Macrophage-derived insulin antagonist ImpL2 induces lipoprotein mobilization upon bacterial infection.

The immune response is an energy-demanding process that must be coordinated with systemic metabolic changes redirecting nutrients from stores to the immune system. Although this interplay is fundamental for the function of the immune system, the underlying mechanisms remain elusive. Our data show that the pro-inflammatory polarization of Drosophila macrophages is coupled to the production of the insulin antagonist ImpL2 through the activity of the transcription factor HIF1α. ImpL2 production, reflecting nutritional demands of activated macrophages, subsequently impairs insulin signaling in the fat body, thereby triggering FOXO-driven mobilization of lipoproteins. This metabolic adaptation is fundamental for the function of the immune system and an individual's resistance to infection. We demonstrated that analogically to Drosophila, mammalian immune-activated macrophages produce ImpL2 homolog IGFBP7 in a HIF1α-dependent manner and that enhanced IGFBP7 production by these cells induces mobilization of lipoproteins from hepatocytes. Hence, the production of ImpL2/IGFBP7 by macrophages represents an evolutionarily conserved mechanism by which macrophages alleviate insulin signaling in the central metabolic organ to secure nutrients necessary for their function upon bacterial infection.

The 2020 report of The Lancet Countdown on health and climate change: responding to converging crises.

TRANSLATIONS: For the Chinese, French, German, and Spanish translations of the abstract see Supplementary Materials section.

Massively parallel characterization of restriction endonucleases.

Restriction endonucleases are highly specific in recognizing the particular DNA sequence they act on. However, their activity is affected by sequence context, enzyme concentration and buffer composition. Changes in these factors may lead to either ineffective cleavage at the cognate restriction site or relaxed specificity allowing cleavage of degenerate 'star' sites. Additionally, uncharacterized restriction endonucleases and engineered variants present novel activities. Traditionally, restriction endonuclease activity is assayed on simple substrates such as plasmids and synthesized oligonucleotides. We present and use high-throughput Illumina sequencing-based strategies to assay the sequence specificity and flanking sequence preference of restriction endonucleases. The techniques use fragmented DNA from sequenced genomes to quantify restriction endonuclease cleavage on a complex genomic DNA substrate in a single reaction. By mapping millions of restriction site-flanking reads back to the Escherichia coli and Drosophila melanogaster genomes we were able to quantitatively characterize the cognate and star site activity of EcoRI and MfeI and demonstrate genome-wide decreases in star activity with engineered high-fidelity variants EcoRI-HF and MfeI-HF, as well as quantify the influence on MfeI cleavage conferred by flanking nucleotides. The methods presented are readily applicable to all type II restriction endonucleases that cleave both strands of double-stranded DNA.

Modelling socio-environmental sensitivities: how public responses to low carbon energy technologies could shape the UK energy system.

Low carbon energy technologies are not deployed in a social vacuum; there are a variety of complex ways in which people understand and engage with these technologies and the changing energy system overall. However, the role of the public's socio-environmental sensitivities to low carbon energy technologies and their responses to energy deployments does not receive much serious attention in planning decarbonisation pathways to 2050. Resistance to certain resources and technologies based on particular socio-environmental sensitivities would alter the portfolio of options available which could shape how the energy system achieves decarbonisation (the decarbonisation pathway) as well as affecting the cost and achievability of decarbonisation. Thus, this paper presents a series of three modelled scenarios which illustrate the way that a variety of socio-environmental sensitivities could impact the development of the energy system and the decarbonisation pathway. The scenarios represent risk aversion (DREAD) which avoids deployment of potentially unsafe large-scale technology, local protectionism (NIMBY) that constrains systems to their existing spatial footprint, and environmental awareness (ECO) where protection of natural resources is paramount. Very different solutions for all three sets of constraints are identified; some seem slightly implausible (DREAD) and all show increased cost (especially in ECO).

Reference-free SNP discovery for the Eurasian beaver from restriction site-associated DNA paired-end data.

In this study, we used restriction site-associated DNA (RAD) sequencing to discover SNP markers suitable for population genetic and parentage analysis with the aim of using them for monitoring the reintroduction of the Eurasian beaver (Castor fibre) to Scotland. In the absence of a reference genome for beaver, we built contigs and discovered SNPs within them using paired-end RAD data, so as to have sufficient flanking region around the SNPs to conduct marker design. To do this, we used a simple pipeline which catalogued the Read 1 data in stacks and then used the assembler cortex_var to conduct de novo assembly and genotyping of multiple samples using the Read 2 data. The analysis of around 1.1 billion short reads of sequence data was reduced to a set of 2579 high-quality candidate SNP markers that were polymorphic in Norwegian and Bavarian beaver. Both laboratory validation of a subset of eight of the SNPs (1.3% error) and internal validation by confirming patterns of Mendelian inheritance in a family group (0.9% error) confirmed the success of this approach.

