Biology subjects

Anderson, R. J.

Publications and source records attributed to Anderson, R. J..

16 recordsLinked to original sources

bioRxiv2026

Calibration-Aware and Interpretable Graph Learning for Multi-Cohort Diffusion Connectome Brain-Age Modeling

Brain-age models derived from diffusion MRI-based structural connectomes may provide imaging biomarkers of accelerated brain aging, but their biological interpretation and transportability across heterogeneous populations remain uncertain. We developed a calibration-aware and hierarchically interpretable graph-learning framework and evaluated it across four independent aging and Alzheimer's disease-related cohorts: ADNI, Duke/UNC ADRC, HABS-HD, and AD-DECODE. The analysis included 1,093 connectome sessions from 789 participants. Cohort-specific graph neural networks were trained using participant-grouped cross-validation across five imaging and multimodal feature configurations. Prediction performance varied more strongly across cohorts than across feature sets, with imaging-only out-of-fold mean absolute error ranging from 4.72 years in ADNI to 9.75 years in AD-DECODE. The imaging-only graph neural network was competitive with ridge, elastic-net, and gradient-boosted regression models trained on matched vectorized connectome features, but was not uniformly superior. Age-bias-corrected brain-age gap was most consistently associated with reduced diffusion-derived microstructural integrity and structural-network organization across cohorts. In longitudinal analyses, corrected brain-age gap showed moderate-to-good within-person preservation in ADNI and HABS-HD, with intraclass correlation coefficients of 0.67 and 0.81, respectively; higher baseline values also predicted subsequent microstructural and network deterioration in ADNI. Multiscale SHAP analysis identified distributed contributions from global graph topology, regional imaging features, edge-derived regional summaries, and individual structural connections involving thalamic, striatal, frontal, parietal, cerebellar, hippocampal, and entorhinal circuitry. External transfer was highly sensitive to cohort shift: across 12 off-diagonal train-test evaluations, median mean absolute error decreased from 17.39 to 8.22 years after target-cohort linear recalibration, whereas median Pearson correlation remained 0.17. Because recalibration used target-cohort chronological age, it was interpreted as a diagnostic sensitivity analysis rather than deployable external validation. Together, these findings support calibration-aware diffusion-connectome brain age as an interpretable imaging biomarker of structural brain aging and prospective microstructural and network vulnerability, while emphasizing the need for cohort-specific calibration before external application.

neuroscience↗

bioRxiv2026

Porphyrin driven redox tuning in structurally defined de novo heme proteins

Designing redox proteins with predictable and tuneable electron transfer properties is a major goal in de novo bioenergetics. Here we show that replacing heme B with a series of structurally conservative non-natural metalloporphyrins enables broad modulation of redox potentials over 400 mV in the de novo designed monoheme m4D2 and diheme 4D2 T19D. The non-natural porphyrins bind with high affinity and do not compromise either the heme binding site or global protein structure, as evidenced by X-ray crystallography and NMR spectroscopy. We also report the native-like NMR structure of m4D2 loaded with the non-natural and symmetric iron 2,4-dimethyldeuteroporphyrin IX, confirming our modular approach to tetrahelical redox protein design. This work establishes a versatile platform for constructing tuneable electron carriers for engineered bioenergetic pathways and bioelectronic applications.

biochemistry↗

bioRxiv2026

Optimization of a liver Trm cell-inducing mRNA vaccine by reduction of type I interferon response

CD8+ tissue-resident memory T cells provide rapid frontline protection at pathogen invasion sites, making them attractive targets for vaccine-mediated immunity. We previously developed an NKT cell-adjuvanted mRNA lipoplex vaccine capable of inducing liver Trm cells and sterile protection against malaria in mice. Here, we show that type I interferon (IFN-I) signalling through dendritic cells -- not T cells -- is a key brake on liver Trm induction by this vaccine. Optimising mRNA manufacturing to reduce immunostimulatory contaminants substantially dampened IFN-I production, boosted antigen expression in the lymphoid tissues, and drove significantly greater Trm accumulation. These enhanced responses translated into superior protection against parasite challenge. Our findings identify DC-intrinsic IFN-I signalling as a tractable target, and mRNA manufacturing quality as a critical and underappreciated lever, for maximising Trm-based vaccine efficacy.

