Clinical evaluation.

<p><b>A: Pedigree of the five-generation Chinese family with autosomal dominant congenital cataract (ADCC).</b> Squares and circles indicate males and females, respectively. Filled symbols indicate affected members and empty symbols indicate unaffected individuals. The diagonal line indicates a deceased family member and the arrow indicates the proband. Family members whose DNA was analyzed by sequencing and restriction enzyme digestion are indicated by asterisks. <b>B: Photograph of the right eye of the proband.</b> The photograph (diffuse illumination) of the proband (V: 1) before surgery shows a posterior polar cataract with cotton-like opacities in the posterior subcapsular cortex. The same phenotype was noted bilaterally.</p

Subcellular localization of WT-AQP0 and Y219*-AQP0in HEK 293T cells, 24 h after transient transfection.

<p>Bar 5μm. <b>A: Localization of singly transfected FLAG-tagged wild-type (WT) and mutated (219*) AQP0 proteins.</b> Photomicrographs show the distribution of immunoreactive FLAG-tagged AQP0 (green) and DAPI-stained nuclei (blue) <b>B: Localization of co-transfected FLAG-WT-AQP0 and Myc-219*-AQP0 proteins.</b> Photomicrographs show the distribution of immunoreactiveMyc-tagged 219*-AQP0 (red), FLAG-tagged WT-AQP0 (green) and DAPI-stained nuclei (blue)</p

Protein expression levels of WT- and Y219*-AQP0 transfected into HEK 293T cells.

<p>Western blots were performed with the anti-FLAG as indicated.β-actin was used as the loading control. Cells with the mutated (Y219*) AQP0 construct showed an 87% reduction in AQP0 protein level compared to cells with wild-type AQP0. ***P<0.01</p

A novel nonsense mutation (c.657C > G; p.Y219*) in <i>MIP</i>/AQP0 in a Chinese family with posterior polar ADCC.

<p><b>A: DNA sequences of <i>MIP</i> in unaffected and affected individuals.</b> The upper chromatogram of the DNA sequence from an unaffected individual (III: 3) shows only the wild-type AQP0 allele, which encodes tyrosine (TAC) at codon 219. The lower sequence chromatogram from the affected proband (V: 1) shows both C and G (S) at position 657 (arrow); thus, the mutant allele contained a C to G transition, which altered the Tyr to a stop codon (TAG).<b>B: A schematic diagram showing the presumed membrane topology of aquaporin0 (AQP0).</b> The depicted mutated portion (gray circles) illustrates the premature truncation of the protein. Amino acid residue 219 is located within the 6th transmembrane domain (blue arrow). <b>C: RFLP analysis shows the C>G transversion, which co-segregated with disease in a family.</b> The PCR product was 187bp in length and contained two <i>Rsa</i>I sites (GTAC). The unaffected allele yielded three fragments (12bp, 45bp, and 130bp) after <i>Rsa</i>I digestion, whereas the affected allele yielded four (12bp, 45bp, 130bp, and 175bp). Only the affected allele displayed the 175bp band (arrow). M indicates the DNA ladder.</p

Additional file 2: of Evidence of reduced recombination rate in human regulatory domains

Supplementary methods. (DOCX 65 kb

Additional file 3: Table S1. of Evidence of reduced recombination rate in human regulatory domains

Datasets used in this study. (XLSX 34 kb

DataSheet_1_DNAAF5 promotes hepatocellular carcinoma malignant progression by recruiting USP39 to improve PFKL protein stability.docx

PurposesDynein axonemal assembly factor 5 (DNAAF5) is the transcription factor of regulating the cytoskeleton and hydrodynamic protein complex assembly, however, it was not well elucidated in the malignant progression of hepatocellular carcinoma (HCC).MethodsWe investigated the role of DNAAF5 in hepatocellular carcinoma by using multiple groups of clinical tissues combined with data from the TCGA database. Then we overexpressed DNAAF5 in hepatocellular carcinoma tumor tissues, which correlates with poor patient survival outcomes. Furthermore, we constructed stable cell lines of HCC cells to confirm the cancer-promoting effects of DNAAF5 in hepatocellular carcinoma. To explore the mechanisms of DNAAF5, transcriptome sequencing combined with mass spectrometry was also performed, which showed that DNAAF5 affects its downstream signaling pathway by interacting with PFKL and that DNAAF5 regulates PFKL protein stability by recruiting the deubiquitination protein, USP39. To corroborate these findings, the same series of tissue microarrays were used to confirm correlations between DNAAF5 and PFKL expressions. In animal experiments, DNAAF5 also promoted the proliferation of HCC cells.ResultsWe found that DNAAF5 expressions were markedly higher in HCC tissues, compared to the adjacent normal tissues. Increased levels of DNAAF5 were associated with significantly worse prognostic outcomes for HCC patients. Cell function experiments showed that HCC cells of overexpressing DNAAF5 exhibited faster proliferation rates, stronger clone formation abilities and higher drug resistance rates. However, tumor cell proliferation rates and colony formation were significantly decreased after DNAAF5 knockout, accompanied by an increase in sensitivity to sorafenib. In addition, the results of our study showed that DNAAF5 accelerates PFKL protein deubiquitination by recruiting USP39 in HCC cells. Furthermore, The overexpression of DNAAF5 could promote HCC cell proliferation in vivo and in vitro, whereas USP39 knockdown inhibited this effect. Overall, DNAAF5 serves as a scaffold protein to recruit USP39 to form a ternary complex by directly binding the PFKL protein, thereby improving the stability of the latter, which promotes the malignant process of hepatocellular carcinoma.ConclusionsThese findings revealed DNAAF5 was negatively correlated with the prognosis of patients with hepatocellular carcinoma. It underlying mechanism showed that DNAAF5 directly binds PFKL and recruits the deubiquitinated protein (USP39) to improve the stability of the PFKL protein, thus enhancing abnormal glycolysis in HCC cells.</p

