Local chromatin fiber folding represses transcription and loop extrusion in quiescent cells.

A longstanding hypothesis is that chromatin fiber folding mediated by interactions between nearby nucleosomes represses transcription. However, it has been difficult to determine the relationship between local chromatin fiber compaction and transcription in cells. Further, global changes in fiber diameters have not been observed, even between interphase and mitotic chromosomes. We show that an increase in the range of local inter-nucleosomal contacts in quiescent yeast drives the compaction of chromatin fibers genome-wide. Unlike actively dividing cells, inter-nucleosomal interactions in quiescent cells require a basic patch in the histone H4 tail. This quiescence-specific fiber folding globally represses transcription and inhibits chromatin loop extrusion by condensin. These results reveal that global changes in chromatin fiber compaction can occur during cell state transitions, and establish physiological roles for local chromatin fiber folding in regulating transcription and chromatin domain formation.

HiCRep.py: fast comparison of Hi-C contact matrices in Python.

MOTIVATION: Hi-C is the most widely used assay for investigating genome-wide 3D organization of chromatin. When working with Hi-C data, it is often useful to calculate the similarity between contact matrices in order to assess experimental reproducibility or to quantify relationships among Hi-C data from related samples. The HiCRep algorithm has been widely adopted for this task, but the existing R implementation suffers from run time limitations on high-resolution Hi-C data or on large single-cell Hi-C datasets. RESULTS: We introduce a Python implementation of HiCRep and demonstrate that it is much faster and consumes much less memory than the existing R implementation. Furthermore, we give examples of HiCRep's ability to accurately distinguish replicates from non-replicates and to reveal cell type structure among collections of Hi-C data. AVAILABILITY AND IMPLEMENTATION: HiCRep.py and its documentation are available with a GPL license at https://github.com/Noble-Lab/hicrep. The software may be installed automatically using the pip package installer. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Computational methods for analyzing and modeling genome structure and organization.

Recent advances in chromosome conformation capture technologies have led to the discovery of previously unappreciated structural features of chromatin. Computational analysis has been critical in detecting these features and thereby helping to uncover the building blocks of genome architecture. Algorithms are being developed to integrate these architectural features to construct better three-dimensional (3D) models of the genome. These computational methods have revealed the importance of 3D genome organization to essential biological processes. In this article, we review the state of the art in analytic and modeling techniques with a focus on their application to answering various biological questions related to chromatin structure. We summarize the limitations of these computational techniques and suggest future directions, including the importance of incorporating multiple sources of experimental data in building a more comprehensive model of the genome. This article is categorized under: Analytical and Computational Methods > Computational Methods Laboratory Methods and Technologies > Genetic/Genomic Methods Models of Systems Properties and Processes > Mechanistic Models.

Unsupervised embedding of single-cell Hi-C data.

Motivation: Single-cell Hi-C (scHi-C) data promises to enable scientists to interrogate the 3D architecture of DNA in the nucleus of the cell, studying how this structure varies stochastically or along developmental or cell-cycle axes. However, Hi-C data analysis requires methods that take into account the unique characteristics of this type of data. In this work, we explore whether methods that have been developed previously for the analysis of bulk Hi-C data can be applied to scHi-C data. We apply methods designed for analysis of bulk Hi-C data to scHi-C data in conjunction with unsupervised embedding. Results: We find that one of these methods, HiCRep, when used in conjunction with multidimensional scaling (MDS), strongly outperforms three other methods, including a technique that has been used previously for scHi-C analysis. We also provide evidence that the HiCRep/MDS method is robust to extremely low per-cell sequencing depth, that this robustness is improved even further when high-coverage and low-coverage cells are projected together, and that the method can be used to jointly embed cells from multiple published datasets.

Single-cell mRNA quantification and differential analysis with Census.