immunology↗

bioRxiv2026

Temporal expression of liver-stage malaria antigens shapes vaccine efficacy

Vaccine-induced cytotoxic T cells can prevent malaria by killing parasite-infected hepatocytes during the liver stage. While several antigenic targets have been identified, little consideration has been given to their temporal expression. Here, we identified SERA1 of Plasmodium berghei as a late liver-stage target in rodent malaria and further showed that the classic vaccine antigen thrombospondin-related adhesion protein (TRAP) is only an early target. While vaccination with either antigen alone was modestly protective, combining these antigens enabled killing over the entire liver-stage, greatly improving efficacy. Given the relatively long liver-stage in human malaria, our findings imply TRAP-dependent vaccines likely utilize only a small proportion of the available liver-stage to eradicate parasites. Our findings further indicate that considerations of temporal coverage when selecting vaccine antigens will improve efficacy. One-Sentence SummaryTemporally defining presentation of liver-stage antigens informs rational combinations that maximize malaria vaccine efficacy.

immunology↗

bioRxiv2025

Smelling the Risk: Early Olfactory Deficits, Brain Networks, and Blood Markers of Alzheimers Disease Risk in Humanized APOE Mice

Olfactory impairment is a hallmark of early Alzheimers disease (AD), but the underlying mechanisms connecting sensory decline to genetic and environmental risk factors remain unclear. Our integrative analysis combines ethologically relevant olfactory behavior assays, high-resolution diffusion MRI connectomics, and blood transcriptomics in a large cohort of humanized APOE mice stratified by APOE genotype (APOE2, APOE3, APOE4), age, sex, high-fat diet, and immune background (HN). Behaviorally, APOE4 mice exhibited accelerated deficits in odor salience, novelty detection, and memory, especially when exposed to a high-fat diet, whereas APOE2 mice showed resilience (ANOVA: APOE x HN, F(2,1669)=77.25, p<0.001, eta squared effect size = 0.08). Notably, age and diet exerted compounding effects, with older and HFD-fed mice displaying reduced odor-guided exploration (diet x age: F(1,1669)=16.04, p<0.001, eta squared effect size = 0.01). Memory analyses revealed robust genotype- and age-dependent impairments: at 24- and 48-hour delays, recognition indices were significantly lower in APOE4 mice compared to APOE2 (long-term memory: APOE x HN, F(2,395)=5.6, p=0.004). Elastic Net-regularized multi-set canonical correlation analysis (MCCA) linked behavior to brain network substrates, revealing subnetworks whose connectivity explained up to 24 percent of behavioral variance (sum of canonical correlations: 1.27, 95% CI [1.18, 1.85], p<0.0001). High-weighted connections between the ventral orbital cortex, somatosensory cortex, and cerebellar-brainstem pathways were identified as critical nodes for risk or compensation. Integrative blood transcriptomics revealed eigengene modules strongly correlated with imaging changes in olfactory-memory circuits (for example, eigengene 2 vs. subiculum diffusivity: r = -0.5, p < 1e-30, explaining up to 24 percent of variance). Gene ontology analysis pinpointed shared pathways in synaptic signaling, translation, and metabolic regulation across brain and blood. Notably, glutamatergic and synaptic pathways were enriched among genes linking peripheral and central compartments. Collectively, these results demonstrate that olfactory behavior, quantitatively shaped by genotype, age, diet, and immune status, serves as a sensitive and translatable early biomarker of Alzheimers disease risk. Our systems-level approach identifies specific brain networks and peripheral molecular signatures underlying sensory-cognitive vulnerability, providing a robust framework for early detection and targeted intervention in AD.

neuroscience↗

bioRxiv2025

Neuroimaging Biomarkers of Neuroprotection: Impact of Voluntary versus Enforced Exercise in Alzheimers Disease Models

