Biology subjects

Paurus, V. L.

Publications and source records attributed to Paurus, V. L..

7 recordsLinked to original sources

bioRxiv2026

Multiomics dissection of the CO2-dependent fast growth of Picochlorum celeri

With a sub-3-micrometer cell and a 27-Mbp diploid genome, the small but mighty Picochlorum celeri doubles within three hours and exceeds 30 g m-2 d-1 outdoors with CO2 supplied. In climate-simulating photobioreactors, productivity declined from 39 g m-2 d-1 under 2.25% CO2 to near zero in air and resumed within hours of CO2 restoration with photosystem II photochemistry largely retained. We profiled the transcriptome, proteome, phosphoproteome, ubiquitinome, acetylome, metabolome and lipidome, including the first site-resolved green-algal ubiquitinome. In air, the Rubisco large subunit tripled to 7.6% of protein while its two small-subunit isoforms, 70% identical, exchanged near-reciprocally, a novel CO2-dependent switch that we propose retunes Rubisco to low CO2. Photorespiratory enzymes increased in abundance whereas proteins involved in external nitrogen assimilation decreased, consistent with increased reliance on internal nitrogen recycling as carbon fixation became limiting. Transcript and protein responses were nearly uncoupled, with cytosolic ribosomal proteins declining despite increased transcripts and plastid ribosomal proteins spared. Phosphorylation was extensively remodeled, as were the lysine modifications ubiquitination, acetylation and CO2-dependent carbamylation, where a CO2-competition assay identified 86 bicarbonate-protected lysines, including RbcL K252. CO2 limitation therefore imposes a reversible growth arrest that reduces cytosolic translational investment while preserving photosynthetic capacity for rapid recovery.

plant biology↗

bioRxiv2026

Saprotrophic soil fungus dissolves lithium lepidolite through inositol rescue metabolism

Microbial weathering of minerals represents a key biogeochemical process through which microorganisms access essential nutrients locked within rock and ore substrates. In this study, we investigated the metabolic response of a model fungus during growth on lithium (Li) ore to elucidate the mechanisms underpinning biologically mediated mineral weathering and subsequent metal mobilization. Our results revealed a distinct metabolic shift in the fungus when cultivated on Li ore, characterized by altered patterns of organic acid production and energy metabolism. This shift coincided with measurable weathering of the Li ore matrix and the consequent release of soluble Li into the surrounding environment, demonstrating a direct link between fungal metabolic reprogramming and mineral breakdown. Importantly, these observations are consistent with metabolic shifts documented in previous studies of mineral weathering by this fungus, suggesting that such responses constitute a conserved and reproducible strategy employed during the colonization of mineral substrates. Although the present work was conducted using a single model microorganism under controlled conditions, the findings carry broader ecological implications. Natural microbial communities inhabiting mineral-rich environments may undergo analogous metabolic shifts when weathering minerals to acquire limiting nutrients, thereby contributing to large-scale elemental cycling and metal release. Understanding these processes not only advances fundamental knowledge of microbe-mineral interactions but also informs emerging applications in biomining and bio-based recovery of critical metals such as lithium. Collectively, this study highlights the central role of microbial metabolism in driving mineral weathering and offers a framework for predicting and harnessing similar processes within complex microbial communities. IMPORTANCEThe model fungus exhibits a distinct, reproducible shift in metabolism--particularly in organic acid production and energy pathways--when grown on Li ore, directly linking cellular metabolism to mineral breakdown. This metabolic shift promotes weathering of the ore matrix and the release of soluble lithium, demonstrating a biological route for liberating a critical metal from its mineral host. Because similar shifts occur across different minerals, natural microbial communities likely employ comparable strategies to weather minerals and acquire limiting nutrients, contributing to global elemental cycling. These findings provide a foundation for sustainable biomining and bio-based recovery of lithium and other critical metals, offering a lower-impact alternative to conventional extraction methods.

