Biology subjects

Vera, D.

Publications and source records attributed to Vera, D..

10 recordsLinked to original sources

bioRxiv2025

Multiomic clocks to predict phenotypic age in mice

Biological age refers to a persons overall health in aging, as distinct from their chronological age. Diverse measures of biological age, referred to as "clocks", have been developed in recent years and enable risk assessments, and an estimation of the efficacy of longevity interventions in animals and humans. While most clocks are trained to predict chronological age, clocks have been developed to predict more complex composite biological age outcomes, at least in humans. These composite outcomes can be made up of a combination of phenotypic data, chronological age, and disease or mortality risk. Here, we develop the first such composite biological age measure for mice: the mouse phenotypic age model (Mouse PhenoAge). This outcome is based on frailty measures, complete blood counts, and mortality risk in a longitudinally assessed cohort of male and female C57BL/6 mice. We then develop clocks to predict Mouse PhenoAge, based on multi-omic models using metabolomic and DNA methylation data. Our models accurately predict Mouse PhenoAge, and residuals of the models are associated with remaining lifespan, even for mice of the same chronological age. These methods offer novel ways to accurately predict mortality in laboratory mice thus reducing the need for lengthy and costly survival studies.

systems biology↗

bioRxiv2024

Sleep-wake states are encoded across emotion-regulation regions of the mouse brain

Emotional dysregulation is highly comorbid with sleep disturbances. Sleep is comprised of unique physiological states that are reflected by conserved brain oscillations. Though the role of these state-dependent oscillations in cognitive function has been well established, less is known regarding the nature of state-dependent oscillations across brain regions that strongly contribute to emotional function. To characterize these dynamics, we recorded local field potentials simultaneously from multiple cortical and subcortical regions implicated in sleep and emotion-regulation and characterize widespread patterns of spectral power and synchrony between brain regions during sleep/wake states. First, we showed that these brain regions encode sleep state, albeit to various degrees of accuracy. We then identified network-based classifiers of sleep based on the combination of features from all recorded brain regions. Spectral power and synchrony from brain networks allowed for automatic, accurate and rapid discrimination of wake, non-REM sleep (NREM) and rapid eye movement (REM) sleep. When we examined the impact of commonly prescribed sleep promoting medications on neural dynamics across these regions, we found disparate alterations to both cortical and subcortical activity across all three states. Finally, a we found that a stress manipulation that disrupts circadian rhythm produced increased sleep fragmentation without altering the underlying average brain dynamics across sleep-wake states. Thus, we characterized state dependent brain dynamics across regions canonically associated with emotions. Significance StatementSleep and emotion regulation are known to be intertwined at the level of behavior and in neuropsychiatric illnesses. Here, we examined how brain regions involved in emotion regulation encode wake and sleep states by performing multi-site electrophysiological recordings in mice. We developed sleep-wake state classifiers that rapidly labeled sleep-wake states from brain activity alone. We then identified how commonly prescribed sleep-inducing medications have unique impacts on brain activity throughout these emotion-regulation regions. Finally, we explored the impact of circadian rhythm disruption on sleep architecture and brain activity. Together, these data shed light on how brain regions which regulate emotion behave during sleep so that one day, treatments to improve both sleep and emotional well-being may be developed.

neuroscience↗

bioRxiv2023

Neuronal Ndst1 depletion accelerates prion protein clearance and slows neurodegeneration in prion infection

