Biology subjects

Campbell, M. G.

Publications and source records attributed to Campbell, M. G..

15 recordsLinked to original sources

bioRxiv2026

A codon-sensitive conformational switch gates commitment to translation start sites

Human translation initiation requires single-nucleotide precision to establish the reading frame, yet initiation at non-AUG codons plays key roles in gene expression. How the initiation machinery balances precision with this regulated flexibility remains unclear. Here, we define a conformational branchpoint governed by the human initiation factor eIF5 that gates commitment to start codons. Using single-molecule and structural approaches, we demonstrate that eIF5 reversibly occupies two conformations, which depends on a strictly conserved loop in the protein that monitors start codon identity. AUG codons favor the conformation that is stabilized by an eIF5-stimulated GTP hydrolysis step, which commits the complex to the start site. Non-AUG codons favor a standby conformation that destabilizes eIF5 and likely overlaps the binding site of an ancient structural homolog. This branchpoint complements enforcement of start codon fidelity by upstream steps and intrinsically controls the efficiency of non-AUG initiation.

biophysics↗

bioRxiv2026

Remote homology and functional genetics unmask deeply preserved Scm3/HJURP orthologs in metazoans

AbstractIn most animals and fungi, centromere identity and function depend on the Scm3/HJURP chaperone, which deposits CENPA at centromeres. However, Scm3/HJURP orthologs appeared to be missing in insects, nematodes, many vertebrates, and other metazoans, suggesting radical chaperone replacement in these lineages. Here, we combine remote homology detection, AlphaFold-based structural modeling, and functional genetics in zebrafish and Caenorhabditis elegans to identify previously unknown Scm3/HJURP orthologs that localize to centromeres and whose loss causes catastrophic mitotic failure. We further show that Drosophila CAL1, long considered a functional analog, is instead a highly diverged Scm3/HJURP ortholog. Despite rapid primary-sequence divergence, predicted and known structures reveal a broadly conserved CENPA-H4-binding scm3 fold across fungi, vertebrates, nematodes, insects, and most metazoans. Our work demonstrates how rapid divergence can obscure the broad conservation of essential centromere machinery and provides a generalizable strategy for unmasking missing orthologs. TeaserAnimals encode a rapidly evolving, essential cell cycle gene previously thought to be absent.

evolutionary biology↗

bioRxiv2025

A hardwired neural circuit for temporal difference learning

The neurotransmitter dopamine plays a major role in learning by acting as a teaching signal to update the brains predictions about rewards. A leading theory proposes that this process is analogous to a reinforcement learning algorithm called temporal difference (TD) learning, and that dopamine acts as the error term within the TD algorithm (TD error). Although many studies have demonstrated similarities between dopamine activity and TD errors1-5, the mechanistic basis for dopaminergic TD learning remains unknown. Here, we combined large-scale neural recordings with patterned optogenetic stimulation to examine whether and how the key steps in TD learning are accomplished by the circuitry connecting dopamine neurons and their targets. Replacing natural rewards with optogenetic stimulation of dopamine axons in the nucleus accumbens (NAc) in a classical conditioning task gradually generated TD error-like activity patterns in dopamine neurons by specifically modifying the task-related activity of NAc neurons expressing the D1 dopamine receptor (D1 neurons). In turn, patterned optogenetic stimulation of NAc D1 neurons in naive animals drove dopamine neuron spiking according to the TD error of the stimulation pattern, indicating that TD computations are hardwired into this circuit. The transformation from D1 neurons to dopamine neurons could be described by a biphasic linear filter, with a rapid positive and delayed negative phase, that effectively computes a temporal difference. This finding suggests that the time horizon over which the TD algorithm operates--the temporal discount factor--is set by the balance of the positive and negative components of the linear filter, pointing to a circuit-level mechanism for temporal discounting. These results provide a new conceptual framework for understanding how the computations and parameters governing animal learning arise from neurobiological components.

neuroscience↗

bioRxiv2025

SARS-CoV-2 nsp1 mediates broad inhibition of translation in mammals

SARS-CoV-2 nonstructural protein 1 (nsp1) promotes innate immune evasion by inhibiting host translation in human cells. However, the role of nsp1 in other host species remains elusive, especially in bats which are natural reservoirs of sarbecoviruses and possess a markedly different innate immune system than humans. Here, we reveal that SARS-CoV-2 nsp1 potently inhibits translation in bat cells from Rhinolophus lepidus, belonging to the same genus as known sarbecovirus reservoirs hosts. We determined a cryo-electron microscopy structure of SARS-CoV-2 nsp1 bound to the Rhinolophus lepidus 40S ribosome and show that it blocks the mRNA entry channel via targeting a highly conserved site among mammals. Accordingly, we found that nsp1 blocked protein translation in mammalian cell lines from several species, underscoring its broadly inhibitory activity and conserved role in numerous SARS-CoV-2 hosts. Our findings illuminate the arms race between coronaviruses and mammalian host immunity (including bats), providing a foundation for understanding the determinants of viral maintenance in bat hosts and spillovers.

