Subcapsular Sinus Macrophages Promote Melanoma Metastasis to the Sentinel Lymph Nodes via an IL1α-STAT3 Axis.

During melanoma metastasis, tumor cells originating in the skin migrate via lymphatic vessels to the sentinel lymph node (sLN). This process facilitates tumor cell spread across the body. Here, we characterized the innate inflammatory response to melanoma in the metastatic microenvironment of the sLN. We found that macrophages located in the subcapsular sinus (SS) produced protumoral IL1α after recognition of tumoral antigens. Moreover, we confirmed that the elimination of LN macrophages or the administration of an IL1α-specific blocking antibody reduced metastatic spread. To understand the mechanism of action of IL1α in the context of the sLN microenvironment, we applied single-cell RNA sequencing to microdissected metastases obtained from animals treated with the IL1α-specific blocking antibody. Among the different pathways affected, we identified STAT3 as one of the main targets of IL1α signaling in metastatic tumor cells. Moreover, we found that the antitumoral effect of the anti-IL1α was not mediated by lymphocytes because Il1r1 knockout mice did not show significant differences in metastasis growth. Finally, we found a synergistic antimetastatic effect of the combination of IL1α blockade and STAT3 inhibition with stattic, highlighting a new immunotherapy approach to preventing melanoma metastasis.

Clonal structure, stability and dynamics of human memory B cells and circulating plasmablasts.

Memory B cells persist for a lifetime and rapidly differentiate into antibody-producing plasmablasts and plasma cells upon antigen re-encounter. The clonal relationship and evolution of memory B cells and circulating plasmablasts is not well understood. Using single-cell sequencing combined with isolation of specific antibodies, we found that in two healthy donors, the memory B cell repertoire was dominated by large IgM, IgA and IgG2 clonal families, whereas IgG1 families, including those specific for recall antigens, were of small size. Analysis of multiyear samples demonstrated stability of memory B cell clonal families and revealed that a large fraction of recently generated plasmablasts was derived from long-term memory B cell families and was found recurrently. Collectively, this study provides a systematic description of the structure, stability and dynamics of the human memory B cell pool and suggests that memory B cells may be active at any time point in the generation of plasmablasts.

metaLINCS: an R package for meta-level analysis of LINCS L1000 drug signatures using stratified connectivity mapping.

Summary: Accessing the collection of perturbed gene expression profiles, such as the LINCS L1000 connectivity map, is usually performed at the individual dataset level, followed by a summary performed by counting individual hits for each perturbagen. With the metaLINCS R package, we present an alternative approach that combines rank correlation and gene set enrichment analysis to identify meta-level enrichment at the perturbagen level and, in the case of drugs, at the mechanism of action level. This significantly simplifies the interpretation and highlights overarching themes in the data. We demonstrate the functionality of the package and compare its performance against those of three currently used approaches. Availability and implementation: metaLINCS is released under GPL3 license. Source code and documentation are freely available on GitHub (https://github.com/bigomics/metaLINCS). Supplementary information: Supplementary data are available at Bioinformatics Advances online.

Dynamics in protein translation sustaining T cell preparedness.

In response to pathogenic threats, naive T cells rapidly transition from a quiescent to an activated state, yet the underlying mechanisms are incompletely understood. Using a pulsed SILAC approach, we investigated the dynamics of mRNA translation kinetics and protein turnover in human naive and activated T cells. Our datasets uncovered that transcription factors maintaining T cell quiescence had constitutively high turnover, which facilitated their depletion following activation. Furthermore, naive T cells maintained a surprisingly large number of idling ribosomes as well as 242 repressed mRNA species and a reservoir of glycolytic enzymes. These components were rapidly engaged following stimulation, promoting an immediate translational and glycolytic switch to ramp up the T cell activation program. Our data elucidate new insights into how T cells maintain a prepared state to mount a rapid immune response, and provide a resource of protein turnover, absolute translation kinetics and protein synthesis rates in T cells ( https://www.immunomics.ch ).

Omics Playground: a comprehensive self-service platform for visualization, analytics and exploration of Big Omics Data.

As the cost of sequencing drops rapidly, the amount of 'omics data increases exponentially, making data visualization and interpretation-'tertiary' analysis a bottleneck. Specialized analytical tools requiring technical expertise are available. However, consolidated and multi-faceted tools that are easy to use for life scientists is highly needed and currently lacking. Here we present Omics Playground, a user-friendly and interactive self-service bioinformatics platform for the in-depth analysis, visualization and interpretation of transcriptomics and proteomics data. It provides a large number of different tools in which special attention has been paid to single cell data. With Omics Playground, life scientists can easily perform complex data analysis and visualization without coding, and significantly reduce the time to discovery.

Correction to: cuRnet: an R package for graph traversing on GPU.

After publication of this supplement article [1], it was brought to our attention that reference 10 and reference 12 in the article are incorrect.

cuRnet: an R package for graph traversing on GPU.