Exercise is a promising strategy for preventing or delaying Alzheimers disease (AD), yet its mechanisms remain unclear. We investigated how exercise influences brain structure, function, and behavior in a familial AD model. Mice underwent voluntary, voluntary plus enforced exercise, or remained sedentary. Neuroimaging included in vivo manganese-enhanced MRI (MEMRI). perfusion, and ex vivo diffusion MRI to assess morphometry, activity, cerebral blood flow (CBF), microstructural integrity and connectivity. Both exercise regimens induced structural and functional brain adaptations while reducing anhedonia. Voluntary exercise increased cortical and limbic volumes, particularly in the hippocampus, cingulate, and entorhinal cortex, supporting cognitive and emotional regulation. Adding enforced exercise influenced subcortical and sensory regions, including visual, motor and associative areas, supporting sensory-motor integration. MEMRI revealed increased activity in sensorimotor, limbic, and associative cortices, with voluntary exercise enhancing limbic and associative regions, and enforced exercise strengthening sensorimotor and subcortical circuits. White matter integrity improved in memory-associate pathways such as the corpus callosum, cingulum, and hippocampal commissure. Synaptic remodeling was observed in the cingulate cortex, anterior thalamic nuclei, and amygdala. Voluntary exercise enhanced CBF in the motor cortex and hippocampus, while enforced exercise limited these increases. Connectivity analyses revealed exercise-responsive networks spanning the cingulate cortex, entorhinal cortex, anterior thalamic nuclei, and basolateral amygdala, and associated tracts. Graph analyses linked running distance with increased thalamic, brainstem, and cerebellar connectivity, associating exercise intensity with plasticity. These findings highlight the ability of chronic exercise to modulate neuroimaging biomarkers through distinct but complementary pathways, reinforcing its potential as a neuroprotective intervention for AD. HighlightsO_LIExercise alters MRI biomarkers via distinct and partially overlapping mechanisms. C_LIO_LIVoluntary exercise boosts cortical and limbic regions for emotion and cognition. C_LIO_LIEnforced exercise strengthens subcortical and sensory areas for motor control. C_LIO_LIFA increases suggest memory tract reinforcement and grey matter remodelling. C_LIO_LIGraph analysis reveals plasticity in memory, emotion, and reward circuits. C_LI

neuroscience↗

bioRxiv2025

Network Biomarkers of Alzheimer's Disease Risk Derived from Joint Volume and Texture Covariance Patterns in Mouse Models

Alzheimers disease (AD) lacks effective cures and is typically detected after substantial pathological changes have occurred, making intervention challenging. Early detection and understanding of risk factors and their downstream effects are therefore crucial. Animal models provide valuable tools to study these prodromal stages. We investigated various levels of genetic risk for AD using mice expressing the three major human APOE alleles in place of mouse APOE. We leverage these mouse models utilizing high-resolution magnetic resonance diffusion imaging, due to its ability to provide multiple parameters that can be analysed jointly. We examine how APOE genotype interacts with age, sex, diet, and immunity to yield jointly discernable changes in regional brain volume and fractional anisotropy, a sensitive metric for brain water diffusion. Our results demonstrate that genotype strongly influences the caudate putamen, pons, cingulate cortex, and cerebellum, while sex affects the amygdala and piriform cortex bilaterally. Immune status impacts numerous regions, including the parietal association cortices, thalamus, auditory cortex, V1, and bilateral dentate cerebellar nuclei. Risk factor interactions particularly affect the amygdala, thalamus, and pons. APOE2 mice on a regular diet exhibited the fewest temporal changes, suggesting resilience, while APOE3 mice showed minimal effects from a high-fat diet (HFD). HFD amplified aging effects across multiple brain regions. The interaction of AD risk factors, including diet, revealed significant changes in the periaqueductal gray, pons, amygdala, inferior colliculus, M1, and ventral orbital cortex. Future studies should investigate the mechanisms underlying these coordinated changes in volume and texture, potentially by examining network similarities in gene expression and metabolism, and their relationship to structural pathways involved in neurodegenerative disease progression.

neuroscience↗

bioRxiv2023

Feature attention graph neural network for estimating brain age and identifying important neural connections in mouse models of genetic risk for Alzheimer's disease

