Aβ Peptide Fibrillar Architectures Controlled by Conformational Constraints of the Monomer

Anomalous self-assembly of the Aβ peptide into fibrillar amyloid deposits is strongly correlated with the development of Alzheimer's disease. Aβ fibril extension follows a template guided “dock and lock” mechanism where polymerisation is catalysed by the fibrillar ends. Using surface plasmon resonance (SPR) and quenched hydrogen-deuterium exchange NMR (H/D-exchange NMR), we have analysed the fibrillar structure and polymerisation properties of both the highly aggregation prone Aβ1–40 Glu22Gly (Aβ40Arc) and wild type Aβ1–40 (Aβ40WT). The solvent protection patterns from H/D exchange experiments suggest very similar structures of the fibrillar forms. However, through cross-seeding experiments monitored by SPR, we found that the monomeric form of Aβ40WT is significantly impaired to acquire the fibrillar architecture of Aβ40Arc. A detailed characterisation demonstrated that Aβ40WT has a restricted ability to dock and isomerise with high binding affinity onto Aβ40Arc fibrils. These results have general implications for the process of fibril assembly, where the rate of polymerisation, and consequently the architecture of the formed fibrils, is restricted by conformational constraints of the monomers. Interestingly, we also found that the kinetic rate of fibril formation rather than the thermodynamically lowest energy state determines the overall fibrillar structure

Digital Health Solutions to Reduce the Burden of Atherosclerotic Cardiovascular Disease Proposed by the CARRIER Consortium

Digital health is a promising tool to support people with an elevated risk for atherosclerotic cardiovascular disease (ASCVD) and patients with an established disease to improve cardiovascular outcomes. Many digital health initiatives have been developed and employed. However, barriers to their large-scale implementation have remained. This paper focuses on these barriers and presents solutions as proposed by the Dutch CARRIER (ie, Coronary ARtery disease: Risk estimations and Interventions for prevention and EaRly detection) consortium. We will focus in 4 sections on the following: (1) the development process of an eHealth solution that will include design thinking and cocreation with relevant stakeholders; (2) the modeling approach for two clinical prediction models (CPMs) to identify people at risk of developing ASCVD and to guide interventions; (3) description of a federated data infrastructure to train the CPMs and to provide the eHealth solution with relevant data; and (4) discussion of an ethical and legal framework for responsible data handling in health care. The Dutch CARRIER consortium consists of a collaboration between experts in the fields of eHealth development, ASCVD, public health, big data, as well as ethics and law. The consortium focuses on reducing the burden of ASCVD. We believe the future of health care is data driven and supported by digital health. Therefore, we hope that our research will not only facilitate CARRIER consortium but may also facilitate other future health care initiatives

Calculations of binding affinity between C8-substituted GTP analogs and the bacterial cell-division protein FtsZ

The FtsZ protein is a self-polymerizing GTPase that plays a central role in bacterial cell division. Several C8-substituted GTP analogs are known to inhibit the polymerization of FtsZ by competing for the same binding site as its endogenous activating ligand GTP. Free energy calculations of the relative binding affinities to FtsZ for a set of five C8-substituted GTP analogs were performed. The calculated values agree well with the available experimental data, and the main contribution to the free energy differences is determined to be the conformational restriction of the ligands. The dihedral angle distributions around the glycosidic bond of these compounds in water are known to vary considerably depending on the physicochemical properties of the substituent at C8. However, within the FtsZ protein, this substitution has a negligible influence on the dihedral angle distributions, which fall within the narrow range of −140° to −90° for all investigated compounds. The corresponding ensemble average of the coupling constants 3J(C4,H1′) is calculated to be 2.95 ± 0.1 Hz. The contribution of the conformational selection of the GTP analogs upon binding was quantified from the corresponding populations. The obtained restraining free energy values follow the same trend as the relative binding affinities to FtsZ, indicating their dominant contribution

Multilevel modeling for data streams with dependent observations

The technological developments of the last decades, e.g., the introduction of the smartphone, have created opportunities to efficiently collect data of many individuals over an extensive period of time. While these technologies allow for intensive longitudinal measurements, they also come with new challenges: data sets collected using these technologies could become extremely large, and in many applications the data collection is never truly `finished'. As a result, the data keep streaming in and analyzing data streams using the standard computation of well-known models becomes inefficient as the computation has to be repeated each time a new data point enters to remain up to date. In this thesis, methods to analyze data streams are developed. The introduction of these methods allows researchers to broaden the scope of their research, by using data streams. In this dissertation, I introduce a commonly used approach to deal with data streams: online learning, a method to update the result of an analysis while the data are entering, without revisiting the previous data points. This approach can deal with data streams, because 1) it is no longer required to store all data in memory; and 2) because the approach updates the results it can easily remain up to date when new data enter. A large range of statistical models can be estimated using this approach, e.g., a linear regression and correlations. However, when observations are clustered, for instance because the same individuals are repeatedly observed, rewriting the estimation procedure to be feasible in a data stream becomes more difficult. This data structure with clustered observations causes dependencies between data points belonging to the same person. Ignoring these dependencies violates an important statistical assumption of independent observations. When a researcher does not deal with the dependencies between the data points, the results are likely to be biased (i.e., inaccurate). There are models which take the dependencies between the observations into account, called multilevel models. These multilevel models, however, are computationally complex to estimate in a data stream, because the estimation procedure requires multiple passes over the data set. These multiple passes over the data set in combination with the continuous stream of new data points make it difficult to estimate the multilevel model in data streams. In this thesis, I developed an online-learning algorithm that updates the multilevel model, while new data enter and without passing over all the data repeatedly. This algorithm, called SEMA (acronym for Streaming Expectation Maximization Approximation), updates the results of the multilevel model with each new data point, without storing the data points. Using this algorithm, researchers can analyze data streams efficiently, while keeping the structure of the data stream into account