Biology subjects

Garcia, B. M.

Publications and source records attributed to Garcia, B. M..

7 recordsLinked to original sources

bioRxiv2026

Habitat and Hydrodynamics Influence Coral Reef and Seagrass Microbial and Exometabolite Dynamics

Coral reef and seagrass ecosystems provide critical storm protection and economic revenue to tropical coastal communities. Effective monitoring and restoration strategies are essential given increasing impacts from climate change and human development. Microorganisms, and the metabolites they produce and consume, are key drivers of coastal ecosystem function. However, microbially-mediated metabolite recycling remains poorly understood, limiting its inclusion in conservation and restoration strategies. Here we examine how seawater exometabolites and microorganisms in coastal ecosystems vary in relation to habitat (seagrass or coral reef), temporal scales and hydrodynamics. We characterized benthic seawater from two St. John, U.S. Virgin Islands coral reefs (Yawzi and Tektite) and one seagrass meadow at dawn and mid-day over four consecutive days. Using quantitative metabolomics and SSU rRNA gene amplicon sequencing, we found that habitat served as the primary driver of exometabolite and microbial community composition, while daily changes and hydrodynamics strongly influenced system variability. Hydrodynamic modeling indicated offshore water intrusion at mid-day at Yawzi reef, likely driving exometabolite and microbial shifts towards oligotrophic taxa (e.g., SAR11, SAR86). In contrast, a high percentage of coastal source water in the seagrass habitat maintained stable exometabolite pools and supported diverse microbial communities. These findings demonstrate that coastal habitat and hydrodynamics strongly influence exometabolite and microbial assemblages, with lesser contributions from temporal changes. Integrating exometabolites, microorganisms, and hydrodynamics provides new insights into coastal ecosystem functioning useful for habitat monitoring and restoration strategies.

ecology↗

bioRxiv2025

Metabolic immunity to infection is driven by mitochondrial one-carbon metabolism

As large consumers of cellular metabolites, mitochondria are positioned to compete with invading microbes for the nutrients they require to grow. Yet, little is known of whether cells weaponize mitochondrial metabolism during infection. We found that the transcription factor ATF4 activated a mitochondrial metabolic defense based on the essential B vitamin folate. During infection with the human parasite Toxoplasma gondii, ATF4 increased mitochondrial DNA (mtDNA) levels by driving the one-carbon (1C) metabolism processes that occur in mitochondria and use folate. The activation of ATF4 depended on host detection of parasite effector proteins, and restricted parasite growth by limiting its access to folate(s) required for dTMP synthesis. Impairing host mitochondrial 1C metabolism downstream of ATF4 promoted parasite growth, while forcing host dependence on mitochondrial 1C metabolism had the opposite effect. ATF4 activation also promoted a host-protective response in a mouse model of Toxoplasma infection. Thus, ATF4 rewires mitochondrial metabolism to activate a folate-based metabolic immunity against Toxoplasma. Our work paves the way for future studies exploring noncanonical defense strategies mediated by mitochondria and the role of folate metabolism during infectious disease. One-Sentence SummaryATF4 rewires mitochondrial metabolism during infection to drive a host-protective response based on folate competition.

cell biology↗

bioRxiv2023

Advancing Reef Monitoring Techniques through Exometabolomics: Quantification of Labile Dissolved Organic Metabolites on Coral Reefs

Extracellular chemical cues constitute much of the language of life among marine organisms from microbes to mammals. These chemical cues shape foraging and feeding strategies, symbiotic interactions, selection of mates and habitats, and the transfer of energy and nutrients within and among ecosystems. Changes in the chemical pool flux within these ecosystems serve as invisible signals of overall ecosystem health and, conversely, disruption to this finely tuned equilibrium. In coral reef systems, the full scope and magnitude of the chemicals involved in maintaining reef equilibria are largely unknown. In particular, processes involving small, polar molecules, which form the majority components of labile dissolved organic carbon (DOC), are often poorly captured using traditional techniques and therefore remain unconstrained. In this study, we employed a recently developed chemical derivatization method and mass spectrometry-based targeted exometabolomics to capture and quantify polar dissolved phase metabolites on five coral reefs in the U.S. Virgin Islands. We detected and quantified 45 polar exometabolites, and further demonstrate their variability across the investigated geographic landscape and contextualize these findings in terms of geographic and benthic cover differences. By comparing our results to previously published coral reef exometabolomes that did not employ chemical derivatization, we show the novel quantification of 23 metabolites which were previously undetected in the coral reef exometabolome, including compounds involved in central carbon metabolism (e.g., glutamate) and novel metabolites such as homoserine betaine. We highlight the immense potential of chemical derivatization-based exometabolomics for quantifying labile chemical cues on coral reefs and measuring molecular level responses to environmental stressors. Overall, improving our understanding of the composition and dynamics of reef exometabolites is vital for effective ecosystem monitoring and management strategies. SynopsisMinimal research exists on the polar exometabolites on coral reefs, an essential component of reef health and the majority component of labile DOC. This study reports 23 newly quantified polar metabolites on coral reefs and highlights significant differences in the exometabolome composition across geographically distinct reefs and bays.

systems biology↗

bioRxiv2022

Ion Mobility for Unknown Metabolite Identification: Hope or Hype?