Alzheimers disease (AD) remains one of the most extensively researched neurodegenerative disorders due to its widespread prevalence and complex risk factors. Age is a crucial risk factor for AD, which can be estimated by the disparity between physiological age and estimated brain age. To model AD risk more effectively, integrating biological, genetic, and cognitive markers is essential. Here, we utilized mouse models expressing the major APOE human alleles and human nitric oxide synthase 2 to replicate genetic risk for AD and a humanized innate immune response. We estimated brain age employing a multivariate dataset that includes brain connectomes, APOE genotype, subject traits such as age and sex, and behavioral data. Our methodology used Feature Attention Graph Neural Networks (FAGNN) for integrating different data types. Behavioral data were processed with a 2D Convolutional Neural Network (CNN), subject traits with a 1D CNN, brain connectomes through a Graph Neural Network using quadrant attention module. The model yielded a mean absolute error for age prediction of 31.85 days, with a root mean squared error of 41.84 days, outperforming other, reduced models. In addition, FAGNN identified key brain connections involved in the aging process. The highest weights were assigned to the connections between cingulum and corpus callosum, striatum, hippocampus, thalamus, hypothalamus, cerebellum, and piriform cortex. Our study demonstrates the feasibility of predicting brain age in models of aging and genetic risk for AD. To verify the validity of our findings, we compared Fractional Anisotropy (FA) along the tracts of regions with the highest connectivity, the Return-to-Origin Probability (RTOP), Return-to-Plane Probability (RTPP), and Return-to-Axis Probability (RTAP), which showed significant differences between young, middle-aged, and old age groups. Younger mice exhibited higher FA, RTOP, RTAP, and RTPP compared to older groups in the selected connections, suggesting that degradation of white matter tracts plays a critical role in aging and for FAGNNs selections. Our analysis suggests a potential neuroprotective role of APOE2, relative to APOE3 and APOE4, where APOE2 appears to mitigate age-related changes. Our findings highlighted a complex interplay of genetics and brain aging in the context of AD risk modeling.

bioengineering↗

bioRxiv2023

CAG repeat expansions create splicing acceptor sites and produce aberrant repeat-containing RNAs

Expansions of CAG trinucleotide repeats cause several rare neurodegenerative diseases. The disease-causing repeats are translated in multiple reading frames, without an identifiable initiation codon. The molecular mechanism of this repeat-associated non-AUG (RAN) translation is not known. We find that expanded CAG repeats create new splice acceptor sites. Splicing of proximal donors to the repeats produces unexpected repeat-containing transcripts. Upon splicing, depending on the sequences surrounding the donor, CAG repeats may become embedded in AUG-initiated open reading frames. Canonical AUG-initiated translation of these aberrant RNAs accounts for proteins that are attributed to RAN translation. Disruption of the relevant splice donors or the in-frame AUG initiation codons is sufficient to abrogate RAN translation. Our findings provide a molecular explanation for the abnormal translation products observed in CAG trinucleotide repeat expansion disorders and add to the repertoire of mechanisms by which repeat expansion mutations disrupt cellular functions.

molecular biology↗

bioRxiv2023

APOE, Immune Factors, Sex, and Diet Interact to Shape Brain Networks in Mouse Models of Aging

Alzheimers disease (AD) presents complex challenges due to its multifactorial nature, poorly understood etiology, and late detection. The mechanisms through which genetic, fixed and modifiable risk factors influence susceptibility to AD are under intense investigation, yet the impact of unique risk factors on brain networks is difficult to disentangle, and their interactions remain unclear. To model multiple risk factors including APOE genotype, age, sex, diet, and immunity we leveraged mice expressing the human APOE and NOS2 genes, conferring a reduced immune response compared to mouse Nos2. Employing graph analyses of brain connectomes derived from accelerated diffusion-weighted MRI, we assessed the global and local impact of risk factors in the absence of AD pathology. Aging and a high-fat diet impacted extensive networks comprising AD-vulnerable regions, including the temporal association cortex, amygdala, and the periaqueductal gray, involved in stress responses. Sex impacted networks including sexually dimorphic regions (thalamus, insula, hypothalamus) and key memory-processing areas (fimbria, septum). APOE genotypes modulated connectivity in memory, sensory, and motor regions, while diet and immunity both impacted the insula and hypothalamus. Notably, these risk factors converged on a circuit comprising 63 of 54,946 total connections (0.11% of the connectome), highlighting shared vulnerability amongst multiple AD risk factors in regions essential for sensory integration, emotional regulation, decision making, motor coordination, memory, homeostasis, and interoception. These network-based biomarkers hold translational value for distinguishing high-risk versus low-risk participants at preclinical AD stages, suggest circuits as potential therapeutic targets, and advance our understanding of network fingerprints associated with AD risk. Significance StatementCurrent interventions for Alzheimers disease (AD) do not provide a cure, and are delivered years after neuropathological onset. Addressing the impact of risk factors on brain networks holds promises for early detection, prevention, and revealing putative therapeutic targets at preclinical stages. We utilized six mouse models to investigate the impact of factors, including APOE genotype, age, sex, immunity, and diet, on brain networks. Large structural connectomes were derived from high resolution compressed sensing diffusion MRI. A highly parallelized graph classification identified subnetworks associated with unique risk factors, revealing their network fingerprints, and a common network composed of 63 connections with shared vulnerability to all risk factors. APOE genotype specific immune signatures support the design of interventions tailored to risk profiles.

