Biology subjects

Garrett, T. J.

Publications and source records attributed to Garrett, T. J..

10 recordsLinked to original sources

bioRxiv2026

Fecal metabolomics reveals preferential complex carbohydrate utilization and guides cultivation of murine gut Firmicutes

The murine gut microbiota provides robust colonization resistance against Clostridioides difficile infection (CDI), yet murine-associated microbes remain notoriously difficult to cultivate in vitro, limiting mechanistic investigation. To identify the ecological and nutritional basis of this cultivation barrier, we leveraged CDI susceptibility as a functional readout of microbial community metabolism to infer in vivo nutrient utilization. Germ-free C57BL/6 mice colonized with varying dilutions of ethanol-treated murine microbiota were challenged with C. difficile resulting in a spectrum of CDI outcomes. Comparative metabolomics of pre-challenge fecal samples revealed a consistent carbohydrate signature: glucose accumulated in communities that resisted C. difficile challenge, whereas complex carbohydrates, including raffinose, sucrose, trehalose, lactose, sorbitol, and mannitol, were significantly depleted. The broad depletion of these complex carbohydrates supports their functional importance within the collective microbial community. Conventional glucose-based media (CMA, BHI+I, RCMT) failed to support robust growth or subculture of murine gut microbiota. Guided by the metabolomics findings, we developed Peptone Yeast Extract with Six Salts and Sugars (PYE6S), a glucose-free medium supplemented with the complex carbohydrates identified as depleted. PYE6S enabled cultivation of 22 unique Firmicutes ASVs, 82% of which lacked named cultured representatives in reference databases. These findings suggest a plausible explanation for why conventional media fail and support a metabolomics-guided framework for rational cultivation of host-associated microbiota across diverse systems. This strategy may be extended to guide media design for other host-associated microbiotas.

microbiology↗

bioRxiv2026

The "dark magic mushroom" co-produces amatoxins and psilocybin

The most famous chemicals produced by mushrooms are the psychedelic compound psilocybin from "magic mushrooms" and amatoxins from deadly poisonous mushrooms. These compounds are known to occur in multiple phylogenetically disjunct fungal lineages but have never been shown to co-occur within a single species. Here we show that the "dark magic mushroom" Galerina indica produces both psilocybin and amatoxins. Mass spectrometry revealed psilocybin and amatoxins in mushroom tissues, and genomic analyses identified corresponding biosynthetic genes. Phylogenetic analyses suggest that G. indica acquired psilocybin biosynthesis via horizontal gene transfer after amatoxin biosynthesis was already established, and that psilocybin biosynthesis was acquired twice independently within Galerina. Intriguingly, acquisition of psilocybin biosynthesis in G. indica may have coincided with reduced amatoxin potency. These findings reveal how horizontal gene transfer can combine powerful bioactive systems in a single species, potentially altering the ecological roles of both compound classes and the evolutionary fitness of the species.

evolutionary biology↗

bioRxiv2026

maxiM/Ze: An Image Recognition Approach for Visualizing and Processing Mass Spectrometry Based Metabolomics Data

Informatics is essential in metabolomics to analyze and interpret complex data for the advancement of biological insights. However, many current data-processing tools are time-consuming, require careful parameter selection, and depend heavily on user expertise, making reproducibility a challenge. To address these challenges, we developed maxiM/Ze, a Python-based application that utilizes image recognition algorithms to process liquid chromatography-high resolution mass spectrometry (LC-HRMS) metabolomics data prior to statistical analysis. The software implements an automated sequential pipeline that includes mass detection, extracted ion chromatogram (EIC) generation, peak alignment, and data visualization. By converting extracted ion chromatograms into PNG images, maxiM/Ze applies image processing techniques from OpenCV, including Canny edge detection, watershed segmentation, and Pearson correlation-based clustering, to align peaks across samples with minimal user input. Validation against Compound Discoverer 3.4 and mzmine 4.8.30 using eight replicate pooled plasma samples demonstrated competitive feature detection (12,067 features), annotation (219 unique compounds), and reproducibility (median CV of 35.8%) across platforms. The application is prepared for release on both Mac OS and Windows platforms, with the goal of improving reproducibility in metabolomics data analysis.

bioinformatics↗

bioRxiv2026

Regression-based Modeling of Spearman's Rho for Longitudinal Metabolomics and Mental Wellness in Breast Cancer Patients

