Minor Spliceosomal 65K/RNPC3 Interacts with ANKRD11 and Mediates HDAC3-Regulated Histone Deacetylation and Transcription.

RNA splicing is crucial in the multilayer regulatory networks for gene expression, making functional interactions with DNA- and other RNA-processing machineries in the nucleus. However, these established couplings are all major spliceosome-related; whether the minor spliceosome is involved remains unclear. Here, through affinity purification using Drosophila lysates, an interaction is identified between the minor spliceosomal 65K/RNPC3 and ANKRD11, a cofactor of histone deacetylase 3 (HDAC3). Using a CRISPR/Cas9 system, Deletion strains are constructed and found that both Dm65KΔ/Δ and Dmankrd11Δ/Δ mutants have reduced histone deacetylation at Lys9 of histone H3 (H3K9) and Lys5 of histone H4 (H4K5) in their heads, exhibiting various neural-related defects. The 65K-ANKRD11 interaction is also conserved in human cells, and the HsANKRD11 middle-uncharacterized domain mediates Hs65K association with HDAC3. Cleavage under targets and tagmentation (CUT&Tag) assays revealed that HsANKRD11 is a bridging factor, which facilitates the synergistic common chromatin-binding of HDAC3 and Hs65K. Knockdown (KD) of HsANKRD11 simultaneously decreased their common binding, resulting in reduced deacetylation of nearby H3K9. Ultimately, this study demonstrates that expression changes of many genes caused by HsANKRD11-KD are due to the decreased common chromatin-binding of HDAC3 and Hs65K and subsequently reduced deacetylation of H3K9, illustrating a novel and conserved coupling mechanism that links the histone deacetylation with minor spliceosome for the regulation of gene expression.

A network meta-analysis of eight chemotherapy regimens for treatment of advanced ovarian cancer.

This study compared the short-term efficacies of different chemotherapy regimens in the treatment of advanced ovarian cancer (AOC) through pair-wise and network meta-analyses (NMA). Randomized controlled trials (RCTs) identified in a comprehensive online literature search met our inclusion criteria. Direct and indirect evidence was combined to compare odds ratios (OR) and surfaces under the cumulative ranking curves (SUCRA) across the different treatment regimens. Twelve eligible RCTs were finally included, involving eight regimens (Paclitaxel + Carboplatin [PC], Gemcitabine + Carboplatin [GC], Carboplatin, Pegylated Liposomal Doxorubicin + Carboplatin [PLD + Carboplatin], Paclitaxel, Paclitaxel + Carboplatin + Topotecan [PC + Topotecan], Paclitaxel + Carboplatin + Epirubicin [PC + Epirubicin] and Docetaxel + Carboplatin [DC]). The NMA results revealed that in terms of overall response rate (ORR) and disease control rate (DCR), PC (ORR: OR=2.59, 95%CI=1.20-6.22; DCR: OR=2.58, 95%CI=1.05-6.82) and GC (ORR: OR=2.08, 95%CI=1.08-4.37; DCR: OR=2.43, 95%CI=1.07-5.80) were more effective against AOC than Carboplatin alone. Similarly, PC (OR=0.21, 95%CI=0.05-0.69), GC (OR=0.31, 95%CI=0.09-0.90) and PLD + Carboplatin (OR=0.22, 95%CI=0.04-0.92) slowed disease progression better than Carboplatin alone. We also found that PC was more efficacious against AOC than Carboplatin or Paclitaxel single-agent chemotherapy. Combination chemotherapy is thus recommended for AOC, and should guide subsequent drug development and treatment strategies.

Application of Empirical Mode Decomposition and Independent Component Analysis for the Interpretation of Rock-Mineral Spectrum.

Rock-mineral spectrum is a mixture of varied mineral spectra, through which we can obtain information about its components quickly and conveniently without any damage to the sample. Empirical mode decomposition (EMD) cannot directly decompose source signals from information of the mixture, and independent component analysis (ICA) requires the number of mixed signals to be no less than the number of source signals. Combining these two methods, mixed signals can be decomposed using EMD method to obtain intrinsic mode function (IMF), while certain IMFs together with mixed signals can be used as input data matrix of ICA to obtain the source signals. This method overcomes the shortcomings of IMF and ICA. Studies have shown that, the higher content of source signals contained in the mixed signal, the better estimation can be obtained through EMD and ICA. The number of IMFs that participate in ICA decomposition determines the number of approximation of source signals. The accuracy of source signal estimation increases with the correlation coefficient between IMF and mixed signals. By applying this method to quantitative analysis of rock-mineral spectrum, information of the component minerals in rock-mineral can be obtained, which improves the efficiency of component analysis in detecting rock-minerals outside.