neuroscience↗

bioRxiv2023

A taxonomically informed DNA reference library to facilitate future biodiversity assessments and monitoring: a case study using seaweeds along a tropical-temperate transition zone in South Africa

The role of seaweeds in the blue bioeconomy has stimulated research efforts around the world but proper species identification and biodiversity assessments, remain a challenge. The South African coast hosts the confluence of the Indian Ocean and Atlantic Ocean, creating a dynamic evolutionary environment that has over time yielded a rich diversity of seaweeds with the highest seaweed diversity occurring along the Agulhas Marine Province. Although South Africa harbours one of the richer seaweed floras in the world, only 24% of the known species are represented by DNA barcodes. We therefore initiated the construction of a taxonomically guided DNA reference library for seaweeds in South Africa with the aim of continuously adding to it in the future. To do this, a seaweed biodiversity survey of the Rhodophyta occurring along a temperate-tropical biogeographic transition zone situated within the Agulhas Marine Province (AMP) in South Africa was carried out. Seaweeds were identified in the field using available field or taxonomic guides and herbarium vouchers were prepared. Subsamples were preserved for DNA analyses and three DNA barcodes (LSU D2-D3; rbcL-3P; COI 5P) were amplified. Sequences were verified on BLAST and preliminary phylogenetic analyses or comparison with the literature were carried out where necessary. A total of 220 barcodes was generated for 88 species and one species variety, including 17 species from or near their type localities and eight generitypes. Novel barcodes were generated for 73 species, nearly half of which were species endemic to Southern Africa. In addition, 21 taxa representing new, potentially new, or reinstated species and at least two new genera were identified as well as one new distribution recorded, all of which require further study. This study significantly adds to the foundational biodiversity knowledge of the South African seaweed flora and highlights new avenues for further research.

molecular biology↗

bioRxiv2022

Genetic Substrates of Brain Vulnerability and Resilience in APOE2 Mice Transitioning from Midlife to Old Age

