Biology subjects

Clendinen, C. S.

Publications and source records attributed to Clendinen, C. S..

5 recordsLinked to original sources

bioRxiv2026

Studying Effects of PDA Media Strengths on the Growth of Colletotrichum sublineola Using MPLEx-Based Integrative Proteomics and Metabolomics Analyses

Colletotrichum sublineola (Cs), the hemibiotrophic fungus that causes sorghum anthracnose, impacts sorghum grain and biomass crop production worldwide. Although nutrient availability is known to influence development in filamentous fungi, including Colletotrichum species, how in vitro nutrient limitation reprograms the Cs cellular state remains unclear. We cultured Cs on full-strength, half-strength, and one-tenth-strength potato dextrose agar (PDA) to define responses across a nutrient gradient. Nutrient limitation induced a pronounced high-sporulation phenotype, with one-tenth-strength PDA producing the strongest conidiation response, followed by half-strength PDA. To study the underlying molecular programs in each condition, we employed a multiplexed metabolite, protein, and lipid extraction (MPLEx) protocol for global proteomics and metabolomics. Global proteomics resulted in 4,590 protein identifications, including 204 unique to one-tenth-strength PDA. Among them are proteins linked to sporulation, vesicular transport, glycosylphosphatidylinositol (GPI)-anchor biosynthesis, and common in fungal extracellular membrane (CFEM)-domain proteins. Differential abundance and pathway analyses revealed a broad reduction of central carbon and energy metabolism, including glycolysis/gluconeogenesis, pentose phosphate, pyruvate metabolism, and glyoxylate pathways, together with increased ribosome-related processes, cAMP signaling, and cell-surface remodeling in one-tenth-strength PDA conditions. In addition, correlative metabolomics supported selective metabolic depletion and resource reallocation toward stress adaptation, membrane remodeling, and conidiation, supporting proteomics findings. Together, these data support a starvation-adapted Cs developmental state associated with enhanced sporulation, cellular pathway reprogramming, and potential virulence linked preparedness under nutrient-limited growth conditions in vitro. Graphical Abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=114 SRC="FIGDIR/small/724728v2_ufig1.gif" ALT="Figure 1"> View larger version (33K): org.highwire.dtl.DTLVardef@f9039borg.highwire.dtl.DTLVardef@5bef0eorg.highwire.dtl.DTLVardef@1abb7b6org.highwire.dtl.DTLVardef@aa11a9_HPS_FORMAT_FIGEXP M_FIG C_FIG

molecular biology↗

bioRxiv2026

Rational reduction of a sorghum SynCom that preserves growth promotion reveals flavonoid-mediated plant microbe interactions

O_LIPlant growth is influenced by the composition of its associated microbiome. The inherent complexity and functional redundancy of natural plant microbiomes presents a formidable barrier to understanding the myriad biological interactions therein. Efforts have been made to develop synthetic microbial communities (SynComs) that can provide a rigorous and generalizable framework for the rational design of next-generation microbial products for sustainable agriculture. We test multiple strategies for stable, plant growth promoting SynCom design and evaluate the phenotypic and molecular impacts of a successful plant-SynCom interaction. C_LIO_LIWe designed 4 distinct, reduced-complexity variants of SynCom SRC1 and assessed their capacities for colonization, stability, and plant growth promotion. To understand the impact on plant performance of our highest performing SynCom variant, we characterized the hosts longitudinal transcriptional response to SynCom inoculation and corroborated the results with metabolomics analysis. C_LIO_LIThe top performing SynCom stably colonized sorghum roots and rhizospheres, elicited plant growth promotion, and induced dynamic spatiotemporal gene transcription in sorghum roots and shoots defined by modulation of growth-defense tradeoff machinery and enhanced flavonoid production. C_LIO_LIThe resultant reduced-complexity SynCom is a highly stable, soil-independent, plant growth promoting, and demonstrates the utility of colonization-based selection criteria, integrated with longitudinal transcriptomic and metabolomic characterization. C_LI

plant biology↗

bioRxiv2024

PyINETA: Open-source platform for INADEQUATE-JRES integration in NMR metabolomics

