Biology subjects

Benfield, T.

Publications and source records attributed to Benfield, T..

3 recordsLinked to original sources

bioRxiv2025

Disrupted α-ketoglutarate homeostasis trains monocyte-derived macrophages towards M2-like phenotype in long-term treated HIV-infection

Cells of the myeloid lineage, particularly monocytes and macrophages, are central to HIV pathogenesis, contributing to viral persistence and immune regulation during suppressive therapy. We hypothesized that metabolic reprogramming and altered chemokine signaling in people with HIV (PWH) on long-term ART impair monocyte trafficking and macrophage polarization. Using single-cell RNA sequencing, immunophenotyping, and metabolic modeling, we identified altered receptor expression and disrupted metabolic flux linked to reduced monocyte migration. Plasma secretome profiling revealed a nonclassical inflammatory microenvironment, while integrative multi-omics and single-cell proteomics of monocyte-derived macrophages (MDMs) demonstrated metabolic rewiring of the Glycolysis-TCA Anaplerosis Axis, orchestrated in part by elevated -ketoglutarate (AKG). Differentiation with PWH serum or AKG, skewed MDMs toward an M2-like phenotype, and enhanced HIV susceptibility. Together, these systems-level and mechanistic analyses reveal that metabolic training drives macrophage dysfunction in well-treated PWH, sustaining low-grade inflammation and highlighting potential therapeutic targets.

systems biology↗

bioRxiv2022

Network-based multi-omics integration reveals metabolic risk profile within treated HIV-infection

Multiomics technologies improve the biological understanding of health status in people living with HIV on antiretroviral therapy (PLWHART). Still, a systematic and in-depth characterization of metabolic risk profile during successful long-term treatment is lacking. Here, we used multi-omics (plasma lipidomic and metabolomic, and fecal 16s microbiome) data-driven stratification and characterization to identify the metabolic at-risk profile within PLWHART. Through network analysis and similarity network fusion (SNF), we identified three groups of PLWHART (SNF-1 to 3). The PLWHART at SNF-2 (45%) was a severe at-risk metabolic profile with increased visceral adipose tissue, BMI, higher incidence of metabolic syndrome (MetS), and increased di- and triglycerides despite having higher CD4+ T-cell counts than the other two clusters. However, the healthy-like and severe at-risk group had a similar metabolic profile differing from HC, with dysregulation of amino acid metabolism. At the microbiome profile, the healthy-like group had a lower -diversity, a lower proportion of MSM, and was enriched in Bacteroides. In contrast, in at-risk groups, there was an increase in Prevotella, with a high proportion of men who have sex with men (MSM) confirming the influence of sexual orientation on the microbiome profile The multi-omics integrative analysis reveals a complex microbial interplay by microbiome-derived metabolites in PLWHART. PLWHART those are severely at-risk clusters may benefit from personalized medicine and lifestyle intervention to improve their metabolic profile. SignificanceThe network and factorization-based integrative analysis of plasma metabolomics, lipidomics, and microbiome profile identified three different diseases state -omics phenotypes within PLWHART driven by metabolomics, lipidomics, and microbiome that a single omics or clinical feature could not explain. The severe at-risk group has a dysregulated metabolic profile that potentiates metabolic diseases that could be barriers to healthy aging. The at-risk group may benefit from personalized medicine and lifestyle intervention to improve their metabolic profile.

bioinformatics↗

bioRxiv2018

Joint sequencing of human and pathogen genomes reveals the genetics of pneumococcal meningitis

Streptococcus pneumoniae is a common nasopharyngeal colonizer, but can also cause life-threatening invasive diseases such as empyema, bacteremia and meningitis. Genetic variation of host and pathogen is known to play a role in invasive pneumococcal disease, though to what extent is unknown. In a genome-wide association study of human and pathogen we show that human variation explains almost half of variation in susceptibility to pneumococcal meningitis and one-third of variation in severity, and identified variants in CCDC33 associated with susceptibility. Pneumococcal variation explained a large amount of invasive potential, but serotype explained only half of this variation. Newly developed methods identified pneumococcal genes involved in invasiveness including pspC and zmpD, and allowed a human-bacteria interaction analysis, finding associations between pneumococcal lineage and STK32C.

genomics↗