Biology subjects

Hayes, R.

Publications and source records attributed to Hayes, R..

12 recordsLinked to original sources

bioRxiv2026

Processing strategies for improving cortical thickness correspondence between low-field and high-field MRI in young people

Portable low-field MRI systems are a promising complement to conventional high-field systems, enabling broader access to MRI. However, correspondence in cortical thickness estimates between low- and high-field MRI in young people remains limited despite its importance for neurodevelopment and psychopathology. To evaluate how multiple low-field image processing approaches improve cortical thickness correspondence with high-field MRI in a large sample of young individuals, we collected T1-weighted (T1w) and T2-weighted (T2w) data using both low-field (64mT) and high-field (3T) MRI within one week of each other from a community sample of young people. We applied deep learning-based super-resolution methods (SynthSR v1 and SynthSR v2) followed by recon-all, or other reconstruction methods (recon-all-clinical and recon-any), to low-field data acquired across multiple sequences (T1w and T2w) and orientations (axial, coronal, sagittal, and multi-orientation average), with and without resampling and/or co-registration. We assessed global, lobar, and regional cortical thickness correspondence with 3T MRI measures based on the Desikan-Killiany atlas using Pearson and intraclass correlations. We compared pipelines using Steiger's Z-tests and Fisher's Z-tests. A total of 150 individuals (mean age, 18.63+/-5.07; 80 female) were included. We observed the highest global correspondence with recon-all-clinical applied to coronal T1w images (r=0.40, pFDR=3.02e-05), which significantly exceeded the best global correspondence in our previous study (Fisher's Z=1.99, p=0.047). At the lobar and regional levels, multi-orientation T2w images processed with recon-all-clinical showed the highest correspondence across the greatest number of regions (4/12 lobes; 13/68 regions). This pipeline showed the highest correspondence and largest improvements relative to recon-all alone in frontal, cingulate, and temporal regions, including the right pars triangularis (r=0.52, pFDR=4.78e-11; Steiger's Z=4.78, pFDR=4.25e-06), right caudal anterior cingulate (r=0.47, pFDR=3.83e-09; Steiger's Z=5.46, pFDR=1.32e-07), and left parahippocampal regions (r=0.58, pFDR=2.98e-14; Steiger's Z=5.17, pFDR=6.01e-07). We observed significantly improved cortical thickness correspondence in low-field MRI in young people relative to recon-all alone and our prior work. The recon-all-clinical pipeline yielded moderate correspondence, particularly in frontal, cingulate, and temporal regions. Our results demonstrate a methodological improvement in the use of low-field MRI for assessing cortical thickness in young people, providing a quantitative benchmark for current tools in this area.

neuroscience↗

bioRxiv2025

Using a wearable EEG device to examine age trends in sleep macro- and micro-architecture across adolescence

Adolescence is a period of distinct maturational changes in sleep physiology. Age-related trends in sleep physiology have been captured using laboratory-based polysomnography, a method limited by logistical burden and high cost. We tested the ability of the accessible Dreem3 sleep EEG headband to replicate established age effects in sleep physiology from late childhood through early adulthood. Typically developing youth (N=100, 9-26 years) completed 3-4 consecutive nights of at-home sleep recording. We estimated age-related trends across eight macro-architecture and 15 micro-architecture variables with known age effects, and conducted exploratory analyses of 24 additional variables. Dreem replicated established age trends, including increases in non-rapid eye movement (NREM) stage 2, and decreases in N3, time in bed, NREM delta and theta power with increasing age. Exploratory analysis revealed age effects in twelve variables, including decreases in spindle activity with increasing age. Sleep EEG wearables offer an accessible way to characterize sleep physiology development.

neuroscience↗

bioRxiv2024

Accurate in silico predictions of modified RNA interactions to a prototypical RNA-binding protein with {lambda}-dynamics

