Biology subjects

Rodriguez, L. C.

Publications and source records attributed to Rodriguez, L. C..

5 recordsLinked to original sources

bioRxiv2026

A life-history axis of resource acquisition in bacteria: depolymerisation versus uptake

Microbial decomposition of complex organic matter is a major control on the global carbon cycle, and its efficiency hinges on how microbes acquire resources. Decomposition requires costly extracellular enzymes to break down complex polymers, whereas the resulting soluble products are captured far more cheaply through membrane transporters. How microbes balance investment between these two processes is a fundamental axis of microbial life history, yet it has remained difficult to quantify across their vast diversity. Using genomic repertoires for carbohydrate depolymerisation and uptake, we developed a simple ratio distinguishing depolymerisation-optimised from uptake-optimised strategies, and applied it to more than 249,000 environmental and host-associated bacterial genomes. These strategies were strongly phylogenetically structured and differed by habitat, with uptake-optimised bacteria unexpectedly more prevalent in diffusion-limited soils than in aquatic environments. In an 18-month field-scale plant litter decomposition experiment, uptake-optimised bacteria dominated during early decomposition, when fungi were more abundant and strongly expressed hydrolytic enzymes, shifting towards depolymerisation-optimised bacteria at later stages. A simple genomic ratio therefore captures ecologically meaningful resource-acquisition strategies that vary with phylogeny, habitat and successional stage. The balance of these strategies could shape community-level extracellular enzyme investment, with consequences for decomposition and carbon cycling.

microbiology↗

bioRxiv2025

Building an Ecosystem of Seizure Localization Methods: Neural Fragility as the First Step

The current treatment for drug-resistant epilepsy (DRE) is surgical intervention, which relies on accurate identification of the seizure onset zone (SOZ) using intracranial EEG (iEEG) data. iEEG analysis with computational epileptogenic zone identification algorithms (CEZIAs) is a promising step towards better SOZ localization and surgical outcomes. A key step in validation and adoption of CEZIAs is to allow for widespread shared development and validation of code and data. We describe a set of three R packages to achieve this goal. Our ecosystem of seizure localization methods involves a straightforward analysis pipeline, standardized data formatting and storage, and completely documented and open-source code. The TableContainer package allows for easy storage and manipulation of table data, serving as groundwork for the Epoch package, which is specifically geared towards iEEG data. The Epoch package allows for cropping, resampling, and visualization of iEEG data and provides publicly downloadable iEEG data for reproducibility. Finally, the EZFragility package uses these two foundational packages to analyze iEEGs for SOZ localization using the Neural Fragility method described by Li et al. EZFragility was built using the same core principles as the original method but included several enhancements in computational efficiency and user experience. It accurately reproduces neural fragility results for both sample patients used in the original paper. This project serves as the first step towards building an open-source, reproducible ecosystem of seizure localization methods in R. Future steps include the addition of other CEZIAs using the framework and sample data already made available by these packages. Significance StatementLocalization of the seizure onset zone (SOZ) is a critical step in surgical treatment of drug-resistant epilepsy. Computational epileptogenic zone identification algorithms (CEZIAs) are promising potential tools to aid in clinical decision-making. However, shared development and verification of CEZIAs is difficult due to obscured source code, variable data structures, and limited data availability. The EZFragility software package1 is the first step in building a collaborative ecosystem of CEZIAs that can be downloaded, tested, and used without these roadblocks. EZFragility1 and its dependent packages TableContainer2 and Epoch3 are freely available on the Comprehensive R Archive Network (CRAN), with source code viewable on GitHub. They provide an open-source framework for CEZIA code, data formatting, and data access, all with extensive documentation.