SO_SCPLOWUMMARYC_SCPLOWChemotherapy in breast cancer (BC) can substantially affect mental wellness. Advances in metabolomics enable comprehensive profiling of metabolic changes over time during and after treatment, offering insights into biological mechanisms linking chemotherapy to mental health outcomes. To study the association between metabolite profiles and mental wellness, correlation-based analyses are particularly useful. Spearmans rho is a widely used correlation measure and popular alternative to Pearsons correlation, since it also applies to non-linear association between variables. However, existing methods are not designed for longitudinal data and do not allow for covariate adjustments. In this paper, we propose a novel regression-based framework grounded in a class of semiparametric models, the functional response models, to extend this popular correlation measure to longitudinal settings with missing data under the missing at random assumption. This framework facilitates inferences about temporal changes in correlations over time and association of explanatory variables for such changes. We use simulation studies to evaluate performance of the approach with moderate sample sizes. We apply the approach to a one-year longitudinal substudy of the EPIGEN study to examine the longitudinal association between metabolite profiles and mental wellness in BC patients undergoing chemotherapy. The identified metabolites may serve as candidates for future in-depth bioinformatics analyses and translational investigations.

cancer biology↗

bioRxiv2025

CerS2 is a druggable target in triple-negative breast cancer

Triple-negative breast cancer (TNBC) remains a significant clinical challenge due to its aggressive nature and lack of effective targeted therapies. The enzyme ceramide synthase 2 (CerS2), which synthesizes pro-apoptotic very long-chain ceramides (VLCCs), represents a promising therapeutic target. Here, we identify and characterize DH20931, a novel, first-in-class small-molecule agonist of CerS2. We demonstrate that DH20931 directly activates CerS2 with nanomolar potency, leading to significant VLCC accumulation in breast cancer cells. This lipotoxic event induces endoplasmic reticulum (ER) stress and triggers apoptosis via the canonical ATF4/CHOP/PUMA signaling pathway. Mechanistically, we uncover a novel interaction between CerS2 and the ER calcium channel, Inositol 1,4,5-trisphosphate receptor 1 (IP3R1). We demonstrate that DH20931 promotes this interaction, enhancing ER-mitochondria proximity and facilitating a CerS2-dependent flux of calcium (Ca{superscript 2}) from the ER into mitochondria. This subsequent mitochondrial Ca{superscript 2} overload serves as a critical trigger for apoptosis. In preclinical evaluations, DH20931 potently inhibited the growth of TNBC cells in 2D and 3D cultures and significantly suppressed tumor progression in orthotopic and patient-derived xenograft (PDX) models, all while exhibiting a favorable safety profile. Our findings validate CerS2 as a druggable target in TNBC and establish a novel therapeutic strategy that leverages a coordinated attack on cancer cells through ER stress and calcium-mediated mitochondrial dysfunction. Graphical Abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=100 SRC="FIGDIR/small/670525v1_ufig1.gif" ALT="Figure 1"> View larger version (33K): org.highwire.dtl.DTLVardef@2db377org.highwire.dtl.DTLVardef@6175d4org.highwire.dtl.DTLVardef@7edb8corg.highwire.dtl.DTLVardef@80042e_HPS_FORMAT_FIGEXP M_FIG C_FIG HighlightsO_LIDH20931 is a first-in-class, potent agonist of Ceramide Synthase 2 (CerS2). C_LIO_LICerS2 activation induces ER stress and engages the ATF4/CHOP/PUMA apoptotic pathway. C_LIO_LIDH20931 promotes a novel CerS2-IP3R1 interaction, causing lethal mitochondrial calcium overload. C_LIO_LITargeting CerS2 shows significant preclinical efficacy against triple-negative breast cancer. C_LI

cancer biology↗

bioRxiv2023

Matrix Linear Models for connecting metabolite composition to individual characteristics

AO_SCPLOWBSTRACTC_SCPLOWHigh-throughput metabolomics data provide a detailed molecular window into biological processes. We consider the problem of assessing how the association of metabolite levels with individual (sample) characteristics such as sex or treatment may depend on metabolite characteristics such as pathway. Typically this is one in a two-step process: In the first step we assess the association of each metabolite with individual characteristics. In the second step an enrichment analysis is performed by metabolite characteristics among significant associations. We combine the two steps using a bilinear model based on the matrix linear model (MLM) framework we have previously developed for high-throughput genetic screens. Our framework can estimate relationships in metabolites sharing known characteristics, whether categorical (such as type of lipid or pathway) or numerical (such as number of double bonds in triglycerides). We demonstrate how MLM offers flexibility and interpretability by applying our method to three metabolomic studies. We show that our approach can separate the contribution of the overlapping triglycerides characteristics, such as the number of double bonds and the number of carbon atoms. The proposed method have been implemented in the open-source Julia package, MatrixLM. Data analysis scripts with example data analyses are also available.

