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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.