RNA-binding proteins shape biology through their widespread functions in RNA biochemistry. Their function requires the recognition of specific RNA motifs for targeted binding. These RNA binding elements can be composed of both unmodified and chemically modified RNAs, of which over 170 chemical modifications have been identified in biology. Unmodified RNA sequence preferences for RNA-binding proteins have been widely studied, with numerous methods available to identify their preferred sequence motifs. However, only a few techniques can detect preferred RNA modifications, and no current method can comprehensively screen the vast array of hundreds of natural RNA modifications. Prior work demonstrated that {lambda}-dynamics is an accurate in silico method to predict RNA base binding preferences of an RNA-binding antibody. This work extends that effort by using {lambda}-dynamics to predict unmodified and modified RNA binding preferences of human Pumilio, a prototypical RNA binding protein. A library of RNA modifications was screened at eight nucleotide positions along the RNA to identify modifications predicted to affect Pumilio binding. Computed binding affinities were compared with experimental data to reveal high predictive accuracy. In silico force field accuracies were also evaluated between CHARMM and Amber RNA force fields to determine the best parameter set to use in binding calculations. This work demonstrates that {lambda}-dynamics can predict RNA interactions to a bona fide RNA-binding protein without the requirements of chemical reagents or new methods to experimentally test binding at the bench. Advancing in silico methods like {lambda}-dynamics will unlock new frontiers in understanding how RNA modifications shape RNA biochemistry.

biochemistry↗

bioRxiv2023

The association between cortical gyrification and sleep in adolescents and young adults.

Study objectivesHealthy sleep is important for adolescent neurodevelopment, and relationships between brain structure and sleep can vary in strength over this maturational window. Although cortical gyrification is increasingly considered a useful index for understanding cognitive and emotional outcomes in adolescence, and sleep is also a strong predictor of such outcomes, we know relatively little about associations between cortical gyrification and sleep. MethodsUsing Local gyrification index (lGI) of 34 bilateral brain regions and regularized regression for feature selection, we examined gyrification-sleep relationships in the Neuroimaging and Pediatric Sleep databank (252 participants; 9-26 years; 58.3% female) and identified developmentally invariant (stable across age) or developmentally specific (observed only during discrete age intervals) brain-sleep associations. Naturalistic sleep characteristics (duration, timing, continuity, and regularity) were estimated from wrist actigraphy. ResultsFor most brain regions, greater lGI was associated with longer sleep duration, earlier sleep timing, lower variability in sleep regularity, and shorter time awake after sleep onset. lGI in frontoparietal network regions showed associations with sleep patterns that were stable across age. However, in default mode network regions, lGI was only associated with sleep patterns from late childhood through early-to-mid adolescence, a period of vulnerability for mental health disorders. ConclusionsWe detected both developmentally invariant and developmentally specific ties between local gyrification and naturalistic sleep patterns. Default mode network regions may be particularly susceptible to interventions promoting more optimal sleep during childhood and adolescence.

neuroscience↗

bioRxiv2022

Microbial Risk Score for Capturing Microbial Characteristics, Integrating Multi-omics Data, and Predicting Disease Risk

