Centromeres License the Mitotic Condensation of Yeast Chromosome Arms.

During mitosis, chromatin condensation shapes chromosomes as separate, rigid, and compact sister chromatids to facilitate their segregation. Here, we show that, unlike wild-type yeast chromosomes, non-chromosomal DNA circles and chromosomes lacking a centromere fail to condense during mitosis. The centromere promotes chromosome condensation strictly in cis through recruiting the kinases Aurora B and Bub1, which trigger the autonomous condensation of the entire chromosome. Shugoshin and the deacetylase Hst2 facilitated spreading the condensation signal to the chromosome arms. Targeting Aurora B to DNA circles or centromere-ablated chromosomes or releasing Shugoshin from PP2A-dependent inhibition bypassed the centromere requirement for condensation and enhanced the mitotic stability of DNA circles. Our data indicate that yeast cells license the chromosome-autonomous condensation of their chromatin in a centromere-dependent manner, excluding from this process non-centromeric DNA and thereby inhibiting their propagation.

Mitotic chromosome condensation resets chromatin to safeguard transcriptional homeostasis during interphase.

Mitotic entry correlates with the condensation of the chromosomes, changes in histone modifications, exclusion of transcription factors from DNA, and the broad downregulation of transcription. However, whether mitotic condensation influences transcription in the subsequent interphase is unknown. Here, we show that preventing one chromosome to condense during mitosis causes it to fail resetting of transcription. Rather, in the following interphase, the affected chromosome contains unusually high levels of the transcription machinery, resulting in abnormally high expression levels of genes in cis, including various transcription factors. This subsequently causes the activation of inducible transcriptional programs in trans, such as the GAL genes, even in the absence of the relevant stimuli. Thus, mitotic chromosome condensation exerts stringent control on interphase gene expression to ensure the maintenance of basic cellular functions and cell identity across cell divisions. Together, our study identifies the maintenance of transcriptional homeostasis during interphase as an unexpected function of mitosis and mitotic chromosome condensation.

Using birth-death processes to infer tumor subpopulation structure from live-cell imaging drug screening data.

Tumor heterogeneity is a complex and widely recognized trait that poses significant challenges in developing effective cancer therapies. In particular, many tumors harbor a variety of subpopulations with distinct therapeutic response characteristics. Characterizing this heterogeneity by determining the subpopulation structure within a tumor enables more precise and successful treatment strategies. In our prior work, we developed PhenoPop, a computational framework for unravelling the drug-response subpopulation structure within a tumor from bulk high-throughput drug screening data. However, the deterministic nature of the underlying models driving PhenoPop restricts the model fit and the information it can extract from the data. As an advancement, we propose a stochastic model based on the linear birth-death process to address this limitation. Our model can formulate a dynamic variance along the horizon of the experiment so that the model uses more information from the data to provide a more robust estimation. In addition, the newly proposed model can be readily adapted to situations where the experimental data exhibits a positive time correlation. We test our model on simulated data (in silico) and experimental data (in vitro), which supports our argument about its advantages.

screenwerk: a modular tool for the design and analysis of drug combination screens.

MOTIVATION: There is a rapidly growing interest in high-throughput drug combination screening to identify synergizing drug interactions for treatment of various maladies, such as cancer and infectious disease. This creates the need for pipelines that can be used to design such screens, perform quality control on the data and generate data files that can be analyzed by synergy-finding bioinformatics applications. RESULTS: screenwerk is an open-source, end-to-end modular tool available as an R-package for the design and analysis of drug combination screens. The tool allows for a customized build of pipelines through its modularity and provides a flexible approach to quality control and data analysis. screenwerk is adaptable to various experimental requirements with an emphasis on precision medicine. It can be coupled to other R packages, such as bayesynergy, to identify synergistic and antagonistic drug interactions in cell lines or patient samples. screenwerk is scalable and provides a complete solution for setting up drug sensitivity screens, read raw measurements and consolidate different datasets, perform various types of quality control and analyze, report and visualize the results of drug sensitivity screens. AVAILABILITY AND IMPLEMENTATION: The R-package and technical documentation is available at https://github.com/Enserink-lab/screenwerk; the R source code is publicly available at https://github.com/Enserink-lab/screenwerk under GNU General Public License v3.0; bayesynergy is accessible at https://github.com/ocbe-uio/bayesynergy. Selected modules are available through Galaxy, an open-source platform for FAIR data analysis at https://oncotools.elixir.no. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Waves of sumoylation support transcription dynamics during adipocyte differentiation.