Select prion diseases are characterized by widespread cerebral plaque-like deposits of amyloid fibrils enriched in heparan sulfate (HS), a major extracellular matrix component. HS facilitates fibril formation in vitro, yet how HS impacts fibrillar plaque growth within the brain is unclear. Here we found that prion-bound HS chains are highly sulfated, and that the sulfation is essential for HS accelerating prion conversion in vitro. Using conditional knockout mice to deplete the HS sulfation enzyme, Ndst1 (N-deacetylase, N-sulfotransferase), from neurons or astrocytes, we investigated how reducing HS sulfation impacts survival and prion aggregate distribution during a prion infection. Neuronal Ndst1-depleted mice survived longer and showed fewer and smaller parenchymal plaques, shorter fibrils, and increased vascular amyloid, consistent with enhanced aggregate transit toward perivascular drainage channels. The prolonged survival was strain-dependent, affecting mice infected with extracellular, plaque-forming, but not membrane bound, prion strains. Live PET imaging revealed rapid clearance of prion protein monomers into the CSF in mice expressing unsulfated HS, further suggesting that HS sulfate groups hinder transit of extracellular prion monomers. Our results directly show how a host cofactor slows the spread of prion protein through the extracellular space and identify an enzyme target to facilitate aggregate clearance. Author summaryPrions cause a rapidly progressive neurologic disease and death with no curative treatment available. Prion aggregates accumulate exponentially in the brain in affected individuals triggering neuronal loss and neuroinflammation. Yet the additional molecules that facilitate aggregation are largely unknown, and their identification may lead to new therapeutic targets. We have found that prions in the brain preferentially bind to a highly sulfated endogenous polysaccharide, known as heparan sulfate (HS). Here we use genetically modified mice that express poorly sulfated neuron-derived HS, and infect mice with different prions strains. We find that the mice infected with a plaque-forming prion strain show a prolonged survival and fewer plaques compared to the controls. We also found that the prion protein was efficiently transported in the interstitial fluid in mice having poorly sulfated HS, suggesting that the prion protein is more readily cleared from the brain. Our study provides insight into how HS retains prion aggregates in the brain to accelerate disease and indicates the specific HS biosynthetic enzymes to target for enhancing protein clearance.

pathology↗

bioRxiv2022

Novel feature selection methods for construction of accurate epigenetic clocks

Epigenetic clocks allow the accurate prediction of age based on the methylation status of specific CpG sites in a variety of tissues. These predictive models can be used to distinguish the biological age of an organism from its chronological age, and are a powerful tool to measure the effectiveness of aging interventions. There is a growing need for methods to efficiently construct epigenetic clocks. The most common approach is to create clocks using elastic net regression modelling of all measured CpG sites, without first identifying specific features or CpGs of interest. The addition of feature selection approaches provides the opportunity to reduce the cost and time of clock development by decreasing the number of CpG sites included in clocks. Here, we apply both classic feature selection methods and novel combinatorial methods to the development of epigenetic clocks. We perform feature selection on the human whole blood methylation dataset of [~]470,000 CpG features published by Hannum and colleagues (2015). We develop clocks to predict age, using a variety of feature selection approaches, and all clocks have R2 correlation scores of greater than 0.73. The most predictive clock uses 35 CpG sites for a R2 correlation score of 0.87. The five most frequent sites across all clocks are also modelled to build a clock with a R2 correlation score of 0.83. These two clocks are validated on two external datasets where they maintain excellent predictive accuracy and outperform Hannum et als model in accuracy of age prediction despite using significantly less CpGs. We also identify the associated gene regulatory regions of these CpG sites, which may be possible targets for future aging studies. These novel feature selection algorithms will lower the number of sites needed to be sequenced to build clocks and allow conventionally expensive aging epigenetic studies to cost a fraction of what it would normally.

