Additional file 3: of Interspecies evolutionary divergence in Liriodendron, evidence from the nucleotide variations of LcDHN-like gene

311 LcDHN-like gDNA sequences in Liriodendron. (DOCX 152 kb

Additional file 2: of Interspecies evolutionary divergence in Liriodendron, evidence from the nucleotide variations of LcDHN-like gene

5’ RACE, 3’ RACE and cDNA sequences of LcDHN-like gene. (DOCX 15 kb

Additional file 1: of Interspecies evolutionary divergence in Liriodendron, evidence from the nucleotide variations of LcDHN-like gene

Table S1. Primers and PCR protocol for RACE amplification and ORF testing. Table S2. Characteristics of properties and structure about LcDHN-like proteins in Liriodendron. P1-P17, proteins from L. chinense; P18-P58, proteins from L. tulipifera. (DOCX 27 kb

Data_Sheet_1_Rhizosphere-associated soil microbiome variability in Verticillium wilt-affected Cotinus coggygria.pdf

IntroductionVerticillium wilt is the most devastating soil-borne disease affecting Cotinus coggygria in the progress of urban landscape construction in China.MethodsTo assess the variability of the rhizosphere-associated soil microbiome in response to Verticillium wilt occurrence, we investigated the microbial diversity, taxonomic composition, biomarker species, and co-occurrence network of the rhizosphere-associated soil in Verticillium wilt-affected C. coggygria using Illumina sequencing.ResultsThe alpha diversity indices of the rhizosphere bacteria in Verticillium wilt-affected plants showed no significant variability compared with those in healthy plants, except for a moderate increase in the Shannon and Invsimpson indices, while the fungal alpha diversity indices were significantly decreased. The abundance of certain dominant or crucial microbial taxa, such as Arthrobacter, Bacillus, Streptomyces, and Trichoderma, displayed significant variations among different soil samples. The bacterial and fungal community structures exhibited distinct variability, as evidenced by the Bray–Curtis dissimilarity matrices. Co-occurrence networks unveiled intricate interactions within the microbial community of Verticillium wilt-affected C. coggygria, with greater edge numbers and higher network density. The phenomenon was more evident in the fungal community, showing increased positive interaction, which may be associated with the aggravation of Verticillium wilt with the aid of Fusarium. The proportions of bacteria involved in membrane transport and second metabolite biosynthesis functions were significantly enriched in the diseased rhizosphere soil samples.DiscussionThese findings suggested that healthy C. coggygria harbored an obviously higher abundance of beneficial microbial consortia, such as Bacillus, while Verticillium wilt-affected plants may recruit antagonistic members such as Streptomyces in response to Verticillium dahliae infection. This study provides a theoretical basis for understanding the soil micro-ecological mechanism of Verticillium wilt occurrence, which may be helpful in the prevention and control of the disease in C. coggygria from the microbiome perspective.</p

Table_1_Investigate the genetic mechanisms of diabetic kidney disease complicated with inflammatory bowel disease through data mining and bioinformatic analysis.xlsx

BackgroundPatients with diabetic kidney disease (DKD) often have gastrointestinal dysfunction such as inflammatory bowel disease (IBD). This study aims to investigate the genetic mechanism leading to IBD in DKD patients through data mining and bioinformatics analysis.MethodsThe disease-related genes of DKD and IBD were searched from the five databases of OMIM, GeneCards, PharmGkb, TTD, and DrugBank, and the intersection part of the two diseases were taken to obtain the risk genes of DKD complicated with IBD. A protein–protein interaction (PPI) network analysis was performed on risk genes, and three topological parameters of degree, betweenness, and closeness of nodes in the network were used to identify key risk genes. Finally, Gene Ontology (GO) analysis and Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis were performed on the risk genes to explore the related mechanism of DKD merging IBD.ResultsThis study identified 495 risk genes for DKD complicated with IBD. After constructing a protein–protein interaction network and screening for three times, six key risk genes were obtained, including matrix metalloproteinase 2 (MMP2), hepatocyte growth factor (HGF), fibroblast growth factor 2 (FGF2), interleukin (IL)-18, IL-13, and C–C motif chemokine ligand 5 (CCL5). Based on GO enrichment analysis, we found that DKD genes complicated with IBD were associated with 3,646 biological processes such as inflammatory response regulation, 121 cellular components such as cytoplasmic vesicles, and 276 molecular functions such as G-protein-coupled receptor binding. Based on KEGG enrichment analysis, we found that the risk genes of DKD combined with IBD were associated with 181 pathways, such as the PI3K-Akt signaling pathway, advanced glycation end product–receptor for AGE (AGE-RAGE) signaling pathway and hypoxia-inducible factor (HIF)-1 signaling pathway.ConclusionThere is a genetic mechanism for the complication of IBD in patients with CKD. Oxidative stress, chronic inflammatory response, and immune dysfunction were possible mechanisms for DKD complicated with IBD.</p

