Influence of food quality on larval growth of Atlantic bluefin tuna (Thunnus thynnus) in the Gulf of Mexico

Larval abundances of Atlantic bluefin tuna (ABT) in the Gulf of Mexico are currently utilized to inform future recruitment by providing a proxy for the spawning potential of western ABT stock. Inclusion of interannual variations in larval growth is a key advance needed to translate larval abundance to recruitment success. However, little is known about the drivers of growth variations during the first weeks of life. We sampled patches of western ABT larvae in 3–4 day Lagrangian experiments in May 2017 and 2018, and assessed age and growth rates from sagittal otoliths relative to size categories of zooplankton biomass and larval feeding behaviors from stomach contents. Growth rates were similar, on average, between patches (0.37 versus 0.39 mm d−1) but differed significantly through ontogeny and were correlated with a food limitation index, highlighting the importance of prey availability. Otolith increment widths were larger for postflexion stages in 2018, coincident with high feeding on preferred prey (mainly cladocerans) and presumably higher biomass of more favorable prey type. Faster growth reflected in the otolith microstructures may improve survival during the highly vulnerable larval stages of ABT, with direct implications for recruitment processes.En prensa1,74

Bluefin Tuna Larvae in Oligotrophic Ocean Foodwebs, Investigations of Nutrients to Zooplankton: Overview of the BLOOFINZ-Gulf of Mexico program

Western Atlantic bluefin tuna (ABT) undertake long-distance migrations from rich feeding grounds in the North Atlantic to spawn in oligotrophic waters of the Gulf of Mexico (GoM). Stock recruitment is strongly affected by interannual variability in the physical features associated with ABT larvae, but the nutrient sources and food-web structure of preferred habitat, the edges of anticyclonic loop eddies, are unknown. Here, we describe the goals, physical context, design and major findings of an end-to-end process study conducted during peak ABT spawning in May 2017 and 2018. Mesoscale features in the oceanic GoM were surveyed for larvae, and five multi-day Lagrangian experiments measured hydrography and nutrients; plankton biomass and composition from bacteria to zooplankton and fish larvae; phytoplankton nutrient uptake, productivity and taxon-specific growth rates; micro- and mesozooplankton grazing; particle export; and ABT larval feeding and growth rates. We provide a general introduction to the BLOOFINZ-GoM project (Bluefin tuna Larvae in Oligotrophic Ocean Foodwebs, Investigation of Nitrogen to Zooplankton) and highlight the finding, based on backtracking of experimental waters to their positions weeks earlier, that lateral transport from the continental slope region may be more of a key determinant of available habitat utilized by larvae than eddy edges per se.Postprint1,74

Trade-Offs Between Risks of Predation and Starvation In Larvae Make the Shelf Break an Optimal Spawning Location For Atlantic Bluefin Tuna

Atlantic bluefin tuna (ABT) (Thunnus thynnus) travel long distances to spawn in oligotrophic regions of the Gulf of Mexico (GoM) which suggests these regions offer some unique benefit to offspring survival. To better understand how larval survival varies within the GoM a spatially explicit, Lagrangian, individual-based model was developed that simulates dispersal and mortality of ABT early life stages within realistic predator and prey fields during the spawning periods from 1993 to 2012. The model estimates that starvation is the largest cumulative source of mortality associated with an early critical period. However, elevated predation on older larvae is identified as the main factor limiting survival to late postflexion. As a result, first-feeding larvae have higher survival on the shelf where food is abundant, whereas older larvae have higher survival in the open ocean with fewer predators, making the shelf break an optimal spawning area. The modeling framework developed in this study explicitly simulates both physical and biological factors that impact larval survival and hence could be used to support ecosystem based management efforts for ABT under current and future climate conditions

Paraneoplastic thrombocytosis in ovarian cancer

<p>Background: The mechanisms of paraneoplastic thrombocytosis in ovarian cancer and the role that platelets play in abetting cancer growth are unclear.</p> <p>Methods: We analyzed clinical data on 619 patients with epithelial ovarian cancer to test associations between platelet counts and disease outcome. Human samples and mouse models of epithelial ovarian cancer were used to explore the underlying mechanisms of paraneoplastic thrombocytosis. The effects of platelets on tumor growth and angiogenesis were ascertained.</p> <p>Results: Thrombocytosis was significantly associated with advanced disease and shortened survival. Plasma levels of thrombopoietin and interleukin-6 were significantly elevated in patients who had thrombocytosis as compared with those who did not. In mouse models, increased hepatic thrombopoietin synthesis in response to tumor-derived interleukin-6 was an underlying mechanism of paraneoplastic thrombocytosis. Tumorderived interleukin-6 and hepatic thrombopoietin were also linked to thrombocytosis in patients. Silencing thrombopoietin and interleukin-6 abrogated thrombocytosis in tumor-bearing mice. Anti–interleukin-6 antibody treatment significantly reduced platelet counts in tumor-bearing mice and in patients with epithelial ovarian cancer. In addition, neutralizing interleukin-6 significantly enhanced the therapeutic efficacy of paclitaxel in mouse models of epithelial ovarian cancer. The use of an antiplatelet antibody to halve platelet counts in tumor-bearing mice significantly reduced tumor growth and angiogenesis.</p> <p>Conclusions: These findings support the existence of a paracrine circuit wherein increased production of thrombopoietic cytokines in tumor and host tissue leads to paraneoplastic thrombocytosis, which fuels tumor growth. We speculate that countering paraneoplastic thrombocytosis either directly or indirectly by targeting these cytokines may have therapeutic potential. </p&gt

