Multiomic Metabolic Enrichment Network Analysis Reveals Metabolite–Protein Physical Interaction Subnetworks Altered in Cancer

Metabolism is recognized as an important driver of cancer progression and other complex diseases, but global metabolite profiling remains a challenge. Protein expression profiling is often a poor proxy since existing pathway enrichment models provide an incomplete mapping between the proteome and metabolism. To overcome these gaps, we introduce multiomic metabolic enrichment network analysis (MOMENTA), an integrative multiomic data analysis framework for more accurately deducing metabolic pathway changes from proteomics data alone in a gene set analysis context by leveraging protein interaction networks to extend annotated metabolic models. We apply MOMENTA to proteomic data from diverse cancer cell lines and human tumors to demonstrate its utility at revealing variation in metabolic pathway activity across cancer types, which we verify using independent metabolomics measurements. The novel metabolic networks we uncover in breast cancer and other tumors are linked to clinical outcomes, underscoring the pathophysiological relevance of the findings.     

Ionotropic Receptors as a Driving Force behind Human Synapse Establishment

The origin of nervous systems is a main theme in biology and its mechanisms are largely underlied by synaptic neurotransmission. One problem to explain synapse establishment is that synaptic orthologs are present in multiple aneural organisms. We questioned how the interactions among these elements evolved and to what extent it relates to our understanding of the nervous systems complexity. We identified the human neurotransmission gene network based on genes present in GABAergic, glutamatergic, serotonergic, dopaminergic, and cholinergic systems. The network comprises 321 human genes, 83 of which act exclusively in the nervous system. We reconstructed the evolutionary scenario of synapse emergence by looking for synaptic orthologs in 476 eukaryotes. The Human–Cnidaria common ancestor displayed a massive emergence of neuroexclusive genes, mainly ionotropic receptors, which might have been crucial to the evolution of synapses. Very few synaptic genes had their origin after the Human–Cnidaria common ancestor. We also identified a higher abundance of synaptic proteins in vertebrates, which suggests an increase in the synaptic network complexity of those organisms.     

Using Echo Ultrasound from Schooling Fish to Detect and Classify Fish Types

Fish finders have already been widely available in the fishing market for a number of years.However,the sizes of these fishfinders are too big and their prices are expensive to suit for the research of robotic fish or mini-submarine.The goal of thisresearch is to propose a low-cost fish detector and classifier which suits for underwater robot or submarine as a proximity sensor.With some pre-condition in hardware and algorithms,the experimental results show that the proposed design has good per-formance,with a detection rate of 100 % and a classification rate of 94 %.Both the existing type of fish and the group behaviorcan be revealed by statistical interpretations such as hovering passion and sparse swimming mode.     

On the Stability of Clathrate Hydrates Encaging Polar Guest Molecules: Contrast in the Hydrogen Bonds of Methylamine and Methanol Hydrates

Abstract

The stability of clathrate hydrates encaging highly polar guests has been investigated in order to explain the experimental observation that some amines form clathrate hydrates but alcohols act as inhibitor to hydrate formation. We choose methylamine and methanol as guest species and examine the stable structure, at which the total potential energy has a minimum value. At the local minima of those two hydrates, the potential energies of water-water and guest-water, and their hydrogen bonded networks are compared. It is found that methanol does not retain the host lattice structure, while the host-network structure is kept in the presence of methylamine. It is shown that the difference in the magnitude of the partial charge on the hydrogen atom between the hydroxyl and amino groups plays a much more significant role on the stability of both clathrate hydrates than the difference in molecular geometry. This is supported from the result of a methylamine-like model that has the same partial charges on the atoms in the hydrophilic site as methanol.     

