Biology subjects

Cutler, J.

Publications and source records attributed to Cutler, J..

7 recordsLinked to original sources

bioRxiv2026

Combined Menin and XPO1 inhibition drive synergistic antileukemic activity in KMT2Ar and NPM1-m AML

Menin scaffolds the oncogenic histone-lysine-N-methyltransferase (KMT2A)-fusion protein (FP) complex in KMT2A-r and wild-type KMT2A complex in NPM1-m acute myeloid leukemia (AML). Menin inhibitors (MIs) are effective in KMT2A-r AML and NPM1-m AML. However, not all patients respond to MIs as monotherapy. In this preclinical study, we demonstrate that the MI ziftomenib, in combination with the XPO1 inhibitor selinexor, synergistically inhibited the growth of multiple KMT2A-r and NPM1-m AML cell lines (CI<1). The combination suppressed colony formation in primary CD34+ KMT2A-r progenitor cells without affecting normal stem cells. Robust apoptosis and decreased G2/M populations were also evident. The combination downregulated HOXA9 and MEIS1 while upregulating monocytic differentiation marker CD11b in both the AML molecular signatures. RNA sequencing and proteomic analysis in KMT2A-r revealed suppression of multiple bona fide menin-KMT2A target genes. Our mechanistic studies also identified a novel role of XPO1 in stabilizing menins binding to chromatin and its interactions with KMT2A and KMT2A/MLLT3. XPO1 inhibitor-mediated disruption of these interactions, particularly in combination with ziftomenib, synergistically impairs oncogenic transcriptional programs. In vivo, combination therapy improved survival in both MV4;11 and OCI-AML3 cell line and primary patient-derived KMT2A-r and NPM1-m AML xenograft models in NSG mice, effective even at reduced drug doses. These preclinical findings demonstrate that simultaneous inhibition of the menin-KMT2A interaction and XPO1 can be a more effective translational strategy for treating KMT2A-r and NPM1-m AML than MI monotherapy to deepen responses and delay/prevent relapses.

cancer biology↗

bioRxiv2025

Dynamic extracellular interactions with AMPA receptors

Synaptic plasticity in the central nervous system enables the encoding, storing, and integrating new information. AMPA-type glutamate receptors (AMPARs) are ligand-gated ion channels that mediate most fast excitatory synaptic transmission in the brain, and plasticity of AMPARs signaling underlies the long-lasting changes in synaptic efficacy and strength important for learning and memory.1,2 Recent work has indicated that the enigmatic N-terminal domain (NTD) of AMPARs may be a critical regulator of synaptic targeting and plasticity of AMPARs. However, few synaptic proteins have been identified that regulate AMPAR plasticity through interactions with AMPAR NTDs. Moreover, the scope of AMPAR NTD interactors that are important for synaptic plasticity remains unknown. Here, we present the dynamic, extracellular interactome for AMPARs during synaptic plasticity. Using surface-restricted proximity labeling and BioSITe-based proteomics, we identified 70 proteins that were differentially labeled by APEX2-tagged AMPARs after induction of chemical Long-term potentiation of synapses (cLTP) in cultured neurons. Included in this list, were four members of the IgLON family of GPI-anchored proteins (Ntm, OBCAM/Opcml, Negr1, Lsamp). We show OBCAM and NTM directly interact with the extracellular domains of AMPARs. Moreover, overexpression of NTM significantly attenuates the mobility of surface AMPARs in dendritic spines. These data represent a significant first step at uncovering the unexplored extracellular regulation of AMPARs, with broad implications for synapse function and synaptic plasticity. Significance StatementOver the past 30 years, significant effort has been focused on understanding the mechanisms that induce long-lasting changes in synapse strength (synaptic plasticity) that drive learning and memory. While many studies have investigated intracellular mechanisms that enable plasticity, especially those acting on AMPA-type glutamate receptors (AMPARs), significantly less is known regarding extracellular mechanisms that shape changes in synapse function. Here, we identified 70 proteins that differentially associate with the extracellular region of AMPARs during chemically-induced synaptic plasticity. We show that OBCAM and NTM directly interact with the NTD of AMPARs and regulate their mobility on the surface of neurons. These data advance our understanding of extracellular AMPAR regulation, with broad implications for synapse function and synaptic plasticity.

