DJ-1 depletion prevents immunoaging in T-cell compartments.

Decline in immune function during aging increases susceptibility to different aging-related diseases. However, the underlying molecular mechanisms, especially the genetic factors contributing to imbalance of naïve/memory T-cell subpopulations, still remain largely elusive. Here, we show that loss of DJ-1 encoded by PARK7/DJ-1, causing early-onset familial Parkinson's disease (PD), unexpectedly diminished signs of immunoaging in T-cell compartments of both human and mice. Compared with two gender-matched unaffected siblings of similar ages, the index PD patient with DJ-1 deficiency showed a decline in many critical immunoaging features, including almost doubled non-senescent T cells. The observation was further consolidated by the results in 45-week-old DJ-1 knockout mice. Our data demonstrated that DJ-1 regulates several immunoaging features via hematopoietic-intrinsic and naïve-CD8-intrinsic mechanisms. Mechanistically, DJ-1 depletion reduced oxidative phosphorylation (OXPHOS) and impaired TCR sensitivity in naïve CD8 T cells at a young age, accumulatively leading to a reduced aging process in T-cell compartments in older mice. Our finding suggests an unrecognized critical role of DJ-1 in regulating immunoaging, discovering a potent target to interfere with immunoaging- and aging-associated diseases.

Neurodegeneration and neuroinflammation are linked, but independent of alpha-synuclein inclusions, in a seeding/spreading mouse model of Parkinson's disease.

A key pathological process in Parkinson's disease (PD) is the transneuronal spreading of α-synuclein. Alpha-synuclein (α-syn) is a presynaptic protein that, in PD, forms pathological inclusions. Other hallmarks of PD include neurodegeneration and microgliosis in susceptible brain regions. Whether it is primarily transneuronal spreading of α-syn particles, inclusion formation, or other mechanisms, such as inflammation, that cause neurodegeneration in PD is unclear. We used a model of spreading of α-syn induced by striatal injection of α-syn preformed fibrils into the mouse striatum to address this question. We performed quantitative analysis for α-syn inclusions, neurodegeneration, and microgliosis in different brain regions, and generated gene expression profiles of the ventral midbrain, at two different timepoints after disease induction. We observed significant neurodegeneration and microgliosis in brain regions not only with, but also without α-syn inclusions. We also observed prominent microgliosis in injured brain regions that did not correlate with neurodegeneration nor with inclusion load. Using longitudinal gene expression profiling, we observed early gene expression changes, linked to neuroinflammation, that preceded neurodegeneration, indicating an active role of microglia in this process. Altered gene pathways overlapped with those typical of PD. Our observations indicate that α-syn inclusion formation is not the major driver in the early phases of PD-like neurodegeneration, but that microglia, activated by diffusible, oligomeric α-syn, may play a key role in this process. Our findings uncover new features of α-syn induced pathologies, in particular microgliosis, and point to the necessity for a broader view of the process of α-syn spreading.

Community-driven roadmap for integrated disease maps.

The Disease Maps Project builds on a network of scientific and clinical groups that exchange best practices, share information and develop systems biomedicine tools. The project aims for an integrated, highly curated and user-friendly platform for disease-related knowledge. The primary focus of disease maps is on interconnected signaling, metabolic and gene regulatory network pathways represented in standard formats. The involvement of domain experts ensures that the key disease hallmarks are covered and relevant, up-to-date knowledge is adequately represented. Expert-curated and computer readable, disease maps may serve as a compendium of knowledge, allow for data-supported hypothesis generation or serve as a scaffold for the generation of predictive mathematical models. This article summarizes the 2nd Disease Maps Community meeting, highlighting its important topics and outcomes. We outline milestones on the roadmap for the future development of disease maps, including creating and maintaining standardized disease maps; sharing parts of maps that encode common human disease mechanisms; providing technical solutions for complexity management of maps; and Web tools for in-depth exploration of such maps. A dedicated discussion was focused on mathematical modeling approaches, as one of the main goals of disease map development is the generation of mathematically interpretable representations to predict disease comorbidity or drug response and to suggest drug repositioning, altogether supporting clinical decisions.