Additional file 1: Figure S1. of Evidence of reduced recombination rate in human regulatory domains

Scatter plot of recombination rate within genetic, physical, and activity links. Figure S2. Recombination valleys within non-overlapped genetic, physical, and activity links. Figure S3. Differences in recombination rate between best meQTL pairs and locally adjacent pairs. Figure S4. Recombination valleys in eQTLs in different tissues and cell lines. Figure S5. Recombination valleys within functional links at different thresholds. Figure S6. Recombination valleys in different recombination rate maps. Figure S7. Recombination valleys after controlling for physical length, G + C percentage, CpG density, SNP density, PRDM9 motif frequency, gene density, and distance to TSS. Figure S8. Recombination valleys exist in intergenic regions and non-coding bases. Figure S9. Recombination rate between Hi-C pairs and matched random intervals within the same HiCCUPS loops. Figure S10. eQTL evidence supported by chromatin conformation signals in the same cell line shows stronger depletion of recombination rate. Figure S11. Relationship between recombination valleys and CTCF. Figure S12 Recombination valleys between physical links, activity links without CTCF motifs, and matched random intervals also without CTCF motifs. Figure S13. Recombination valleys are most prominent at enhancer–TSS links, DNase–TSS links Hi-C links, and ChIA-PET PolII/PolII links associated with housekeeping genes. Figure S14. Recombination valleys are prominent at early embryonic developmental genes, but not at other cell type-specific genes. Figure S15. Recombination valleys are prominent at housekeeping genes in highly expressed and minimally expressed genes at the oocyte stage. Figure S16. Recombination valleys are most prominent at constitutive eQTL links. Figure S17. Recombination valleys in mouse regulatory domains. Figure S18. Recombination valleys are correlated with hotspot density and DNA methylation. Figure S19. Mechanistic model for recombination valley in regulatory domains. Figure S20. Relationship between recombination rate and DNA methylation quantile within 500-kb windows and within genetic links at different early development stages. Figure S21. Global relationship between DNA methylation, DNA double stranded break initiation frequency, and DNA double stranded break repair efficiency. Figure S22 Recombination rate predictions within functional links. (ZIP 46345 kb

Evaluation of Reference Genes for Normalization of Gene Expression Using Quantitative RT-PCR under Aluminum, Cadmium, and Heat Stresses in Soybean

<div><p>Quantitative reverse transcription polymerase chain reaction (qRT-PCR) is widely used to analyze the relative gene expression level, however, the accuracy of qRT-PCR is greatly affected by the stability of reference genes, which is tissue- and environment- dependent. Therefore, choosing the most stable reference gene in a specific tissue and environment is critical to interpret gene expression patterns. Aluminum (Al), cadmium (Cd), and heat stresses are three important abiotic factors limiting soybean (<i>Glycine max</i>) production in southern China. To identify the suitable reference genes for normalizing the expression levels of target genes by qRT-PCR in soybean response to Al, Cd and heat stresses, we studied the expression stability of ten commonly used housekeeping genes in soybean roots and leaves under these three abiotic stresses, using five approaches, BestKeeper, Delta Ct, geNorm, NormFinder and RefFinder. We found <i>TUA4</i> is the most stable reference gene in soybean root tips under Al stress. Under Cd stress, <i>Fbox</i> and <i>UKN2</i> are the most stable reference genes in roots and leaves, respectively, while <i>60S</i> is the most suitable reference gene when analyzing both roots and leaves together. For heat stress, <i>TUA4</i> and <i>UKN2</i> are the most stable housekeeping genes in roots and leaves, respectively, and <i>UKN2</i> is the best reference gene for analysis of roots and leaves together. To validate the reference genes, we quantified the relative expression levels of six target genes that were involved in soybean response to Al, Cd or heat stresses, respectively. The expression patterns of these target genes differed between using the most and least stable reference genes, suggesting the selection of a suitable reference gene is critical for gene expression studies.</p></div

The expression stability of the ten soybean reference genes across all treatments in this study.

<p>Y-axis represents the average expression stability (M) values analyzed by geNorm. (A) Leaf samples, (B) Root samples, (C) Leaf and root samples together.</p