Understanding the interplay between genotype, age, and sex has potential to reveal factors that determine the switch between successful and pathological aging. APOE allelic variation modulate brain vulnerability and cognitive resilience during aging and Alzheimer disease (AD). The APOE4 allele confers the most risk and has been extensively studied with respect to the control APOE3 allele. The APOE2 allele has been less studied, and the mechanisms by which it confers cognitive resilience and neuroprotection remain largely unknown. Using mouse models with targeted replacement of the murine APOE gene with the human major APOE2 alleles we sought to identify changes during a critical period of middle to old age transition, in a mouse model of resilience to AD. Age but not female sex was important in modulating learning and memory estimates based on Morris water maze metrics. A small but significant 3% global brain atrophy due to aging was reflected by regional atrophy in the cingulate cortex 24, fornix and hippocampal commissure (>9%). Females had larger regional volumes relative to males for the bed nucleus of stria terminalis, subbrachial nucleus, postsubiculum (~10%), and claustrum (>5%), while males had larger volumes for the orbitofrontal cortex, frontal association cortex, and the longitudinal fasciculus of pons (>9%). Age promoted atrophy in both white (anterior commissure, corpus callosum, etc.), and gray matter, in particular the olfactory cortex, frontal association area 3, thalamus, hippocampus and cerebellum. A negative age by sex interaction was noted for the olfactory areas, piriform cortex, amygdala, ventral hippocampus, entorhinal cortex, and cerebellum, suggesting faster decline in females. Fractional anisotropy indicated an advantage for younger females for the cingulate cortex, insula, dorsal thalamus, ventral hippocampus, amygdala, visual and entorhinal cortex, and cerebellum, but there was faster decline with age. Interestingly white matter tracts were largely spared in females during aging. We used vertex screening to find associations between connectome and traits such as age and sex, and sparse multiple canonical correlation analysis to integrate our analyses over connectomes, traits, and RNA-seq. Brain subgraphs favored in males included the secondary motor cortex and superior cerebellar peduncle, while those for females included hippocampus and primary somatosensory cortex. Age related connectivity loss affected the hippocampus and primary somatosensory cortex. We validated these subgraphs using neural networks, showing increased accuracy for sex prediction from 81.9% when using the whole connectome as a predictor, to 94.28% when using the subgraphs estimated through vertex screening. Transcriptomic analyses revealed the largest fold change (FC) for age related genes was for Cpt1c (log2FC = 7.1), involved in transport of long-chain fatty acids into mitochondria and neuronal oxidative metabolism. Arg1, a critical regulator of innate and adaptive immune responses (log2FC = 4.9) also showed age specific changes. Amongst the sex related genes, the largest FC were observed for Maoa (log2FC = 4.9) involved in the degradation of the neurotransmitters serotonin, epinephrine, norepinephrine, and dopamine, and implicated in response to stress. Four genes were common for age and sex related vulnerability: Myo1e (log2FC = -1.5), Creld2 (log2FC = 1.4), Ptprt (log2FC = 2.9), and Pex1 (log2FC = 3.6). We tested whether blood gene expression help track phenotype changes with age and sex. Genes with the highest weight after connectome filtering included Ankzfp1 with a role in maintaining mitochondrial integrity under stress, as well as Pex1, Cep250, Nat14, Arg1, and Rangrf. Connectome filtered genes pointed to pathways relate to stress response, transport, and metabolic processes. Our modeling approaches using sparse canonical correlation analysis help relate quantitative traits to vulnerable brain networks, and blood markers for biological processes. Our study shows the APOE2 impact on neurocognition, brain networks, and biological pathways during a critical middle to old age transition in an animal model of resilience. Identifying changes in vulnerable brain and gene networks and markers of resilience may help reveal targets for therapies that support successful aging.

neuroscience↗

bioRxiv2022

Vulnerable Brain Networks Associated with Risk for Alzheimer's Disease

Brain connectomes provide untapped potential for identifying individuals at risk for Alzheimers disease (AD), and can help provide novel targets based on selective circuit vulnerability. Age, APOE4 genotype, and female sex are thought to contribute to the selective vulnerability of brain networks in Alzheimers disease, in a manner that differentiates pathological versus normal aging. These brain networks may predict pathology otherwise hard to detect, decades before overt disease manifestation and cognitive decline. Uncovering network based biomarkers at prodromal, asymptomatic stages may offer new windows of opportunity for interventions, either therapeutic or preventive. We used a sample of 72 people across the age span to model the relationship between Alzheimers disease risk and vulnerable brain networks. Sparse Canonical Correlation analysis (SCCA) revealed relationships between brain subgraphs and AD risk, with bootstrap based confidence intervals. When constructing a composite AD risk factor based on sex, age, genotype, the highest weight was associated with genotype. Next, we mapped networks associated with auditory, visual, and olfactory memory, and identified networks extending beyond the main nodes known to be involved in these functions. The inclusion of cognitive metrics in a composite risk factor pointed to vulnerable networks, and associated with the specific memory tests. These regions with the highest cumulative degree of connectivity in our studies were the pericalcarine, insula, banks of the superior sulcus and cerebellum. To help scale up our approach, we extended Tensor Network Principal Component Analysis (TNPCA) to evaluate AD risk related subgraphs, introducing CCA components and sparsity. When constructing a composite AD risk factor based on sex, age, and genotype, and family risk factor the most significant risk was associated with age. Our sparse regression based predictive models revealed vulnerable networks associated with known risk factors. The prediction error was 17% for genotype, 24% for family risk factor, and 5 years for age. Age prediction in groups including MCI and AD subjects involved several regions that were not prominent for age prediction otherwise. These regions included the middle and transverse temporal, paracentral and superior banks of temporal sulcus, as well as the amygdala and parahippocampal gyrus. The joint estimation of AD risk and connectome based mappings involved the cuneus, temporal, and cingulate cortices known to be associated with AD, and add new candidates, such as the cerebellum, whose role in AD is to be understood. Our predictive modeling approaches for AD risk factors represent a stepping stone towards single subject prediction, based on distances from normative graphs.

