Biology subjects

Clarke, R.

Publications and source records attributed to Clarke, R..

At least 19 recordsLinked to original sources

bioRxiv2026

Stochastic Boolean Model of Death Signaling in MCF7:5C Predicts Cell Death Inducers and Inhibitors

Disrupted cell death signaling contributes significantly to the inappropriate survival of cancer cells, enabling them to evade the apoptotic processes that normally eliminate damaged or abnormal cells. To assess the impact of disrupted cell death signaling on cancer cell survival, we developed a continuous-time stochastic Boolean model of apoptosis signaling in MCF7:5C human breast cancer cells that are resistant to estrogen deprivation and undergo apoptosis in response to estrogen. The model was calibrated to replicate the dynamics of key apoptosis regulators in MCF7:5C cells before and after 17{beta}-estradiol (E2) treatment. Subsequently, the calibrated model was used to predict both the single and double perturbations that inhibit cell death in estradiol-treated MCF7:5C cells, and additional single interventions capable of reinducing apoptosis. For example, strains with single-gene deletion of PERK, eIF2, CHOP, or ATF4 enabled E2-treated MCF7:5C cells to evade apoptosis. However, additional inhibition of SRC, PI3K, ESR1, CEBPB, or MAPK8 restored apoptotic responses in these resistant cells. We identified 30 resistant cell strains with double mutations and 506 drug-target combinations capable of inducing four distinct modes of cell death: direct CASP7 activation, extrinsic apoptosis, intrinsic apoptosis, and combined intrinsic/extrinsic apoptosis. Overall, our model successfully explains the drug response behavior of MCF7:5C cells, predicts mechanisms of drug resistance, and suggests treatment strategies to overcome this resistance.

cancer biology↗

bioRxiv2026

ARID1A orchestrates the activity of FOXA1 and AP-2 transcription factors in lobular breast cancer cells

Mutations in components of the SWItch/Sucrose Non-Fermentable (SWI/SNF) chromatin-remodelling complex are among the most common genetic alterations in human cancers, yet their functional consequences are difficult to predict as they are highly context-dependent. ARID1A, a core subunit of the canonical BAF complex, is the most frequently mutated SWI/SNF gene and is recurrently altered in breast cancer, with a notable enrichment in the lobular carcinoma subtype. While previous studies in ductal breast cancer have linked ARID1A loss to deregulated oestrogen receptor (ER) signalling and endocrine resistance through its association with the pioneer factor FOXA1, the role of ARID1A in lobular cancer remains poorly understood. Here, we define the genomic, transcriptomic, and chromatin accessibility landscapes governed by ARID1A in lobular breast cancer cells. We show that ARID1A and its catalytic partner SMARCA4 co-occupy distal regulatory regions enriched for forkhead and AP-2 transcription factor motifs. ARID1A depletion leads predominantly to loss of chromatin accessibility at these putative enhancer regions and downregulation of associated genes, indicating a primary role in maintaining a permissive regulatory landscape. Extensive co-binding and reciprocal dependencies between ARID1A, FOXA1, and AP-2 transcription factors reveal a coordinated regulatory network distinct from that observed in ductal breast cancers. ARID1A loss does not impair ER-mediated transcriptional responses in the lobular subtype but instead alters basal expression of a subset of oestrogen-responsive genes. Importantly, ARID1A, FOXA1, and AP-2 jointly regulate genes implicated in skeletal system development and bone metastasis, mirroring mutational patterns observed in lobular breast cancer patient datasets. These findings highlight a unique ARID1A-centred transcriptional programme in lobular breast cancer with potential implications for metastatic behaviour and therapeutic vulnerability.

Cancer Biology↗

bioRxiv2026

Rainfall immediately before and after fire promotes long-term occurrence of a rare, fire-sensitive passerine.

