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Many complex diseases exhibit pronounced sex differences that can affect both the initial risk of developing the disease, as well as clinical disease symptoms, molecular manifestations, disease progression, and the risk of developing comorbidities. Despite this, computational studies of molecular data for complex diseases often treat sex as a confounding variable, aiming to filter out sex-specific effects rather than attempting to interpret them. A more systematic, in-depth exploration of sex-specific disease mechanisms could significantly improve our understanding of pathological and protective processes with sex-dependent profiles. This survey discusses dedicated bioinformatics approaches for the study of molecular sex differences in complex diseases. It highlights that, beyond classical statistical methods, approaches are needed that integrate prior knowledge of relevant hormone signaling interactions, gene regulatory networks, and sex linkage of genes to provide a mechanistic interpretation of sex-dependent alterations in disease. The review examines and compares the advantages, pitfalls and limitations of various conventional statistical and systems-level mechanistic analyses for this purpose, including tailored pathway and network analysis techniques. Overall, this survey highlights the potential of specialized bioinformatics techniques to systematically investigate molecular sex differences in complex diseases, to inform biomarker signature modeling, and to guide more personalized treatment approaches., (© The Author(s) 2024. Published by Oxford University Press.)

Parkinson's disease (PD) presents diverse symptoms and comorbidities, complicating its diagnosis and management. The primary objective of this cross-sectional, monocentric study was to assess digital gait sensor data's utility for monitoring and diagnosis of motor and gait impairment in PD. As a secondary objective, for the more challenging tasks of detecting comorbidities, non-motor outcomes, and disease progression subgroups, we evaluated for the first time the integration of digital markers with metabolomics and clinical data. Using shoe-attached digital sensors, we collected gait measurements from 162 patients and 129 controls in a single visit. Machine learning models showed significant diagnostic power, with AUC scores of 83-92% for PD vs. control and up to 75% for motor severity classification. Integrating gait data with metabolomics and clinical data improved predictions for challenging-to-detect comorbidities such as hallucinations. Overall, this approach using digital biomarkers and multimodal data integration can assist in objective disease monitoring, diagnosis, and comorbidity detection., (© 2024. The Author(s).)

MicroRNAs act via targeted suppression of messenger RNA translation in the DNA-RNA-protein axis. The dysregulation of microRNA(s) reflects the epigenetic changes affecting the cellular processes in multiple disorders. To understand the complex effect of dysregulated microRNAs linked to neurodegeneration, we performed a cross-sectional microRNA expression analysis in idiopathic Parkinson's disease ( n = 367), progressive supranuclear palsy ( n = 35) and healthy controls ( n = 416) from the Luxembourg Parkinson's Study, followed by prediction modelling, enriched pathway analysis and target simulation of dysregulated microRNAs using probabilistic Boolean modelling. Forty-six microRNAs were identified to be dysregulated in Parkinson's disease versus controls and 16 in progressive supranuclear palsy versus controls with 4 overlapping significantly dysregulated microRNAs between the comparisons. Predictive power of microRNA subsets (including up to 100 microRNAs) was modest for differentiating Parkinson's disease or progressive supranuclear palsy from controls (maximal cross-validated area under the receiver operating characteristic curve 0.76 and 0.86, respectively) and low for progressive supranuclear palsy versus Parkinson's disease (maximal cross-validated area under the receiver operating characteristic curve 0.63). The enriched pathway analysis revealed natural killer cell pathway to be dysregulated in both, Parkinson's disease and progressive supranuclear palsy versus controls, indicating that the immune system might play an important role in both diseases. Probabilistic Boolean modelling of pathway dynamics affected by dysregulated microRNAs in Parkinson's disease and progressive supranuclear palsy revealed partially overlapping dysregulation in activity of the transcription factor EB, endoplasmic reticulum stress signalling, calcium signalling pathway, dopaminergic transcription and peroxisome proliferator-activated receptor gamma coactivator-1α activity, though involving different mechanisms. These findings indicated a partially convergent (sub)cellular end-point dysfunction at multiple levels in Parkinson's disease and progressive supranuclear palsy, but with distinctive underlying molecular mechanisms., Competing Interests: The authors report no competing interests., (© The Author(s) 2024. Published by Oxford University Press on behalf of the Guarantors of Brain.)

The progression of Parkinson's disease (PD) is heterogeneous across patients, affecting counseling and inflating the number of patients needed to test potential neuroprotective treatments. Moreover, disease subtypes might require different therapies. This work uses a data-driven approach to investigate how observed heterogeneity in PD can be explained by the existence of distinct PD progression subtypes. To derive stable PD progression subtypes in an unbiased manner, we analyzed multimodal longitudinal data from three large PD cohorts and performed extensive cross-cohort validation. A latent time joint mixed-effects model (LTJMM) was used to align patients on a common disease timescale. Progression subtypes were identified by variational deep embedding with recurrence (VaDER). In each cohort, we identified a fast-progressing and a slow-progressing subtype, reflected by different patterns of motor and non-motor symptoms progression, survival rates, treatment response, features extracted from DaTSCAN imaging and digital gait assessments, education, and Alzheimer's disease pathology. Progression subtypes could be predicted with ROC-AUC up to 0.79 for individual patients when a one-year observation period was used for model training. Simulations demonstrated that enriching clinical trials with fast-progressing patients based on these predictions can reduce the required cohort size by 43%. Our results show that heterogeneity in PD can be explained by two distinct subtypes of PD progression that are stable across cohorts. These subtypes align with the brain-first vs. body-first concept, which potentially provides a biological explanation for subtype differences. Our predictive models will enable clinical trials with significantly lower sample sizes by enriching fast-progressing patients., (© 2024. The Author(s).)

Parkinson's disease (PD) is a highly heterogeneous disorder influenced by several environmental and genetic factors. Effective disease-modifying therapies and robust early-stage biomarkers are still lacking, and an improved understanding of the molecular changes in PD could help to reveal new diagnostic markers and pharmaceutical targets. Here, we report results from a cohort-wide blood plasma metabolic profiling of PD patients and controls in the Luxembourg Parkinson's Study to detect disease-associated alterations at the level of systemic cellular process and network alterations. We identified statistically significant changes in both individual metabolite levels and global pathway activities in PD vs. controls and significant correlations with motor impairment scores. As a primary observation when investigating shared molecular sub-network alterations, we detect pronounced and coordinated increased metabolite abundances in xanthine metabolism in de novo patients, which are consistent with previous PD case/control transcriptomics data from an independent cohort in terms of known enzyme-metabolite network relationships. From the integrated metabolomics and transcriptomics network analysis, the enzyme hypoxanthine phosphoribosyltransferase 1 (HPRT1) is determined as a potential key regulator controlling the shared changes in xanthine metabolism and linking them to a mechanism that may contribute to pathological loss of cellular adenosine triphosphate (ATP) in PD. Overall, the investigations revealed significant PD-associated metabolome alterations, including pronounced changes in xanthine metabolism that are mechanistically congruent with alterations observed in independent transcriptomics data. The enzyme HPRT1 may merit further investigation as a main regulator of these network alterations and as a potential therapeutic target to address downstream molecular pathology in PD., (© 2024. The Author(s).)

