Biology subjects

Fontana, S.

Publications and source records attributed to Fontana, S..

8 recordsLinked to original sources

bioRxiv2026

Developmental and molecular features shaping broad and synergistic actions of human autoantibodies targeting interferon-α

Neutralizing autoantibodies against interferon- (IFN) are associated with severe viral infections but may improve outcomes in IFN-driven autoimmunity. How these antibodies emerge, mature, and function at the molecular level remains unclear. Here, we tracked development of plasma neutralizing activity against all 12 IFNs in multiple human donors over years. Plasma neutralization frequently initiates with narrow IFN specificity, often IFN8, but increases in breadth and potency with individual kinetics. Monoclonal antibody analyses reveal that affinity maturation generates epitope-specific antibody classes, highlighting parallel emergence of subtype-restricted and broadly-reactive antibodies. Biophysical and structural characterization of human antibody-IFN complexes uncovers determinants of initial specificity and breadth development. Strikingly, antibodies engaging non-competing, neutralizing epitopes can act co-operatively to block IFN signaling, and some combinations trigger Fc-effector functions via cell surface IFN:IFNAR2 interactions that may eliminate IFN-responsive cells. Our findings reveal features shaping anti-IFN autoantibody repertoires and unappreciated synergistic mechanisms that amplify their potency and pathogenic potential.

immunology↗

bioRxiv2025

Genome-wide DNA methylation study reveals specific signatures in the affected arterial tissue of giant cell arteritis patients

ObjectivesGiant cell arteritis (GCA) is a large-vessel vasculitis, potentially causing complications such as blindness and strokes. This study aims to gain insights into the pathogenesis of GCA by identifying specific DNA methylation signatures in the arterial tissue of patients with this vasculitis. MethodsDNA methylation profiling was analyzed in 79 temporal artery biopsy samples (69 patients with GCA and 10 controls) by performing an epigenome-wide association study (EWAS). Differential analysis was performed to identify differentially methylated positions (DMPs) and regions (DMRs). Lastly, we compared our findings with previous transcriptomics and epigenomics studies on GCA-affected arteries. ResultsEWAS identified 3,644 DMPs (FDR < 0.05, |{Delta}{beta}| > 0.3), indicating a profound alteration within GCA-affected arterial tissue. These DMPs were annotated to 1,517 potentially dysregulated genes. 282 additional genes were identified by annotation of significant DMRs. Pathway enrichment analysis revealed a significant alteration of inflammatory mechanisms, such as interleukins 2 and 7, as well as pathways related to vascular remodeling. Omics study comparison revealed 37 genes consistently affected across datasets, many of them linked to immune signaling and T cell regulation. Notably, markers of exhausted T cells, including SLAMF6 and HAVCR2, were present among them. ConclusionsOur study identified GCA-specific DNA methylation signatures in arterial tissue, revealing disrupted inflammatory and vascular pathways, and suggesting the involvement of exhausted T cells in this condition. These findings offer new insights into GCA pathogenesis and provide new potential targets for the treatment of this debilitating disease. Key messagesO_ST_ABSWhat is already known on this topicC_ST_ABS{square} Affected arteries by giant cell arteritis (GCA) exhibit unique epigenetic signatures, reflecting significant disruptions in gene regulation. However, small sample sizes have constrained the clinical translation and broader interpretation of these findings, leaving key gaps in understanding GCA pathogenesis and identifying therapeutic targets. What this study adds{square} This study provides the largest-to-date epigenome-wide DNA methylation profiling in GCA-affected arteries, uncovering thousands of epigenetic changes and revealing profound disruptions in inflammatory and vascular pathways, including IL-2, IL-7, and CXCR4 signaling. {square}We compiled 37 genes consistently affected across different omics datasets in GCA-affected arteries, providing a robust list of candidates for further research into GCA pathogenesis and treatment. {square}Our findings provide evidence of T cell exhaustion in GCA-affected arteries, supported by consistent changes in key markers such as SLAMF6 and HAVCR2 (TIM-3), suggesting a novel mechanism of immune dysregulation in GCA. How this study might affect research, practice or policy{square} This study nominates exhausted T cells, the NLRP3 inflammasome, and CXCR4 signaling as novel contributors in GCA pathogenesis, suggesting new therapeutic targets for further research in the treatment of this disease.

