A pairwise cytokine code explains the organism-wide response to sepsis.

Sepsis is a systemic response to infection with life-threatening consequences. Our understanding of the molecular and cellular impact of sepsis across organs remains rudimentary. Here, we characterize the pathogenesis of sepsis by measuring dynamic changes in gene expression across organs. To pinpoint molecules controlling organ states in sepsis, we compare the effects of sepsis on organ gene expression to those of 6 singles and 15 pairs of recombinant cytokines. Strikingly, we find that the pairwise effects of tumor necrosis factor plus interleukin (IL)-18, interferon-gamma or IL-1ß suffice to mirror the impact of sepsis across tissues. Mechanistically, we map the cellular effects of sepsis and cytokines by computing changes in the abundance of 195 cell types across 9 organs, which we validate by whole-mouse spatial profiling. Our work decodes the cytokine cacophony in sepsis into a pairwise cytokine message capturing the gene, cell and tissue responses of the host to the disease.

A flexible empirical Bayes approach to multivariate multiple regression, and its improved accuracy in predicting multi-tissue gene expression from genotypes.

Predicting phenotypes from genotypes is a fundamental task in quantitative genetics. With technological advances, it is now possible to measure multiple phenotypes in large samples. Multiple phenotypes can share their genetic component; therefore, modeling these phenotypes jointly may improve prediction accuracy by leveraging effects that are shared across phenotypes. However, effects can be shared across phenotypes in a variety of ways, so computationally efficient statistical methods are needed that can accurately and flexibly capture patterns of effect sharing. Here, we describe new Bayesian multivariate, multiple regression methods that, by using flexible priors, are able to model and adapt to different patterns of effect sharing and specificity across phenotypes. Simulation results show that these new methods are fast and improve prediction accuracy compared with existing methods in a wide range of settings where effects are shared. Further, in settings where effects are not shared, our methods still perform competitively with state-of-the-art methods. In real data analyses of expression data in the Genotype Tissue Expression (GTEx) project, our methods improve prediction performance on average for all tissues, with the greatest gains in tissues where effects are strongly shared, and in the tissues with smaller sample sizes. While we use gene expression prediction to illustrate our methods, the methods are generally applicable to any multi-phenotype applications, including prediction of polygenic scores and breeding values. Thus, our methods have the potential to provide improvements across fields and organisms.

Accelerated dimensionality reduction of single-cell RNA sequencing data with fastglmpca.

SUMMARY: Motivated by theoretical and practical issues that arise when applying Principal component analysis (PCA) to count data, Townes et al. introduced "Poisson GLM-PCA", a variation of PCA adapted to count data, as a tool for dimensionality reduction of single-cell RNA sequencing (scRNA-seq) data. However, fitting GLM-PCA is computationally challenging. Here we study this problem, and show that a simple algorithm, which we call "Alternating Poisson Regression" (APR), produces better quality fits, and in less time, than existing algorithms. APR is also memory-efficient and lends itself to parallel implementation on multi-core processors, both of which are helpful for handling large scRNA-seq datasets. We illustrate the benefits of this approach in three publicly available scRNA-seq datasets. The new algorithms are implemented in an R package, fastglmpca. AVAILABILITY AND IMPLEMENTATION: The fastglmpca R package is released on CRAN for Windows, macOS and Linux, and the source code is available at github.com/stephenslab/fastglmpca under the open source GPL-3 license. Scripts to reproduce the results in this paper are also available in the GitHub repository and on Zenodo.

Fine-mapping from summary data with the "Sum of Single Effects" model.

In recent work, Wang et al introduced the "Sum of Single Effects" (SuSiE) model, and showed that it provides a simple and efficient approach to fine-mapping genetic variants from individual-level data. Here we present new methods for fitting the SuSiE model to summary data, for example to single-SNP z-scores from an association study and linkage disequilibrium (LD) values estimated from a suitable reference panel. To develop these new methods, we first describe a simple, generic strategy for extending any individual-level data method to deal with summary data. The key idea is to replace the usual regression likelihood with an analogous likelihood based on summary data. We show that existing fine-mapping methods such as FINEMAP and CAVIAR also (implicitly) use this strategy, but in different ways, and so this provides a common framework for understanding different methods for fine-mapping. We investigate other common practical issues in fine-mapping with summary data, including problems caused by inconsistencies between the z-scores and LD estimates, and we develop diagnostics to identify these inconsistencies. We also present a new refinement procedure that improves model fits in some data sets, and hence improves overall reliability of the SuSiE fine-mapping results. Detailed evaluations of fine-mapping methods in a range of simulated data sets show that SuSiE applied to summary data is competitive, in both speed and accuracy, with the best available fine-mapping methods for summary data.