Attributes of fire regimes are known to drive habitat suitability for many species in fire-prone environments. Comparatively little is known about how abiotic conditions (e.g. rainfall events, cumulative rainfall, drought) at the time of fire may affect long-term (>2-years) post-fire occurrence. We sought to a) establish whether the post-fire development of heathland habitat for the endangered mallee emu-wren is influenced by rainfall within 12-months before or after the most-recent fire, b) identify the preferred fire-age of heathland vegetation for the mallee emu-wren, and c) map those habitats most likely to support the species across a large reserve ([~]271,000 ha), Ngarkat Conservation Park, from which it has been extirpated. Using historical presence records, collected prior to the extirpation of mallee emu-wrens from the study area, we implemented a random-forest modelling approach to predict relative likelihood of occurrence (considered a proxy for probability of suitable habitat). Rainfall in the 12-months before and after fire had a positive effect on relative likelihood of mallee emu-wren occurrence. The development of high-quality mallee emu-wren habitat required at least 420 mm of rainfall in the 12-months prior to the most recent fire. Only 35% of Ngarkat received rainfall above this threshold prior to the most recent fire. Rainfall in the 12-months after fire positively influenced relative likelihood of mallee emu-wren occurrence, though the effect was less pronounced than pre-fire rainfall. Relative likelihood of mallee emu-wren occurrence peaked 15 years after fire, with an [~]10-year peak time window of relative occurrence (10-20-years). This study highlights that abiotic conditions at the time of fire, particularly rainfall in the 12-months preceding fire, have long-lasting impacts on relative probability of occurrence for this fire-sensitive species. Targeting fire management in ways that maximise post-fire occurrence of the mallee emu-wren - particularly by burning senesced habitat following periods of elevated rainfall - has potential to enhance conservation outcomes. Given the substantial and long-term impact of rainfall around the time of a fire identified in this study, short-term climatic conditions deserve greater attention in a range of ecosystems where managers aim to use fire to manipulate habitat for the benefit of fire-sensitive species.

ecology↗

bioRxiv2025

Synthetic cargo adaptors reveal molecular features that can enhance dynein activation

Cytoplasmic dynein-1 (dynein) facilitates the microtubule-based retrograde trafficking of all cellular cargo. To become active, dynein binds dynactin and one of many cargo-specific adaptors to form the active transport complex. Despite having similar structures, active transport complexes assembled with different adaptors move with different properties in vitro. To explore how adaptors differentially activate dynein, we engineered a library of synthetic adaptors and characterized their ability to activate dynein using in vitro reconstitution and cell-based trafficking assays. We found that the apparent motility of dynein is highly plastic and tunable by the adaptor sequence and that it is possible to engineer adaptors that outperform endogenous adaptors ability to generate highly motile active transport complexes. We also found that different adaptors support distinct trafficking behavior and cargo movement in cells. These findings provide insight into how dynein motility is modulated to meet the unique trafficking requirements of all cellular cargo.

biophysics↗

bioRxiv2024

Acoustic Recognition of Individual Animals in thePresence of Unknown Individuals

Passive acoustic monitoring is firmly established as an effective non-invasive technique for wildlife monitoring. The analysis of animal vocalizations recorded in their natural habitats is commonly used to monitor species occupancy, distribution mapping and community composition. However, the ability to distinguish between individual animals by their vocalisations remains under-explored and presents an exciting opportunity to study individual animal behavior and population demographics in more detail. In this work, we investigate bioacoustic individual-level recognition. In contrast to existing work, we focus on settings where only a subset of the existing population is known and labeled. This is crucial because wildlife populations are constantly changing so that solutions operating only within a known set of individuals are not realistically applicable in the wild. Using two novel datasets, we show that models initially trained to classify only known individuals can also be extended to detect new and previously unknown individuals not included in the training set. We demonstrate that feature extractors pretrained on species classification can be successfully adapted for this task. Extending individual-level recognition to unknown individuals, so-called out-of-distribution classification, is a crucial step towards making individual recognition a realistic possibility in the wild. HighlightsO_LIWe show that features learned by models pretrained on bird species data can be transferred to individual classification tasks with minimal effort. C_LIO_LIWe define and explore the out-of-distribution classification problem on individual animal vocalisations and address various subtleties and eco-logical use cases. C_LIO_LIWe compile and contribute two additional datasets to facilitate further research on individual acoustic recognition. C_LI

bioinformatics↗

bioRxiv2024

A modular system for programming multistep activation of endogenous genes in stem cells

