Biology subjects

Barnes, W.

Publications and source records attributed to Barnes, W..

5 recordsLinked to original sources

bioRxiv2025

Bacteriophage replication strategies are associated with organic matter energy content on coral reefs

Bacteriophages, viruses that infect bacteria, play a crucial role in carbon cycling within marine environments. In coral reefs, dissolved organic matter (DOM) released by benthic primary producers such as algae fuels heterotrophic microbial growth, which can be detrimental to corals. While this microbialization process has been associated with the abundance and replication strategies of bacteriophages, the direct relationship between reef DOM composition and bacteriophage communities remains unclear. Here, we combine metabolomics, metagenomes, and viromes to demonstrate that phage abundances have significant relationships with DOM composition on the reefs of Curacao, Southern Caribbean. By constructing co-occurrence networks between free or cell-associated viruses and exometabolites, we identified thousands of statistically significant associations between phages and organic compounds. While total viral abundances did not significantly correlate with overall dissolved organic carbon (DOC) concentration, cell-associated phages had significantly more positive associations with compounds that had a reduced nominal oxidative state of carbon (NOSC). Furthermore, temperate phages were more frequently correlated with metabolites exhibiting higher Gibbs energy than lytic phages. Six of the ten viruses with the highest number of positive associations with metabolites were temperate (i.e., encoded an integrase or were identified as a prophage), despite this network consisting of approximately 90% lytic viruses. These temperate viruses were predicted to infect members of the genus Sphingobium. Together, these findings reveal a connection between phage replication strategies and DOM energy availability with potential implications for coral reef biogeochemistry. SignificanceCoral reefs are highly dynamic ecosystems where microbial communities and organic matter cycles are intricately linked. This study provides new insights into how bacteriophages interact with dissolved organic matter (DOM) composition, revealing that cell-associated bacteriophages, particularly temperate phages, are associated with more energy-rich organic compounds. These findings suggest that DOM may have a bottom-up influence on the lysis-lysogeny decision of temperate phages infecting their host or that lysogeny may play an underappreciated role in shaping the reef carbon cycle. Energy-rich organic compounds have generally been associated with increased algal abundances and coral decline. By demonstrating significant connections between viral infection strategies and the energy content of DOM, our results highlight the potential for phages to influence coral reef biogeochemistry and health.

microbiology↗

bioRxiv2023

LipidSIM: inferring mechanistic lipid biosynthesis perturbations from lipidomics with a flexible, low-parameter, systematic Markov Modeling framework

Lipid metabolism is a complex and dynamic system involving numerous enzymes at the junction of multiple metabolic pathways. Disruption of these pathways leads to systematic dyslipidemia, a hallmark of many pathological developments, such as nonalcoholic steatohepatitis and diabetes. Recent advances in computational tools can provide insights into the dysregulation of lipid biosynthesis, but limitations remain due to the complexity of lipidomic data, limited knowledge of interactions among involved enzymes, and technical challenges in standardizing across different lipid types. In this study, we present a low-parameter, biologically interpretable framework named Lipid Synthesis Investigative Markov model (LipidSIM), which models and predicts the source of perturbations in lipid biosynthesis from lipidomic data. LipidSIM achieves this by accounting for the interdependency between the lipid species via the lipid biosynthesis network and generates testable hypotheses regarding changes in lipid biosynthetic reactions. This feature allows the integration of lipidomics with other omics types, such as transcriptomics, to elucidate the direct driving mechanisms of altered lipidomes due to treatments or disease progression. To demonstrate the value of LipidSIM, we first applied it to hepatic lipidomics following Keap1 knockdown and found changes in mRNA expression of the lipid pathways were consistent with the LipidSIM-predicted fluxes. Second, we used it to study lipidomic changes following intraperitoneal injection of CCl4 to induce fast NAFLD/NASH development and the progression of fibrosis and hepatic cancer. Finally, to show the power of LipidSIM for classifying samples with dyslipidemia, we used a Dgat2-knockdown study dataset. Thus, we show that as it demands no a priori knowledge of enzyme kinetics, LipidSIM is a valuable and intuitive framework for extracting biological insights from complex lipidomic data.

bioinformatics↗

bioRxiv2022

A high-throughput COPD bronchosphere model for disease-relevant phenotypic compound screening

COPD is the third leading cause of death worldwide, but current therapies for COPD are only effective at treating the symptoms of the disease rather than targeting the underlying pathways that are driving the pathogenic changes. The lack of targeted therapies for COPD is in part due to a lack of knowledge about drivers of disease progression and the difficulty in building relevant and high throughput models that can recapitulate the phenotypic and transcriptomic changes associated with pathogenesis of COPD. To identify these drivers, we have developed a cigarette smoke extract (CSE)-treated bronchosphere assay in 384-well plate format that exhibits CSE-induced decreases in size and increase in luminal secretion of MUC5AC. Transcriptomic changes in CSE-treated bronchospheres resemble changes that occur in human smokers both with and without COPD compared to healthy groups, indicating that this model can capture human smoking signature. To identify new targets, we ran a small molecule compound deck screening with diversity in target mechanisms of action and identified hit compounds that attenuated CSE induced changes, either decreasing spheroid size or increasing secreted mucus. This work provides insight into the utility of this bronchosphere model in examining human respiratory diseases, the pathways implicated by CSE, and compounds with known mechanisms of action for therapeutic development.

cell biology↗

bioRxiv2020

Predictability in natural images determines V1 firing rates and synchronization: A deep neural network approach

Predictive coding is an important candidate theory of self-supervised learning in the brain. Its central idea is that neural activity results from an integration and comparison of bottom-up inputs with contextual predictions, a process in which firing rates and synchronization may play distinct roles. Here, we quantified stimulus predictability for natural images based on self-supervised, generative neural networks. When the precise pixel structure of a stimulus falling into the V1 receptive field (RF) was predicted by the spatial context, V1 exhibited characteristic{gamma} -synchronization (30-80Hz), despite no detectable modulation of firing rates. In contrast to{gamma} , {beta}-synchronization emerged exclusively for unpredictable stimuli. Natural images with high structural predictability were characterized by high compressibility and low dimensionality. Yet, perceptual similarity was mainly determined by higher-level features of natural stimuli, not by the precise pixel structure. When higher-level features of the stimulus in the receptive field were predicted by the context, neurons showed a strong reduction in firing rates and an increase in surround suppression that was dissociated from synchronization patterns. These findings reveal distinct roles of synchronization and firing rates in the predictive coding of natural images.

neuroscience↗

bioRxiv2018

Surface color and predictability determine contextual modulation of V1 firing and gamma oscillations

The integration of direct bottom-up inputs with contextual information is a canonical motif in neocortical circuits. In area V1, neurons may reduce their firing rates when the (classical) receptive field input can be predicted by the spatial context. We previously hypothesized that gamma-synchronization (30-80Hz) provides a complementary signal to rates, encoding whether stimuli are predicted from spatial context by preferentially synchronizing neuronal populations receiving predictable inputs. Here we investigated how rates and synchrony are modulated by predictive context. Large uniform surfaces, which have high spatial predictability, strongly suppressed firing yet induced prominent gamma-synchronization, but only when they were colored. Yet, chromatic mismatches between center and surround, breaking predictability, strongly reduced gamma-synchronization while increasing firing rates. Differences between colors, including strong gamma-responses to red, arose because of stimulus adaptation to a full-screen background, with a prominent difference in adaptation between M- and L-cone signaling pathways. Thus, synchrony signals whether RF inputs are predicted from spatial context and may encode relationships across space, while firing rates increase when stimuli are unpredicted from the context.

neuroscience↗