Biology subjects

Edfors, F.

Publications and source records attributed to Edfors, F..

14 recordsLinked to original sources

bioRxiv2026

Reference-free protein sequencing by consensus assembly of redundant de novo peptide reads

Reading a proteins sequence from tandem mass spectra without a reference is limited by single-spectrum accuracy, most acutely across the hypervariable complementaritydetermining regions of antibodies. Broadly specific proteases tile a protein with long, overlapping peptides, so every residue is covered by many independent de novo reads. borgonovo assembles their per-step probability profiles into a reference-free per-residue consensus, seeding templates from mass-closure-consistent reads and recruiting the rest by substitution- tolerant alignment and per-column voting. Re-decoding each spectrum with a prior from its consensus position lifts amino acid accuracy on placed spectra from 0.80 to 0.87. On the therapeutic antibody trastuzumab, nine proteases cover its heavy and light chains completely at 0.88 fixed-window identity, and 0.93 on the pruned assembly once local indels are accommodated. Applied unchanged to five secretome proteins and trastuzumab with three proteases, it reaches 0.87 mean fixed-window identity over 82% coverage. borgonovo is open source and works with most de novo sequencers, so redundant digestion turns any of them into a protein sequencer where no reference exists.

bioinformatics↗

bioRxiv2026

Identifying Novel Targets of the Stringent Response in Plants and Cyanobacteria using chemoproteomics

Survival in dynamic environments requires photosynthetic organisms to rapidly sense and respond to stress. The stringent response, mediated by the signaling molecule guanosine-3,5-bisdiphosphate (ppGpp), is crucial for acclimation to environmental changes such as darkness and nitrogen limitation. While it has been extensively characterized in heterotrophic bacteria such as Escherichia coli, the molecular mechanisms and regulatory targets of ppGpp in photosynthetic organisms remain less understood. Here, we report large-scale chemoproteomic identification of ppGpp-binding proteins across plant chloroplasts and cyanobacteria, revealing both conserved and novel targets compared to E. coli. In plants, we found that ppGpp regulates pyrimidine metabolism by inhibiting the chloroplastic enzyme aspartate transcarbamoylase (PyrB). In cyanobacteria, we found that ppGpp activates glucose-1-phosphate adenylyltransferase (GlgC) involved in glycogen synthesis, activates citrate synthase (GltA), and induces carboxysome aggregation. These findings expand the known ppGpp regulatory network in photosynthetic organisms and provide a foundation for understanding how ppGpp coordinates adaptation to nutrient and environmental stresses.

systems biology↗

bioRxiv2026

Machine learning uncovers circulating biomarkers and molecular heterogeneity in obesity and type 2 diabetes

Obesity and Type 2 Diabetes (T2D) are heterogeneous metabolic disorders whose molecular diversity is incompletely defined. We analyzed circulating proteomic profiles from 129 individuals belonging to Control, Obesity, and T2D groups and applied complementary machine-learning approaches, including random forest, multinomial logistic regression with LASSO regularization, support vector machines, and ensemble voting to identify proteins distinguishing the clinical groups. Convergent model outputs revealed a partially overlapping panel of discriminative proteins. Model performance was evaluated in an independent dataset from the Human Protein Atlas (n=834) comprising healthy individuals, patients with Obesity, T2D, or other metabolic diseases. Unsupervised clustering further identified multiple proteomic subgroups within each clinical category, indicating substantial intragroup heterogeneity. Bootstrap random forest with null-model benchmarking highlighted stable cluster-discriminative proteins. These findings demonstrate that integrating circulating proteomics with machine learning can resolve molecular heterogeneity in Obesity and T2D and nominate candidate biomarkers for metabolic disease stratification.

biochemistry↗

bioRxiv2026

Systems-level longitudinal immune profiling reveals individualized immunotypes and genetic associations