neuroscience↗

bioRxiv2021

Distinct neural representations for prosocial and self-benefitting effort

Prosocial behaviours - actions that benefit others - are central to individual and societal well-being. Most prosocial acts are effortful. Yet, how the brain encodes effort costs when actions benefit others is unknown. Here, using a combination of multivariate representational similarity analysis and model-based univariate analysis during fMRI, we reveal how the costs of prosocial efforts are processed. Strikingly, we identified a unique neural signature of effort in the anterior cingulate gyrus for prosocial acts both when choosing to help others and when exerting force for their benefit. This pattern was absent for similar self-benefitting behaviour and correlated with individual levels of empathy. In contrast, the ventral tegmental area and the ventral insula signalled subjective value preferentially when choosing whether to exert effort to benefit oneself. These findings demonstrate partially distinct brain areas guide the evaluation and exertion of effort costs when acts are prosocial or self-benefitting.

neuroscience↗

bioRxiv2021

Oxytocin modulates neurocomputational mechanisms underlying prosocial reinforcement learning

Humans often act in the best interests of others. However, how we learn which actions result in good outcomes for other people and the neurochemical systems that support this prosocial learning remain poorly understood. Using computational models of reinforcement learning, functional magnetic resonance imaging and dynamic causal modelling, we examined how different doses of intranasal oxytocin, a neuropeptide linked to social cognition, impact how people learn to benefit others (prosocial learning) and whether this influence could be dissociated from how we learn to benefit ourselves (self-oriented learning). We show that a low dose of oxytocin prevented decreases in prosocial performance over time, despite no impact on self-oriented learning. Critically, oxytocin produced dose-dependent changes in the encoding of prediction errors (PE) in the midbrain-subgenual anterior cingulate cortex (sgACC) pathway specifically during prosocial learning. Our findings reveal a new role of oxytocin in prosocial learning by modulating computations of PEs in the midbrain-sgACC pathway.

neuroscience↗

bioRxiv2020

A feature-based network analysis and fMRI meta-analysis of the task structure underlying prosocial decision-making reveals three distinct clusters: cooperation, equity, and altruism

Tasks that measure correlates of prosocial decision-making share one common feature: agents can make choices that increase the welfare of a beneficiary. However, prosocial decisions vary widely as a function of other task features. The diverse ways that prosociality is defined and the heterogeneity of prosocial decisions have created challenges for interpreting findings across studies and identifying their neural correlates. To overcome these challenges, we aimed to organize the prosocial decision-making task-space of neuroimaging studies. We conducted a systematic search for studies in which participants made decisions to increase the welfare of others during fMRI. We identified shared and distinct features of these tasks and employed an unsupervised graph-based approach to assess how various forms of prosocial decision-making are related in terms of their low-level components (e.g., task features like potential cost to the agent or potential for reciprocity). Analyses uncovered three clusters of prosocial decisions, which we labeled cooperation, equity, and altruism. This feature-based representation of the task structure was supported by results of a neuroimaging meta-analysis that each type of prosocial decisions recruited diverging neural systems. Results clarify some of the existing heterogeneity in how prosociality is conceptualized and generate insight for future research and task paradigm development.

neuroscience↗

bioRxiv2020

Ageing disrupts reinforcement learning whilst learning to help others is preserved

Reinforcement learning is a fundamental mechanism displayed by many species. However, adaptive behaviour depends not only on learning about actions and outcomes that affect ourselves, but also those that affect others. Here, using computational reinforcement learning models, we tested whether young (age 18-36) and older (age 60-80, total n=152) adults can learn to gain rewards for themselves, another person (prosocial), or neither individual (control). Detailed model comparison showed that a model with separate learning rates for each recipient best explained behaviour. Young adults were faster to learn when their actions benefitted themselves, compared to helping others. Strikingly, compared to younger adults, older adults showed preserved prosocial learning rates but reduced self-relevant learning rates. Moreover, psychopathic traits were lower in older adults and negatively correlated with prosocial learning. These findings suggest learning how to benefit others is preserved across the lifespan with implications for reinforcement learning and theories of healthy ageing.

neuroscience↗

bioRxiv2018

Adjusting for Variable Brain Coverage in Voxel-Based fMRI Meta-Analysis

Meta-analyses of fMRI studies are vital to establish consistent findings across the literature. However, fMRI data are susceptible to signal dropout (i.e. incomplete brain coverage), which varies across studies and brain regions. In other words, for some brain regions, only a variable subset of the studies included in an fMRI meta-analysis have data present. These missing data can mean activations in fMRI meta-analysis are underestimated (type II errors). Here we present SPM (MATLAB) code to run a novel method of adjusting random-effects models for meta-analytic averaging of a group of studies and mixed-effects models for comparison between two groups of studies. In two separate datasets, meta-analytic effect sizes and z-scores were larger in the adjusted, compared to the unadjusted analysis. Relevantly, these changes were in regions such as the ventromedial prefrontal cortex where coverage was lowest. Limitations of the method, including issues of how to threshold the adjusted maps are discussed. Code and demonstration data for the adjusted method are available at https://doi.org/10.25377/sussex.c.4223411.

neuroscience↗