biochemistry↗

bioRxiv2025

Structural and functional characterization of integrin a5-targeting antibodies for anti-angiogenic therapy

Integrins are heterodimeric receptors important for cell adhesion and signaling. Integrin 5{beta}1 is a key mediator of angiogenesis and its dysregulation is associated with tumor progression and metastasis. Despite numerous efforts, 5{beta}1-targeting therapeutics have been unsuccessful due to poor efficacy and off-target effects. A contributing factor is our limited understanding of how integrin conformation influences interactions with therapeutics. Using cell-based functional assays, patient-derived xenografts, biophysics, and electron microscopy, we shed light on these relationships by characterizing two anti-5{beta}1 antibodies, BIIG2 and MINT1526A. We show that both antibodies bind 5{beta}1 with nanomolar affinity, reduce tube formation in vitro, and bind overlapping epitopes that block fibronectin binding. However, using electron microscopy, we reveal that while BIIG2 binding does not substantially alter the conformational states, MINT1526A preferentially recognizes the bent conformation and restricts the conformational ensemble. These insights can guide which aspects to prioritize to improve the design of future integrin-targeted therapeutics.

biochemistry↗

bioRxiv2024

De Novo Design of Integrin α5β1 Modulating Proteins for Regenerative Medicine

Integrin 5{beta}1 is crucial for cell attachment and migration in development and tissue regeneration, and 5{beta}1 binding proteins could have considerable utility in regenerative medicine and next-generation therapeutics. We use computational protein design to create de novo 5{beta}1-specific modulating miniprotein binders, called NeoNectins, that bind to and stabilize the open state of 5{beta}1. When immobilized onto titanium surfaces and throughout 3D hydrogels, the NeoNectins outperform native fibronectin and RGD peptide in enhancing cell attachment and spreading, and NeoNectin-grafted titanium implants outperformed fibronectin and RGD-grafted implants in animal models in promoting tissue integration and bone growth. NeoNectins should be broadly applicable for tissue engineering and biomedicine. One-Sentence SummaryA de novo-designed fibronectin substitute, NeoNectin, is specific for integrin 5{beta}1 and can be incorporated into biomaterials for regenerative medicine.

bioengineering↗

bioRxiv2023

Evolutionary origin and structural ligand mimicry by the inserted domain of alpha-integrin proteins

Heterodimeric integrin proteins transmit signals through conformational changes upon ligand binding between their alpha () and beta ({beta}) subunits. Early in chordate evolution, some subunits acquired an "inserted" (I) domain, which expanded their ligand binding capacity but simultaneously obstructed the ancestral ligand-binding pocket. While this would seemingly impede conventional ligand-mediated integrin activation, it was proposed that the I domain itself could serve both as a ligand replacement and an activation trigger. Here, we provide compelling evidence in support of this longstanding hypothesis using high-resolution cryo-electron microscopy structures of two distinct integrin complexes: the ligand-free and E-cadherin-bound states of the E{beta}7 integrin with the I domain, as well as the 4{beta}7 integrin lacking the I domain in both a ligand-free state and bound to MadCAM-1. We trace the evolutionary origin of the I domain to an ancestral collagen-collagen interaction domain. Our analyses illuminate how the I domain intrinsically mimics an extrinsic ligand, enabling integrins to undergo the canonical allosteric cascade of conformational activation and dramatically expanding the range of cellular communication mechanisms in vertebrates.

molecular biology↗

bioRxiv2023

Competitive integration of time and reward explains value-sensitive foraging decisions and frontal cortex ramping dynamics

Patch foraging presents a ubiquitous decision-making process in which animals decide when to abandon a resource patch of diminishing value to pursue an alternative. We developed a virtual foraging task in which mouse behavior varied systematically with patch value. Mouse behavior could be explained by a model integrating time and rewards antagonistically, scaled by a latent patience state. The model accounted for deviations from predictions of optimal foraging theory. Neural recordings throughout frontal areas revealed encoding of decision variables from the integrator model, most robustly in frontal cortex. Regression modeling followed by unsupervised clustering identified a subset of ramping neurons. These neurons firing rates ramped up gradually (up to tens of seconds), were inhibited by rewards, and were better described as a continuous ramp than a discrete stepping process. Together, these results identify integration via frontal cortex ramping dynamics as a candidate mechanism for solving patch foraging problems.

