Tmem119-EGFP and Tmem119-CreERT2 transgenic mice for labeling and manipulating microglia, bioRxiv, 2019-05-02

AbstractMicroglia are specialized brain-resident macrophages with important functions in health and disease. To improve our understanding of these cells, the research community needs genetic tools to identify and control them in a manner that distinguishes them from closely related cell-types. We have targeted the recently discovered microglia-specific Tmem119 gene to generate knock-in mice expressing EGFP (JAX#031823) or CreERT2 (JAX#031820) for the identification and manipulation of microglia, respectively. Genetic characterization of the locus and qPCR-based analysis demonstrate correct positioning of the transgenes and intact expression of endogenous Tmem119 in the knock-in mouse models. Immunofluorescence analysis further shows that parenchymal microglia, but not other brain macrophages, are completely and faithfully labeled in the EGFP-line at different time points of development. Flow cytometry indicates highly selective expression of EGFP in CD11b+CD45lo microglia. Similarly, immunofluorescence and flow cytometry analyses using a Cre-dependent reporter mouse line demonstrate activity of CreERT2 primarily in microglia upon tamoxifen administration with the caveat of activity in leptomeningeal cells. Finally, flow cytometric analyses reveal absence of EGFP expression and minimal activity of CreERT2 in blood monocytes of the Tmem119-EGFP and Tmem119-CreERT2 lines, respectively. These new transgenic lines extend the microglia toolbox by providing the currently most specific genetic labeling and control over these cells in the myeloid compartment of mice.Visual abstract<jatsfig id=ufig1 position=float orientation=portrait fig-type=figure><jatsgraphic xmlnsxlink=httpwww.w3.org1999xlink xlinkhref=624825v2_ufig1 position=float orientation=portrait >Significance statementTools that specifically label and manipulate only microglia are currently unavailable, but are critically needed to further our understanding of this cell type. Complementing and significantly extending recently introduced microglia-specific immunostaining methods that have quickly become a new standard in the field, we generated two mouse lines that label and control gene expression in microglia with high specificity and made them publicly available. Using these readily accessible mice, the research community will be able to study microglia biology with improved specificity.

biorxiv neuroscience 0-100-users 2019

Biological structure and function emerge from scaling unsupervised learning to 250 million protein sequences, bioRxiv, 2019-04-30

AbstractIn the field of artificial intelligence, a combination of scale in data and model capacity enabled by unsupervised learning has led to major advances in representation learning and statistical generation. In biology, the anticipated growth of sequencing promises unprecedented data on natural sequence diversity. Learning the natural distribution of evolutionary protein sequence variation is a logical step toward predictive and generative modeling for biology. To this end we use unsupervised learning to train a deep contextual language model on 86 billion amino acids across 250 million sequences spanning evolutionary diversity. The resulting model maps raw sequences to representations of biological properties without labels or prior domain knowledge. The learned representation space organizes sequences at multiple levels of biological granularity from the biochemical to proteomic levels. Unsupervised learning recovers information about protein structure secondary structure and residue-residue contacts can be identified by linear projections from the learned representations. Training language models on full sequence diversity rather than individual protein families increases recoverable information about secondary structure. The unsupervised models can be adapted with supervision from quantitative mutagenesis data to predict variant activity. Predictions from sequences alone are comparable to results from a state-of-the-art model of mutational effects that uses evolutionary and structurally derived features.

biorxiv synthetic-biology 200-500-users 2019

Long-Term Exposure to Elevated Lipoprotein(a) Levels, Parental Lifespan and Risk of Mortality, bioRxiv, 2019-04-30

ABSTRACTBackgroundElevated Lipoprotein(a) (Lp[a]) levels are associated with a broad range of atherosclerotic cardiovascular diseases (CVD). The impact of high Lp(a) levels on human longevity is however controversial. Our objectives were to determine whether genetically-determined Lp(a) levels are associated with parental lifespan and to assess the association between measured and genetically-determined Lp(a) levels and long-term all-cause and cardiovascular mortality.MethodsWe determined the association between a genetic risk score of 26 single nucleotide polymorphisms weighted for their impact on Lp(a) levels (wGRS) and parental lifespan (at least one long-lived parent; father still alive and older than 90 or father’s age of death ≥90 or mother still alive and older than 93 or mother’s age of death ≥93) in 139,362 participants from the UK Biobank. A total of 17,686 participants were considered as having high parental lifespan. We also investigated the association between Lp(a) levels and all-cause and cardiovascular mortality in 18,720 participants from the EPIC-Norfolk study.ResultsIn the UK Biobank, increases in the wGRS (weighted for a 50 mgdL increase in Lp(a) levels) were inversely associated with a high parental lifespan (odds ratio=0.92, 95% confidence interval [CI]=0.89-0.94, p=2.7×10−8). During the 20-year follow-up of the EPIC-Norfolk study, 5686 participants died (2412 from CVD-related causes). Compared to participants with Lp(a) levels &lt;50 mgdL, those with Lp(a) levels ≥50 mgdL had an increased hazard ratio (HR) for all-cause (HR=1.17, 95% CI=1.08-1.27) and cardiovascular (HR=1.54, 95% CI=1.37-1.72) mortality. Compared to individuals with Lp(a) levels below the 50th percentile of the Lp(a) distribution (in whom event rates were 29.8% and 11.3%, respectively for all-cause and cardiovascular mortality), those with Lp(a) levels equal or above the 95th percentile of the population distribution (≥70 mgdL) had HRs of 1.22 (95% CI=1.09-1.37, event rate 37.5%) and 1.71 (95% CI=1.46-2.00, event rate 20.0%), for all-cause mortality and cardiovascular mortality, respectively.ConclusionsResults of this study suggest a potentially causal effect of Lp(a) on human longevity, support the use of parental lifespan as a tool to study the genetic determinants of human longevity, and provide a rationale for a trial of Lp(a)-lowering therapy in individuals with high Lp(a) levels.

biorxiv genetics 0-100-users 2019

 

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