Although genomes encode instructions for mammalian cell differentiation with rich syntactic relationships, existing methods for genetically programming cells have modest capabilities for stepwise regulation of genes. Here, we developed a sequential genetic system that enables transcriptional activation of endogenous genes in a preprogrammed, stepwise manner. The system relies on the removal of an RNA polymerase III termination signal to induce both the transcriptional activation and the DNA endonuclease activities of a Cas9-VPR protein to effect stepwise progression through cascades of gene activation events. The efficiency of the cascading system enables a new dimension for cellular programming by allowing the manipulation of the sequential order of gene activation for directing the differentiation of human stem cells. One-Sentence SummaryDevelopment of a synthetic biology system for preprogrammed, stepwise activation of endogenous genes.

synthetic biology↗

bioRxiv2023

Proteomics of Patient-derived Breast Tumours Identifies a Pro-migratory Osteomodulin-Cyclin Dependent Kinase 1 Axis which Drives Bone Metastasis

Metastasis to different organs remains the main cause of mortality in breast cancer. Molecular predictors of metastasis are limited as well as therapeutic options. Here, we conducted quantitative proteomics and phosphoproteomics analysis of patient-derived tumours, identifying osteomodulin (OMD) as a dysregulated protein and associated with bone metastases. Cancer-associated fibroblasts secrete OMD which increases breast cancer migration in vitro and promotes the formation of bone metastases in vivo. Downstream of OMD, phosphoproteomics identified the activation of cyclin-dependent kinase 1 (CDK1). The OMD-CDK1 signalling axis drives a pro-migratory and pro-survival phenotype in vitro and bone metastasis in vivo. Our findings highlight the importance of OMD and CDK1 in breast cancer bone metastasis and proposes an alternative therapeutic avenue for the treatment and the prevention of organ-specific metastases.

cancer biology↗

bioRxiv2023

ABDS: tool suite for analyzing biologically diverse samples

MotivationAnalytics tools are essential to identify informative molecular features about different phenotypic groups. Among the most fundamental tasks are missing value imputation, signature gene detection, and expression pattern visualization. However, most commonly used analytics tools may be problematic for characterizing biologically diverse samples when either signature genes possess uneven missing rates across different groups yet involving complex missing mechanisms, or multiple biological groups are simultaneously compared and visualized. ResultsWe develop ABDS tool suite tailored specifically to analyzing biologically diverse samples. Mechanism-integrated group-wise imputation is developed to recruit signature genes involving informative missingness, cosine-based one-sample test is extended to detect enumerated signature genes, and unified heatmap is designed to comparably display complex expression patterns. We discuss the methodological principles and demonstrate the conceptual advantages of the three software tools. We also showcase the biomedical applications of these individual tools. Implemented in open-source R scripts, ABDS tool suite complements rather than replaces the existing tools and will allow biologists to more accurately detect interpretable molecular signals among diverse phenotypic samples. Availability and implementationThe R Scripts of ABDS tool suite is freely available at https://github.com/niccolodpdu/ABDS. Contactyuewang@vt.edu Supplementary informationSupplementary materials are available at Bioinformatics Advances online.

bioinformatics↗

bioRxiv2023

Subtype-specific circadian clock dysregulation modulates breast cancer biology, invasiveness, and prognosis.

