Biology subjects

Pellegrino, R.

Publications and source records attributed to Pellegrino, R..

10 recordsLinked to original sources

bioRxiv2026

Odors Smell Like Their Components: A Linear Framework for Predicting Olfactory Mixture Perception

Most smells, whether in food, perfume, or the environment, are complex mixtures of many odor molecules. A principled framework for modeling natural mixtures has been considered intractable because mixtures are widely assumed to generate nonlinear perceptual interactions that mirror nonlinear responses at the receptor and neural levels. We quantified perceptual interactions across 432 mixtures composed of 144 component odorants using a trained human panel, and nearly all mixtures were explained by a linear average of their component quality profiles. Within this linear modeling framework, averaging component profiles predicted mixture perception significantly better than the previous state-of-the-art and approached the ceiling set by measurement noise. Even mixtures previously reported to exhibit emergence were equally well predicted. These results suggest that the main challenge in modeling odor perception is characterizing individual components, rather than exhaustively mapping odor-odor interactions. Just as additive color mixing enabled colorimetry, linear mixing in olfaction opens the door to quantitative odorimetry, allowing mixtures to be predicted, reconstructed, and optimized computationally.

neuroscience↗

bioRxiv2026

Odor Sampling Bags Enable Reliable Delivery of Controlled Odor Concentrations

Precise control of odorant concentration is essential for reliable olfactory research, yet existing odorant delivery methods often suffer from solvent interactions and dilution from ambient air, limiting stimulus consistency in olfactory research. We developed an odor sampling bag system using Nalophan plastic to create a closed headspace with air as the carrier medium, eliminating solvent-related variability and ambient air dilution. In two independent experiments, 15 trained panelists each rated the perceived intensity of seven concentrations of benzaldehyde and 2-heptanone using both gas-sampling bags and glass jars. Bags produced higher maximum perceptual intensities (p < 0.001) and greater test-retest reliability than jars (Experiment 1: r = 0.89 vs. 0.81, p < 0.001; Experiment 2: r = 0.86 vs. 0.72, p < 0.001). Notably, the two tested odorants showed different maximum intensities in bags (p < 0.001) but not jars (p = 0.85), suggesting bags better preserve odorant-specific concentration differences. Photoionization detector measurements confirmed stable headspace concentrations over time, comparable to industry-standard Tedlar bags. This cost-effective approach offers improved stimulus control for olfactory psychophysics research.

neuroscience↗

bioRxiv2025

High-Fidelity Tuning of Olfactory Mixture Distances in the Perceptual Space of Smell Through a Community Effort

A central goal in sensory science is to establish quantitative mappings between physical stimuli and perceptual responses. While such mappings are well characterized in vision and audition, they remain poorly defined in olfaction, limiting progress toward understanding the representations of smell. Predicting perceptual similarity between odor mixtures offers a promising route to formalize these relationships. To advance this effort, the DREAM (Dialogue for Reverse Engineering Assessment and Methods) Olfactory Mixtures Prediction Challenge assembled a curated, cross-study dataset describing the similarity of 507 mixture pairs and an unpublished test set of 46 mixture pairs. Teams competed to predict the perceptual similarity of mixture pairs, and then collaborated post-challenge to create an ensemble combining top-performing models that notably improves predictions over the existing state-of-the-art models. Moreover, ensemble model maintains high predictive accuracy in novel validation set. Our model provides a reproducible framework for neuroscientists, chemists, and engineers to compare odor mixtures and provides a foundation for future efforts towards better understanding the olfactory properties of mixtures.

neuroscience↗

bioRxiv2025

Functional Lipid Analysis via Index-Based Lipidomics Profile: A New Computational Module in LipidOne

Understanding the functional roles of lipids is essential for interpreting metabolic phenotypes in health, disease, and dietary interventions. Here, we present a major update to LipidOne, a user-friendly web-based platform for lipidomic data interpretation (lipidone.eu), introducing the novel analytical module: Functional Lipid Analysis (FLA). This component enables the assessment of the quantitative features of lipidomic datasets through a biologically structured, index-based approach. The FLA module computes 42 indices representing specific lipid function-- including membrane structure, energy storage, and signaling. These indices are derived from lipid classes, molecular species, and fatty acyl-, alkyl-, and alkenyl-chain composition. Each index is statistically compared across experimental groups and analyzed and visualized through dedicated tools, including bar plots, volcano plots, PCA, PLS-DA, heatmaps, and functional radar charts. Every index is semantically annotated with biologically meaningful phrases, allowing users to move beyond numerical variations and toward mechanistic insights of lipid function. In the FLA module, index variations are further linked to predicted protein mediators, bridging lipid alterations to enzymatic pathways and enabling network-based interpretations. This integrative strategy lays the foundation for a systems biology perspective, connecting lipidomics to proteomics and or transcriptomics yielding functional pathway analysis. We demonstrate the utility of this framework using datasets from two published works. In both cases, FLA confirmed the authors conclusions and yielded additional, biologically coherent functional readouts not originally emphasized. By shifting the focus from individual lipid species to interpretable biochemical indices, LipidOne 2.3 offers a reproducible, scalable, and biologically informed platform for systems-level lipid biology and hypothesis generation.

