Biology subjects

Wijaya, L.

Publications and source records attributed to Wijaya, L..

3 recordsLinked to original sources

bioRxiv2023

Microbiome remodeling through bacterial competition and host behavior enables rapid adaptation to environmental toxins.

Human activity is altering the environment in a rapid pace, challenging the adaptive capacities of genetic variation within animal populations. Animals also harbor extensive gut microbiomes, which play diverse roles in host health and fitness and may help expanding host capabilities. The unprecedented scale of human usage of xenobiotics and contamination with environmental toxins describes one challenge against which bacteria with their immense biochemical diversity would be useful, by increasing detoxification capacities. To explore the potential of bacteria-assisted rapid adaptation, we used Caenorhabditis elegans worms harboring a defined microbiome, and neomycin as a model toxin, harmful for the worm host and neutralized to different extents by some microbiome members. Worms raised in the presence of neomycin showed delayed development and decreased survival but were protected when colonized by neomycin-resistant members of the microbiome. Two distinct mechanisms facilitated this protection: gut enrichment driven by altered bacterial competition for the strain best capable of modifying neomycin; and host avoidance behavior, which depended on the conserved JNK homolog KGB-1, enabling preference and acquisition of neomycin-protective bacteria. We further tested the consequences of adaptation, considering that enrichment for protective strains may represent dysbiosis. We found that neomycin-adapted gut microbiomes caused increased susceptibility to infection as well as an increase in gut lipid storage, suggesting metabolic remodeling. Our proof-of-concept experiments support the feasibility of bacteria-assisted host adaptation and suggest that it may be prevalent. The results also highlight trade-offs between toxin adaptation and other traits of fitness.

evolutionary biology↗

bioRxiv2023

A Network-based Transcriptomic Landscape of HepG2 cells to Uncover Causal Gene Cytotoxicity Interactions Underlying Drug-Induced Liver Injury

Drug-induced liver injury (DILI) remains the main reason of drug development attritions largely due to poor mechanistic understanding. Toxicogenomics to interrogate the mechanism of DILI has been broadly performed. Gene network-based transcriptome analysis is a bioinformatics approach that potentially contributes to improving mechanistic interpretation of toxicogenomics data. In this current study, we performed an extensive concentration time course response-toxicogenomics study in the HepG2 cell line exposed to various DILI compounds, reference compounds for stress response pathways, cytokine receptors, and growth factor receptors. We established > 500 conditions subjected to whole transcriptome targeted RNA sequences and applied weighted gene co-regulated network analysis (WGCNA) to the transcriptomics data followed by identification of gene networks (modules) that were strongly modulated upon the exposure of DILI compounds. Preservation analysis on the module responses of HepG2 and PHH demonstrated highly preserved adaptive stress responses gene networks. We correlated gene network with cell death as the progressive cellular outcomes. Causality of the target genes of these modules was evaluated using RNA interference validation experiments. We identified that GTPBP2, HSPA1B, IRF1, SIRT1 and TSC22D3 exhibited strong causality towards cell death. Altogether, we demonstrate the application of large transcriptome datasets combined with network-based analysis and biological validation to uncover the candidate determinants of DILI.

pharmacology and toxicology↗

bioRxiv2018

Deconvoluting Virome-Wide Antiviral Antibody Profiling Data

The ability to comprehensively characterize exposures and immune responses to viral infections will be critical to better understanding human health and disease. We previously described the VirScan system, a phage-display based technology for profiling antibody binding to a comprehensive library of peptides designed to represent the human virome. The previous VirScan analytical approach did not fully account for disproportionate representation of viruses in the library or for antibody cross-reactivity among sequences shared by related viruses. Here we present the AntiViral Antibody Response Deconvolution Algorithm ( AVARDA), a multi-module software package for analyzing VirScan datasets. AVARDA provides a probabilistic assessment of infection at species-level resolution by considering alignment of all library peptides to each other and to all human viruses. We employed AVARDA to analyze VirScan data from a cohort of encephalitis patients with either known viral infections or undiagnosed etiologies. By comparing acute and convalescent sera, AVARDA successfully confirmed or detected antibody responses to human herpesviruses 1, 3, 4, 5, and 6, thereby improving the rate of diagnosing viral encephalitis in this cohort by 62.5%. We further assessed AVARDAs utility in the setting of an epidemiological study, demonstrating its ability to determine infections acquired in a child followed prospectively from infancy. We consider ways in which AVARDAs conceptual framework may be further developed in the future and describe how its analyses may be extended beyond investigations of viral infection. AVARDA, in combination with VirScan and other pan-pathogen serological techniques, is likely to find broad utility in the epidemiology and diagnosis of infectious diseases.

bioinformatics↗