Biology subjects

Awany, D.

Publications and source records attributed to Awany, D..

4 recordsLinked to original sources

bioRxiv2025

Inflammatory Biomarkers of Asymptomatic and Symptomatic Tuberculosis

A large proportion of individuals with tuberculosis (TB) are asymptomatic. The biological and inflammatory underpinnings of asymptomatic TB are unknown and may differ from symptomatic TB. We characterised blood transcriptomic and proteomic profiles in South African community screening vs. health facility-based triage cohorts. Asymptomatic TB shared core transcriptomic and proteomic features with symptomatic TB, including upregulation of innate, interferon and inflammatory pathways and downregulation of T and B cell pathways. Integration of transcriptomic and proteomic data from asymptomatic TB individuals identified two distinct sub-clusters characterized by higher or lower bacterial burden, blood IFN-{gamma} responses, BMI, and chest radiographic abnormalities, suggesting different disease severity. We identified a new blood transcriptomic signature of asymptomatic TB. However, diagnostic performance of transcriptomic and proteomic markers was weaker for asymptomatic TB than symptomatic TB, suggesting that policy development for community-based, asymptomatic TB screening should not adopt biomarkers developed for symptomatic TB triage without further optimization.

immunology↗

bioRxiv2025

Cross-species blood transcriptional correlates of BCG-mediated protection against tuberculosis include innate and adaptive immune processes

The immune mechanisms induced by the Bacillus Calmette-Guerin (BCG) vaccine, and the subset of which mediate protection against tuberculosis (TB), remain poorly understood. This is further complicated by difficulties to verify vaccine-induced protection in humans. Although research in animal models, namely mice and non-human primates (NHPs), has begun to close this knowledge gap, discrepancies in the relative importance of biological pathways across species limit the utility of animal model-derived biological insights in humans. To address these challenges, we applied a systems modeling framework, Translatable Components Regression (TransCompR), to identify human blood transcriptional variability which could predict Mtb challenge outcomes in BCG-vaccinated NHPs. These protection-associated pathways included both innate and adaptive immune activation mechanisms, along with signaling via type I interferons and anti-mycobacterial T helper cytokines. We further partially validated the associations between these mechanisms and protection in humans using publicly available microarray data collected from BCG-vaccinated infants who either developed TB or remained healthy during two years of follow-up. Overall, our work demonstrates how species translation modeling can leverage animal studies to generate hypotheses about the mechanisms that underlie human infectious disease and vaccination outcomes, which may be difficult or impossible to ascertain using human data alone.

systems biology↗

bioRxiv2024

Modelling Microbiome Association with Host Phenotypes Using a Bayesian Dirichlet Process Model

Dysbiosis in the human gut microbiome has been shown to be intimately involved in the pathogenesis of a wide range of communicable and non-communicable diseases. As microbiome wide association study becomes the workhorse for identifying association between microbial taxa and human diseases/traits, proper modelling of microbial taxa abundances is critical. In particular, statistical frameworks need to effectively model correlation among microbial taxa as well as latent heterogeneity across samples. Here, a Bayesian method using the Dirichlet process random effects model is devised for microbiome association study. The proposed method uses a weighted combination of phylogenetic and radial basis function kernels to model taxa effects, and a non-parametrically modelled latent variable to model latent heterogeneity among samples. Using simulated and real microbiome datasets, it is shown that the method has high statistical power for association inference. SoftwareThe R codes to implement the method has been incorporated into a script phy-loDPM.R, and is available online at https://github.com/AwanyDenis/phyloDPM.

bioinformatics↗

bioRxiv2020

Heritability jointly Explained by Host Genotype and Microbiome:Will Improve Traits Prediction?

As we observe the 70th anniversary of the publication by Robertson that formalized the notion of heritability, geneticists remain puzzled by the problem of missing/hidden heritability, where heritability estimates from genome-wide association studies (GWAS) fall short of that from twin-based studies. Many possible explanations have been offered for this discrepancy, including existence of genetic variants poorly captured by existing arrays, dominance, epistasis, and unaccounted-for environmental factors; albeit these remain controversial. We believe a substantial part of this problem could be solved or better understood by incorporating the hosts microbiota information in the GWAS model for heritability estimation; ultimately also increasing human traits prediction for clinical utility. This is because, despite empirical observations such as (i) the intimate role of the microbiome in many complex human phenotypes, (ii) the overlap between genetic variants associated with both microbiome attributes and complex diseases, and (iii) the existence of heritable bacterial taxa, current GWAS models for heritability estimate do not take into account the contributory role of the microbiome. Furthermore, heritability estimate from twin-based studies does not discern microbiome component of the observed total phenotypic variance. Here, we summarize the concept of heritability in GWAS and microbiome-wide association studies (MWAS), focusing on its estimation, from a statistical genetics perspective. We then discuss a possible method to incorporate the microbiome in the estimation of heritability in host GWAS.

bioinformatics↗