C-Jun N-terminal kinase controls TDP-43 accumulation in stress granules induced by oxidative stress

<p>Abstract</p> <p>Background</p> <p>TDP-43 proteinopathies are characterized by loss of nuclear TDP-43 expression and formation of C-terminal TDP-43 fragmentation and accumulation in the cytoplasm. Recent studies have shown that TDP-43 can accumulate in RNA stress granules (SGs) in response to cell stresses and this could be associated with subsequent formation of TDP-43 ubiquinated protein aggregates. However, the initial mechanisms controlling endogenous TDP-43 accumulation in SGs during chronic disease are not understood. In this study we investigated the mechanism of TDP-43 processing and accumulation in SGs in SH-SY5Y neuronal-like cells exposed to chronic oxidative stress. Cell cultures were treated overnight with the mitochondrial inhibitor paraquat and examined for TDP-43 and SG processing.</p> <p>Results</p> <p>We found that mild stress induced by paraquat led to formation of TDP-43 and HuR-positive SGs, a proportion of which were ubiquitinated. The co-localization of TDP-43 with SGs could be fully prevented by inhibition of c-Jun N-terminal kinase (JNK). JNK inhibition did not prevent formation of HuR-positive SGs and did not prevent diffuse TDP-43 accumulation in the cytosol. In contrast, ERK or p38 inhibition prevented formation of both TDP-43 and HuR-positive SGs. JNK inhibition also inhibited TDP-43 SG localization in cells acutely treated with sodium arsenite and reduced the number of aggregates per cell in cultures transfected with C-terminal TDP-43 162-414 and 219-414 constructs.</p> <p>Conclusions</p> <p>Our studies are the first to demonstrate a critical role for kinase control of TDP-43 accumulation in SGs and may have important implications for development of treatments for FTD and ALS, targeting cell signal pathway control of TDP-43 aggregation.</p

SDSS-IV MaNGA-resolved Star Formation and Molecular Gas Properties of Green Valley Galaxies: A First Look with ALMA and MaNGA

We study the role of cold gas in quenching star formation in the green valley by analyzing ALMA 12 CO (1-0) observations of three galaxies with resolved optical spectroscopy from the MaNGA survey. We present resolution-matched maps of the star formation rate and molecular gas mass. These data are used to calculate the star formation efficiency (SFE) and gas fraction (f gas ) for these galaxies separately in the central "bulge" regions and outer disks. We find that, for the two galaxies whose global specific star formation rate (sSFR) deviates most from the star formation main sequence, the gas fraction in the bulges is significantly lower than that in their disks, supporting an "inside-out" model of galaxy quenching. For the two galaxies where SFE can be reliably determined in the central regions, the bulges and disks share similar SFEs. This suggests that a decline in f gas is the main driver of lowered sSFR in bulges compared to disks in green valley galaxies. Within the disks, there exist common correlations between the sSFR and SFE and between sSFR and f gas on kiloparsec scales - the local SFE or f gas in the disks declines with local sSFR. Our results support a picture in which the sSFR in bulges is primarily controlled by f gas , whereas both SFE and f gas play a role in lowering the sSFR in disks. A larger sample is required to confirm if the trend established in this work is representative of the green valley as a whole.The work is supported by the Ministry of Science & Technology of Taiwan under the grant MOST 103-2112-M-001-031-MY3 and 106-2112-M-001-034. R.M. and F.B. acknowledge support by the UK Science and Technology Facilities Council (STFC). R.M. acknowledges ERC Advanced Grant 695671 "QUENCH.

