Biology subjects

Gerber, J.

Publications and source records attributed to Gerber, J..

3 recordsLinked to original sources

bioRxiv2026

An FGF7-FGFR2-KLF4 feedback loop sustains anti-inflammatory signaling in epithelial cells

Chronic or excessive inflammation are hallmarks of many human diseases, but the endogenous factors that limit inflammatory responses are insufficiently characterized. We discovered a broad anti-inflammatory function of fibroblast growth factor 7 (FGF7), which is mediated via epithelial cels. FGF7 suppressed the expression of pro-inflammatory and immunomodulatory genes in cultured human keratinocytes in the absence or presence of pro-inflammatory stimuli and in a psoriasiform inflammation model in mice. Mechanistically, this involves an FGF7-FGF receptor 2 (FGFR2)-MAPK-Kruppel-like factor 4 (KLF4) signaling axis. FGF7 induced significant alterations in the KLF4 interactome in human keratinocytes and suppressed the transcriptional activity of KLF4 at its pro-inflammatory target genes. Concomitantly, expression of FGFR2 and downstream signaling components was promoted by KLF4, identifying a KLF4-dependent regulatory feedback loop that sustains anti-inflammatory FGF signaling. These results suggest activation of FGF7-FGFR2-KLF4 signaling as a strategy for the treatment of inflammatory diseases involving the skin or other epithelial tissues and highlight the role of epithelial cells in the control of inflammation.

cell biology↗

bioRxiv2020

Dynamic brain-to-brain concordance and behavioral mirroring as a mechanism of the patient-clinician interaction

The patient-clinician interaction can powerfully shape treatment out-comes such as pain, but is often considered an intangible 'art-of-medicine', and has largely eluded scientific inquiry. Although brain correlates of social processes such as empathy and theory-of-mind have been studied using single-subject designs, the specific behavioral and neural mechanisms underpinning the patient-clinician interaction are unknown. Using a two-person interactive design, we simultaneously recorded functional MRI (i.e. hyperscanning) in patient-clinician dyads, who interacted via live video while clinicians treated evoked pain in chronic pain patients. Our results show that patient analgesia is mediated by patient-clinician nonverbal behavioral mirroring and brain-to-brain concordance in circuitry implicated in theory-of-mind and social mirroring. Dyad-based analyses showed extensive dynamic coupling of these brain nodes with the partners' brain activity, yet only in dyads where clinical rapport had been established prior to the interaction. These findings point to a putatively key brain-behavioral mechanism for therapeutic alliance and psychosocial analgesia.

neuroscience↗

bioRxiv2018

Interpreting Diagnostic Histories into HIV Infection Time Estimates: Framework and Online Tool

BackgroundIt is frequently of epidemiological and/or clinical interest to estimate the date of HIV infection or time-since-infection of individuals. Yet, for over 15 years, the only widely-referenced infection dating algorithm that utilises diagnostic testing data to estimate time-since-infection has been the Fiebig staging system. This defines a number of stages of early HIV infection through various standard combinations of contemporaneous discordant diagnostic results, using tests of different sensitivity.\n\nObjectiveTo develop a new, more nuanced infection dating algorithm, we generalised the Fiebig approach to accommodate positive and negative diagnostic results generated on the same or different dates, and arbitrary current or future tests - as long as the test sensitivity is known. For this purpose, test sensitivity is conceptualised as the probability that a specimen will produce a positive result, expressed as a function of time since infection. This can be summarised as a median diagnostic delay parameter, together with a measure of inter-subject variability.\n\nMethodsThe present work outlines the analytical framework for infection date estimation using subject-level diagnostic testing histories, and data on test sensitivity. We introduce a publicly-available online HIV infection dating tool that implements this estimation method, bringing together 1) curatorship of HIV test performance data, and 2) infection date estimation functionality, to calculate plausible intervals within which infection likely became detectable for each individual. The midpoints of these intervals are interpreted as infection time point estimates and referred to as Estimated Dates of Detectable Infection (EDDIs).\n\nResultsIn many settings, including most research studies, detailed diagnostic testing data are routinely recorded, and can provide reasonably precise estimates of the timing of HIV infection. We present a simple logic to the interpretation of diagnostic testing histories into infection time estimates, either as a point estimate (EDDI) or an interval (earliest plausible to latest plausible dates of detectable infection), along with a publicly-accessible online tool that supports wide application of this logic.\n\nConclusionsThis tool, available at https://tools.incidence-estimation.org/idt/, is readily updatable as test technology evolves, given the simple architecture of the system and its nature as an open source project.

epidemiology↗