k-SLAM: accurate and ultra-fast taxonomic classification and gene identification for large metagenomic data sets.

k-SLAM is a highly efficient algorithm for the characterization of metagenomic data. Unlike other ultra-fast metagenomic classifiers, full sequence alignment is performed allowing for gene identification and variant calling in addition to accurate taxonomic classification. A k-mer based method provides greater taxonomic accuracy than other classifiers and a three orders of magnitude speed increase over alignment based approaches.

Core binding factor (CBF) is required for Epstein-Barr virus EBNA3 proteins to regulate target gene expression.

ChIP-seq performed on lymphoblastoid cell lines (LCLs), expressing epitope-tagged EBNA3A, EBNA3B or EBNA3C from EBV-recombinants, revealed important principles of EBNA3 binding to chromatin. When combined with global chromatin looping data, EBNA3-bound loci were found to have a singular character, each directly associating with either EBNA3-repressed or EBNA3-activated genes, but not with both. EBNA3A and EBNA3C showed significant association with repressed and activated genes.

Cell-wall remodeling drives engulfment during sporulation.

When starved, the Gram-positive bacterium forms durable spores for survival. Sporulation initiates with an asymmetric cell division, creating a large mother cell and a small forespore. Subsequently, the mother cell membrane engulfs the forespore in a phagocytosis-like process.

A large fraction of HLA class I ligands are proteasome-generated spliced peptides.

The proteasome generates the epitopes presented on human leukocyte antigen (HLA) class I molecules that elicit CD8 T cell responses. Reports of proteasome-generated spliced epitopes exist, but they have been regarded as rare events. Here, however, we show that the proteasome-generated spliced peptide pool accounts for one-third of the entire HLA class I immunopeptidome in terms of diversity and one-fourth in terms of abundance.

Retroviruses integrate into a shared, non-palindromic DNA motif.

Many DNA-binding factors, such as transcription factors, form oligomeric complexes with structural symmetry that bind to palindromic DNA sequences. Palindromic consensus nucleotide sequences are also found at the genomic integration sites of retroviruses and other transposable elements, and it has been suggested that this palindromic consensus arises as a consequence of the structural symmetry in the integrase complex. However, we show here that the palindromic consensus sequence is not present in individual integration sites of human T-cell lymphotropic virus type 1 (HTLV-1) and human immunodeficiency virus type 1 (HIV-1), but arises in the population average as a consequence of the existence of a non-palindromic nucleotide motif that occurs in approximately equal proportions on the plus strand and the minus strand of the host genome.

A Simple Screening Approach To Prioritize Genes for Functional Analysis Identifies a Role for Interferon Regulatory Factor 7 in the Control of Respiratory Syncytial Virus Disease.

Greater understanding of the functions of host gene products in response to infection is required. While many of these genes enable pathogen clearance, some enhance pathogen growth or contribute to disease symptoms. Many studies have profiled transcriptomic and proteomic responses to infection, generating large data sets, but selecting targets for further study is challenging.

An expanded evaluation of protein function prediction methods shows an improvement in accuracy.

Background: A major bottleneck in our understanding of the molecular underpinnings of life is the assignment of function to proteins. While molecular experiments provide the most reliable annotation of proteins, their relatively low throughput and restricted purview have led to an increasing role for computational function prediction. However, assessing methods for protein function prediction and tracking progress in the field remain challenging.

Inferring extrinsic noise from single-cell gene expression data using approximate Bayesian computation.

Background: Gene expression is known to be an intrinsically stochastic process which can involve single-digit numbers of mRNA molecules in a cell at any given time. The modelling of such processes calls for the use of exact stochastic simulation methods, most notably the Gillespie algorithm. However, this stochasticity, also termed "intrinsic noise", does not account for all the variability between genetically identical cells growing in a homogeneous environment.

Accurate Reconstruction of Cell and Particle Tracks from 3D Live Imaging Data.

Spatial structures often constrain the 3D movement of cells or particles in vivo, yet this information is obscured when microscopy data are analyzed using standard approaches. Here, we present methods, called unwrapping and Riemannian manifold learning, for mapping particle-tracking data along unseen and irregularly curved surfaces onto appropriate 2D representations. This is conceptually similar to the problem of reconstructing accurate geography from conventional Mercator maps, but our methods do not require prior knowledge of the environments' physical structure.

Multidisciplinary approaches to understanding collective cell migration in developmental biology.

Mathematical models are becoming increasingly integrated with experimental efforts in the study of biological systems. Collective cell migration in developmental biology is a particularly fruitful application area for the development of theoretical models to predict the behaviour of complex multicellular systems with many interacting parts. In this context, mathematical models provide a tool to assess the consistency of experimental observations with testable mechanistic hypotheses.

Robustness of MEK-ERK Dynamics and Origins of Cell-to-Cell Variability in MAPK Signaling.

Cellular signaling processes can exhibit pronounced cell-to-cell variability in genetically identical cells. This affects how individual cells respond differentially to the same environmental stimulus. However, the origins of cell-to-cell variability in cellular signaling systems remain poorly understood.

Engineering control of bacterial cellulose production using a genetic toolkit and a new cellulose-producing strain.