BackgroundWith the rapid accumulation of microbiome-wide association studies, a great amount of microbiome data are available to study the microbiomes role in human disease and advance the microbiomes potential use for disease prediction. However, the unique features of microbiome data hinder its utility for disease prediction. MethodsMotivated from the polygenic risk score framework, we propose a microbial risk score (MRS) framework to aggregate the complicated microbial profile into a summarized risk score that can be used to measure and predict disease susceptibility. Specifically, the MRS algorithm involves two steps: 1) identifying a sub-community consisting of the signature microbial taxa associated with disease, and 2) integrating the identified microbial taxa into a continuous score. The first step is carried out using the existing sophisticated microbial association tests and pruning and thresholding method in the discovery samples. The second step constructs a community-based MRS by calculating alpha diversity on the identified sub-community in the validation samples. Moreover, we propose a multi-omics data integration method by jointly modeling the proposed MRS and other risk scores constructed from other omics data in disease prediction. ResultsThrough three comprehensive real data analyses using the NYU Langone Health COVID-19 cohort, the gut microbiome health index (GMHI) multi-study cohort, and a large type 1 diabetes cohort separately, we exhibit and evaluate the utility of the proposed MRS framework for disease prediction and multi-omics data integration. In addition, the disease-specific MRSs for colorectal adenoma, colorectal cancer, Crohns disease, and rheumatoid arthritis based on the relative abundances of 5, 6, 12, and 6 microbial taxa respectively are created and validated using the GMHI multi-study cohort. Especially, Crohns disease MRS achieves AUCs of 0.88 ([0.85-0.91]) and 0.86 ([0.78-0.95]) in the discovery and validation cohorts, respectively. ConclusionsThe proposed MRS framework sheds light on the utility of the microbiome data for disease prediction and multi-omics integration, and provides great potential in understanding the microbiomes role in disease diagnosis and prognosis.

microbiology↗

bioRxiv2021

Secondary metabolism drives ecological breadth in the Xylariaceae

Global, large-scale surveys of phylogenetically diverse plant and lichen hosts have revealed an extremely high richness of endophytes in the Xylariales, one of the largest clades of filamentous fungi and a significant source of novel secondary metabolites (SMs). Endophytes may produce host protective antimicrobial or insecticidal SMs, as well as compounds that facilitate symbiotic establishment through suppression or degradation of host immune response, but the ecological roles of most SMs are unknown. Here we characterized metabolic gene clusters in 96 genomes of endophytes and closely related saprotrophs and pathogens in two clades of Xylariales (Xylariaceae s.l. and Hypoxylaceae). Hundreds of genes appear horizontally transferred to xylarialean fungi from distantly related fungi and bacteria, including numerous genes in secondary metabolite gene clusters (SMGCs). Although all xylarialean genomes contain hyperabundant SMGCs, we show that increased gene duplications, horizontal gene transfers (HGTs), and SMGC content in Xylariaceae s.l. taxa are linked to greater phylogenetic host breadth, larger biogeographic distributions, and increased capacity for lignocellulose decomposition compared to Hypoxylaceae taxa. Overall, our results suggest that xylarialean endophytes capable of dual ecological modes (symbiotic and saprotrophic) experience greater selection to diversify SMGCs to both increase competitiveness within microbial communities and facilitate diverse symbiotic interactions.

genomics↗

bioRxiv2020

Developmental Influences on Symptom Expression in Antipsychotic-Naive First-Episode Psychosis

IntroductionThe neurodevelopmental model of psychosis was established over 30 years ago; however, the developmental influence on psychotic symptom expression - how a persons age affects clinical presentation in first-episode psychosis - has not been thoroughly investigated. MethodUsing generalized additive modeling, which allows for linear and non-linear functional forms of age-related change, we leveraged symptom data from a large sample of antipsychotic-naive individuals experiencing a first episode of psychosis (N=340, 12-40 years, 1-12 visits), collected at the University of Pittsburgh from 1990-2017. We examined relationships between age and severity of perceptual and non-perceptual positive symptoms and negative symptoms. We also tested for age-associated effects on change in positive or negative symptom severity following baseline assessment, and explored the time-varying relationship between perceptual and non-perceptual positive symptoms across adolescent development. ResultsIn the cross-sectional and longitudinal data, perceptual positive symptoms significantly decreased with increasing age (F=7.0, p=0.0007; q=0.003) while non-perceptual positive symptoms increased with increasing age (F=4.1, p=0.01, q=0.02). These relationships remained significant when SES, IQ, and illness duration were included as covariates. There were no developmental effects on change in positive or negative symptom severity (all p>0.25). Finally, an association between severity of non-perceptual and perceptual symptoms developed with increasing age, with a significant association starting at age 18. ConclusionThese findings suggest that as cognitive maturation proceeds, perceptual symptoms attenuate while non-perceptual symptoms are enhanced, reflecting influences of developmental processes on psychosis expression. Findings underscore how pathological brain-behavior relationships vary as a function of development.