immunology↗

bioRxiv2024

Single-cell trait diversity explains niche and fitness differences in aquatic microbial communities

Two fundamental questions in ecology are how biodiversity is maintained and how it affects ecosystem functioning. Until now, it has been difficult to study the above mechanisms in natural microbial communities, yet they are important drivers of biogeochemical ecosystem functions. Here, we use a new approach to define and measure biodiversity in complex lake microbial communities (Lake Cadagno, Switzerland) based on the cell-to-cell variation in multiple functionally relevant phenotypic traits. We use stable isotope probing coupled to correlative imaging using confocal laser scanning microscopy (CLSM) and nanometer-scale secondary ion mass spectrometry (NanoSIMS) to obtain morphological (size), physiological (pigments) and metabolic (carbon and nitrogen isotope uptake and sulfur content) traits for a large number of individual cells along the environmental gradient found across lake depth. We show that cell-to-cell trait variation is significantly correlated with cell densities as a proxy for ecosystem functioning, whereas genetic diversity measured at the level of 16S and 18S is not. Our single-cell analysis provides evidence for a simultaneous increase in niche partitioning (measured as increased evenness in pigment composition) and decrease in fitness differences (measured as decreased variability in sulfur content) due to light limitation and competition for nutrients in deep layers of the lake. This leads to a negative relationship between niche and fitness differences. Our results suggest that niche and fitness differences in natural microbial communities can be understood at the level of single-cell traits, providing a mechanistic understanding of the relationship between microbial diversity and ecosystem functioning.

microbiology↗

bioRxiv2022

Tackling Recalcitrant Pseudomonas aeruginosa Infections In Critical Illness via Anti-virulence Monotherapy

Intestinal barrier derangement allows intestinal bacteria and their products to translocate to the systemic circulation. Pseudomonas aeruginosa (PA) superimposed infection in critically ill patients increases gut permeability and leads to gut-driven sepsis. PA infections are challenging due to multi-drug resistance (MDR), biofilms, and/or antibiotic tolerance. Inhibition of the quorum-sensing transcriptional regulator MvfR(PqsR) is a desirable anti-PA strategy as MvfR controls multiple acute and chronic virulence functions. Here we show that MvfR promotes intestinal permeability and report novel potent anti-MvfR compounds, the N-Aryl Malonamides (NAMs) resulting from extensive structure-activity-relationship studies and thorough assessment of the inhibition of MvfR-controlled virulence functions. This novel class of anti-virulence non-native ligand-based agents has a half-maximal inhibitory concentration in the nanomolar range and strong target engagement. Using a NAM lead in monotherapy protects murine intestinal barrier function, abolishes MvfR-regulated small molecules, ameliorates bacterial dissemination, and lowers inflammatory cytokines. This study demonstrates the importance of MvfR in PA-driven intestinal permeability. It underscores the utility of anti-MvfR agents in maintaining gut mucosal integrity, which should be part of any successful strategy to prevent/treat PA infections and associated gut-derived sepsis in critical illness settings. NAMs provide for the development of crucial preventive/therapeutic monotherapy options against untreatable MDR PA infections.

microbiology↗

bioRxiv2021

A multifaceted ecological assessment reveals the invasion of the freshwater red macroalga Montagnia macrospora (Batrachospermales, Rhodophyta) in Taiwan

