Biology subjects

Wall, M.

Publications and source records attributed to Wall, M..

27 records · Page 2Linked to original sources

bioRxiv2020

Deciphering the Agonist Binding Mechanism to the Adenosine A1 Receptor

Despite being amongst the most characterized G protein-coupled receptors (GPCRs), adenosine receptors (ARs) have always been a difficult target in drug design. To date, no agonist other than the natural effector and the diagnostic regadenoson has been approved for human use. Recently, the structure of the adenosine A1 receptor (A1R) was determined in the active, Gi protein complexed state; this has important repercussions for structure-based drug design. Here, we employed supervised molecular dynamics simulations and mutagenesis experiments to extend the structural knowledge of the binding of selective agonists to A1R. Our results identify new residues involved in the association and dissociation pathway, suggest the binding mode of N6-cyclopentyladenosine (CPA) related ligands, and highlight the dramatic effect that chemical modifications can have on the overall binding mechanism.

pharmacology and toxicology↗

bioRxiv2020

Intra-session test-retest reliability of functional connectivity in infants

Resting functional MRI studies of the infant brain are increasingly becoming an important tool in developmental neuroscience. Whereas the test-retest reliability of functional connectivity (FC) measures derived from resting fMRI data have been characterized in the adult and child brain, similar assessments have not been conducted in infants. In this study, we examined the intra-session test-retest reliability of FC measures from 119 infant brain MRI scans from four neurodevelopmental studies. We investigated edge-level and subject-level reliability within one MRI session (between and within runs) measured by the Intraclass correlation coefficient (ICC). First, using an atlas-based approach, we examined whole-brain connectivity as well as connectivity within two common resting fMRI networks - the default mode network (DMN) and the sensorimotor network (SMN). Second, we examined the influence of run duration, study site, and scanning manufacturer (e.g., Philips and General Electric) on ICCs. Lastly, we tested spatial similarity using the Jaccard Index from networks derived from independent component analysis (ICA). Consistent with resting fMRI studies from adults, our findings indicated poor edge-level reliability (ICC = 0.14 - 0.18), but moderate-to-good subject-level intra-session reliability for whole-brain, DMN, and SMN connectivity (ICC = 0.40 - 0.78). We also found significant effects of run duration, site, and scanning manufacturer on reliability estimates. Some ICA-derived networks showed strong spatial reproducibility (e.g., DMN, SMN, and Visual Network), and were labelled based on their spatial similarity to analogous networks measured in adults. These networks were reproducibly found across different study studies. However, other ICA-networks (e.g. Executive Control Network) did not show strong spatial reproducibility, suggesting that the reliability and/or maturational course of functional connectivity may vary by network. In sum, our findings suggest that developmental scientist may be on safe ground examining the functional organization of some major neural networks (e.g. DMN and SMN), but judicious interpretation of functional connectivity is essential to its ongoing success. HighlightsO_LIInfant functional connectivity (FC) shows poor edge-level reliability (ICCs) C_LIO_LIHowever, subject-level infant FC estimates show good-to-excellent ICCs C_LIO_LISpatial reproducibility is better for some resting networks (DMN, SMN) than others (ECN) C_LIO_LIReliability estimates differ across study site and MRI scanner C_LIO_LIConclusion - Infant FC can be a reliable measurement, but judicious use is needed C_LI

bioinformatics↗

bioRxiv2020

Non-opioid analgesia based on Gα signalling bias

The development of therapeutic agonists for G protein-coupled receptors (GPCRs) is hampered by the propensity of GPCRs to couple to multiple intracellular signalling pathways. This promiscuous coupling leads to numerous downstream cellular effects, some of which are therapeutically undesirable. This is especially the case for adenosine A1 receptors (A1Rs) whose clinical potential is undermined by the sedation and cardiorespiratory depression caused by conventional agonists. We have discovered that the A1R-selective agonist, BnOCPA, is a potent and powerful analgesic but does not cause sedation, bradycardia, hypotension or respiratory depression. This unprecedented discrimination between native A1Rs arises from BnOCPAs unique and exquisitely selective activation of Gob among the six Gi/o subtypes, and in the absence of {beta}-arrestin recruitment. BnOCPA thus demonstrates a highly-specific G-selective activation of the native A1R, sheds new light on GPCR signalling, and reveals new possibilities for the development of novel therapeutics based on the far-reaching concept of selective G agonism. Short summaryWe describe the selective activation of an adenosine A1 receptor-mediated intracellular pathway that provides potent analgesia in the absence of sedation or cardiorespiratory depression, paving the way for novel medicines based on the far-reaching concept of selective G agonism.

