Biology subjects

Dahl, L.

Publications and source records attributed to Dahl, L..

4 recordsLinked to original sources

bioRxiv2023

Expanding the GPCR-RAMP interactome

Receptor activity-modifying proteins (RAMPs) can form complexes with G protein-coupled receptors (GPCRs) and regulate their cellular trafficking and pharmacology. RAMP interactions have been identified for about 50 GPCRs, but only a few GPCR-RAMP complexes have been studied in detail. To elucidate a complete interactome between GPCRs and the three RAMPs, we developed a customized library of 215 Dual Epitope-Tagged (DuET) GPCRs representing all GPCR subfamilies. Using a multiplexed suspension bead array (SBA) assay, we identified 122 GPCRs that showed strong evidence for interaction with at least one RAMP. We screened for native interactions in three cell lines and found 23 GPCRs that formed complexes with RAMPs. Mapping the GPCR-RAMP interactome expands the current system-wide functional characterization of RAMP-interacting GPCRs to inform the design of selective GPCR-targeted therapeutics. One-Sentence SummaryNovel complexes between G protein-coupled receptors and interacting proteins point to a system-wide regulation of GPCR function.

biochemistry↗

bioRxiv2022

Multiplexed selectivity screening of anti-GPCR antibodies

G protein-coupled receptors (GPCRs) control critical cellular signaling pathways. Therapeutic agents, such as antibodies (Abs), are being developed to modulate GPCR signaling pathways. However, validating the selectivity of anti-GPCR Abs is challenging due to sequence similarities of individual receptors within GPCR subfamilies. To address this, we developed a multiplexed immunoassay to test >400 anti-GPCR Abs from the Human Protein Atlas targeting a customized library of 215 expressed and solubilized GPCRs representing all GPCR subfamilies. We found that ~61% of Abs were selective for their intended target, ~11% to bind off-target, and ~28% not to bind any GPCR. Antigens of on-target Abs were, on average, significantly longer, more disordered, and less likely to be buried in the interior of the GPCR protein than the other Abs. These results provide important insights into the immunogenicity of GPCR epitopes and form a basis for the design of therapeutic Abs and the detection of pathological auto-antibodies. TEASERA multiplexed library-to-library selectivity analysis of 400 anti-GPCR antibodies within subfamilies of 200 solubilized receptors.

biochemistry↗

bioRxiv2022

Markov State Modelling Reveals Heterogeneous Drug-Inhibition Mechanism of Calmodulin

Calmodulin (CaM) is a calcium sensor which binds and regulates a wide range of target-proteins. This implicitly enables the concentration of calcium to influence many downstream physiological responses, including muscle contraction, learning and depression. The antipsychotic drug trifluoperazine (TFP) is a known CaM inhibitor. By binding to various sites, TFP prevents CaM from associating to target-proteins. However, the molecular and state-dependent mechanisms behind CaM inhibition by drugs such as TFP are largely unknown. Here, we build a Markov state model (MSM) from adaptively sampled molecular dynamics simulations and reveal the structural and dynamical features behind the inhibitory mechanism of TFP-binding to the C-terminal domain of CaM. We specifically identify three major TFP binding-modes from the MSM macrostates, and distinguish their effect on CaM conformation by using a systematic analysis protocol based on biophysical descriptors and tools from machine learning. The results show that depending on the binding orientation, TFP effectively stabilizes features of the calcium-unbound CaM, either affecting the CaM hydrophobic binding pocket, the calcium binding sites or the secondary structure content in the bound domain. The conclusions drawn from this work may in the future serve to formulate a complete model of pharmacological modulation of CaM, which furthers our understanding of how these drugs affect signaling pathways as well as associated diseases. Author summaryCalmodulin (CaM) is a calcium-sensing protein which makes other proteins dependent on the surrounding calcium concentration by binding to these proteins. Such protein-protein interactions with CaM are vital for calcium to control many physiological pathways within the cell. The antipsychotic drug trifluoperazine (TFP) inhibits CaMs ability to bind and regulate other proteins. Here, we use molecular dynamics simulations together with Markov state modeling and machine learning to understand the structural and dynamical features by which TFP bound to the one domain of CaM prevents association to other proteins. We find that TFP encourages CaM to adopt a conformation that is like the one stabilized in absence of calcium: depending on the binding orientation of TFP, the drug indeed either affects the CaM hydrophobic binding pocket, the calcium binding sites or the secondary structure content in the domain. Understanding TFP binding is a first step towards designing better drugs targeting CaM.

biophysics↗

bioRxiv2021

Circulating proteins reveal prior use of menopausal hormonal therapy and increased risk of breast cancer

BackgroundRisk prediction is crucial for early detection and prognosis of breast cancer. Circulating plasma proteins could provide a valuable source to increase the validity of risk prediction models, however, no such markers have yet been identified for clinical use. MethodsEDTA plasma samples from 183 breast cancer cases and 366 age-matched controls were collected prior to diagnosis from the Swedish breast cancer cohort KARMA. The samples were profiled on 700 circulating proteins using an exploratory affinity proteomics approach. Linear association analyses were performed on case-control status and a data-driven analysis strategy was applied to cluster the women on their plasma proteome profiles in an unsupervised manner. The resulting clusters were subsequently annotated for the differences in phenotypic characteristics, clinical parameters, and genetic risk. ResultsUsing the data-driven approach we identified five clusters with distinct proteomic plasma profiles. Women in a particular sub-group (cluster 1) were significantly more likely to have used menopausal hormonal therapy (MHT), more likely to get a breast cancer diagnosis, and were older compared to the remaining clusters. The levels of circulating proteins in cluster 1 were decreased for proteins related to DNA repair and cell replication and increased for proteins related to mammographic density and female tissues. In contrast, classical dichotomous case-control analyses did not reveal any proteins significantly associated with future breast cancer. ConclusionUsing a data-driven approach, we identified a subset of women with circulating proteins associated with previous use of MHT and risk of breast cancer. Our findings point to the potential long-lasting effects of MHT on the circulating proteome even after ending the treatment, and hence provide valuable insights concerning risk predication of breast cancer. HighlightsO_LICurrent risk prediction models use a variety of factors to identify women at risk of developing breast cancer. C_LIO_LIProteins circulating in blood represent an attractive but currently still underrepresented source of candidates serving as molecular risk factors. C_LIO_LIPlasma proteomes from women participating in a prospective breast cancer cohort study were studied for proteomic risk factors related to a future breast cancer diagnosis. C_LIO_LIUsing data-driven approaches, women with future breast cancers and previous use of menopausal hormone therapy were identified based on their circulating proteins. C_LIO_LIMenopausal hormone therapy was found to altered the levels of the circulating proteins even years after the treatment ended. C_LI

systems biology↗