Biology subjects

Calabrese, J. M.

Publications and source records attributed to Calabrese, J. M..

17 recordsLinked to original sources

bioRxiv2026

Scanning transcriptomes for nonlinear, domain-level similarities using hmSEEKR

Long noncoding RNAs (lncRNAs) play roles in gene regulation across kingdoms of life. However, lncRNAs with related functions often lack linear sequence similarity, making it difficult to leverage studies of one lncRNA to inform the understanding of others. We describe a k-mer-based hidden Markov model, hmSEEKR, that enables the scanning of transcriptomes for regions of non-linear sequence similarity to a query domain, without prior knowledge of where within the transcriptome the similarities may be located. When individual lncRNA domains were used as search features, hmSEEKR successfully identified regions in other RNAs that harbor non-linear sequence similarity and bind similar sets of proteins. Applying hmSEEKR to transcriptome-wide searches, we found that certain domains within the lncRNAs XIST, NEAT1, and MALAT1 exhibited widespread regional similarity to both lncRNA and protein-coding genes, while others were more unique, exhibiting similarity to [~]100 genes or fewer. Combinatorial searches uncovered RNAs containing sequential matches to core functional domains of XIST and NEAT1, and eCLIP-inferred protein-interaction networks within these RNAs more closely resembled those of XIST and NEAT1, respectively, than would be expected by chance, suggesting the searches recovered RNAs with similar biological properties. Finally, within annotated sets of cis-activating and cis-repressive lncRNAs, we observed opposing enrichments for similarity to domains associated with transcription-promoting complexes and heterogeneous nuclear ribonucleoprotein (hnRNP) binding, respectively, suggesting the enriched sequences may contribute to regulatory functions. hmSEEKR can be applied with minimal training data and enables the a priori discovery of RNA domains that share nonlinear similarity, offering a sequence-informed approach to discover functional elements within noncoding transcriptomes.

bioinformatics↗

bioRxiv2026

Repeat expansions in C9orf72 rewire the 3D chromatin landscape in ALS

Amyotrophic lateral sclerosis (ALS) is frequently driven by GGGGCC short tandem repeat (STR) expansions in C9orf72, yet the mechanisms by which these expansions lead to neurodegeneration remain incompletely understood. Here, we propose a novel mechanism involving higher-order chromatin architecture where C9orf72-STR expansions induce widespread, neuron-specific gains in chromatin loops that are closely linked to transcriptomic dysregulation in ALS. These ectopic loops colocalize with the genomic binding sites of C9orf72-STR RNAs and the architectural protein CTCF, supporting a model in which RNA-DNA interactions promote aberrant loop formation. Together, our findings demonstrate how C9orf72-STR expansions remodel the neuronal genome and disrupt gene expression, uncovering an RNA-driven mechanism of chromatin reorganization in C9-ALS that connects altered nuclear topology to gene dysregulation in neurodegeneration.

genomics↗

bioRxiv2025

Early Host-Virus RNA Interactions Reveal SPEN-Driven m6A Regulation as a Major Determinant of Henipavirus Infection

Early interactions between viral RNA and host-encoded RNA-binding proteins are pivotal in shaping the trajectory of RNA virus infection. Henipaviruses are emerging, highly lethal BSL-4 pathogens whose mechanisms of pathogenesis remain largely elusive. To illuminate the earliest moments of host-virus interplay, we employed Viral Cross-linking and Solid-phase Purification (VIR-CLASP) to capture host proteins bound to the incoming henipavirus genome within the first hour of infection. This approach establishes the first henipavirus RNA-host protein interactome, revealing 146 human proteins directly associated with the primary viral RNA. Among these, SPEN, RBM15, and RBM15B - canonical regulators of lncRNA Xist - emerged as key host factors that actively promote viral infection. Direct RNA sequencing further uncovered that SPEN depletion induces widespread hypomethylation, affecting ~98% of differentially modified m6A sites, ~87% of which localize to the L mRNA transcript encoding the viral RNA-dependent RNA polymerase. Collectively, these findings expose a critical layer of host dependency at the very onset of infection and reveal a previously unappreciated role for SPEN family proteins in facilitating henipavirus infection.

