BET inhibition blocks inflammation-induced cardiac dysfunction and SARS-CoV-2 infection

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Optimal tuning of weighted kNN- and diffusion-based methods for denoising single cell genomics data

The analysis of single-cell genomics data presents several statistical challenges, and extensive efforts have been made to produce methods for the analysis of this data that impute missing values, address sampling issues and quantify and correct for noise. In spite of such efforts, no consensus on best practices has been established and all current approaches vary substantially based on the available data and empirical tests. The k-Nearest Neighbor Graph (kNN-G) is often used to infer the identities of, and relationships between, cells and is the basis of many widely used dimensionality-reduction and projection methods. The kNN-G has also been the basis for imputation methods using, e.g., neighbor averaging and graph diffusion. However, due to the lack of an agreed-upon optimal objective function for choosing hyperparameters, these methods tend to oversmooth data, thereby resulting in a loss of information with regard to cell identity and the specific gene-to-gene patterns underlying regulatory mechanisms. In this paper, we investigate the tuning of kNN- and diffusion-based denoising methods with a novel non-stochastic method for optimally preserving biologically relevant informative variance in single-cell data. The framework, Denoising Expression data with a Weighted Affinity Kernel and Self-Supervision (DEWÄKSS), uses a self-supervised technique to tune its parameters. We demonstrate that denoising with optimal parameters selected by our objective function (i) is robust to preprocessing methods using data from established benchmarks, (ii) disentangles cellular identity and maintains robust clusters over dimension-reduction methods, (iii) maintains variance along several expression dimensions, unlike previous heuristic-based methods that tend to oversmooth data variance, and (iv) rarely involves diffusion but rather uses a fixed weighted kNN graph for denoising. Together, these findings provide a new understanding of kNN- and diffusion-based denoising methods. Code and example data for DEWÄKSS is available at https://gitlab.com/Xparx/dewakss/-/tree/Tjarnberg2020branch.     

Nonsymmetrical double logistic analysis of ambulatory blood pressure recordings.

We developed an asymmetric double logistic curve-fitting procedure for circadian analysis that can determine the rate of change in variables during the day-to-night separately from the night-to-day transition for use in animal studies. We now have applied this procedure to 24-h systolic (SAP) and diastolic arterial pressure (DAP) and heart rate ambulatory recordings from 302 patients. In 292 cases, all parameters showed a pattern of higher day and lower night values. In men there was a similar rate of transition between day and night or from night to day for both SAP and DAP that lasted 3-4 h, indicating a symmetrical diurnal pattern. By contrast, women showed a faster rate of decrease in mean arterial pressure in the evening compared with men (P < 0.05) and therefore showed an asymmetric diurnal SAP pattern. For both men and women, there was a markedly greater rate of morning increase in heart rate compared with the rate of evening decrease (2.2- and 1.9-fold, respectively, P < 0.001). The logistic method provided a better fit than the square-wave or the cosinor method (P < 0.001) and more appropriately detected nondippers. We conclude that analysis of ambulatory recordings by a new logistic curve-fitting method reveals more rapid reductions in evening SAP in women than men but both have two- to threefold more rapid morning rates of tachycardia. The ability of the double logistic method to determine the diurnal blood pressure rates of change independently is key to determining new markers for cardiovascular risk.     

RNA isolation from yeast using silica matrices.

RNA isolation from yeast is complicated by the need to initially break the cell wall. While this can be accomplished by glass bead disruption or enzyme treatment, these approaches result in DNA contamination and/or the need for incubation periods. We have developed a protocol for the isolation of RNA samples from yeast that minimizes degradation by RNases and incorporates two purification steps: acid phenol extraction and binding to a silica matrix. The procedure requires no precipitation steps, facilitating automation, and can be completed in less than 90 min. The RNA quality is ideal for microarray analysis.     

A comprehensive mouse IBD database for the efficient localization of quantitative trait loci

Traditional fine-mapping approaches in mouse genetics that go from a linkage region to a candidate gene are very costly and time consuming. Shared ancestry regions, along with the combination of genetics and genomics approaches, provide a powerful tool to shorten the time and effort required to identify a causative gene. In this article we present a novel methodology that predicts IBD (identical by descent) regions between pairs of inbred strains using single nucleotide polymorphism (SNP) maps. We have validated this approach by comparing the IBD regions, estimated using different algorithms, to the results derived using the sequence information in the strains present in the Celera Mouse Database. We showed that based on the current publicly available SNP genotypes, large IBD regions (>1 Mb) can be identified successfully. By assembling a list of 21,514 SNPs in 61 common inbred strains, we inferred IBD regions between all pairs of strains and confirmed, for the first time, that existing quantitative trait genes (QTG) and susceptibility genes all lie outside of IBD regions. We also illustrated how knowledge of IBD structures can be applied to strain selection for future crosses. We have made our results available for data mining and download through a public website ( ). Electronic Supplementary Material Electronic Supplementary material is available for this article at and accessible for authorised users.     

Engineering of a target site-specific recombinase by a combined evolution- and structure-guided approach

Site-specific recombinases (SSRs) can perform DNA rearrangements, including deletions, inversions and translocations when their naive target sequences are placed strategically into the genome of an organism. Hence, in order to employ SSRs in heterologous hosts, their target sites have to be introduced into the genome of an organism before the enzyme can be practically employed. Engineered SSRs hold great promise for biotechnology and advanced biomedical applications, as they promise to extend the usefulness of SSRs to allow efficient and specific recombination of pre-existing, natural genomic sequences. However, the generation of enzymes with desired properties remains challenging. Here, we use substrate-linked directed evolution in combination with molecular modeling to rationally engineer an efficient and specific recombinase (sTre) that readily and specifically recombines a sequence present in the HIV-1 genome. We elucidate the role of key residues implicated in the molecular recognition mechanism and we present a rationale for sTre’s enhanced specificity. Combining evolutionary and rational approaches should help in accelerating the generation of enzymes with desired properties for use in biotechnology and biomedicine.