Tight control of gene expression networks required for adipose tissue formation and plasticity is essential for adaptation to energy needs and environmental cues. However, the mechanisms that orchestrate the global and dramatic transcriptional changes leading to adipocyte differentiation remain to be fully unraveled. We investigated the regulation of nascent transcription by the sumoylation pathway during adipocyte differentiation using SLAMseq and ChIPseq. We discovered that the sumoylation pathway has a dual function in differentiation; it supports the initial downregulation of pre-adipocyte-specific genes, while it promotes the establishment of the mature adipocyte transcriptional program. By characterizing endogenous sumoylome dynamics in differentiating adipocytes by mass spectrometry, we found that sumoylation of specific transcription factors like PPARγ/RXR and their co-factors are associated with the transcription of adipogenic genes. Finally, using RXR as a model, we found that sumoylation may regulate adipogenic transcription by supporting the chromatin occurrence of transcription factors. Our data demonstrate that the sumoylation pathway supports the rewiring of transcriptional networks required for formation of functional adipocytes. This study also provides the scientists in the field of cellular differentiation and development with an in-depth resource of the dynamics of the SUMO-chromatin landscape, SUMO-regulated transcription and endogenous sumoylation sites during adipocyte differentiation.

bayesynergy: flexible Bayesian modelling of synergistic interaction effects in in vitro drug combination experiments.

The effect of cancer therapies is often tested pre-clinically via in vitro experiments, where the post-treatment viability of the cancer cell population is measured through assays estimating the number of viable cells. In this way, large libraries of compounds can be tested, comparing the efficacy of each treatment. Drug interaction studies focus on the quantification of the additional effect encountered when two drugs are combined, as opposed to using the treatments separately. In the bayesynergy R package, we implement a probabilistic approach for the description of the drug combination experiment, where the observed dose response curve is modelled as a sum of the expected response under a zero-interaction model and an additional interaction effect (synergistic or antagonistic). Although the model formulation makes use of the Bliss independence assumption, we note that the posterior estimates of the dose-response surface can also be used to extract synergy scores based on other reference models, which we illustrate for the Highest Single Agent model. The interaction is modelled in a flexible manner, using a Gaussian process formulation. Since the proposed approach is based on a statistical model, it allows the natural inclusion of replicates, handles missing data and uneven concentration grids, and provides uncertainty quantification around the results. The model is implemented in the open-source Stan programming language providing a computationally efficient sampler, a fast approximation of the posterior through variational inference, and features parallel processing for working with large drug combination screens.

A cell competition-based small molecule screen identifies a novel compound that induces dual c-Myc depletion and p53 activation.

Breakpoint Cluster Region-Abelson kinase (BCR-Abl) is a driver oncogene that causes chronic myeloid leukemia and a subset of acute lymphoid leukemias. Although tyrosine kinase inhibitors provide an effective treatment for these diseases, they generally do not kill leukemic stem cells (LSCs), the cancer-initiating cells that compete with normal hematopoietic stem cells for the bone marrow niche. New strategies to target cancers driven by BCR-Abl are therefore urgently needed. We performed a small molecule screen based on competition between isogenic untransformed cells and BCR-Abl-transformed cells and identified several compounds that selectively impair the fitness of BCR-Abl-transformed cells. Interestingly, systems-level analysis of one of these novel compounds, DJ34, revealed that it induced depletion of c-Myc and activation of p53. DJ34-mediated c-Myc depletion occurred in a wide range of tumor cell types, including lymphoma, lung, glioblastoma, breast cancer, and several forms of leukemia, with primary LSCs being particularly sensitive to DJ34. Further analyses revealed that DJ34 interferes with c-Myc synthesis at the level of transcription, and we provide data showing that DJ34 is a DNA intercalator and topoisomerase II inhibitor. Physiologically, DJ34 induced apoptosis, cell cycle arrest, and cell differentiation. Taken together, we have identified a novel compound that dually targets c-Myc and p53 in a wide variety of cancers, and with particularly strong activity against LSCs.

