Biology subjects

Pei, F.

Publications and source records attributed to Pei, F..

8 recordsLinked to original sources

bioRxiv2026

Intrinsic Goals for Autonomous Agents: Hierarchical Exploration Captures Future Volitional Action in Drosophila

Animals have a remarkable ability to act autonomously---driven by intrinsic motivation---without external sensory cues or extrinsic objectives, yet the computational mechanisms behind this ability largely remain a mystery. To understand how biological autonomy may emerge from optimizing intrinsic goals, we use recent whole-brain recordings in the fruit fly (\textit{Drosophila melanogaster}) that identify neural representations of spontaneous exploratory decisions more than $10$ seconds in advance. Combined with a neuromechanical digital twin of \textit{Drosophila}, these recordings allow us to test whether intrinsically motivated artificial agents with realistic embodiments quantitatively match fly behavior and internal representations of future volitional action during exploration. We implement a hierarchical, intrinsically motivated agent whose policy operates over a discrete library of motor primitives---fixed low-level controllers that actuate joint dynamics---closely matching ethological theories of behavioral syllables. We train agents with no external reward using $20$ intrinsic motivation algorithms spanning curiosity, entropy, and competency methods. We evaluate frozen policies in a virtual tethered-ball assay modeled after the fruit-fly experiment, allowing us to compare spontaneous locomotor statistics and internal representations and decode future turn direction from both kinematics and neural activations on the same timescales observed in flies. We find that DIAYN---a hierarchical competency algorithm that learns a discrete set of policies maximizing behavioral entropy and the separability of their induced sensory observations---achieves the strongest joint behavioral and neural prospective coding performance with the fruit fly. Surprisingly, DIAYN's learned policies closely capture individual variability in fly exploration strategies, suggesting that this behavioral diversity optimizes a concrete computational goal of maximizing state-space coverage at the \emph{population} level. Furthermore, DIAYN agents' latent activity closely captures the specific sum-difference rule in interhemispheric encoding of future kinematics in biological flies. Together, these results establish hierarchical, intrinsically motivated agents as a powerful framework for reverse-engineering animal autonomy.

neuroscience↗

bioRxiv2026

Genetic regulation of circulating metabolome in cattle

The circulating metabolome is a vital intermediate layer linking genetics to complex phenotypes, yet existing genome-wide studies-even in humans-rarely capture dynamic physiological contexts or cellular regulatory mechanisms. Here, we present the Cattle Metabolome Atlas (https://cattlema.farmgtex.org/), a comprehensive resource of 3,436 plasma metabolites and 4,851 serum metabolites from 4,651 animals with matched sequence-level genotypes across highly dynamic parity and lactation stages. We mapped 728 plasma and 622 serum metabolite quantitative trait loci (mQTL), revealing widespread context-dependent regulatory architectures. Integrating these mQTL with the multi-tissue expression quantitative trait loci (eQTL) and single-cell atlas of 59 tissues demonstrates that 74-79% of mQTL colocalize with eQTL across 29 tissues, prioritizing the liver as the primary systemic hub and resolving metabolic programs at single-cell resolution. Furthermore, we charted 538 causal gene-tissue-metabolite-trait cascades across 11 complex traits in cattle. Finally, cross-species analyses demonstrate partial evolutionary conservation of genetic metabolism between cattle and humans, establishing this atlas as a powerful asset for cattle genetics and genomics, selective breeding, and comparative biology.

genetics↗

bioRxiv2024

CRISPR screens reveal convergent targeting strategies against evolutionarily distinct chemoresistance in cancer

Resistance to chemotherapy has been a major hurdle that limits therapeutic benefits for many types of cancer. Here we systematically identify genetic drivers underlying chemoresistance by performing 30 genome-scale CRISPR knockout screens for seven chemotherapeutic agents in multiple cancer cells. Chemoresistance genes vary between conditions primarily due to distinct genetic background and mechanism of action of drugs, manifesting heterogeneous and multiplexed routes towards chemoresistance. By focusing on oxaliplatin and irinotecan resistance in colorectal cancer, we unravel that evolutionarily distinct chemoresistance can share consensus vulnerabilities identified by 26 second-round CRISPR screens with druggable gene library. We further pinpoint PLK4 as a therapeutic target to overcome oxaliplatin resistance in various models via genetic ablation or pharmacological inhibition, highlighting a single-agent strategy to antagonize evolutionarily distinct chemoresistance. Our study not only provides resources and insights into the molecular basis of chemoresistance, but also proposes potential biomarkers and therapeutic strategies against such resistance.