Label-free, multi-scale imaging of ex-vivo mouse brain using spatial light interference microscopy

Brain connectivity spans over broad spatial scales, from nanometers to centimeters. In order to understand the brain at multi-scale, the neural network in wide-field has been visualized in detail by taking advantage of light microscopy. However, the process of staining or addition of fluorescent tags is commonly required, and the image contrast is insufficient for delineation of cytoarchitecture. To overcome this barrier, we use spatial light interference microscopy to investigate brain structure with high-resolution, sub-nanometer pathlength sensitivity without the use of exogenous contrast agents. Combining wide-field imaging and a mosaic algorithm developed in-house, we show the detailed architecture of cells and myelin, within coronal olfactory bulb and cortical sections, and from sagittal sections of the hippocampus and cerebellum. Our technique is well suited to identify laminar characteristics of fiber tract orientation within white matter, e.g. the corpus callosum. To further improve the macro-scale contrast of anatomical structures, and to better differentiate axons and dendrites from cell bodies, we mapped the tissue in terms of its scattering property. Based on our results, we anticipate that spatial light interference microscopy can potentially provide multiscale and multicontrast perspectives of gross and microscopic brain anatomy.ope

ACL injury prevention, more effective with a different way of motor learning?

What happens to the transference of learning proper jump-landing technique in isolation when an individual is expected to perform at a competitive level yet tries to maintain proper jump-landing technique? This is the key question for researchers, physical therapists, athletic trainers and coaches involved in ACL injury prevention in athletes. The need for ACL injury prevention is clear, however, in spite of these ongoing initiatives and reported early successes, ACL injury rates and the associated gender disparity have not diminished. One problem could be the difficulties with the measurements of injury rates and the difficulties with the implementation of thorough large scale injury prevention programs. A second issue could be the transition from conscious awareness during training sessions on technique in the laboratory to unexpected and automatic movements during a training or game involves complicated motor control adaptations. The purpose of this paper is to highlight the issue of motor learning in relation to ACL injury prevention and to post suggestions for future research. ACL injury prevention programs addressing explicit rules regarding desired landing positions by emphasizing proper alignment of the hip, knee, and ankle are reported in the literature. This may very well be a sensible way, but the use of explicit strategies may be less suitable for the acquisition of the control of complex motor skills (Maxwell et al. J Sports Sci 18:111-120, 2000). Sufficient literature on motor learning and it variations point in that direction

Local Adaptation of Aboveground Herbivores towards Plant Phenotypes Induced by Soil Biota

Background: Soil biota may trigger strong physiological responses in plants and consequently induce distinct phenotypes. Plant phenotype, in turn, has a strong impact on herbivore performance. Here, we tested the hypothesis that aboveground herbivores are able to adapt to plant phenotypes induced by soil biota. Methodology and Principal Findings: We bred spider mites for 15 generations on snap beans with three different belowground biotic interactions: (i) no biota (to serve as control), (ii) arbuscular mycorrhizal fungi and (ii) root-feeding nematodes. Subsequently, we conducted a reciprocal selection experiment using these spider mites, which had been kept on the differently treated plants. Belowground treatments induced changes in plant biomass, nutrient composition and water content. No direct chemical defence through cyanogenesis was detected in any of the plant groups. Growth rates of spider mites were higher on the ecotypes on which they were bred for 15 generations, although the statistical significance disappeared for mites from the nematode treatment when corrected for all multiple comparisons. Conclusion/Significance: These results demonstrate that belowground biota may indeed impose selection on the aboveground insect herbivores mediated by the host plant. The observed adaptation was driven by variable quantitativ

Whole Genomes of Chandipura Virus Isolates and Comparative Analysis with Other Rhabdoviruses

The Chandipura virus (CHPV) belonging to the Vesiculovirus genus and Rhabdoviridae family, has recently been associated with a number of encephalitis epidemics, with high mortality in children, in different parts of India. No full length genome sequences of CHPV isolates were available in GenBank and little is known about the molecular markers for pathogenesis. In the present study, we provide the complete genomic sequences of four isolates from epidemics during 2003–2007. These sequences along with the deduced sequence of the prototype isolate of 1965 were analysed using phylogeny, motif search, homology modeling and epitope prediction methods. Comparison with other rhaboviruses was also done for functional extrapolations. All CHPV isolates clustered with the Isfahan virus and maintained several functional motifs of other rhabdoviruses. A notable difference with the prototype vesiculovirus, Vesicular Stomatitis Virus was in the L-domain flanking sequences of the M protein that are known to be crucial for interaction with host proteins. With respect to the prototype isolate, significant additional mutations were acquired in the 2003–2007 isolates. Several mutations in G mapped onto probable antigenic sites. A mutation in N mapped onto regions crucial for N-N interaction and a putative T-cell epitope. A mutation in the Casein kinase II phosphorylation site in P may attribute to increased rates of phosphorylation. Gene junction comparison revealed changes in the M-G junction of all the epidemic isolates that may have implications on read-through and gene transcription levels. The study can form the basis for further experimental verification and provide additional insights into the virulence determinants of the CHPV

Burning in Banksia Woodlands: How Does the Fire-Free Period Influence Reptile Communities?

