Multi-scale approach for the prediction of atomic scale properties.
Ontology highlight
ABSTRACT:
SUBMITTER: Grisafi A
PROVIDER: S-EPMC8179303 | biostudies-literature |
REPOSITORIES: biostudies-literature
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Project description:An increasingly common method for predicting gene activity is genome-wide chromatin immunoprecipitation of M-bM-^@M-^XactiveM-bM-^@M-^Y chromatin modifications followed by massively parallel sequencing (ChIP-seq). Using a novel ChIP-seq quantification method (cRPKM), we tested the power of such ChIP-seq strategies to predict relative protein and RNA levels at the pre-pro-B and pro-B differentiation stages in early B cell lymphopoiesis. Using a multi-omics approach that compares promoter chromatin status (ChIP-seq; published in GSE:21978) with ongoing active transcription (GRO-seq; published in GSE:40173), steady state mRNA (RNA-seq), inferred mRNA stability, and relative proteome abundance measurements (iTRAQ), we demonstrate that active chromatin modifications at promoters are a good indicator of transcription and steady state mRNA levels. Moreover, we found that promoters with active chromatin modifications exclusively in one of these cell states frequently predicted differentially expressed proteins. However, we found that many genes whose promoters have non-differential but active chromatin modifications also displayed changes in expression of their cognate proteins. This large class of developmentally and differentially regulated proteins that was uncoupled from chromatin status used mostly post-transcriptional mechanisms. Interestingly, the most differentially expressed protein in our B-cell development system, 2410004B18Rik, was regulated by a post-transcriptional mechanism, which further analyses indicated was mediated by an identified miRNA. These data provide a striking example of how our integrated multi-omics data set can be useful in uncovering regulatory mechanisms. Total RNA from mouse pre-pro-B and pro-B cells, depleted of rRNA and small RNAs, was sequenced using a strand specific, single end sequencing strategy.
2013-11-19 | E-GEOD-52450 | biostudies-arrayexpress
Project description:[Figure: see text].
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Project description:An increasingly common method for predicting gene activity is genome-wide chromatin immunoprecipitation of ‘active’ chromatin modifications followed by massively parallel sequencing (ChIP-seq). Using a novel ChIP-seq quantification method (cRPKM), we tested the power of such ChIP-seq strategies to predict relative protein and RNA levels at the pre-pro-B and pro-B differentiation stages in early B cell lymphopoiesis. Using a multi-omics approach that compares promoter chromatin status (ChIP-seq; published in GSE:21978) with ongoing active transcription (GRO-seq; published in GSE:40173), steady state mRNA (RNA-seq), inferred mRNA stability, and relative proteome abundance measurements (iTRAQ), we demonstrate that active chromatin modifications at promoters are a good indicator of transcription and steady state mRNA levels. Moreover, we found that promoters with active chromatin modifications exclusively in one of these cell states frequently predicted differentially expressed proteins. However, we found that many genes whose promoters have non-differential but active chromatin modifications also displayed changes in expression of their cognate proteins. This large class of developmentally and differentially regulated proteins that was uncoupled from chromatin status used mostly post-transcriptional mechanisms. Interestingly, the most differentially expressed protein in our B-cell development system, 2410004B18Rik, was regulated by a post-transcriptional mechanism, which further analyses indicated was mediated by an identified miRNA. These data provide a striking example of how our integrated multi-omics data set can be useful in uncovering regulatory mechanisms.
2013-11-19 | GSE52450 | GEO
Project description:An estimated 50% of depressed patients are inadequately treated by available interventions. Even with an eventual recovery, many patients require a trial and error approach, as there are no reliable guidelines to match patients to optimal treatments and many patients develop treatment resistance over time. This situation derives from the heterogeneity of depression and the lack of biomarkers for stratification by distinct depression subtypes. There is thus a dire need for novel therapies. To address these known challenges, we propose a multi-scale framework for fundamental research on depression, aimed at identifying the brain circuits that are dysfunctional in several animal models of depression as well the changes in gene expression that are associated with these models. When combined with human genetic and imaging studies, our preclinical studies are starting to identify candidate circuits and molecules that are altered both in models of disease and in patient populations. Targeting these circuits and mechanisms can lead to novel generations of antidepressants tailored to specific patient populations with distinctive types of molecular and circuit dysfunction.