Studies in shift workers and model organisms link circadian disruption to breast cancer. However, molecular rhythms in non-cancerous and cancerous human breast tissues are largely unknown. We reconstructed rhythms informatically, integrating locally collected, time-stamped biopsies with public datasets. For non-cancerous tissue, the inferred order of core-circadian genes matches established physiology. Inflammatory, epithelial-mesenchymal transition (EMT), and estrogen responsiveness pathways show circadian modulation. Among tumors, clock correlation analysis demonstrates subtype-specific changes in circadian organization. Luminal A organoids and informatic ordering of Luminal A samples exhibit continued, albeit disrupted rhythms. However, CYCLOPS magnitude, a measure of global rhythm strength, varied widely among Luminal A samples. Cycling of EMT pathway genes was markedly increased in high-magnitude Luminal A tumors. Patients with high-magnitude tumors had reduced 5-year survival. Correspondingly, 3D Luminal A cultures show reduced invasion following molecular clock disruption. This study links subtype-specific circadian disruption in breast cancer to EMT, metastatic potential, and prognosis.

cancer biology↗

bioRxiv2023

Immp2l knockdown increases stimulus-driven instrumental behaviour but does not alter goal-directed learning or neuron density in cortico-striatal circuits in a mouse model of Tourette syndrome and autism spectrum disorder

Cortico-striatal neurocircuits mediate goal-directed and habitual actions which are necessary for adaptive behaviour. It has recently been proposed that some of the core symptoms of autism spectrum disorder (ASD) and Gilles de la Tourette syndrome (GTS), such as tics and repetitive behaviours, may emerge because of imbalances in these neurocircuits. We have recently developed a model of ASD and GTS by knocking down Immp2l, a mitochondrial gene frequently associated with these disorders. The current study sought to determine whether Immp2l knockdown (KD) in male mice alters flexible, goal- or cue-driven behaviour using procedures specifically designed to examine response-outcome and stimulus-response associations, which underlie goal-directed and habitual behaviour, respectively. Whether Immp2l KD alters neuron density in cortico-striatal neurocircuits known to regulate these behaviours was also examined. Immp2l KD mice and wild type-like mice (WT) were trained on Pavlovian and instrumental learning procedures where auditory cues predicted food delivery and lever-press responses earned a food outcome. It was demonstrated that goal-directed learning was not changed for Immp2l KD mice compared to WT mice, as lever-press responses were sensitive to changes in the value of the food outcome, and to contingency reversal and degradation. There was also no difference in the capacity of KD mice to form habitual behaviours compared to WT mice following extending training of the instrumental action. However, Immp2l KD mice were more responsive to auditory stimuli paired with food as indicated by a non-specific increase in lever response rates during Pavlovian-to-instrumental transfer. Finally, there were no alterations to neuron density in striatum or any prefrontal cortex or limbic brain structures examined. Thus, the current study suggests that Immp2l is not necessary for learned maladaptive goal or stimulus driven behaviours in ASD or GTS, but that it may contribute to increased capacity for external stimuli to drive behaviour. Alterations to stimulus-driven behaviour could potentially influence the expression of tics and repetitive behaviours, suggesting that genetic alterations to Immp2l may contribute to these core symptoms in ASD and GTS. Given that this is the first application of this battery of instrumental learning procedures to a mouse model of ASD or GTS, it is an important initial step in determining the contribution of known risk-genes to goal-directed versus habitual behaviours, which should be more broadly applied to other rodent models of ASD and GTS in the future.