Single-cell gene expression studies promise to reveal rare cell types and cryptic states, but the high variability of single-cell RNA-seq measurements frustrates efforts to assay transcriptional differences between cells. We introduce the Census algorithm to convert relative RNA-seq expression levels into relative transcript counts without the need for experimental spike-in controls. Analyzing changes in relative transcript counts led to dramatic improvements in accuracy compared to normalized read counts and enabled new statistical tests for identifying developmentally regulated genes. Census counts can be analyzed with widely used regression techniques to reveal changes in cell-fate-dependent gene expression, splicing patterns and allelic imbalances. We reanalyzed single-cell data from several developmental and disease studies, and demonstrate that Census enabled robust analysis at multiple layers of gene regulation. Census is freely available through our updated single-cell analysis toolkit, Monocle 2.

Generalized and efficient algorithm for computing multipole energies and gradients based on Cartesian tensors.

Accurate representation of intermolecular forces has been the central task of classical atomic simulations, known as molecular mechanics. Recent advancements in molecular mechanics models have put forward the explicit representation of permanent and/or induced electric multipole (EMP) moments. The formulas developed so far to calculate EMP interactions tend to have complicated expressions, especially in Cartesian coordinates, which can only be applied to a specific kernel potential function. For example, one needs to develop a new formula each time a new kernel function is encountered. The complication of these formalisms arises from an intriguing and yet obscured mathematical relation between the kernel functions and the gradient operators. Here, I uncover this relation via rigorous derivation and find that the formula to calculate EMP interactions is basically invariant to the potential kernel functions as long as they are of the form f(r), i.e., any Green's function that depends on inter-particle distance. I provide an algorithm for efficient evaluation of EMP interaction energies, forces, and torques for any kernel f(r) up to any arbitrary rank of EMP moments in Cartesian coordinates. The working equations of this algorithm are essentially the same for any kernel f(r). Recently, a few recursive algorithms were proposed to calculate EMP interactions. Depending on the kernel functions, the algorithm here is about 4-16 times faster than these algorithms in terms of the required number of floating point operations and is much more memory efficient. I show that it is even faster than a theoretically ideal recursion scheme, i.e., one that requires 1 floating point multiplication and 1 addition per recursion step. This algorithm has a compact vector-based expression that is optimal for computer programming. The Cartesian nature of this algorithm makes it fit easily into modern molecular simulation packages as compared with spherical coordinate-based algorithms. A software library based on this algorithm has been implemented in C++11 and has been released.

Thermodynamics of Micelle Formation and Membrane Fusion Modulate Antimicrobial Lipopeptide Activity.

Antimicrobial lipopeptides (AMLPs) are antimicrobial drug candidates that preferentially target microbial membranes. One class of AMLPs, composed of cationic tetrapeptides attached to an acyl chain, have minimal inhibitory concentrations in the micromolar range against a range of bacteria and fungi. Previously, we used coarse-grained molecular dynamics simulations and free energy methods to study the thermodynamics of their interaction with membranes in their monomeric state. Here, we extended the study to the biologically relevant micellar state, using, to our knowledge, a novel reaction coordinate based on hydrophobic contacts. Using umbrella sampling along this reaction coordinate, we identified the critical transition states when micelles insert into membranes. The results indicate that the binding of these AMLP micelles to membranes is thermodynamically favorable, but in contrast to the monomeric case, there are significant free energy barriers. The height of these free energy barriers depends on the membrane composition, suggesting that the AMLPs' ability to selectively target bacterial membranes may be as much kinetic as thermodynamic. This mechanism highlights the importance of considering oligomeric state in solution as criterion when optimizing peptides or lipopeptides as antibiotic leads.

Thermodynamics of antimicrobial lipopeptide binding to membranes: origins of affinity and selectivity.

The development of novel antibiotic drugs is one of the most pressing biomedical problems due to the increasing number of antibiotic-resistant pathogens. Antimicrobial peptides and lipopeptides are a promising category of candidates, but the molecular origins of their antimembrane activity is unclear. Here we explore a series of recently developed antimicrobial lipopeptides, using coarse-grained molecular-dynamics simulations and free energy methods to uncover the thermodynamics governing their binding to membranes. Specifically, we quantify C16-KGGK's binding affinity to the two types of membrane by umbrella sampling. We also examined the origin of C16-KGGK's selectivity for bacterial versus mammalian membranes by systematically varying the peptide sequence and salt concentration. Our data showed that the C16 hydrophobic tail is the main contributor to its affinity to lipid membrane, whereas the peptide portion is mainly responsible for its selectivity. Furthermore, the electrostatic interaction between the cationic peptide and anionic bacterial membrane plays a significant role in the selectivity.