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Project description:Quantitative Susceptibility Mapping (QSM), best known as a surrogate for tissue iron content, is becoming a highly relevant MRI contrast for monitoring cellular and vascular status in aging, addiction, traumatic brain injury and, in general, a wide range of neurological disorders. In this study we present a new Bayesian QSM algorithm, named Multi-Scale Dipole Inversion (MSDI), which builds on the nonlinear Morphology-Enabled Dipole Inversion (nMEDI) framework, incorporating three additional features: (i) improved implementation of Laplace's equation to reduce the influence of background fields through variable harmonic filtering and subsequent deconvolution, (ii) improved error control through dynamic phase-reliability compensation across spatial scales, and (iii) scalewise use of the morphological prior. More generally, this new pre-conditioned QSM formalism aims to reduce the impact of dipole-incompatible fields and measurement errors such as flow effects, poor signal-to-noise ratio or other data inconsistencies that can lead to streaking and shadowing artefacts. In terms of performance, MSDI is the first algorithm to rank in the top-10 for all metrics evaluated in the 2016 QSM Reconstruction Challenge. It also demonstrated lower variance than nMEDI and more stable behaviour in scan-rescan reproducibility experiments for different MRI acquisitions at 3 and 7 Tesla. In the present work, we also explored new forms of susceptibility MRI contrast making explicit use of the differential information across spatial scales. Specifically, we show MSDI-derived examples of: (i) enhanced anatomical detail with susceptibility inversions from short-range dipole fields (hereby referred to as High-Pass Susceptibility Mapping or HPSM), (ii) high specificity to venous-blood susceptibilities for highly regularised HPSM (making a case for MSDI-based Venography or VenoMSDI), (iii) improved tissue specificity (and possibly statistical conditioning) for Macroscopic-Vessel Suppressed Susceptibility Mapping (MVSSM), and (iv) high spatial specificity and definition for HPSM-based Susceptibility-Weighted Imaging (HPSM-SWI) and related intensity projections.
| S-EPMC6215336 | biostudies-literature
Project description:Biodiversity loss--one of the most prominent forms of modern environmental change--has been heavily driven by terrestrial habitat loss and, in particular, the spread and intensification of agriculture. Expanding agricultural land-use has led to the search for strong conservation strategies, with some suggesting that biodiversity conservation in agriculture is best maximized by reducing local management intensity, such as fertilizer and pesticide application. Others highlight the importance of landscape-level approaches that incorporate natural or semi-natural areas in landscapes surrounding farms. Here, we show that both of these practices are valuable to the conservation of biodiversity, and that either local or landscape factors can be most crucial to conservation planning depending on which types of organisms one wishes to save. We performed a quantitative review of 266 observations taken from 31 studies that compared the impacts of localized (within farm) management strategies and landscape complexity (around farms) on the richness and abundance of plant, invertebrate and vertebrate species in agro-ecosystems. While both factors significantly impacted species richness, the richness of sessile plants increased with less-intensive local management, but did not significantly respond to landscape complexity. By contrast, the richness of mobile vertebrates increased with landscape complexity, but did not significantly increase with less-intensive local management. Invertebrate richness and abundance responded to both factors. Our analyses point to clear differences in how various groups of organisms respond to differing scales of management, and suggest that preservation of multiple taxonomic groups will require multiple scales of conservation.
