Additional file 2: Supplementary tables. of Epigenetic and genetic alterations and their influence on gene regulation in chronic lymphocytic leukemia

Table S1. Overlap and enrichment of repeated elements along dREs. Table S2. Enrichment of dREs in the regulatory domains of CLL-up/down-regulated genes. Table S3. Enrichment of gene groups to Gene Ontology biological processes. Table S4. Density of GWAS SNPs associated with agglomerated traits along dRE. Table S5. Association of dREs to individual GWAS traits. Table S6. Prediction results and GWAS knowledge of the cancer-related GWAS SNPs which exhibit the significant allele frequency difference between CLL and normal samples. Table S7. Enrichment of TFBSs along dREs. Table S8. Enrichment of TFBS loss caused by CLL-associated alleles along lost dREs. Table S9. Enrichment of TFBS gain caused by CLL-associated alleles along gained dREs. (XLSX 64 kb

Additional file 1: Supplementary figures. of Epigenetic and genetic alterations and their influence on gene regulation in chronic lymphocytic leukemia

Figure S1. Flowchart for our data analysis. Figure S2. Distribution of dREs. The distributions of the distances between two nearest dREs (red) are shown for (a) gained dREs, b) lost dREs, and c) cross-class dREs (i.e., gained dREs and their nearest lost dREs). Figure S3. Contingency table to estimate the haplotype association between the allele 1 at a LD SNP m and the allele 1 at its tag GWAS SNP m_tag. Figure S4. Evaluation of impact of CLL substitutions on TFBS. MU, the mutant allele, is the allele enriched in CLL with respect to normal B-cells, while WT, the wild - type allele, is the allele depleted in CLL with respect to normal B-cells. Figure S5. Genomic distribution of the assayed CpG sites. The CpG sites located within CpG islands (CGIs) and those not in CGIs are analyzed separately. Figure S6. PCA of methylation levels of CpG sites located at gene regulatory regions. (a) non-promoter CpG sites and (b) promoter CpG sites. The CLL and normal samples are represented by red and grey dots, respectively. Figure S7. Examples of dREs and sREs in the loci of (a) IRF4 and EXOC2, (b) FOXF2 and (c) E4F1 and MLST8. sREs are marked in red bars, while gained and lost sREs are plotted in blue and green, respectively. Also promoter dREs/sREs are indicated by a black asterisk and the name of the corresponding genes. Figure S8. Fraction of REs (REs, lost dREs, gained dREs) and hiMRs (controls) residing in CGIs. Figure S9. Coverage of repeats along REs and hiMRs. Figure S10. Enrichment of different types of repeats in REs with respect to hiMRs. Figure S11. Overlap among the gene groups. Gene groups are defined according to the distribution of REs. “Shared” represents the set of genes containing the sRE(s) in their loci. Similarly “Lost” and “Gained” are the genes harboring the lost and gained dRE(s), respectively. Figure S12. GWAS CLL SNPs located within the detected dREs and sREs. For each SNP, GWAS association is -log10(p value estimated in GWAS studies). In the figures, sREs are represented by red bar, while gained and lost dREs are marked by blue and green bars, respectively. Figure S13. GWAS lymphoma SNPs located with the detected dREs and sREs. For each SNP, GWAS association is -log10(p value estimated in GWAS studies). In the figures, sREs are represented by red bar, while gained and lost dREs are marked by blue and green bars, respectively. Figure S14. rs1976684, a SNP residing in a lost dRE, is in an LD block (p 2 = 1.0, distance = 2564 bp) with rs501764, a GWAS SNP significantly associated with Hodgkin’s lymphoma [1] (Figure S13). The allele G of rs501764 is in a prominent haplotype (OR = 432.6, Fisher’s exact test p = 2 × 10− 133) with the allele G at rs1976684, the pathogenic allele for Hodgkin’s lymphoma [1]. Furthermore, the allele G at rs1976684 recurs significantly in CLL samples as compared to controls (p = 2 × 10− 10). Another line of evidence is that rs1976684 has a strong linkage (r 2 = 1.0) with rs4143094, a colorectal-cancer SNP with the risk allele of T [2]. Also, the disease allele T at rs4143094 is in a significant haplotype with the CLL-rich allele G at rs1976684 (OR = 70.7, Fisher’s exact test p = 3 × 10− 252). Collectively, a lost-dRE SNP rs1976684 is significantly linked to two GWAS SNPs associated with cancers, including lymphoma, a haematological cancer. The CLL-enriched allele of rs1976684 significantly co-occurs with the risk alleles of these GWAS SNPs. Moreover, the mutation from A to G at rs19766684 results in the loss of binding motifs of nuclear receptor subfamily 2 group F member 1 (NR2F1), a TF found to play a crucial role in development and differentiation processes in B-cell [3], further suggesting that rs1976684 is a potential CLL SNP with G as the culprit allele. Figure S15. rs211512, a cancer-associated gained-dRE SNP. rs211512 has a strong LD to rs4925386 (r 2 = 1.0, distance = 7549 bp), a colorectal-cancer GWAS SNP [4]. Its over-represented allele C (p < 10− 16) is in a significant haplotype with the cancer-risk allele at rs12193698 (OR = 1482.16, Fisher’s exact test p < 10− 300). All of these suggest the cancer-association of rs2151512 and its allele C, which is further supported by the observation that the CLL mutation at rs2151512 (replacing T with C) generates the binding motifs for GFI1B. GFI1B is a well-recognized major regulator of early hematopoiesis and hematopoietic stem cells, and has been associated with human blood diseases, including leukemia and lymphoma [5, 6]. The black allele is the one enriched in CLL (i.e., CLL-associated), while the grey allele is the one associated with normal samples. To show the TFBS change caused by this gained dRE SNP, the TFBS exclusively mapped to the black allele is presented here. Figure S16. The results of liver tumor dataset. This dataset consists of DNA methylation profiles of 4 tumor and 4 control samples (Gene Expression Omnibus, GSE70090, [7]). We detected 51988 gained dREs, 22948 lost dREs and 12476 sREs. The gained and lost dREs are enriched around the genes up and down-regulated in liver tumor, respectively. ** means binomial test p < 0.0001, while * is for the case of p <0.05. (DOCX 987 kb