neuroscience↗

bioRxiv2025

Decomposing Predictive Information in Social Dynamics

Social behaviors include some of the most interesting interactions in living systems yet their principled characterization remains unsolved. Here we suggest that at the core of social interactions is the notion of mutual prediction, which we analyze in the context of two male zebrafish engaged in a dominance contest. Using 3D velocity trajectories, we construct the mutual information between a two-animal past and one-animal future, and we quantify the redundant, unique, and synergistic components using partial information decomposition across time windows. We find that predictive information decomposition naturally aligns with important social concepts, such as mirroring and dominance. At contest end, we find asymmetries in self-unique and redundant information that reflect the emergent dominance relationship. Applied to mecp2 zebrafish mutants, an autism model, we find that predictive information is reduced overall, but especially for synergistic flows, which is indicative of difficulties in more complex social dynamics. Significance StatementSocial interactions are rich and diverse, ranging from mirroring to complementary actions. A unifying framework for defining and analyzing such interaction types has long been needed. Here, based on modern information theory, we formulate how the past state of interacting organisms encodes the future state of an individual. This framework provides a natural decomposition of pairwise social dynamics into mirroring, independent action, directed influence, and joint action. Applied to dominance contests in zebrafish, these modes of interaction capture distinct phases of conflict, their assessment strategies, and the resulting dominance relationships. Moreover, our analysis reveals a specific disruption in the social behavior of mutant zebrafish linked to autism, shedding new light on impairments in communication and social learning.

biophysics↗

bioRxiv2025

Sex, not estrous cycle stage, drives differences in the microglial transcriptome in the 5xFAD mouse model of amyloidosis

Alzheimers disease (AD) presents with a sex bias where women are at higher risk and exhibit worse cognitive decline and brain atrophy compared to men. Microglia play a significant role in the pathogenesis and progression of AD and have been shown to be sexually differentiated in health and disease. Whether microglia contribute to the sex differences in AD remains to be elucidated. Herein, we characterize the sex differences in amyloid-beta (A{beta}) plaque pathology and microglia-plaque interaction using the 5xFAD mouse model of amyloidosis and further elucidate the microglial transcriptomic changes that occur in males and females. In females we concentrate on two hormonally distinct stages of the rodent estrous cycle: proestrus and diestrus. Our results indicate that A{beta} plaque morphology is sexually distinct with females having greater plaque volume and lower plaque sphericity compared to males. Microglia also interact with plaques in a sexually distinct manner with females phagocytosing A{beta} to a greater extent compared to males. Furthermore, we found that female microglia are not overtly different at the proestrus or diestrus stages. However, we found stark sex differences between female and male microglia transcriptomes in the 5xFAD brains, where female 5xFAD microglia were enriched in genes involved in glycolytic metabolism, antigen presentation, disease-associated microglia and microglia neurodegenerative phenotype (DAM/MGnD), and interferon signaling compared to male 5xFAD microglia.

neuroscience↗

bioRxiv2021

Forest structure and degradation drive canopy gap sizes across the Brazilian Amazon

Canopy gaps are openings in the forest canopy resulting from branch fall and tree mortality events. Light reaches the lower layers of the canopy through these gaps, enabling understory trees to grow and maintaining the high heterogeneity and biodiversity of tropical forests. The size-frequency distribution of canopy gaps follows a power-law distribution, and the slope of this power-law () is a key indicator of forest structure and dynamics. We detected canopy gaps using a unique LiDAR data set consisting of 650 transects randomly distributed across the Brazilian Amazon Forest providing an unprecedented perspective on forest structural variation over 2500 km2 of forest. We then investigated how varied with forest structure, elevation, soil fertility, water deficit, wind gust speed and lightning intensity. We found that human modified forests had more large gaps than intact forests. Within the intact forests we observed a large-scale Northwest to Southeast pattern in (more large gaps in the Southeast), which aligns with recent work on tree mortality rates. The two most important variables in predicting were median canopy height and maximum modeled height, which had opposite effects on the number of large gaps. Forests with higher median canopy height contain fewer large gaps but the presence of very tall trees was associated with more large gaps. Environmental variables were of secondary importance in our model, with larger gaps occurring in drier forests with high soil fertility, wind speed, and lightning intensity. The distribution of large gaps in the forest canopy varies substantially over the Brazilian Amazon as a result of canopy structure and mortality rates. We mapped this variation and found more large gaps in human modified forests, forests on fertile soils and those exposed to higher wind, lightning and drought stress. Increasing extreme weather events due to climate change may therefore increase the number of large gaps in currently intact forests, causing them to resemble human modified forests.

ecology↗