biochemistry↗

bioRxiv2025

Estimating population range distributions from animal tracking data

O_LIQuantifying the space requirements of a population is a fundamental problem in spatial ecology, particularly as it relates to the identification of important utilization areas and the designation of protected areas for conservation and wildlife management. C_LIO_LITraditionally, population space use estimation techniques scale up from the individual to the population level by aggregating individual animal tracks and then using a single pooled distribution estimator like minimum convex polygons (MCP) or kernel density estimation (KDE). These techniques fail to account for the high levels of temporal autocorrelation in modern tracking datasets, and estimates are often sensitive to the number of individuals sampled. C_LIO_LIWe introduce a new population kernel density estimator (PKDE) that accounts for temporal autocorrelation in tracking data, propagates uncertainty from the individual to the population level, accounts for inter-individual variation when scaling up to the population level, and is not highly sensitive to the number of individuals tracked. Through a combination of simulated data and empirical GPS tracking datasets from three species: (a) grizzly bear (Ursus arctos horribilis); (b) lowland tapir (Tapirus terrestris); and (c) bobcat (Lynx rufus), we demonstrate that PKDE produces minimally biased estimates of population-level space use compared to conventional methods like MCP and KDE. C_LIO_LIThe use of conventional estimators can lead to substantial underestimation of population space usage, making them unsuitable for area-based conservation planning. The statistically efficient PKDE estimator provides relatively unbiased estimates of space use with fewer individuals sampled. This method has been made available as a function in the ctmm R package. C_LI

ecology↗

bioRxiv2023

Nomadic ungulate movements under threat: Declining mobility of Mongolian gazelles in the Eastern Steppe.

Increasing habitat fragmentation and disturbance threaten long distance movements of ungulates. While the effects of impermeable barriers on ungulate migrations have been well researched, quantitative evidence for gradual and long-term changes of mobility in response to anthropogenic disturbance remains relatively rare. We investigated changes in movement behavior of Mongolian gazelle Procapra gutturosa, a nomadic ungulate species native to the Mongolian steppe. Using GPS tracking data collected from 62 gazelle individuals between 2007 and 2021, we quantified 16-day displacement distances for each individual as a metric for long-distance movements. We used generalized linear mixed models, generalized additive models and additive quantile mixed models to assess how anthropogenic and environmental factors affected gazelle movement behavior. Long distance 16-day movements decreased significantly by up to 36 %, from 142 km in 2007 to 92 km in 2021. Changes in gazelle mobility were affected by the increasing number of vehicles in Mongolia, but could not be explained by concurrent changes in other environmental factors like temperature, precipitation or vegetation greenness that often drive ungulate migration behavior. Moreover, we found that gazelle movement decreased close to roads, and that gazelles stayed further away from roads during the snow-free season, when vehicular traffic likely is most intense. Conserving landscape permeability is essential for maintaining populations of highly mobile species. Our study provides evidence for a gradual decline in gazelle mobility over fifteen years as a response to increasing anthropogenic impact. To date, the transportation infrastructure permeating the Eastern Steppe does not pose physical barriers, yet our findings suggest that increasing traffic volume may create semipermeable barriers to gazelle movement. As human activity is increasing throughout the Eastern Steppe, interactions between ungulates and vehicle traffic need to be closely monitored in order to identify, localize, and mitigate semipermeable barrier effects before landscape permeability is severely altered.

ecology↗

bioRxiv2023

movedesign: Shiny R app to evaluate sampling design for animal tracking movement studies

