Biology subjects

Forsström, B.

Publications and source records attributed to Forsström, B..

4 recordsLinked to original sources

bioRxiv2021

Protein allocation and utilization in the versatile chemolithoautotroph Cupriavidus necator

Bacteria must balance the different needs for substrate assimilation, growth functions, and resilience in order to thrive in their environment. Of all cellular macromolecules, the bacterial proteome is by far the most important resource and its size is limited. Here, we investigated how the highly versatile knallgas bacterium Cupriavidus necator reallocates protein resources when grown on different limiting substrates and with different growth rates. We determined protein quantity by mass spectrometry and estimated enzyme utilization by resource balance analysis modeling. We found that C. necator invests a large fraction of its proteome in functions that are hardly utilized. Of the enzymes that are utilized, many are present in excess abundance. One prominent example is the strong expression of CBB cycle genes such as Rubisco during growth on fructose. Modeling and mutant competition experiments suggest that CO2-reassimilation through Rubisco does not provide a fitness benefit for heterotrophic growth, but is rather an investment in readiness for autotrophy. HighlightsO_LIA large fraction of the C. necator proteome is not utilized and not essential C_LIO_LIHighly utilized enzymes are more abundant and less variable C_LIO_LIAutotrophy related enzymes are largely underutilized C_LIO_LIRe-assimilation of CO2 via the CBB cycle is unlikely to provide a fitness benefit C_LI

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↗

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↗

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↗