The Effects Of Bone Marrow Adipocytes On Metabolic Regulation In Metastatic Prostate Cancer

Bone is a preferential site of metastasis from prostate cancer (PCa). Although there have been many advances in therapeutic options for patients suffering from metastatic PCa, this disease remains incurable with an estimated five-year survival of 33%. To design effective therapeutic interventions for metastatic PCa, it is essential that we elucidate the molecular mechanisms responsible for tumor cell adaptation to and the ability to thrive within the bone metastatic niche. Age and obesity, conditions that increase adipocyte numbers in bone marrow, are risk factors for skeletal metastases from PCa; therefore, our laboratory is focused on the interactions between marrow adipocytes and PCa cells. We initially detailed the metabolic alterations that occur in prostate cancer cells in response to interactions with bone marrow adipocytes in multiple in vivo and in vitro models. The following conclusions were drawn as a result of these experiments: 1) Patients with metastatic disease have increased expression of glycolytic and hypoxic genes compared to primary PCa tumors; 2) tumors grown intratibially in vivo in diet induced models of high marrow adiposity have increased expression of glycolytic and hypoxic genes compared to mice with fewer marrow adipocytes; 3) paracrine interactions between tumor cells and adipocytes in vitro induce expression of glycolytic and hypoxic proteins in tumor cells; 4) PCa cells exposed to adipocytes with increased expression of glycolytic markers exhibit enhanced Warburg metabolism with increases in lactate production, decreases in oxidative phosphorylation, and decreases in ATP production without perturbation of mitochondrial integrity or cellular viability; 5) tumor cells stimulate lipolysis within adipocytes but the inhibition of lipolysis does not affect adipocyte-driven changes in PCa cell metabolism due to possible compensatory mechanisms; 6) metabolic effects are driven through the activation of HIF-1α in PCa cells as shown by increased expression of hypoxia-responsive genes and the reversal of adipocyte-induced metabolic changes upon knockdown of tumor cell HIF-1α. Additionally, we found novel signaling pathways are activated in tumor cells due to cross talk between tumor cells and adipocytes. We observed a regulation of COX-2 in adipocytes by tumor-secreted IL-1β that leads to increased PGE2 synthesis and release and this PGE2 signals through the EP receptors on the tumor cells to elicit downstream GSK3β/β-catenin signaling and subsequent HIF-1α activation. We also observed increased SPHK1 in adipocytes exposed to tumor cells as an effect of tumor-stimulated lipolysis within adipocytes, but that S1P was not sufficient to activate HIF-1α signaling in tumor cells or downstream metabolic alterations. In summary, we have discovered novel crosstalk between metastatic prostate tumor cells and bone marrow adipocytes that cause activation of many pathways involved in tumor survival and growth within the bone. We have revealed a functional contribution of bone marrow adipocytes to altered tumor metabolism and signaling in bone. The expected outcome of this research is the validation of the significance of adipocyte-derived lipids in growth and aggressiveness of metastatic PCa in bone. The ultimate goal is utilize findings from this study to explore whether adipocyte-driven metabolic adaptation contributes to chemoresistance of skeletal tumors and whether targeting tumor metabolism offers new options for improved therapy and/or prevention of aggressive disease

Predicting Dynamic Memory Requirements for Scientific Workflow Tasks

With the increasing amount of data available to scientists in disciplines as diverse as bioinformatics, physics, and remote sensing, scientific workflow systems are becoming increasingly important for composing and executing scalable data analysis pipelines. When writing such workflows, users need to specify the resources to be reserved for tasks so that sufficient resources are allocated on the target cluster infrastructure. Crucially, underestimating a task's memory requirements can result in task failures. Therefore, users often resort to overprovisioning, resulting in significant resource wastage and decreased throughput. In this paper, we propose a novel online method that uses monitoring time series data to predict task memory usage in order to reduce the memory wastage of scientific workflow tasks. Our method predicts a task's runtime, divides it into k equally-sized segments, and learns the peak memory value for each segment depending on the total file input size. We evaluate the prototype implementation of our method using workflows from the publicly available nf-core repository, showing an average memory wastage reduction of 29.48% compared to the best state-of-the-art approac

Epigenetic activation of the FLT3 gene by ZNF384 fusion confers a therapeutic susceptibility in acute lymphoblastic leukemia.

FLT3 is an attractive therapeutic target in acute lymphoblastic leukemia (ALL) but the mechanism for its activation in this cancer is incompletely understood. Profiling global gene expression in large ALL cohorts, we identify over-expression of FLT3 in ZNF384-rearranged ALL, consistently across cases harboring different fusion partners with ZNF384. Mechanistically, we discover an intergenic enhancer element at the FLT3 locus that is exclusively activated in ZNF384-rearranged ALL, with the enhancer-promoter looping directly mediated by the fusion protein. There is also a global enrichment of active enhancers within ZNF384 binding sites across the genome in ZNF384-rearranged ALL cells. Downregulation of ZNF384 blunts FLT3 activation and decreases ALL cell sensitivity to FLT3 inhibitor gilteritinib in vitro. In patient-derived xenograft models of ZNF384-rearranged ALL, gilteritinib exhibits significant anti-leukemia efficacy as a monotherapy in vivo. Collectively, our results provide insights into FLT3 regulation in ALL and point to potential genomics-guided targeted therapy for this patient population

Development and Preliminary Clinical Activity of PD-1-Guided CTLA-4 Blocking Bispecific DART Molecule.