neuroscience↗

bioRxiv2023

Improved dual-color GRAB sensors for monitoring dopaminergic activity in vivo

Dopamine (DA) plays multiple roles in a wide range of physiological and pathological processes via a vast network of dopaminergic projections. To fully dissect the spatiotemporal dynamics of DA release in both dense and sparsely innervated brain regions, we developed a series of green and red fluorescent GPCR activation-based DA (GRABDA) sensors using a variety of DA receptor subtypes. These sensors have high sensitivity, selectivity, and signal-to-noise properties with subsecond response kinetics and the ability to detect a wide range of DA concentrations. We then used these sensors in freely moving mice to measure both optogenetically evoked and behaviorally relevant DA release while measuring neurochemical signaling in the nucleus accumbens, amygdala, and cortex. Using these sensors, we also detected spatially resolved heterogeneous cortical DA release in mice performing various behaviors. These next-generation GRABDA sensors provide a robust set of tools for imaging dopaminergic activity under a variety of physiological and pathological conditions.

neuroscience↗

bioRxiv2023

De novo design of highly selective miniprotein inhibitors of integrins αvβ6 and αvβ8

The RGD (Arg-Gly-Asp)-binding integrins v{beta}6 and v{beta}8 are clinically validated cancer and fibrosis targets of considerable therapeutic importance. Compounds that can discriminate between the two closely related integrin proteins and other RGD integrins, stabilize specific conformational states, and have sufficient stability enabling tissue restricted administration could have considerable therapeutic utility. Existing small molecules and antibody inhibitors do not have all of these properties, and hence there is a need for new approaches. Here we describe a method for computationally designing hyperstable RGD-containing miniproteins that are highly selective for a single RGD integrin heterodimer and conformational state, and use this strategy to design inhibitors of v{beta}6 and v{beta}8 with high selectivity. The v{beta}6 and v{beta}8 inhibitors have picomolar affinities for their targets, and >1000-fold selectivity over other RGD integrins. CryoEM structures are within 0.6-0.7[A] root-mean-square deviation (RMSD) to the computational design models; the designed v{beta}6 inhibitor and native ligand stabilize the open conformation in contrast to the therapeutic anti-v{beta}6 antibody BG00011 that stabilizes the bent-closed conformation and caused on-target toxicity in patients with lung fibrosis, and the v{beta}8 inhibitor maintains the constitutively fixed extended-closed v{beta}8 conformation. In a mouse model of bleomycin-induced lung fibrosis, the v{beta}6 inhibitor potently reduced fibrotic burden and improved overall lung mechanics when delivered via oropharyngeal administration mimicking inhalation, demonstrating the therapeutic potential of de novo designed integrin binding proteins with high selectivity.

biochemistry↗

bioRxiv2021

Explaining heterogeneity in medial entorhinal cortex with task-driven neural networks

Medial entorhinal cortex (MEC) supports a wide range of navigational and memory related behaviors. Well-known experimental results have revealed specialized cell types in MEC -- e.g. grid, border, and head-direction cells -- whose highly stereotypical response profiles are suggestive of the role they might play in supporting MEC functionality. However, the majority of MEC neurons do not exhibit stereotypical firing patterns. How should the response profiles of these more "heterogeneous" cells be described, and how do they contribute to behavior? In this work, we took a computational approach to addressing these questions. We first performed a statistical analysis that shows that heterogeneous MEC cells are just as reliable in their response patterns as the more stereotypical cell types, suggesting that they have a coherent functional role. Next, we evaluated a spectrum of candidate models in terms of their ability to describe the response profiles of both stereotypical and heterogeneous MEC cells. We found that recently developed task-optimized neural network models are substantially better than traditional grid cell-centric models at matching most MEC neuronal response profiles -- including those of grid cells themselves -- despite not being explicitly trained for this purpose. Specific choices of network architecture (such as gated nonlinearities and an explicit intermediate place cell representation) have an important effect on the ability of the model to generalize to novel scenarios, with the best of these models closely approaching the noise ceiling of the data itself. We then performed in silico experiments on this model to address questions involving the relative functional relevance of various cell types, finding that heterogeneous cells are likely to be just as involved in downstream functional outcomes (such as path integration) as grid and border cells. Finally, inspired by recent data showing that, going beyond their spatial response selectivity, MEC cells are also responsive to non-spatial rewards, we introduce a new MEC model that performs reward-modulated path integration. We find that this unified model matches neural recordings across all variable-reward conditions. Taken together, our results point toward a conceptually principled goal-driven modeling approach for moving future experimental and computational efforts beyond overly-simplistic single-cell stereotypes.

neuroscience↗

bioRxiv2021

CryoEM and AI reveal a structure of SARS-CoV-2 Nsp2, a multifunctional protein involved in key host processes.