Lymph-derived chemokines direct early neutrophil infiltration in the lymph nodes upon Staphylococcus aureus skin infection.

A large number of neutrophils infiltrate the lymph node (LN) within 4 h after Staphylococcus aureus skin infection (4 h postinfection [hpi]) and prevent systemic S. aureus dissemination. It is not clear how infection in the skin can remotely and effectively recruit neutrophils to the LN. Here, we found that lymphatic vessel occlusion substantially reduced neutrophil recruitment to the LN. Lymphatic vessels effectively transported bacteria and proinflammatory chemokines (i.e., Chemokine [C-X-C motif] motif 1 [CXCL1] and CXCL2) to the LN. However, in the absence of lymph flow, S. aureus alone in the LN was insufficient to recruit neutrophils to the LN at 4 hpi. Instead, lymph flow facilitated the earliest neutrophil recruitment to the LN by delivering chemokines (i.e., CXCL1, CXCL2) from the site of infection. Lymphatic dysfunction is often found during inflammation. During oxazolone (OX)-induced skin inflammation, CXCL1/2 in the LN was reduced after infection. The interrupted LN conduits further disrupted the flow of lymph and impeded its communication with high endothelial venules (HEVs), resulting in impaired neutrophil migration. The impaired neutrophil interaction with bacteria contributed to persistent infection in the LN. Our studies showed that both the flow of lymph from lymphatic vessels to the LN and the distribution of lymph in the LN are critical to ensure optimal neutrophil migration and timely innate immune protection in S. aureus infection.

Not just fibrotic: endothelial-derived TGFß maintains contractile function and lymphatic muscle phenotype during homeostasis.

Cell-cell communication within the lymphatic vasculature during homeostasis is incompletely detailed. Although many discoveries highlight the pathological roles of transforming growth factor-beta (TGFß) in chronic vascular inflammation and associated fibrosis, only a small amount is known surrounding the role of TGFß-signaling in homeostatic lymphatic function. Here, we discovered that pharmacological blockade of TGFß receptor 1 (TGFßR1) negatively impacts rat mesenteric lymphatic vessel pumping, significantly reducing vessel contractility and surrounding lymphatic muscle coverage. We have identified mesenteric lymphatic endothelial cells themselves as a source of endogenous vascular TGFß and that TGFß production is significantly increased in these cells via activation of a number of functional pattern recognition receptors they express. We show that a continuous supply of TGFß is essential to maintain the contractile phenotype of neighboring lymphatic muscle cells and support this conclusion through in vitro analysis of primary isolated lymphatic muscle cells that undergo synthetic differentiation during 2-D cell culture, a phenomenon that could be effectively rescued by supplementation with recombinant TGFß. Finally, we demonstrate that lymphatic endothelial production of TGFß is regulated, in part, by nitric oxide in a manner we propose is essential to counteract the pathological over-production of TGFß. Taken together, these data highlight the essential role of homeostatic TGFß signaling in the maintenance of lymphatic vascular function and highlight possible deleterious consequences of its inhibition.NEW & NOTEWORTHY The growth factor TGFß is commonly associated with its pathological overproduction during tissue fibrosis rather than its homeostatic functions. We expose the lymphatic endothelium as a source of endogenous TGFß, the impact of its production on the maintenance of surrounding lymphatic muscle cell phenotype, and internally regulated mechanisms of its production. Overall, these results highlight the intricate balance of TGFß-signaling as an essential component of maintaining lymphatic contractile function.

Flow-dependent regulation of rat mesenteric lymphatic vessel contractile response requires activation of endothelial TRPV4 channels.