BACKGROUND: R has become the de-facto reference analysis environment in Bioinformatics. Plenty of tools are available as packages that extend the R functionality, and many of them target the analysis of biological networks. Several algorithms for graphs, which are the most adopted mathematical representation of networks, are well-known examples of applications that require high-performance computing, and for which classic sequential implementations are becoming inappropriate. In this context, parallel approaches targeting GPU architectures are becoming pervasive to deal with the execution time constraints. Although R packages for parallel execution on GPUs are already available, none of them provides graph algorithms. RESULTS: This work presents cuRnet, a R package that provides a parallel implementation for GPUs of the breath-first search (BFS), the single-source shortest paths (SSSP), and the strongly connected components (SCC) algorithms. The package allows offloading computing intensive applications to GPU devices for massively parallel computation and to speed up the runtime up to one order of magnitude with respect to the standard sequential computations on CPU. We have tested cuRnet on a benchmark of large protein interaction networks and for the interpretation of high-throughput omics data thought network analysis. CONCLUSIONS: cuRnet is a R package to speed up graph traversal and analysis through parallel computation on GPUs. We show the efficiency of cuRnet applied both to biological network analysis, which requires basic graph algorithms, and to complex existing procedures built upon such algorithms.

PCSF: An R-package for network-based interpretation of high-throughput data.

With the recent technological developments a vast amount of high-throughput data has been profiled to understand the mechanism of complex diseases. The current bioinformatics challenge is to interpret the data and underlying biology, where efficient algorithms for analyzing heterogeneous high-throughput data using biological networks are becoming increasingly valuable. In this paper, we propose a software package based on the Prize-collecting Steiner Forest graph optimization approach. The PCSF package performs fast and user-friendly network analysis of high-throughput data by mapping the data onto a biological networks such as protein-protein interaction, gene-gene interaction or any other correlation or coexpression based networks. Using the interaction networks as a template, it determines high-confidence subnetworks relevant to the data, which potentially leads to predictions of functional units. It also interactively visualizes the resulting subnetwork with functional enrichment analysis.

Automating unambiguous NOE data usage in NVR for NMR protein structure-based assignments.

Nuclear Magnetic Resonance (NMR) Spectroscopy is an important technique that allows determining protein structure in solution. An important problem in protein structure determination using NMR spectroscopy is the mapping of peaks to corresponding amino acids, also known as the assignment problem. Structure-Based Assignment (SBA) is an approach to solve this problem using a template structure that is homologous to the target. Our previously developed approach Nuclear Vector Replacement-Binary Integer Programming (NVR-BIP) computed the optimal solution for small proteins, but was unable to solve the assignments of large proteins. NVR-Ant Colony Optimization (ACO) extended the applicability of the NVR approach for such proteins. One of the input data utilized in these approaches is the Nuclear Overhauser Effect (NOE) data. NOE is an interaction observed between two protons if the protons are located close in space. These protons could be amide protons, protons attached to the alpha-carbon atom in the backbone of the protein, or side chain protons. NVR only uses backbone protons. In this paper, we reformulate the NVR-BIP model to distinguish the type of proton in NOE data and use the corresponding proton coordinates in the extended formulation. In addition, the threshold value over interproton distances is set in a standard manner for all proteins by extracting the NOE upper bound distance information from the data. We also convert NOE intensities into distance thresholds. Our new approach thus handles the NOE data correctly and without manually determined parameters. We accordingly adapt NVR-ACO solution methodology to these changes. Computational results show that our approaches obtain optimal solutions for small proteins. For the large proteins our ant colony optimization-based approach obtains promising results.

A drug similarity network for understanding drug mechanism of action.

Chemogenomic experiments, where genetic and chemical perturbations are combined, provide data for discovering the relationships between genotype and phenotype. Traditionally, analysis of chemogenomic datasets has been done considering the sensitivity of the deletion strains to chemicals, and this has shed light on drug mechanism of action and detecting drug targets. Here, we computationally analyzed a large chemogenomic dataset, which combines more than 300 chemicals with virtually all gene deletion strains in the yeast S. cerevisiae. In addition to sensitivity relation between deletion strains and chemicals, we also considered the deletion strains that are resistant to chemicals. We found a small set of genes whose deletion makes the cell resistant to many chemicals. Curiously, these genes were enriched for functions related to RNA metabolism. Our approach allowed us to generate a network of drugs and genes that are connected with resistance or sensitivity relationships. As a quality assessment, we showed that the higher order motifs found in this network are consistent with biological expectations. Finally, we constructed a biologically relevant network projection pertaining to drug similarities, and analyzed this network projection in detail. We propose this drug similarity network as a useful tool for understanding drug mechanism of action.

Large-scale identification and analysis of suppressive drug interactions.

One drug may suppress the effects of another. Although knowledge of drug suppression is vital to avoid efficacy-reducing drug interactions or discover countermeasures for chemical toxins, drug-drug suppression relationships have not been systematically mapped. Here, we analyze the growth response of Saccharomyces cerevisiae to anti-fungal compound ("drug") pairs. Among 440 ordered drug pairs, we identified 94 suppressive drug interactions. Using only pairs not selected on the basis of their suppression behavior, we provide an estimate of the prevalence of suppressive interactions between anti-fungal compounds as 17%. Analysis of the drug suppression network suggested that Bromopyruvate is a frequently suppressive drug and Staurosporine is a frequently suppressed drug. We investigated potential explanations for suppressive drug interactions, including chemogenomic analysis, coaggregation, and pH effects, allowing us to explain the interaction tendencies of Bromopyruvate.