Cell Cycle-Dependent Transcription: The Cyclin Dependent Kinase Cdk1 Is a Direct Regulator of Basal Transcription Machineries.

The cyclin-dependent kinase Cdk1 is best known for its function as master regulator of the cell cycle. It phosphorylates several key proteins to control progression through the different phases of the cell cycle. However, studies conducted several decades ago with mammalian cells revealed that Cdk1 also directly regulates the basal transcription machinery, most notably RNA polymerase II. More recent studies in the budding yeast Saccharomyces cerevisiae have revisited this function of Cdk1 and also revealed that Cdk1 directly controls RNA polymerase III activity. These studies have also provided novel insight into the physiological relevance of this process. For instance, cell cycle-stage-dependent activity of these complexes may be important for meeting the increased demand for various proteins involved in housekeeping, metabolism, and protein synthesis. Recent work also indicates that direct regulation of the RNA polymerase II machinery promotes cell cycle entry. Here, we provide an overview of the regulation of basal transcription by Cdk1, and we hypothesize that the original function of the primordial cell-cycle CDK was to regulate RNAPII and that it later evolved into specialized kinases that govern various aspects of the transcription machinery and the cell cycle.

Tyrosine kinase inhibitors and interferon-α increase tunneling nanotube (TNT) formation and cell adhesion in chronic myeloid leukemia (CML) cell lines.

Chronic myeloid leukemia (CML) is a stem cell disease of the bone marrow where mechanisms of inter-leukemic communication and cell-to-cell interactions are proposed to be important for optimal therapy response. Tunneling nanotubes (TNTs) are novel intercellular communication structures transporting different cargos with potential implications in therapy resistance. Here, we have investigated TNTs in CML cells and following treatment with the highly effective CML therapeutics tyrosine kinase inhibitors (TKIs) and interferon-α (IFNα). CML cells from chronic phase CML patients as well as the blast crisis phase cell lines, Kcl-22 and K562, formed few or no TNTs. Treatment with imatinib increased TNT formation in both Kcl-22 and K562 cells, while nilotinib or IFNα increased TNTs in Kcl-22 cells only where the TNT increase was associated with adherence to fibronectin-coated surfaces, altered morphology, and reduced movement involving ß1integrin. Ex vivo treated cells from chronic phase CML patients showed limited changes in TNT formation similarly to bone marrow cells from healthy individuals. Interestingly, in vivo nilotinib treatment in a Kcl-22 subcutaneous mouse model resulted in morphological changes and TNT-like structures in the tumor-derived Kcl-22 cells. Our results demonstrate that CML cells express low levels of TNTs, but CML therapeutics increase TNT formation in designated cell models indicating TNT functionality in bone marrow derived malignancies and their microenvironment.

Pharmacologic Control of CAR T Cells.

Chimeric antigen receptor (CAR) therapy is a promising modality for the treatment of advanced cancers that are otherwise incurable. During the last decade, different centers worldwide have tested the anti-CD19 CAR T cells and shown clinical benefits in the treatment of B cell tumors. However, despite these encouraging results, CAR treatment has also been found to lead to serious side effects and capricious response profiles in patients. In addition, the CD19 CAR success has been difficult to reproduce for other types of malignancy. The appearance of resistant tumor variants, the lack of antigen specificity, and the occurrence of severe adverse effects due to over-stimulation of the therapeutic cells have been identified as the major impediments. This has motivated a growing interest in developing strategies to overcome these hurdles through CAR control. Among them, the combination of small molecules and approved drugs with CAR T cells has been investigated. These have been exploited to induce a synergistic anti-cancer effect but also to control the presence of the CAR T cells or tune the therapeutic activity. In the present review, we discuss opportunistic and rational approaches involving drugs featuring anti-cancer efficacy and CAR-adjustable effect.

Desumoylation of RNA polymerase III lies at the core of the Sumo stress response in yeast.