Genomic patterns of introgression in rainbow and westslope cutthroat trout illuminated by overlapping paired-end RAD sequencing.

Rapid and inexpensive methods for genomewide single nucleotide polymorphism (SNP) discovery and genotyping are urgently needed for population management and conservation. In hybridized populations, genomic techniques that can identify and genotype thousands of species-diagnostic markers would allow precise estimates of population- and individual-level admixture as well as identification of 'super invasive' alleles, which show elevated rates of introgression above the genomewide background (likely due to natural selection). Techniques like restriction-site-associated DNA (RAD) sequencing can discover and genotype large numbers of SNPs, but they have been limited by the length of continuous sequence data they produce with Illumina short-read sequencing. We present a novel approach, overlapping paired-end RAD sequencing, to generate RAD contigs of >300-400 bp. These contigs provide sufficient flanking sequence for design of high-throughput SNP genotyping arrays and strict filtering to identify duplicate paralogous loci. We applied this approach in five populations of native westslope cutthroat trout that previously showed varying (low) levels of admixture from introduced rainbow trout (RBT). We produced 77 141 RAD contigs and used these data to filter and genotype 3180 previously identified species-diagnostic SNP loci. Our population-level and individual-level estimates of admixture were generally consistent with previous microsatellite-based estimates from the same individuals. However, we observed slightly lower admixture estimates from genomewide markers, which might result from natural selection against certain genome regions, different genomic locations for microsatellites vs. RAD-derived SNPs and/or sampling error from the small number of microsatellite loci (n = 7). We also identified candidate adaptive super invasive alleles from RBT that had excessively high admixture proportions in hybridized cutthroat trout populations.

A Systematic Method for Detecting Abnormal mRNA Splicing and Assessing Its Clinical Impact in Individuals Undergoing Genetic Testing for Hereditary Cancer Syndromes.

Nearly 14% of disease-causing germline variants result from the disruption of mRNA splicing. Most (67%) DNA variants predicted in silico to disrupt splicing are classified as variants of uncertain significance. An analytic workflow-splice effect event resolver (SPEER)-was developed and validated to use mRNA sequencing to reveal significant deviations in splicing, pinpoint the DNA variants potentially involved, and measure the deleterious effects of the altered splicing on mRNA transcripts, providing evidence for assessing the pathogenicity of the variant. SPEER was used to analyze leukocyte RNA encoding 63 hereditary cancer syndrome-related genes in 20,317 patients. Among 3563 patients (17.5%) with at least one DNA variant predicted to affect splicing, 971 (4.8%) had altered splicing with a deleterious effect on the transcript, and 40 had altered splicing due to a DNA variant located outside of the reportable range of the test. Integrating SPEER results into the interpretation of variants allowed variants of uncertain significance to be reclassified as pathogenic or likely pathogenic in 0.4%, and as benign or likely benign in 5.9%, of the 20,317 patients. SPEER-based evidence was associated with a significantly greater effect on classifications of pathogenic or likely pathogenic and benign or likely benign in nonwhite versus non-Hispanic white patients, illustrating that evidence derived from mRNA splicing analysis may help to reduce ethnic/ancestral disparities in genetic testing.

Sensitivity, specificity, and accuracy of a liquid biopsy approach utilizing molecular amplification pools.

Circulating cell-free DNA (cfDNA) has the potential to be a specific biomarker for the therapeutic management of lung cancer patients. Here, a new sequencing error-reduction method based on molecular amplification pools (MAPs) was utilized to analyze cfDNA in lung cancer patients. We determined the accuracy of MAPs plasma sequencing with respect to droplet digital polymerase chain reaction assays (ddPCR), and tested whether actionable mutation discovery is improved by next-generation sequencing (NGS) in a clinical setting. This study reports data from 356 lung cancer patients receiving plasma testing as part of routine clinical management. Sequencing of cfDNA via MAPs had a sensitivity of 98.5% and specificity 98.9%. The ddPCR assay was used as the reference, since it is an established, accurate assay that can be performed contemporaneously on the same plasma sample. MAPs sequencing detected somatic variants in 261 of 356 samples (73%). Non-actionable clonal hematopoiesis-associated variants were identified via sequencing in 21% of samples. The accuracy of this cfDNA sequencing approach was similar to that of ddPCR assays in a clinical setting, down to an allele frequency of 0.1%. Due to broader coverage and high sensitivity for insertions and deletions, sequencing via MAPs afforded important detection of additional actionable mutations.

An equitable redistribution of unburnable carbon.

The rapid phase-out of fossil fuels is critical to achieving a well-below 2 °C world. An emerging body of research explores the implications of this phase-out for fossil fuel producing countries, including the perceived tension between least-cost and most-equitable pathways. Here we present modelling, which re-distributes remaining fossil fuel production towards developing countries. We show that redistribution is challenging due to large economic disincentives required to shift production, and offers limited economic benefit for developing countries given the long timeframe required to effect change, and the wider impact of rising fuel import and energy systems costs. Furthermore, increases in production shares are offset by shrinking markets for fossil fuels, which are part dependent on carbon capture and storage (CCS). We argue that while there is a weak economic case for redistribution, there is a clear role for equity principles in guiding the development of supply side policy and in development assistance.