Alzheimer's disease (AD) progression and pathology show pronounced sex differences, but the factors driving these remain poorly understood. To gain insights into early AD-associated molecular changes and their sex dependency for tau pathology in the cortex, we performed single-cell RNA-seq in the THY-Tau22 AD mouse model. By examining cell type-specific and cell type-agnostic AD-related gene activity changes and their sex-dimorphism for individual genes, pathways and cellular sub-networks, we identified both statistically significant alterations and interpreted the upstream mechanisms controlling them. Our results confirm several significant sex-dependent alterations in gene activity in the THY-Tau22 model mice compared to controls, with more pronounced alterations in females. Both changes shared across multiple cell types and cell type-specific changes were observed. The differential genes showed significant over-representation of known AD-relevant processes, such as pathways associated with neuronal differentiation, programmed cell death and inflammatory responses. Regulatory network analysis of these genes revealed upstream regulators that modulate many of the downstream targets with sex-dependent changes. Most key regulators have been previously implicated in AD, such as Egr1, Klf4, Chchd2, complement system genes, and myelin-associated glycoproteins. Comparing with similar data from the Tg2576 AD mouse model and human AD patients, we identified multiple genes with consistent, cell type-specific and sex-dependent alterations across all three datasets. These shared changes were particularly evident in the expression of myelin-associated genes such as Mbp and Plp1 in oligodendrocytes. In summary, we observed significant cell type-specific transcriptomic changes in the THY-Tau22 mouse model, with a strong over-representation of known AD-associated genes and processes. These include both sex-neutral and sex-specific patterns, characterized by consistent shifts in upstream master regulators and downstream target genes. Collectively, these findings provide insights into mechanisms influencing sex-specific susceptibility to AD and reveal key regulatory proteins that could be targeted for developing treatments addressing sex-dependent AD pathology., (© 2024. The Author(s).)

Background: Deep phenotyping of Parkinson's disease (PD) is essential to investigate this fastest-growing neurodegenerative disorder. Since 2015, over 800 individuals with PD and atypical parkinsonism along with more than 800 control subjects have been recruited in the frame of the observational, monocentric, nation-wide, longitudinal-prospective Luxembourg Parkinson's study., Objective: To profile the baseline dataset and to explore risk factors, comorbidities and clinical profiles associated with PD, atypical parkinsonism and controls., Methods: Epidemiological and clinical characteristics of all 1,648 participants divided in disease and control groups were investigated. Then, a cross-sectional group comparison was performed between the three largest groups: PD, progressive supranuclear palsy (PSP) and controls. Subsequently, multiple linear and logistic regression models were fitted adjusting for confounders., Results: The mean (SD) age at onset (AAO) of PD was 62.3 (11.8) years with 15% early onset (AAO < 50 years), mean disease duration 4.90 (5.16) years, male sex 66.5% and mean MDS-UPDRS III 35.2 (16.3). For PSP, the respective values were: 67.6 (8.2) years, all PSP with AAO > 50 years, 2.80 (2.62) years, 62.7% and 53.3 (19.5). The highest frequency of hyposmia was detected in PD followed by PSP and controls (72.9%; 53.2%; 14.7%), challenging the use of hyposmia as discriminating feature in PD vs. PSP. Alcohol abstinence was significantly higher in PD than controls (17.6 vs. 12.9%, p  = 0.003)., Conclusion: Luxembourg Parkinson's study constitutes a valuable resource to strengthen the understanding of complex traits in the aforementioned neurodegenerative disorders. It corroborated several previously observed clinical profiles, and provided insight on frequency of hyposmia in PSP and dietary habits, such as alcohol abstinence in PD. Clinical trial registration : clinicaltrials.gov, NCT05266872., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest. The author(s) declared that they were an editorial board member of Frontiers, at the time of submission. This had no impact on the peer review process and the final decision., (Copyright © 2023 Pavelka, Rawal, Ghosh, Pauly, Pauly, Hanff, Kolber, Jónsdóttir, Mcintyre, Azaiz, Thiry, Vilasboas, Soboleva, Giraitis, Tsurkalenko, Sapienza, Diederich, Klucken, Glaab, Aguayo, Jubal, Perquin, Vaillant, May, Gantenbein, Satagopam, Krüger and on behalf of the NCER-PD Consortium.)

The mechanisms underlying Parkinson's disease (PD) etiology are only partially understood despite intensive research conducted in the field. Recent evidence suggests that early neurodevelopmental defects might play a role in cellular susceptibility to neurodegeneration. To study the early developmental contribution of GBA mutations in PD we used patient-derived iPSCs carrying a heterozygous N370S mutation in the GBA gene. Patient-specific midbrain organoids displayed GBA-PD relevant phenotypes such as reduction of GCase activity, autophagy impairment, and mitochondrial dysfunction. Genome-scale metabolic (GEM) modeling predicted changes in lipid metabolism which were validated with lipidomics analysis, showing significant differences in the lipidome of GBA-PD. In addition, patient-specific midbrain organoids exhibited a decrease in the number and complexity of dopaminergic neurons. This was accompanied by an increase in the neural progenitor population showing signs of oxidative stress-induced damage and premature cellular senescence. These results provide insights into how GBA mutations may lead to neurodevelopmental defects thereby predisposing to PD pathology., (© 2023. The Author(s).)

Motivation: In many high-dimensional prediction or classification tasks, complementary data on the features are available, e.g. prior biological knowledge on (epi)genetic markers. Here we consider tasks with numerical prior information that provide an insight into the importance (weight) and the direction (sign) of the feature effects, e.g. regression coefficients from previous studies., Results: We propose an approach for integrating multiple sources of such prior information into penalized regression. If suitable co-data are available, this improves the predictive performance, as shown by simulation and application., Availability and Implementation: The proposed method is implemented in the R package transreg (https://github.com/lcsb-bds/transreg, https://cran.r-project.org/package=transreg)., (© The Author(s) 2023. Published by Oxford University Press.)

