Biology subjects

Godoy, J.

Publications and source records attributed to Godoy, J..

5 recordsLinked to original sources

bioRxiv2026

Global Neural Oscillations Underlie Performance Variability and Attentional State Fluctuations in Humans

Fluctuations in attentional states, such as mind-wandering (MW), are associated with critical variability in task performance. While fMRI studies highlight the opposing roles of task-positive (e.g., dorsal attention network) and task-negative (e.g., default mode network) systems, the electrophysiological mechanisms underlying these dynamics remain poorly understood. Using intracranial electrocorticography in humans performing a sustained attention task, we identified global oscillatory dynamics linked to attentional shifts. MW was characterized by (i) reduced theta ({theta}) and alpha ({square}) power, (ii) decreased aperiodic signal components, indicating a shift toward cortical inhibition, (iii) enhanced phase synchronization across networks, and (iv) strengthened {theta} phase-behavior correlations ({rho}). These features support a non-network-specific framework in which low-frequency {theta} dynamics--captured by both {theta} power and {rho}--are associated with attentional fluctuations, while aperiodic offset relates to attentional state indirectly through its association with {rho} (Structural Equation Modeling: power[->]state {beta} = -0.118, p = 0.002; {rho}[->]state {beta} = 0.246, p < 0.001; offset[->]{rho} {beta} = -0.222, p < 0.001). Our study provides a unified neurophysiological framework for understanding how spontaneous neural activity can drive attentional fluctuations and performance variability, with implications for research on attention, learning, and neuropsychiatric disorders.

neuroscience↗

bioRxiv2024

Phenotyping the hidden half: Combining UAV phenotyping and machine learning to predict barley root traits in the field

Improving crop root systems for enhanced adaptation and productivity remains challenging due to limitations in scalable non-destructive phenotyping approaches, inconsistent translation of root phenotypes from controlled environment to the field, and a lack of understanding of the genetic controls. This study serves as a proof of concept, evaluating a panel of Australian barley breeding lines and cultivars (Hordeum vulgare L) in two field experiments. Integrated ground-based root and shoot phenotyping was performed at key growth stages. UAV-captured vegetation indices (VIs) were explored for their potential to predict root distribution and above-ground biomass. Machine learning models, trained on a subset of 20 diverse lines, with the most accurate model applied to predict traits across a broader panel of 395 lines. Unlike previous studies focusing on above-ground traits or indirect proxies, this research directly predicts root traits in field conditions using VIs, machine learning and root phenotyping. Root trait predictions for the broader panel enabled genomic analysis using a haplotype-based approach, identifying key genetic drivers, including EGT1 and EGT2 which regulate root gravitropism. This approach offers the potential to advance root research across various crops and integrate root traits into breeding programs, fostering the development of varieties adapted to future environments. HighlightIntegrating UAV phenotyping and machine learning can be used to predict RSA traits non-destructively and offers a new approach to support root research and crop improvement.

physiology↗

bioRxiv2022

Uncovering Hidden Enhancers Through Unbiased In Vivo Testing

Transcriptional enhancers are a predominant class of noncoding regulatory elements that activate cell type-specific gene expression. Tissue-specific enhancer-associated chromatin signatures have proven useful to identify candidate enhancer elements at a genome-wide scale, but their sensitivity for the comprehensive detection of all enhancers active in a given tissue in vivo remains unclear. Here we show that a substantial proportion of in vivo enhancers are hidden from discovery by conventional chromatin profiling methods. In an initial comparison of over 1,200 in vivo validated tissue-specific enhancers with tissue-matched mouse developmental epigenome data, 14% (n=286) of active enhancers did not show canonical enhancer-associated chromatin signatures in the tissue in which they are active. To assess the prevalence of enhancers not detectable by conventional chromatin profiling approaches in more detail, we used a high throughput transgenic enhancer reporter assay to systematically screen over 1.3 Mb of mouse genomic sequence at two critical developmental loci, assessing a total of 281 consecutive 5kb regions for in vivo enhancer activity in mouse embryos. We observed reproducible enhancer-reporter activity in 88 tissue-specific elements, 26% of which did not show canonical enhancer-associated chromatin signatures in the corresponding tissues. Overall, we find these hidden enhancers are indistinguishable from marked enhancers based on levels of evolutionary conservation, enrichment of transcription factor families, and genomic positioning relative to putative target genes. In combination, our retrospective and prospective studies assessed only 0.1% of the mouse genome and identified 309 tissue-specific enhancers that are hidden from current chromatin-based enhancer identification approaches. Our findings suggest the existence of tens of thousands of active enhancers throughout the genome that remain undetected by current chromatin profiling approaches and are an unappreciated source of additional genome function of import in interpreting growing whole human genome sequencing data.