microbiology↗

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↗

bioRxiv2025

Discovery of non-opioid receptor protein targets of fentanyl across tissues from animal models and humans using photoaffinity probes

Synthetic opioids such as fentanyl and related analogs have been widely used for pain management. However, their negative side effects, including respiratory depression and high potential for addiction, underscore the need for a deeper understanding of fentanyls interactions with proteins throughout the human body. Fentanyl analogs bind and activate opioid receptors in the central and peripheral nervous systems, triggering numerous downstream signaling pathways. Increasingly, fentanyl has been shown to interact with non-opioid receptors, and elucidation of these non-canonical fentanyl-protein interactions may provide insights into the mechanisms contributing to fentanyls adverse effects and illuminate novel countermeasure strategies. To identify proteins in mammalian tissues that may interact with fentanyl, we designed and synthesized three affinity-based probes (AfBPs) that include the fentanyl core and feature a diazirine photoaffinity group and alkyne handle for click chemistry at different positions. Molecular docking simulations predicted that these AfBPs bind the mu opioid receptor similarly to fentanyl. Affinity-based protein profiling using the FA-T1 probe in vitro in tissues from six animal species identified histamine N-methyltransferase (HNMT), endophilin-B1 (SH3GLB1), fructosamine-3-kinase (FN3K), cutA divalent cation tolerance analog (CUTA), and monoamine oxidase B (MAOB) among the top proteins that bind fentanyl in multiple species and tissue types. Molecular docking of fentanyl and remifentanil with these protein structures identified putative binding sites. The interaction of fentanyl with specific proteins was empirically assessed through protein structural analyses. These findings highlight potential fentanyl-protein interactions that may contribute to the acute and long-term impacts of fentanyl exposures.

pharmacology and toxicology↗

bioRxiv2023

A Phosphoproteomics Data Resource for Systems-level Modeling of Kinase Signaling Networks

Building mechanistic models of kinase-driven signaling pathways requires quantitative measurements of protein phosphorylation across physiologically relevant conditions, but this is rarely done because of the insensitivity of traditional technologies. By using a multiplexed deep phosphoproteome profiling workflow, we were able to generate a deep phosphoproteomics dataset of the EGFR-MAPK pathway in non-transformed MCF10A cells across physiological ligand concentrations with a time resolution of <12 min and in the presence and absence of multiple kinase inhibitors. An improved phosphosite mapping technique allowed us to reliably identify >46,000 phosphorylation sites on >6600 proteins, of which >4500 sites from 2110 proteins displayed a >2-fold increase in phosphorylation in response to EGF. This data was then placed into a cellular context by linking it to 15 previously published protein databases. We found that our results were consistent with much, but not all previously reported data regarding the activation and negative feedback phosphorylation of core EGFR-ERK pathway proteins. We also found that EGFR signaling is biphasic with substrates downstream of RAS/MAPK activation showing a maximum response at <3ng/ml EGF while direct substrates, such as HGS and STAT5B, showing no saturation. We found that RAS activation is mediated by at least 3 parallel pathways, two of which depend on PTPN11. There appears to be an approximately 4-minute delay in pathway activation at the step between RAS and RAF, but subsequent pathway phosphorylation was extremely rapid. Approximately 80 proteins showed a >2-fold increase in phosphorylation across all experiments and these proteins had a significantly higher median number of phosphorylation sites (~18) relative to total cellular phosphoproteins (~4). Over 60% of EGF-stimulated phosphoproteins were downstream of MAPK and included mediators of cellular processes such as gene transcription, transport, signal transduction and cytoskeletal arrangement. Their phosphorylation was either linear with respect to MAPK activation or biphasic, corresponding to the biphasic signaling seen at the level of the EGFR. This deep, integrated phosphoproteomics data resource should be useful in building mechanistic models of EGFR and MAPK signaling and for understanding how downstream responses are regulated.

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↗