The SARS-CoV-2 protein Nsp2 has been implicated in a wide range of viral processes, but its exact functions, and the structural basis of those functions, remain unknown. Here, we report an atomic model for full-length Nsp2 obtained by combining cryo-electron microscopy with deep learning-based structure prediction from AlphaFold2. The resulting structure reveals a highly-conserved zinc ion-binding site, suggesting a role for Nsp2 in RNA binding. Mapping emerging mutations from variants of SARS-CoV-2 on the resulting structure shows potential host-Nsp2 interaction regions. Using structural analysis together with affinity tagged purification mass spectrometry experiments, we identify Nsp2 mutants that are unable to interact with the actin-nucleation-promoting WASH protein complex or with GIGYF2, an inhibitor of translation initiation and modulator of ribosome-associated quality control. Our work suggests a potential role of Nsp2 in linking viral transcription within the viral replication-transcription complexes (RTC) to the translation initiation of the viral message. Collectively, the structure reported here, combined with mutant interaction mapping, provides a foundation for functional studies of this evolutionary conserved coronavirus protein and may assist future drug design.

biophysics↗

bioRxiv2020

Dynamic and reversible remapping of network representations in an unchanging environment

In response to environmental changes, the medial entorhinal cortex alters its single-cell firing properties. This flexibility in neural coding is hypothesized to support navigation and memory by dividing sensory experience into unique contextual episodes. However, it is unknown how the entorhinal circuit transitions between different representations, particularly when sensory information is not delineated into discrete contexts. Here, we describe spontaneous and abrupt transitions between multiple spatial maps of an unchanging task and environment. These remapping events were synchronized across hundreds of medial entorhinal neurons and correlated with changes in running speed. While remapping altered spatial coding in individual neurons, we show that features of the environment were statistically preserved at the population-level, enabling simple decoding strategies. These findings provoke a reconsideration of how medial entorhinal cortex dynamically represents space and broadly suggest a remarkable capacity for higher-order cortical circuits to rapidly and substantially reorganize their neural representations.

neuroscience↗

bioRxiv2020

Distinct algorithms for combining landmarks and path integration in medial entorhinal, visual andretrosplenial cortex

During navigation, animals estimate their position using path integration and landmarks, engaging many brain areas. Whether these areas follow specialized or universal cue integration principles remains unknown. Here, we combined electrophysiology with virtual reality to quantify cue integration across thousands of neurons in three areas that support navigation: primary visual (V1), retrosplenial (RSC) and medial entorhinal cortex (MEC). Path integration influenced position estimates in MEC more than in V1 and RSC. V1 coded position retrospectively, likely reflecting delays in sensory processing, whereas MEC coded position prospectively, and RSC was intermediate between the two. In combining path integration with landmarks, MEC showed signatures of Kalman filtering, and we report a distance-tuned neural population that could implement such filtering through attractor dynamics. Our results show that during navigation, MEC serves as a specialized cortical hub for reconciling path integration and landmarks to estimate position and suggest an algorithm for calculating these estimates.

neuroscience↗

bioRxiv2020

Enhancing SNR and generating contrast for cryo-EM images with convolutional neural networks

In cryogenic electron microscopy (cryo-EM) of radiation-sensitive biological samples, both the signal-to-noise ratio (SNR) and the contrast of images are critically important in the image processing pipeline. Classic methods improve low-frequency image contrast experimentally, by imaging with high defocus, or computationally, by applying various types of low-pass filter. These contrast improvements typically come at the expense of high-frequency SNR, which is suppressed by high-defocus imaging and removed by low pass filtration. Here, we demonstrate that a convolutional neural network (CNN) denoising algorithm can be used to significantly enhance SNR and generate contrast in cryo-EM images. We provide a quantitative evaluation of bias introduced by the denoising procedure and its influences on image processing and three-dimensional reconstructions. Our study suggests that besides enhancing the visual contrast of cryo-EM images, the enhanced SNR of denoised images may facilitate better outcomes in the other parts of the image processing pipeline, such as classification and 3D alignment. Overall, our results provide a ground of using denoising CNNs in the cryo-EM image processing pipeline.

biophysics↗