O_LIProjects focused on movement behavior and home range are commonplace, but beyond a focus on choosing appropriate research questions, there are no clear guidelines for such studies. Without these guidelines, designing an animal tracking study to produce reliable estimates of space-use and movement properties (necessary to answer basic movement ecology questions), is often done in an ad hoc manner. C_LIO_LIWe developed movedesign, a user-friendly Shiny application, which can be utilized to investigate the precision of three estimates regularly reported in movement and spatial ecology studies: home range area, speed, and distance traveled. Conceptually similar to statistical power analysis, this application enables users to assess the degree of estimate precision that may be achieved with a given sampling design; i.e., the choices regarding data resolution (sampling interval) and battery life (sampling duration). C_LIO_LILeveraging the ctmm R package, we utilize two methods proven to handle many common biases in animal movement datasets: autocorrelated Kernel Density Estimators (AKDE) and continuous-time speed and distance (CTSD) estimators. Longer sampling durations are required to reliably estimate home range areas via the detection of a sufficient number of home range crossings. In contrast, speed and distance estimation requires a sampling interval short enough to ensure that a statistically significant signature of the animals velocity remains in the data. C_LIO_LIThis application addresses key challenges faced by researchers when designing tracking studies, including the trade-off between long battery life and high resolution of GPS locations collected by the devices, which may result in a compromise between reliably estimating home range or speed and distance. movedesign has broad applications for researchers and decision-makers, supporting them to focus efforts and resources in achieving the optimal sampling design strategy for their research questions, prioritizing the correct deployment decisions for insightful and reliable outputs, while understanding the trade-off associated with these choices. C_LI

ecology↗

bioRxiv2023

A network based approach to identifying correlations between phylogeny, morphological traits and occurrence information of fish Species in US river basins.

The complex network framework has been successfully used to model interactions between entities in Complex Systems in the Biological Sciences such as Proteomics, Genomics, Neuroscience, and Ecology. Networks of organisms at different spatial scales and in different ecosystems have provided insights into community assembly patterns and emergent properties of ecological systems. In the present work, we investigate two questions pertaining to fish species assembly rules in US river basins, a) if morphologically similar fish species also tend to be phylogenetically closer, and b) to what extent are co-occurring species that are phylogentically close also morphologically similar? For the first question, we construct a network of Hydrologic Unit Code 8 (HUC8) regions as nodes with interaction strengths (edges) governed by the number of common species. For each of the modules of this network, which are found to be geographically separated, there is differential yet significant evidence that phylogenetic distance predicts morphological distance. For the second question, we construct and analyze nearest neighbor directed networks of species based on their morphological distances and phylogenetic distances. Through module detection on these networks and comparing the module-level mean phylogenetic distance and mean morphological distance with the number of basins of common occurrence of species in modules, we find that both phylogeny and morphology of species have significant roles in governing species co-occurrence, i.e. phylogenetically and morphologically distant species tend to co-exist more. In addition, between the two quantities (morphological distance and phylogentic distance), we find that morphological distance is a stronger determinant of species co-occurrences.

ecology↗

bioRxiv2022

Mitigating pseudoreplication and bias in resource selection functions with autocorrelation-informed weighting

O_LIResource selection functions are among the most commonly used statistical tools in both basic and applied animal ecology. They are typically parameterized using animal tracking data, and advances in animal tracking technology have led to increasing levels of autocorrelation between locations in such data sets. Because resource selection functions assume that data are independent and identically distributed, such autocorrelation can cause misleadingly narrow confidence intervals and biased parameter estimates. C_LIO_LIData thinning, generalized estimating equations, and step selection functions have been suggested as techniques for mitigating the statistical problems posed by autocorrelation, but these approaches have notable limitations that include statistical inefficiency, unclear or arbitrary targets for adequate levels of statistical independence, constraints in input data, and (in the case of step selection functions) scale-dependent inference. To remedy these problems, we introduce a method for likelihood weighting of animal locations to mitigate the negative consequences of autocorrelation on resource selection functions. C_LIO_LIIn this study, we demonstrate that this method weights each observed location in an animals movement track according to its level of non-independence, expanding confidence intervals and reducing bias that can arise when there are missing data in the movement track. C_LIO_LIEcologists and conservation biologists can use this method to improve the quality of inferences derived from resource selection functions. We also provide a complete, annotated analytical workflow to help new users apply our method to their own animal tracking data using the ctmm R package. C_LI

ecology↗

bioRxiv2022

Species invasiveness and community invasibility of US freshwater fish fauna revealed via trait-based analysis