A rare loss-of-function variant of ADAM17 is associated with late-onset familial Alzheimer disease.

Common variants of about 20 genes contributing to AD risk have so far been identified through genome-wide association studies (GWAS). However, there is still a large proportion of heritability that might be explained by rare but functionally important variants. One of the so far identified genes with rare AD causing variants is ADAM10. Using whole-genome sequencing we now identified a single rare nonsynonymous variant (SNV) rs142946965 [p.R215I] in ADAM17 co-segregating with an autosomal-dominant pattern of late-onset AD in one family. Subsequent genotyping and analysis of available whole-exome sequencing data of additional case/control samples from Germany, UK, and USA identified five variant carriers among AD patients only. The mutation inhibits pro-protein cleavage and the formation of the active enzyme, thus leading to loss-of-function of ADAM17 alpha-secretase. Further, we identified a strong negative correlation between ADAM17 and APP gene expression in human brain and present in vitro evidence that ADAM17 negatively controls the expression of APP. As a consequence, p.R215I mutation of ADAM17 leads to elevated Aß formation in vitro. Together our data supports a causative association of the identified ADAM17 variant in the pathogenesis of AD.

A comprehensive and comparative phenotypic analysis of the collaborative founder strains identifies new and known phenotypes.

The collaborative cross (CC) is a large panel of mouse-inbred lines derived from eight founder strains (NOD/ShiLtJ, NZO/HILtJ, A/J, C57BL/6J, 129S1/SvImJ, CAST/EiJ, PWK/PhJ, and WSB/EiJ). Here, we performed a comprehensive and comparative phenotyping screening to identify phenotypic differences and similarities between the eight founder strains. In total, more than 300 parameters including allergy, behavior, cardiovascular, clinical blood chemistry, dysmorphology, bone and cartilage, energy metabolism, eye and vision, immunology, lung function, neurology, nociception, and pathology were analyzed; in most traits from sixteen females and sixteen males. We identified over 270 parameters that were significantly different between strains. This study highlights the value of the founder and CC strains for phenotype-genotype associations of many genetic traits that are highly relevant to human diseases. All data described here are publicly available from the mouse phenome database for analyses and downloads.

Presenting and sharing clinical data using the eTRIKS Standards Master Tree for tranSMART.

MOTIVATION: Standardization and semantic alignment have been considered one of the major challenges for data integration in clinical research. The inclusion of the CDISC SDTM clinical data standard into the tranSMART i2b2 via a guiding master ontology tree positively impacts and supports the efficacy of data sharing, visualization and exploration across datasets. RESULTS: We present here a schema for the organization of SDTM variables into the tranSMART i2b2 tree along with a script and test dataset to exemplify the mapping strategy. The eTRIKS master tree concept is demonstrated by making use of fictitious data generated for four patients, including 16 SDTM clinical domains. We describe how the usage of correct visit names and data labels can help to integrate multiple readouts per patient and avoid ETL crashes when running a tranSMART loading routine. AVAILABILITY AND IMPLEMENTATION: The eTRIKS Master Tree package and test datasets are publicly available at https://doi.org/10.5281/zenodo.1009098 and a functional demo installation at https://public.etriks.org/transmart/datasetExplorer/ under eTRIKS-Master Tree branch, where the discussed examples can be visualized.

Single-cell transcriptomics reveals distinct inflammation-induced microglia signatures.

Microglia are specialized parenchymal-resident phagocytes of the central nervous system (CNS) that actively support, defend and modulate the neural environment. Dysfunctional microglial responses are thought to worsen CNS diseases; nevertheless, their impact during neuroinflammatory processes remains largely obscure. Here, using a combination of single-cell RNA sequencing and multicolour flow cytometry, we comprehensively profile microglia in the brain of lipopolysaccharide (LPS)-injected mice. By excluding the contribution of other immune CNS-resident and peripheral cells, we show that microglia isolated from LPS-injected mice display a global downregulation of their homeostatic signature together with an upregulation of inflammatory genes. Notably, we identify distinct microglial activated profiles under inflammatory conditions, which greatly differ from neurodegenerative disease-associated profiles. These results provide insights into microglial heterogeneity and establish a resource for the identification of specific phenotypes in CNS disorders, such as neuroinflammatory and neurodegenerative diseases.

