An E3-like Factor that Promotes SUMO Conjugation to the Yeast Septins

AbstractCovalent attachment of the ubiquitin-related protein SUMO to other proteins participates in many processes including signal transduction, transcriptional regulation, and growth control. We report the characterization of Siz1 as an E3-like factor in the SUMO pathway. Siz1 is required for SUMO attachment to the S. cerevisiae septins in vivo and strongly stimulates septin sumoylation in vitro. Siz1 and the related protein Siz2 promote SUMO conjugation to different substrates at different stages of the cell cycle and, together, are required for most SUMO conjugation in yeast. Siz1, Siz2, and the PIAS (protein inhibitor of activated STAT) proteins form a conserved family defined by an unusual RING-related motif. Our results suggest that this family functions by promoting SUMO conjugation to specific substrates

Multisite Phosphorylation of the Sum1 Transcriptional Repressor by S-Phase Kinases Controls Exit from Meiotic Prophase in Yeast.

Activation of the meiotic transcription factor Ndt80 is a key regulatory transition in the life cycle of Saccharomyces cerevisiae because it triggers exit from pachytene and entry into meiosis. The NDT80 promoter is held inactive by a complex containing the DNA-binding protein Sum1 and the histone deacetylase Hst1. Meiosis-specific phosphorylation of Sum1 by the protein kinases Cdk1, Ime2, and Cdc7 is required for NDT80 expression. Here, we show that the S-phase-promoting cyclin Clb5 activates Cdk1 to phosphorylate most, and perhaps all, of the 11 minimal cyclin-dependent kinase (CDK) phospho-consensus sites (S/T-P) in Sum1. Nine of these sites can individually promote modest levels of meiosis, yet these sites function in a quasiadditive manner to promote substantial levels of meiosis. Two Cdk1 sites and an Ime2 site individually promote high levels of meiosis, likely by preparing Sum1 for phosphorylation by Cdc7. Chromatin immunoprecipitation reveals that the phosphorylation sites are required for removal of Sum1 from the NDT80 promoter. We also find that Sum1, but not its partner protein Hst1, is required to repress NDT80 transcription. Thus, while the phosphorylation of Sum1 may lead to dissociation from DNA by influencing Hst1, it is the presence of Sum1 on DNA that determines whether NDT80 will be expressed

Multiple domains in Siz SUMO ligases contribute to substrate selectivity.

Saccharomyces cerevisiae contains two Siz/PIAS SUMO E3 ligases, Siz1 and Siz2/Nfi1, and one other known ligase, Mms21. Although ubiquitin ligases are highly substrate-specific, the degree to which SUMO ligases target distinct sets of substrates is unknown. Here we show that although Siz1 and Siz2 each have unique substrates in vivo, sumoylation of many substrates can be stimulated by either protein. Furthermore, in the absence of both Siz proteins, many of the same substrates are still sumoylated at low levels. Some of this residual sumoylation depends on MMS21. Siz1 targets its unique substrates through at least two distinct domains. Sumoylation of PCNA (proliferating cell nuclear antigen) and the splicing factor Prp45 requires part of the N-terminal region of Siz1, the ;PINIT\u27 domain, whereas sumoylation of the bud neck-associated septin proteins Cdc3, Cdc11 and Shs1/Sep7 requires the C-terminal domain of Siz1, which is also sufficient for cell cycle-dependent localization of Siz1 to the bud neck. Remarkably, the non-sumoylated septins Cdc10 and Cdc12 also undergo Siz1-dependent sumoylation if they are fused to the short PsiKXE SUMO attachment-site sequence. Collectively, these results suggest that local concentration of the E3, rather than a single direct interaction with the substrate polypeptide, is the major factor in substrate selectivity by Siz proteins

Binding of the Bacillus subtilis LexA protein to the SOS operator

The Bacillus subtilis LexA protein represses the SOS response to DNA damage by binding as a dimer to the consensus operator sequence 5′-CGAACN(4)GTTCG-3′. To characterize the requirements for LexA binding to SOS operators, we determined the operator bases needed for site-specific binding as well as the LexA amino acids required for operator recognition. Using mobility shift assays to determine equilibrium constants for B.subtilis LexA binding to recA operator mutants, we found that several single base substitutions within the 14 bp recA operator sequence destabilized binding enough to abolish site-specific binding. Our results show that the AT base pairs at the third and fourth positions from the 5′ end of a 7 bp half-site are essential and that the preferred binding site for a LexA dimer is 5′-CGAACATATGTTCG-3′. Binding studies with LexA mutants, in which the solvent accessible amino acid residues in the putative DNA binding domain were mutated, indicate that Arg-49 and His-46 are essential for binding and that Lys-53 and Ala-48 are also involved in operator recognition. Guided by our mutational analyses as well as hydroxyl radical footprinting studies of the dinC and recA operators we docked a computer model of B.subtilis LexA on the preferred operator sequence in silico. Our model suggests that binding by a LexA dimer involves bending of the DNA helix within the internal 4 bp of the operator

