Imaging of Functional Connectivity in the Mouse Brain

Functional neuroimaging (e.g., with fMRI) has been difficult to perform in mice, making it challenging to translate between human fMRI studies and molecular and genetic mechanisms. A method to easily perform large-scale functional neuroimaging in mice would enable the discovery of functional correlates of genetic manipulations and bridge with mouse models of disease. To satisfy this need, we combined resting-state functional connectivity mapping with optical intrinsic signal imaging (fcOIS). We demonstrate functional connectivity in mice through highly detailed fcOIS mapping of resting-state networks across most of the cerebral cortex. Synthesis of multiple network connectivity patterns through iterative parcellation and clustering provides a comprehensive map of the functional neuroarchitecture and demonstrates identification of the major functional regions of the mouse cerebral cortex. The method relies on simple and relatively inexpensive camera-based equipment, does not require exogenous contrast agents and involves only reflection of the scalp (the skull remains intact) making it minimally invasive. In principle, fcOIS allows new paradigms linking human neuroscience with the power of molecular/genetic manipulations in mouse models

Network Inference Algorithms Elucidate Nrf2 Regulation of Mouse Lung Oxidative Stress

A variety of cardiovascular, neurological, and neoplastic conditions have been associated with oxidative stress, i.e., conditions under which levels of reactive oxygen species (ROS) are elevated over significant periods. Nuclear factor erythroid 2-related factor (Nrf2) regulates the transcription of several gene products involved in the protective response to oxidative stress. The transcriptional regulatory and signaling relationships linking gene products involved in the response to oxidative stress are, currently, only partially resolved. Microarray data constitute RNA abundance measures representing gene expression patterns. In some cases, these patterns can identify the molecular interactions of gene products. They can be, in effect, proxies for protein–protein and protein–DNA interactions. Traditional techniques used for clustering coregulated genes on high-throughput gene arrays are rarely capable of distinguishing between direct transcriptional regulatory interactions and indirect ones. In this study, newly developed information-theoretic algorithms that employ the concept of mutual information were used: the Algorithm for the Reconstruction of Accurate Cellular Networks (ARACNE), and Context Likelihood of Relatedness (CLR). These algorithms captured dependencies in the gene expression profiles of the mouse lung, allowing the regulatory effect of Nrf2 in response to oxidative stress to be determined more precisely. In addition, a characterization of promoter sequences of Nrf2 regulatory targets was conducted using a Support Vector Machine classification algorithm to corroborate ARACNE and CLR predictions. Inferred networks were analyzed, compared, and integrated using the Collective Analysis of Biological Interaction Networks (CABIN) plug-in of Cytoscape. Using the two network inference algorithms and one machine learning algorithm, a number of both previously known and novel targets of Nrf2 transcriptional activation were identified. Genes predicted as novel Nrf2 targets include Atf1, Srxn1, Prnp, Sod2, Als2, Nfkbib, and Ppp1r15b. Furthermore, microarray and quantitative RT-PCR experiments following cigarette-smoke-induced oxidative stress in Nrf2+/+ and Nrf2−/− mouse lung affirmed many of the predictions made. Several new potential feed-forward regulatory loops involving Nrf2, Nqo1, Srxn1, Prdx1, Als2, Atf1, Sod1, and Park7 were predicted. This work shows the promise of network inference algorithms operating on high-throughput gene expression data in identifying transcriptional regulatory and other signaling relationships implicated in mammalian disease

Search for new phenomena in final states with an energetic jet and large missing transverse momentum in pp collisions at √ s = 8 TeV with the ATLAS detector

Results of a search for new phenomena in final states with an energetic jet and large missing transverse momentum are reported. The search uses 20.3 fb−1 of √ s = 8 TeV data collected in 2012 with the ATLAS detector at the LHC. Events are required to have at least one jet with pT > 120 GeV and no leptons. Nine signal regions are considered with increasing missing transverse momentum requirements between Emiss T > 150 GeV and Emiss T > 700 GeV. Good agreement is observed between the number of events in data and Standard Model expectations. The results are translated into exclusion limits on models with either large extra spatial dimensions, pair production of weakly interacting dark matter candidates, or production of very light gravitinos in a gauge-mediated supersymmetric model. In addition, limits on the production of an invisibly decaying Higgs-like boson leading to similar topologies in the final state are presente

Multimodal Functional Network Connectivity: An EEG-fMRI Fusion in Network Space

EEG and fMRI recordings measure the functional activity of multiple coherent networks distributed in the cerebral cortex. Identifying network interaction from the complementary neuroelectric and hemodynamic signals may help to explain the complex relationships between different brain regions. In this paper, multimodal functional network connectivity (mFNC) is proposed for the fusion of EEG and fMRI in network space. First, functional networks (FNs) are extracted using spatial independent component analysis (ICA) in each modality separately. Then the interactions among FNs in each modality are explored by Granger causality analysis (GCA). Finally, fMRI FNs are matched to EEG FNs in the spatial domain using network-based source imaging (NESOI). Investigations of both synthetic and real data demonstrate that mFNC has the potential to reveal the underlying neural networks of each modality separately and in their combination. With mFNC, comprehensive relationships among FNs might be unveiled for the deep exploration of neural activities and metabolic responses in a specific task or neurological state