Ion mobility (IM) spectrometry provides semi-orthogonal data to mass spectrometry (MS), showing promise for identifying unknown metabolites in complex non-targeted metabolomics datasets. While current literature has showcased IM-MS for identifying unknowns under near ideal circumstances, less work has been conducted to evaluate the performance of this approach in metabolomics studies involving highly complex samples with difficult matrices. Here, we present a workflow incorporating de novo molecular formula annotation and MS/MS structure elucidation using SIRIUS 4 with experimental IM collision cross-section (CCS) measurements and machine learning CCS predictions to identify differential unknown metabolites in mutant strains of Caenorhabditis elegans. For many of those ion features this workflow enabled the successful filtering of candidate structures generated by in silico MS/MS predictions, though in some cases annotations were challenged by significant hurdles in instrumentation performance and data analysis. While for 37% of differential features we were able to successfully collect both MS/MS and CCS data, fewer than half of these features benefited from a reduction in the number of possible candidate structures using CCS filtering due to poor matching of the machine learning training sets, limited accuracy of experimental and predicted CCS values, and lack of candidate structures resulting from the MS/MS data. When using a CCS error cutoff of {+/-}3%, an average 28% of candidate structures could be successfully filtered. Herein, we identify and describe the bottlenecks and limitations associated with the identification of unknowns in non-targeted metabolomics using IM-MS to focus and provide insight on areas requiring further improvement.

biochemistry↗

bioRxiv2022

An anchored experimental design and meta-analysis approach to address batch effects in large-scale metabolomics

Large-scale untargeted metabolomics studies suffer from individual variation, batch effects and instrument variability, making comparisons of common spectral features across studies difficult. One solution is to compare studies after compound identification. However, compound identification is expensive and time consuming. We successfully identify common spectral features across multiple studies, with a generalizable experimental design approach. First, we included an anchor strain, PD1074, during sample and data collection. Second, we collected data in blocks with multiple controls. These anchors enabled us to successfully integrate three studies of Caenorhabditis elegans for nuclear magnetic resonance (NMR) spectroscopy and liquid chromatography-mass spectrometry (LC-MS) data from five different assays. We found 34% and 14% of features to be significant in LC-MS and NMR, respectively. Between 20-50% of spectral features differ in a mutant and among a set of genetically diverse natural strains, suggesting this reduced set of spectral features are excellent targets for compound identification. GRAPHICAL ABSTRACT O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=108 SRC="FIGDIR/small/485859v1_ufig1.gif" ALT="Figure 1"> View larger version (47K): org.highwire.dtl.DTLVardef@84abe1org.highwire.dtl.DTLVardef@3066fforg.highwire.dtl.DTLVardef@1ba166corg.highwire.dtl.DTLVardef@164cf0_HPS_FORMAT_FIGEXP M_FIG C_FIG Fourteen C. elegans strains are used in three individual studies. PD1074, the anchor control strain (orange), is grown alongside test strains (green, yellow, purple). Multiple biological replicates of PD1074 captures environmental variation in growth conditions. Non-polar and polar metabolic data across the three studies (i.e., natural strains, central metabolism mutants, and UGT mutants) were collected by nuclear magnetic resonance (NMR) spectroscopy and liquid chromatography-mass spectrometry (LC-MS). Data acquisition controls in each block included biological reference material and pooled PD1074 samples. Biological replicates of PD1074 (n = 42 for LC-MS, n = 52 for NMR) were included in all batches. Meta-analysis provided comparable inferences to mixed effects models, and the estimated relative effects of each test strain to PD1074 and straightforward comparisons of test strains across experiments.

systems biology↗

bioRxiv2021

Long-Term Metabolomics Reference Material

The use of quality control samples in metabolomics ensures data quality, reproducibility and comparability between studies, analytical platforms and laboratories. Long-term, stable and sustainable reference materials (RMs) are a critical component of the QA/QC system, however, the limited selection of currently available matrix matched RMs reduce their applicability for widespread use. To produce a RM in any context, for any matrix that is robust to changes over the course of time we developed IBAT (Iterative Batch Averaging meThod). To illustrate this method, we generated 11 independently grown E. coli batches and made a RM over the course of 10 IBAT iterations. We measured the variance of these materials by NMR and showed that IBAT produces a stable and sustainable RM over time. This E. coli RM was then used as food source to produce a C. elegans RM for a metabolomics experiment. The metabolite extraction of this material alongside 41 independently grown individual C. elegans samples of the same genotype, allowed to estimate the proportion of sample variation in pre-analytical steps. From the NMR data, we found that 40% of the metabolite variance is due to the metabolite extraction process and analysis and 60% is due to sample-to-sample variance. The availability of RMs in untargeted metabolomics is one of the predominant needs of the metabolomics community that reach beyond quality control practices. IBAT addresses this need by facilitating the production of biologically relevant RMs and increasing their widespread use.

biochemistry↗

bioRxiv2018

Mitofusin 1 is required for the oocyte-granulosa cell communication that regulates oogenesis

Mitochondrial function, largely regulated by the dynamics of this organelle, is inextricably linked to oocyte health. While the proteins that modulate mitochondrial fusion, Mitofusin 1 (MFN1) and 2 (MFN2), are required for embryogenesis, their role in oocyte development remains unclear. Here we show that the oocyte-specific deletion of Mfn1, but not Mfn2, results in a complete loss of oocyte growth and ovulation due to a block in folliculogenesis at the preantral-to-antral follicle transition. We pinpoint the loss of oocyte ovulation to disrupted oocyte-somatic cell communication - Mfn1-null oocytes are deficient for the production of the important somatic cell signaling factor GDF9. Unexpectedly, the double loss of Mfn1 and Mfn2 mitigates the effects on oocyte growth and ovulation, which is explained by a partial rescue of oocyte-somatic cell communication and folliculogenesis. Together, this work demonstrates that mitochondrial function influences communication of oocyte with follicular somatic cells and suggests that the balanced expression of modulators of mitochondrial dynamics is critical for proper oocyte development.

developmental biology↗