genomics↗

bioRxiv2021

Novel design of imputation-enabled SNP arrays for breeding and research applications supporting multi-species hybridisation

Array-based SNP genotyping platforms have low genotype error and missing data rates compared to genotyping-by-sequencing technologies. However, design decisions used to create array-based SNP genotyping assays for both research and breeding applications are critical to their success. We describe a novel approach applicable to any animal or plant species for the design of cost-effective imputation-enabled SNP genotyping arrays with broad utility and demonstrate its application through the development of the Infinium Wheat Barley 40K SNP array. We show the approach delivers high-quality and high-resolution data for wheat and barley, including when samples are jointly hybridised. The new array aims to maximally capture haplotypic diversity in globally diverse wheat and barley germplasm while minimising ascertainment bias. Comprising mostly biallelic markers designed to be species-specific and single-copy, it permits highly accurate imputation in diverse germplasm to improve statistical power for GWAS and genomic selection. The SNP content captures tetraploid wheat (A- and B-genome) and Ae. tauschii (D-genome) diversity and delineates synthetic and tetraploid wheat from other wheats, as well as tetraploid species and subgroups. The content includes SNP tagging key trait loci in wheat and barley and that directly connect to other genotyping platforms and legacy datasets. The utility of the array is enhanced through the web-based tool Pretzel (https://plantinformatics.io/) which enables the arrays content to be visualised and interrogated interactively in the context of numerous genetic and genomic resources to more seamlessly connect research and breeding. The array is available for use by the international wheat and barley community. Short summaryDesigning SNP genotyping arrays for closely related species with broad applicability in both research and breeding is challenging. Here we describe a novel generic approach to select SNP content for such arrays and demonstrate its utility in wheat and barley to: O_LIcapture haplotypic diversity while minimising ascertainment bias; C_LIO_LIaccurately impute to high SNP density in diverse germplasm; C_LIO_LIgenerate high-quality high-resolution genotypic data; and C_LIO_LIjointly hybridise samples to the same bead chip array. C_LI

genetics↗

bioRxiv2021

Topologically Associating Domain Boundaries are Commonly Required for Normal Genome Function

Topologically associating domain (TAD) boundaries are thought to partition the genome into distinct regulatory territories. Anecdotal evidence suggests that their disruption may interfere with normal gene expression and cause disease phenotype1-3, but the overall extent to which this occurs remains unknown. Here we show that TAD boundary deletions commonly disrupt normal genome function in vivo. We used CRISPR genome editing in mice to individually delete eight TAD boundaries (11-80kb in size) from the genome in mice. All deletions examined resulted in at least one detectable molecular or organismal phenotype, which included altered chromatin interactions or gene expression, reduced viability, and anatomical phenotypes. For 5 of 8 (62%) loci examined, boundary deletions were associated with increased embryonic lethality or other developmental phenotypes. For example, a TAD boundary deletion near Smad3/Smad6 caused complete embryonic lethality, while a deletion near Tbx5/Lhx5 resulted in a severe lung malformation. Our findings demonstrate the importance of TAD boundary sequences for in vivo genome function and suggest that noncoding deletions affecting TAD boundaries should be carefully considered for potential pathogenicity in clinical genetics screening.

genomics↗