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Project description:The degree to which we can understand the multi-scale organization of cellular life is tied to how well our models can represent this organization and the processes that drive its evolution. This paper uses Vivarium-an engine for composing heterogeneous computational biology models into integrated, multi-scale simulations. Vivarium's approach is demonstrated by combining several sub-models of biophysical processes into a model of chemotactic E. coli that exchange molecules with their environment, express the genes required for chemotaxis, swim, grow, and divide. This model is developed incrementally, highlighting cross-compartment mechanisms that link E. coli to its environment, with models for: (1) metabolism and transport, with transport moving nutrients across the membrane boundary and metabolism converting them to useful metabolites, (2) transcription, translation, complexation, and degradation, with stochastic mechanisms that read real gene sequence data and consume base pairs and ATP to make proteins and complexes, and (3) the activity of flagella and chemoreceptors, which together support navigation in the environment.
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Project description:Besides epitaxial mismatch that can be accommodated by lattice distortions and/or octahedral rotations, ferroelectric-ferromagnetic interfaces are affected by symmetry mismatch and subsequent magnetic ordering. Here, we have investigated La0.67 Sr0.33 MnO3 (LSMO) samples with varying underlying unit cells (uc) of BaTiO3 (BTO) layer on (001) and (110) oriented substrates in order to elucidate the role of symmetry mismatch. Lattice mismatch for 3 uc of BTO and symmetry mismatch for 10 uc of BTO, both associated with local MnO6 octahedral distortions of the (001) LSMO within the first few uc, are revealed by scanning transmission electron microscopy. Interestingly, we find exchange bias along the in-plane [110]/[100] directions only for the (001) oriented samples. Polarized neutron reflectivity measurements confirm the existence of a layer with zero net moment only within (001) oriented samples. First principle density functional calculations show that even though the bulk ground state of LSMO is ferromagnetic, a large lattice constant together with an excess of La can stabilize an antiferromagnetic LaMnO3-type phase at the interface region and explain the experimentally observed exchange bias. Atomic scale tuning of MnO6 octahedra can thus be made possible via symmetry mismatch at heteroepitaxial interfaces. This aspect can act as a vital parameter for structure-driven control of physical properties.
| S-EPMC5587576 | biostudies-literature
Project description:BackgroundThe set of indispensable genes that are required by an organism to grow and sustain life are termed as essential genes. There is a strong interest in identification of the set of essential genes, particularly in pathogens, not only for a better understanding of the pathogen biology, but also for identifying drug targets and the minimal gene set for the organism. Essentiality is inherently a systems property and requires consideration of the system as a whole for their identification. The available experimental approaches capture some aspects but each method comes with its own limitations. Moreover, they do not explain the basis for essentiality in most cases. A powerful prediction method to recognize this gene pool including rationalization of the known essential genes in a given organism would be very useful. Here we describe a multi-level multi-scale approach to identify the essential gene pool in a deadly pathogen, Mycobacterium tuberculosis.ResultsThe multi-level workflow analyses the bacterial cell by studying (a) genome-wide gene expression profiles to identify the set of genes which show consistent and significant levels of expression in multiple samples of the same condition, (b) indispensability for growth by using gene expression integrated flux balance analysis of a genome-scale metabolic model, (c) importance for maintaining the integrity and flow in a protein-protein interaction network and (d) evolutionary conservation in a set of genomes of the same ecological niche. In the gene pool identified, the functional basis for essentiality has been addressed by studying residue level conservation and the sub-structure at the ligand binding pockets, from which essential amino acid residues in that pocket have also been identified. 283 genes were identified as essential genes with high-confidence. An agreement of about 73.5% is observed with that obtained from the experimental transposon mutagenesis technique. A large proportion of the identified genes belong to the class of intermediary metabolism and respiration.ConclusionsThe multi-scale, multi-level approach described can be generally applied to other pathogens as well. The essential gene pool identified form a basis for designing experiments to probe their finer functional roles and also serve as a ready shortlist for identifying drug targets.
| S-EPMC4234997 | biostudies-literature