Diffusion about the mean drift location in a biased random walk

Random walks are used to model movement in a wide variety of contexts: from the movement of cells undergoing chemotaxis to the migration of animals. In a two- dimensional biased random walk, the diffusion about the mean drift position is entirely dependent on the moments of the angular distribution used to determine the movement direction at each step. Here we consider biased random walks using several different angular distributions and derive expressions for the diffusion coefficients in each direction based on either a fixed or variable movement speed, and we use these to generate a probability density function for the long-time spatial distribution. we demonstrate how diffusion is typically anisotropic around the mean drift position and illustrate these theoretical results using computer simulations. we relate these results to earlier studies of swimming microorganisms and explain how the results can be generalized to other types of animal movement. © 2010 by the Ecological Society of America

DNA sequence-dependent formation of heterochromatin nanodomains.

The mammalian epigenome contains thousands of heterochromatin nanodomains (HNDs) marked by di- and trimethylation of histone H3 at lysine 9 (H3K9me2/3), which have a typical size of 3-10 nucleosomes. However, what governs HND location and extension is only partly understood. Here, we address this issue by introducing the chromatin hierarchical lattice framework (ChromHL) that predicts chromatin state patterns with single-nucleotide resolution. ChromHL is applied to analyse four HND types in mouse embryonic stem cells that are defined by histone methylases SUV39H1/2 or GLP, transcription factor ADNP or chromatin remodeller ATRX. We find that HND patterns can be computed from PAX3/9, ADNP and LINE1 sequence motifs as nucleation sites and boundaries that are determined by DNA sequence (e.g. CTCF binding sites), cooperative interactions between nucleosomes as well as nucleosome-HP1 interactions. Thus, ChromHL rationalizes how patterns of H3K9me2/3 are established and changed via the activity of protein factors in processes like cell differentiation

Microbial ligand costimulation drives neutrophilic steroid-refractory asthma

Funding: The authors thank the Wellcome Trust (102705) and the Universities of Aberdeen and Cape Town for funding. This research was also supported, in part, by National Institutes of Health GM53522 and GM083016 to DLW. KF and BNL are funded by the Fonds Wetenschappelijk Onderzoek, BNL is the recipient of an European Research Commission consolidator grant and participates in the European Union FP7 programs EUBIOPRED and MedALL. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.Peer reviewedPublisher PD