cancer biology↗

bioRxiv2021

Atomic-Resolution Prediction of Degrader-mediated Ternary Complex Structures by Combining Molecular Simulations with Hydrogen Deuterium Exchange

Targeted protein degradation (TPD) has emerged as a powerful approach in drug discovery for removing (rather than inhibiting) proteins implicated in diseases. A key step in this approach is the formation of an induced proximity complex, where a degrader molecule recruits an E3 ligase to the protein of interest (POI), facilitating the transfer of ubiquitin to the POI and initiating the proteasomal degradation process. Here, we address three critical aspects of the TPD process: 1) formation of the ternary complex induced by a degrader molecule, 2) conformational heterogeneity of the ternary complex, and 3) assessment of ubiquitination propensity via the full Cullin Ring Ligase (CRL) macromolecular assembly. The novel approach presented here combines experimental biophysical data--in this case hydrogen-deuterium exchange mass spectrometry (HDX-MS, which measures the solvent exposure of protein residues)--with all-atom explicit solvent molecular dynamics (MD) simulations aided by enhanced sampling techniques to predict structural ensembles of ternary complexes at atomic resolution. We present results demonstrating the efficiency, accuracy, and reliability of our approach to predict ternary structure ensembles using the bromodomain of SMARCA2 (SMARCA2BD) with the E3 ligase VHL as the system of interest. The simulations reproduce X-ray crystal structures - including prospective simulations validated on a new structure that we determined in this work (PDB ID: 7S4E) - with root mean square deviations (RMSD) of 1.1 to 1.6 [A]. The simulations also reveal a structural ensemble of low-energy conformations of the ternary complex within a broad energy basin. To further characterize the structural ensemble, we used snapshots from the aforementioned simulations as seeds for Hamiltonian replica exchange molecular dynamics (HREMD) simulations, and then perform 7.1 milliseconds of aggregate simulation time using Folding@home. The resulting free energy surface identifies the crystal structure conformation within a broad low-energy basin and the dynamic ensemble is consistent with solution-phase biophysical experimental data (HDX-MS and small-angle x-ray scattering, SAXS). Finally, we graft structures from the ternary complexes onto the full CRL and perform enhanced sampling simulations, where we find that differences in degradation efficiency can be explained by the proximity distribution of lysine residues on the POI relative to the E2-loaded ubiquitin. Several of the top predicted ubiquitinated lysine residues are validated prospectively through a ubiquitin mapping proteomics experiment.

biophysics↗

bioRxiv2020

Mechanisms of Action of Autophagy Modulators Dissected by Quantitative Systems Pharmacology Analysis

Autophagy plays an essential role in cell survival/death and functioning. Modulation of autophagy has been recognized as a promising therapeutic strategy against diseases/disorders associated with uncontrolled growth or accumulation of biomolecular aggregates, organelles or cells including those caused by cancer, aging, neurodegeneration, and liver diseases such as 1-antitrypsin deficiency. Numerous pharmacological agents that enhance or suppress autophagy have been discovered. However, their molecular mechanisms of action are far from clear. Here we collected a set of 225 autophagy modulators and carried out a comprehensive quantitative systems pharmacology (QSP) analysis of their targets using both existing databases and predictions made by our machine learning algorithm. Autophagy modulators include several highly promiscuous drugs (e.g. artenimol and olanzapine acting as activator, fostamatinib as inhibitor, or melatonin as dual-modulator), as well as selected drugs uniquely targeting specific proteins ([~]30% of modulators). They are mediated by three layers of regulation: (i) pathways involving core autophagy-related (ATG) proteins such as mTOR, AKT, and AMPK; (ii) upstream signaling events that regulates the activity of ATG pathways such as calcium-, cAMP-, and MAPK-signaling pathways; and (iii) transcription factors regulating the expression ATG proteins such as TFEB, TFE3, HIF-1, FoxO, and NF-{kappa}B. Our results suggest that PKA serves as a linker bridging between various signal transduction events and autophagy. These new insights contribute to a better assessment of the mechanism of action of autophagy modulators as well as their side effects, development of novel polypharmacological strategies, and identification of drug repurposing opportunities.