A Genome-Wide Screen Identifies Genes That Affect Somatic Homolog Pairing in Drosophila

In Drosophila and other Dipterans, homologous chromosomes are in close contact in virtually all nuclei, a phenomenon known as somatic homolog pairing. Although homolog pairing has been recognized for over a century, relatively little is known about its regulation. We performed a genome-wide RNAi-based screen that monitored the X-specific localization of the male-specific lethal (MSL) complex, and we identified 59 candidate genes whose knockdown via RNAi causes a change in the pattern of MSL staining that is consistent with a disruption of X-chromosomal homolog pairing. Using DNA fluorescent in situ hybridization (FISH), we confirmed that knockdown of 17 of these genes has a dramatic effect on pairing of the 359 bp repeat at the base of the X. Furthermore, dsRNAs targeting Pr-set7, which encodes an H4K20 methyltransferase, cause a modest disruption in somatic homolog pairing. Consistent with our results in cultured cells, a classical mutation in one of the strongest candidate genes, pebble (pbl), causes a decrease in somatic homolog pairing in developing embryos. Interestingly, many of the genes identified by our screen have known roles in diverse cell-cycle events, suggesting an important link between somatic homolog pairing and the choreography of chromosomes during the cell cycle

An Older, More Quiescent Universe from Panchromatic SED Fitting of the 3D-HST Survey

Galaxy observations are influenced by many physical parameters: stellar masses, star formation rates (SFRs), star formation histories (SFHs), metallicities, dust, black hole activity, and more. As a result, inferring accurate physical parameters requires high-dimensional models which capture or marginalize over this complexity. Here we re-assess inferences of galaxy stellar masses and SFRs using the 14-parameter physical model Prospector-$\alpha$ built in the Prospector Bayesian inference framework. We fit the photometry of 58,461 galaxies from the 3D-HST catalogs at $0.5 < z < 2.5$. The resulting stellar masses are $\sim0.1-0.3$ dex larger than the fiducial masses while remaining consistent with dynamical constraints. This change is primarily due to the systematically older SFHs inferred with Prospector. The SFRs are $\sim0.1-1+$ dex lower than UV+IR SFRs, with the largest offsets caused by emission from "old" ($t>100$ Myr) stars. These new inferences lower the observed cosmic star formation rate density by $\sim0.2$ dex and increase the observed stellar mass growth by $\sim 0.1$ dex, finally bringing these two quantities into agreement and implying an older, more quiescent Universe than found by previous studies at these redshifts. We corroborate these results by showing that the Prospector-$\alpha$ SFHs are both more physically realistic and are much better predictors of the evolution of the stellar mass function. Finally, we highlight examples of observational data which can break degeneracies in the current model; these observations can be incorporated into priors in future models to produce new & more accurate physical parameters.Comment: Replaced w/ accepted versio

Creating research-ready partnerships: The initial development of seven implementation laboratories to advance cancer control

BACKGROUND: In 2019-2020, with National Cancer Institute funding, seven implementation laboratory (I-Lab) partnerships between scientists and stakeholders in \u27real-world\u27 settings working to implement evidence-based interventions were developed within the Implementation Science Centers in Cancer Control (ISC3) consortium. This paper describes and compares approaches to the initial development of seven I-Labs in order to gain an understanding of the development of research partnerships representing various implementation science designs. METHODS: In April-June 2021, members of the ISC3 Implementation Laboratories workgroup interviewed research teams involved in I-Lab development in each center. This cross-sectional study used semi-structured interviews and case-study-based methods to collect and analyze data about I-Lab designs and activities. Interview notes were analyzed to identify a set of comparable domains across sites. These domains served as the framework for seven case descriptions summarizing design decisions and partnership elements across sites. RESULTS: Domains identified from interviews as comparable across sites included engagement of community and clinical I-Lab members in research activities, data sources, engagement methods, dissemination strategies, and health equity. The I-Labs use a variety of research partnership designs to support engagement including participatory research, community-engaged research, and learning health systems of embedded research. Regarding data, I-Labs in which members use common electronic health records (EHRs) leverage these both as a data source and a digital implementation strategy. I-Labs without a shared EHR among partners also leverage other sources for research or surveillance, most commonly qualitative data, surveys, and public health data systems. All seven I-Labs use advisory boards or partnership meetings to engage with members; six use stakeholder interviews and regular communications. Most (70%) tools or methods used to engage I-Lab members such as advisory groups, coalitions, or regular communications, were pre-existing. Think tanks, which two I-Labs developed, represented novel engagement approaches. To disseminate research results, all centers developed web-based products, and most (n = 6) use publications, learning collaboratives, and community forums. Important variations emerged in approaches to health equity, ranging from partnering with members serving historically marginalized populations to the development of novel methods. CONCLUSIONS: The development of the ISC3 implementation laboratories, which represented a variety of research partnership designs, offers the opportunity to advance understanding of how researchers developed and built partnerships to effectively engage stakeholders throughout the cancer control research lifecycle. In future years, we will be able to share lessons learned for the development and sustainment of implementation laboratories