Biology subjects

Choo-Wosoba, H.

Publications and source records attributed to Choo-Wosoba, H..

4 recordsLinked to original sources

bioRxiv2024

Altruistic feeding and cell-cell signaling during bacterial differentiation actively enhance phenotypic heterogeneity

Starvation triggers bacterial spore formation, a committed differentiation program that transforms a vegetative cell into a dormant spore. Cells in a population enter sporulation non-uniformly to secure against the possibility that favorable growth conditions, which puts sporulation-committed cells at a disadvantage, may resume. This heterogeneous behavior is initiated by a passive mechanism: stochastic activation of a master transcriptional regulator. Here, we identify a cell-cell communication pathway that actively promotes phenotypic heterogeneity, wherein Bacillus subtilis cells that start sporulating early utilize a calcineurin-like phosphoesterase to release glycerol, which simultaneously acts as a signaling molecule and a nutrient to delay non-sporulating cells from entering sporulation. This produced a more diverse population that was better poised to exploit a sudden influx of nutrients compared to those generating heterogeneity via stochastic gene expression alone. Although conflict systems are prevalent among microbes, genetically encoded cooperative behavior in unicellular organisms can evidently also boost inclusive fitness.

microbiology↗

bioRxiv2021

An intranasally administrated SARS-CoV-2 beta variant subunit booster vaccine prevents beta variant viral replication in rhesus macaques

Emerging of SARS-CoV-2 variants and waning of vaccine/infection-induced immunity poses threats to curbing the COVID-19 pandemic. An effective, safe, and convenient booster vaccine will be needed. We hypothesized that a variant-modified mucosal booster vaccine might induce local immunity to prevent SARS-CoV-2 infection at the port of entry. The beta-variant is hardest to cross-neutralize. Herein we assessed the protective efficacy of an intranasal booster composed of beta variant-spike protein S1 with IL-15 and TLR agonists in previously immunized macaques. The macaques were first vaccinated with Wuhan strain S1 with the same adjuvant. One year later, negligibly detectable SARS-CoV-2-specific antibody remained. Nevertheless, the booster induced vigorous humoral immunity including serum- and bronchoalveolar lavage (BAL)-IgG, secretory nasal- and BAL-IgA, and neutralizing antibody against the original strain and/or beta variant. Beta-variant S1-specifc CD4+ and CD8+ T cell responses were also elicited in PBMC and BAL. Following SARS-CoV-2 beta variant challenge, the vaccinated group demonstrated significant protection against viral replication in the upper and lower respiratory tracts, with almost full protection in the nasal cavity. The fact that one intranasal beta-variant booster administrated one year after the first vaccination provoked protective immunity against beta variant infections may inform future SARS-CoV-2 booster design and administration timing.

immunology↗

bioRxiv2019

A Hidden Markov Modeling Approach forIdentifying Tumor Subclones in Next-GenerationSequencing Studies

Allele-specific copy number alteration (ASCNA) analysis is for identifying copy number abnormalities in tumor cells. Unlike normal cells, tumor cells are heterogeneous as a combination of dominant and minor subclones with distinct copy number profiles. Estimating the clonal proportion and identifying mainclone and subclone genotypes across the genome is important for understanding tumor progression. Several ASCNA tools have recently been developed, but they have been limited to the identification of subclone regions, and not the genotype of subclones. In this paper, we propose subHMM, a hidden Markov model-based approach that estimates both subclone region as well as region-specific subclone genotype and clonal proportion. We specify a hidden state variable representing the conglomeration of clonal genotype and subclone status. We propose a two-step algorithm for parameter estimation, where in the first step, a standard hidden Markov model with this conglomerated state variable is fit. Then, in the second step, region-specific estimates of the clonal proportions are obtained by maximizing region-specific pseudo-likelihoods. We apply subHMM to study renal cell carcinoma datasets in The Cancer Genome Atlas. In addition, we conduct simulation studies that show the good performance of the proposed approach. The R package is available online at https://dceg.cancer.gov/tools/analysis/subhmm. somatic copy number alteration; tumor heterogeneity; E-M algorithm; forward-backward algorithm.

cancer biology↗

bioRxiv2018

hsegHMM: Hidden Markov Model-based Allele-specific Copy Number Alteration Analysis Accounting for Hypersegmentation

BackgroundSomatic copy number alternation (SCNA) is a common feature of the cancer genome and is associated with cancer etiology and prognosis. The allele-specific SCNA analysis of a tumor sample aims to identify the allele-specific copy numbers of both alleles, adjusting for the ploidy and the tumor purity. Next generation sequencing platforms produce abundant read counts at the base-pair resolution across the exome or whole genome which is susceptible to hypersegmentation, a phenomenon where numerous regions with very short length are falsely identified as SCNA.\n\nResultsWe propose hsegHMM, a hidden Markov model approach that accounts for hypersegmentation for allele-specific SCNA analysis. hsegHMM provides statistical inference of copy number profiles by using an effcient E-M algorithm procedure. Through simulation and application studies, we found that hsegHMM handles hypersegmentation effectively with a t-distribution as a part of the emission probability distribution structure and a carefully defined state space. We also compared hsegHMM with FACETS which is a current method for allele-specific SCNA analysis. For the application, we use a renal cell carcinoma sample from The Cancer Genome Atlas (TCGA) study.\n\nConclusionsWe demonstrate the robustness of hsegHMM to hypersegmentation. Furthermore, hsegHMM provides the quantification of uncertainty in identifying allele-specific SCNAs over the entire chromosomes. hsegHMM performs better than FACETS when read depth (coverage) is uneven across the genome.

bioinformatics↗