bioRxiv Science⌕ Search

Biology subjects

Shim, H. S.

Publications and source records attributed to Shim, H. S..

3 recordsLinked to original sources

A paradoxical population structure of var DBLα types in Africa

The var multigene family encodes the P. falciparum erythrocyte membrane protein 1 (PfEMP1), which is important in host-parasite interaction as a virulence factor and major surface antigen of the blood stages of the parasite, responsible for maintaining chronic infection. Whilst important in the biology of P. falciparum, these genes (50 to 60 genes per parasite genome) are routinely excluded from whole genome analyses due to their hyper-diversity, achieved primarily through recombination. The PfEMP1 head structure almost always consists of a DBL-CIDR tandem. Categorised into different groups (upsA, upsB, upsC), different head structures have been associated with different ligand-binding affinities and disease severities. We study how conserved individual DBL types are at the country, regional, and local scales in Sub-Saharan Africa. Using publicly-available sequence datasets and a novel ups classification algorithm, cUps, we performed an in silico exploration of DBL conservation through time and space in Africa. In all three ups groups, the population structure of DBL types in Africa consists of variants occurring at rare, low, moderate, and high frequencies. Non-rare variants were found to be temporally stable in a local area in endemic Ghana. When inspected across different geographical scales, we report different levels of conservation; while some DBL types were consistently found in high frequencies in multiple African countries, others were conserved only locally, signifying local preservation of specific types. Underlying this population pattern is the composition of DBL types within each isolate DBL repertoire, revealed to also consist of a mix of types found at rare, low, moderate, and high frequencies in the population. We further discuss the adaptive forces and balancing selection, including host genetic factors, potentially shaping the evolution and diversity of DBL types in Africa.

genetics↗

In vitro broad-spectrum antiviral activity of MIT-001, a mitochondria-targeted reactive oxygen species scavenger, against severe acute respiratory syndrome coronavirus 2 and multiple zoonotic viruses

The COVID-19 pandemic caused by SARS-CoV-2 becomes a serious threat to global health and requires the development of effective antiviral therapies. Current therapies that target viral proteins have limited efficacy with side effects. In this study, we investigated the antiviral activity of MIT-001, a small molecule reactive oxygen species (ROS) scavenger targeting mitochondria, against SARS-CoV-2 and other zoonotic viruses in vitro. The antiviral activity of MIT-001 was quantified by RT-qPCR and plaque assay. We also evaluated the functional analysis of MIT-001 by JC-1 staining to measure mitochondrial depolarization, total RNA sequencing to investigate gene expression changes, and immunoblot to quantify protein expression levels. The results showed that MIT-001 effectively inhibited the replication of B.1.617.2 and BA.1 strains, Zika virus, Seoul virus, and Vaccinia virus. Treatment with MIT-001 restored the expression of heme oxygenase-1 (HMOX1) and NAD(P)H: quinone oxidoreductase 1 (NqO1) genes, anti-oxidant enzymes reduced by SARS-CoV-2, to normal levels. The presence of MIT-001 also alleviated mitochondrial depolarization caused by SARS-CoV-2 infection. These findings highlight the potential of MIT-001 as a broad-spectrum antiviral compound that targets for zoonotic RNA and DNA viruses, providing a promising therapeutic approach to combat viral infection.

microbiology↗

Robust differential composition and variability analysis for multisample cell omics

Cell omics such as single-cell genomics, proteomics and microbiomics allow the characterisation of tissue and microbial community composition, which can be compared between conditions to identify biological drivers. This strategy has been critical to unveiling markers of disease progression such as cancer and pathogen infection. For cell omic data, no method for differential variability analysis exists, and methods for differential composition analysis only take a few fundamental data properties into account. Here we introduce sccomp, a generalised method for differential composition and variability analyses able to jointly model data count distribution, compositionality, group-specific variability and proportion mean-variability association, with awareness against outliers. Sccomp is an extensive analysis framework that allows realistic data simulation and cross-study knowledge transfer. Here, we demonstrate that mean-variability association is ubiquitous across technologies showing the inadequacy of the very popular Dirichlet-multinomial modelling and provide mandatory principles for differential variability analysis. We show that sccomp accurately fits experimental data, with a 50% incremental improvement over state-of-the-art algorithms. Using sccomp, we identified novel differential constraints and composition in the microenvironment of primary breast cancer. Significance statementDetermining the composition of cell populations is made possible by technologies like single-cell transcriptomics, CyTOF and microbiome sequencing. Such analyses are now widespread across fields (~800 publications/month, Scopus). However, existing methods for differential abundance do not model all data features, and cell-type/taxa specific differential variability is not yet possible. Increase in the variability of tissue composition and microbial communities is a well-known indicator of loss of homeostasis and disease. A suitable statistical method would enable new types of analyses to identify component-specific loss of homeostasis for the first time. This and other innovations are now possible through our discovery of the mean-variability association for compositional data. Based on this fundamental observation, we have developed a new statistical model, sccomp, that enables differential variability analysis for composition data, improved differential abundance analyses, with cross-sample information borrowing, outlier identification and exclusion, realistic data simulation, based on experimental datasets, cross-study knowledge transfer.

bioinformatics↗