While biological invasions are recognized as a major threat to global biodiversity, determining species abilities to invade new areas (species invasiveness) and the vulnerability of those areas to invasions (community invasibility) are still poorly understood. Here, we used trait-based analysis to profile invasive species and quantify the community invasibility for >1,800 North American freshwater fish communities. We show that species with higher reproduction rates, longer life spans and larger sizes tend to be more invasive. Community invasibility peaked when the functional distance among native species was high, leaving unoccupied functional space for the establishment of potential invaders. Invasion success is therefore governed by both the functional traits of non-native species determining their invasiveness, and by the functional characteristics of the invaded community determining its invasibility. Considering those two determinants together will allow better predictions of invasions.

ecology↗

bioRxiv2021

Population-level inference for home-range areas

O_LIHome-range estimates are a common product of animal tracking data, as each range informs on the area needed by a given individual. Population-level inference on home-range areas--where multiple individual home-ranges are considered to be sampled from a population--is also important to evaluate changes over time, space, or covariates, such as habitat quality or fragmentation, and for comparative analyses of species averages. Population-level home-range parameters have traditionally been estimated by first assuming that the input tracking data were sampled independently when calculating home ranges via conventional kernel density estimation (KDE) or minimal convex polygon (MCP) methods, and then assuming that those individual home ranges were measured exactly when calculating the population-level estimates. This conventional approach does not account for the temporal autocorrelation that is inherent in modern tracking data, nor for the uncertainties of each individual home-range estimate, which are often large and heterogeneous. C_LIO_LIHere, we introduce a statistically and computationally efficient framework for the population-level analysis of home-range areas, based on autocorrelated kernel density estimation (AKDE), that can account for variable temporal autocorrelation and estimation uncertainty. C_LIO_LIWe apply our method to empirical examples on lowland tapir (Tapirus terrestris), kinkajou (Potos flavus), white-nosed coati (Nasua narica), white-faced capuchin monkey (Cebus capucinus), and spider monkey (Ateles geoffroyi), and quantify differences between species, environments, and sexes. C_LIO_LIOur approach allows researchers to more accurately compare different populations with different movement behaviors or sampling schedules, while retaining statistical precision and power when individual home-range uncertainties vary. Finally, we emphasize the estimation of effect sizes when comparing populations, rather than mere significance tests. C_LI

ecology↗

bioRxiv2020

A comprehensive framework for handling location error in animal tracking data

Animal tracking data are being collected more frequently, in greater detail, and on smaller taxa than ever before. These data hold the promise to increase the relevance of animal movement for understanding ecological processes, but this potential will only be fully realized if their accompanying location error is properly addressed. Historically, coarsely-sampled movement data have proved invaluable for understanding large scale processes (e.g., home range, habitat selection, etc.), but modern fine-scale data promise to unlock far more ecological information. While GPS location error can often be ignored in coarsely sampled data, fine-scale data require more care, and tools to do this have not kept pace. Current approaches to dealing with location error largely fall into two categories--either discarding the least accurate location estimates prior to analysis or simultaneously fitting movement and error parameters in a hidden-state model. In some cases these approaches can provide a level of correction, but they have known limitations, and in some cases they can be worse than doing nothing. Here, we provide a general framework to account for location error in the analysis of triangulated and trilatcralizcd animal tracking data, which includes GPS, Argos Doppler-shift, triangulated VHF, trilatcralized acoustic and cellular location data. We apply our error-modelselection framework to 190 GPS, cellular, and acoustic devices representing 27 models from 14 manufacturers. Collectively, these devices were used to track a wide range of taxa comprising birds, fish, reptiles, and mammals of different sizes and with different behaviors, in urban, suburban, and wild settings. In almost half of the tested device models, error-model selection was necessary to obtain the best performing error model, and in almost a quarter of tested device models, the reported DOP values were actually misinformative. Then, using empirical tracking data from multiple species, we provide an overview of modern, error-informed movement analyses, including continuous-time path reconstruction, home-range distribution, home-range overlap, speed, and distance estimation. Adding to these techniques, we introduce new error-informed estimators for outlier detection and autocorrelation visualization. Because error-induced biases depend on many factors--sampling schedule, movement characteristics, tracking device, habitat, etc.--differential bias can easily confound biological inference and lead researchers to draw false conclusions. We demonstrate how error-informed analyses on calibrated tracking data can provide more accurate estimates are that are insensitive to location error, and allow researchers to use all of their data.