bioinformatics↗

bioRxiv2019

The regulatory landscape of early maize inflorescence development

BackgroundThe functional genome of agronomically important plant species remains largely unexplored, yet presents a virtually untapped resource for targeted crop improvement. Functional elements of regulatory DNA revealed through profiles of chromatin accessibility can be harnessed for fine-tuning gene expression to optimal phenotypes in specific environments. ResultHere, we investigate the non-coding regulatory space in the maize (Zea mays) genome during early reproductive development of pollen- and grain-bearing inflorescences. Using an assay for differential sensitivity of chromatin to micrococcal nuclease (MNase) digestion, we profile accessible chromatin and nucleosome occupancy in these largely undifferentiated tissues and classify at least 1.6 percent of the genome as accessible, with the majority of MNase hypersensitive sites marking proximal promoters, but also 3 ends of maize genes. This approach maps regulatory elements to footprint-level resolution. Integration of complementary transcriptome profiles and transcription factor occupancy data are used to annotate regulatory factors, such as combinatorial transcription factor binding motifs and long non-coding RNAs, that potentially contribute to organogenesis, including tissue-specific regulation between male and female inflorescence structures. Finally, genome-wide association studies for inflorescence architecture traits based solely on functional regions delineated by MNase hypersensitivity reveals new SNP-trait associations in known regulators of inflorescence development as well as new candidates. ConclusionsThese analyses provide a comprehensive look into the cis-regulatory landscape during inflorescence differentiation in a major cereal crop, which ultimately shapes architecture and influences yield potential.

plant biology↗

bioRxiv2018

Identification of cis elements for spatio-temporal control of DNA replication

The temporal order of DNA replication (replication timing, RT) is highly coupled with genome architecture, but cis-elements regulating spatio-temporal control of replication have remained elusive. We performed an extensive series of CRISPR mediated deletions and inversions and high-resolution capture Hi-C of a pluripotency associated domain (DppA2/4) in mouse embryonic stem cells. Whereas CTCF mediated loops and chromatin domain boundaries were dispensable, deletion of three intra-domain prominent CTCF-independent 3D contact sites caused a domain-wide delay in RT, shift in sub-nuclear chromatin compartment and loss of transcriptional activity, These \"early replication control elements\" (ERCEs) display prominent chromatin features resembling enhancers/promoters and individual and pair-wise deletions of the ERCEs confirmed their partial redundancy and interdependency in controlling domain-wide RT and transcription. Our results demonstrate that discrete cis-regulatory elements mediate domain-wide RT, chromatin compartmentalization, and transcription, representing a major advance in dissecting the relationship between genome structure and function.\n\nHighlightsO_LIcis-elements (ERCEs) regulate large scale chromosome structure and function\nC_LIO_LIMultiple ERCEs cooperatively control domain-wide replication\nC_LIO_LIERCEs harbor prominent active chromatin features and form CTCF-independent loops\nC_LIO_LIERCEs enable genetic dissection of large-scale chromosome structure-function.\nC_LI

molecular biology↗

bioRxiv2017

Allele-specific control of replication timing and genome organization during development

DNA replication occurs in a defined temporal order known as the replication-timing (RT) program. RT is regulated during development in discrete chromosomal units, coordinated with transcriptional activity and 3D genome organization. Here, we derived distinct cell types from F1 hybrid musculus X castaneus mouse crosses and exploited the high single nucleotide polymorphism (SNP) density to characterize allelic differences in RT (Repli-seq), genome organization (Hi-C and promoter-capture Hi-C), gene expression (nuclear RNA-seq) and chromatin accessibility (ATAC-seq). We also present HARP: a new computational tool for sorting SNPs in phased genomes to efficiently measure allele-specific genome-wide data. Analysis of 6 different hybrid mESC clones with different genomes (C57BL/6, 129/sv and CAST/Ei), parental configurations and gender revealed significant RT asynchrony between alleles across ~12 % of the autosomal genome linked to sub-species genomes but not to parental origin, growth conditions or gender. RT asynchrony in mESCs strongly correlated with changes in Hi-C compartments between alleles but not SNP density, gene expression, imprinting or chromatin accessibility. We then tracked mESC RT asynchronous regions during development by analyzing differentiated cell types including extraembryonic endoderm stem (XEN) cells, 4 male and female primary mouse embryonic fibroblasts (MEFs) and neural precursors (NPCs) differentiated in vitro from mESCs with opposite parental configurations. Surprisingly, we found that RT asynchrony and allelic discordance in Hi-C compartments seen in mESCs was largely lost in all differentiated cell types, coordinated with a more uniform Hi-C compartment arrangement, suggesting that genome organization of homologues converges to similar folding patterns during cell fate commitment.