Invasive freshwater macroalgae are rarely described. Recently, a freshwater red alga, Montagnia macrospora, was introduced from South America to East Asia via the global aquarium trade. The earliest occurrence record of this alga in Taiwan is dated 2005. To determine whether M. macrospora has become an invasive species in Taiwan and to understand its traits that facilitated its invasion, we took a total-evidence approach that combines examination of ecological background and population genetic analysis. Our island-wide survey showed that M. macrospora alga was widespread in the field across Taiwan, where the climate greatly differs from that of South America. Our population genetic analysis revealed that the cox2-3 sequences of all the specimens of M. macrospora from Taiwan were identical, consistent with the hypothesis that the alga expanded through asexual reproduction. Moreover, during our long-term ecological assessments and field surveys, we observed that M. macrospora is an ecological generalist that can self-sustain for a decade and bloom. Taken together, our data suggest that M. macrospora has successfully invaded the freshwater ecosystems in Taiwan due to its ability to disperse asexually and to grow under broad environmental conditions. We hope that our study brings attention to invasive freshwater algae, which have been overlooked in conservation planning and management.

ecology↗

bioRxiv2018

Resource supply drives even spacing of individuals along multiple trait axes in light-limited phytoplankton populations

Individual-level variation arising from responses to environmental gradients influences population and community dynamics. How such responses empirically relate to the mechanisms that govern species coexistence is however not well understood. Previous results from lake phytoplankton communities suggested that the evenness of organisms in multidimensional trait space increases with resource limitation, possibly due to resource partitioning at the individual level. Here we experimentally tested the emergence of this pattern by growing two phytoplankton species (Pseudokirchneriella subcapitata, Microcystis aeruginosa) under a gradient of light intensity, in monoculture and jointly. Under low light (resource) conditions, the populations diversified into a wide range of phenotypes, which were evenly distributed in multidimensional trait space (defined by four pigment-related trait dimensions), confirming the observed field pattern. Our results provide prime experimental evidence that resource limitation induces even spacing of conspecific and heterospecific microbial phenotypes along trait axes, and advances our understanding of trait-based coexistence.

ecology↗

bioRxiv2018

Quantifying cell densities and biovolumes of phytoplankton communities and functional groups using scanning flow cytometry, machine learning and unsupervised clustering

Scanning flow cytometry (SFCM) is characterized by the measurement of time-resolved pulses of fluorescence and scattering, enabling the high-throughput quantification of phytoplankton morphology and pigmentation. Quantifying variation at the single cell and colony level improves our ability to understand dynamics in natural communities. Automated high-frequency monitoring of these communities is presently limited by the absence of repeatable, rapid protocols to analyse SFCM datasets, where images of individual particles are not available. Here we demonstrate a repeatable, semi-automated method to (1) rapidly clean SFCM data from a phytoplankton community by removing signals that do not belong to live phytoplankton cells, (2) classify individual cells into trait clusters that correspond to functional groups, and (3) quantify the biovolumes of individual cells, the total biovolume of the whole community and the total biovolumes of the major functional groups. Our method involves the development of training datasets using lab cultures, the use of an unsupervised clustering algorithm to identify trait clusters, and machine learning tools (random forests) to (1) evaluate variable importance, (2) classify data points, and (3) estimate biovolumes of individual cells. We provide example datasets and R code for our analytical approach that can be adapted for analysis of datasets from other flow cytometers or scanning flow cytometers.

ecology↗

bioRxiv2017

The predictability of a lake phytoplankton community, from hours to years

Forecasting anthropogenic changes to ecological communities is one of the central challenges in ecology. However, nonlinear dependencies, biotic interactions and data limitations have limited our ability to assess how predictable communities are. Here we used a machine learning approach and environmental monitoring data (biological, physical and chemical) to assess the predictability of phytoplankton cell density in one lake across an unprecedented range of time scales. Communities were highly predictable over hours to months: model R2 decreased from 0. 89 at 4 hours to 0.75 at 1 month, and in a long-term dataset lacking fine spatial resolution, from 0.46 at 1 month to 0.32 at 10 years. When cyanobacterial and eukaryotic algal cell density were examined separately, model-inferred environmental growth dependencies matched laboratory studies, and suggested novel trade-offs governing their competition. High-frequency monitoring and machine learning can help elucidate the mechanisms underlying ecological dynamics and set prediction targets for process-based models.

ecology↗