EGFR Ligands Differentially Stabilize Receptor Dimers to Specify Signaling Kinetics.

Epidermal growth factor receptor (EGFR) regulates many crucial cellular programs, with seven different activating ligands shaping cell signaling in distinct ways. Using crystallography and other approaches, we show how the EGFR ligands epiregulin (EREG) and epigen (EPGN) stabilize different dimeric conformations of the EGFR extracellular region. As a consequence, EREG or EPGN induce less stable EGFR dimers than EGF-making them partial agonists of EGFR dimerization. Unexpectedly, this weakened dimerization elicits more sustained EGFR signaling than seen with EGF, provoking responses in breast cancer cells associated with differentiation rather than proliferation. Our results reveal how responses to different EGFR ligands are defined by receptor dimerization strength and signaling dynamics. These findings have broad implications for understanding receptor tyrosine kinase (RTK) signaling specificity. Our results also suggest parallels between partial and/or biased agonism in RTKs and G-protein-coupled receptors, as well as new therapeutic opportunities for correcting RTK signaling output.

Causal Influence of Visual Cues on Hippocampal Directional Selectivity.

Hippocampal neurons show selectivity with respect to visual cues in primates, including humans, but this has never been found in rodents. To address this long-standing discrepancy, we measured hippocampal activity from rodents during real-world random foraging. Surprisingly, ∼ 25% of neurons exhibited significant directional modulation with respect to visual cues. To dissociate the contributions of visual and vestibular cues, we made similar measurements in virtual reality, in which only visual cues were informative. Here, we found significant directional modulation despite the severe loss of vestibular information, challenging prevailing theories of directionality. Changes in the amount of angular information in visual cues induced corresponding changes in head-directional modulation at the neuronal and population levels. Thus, visual cues are sufficient for-and play a predictable, causal role in-generating directionally selective hippocampal responses. These results dissociate hippocampal directional and spatial selectivity and bridge the gap between primate and rodent studies.

Linking hippocampal multiplexed tuning, Hebbian plasticity and navigation.

Three major pillars of hippocampal function are spatial navigation1, Hebbian synaptic plasticity2 and spatial selectivity3. The hippocampus is also implicated in episodic memory4, but the precise link between these four functions is missing. Here we report the multiplexed selectivity of dorsal CA1 neurons while rats performed a virtual navigation task using only distal visual cues5, similar to the standard water maze test of spatial memory1. Neural responses primarily encoded path distance from the start point and the head angle of rats, with a weak allocentric spatial component similar to that in primates but substantially weaker than in rodents in the real world. Often, the same cells multiplexed and encoded path distance, angle and allocentric position in a sequence, thus encoding a journey-specific episode. The strength of neural activity and tuning strongly correlated with performance, with a temporal relationship indicating neural responses influencing behaviour and vice versa. Consistent with computational models of associative and causal Hebbian learning6,7, neural responses showed increasing clustering8 and became better predictors of behaviourally relevant variables, with the average neurometric curves exceeding and converging to psychometric curves. Thus, hippocampal neurons multiplex and exhibit highly plastic, task- and experience-dependent tuning to path-centric and allocentric variables to form episodic sequences supporting navigation.

The neuron as a direct data-driven controller.

In the quest to model neuronal function amid gaps in physiological data, a promising strategy is to develop a normative theory that interprets neuronal physiology as optimizing a computational objective. This study extends current normative models, which primarily optimize prediction, by conceptualizing neurons as optimal feedback controllers. We posit that neurons, especially those beyond early sensory areas, steer their environment toward a specific desired state through their output. This environment comprises both synaptically interlinked neurons and external motor sensory feedback loops, enabling neurons to evaluate the effectiveness of their control via synaptic feedback. To model neurons as biologically feasible controllers which implicitly identify loop dynamics, infer latent states, and optimize control we utilize the contemporary direct data-driven control (DD-DC) framework. Our DD-DC neuron model explains various neurophysiological phenomena: the shift from potentiation to depression in spike-timing-dependent plasticity with its asymmetry, the duration and adaptive nature of feedforward and feedback neuronal filters, the imprecision in spike generation under constant stimulation, and the characteristic operational variability and noise in the brain. Our model presents a significant departure from the traditional, feedforward, instant-response McCulloch-Pitts-Rosenblatt neuron, offering a modern, biologically informed fundamental unit for constructing neural networks.