MIC-MAC: An automated pipeline for high-throughput characterization and classification of three-dimensional microglia morphologies in mouse and human postmortem brain samples.

The phenotypic changes of microglia in brain diseases are particularly diverse and their role in disease progression, beneficial, or detrimental, is still elusive. High-throughput molecular approaches such as single-cell RNA-sequencing can now resolve the high heterogeneity in microglia population for a specific physiological condition, however, the relation between the different microglial signatures and their surrounding brain microenvironment is barely understood. Thus, better tools to characterize the phenotypic variations of microglia in situ are needed, particularly for human brain postmortem samples analysis. To address this challenge, we developed MIC-MAC, a Microglia and Immune Cells Morphologies Analyser and Classifier pipeline that semiautomatically segments, extracts, and classifies all microglia and immune cells labeled in large three-dimensional (3D) confocal image stacks of mouse and human brain samples. Our imaging-based approach enables automatic 3D-morphology characterization and classification of thousands of individual microglia in situ and revealed species- and disease-specific morphological phenotypes in mouse aging, human Alzheimer's disease, and dementia with Lewy Bodie's samples. MIC-MAC is a precision diagnostic tool that allows a rapid, unbiased, and large-scale analysis of microglia morphological states in mouse models and patient brain samples.

Large-scale validation of miRNAs by disease association, evolutionary conservation and pathway activity.

The validation of microRNAs (miRNAs) identified by next generation sequencing involves amplification-free and hybridization-based detection of transcripts as criteria for confirming valid miRNAs. Since respective validation is frequently not performed, miRNA repositories likely still contain a substantial fraction of false positive candidates while true miRNAs are not stored in the repositories yet. Especially if downstream analyses are performed with these candidates (e.g. target or pathway prediction), the results may be misleading. In the present study, we evaluated 558 mature miRNAs from miRBase and 1,709 miRNA candidates from next generation sequencing experiments by amplification-free hybridization and investigated their distributions in patients with various disease conditions. Notably, the most significant miRNAs in diseases are often not contained in the miRBase. However, these candidates are evolutionary highly conserved. From the expression patterns, target gene and pathway analyses and evolutionary conservation analyses, we were able to shed light on the complexity of miRNAs in humans. Our data also highlight that a more thorough validation of miRNAs identified by next generation sequencing is required. The results are available in miRCarta ( https://mircarta.cs.uni-saarland.de ).

From hype to reality: data science enabling personalized medicine.

BACKGROUND: Personalized, precision, P4, or stratified medicine is understood as a medical approach in which patients are stratified based on their disease subtype, risk, prognosis, or treatment response using specialized diagnostic tests. The key idea is to base medical decisions on individual patient characteristics, including molecular and behavioral biomarkers, rather than on population averages. Personalized medicine is deeply connected to and dependent on data science, specifically machine learning (often named Artificial Intelligence in the mainstream media). While during recent years there has been a lot of enthusiasm about the potential of 'big data' and machine learning-based solutions, there exist only few examples that impact current clinical practice. The lack of impact on clinical practice can largely be attributed to insufficient performance of predictive models, difficulties to interpret complex model predictions, and lack of validation via prospective clinical trials that demonstrate a clear benefit compared to the standard of care. In this paper, we review the potential of state-of-the-art data science approaches for personalized medicine, discuss open challenges, and highlight directions that may help to overcome them in the future. CONCLUSIONS: There is a need for an interdisciplinary effort, including data scientists, physicians, patient advocates, regulatory agencies, and health insurance organizations. Partially unrealistic expectations and concerns about data science-based solutions need to be better managed. In parallel, computational methods must advance more to provide direct benefit to clinical practice.

Rare GABRA3 variants are associated with epileptic seizures, encephalopathy and dysmorphic features.