cell biology↗

bioRxiv2017

iSeg: an efficient algorithm for segmentation of genomic and epigenomic data

BackgroundIdentification of functional elements of a genome often requires dividing a sequence of measurements along a genome into segments where adjacent segments have different properties, such as different mean values. This problem is often called the segmentation problem in the field of genomics, and the change-point problem in other scientific disciplines. Despite dozens of algorithms developed to address this problem in genomics research, methods with improved accuracy and speed are still needed to effectively tackle both existing and emerging genomic and epigenomic segmentation problems.\n\nResultsWe designed an efficient algorithm, called iSeg, for segmentation of genomic and epigenomic profiles. iSeg first utilizes dynamic programming to identify candidate segments and test for significance. It then uses a novel data structure based on two coupled balanced binary trees to detect overlapping significant segments and update them simultaneously during searching and refinement stages. Refinement and merging of significant segments are performed at the end to generate the final set of segments. By using an objective function based on the p-values of the segments, the algorithm can serve as a general computational framework to be combined with different assumptions on the distributions of the data. As a general segmentation method, it can segment different types of genomic and epigenomic data, such as DNA copy number variation, nucleosome occupancy, nuclease sensitivity, and differential nuclease sensitivity data. Using simple t-tests to compute p-values across multiple datasets of different types, we evaluate iSeg using both simulated and experimental datasets and show that it performs satisfactorily when compared with some other popular methods, which often employ more sophisticated statistical models. Implemented in C++, iSeg is also very computationally efficient, well suited for large numbers of input profiles and data with very long sequences.\n\nConclusionsWe have developed an effective and efficient general-purpose segmentation tool for sequential data and illustrated its use in segmentation of genomic and epigenomic profiles.

bioinformatics↗

bioRxiv2017

SRSF shape analysis for sequencing data reveal newdifferentiating patterns

MotivationSequencing-based methods to examine fundamental features of the genome, such as gene expression and chromatin structure, rely on inferences from the abundance and distribution of reads derived from Illumina sequencing. Drawing sound inferences from such experiments relies on appropriate mathematical methods to model the distribution of reads along the genome, which has been challenging due to the scale and nature of these data.\n\nResultsWe propose a new framework (SRSFseq) based on Square Root Slope Functions shape analysis to analyse Illumina sequencing data. In the new approach the basic unit of information is the density of mapped reads over region of interest located on the known reference genome. The densities are interpreted as shapes and a new shape analysis model is proposed. An equivalent of a Fisher test is used to quantify the significance of shape differences in read distribution patterns between groups of density functions in different experimental conditions. We evaluated the performance of this new framework to analyze RNA-seq data at the exon level, which enabled the detection of variation in read distributions and abundances between experimental conditions not detected by other methods. Thus, the method is a suitable supplement to the state of the are count based techniques. The variety of density representations and flexibility of mathematical design allow the model to be easily adapted to other data types or problems in which the distribution of reads is to be tested. The functional interpretation and SRSF phase-amplitude separation technique gives an efficient noise reduction procedure improving the sensitivity and specificity of the method.

bioinformatics↗

bioRxiv2017

Repli-seq: genome-wide analysis of replication timing by next-generation sequencing

Cycling cells duplicate their DNA content during S phase, following a defined program called replication timing (RT). Early and late replicating regions differ in terms of mutation rates, transcriptional activity, chromatin marks and sub-nuclear position. Moreover, RT is regulated during development and is altered in disease. Exploring mechanisms linking RT to other cellular processes in normal and diseased cells will be facilitated by rapid and robust methods with which to measure RT genome wide. Here, we describe a protocol to analyse genome-wide RT by next-generation sequencing (NGS). This protocol yields highly reproducible results across laboratories and platforms. We also provide the computational pipelines for analysis, parsing phased genomes using single nucleotide polymorphisms (SNP) for analyzing imprinted RT, and for direct comparison to Repli-chip data obtained by analyzing nascent DNA by microarrays.

genomics↗