The CCP4 suite: integrative software for macromolecular crystallography

The Collaborative Computational Project No. 4 (CCP4) is a UK-led international collective with a mission to develop, test, distribute and promote software for macromolecular crystallography. The CCP4 suite is a multiplatform collection of programs brought together by familiar execution routines, a set of common libraries and graphical interfaces. The CCP4 suite has experienced several considerable changes since its last reference article, involving new infrastructure, original programs and graphical interfaces. This article, which is intended as a general literature citation for the use of the CCP4 software suite in structure determination, will guide the reader through such transformations, offering a general overview of the new features and outlining future developments. As such, it aims to highlight the individual programs that comprise the suite and to provide the latest references to them for perusal by crystallographers around the world.Jon Agirre is a Royal Society University Research Fellow (UF160039 and URF\R\221006). Mihaela Atanasova is funded by the UK Engineering and Physical Sciences Research Council (EPSRC; EP/R513386/1). Haroldas Bagdonas is funded by The Royal Society (RGF/R1/181006). Jose´ Javier Burgos-Ma´rmol and Daniel J. Rigden are supported by the BBSRC (BB/S007105/1). Robbie P. Joosten is funded by the European Union’s Horizon 2020 research and innovation programme under grant agreement No. 871037 (iNEXTDiscovery) and by CCP4. This work was supported by the Medical Research Council as part of United Kingdom Research and Innovation, also known as UK Research and Innovation: MRC file reference No. MC_UP_A025_1012 to Garib N. Murshudov, which also funded Keitaro Yamashita, Paul Emsley and Fei Long. Robert A. Nicholls is funded by the BBSRC (BB/S007083/1). Soon Wen Hoh is funded by the BBSRC (BB/T012935/1). Kevin D. Cowtan and Paul S. Bond are funded in part by the BBSRC (BB/S005099/1). John Berrisford and Sameer Velankar thank the European Molecular Biology Laboratory–European Bioinformatics Institute, who supported this work. Andrea Thorn was supported in the development of AUSPEX by the German Federal Ministry of Education and Research (05K19WWA and 05K22GU5) and by Deutsche Forschungsgemeinschaft (TH2135/2-1). Petr Kolenko and Martin Maly´ are funded by the MEYS CR (CZ.02.1.01/0.0/0.0/16_019/0000778). Martin Maly´ is funded by the Czech Academy of Sciences (86652036) and CCP4/STFC (521862101). Anastassis Perrakis acknowledges funding from iNEXT (grant No. 653706), iNEXT-Discovery (grant No. 871037), West-Life (grant No. 675858) and EOSC-Life (grant No. 824087) funded by the Horizon 2020 program of the European Commission. Robbie P. Joosten has been the recipient of a Veni grant (722.011.011) and a Vidi grant (723.013.003) from the Netherlands Organization for Scientific Research (NWO). Maarten L. Hekkelman, Robbie P. Joosten and Anastassis Perrakis thank the Research High Performance Computing facility of the Netherlands Cancer Institute for providing and maintaining computation resources and acknowledge the institutional grant from the Dutch Cancer Society and the Dutch Ministry of Health, Welfare and Sport. Tarik R. Drevon is funded by the BBSRC (BB/S007040/1). Randy J. Read is supported by a Principal Research Fellowship from the Wellcome Trust (grant 209407/Z/17/Z). Atlanta G. Cook is supported by a Wellcome Trust SRF (200898) and a Wellcome Centre for Cell Biology core grant (203149). Isabel Uso´n acknowledges support from STFC-UK/CCP4: ‘Agreement for the integration of methods into the CCP4 software distribution, ARCIMBOLDO_LOW’ and Spanish MICINN/AEI/FEDER/UE (PID2021-128751NB-I00). Pavol Skubak and Navraj Pannu were funded by the NWO Applied Sciences and Engineering Domain and CCP4 (grant Nos. 13337 and 16219). Bernhard Lohkamp was supported by the Ro¨ntgen A˚ ngstro¨m Cluster (grant 349-2013-597). Nicholas Pearce is currently funded by the SciLifeLab and Wallenberg Data Driven Life Science Program (grant KAW 2020.0239) and has previously been funded by a Veni Fellowship (VI.Veni.192.143) from the Dutch Research Council (NWO), a Long-term EMBO fellowship (ALTF 609-2017) and EPSRC grant EP/G037280/1. David M. Lawson received funding from BBSRC Institute Strategic Programme Grants (BB/P012523/1 and BB/P012574/1). Lucrezia Catapano is the recipient of an STFC/CCP4-funded PhD studentship (Agreement No: 7920 S2 2020 007).Peer reviewe

Nucleosome reorganisation in breast cancer tissues.

Background Nucleosome repositioning in cancer is believed to cause many changes in genome organisation and gene expression. Understanding these changes is important to elucidate fundamental aspects of cancer. It is also important for medical diagnostics based on cell-free DNA (cfDNA), which originates from genomic DNA regions protected from digestion by nucleosomes. Results We have generated high-resolution nucleosome maps in paired tumour and normal tissues from the same breast cancer patients using MNase-assisted histone H3 ChIP-seq and compared them with the corresponding cfDNA from blood plasma. This analysis has detected single-nucleosome repositioning at key regulatory regions in a patient-specific manner and common cancer-specific patterns across patients. The nucleosomes gained in tumour versus normal tissue were particularly informative of cancer pathways, with ~ 20-fold enrichment at CpG islands, a large fraction of which marked promoters of genes encoding DNA-binding proteins. The tumour tissues were characterised by a 5-10 bp decrease in the average distance between nucleosomes (nucleosome repeat length, NRL), which is qualitatively similar to the differences between pluripotent and differentiated cells. This effect was correlated with gene activity, differential DNA methylation and changes in local occupancy of linker histone variants H1.4 and H1X. Conclusions Our study offers a novel resource of high-resolution nucleosome maps in breast cancer patients and reports for the first time the effect of systematic decrease of NRL in paired tumour versus normal breast tissues from the same patient. Our findings provide a new mechanistic understanding of nucleosome repositioning in tumour tissues that can be valuable for patient diagnostics, stratification and monitoring

New genetic loci link adipose and insulin biology to body fat distribution.

