Gouda, N. | bioRxiv Science

bioRxiv2024

DnaB and DciA: Mechanisms of Helicase Loading and Translocation on ssDNA

O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=83 SRC="FIGDIR/small/622779v2_ufig1.gif" ALT="Figure 1"> View larger version (26K): org.highwire.dtl.DTLVardef@b0641dorg.highwire.dtl.DTLVardef@7caf59org.highwire.dtl.DTLVardef@1dc0fccorg.highwire.dtl.DTLVardef@913ce2_HPS_FORMAT_FIGEXP M_FIG O_FLOATNOGraphical AbstractC_FLOATNO The Vibrio cholerae (Vc) DnaB replicative helicase structure bound to single-stranded (ss) DNA is depicted in the ribbon (top left) and sphere-cylinder representation (top right). In the bottom center is a native mass spectrum showing Vc DnaB helicase loading onto single-stranded DNA (ssDNA). C_FIG Replicative helicases are assembled on chromosomes by helicase loaders before initiation of DNA replication. Here, we investigate mechanisms used by the bacterial Vibrio cholerae (Vc) DnaB replicative helicase and the DciA helicase loader. Structural analysis of the ATP{gamma}S form of the VcDnaB-ssDNA complex reveals a configuration distinct from that seen with GDP*AlF4. With ATP{gamma}S, the amino-terminal (NTD) tier, previously found as an open spiral in the GDP*AlF4 complex, adopts a closed planar arrangement. Further, the DnaB subunit at the top of the carboxy-terminal spiral (CTD) tier is displaced by [~]25 [A] between the two forms. We suggest that remodeling the NTD layer between closed planar and open spiral configurations and migration of two distinct CTDs to the top of the DnaB spiral, repeated three times, mediates hand-over-hand translocation. Biochemical analysis suggests that VcDciA leverages its Lasso domain to contact DnaB near its Docking-Linker-Helix interface. Up to three copies of VcDciA bind to VcDnaB and suppress its ATPase activity during loading onto physiological DNA substrates. Our data suggest that DciA loads DnaB onto DNA using the ring-opening mechanism.

biochemistry↗

bioRxiv2016

SCODE: An efficient regulatory network inference algorithm from single-cell RNA-Seq during differentiation

The analysis of RNA-Seq data from individual differentiating cells enables us to reconstruct the differentiation process and the degree of differentiation (in pseudo-time) of each cell. Such analyses can reveal detailed expression dynamics and functional relationships for differentiation. To further elucidate differentiation processes, more insight into gene regulatory networks is required. The pseudo-time can be regarded as time information and, therefore, single-cell RNA-Seq data are time-course data with high time resolution. Although time-course data are useful for inferring networks, conventional inference algorithms for such data suffer from high time complexity when the number of samples and genes is large. Therefore, a novel algorithm is necessary to infer networks from single-cell RNA-Seq during differentiation.\n\nIn this study, we developed the novel and efficient algorithm SCODE to infer regulatory networks, based on ordinary differential equations. We applied SCODE to three single-cell RNA-Seq datasets and confirmed that SCODE can reconstruct observed expression dynamics. We evaluated SCODE by comparing its inferred networks with use of a DNaseI-footprint based network. The performance of SCODE was best for two of the datasets and nearly best for the remaining dataset. We also compared the runtimes and showed that the runtimes for SCODE are significantly shorter than for alternatives. Thus, our algorithm provides a promising approach for further single-cell differentiation analyses.\n\nThe R source code of SCODE is available at https://github.com/hmatsu1226/SCODE.

bioinformatics↗