28 research outputs found
Transcriptomic and Proteomic Analysis of Marine Nematode <i>Litoditis marina</i> Acclimated to Different Salinities
Salinity is a critical abiotic factor for all living organisms. The ability to adapt to different salinity environments determines an organism’s survival and ecological niches. Litoditis marina is a euryhaline marine nematode widely distributed in coastal ecosystems all over the world, although numerous genes involved in its salinity response have been reported, the adaptive mechanisms underlying its euryhalinity remain unexplored. Here, we utilized worms which have been acclimated to either low-salinity or high-salinity conditions and evaluated their basal gene expression at both transcriptomic and proteomic levels. We found that several conserved regulators, including osmolytes biosynthesis genes, transthyretin-like family genes, V-type H+-transporting ATPase and potassium channel genes, were involved in both short-term salinity stress response and long-term acclimation processes. In addition, we identified genes related to cell volume regulation, such as actin regulatory genes, Rho family small GTPases and diverse ion transporters, which might contribute to hyposaline acclimation, while the glycerol biosynthesis genes gpdh-1 and gpdh-2 accompanied hypersaline acclimation in L. marina. This study paves the way for further in-depth exploration of the adaptive mechanisms underlying euryhalinity and may also contribute to the study of healthy ecosystems in the context of global climate change
RFX transcription factor DAF-19 regulates 5-HT and innate immune responses to pathogenic bacteria in Caenorhabditis elegans.
In Caenorhabditis elegans the Toll-interleukin receptor domain adaptor protein TIR-1 via a conserved mitogen-activated protein kinase (MAPK) signaling cascade induces innate immunity and upregulates serotonin (5-HT) biosynthesis gene tph-1 in a pair of ADF chemosensory neurons in response to infection. Here, we identify transcription factors downstream of the TIR-1 signaling pathway. We show that common transcription factors control the innate immunity and 5-HT biosynthesis. We demonstrate that a cysteine to tyrosine substitution in an ARM motif of the HEAT/Arm repeat region of the TIR-1 protein confers TIR-1 hyperactivation, leading to constitutive tph-1 upregulation in the ADF neurons, increased expression of intestinal antimicrobial genes, and enhanced resistance to killing by the human opportunistic pathogen Pseudomonas aeruginosa PA14. A forward genetic screen for suppressors of the hyperactive TIR-1 led to the identification of DAF-19, an ortholog of regulatory factor X (RFX) transcription factors that are required for human adaptive immunity. We show that DAF-19 concerts with ATF-7, a member of the activating transcription factor (ATF)/cAMP response element-binding B (CREB) family of transcription factors, to regulate tph-1 and antimicrobial genes, reminiscent of RFX-CREB interaction in human immune cells. daf-19 mutants display heightened susceptibility to killing by PA14. Remarkably, whereas the TIR-1-MAPK-DAF-19/ATF-7 pathway in the intestinal immunity is regulated by DKF-2/protein kinase D, we found that the regulation of tph-1 expression is independent of DKF-2 but requires UNC-43/Ca(2+)/calmodulin-dependent protein kinase (CaMK) II. Our results suggest that pathogenic cues trigger a common core-signaling pathway via tissue-specific mechanisms and demonstrate a novel role for RFX factors in neuronal and innate immune responses to infection
A Homolog of the Cell Apoptosis Susceptibility Gene Involved in Ovary Development of Chinese Shrimp Fenneropenaeus chinensis1
Spatiotemporal Evolution and Prediction of AOT in Coal Resource Cities: A Case Study of Shanxi Province, China
As aerosols in the air have a great influence on the health of residents of coal resource-based cities, these municipalities are confronting the dilemma of air pollution that is caused by the increase of suspended particles in the atmosphere and their development process. Aerosol optical thickness could be used to explore the aerosol temporal and spatial variations and to develop accurate prediction models, which is of great significance to the control of air pollution in coal resource-based cities. This paper explored the temporal spatial variation characteristics of aerosols in coal resource-based regions. A total of 11 typical coal-resource prefecture-level cities in the Shanxi Province were studied and inverted the aerosol optical thickness (AOT) among these cities based on MODIS (Moderate Resolution Imaging Spectroradiometer) data and analyzed the significant factors affecting AOT. Through inputting significant correlation factors as the input variables of NARX (nonlinear auto regressive models with exogenous inputs) neural network, the monthly average AOTs in the Shanxi Province were predicted between 2011 and 2019. The results showed that, in terms of time series, AOT increased from January to July and decreased from July to December, the maximum AOT was 0.66 in summer and the minimum was 0.2 in autumn, and it was related to the local