bioinformatics↗

bioRxiv2025

Mice and humans evaluate odor stimulus strength using common psychophysical principles

Sensory systems translate physical stimuli from the environment--such as light, sound, or chemicals--into signals that the brain can interpret. Across these systems, the amplitude of a stimulus is represented by its perceived intensity. Although previous research has extensively studied how the brain represents physical stimuli, less is known about how it represents perceptual variables such as stimulus intensity. This is primarily due to the difficulty in measuring perceptual responses in animal models, where neural recordings are more accessible. In this study, we use mouse olfaction as a model system to develop a framework for measuring perceived odor intensity. We begin by employing a two-odor concentration classification task to demonstrate that both mice and humans assess stimulus amplitude using a common perceptual scale. We then show that this scale corresponds to intensity. Finally, we apply this method to determine isointense concentrations of different odorants in mice. Our approach offers a powerful tool for testing hypotheses about the neural mechanisms underlying perceived odor intensity, potentially enhancing our understanding of olfactory processing and its neural substrates.

neuroscience↗

bioRxiv2025

A quantitative framework for predicting odor intensity across molecule and mixtures

In vision and hearing, standardized units such as lumens (for brightness) and decibels (for loudness) allow consistent quantification of stimulus intensity, enabling precise control of sensory experiences. Olfaction, by contrast, currently lacks a robust quantitative framework linking physical stimulus properties directly to perceived odor intensity, complicating efforts to accurately characterize and manipulate aromas. To bridge this gap, we used a precisely controlled odor delivery system combined with deep learning models to predict the intensity of both single molecules and mixtures from physical properties. These models allowed us to develop an automated, quantitative method that accurately identifies which volatile components meaningfully contribute to aroma perception, overcoming the limitations of traditional heuristic approaches such as odor activity values and demonstrating practical utility in complex naturalistic odors.

neuroscience↗

bioRxiv2023

KIF23 regulation by miR-107 controls replicative tumor cell fitness in mouse and human hepatocellular carcinoma

Background & AimsIn hepatocellular carcinoma, there is a lack of successful translation of experimental targets identified in mouse models to human patients. In this study, we used a comprehensive transcriptomic approach in mice to identify novel potential targets for therapeutic intervention in humans. MethodsWe analysed combined genome-wide miRNA and mRNA expression data in three pathogenically distinct mouse models of liver cancer. Effects of target genes on hepatoma cell fitness were evaluated by proliferation, survival and motility assays. TCGA and GEO databases, in combination with tissue microarrays (TMA), were used to validate the mouse targets and their impact on human HCC prognosis. Finally, the functional effects of the identified targets on tumorigenesis and tumor therapy were tested in hydrodynamic tail vein injection (HDTVi)-based preclinical HCC models in vivo. ResultsThe expression of miR-107 was found to be significantly reduced in mouse models of liver tumors of various etiologies and in cohorts of human HCC patients. Overexpression of miR-107 or inhibition of its novel target Kinesin family member 23 (Kif23) significantly reduced proliferation by interfering with cytokinesis, thereby controlling survival and motility of mouse and human hepatoma cells. In humans, KIF23 expression was found to be a prognostic marker in liver cancer, with high expression associated with poor prognosis. HDTVi of vectors carrying either pre-miR- 107 or anti-Kif23 shRNA inhibited the development of highly aggressive cMyc-NRas- induced liver cancers in mice. ConclusionsDisruption of the miR-107/Kif23 axis inhibited hepatoma cell proliferation in vitro and prevented oncogene-induced liver cancer development in vivo, offering a novel potential avenue for the treatment of HCC in humans. Impact and implicationsA comprehensive analysis integrating in silico prediction, miRNA and mRNA data in three pathogenically distinct mouse models provided novel targets for the treatment of human HCC, bridging the translational gap between mouse data and human HCC. Our functional findings on the novel miR-107/Kif23 module provide important new insights into the control of mitosis in liver cancer cells. The findings that miR-107 overexpression or Kif23 inhibition had a dramatic functional effect on inhibiting the growth of liver cancer cells in vitro and in vivo suggest that the miR-107/Kif23 axis may be a promising novel target and potential adjunct to sequential systemic therapy of HCC. Graphical abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=164 SRC="FIGDIR/small/565448v1_ufig1.gif" ALT="Figure 1"> View larger version (45K): org.highwire.dtl.DTLVardef@afda02org.highwire.dtl.DTLVardef@111da7borg.highwire.dtl.DTLVardef@11eeeecorg.highwire.dtl.DTLVardef@1d7d9f7_HPS_FORMAT_FIGEXP M_FIG C_FIG HighlightsO_LImiR-107 is globally downregulated in mouse liver cancers of different etiologies and represents a potential biomarker in human HCC. C_LIO_LIIntegration of in-silico-prediction, miRNA and mRNA transcriptomics identified KIF23, a mitotic spindle-associated protein, as a specific target mediating the biological effects of miR-107. C_LIO_LIThe miR-107/KIF23 module promotes replicative fitness of liver cancer cells through an essential function in cytokinesis C_LIO_LIMice receiving shRNA targeting Kif23 were completely protected from oncogene-induced liver cancer. C_LI