Functional Disconnection and Compensation in Mild Cognitive Impairment: Evidence from DLPFC Connectivity Using Resting-State fMRI

The known regional abnormality of the dorsolateral prefrontal cortex (DLPFC) and its role in various neural circuits in mild cognitive impairment (MCI) has given prominence to its importance in studies on the disconnection associated with MCI. The purpose of the current study was to examine the DLPFC functional connectivity patterns during rest in MCI patients and the impact of regional grey matter (GM) atrophy on the functional results. Structural and functional MRI data were collected from 14 MCI patients and 14 age, gender-matched healthy controls. We found that both the bilateral DLPFC showed reduced functional connectivity with the inferior parietal lobule (IPL), superior/medial frontal gyrus and sub-cortical regions (e.g., thalamus, putamen) in MCI patients when compared with healthy controls. Moreover, the DLPFC connectivity with the IPL and thalamus significantly correlated with the cognitive performance of patients as measured by mini-mental state examination (MMSE), clock drawing test (CDT), and California verbal learning test (CVLT) scores. When taking GM atrophy as covariates, these results were approximately consistent with those without correction, although there may be a decrease in the statistical power. These results suggest that the DLPFC disconnections may be the substrates of cognitive impairments in MCI patients. In addition, we also found enhanced functional connectivity between the left DLPFC and the right prefrontal cortex in MCI patients. This is consistent with previous findings of MCI-related increased activation during cognitive tasks, and may represent a compensatory mechanism in MCI patients. Together, the present study demonstrated the coexistence of functional disconnection and compensation in MCI patients using DLPFC functional connectivity analysis, and thus might provide insights into biological mechanism of the disease

Driving and Driven Architectures of Directed Small-World Human Brain Functional Networks

Recently, increasing attention has been focused on the investigation of the human brain connectome that describes the patterns of structural and functional connectivity networks of the human brain. Many studies of the human connectome have demonstrated that the brain network follows a small-world topology with an intrinsically cohesive modular structure and includes several network hubs in the medial parietal regions. However, most of these studies have only focused on undirected connections between regions in which the directions of information flow are not taken into account. How the brain regions causally influence each other and how the directed network of human brain is topologically organized remain largely unknown. Here, we applied linear multivariate Granger causality analysis (GCA) and graph theoretical approaches to a resting-state functional MRI dataset with a large cohort of young healthy participants (n = 86) to explore connectivity patterns of the population-based whole-brain functional directed network. This directed brain network exhibited prominent small-world properties, which obviously improved previous results of functional MRI studies showing weak small-world properties in the directed brain networks in terms of a kernel-based GCA and individual analysis. This brain network also showed significant modular structures associated with 5 well known subsystems: fronto-parietal, visual, paralimbic/limbic, subcortical and primary systems. Importantly, we identified several driving hubs predominantly located in the components of the attentional network (e.g., the inferior frontal gyrus, supplementary motor area, insula and fusiform gyrus) and several driven hubs predominantly located in the components of the default mode network (e.g., the precuneus, posterior cingulate gyrus, medial prefrontal cortex and inferior parietal lobule). Further split-half analyses indicated that our results were highly reproducible between two independent subgroups. The current study demonstrated the directions of spontaneous information flow and causal influences in the directed brain networks, thus providing new insights into our understanding of human brain functional connectome

Connectivity of the Primate Superior Colliculus Mapped by Concurrent Microstimulation and Event-Related fMRI

Background: Neuroanatomical studies investigating the connectivity of brain areas have heretofore employed procedures in which chemical or viral tracers are injected into an area of interest, and connected areas are subsequently identified using histological techniques. Such experiments require the sacrifice of the animals and do not allow for subsequent electrophysiological studies in the same subjects, rendering a direct investigation of the functional properties of anatomically identified areas impossible. Methodology/Principal Findings: Here, we used a combination of microstimulation and fMRI in an anesthetized monkey preparation to study the connectivity of the superior colliculus (SC). Microstimulation of the SC resulted in changes in the blood oxygenation level-dependent (BOLD) signals in the SC and in several cortical and subcortical areas consistent with the known connectivity of the SC in primates. Conclusions/Significance: These findings demonstrates that the concurrent use of microstimulation and fMRI can be used to identify brain networks for further electrophysiological or fMRI investigation

Application of the PM6 semi-empirical method to modeling proteins enhances docking accuracy of AutoDock