The human immune system exhibits substantial inter-individual variation, yet how this variability is coordinated across the system and persists over time remains incompletely defined. Here, we integrated whole-genome sequencing with longitudinal multi-omics immune profiling in 101 healthy individuals followed over two years, combining mass cytometry-based immune cell profiling, PBMC transcriptomics, plasma proteomics, and clinical measurements. Coordinated variation across cellular composition, gene expression, and circulating proteins revealed reproducible immune patterns within individuals across visits. Integrative analyses identified three major immunotypes: adaptive lymphoid (CD4 T cell and B cell enriched), myeloid-inflammation (monocyte and dendritic cell-driven), and cytotoxic (CD8 T cell-dominated), each associated with distinct metabolic and inflammatory profiles. Genome-wide association analyses identified cell-type-specific quantitative trait loci primarily affecting memory lymphocytes, and a polygenic score for memory B cells correlated with both cellular abundance and transcriptional activity. Together, these findings provide a systems-level view for understanding baseline immune heterogeneity in human populations. TeaserLongitudinally stable immune individuality reflects coordinated modules shaped by systemic physiology and genetics

systems biology↗

bioRxiv2024

Longitudinal Exposomics in a Multiomic Wellness Cohort Reveals Distinctive and Dynamic Environmental Chemical Mixtures in Blood

Chemical exposomes can now be comprehensively measured in human blood, but robust application of chemical exposomics in cohort studies requires knowledge of the longitudinal stability and interindividual variability of exogenous molecular profiles. Here we applied chemical exposomics to plasma of 46 adults, each sampled six times over two years in a multiomic wellness cohort. New chemicals were discovered, distinctive co-exposure patterns were observed, and intra-class correlation coefficients (ICC) for 519 confidently annotated substances are reported to support study design. Longitudinal stability of the chemical exposome (mean ICC 0.30) was significantly lower than the proteome, metabolome, lipidome or microbiome, and must be measured more frequently than other molecular profiles in health studies. Mixed-effects models nevertheless revealed significant associations between testosterone and perfluoroalkyl substances, and significant time-trends for low and high stability exposures alike. Complex exposome data structures were visualized and explored, demonstrating great potential for longitudinal exposomics in precision health research. TeaserThe first cohort-level application of longitudinal exposomics revealed novel and dynamic co-exposures in blood of relevance to precision health.

pharmacology and toxicology↗

bioRxiv2023

Assessing the role of trypsin in quantitative plasma- and single-cell proteomics towards clinical application

Mass spectrometry-based bottom-up proteomics is rapidly evolving and routinely applied in large biomedical studies. Proteases are a central component of every bottom-up proteomics experiment, digesting proteins into peptides. Trypsin has been the most widely applied protease in proteomics, due to its characteristics. With ever-larger cohort sizes and possible future clinical application of mass spectrometry-based proteomics, the technical impact of trypsin becomes increasingly relevant. To assess possible biases introduced by trypsin digestion, we evaluated the impact of eight commercially available trypsins in a variety of bottom-up proteomics experiments, and across a range of protease concentrations and storage times. To investigate the universal impact of these technical attributes, we included bulk HeLa-cell lysate, human plasma and single HEK293 cells, which were analyzed over a range of Selected Reaction Monitoring (SRM), Data-Independent Acquisition (DIA), and Data-Dependent Acquisition (DDA) instrument methods on three LC-MS instruments. Quantification methods employed encompassed both label-free approaches and absolute quantification utilizing spike-in heavy-labeled recombinant protein fragment standards. Based on this extensive dataset, we report variations between commercial trypsins, their source, as well as their concentration. Furthermore, we provide suggestions on the handling of trypsin in large scale studies.

molecular biology↗

bioRxiv2022

Genome-wide single cell annotation of the human protein-coding genes

An important quest for the life science community is to deliver a complete annotation of the human building-blocks of life, the genes and the proteins. Here, we report on a genome-wide effort to annotate all protein-coding genes based on single cell transcriptomics data representing all major tissues and organs in the human body, integrated with data from bulk transcriptomics and antibody-based tissue profiling. Altogether, 25 tissues have been analyzed with single cell transcriptomics resulting in genome-wide expression in 444 single cell types using a strategy involving pooling data from individual cells to obtain genome-wide expression profiles of individual cell type. We introduce a new genome-wide classification tool based on clustering of similar expression profiles across single cell types, which can be visualized using dimensional reduction maps (UMAP). The clustering classification is integrated with a new "tau" score classification for all protein-coding genes, resulting in a measure of single cell specificity across all cell types for all individual genes. The analysis has allowed us to annotate all human protein-coding genes with regards to function and spatial distribution across individual cell types across all major tissues and organs in the human body. A new version of the open access Human Protein Atlas (www.proteinatlas.org) has been launched to enable researchers to explore the new genome-wide annotation on an individual gene level.

systems biology↗

bioRxiv2022

Metabolite interactions in the bacterial Calvin cycle and implications for flux regulation

