 1, leucine-rich repeat containing genes, either on sense or antisense strands. This study constitutes the first comparison of An. arabiensis molecular interaction with P. vivax and P. falciparum, investigating both coding and long non-coding RNAs for the identification of potential transcripts, that could lead to the development of novel approaches to simultaneously block the transmission of vivax and falciparum malaria.

microbiology↗

bioRxiv2021

The dengue virus NS1 protein conveys pro-inflammatory signals by docking onto human high-density lipoproteins

The nonstructural NS1 protein is a virulence factor secreted by dengue virus (DENV)-infected cells. NS1 is known to alter the complement system, activate immune cells and perturb endothelial barriers. Here we show that pro-inflammatory signals are triggered by a high affinity complex formed between NS1 and human high-density lipoproteins (HDL). Electron microscopy images of the NS1-HDL complexes show spherical HDL particles with rod-shaped NS1 protrusions on their surface. These complexes are readily detectable in the plasma of hospitalized dengue patients using anti-apolipoprotein A-I (ApoA-I) antibodies specific of the HDL moiety. The functional reprogramming of HDL particles by the NS1 protein as a means to exacerbate systemic inflammation during DENV infection provides a new paradigm linking the human lipoprotein network to dengue pathogenesis.

microbiology↗

bioRxiv2020

Experimental adaptation of dengue virus 1 to Aedes albopictus mosquitoes by in vivo selection

In most of the world, Dengue virus (DENV) is mainly transmitted by the mosquito Aedes aegypti while in Europe, Aedes albopictus is responsible for human DENV cases since 2010. Identifying mutations that make DENV more competent for transmission by Ae. albopictus will help to predict emergence of epidemic strains. Ten serial passages in vivo in Ae. albopictus led to select DENV-1 strains with greater infectivity for this vector in vivo and in cultured mosquito cells. These changes were mediated by multiple adaptive mutations in the virus genome, including a mutation at position 10,418 in the DENV 3UTR within an RNA stem-loop structure involved in subgenomic flavivirus RNA (sfRNA) production. Using reverse genetics, we showed that the 10,418 mutation alone does not confer a detectable increase in transmission efficiency in vivo. These results reveal the complex adaptive landscape of DENV transmission by mosquitoes and emphasize the role of epistasis in shaping evolutionary trajectories of DENV variants.

microbiology↗

bioRxiv2019

Genetic diversity of Collaborative Cross mice controls viral replication, clinical severity and brain pathology induced by Zika virus infection, independently of Oas1b

The explosive spread of Zika virus (ZIKV) has been associated with major variations in severe disease and congenital afflictions among infected populations, suggesting an influence of host genes. We investigated how genome-wide variants could impact susceptibility to ZIKV infection in mice. We first describe that the susceptibility of Ifnar1 knockout mice is largely influenced by their genetic background. We then show that the broad genetic diversity of Collaborative Cross mice, which receptor to type I interferon (IFNAR) was blocked by anti-IFNAR antibody, expressed phenotypes ranging from complete resistance to severe symptoms and death with large variations in the peak and rate of decrease of plasma viral load, in brain viral load, in brain histopathology and in viral replication rate in infected cells. Differences of susceptibility between CC strains were correlated between Zika, Dengue and West Nile viruses. We identified highly susceptible and resistant mouse strains as new models to investigate the mechanisms of human ZIKV disease and other flavivirus infections. Genetic analyses revealed that phenotypic variations are driven by multiple genes with small effects, reflecting the complexity of ZIKV disease susceptibility in human population. Notably, our results rule out a role of the Oas1b gene in the susceptibility to ZIKV. Altogether, this study emphasizes the role of host genes in the pathogeny of ZIKV infection and lays the foundation for further genetic and mechanistic studies.\n\nIMPORTANCEIn recent outbreaks, ZIKV has infected millions of people and induced rare but potentially severe complications, including Guillain-Barre syndrome and encephalitis in adults. While several viral sequence variants were proposed to enhance the pathogenicity of ZIKV, the influence of host genetic variants in the clinical heterogeneity remains mostly unexplored. We have addressed this question using a mouse panel which models the genetic diversity of human population and a ZIKV strain from a recent clinical isolate. Through a combination of in vitro and in vivo approaches, we demonstrate that multiple host genetic variants determine viral replication in infected cells, and clinical severity, kinetics of blood viral load and brain pathology in mice. We describe new mouse models expressing high susceptibility or resistance to ZIKV and to other flaviviruses. These models will facilitate the identification and mechanistic characterization of host genes that influence ZIKV pathogenesis.

genetics↗

bioRxiv2017

A methodology for unsupervised clustering using iterative pruning to capture fine-scale structure

SNP-based information is used in several existing clustering methods to detect shared genetic ancestry or to identify population substructure. Here, we present a methodology for unsupervised clustering using iterative pruning to capture fine-scale structure called IPCAPS. Our method supports ordinal data which can be applied directly to SNP data to identify fine-scale population structure. We compare our method to existing tools for detecting fine-scale structure via simulations. The simulated data do not take into account haplotype information, therefore all markers are independent. Although haplotypes may be more informative than SNPs, especially in fine-scale detection analyses, the haplotype inference process often remains too computationally intensive. Therefore, our strategy has been to restrict attention to SNPs and to investigate the scale of the structure we are able to detect with them. We show that the experimental results in simulated data can be highly accurate and an improvement to existing tools. We are convinced that our method has a potential to detect fine-scale structure.

bioinformatics↗

bioRxiv2017

IPCAPS: an R package for iterative pruning to capture population structure

BackgroundResolving population genetic structure is challenging, especially when dealing with closely related or geographically confined populations. Although Principal Component Analysis (PCA)-based methods and genomic variation with single nucleotide polymorphisms (SNPs) are widely used to describe shared genetic ancestry, improvements can be made especially when fine-scale population structure is the target.\n\nResultsThis work presents an R package called IPCAPS, which uses SNP information for resolving possibly fine-scale population structure. The IPCAPS routines are built on the iterative pruning Principal Component Analysis (ipPCA) framework that systematically assigns individuals to genetically similar subgroups. In each iteration, our tool is able to detect and eliminate outliers, hereby avoiding severe misclassification errors.\n\nConclusionsIPCAPS supports different measurement scales for variables used to identify substructure. Hence, panels of gene expression and methylation data can be accommodated as well. The tool can also be applied in patient sub-phenotyping contexts. IPCAPS is developed in R and is freely available from bio3.giga.ulg.ac.be/ipcaps

bioinformatics↗