monsoon, temperature, and humidity. While as far as the space alignment is concerned, the figure for AOT in Shanxi Province varied significantly. High AOT was mainly concentrated in the centre and south and low AOT was focused on the northwestern part. Among the positively correlated factors, the correlation coefficient of population density and temperature exceeded 0.8, which was highly positive, and among the negatively correlated factors, the correlation coefficient of NDVI exceeded -0.8, which was highly negative. After improving the model by adding the important factors that were mentioned before, the error between the predicted mean value and the actual mean value was no more than 0.06. Considering this charge, the NARX neural network with multiple inputs can contribute to better prediction results
Spatiotemporal Evolution and Prediction of AOT in Coal Resource Cities: A Case Study of Shanxi Province, China
As aerosols in the air have a great influence on the health of residents of coal resource-based cities, these municipalities are confronting the dilemma of air pollution that is caused by the increase of suspended particles in the atmosphere and their development process. Aerosol optical thickness could be used to explore the aerosol temporal and spatial variations and to develop accurate prediction models, which is of great significance to the control of air pollution in coal resource-based cities. This paper explored the temporal spatial variation characteristics of aerosols in coal resource-based regions. A total of 11 typical coal-resource prefecture-level cities in the Shanxi Province were studied and inverted the aerosol optical thickness (AOT) among these cities based on MODIS (Moderate Resolution Imaging Spectroradiometer) data and analyzed the significant factors affecting AOT. Through inputting significant correlation factors as the input variables of NARX (nonlinear auto regressive models with exogenous inputs) neural network, the monthly average AOTs in the Shanxi Province were predicted between 2011 and 2019. The results showed that, in terms of time series, AOT increased from January to July and decreased from July to December, the maximum AOT was 0.66 in summer and the minimum was 0.2 in autumn, and it was related to the local monsoon, temperature, and humidity. While as far as the space alignment is concerned, the figure for AOT in Shanxi Province varied significantly. High AOT was mainly concentrated in the centre and south and low AOT was focused on the northwestern part. Among the positively correlated factors, the correlation coefficient of population density and temperature exceeded 0.8, which was highly positive, and among the negatively correlated factors, the correlation coefficient of NDVI exceeded -0.8, which was highly negative. After improving the model by adding the important factors that were mentioned before, the error between the predicted mean value and the actual mean value was no more than 0.06. Considering this charge, the NARX neural network with multiple inputs can contribute to better prediction results
GPB-1 regulates baseline ADF <i>tph-1</i>::<i>gfp</i> expression in a GOA-1-dependent manner.
<p><b>(A)</b> Both GOA-1 deletion (<i>goa-1lf</i>) and GTP-bound hyperactive GOA-1(Q205L) (<i>goa-1gf</i>) mutations elevated ADF <i>tph-1</i>::<i>gfp</i> compared to WT. <i>gpb-1(yz71)</i> and <i>ocr-2</i> TRPV mutations suppressed <i>tph-1</i>::<i>gfp</i> in <i>goa-1(lf)</i> and <i>goa-1(gf)</i> backgrounds. <b>(B)</b> Dominant negative GDP-bound GOA-1(S47C) transgene (<i>goa-1DN</i>) diminished ADF <i>tph-1</i> expression in three independent transgenic lines. <i>goa-1(DN)</i> transgenic animals remained capable of enhancing ADF <i>tph-1</i>::<i>gfp</i> when induced to form dauers or in <i>tir-1(gf)</i> background. For each assay, the value of ADF GFP fluorescence in dauers and that of mutants and transgenic animals is normalized to the value of WT animals under optimal conditions. Data represent the average of ≥ 2 trials ± SEM. The differences between WT and mutants are marked on the top of bar, and the differences between comparison groups are indicated, *p < 0.05, ** p < 0.01, *** p < 0.001, t-test for two group comparisons, and ANOVA for multi-group/condition comparisons. <b>(C)</b> ADF-specific marker <i>Psrh-142</i>::<i>mCherry</i> was expressed and showed characteristic ADF axon and dendritic morphology in <i>goa-1(DN)</i> transgenic animals. Animals at all developmental stages were analyzed, and images of L4 animals are shown. <b>(D)</b> A schematic of GPB-1 and GOA-1 interaction on <i>tph-1</i> expression. Effector molecules compete with G<sub>o</sub>α for Gβ binding to drive <i>tph-1</i> expression. In <i>goa-1(lf)</i> an <i>goa-1(gf)</i> mutants, the effector constitutively binds to Gβ. GDP-bound conformation of the <i>GOA-1(DN)</i> protein blocks the effector binding. GPB-1(G162E) obstructs the interactions with both GOA-1 and the effector, leading to constitutively diminished <i>tph-1</i> regardless of the presence of GDP- or GTP-bound G<sub>o</sub>α.</p
ADF-produced 5-HT modulates specific 5-HT-regulated innate behaviors.
