Biology subjects

Kessler, D.

Publications and source records attributed to Kessler, D..

6 recordsLinked to original sources

bioRxiv2026

The floral illusion: A parasitic beetle mimics the scent of flowers to attract bees

Animals are not known to biosynthesize floral signals to manipulate pollinators, although such mimicry could profoundly shape plant-pollinator interactions. Larvae of the poisonous European blister beetle Meloe proscarabaeus parasitize multiple solitary bee species, yet the mechanism enabling host attraction has remained unresolved. Here we show that these larvae lure bees by emitting a bouquet of volatile compounds that closely resembles floral scent. Chemical analyses reveal a complex blend of monoterpenoids derived from (S)-linalool, a ubiquitous floral volatile. Behavioral assays demonstrate that these compounds function as floral cues, eliciting attraction in bees. Transcriptomic and functional analyses identify cytochrome P450 enzymes that oxidize (S)-linalool, demonstrating that larvae biosynthesize these plant-like volatiles de novo. Together, these findings broaden the scope of interkingdom chemical mimicry and uncover a striking form of sensory deception in which an insect chemically assumes the signal identity of a flower, revealing that animals can evolve biosynthetic pathways to exploit plant-pollinator communication.

ecology↗

bioRxiv2025

Hierarchical and Spatial Mapping of Whole-Brain c-Fos Activity Reveals Distinct Opioid and Withdrawal Neuronal Ensembles

How opioid exposure and withdrawal states shape brain activity at the systems and circuit level remains poorly understood. Here, we use whole-brain, cellular-resolution c-Fos mapping to define brain-wide activity patterns and neuronal ensembles associated with morphine administration and withdrawal. To account for the brains anatomically nested structure, we developed and applied a hierarchical statistical framework that detects region-specific changes in activity and outperforms conventional methods that treat brain regions as individual, unrelated units. These distributed signals formed ensembles with consistent and anatomically structured patterns of activity, both within subregions and across multiple connected brain areas. By combining TRAP2-based activity tagging with acute whole-brain c-Fos staining, we identified morphine- and withdrawal-activated ensembles and found that they are largely non-overlapping at the single-cell level, even within the same brain region. Integration with existing spatial transcriptomics datasets identified molecular markers for these state-specific ensembles in key brain areas such as the nucleus of accumbens, amygdala and ventral tegmental areas. Lastly, by integrating Allen mouse whole-brain transcriptional datasets, we identified the molecular identity of the morphine- administration and withdrawal ensembles. These findings define dissociable neuronal ensembles that encode opposing drug states and introduce a scalable framework for linking whole-brain activity to molecular and circuit-level mechanisms. HighlightsO_LIWhole-brain neural activity mapping identifies the regional and spatial difference of morphine-administration and withdrawal neuronal ensembles. C_LIO_LIBRANCH, a hierarchical statistical testing framework, offers increased sensitivity and anatomical interpretability for whole-brain datasets. C_LIO_LITRAP2-based activity tagging reveals brain-wide separation of acute morphine and withdrawal ensembles at the cellular level. C_LIO_LIA systematic analysis of the whole brain neural activity data combined with spatial transcriptomic data revealed molecular features of opioid-related neural ensembles. C_LI

neuroscience↗

bioRxiv2023

Pre-zygotic mate selection affects progeny fitness and is only partially correlated with the expression of NaS-like RNases

O_LINicotiana attenuata styles preferentially select pollen from accessions with corresponding expression patterns of NaS-like-RNases (SLRs), and the post-pollination ethylene burst (PPEB) is an accurate predictor of seed siring success. However, the ecological consequences of mate selection, its effect on the progeny, and the role of SLRs in the control of ethylene signaling are still not well understood. C_LIO_LIWe explored the link between the magnitude of the ethylene burst and transcript and protein abundance of the SLRs in a set of recombinant inbred lines (RILs) and investigated the fitness consequences of mate selection for the next generation. Genome Wide Association Study (GWAS) identified novel candidate genes potentially involved in the control of mate selection. C_LIO_LIWe found that high levels of PPEB are associated with the absence of SLR2 but not with the expression of SLR1 in most of the tested RILs. Maternal genotypes that favor certain pollen produce offspring with longer roots when pollinated with these donors, but the selection for beneficial traits is abolished when the maternal genotype selects only against certain pollen donors. C_LIO_LIWe conclude that mate selection mechanisms affect the offspring number and performance in ways that might be adaptive. C_LI

ecology↗

bioRxiv2020

Guiding and interpreting brain network classification with transcriptional data

The investigation of brain networks has yielded many insights that have helped to characterise many neurological and psychiatric disorders. In particular, network classification of functional magnetic resonance imaging (fMRI) data is an important tool for the identification of prognostic and diagnostic biomarkers of brain connectivity disorders such as schizophrenia and depression. However, existing generic network classification methods provide no direct information on the underlying molecular mechanisms of the selected functional connectivity features when applied to fMRI data. To address this, we propose a novel fMRI network classification method that incor-porates brain transcriptional data using a user-specified gene set collection (GSC) to construct feature groups for use in classification of brain connectivity data. The use of GSCs are an opportunity to incorporate knowledge of potential molecular mechanisms which may be associated with a disease. The inclusion of transcriptional data yields improved prediction accuracy on publicly available schizophrenia fMRI data for several of the GSCs we consider. We also introduce a post-hoc interpretation framework to provide transcriptional-data-guided biological interpretations for discriminative functional connectivity features identified by existing fMRI network classification methods.

bioinformatics↗

bioRxiv2019

Native mass spectrometry can effectively predict PROTAC efficacy

Protein degraders, also known as proteolysis targeting chimeras (PROTACs), are bifunctional small molecules that bring an E3 ubiquitin ligase and a protein of interest (POI) into proximity, thus promoting ubiquitination and degradation of the targeted POI [1-3]. Despite their great promise as next-generation pharmaceutical drugs, the development of new PROTACs is challenged by the complexity of the system, which involves binary and ternary interactions between components. Here, we demonstrate the strength of native mass spectrometry (nMS), a label-free technique, to provide novel insight into PROTAC-mediated protein interactions. We show that nMS can monitor the formation of ternary E3-PROTAC-POI complexes and detect various intermediate species in a single experiment. A unique benefit of the method is its ability to reveal preferentially formed E3-PROTAC-POI combinations in competition experiments with multiple substrate proteins, thereby positioning it as an ideal high-throughput screening strategy during the development of new PROTACs.

biochemistry↗

bioRxiv2018

Fundamental Units of Inter-Individual Variation in Resting State Connectomes

Resting state functional connectomes are massive and complex. It is an open question, however, whether connectomes differ across individuals in a correspondingly massive number of ways, or whether most differences take a small number of characteristic forms. We systematically investigated this question and found clear evidence of low-rank structure in which a modest number of connectomic components, around 50-150, account for a sizable portion of inter-individual connectomic variation. This number was convergently arrived at with multiple methods including estimation of intrinsic dimensionality and assessment of reconstruction of out-of-sample data. We demonstrate that these connectomic components enable prediction of a broad array of neurocognitive and clinical variables. In addition, using stochastic block modeling-based methods, we show these components exhibit extensive community structure reflecting interrelationships between intrinsic connectivity networks. We propose that these connectivity components form an effective basis set for quantifying and interpreting inter-individual connectomic differences, and for predicting behavioral/clinical phenotypes.

neuroscience↗