Revealing Pathway Dynamics in Heart Diseases by Analyzing Multiple Differential Networks

Development of heart diseases is driven by dynamic changes in both the activity and connectivity of gene pathways. Understanding these dynamic events is critical for understanding pathogenic mechanisms and development of effective treatment. Currently, there is a lack of computational methods that enable analysis of multiple gene networks, each of which exhibits differential activity compared to the network of the baseline/healthy condition. We describe the iMDM algorithm to identify both unique and shared gene modules across multiple differential co-expression networks, termed M-DMs (multiple differential modules). We applied iMDM to a time-course RNA-Seq dataset generated using a murine heart failure model generated on two genotypes. We showed that iMDM achieves higher accuracy in inferring gene modules compared to using single or multiple co-expression networks. We found that condition-specific M-DMs exhibit differential activities, mediate different biological processes, and are enriched for genes with known cardiovascular phenotypes. By analyzing M-DMs that are present in multiple conditions, we revealed dynamic changes in pathway activity and connectivity across heart failure conditions. We further showed that module dynamics were correlated with the dynamics of disease phenotypes during the development of heart failure. Thus, pathway dynamics is a powerful measure for understanding pathogenesis. iMDM provides a principled way to dissect the dynamics of gene pathways and its relationship to the dynamics of disease phenotype. With the exponential growth of omics data, our method can aid in generating systems-level insights into disease progression.     

Topographical Estimation of Visual Population Receptive Fields by fMRI

Visual cortex is retinotopically organized so that neighboring populations of cells map to neighboring parts of the visual field. Functional magnetic resonance imaging allows us to estimate voxel-based population receptive fields (pRF), i.e., the part of the visual field that activates the cells within each voxel. Prior, direct, pRF estimation methods¹ suffer from certain limitations: 1) the pRF model is chosen a-priori and may not fully capture the actual pRF shape, and 2) pRF centers are prone to mislocalization near the border of the stimulus space. Here a new topographical pRF estimation method² is proposed that largely circumvents these limitations. A linear model is used to predict the Blood Oxygen Level-Dependent (BOLD) signal by convolving the linear response of the pRF to the visual stimulus with the canonical hemodynamic response function. PRF topography is represented as a weight vector whose components represent the strength of the aggregate response of voxel neurons to stimuli presented at different visual field locations. The resulting linear equations can be solved for the pRF weight vector using ridge regression³, yielding the pRF topography. A pRF model that is matched to the estimated topography can then be chosen post-hoc, thereby improving the estimates of pRF parameters such as pRF-center location, pRF orientation, size, etc. Having the pRF topography available also allows the visual verification of pRF parameter estimates allowing the extraction of various pRF properties without having to make a-priori assumptions about the pRF structure. This approach promises to be particularly useful for investigating the pRF organization of patients with disorders of the visual system.     

Endo-β-N-acetylglucosamidases (ENGases) in the fungus Trichoderma atroviride: Possible involvement of the filamentous fungi-specific cytosolic ENGase in the ERAD process

N-Glycosylation is an important post-translational modification of proteins, which mainly occurs in the endoplasmic reticulum (ER). Glycoproteins that are unable to fold properly are exported to the cytosol for degradation by a cellular system called ER-associated degradation (ERAD). Once misfolded glycoproteins are exported to the cytosol, they are subjected to deglycosylation by peptide:N-glycanase (PNGase) to facilitate the efficient degradation of misfolded proteins by the proteasome. Interestingly, the ortholog of PNGase in some filamentous fungi was found to be an inactive deglycosylating enzyme. On the other hand, it has been shown that in filamentous fungi genomes, usually two different fungi-specific endo-β-N-acetylglucosamidases (ENGases) can be found; one is predicted to be localized in the cytosol and the other to have a signal sequence, while the functional importance of these enzymes remains to be clarified. In this study the ENGases of the filamentous fungus Trichoderma atroviride was characterized. By heterologous expression of the ENGases Eng18A and Eng18B in Saccharomyces cerevisiae, it was found that both ENGases are active deglycosylating enzymes. Interestingly, only Eng18B was able to enhance the efficient degradation of the RTL protein, a PNGase-dependent ERAD substrate, implying the involvement of this enzyme in the ERAD process. These results indicate that T. atroviride Eng18B may deglycosylate misfolded glycoproteins, substituting the function of the cytoplasmic PNGase in the ERAD process.     

