Biology subjects

Kjeldsen, F.

Publications and source records attributed to Kjeldsen, F..

9 recordsLinked to original sources

bioRxiv2026

Oral administration of Angiotensin-(1-7) included in hydroxypropyl β-cyclodextrin attenuates cardiac hypertrophy and inflammation in a mouse model of Takotsubo cardiomyopathy

Background Takotsubo cardiomyopathy (TTC) is a stress-induced cardiomyopathy characterized by acute cardiac dysfunction, inflammation, and hypertrophy, yet no therapy directly targets the underlying myocardial injury. Clinical studies have reported elevated circulating Angiotensin II (Ang II) levels in TTC patients, suggesting dysregulation of the renin-angiotensin system (RAS). Given the established cardioprotective effects of Angiotensin-(1-7) [Ang-(1-7)] in other cardiovascular diseases, we hypothesized that oral administration of Ang-(1-7) could attenuate acute cardiac remodeling during TTC. Methods Female C57BL/6J mice were subjected to a single high-dose isoproterenol (ISO) injection (300 mg/Kg, intraperitoneal) to induce TTC and received daily oral treatment with Ang-(1-7) complexed with hydroxypropyl {beta}-cyclodextrin [Ang-(1-7)-CD] or vehicle (CD), for 4 days, beginning one day before ISO administration. Cardiac proteome alterations were characterized by tandem mass tag (TMT)-based quantitative proteomics. Cardiac hypertrophy was assessed by heart weight normalized to tibial length and cardiomyocyte cross-sectional area. Myocardial inflammation was evaluated by histopathology, CD68 immunostaining, and flow cytometric analysis of infiltrating leukocyte populations. Results We observed a distinct cardiac proteome signature in Ang-(1-7)-treated ISO mice, predicting inhibition of pathways associated with cardiac hypertrophy and adverse remodeling. These molecular changes were accompanied by reduced heart weight normalized to tibial length and smaller cardiomyocyte cross-sectional area compared with vehicle-treated ISO mice. Oral Ang-(1-7) also attenuated myocardial inflammation, as evidenced by lower inflammatory scores, reduced CD68+ macrophage infiltration, decreased numbers of CD45+ leukocytes, and marked suppression of Ly6G+ neutrophil and CCR2+ monocyte recruitment to the heart. These protective effects were associated with improved survival following ISO challenge. MS data are available via ProteomeXchange (PXD082872). Conclusions Oral administration of Ang-(1-7) attenuated acute cardiac hypertrophy and inflammation in experimental TTC while inducing cardiac proteome changes consistent with reduced adverse remodeling. These findings identify activation of the protective RAS axis as a promising therapeutic strategy for limiting myocardial injury during TTC.

physiology↗

bioRxiv2025

DeepLC introduces transfer learning for accurate LC retention time prediction and adaptation to substantially different modifications and setups

While LC retention time prediction of peptides and their modifications has proven useful, widespread adoption and optimal performance are hindered by variations in experimental parameters. These variations can render retention time prediction models inaccurate and dramatically reduce the value of predictions for identification, validation, and DIA spectral library generation. To date, mitigation of these issues has been attempted through calibration or by training bespoke models for specific experimental setups, with only partial success. We here demonstrate that transfer learning can successfully overcome these limitations by leveraging pre-trained model parameters. Remarkably, this approach can even fit highly performant models to substantially different peptide modifications and LC conditions than those on which the model was originally trained. This impressive adaptability of transfer learning makes it a highly robust solution for accurate peptide retention time prediction across a very wide variety of imaginable proteomics workflows.

bioinformatics↗

bioRxiv2024

Quantitative assessment of strain isolates and microbiomes using fast MS/MS-free metaproteomics

BACKGROUNDMicrobial communities play a crucial role in human health and environmental regulation, but present an especial challenge for the analytical science due to their diversity and dynamic range. Tandem mass spectrometry provides functional insights on microorganisms life cycle, but still lacks throughput and sensitivity. MALDI TOF is widely used for ultrafast identification of species, but does not assess their functional activity. Development of ultrafast mass spectrometry methods and bioinformatic approaches applicable for both accurate identification and functional assessment of microbial communities based on their protein content is of high interest. RESULTSWe show for the first time that both identity and functional activity of microorganisms and their communities can be accurately determined in experiments as short as 7 minutes per sample, using the basic Orbitrap MS configuration without peptide fragmentation. The approach was validated using strain isolates, mock microbiomes composed of bacteria spiked at known concentrations and human fecal microbiomes. Our new bioinformatic algorithm identifies the bacterial species with an accuracy of 95 %, when no prior information on the sample is available. Microbiome composition was resolved at the genus level with the mean difference between the actual and identified components of 12 %. For mock microbiomes, Pearson coefficient of up to 0.97 was achieved in estimates of strain biomass change. By the example of Rhodococcus biodegradation of n-alkanes, phenols and its derivatives, we showed the accurate assessment of functional activity of strain isolates, compared with the standard label-free and label-based approaches. SIGNIFICANCEOur approach makes microbial proteomics fast, functional and insightful using the Orbitrap instruments even without employing peptide fragmentation technology. The approach can be applied to any microorganisms and can take a niche in routine functional assessment of microbial pathogens and consortiums in clinical diagnostics together with MALDI TOF MS and 16S rRNA gene sequencing.

