Aluminum hydroxide (Al(OH)3) and aluminum phosphate (AlPO4) are widely used adjuvants in human vaccines. However, a rationale to choose one or the other is lacking since the differences between... Show moreAluminum hydroxide (Al(OH)3) and aluminum phosphate (AlPO4) are widely used adjuvants in human vaccines. However, a rationale to choose one or the other is lacking since the differences between molecular mechanisms of action of these adjuvants are unknown. In the current study, we compared the innate immune response induced by both adjuvants in vitro and in vivo. Proteome analysis of human primary monocytes was used to determine the immunological pathways activated by these adjuvants. Subsequently, analysis of immune cells present at the site of injection and proteome analysis of the muscle tissue revealed the differentially regulated processes related to the innate immune response in vivo. Incubation with Al(OH)3 specifically enhanced the activation of antigen processing and presentation pathways in vitro. In vivo experiments showed that only intramuscular (I.M.) immunization with Al(OH)3 attracted neutrophils, while I.M. immunization with AlPO4 attracted monocytes/macrophages to the site of injection. In addition, only I.M. immunization with Al(OH)3 enhanced the process of hemostasis after 96 hours, possibly related to neutrophilic extracellular trap formation. Both adjuvants differentially regulated various immune system-related processes. The results show that Al(OH)3 and AlPO4 act differently on the innate immune system. We speculate that these different regulations affect the interaction with cells, due to the different physicochemical properties of both adjuvants. Show less
Brown, A.G.A.; Vallenari, A.; Prusti, T.; Bruijne, J.H.J. de; Babusiaux, C.; Biermann, M.; ... ; Licata, E. et al. 2021
Motivation: When phase III clinical drug trials fail their endpoint, enormous resources are wasted. Moreover, even if a clinical trial demonstrates a significant benefit, the observed effects are... Show moreMotivation: When phase III clinical drug trials fail their endpoint, enormous resources are wasted. Moreover, even if a clinical trial demonstrates a significant benefit, the observed effects are often small and may not outweigh the side effects of the drug. Therefore, there is a great clinical need for methods to identify genetic markers that can identify subgroups of patients which are likely to benefit from treatment as this may (i) rescue failed clinical trials and/or (ii) identify subgroups of patients which benefit more than the population as a whole. When single genetic biomarkers cannot be found, machine learning approaches that find multivariate signatures are required. For single nucleotide polymorphism (SNP) profiles, this is extremely challenging owing to the high dimensionality of the data. Here, we introduce RAINFOREST (tReAtment benefIt prediction using raNdom FOREST), which can predict treatment benefit from patient SNP profiles obtained in a clinical trial setting.Results: We demonstrate the performance of RAINFOREST on the CAIRO2 dataset, a phase III clinical trial which tested the addition of cetuximab treatment for metastatic colorectal cancer and concluded there was no benefit. However, we find that RAINFOREST is able to identify a subgroup comprising 27.7% of the patients that do benefit, with a hazard ratio of 0.69 (P = 0.04) in favor of cetuximab. The method is not specific to colorectal cancer and could aid in reanalysis of clinical trial data and provide a more personalized approach to cancer treatment, also when there is no clear link between a single variant and treatment benefit. Show less
Jong, J.M.G.H.J. de; Bruijne, J.de; Ridder, J. de 2020
Context. Benford’s law states that for scale- and base-invariant data sets covering a wide dynamic range, the distribution of the first significant digit is biased towards low values. This has been... Show moreContext. Benford’s law states that for scale- and base-invariant data sets covering a wide dynamic range, the distribution of the first significant digit is biased towards low values. This has been shown to be true for wildly different datasets, including financial, geographical, and atomic data. In astronomy, earlier work showed that Benford’s law also holds for distances estimated as the inverse of parallaxes from the ESA HIPPARCOS mission.Aims. We investigate whether Benford’s law still holds for the 1.3 billion parallaxes contained in the second data release of Gaia (Gaia DR2). In contrast to previous work, we also include negative parallaxes. We examine whether distance estimates computed using a Bayesian approach instead of parallax inversion still follow Benford’s law. Lastly, we investigate the use of Benford’s law as a validation tool for the zero-point of the Gaia parallaxes.Methods. We computed histograms of the observed most significant digit of the parallaxes and distances, and compared them with the predicted values from Benford’s law, as well as with theoretically expected histograms. The latter were derived from a simulated Gaia catalogue based on the Besançon galaxy model.Results. The observed parallaxes in Gaia DR2 indeed follow Benford’s law. Distances computed with the Bayesian approach of Bailer-Jones et al. (2018, AJ, 156, 58) no longer follow Benford’s law, although low-value ciphers are still favoured for the most significant digit. The prior that is used has a significant effect on the digit distribution. Using the simulated Gaia universe model snapshot, we demonstrate that the true distances underlying the Gaia catalogue are not expected to follow Benford’s law, essentially because the interplay between the luminosity function of the Milky Way and the mission selection function results in a bi-modal distance distribution, corresponding to nearby dwarfs in the Galactic disc and distant giants in the Galactic bulge. In conclusion, Gaia DR2 parallaxes only follow Benford’s Law as a result of observational errors. Finally, we show that a zero-point offset of the parallaxes derived by optimising the fit between the observed most-significant digit frequencies and Benford’s law leads to a value that is inconsistent with the value that is derived from quasars. The underlying reason is that such a fit primarily corrects for the difference in the number of positive and negative parallaxes, and can thus not be used to obtain a reliable zero-point. Show less
The recent boom in microfluidics and combinatorial indexing strategies, combined with low sequencing costs, has empowered single-cell sequencing technology. Thousands-or even millions-of cells... Show moreThe recent boom in microfluidics and combinatorial indexing strategies, combined with low sequencing costs, has empowered single-cell sequencing technology. Thousands-or even millions-of cells analyzed in a single experiment amount to a data revolution in single-cell biology and pose unique data science problems. Here, we outline eleven challenges that will be central to bringing this emerging field of single-cell data science forward. For each challenge, we highlight motivating research questions, review prior work, and formulate open problems. This compendium is for established researchers, newcomers, and students alike, highlighting interesting and rewarding problems for the coming years. Show less
Taskesen, E.; Huisman, S.M.H.; Mahfouz, A.; Krijthe, J.H.; Ridder, J. de; Stolpe, A. van de; ... ; Reinders, M.J.T. 2018
