Biology subjects

Jonsson, P.

Publications and source records attributed to Jonsson, P..

4 recordsLinked to original sources

bioRxiv2018

Hydrodynamic trapping measures the interaction between membrane-associated molecules

How membrane proteins distribute and behave on the surface of cells is determined by the molecules interaction potential. However, measuring this potential, and how it varies with protein-to-protein distance, has been challenging. We here present how a method we call hydrodynamic trapping can achieve this. Our method uses the focused liquid flow from a micropipette to locally accumulate molecules protruding from a lipid membrane. The interaction potential, as well as information about the dimensions of the studied molecule, are obtained by relating the degree of accumulation to the strength of the trap. We have used this to study four representative proteins, with different height-to-width ratios and protein properties; from the globular streptavidin, to the rod-like immune cell proteins CD2, CD4 and CD45. The obtained data illustrates how protein shape, glycosylation and flexibility influence the behaviour of membrane proteins as well as underline the general applicability of the method.

biophysics↗

bioRxiv2018

Statistical loadings and latent significance simplify and improve interpretation of multivariate projection models

Multivariate projection methods are unique in being both multivariable by combining many variables into stronger predictive features (latent variables), and multivariate for being able to model systematic variation both related and orthogonal to an observed response. Orthogonal partial least squares (OPLS) is a versatile multivariate projection method for analysis of correlation, discrimination and effect changes. However, currently OPLS is not fully using its multivariate potential since orthogonal systematic variation is not considered in model interpretation, resulting in univariate interpretation of variable significance. We present a strategy for improved interpretation of OPLS models based upon a post-hoc linear regression analysis that can be used with or without the orthogonal OPLS score(s) as a covariate to make the interpretation multivariate or univariate respectively. By selecting the observed response y or estimated response yhat as a one of the factors in the linear regression the results are related to either of the OPLS loadings w or p. Furthermore, converting the OPLS loading values to statistical t-values creates a direct link to statistical significance. Finally, by applying three different Boolean loadings W, P and W{wedge}P variable significance can be summarized based on three criteria. W and P reveal if the values in w or p respectively are outside the statistical limits with W{wedge}P being the logical conjunction of W and P (significant if outside limits in both W and P). Two examples are used to verify the proposed strategy. First, a synthetic example, simulating a mix of mass spectra, and second a clinical metabolomics study of a dietary intervention. In the simulated example we show that multivariate interpretation gives higher accuracy for estimation of true differences, mainly due to higher true positive rate. Furthermore, we highlight how application of W{wedge}P for summarizing variable significance leads to higher accuracy. For the metabolomics example, we show that a more detailed interpretation, i.e. larger number of significant metabolites of relevance, is obtained using the multivariate interpretation. In summary, the suggested strategy provides means for facilitated interpretation of OPLS models, beyond univariate statistics, and offers a multivariate tool for discovery of biomarker patterns, i.e. latent biomarkers.

bioinformatics↗

bioRxiv2018

Genetic meta-analysis identifies 10 novel loci and functional pathways for Alzheimer’s disease risk

Late onset Alzheimers disease (AD) is the most common form of dementia with more than 35 million people affected worldwide, and no curative treatment available. AD is highly heritable and recent genome-wide meta-analyses have identified over 20 genomic loci associated with AD, yet only explaining a small proportion of the genetic variance indicating that undiscovered loci exist. Here, we performed the largest genome-wide association study of clinically diagnosed AD and AD-by-proxy (71,880 AD cases, 383,378 controls). AD-by-proxy status is based on parental AD diagnosis, and showed strong genetic correlation with AD (rg=0.81). Genetic meta analysis identified 29 risk loci, of which 9 are novel, and implicating 215 potential causative genes. Independent replication further supports these novel loci in AD. Associated genes are strongly expressed in immune-related tissues and cell types (spleen, liver and microglia). Furthermore, gene-set analyses indicate the genetic contribution of biological mechanisms involved in lipid-related processes and degradation of amyloid precursor proteins. We show strong genetic correlations with multiple health-related outcomes, and Mendelian randomisation results suggest a protective effect of cognitive ability on AD risk. These results are a step forward in identifying more of the genetic factors that contribute to AD risk and add novel insights into the neurobiology of AD to guide new drug development.

genetics↗

bioRxiv2017

Constraining CD45 exclusion at close-contacts provides a mechanism for discriminatory T-cell receptor signalling

The T-cell receptor (TCR) triggers the elimination of pathogens and tumors by T lymphocytes. In order for this to avoid damage to the host, the receptor has to discriminate between thousands of peptide ligands presented by each host cell. Exactly how the TCR does this is unknown. In resting T-cells, the TCR is largely unphosphorylated due to the dominance of phosphatases over kinases expressed at the cell surface. When agonist peptides are presented to the TCR by major histocompatibility complex (MHC) proteins expressed by antigen-presenting cells (APCs), very fast receptor triggering occurs, leading to TCR phosphorylation. Recent work suggests that this depends on the local exclusion of the phosphatases from regions of contact of the T cells with the APCs. Here, we develop and test a quantitative treatment of receptor triggering reliant only upon TCR dwell-time in phosphatase-depleted cell-cell contacts constrained in area by cell topography. Using the model and experimentally-derived parameters, we find that ligand discrimination is possible but that it depends crucially on individual contacts being 400 nm in diameter or smaller, i.e. the size generated by microvilli. The model not only correctly predicts the relative signaling potencies of known agonists and non-agonists, but achieves this in the absence of conventional, multi-step kinetic proof-reading. Our work provides a simple, quantitative and predictive molecular framework for understanding why TCR triggering is so selective and fast, and reveals that for some receptors, cell topography crucially influences signaling outcomes.\n\nSignificance statementOne approach to testing biological theories is to determine if they are predictive. A simple, theoretical treatment of TCR triggering suggests that ligand discrimination by the receptor relies on just two physical principles: (1) the time TCRs spend in cell-cell contacts depleted of large tyrosine phosphatases; and (2) constraints on contact size imposed by T cells using finger-like protrusions to interrogate their targets. The theory not only allows agonistic and non-agonistic TCR ligands to be distinguished but predicts the relative signalling potencies of agonists with remarkable accuracy. This suggests that the theory captures the essential features of receptor triggering.

immunology↗