Fire is an important management tool for both hazard reduction burning and maintenance of biodiversity. The impact of time since last fire on fauna is an important factor to understand as land managers often aim for prescribed burning regimes with specific fire-free intervals. However, our current understanding of the impact of time since last fire on fauna is largely unknown and likely dependent on vegetation type. We examined the responses of reptiles to fire age in banksia woodlands, and the interspersed melaleuca damplands among them, north of Perth, Western Australia, where the current prescribed burning regime is targeting a fire-free period of 8–12 years. The response of reptiles to fire was dependent on vegetation type. Reptiles were generally more abundant (e.g. Lerista elegans and Ctenophorus adelaidensis) and specious in banksia sites. Several species (e.g. Menetia greyii, Cryptoblepharus buchananii) preferred long unburnt melaleuca sites (>16 years since last fire, YSLF) compared to recently burnt sites (<12 YSLF). Several of the small elapids (e.g. the WA priority listed species Neelaps calonotus) were only detected in older-aged banksia sites (>16 YSLF). The terrestrial dragon C. adelaidensis and the skink Morethia obscura displayed a strong response to fire in banksia woodlands only. Highest abundances of the dragon were detected in the recently burnt (<7 YSLF) and long unburnt (>35 YSLF) banksia woodlands, while the skink was more abundant in older sites. Habitats from a range of fire ages are required to support the reptiles we detected, especially the longer unburnt (>16 YSLF) melaleuca habitat. Current burning prescriptions are reducing the availability of these older habitats

Machine learning for regulatory analysis and transcription factor target prediction in yeast

High throughput technologies, including array-based chromatin immunoprecipitation, have rapidly increased our knowledge of transcriptional maps—the identity and location of regulatory binding sites within genomes. Still, the full identification of sites, even in lower eukaryotes, remains largely incomplete. In this paper we develop a supervised learning approach to site identification using support vector machines (SVMs) to combine 26 different data types. A comparison with the standard approach to site identification using position specific scoring matrices (PSSMs) for a set of 104 Saccharomyces cerevisiae regulators indicates that our SVM-based target classification is more sensitive (73 vs. 20%) when specificity and positive predictive value are the same. We have applied our SVM classifier for each transcriptional regulator to all promoters in the yeast genome to obtain thousands of new targets, which are currently being analyzed and refined to limit the risk of classifier over-fitting. For the purpose of illustration we discuss several results, including biochemical pathway predictions for Gcn4 and Rap1. For both transcription factors SVM predictions match well with the known biology of control mechanisms, and possible new roles for these factors are suggested, such as a function for Rap1 in regulating fermentative growth. We also examine the promoter melting temperature curves for the targets of YJR060W, and show that targets of this TF have potentially unique physical properties which distinguish them from other genes. The SVM output automatically provides the means to rank dataset features to identify important biological elements. We use this property to rank classifying k-mers, thereby reconstructing known binding sites for several TFs, and to rank expression experiments, determining the conditions under which Fhl1, the factor responsible for expression of ribosomal protein genes, is active. We can see that targets of Fhl1 are differentially expressed in the chosen conditions as compared to the expression of average and negative set genes. SVM-based classifiers provide a robust framework for analysis of regulatory networks. Processing of classifier outputs can provide high quality predictions and biological insight into functions of particular transcription factors. Future work on this method will focus on increasing the accuracy and quality of predictions using feature reduction and clustering strategies. Since predictions have been made on only 104 TFs in yeast, new classifiers will be built for the remaining 100 factors which have available binding data