Post-translational modification by small ubiquitin-like modifier (Sumo) regulates many cellular processes, including the adaptive response to various types of stress, referred to as the Sumo stress response (SSR). However, it remains unclear whether the SSR involves a common set of core proteins regardless of the type of stress or whether each particular type of stress induces a stress-specific SSR that targets a unique, largely nonoverlapping set of Sumo substrates. In this study, we used MS and a Gene Ontology approach to identify differentially sumoylated proteins during heat stress, hyperosmotic stress, oxidative stress, nitrogen starvation, and DNA alkylation in Saccharomyces cerevisiae cells. Our results indicate that each stress triggers a specific SSR signature centered on proteins involved in transcription, translation, and chromatin regulation. Strikingly, whereas the various stress-specific SSRs were largely nonoverlapping, all types of stress tested here resulted in desumoylation of subunits of RNA polymerase III, which correlated with a decrease in tRNA synthesis. We conclude that desumoylation and subsequent inhibition of RNA polymerase III constitutes the core of all stress-specific SSRs in yeast.

Cdk1 gates cell cycle-dependent tRNA synthesis by regulating RNA polymerase III activity.

tRNA genes are transcribed by RNA polymerase III (RNAPIII). During recent years it has become clear that RNAPIII activity is strictly regulated by the cell in response to environmental cues and the homeostatic status of the cell. However, the molecular mechanisms that control RNAPIII activity to regulate the amplitude of tDNA transcription in normally cycling cells are not well understood. Here, we show that tRNA levels fluctuate during the cell cycle and reveal an underlying molecular mechanism. The cyclin Clb5 recruits the cyclin dependent kinase Cdk1 to tRNA genes to boost tDNA transcription during late S phase. At tDNA genes, Cdk1 promotes the recruitment of TFIIIC, stimulates the interaction between TFIIIB and TFIIIC, and increases the dynamics of RNA polymerase III in vivo. Furthermore, we identified Bdp1 as a putative Cdk1 substrate in this process. Preventing Bdp1 phosphorylation prevented cell cycle-dependent recruitment of TFIIIC and abolished the cell cycle-dependent increase in tDNA transcription. Our findings demonstrate that under optimal growth conditions Cdk1 gates tRNA synthesis in S phase by regulating the RNAPIII machinery, revealing a direct link between the cell cycle and RNAPIII activity.

TORC1-dependent sumoylation of Rpc82 promotes RNA polymerase III assembly and activity.

Maintaining cellular homeostasis under changing nutrient conditions is essential for the growth and development of all organisms. The mechanisms that maintain homeostasis upon loss of nutrient supply are not well understood. By mapping the SUMO proteome in Saccharomyces cerevisiae, we discovered a specific set of differentially sumoylated proteins mainly involved in transcription. RNA polymerase III (RNAPIII) components, including Rpc53, Rpc82, and Ret1, are particularly prominent nutrient-dependent SUMO targets. Nitrogen starvation, as well as direct inhibition of the master nutrient response regulator target of rapamycin complex 1 (TORC1), results in rapid desumoylation of these proteins, which is reflected by loss of SUMO at tRNA genes. TORC1-dependent sumoylation of Rpc82 in particular is required for robust tRNA transcription. Mechanistically, sumoylation of Rpc82 is important for assembly of the RNAPIII holoenzyme and recruitment of Rpc82 to tRNA genes. In conclusion, our data show that TORC1-dependent sumoylation of Rpc82 bolsters the transcriptional capacity of RNAPIII under optimal growth conditions.

Sumoylation of Rap1 mediates the recruitment of TFIID to promote transcription of ribosomal protein genes.

Transcription factors are abundant Sumo targets, yet the global distribution of Sumo along the chromatin and its physiological relevance in transcription are poorly understood. Using Saccharomyces cerevisiae, we determined the genome-wide localization of Sumo along the chromatin. We discovered that Sumo-enriched genes are almost exclusively involved in translation, such as tRNA genes and ribosomal protein genes (RPGs). Genome-wide expression analysis showed that Sumo positively regulates their transcription. We also discovered that the Sumo consensus motif at RPG promoters is identical to the DNA binding motif of the transcription factor Rap1. We demonstrate that Rap1 is a molecular target of Sumo and that sumoylation of Rap1 is important for cell viability. Furthermore, Rap1 sumoylation promotes recruitment of the basal transcription machinery, and sumoylation of Rap1 cooperates with the target of rapamycin kinase complex 1 (TORC1) pathway to promote RPG transcription. Strikingly, our data reveal that sumoylation of Rap1 functions in a homeostatic feedback loop that sustains RPG transcription during translational stress. Taken together, Sumo regulates the cellular translational capacity by promoting transcription of tRNA genes and RPGs.