Evidence of epistasis in regions of long-range linkage disequilibrium across five complex diseases in the UK Biobank and eMERGE datasets.

Leveraging linkage disequilibrium (LD) patterns as representative of population substructure enables the discovery of additive association signals in genome-wide association studies (GWASs). Standard GWASs are well-powered to interrogate additive models; however, new approaches are required for invesigating other modes of inheritance such as dominance and epistasis. Epistasis, or non-additive interaction between genes, exists across the genome but often goes undetected because of a lack of statistical power. Furthermore, the adoption of LD pruning as customary in standard GWASs excludes detection of sites that are in LD but might underlie the genetic architecture of complex traits. We hypothesize that uncovering long-range interactions between loci with strong LD due to epistatic selection can elucidate genetic mechanisms underlying common diseases. To investigate this hypothesis, we tested for associations between 23 common diseases and 5,625,845 epistatic SNP-SNP pairs (determined by Ohta's D statistics) in long-range LD (>0.25 cM). Across five disease phenotypes, we identified one significant and four near-significant associations that replicated in two large genotype-phenotype datasets (UK Biobank and eMERGE). The genes that were most likely involved in the replicated associations were (1) members of highly conserved gene families with complex roles in multiple pathways, (2) essential genes, and/or (3) genes that were associated in the literature with complex traits that display variable expressivity. These results support the highly pleiotropic and conserved nature of variants in long-range LD under epistatic selection. Our work supports the hypothesis that epistatic interactions regulate diverse clinical mechanisms and might especially be driving factors in conditions with a wide range of phenotypic outcomes.

Electronic health records and polygenic risk scores for predicting disease risk.

Accurate prediction of disease risk based on the genetic make-up of an individual is essential for effective prevention and personalized treatment. Nevertheless, to date, individual genetic variants from genome-wide association studies have achieved only moderate prediction of disease risk. The aggregation of genetic variants under a polygenic model shows promising improvements in prediction accuracies. Increasingly, electronic health records (EHRs) are being linked to patient genetic data in biobanks, which provides new opportunities for developing and applying polygenic risk scores in the clinic, to systematically examine and evaluate patient susceptibilities to disease. However, the heterogeneous nature of EHR data brings forth many practical challenges along every step of designing and implementing risk prediction strategies. In this Review, we present the unique considerations for using genotype and phenotype data from biobank-linked EHRs for polygenic risk prediction.

KRAGEN: a knowledge graph-enhanced RAG framework for biomedical problem solving using large language models.

MOTIVATION: Answering and solving complex problems using a large language model (LLM) given a certain domain such as biomedicine is a challenging task that requires both factual consistency and logic, and LLMs often suffer from some major limitations, such as hallucinating false or irrelevant information, or being influenced by noisy data. These issues can compromise the trustworthiness, accuracy, and compliance of LLM-generated text and insights. RESULTS: Knowledge Retrieval Augmented Generation ENgine (KRAGEN) is a new tool that combines knowledge graphs, Retrieval Augmented Generation (RAG), and advanced prompting techniques to solve complex problems with natural language. KRAGEN converts knowledge graphs into a vector database and uses RAG to retrieve relevant facts from it. KRAGEN uses advanced prompting techniques: namely graph-of-thoughts (GoT), to dynamically break down a complex problem into smaller subproblems, and proceeds to solve each subproblem by using the relevant knowledge through the RAG framework, which limits the hallucinations, and finally, consolidates the subproblems and provides a solution. KRAGEN's graph visualization allows the user to interact with and evaluate the quality of the solution's GoT structure and logic. AVAILABILITY AND IMPLEMENTATION: KRAGEN is deployed by running its custom Docker containers. KRAGEN is available as open-source from GitHub at: https://github.com/EpistasisLab/KRAGEN.

Artificial intelligence: revolutionizing cardiology with large language models.

Natural language processing techniques are having an increasing impact on clinical care from patient, clinician, administrator, and research perspective. Among others are automated generation of clinical notes and discharge letters, medical term coding for billing, medical chatbots both for patients and clinicians, data enrichment in the identification of disease symptoms or diagnosis, cohort selection for clinical trial, and auditing purposes. In the review, an overview of the history in natural language processing techniques developed with brief technical background is presented. Subsequently, the review will discuss implementation strategies of natural language processing tools, thereby specifically focusing on large language models, and conclude with future opportunities in the application of such techniques in the field of cardiology.