Data_Sheet_1_Identification of diagnostic gene biomarkers and immune infiltration in patients with diabetic kidney disease using machine learning strategies and bioinformatic analysis.docx

ObjectiveDiabetic kidney disease (DKD) is the leading cause of chronic kidney disease and end-stage renal disease worldwide. Early diagnosis is critical to prevent its progression. The aim of this study was to identify potential diagnostic biomarkers for DKD, illustrate the biological processes related to the biomarkers and investigate the relationship between them and immune cell infiltration.Materials and methodsGene expression profiles (GSE30528, GSE96804, and GSE99339) for samples obtained from DKD and controls were downloaded from the Gene Expression Omnibus database as a training set, and the gene expression profiles (GSE47185 and GSE30122) were downloaded as a validation set. Differentially expressed genes (DEGs) were identified using the training set, and functional correlation analyses were performed. The least absolute shrinkage and selection operator (LASSO), support vector machine-recursive feature elimination (SVM-RFE), and random forests (RF) were performed to identify potential diagnostic biomarkers. To evaluate the diagnostic efficacy of these potential biomarkers, receiver operating characteristic (ROC) curves were plotted separately for the training and validation sets, and immunohistochemical (IHC) staining for biomarkers was performed in the DKD and control kidney tissues. In addition, the CIBERSORT, XCELL and TIMER algorithms were employed to assess the infiltration of immune cells in DKD, and the relationships between the biomarkers and infiltrating immune cells were also investigated.ResultsA total of 95 DEGs were identified. Using three machine learning algorithms, DUSP1 and PRKAR2B were identified as potential biomarker genes for the diagnosis of DKD. The diagnostic efficacy of DUSP1 and PRKAR2B was assessed using the areas under the curves in the ROC analysis of the training set (0.945 and 0.932, respectively) and validation set (0.789 and 0.709, respectively). IHC staining suggested that the expression levels of DUSP1 and PRKAR2B were significantly lower in DKD patients compared to normal. Immune cell infiltration analysis showed that B memory cells, gamma delta T cells, macrophages, and neutrophils may be involved in the development of DKD. Furthermore, both of the candidate genes are associated with these immune cell subtypes to varying extents.ConclusionDUSP1 and PRKAR2B are potential diagnostic markers of DKD, and they are closely associated with immune cell infiltration.</p

Hydrogen bonds^a existing in T3 loop (residues from 88 to 101) of wild type PG8fn and two mutants (N94Q, N94A), and their occupancies during the last 20 ns of MD simulations.

<p>^<i>a</i> Only H-bonds with occupancies >50% are shown.</p><p>^<i>b</i> Not observed.</p><p>Hydrogen bonds<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0135413#t001fn001" target="_blank">^a</a> existing in T3 loop (residues from 88 to 101) of wild type PG8fn and two mutants (N94Q, N94A), and their occupancies during the last 20 ns of MD simulations.</p

The consensus sequence logo of the loop T3 regions of 1,000 GH28 endo-PGs generated using the WEBLOGO.

<p>The consensus sequence logo of the loop T3 regions of 1,000 GH28 endo-PGs generated using the WEBLOGO.</p

Kinetic values of wild type PG8fn and its mutants ^a.

<p>^<i>a</i> The kinetic values are shown as means ± standard deviations (n = 3).</p><p>^<i>b</i> The <i>k</i>_<i>cat</i> values were calculated by considering the enzyme to be a monomeric form.</p><p>^<i>c</i> △(△G) = −RT·ln[(<i>k</i>_<i>cat</i>/<i>K</i>_<i>m</i>)_mut/(<i>k</i>_<i>cat</i>/<i>K</i>_<i>m</i>)_wt], where (<i>k</i>_<i>cat</i>/<i>K</i>_<i>m</i>)_mut and (<i>k</i>_<i>cat</i>/<i>K</i>_<i>m</i>)_wt are the <i>k</i>_<i>cat</i>/<i>K</i>_<i>m</i> ratios of the mutant and wild type enzyme, respectively, R is the ideal gas constant, and T is the temperature in Kelvin.</p><p>Kinetic values of wild type PG8fn and its mutants <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0135413#t002fn001" target="_blank">^a</a>.</p

Illustration of the substrate pentagalacturonic acid docked to the wild type PG8fn catalytic pocket.

<p>The system was constructed using PyMOL. The protein surface is shown in transparent gray. The catalytic region forms a tunnel through which the substrate passes. Hydrogen bond is depicted as blue dashed lines. Asn94 is marked in green. Catalytic triads in the active center are marked in cyan. The key amino acids interacted with GalpA at –1/+1 subsites are marked in orange.</p