neuroscience↗

bioRxiv2023

Temporal regulation of AgRP neurons mediates context-induced feeding

An environment can have a powerful influence over appetite and feeding behaviour. For example, an environmental context, which reliably predicts food, will increase the appetitive food drive to the same environment context. Interestingly, mice are required to be hungry to develop such a context-induced feeding (CIF) response, suggesting the neural circuits sensitive to hunger play an important role to associate an internal energy state with a particular environment context. Hunger-sensing Agouti related peptide (AgRP) neurons are activated by circulating signals of energy deficit and reset to a silenced state by gut feedback mechanisms following food consumption. We hypothesised that AgRP neurons are both necessary and sufficient to drive CIF in the absence of hunger. While fasting increased CIF, chemogenetic inhibition of AgRP neurons during context acquisition prevented this effect. Intriguingly, chemogenetic activation of AgRP neurons during context acquisition did not increase CIF, suggesting precise temporal firing properties may be required. Indeed, photostimulation of AgRP neurons, only during context exposure (ON-OFF in context), increased CIF. Moreover, AgRP photostimulation prior to context exposure, coupled with the termination of photostimulation in the context in the absence of food consumption, was sufficient to drive a subsequent CIF. Our results suggest that AgRP neurons regulate the acquisition of CIF when the temporal firing properties are matched to context exposure. These results further highlight that acute AgRP inhibition is a salient neural event underscoring the effect of hunger on associative learning.

neuroscience↗

bioRxiv2022

Borrowing ecological theory to infer interactions between sensitive and resistant breast cancer cell populations

While some forms of breast cancer are highly responsive to treatment, endocrine therapy-resistant breast cancers are disproportionately lethal. There has been significant progress in understanding how endocrine therapy-resistant strains evolve from therapy-susceptible strains of cancer, but little is understood about the proliferation of resistance through cancer cell populations, or the interactions that occur between populations of resistant and sensitive cells. In this study, we characterize the nature of the ecological interaction between populations of resistant and susceptible breast cancer cells to reveal novel methods of controlling drug resistance. Using in-vitro data on fluorescent-tagged resistant and susceptible cells, we use an image processing algorithm to identify and count cell growth till equilibrium. We then borrow theory from population biology to infer the type of ecological interaction that occurs between populations of resistant and sensitive cells. In particular, we use a Bayesian approach to fit single culture cell populations to infer density-dependent growth parameters (growth rate, carrying capacity) and a Generalized Lotka-Volterra model to understand how susceptible and resistant co-culture populations may be depressing or supporting growth of the other. Our results identify a net mutualistic interaction between the susceptible and resistant cancer strains, demonstrating that there are ecological dynamics to cancer resistance. Our findings also suggest that ecological dynamics change in the presence of therapy, and that an adaptive treatment protocol can induce cycling behavior suggesting that heterogeneous ecological effects contribute to empirically observed adaptive-therapeutic dynamics.

cell biology↗

bioRxiv2021

A general deep learning model for bird detection in high resolution airborne imagery

Advances in artificial intelligence for computer vision hold great promise for increasing the scales at which ecological systems can be studied. The distribution and behavior of individuals is central to ecology, and computer vision using deep neural networks can learn to detect individual objects in imagery. However, developing supervised models for ecological monitoring is challenging because it needs large amounts of human-labeled training data, requires advanced technical expertise and computational infrastructure, and is prone to overfitting. This limits application across space and time. One solution is developing generalized models that can be applied across species and ecosystems. Using over 250,000 annotations from 13 projects from around the world, we develop a general bird detection model that achieves over 65% recall and 50% precision on novel aerial data without any local training despite differences in species, habitat, and imaging methodology. Fine-tuning this model with only 1000 local annotations increase these values to an average of 84% recall and 69% precision by building on the general features learned from other data sources. Retraining from the general model improves local predictions even when moderately large annotation sets are available and makes model training faster and more stable. Our results demonstrate that general models for detecting broad classes of organisms using airborne imagery are achievable. These models can reduce the effort, expertise, and computational resources necessary for automating the detection of individual organisms across large scales, helping to transform the scale of data collection in ecology and the questions that can be addressed.

ecology↗

bioRxiv2021

Determining molecular archetype composition and expression from bulk tissues with unsupervised deconvolution