Better informing everyday fall risk assessment: experimental studies with contemporary technologies.

BACKGROUND: Age-related mobility issues and frailty are a major public health concern because of an increased risk of falls. Subjective assessment of fall risk in the clinic is limited, failing to account for an individual's habitual activities in the home or community. Equally, objective mobility trackers for use in the home and community lack extrinsic (ie, environmental) data capture to comprehensively inform fall risk. We propose a contemporary approach that combines artificial intelligence (AI) and video glasses to augment current methods of fall risk assessment. METHODS: Two case studies were performed to provide a framework to assess extrinsic factors within fall risk assessment via video glasses. The first was AI-based detection of environment and terrain type. We developed convolutional neural networks (CNN) via a bespoke dataset (>145 000 images) captured from different settings (eg, offices, high streets) via free-licenced video on social media. AI automated a textual description to uphold privacy while describing the scene (eg, indoor and carpet). In the second case study, we provided video glasses to participants within a university campus (two men, 17 women; aged 21-60 years) to capture data for automatically labelling environment and objects (eg, fall hazards) via a CNN object detection algorithm. The case studies ran from Dec 5, 2022, to March 24, 2023. FINDINGS: To date, results show promise for the efficient, and accurate AI-based approach to better inform fall risk. Each component of the framework achieved at least 75% accuracy across a range of walks (indoor and outdoor and multiple terrains) from a dataset of 6283 new images. The AI achieved a mean average precision score of 0·93 for the identification of fall risk hazards. INTERPRETATIONS: The AI-based approach provides a contemporary means to better inform fall risk while providing an ethical means to uphold privacy. The proposed approach could have significant implications for improving overall health and quality of life, enabling ageing in place through habitual data collection with contemporary wearables to decentralise fall risk assessment. A limitation was the lack of data collection on older adults within real world, unscripted settings. However, the next phase of this research is the deployment of the AI on real-world data from a cohort of more than 40 participants within UK-based homes. FUNDING: National Institute of Health and Care Research (NIHR) Applied Research Collaboration (ARC) North-East and North Cumbria (NENC), Faculty of Engineering and Environment at Northumbria University.

Aliro: an automated machine learning tool leveraging large language models.

MOTIVATION: Biomedical and healthcare domains generate vast amounts of complex data that can be challenging to analyze using machine learning tools, especially for researchers without computer science training. RESULTS: Aliro is an open-source software package designed to automate machine learning analysis through a clean web interface. By infusing the power of large language models, the user can interact with their data by seamlessly retrieving and executing code pulled from the large language model, accelerating automated discovery of new insights from data. Aliro includes a pre-trained machine learning recommendation system that can assist the user to automate the selection of machine learning algorithms and its hyperparameters and provides visualization of the evaluated model and data. AVAILABILITY AND IMPLEMENTATION: Aliro is deployed by running its custom Docker containers. Aliro is available as open-source from GitHub at: https://github.com/EpistasisLab/Aliro.

Optimization of the flip angles of narrow-band editing pulses in J-difference edited MRS of lactate at 3T.

PURPOSE: Application of highly selective editing RF pulses provides a means of minimizing co-editing of contaminants in J-difference MRS (MEGA), but it causes reduction in editing yield. We examined the flip angles (FAs) of narrow-band editing pulses to maximize the lactate edited signal with minimal co-editing of threonine. METHODS: The effect of editing-pulse FA on the editing performance was examined, with numerical and phantom analyses, for bandwidths of 17.6-300 Hz in MEGA-PRESS editing of lactate at 3T. The FA and envelope of 46 ms Gaussian editing pulses were tailored to maximize the lactate edited signal at 1.3 ppm and minimize co-editing of threonine. The optimized editing-pulse FA MEGA scheme was tested in brain tumor patients. RESULTS: Simulation and phantom data indicated that the optimum FA of MEGA editing pulses is progressively larger than 180° as the editing-pulse bandwidth decreases. For 46 ms long 17.6 Hz bandwidth Gaussian pulses and other given sequence parameters, the lactate edited signal was maximum at the first and second editing-pulse FAs of 241° and 249°, respectively. The edit-on and difference-edited lactate peak areas of the optimized FA MEGA were greater by 43% and 25% compared to the 180°-FA MEGA, respectively. In-vivo data confirmed the simulation and phantom results. The lesions of the brain tumor patients showed elevated lactate and physiological levels of threonine. CONCLUSION: The lactate MEGA editing yield is significantly increased with editing-pulse FA much larger than 180° when the editing-pulse bandwidth is comparable to the lactate quartet frequency width.