neuroscience↗

bioRxiv2020

Depletion of glycosaminoglycans decreases contact stiffness of single collagen I fibrils in aqueous solution

Fibrillar collagen-integrin interactions in the extracellular matrix (ECM) regulate a multitude of cellular processes and cell signalling. Collagen I fibrils serve as the molecular scaffolding for connective tissues throughout the human body and are the most abundant protein building blocks in the ECM. The ECM environment is diverse, made up of several ECM proteins, enzymes, and proteoglycans. The contents of the ECM environment are modulated by disease and aging and may influence these critical collagen-integrin interactions. In particular, glycosaminoglycans (GAGs), anionic polysaccharides that decorate proteoglycans, become depleted in the ECM with natural aging and their mis-regulation has been linked to cancers and other diseases. The impact of GAG concentration in the ECM environment on collagen interactions is not well understood. Here, we integrate protein adhesion assays with liquid high resolution atomic force microscopy (AFM) to assess the affects of GAG depletion on the interaction of collagen I fibrils with the integrin 2I domain. Adhesion assays demonstrate that 2I preferentially binds to GAG-depleted collagen I fibrils. By amplitude modulated AFM in air and in solution, we find that GAG-depleted collagen I fibrils retain structural features of the native fibrils, including their characteristic D-banding pattern, a key structural motif. AFM fast force mapping in solution shows that GAG depletion reduces the stiffness of individual fibrils, lowering the indentation modulus by half compared to native fibrils. Together these results shed new light on how GAGs influence collagen I fibril- integrin interactions and may aid in strategies to treat diseases that result from GAG misregulation. Statement for broader audienceAging and disease result in mis-regulation of glycosaminoglycan (GAG) levels in the extracellular matrix (ECM), which may affect fibrillar collagen interactions that are vital for cellular processes. Here, we characterize the impact of GAG depletion on collagen-integrin 2I domain interactions and collagen fibril topography and stiffness. We show that GAG depletion increases collagen-2I binding and reduces stiffness in comparison to native fibrils. These results may inform on strategies for treating GAG mis-regulation.

biophysics↗

bioRxiv2018

Modelling the distribution of Aedes aegypti and Aedes albopictus using climate, host density and interspecies competitive effects

Florida faces the challenge of repeated introduction and autochthonous transmission of arboviruses transmitted by Aedes aegypti and Aedes albopictus. Empirically-based predictive models of the spatial distribution of these species would aid surveillance and vector control efforts. To predict the occurrence and abundance of these species, we fit mixed-effects zero-inflated negative binomial regression to a mosquito surveillance dataset with records from more than 200,000 trap days, covering 73% of the land area and ranging from 2004 to 2018 in Florida. We found an asymmetrical competitive interaction between adult populations of Aedes aegypti and Aedes albopictus for the sampled sites. Wind speed was negatively associated with the occurrence and abundance of both vectors. Our model predictions show high accuracy (72.9% to 94.5%) in the validation tests leaving out a random 10% subset of sites and data from 2018, suggesting a potential for predicting the distribution of the two Aedes vectors.

ecology↗

bioRxiv2018

A comprehensive genomics solution for HIV surveillance and clinical monitoring in a global health setting