Annotating compounds with high confidence is a critical element in metabolomics. 13C-detection NMR experiment INADEQUATE (incredible natural abundance double-quantum transfer experiment) stands out as a powerful tool for structural elucidation, whereas this valuable experiment is not often included in metabolomics studies. This is partly due to the lack of community platform that provides structural information based INADEQUATE. Also, it is often the case that a single study uses various NMR experiments synergistically to improve the quality of information or balance total NMR experiment time, but there is no public platform that can integrate the outputs of INADEQUATE and other NMR experiments either. Here, we introduce PyINETA, Python-based INADEQUATE network analysis. PyINETA is an open-source platform that provides structural information of molecules using INADEQUATE, conducts database search, and integrates information of INADEQUATE and a complementary NMR experiment 13C J-resolved experiment (13C-JRES). Those steps are carried out automatically, and PyINETA keeps track of all the pipeline parameters and outputs, ensuring the transparency of annotation in metabolomics. Our evaluation of PyINETA using a model mouse study showed that our pipeline successfully integrated INADEQUATE and 13C-JRES. The results showed that 13C-labeled amino acids that were fed to mice were transferred to different tissues, and, also, they were transformed to other metabolites. The distribution of those compounds was tissue-specific, showing enrichment of particular metabolites in liver, spleen, pancreas, muscle, or lung. The value of PyINETA was not limited to those known compounds; PyINETA also provided fragment information for unknown compounds. PyINETA is available on NMRbox.

systems biology↗

bioRxiv2023

Gaussian Mixture Modeling Extensions for Improved False Discovery Rate Estimation in GC-MS Metabolomics

The ability to reliably identify small molecules (e.g. metabolites) is key towards driving scientific advancement in metabolomics. Gas chromatography-mass spectrometry (GC-MS) is an analytic method that may be applied to facilitate this process. The typical GC-MS identification workflow involves quantifying the similarity of an observed sample spectrum and other features (e.g. retention index) to that of several references, noting the compound of the best-matching reference spectrum as the identified metabolite. While a deluge of similarity metrics exists, none quantify the error rate of generated identifications, thereby presenting an unknown risk of false identification or discovery. To quantify this unknown risk, we propose a model-based framework for estimating the false discovery rate (FDR) among a set of identifications. Extending a traditional mixture modeling framework, our method incorporates both similarity score and experimental information in estimating the FDR. We apply these models to identification lists derived from across 548 samples of varying complexity and sample type (e.g. fungal species, standard mixtures, etc.), comparing their performance to that of the traditional Gaussian mixture model (GMM). Through simulation, we additionally assess the impact of reference library size on the accuracy of FDR estimates. In comparing the best performing model extensions to the GMM, our results indicate relative decreases in median absolute estimation error (MAE) ranging from 12% to 70%, based on comparisons of the median MAEs across all hit-lists. Results indicate that these relative performance improvements generally hold despite library size, however FDR estimation error typically worsens as the set of reference compounds diminishes. For TOC graphic only O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=74 SRC="FIGDIR/small/527348v1_ufig1.gif" ALT="Figure 1"> View larger version (11K): org.highwire.dtl.DTLVardef@ab9e2eorg.highwire.dtl.DTLVardef@11e0d8aorg.highwire.dtl.DTLVardef@ae2acorg.highwire.dtl.DTLVardef@a7ae20_HPS_FORMAT_FIGEXP M_FIG C_FIG

bioinformatics↗

bioRxiv2023

Evaluating Retention Index Score Assumptions to Refine GC-MS Metabolite Identification

As metabolomics grows into a high-throughput and high demand research field, current metrics for the identification of small molecules in GC-MS still require manual verification. Though steps have been taken to improve scoring metrics by combining spectral similarity (SS) and retention index (RI), the problem persists. A large body of literature has analyzed and refined SS scores, but few studies have explicitly studied improvements to RI scores. Here, we examined whether uninvestigated assumptions of the RI score are valid and propose ways to improve them. Query RI were matched to library RI with a generous window of +/-35 to avoid unintentional removal of valid compound identifications. Each match was manually verified as a true positive, true negative or unknown. Metabolites with at least 30 true positive identifications were included in downstream analyses, resulting in a total of 87 metabolites from samples of varying complexity and type (e.g., amino acid mixtures, human urine, fungal species, etc.). Our results showed that the RI score assumptions of normality, consistent variance across metabolites, and a mean error centered at 0 are often violated. We demonstrated through a cross-validation analysis that modifying these underlying assumptions according to empirical, metabolite-specific distributions improved the true positive and negative rankings. Further, we statistically determined the minimum number of samples required to estimate distributional parameters for scoring metrics. Overall, this work proposes a robust statistical pipeline to reduce the time bottleneck of metabolite identification by improving RI scores and thus minimizes the effort to complete manual verification.

bioinformatics↗