MOESM1 of Integrated intracellular metabolic profiling and pathway analysis approaches reveal complex metabolic regulation by Clostridium acetobutylicum

Additional file 1. All the metabolites detected in our study and the results of pathway analysis

Insights into the Enhanced Reversibility of Graphite Anode Upon Fast Charging Through Li Reservoir

Increasing the charging rate and reducing the charging time for Li-ion batteries are crucial to realize the mainstream of electric vehicles. However, it is formidable to avoid the Li plating on graphite anode upon fast charging. Despite the tremendous progress in Li detection techniques, the fundamental mechanism of Li plating and its chemical/electrochemical responses upon cycling still remains elusive. Herein, we present a comprehensive electrochemical method to investigate the fast charging behavior of graphite electrode. A detailed analysis is directed toward understanding the changes in phase, composition, and morphology of the fast-charged graphite. By applying a resting process, we scrutinize the further reactions of the plated Li, which readily transforms into irreversible (dead) Li. We further develop a modified graphite electrode with a thin Ag coating as the Li reservoir. The plated Li can be “absorbed” by the Ag layer to form the Li–Ag solid solution that suppresses the formation of dead Li and provides structural stability, thus promoting the further lithiation of graphite and enhancing the reversibility. This work not only provides additional insights into the fast charging behavior of graphite electrode but also demonstrates a potential strategy to improve the fast charging performance of graphite anode

MOESM1 of Integrating multi-omics analyses of Nonomuraea dietziae to reveal the role of soybean oil in [(4′-OH)MeLeu]4-CsA overproduction