High-throughput viral genetic sequencing is needed to monitor the spread of drug resistance, direct optimal antiretroviral regimes, and to identify transmission dynamics in generalised HIV epidemics. Public health efforts to sequence HIV genomes at scale face three major technical challenges: (i) minimising assay cost and protocol complexity, (ii) maximising sensitivity, and (iii) recovering accurate and unbiased sequences of both the genome consensus and the within-host viral diversity. Here we present a novel, high-throughput, virus-enriched sequencing method and computational pipeline tailored specifically to HIV (veSEQ-HIV), which addresses all three technical challenges, and can be used directly on leftover blood drawn for routine CD4 testing. We demonstrate its performance on 1,620 plasma samples collected from consenting individuals attending 10 large urban clinics in Zambia, partners of HPTN 071 (PopART). We show that veSEQ-HIV consistently recovers complete HIV genomes from the majority of samples of different subtypes, and is also quantitative: the number of HIV reads per sample obtained by veSEQ-HIV estimates viral load without the need for additional testing. Both quantitativity and sensitivity were assessed on a subset of 126 samples with clinically measured viral loads, and with standardized quantification controls (VL 100 - 5,000,000 RNA copies/ml). Complete HIV genomes were recovered from 93% (85/91) of samples when viral load was over 1,000 copies per ml. The quantitative nature of the assay implies that variant frequencies estimated with veSEQ-HIV are representative of true variant frequencies in the sample. Detection of minority variants can be exploited for epidemiological analysis of transmission and drug resistance, and we show how the information contained in individual reads of a veSEQ-HIV sample can be used to detect linkage between multiple mutations associated with resistance to antiretroviral therapy. Less than 2% of reads obtained by veSEQ-HIV were identified as in silico contamination events using updates to the phyloscanner software (phyloscanner clean) that we show to be 95% sensitive and 99% specific at decontaminating NGS data. The cost of the assay -- approximately 45 USD per sample -- compares favourably with existing VL and HIV genotyping tests, and provides the additional value of viral load quantification and inference of drug resistance with a single test. veSEQ-HIV is well suited to large public health efforts and is being applied to all [~]9000 samples collected for the HPTN 071-2 (PopART Phylogenetics) study.

genomics↗

bioRxiv2018

Ligand Modulates Cross-Coupling between Riboswitch Folding and Transcriptional Pausing

HighlightsO_LIsmFRET, biochemistry and simulations probe co-transcriptional riboswitch folding\nC_LIO_LINascent RNA folding is disfavored by the DNA template and aided by RNAP\nC_LIO_LITranscriptional pausing is stabilized by RNA pseudoknot and destabilized by ligand\nC_LI\n\nSUMMARYNumerous classes of riboswitches have been found to regulate bacterial gene expression in response to physiological cues, offering new paths to anti-bacterial drugs. As common studies of isolated riboswitches lack the functional context of the transcription machinery, we here combine single-molecule, biochemical and simulation approaches to investigate the coupling between co-transcriptional folding of the pseudoknot-structured preQ1 riboswitch and RNA polymerase (RNAP) pausing. We show that pausing at a site immediately downstream of the riboswitch requires a ligand-free pseudoknot in the nascent RNA, a precisely spaced sequence resembling the pause consensus, and electrostatic and steric interactions with the RNAP exit channel. While interactions with RNAP stabilize the native fold of the riboswitch, binding of the ligand signals RNAP release from the pause. Our results demonstrate that the nascent riboswitch and its ligand actively modulate the function of RNAP and vice versa, a paradigm likely to apply to other cellular RNA transcripts.

biophysics↗

bioRxiv2018

The Aquilegia genome: adaptive radiation and an extraordinarily polymorphic chromosome with a unique history

The columbine genus Aquilegia is a classic example of an adaptive radiation, involving a wide variety of pollinators and habitats. Here we present the genome assembly of A. coerulea Goldsmith, complemented by high-coverage sequencing data from 10 wild species covering the world-wide distribution. Our analyses reveal extensive allele sharing among species and demonstrate that introgression and selection played a role in the Aquilegia radiation. We also present the remarkable discovery that the evolutionary history of an entire chromosome differs from that of the rest of the genome - a phenomenon which we do not fully understand, but which highlights the need to consider chromosomes in an evolutionary context.

evolutionary biology↗