GRP94 rewires and buffers the FLT3-ITD signaling network and promotes survival of acute myeloid leukemic stem cells.

Internal tandem duplications in the tyrosine kinase receptor FLT3 (FLT3-ITD) are among the most common lesions in acute myeloid leukemia and there exists a need for new forms of treatment. Using ex vivo drug sensitivity screening, we found that FLT3-ITD+ patient cells are particularly sensitive to HSP90 inhibitors. While it is well known that HSP90 is important for FLT3-ITD stability, we found that HSP90 family members play a much more complex role in FLT3-ITD signaling than previously appreciated. First, we found that FLT3-ITD activates the unfolded protein response, leading to increased expression of GRP94/HSP90B1. This results in activation of a nefarious feedback loop, in which GRP94 rewires FLT3-ITD signaling by binding and retaining FLT3-ITD in the endoplasmic reticulum, leading to aberrant activation of downstream signaling pathways and further inducing the unfolded protein response. Second, HSP90 family proteins protect FLT3-ITD+ acute myeloid leukemia cells against apoptosis by alleviating proteotoxic stress, and treatment with HSP90 inhibitors results in proteotoxic overload that triggers unfolded protein response-induced apoptosis. Importantly, leukemic stem cells are strongly dependent upon HSP90 for their survival, and the HSP90 inhibitor ganetespib causes leukemic stem cell exhaustion in patient-derived mouse xenograft models. Taken together, our study reveals a molecular basis for HSP90 addiction of FLT3-ITD+ acute myeloid leukemia cells and provides a rationale for including HSP90 inhibitors in the treatment regime for FLT3-ITD+ acute myeloid leukemia.

SUMO-regulated transcription: challenging the dogma.

The small ubiquitin-like modifier SUMO regulates many aspects of cellular physiology to maintain cell homeostasis, both under normal conditions and during cell stress. Components of the transcriptional apparatus and chromatin are among the most prominent SUMO substrates. The prevailing view is that SUMO serves to repress transcription. However, as we will discuss in this review, this model needs to be refined, because recent studies have revealed that SUMO can also have profound positive effects on transcription.

Regulation of Cellular Processes by SUMO: Understudied Topics.

SUMO plays a multiple role in maintenance of cellular homeostasis, both under normal conditions and under cell stress . Considerable effort has been devoted to unraveling the functions of SUMO in regulation of transcription and preservation of genome stability. However, it is clear from high-throughput SUMO proteome studies that SUMO likely regulates many more cellular processes. The function of SUMO in these processes has hardly been explored. This review will focus on the emerging function of SUMO in regulation of several of these processes.

Identification and characterization of a novel evolutionarily conserved lysine-specific methyltransferase targeting eukaryotic translation elongation factor 2 (eEF2).

The components of the cellular protein translation machinery, such as ribosomal proteins and translation factors, are subject to numerous post-translational modifications. In particular, this group of proteins is frequently methylated. However, for the majority of these methylations, the responsible methyltransferases (MTases) remain unknown. The human FAM86A (family with sequence similarity 86) protein belongs to a recently identified family of protein MTases, and we here show that FAM86A catalyzes the trimethylation of eukaryotic elongation factor 2 (eEF2) on Lys-525. Moreover, we demonstrate that the Saccharomyces cerevisiae MTase Yjr129c, which displays sequence homology to FAM86A, is a functional FAM86A orthologue, modifying the corresponding residue (Lys-509) in yeast eEF2, both in vitro and in vivo. Finally, Yjr129c-deficient yeast cells displayed phenotypes related to eEF2 function (i.e. increased frameshifting during protein translation and hypersensitivity toward the eEF2-specific drug sordarin). In summary, the present study establishes the function of the previously uncharacterized MTases FAM86A and Yjr129c, demonstrating that these enzymes introduce a functionally important lysine methylation in eEF2. Based on the previous naming of similar enzymes, we have redubbed FAM86A and Yjr129c as eEF2-KMT and Efm3, respectively.