The vast majority of Parkinson's disease cases are idiopathic. Unclear etiology and multifactorial nature complicate the comprehension of disease pathogenesis. Identification of early transcriptomic and metabolic alterations consistent across different idiopathic Parkinson's disease (IPD) patients might reveal the potential basis of increased dopaminergic neuron vulnerability and primary disease mechanisms. In this study, we combine systems biology and data integration approaches to identify differences in transcriptomic and metabolic signatures between IPD patient and healthy individual-derived midbrain neural precursor cells. Characterization of gene expression and metabolic modeling reveal pyruvate, several amino acid and lipid metabolism as the most dysregulated metabolic pathways in IPD neural precursors. Furthermore, we show that IPD neural precursors endure mitochondrial metabolism impairment and a reduced total NAD pool. Accordingly, we show that treatment with NAD precursors increases ATP yield hence demonstrating a potential to rescue early IPD-associated metabolic changes., (© 2023. The Author(s).)

Background: Astrocytes have recently gained attention as key contributors to the pathogenesis of neurodegenerative disorders including Parkinson's disease. To investigate human astrocytes in vitro, numerous differentiation protocols have been developed. However, the properties of the resulting glia are inconsistent, which complicates the selection of an appropriate method for a given research question. Thus, we compared two approaches for the generation of iPSC-derived astrocytes. We phenotyped glia that were obtained employing a widely used long, serum-free ("LSF") method against an in-house established short, serum-containing ("SSC") protocol which allows for the generation of astrocytes and midbrain neurons from the same precursor cells., Results: We employed high-content confocal imaging and RNA sequencing to characterize the cultures. The astrocytes generated with the LSF or SSC protocols differed considerably in their properties: while the former cells were more labor-intense in their generation (5 vs 2 months), they were also more mature. This notion was strengthened by data resulting from cell type deconvolution analysis that was applied to bulk transcriptomes from the cultures to assess their similarity with human postmortem astrocytes., Conclusions: Overall, our analyses highlight the need to consider the advantages and disadvantages of a given differentiation protocol, when designing functional or drug discovery studies involving iPSC-derived astrocytes., (© 2023. BioMed Central Ltd., part of Springer Nature.)

In many biomedical applications, we are more interested in the predicted probability that a numerical outcome is above a threshold than in the predicted value of the outcome. For example, it might be known that antibody levels above a certain threshold provide immunity against a disease, or a threshold for a disease severity score might reflect conversion from the presymptomatic to the symptomatic disease stage. Accordingly, biomedical researchers often convert numerical to binary outcomes (loss of information) to conduct logistic regression (probabilistic interpretation). We address this bad statistical practice by modelling the binary outcome with logistic regression, modelling the numerical outcome with linear regression, transforming the predicted values from linear regression to predicted probabilities, and combining the predicted probabilities from logistic and linear regression. Analysing high-dimensional simulated and experimental data, namely clinical data for predicting cognitive impairment, we obtain significantly improved predictions of dichotomised outcomes. Thus, the proposed approach effectively combines binary with numerical outcomes to improve binary classification in high-dimensional settings. An implementation is available in the R package cornet on GitHub (https://github.com/rauschenberger/cornet) and CRAN (https://CRAN.R-project.org/package=cornet)., Competing Interests: No potential conflict of interest was reported by the author(s)., (© 2023 Luxembourg Centre for Systems Biomedicine (LCSB), University of Luxembourg. Published by Informa UK Limited, trading as Taylor & Francis Group.)

A growing body of evidence links gut microbiota changes with inflammatory bowel disease (IBD), raising the potential benefit of exploiting metagenomics data for non-invasive IBD diagnostics. The sbv IMPROVER metagenomics diagnosis for inflammatory bowel disease challenge investigated computational metagenomics methods for discriminating IBD and nonIBD subjects. Participants in this challenge were given independent training and test metagenomics data from IBD and nonIBD subjects, which could be wither either raw read data (sub-challenge 1, SC1) or processed Taxonomy- and Function-based profiles (sub-challenge 2, SC2). A total of 81 anonymized submissions were received between September 2019 and March 2020. Most participants' predictions performed better than random predictions in classifying IBD versus nonIBD, Ulcerative Colitis (UC) versus nonIBD, and Crohn's Disease (CD) versus nonIBD. However, discrimination between UC and CD remains challenging, with the classification quality similar to the set of random predictions. We analyzed the class prediction accuracy, the metagenomics features by the teams, and computational methods used. These results will be openly shared with the scientific community to help advance IBD research and illustrate the application of a range of computational methodologies for effective metagenomic classification., (© 2023. The Author(s).)

Parkinson's disease (PD) is a heterogeneous disorder, and among the factors which influence the symptom profile, biological sex has been reported to play a significant role. While males have a higher age-adjusted disease incidence and are more frequently affected by muscle rigidity, females present more often with disabling tremors. The molecular mechanisms involved in these differences are still largely unknown, and an improved understanding of the relevant factors may open new avenues for pharmacological disease modification. To help address this challenge, we conducted a meta-analysis of disease-associated molecular sex differences in brain transcriptomics data from case/control studies. Both sex-specific (alteration in only one sex) and sex-dimorphic changes (changes in both sexes, but with opposite direction) were identified. Using further systems level pathway and network analyses, coordinated sex-related alterations were studied. These analyses revealed significant disease-associated sex differences in mitochondrial pathways and highlight specific regulatory factors whose activity changes can explain downstream network alterations, propagated through gene regulatory cascades. Single-cell expression data analyses confirmed the main pathway-level changes observed in bulk transcriptomics data. Overall, our analyses revealed significant sex disparities in PD-associated transcriptomic changes, resulting in coordinated modulations of molecular processes. Among the regulatory factors involved, NR4A2 has already been reported to harbor rare mutations in familial PD and its pharmacological activation confers neuroprotective effects in toxin-induced models of Parkinsonism. Our observations suggest that NR4A2 may warrant further research as a potential adjuvant therapeutic target to address a subset of pathological molecular features of PD that display sex-associated profiles., (© 2023. The Author(s).)

Specialized pro-resolving mediators (SPMs) are multifunctional lipid mediators that participate in the resolution of inflammation. We have recently described that oral epithelial cells (OECs) express receptors of the SPM resolvin RvD1 n-3 DPA and that cultured OECs respond to RvD1 n-3 DPA addition by intracellular calcium release, nuclear receptor translocation and transcription of genes coding for antimicrobial peptides. The aim of the present study was to assess the functional outcome of RvD1 n-3 DPA -signaling in OECs under inflammatory conditions. To this end, we performed transcriptomic analyses of TNF-α-stimulated cells that were subsequently treated with RvD1 n-3 DPA and found significant downregulation of pro-inflammatory nuclear factor kappa B (NF-κB) target genes. Further bioinformatics analyses showed that RvD1 n-3 DPA inhibited the expression of several genes involved in the NF-κB activation pathway. Confocal microscopy revealed that addition of RvD1 n-3 DPA to OECs reversed TNF-α-induced nuclear translocation of NF-κB p65. Co-treatment of the cells with the exportin 1 inhibitor leptomycin B indicated that RvD1 n-3 DPA increases nuclear export of p65. Taken together, our observations suggest that SPMs also have the potential to be used as a therapeutic aid when inflammation is established.