Complex tissues are composite ecological systems whose components interact with each other to create a unique physiological or pathophysiological state distinct from that found in other tissue microenvironments. To explore this ground yet dynamic state, molecular profiling of bulk tissues and mathematical deconvolution can be jointly used to characterize heterogeneity as an aggregate of molecularly distinct tissue or cell subtypes. We first introduce an efficient and fully unsupervised deconvolution method, namely the Convex Analysis of Mixtures - CAM3.0, that may aid biologists to confirm existing or generate novel scientific hypotheses about complex tissues in many biomedical contexts. We then evaluate the CAM3.0 functional pipelines using both simulations and benchmark data. We also report diverse case studies on bulk tissues with unknown number, proportion and expression patterns of the molecular archetypes. Importantly, these preliminary results support the concept that expression patterns of molecular archetypes often reflect the interactive not individual contributions of many known or novel cell types, and unsupervised deconvolution would be more powerful in uncovering novel multicellular or subcellular archetypes.

bioinformatics↗

bioRxiv2021

DDN2.0: R and Python packages for differential dependency network analysis of biological systems

Data-driven differential dependency network analysis identifies in a complex and often unknown overall molecular circuitry a network of differentially connected molecular entities (pairwise selective coupling or uncoupling depending on the specific phenotypes or experimental conditions) (Herrington, et al. 2018; Zhang, et al., 2009; Zhang and Wang, 2010; Zhang, et al., 2016). Such differential dependency networks are typically used to assist in the inference of potential key pathways. Based on our previously developed Differential Dependency Network (DDN) method, we report here the fully implemented R and Python software tool packages for public use. The DDN2.0 algorithm uses a fused Lasso model and block-wise coordinate descent to estimate both the common and differential edges of dependency networks. The identified DDN can help to provide plausible interpretation of data, gain new insight of disease biology, and generate novel hypotheses for further validation and investigations. To address the imbalanced sample group problem, we propose a sample-size normalized formulation to correct systematic bias. To address high computational complexity, we propose four strategies to accelerate DDN2.0 learning. The experimental results show that new DDN2.0+ learning speed with combined four accelerating strategies is hundreds of times faster than that of DDN2.0 algorithm on medium-sized data (Fu, 2019). To detect intra-omics and inter-omics network rewiring, we propose multiDDN using a multi-layer signaling model to integrate multi-omics data. The simulation study shows that the multiDDN method can achieve higher accuracy of detecting network rewiring (Fu, 2019).

bioinformatics↗

bioRxiv2021

Glucose-sensing in AgRP neurons integrates homeostatic energy state with dopamine signalling in the striatum.

Hunger increases the motivation of an organism to seek out and consume highly palatable energy dense foods. While hunger-sensing Agouti-related peptide (AgRP) neurons influence this process, whether metabolic detection of homeostatic state via metabolic sensing in AgRP neurons potentiates motivation through the midbrain dopamine system is unexplored. Here, we used the AgRP-specific deletion of carnitine acetyltransferase (Crat), a metabolic enzyme regulating glucose and fatty acid oxidation, as a model of impaired metabolic-sensing in AgRP neurons. We then tested the hypothesis that appropriate metabolic-sensing in AgRP neurons is required to increase food reward motivation by modulating accumbal or striatal dopamine release. Electrophysiological studies confirm that Crat deletion in AgRP neurons (KO) impairs normal ex vivo glucose-sensing, and in vivo photometry experiments show that AgRP neurons in KO mice do not exhibit normal responses to repeated palatable food presentation and consumption, highlighting that this model is appropriate to test the hypothesis. Fiber photometry experiments, using the dopamine sensor GRAB-DA, revealed that impaired metabolic-sensing reduces acute dopamine release (seconds) in the nucleus accumbens, but not the dorsal striatum, to palatable food consumption and during operant responding. Positron electron tomography (PET) methods indicated that impaired metabolic-sensing in AgRP neurons suppressed radiolabelled 18F-fDOPA accumulation after [~]30 minutes in the dorsal striatum but not the ventral striatum, suggesting a role for AgRP neurons to restrict a long term post-ingestive dopamine response in the dorsal striatum. Finally, impaired metabolic-sensing in AgRP neurons suppresses motivated operant responding for sucrose rewards. Notably, these behavioural effects are potentiated in the hungry state and therefore highlight that metabolic-sensing in AgRP neurons is required for the appropriate temporal integration and transmission of homeostatic hunger-sensing to dopamine signalling in the striatum.