Ten simple rules for managing laboratory information.

Information is the cornerstone of research, from experimental (meta)data and computational processes to complex inventories of reagents and equipment. These 10 simple rules discuss best practices for leveraging laboratory information management systems to transform this large information load into useful scientific findings.

Preference matrix guided sparse canonical correlation analysis for mining brain imaging genetic associations in Alzheimer's disease.

Investigating the relationship between genetic variation and phenotypic traits is a key issue in quantitative genetics. Specifically for Alzheimer's disease, the association between genetic markers and quantitative traits remains vague while, once identified, will provide valuable guidance for the study and development of genetics-based treatment approaches. Currently, to analyze the association of two modalities, sparse canonical correlation analysis (SCCA) is commonly used to compute one sparse linear combination of the variable features for each modality, giving a pair of linear combination vectors in total that maximizes the cross-correlation between the analyzed modalities. One drawback of the plain SCCA model is that the existing findings and knowledge cannot be integrated into the model as priors to help extract interesting correlations as well as identify biologically meaningful genetic and phenotypic markers. To bridge this gap, we introduce preference matrix guided SCCA (PM-SCCA) that not only takes priors encoded as a preference matrix but also maintains computational simplicity. A simulation study and a real-data experiment are conducted to investigate the effectiveness of the model. Both experiments demonstrate that the proposed PM-SCCA model can capture not only genotype-phenotype correlation but also relevant features effectively.

Novel EDGE encoding method enhances ability to identify genetic interactions.

Assumptions are made about the genetic model of single nucleotide polymorphisms (SNPs) when choosing a traditional genetic encoding: additive, dominant, and recessive. Furthermore, SNPs across the genome are unlikely to demonstrate identical genetic models. However, running SNP-SNP interaction analyses with every combination of encodings raises the multiple testing burden. Here, we present a novel and flexible encoding for genetic interactions, the elastic data-driven genetic encoding (EDGE), in which SNPs are assigned a heterozygous value based on the genetic model they demonstrate in a dataset prior to interaction testing. We assessed the power of EDGE to detect genetic interactions using 29 combinations of simulated genetic models and found it outperformed the traditional encoding methods across 10%, 30%, and 50% minor allele frequencies (MAFs). Further, EDGE maintained a low false-positive rate, while additive and dominant encodings demonstrated inflation. We evaluated EDGE and the traditional encodings with genetic data from the Electronic Medical Records and Genomics (eMERGE) Network for five phenotypes: age-related macular degeneration (AMD), age-related cataract, glaucoma, type 2 diabetes (T2D), and resistant hypertension. A multi-encoding genome-wide association study (GWAS) for each phenotype was performed using the traditional encodings, and the top results of the multi-encoding GWAS were considered for SNP-SNP interaction using the traditional encodings and EDGE. EDGE identified a novel SNP-SNP interaction for age-related cataract that no other method identified: rs7787286 (MAF: 0.041; intergenic region of chromosome 7)-rs4695885 (MAF: 0.34; intergenic region of chromosome 4) with a Bonferroni LRT p of 0.018. A SNP-SNP interaction was found in data from the UK Biobank within 25 kb of these SNPs using the recessive encoding: rs60374751 (MAF: 0.030) and rs6843594 (MAF: 0.34) (Bonferroni LRT p: 0.026). We recommend using EDGE to flexibly detect interactions between SNPs exhibiting diverse action.

The Alzheimer's Knowledge Base: A Knowledge Graph for Alzheimer Disease Research.

