Biology subjects

Jager, S.

Publications and source records attributed to Jager, S..

8 recordsLinked to original sources

bioRxiv2026

Disentangling maternal pregnancy and fetal-specific plasma signatures through integrated proteomics and site-specific N-glycoproteomics

During pregnancy, mother and fetus co-exist albeit as two physiologically distinct individuals, connected through the placenta and the umbilical cord. This connection allows for the exchange of vital substances, while individual blood homeostasis remains intact. We hypothesized that comparing maternal and cord plasma (N-glyco)proteomes would provide insights into the biological communication and unique physiological state of each individual. As comparing only matched maternal and umbilical cord blood plasma misses a crucial identifier in determining whether observed changes are related to infancy, pregnancy or both, we designed a triad study by adding a non-pregnant women control group. This allowed us to distinguish pregnancy-specific proteins and their potentially altered N-glycosylation from those in infancy. We found that the fetus and mother have distinct proteomes, N-glycoproteoforms and alternative complement regulation. Moreover, we observed that pregnancy in women led to specific N-glycosylation changes on a select set of primarily HDL-associated and acute-phase proteins. Lastly, our data provide evidence for an early fetal development of the humoral immune system as especially fetal IgM displayed distinctive N-glycosylation signatures.

developmental biology↗

bioRxiv2026

mRNA-GPT: A Generative Model for Full-Length mRNA Design and Optimization

We introduce mRNA-GPT, a generative model for end-to-end full-length mRNA sequence design and optimization. Unlike existing approaches that optimize isolated regions, mRNA-GPT jointly optimizes across all three regions (5' UTR, CDS, and 3' UTR) to capture long-range sequence dependencies and cross-region regulatory interactions critical for therapeutic efficacy. The model is pre-trained on 30 million full-length natural mRNA sequences across diverse species and organisms, establishing a robust foundation for sequence generation. We employ Reinforcement Learning (RL), specifically Proximal Policy Optimization (PPO) with oracle-based reward signals, to directly and iteratively optimize target properties, such as half-life and translation efficiency. mRNA-GPT supports flexible generation modes: single regions (UTR or CDS alone), full-length sequences, or generation of any region conditioned on any other region. Through multi-objective optimization, mRNA-GPT achieves Pareto-optimal designs, i.e., solutions for which no single objective can be improved without degrading another, that balance competing properties without sacrificing performance on either objective. mRNA-GPT demonstrates superior design capabilities compared to state-of-the-art methods, achieving enhanced performance in 3' UTR stability optimization, CDS translation rate enhancement, and comprehensive full-length sequence design.

bioinformatics↗

bioRxiv2026

Expanding Glycopeptide Identification with Match-Between-Glycans in FragPipe

Glycosylation is one of the most important, but also most complex, post-translational modifications of proteins, playing a pivotal role in various pathological processes. Mass spectrometry-based large-scale glycoproteomics analysis offers a powerful approach to explore the fundamental roles of glycosylation in both physiological and pathological contexts. Traditionally, DDA glycopeptide assignment relies on information-dense MS2 spectra, containing sufficient fragmentation information to identify both the peptide and glycan moieties. Achieving this fragmentation can be difficult, especially for low-abundant glycopeptides and/or large, complex glycans. These glycopeptides are often not assigned using current data analysis software, yet they can be of biological relevance. Here, we introduce a method called match-between-glycans (MBG), which expands glycopeptide identification while maintaining the existing glycoproteome analysis workflow. MBG enables expanding the set of identified glycopeptides to include those without MS2 spectra, or with lower quality MS2 spectra, by looking for MS1 signals displaced from other identified glycopeptides by one or multiple monosaccharide unit(s). MBG can also identify glycans not included in the glycan database, such as those containing adducts or modifications, allowing these glycans to be recovered without a drastic expansion of the search space. Combined with target-decoy FDR control, we show this method is capable of accurately expanding glycopeptide identifications and providing a more complete quantitative profile of glycosylation at each glycosite. MBG is fully integrated into the glycoproteomics workflows in FragPipe, allowing seamless, one-click operation.

bioinformatics↗

bioRxiv2025

A universal spectrum annotator for complex peptidoforms in mass spectrometry-based proteomics

Accurate and comprehensive peptide spectrum annotation is a crucial step to interpret mass spectrometry-based proteomics data. While peak assignment in peptide fragmentation spectra is central to a broad range of proteomics applications, current tools tend to be specialized to a specific task. Here we present a more comprehensive tool, the Annotator, which unifies spectrum annotation for bottom-up, middle-down, top-down, cross-linked, and glycopeptide fragmentation mass spectra, from all fragmentation methods, including all ion types: a/b/c, x/y/z, d/v/w, and immonium ions. The Annotator integrates all known post-translational modifications from common databases and additionally allows for the definition of custom fragmentation models and modifications. Modifications allow for diagnostic fragment ions, site specific neutral losses, and multiple breakage sites for cross-linkers. The underlying library used for the theoretical fragmentation and matching is based on the unified peptidoform notation ProForma 2.0 and made available as a Rust library (rustyms) with Python bindings. This enables spectrum annotation of diverse and complex peptidoforms across the broad range of mass spectrometry-based proteomics applications.

