Biology subjects

Geber, A.

Publications and source records attributed to Geber, A..

7 recordsLinked to original sources

bioRxiv2026

Single cell analysis of neonatal naïve CD8α+ T cells reveals novel subsets bridging the innate-adaptive spectrum

There is growing evidence that neonates harbor innate-like CD8a+ T cell subsets that contribute to both protection and hyper-inflammatory states. It remains unclear, however, where these innate-like features are found among the many conventional and unconventional T cell populations that can upregulate the CD8 receptor. Further delineation of these unique populations and functions, with a focus on CD8ab co-expression, will enable studies that seek to understand the unique immune features in conventional T cell populations that are present during fetal and early postnatal life. We used cord blood from infants across the full viable gestational age range to examine phenotypic and transcriptional heterogeneity, with a particular focus on the naive T cell pool. We report a set of fetally-derived and innate-like naive CD8{beta}+ T cells ( FITs) that are marked by their KLRG1+CD161+ phenotype, unique transcriptomic features and which are sparsely detected in adult peripheral blood. Additionally, using T cell receptor repertoire profiling, we can distinguish FITs from well-described and semi-invariant unconventional T cell populations such as mucosa-associated invariant T cells. Our delineation of FITs unique features will enable future investigation into their ontogeny and tissue distribution, and ultimately their role in immune-related outcomes in preterm infants.

immunology↗

bioRxiv2022

Mouse lung Tissue Resident Memory (TRM) CD8 T cell subsets have distinct metabolic profiles from each other and from non-TRM

Tissue-resident memory CD8 T cells (TRM) principally reside in peripheral non-lymphoid tissues such as lung and skin and confer protection against a variety of illnesses ranging from infections to cancers. The functions of different memory CD8 T cell subsets have been linked with distinct metabolic pathways and differ from other CD8 T cell subsets. For example, skin-derived memory T cells undergo fatty acid oxidation and oxidative phosphorylation to a greater degree than circulating memory and naive cells. Lung TRM cells defined by the cell surface expression of integrins exist as distinct subsets that differ in gene expression and function. We hypothesize that TRM subsets with different integrin profiles will utilize unique metabolic programs. To test this, differential expression and pathway analysis were conducted on RNAseq datasets from mouse lung TRM yielding significant differences related to metabolism. Next, metabolic models were constructed and the predictions were interrogated using functional metabolite uptake assays. The levels of oxidative phosphorylation, mitochondrial mass, and neutral lipids were measured. Furthermore, to investigate the potential relationships to TRM development, T-cell differentiation studies were conducted in vitro with varying concentrations of metabolites. These demonstrated that lipid conditions impact T cell survival, and that glucose concentration impacts the expression of canonical TRM marker CD49a, with no effect on central memory-like T-cell marker CCR7. In summary, it is demonstrated that mouse resident memory T cells subsets defined by integrin-expression in the lung have unique metabolic profiles and that nutrient abundance can alter differentiation.

immunology↗

bioRxiv2022

Fluximplied: A novel approach integrates rate limiting steps and differential expression for pathway analysis

BackgroundMany tools exist to perform a rigorous pathway analysis, though traditional gene set enrichment analysis remains among the most common. While useful for many applications, one common situation where it is less so is the metabolic profiling of bulk omics datasets. Rate limiting steps in more linear pathways are the main determinant of flux through these pathways, but differential expression of the enzymes that catalyze these steps is usually not differentially weighted in pathway analysis. Fluximplied was built to perform pathway analysis with rate limiting steps in mind to assess the implied flux through a number of well validated metabolic pathways. ResultsA database of rate limiting steps and their associated pathway was constructed. Using publicly available human RNA sequencing data from liver, putamen, and adipose tissue, fluximplied generally corroborated the pathway analysis. When comparing two CD8 T cell subsets from mouse lung, our algorithm confirmed previous findings that were not found previously. ConclusionFluximplied is an accessible tool for pathway analysis which is intended to assist the user with hypothesis generation. Unlike traditional approaches to pathway analysis, it specifically queries a database of rate limiting steps in order to infer flux through canonical metabolic pathways.

bioinformatics↗

bioRxiv2019

Prediction pipeline for discovery of regulatory motifs associated with Brugia malayi molting

