Challenges for Restoration of Coastal Marine Ecosystems in the Anthropocene

Coastal marine ecosystems provide critical goods and services to humanity but many are experiencing rapid degradation. The need for effective restoration tools capable of promoting large-scale recovery of coastal ecosystems in the face of intensifying climatic stress has never been greater. We identify four major challenges for more effective implementation of coastal marine ecosystem restoration (MER): (1) development of effective, scalable restoration methods, (2) incorporation of innovative tools that promote climate adaptation, (3) integration of social and ecological restoration priorities, and (4) promotion of the perception and use of coastal MER as a scientifically credible management approach. Tackling these challenges should improve restoration success rates, heighten their recognition, and accelerate investment in and promotion of coastal MER. To reverse the accelerating decline of marine ecosystems, we discuss potential directions for meeting these challenges by applying coastal MER tools that are science-based and actionable. For coastal restoration to have a global impact, it must incorporate social science, technological and conceptual advances, and plan for future climate scenarios

A primary breast cancer with distinct foci of estrogen receptor-alpha positive and negative cells derived from the same clonal origin as revealed by whole exome sequencing

© 2018, Springer Science+Business Media, LLC, part of Springer Nature. Background/purpose: Tumor heterogeneity is a now well-recognized phenomenon that can affect the classification, prognosis and treatment of human cancers. Heterogeneity is often described in primary breast cancers based upon histologic subtypes, hormone- and HER2-receptor status, and immunolabeling for various markers, which can be seen within a single tumor as mixed cellular populations, or as separate discrete foci. Experimental design/methods: Here, we present a case report of a patient’s primary breast cancer that had two separate but adjacent histologic components, one that was estrogen receptor (ER) positive, and the other ER negative. Each component was subjected to whole exome sequencing and compared for gene identity to determine clonal origin. Results: Using prior bioinformatic tools, we demonstrated that both the ER positive and negative components shared many variants, including passenger and driver alterations. Copy number variations also supported the two components were derived from a single common clone. Conclusions: These analyses strongly suggest that the two ER components of this patient’s breast cancer were derived from the same clonal origin. Our results have implications for the evolution of breast cancers with mixed histologies, and how they might be best managed for optimal therapy

Reordering buffer management with advice

In the reordering buffer management problem, a sequence of colored items arrives at a service station to be processed. Each color change between two consecutively processed items generates some cost. A reordering buffer of capacity k items can be used to preprocess the input sequence in order to decrease the number of color changes. The goal is to find a scheduling strategy that, using the reordering buffer, minimizes the number of color changes in the given sequence of items. We consider the problem in the setting of online computation with advice. In this model, the color of an item becomes known only at the time when the item enters the reordering buffer. Additionally, together with each item entering the buffer, we get a fixed number of advice bits, which can be seen as information about the future or as information about an optimal solution (or an approximation thereof) for the whole input sequence. We show that for any ε>0 there is a (1+ε)-competitive algorithm for the problem which uses only a constant (depending on ε) number of advice bits per input item. This also immediately implies a (1+ε)-approximation algorithm which has 2O(nlog1/ε) running time (this should be compared to the trivial optimal algorithm which has a running time of kO(n)). We complement the above result by presenting a lower bound of Ω(logk) bits of advice per request for any 1-competitive algorithm

Clusters versus Affinity-Based Approaches in F. tularensis Whole Genome Search of CTL Epitopes

Deciphering the cellular immunome of a bacterial pathogen is challenging due to the enormous number of putative peptidic determinants. State-of-the-art prediction methods developed in recent years enable to significantly reduce the number of peptides to be screened, yet the number of remaining candidates for experimental evaluation is still in the range of ten-thousands, even for a limited coverage of MHC alleles. We have recently established a resource-efficient approach for down selection of candidates and enrichment of true positives, based on selection of predicted MHC binders located in high density “hotspots" of putative epitopes. This cluster-based approach was applied to an unbiased, whole genome search of Francisella tularensis CTL epitopes and was shown to yield a 17–25 fold higher level of responders as compared to randomly selected predicted epitopes tested in Kb/Db C57BL/6 mice. In the present study, we further evaluate the cluster-based approach (down to a lower density range) and compare this approach to the classical affinity-based approach by testing putative CTL epitopes with predicted IC50 values of <10 nM. We demonstrate that while the percent of responders achieved by both approaches is similar, the profile of responders is different, and the predicted binding affinity of most responders in the cluster-based approach is relatively low (geometric mean of 170 nM), rendering the two approaches complimentary. The cluster-based approach is further validated in BALB/c F. tularensis immunized mice belonging to another allelic restriction (Kd/Dd) group. To date, the cluster-based approach yielded over 200 novel F. tularensis peptides eliciting a cellular response, all were verified as MHC class I binders, thereby substantially increasing the F. tularensis dataset of known CTL epitopes. The generality and power of the high density cluster-based approach suggest that it can be a valuable tool for identification of novel CTLs in proteomes of other bacterial pathogens

Inhibition of Human Acetyl- and Butyrylcholinesterase by Novel Carbamates of (−)- and (+)-Tetrahydrofurobenzofuran and Methanobenzodioxepine