bioinformatics↗

bioRxiv2024

Structural polymorphism of alpha-synuclein fibrils alters pathway of Hsc70 mediated disaggregation

The pathological aggregation of -synuclein into amyloid fibrils is a hallmark of synucleinopathies including Parkinsons disease. Despite this commonality, synucleinopathies display divergent disease phenotypes that have been attributed to disease specific three-dimensional structures of -synuclein fibrils, each with a unique toxic gain-of-function profile. The Hsc70 chaperone is remarkable in its ability to disassemble pre-existing amyloid fibrils of different proteins in an ATP and co-chaperone dependent manner. We find however, using six well-defined conformational polymorphs of -synuclein fibrils, that the activity of the Hsc70 disaggregase machinery is sensitive to differences in the amyloid conformation, confirming that fibril polymorphism directly affects interactions with the proteostasis network. Amyloid conformation influences not only how efficiently fibrils are cleared by the Hsc70 machinery but also the preferred pathway of disaggregation. We further show that, in vitro, the active processing of fibrils by the Hsc70 machinery inadvertently produces seeding competent species that further promote protein aggregation. Amyloid conformation thus is an important feature that can tilt the balance between beneficial or detrimental protein quality control activities in the context of disease.

biochemistry↗

bioRxiv2024

Narrow window data-independent acquisition on the Orbitrap Astral Mass Spectrometer enables fast and deep coverage of the plasma glycoproteome

Recently, a conceptually new mass analyzer was introduced by pairing a quadrupole Orbitrap mass spectrometer with an asymmetric track lossless (Astral) analyzer. This system provides >200-Hz MS/MS scanning speed, high resolving power and sensitivity, and low-ppm mass accuracy. This instrument allows a narrow-window data-independent (nDIA) strategy, improving sensitivity and reproducibility even when using very short LC gradients. Although this represents a new technical milestone in peptide-centric proteomics, this new system has not yet been evaluated for the analyses of very complex and clinically important proteomes, such as represented by the plasma glycoproteome. Here, we evaluated the Orbitrap Astral mass spectrometer for the analysis of the plasma glycoproteome, and pioneer a dedicated nDIA workflow, themed nGlycoDIA. With substantially adjusted parameters and varying collision energies, nGlycoDIA has clear benefits for plasma glycoproteomics. We tested our method both in glycopeptide enriched and crude plasma, leading to the identification of more than 3000 unique glycoPSMs from 181 glycoproteins, covering a dynamic range of 7 orders of magnitude in the enriched plasma sample in just 40 minutes. In addition, we detect for the first time several glycosylated cytokines that have a reported plasma concentration in the ng/L range. Furthermore, shortening the gradient to 10 min still allows the detection of almost 2500 unique glycoPSMs from enriched plasma, indicating that high-throughput indepth clinical plasma glycoproteomics may be within reach.

biochemistry↗

bioRxiv2023

Deep Batch Active Learning for Drug Discovery

A key challenge in drug discovery is to optimize, in silico, various absorption and affinity properties of small molecules. One strategy that was proposed for such optimization process is active learning. In active learning molecules are selected for testing based on their likelihood of improving model performance. To enable the use of active learning with advanced neural network models we developed two novel active learning batch selection methods. These methods were tested on several public datasets for different optimization goals and with different sizes. We have also curated new affinity datasets that provide chronological information on state-of-the-art experimental strategy. As we show, for all datasets the new active learning methods greatly improved on existing and current batch selection methods leading to significant potential saving in the number of experiments needed to reach the same model performance. Our methods are general and can be used with any package including the popular DeepChem library.

bioinformatics↗

bioRxiv2019

Optimization of the experimental parameters of the ligase cycling reaction

The ligase cycling reaction (LCR) is a scarless and efficient method to assemble plasmids from fragments of DNA. This assembly method is based on the hybridization of DNA fragments with complementary oligonucleotides, so-called bridging oligos (BOs), and an experimental procedure of thermal denaturation, annealing and ligation. In this study, we explore the effect of molecular crosstalk of BOs and various experimental parameters on the LCR by utilizing a fluorescence-based screening system. The results indicate an impact of the melting temperatures of BOs on the overall success of the LCR assembly. Secondary structure inhibitors, such as DMSO and betaine, are shown to negatively impact the number of correctly assembled plasmids. Adjustments of the annealing, ligation and BO-melting temperature further improved the LCR. The optimized LCR was confirmed by validation experiments. Based on these findings, a step-by-step protocol is offered within this study to ensure a routine for high efficient LCR assemblies.

bioengineering↗