microbiology↗

bioRxiv2023

Vagus nerve mediated liver-brain axis is a major regulator of the metabolic landscape in the liver

BackgroundThe liver serves as a major energetic reservoir for other tissues and its metabolic function is controlled by humoral and neural factors. The vagus nerve innervating the gastrointestinal tract plays an important role in regulating peripheral metabolism and energy expenditure. Although the liver receives vagus nerve fibers, the impact of this circuitry in the regulation of hepatic metabolism is still poorly understood. MethodsHerein, we used a combination of quantitative proteomics and in vivo imaging techniques to investigate the impact of the vagus nerve on liver metabolism. ResultsVagus nerve shapes the metabolic framework of the liver, as surgical ablation (vagotomy; VNX) of this circuitry led to a significant alteration of the hepatic proteome landscape. Differential protein expression and pathway enrichment analyses showed that glycolytic and fatty acid biosynthesis were increased following VNX, whereas {beta}-oxidation was decreased. This metabolic shift facilitated lipid accumulation in hepatocytes. Furthermore, VNX worsened liver steatosis following high-carbohydrate or high-fat dietary challenges. ConclusionsThis study describes the liver-brain axis mediated by the vagus nerve as an important regulator of the hepatic metabolic landscape. HighlightsO_LIVagus nerve is a novel regulator of the hepatic metabolic landscape. C_LIO_LIAblation of vagus nerve neural circuit by vagotomy resulted in a metabolic shift towards glycolysis and fatty acid biosynthesis. C_LIO_LILipid accumulation was increased in vagotomized mice fed with a standard diet. C_LIO_LILiver steatosis was increased following dietary challenges with high-carbohydrate or high-fat diets. C_LIO_LIVagus nerve can be a promising new target for NAFLD treatment. C_LI

physiology↗

bioRxiv2023

Exploiting Charge-State Distribution to Probe Salt-Bridge Structures in Gas-Phase Phosphopeptides and Enhance Proteomics Analyses

Charging of analytes is a prerequisite for performing mass spectrometry analysis. In proteomics, electrospray ionization stands as the dominant technique for this process. Although the observation of differences in the peptide charge state distribution (CSD) is well-known among experimentalists, its analytical value remains underexplored. To investigate the utility of this dimension, we analyzed several public datasets, comprising over 250,000 peptide CSD profiles from the human proteome. We found that the dimension of the CSD demonstrates high reproducibility across multiple laboratories, mass analyzers, and extensive time intervals. The general observation was that the CSD enabled effective partitioning of the peptide property space, resulting in enhanced discrimination between sequence or constitutional peptide isomers. Next, by evaluating the CSD values of phosphorylated peptides, we were able to differentiate between phosphopeptides that indicate the formation of intramolecular structures in the gas phase and those that do not. The reproducibility of the CSD values (mean cosine similarity above 0.97 for most of the experiments), qualified CSD data suitable to train a deep learning model capable of accurately predicting CSD values (mean cosine similarity - 0.98). When we applied the CSD dimension to MS1- and MS2-based proteomics experiments, we consistently observed around a 5% increase in protein and peptide identification rate. Even though the CSD dimension is not as effective a discriminator as the widely-used retention time dimension, it still holds potential for application in direct infusion proteomics.

