Biology subjects

Zampieri, M.

Publications and source records attributed to Zampieri, M..

7 recordsLinked to original sources

bioRxiv2026

A perturbation atlas of mouse neural organoids resolves macroglial reactivity and nominates drivers of degeneration

Understanding neuro-glial roles in homeostasis and disease requires scalable central nervous system (CNS) models, particularly as macroglia like astrocytes and Muller glia remain challenging to study in slow-developing human systems. Here, we generate and map the cellular diversity of mouse neural organoids spanning multiple brain regions and the retina. Benchmarking transcriptomic maturation against in vivo references captures postnatal neuro-macroglial states within a month. We show that macroglia are highly responsive to inflammatory cues and adopt specific transcriptomic states also found in vivo. Through a single-cell perturbation screen targeting 13 Muller-glia-specific pathways, we map how certain inflammatory cytokines drive degeneration-like stress programs in photoreceptors and distinct reactive states in Muller glia, including an Oncostatin M-driven chaperone-associated state. Mechanistically, we identify gp130-STAT3 signaling as a targetable driver of macroglial reactivity and photoreceptor distress. Together, these single-cell atlases provide a foundation for dissecting cellular crosstalk and neuro-glial pathology in the mature CNS.

neuroscience↗

bioRxiv2026

Model-based inference of enzyme inhibitions from perturbation-induced metabolic dynamics

In microbes, metabolism plays a key role in the first rapid adaptation to sudden challenges such as nutrient limitations or toxic compounds. While metabolomics enables the profiling of stimuli-induced metabolic changes, computational approaches that can interpret these data to mechanistically explain how perturbations propagate through metabolism to produce the observed changes are lagging behind. Here, we developed a computational framework, called Inference from Metabolic Fingerprints (IMF), to model the immediate dynamic response to a metabolic perturbation and systematically infer its entry point (i.e. enzymatic target). IMF assumes small perturbations and linearizes the nonlinear dynamics around a reference steady state. This allows IMF to scale with large metabolic networks and bypass missing kinetic parameters by allowing for fast and efficient ensemble sampling. We apply IMF to a model of central metabolism in Escherichia coli. Using in-silico and experimental data, we demonstrate the ability to infer the target of metabolic perturbations in spite of unknown kinetic parameters and incomplete metabolic data. Hence, we show that IMF is an effective approach for designing, analyzing and interpreting time-resolved metabolomics.

systems biology↗

bioRxiv2025

The glutamine-aspartate metabolic tradeoff between fast growth and metabolic flexibility.

Cancer cells develop unique addictions to nutrients, offering attractive opportunities for clinical intervention. However, how cancer cells orchestrate and benefit from metabolic rewiring remain fundamental open questions. Here, we developed a systematic framework to investigate nutrient dependencies and applied it to study glutamine addiction. By measuring metabolite exchange rates across 54 cancer cell lines with different levels of dependency on extracellular glutamine and in human stem cells, we discovered that a dualistic regulation of glutamate biosynthesis from glutamine or aspartate, driven by the tissue of origin, generates a tradeoff between fast growth and metabolic flexibility. Monitoring dynamic metabolic adaptation to glutamine deprivation showed that the role of glutamine as limiting substrate in purine biosynthesis is not responsible for glutamine dependency. Rather, growth arrest is mediated by glutamine as a signaling molecule. Our approach opens new opportunities to map, mechanistically investigate and pharmacologically interfere with metabolic dependencies, beyond glutamine addiction.

cancer biology↗

bioRxiv2019

Model-based integration of genomics and metabolomics reveals SNP functionality in Mycobacterium tuberculosis

Human tuberculosis is caused by members of the Mycobacterium tuberculosis complex (MTBC) and presents variable disease outcomes. The variation has primarily been attributed to host and environmental factors, but recent evidence indicates an additional role of genetic diversity among MTBC clinical strains. Here, we used metabolomics to unravel the potential role of genetic variations in conferring strain-specific adaptive capacity and vulnerability. To systematically identify functionality of single nucleotide polymorphisms (SNPs), we developed a constraint-based approach that integrates metabolomic and genomic data. Model-based predictions were systematically tested against independent metabolome data; they correctly classified SNP effects in pyruvate kinase and suggested a genetic basis for strain-specific sensitivity to the antibiotic para-aminosalicylic acid. Our method is broadly applicable to mutations in enzyme-encoding genes across microbial life, opening new possibilities for identifying strain-specific metabolic vulnerabilities that could lead to more selective treatment strategies.

systems biology↗

bioRxiv2018

Resource misallocation as a mediator of fitness costs in antibiotic resistance.

Antimicrobial resistance poses a threat to global health and the economy. It is widely accepted that, in the absence of antibiotics, drug resistance mutations carry a fitness cost. In the case of rifampicin resistance in fast-growing bacteria, this cost stems from a reduced transcription rate of the RNA polymerase resulting in slower ribosome biosynthesis. However, this relationship does not apply in the slow-growing Mycobacterium tuberculosis, where the true mechanism of fitness cost of rifampicin resistance as well as the impact of compensatory evolution remain unknown. Here we show, using global transcriptomic and proteomic profiling of selected M. tuberculosis mutants and clinical strains, that the fitness cost of rifampicin resistance in M. tuberculosis is the result of the physiological burden caused by aberrant gene expression. We further show that the perceived burden can be increased, effectively suppressing the emergence of drug resistance.

microbiology↗

bioRxiv2018

A framework for large-scale dynamic metabolome drug profiling in mammalian cells: a case study analysis of the anti-cancer drug dichloroacetate

Metabolic profiling of cell line collections have become an invaluable tool to study disease etiology, drug modes of action and personalized medicine. However, large-scale in vitro dynamic metabolic profiling is limited by time-consuming sampling and complex measurement procedures. By adapting an MS-based metabolomics workflow for high-throughput profiling of diverse adherent mammalian cells, we establish a technique for the rapid measurement and analysis of drug-induced dynamic changes in intracellular metabolites. This methodology is scalable to large compound libraries and is here applied to study the mechanism underlying the toxic effect of dichloroacetate in ovarian cancer cell lines. System-level analysis of the metabolic responses revealed a key and unexpected role of CoA imbalance in dichloroacetate toxicity. The herein proposed strategy for large-scale drug metabolic profiling is complementary to other molecular profiling techniques, opening new scientific and drug-discovery opportunities.

systems biology↗

bioRxiv2018

Charting the cross-functional map between transcription factors and cancer metabolism

Transcriptional reprogramming of cellular metabolism is a hallmark feature of cancer. However, a systematic approach to study the role of transcription factors (TFs) in mediating cancer metabolic rewiring is missing. Here, we chart a genome-scale map of TF-metabolite associations in human using a new combined computational-experimental framework for large-scale metabolic profiling of adherent cell lines, and the integration of newly generated intracellular metabolic profiles of 54 cancer cell lines with transcriptomic and proteomic data. We unravel a large space of dependencies between TFs and central metabolic pathways, suggesting that the regulation of carbon metabolism in tumors may be more diverse and flexible than previously appreciated. This map provides an unprecedented resource to predict TFs responsible for metabolic transformation in patient-derived tumor samples, opening new opportunities in designing modulators of oncogenic TFs and in understanding disease etiology.

systems biology↗