BACKGROUND: As global populations age and become susceptible to neurodegenerative illnesses, new therapies for Alzheimer disease (AD) are urgently needed. Existing data resources for drug discovery and repurposing fail to capture relationships central to the disease's etiology and response to drugs. OBJECTIVE: We designed the Alzheimer's Knowledge Base (AlzKB) to alleviate this need by providing a comprehensive knowledge representation of AD etiology and candidate therapeutics. METHODS: We designed the AlzKB as a large, heterogeneous graph knowledge base assembled using 22 diverse external data sources describing biological and pharmaceutical entities at different levels of organization (eg, chemicals, genes, anatomy, and diseases). AlzKB uses a Web Ontology Language 2 ontology to enforce semantic consistency and allow for ontological inference. We provide a public version of AlzKB and allow users to run and modify local versions of the knowledge base. RESULTS: AlzKB is freely available on the web and currently contains 118,902 entities with 1,309,527 relationships between those entities. To demonstrate its value, we used graph data science and machine learning to (1) propose new therapeutic targets based on similarities of AD to Parkinson disease and (2) repurpose existing drugs that may treat AD. For each use case, AlzKB recovers known therapeutic associations while proposing biologically plausible new ones. CONCLUSIONS: AlzKB is a new, publicly available knowledge resource that enables researchers to discover complex translational associations for AD drug discovery. Through 2 use cases, we show that it is a valuable tool for proposing novel therapeutic hypotheses based on public biomedical knowledge.

Enhancing fall risk assessment: instrumenting vision with deep learning during walks.

BACKGROUND: Falls are common in a range of clinical cohorts, where routine risk assessment often comprises subjective visual observation only. Typically, observational assessment involves evaluation of an individual's gait during scripted walking protocols within a lab to identify deficits that potentially increase fall risk, but subtle deficits may not be (readily) observable. Therefore, objective approaches (e.g., inertial measurement units, IMUs) are useful for quantifying high resolution gait characteristics, enabling more informed fall risk assessment by capturing subtle deficits. However, IMU-based gait instrumentation alone is limited, failing to consider participant behaviour and details within the environment (e.g., obstacles). Video-based eye-tracking glasses may provide additional insight to fall risk, clarifying how people traverse environments based on head and eye movements. Recording head and eye movements can provide insights into how the allocation of visual attention to environmental stimuli influences successful navigation around obstacles. Yet, manual review of video data to evaluate head and eye movements is time-consuming and subjective. An automated approach is needed but none currently exists. This paper proposes a deep learning-based object detection algorithm (VARFA) to instrument vision and video data during walks, complementing instrumented gait. METHOD: The approach automatically labels video data captured in a gait lab to assess visual attention and details of the environment. The proposed algorithm uses a YoloV8 model trained on with a novel lab-based dataset. RESULTS: VARFA achieved excellent evaluation metrics (0.93 mAP50), identifying, and localizing static objects (e.g., obstacles in the walking path) with an average accuracy of 93%. Similarly, a U-NET based track/path segmentation model achieved good metrics (IoU 0.82), suggesting that the predicted tracks (i.e., walking paths) align closely with the actual track, with an overlap of 82%. Notably, both models achieved these metrics while processing at real-time speeds, demonstrating efficiency and effectiveness for pragmatic applications. CONCLUSION: The instrumented approach improves the efficiency and accuracy of fall risk assessment by evaluating the visual allocation of attention (i.e., information about when and where a person is attending) during navigation, improving the breadth of instrumentation in this area. Use of VARFA to instrument vision could be used to better inform fall risk assessment by providing behaviour and context data to complement instrumented e.g., IMU data during gait tasks. That may have notable (e.g., personalized) rehabilitation implications across a wide range of clinical cohorts where poor gait and increased fall risk are common.

Artificial Intelligence and Technology Collaboratories: Innovating aging research and Alzheimer's care.