ecology↗

bioRxiv2020

ctmmweb: A graphical user interface for autocorrelation-informed home range estimation

Estimating animal home ranges is a primary purpose of collecting tracking data. All conventional home range estimators in widespread usage, including minimum convex polygons and kernel density estimators, assume independently sampled data. In stark contrast, modern GPS animal tracking datasets are almost always strongly autocorrelated. This incongruence between estimator assumptions and empirical reality leads to systematically underestimated home ranges. Autocorrelated kernel density estimation (AKDE) resolves this conflict by modeling the observed autocorrelation structure of tracking data during home range estimation, and has been shown to perform accurately across a broad range of tracking datasets. However, compared to conventional estimators, AKDE requires additional modeling steps and has heretofore only been accessible via the command-line ctmm R package. Here, we introduce ctmmweb, which provides a point-and-click graphical interface to ctmm, and streamlines AKDE, its prerequisite autocorrelation modeling steps, and a number of additional movement analyses. We demonstrate ctmmwebs capabilities, including AKDE home range estimation and subsequent home range overlap analysis, on a dataset of four jaguars from the Brazilian Pantanal. We intend ctmmweb to open AKDE and related autocorrelation-explicit analyses to a wider audience of wildlife and conservation professionals.

ecology↗

bioRxiv2020

RNP-MaP: In-cell analysis of protein interaction networks defines functional hubs in RNA

RNAs interact with networks of proteins to form complexes (RNPs) that govern many biological processes, but these networks are currently impossible to examine in a comprehensive way. We developed a live-cell chemical probing strategy for mapping protein interaction networks in any RNA with single-nucleotide resolution. This RNP-MaP strategy (RNP network analysis by mutational profiling) simultaneously detects binding by and cooperative interactions involving multiple proteins with single RNA molecules. RNP-MaP revealed that two structurally related, but sequence-divergent noncoding RNAs, RNase P and RMRP, share nearly identical RNP networks and, further, that protein interaction network hubs identify function-critical sites in these RNAs. RNP-MaP identified numerous protein interaction networks within the XIST long noncoding RNA that are conserved between mouse and human RNAs and distinguished communities of proteins that network together on XIST. RNP-MaP data show that the Xist E region is densely networked by protein interactions and that PTBP1, MATR3, and TIA1 proteins each interface with the XIST E region via two distinct interaction modes; and we find that the XIST E region is sufficient to mediate RNA foci formation in cells. RNP-MaP will enable discovery and mechanistic analysis of protein interaction networks across any RNA in cells.

biochemistry↗

bioRxiv2019

How range residency and long-range perception change encounter rates

Encounter rates link movement strategies to intra- and inter-specific interactions, and therefore translate individual movement behavior into higher-level ecological processes. Indeed, a large body of interacting population theory rests on the law of mass action, which can be derived from assumptions of Brownian motion in an enclosed container with exclusively local perception. These assumptions imply completely uniform space use, individual home ranges equivalent to the population range, and encounter dependent on movement paths actually crossing. Mounting empirical evidence, however, suggests that animals use space non-uniformly, occupy home ranges substantially smaller than the population range, and are often capable of nonlocal perception. Here, we explore how these empirically supported behaviors change pairwise encounter rates. Specifically, we derive novel analytical expressions for encounter rates under Ornstein-Uhlenbeck motion, which features non-uniform space use and allows individual home ranges to differ from the population range. We compare OU-based encounter predictions to those of Reflected Brownian Motion, from which the law of mass action can be derived. For both models, we further explore how the interplay between the scale of perception and home range size affects encounter rates. We find that neglecting realistic movement and perceptual behaviors can systematically bias encounter rate predictions.

ecology↗

bioRxiv2019

Non-linear sequence similarity between the Xist and Rsx long noncoding RNAs suggests shared functions of tandem repeat domains