Metabolite-level regulation of enzyme activity is important for microbes to cope with environmental shifts. Knowledge of such regulations can also guide strain engineering to improve industrial phenotypes. Recently developed chemoproteomics workflows allow for genome-wide detection of metabolite-protein interactions that may regulate pathway activity. We applied limited proteolysis small molecule mapping (LiP-SMap) to identify and compare metabolite-protein interactions in the proteomes of two cyanobacteria and two lithoautotrophic bacteria that fix CO2 using the Calvin cycle. Clustering analysis of the hundreds of detected interactions showed that some metabolites interacted in a species-specific manner, such as interactions of glucose-6-phosphate in Cupriavidus necator and of glyoxylate in Synechocystis sp PCC 6803. These are interpreted in light of the different central carbon conversion pathways present. Metabolites interacting with the Calvin cycle enzymes fructose-1,6/sedoheptulose-1,7-bisphosphatase (F/SBPase) and transketolase were tested for effects on catalytic activity in vitro. The Calvin cycle intermediate glyceraldehyde-3-phosphate activated both Synechocystis and Cupriavidus F/SBPase, which suggests a feed-forward activation of the cycle in both photoautotrophs and chemolithoautotrophs. In contrast to the stimulating effect in reduced conditions, glyceraldehyde-3-phosphate inactivated the Synechocystis F/SBPase in oxidized conditions by accelerating protein aggregation. Thus, metabolite-level regulation of the Calvin cycle is more prevalent than previously appreciated and may act in addition to redox regulation.

systems biology↗

bioRxiv2021

Harnessing secretory pathway differences between HEK293 and CHO to rescue production of difficult to express proteins

Biologics represent the fastest growing group of therapeutics, but many advanced recombinant protein moieties remain difficult to produce. Here, we identify bottlenecks limiting expression of recombinant human proteins through a systems biology analysis of the transcriptomes of CHO and HEK293 during recombinant overexpression. Surprisingly, one third of the challenging human proteins displayed improved secretion upon host cell swapping from CHO to HEK293. While most components of the secretory machinery showed comparable expression levels in both expression hosts, genes with significant expression variation were identified. Among these, ATF4, SRP9, JUN, PDIA3 and HSPA8 were validated as productivity boosters in CHO. Further, more heavily glycosylated products benefitted more from the elevated activities of the N- and O-glycosyltransferases found in HEK293. Collectively, our results demonstrate the utilization of HEK293 for expression rescue of human proteins and suggest a methodology for identification of secretory pathway components improving recombinant protein yield in HEK293 and CHO.

systems biology↗

bioRxiv2020

Facets of individual-specific health signatures determined from longitudinal plasma proteome profiling

BackgroundPrecision medicine approaches aim to tackle diseases on an individual level through molecular profiling. Despite the growing knowledge about diseases and the reported diversity of molecular phenotypes, the descriptions of human health on an individual level have been far less elaborate. MethodsTo provide insights into the longitudinal protein signatures of well-being, we profiled blood plasma collected over one year from 101 clinically healthy individuals using multiplexed antibody assays. After applying an antibody validation scheme, we utilized > 700 protein profiles for in-depth analyses of the individuals short-term health trajectories. FindingsWe found signatures of circulating proteomes to be highly individual-specific. Considering technical and longitudinal variability, we observed both stable and fluctuating proteins in the circulation, as well as networks of proteins that covaried over time. For each participant, there were unique protein profiles and some of these could be explained by associations to genetic variants. InterpretationThis study demonstrates that there was noticeable diversity among clinically healthy subjects, and facets of individual-specific signatures emerged by monitoring the variability of the circulating proteomes over time. Longitudinal profiling of circulating proteomes has the potential to enable a more personal hence precise assessment of health states, and thereby provide a valuable component of precision medicine approaches. FundingThis work was supported by the Erling Persson Foundation for the KTH Centre for Precision Medicine and the Swedish Heart and Lung Foundation for the SCAPIS project. We also acknowledge the Knut and Alice Wallenberg Foundation for funding the Human Protein Atlas project, Science for Life Laboratory for Plasma Profiling Facility, and the Swedish Research Council (Grant no 2017-00641).