Genetic epilepsies are caused by mutations in a range of different genes, many of them encoding ion channels, receptors or transporters. While the number of detected variants and genes increased dramatically in the recent years, pleiotropic effects have also been recognized, revealing that clinical syndromes with various degrees of severity arise from a single gene, a single mutation, or from different mutations showing similar functional defects. Accordingly, several genes coding for GABAA receptor subunits have been linked to a spectrum of benign to severe epileptic disorders and it was shown that a loss of function presents the major correlated pathomechanism. Here, we identified six variants in GABRA3 encoding the α3-subunit of the GABAA receptor. This gene is located on chromosome Xq28 and has not been previously associated with human disease. Five missense variants and one microduplication were detected in four families and two sporadic cases presenting with a range of epileptic seizure types, a varying degree of intellectual disability and developmental delay, sometimes with dysmorphic features or nystagmus. The variants co-segregated mostly but not completely with the phenotype in the families, indicating in some cases incomplete penetrance, involvement of other genes, or presence of phenocopies. Overall, males were more severely affected and there were three asymptomatic female mutation carriers compared to only one male without a clinical phenotype. X-chromosome inactivation studies could not explain the phenotypic variability in females. Three detected missense variants are localized in the extracellular GABA-binding NH2-terminus, one in the M2-M3 linker and one in the M4 transmembrane segment of the α3-subunit. Functional studies in Xenopus laevis oocytes revealed a variable but significant reduction of GABA-evoked anion currents for all mutants compared to wild-type receptors. The degree of current reduction correlated partially with the phenotype. The microduplication disrupted GABRA3 expression in fibroblasts of the affected patient. In summary, our results reveal that rare loss-of-function variants in GABRA3 increase the risk for a varying combination of epilepsy, intellectual disability/developmental delay and dysmorphic features, presenting in some pedigrees with an X-linked inheritance pattern.

Recessive mutations in SLC35A3 cause early onset epileptic encephalopathy with skeletal defects.

We describe the clinical and whole genome sequencing (WGS) study of a non-consanguineous Italian family in which two siblings, a boy and a girl, manifesting a severe epileptic encephalopathy (EE) with skeletal abnormalities, carried novel SLC35A3 compound heterozygous mutations. Both siblings exhibited infantile spasms, associated with focal, and tonic vibratory seizures from early infancy. EEG recordings showed a suppression-burst (SB) pattern and multifocal paroxysmal activity in both. In addition both had quadriplegia, acquired microcephaly, and severe intellectual disability. General examination showed distal arthrogryposis predominant in the hands in both siblings and severe left dorso-lumbar convex scoliosis in one. WGS of the siblings-parents quartet identified novel compound heterozygous mutations in SLC35A3 in both children. SLC35A3 encodes the major Golgi uridine diphosphate N-acetylglucosamine transporter. With this study, we add SLC35A3 to the gene list of epilepsies. Neurological symptoms and skeletal abnormalities might result from impaired glycosylation of proteins involved in normal development and function of the central nervous system and skeletal apparatus.

Loss of SYNJ1 dual phosphatase activity leads to early onset refractory seizures and progressive neurological decline.

SYNJ1 encodes a polyphosphoinositide phosphatase, synaptojanin 1, which contains two consecutive phosphatase domains and plays a prominent role in synaptic vesicle dynamics. Autosomal recessive inherited variants in SYNJ1 have previously been associated with two different neurological diseases: a recurrent homozygous missense variant (p.Arg258Gln) that abolishes Sac1 phosphatase activity was identified in three independent families with early onset parkinsonism, whereas a homozygous nonsense variant (p.Arg136*) causing a severe decrease of mRNA transcript was found in a single patient with intractable epilepsy and tau pathology. We performed whole exome or genome sequencing in three independent sib pairs with early onset refractory seizures and progressive neurological decline, and identified novel segregating recessive SYNJ1 defects. A homozygous missense variant resulting in an amino acid substitution (p.Tyr888Cys) was found to impair, but not abolish, the dual phosphatase activity of SYNJ1, whereas three premature stop variants (homozygote p.Trp843* and compound heterozygote p.Gln647Argfs*6/p.Ser1122Thrfs*3) almost completely abolished mRNA transcript production. A genetic follow-up screening in a large cohort of 543 patients with a wide phenotypical range of epilepsies and intellectual disability revealed no additional pathogenic variants, showing that SYNJ1 deficiency is rare and probably linked to a specific phenotype. While variants leading to early onset parkinsonism selectively abolish Sac1 function, our results provide evidence that a critical reduction of the dual phosphatase activity of SYNJ1 underlies a severe disorder with neonatal refractory epilepsy and a neurodegenerative disease course. These findings further expand the clinical spectrum of synaptic dysregulation in patients with severe epilepsy, and emphasize the importance of this biological pathway in seizure pathophysiology.