Competing Interests: The authors have declared that no competing interests exist.

We describe the generation of an isogenic control cell line DJ-1-delP GC13 from an induced pluripotent stem cell (iPSC) line DJ-1-delP LCSBi008-A that was derived from fibroblasts obtained from a Parkinson's disease (PD) patient. Using CRISPR/Cas9 technology, we corrected the disease causing c.471&#95;473delGCC homozygous mutation in the PARK7 gene leading to p.158P deletion in the encoded protein DJ-1. The generated isogenic pair will be used for phenotypic analysis of PD-patient derived neurons and astrocytes., (Copyright © 2022 The Author(s). Published by Elsevier B.V. All rights reserved.)

Recent years have witnessed a strongly increasing interest in digital technology within medicine (sensor devices, specific smartphone apps) and specifically also neurology. Quantitative measures derived from digital technology could provide Digital Biomarkers (DMs) enabling a quantitative and continuous monitoring of disease symptoms, also outside clinics. This includes the possibility to continuously and sensitively monitor the response to treatment, hence opening the opportunity to adapt medication pathways quickly. In addition, DMs may in the future allow early diagnosis, stratification of patient subgroups and prediction of clinical outcomes. Thus, DMs could complement or in certain cases even replace classical examiner-based outcome measures and molecular biomarkers measured in cerebral spinal fluid, blood, urine, saliva, or other body liquids. Altogether, DMs could play a prominent role in the emerging field of precision medicine. However, realizing this vision requires dedicated research. First, advanced data analytical methods need to be developed and applied, which extract candidate DMs from raw signals. Second, these candidate DMs need to be validated by (a) showing their correlation to established clinical outcome measures, and (b) demonstrating their diagnostic and/or prognostic value compared to established biomarkers. These points again require the use of advanced data analytical methods, including machine learning. In addition, the arising ethical, legal and social questions associated with the collection and processing of sensitive patient data and the use of machine learning methods to analyze these data for better individualized treatment of the disease, must be considered thoroughly. Using Parkinson's Disease (PD) as a prime example of a complex multifactorial disorder, the purpose of this article is to critically review the current state of research regarding the use of DMs, discuss open challenges and highlight emerging new directions., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2022 Fröhlich, Bontridder, Petrovska-Delacréta, Glaab, Kluge, Yacoubi, Marín Valero, Corvol, Eskofier, Van Gyseghem, Lehericy, Winkler and Klucken.)

In the summer of 2021, European governments removed most NPIs after experiencing prolonged second and third waves of the COVID-19 pandemic. Most countries failed to achieve immunization rates high enough to avoid resurgence of the virus. Public health strategies for autumn and winter 2021 have ranged from countries aiming at low incidence by re-introducing NPIs to accepting high incidence levels. However, such high incidence strategies almost certainly lead to the very consequences that they seek to avoid: restrictions that harm people and economies. At high incidence, the important pandemic containment measure 'test-trace-isolate-support' becomes inefficient. At that point, the spread of SARS-CoV-2 and its numerous harmful consequences can likely only be controlled through restrictions. We argue that all European countries need to pursue a low incidence strategy in a coordinated manner. Such an endeavour can only be successful if it is built on open communication and trust., Competing Interests: TC was supported by the EU Commission, grant agreement No 101016233 (PERISCOPE). SB was supported by Netzwerk Universitätsmedizin, project egePan (01KX2021). ACV's institution was supported by Ministry of Culture and Science of the German State of North Rhine-Westphalia. EGl was supported by the Luxembourg National Research Fund (FNR) with Public funding support with payments to the host institute as part of the COVID-19 Fast-Track grant research project CovScreen (COVID-19/20201/14715687). EGr has received payments for a manuscript on the history of pandemics. JK is employed by a project funded by the European Research Council, European Union's Horizon 2020 research and innovation programme (grant agreement no. 724460). CL received grants from the University of Oxford, National Centre for Smoking Cessation and Training, UK, Horizon 2020, EUROPEAN COMMISSION, and Pfizer Inc, royalties from Olvos Science, payment for expert testimony from Word Health Organization and European Commission, has a patent for Cretan Iama Olvos Science, and is on the advisory board for Pfizer Helas and Vianex SA. GNP received grants and royalties from Novartis, FNIH, Gilead Grants, managed through NIH, and is the chair of the Nemitsas Prize Award Committee. MPi was supported by Wellcome Trust [grant numbers: 209519/Z/17/Z; WT106612MA], MRC [grant number: MR/S035818/1], ESRC [grant numbers: ES/T014164/1; ES/S013873/1], and British Academy [EN160164]. ESz's lab receives funding for other projects from Merck Healthcare. ST's institution received grants due to his role as Co-investigator-PI in study under the European Union's Horizon 2020 research and innovation programme, Grant Agreement, No 883441, under the agreement and control of the Special Committee for Research Grants of the University of Athens, Athens, Greece. PWilmes’ institution received grants from the European Commission's Horizon 2020 programme including the European Research Council (CoG 863664), the Luxembourg National Research Fund, and the University of Luxembourg, and owns patents. PWilmes received honoraria for being on two PhD juries at the University of Copenhagen and for the Maud Menten lecture at the University of Western Ontario, and for membership of the scientific steering committee for a clinical trial by 4D Pharma plc. and he is Co-speaker of the Research Luxembourg COVID-19 Task Force. Vice-president of the Luxembourg Society for Microbiology. All these were unrelated to this article. All other authors declare no competing interests., (© 2021 The Authors.)

Objective: To review biomarker discovery studies using omics data for patient stratification which led to clinically validated FDA-cleared tests or laboratory developed tests, in order to identify common characteristics and derive recommendations for future biomarker projects., Design: Scoping review., Methods: We searched PubMed, EMBASE and Web of Science to obtain a comprehensive list of articles from the biomedical literature published between January 2000 and July 2021, describing clinically validated biomarker signatures for patient stratification, derived using statistical learning approaches. All documents were screened to retain only peer-reviewed research articles, review articles or opinion articles, covering supervised and unsupervised machine learning applications for omics-based patient stratification. Two reviewers independently confirmed the eligibility. Disagreements were solved by consensus. We focused the final analysis on omics-based biomarkers which achieved the highest level of validation, that is, clinical approval of the developed molecular signature as a laboratory developed test or FDA approved tests., Results: Overall, 352 articles fulfilled the eligibility criteria. The analysis of validated biomarker signatures identified multiple common methodological and practical features that may explain the successful test development and guide future biomarker projects. These include study design choices to ensure sufficient statistical power for model building and external testing, suitable combinations of non-targeted and targeted measurement technologies, the integration of prior biological knowledge, strict filtering and inclusion/exclusion criteria, and the adequacy of statistical and machine learning methods for discovery and validation., Conclusions: While most clinically validated biomarker models derived from omics data have been developed for personalised oncology, first applications for non-cancer diseases show the potential of multivariate omics biomarker design for other complex disorders. Distinctive characteristics of prior success stories, such as early filtering and robust discovery approaches, continuous improvements in assay design and experimental measurement technology, and rigorous multicohort validation approaches, enable the derivation of specific recommendations for future studies., Competing Interests: Competing interests: None declared, (© Author(s) (or their employer(s)) 2021. Re-use permitted under CC BY-NC. No commercial re-use. See rights and permissions. Published by BMJ.)