Characterization of a potent antimicrobial lipopeptide via coarse-grained molecular dynamics.

The prevalence of antibiotic-resistant pathogens is a major medical concern, prompting increased interest in the development of novel antimicrobial compounds. One such set of naturally occurring compounds, known as antimicrobial peptides (AMPs), have broad-spectrum activity, but come with many limitations for clinical use. Recent work has resulted in a set of antimicrobial lipopeptides (AMLPs) with micromolar minimum inhibitory concentrations and excellent selectivity for bacterial membranes. To characterize a potent, synthetic lipopeptide, C16-KGGK, we used multi-microsecond coarse-grained simulations with the MARTINI forcefield, with a total simulation time of nearly 46µs. These simulations show rapid binding of C16-KGGK, which forms micelles in solution, to model bacterial lipid bilayers. Furthermore, upon binding to the surface of the bilayer, these lipopeptides alter the local lipid organization by recruiting negatively charged POPG lipids to the site of binding. It is likely that this drastic reorganization of the bilayer has major effects on bilayer dynamics and cellular processes that depend on specific bilayer compositions. By contrast, the simulations revealed no association between the lipopeptides and model mammalian bilayers. These simulations provide biophysical insights into lipopeptide selectivity and suggest a possible mechanism for antimicrobial action. This article is part of a Special Issue entitled: Membrane protein structure and function.

Expression, purification and preliminary crystallographic analysis of human thyroid hormone responsive protein.

Thyroid hormone responsive protein (Thrsp, also known as Spot 14 and S14) is a carbohydrate-inducible and thyroid-hormone-inducible nuclear protein specific to liver, adipose and lactating mammary tissues. Thrsp functions to activate genes encoding fatty-acid synthesis enzymes. Recent studies have shown that in some cancers human Thrsp (hS14) localizes to the nucleus and is amplified, suggesting that it plays a role in the regulation of lipogenic enzymes during tumourigenesis. Thrsp, a member of the Spot 14 superfamily, is an acidic homodimeric protein with no sequence similarity to other mammalian gene products and its biochemical function is elusive. To shed light on the structure-function relationship of this protein, human Thrsp was crystallized. Recombinant human Thrsp (hThrsp), the N-terminally truncated human Thrsp(10-146) (hThrsp9) and their selenomethionyl (SeMet) derivatives were expressed in Escherichia coli, purified and crystallized using the hanging-drop vapour-diffusion method. Diffraction-quality crystals were grown at 293 K using Li(2)SO(4) as a precipitant. Using synchrotron radiation, data for the hThrsp SeMet derivative, hThrsp9 and its SeMet derivative were collected to 4.0, 3.0 and 3.6 Šresolution, respectively, at 100 K. The crystals of full-length hThrsp and its SeMet derivative belonged to space group P4(1)2(1)2, with approximate unit-cell parameters a = b = 123.9, c = 242.1 Å, α = ß = γ = 90.0°. In contrast, the crystals of the truncated hThrsp9 and its SeMet derivative belonged to space group P2(1)2(1)2(1), with approximate unit-cell parameters a = 91.6, b = 100.8, c = 193.7 Å, α = ß = γ = 90.0°. A molecular-replacement solution calculated using a murine Spot 14 structure as a search model indicated the presence of six molecules per asymmetric unit, comprising three hThrsp homodimers.

3-Benzyl-oxypyridin-2-amine.

In the title compound, C(12)H(12)N(2)O, the dihedral angle between the planes of the pyridine and phenyl rings plane is 35.94 (12)°. In the crystal structure, centrosymmetrically related mol-ecules are linked by a pair of N-H⋯N hydrogen bonds, forming a dimer with an R(2) (2)(8) ring motif. In addition, there is an intra-molecular N-H⋯O inter-action.