systems biology↗

bioRxiv2019

Cdc48/VCP and endocytosis regulate TDP-43 and FUS toxicity and turnover

Amyotrophic lateral sclerosis (ALS) is a fatal motor neuron degenerative disease. TDP-43 (TAR DNA-binding protein 43) and FUS (fused in sarcoma) are aggregation-prone RNA-binding proteins that in ALS can mis-localize to the cytoplasm of affected motor neuron cells, often forming cytoplasmic aggregates in the process. Such mis-localization and aggregation are implicated in ALS pathology, though the mechanisms of TDP-43 and FUS cytoplasmic toxicity remains unclear. Recently, we determined that the endocytic function aids turnover of TDP-43 and reduces TDP-43 toxicity. Here, we identified that Cdc48 and Ubx3, a Cdc48 co-factor implicated in endocytic function, regulates the turnover and toxicity of TDP-43 and FUS expressed in S. cerevisiae. Cdc48 physically interacts and co-localizes with TDP-43, as does VCP in ALS patient tissue. In yeast, FUS toxicity also depends strongly on endocytic function, but not autophagy under normal conditions. FUS expression also impairs endocytic function, as previously observed with TDP-43. Taken together, our data identifies a role for Cdc48/VCP and endocytosis function in regulating TDP-43 and FUS toxicity and turnover. Furthermore, endocytic dysfunction may be a common defect affecting cytoplasmic clearance of ALS aggregation-prone proteins and may represent a novel therapeutic target of promise.

molecular biology↗

bioRxiv2018

Quantitative systems pharmacological analysis of drugs of abuse reveals the pleiotropy of their targets and the effector role of mTORC1

Existing treatments against drug addiction are often ineffective due to the complexity of the networks of protein-drug and protein-protein interactions (PPIs) that mediate the development of drug addiction and related neurobiological disorders. There is an urgent need for understanding the molecular mechanisms that underlie drug addiction toward designing novel preventive or therapeutic strategies. The rapidly accumulating data on addictive drugs and their targets as well as advances in machine learning methods and computing technology now present an opportunity to systematically mine existing data and draw inferences on potential new strategies. To this aim, we carried out a comprehensive analysis of cellular pathways implicated in a diverse set of 50 drugs of abuse using quantitative systems pharmacology methods. The analysis of the drug/ligand-target interactions compiled in DrugBank and STITCH databases revealed 142 known and 48 newly predicted targets, which have been further analyzed to identify the KEGG pathways enriched at different stages of drug addiction cycle, as well as those implicated in cell signaling and regulation events associated with drug abuse. Apart from synaptic neurotransmission pathways detected as a common upstream signaling module that senses the early effects of drugs of abuse, pathways involved in neuroplasticity are distinguished as determinants of neuronal morphological changes. Notably, many signaling pathways converge on important targets such as mTORC1. The latter is proposed to act as a universal effector of the persistent restructuring of neurons in response to continued use of drugs of abuse.

systems biology↗

bioRxiv2018

Autism sensory dysfunction in an evolutionarily conserved system

There is increasing evidence for a strong genetic basis for autism, with many genetic models being developed in an attempt to replicate autistic symptoms in animals. However, current animal behaviour paradigms rarely match the social and cognitive behaviours exhibited by autistic individuals. Here we instead assay another functional domain - sensory processing - known to be affected in autism to test a novel genetic autism model in Drosophila melanogaster. We show similar visual response alterations and a similar development trajectory in Nhe3 mutant flies (total N=72) and in autistic human participants (total N=154). We report a dissociation between first- and second-order electrophysiological visual responses to steady-state stimulation in adult mutant fruit flies that is strikingly similar to the response pattern in human adults with ASD as well as that of a large sample of neurotypical individuals with high numbers of autistic traits. We explain this as a genetically driven, selective signalling alteration in transient visual dynamics. In contrast to adults, autistic children show a decrease in the first-order response that is matched by the fruit fly model, suggesting that a compensatory change in processing occurs during development. Our results provide the first animal model of autism comprising a differential developmental phenotype in visual processing.

neuroscience↗