Motivation: Multivariate (multi-target) regression has the potential to outperform univariate (single-target) regression at predicting correlated outcomes, which frequently occur in biomedical and clinical research. Here we implement multivariate lasso and ridge regression using stacked generalization., Results: Our flexible approach leads to predictive and interpretable models in high-dimensional settings, with a single estimate for each input-output effect. In the simulation, we compare the predictive performance of several state-of-the-art methods for multivariate regression. In the application, we use clinical and genomic data to predict multiple motor and non-motor symptoms in Parkinson's disease patients. We conclude that stacked multivariate regression, with our adaptations, is a competitive method for predicting correlated outcomes., Availability and Implementation: The R package joinet is available on GitHub (https://github.com/rauschenberger/joinet) and cran (https://cran.r-project.org/package=joinet)., Supplementary Information: Supplementary data are available at Bioinformatics online., (© The Author(s) 2021. Published by Oxford University Press.)

Parkinson's disease (PD) is a neurodegenerative disease with unknown cause in the majority of patients, who are therefore considered "idiopathic" (IPD). PD predominantly affects dopaminergic neurons in the substantia nigra pars compacta (SNpc), yet the pathology is not limited to this cell type. Advancing age is considered the main risk factor for the development of IPD and greatly influences the function of microglia, the immune cells of the brain. With increasing age, microglia become dysfunctional and release pro-inflammatory factors into the extracellular space, which promote neuronal cell death. Accordingly, neuroinflammation has also been described as a feature of PD. So far, studies exploring inflammatory pathways in IPD patient samples have primarily focused on blood-derived immune cells or brain sections, but rarely investigated patient microglia in vitro . Accordingly, we decided to explore the contribution of microglia to IPD in a comparative manner using, both, iPSC-derived cultures and postmortem tissue. Our meta-analysis of published RNAseq datasets indicated an upregulation of IL10 and IL1B in nigral tissue from IPD patients. We observed increased expression levels of these cytokines in microglia compared to neurons using our single-cell midbrain atlas. Moreover, IL10 and IL1B were upregulated in IPD compared to control microglia. Next, to validate these findings in vitro , we generated IPD patient microglia from iPSCs using an established differentiation protocol. IPD microglia were more readily primed as indicated by elevated IL1B and IL10 gene expression and higher mRNA and protein levels of NLRP3 after LPS treatment. In addition, IPD microglia had higher phagocytic capacity under basal conditions-a phenotype that was further exacerbated upon stimulation with LPS, suggesting an aberrant microglial function. Our results demonstrate the significance of microglia as the key player in the neuroinflammation process in IPD. While our study highlights the importance of microglia-mediated inflammatory signaling in IPD, further investigations will be needed to explore particular disease mechanisms in these cells., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2021 Badanjak, Mulica, Smajic, Delcambre, Tranchevent, Diederich, Rauen, Schwamborn, Glaab, Cowley, Antony, Pereira, Venegas and Grünewald.)

We need to effectively combine the knowledge from surging literature with complex datasets to propose mechanistic models of SARS-CoV-2 infection, improving data interpretation and predicting key targets of intervention. Here, we describe a large-scale community effort to build an open access, interoperable and computable repository of COVID-19 molecular mechanisms. The COVID-19 Disease Map (C19DMap) is a graphical, interactive representation of disease-relevant molecular mechanisms linking many knowledge sources. Notably, it is a computational resource for graph-based analyses and disease modelling. To this end, we established a framework of tools, platforms and guidelines necessary for a multifaceted community of biocurators, domain experts, bioinformaticians and computational biologists. The diagrams of the C19DMap, curated from the literature, are integrated with relevant interaction and text mining databases. We demonstrate the application of network analysis and modelling approaches by concrete examples to highlight new testable hypotheses. This framework helps to find signatures of SARS-CoV-2 predisposition, treatment response or prioritisation of drug candidates. Such an approach may help deal with new waves of COVID-19 or similar pandemics in the long-term perspective., (© 2021 The Authors. Published under the terms of the CC BY 4.0 license.)

How will the coronavirus disease 2019 (COVID-19) pandemic develop in the coming months and years? Based on an expert survey, we examine key aspects that are likely to influence the COVID-19 pandemic in Europe. The challenges and developments will strongly depend on the progress of national and global vaccination programs, the emergence and spread of variants of concern (VOCs), and public responses to non-pharmaceutical interventions (NPIs). In the short term, many people remain unvaccinated, VOCs continue to emerge and spread, and mobility and population mixing are expected to increase. Therefore, lifting restrictions too much and too early risk another damaging wave. This challenge remains despite the reduced opportunities for transmission given vaccination progress and reduced indoor mixing in summer 2021. In autumn 2021, increased indoor activity might accelerate the spread again, whilst a necessary reintroduction of NPIs might be too slow. The incidence may strongly rise again, possibly filling intensive care units, if vaccination levels are not high enough. A moderate, adaptive level of NPIs will thus remain necessary. These epidemiological aspects combined with economic, social, and health-related consequences provide a more holistic perspective on the future of the COVID-19 pandemic., Competing Interests: ENI, VP, SB, and SBM were supported by the Max Planck Society. VP received honoraria for lectures and presentations on COVID-19 mitigation strategies. PB was supported by the Epipose project from the European Union's SC1-PHE-CORONAVIRUS-2020 programme (grant agreement number 101003688), and consulting fees were paid to his institution by Pfizer and Pfizer Belgium. ACV was supported by the Ministry of Culture and Science of the German State of North Rhine-Westphalia and the German Federal Ministry of Education and Research. TC was supported by the European Union's Horizon 2020 research and innovation programme project PERISCOPE (grant agreement number 101016233). EGl was supported by the Luxembourg National Research Fund. EGr received fees from the German Board of Pharmacists for educational events on COVID-19 and is the president of the German Society for Epidemiology. MK was supported by ZonMw grants number 10430022010001 and number 91216062, and the European Union's Horizon 2020 research and innovation programme project CORESMA (grant agreement number 101003480). NL was supported by European Union's Horizon 2020 research and innovation programme project EpiPose (grant agreement number 101003688), and the Swiss National Science Foundation (project number 176233). MM is a member of UK Independent SAGE. SBM was supported by egePan 01KX7021. MPi was supported by the UK Economic and Social Research Council (ESRC) [ES/S013873/1; ES/T014164/1], the UK Medical Research Council (MRC) [MR/S035818/1], FWO, and Wellcome Trust [209519/Z/17/Z; 106612/Z/14/Z]. BP is a member of the Austrian National Bioethics Commission, and the European Group on Ethics in Science and New Technologies, advising the Austrian Government and the EU Commission respectively. Other research projects in the lab of ESz are partly funded by Merck Healthcare KGaA. All other authors have no competing interests to declare., (© 2021 The Author(s).)