A new enantiomeric synthesis utilizing classical resolution provided two novel series of optically active inhibitors of cholinesterase: (−)- and (+)- O-carbamoyl phenols of tetrahydrofurobenzofuran and methanobenzodioxepine. An additional two series of (−)- and (+)-O-carbamoyl phenols of pyrroloindole and furoindole were obtained by known procedures, and their anticholinesterase actions were similarly quantified against freshly prepared human acetyl- (AChE) and butyrylcholinesterase (BChE). Both enantiomeric forms of each series demonstrated potent cholinesterase inhibitory activity (with IC50 values as low as 10 nM for AChE and 3 nM for BChE), with the exception of the (+)-O-carbamoyl phenols of pyrroloindole that lacked activity (IC50 values > 1 µM). Based on the biological data of these four series, a SAR analysis was provided by molecular volume calculations. In addition, a probable transition state model was established according to the known X-ray structure of a transition state complex of Torpedo californica AChE-m-(N,N,N,trimethylammonio)-2,2,2-trifluoroacetophenone (TcAChE-TMTFA). This model proved valuable in explaining the enantio-selectivity and enzyme subtype selectivity of each series. These carbamates are more or similarly potent to anticholinesterases in current clinical use; providing not only inhibitors of potential clinical relevance but also pharmacological tools to define drug-enzyme binding interactions within an enzyme crucial in the maintenance of cognition and numerous systemic physiological functions in health, aging and disease

Epigenetic Profiling and Response to CD19 Chimeric Antigen Receptor T-Cell Therapy in B-Cell Malignancies

Background: Chimeric antigen receptor (CAR) T cells directed against CD19 (CART19) are effective in B-cell malignancies, but little is known about the molecular factors predicting clinical outcome of CART19 therapy. The increasingly recognized relevance of epigenetic changes in cancer immunology prompted us to determine the impact of the DNA methylation profiles of CART19 cells on the clinical course. Methods: We recruited 114 patients with B-cell malignancies, comprising 77 patients with acute lymphoblastic leukemia and 37 patients with non-Hodgkin lymphoma who were treated with CART19 cells. Using a comprehensive DNA methylation microarray, we determined the epigenomic changes that occur in the patient T cells upon transduction of the CAR vector. The effects of the identified DNA methylation sites on clinical response, cytokine release syndrome, immune effector cell-associated neurotoxicity syndrome, event-free survival, and overall survival were assessed. All statistical tests were 2-sided. Results: We identified 984 genomic sites with differential DNA methylation between CAR-untransduced and CAR-transduced T cells before infusion into the patient. Eighteen of these distinct epigenetic loci were associated with complete response (CR), adjusting by multiple testing. Using the sites linked to CR, an epigenetic signature, referred to hereafter as the EPICART signature, was established in the initial discovery cohort (n = 79), which was associated with CR (Fisher exact test, P < .001) and enhanced event-free survival (hazard ratio [HR] = 0.36; 95% confidence interval [CI] = 0.19 to 0.70; P = .002; log-rank P = .003) and overall survival (HR = 0.45; 95% CI = 0.20 to 0.99; P = .047; log-rank P = .04;). Most important, the EPICART profile maintained its clinical course predictive value in the validation cohort (n = 35), where it was associated with CR (Fisher exact test, P < .001) and enhanced overall survival (HR = 0.31; 95% CI = 0.11 to 0.84; P = .02; log-rank P = .02). Conclusions: We show that the DNA methylation landscape of patient CART19 cells influences the efficacy of the cellular immunotherapy treatment in patients with B-cell malignancy.Supported by CERCA Programme/Generalitat de Catalunya, Health Department PERIS #SLT/002/16/00374, AGAUR-project #2017SGR1080; MCI/AEI/ERDF project #RTI2018-094049-B-I00; ERC EPIPHARM; Cellex Foundation; “la Caixa” Foundation (LCF/PR/GN18/51140001 and LCF/PR/GN18/50310007), RF-2016–02364388, Accelerator Award—Cancer Research UK/AIRC—INCAR Associazione Italiana Ricerca per la Ricerca sul Cancro (AIRC) Project 5 × 1000 no. 9962, AIRC IG 2018 id. 21724, AIRC MFAG id. 21769 and id. 20450; MIUR (Grant PRIN 2017); and RCR-2019–23669115

Modeling Stochasticity and Variability in Gene Regulatory Networks

Modeling stochasticity in gene regulatory networks is an important and complex problem in molecular systems biology. To elucidate intrinsic noise, several modeling strategies such as the Gillespie algorithm have been used successfully. This paper contributes an approach as an alternative to these classical settings. Within the discrete paradigm, where genes, proteins, and other molecular components of gene regulatory networks are modeled as discrete variables and are assigned as logical rules describing their regulation through interactions with other components. Stochasticity is modeled at the biological function level under the assumption that even if the expression levels of the input nodes of an update rule guarantee activation or degradation there is a probability that the process will not occur due to stochastic effects. This approach allows a finer analysis of discrete models and provides a natural setup for cell population simulations to study cell-to-cell variability. We applied our methods to two of the most studied regulatory networks, the outcome of lambda phage infection of bacteria and the p53-mdm2 complex.Comment: 23 pages, 8 figure

Blockchains for Business Process Management - Challenges and Opportunities

Blockchain technology promises a sizable potential for executing inter-organizational business processes without requiring a central party serving as a single point of trust (and failure). This paper analyzes its impact on business process management (BPM). We structure the discussion using two BPM frameworks, namely the six BPM core capabilities and the BPM lifecycle. This paper provides research directions for investigating the application of blockchain technology to BPM.Comment: Preprint for ACM TMI