Additional file 1: Figure S1. HPLC analysis of CsA and [(4’-OH)MeLeu]4-CsA. Figure S2. NMR analysis of CsA and [(4’-OH)MeLeu]4-CsA. Figure S3. HRMS analysis of CsA and [(4’-OH)MeLeu]4-CsA. Figure S4. 2DE-based proteomic profiles of N. dietziae. Proteins are extracted at different growth phases and media. Arrows point to the significantly differential proteins under MO condition and their characteristics are shown in Table 1. Table S1. Primers for qRT-PCR of the CYPs

Comparison of cell growth profiles for different isobutanol-producing <i>B. subtilis</i>.

<p>The experiments were carried out in LBGSM-I medium under microaerobic conditions. Strains were cultivated in the medium supplemented with 3 g/L sodium acetic acid. Data were expressed as average values and standard deviations (SD) of three parallel studies.</p

Model-Driven Redox Pathway Manipulation for Improved Isobutanol Production in <i>Bacillus subtilis</i> Complemented with Experimental Validation and Metabolic Profiling Analysis

<div><p>To rationally guide the improvement of isobutanol production, metabolic network and metabolic profiling analysis were performed to provide global and profound insights into cell metabolism of isobutanol-producing <i>Bacillus subtilis</i>. The metabolic flux distribution of strains with different isobutanol production capacity (BSUL03, BSUL04 and BSUL05) drops a hint of the importance of NADPH on isobutanol biosynthesis. Therefore, the redox pathways were redesigned in this study. To increase NADPH concentration, glucose-6-phosphate isomerase was inactivated (BSUL06) and glucose-6-phosphate dehydrogenase was overexpressed (BSUL07) successively. As expected, NADPH pool size in BSUL07 was 4.4-fold higher than that in parental strain BSUL05. However, cell growth, isobutanol yield and production were decreased by 46%, 22%, and 80%, respectively. Metabolic profiling analysis suggested that the severely imbalanced redox status might be the primary reason. To solve this problem, gene <i>udhA</i> of <i>Escherichia coli</i> encoding transhydrogenase was further overexpressed (BSUL08), which not only well balanced the cellular ratio of NAD(P)H/NAD(P)⁺, but also increased NADH and ATP concentration. In addition, a straightforward engineering approach for improving NADPH concentrations was employed in BSUL05 by overexpressing exogenous gene <i>pntAB</i> and obtained BSUL09. The performance for isobutanol production by BSUL09 was poorer than BSUL08 but better than other engineered strains. Furthermore, in fed-batch fermentation the isobutanol production and yield of BSUL08 increased by 11% and 19%, up to the value of 6.12 g/L and 0.37 C-mol isobutanol/C-mol glucose (63% of the theoretical value), respectively, compared with parental strain BSUL05. These results demonstrated that model-driven complemented with metabolic profiling analysis could serve as a useful approach in the strain improvement for higher bio-productivity in further application.</p></div

MiNeGS can identify functional coordinated NFPs with high significance.

<p>MiNeGS can identify functional coordinated NFPs with high significance.</p

Strains and plasmids used in this work.

a<p>CGSC: Coli Gentic Stock Center.</p>b<p>BGSC: Bacillus Gentic Stock Center.</p

The metabolic flux distribution in isobutanol-producing strain <i>B. subtilis</i>.

<p>Data represented <i>in silico</i> flux distribution of different isobutanol-producing strains (top BSUL03, middle BSUL04, down BSUL05). Bold red and green lines represented the increased and decreased flux, respectively. The blue marks represented the targets for redox pathway engineering in this work. Abbreviations were listed in previous work <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0093815#pone.0093815-Li2" target="_blank">[10]</a>. Part of the flux data in central metabolism were taken from previous work <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0093815#pone.0093815-Li2" target="_blank">[10]</a>.</p