45th Annual Larval Fish Conference & 13th International Larval Biology Symposium San Diego, California 29 August – 1 September, 2022

INDITU

Plankton food webs of the Gulf of Mexico spawning grounds of Atlantic Bluefin tuna

We used linear inverse ecosystem modeling techniques to assimilate data from extensive Lagrangian field experiments into a mass-balance constrained food web for the Gulf of Mexico open-ocean ecosystem. This region is highly oligotrophic, yet Atlantic bluefin tuna (ABT) travel long distances from feeding grounds in the North Atlantic to spawn there. Our results show extensive nutrient regeneration fueling primary productivity (mostly by cyanobacteria and other picophytoplankton) in the upper euphotic zone. The food web is dominated by themicrobial loop (>70% of net primary productivity is respired by heterotrophic bacteria and protists that feed on them). By contrast, herbivorous food web pathways from phytoplankton to metazoan zooplankton process <10% of the net primary production in the mixed layer. Nevertheless, ABT larvae feed preferentially on podonid cladocerans and other suspensionfeeding zooplankton, which in turn derive much of their nutrition from nano- and micro-phytoplankton (mixotrophic flagellates, and to a lesser extent, diatoms). This allows ABT larvae to maintain a comparatively low trophic level (∼4.2 for preflexion and postflexion larvae), which increases trophic transfer from phytoplankton to larval fish.ECOLATUNECOlogía trófica comparativa de LArvas de aTUN rojo atlántico (Thunnus thynnus) de las áreas de puesta del Medterraneo-NO y el Golfo de México.S

A flexible ChIP-sequencing simulation toolkit

Abstract Background A major challenge in evaluating quantitative ChIP-seq analyses, such as peak calling and differential binding, is a lack of reliable ground truth data. Accurate simulation of ChIP-seq data can mitigate this challenge, but existing frameworks are either too cumbersome to apply genome-wide or unable to model a number of important experimental conditions in ChIP-seq. Results We present ChIPs, a toolkit for rapidly simulating ChIP-seq data using statistical models of key experimental steps. We demonstrate how ChIPs can be used for a range of applications, including benchmarking analysis tools and evaluating the impact of various experimental parameters. ChIPs is implemented as a standalone command-line program written in C++ and is available from https://github.com/gymreklab/chips . Conclusions ChIPs is an efficient ChIP-seq simulation framework that generates realistic datasets over a flexible range of experimental conditions. It can serve as an important component in various ChIP-seq analyses where ground truth data are needed

A flexible ChIP-sequencing simulation toolkit.

BackgroundA major challenge in evaluating quantitative ChIP-seq analyses, such as peak calling and differential binding, is a lack of reliable ground truth data. Accurate simulation of ChIP-seq data can mitigate this challenge, but existing frameworks are either too cumbersome to apply genome-wide or unable to model a number of important experimental conditions in ChIP-seq.ResultsWe present ChIPs, a toolkit for rapidly simulating ChIP-seq data using statistical models of key experimental steps. We demonstrate how ChIPs can be used for a range of applications, including benchmarking analysis tools and evaluating the impact of various experimental parameters. ChIPs is implemented as a standalone command-line program written in C++ and is available from https://github.com/gymreklab/chips .ConclusionsChIPs is an efficient ChIP-seq simulation framework that generates realistic datasets over a flexible range of experimental conditions. It can serve as an important component in various ChIP-seq analyses where ground truth data are needed

Deep neural networks identify sequence context features predictive of transcription factor binding.

Transcription factors (TFs) bind DNA by recognizing specific sequence motifs, typically of length 6-12bp. A motif can occur many thousands of times in the human genome, but only a subset of those sites are actually bound. Here we present a machine learning framework leveraging existing convolutional neural network architectures and model interpretation techniques to identify and interpret sequence context features most important for predicting whether a particular motif instance will be bound. We apply our framework to predict binding at motifs for 38 TFs in a lymphoblastoid cell line, score the importance of context sequences at base-pair resolution, and characterize context features most predictive of binding. We find that the choice of training data heavily influences classification accuracy and the relative importance of features such as open chromatin. Overall, our framework enables novel insights into features predictive of TF binding and is likely to inform future deep learning applications to interpret non-coding genetic variants

Haptools: a toolkit for admixture and haplotype analysis.

SummaryLeveraging local ancestry and haplotype information in genome-wide association studies and downstream analyses can improve the utility of genomics for individuals from diverse and recently admixed ancestries. However, most existing simulation, visualization and variant analysis frameworks are based on variant-level analysis and do not automatically handle these features. We present haptools, an open-source toolkit for performing local ancestry aware and haplotype-based analysis of complex traits. Haptools supports fast simulation of admixed genomes, visualization of admixture tracks, simulation of haplotype- and local ancestry-specific phenotype effects and a variety of file operations and statistics computed in a haplotype-aware manner.Availability and implementationHaptools is freely available at https://github.com/cast-genomics/haptools.DocumentationDetailed documentation is available at https://haptools.readthedocs.io.Supplementary informationSupplementary data are available at Bioinformatics online