Body fat distribution is a heritable trait and a well-established predictor of adverse metabolic outcomes, independent of overall adiposity. To increase our understanding of the genetic basis of body fat distribution and its molecular links to cardiometabolic traits, here we conduct genome-wide association meta-analyses of traits related to waist and hip circumferences in up to 224,459 individuals. We identify 49 loci (33 new) associated with waist-to-hip ratio adjusted for body mass index (BMI), and an additional 19 loci newly associated with related waist and hip circumference measures (P < 5 × 10(-8)). In total, 20 of the 49 waist-to-hip ratio adjusted for BMI loci show significant sexual dimorphism, 19 of which display a stronger effect in women. The identified loci were enriched for genes expressed in adipose tissue and for putative regulatory elements in adipocytes. Pathway analyses implicated adipogenesis, angiogenesis, transcriptional regulation and insulin resistance as processes affecting fat distribution, providing insight into potential pathophysiological mechanisms

The CCP4 suite : integrative software for macromolecular crystallography

The Collaborative Computational Project No. 4 (CCP4) is a UK-led international collective with a mission to develop, test, distribute and promote software for macromolecular crystallography. The CCP4 suite is a multiplatform collection of programs brought together by familiar execution routines, a set of common libraries and graphical interfaces. The CCP4 suite has experienced several considerable changes since its last reference article, involving new infrastructure, original programs and graphical interfaces. This article, which is intended as a general literature citation for the use of the CCP4 software suite in structure determination, will guide the reader through such transformations, offering a general overview of the new features and outlining future developments. As such, it aims to highlight the individual programs that comprise the suite and to provide the latest references to them for perusal by crystallographers around the world

pH-induced gene regulation of solvent production by Clostridium acetobutylicum in continuous culture:Parameter estimation and sporulation modelling

The acetone–butanol (AB) fermentation process in the anaerobic endospore-forming Gram-positive bacterium Clostridium acetobutylicum is useful as a producer of biofuels, particularly butanol. Recent work has concentrated on trying to improve the efficiency of the fermentation method, either through changes in the environmental conditions or by modifying the genome to selectively favour the production of one particular solvent over others. Fermentation of glucose by C. acetobutylicum occurs in two stages: initially the acids acetate and butyrate are produced and excreted and then, as the external pH falls, acetate and butyrate are ingested and further metabolised into the solvents acetone, butanol and ethanol. In order to optimise butanol production, it is important to understand how pH affects the enzyme-controlled reactions in the metabolism process. We adapt an ordinary differential equation model of the metabolic network with regulation at the genetic level for the required enzymes; parametrising the model using experimental data generated from continuous culture, we improve on previous point predictions (S. Haus, S. Jabbari, T. Millat, H. Janssen, R.-J. Fisher, H. Bahl, J. R. King, O. Wolkenhauer, A systems biology approach to investigate the effect of pH-induced gene regulation on solvent production by Clostridium acetobutylicum in continuous culture, BMC Systems Biology 5 (2011)) [1] both by using a different optimisation approach and by computing confidence intervals and correlation coefficients. We find in particular that the parameters are ill-determined from the data and that two separate clusters of parameters appear correlated, reflecting the importance of two metabolic intermediates. We extend the model further to include another aspect of the clostridial survival mechanism, sporulation, and by computation of the Akaike Information Criterion values find that the there is some evidence for the presence of sporulation during the shift

Background modelling of diffraction data in the presence of ice rings

An algorithm for modelling the background for each Bragg reflection in a series of X-ray diffraction images containing Debye–Scherrer diffraction from ice in the sample is presented. The method involves the use of a global background model which is generated from the complete X-ray diffraction data set. Fitting of this model to the background pixels is then performed for each reflection independently. The algorithm uses a static background model that does not vary over the course of the scan. The greatest improvement can be expected for data where ice rings are present throughout the data set and the local background shape at the size of a spot on the detector does not exhibit large time-dependent variation. However, the algorithm has been applied to data sets whose background showed large pixel variations (variance/mean > 2) and has been shown to improve the results of processing for these data sets. It is shown that the use of a simple flat-background model as in traditional integration programs causes systematic bias in the background determination at ice-ring resolutions, resulting in an overestimation of reflection intensities at the peaks of the ice rings and an underestimation of reflection intensities either side of the ice ring. The new global background-model algorithm presented here corrects for this bias, resulting in a noticeable improvement in R factors following refinement