Combination immunotherapy with antibodies directed against PD-1 and CTLA-4 shows improved clinical benefit across cancer indications compared to single agents, albeit with increased toxicity. Leveraging the observation that PD-1 and CTLA-4 are co-expressed by tumor-infiltrating lymphocytes, an investigational PD-1 x CTLA-4 bispecific DART molecule, MGD019, is engineered to maximize checkpoint blockade in the tumor microenvironment via enhanced CTLA-4 blockade in a PD-1-binding-dependent manner

Molecular Epidemiology and Evolutionary Trajectory of Emerging Echovirus 30, Europe

In 2018, an upsurge in echovirus 30 (E30) infections was reported in Europe. We conducted a large-scale epidemiologic and evolutionary study of 1,329 E30 strains collected in 22 countries in Europe during 2016-2018. Most E30 cases affected persons 0-4 years of age (29%) and 25-34 years of age (27%). Sequences were divided into 6 genetic clades (G1-G6). Most (53%) sequences belonged to G1, followed by G6 (23%), G2 (17%), G4 (4%), G3 (0.3%), and G5 (0.2%). Each clade encompassed unique individual recombinant forms; G1 and G4 displayed >= 2 unique recombinant forms. Rapid turnover of new clades and recombinant forms occurred over time. Clades G1 and G6 dominated in 2018, suggesting the E30 upsurge was caused by emergence of 2 distinct clades circulating in Europe. Investigation into the mechanisms behind the rapid turnover of E30 is crucial for clarifying the epidemiology and evolution of these enterovirus infections.Peer reviewe

Recommendations for enterovirus diagnostics and characterisation within and beyond Europe.

Enteroviruses (EV) can cause severe neurological and respiratory infections, and occasionally lead to devastating outbreaks as previously demonstrated with EV-A71 and EV-D68 in Europe. However, these infections are still often underdiagnosed and EV typing data is not currently collected at European level. In order to improve EV diagnostics, collate data on severe EV infections and monitor the circulation of EV types, we have established European non-polio enterovirus network (ENPEN). First task of this cross-border network has been to ensure prompt and adequate diagnosis of these infections in Europe, and hence we present recommendations for non-polio EV detection and typing based on the consensus view of this multidisciplinary team including experts from over 20 European countries. We recommend that respiratory and stool samples in addition to cerebrospinal fluid (CSF) and blood samples are submitted for EV testing from patients with suspected neurological infections. This is vital since viruses like EV-D68 are rarely detectable in CSF or stool samples. Furthermore, reverse transcriptase PCR (RT-PCR) targeting the 5'noncoding regions (5'NCR) should be used for diagnosis of EVs due to their sensitivity, specificity and short turnaround time. Sequencing of the VP1 capsid protein gene is recommended for EV typing; EV typing cannot be based on the 5'NCR sequences due to frequent recombination events and should not rely on virus isolation. Effective and standardized laboratory diagnostics and characterisation of circulating virus strains are the first step towards effective and continuous surveillance activities, which in turn will be used to provide better estimation on EV disease burden

Re-emergence of enterovirus D68 in Europe after easing the COVID-19 lockdown, September 2021

We report a rapid increase in enterovirus D68 (EV-D68) infections, with 139 cases reported from eight European countries between 31 July and 14 October 2021. This upsurge is in line with the seasonality of EV-D68 and was presumably stimulated by the widespread reopening after COVID-19 lockdown. Most cases were identified in September, but more are to be expected in the coming months. Reinforcement of clinical awareness, diagnostic capacities and surveillance of EV-D68 is urgently needed in Europe

Predicting Dynamic Memory Requirements for Scientific Workflow Tasks

With the increasing amount of data available to scientists in disciplines as diverse as bioinformatics, physics, and remote sensing, scientific workflow systems are becoming increasingly important for composing and executing scalable data analysis pipelines. When writing such workflows, users need to specify the resources to be reserved for tasks so that sufficient resources are allocated on the target cluster infrastructure. Crucially, underestimating a task’s memory requirements can result in task failures. Therefore, users often resort to overprovisioning, resulting in significant resource wastage and decreased throughput. In this paper, we propose a novel online method that uses monitoring time series data to predict task memory usage in order to reduce the memory wastage of scientific workflow tasks. Our method predicts a task’s runtime, divides it into k equally sized segments, and learns the peak memory value for each segment depending on the total file input size. We evaluate the prototype implementation of our method using workflows from the publicly available nf-core repository, showing an average memory wastage reduction of 29.48% compared to the best state of-the-art approac