OBJECTIVES: The objective of our study is to evaluate the involvement of the transient receptor potential vanilloid 4 (TRPV4) in the alteration of lymphatic pumping in response to flow and determine the signaling pathways involved. METHODS: We used immunofluorescence imaging and western blotting to assess TRPV4 expression in rat mesenteric lymphatic vessels. We examined inhibition of TRPV4 with HC067047, nitric oxide synthase (NOS) with L-NNA and cyclooxygenases (COXs) with indomethacin on the contractile response of pressurized lymphatic vessels to flow changes induced by a stepwise increase in pressure gradients, and the functionality of endothelial TRPV4 channels by measuring the intracellular Ca2+ response of primary lymphatic endothelial cell cultures to the selective agonist GSK1016790A. RESULTS: TRPV4 protein was expressed in both the endothelial and the smooth muscle layer of rat mesenteric lymphatics with high endothelial expression around the valve sites. When maintained under constant transmural pressure, most lymphatic vessels displayed a decrease in contraction frequency under conditions of flow and this effect was ablated through inhibition of NOS, COX or TRPV4. CONCLUSIONS: Our findings demonstrate a critical role for TRPV4 in the decrease in contraction frequency induced in lymphatic vessels by increases in flow rate via the production and action of nitric oxide and dilatory prostanoids.

Characterizing and inferring quantitative cell cycle phase in single-cell RNA-seq data analysis.

Cellular heterogeneity in gene expression is driven by cellular processes, such as cell cycle and cell-type identity, and cellular environment such as spatial location. The cell cycle, in particular, is thought to be a key driver of cell-to-cell heterogeneity in gene expression, even in otherwise homogeneous cell populations. Recent advances in single-cell RNA-sequencing (scRNA-seq) facilitate detailed characterization of gene expression heterogeneity and can thus shed new light on the processes driving heterogeneity. Here, we combined fluorescence imaging with scRNA-seq to measure cell cycle phase and gene expression levels in human induced pluripotent stem cells (iPSCs). By using these data, we developed a novel approach to characterize cell cycle progression. Although standard methods assign cells to discrete cell cycle stages, our method goes beyond this and quantifies cell cycle progression on a continuum. We found that, on average, scRNA-seq data from only five genes predicted a cell's position on the cell cycle continuum to within 14% of the entire cycle and that using more genes did not improve this accuracy. Our data and predictor of cell cycle phase can directly help future studies to account for cell cycle-related heterogeneity in iPSCs. Our results and methods also provide a foundation for future work to characterize the effects of the cell cycle on expression heterogeneity in other cell types.

The Bayesian lens and Bayesian blinkers.

I discuss the benefits of looking through the 'Bayesian lens' (seeking a Bayesian interpretation of ostensibly non-Bayesian methods), and the dangers of wearing 'Bayesian blinkers' (eschewing non-Bayesian methods as a matter of philosophical principle). I hope that the ideas may be useful to scientists trying to understand widely used statistical methods (including confidence intervals and [Formula: see text]-values), as well as teachers of statistics and practitioners who wish to avoid the mistake of overemphasizing philosophy at the expense of practical matters. This article is part of the theme issue 'Bayesian inference: challenges, perspectives, and prospects'.

Bayesian multivariate reanalysis of large genetic studies identifies many new associations.

Genome-wide association studies (GWAS) have now been conducted for hundreds of phenotypes of relevance to human health. Many such GWAS involve multiple closely-related phenotypes collected on the same samples. However, the vast majority of these GWAS have been analyzed using simple univariate analyses, which consider one phenotype at a time. This is despite the fact that, at least in simulation experiments, multivariate analyses have been shown to be more powerful at detecting associations. Here, we conduct multivariate association analyses on 13 different publicly-available GWAS datasets that involve multiple closely-related phenotypes. These data include large studies of anthropometric traits (GIANT), plasma lipid traits (GlobalLipids), and red blood cell traits (HaemgenRBC). Our analyses identify many new associations (433 in total across the 13 studies), many of which replicate when follow-up samples are available. Overall, our results demonstrate that multivariate analyses can help make more effective use of data from both existing and future GWAS.