physiology↗

bioRxiv2019

Liquid-like and rigid-body motions in molecular-dynamics simulations of a crystalline protein

To gain insight into crystalline protein dynamics, we performed molecular-dynamics (MD) simulations of a periodic 2x2x2 supercell of staphylococcal nuclease. We used the resulting MD trajectories to simulate X-ray diffraction and to study collective motions. The agreement of simulated X-ray diffraction with the data is comparable to previous MD simulation studies. We studied collective motions by analyzing statistically the covariance of alpha-carbon position displacements. The covariance decreases exponentially with the distance between atoms, which is consistent with a liquid-like motions (LLM) model, in which the protein behaves like a soft material. To gain finer insight into the collective motions, we examined the covariance behavior within a protein molecule (intra-protein) and between different protein molecules (inter-protein). The inter-protein atom pairs, which dominate the overall statistics, exhibit LLM behavior; however, the intra-protein pairs exhibit behavior that is consistent with a superposition of LLM and rigid-body motions (RBM). Our results indicate that LLM behavior of global dynamics is present in MD simulations of a protein crystal. They also show that RBM behavior is detectable in the simulations but that it is subsumed by the LLM behavior. Finally the results provide clues about how correlated motions of atom pairs both within and across proteins might manifest in diffraction data. Overall our findings increase our understanding of the connection between molecular motions and diffraction data, and therefore advance efforts to extract information about functionally important motions from crystallography experiments.

biophysics↗

bioRxiv2019

Cep55 overexpression promotes genomic instability and tumorigenesis in mice

High expression of centrosomal protein CEP55 has been correlated with clinico-pathological parameters across multiple human cancers. Despite significant in vitro studies and association of aberrantly overexpressed CEP55 with worse prognosis, its causal role in vivo tumorigenesis remains elusive. Here, using a ubiquitously overexpressing transgenic mouse model, we show that Cep55 overexpression causes spontaneous tumorigenesis and accelerates Trp53+/- induced tumours in vivo. At the cellular level, using mouse embryonic fibroblasts (MEFs), we demonstrate that Cep55 overexpression induces proliferation advantage by modulating multiple cellular signalling networks including the PI3K/AKT pathway. Notably, the Cep55 overexpressing MEFs demonstrate high level of mitotic chromosomal instability (CIN) due to stabilized microtubules. Interestingly, Cep55 overexpressing MEFs have a compromised Chk1-dependent S-phase checkpoint, causing increased replication speed and DNA damage, resulting in a prolonged aberrant mitotic division. Importantly, this phenotype was rescued by pharmacological inhibition of Pi3k/Akt or expression of mutant Chk1 (S280A), that is insensitive to regulation by active AKT, in Cep55 overexpressing cell. Collectively, our data demonstrates causative effects of deregulated Cep55 on genome stability and tumorigenesis which have potential implications for tumour initiation and therapy.

cancer biology↗

bioRxiv2019

STARRPeaker: Uniform processing and accurate identification of whole human STARR-seq active regions

BackgroundHigh-throughput reporter assays, such as self-transcribing active regulatory region sequencing (STARR-seq), allow for unbiased and quantitative assessment of enhancers at a genome-wide scale. Recent advances in STARR-seq technology have employed progressively more complex genomic libraries and increased sequencing depths, to assay larger sized regions, up to the entire human genome. These advances necessitate a reliable processing pipeline and peak-calling algorithm. ResultsMost STARR-seq studies have relied on chromatin immunoprecipitation sequencing (ChIP-seq) processing pipelines. However, there are key differences in STARR-seq versus ChIP-seq. First, STARR-seq uses transcribed RNA to measure the activity of an enhancer, making an accurate determination of the basal transcription rate important. Second, STARR-seq coverage is highly non-uniform, overdispersed, and often confounded by sequencing biases, such as GC content and mappability. Lastly, here, we observed a clear correlation between RNA thermodynamic stability and STARR-seq readout, suggesting that STARR-seq may be sensitive to RNA secondary structure and stability. Considering these findings, we developed a negative-binomial regression framework for uniformly processing STARR-seq data, called STARRPeaker. In support of this, we generated whole-genome STARR-seq data from the HepG2 and K562 human cell lines and applied STARRPeaker to call enhancers. ConclusionsWe show STARRPeaker can unbiasedly detect active enhancers from both captured and whole-genome STARR-seq data. Specifically, we report [~]33,000 and [~]20,000 candidate enhancers from HepG2 and K562, respectively. Moreover, we show that STARRPeaker outperforms other peak callers in terms of identifying known enhancers with fewer false positives. Overall, we demonstrate an optimized processing framework for STARR-seq experiments can identify putative enhancers while addressing potential confounders.

