Biology subjects

Jerger, A.

Publications and source records attributed to Jerger, A..

3 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

ChatGEM: An Agentic Architecture Enabling Interactive Simulation of Genome-Scale Metabolic Models

Genome-scale metabolic models (GEMs) are powerful tools for predicting cellular phenotypes and guiding microbial strain engineering, yet broad adoption remains challenging due to the computational expertise required. To overcome that, we present ChatGEM, an agentic platform that enables interactive GEM simulation through natural language. Built on the multi-agent ADEPT framework, ChatGEM integrates COBRApy within a retrieval-augmented generation (RAG) architecture that coordinates code generation and execution through specialized agents. Benchmarking across three tasks of increasing complexity showed that RAG-enabled code generation improved the mean overall performance score from 2.63 to 4.20 while reducing the execution time significantly starting from routine to complex tasks. Application of ChatGEM using an enzyme-constrained GEM (ecGEM) for four engineered Pseudomonas putida KT2440 strains identified the constitutive strain as the optimal chassis for succinate overproduction using a succinate leakage index - a prediction observed experimentally. Therefore, ChatGEM democratizes metabolic modeling by enabling researchers without computational expertise to perform sophisticated GEM-based analyses through natural language, and, hence, accelerating scientific discovery.

systems biology↗

bioRxiv2024

CLEAN-Contact: Contrastive Learning-enabled Enzyme Functional Annotation Prediction with Structural Inference

Recent years have witnessed the remarkable progress of deep learning within the realm of scientific disciplines, yielding a wealth of promising outcomes. A prominent challenge within this domain has been the task of predicting enzyme function, a complex problem that has seen the development of numerous computational methods, particularly those rooted in deep learning techniques. However, the majority of these methods have primarily focused on either amino acid sequence data or protein structure data, neglecting the potential synergy of combining of both modalities. To address this gap, we propose a novel Contrastive Learning framework for Enzyme functional ANnotation prediction combined with protein amino acid sequences and Contact maps (CLEAN-Contact). We rigorously evaluated the performance of our CLEAN-Contact framework against the state-of-the-art enzyme function prediction model using multiple benchmark datasets. Using CLEAN-Contact, we predicted novel enzyme functions within the proteome of Prochlorococcus marinus MED4. Our findings convincingly demonstrate the substantial superiority of our CLEAN-Contact framework, marking a significant step forward in enzyme function prediction accuracy.

bioinformatics↗