biochemistry↗

bioRxiv2019

Systematic Development Of Sandwich Immunoassays For The Plasma Secretome

The plasma proteome offers a clinically useful window into human health and disease. With recent progress made on the development of highly multiplexed immunoassays with high sample throughput, a remaining need is to establish a pipeline for validating the individual proteins that build such bio-signatures by using targeted assays. In order to streamline such efforts, we developed a workflow to build dual binder sandwich immunoassays (SIA) and chose to evaluate this on proteins predicted to be secreted form cells and tissues. Utilizing the multiplexing capacities of the bead array technology, we first screened ~ 1,800 unique antibody pairs against 209 protein targets and collected data from dilution series of recombinant proteins as well as EDTA plasma. Employing 624 unique antibodies from the Human Protein Atlas, we obtained dilution-dependent curves in plasma and concentration-dependent curves of full-length proteins for 102 (49%) of the targets. For 22 protein assays, the longitudinal, inter-individual and technical performance was determined in a set of plasma samples collected from 18 healthy subjects every third month over one year. Lastly, we compared 14 of these assays with SIAs composed of other binders, proximity extension assays and affinity-free targeted mass spectrometry. Our workflow provides a multiplexed approach to screen for SIA pairs that suggests using at least three antibodies per target. This design is applicable for a wider range of targets of the plasma proteome, while the assays can be applied for discovery but also to validate emerging candidates derived from other platforms.

biochemistry↗

bioRxiv2018

The human secretome - the proteins secreted from human cells

The proteins secreted by human tissues (the secretome) are important for the basic understanding of human biology, but also for identification of potential targets for future diagnosis and therapy. Here, we present an annotation of all predicted secreted proteins (n=2,623) with information about their spatial distribution in the human body. A high-throughput mammalian cell factory was established to create a resource of recombinant full-length proteins. This resource was used for phenotypic assays involving {beta}-cell dedifferentiation and for development of targeted proteomics assays. A comparison between host cells, including omics analysis, shows that many of the proteins that failed to be generated in CHO cells could be rescued in human HEK293 cells. In conclusion, the human secretome has been mapped and characterized to facilitate further exploration of the human secretome.

molecular biology↗

bioRxiv2018

Screening a Resource of Recombinant Protein Fragments for Targeted Proteomics

The availability of proteomics resources hosting protein and peptide standards, as well as the data describing their analytical performances, will continue to enhance our current capabilities to develop targeted proteomics methods for quantitative biology. This study describes the analysis of a resource of 26,840 individually purified recombinant protein fragments corresponding to more than 16,000 human protein-coding genes. The resource was screened to identify proteotypic peptides suitable for targeted proteomics efforts and we report LC-MS/MS assay coordinates for more than 25,000 proteotypic peptides, corresponding to more than 10,000 unique proteins. Additionally, peptide formation and digestion kinetics were, for a subset of the standards, monitored using a time-course protocol involving parallel digestion of isotope-labelled recombinant protein standards and endogenous human plasma proteins. We show that the strategy by adding isotope-labelled recombinant proteins prior to trypsin digestion enables short digestion protocols ([≤]60 min) with robust quantitative precision. In a proof-of-concept study, we quantified 23 proteins in human plasma using assay parameters defined in our study and used the standards to describe distinct clusters of individuals linked to different levels of LPA, APOE, SERPINA5 and TFRC. In summary, we describe the use and utility of a resource of recombinant proteins to identify proteotypic peptides useful for targeted proteomics assay development.

biochemistry↗

bioRxiv2017

A protein standard that emulates homology for the characterization of protein inference algorithms

A natural way to benchmark the performance of an analytical experimental setup is to use samples of known content, and see to what degree one can correctly infer the content of such a sample from the data. For shotgun proteomics, one of the inherent problems of interpreting data is that the measured analytes are peptides and not the actual proteins themselves. As some proteins share proteolytic peptides, there might be more than one possible causative set of proteins resulting in a given set of peptides and there is a need for mechanisms that infer proteins from lists of detected peptides. A weakness of commercially available samples of known content is that they consist of proteins that are deliberately selected for producing tryptic peptides that are unique to a single protein. Unfortunately, such samples do not expose any complications in protein inference. For a realistic benchmark of protein inference procedures, there is, therefore, a need for samples of known content where the present proteins share peptides with known absent proteins. Here, we present such a standard, that is based on E. coli expressed human protein fragments. To illustrate the usage of this standard, we benchmark a set of different protein inference procedures on the data. We observe that inference procedures excluding shared peptides provide more accurate estimates of errors compared to methods that include information from shared peptides, while still giving a reasonable performance in terms of the number of identified proteins. We also demonstrate that using a sample of known protein content without proteins with shared tryptic peptides can give a false sense of accuracy for many protein inference methods.

bioinformatics↗