Novel Monte Carlo dose calculation algorithm for robotic radiosurgery with multi leaf collimator: Dosimetric evaluation

PurposeAt introduction in 2014, dose calculation for the first MLC on a robotic SRS/SBRT platform was limited to a correction-based Finite-Size Pencil Beam (FSPB) algorithm. We report on the dosimetric accuracy of a novel Monte Carlo (MC) dose calculation algorithm for this MLC, included in the Precision™ treatment planning system.MethodsA phantom was built of one slab (5.0 cm) of lung-equivalent material (0.09…0.29 g/cc) enclosed by 3.5 cm (above) and 5 cm (below) slabs of solid water (1.045 g/cc). This was irradiated using rectangular (15.4 × 15.4 mm² to 53.8 × 53.7 mm²) and two irregular MLC-fields. Radiochromic film (EBT3) was positioned perpendicular to the slabs and parallel to the beam. Calculated dose distributions were compared to film measurements using line scans and 2D gamma analysis.ResultsMeasured and MC calculated percent depth dose curves showed a characteristic dose drop within the low-density region, which was not correctly reproduced by FSPB. Superior average gamma pass rates (2%/1 mm) were found for MC (91.2 ± 1.5%) compared to FSPB (55.4 ± 2.7%). However, MC calculations exhibited localized anomalies at mass density transitions around 0.15 g/cc, which were traced to a simplification in electron transport. Absence of these anomalies was confirmed in a modified build of the MC engine, which increased gamma pass rates to 96.6 ± 1.2%.ConclusionsThe novel MC algorithm greatly improves dosimetric accuracy in heterogeneous tissue, potentially expanding the clinical use of robotic radiosurgery with MLC. In-depth, independent validation is paramount to identify and reduce the residual uncertainties in any software solution.     

Solution Structure of the HIV-1 Intron Splicing Silencer and Its Interactions with the UP1 Domain of Heterogeneous Nuclear Ribonucleoprotein (hnRNP) A1

Splicing patterns in human immunodeficiency virus type 1 (HIV-1) are maintained through cis regulatory elements that recruit antagonistic host RNA-binding proteins. The activity of the 3′ acceptor site A7 is tightly regulated through a complex network of an intronic splicing silencer (ISS), a bipartite exonic splicing silencer (ESS3a/b), and an exonic splicing enhancer (ESE3). Because HIV-1 splicing depends on protein-RNA interactions, it is important to know the tertiary structures surrounding the splice sites. Herein, we present the NMR solution structure of the phylogenetically conserved ISS stem loop. ISS adopts a stable structure consisting of conserved UG wobble pairs, a folded 2X2 (GU/UA) internal loop, a UU bulge, and a flexible AGUGA apical loop. Calorimetric and biochemical titrations indicate that the UP1 domain of heterogeneous nuclear ribonucleoprotein A1 binds the ISS apical loop site-specifically and with nanomolar affinity. Collectively, this work provides additional insights into how HIV-1 uses a conserved RNA structure to commandeer a host RNA-binding protein.     

Constructing more informative plant–pollinator networks: visitation and pollen deposition networks in a heathland plant community

Interaction networks are widely used as tools to understand plant–pollinator communities, and to examine potential threats to plant diversity and food security if the ecosystem service provided by pollinating animals declines. However, most networks to date are based on recording visits to flowers, rather than recording clearly defined effective pollination events. Here we provide the first networks that explicitly incorporate measures of pollinator effectiveness (PE) from pollen deposition on stigmas per visit, and pollinator importance (PI) as the product of PE and visit frequency. These more informative networks, here produced for a low diversity heathland habitat, reveal that plant–pollinator interactions are more specialized than shown in most previous studies. At the studied site, the specialization index was lower for the visitation network than the PE network, which was in turn lower than for the PI network. Our study shows that collecting PE data is feasible for community-level studies in low diversity communities and that including information about PE can change the structure of interaction networks. This could have important consequences for our understanding of threats to pollination systems.     

pathmatrix: a geographical information system tool to compute effective distances among samples

pathmatrix is a tool used to compute matrices of effective geographical distances among samples using a least‐cost path algorithm. This program is dedicated to the study of the role of the environment on the spatial genetic structure of populations. Punctual locations (e.g. individuals) or zones encompassing sample data points (e.g. demes) are used in conjunction with a species‐specific friction map representing the cost of movement through the landscape. Matrices of effective distances can then be exported to population genetic software to test, for example, for isolation by distance. pathmatrix is an extension to the geographical information system (GIS) software arcview 3.x.