Biology subjects

Rick, J.

Publications and source records attributed to Rick, J..

5 recordsLinked to original sources

bioRxiv2022

Grazers affect the composition of dissolved storage glycans and thereby bacterioplankton composition during a biphasic North Sea spring algae bloom

Blooms of marine microalgae play a pivotal role in global carbon cycling. Such blooms entail successive blooms of specialized clades of planktonic bacteria that remineralize algal biomass. We investigated the bacterioplankton response to a bloom in the German Bight in spring 2020. Metagenome sequencing at 30 time-points allowed reconstruction of 251 metagenome-assembled genomes (MAGs), 245 representing as yet uncultured species, while corresponding metatranscriptome sequencing highlighted 50 particularly active MAGs. Together with algae, copepod, protist and bacteria diversity and abundance data in combination with physico-chemical data and antibody-based saccharide measurements, we demonstrate (i) how dissolved primary photoassimilated algal and secondary bacterial storage glycans shape the bacterioplankton community composition, and (ii) how grazing on higher trophic levels determines the release of these abundant glycans. We thus elucidate principles governing how bacterioplankton clades respond to algal blooms and collectively remineralize gigatons of carbon annually on a global scale.

ecology↗

bioRxiv2021

Evaluation of Machine Learning predictions of a highly resolved long time series of Chlorophyll-a concentration

Pelagic Chlorophyll-a concentrations are key for evaluation of the environmental status and productivity of marine systems. In this study, chlorophyll-a concentrations for the Helgoland Roads Time Series were modeled using a number of measured water and environmental parameters. We chose three common Machine Learning algorithms from the literature: Support Vector Machine Regressor, Neural Networks Multi-layer Perceptron Regressor and Random Forest Regressor. Results showed that Support Vector Machine Regressor slightly outperformed other models. The evaluation with a test dataset and verification with an independent validation dataset for chlorophyll-a concentrations showed a good generalization capacity, evaluated by the root mean squared errors of less than 1 g L-1. Feature selection and engineering are important and improved the models significantly, as measured in performance, improving by a minimum of 48% the adjusted R2. We tested SARIMA in comparison and found that the univariate nature of SARIMA does not allow for better results than the Machine Learning models. Additionally, the computer processing time needed was much higher (prohibitive) for SARIMA.

ecology↗

bioRxiv2021

Identification of Cancer-Associated Fibroblasts in Glioblastoma and Defining Their Pro-tumoral Effects

Despite their identification in some cancers, pro-tumoral cancer-associated fibroblasts (CAFs) were presumed absent in glioblastoma given the lack of brain fibroblasts. Serial trypsinization of primary glioblastoma cultures yielded cells with CAF morphology, CAF transcriptomic profile, and mesenchymal lineage in single-cell RNA-seq. Glioblastoma CAFs were attracted to glioblastoma stem cells (GSCs) and CAFs enriched GSCs. We created a resource of inferred crosstalk by mapping expression of receptors to their cognate ligands, identifying PDGF-{beta} and TGF-{beta} as mediators of GSC effects on CAFs, and osteopontin and hepatocyte growth factor as mediators of CAF-induced GSC enrichment. Glioblastoma CAFs also induced M2 macrophage polarization by producing the EDA fibronectin variant. Glioblastoma CAFs were enriched in the subventricular zone which houses neural stem cells that produce GSCs. Including CAFs in GSC-derived xenografts induced in vivo growth. These findings are among the first to identify glioblastoma CAFs and their GSC interactions, making them an intriguing target.

cancer biology↗

bioRxiv2019

Genome-wide association study of Parkinson's disease progression biomarkers in 12 longitudinal patients' cohorts

BackgroundSeveral reports have identified different patterns of Parkinsons disease progression in individuals carrying missense variants in the GBA or LRRK2 genes. The overall contribution of genetic factors to the severity and progression of Parkinsons disease, however, has not been well studied. ObjectivesTo test the association between genetic variants and the clinical features and progression of Parkinsons disease on a genome-wide scale. MethodsWe accumulated individual data from 12 longitudinal cohorts in a total of 4,093 patients with 25,254 observations over a median of 3.81 years. Genome-wide associations were evaluated for 25 cross-sectional and longitudinal phenotypes. Specific variants of interest, including 90 recently-identified disease risk variants, were also investigated for the associations with these phenotypes. ResultsTwo variants were genome-wide significant. Rs382940(T>A), within the intron of SLC44A1, was associated with reaching Hoehn and Yahr stage 3 or higher faster (HR 2.04 [1.58, 2.62], P-value = 3.46E-8). Rs61863020(G>A), an intergenic variant and eQTL for ADRA2A, was associated with a lower prevalence of insomnia at baseline (OR 0.63 [0,52, 0.75], P-value = 4.74E-8). In the targeted analysis, we found nine associations between known Parkinsons risk variants and more severe motor/cognitive symptoms. Also, we replicated previous reports of GBA coding variants (rs2230288: p.E365K, rs75548401: p.T408M) being associated with greater motor and cognitive decline over time, and APOE E4 tagging variant (rs429358) being associated with greater cognitive deficits in patients. ConclusionsWe identified novel genetic factors associated with heterogeneity of progression in Parkinsons disease. The results provide new insights into the pathogenesis of Parkinsons disease as well as patient stratification for clinical trials.

genetics↗

bioRxiv2018

A novel xenograft model reveals invasive mesenchymal transition and ineffective angiogenic response during the evolution of resistance to anti-angiogenic therapy

Bevacizumab treatment of glioblastoma is limited by transient responses and acquired resistance. Because of the lengthy duration of treatment that can precede resistance in patients, in order to study changes underlying the evolution of bevacizumab resistance, we created a novel multigenerational xenograft model of acquired bevacizumab resistance. Glioblastoma xenografts were treated with bevacizumab or IgG, and the fastest growing tumor re-implanted into new mice, generating paired isogeneic responsive or resistant multigenerational xenografts. Microarray analysis revealed significant overexpression across generations of the mesenchymal subtype gene signature, paralleling results from patient bevacizumab-resistant glioblastomas (BRGs) that exhibited increasing mesenchymal gene expression correlating with increased bevacizumab treatment duration. Key mesenchymal markers, including YKL-40, CD44, SERPINE1, and TIMP1 were upregulated across generations, with altered morphology, increased invasiveness, and increased neurosphere formation confirmed in later xenograft generations. Interestingly, YKL-40 levels were elevated in serum of patients with bevacizumab-resistant vs. bevacizumab-naive glioblastomas. Finally, despite upregulation of VEGF-independent pro-angiogenic genes across xenograft generations, immunostaining revealed increased hypoxia and decreased vessel density with increasing generation of treatment, mirroring our findings in patient BRGs and suggesting tumor growth despite effective devascularization caused by VEGF blockade. Besides identifying novel targets for preventing the evolution of resistance and offering a xenograft model for testing resistance inhibitors, our work suggests YKL-40 as a blood biomarker of bevacizumab resistance worthy of further evaluation.

cancer biology↗