The marsupial inactive X chromosome expresses a long noncoding RNA (lncRNA) called Rsx that has been proposed to be the functional analogue of eutherian Xist. Despite the possibility that Xist and Rsx encode related functions, the two lncRNAs harbor no linear sequence similarity. However, both lncRNAs harbor domains of tandemly repeated sequence. In Xist, these repeat domains are known to be critical for function. Using k-mer based comparison, we show that the repeat domains of Xist and Rsx unexpectedly partition into two major clusters that each harbor substantial levels of non-linear sequence similarity. Xist Repeats B, C and D were most similar to each other and to Rsx Repeat 1, whereas Xist Repeats A and E were most similar to each other and to Rsx Repeats 2, 3, and 4. Similarities at the level of k-mers corresponded to domain-specific enrichment of protein-binding motifs. Within individual domains, protein-binding motifs were often enriched to extreme levels. Our data support the hypothesis that Xist and Rsx encode similar functions through different spatial arrangements of functionally analogous protein-binding domains. We propose that the two clusters of repeat domains in Xist and Rsx function in part to cooperatively recruit PRC1 and PRC2 to chromatin. The physical manner in which these domains engage with protein cofactors may be just as critical to the function of the domains as the protein cofactors themselves. The general approaches we outline in this report should prove useful in the study of any set of RNAs.

genetics↗

bioRxiv2018

A piggyBac-based toolkit for inducible genome editing in mammalian cells

We describe the development and application of a novel series of vectors that facilitate CRISPR-Cas9-mediated genome editing in mammalian cells, which we call CRISPR-Bac. CRISPR-Bac leverages the piggyBac transposon to randomly insert CRISPR-Cas9 components into mammalian genomes. In CRISPR-Bac, a single piggyBac cargo vector containing a doxycycline-inducible Cas9 or catalytically-dead Cas9 (dCas9) variant and a gene conferring resistance to Hygromycin B is co-transfected with a plasmid expressing the piggyBac transposase. A second cargo vector, expressing a single-guide RNA (sgRNA) of interest, the reverse-tetracycline TransActivator (rtTA), and a gene conferring resistance to G418, is also cotransfected. Subsequent selection on Hygromycin B and G418 generates polyclonal cell populations that stably express Cas9, rtTA, and the sgRNA(s) of interest. Using Mus musculus-derived embryonic and trophoblast stem cells, we show that CRISPR-Bac can be used to knockdown proteins of interest, to create targeted genetic deletions with high efficiency, and to activate or repress transcription of protein-coding genes and an imprinted long noncoding RNA. The ratio of sgRNA-to-Cas9-to-transposase can be adjusted in transfections to alter the average number of cargo insertions into the genome. sgRNAs targeting multiple genes can be inserted in a single transfection. CRISPR-Bac is a versatile platform for genome editing that simplifies the generation of mammalian cells that stably express the CRISPR-Cas9 machinery.

molecular biology↗

bioRxiv2018

SWI/SNF remains localized to chromatin in the presence of SCHLAP1

SCHLAP1 is a long-noncoding RNA that is prognostic for progression to metastatic prostate cancer and promotes an invasive phenotype. SCHLAP1 is reported to function by depleting the core SWI/SNF subunit, SMARCB1, from the genome. SWI/SNF is a large, multi-subunit, chromatin remodeling complex that can be combinatorially assembled to yield hundreds to thousands of distinct complexes. Here, we investigated the hypothesis that SCHLAP1 affects only specific forms of SWI/SNF and that the remaining SWI/SNF complexes were important for the increased invasion in SCHLAP1 expressing prostate cells. Using several assays we found that SWI/SNF is not depleted from the genome by SCHLAP1 expression. We find that SCHLAP1 induces changes to chromatin openness but is not sufficient to drive changes in histone modifications. Additionally, we show that SWI/SNF binds many coding and non-coding RNAs. Together these results suggest that SCHLAP1 has roles independent of canonical SWI/SNF and that SWI/SNF broadly interacts with RNA.

cancer biology↗