Motivation: Machine learning in the biomedical sciences should ideally provide predictive and interpretable models. When predicting outcomes from clinical or molecular features, applied researchers often want to know which features have effects, whether these effects are positive or negative and how strong these effects are. Regression analysis includes this information in the coefficients but typically renders less predictive models than more advanced machine learning techniques., Results: Here, we propose an interpretable meta-learning approach for high-dimensional regression. The elastic net provides a compromise between estimating weak effects for many features and strong effects for some features. It has a mixing parameter to weight between ridge and lasso regularization. Instead of selecting one weighting by tuning, we combine multiple weightings by stacking. We do this in a way that increases predictivity without sacrificing interpretability., Availability and Implementation: The R package starnet is available on GitHub (https://github.com/rauschenberger/starnet) and CRAN (https://CRAN.R-project.org/package=starnet)., (© The Author(s) 2020. Published by Oxford University Press.)

In development, differentiation from a pluripotent state results in global epigenetic changes, although the extent to which this occurs in induced pluripotent stem cell-based neuronal models has not been extensively characterized. In the present study, induced pluripotent stem cell colonies (33Qn1 line) were differentiated and collected at four time-points, with DNA methylation assessed using the Illumina Infinium Human Methylation EPIC BeadChip array. Dynamic changes in DNA methylation occurring during differentiation were investigated using a data-driven trajectory inference method. We identified a large number of Bonferroni-significant loci that showed progressive alterations in DNA methylation during neuronal differentiation. A gene-gene interaction network analysis identified 60 densely connected genes that were influential in the differentiation of neurons, with STAT3 being the gene with the highest connectivity., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest. The handling editor declared a shared affiliation with several of the authors JI, EP, TK, AJ, JB, KL, at time of review., (Copyright © 2021 Imm, Pishva, Ali, Kerrigan, Jeffries, Burrage, Glaab, Cope, Jones, Allen and Lunnon.)

Single-cell RNA-sequencing (scRNAseq) technologies are rapidly evolving. Although very informative, in standard scRNAseq experiments, the spatial organization of the cells in the tissue of origin is lost. Conversely, spatial RNA-seq technologies designed to maintain cell localization have limited throughput and gene coverage. Mapping scRNAseq to genes with spatial information increases coverage while providing spatial location. However, methods to perform such mapping have not yet been benchmarked. To fill this gap, we organized the DREAM Single-Cell Transcriptomics challenge focused on the spatial reconstruction of cells from the Drosophila embryo from scRNAseq data, leveraging as silver standard, genes with in situ hybridization data from the Berkeley Drosophila Transcription Network Project reference atlas. The 34 participating teams used diverse algorithms for gene selection and location prediction, while being able to correctly localize clusters of cells. Selection of predictor genes was essential for this task. Predictor genes showed a relatively high expression entropy, high spatial clustering and included prominent developmental genes such as gap and pair-rule genes and tissue markers. Application of the top 10 methods to a zebra fish embryo dataset yielded similar performance and statistical properties of the selected genes than in the Drosophila data. This suggests that methods developed in this challenge are able to extract generalizable properties of genes that are useful to accurately reconstruct the spatial arrangement of cells in tissues., (© 2020 Tanevski et al.)

Parkinson's disease (PD) is a complex and heterogeneous disorder involving multiple genetic and environmental influences. Although a wide range of PD risk factors and clinical markers for the symptomatic motor stage of the disease have been identified, there are still no reliable biomarkers available for the early pre-motor phase of PD and for predicting disease progression. High-throughput RNA-based biomarker profiling and modeling may provide a means to exploit the joint information content from a multitude of markers to derive diagnostic and prognostic signatures. In the field of PD biomarker research, currently, no clinically validated RNA-based biomarker models are available, but previous studies reported several significantly disease-associated changes in RNA abundances and activities in multiple human tissues and body fluids. Here, we review the current knowledge of the regulation and function of non-coding RNAs in PD, focusing on microRNAs, long non-coding RNAs, and circular RNAs. Since there is growing evidence for functional interactions between the heart and the brain, we discuss the benefits of studying the role of non-coding RNAs in organ interactions when deciphering the complex regulatory networks involved in PD progression. We finally review important concepts of harmonization and curation of high throughput datasets, and we discuss the potential of systems biomedicine to derive and evaluate RNA biomarker signatures from high-throughput expression data.

Background: Parkinson's Disease (PD) and Hutchinson-Gilford Progeria Syndrome (HGPS) are two heterogeneous disorders, which both display molecular and clinical alterations associated with the aging process. However, similarities and differences between molecular changes in these two disorders have not yet been investigated systematically at the level of individual biomolecules and shared molecular network alterations., Methods: Here, we perform a comparative meta-analysis and network analysis of human transcriptomics data from case-control studies for both diseases to investigate common susceptibility genes and sub-networks in PD and HGPS. Alzheimer's disease (AD) and primary melanoma (PM) were included as controls to confirm that the identified overlapping susceptibility genes for PD and HGPS are non-generic., Results: We find statistically significant, overlapping genes and cellular processes with significant alterations in both diseases. Interestingly, the majority of these shared affected genes display changes with opposite directionality, indicating that shared susceptible cellular processes undergo different mechanistic changes in PD and HGPS. A complementary regulatory network analysis also reveals that the altered genes in PD and HGPS both contain targets controlled by the upstream regulator CDC5L., Conclusions: Overall, our analyses reveal a significant overlap of affected cellular processes and molecular sub-networks in PD and HGPS, including changes in aging-related processes that may reflect key susceptibility factors associated with age-related risk for PD.