This perspective outlines the Artificial Intelligence and Technology Collaboratories (AITC) at Johns Hopkins University, University of Pennsylvania, and University of Massachusetts, highlighting their roles in developing AI-based technologies for older adult care, particularly targeting Alzheimer's disease (AD). These National Institute on Aging (NIA) centers foster collaboration among clinicians, gerontologists, ethicists, business professionals, and engineers to create AI solutions. Key activities include identifying technology needs, stakeholder engagement, training, mentoring, data integration, and navigating ethical challenges. The objective is to apply these innovations effectively in real-world scenarios, including in rural settings. In addition, the AITC focuses on developing best practices for AI application in the care of older adults, facilitating pilot studies, and addressing ethical concerns related to technology development for older adults with cognitive impairment, with the ultimate aim of improving the lives of older adults and their caregivers. HIGHLIGHTS: Addressing the complex needs of older adults with Alzheimer's disease (AD) requires a comprehensive approach, integrating medical and social support. Current gaps in training, techniques, tools, and expertise hinder uniform access across communities and health care settings. Artificial intelligence (AI) and digital technologies hold promise in transforming care for this demographic. Yet, transitioning these innovations from concept to marketable products presents significant challenges, often stalling promising advancements in the developmental phase. The Artificial Intelligence and Technology Collaboratories (AITC) program, funded by the National Institute on Aging (NIA), presents a viable model. These Collaboratories foster the development and implementation of AI methods and technologies through projects aimed at improving care for older Americans, particularly those with AD, and promote the sharing of best practices in AI and technology integration. Why Does This Matter? The National Institute on Aging (NIA) Artificial Intelligence and Technology Collaboratories (AITC) program's mission is to accelerate the adoption of artificial intelligence (AI) and new technologies for the betterment of older adults, especially those with dementia. By bridging scientific and technological expertise, fostering clinical and industry partnerships, and enhancing the sharing of best practices, this program can significantly improve the health and quality of life for older adults with Alzheimer's disease (AD).

Genetic heterogeneity: Challenges, impacts, and methods through an associative lens.

Genetic heterogeneity describes the occurrence of the same or similar phenotypes through different genetic mechanisms in different individuals. Robustly characterizing and accounting for genetic heterogeneity is crucial to pursuing the goals of precision medicine, for discovering novel disease biomarkers, and for identifying targets for treatments. Failure to account for genetic heterogeneity may lead to missed associations and incorrect inferences. Thus, it is critical to review the impact of genetic heterogeneity on the design and analysis of population level genetic studies, aspects that are often overlooked in the literature. In this review, we first contextualize our approach to genetic heterogeneity by proposing a high-level categorization of heterogeneity into "feature," "outcome," and "associative" heterogeneity, drawing on perspectives from epidemiology and machine learning to illustrate distinctions between them. We highlight the unique nature of genetic heterogeneity as a heterogeneous pattern of association that warrants specific methodological considerations. We then focus on the challenges that preclude effective detection and characterization of genetic heterogeneity across a variety of epidemiological contexts. Finally, we discuss systems heterogeneity as an integrated approach to using genetic and other high-dimensional multi-omic data in complex disease research.

Food and Drug Administration Public Workshop Summary-Development Considerations of Antifungal Drugs to Address Unmet Medical Need.

Pressing challenges in the treatment of invasive fungal infections (IFIs) include emerging and rare pathogens, resistant/refractory infections, and antifungal armamentarium limited by toxicity, drug-drug interactions, and lack of oral formulations. Development of new antifungal drugs is hampered by the limitations of the available diagnostics, clinical trial endpoints, prolonged trial duration, difficulties in patient recruitment, including subpopulations (eg, pediatrics), and heterogeneity of the IFIs. On 4 August 2020, the US Food and Drug Administration convened a workshop that included IFI experts from academia, industry, and other government agencies to discuss the IFI landscape, unmet need, and potential strategies to facilitate the development of antifungal drugs for treatment and prophylaxis. This article summarizes the key topics presented and discussed during the workshop, such as incentives and research support for drug developers, nonclinical development, clinical trial design challenges, lessons learned from industry, and potential collaborations to facilitate antifungal drug development.

Animal pharmacokinetics/pharmacodynamics (PK/PD) infection models for clinical development of antibacterial drugs: lessons from selected cases.

In the wake of emerging antimicrobial resistance, antibacterial drug development has become more critical. At the same time, development of antibacterial drugs targeting specific pathogens or resistance phenotypes that may have low prevalence presents challenges because it is difficult to conduct large, randomized controlled trials for such drugs. Animal models have increasingly supported clinical development of antibacterials; however, more work is needed to optimize the design and application of these animal models to ensure clear and actionable translation to further human investigation. This review discusses recent case studies of animal infection models used to support antibacterial drug development in order to illuminate considerations for future development of novel antibacterial drugs.