Filarial nematodes can cause debilitating diseases in humans. They have complicated life cycles involving an insect vector and mammalian hosts, and they go through a number of developmental molts. While whole genome sequences of parasitic worms are now available, very little is known about transcription factor (TF) binding sites and their cognate transcription factors that play a role in regulating development. To address this gap, we developed a novel motif prediction pipeline, Emotif Alpha, that integrates ten different motif discovery algorithms, multiple statistical tests, and a comparative analysis of conserved elements between the filarial worms Brugia malayi and Onchocerca volvulus, and the free-living nematode Caenorhabditis elegans. We identified stage-specific TF binding motifs in B. malayi, with a particular focus on those potentially involved in the L3-L4 molt, a stage important for the establishment of infection in the mammalian host. Using an in vitro molting system, we tested and validated three of these motifs demonstrating the accuracy of the motif prediction pipeline.

bioinformatics↗

bioRxiv2019

Modeling the metabolic interplay between a parasitic worm and its bacterial endosymbiont allows the identification of novel drug targets

The filarial nematode Brugia malayi represents a leading cause of disability in the developing world, causing lymphatic filariasis in nearly 40 million people. Currently available drugs are not well-suited to mass drug administration efforts, so new treatments are urgently required. One potential vulnerability is the endosymbiotic bacteria Wolbachia--present in many filariae--which is vital to the worm.\n\nGenome scale metabolic networks have been used to study prokaryotes and protists and have proven valuable in identifying therapeutic targets, but only recently have been applied to eukaryotic organisms. Here, we present iDC625, the first compartmentalized metabolic model of a parasitic worm. We used this model to show how metabolic pathway usage allows the worm to adapt to different environments, and predict a set of 99 reactions essential to the survival of B. malayi. We validated three of those reactions with drug tests and demonstrated novel antifilarial properties for all three compounds.

systems biology↗

bioRxiv2019

Microbial composition of the human nasopharynx varies according to influenza virus type and vaccination status.

Factors that contribute to enhanced susceptibility to severe bacterial disease after influenza infection are not well defined, but likely include the microbiome of the respiratory tract. Vaccination against influenza, while having variable effectiveness, could also play a role in microbial community stability. We collected nasopharyngeal samples from 215 individuals infected with influenza A/H3N2 or influenza B and profiled the microbiota by target sequencing of the 16S rRNA gene. We identified signature taxonomic groups by performing linear discriminant analysis and effective size comparisons (LEfSe) and defined bacterial community types using Dirichlet Multinomial Mixture (DMM) models. Influenza infection was shown to be significantly associated with microbial composition of the nasopharynx according to the virus type and the vaccination status of the patient. We identified 4 microbial community types across the combined cohort of influenza patients and healthy individuals with one community type most representative of the influenza-infected group. We also identified microbial taxa for which relative abundance was significantly higher in the unvaccinated elderly group; these taxa include species known to be associated with pneumonia.\n\nImportanceOur results suggest that there is a significant association between the composition of the microbiota in the nasopharynx and the influenza virus type causing the infection. We observe that vaccination status, especially in more senior individuals, also has an association with the microbial community profile. This indicates that vaccination against influenza, even when ineffective to prevent disease, could play a role in controlling secondary bacterial complications.

microbiology↗

bioRxiv2018

Cell-to-cell variation in defective virus expression and effects on host responses during influenza virus infection

Virus and host factors contribute to cell-to-cell variation in viral infections and determine the outcome of the overall infection. However, the extent of the variability at the single cell level and how it impacts virus-host interactions at a systems level are not well understood. To characterize the dynamics of viral transcription and host responses, we used single-cell RNA sequencing to quantify at multiple time points the host and viral transcriptomes of human A549 cells and primary bronchial epithelial cells infected with influenza A virus. We observed substantial variability of viral transcription between cells, including the accumulation of defective viral genomes (DVGs) that impact viral replication. We show a correlation between DVGs and viral-induced variation of the host transcriptional program and an association between differential induction of innate immune response genes and attenuated viral transcription in subpopulations of cells. These observations at the single cell level improve our understanding of the complex virus-host interplay during influenza infection.\n\nIMPORTANCEDefective influenza virus particles generated during viral replication carry incomplete viral genomes and can interfere with the replication of competent viruses. These defective genomes are thought to modulate disease severity and pathogenicity of the influenza infection. Different defective viral genomes also introduce another source of variation across a heterogeneous cell population. Evaluating the impact of defective virus genomes on host cell responses cannot be fully resolved at the population level, requiring single cell transcriptional profiling. Here we characterized virus and host transcriptomes in individual influenza-infected cells, including that of defective viruses that arise during influenza A virus infection. We established an association between defective virus transcription and host responses and validated interfering and immunostimulatory functions of identified dominant defective virus genome species in vitro. This study demonstrates the intricate effects of defective viral genomes on host transcriptional responses and highlights the importance of capturing host-virus interactions at the single-cell level.

microbiology↗