Background: Although most cases of Parkinson´s disease (PD) are idiopathic with unknown cause, an increasing number of genes and genetic risk factors have been discovered that play a role in PD pathogenesis. Many of the PD-associated proteins are involved in mitochondrial quality control, e.g., PINK1, Parkin, and LRRK2, which were recently identified as regulators of mitochondrial-endoplasmic reticulum (ER) contact sites (MERCs) linking mitochondrial homeostasis to intracellular calcium handling. In this context, Miro1 is increasingly recognized to play a role in PD pathology. Recently, we identified the first PD patients carrying mutations in RHOT1 , the gene coding for Miro1. Here, we describe two novel RHOT1 mutations identified in two PD patients and the characterization of the cellular phenotypes., Methods: Using whole exome sequencing we identified two PD patients carrying heterozygous mutations leading to the amino acid exchanges T351A and T610A in Miro1. We analyzed calcium homeostasis and MERCs in detail by live cell imaging and immunocytochemistry in patient-derived fibroblasts., Results: We show that fibroblasts expressing mutant T351A or T610A Miro1 display impaired calcium homeostasis and a reduced amount of MERCs. All fibroblast lines from patients with pathogenic variants in Miro1, revealed alterations of the structure of MERCs., Conclusion: Our data suggest that Miro1 is important for the regulation of the structure and function of MERCs. Moreover, our study supports the role of MERCs in the pathogenesis of PD and further establishes variants in RHOT1 as rare genetic risk factors for neurodegeneration.

Parkinson's disease (PD) exhibits systemic effects on the human metabolism, with emerging roles for the gut microbiome. Here, we integrate longitudinal metabolome data from 30 drug-naive, de novo PD patients and 30 matched controls with constraint-based modeling of gut microbial communities derived from an independent, drug-naive PD cohort, and prospective data from the general population. Our key results are (1) longitudinal trajectory of metabolites associated with the interconversion of methionine and cysteine via cystathionine differed between PD patients and controls; (2) dopaminergic medication showed strong lipidomic signatures; (3) taurine-conjugated bile acids correlated with the severity of motor symptoms, while low levels of sulfated taurolithocholate were associated with PD incidence in the general population; and (4) computational modeling predicted changes in sulfur metabolism, driven by A. muciniphila and B. wadsworthia, which is consistent with the changed metabolome. The multi-omics integration reveals PD-specific patterns in microbial-host sulfur co-metabolism that may contribute to PD severity., (Copyright © 2019 The Authors. Published by Elsevier Inc. All rights reserved.)

Ubiquitin specific peptidase 9 (USP9) is a deubiquitinase encoded by a sex-linked gene with a Y-chromosomal form ( USP9Y ) and an X-chromosomal form ( USP9X ) that escapes X-inactivation. Since USP9 is a key regulatory gene with sex-linked expression in the human brain, the gene may be of interest for researchers studying molecular gender differences and ubiquitin signaling in the brain. To assess the downstream effects of knocking down USP9X and USP9Y on a transcriptome-wide scale, we have conducted microarray profiling experiments using the human DU145 prostate cancer cell culture model, after confirming the robust expression of both USP9X and USP9Y in this model. By designing shRNA constructs for the specific knockdown of USP9X and the joint knockdown of USP9X and USP9Y , we have compared gene expression changes in both knockdowns to control conditions to infer potential shared and X- or Y-form specific alterations. Here, we provide details of the corresponding microarray profiling data, which has been deposited in the Gene Expression Omnibus database (GEO series accession number GSE79376). A biological interpretation of the data in the context of a potential involvement of USP9 in Alzheimer's disease has previously been presented in Köglsberger et al. (2016). To facilitate the re-use and re-analysis of the data for other applications, e.g. the study of ubiquitin signaling and protein turnover control, and the regulation of molecular gender differences in the human brain and brain-related disorders, we provide a more in-depth discussion of the data properties, specifications and possible use cases.

Background: Quantitative trait locus (QTL) mapping using bulk segregants is an effective approach for identifying genetic variants associated with phenotypes of interest in model organisms. By exploiting next-generation sequencing technology, the QTL mapping accuracy can be improved significantly, providing a valuable means to annotate new genetic variants. However, setting up a comprehensive analysis framework for this purpose is a time-consuming and error-prone task, posing many challenges for scientists with limited experience in this domain., Results: Here, we present BSA4Yeast, a comprehensive web application for QTL mapping via bulk segregant analysis of yeast sequencing data. The software provides an automated and efficiency-optimized data processing, up-to-date functional annotations, and an interactive web interface to explore identified QTLs., Conclusions: BSA4Yeast enables researchers to identify plausible candidate genes in QTL regions efficiently in order to validate their genetic variations experimentally as causative for a phenotype of interest. BSA4Yeast is freely available at https://bsa4yeast.lcsb.uni.lu., (© The Author(s) 2019. Published by Oxford University Press.)

Parkinson's disease (PD)-specific neurons, grown in standard 2D cultures, typically only display weak endophenotypes. The cultivation of PD patient-specific neurons, derived from induced pluripotent stem cells carrying the LRRK2-G2019S mutation, is optimized in 3D microfluidics. The automated image analysis algorithms are implemented to enable pharmacophenomics in disease-relevant conditions. In contrast to 2D cultures, this 3D approach reveals robust endophenotypes. High-content imaging data show decreased dopaminergic differentiation and branching complexity, altered mitochondrial morphology, and increased cell death in LRRK2-G2019S neurons compared to isogenic lines without using stressor agents. Treatment with the LRRK2 inhibitor 2 (Inh2) rescues LRRK2-G2019S-dependent dopaminergic phenotypes. Strikingly, a holistic analysis of all studied features shows that the genetic background of the PD patients, and not the LRRK2-G2019S mutation, constitutes the strongest contribution to the phenotypes. These data support the use of advanced in vitro models for future patient stratification and personalized drug development.

The mitochondrial proteins TRAP1 and HTRA2 have previously been shown to be phosphorylated in the presence of the Parkinson's disease kinase PINK1 but the downstream signalling is unknown. HTRA2 and PINK1 loss of function causes parkinsonism in humans and animals. Here, we identified TRAP1 as an interactor of HTRA2 using an unbiased mass spectrometry approach. In our human cell models, TRAP1 overexpression is protective, rescuing HTRA2 and PINK1-associated mitochondrial dysfunction and suggesting that TRAP1 acts downstream of HTRA2 and PINK1. HTRA2 regulates TRAP1 protein levels, but TRAP1 is not a direct target of HTRA2 protease activity. Following genetic screening of Parkinson's disease patients and healthy controls, we also report the first TRAP1 mutation leading to complete loss of functional protein in a patient with late onset Parkinson's disease. Analysis of fibroblasts derived from the patient reveal that oxygen consumption, ATP output and reactive oxygen species are increased compared to healthy individuals. This is coupled with an increased pool of free NADH, increased mitochondrial biogenesis, triggering of the mitochondrial unfolded protein response, loss of mitochondrial membrane potential and sensitivity to mitochondrial removal and apoptosis. These data highlight the role of TRAP1 in the regulation of energy metabolism and mitochondrial quality control. Interestingly, the diabetes drug metformin reverses mutation-associated alterations on energy metabolism, mitochondrial biogenesis and restores mitochondrial membrane potential. In summary, our data show that TRAP1 acts downstream of PINK1 and HTRA2 for mitochondrial fine tuning, whereas TRAP1 loss of function leads to reduced control of energy metabolism, ultimately impacting mitochondrial membrane potential. These findings offer new insight into mitochondrial pathologies in Parkinson's disease and provide new prospects for targeted therapies., (© The Author (2017). Published by Oxford University Press on behalf of the Guarantors of Brain. All rights reserved. For Permissions, please email: journals.permissions@oup.com.)