Influenza H3N2 infection of the collaborative cross founder strains reveals highly divergent host responses and identifies a unique phenotype in CAST/EiJ mice.

BACKGROUND: Influenza A virus is a zoonotic pathogen that poses a major threat to human and animal health. The severe course of influenza infection is not only influenced by viral virulence factors but also by individual differences in the host response. To determine the extent to which the genetic background can modulate severity of an infection, we studied the host responses to influenza infections in the eight genetically highly diverse Collaborative Cross (CC) founder mouse strains. RESULTS: We observed highly divergent host responses between the CC founder strains with respect to survival, body weight loss, hematological parameters in the blood, relative lung weight and viral load. Mouse strain was the main factor with highest effect size on body weight loss after infection, demonstrating that this phenotype was highly heritable. Sex represented another significant main effect, although it was less strong. Analysis of survival rates and mean time to death suggested three groups of susceptibility phenotypes: highly susceptible (A/J, CAST/EiJ, WSB/EiJ), intermediate susceptible (C57BL/6J, 129S1/SvImJ, NOD/ShiLtJ) and highly resistant strains (NZO/HlLtJ, PWK/PhJ). These three susceptibility groups were significantly different with respect to death/survival counts. Viral load was significantly different between susceptible and resistant strains but not between intermediate and highly susceptible strains. CAST/EiJ mice showed a unique phenotype. Despite high viral loads in their lungs, CAST/EiJ mice exhibited low counts of infiltrating granulocytes and showed increased numbers of macrophages in the lung. Histological studies of infected lungs and transcriptome analyses of peripheral blood cells and lungs confirmed an abnormal response in the leukocyte recruitment in CAST/EiJ mice. CONCLUSIONS: The eight CC founder strains exhibited a large diversity in their response to influenza infections. Therefore, the CC will represent an ideal mouse genetic reference population to study the influence of genetic variation on the susceptibility and resistance to influenza infections which will be important to understand individual variations of disease severity in humans. The unique phenotype combination in the CAST/EiJ strain resembles human leukocyte adhesion deficiency and may thus represent a new mouse model to understand this and related abnormal immune responses to infections in humans.

The mouse brain metabolome: region-specific signatures and response to excitotoxic neuronal injury.

Neurodegeneration is a multistep process characterized by a multitude of molecular entities and their interactions. Systems analyses, or omics approaches, have become an important tool in characterizing this process. Although RNA and protein profiling made their entry into this field a couple of decades ago, metabolite profiling is a more recent addition. The metabolome represents a large part or all metabolites in a tissue, and gives a snapshot of its physiology. By using gas chromatography coupled to mass spectrometry, we analyzed the metabolic profile of brain regions of the mouse, and found that each region is characterized by its own metabolic signature. We then analyzed the metabolic profile of the mouse brain after excitotoxic injury, a mechanism of neurodegeneration implicated in numerous neurological diseases. More important, we validated our findings by measuring, histologically and molecularly, actual neurodegeneration and glial response. We found that a specific global metabolic signature, best revealed by machine learning algorithms, rather than individual metabolites, was the most robust correlate of neuronal injury and the accompanying gliosis, and this signature could serve as a global biomarker for neurodegeneration. We also observed that brain lesioning induced several metabolites with neuroprotective properties. Our results deepen the understanding of metabolic changes accompanying neurodegeneration in disease models, and could help rapidly evaluate these changes in preclinical drug studies.