neuroscience↗

bioRxiv2021

Sample-wise unsupervised deconvolution of complex tissues

MotivationComplex biological tissues are often a heterogeneous mixture of several molecularly distinct cell or tissue subtypes. Both subtype compositions and expressions in individual samples can vary across different biological states or conditions. Computational deconvolution aims to dissect patterns of bulk gene expression data into subtype compositions and subtype-specific expressions. Typically, existing deconvolution methods can only estimate averaged subtype-specific expressions in a population, while detecting differential expressions or co-expression networks in particular subtypes requires unique subtype expression estimates in individual samples. Different from population-level deconvolution, however, individual-level deconvolution is mathematically an underdetermined problem because there are more variables than observations. ResultsWe report a sample-wise Convex Analysis of Mixtures (swCAM) method that can estimate subtype proportions and subtype-specific expressions in individual samples from bulk tissue transcriptomes. We extend our previous CAM framework to include a new term accounting for between-sample variations and formulate swCAM as a nuclear-norm and{ell} 2,1-norm regularized matrix factorization problem. We determine hyperparameter values using a cross-validation scheme with random entry exclusion and obtain a swCAM solution using an efficient alternating direction method of multipliers. The swCAM is implemented in open-source R scripts. Experimental results on realistic simulation data show that swCAM can accurately estimate subtype-specific expressions in individual samples and successfully extract co-expression networks in particular subtypes that are otherwise unobtainable using bulk expression data. Application of swCAM to bulk-tissue data of 320 samples from bipolar disorder patients and controls identified changes in cell proportions, expression and coexpression modules in patient neurons. Mitochondria related genes showed significant changes suggesting an important role of energy dysregulation in bipolar disorder. Availability and implementationThe R Scripts of swCAM is freely available at https://github.com/Lululuella/swCAM. A users guide and a vignette are provided. Contactyuewang@vt.edu Supplementary informationSupplementary data are available at Bioinformatics online.

bioinformatics↗

bioRxiv2020

Feeding Experimentation Device version 3 (FED3): An open-source home-cage compatible device for measuring food intake and operant behavior

Feeding is critical for survival and disruption in the mechanisms that govern food intake underlie disorders such as obesity and anorexia nervosa. It is important to understand both food intake and food motivation to reveal mechanisms underlying feeding disorders. Operant behavioral testing can be used to measure the motivational component to feeding, but most food intake monitoring systems do not measure operant behavior. Here, we present a new solution for monitoring both food intake and motivation: The Feeding Experimentation Device version 3 (FED3). FED3 measures food intake and operant behavior in rodent home-cages, enabling longitudinal studies of feeding behavior with minimal experimenter intervention. It has a programmable output for synchronizing behavior with optogenetic stimulation or neural recordings. Finally, FED3 design files are open-source and freely available, allowing researchers to modify FED3 to suit their needs. In this paper we demonstrate the utility of FED3 in a range of experimental paradigms. In BriefUsing a novel, high-throughput home cage feeding platform, FED3, Matikainen-Ankney et al. quantify food intake and operant learning in groups of mice conducted at multiple institutions across the globe. Results include rates of operant efficiency, circadian feeding patterns, and operant optogenetic self-stimulation. Highlights O_LIThe Feeding Experimentation Device version 3(FED3) records food intake and operant behavior in rodent home cages. C_LIO_LIAnalysis of food intake includes total intake, meal pattern analysis, and circadian analysis of feeding patterns. C_LIO_LIFED3 also allows for operant behavioral assays to examine food learning and motivation. C_LI

animal behavior and cognition↗