bioinformatics↗

bioRxiv2019

Electronic Health Records Based Prediction of Future Incidence of Alzheimers Disease Using Machine Learning

Nationwide population-based cohort provides a new opportunity to build a completely automated risk prediction model based on individuals history of health and healthcare beyond existing risk prediction models. We tested the possibility of machine learning models to predict future incidence of Alzheimers disease (AD) using large-scale administrative health data. From the Korean National Health Insurance Service database between 2002 and 2010, we obtained de-identified health data in elders above 65 years (N=40,736) containing 4,894 unique clinical features including ICD-10 codes, medication codes, laboratory values, history of personal and family illness, and socio-demographics. To define incident AD two operational definitions were considered: "definite AD" with diagnostic codes and dementia medication (n=614) and "probable AD" with only diagnosis (n=2,026). We trained and validated a random forest, support vector machine, and logistic regression to predict incident AD in 1,2,3, and 4 subsequent years. For predicting future incidence of AD in balanced samples (bootstrapping), the machine learning models showed reasonable performance in 1-year prediction with AUC of 0.775 and 0.759, based on "definite AD" and "probable AD" outcomes, respectively; in 2-year, 0.730 and 0.693; in 3-year, 0.677 and 0.644; in 4-year, 0.725 and 0.683. The results were similar when the entire (unbalanced) samples were used. Important clinical features selected in logistic regression included hemoglobin level, age, and urine protein level. This study may shed a light on the utility of the data-driven machine learning model based on large-scale administrative health data in AD risk prediction, which may enable better selection of individuals at risk for AD in clinical trials or early detection in clinical settings.

bioinformatics↗

bioRxiv2017

Bringing Diffuse X-ray Scattering Into Focus

X-ray crystallography is experiencing a renaissance as a method for probing the protein conformational ensemble. The inherent limitations of Bragg analysis, however, which only reveals the mean structure, have given way to a surge in interest in diffuse scattering, which is caused by structure variations. Diffuse scattering is present in all macromolecular crystallography experiments. Recent studies are shedding light on the origins of diffuse scattering in protein crystallography, and provide clues for leveraging diffuse scattering to model protein motions with atomic detail.

biophysics↗

bioRxiv2017

Internal Protein Motions in Molecular Dynamics Simulations of Bragg and Diffuse X-ray Scattering

SynopsisA molecular dynamics simulation of diffuse X-ray scattering from staphylococcal nuclease crystals is greatly improved when the unit cell model is expanded to a 2x2x2 layout of eight unit cells. The dynamics are dominated by internal protein motions rather than rigid packing interactions.\n\nAbstractMolecular dynamics (MD) simulations of Bragg and diffuse X-ray scattering provide a means of obtaining experimentally validated models of protein conformational ensembles. This paper shows that, compared to a single periodic unit cell model, the accuracy in simulating diffuse scattering is increased when the crystal is modeled as a periodic supercell, consisting of a 2x2x2 layout of eight unit cells. The MD simulations capture the general dependence of correlations on the separation of atoms. There is substantial agreement between the simulated Bragg reflections and the crystal structure; there are local deviations, however, indicating both the limitation of using a single structure to model disordered regions of the protein and local deviations of the average structure away from the crystal structure. Although it was anticipated that a longer duration simulation might be required to achieve convergence of the diffuse scattering calculation using the supercell model, only a microsecond is required, the same as for the unit cell. Rigid protein motions only account for a small fraction of the variation in atom positions from the simulation. The results indicate that protein crystal dynamics can be dominated by internal motions rather than packing interactions, and that MD simulations can be combined with Bragg and diffuse X-ray scattering to model the protein conformational ensemble.

biophysics↗