Evidence that Aspartate Aminotransferase Activity and Ketodicarboxylate Carrier Function Are Essential for Biosynthesis of Transmitter Glutamate

Based on the selective inhibition of glutamate release in cerebellar granule cells in primary cultures by the aspartate aminotransferase inhibitor, aminooxyacetic acid, and by the ketodicarboxylate carrier inhibitor, phenylsuccinate, a novel model for synthesis of transmitter glutamate is suggested: Glutamate is formed from glutamine in the mitochondrial intramembrane space by phosphate-activated glutaminase, transported across the inner membrane in exchange with aspartate, transaminated in the matrix to alpha-ketoglutarate, which via the ketodicarboxylate carrier is transferred to the cytoplasm, and transaminated to form transmitter glutamate. Such a mechanism would explain the functional role of aspartate aminotransferase in glutamatergic neurons.     

Determination of volatile substances in olives and their effect on reproduction of the olive fruit fly

Females of the olive fruit fly Bactrocera oleae lay their eggs in olives mainly using fruit volatile stimuli. Using GC-MS analysis, we determined the chemical composition of the volatile blend emitted from field-collected olive fruit of cv. Megaritiki, at different stages of maturity. GC-MS analysis demonstrated qualitative and quantitative differences in the headspace blend emitted by the olive fruit. Certain chemicals such as toluene, n-octane, α-pinene, limonene, ethyl hexanol, nonanal n-dodecane, decanal and n-tetradecane were detected in greater amounts, irrespective of the growth stage of the fruit. The flies’ exposure to a number of these chemicals, such as n-octane and α-pinene, as well as to a mixture consisting of n-octane, α-pinene, limonene, ethyl hexanol, nonanal, n-dodecane, decanal and n–tetradecane favoured successful mating and egg production. The results may contribute to the improvement of the mass rearing of the fly, through these findings, which illustrate the positive effect that certain fruit volatile chemicals have on the fly's reproduction and offer a better understanding of the relation between the fly and the host fruit.     

Data-driven flow-map models for data-efficient discovery of dynamics and fast uncertainty quantification of biological and biochemical systems

Computational models are increasingly used to investigate and predict the complex dynamics of biological and biochemical systems. Nevertheless, governing equations of a biochemical system may not be (fully) known, which would necessitate learning the system dynamics directly from, often limited and noisy, observed data. On the other hand, when expensive models are available, systematic and efficient quantification of the effects of model uncertainties on quantities of interest can be an arduous task. This paper leverages the notion of flow-map (de)compositions to present a framework that can address both of these challenges via learning data-driven models useful for capturing the dynamical behavior of biochemical systems. Data-driven flow-map models seek to directly learn the integration operators of the governing differential equations in a black-box manner, irrespective of structure of the underlying equations. As such, they can serve as a flexible approach for deriving fast-to-evaluate surrogates for expensive computational models of system dynamics, or, alternatively, for reconstructing the long-term system dynamics via experimental observations. We present a data-efficient approach to data-driven flow-map modeling based on polynomial chaos Kriging. The approach is demonstrated for discovery of the dynamics of various benchmark systems and a coculture bioreactor subject to external forcing, as well as for uncertainty quantification of a microbial electrosynthesis reactor. Such data-driven models and analyses of dynamical systems can be paramount in the design and optimization of bioprocesses and integrated biomanufacturing systems.     

Phosphorylation at Ser244 by CK1 determines nuclear localization and substrate targeting of PKD2

Protein kinase D2 (PKD2), a member of the PKD family of serine/threonine kinases, is localized in various subcellular compartments including the nucleus where the kinase accumulates upon activation of G-protein-coupled receptors. We define three critical post-translational modifications required for nuclear accumulation of PKD2 in response to activation of the CCK2 receptor (CCK2R): phosphorylation at Ser706 and Ser710 within the activation loop by PKC eta leading to catalytic activity and phosphorylation at Ser244 within the zinc-finger domain, which is crucial for blocking nuclear export of active PKD2 by preventing its interaction with the Crm-1 export machinery. We identify CK1delta and epsilon as upstream activated kinases by CCK2R that phosphorylate PKD2 at Ser244. Moreover, nuclear accumulation of active PKD2 is a prerequisite for efficient phosphorylation of its nuclear substrate, HDAC7. Only nuclear, active PKD2 mediates CCK2R-induced HDAC7 phosphorylation and Nur77 expression. Thus, we define a novel, compartment-specific signal transduction pathway downstream of CCK2R that phosphorylates PKD2 at three specific sites, results in nuclear accumulation of the active kinase and culminates in efficient phosphorylation of nuclear PKD2 substrates in human gastric cancer cells.