cancer biology↗

bioRxiv2023

Uncovering novel roles of miR-122 in the pathophysiology of the liver: Potential interaction with NRF1 and E2F4 signaling

MicroRNA miR-122 plays a pivotal role in liver function. Despite numerous studies investigating this miRNA, the global network of genes regulated by miR-122 and its contribution to the underlying pathophysiological mechanisms remain largely unknown. To gain a deeper understanding of miR-122 activity, we employed two complementary approaches. Firstly, through transcriptome analysis of polyribosome-bound RNAs, we discovered that miR-122 exhibits potential antagonistic effects on specific transcription factors known to be dysregulated in liver disease, including nuclear respiratory factor-1 (NRF1) and the E2F Transcription Factor 4 (E2F4). Secondly, through proteome analysis of hepatoma cell transfected with either miR-122 mimic or antagomiR we discovered changes in several proteins associated with increased malignancy. Interestingly, many of these proteins were reported to be transcriptionally regulated by NRF1 and E2F4, six of which we validated as miR-122 targets. Among these, a negative correlation was observed between miR-122 and glucose-6-phosphate dehydrogenase levels in the livers of patients with hepatitis B virus-associated hepatocellular carcinoma. This study provides novel insights into potential alterations of molecular pathway occurring at the early stages of liver disease, driven by the dysregulation of miR-122 and its associated genes.

cancer biology↗

bioRxiv2022

Pyrfume: A Window to the World's Olfactory Data

Advances in theoretical understanding are frequently unlocked by access to large, diverse experimental datasets. Olfactory neuroscience and psychophysics remain years behind the other senses in part because rich datasets linking olfactory stimuli with their corresponding percepts, behaviors, and neural pathways underlying this transformation, remain scarce. Here we present a concerted effort to unlock and unify dozens of stimulus-linked olfactory datasets across species and modalities under a unified framework called Pyrfume. We present examples of how researchers might use Pyrfume to conduct novel analyses uncovering new principles, introduce trainees to the field, or construct benchmarks for machine olfaction.

neuroscience↗

bioRxiv2018

Retronasal Habituation: Characterization and Impact on Flavor Perception using Time-Intensity

Olfactory habituation results from prolonged exposure to an odor, leading to perceptual changes defined by several characteristics. To date, human habituation research has focused on orthonasal olfaction which is perceived externally while ignoring internal routes of odor perception related to flavor. In our study, we conducted two experiments to characterize retronasal olfactory habituation and measured its impact on flavor perception. In Experiment 1, 22 participants rhythmically breathed a food odor (lime), non-food odor (lavender), and blank (propylene glycol) that was presented using an orally-adhered strip, while rating the odor intensity using the time-intensity procedure. After a 10-minute exposure, the participants ate a lime-flavored gummy and rated the lime flavor. In Experiment 2, the same procedure was performed for a low-level lime odor, a simple (lime oil) and complex (lime oil + sucrose + citric acid) beverage as the flavor stimuli. Our results demonstrated two known principles of habituation for retronasally presented odors: 1) prolonged exposure lead to decreased perception, 2) weaker stimuli lead to more rapid habituation. Additionally, we found that the non-food odor habituated slower than the food odor; however, the participants seemed to recover simultaneously upon food and beverage consumption leading to no change in flavor perception.

neuroscience↗