<p><b>(A)</b><i>tph-1</i> expressed in ADF not in NSM rescued pumping rates of <i>tph-1</i> mutants. <b>(B)</b><i>tph-1</i> expressed in ADF and in NSM concerted to reduce locomotory rates. <b>(C—D</b>) <i>tph-1</i> expressed in ADF and NSM is not required for 5-HT-regulation of egg-laying rates or egg accumulation in the uterus. Data represent the summary of 4–9 trials. Differences between the groups are indicated, and the differences to WT are marked above the bars, ** p < 0.01, *** p < 0.001 (ANOVA followed by Bonferroni test).</p
<i>tph-1</i>::<i>gfp</i> expression in <i>gpb-1(yz71)</i> mutants under optimal and aversive conditions.
<p><b>(A)</b> Dauer formation induced by aversive growth conditions caused ADF <i>tph-1</i>::<i>gfp</i> upregulation in WT and <i>yz71</i>mutants, as compared to their respective L4 stage siblings. <b>(B)</b> Mutation in cilia structural gene <i>che-2</i> was capable of triggering ADF <i>tph-1</i>::<i>gfp</i> upregulation in <i>yz71</i> mutants. <b>(C)</b> Pathogen PA14 failed to induce ADF <i>tph-1</i>::<i>gfp</i> upregulation in <i>yz71</i> mutants, as comparing <i>tph-1</i>::<i>gfp</i> between 1<sup>st</sup> day adults fed PA14 and non-pathogenic bacterial control OP50 for 6 hr. <i>gpb-1(g)</i> transgene restored the PA14 response in <i>yz71</i> mutants. <b>(D)</b> Comparing ADF <i>tph-1</i>::<i>gfp</i> between <i>gpb-1(yz71)</i> and <i>ocr-2</i> TRPV channel mutants. In both mutants, ADF <i>tph-1</i>::<i>gfp</i> was diminished under optimal growth conditions, but enhanced in <i>tir-1(yz68gf)</i> background as compared to the <i>yz71</i> and <i>ocr-2</i> single mutants. <i>yz71</i> and <i>ocr-2</i> did not produce an additive effect. For each assay, the value of ADF GFP fluorescence in WT animals under a stress paradigm and that of mutants is normalized to the value of WT animals under optimal conditions. Data represent the average of ≥ 3 trials ± SEM, * p < 0.05, *** p < 0.001, t-test for two group comparisons, and ANOVA for multi-group/condition comparisons.</p
G162E occurs at the Gβ surface that binds to Gα switch II helix and Gβ effectors.
<p><b>(A)</b> Comparing amino acid sequence surrounding G162 (red boxed) between <i>C</i>. <i>elegans</i> GPB-1, bovine (B.t.) Gβ1, and human (H.s.) Gβ1 and Gβ2, all of which contain 340 amino acid residues and the numbers refer to residue positions in the corresponding proteins. <b>(B) Modeling G162E substitution over bovine β1. (Bi)</b> Cartoon presentation of the crystallographic structure of rat G<sub>i</sub>α<sub>1</sub> and bovine β1γ2 heterotrimer rendered with PyMOL software. β subunit is colored cyan, and γ subunit colored orange. G162 (the side chain colored red) is located on the surface of the narrow side of the propeller architecture of Gβ. <b>(Bii)</b> Another view of the heterotrimer showing the interactions between Gβ and GDP-bound Gα (green). G162 is located on the surface that binds to the Gα switch II helix (yellow). GDP is in white, and the nearby Gα-S47 that is critical for the GTP binding is colored magenta. <b>(Biii)</b> An enlarged view showing the interface between Gβ and Gα switch II helix. Gβ-G162 sits nearby Gβ-Y145, a critical residue in an effector binding hotspot [<a href="http://www.plosgenetics.org/article/info:doi/10.1371/journal.pgen.1005540#pgen.1005540.ref017" target="_blank">17</a>]. Gβ-G162 is in close proximity to Gα-S206 in the switch II helix. The conformation of Gα-G202 and G203 is critical for GTP binding [<a href="http://www.plosgenetics.org/article/info:doi/10.1371/journal.pgen.1005540#pgen.1005540.ref032" target="_blank">32</a>]. <b>(Biv)</b> An enlarged view showing the Gβ and Gα switch II interface, with G162 changed to E in the Gβ sequence. <b>(C)</b> GPB-1 immunostaining. <b>Ci</b> and <b>Ciii</b>. In both WT and <i>yz71</i> two-cell embryos, GPB-1 was enriched in the asters (arrowheads) and in the region between cells. <b>Cii</b> and <b>Civ</b>. In WT and <i>yz71</i> animals, GPB-1 can be detected in the cell membrane in neurons. <b>Cv</b>. <i>gpb-1(g)</i> overexpression transgenic animals showing GPB-1 localized to axons and dendrites.</p