bioinformatics↗

bioRxiv2021

The DAXX-SREBP axis promotes oncogenic lipogenesis and tumorigenesis

De novo lipogenesis produces lipids for membrane biosynthesis and cell signaling. Elevated lipogenesis is a major metabolic feature in cancer cells. In breast and other cancer types, genes involved in lipogenesis are highly upregulated, but the mechanisms that control their expression remain poorly understood. DAXX modulates gene expression through binding to diverse transcription factors although the functional impact of these diverse interactions remains to be defined. Our recent analysis indicates that DAXX is overexpressed in diverse cancer types. However, mechanisms underlying DAXXs oncogenic function remains elusive. Using global integrated transcriptomic and lipidomic analyses, we show that DAXX plays a key role in lipid metabolism. DAXX depletion attenuates, while its overexpression enhances, lipogenic gene expression, lipid synthesis and tumor growth. Mechanistically, DAXX interacts with SREBP1 and SREBP2 and activates SREBP-mediated transcription. DAXX associates with lipogenic gene promoters through SREBPs. Underscoring the critical roles for the DAXX-SREBP interaction for lipogenesis, SREBP2 knockdown attenuates tumor growth in cells with DAXX overexpression, and a DAXX mutant unable to bind SREBPs are incapable of promoting lipogenesis and tumor growth. Our results identify the DAXX-SREBP axis as an important pathway for tumorigenesis.

cancer biology↗

bioRxiv2020

Antibiotics effects on the fecal metabolome in preterm infants

Within a randomized prospective pilot study of preterm infants born less than 33 weeks gestation, fecal samples were collected weekly and metabolomic analysis was performed. The objective is to evaluate for differences in fecal metabolites in infants exposed to antibiotics vs not exposed to antibiotics in the first 48hours after birth. Significant differences were seen in the antibiotics vs no antibiotics group, including pathways related to vitamin biosynthesis, bile acids, amino acid metabolism and neurotransmitters. Early antibiotic exposure in preterm infants may alter metabolites in the intestinal tract of preterm infants. Broader multi-omic studies that address mechanisms will guide more prudent antibiotic use in this population.

microbiology↗

bioRxiv2020

Antibiotics may influence gut microbiome signaling to the brain in preterm neonates

Antibiotic use in neonates can have detrimental effects on the developing gut microbiome, increasing the risk of morbidity. A majority of preterm neonates receive antibiotics after birth without clear evidence to guide this practice. Here microbiome, metabolomic, and immune marker results from the Routine Early Antibiotic use in SymptOmatic preterm Neonates (REASON) study are presented. The REASON study is the first trial to randomize symptomatic preterm neonates to receive or not receive antibiotics in the first 48 hours after birth. Using 16S rRNA sequencing of stool samples collected longitudinally for 91 neonates, the effect of such antibiotic use on microbiome diversity is assessed. The results illustrate that type of nutrition shapes the early infant gut microbiome. By integrating data for the gut microbiome, stool metabolites, stool immune markers, and inferred metabolic pathways, an association was discovered between Veillonella and the neurotransmitter gamma-aminobutyric acid (GABA). These results suggest early antibiotic use may impact the gut-brain axis with the potential for consequences in early life development, a finding that needs to be validated in a larger cohort.

microbiology↗

bioRxiv2017

Untargeted Metabolomic Analysis In Naturally Occurring Canine Diabetes Mellitus Identifies Similarities To Human Type 1 Diabetes

While predominant as a disease entity, knowledge voids exist regarding the pathogenesis of canine diabetes. To test the hypothesis that diabetic dogs have similar metabolomic perturbations to humans with type 1 diabetes (T1D), we analyzed serum metabolomic profiles of breed- and body weight-matched, diabetic (n=6) and healthy (n=6) dogs by liquid chromatography-mass spectrometry (LC-MS) profiling. We report distinct clustering of diabetic and control groups based on heat map analysis of known and unknown metabolites. Random forest classification identified 5/6 dogs per group correctly with overall out of bag error rate=16.7%. Diabetic dogs demonstrated significant upregulation of glycolysis/gluconeogenesis intermediates (e.g., glucose/fructose, C6H12O6, keto-hexose, deoxy-hexose, (P<0.01)), with significant downregulation of tryptophan metabolism metabolites (e.g., picolinic acid, indoxyl sulfate, anthranilate, (P<0.01)). Multiple amino acids (AA), AA metabolites, and bile acids were also significantly lower in diabetic versus healthy dogs (P<0.05) with the exception of the branched chain AA valine, which was elevated in diabetic animals (P<0.05). Metabolomic profiles in diabetic versus healthy dogs shared similarities with those reported in human T1D (e.g., alterations in glycolysis/gluconeogensis metabolites, bile acids, and elevated branched chain AA). Further studies are warranted to evaluate the utility of canine diabetes to provide novel mechanistic insights to the human disorder.\n\nAbbreviations

systems biology↗