<p>Abstract</p> <p>Background</p> <p>Molecular docking methods are commonly used for predicting binding modes and energies of ligands to proteins. For accurate complex geometry and binding energy estimation, an appropriate method for calculating partial charges is essential. AutoDockTools software, the interface for preparing input files for one of the most widely used docking programs AutoDock 4, utilizes the Gasteiger partial charge calculation method for both protein and ligand charge calculation. However, it has already been shown that more accurate partial charge calculation - and as a consequence, more accurate docking- can be achieved by using quantum chemical methods. For docking calculations quantum chemical partial charge calculation as a routine was only used for ligands so far. The newly developed Mozyme function of MOPAC2009 allows fast partial charge calculation of proteins by quantum mechanical semi-empirical methods. Thus, in the current study, the effect of semi-empirical quantum-mechanical partial charge calculation on docking accuracy could be investigated.</p> <p>Results</p> <p>The docking accuracy of AutoDock 4 using the original AutoDock scoring function was investigated on a set of 53 protein ligand complexes using Gasteiger and PM6 partial charge calculation methods. This has enabled us to compare the effect of the partial charge calculation method on docking accuracy utilizing AutoDock 4 software. Our results showed that the docking accuracy in regard to complex geometry (docking result defined as accurate when the RMSD of the first rank docking result complex is within 2 Å of the experimentally determined X-ray structure) significantly increased when partial charges of the ligands and proteins were calculated with the semi-empirical PM6 method.</p> <p>Out of the 53 complexes analyzed in the course of our study, the geometry of 42 complexes were accurately calculated using PM6 partial charges, while the use of Gasteiger charges resulted in only 28 accurate geometries. The binding affinity estimation was not influenced by the partial charge calculation method - for more accurate binding affinity prediction development of a new scoring function for AutoDock is needed.</p> <p>Conclusion</p> <p>Our results demonstrate that the accuracy of determination of complex geometry using AutoDock 4 for docking calculation greatly increases with the use of quantum chemical partial charge calculation on both the ligands and proteins.</p

Magnetic Resonance Imaging in Huntington's Disease.

Magnetic resonance imaging (MRI) is a noninvasive technique used routinely to image the body in both clinical and research settings. Through the manipulation of radio waves and static field gradients, MRI uses the principle of nuclear magnetic resonance to produce images with high spatial resolution, appropriate for the investigation of brain structure and function

Defining functional diversity for lignocellulose degradation in a microbial community using multi-omics studies

Abstract\ud \ud Background\ud Lignocellulose is one of the most abundant forms of fixed carbon in the biosphere. Current industrial approaches to the degradation of lignocellulose employ enzyme mixtures, usually from a single fungal species, which are only effective in hydrolyzing polysaccharides following biomass pre-treatments. While the enzymatic mechanisms of lignocellulose degradation have been characterized in detail in individual microbial species, the microbial communities that efficiently breakdown plant materials in nature are species rich and secrete a myriad of enzymes to perform “community-level” metabolism of lignocellulose. Single-species approaches are, therefore, likely to miss important aspects of lignocellulose degradation that will be central to optimizing commercial processes.\ud \ud \ud Results\ud Here, we investigated the microbial degradation of wheat straw in liquid cultures that had been inoculated with wheat straw compost. Samples taken at selected time points were subjected to multi-omics analysis with the aim of identifying new microbial mechanisms for lignocellulose degradation that could be applied in industrial pre-treatment of feedstocks. Phylogenetic composition of the community, based on sequenced bacterial and eukaryotic ribosomal genes, showed a gradual decrease in complexity and diversity over time due to microbial enrichment. Taxonomic affiliation of bacterial species showed dominance of Bacteroidetes and Proteobacteria and high relative abundance of genera Asticcacaulis, Leadbetterella and Truepera. The eukaryotic members of the community were enriched in peritrich ciliates from genus Telotrochidium that thrived in the liquid cultures compared to fungal species that were present in low abundance. A targeted metasecretome approach combined with metatranscriptomics analysis, identified 1127 proteins and showed the presence of numerous carbohydrate-active enzymes extracted from the biomass-bound fractions and from the culture supernatant. This revealed a wide array of hydrolytic cellulases, hemicellulases and carbohydrate-binding modules involved in lignocellulose degradation. The expression of these activities correlated to the changes in the biomass composition observed by FTIR and ssNMR measurements.\ud \ud \ud Conclusions\ud A combination of mass spectrometry-based proteomics coupled with metatranscriptomics has enabled the identification of a large number of lignocellulose degrading enzymes that can now be further explored for the development of improved enzyme cocktails for the treatment of plant-based feedstocks. In addition to the expected carbohydrate-active enzymes, our studies reveal a large number of unknown proteins, some of which may play a crucial role in community-based lignocellulose degradation.This work was funded by Biotechnology and Biological Sciences Research\ud Council (BBSRC) Grants BB/1018492/1, BB/K020358/1 and BB/P027717/1, the\ud BBSRC Network in Biotechnology and Bioenergy BIOCATNET and São Paulo\ud Research Foundation (FAPESP) Grant 10/52362-5. ERdA thanks EMBRAPA\ud Instrumentation São Carlos and Dr. Luiz Alberto Colnago for providing the\ud NMR facility and CNPq Grant 312852/2014-2. The authors would like to thank\ud Deborah Rathbone and Susan Heywood from the Biorenewables Develop‑\ud ment Centre for technical assistance in rRNA amplicon sequencing