neuroscience↗

bioRxiv2022

Absolute Winding Number Differentiates Spatial Navigation Strategies with Genetic Risk for Alzheimer's Disease

Spatial navigation and orientation are emerging as promising markers for altered cognition in prodromal Alzheimers disease, and even in cognitively normal individuals at risk for Alzheimers disease. The different APOE gene alleles confer various degrees of risk. The APOE2 allele is considered protective, APOE3 is seen as control, while APOE4 carriage is the major known genetic risk for Alzheimers disease. We have used mouse models carrying the three humanized APOE alleles and tested them in a spatial memory task in the Morris water maze. We introduce a new metric, the absolute winding number, to characterize the spatial search strategy, through the shape of the swim path. We show that this metric is robust to noise, and works for small group samples. Moreover, the absolute winding number better differentiated APOE3 carriers, through their straighter swim paths relative to both APOE2 and APOE4 genotypes. Finally, this novel metric was sensitive to sex differences, supporting increased vulnerability in females. We hypothesized differences in spatial memory and navigation strategies are linked to differences in brain networks, and showed that different genotypes have different reliance on the hippocampal and caudate putamen circuits, pointing to a role for white matter connections. Moreover, differences were most pronounced in females. This departure from a hippocampal centric to a brain network approach may open avenues for identifying regions linked to increased risk for Alzheimers disease, before overt disease manifestation. Further exploration of novel biomarkers based on spatial navigation strategies may enlarge the windows of opportunity for interventions. The proposed framework will be significant in dissecting vulnerable circuits associated with cognitive changes in prodromal Alzheimers disease.

neuroscience↗

bioRxiv2021

Rigidifying a de novo enzyme increases activity and induces a negative activation heat capacity

Conformational sampling profoundly impacts the overall activity and temperature dependence of enzymes. Peroxidases have emerged as versatile platforms for high value biocatalysis owing to their broad palette of potential biotransformations. Here, we explore the role of conformational sampling in mediating a de novo peroxidases activity. We demonstrate that 2,2,2-triflouoroethanol (TFE) affects the equilibrium of enzyme conformational states, tending towards a more globally rigid structure. This is correlated with increases both stability and activity. Notably, these effects are concomitant with the emergence of curvature in the temperature-activity profile, trading off activity gains at ambient temperature with losses at high temperatures. We apply macromolecular rate theory (MMRT) to understand enzyme temperature dependence data. These data point to an increase in protein rigidity associated with a difference in the distribution of protein dynamics between the ground and transition state. We compare the thermodynamics of the de novo enzyme activity to those of a natural peroxidase, horseradish peroxidase. We find that the native enzyme resembles the rigidified de novo enzyme in terms of the thermodynamics of enzyme catalysis and the putative distribution of protein dynamics between the ground and transition state. The addition of TFE apparently causes C45 to behave more like the natural enzyme. Our data suggest robust, generic strategies for improving biocatalytic activity by manipulating protein rigidity; for functional de novo protein catalysts in particular, this can provide more enzyme-like catalysts without further rational engineering, computational redesign or directed evolution.

biochemistry↗

bioRxiv2019

Node-Specific Heritability in the Mouse Connectome

Genome-wide association studies have demonstrated significant links between human brain structure and common DNA variants. Similar studies with rodents have been challenging because of smaller brain volumes. Using high field MRI (9.4T) and compressed sensing, we have achieved microscopic resolution and sufficiently high throughput for rodent population studies. We generated whole brain structural MRI and diffusion connectomes for four diverse isogenic lines of mice (C57BL/6J, DBA/2J, CAST/EiJ, and BTBR) at spatial resolution 20,000 times higher than human connectomes. We derived volumes, scalar diffusion metrics, and estimates of residual technical error for 166 regions in each hemisphere and connectivity between the regions. Volumes of discrete brain regions had the highest mean heritability (0.71 {+/-} 0.23 SD, n = 332), followed by fractional anisotropy (0.54 {+/-} 0.26), radial diffusivity (0.34 {+/-} 0.022), and axial diffusivity (0.28 {+/-} 0.19). Connection profiles were statistically different in 280 of 322 nodes across all four strains. Nearly 150 of the connection profiles were statistically different between the C57BL/6J, DBA/2J, and CAST/EiJ lines.

neuroscience↗