Proteomes of even well characterized organisms still contain a high percentage of proteins with unknown or uncertain molecular and/or biological function. A significant fraction of those proteins is predicted to have catalytic properties. Here we aimed at identifying the function of the Saccharomyces cerevisiae Ydr109c protein and its human homolog FGGY, both of which belong to the broadly conserved FGGY family of carbohydrate kinases. Functionally identified members of this family phosphorylate 3- to 7-carbon sugars or sugar derivatives, but the endogenous substrate of S. cerevisiae Ydr109c and human FGGY has remained unknown. Untargeted metabolomics analysis of an S. cerevisiae deletion mutant of YDR109C revealed ribulose as one of the metabolites with the most significantly changed intracellular concentration as compared with a wild-type strain. In human HEK293 cells, ribulose could only be detected when ribitol was added to the cultivation medium, and under this condition, FGGY silencing led to ribulose accumulation. Biochemical characterization of the recombinant purified Ydr109c and FGGY proteins showed a clear substrate preference of both kinases for d-ribulose over a range of other sugars and sugar derivatives tested, including l-ribulose. Detailed sequence and structural analyses of Ydr109c and FGGY as well as homologs thereof furthermore allowed the definition of a 5-residue d-ribulokinase signature motif (TCSLV). The physiological role of the herein identified eukaryotic d-ribulokinase remains unclear, but we speculate that S. cerevisiae Ydr109c and human FGGY could act as metabolite repair enzymes, serving to re-phosphorylate free d-ribulose generated by promiscuous phosphatases from d-ribulose 5-phosphate. In human cells, FGGY can additionally participate in ribitol metabolism., (© 2017 by The American Society for Biochemistry and Molecular Biology, Inc.)

Conversion of astrocytes to neurons, via de-differentiation to neural stem cells (NSC), may be a new approach to treat neurodegenerative diseases and brain injuries. The signaling factors affecting such a cell conversion are poorly understood, and they are hard to identify in complex disease models or conventional cell cultures. To address this question, we developed a serum-free, strictly controlled culture system of pure and homogeneous "astrocytes generated from murine embryonic stem cells (ESC)." These stem cell derived astrocytes (mAGES), as well as standard primary astrocytes resumed proliferation upon addition of FGF. The signaling of FGF receptor tyrosine kinase converted GFAP-positive mAGES to nestin-positive NSC. ERK phosphorylation was necessary, but not sufficient, for cell cycle re-entry, as EGF triggered no de-differentiation. The NSC obtained by de-differentiation of mAGES were similar to those obtained directly by differentiation of ESC, as evidenced by standard phenotyping, and also by transcriptome mapping, metabolic profiling, and by differentiation to neurons or astrocytes. The de-differentiation was negatively affected by inflammatory mediators, and in particular, interferon-γ strongly impaired the formation of NSC from mAGES by a pathway involving phosphorylation of STAT1, but not the generation of nitric oxide. Thus, two antagonistic signaling pathways were identified here that affect fate conversion of astrocytes independent of genetic manipulation. The complex interplay of the respective signaling molecules that promote/inhibit astrocyte de-differentiation may explain why astrocytes do not readily form neural stem cells in most diseases. Increased knowledge of such factors may provide therapeutic opportunities to favor such conversions. Stem Cells 2016;34:2861-2874., (© 2016 AlphaMed Press.)

Changes in the human gastrointestinal microbiome are associated with several diseases. To infer causality, experiments in representative models are essential, but widely used animal models exhibit limitations. Here we present a modular, microfluidics-based model (HuMiX, human-microbial crosstalk), which allows co-culture of human and microbial cells under conditions representative of the gastrointestinal human-microbe interface. We demonstrate the ability of HuMiX to recapitulate in vivo transcriptional, metabolic and immunological responses in human intestinal epithelial cells following their co-culture with the commensal Lactobacillus rhamnosus GG (LGG) grown under anaerobic conditions. In addition, we show that the co-culture of human epithelial cells with the obligate anaerobe Bacteroides caccae and LGG results in a transcriptional response, which is distinct from that of a co-culture solely comprising LGG. HuMiX facilitates investigations of host-microbe molecular interactions and provides insights into a range of fundamental research questions linking the gastrointestinal microbiome to human health and disease.

For many complex diseases, an earlier and more reliable diagnosis is considered a key prerequisite for developing more effective therapies to prevent or delay disease progression. Classical statistical learning approaches for specimen classification using omics data, however, often cannot provide diagnostic models with sufficient accuracy and robustness for heterogeneous diseases like cancers or neurodegenerative disorders. In recent years, new approaches for building multivariate biomarker models on omics data have been proposed, which exploit prior biological knowledge from molecular networks and cellular pathways to address these limitations. This survey provides an overview of these recent developments and compares pathway- and network-based specimen classification approaches in terms of their utility for improving model robustness, accuracy and biological interpretability. Different routes to translate omics-based multifactorial biomarker models into clinical diagnostic tests are discussed, and a previous study is presented as example., (© The Author 2015. Published by Oxford University Press.)

Virtual screening, the search for bioactive compounds via computational methods, provides a wide range of opportunities to speed up drug development and reduce the associated risks and costs. While virtual screening is already a standard practice in pharmaceutical companies, its applications in preclinical academic research still remain under-exploited, in spite of an increasing availability of dedicated free databases and software tools. In this survey, an overview of recent developments in this field is presented, focusing on free software and data repositories for screening as alternatives to their commercial counterparts, and outlining how available resources can be interlinked into a comprehensive virtual screening pipeline using typical academic computing facilities. Finally, to facilitate the set-up of corresponding pipelines, a downloadable software system is provided, using platform virtualization to integrate pre-installed screening tools and scripts for reproducible application across different operating systems., (© The Author 2015. Published by Oxford University Press.)