Bacterial cellulose is a strong and ultrapure form of cellulose produced naturally by several species of the Acetobacteraceae Its high strength, purity, and biocompatibility make it of great interest to materials science; however, precise control of its biosynthesis has remained a challenge for biotechnology. Here we isolate a strain of Komagataeibacter rhaeticus (K. rhaeticus iGEM) that can produce cellulose at high yields, grow in low-nitrogen conditions, and is highly resistant to toxic chemicals.

MEANS: python package for Moment Expansion Approximation, iNference and Simulation.

Motivation: Many biochemical systems require stochastic descriptions. Unfortunately these can only be solved for the simplest cases and their direct simulation can become prohibitively expensive, precluding thorough analysis. As an alternative, moment closure approximation methods generate equations for the time-evolution of the system's moments and apply a closure ansatz to obtain a closed set of differential equations; that can become the basis for the deterministic analysis of the moments of the outputs of stochastic systems.

IGSF10 mutations dysregulate gonadotropin-releasing hormone neuronal migration resulting in delayed puberty.

Early or late pubertal onset affects up to 5% of adolescents and is associated with adverse health and psychosocial outcomes. Self-limited delayed puberty (DP) segregates predominantly in an autosomal dominant pattern, but the underlying genetic background is unknown. Using exome and candidate gene sequencing, we have identified rare mutations in IGSF10 in 6 unrelated families, which resulted in intracellular retention with failure in the secretion of mutant proteins.

Genome sequence and plasmid transformation of the model high-yield bacterial cellulose producer Gluconacetobacter hansenii ATCC 53582.

Bacterial cellulose is a strong, highly pure form of cellulose that is used in a range of applications in industry, consumer goods and medicine. Gluconacetobacter hansenii ATCC 53582 is one of the highest reported bacterial cellulose producing strains and has been used as a model organism in numerous studies of bacterial cellulose production and studies aiming to increased cellulose productivity. Here we present a high-quality draft genome sequence for G.

Know the Single-Receptor Sensing Limit? Think Again.

How cells reliably infer information about their environment is a fundamentally important question. While sensing and signaling generally start with cell-surface receptors, the degree of accuracy with which a cell can measure external ligand concentration with even the simplest device-a single receptor-is surprisingly hard to pin down. Recent studies provide conflicting results for the fundamental physical limits.

CD8(+) T cells of Listeria monocytogenes-infected mice recognize both linear and spliced proteasome products.

CD8(+) T cells responding to infection recognize pathogen-derived epitopes presented by MHC class-I molecules. While most of such epitopes are generated by proteasome-mediated antigen cleavage, analysis of tumor antigen processing has revealed that epitopes may also derive from proteasome-catalyzed peptide splicing (PCPS). To determine whether PCPS contributes to epitope processing during infection, we analyzed the fragments produced by purified proteasomes from a Listeria monocytogenes polypeptide.

Bacterial chemotaxis: information processing, thermodynamics, and behavior.

Escherichia coli has long been used as a model organism due to the extensive experimental characterization of its pathways and molecular components. Take chemotaxis as an example, which allows bacteria to sense and swim in response to chemicals, such as nutrients and toxins. Many of the pathway's remarkable sensing and signaling properties are now concisely summarized in terms of design (or engineering) principles.

Protein Connectivity in Chemotaxis Receptor Complexes.

The chemotaxis sensory system allows bacteria such as Escherichia coli to swim towards nutrients and away from repellents. The underlying pathway is remarkably sensitive in detecting chemical gradients over a wide range of ambient concentrations. Interactions among receptors, which are predominantly clustered at the cell poles, are crucial to this sensitivity.

The T210M Substitution in the HLA-a*02:01 gp100 Epitope Strongly Affects Overall Proteasomal Cleavage Site Usage and Antigen Processing.

MHC class I-restricted epitopes, which carry a tumor-specific mutation resulting in improved MHC binding affinity, are preferred T cell receptor targets in innovative adoptive T cell therapies. However, T cell therapy requires efficient generation of the selected epitope. How such mutations may affect proteasome-mediated antigen processing has so far not been studied.

AlloPred: prediction of allosteric pockets on proteins using normal mode perturbation analysis.

Background: Despite being hugely important in biological processes, allostery is poorly understood and no universal mechanism has been discovered. Allosteric drugs are a largely unexplored prospect with many potential advantages over orthosteric drugs. Computational methods to predict allosteric sites on proteins are needed to aid the discovery of allosteric drugs, as well as to advance our fundamental understanding of allostery.

Inference of random walk models to describe leukocyte migration.

While the majority of cells in an organism are static and remain relatively immobile in their tissue, migrating cells occur commonly during developmental processes and are crucial for a functioning immune response. The mode of migration has been described in terms of various types of random walks. To understand the details of the migratory behaviour we rely on mathematical models and their calibration to experimental data.

Quantitative time-resolved analysis reveals intricate, differential regulation of standard- and immuno-proteasomes.

Proteasomal protein degradation is a key determinant of protein half-life and hence of cellular processes ranging from basic metabolism to a host of immunological processes. Despite its importance the mechanisms regulating proteasome activity are only incompletely understood. Here we use an iterative and tightly integrated experimental and modelling approach to develop, explore and validate mechanistic models of proteasomal peptide-hydrolysis dynamics.