biochemistry↗

bioRxiv2023

Potential of Negative Ion Mode Proteomics: MS1-Only Approach

Current proteomics approaches rely almost exclusively on using positive ionization mode, which results in inefficient ionization of many acidic peptides. With an equal quantity of acidic and basic proteins and, correspondingly, the similar number for their derived peptides in case of the human proteome, this inefficient ionization poses both a substantial challenge and a potential. In this work, we study the efficiency of protein identification in the bottom-up proteomic analysis performed in negative ionization mode, using the recently introduced MS1-only ultra-fast data acquisition method DirectMS1. This method is based on accurate peptide mass measurements and predicted retention times. Our method achieves the highest rate of protein identifications in negative ion mode to date, with over 1,000 proteins identified in a human cell line at a 1% false discovery rate using a single-shot 10-min separation gradient, which is comparable with hours-long MS/MS-based analyses. Evaluating the proteins as a function of pI indicated preferable identification of the acidic part of the proteome. Optimization of separation and mass spectrometric experimental conditions facilitated the performance of the method with the best results in terms of spray stability and signal abundance obtained using mobile buffers at 2.5 mM imidazole and 3% isopropanol. The work also highlighted the complementarity of data acquired in positive and negative modes: Combining the results for all replicates for both polarities, the number of identified proteins increased up to 1,774. Finally, we performed analysis of the methods efficiency when different proteases are used for protein digestion. Among the four studied proteases (LysC, GluC, AspN, and trypsin), we found that trypsin and LysC performed best in terms of protein identification yield. Thus, digestion procedures used for positive mode proteomics can be efficiently utilized for analysis in negative ion mode.

molecular biology↗

bioRxiv2022

DirectMS1Quant: ultrafast quantitative proteomics with MS/MS-free mass spectrometry

Recently, we presented the DirectMS1 method of ultrafast proteome-wide analysis based on minute-long LC gradients and MS1-only mass spectra acquisition. Currently, the method provides the depth of human cell proteome coverage of 2500 proteins at 1% false discovery rate (FDR) when using 5-min LC gradients and 7.3 min runtime in total. While the standard MS/MS approaches provide 4000 to 5000 protein identifications within a couple of hours of instrumentation time, we advocate here that the higher number of identified proteins does not always translate into better quantitation quality of the proteome analysis. To further elaborate on this issue we performed one-by-one comparison of quantitation results obtained using DirectMS1 with three popular MS/MS-based quantitation methods: label-free quantification (LFQ), tandem mass tag (TMT), both based on data dependent acquisition (DDA), and data independent acquisition (DIA). For the comparison we performed a series of proteome-wide analysis of well-characterized (ground truth) and biological relevant samples, including a mix of UPS1 proteins spiked at different concentrations into E. coli digest used as a background and a set of glioblastoma cell lines. MS1-only data was analyzed using a novel quantitation workflow called DirectMS1Quant developed in this work. The results obtained in this study demonstrated comparable quantitation efficiency of 5 min DirectMS1 with both TMT and DIA methods utilizing 10 to 20-fold longer instrumentation time.

bioinformatics↗

bioRxiv2020

Boosting the MS1-only proteomics with machine learning allows 2000 protein identifications in 5-minute proteome analysis

Proteome-wide analyses most often rely on tandem mass spectrometry imposing considerable instrumental time consumption that is one of the main obstacles in a broader acceptance of proteomics in biomedical and clinical research. Recently, we presented a fast proteomic method termed DirectMS1 based on MS1-only mass spectra acquisition and data processing. The method allowed significant squeezing of the proteome-wide analysis to a few minute time frame at the depth of quantitative proteome coverage of 1000 proteins at 1% FDR. In this work, to further increase the capabilities of the DirectMS1 method, we explored the opportunities presented by the recent progress in the machine learning area and applied the LightGBM tree-based learning algorithm into the scoring of peptide-feature matches when processing MS1 spectra. Further, we integrated the peptide feature identification algorithm of DirectMS1 with the recently introduced peptide retention time prediction utility, DeepLC. Additional approaches to improve performance of the DirectMS1 method are discussed and demonstrated, such as FAIMS coupled to the Orbitrap mass analyzer. As a result of all improvements to DirectMS1, we succeeded in identifying more than 2000 proteins at 1% FDR from the HeLa cell line in a 5 minute LC-MS1 analysis.

bioinformatics↗

bioRxiv2019

DirectMS1: MS/MS-free identification of 1000 proteins of cellular proteomes in 5 minutes

Proteome characterization relies heavily on tandem mass spectrometry (MS/MS) and is thus associated with instrumentation complexity, lengthy analysis time, and limited duty-cycle. It was always tempting to implement approaches which do not require MS/MS, yet, they were constantly failing in achieving meaningful depth of quantitative proteome coverage within short experimental times, which is particular important for clinical or biomarker discovery applications. Here, we report on the first successful attempt to develop a truly MS/MS-free and label-free method for bottom-up proteomics. We demonstrate identification of 1000 protein groups for a standard HeLa cell line digest using 5-minute LC gradients. The amount of loaded sample was varied in a range from 1 ng to 500 ng, and the method demonstrated 10-fold higher sensitivity compared with the standard MS/MS-based approach. Due to significantly higher sequence coverage obtained by the developed method, it outperforms all popular MS/MS-based label-free quantitation approaches.

biochemistry↗