Search bioRxiv⌕ Search
Home/Archive/bioRxiv

bioRxiv · 10.64898/2026.06.16.732715

A Multisensor Framework Reveals Redox Constraints on Glycolysis in vivo

Ravikumar, S.·Wolfe, A.·Colon-Ramos, D.
View original source ↗Export metadata ↓

Abstract

Genetically encoded biosensors have transformed the study of metabolism, yet measurements of individual metabolites often provide an incomplete view of pathway regulation. Here, we develop a multisensor framework in Caenorhabditis elegans neurons to interpret glycolytic dynamics and redox state in vivo. We combine biosensors for NADH/NAD, fructose-1,6-bisphosphate, lactate, and pyruvate to resolve metabolic responses during hypoxia and redox perturbation. To causally test how redox state modulates glycolysis in vivo, we cell-specifically expressed the NADH-producing enzyme EcSTH and the NADH oxidase LbNOX to bidirectionally tune neuronal NADH/NAD balance. These perturbations revealed that redox modulation is sufficient to constrain or relieve lower glycolytic activity. Elevation of NADH/NAD promoted accumulation of upper glycolytic intermediates while suppressing lower glycolytic responses during energetic stress, consistent with inhibition at the NAD-dependent GAPDH step. Conversely, oxidation of NADH relieved this constraint and shifted metabolite pools consistent with enhanced lower glycolytic activity. Elevated NADH/NAD ratios also impaired synaptic vesicle organization, linking redox-mediated glycolytic inhibition to neuronal function. As a case study for how integrated biosensor approaches can provide semi-quantitative insight into pathway-level metabolic regulation, we genetically perturbed endogenous NADH recycling pathways. These experiments revealed a hierarchical organization of neuronal redox buffering, with lactate dehydrogenase (LDH-1) serving as the dominant route for NAD regeneration during hypoxia and glycerol-3-phosphate dehydrogenase (GPDH-2) providing a secondary compensatory pathway. Graded impairment of NADH recycling resulted in corresponding increases in fructose-1,6-bisphosphate accumulation and synaptic defects, consistent with progressive inhibition of lower glycolysis. Together, these results establish a tractable in vivo system to probe causal relationships between redox state, glycolytic dynamics, and cellular physiology.

Explore related subjects

Molecular BiologyPreprint

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

Ravikumar, S., Wolfe, A., Colon-Ramos, D.. 2026-06-17. A Multisensor Framework Reveals Redox Constraints on Glycolysis in vivo. https://doi.org/10.64898/2026.06.16.732715

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related preprints

bioRxiv2026

Functional primary human 3D skeletal muscle organoids enable exercise and metabolic research

Human skeletal muscle is the principal site of insulin-stimulated glucose disposal and a major mediator of exercise-induced metabolic benefits, yet human models that preserve metabolic and exercise responsiveness remain limited. We generated primary human skeletal muscle organoids from donor-derived CD56+ myoblasts using a collagen-based extracellular matrix and serum-free IGF1-guided differentiation. The organoids formed aligned contractile tissues containing oxidative and glycolytic fiber type-like myotubes, displayed enhanced mitochondrial respiration, insulin-stimulated glucose uptake, and reproducible force generation. Electrical pulse stimulation induced AMPK activation, increased glucose utilization and lactate production, and upregulated canonical exercise-responsive genes including NR4A3 and PPARGC1A. Notably, transcriptional responses to in vitro exercise overlapped with acute exercise responses observed in skeletal muscle biopsies from the same donors. The organoids further detected functional impairments of skeletal muscle performance induced by TGF-{beta}1 and metformin and increased speed generation by testosterone treatment. These findings establish a donor-specific human skeletal muscle platform that recapitulates key features of insulin action and exercise adaptation and may enable mechanistic studies of skeletal muscle metabolism, exercise responsiveness, and therapeutic interventions relevant to diabetes.

Molecular Biology↗
bioRxiv2026

TRIDENT (Taxonomic Resolution and IDentification using Environmental dNa Traces): An Optimized Algorithm for Vertebrate Taxonomic Assignments in eDNA Metabarcoding, Integrating Molecular, Taxonomic, and Ecological Criteria

Environmental DNA (eDNA) metabarcoding has become a powerful approach for large-scale biodiversity assessment, yet taxonomic assignment remains one of its most critical error-prone steps. Current bioinformatic pipelines rely on molecular similarity searches against reference databases, but assignment accuracy is constrained not only by short marker length and database incompleteness, but also by fundamental limitations, including recent species radiations, incomplete lineage sorting, introgression, NUMTs, and the imperfect correspondence between genetic variation and species boundaries. Here, we present TRIDENT (Taxonomic Resolution and IDentification using Environmental dNa Traces), an automated and simple protocol designed to improve taxonomic assignments in eDNA metabarcoding. Initially developed for marine vertebrates, TRIDENT may be used with any barcode and integrates three complementary sources of evidence: molecular similarity (NCBI/GenBank and BOLD), curated taxonomic information (WoRMS), and ecological plausibility derived from biogeographic occurrence data (GBIF). The workflow sequentially constructs candidate taxon lists based on sequence similarity, expands them through taxonomic hierarchies, and filters them using spatial occurrence constraints. It further identifies possible taxa lacking reference barcodes and evaluates their plausibility through CO1-based similarity if data exist in BOLD. TRIDENT has been implemented as a source-available Python tool and tested using empirical eDNA datasets from marine vertebrates as well as simulated communities. Results demonstrate that the tool produces taxonomic assignments consistent with expert manual curation while substantially reducing processing time and attention errors caused by manual processing of large datasets. By combining molecular, taxonomic, and ecological criteria within a single framework, TRIDENT improves transparency and reproducibility and provides a robust and flexible solution strengthening confidence in taxonomic identifications in eDNA-based biodiversity assessments.

Molecular Biology↗
bioRxiv2026

Identifying and Addressing Systematic Data Leakage in Protein-Ligand Affinity Benchmarks

Accurate prediction of protein-ligand binding affinity is a crucial goal in structure-based drug discovery, with the potential to significantly shorten development timelines. Recently, a new wave of machine learning models based on co-folding, such as Boltz-2 and IsoDDE, has demonstrated performance that matches or exceeds that of gold-standard physics-based methods like Free Energy Perturbation (FEP). This paper provides a critical assessment of these claims, revealing that current benchmarks are heavily influenced by data leakage, and proposes a new benchmark that explicitly controls for data leakage. We demonstrate that splitting by protein-sequence identity is inherently insufficient to prevent data leakage due to "target mirroring," in which homologous proteins with low overall sequence identity still exhibit highly correlated binding profiles. Our meta-analysis of documents in the ChEMBL 36 database identifies more than 6,000 such assay pairs and finds that leakage persists for sequence-identity thresholds as low as 0.2, well below the values commonly used in benchmarks today. Additionally, we show that a ligand-only baseline model, which lacks protein structural information, achieves surprisingly high performance on the FEP+ 4 and OpenFE benchmarks (r = 0.66 and r = 0.36, respectively). Our results indicate that current benchmarks tend to reward models for memorizing training data and exploiting localized leakage rather than truly learning biophysical principles. To address this issue, we propose the Novelty-Tiered Affinity Benchmark, in which the test data is partitioned into ligand novelty tiers. In the most challenging tier (Tanimoto similarity < 0.35), ligand-only models perform notably worse (r = 0.14), offering a clear baseline for evaluating genuine generalization. We argue that the field must move beyond sequence-based splits to ensure that AI-driven discovery translates into successful prospective laboratory research.

Molecular Biology↗