Quantifying a strategic view of diabetes technology impacts: a system dynamics approach.

BACKGROUND: Diabetes and its related complications and comorbidities (C&Cs) represent a significant and increasing healthcare burden. Absence of a systematic manner to evaluate value propositions of technologies from various stakeholders' perspectives impedes the best uses of current and emerging technologies. METHODS: A system dynamics methodology was used to develop a comprehensive causal simulator of the U.S. population and give proof of principle that entire portfolios of present and future technologies can be evaluated for direct and indirect impacts. An explicit semiquantitative methodology was used for surveying, prioritizing, and grouping C&Cs, patient subgroups, and technologies, utilizing diverse publicly available clinical literature. RESULTS: The resulting model simulates the incidence and prevalence of diabetes and 10 of its most commonly associated C&Cs, reflecting their interrelated upstream and downstream relationships. The simulator enables systematic evaluation of tens of thousands of potential combinations of emerging technologies and technology leverage points that can be used to improve patient outcomes and guide technology investments. Feasibility was demonstrated through single, pair-wise, and targeted analyses of technologies. CONCLUSIONS: This effort demonstrated the feasibility of linking complex, interconnected disease states, impact points, outcomes, and interventions with a variety of outcome metrics, to an extent greater than existing models developed for other purposes. The project demonstrated the ability to identify priority technologies and pipeline therapies and leverage points among diabetes interventions. It demonstrated more effective knowledge management of diverse information essential for formulating strategy that could be applied in a wide range of therapeutic applications and technology innovation uses.

ERASE-Seq: Leveraging replicate measurements to enhance ultralow frequency variant detection in NGS data.

The accurate detection of ultralow allele frequency variants in DNA samples is of interest in both research and medical settings, particularly in liquid biopsies where cancer mutational status is monitored from circulating DNA. Next-generation sequencing (NGS) technologies employing molecular barcoding have shown promise but significant sensitivity and specificity improvements are still needed to detect mutations in a majority of patients before the metastatic stage. To address this we present analytical validation data for ERASE-Seq (Elimination of Recurrent Artifacts and Stochastic Errors), a method for accurate and sensitive detection of ultralow frequency DNA variants in NGS data. ERASE-Seq differs from previous methods by creating a robust statistical framework to utilize technical replicates in conjunction with background error modeling, providing a 10 to 100-fold reduction in false positive rates compared to published molecular barcoding methods. ERASE-Seq was tested using spiked human DNA mixtures with clinically realistic DNA input quantities to detect SNVs and indels between 0.05% and 1% allele frequency, the range commonly found in liquid biopsy samples. Variants were detected with greater than 90% sensitivity and a false positive rate below 0.1 calls per 10,000 possible variants. The approach represents a significant performance improvement compared to molecular barcoding methods and does not require changing molecular reagents.

Oil spill response capabilities and technologies for ice-covered Arctic marine waters: A review of recent developments and established practices.

Renewed political and commercial interest in the resources of the Arctic, the reduction in the extent and thickness of sea ice, and the recent failings that led to the Deepwater Horizon oil spill, have prompted industry and its regulatory agencies, governments, local communities and NGOs to look at all aspects of Arctic oil spill countermeasures with fresh eyes. This paper provides an overview of present oil spill response capabilities and technologies for ice-covered waters, as well as under potential future conditions driven by a changing climate. Though not an exhaustive review, we provide the key research results for oil spill response from knowledge accumulated over many decades, including significant review papers that have been prepared as well as results from recent laboratory tests, field programmes and modelling work. The three main areas covered by the review are as follows: oil weathering and modelling; oil detection and monitoring; and oil spill response techniques.

Leads in Arctic pack ice enable early phytoplankton blooms below snow-covered sea ice.

The Arctic icescape is rapidly transforming from a thicker multiyear ice cover to a thinner and largely seasonal first-year ice cover with significant consequences for Arctic primary production. One critical challenge is to understand how productivity will change within the next decades. Recent studies have reported extensive phytoplankton blooms beneath ponded sea ice during summer, indicating that satellite-based Arctic annual primary production estimates may be significantly underestimated. Here we present a unique time-series of a phytoplankton spring bloom observed beneath snow-covered Arctic pack ice. The bloom, dominated by the haptophyte algae Phaeocystis pouchetii, caused near depletion of the surface nitrate inventory and a decline in dissolved inorganic carbon by 16 ± 6 g C m-2. Ocean circulation characteristics in the area indicated that the bloom developed in situ despite the snow-covered sea ice. Leads in the dynamic ice cover provided added sunlight necessary to initiate and sustain the bloom. Phytoplankton blooms beneath snow-covered ice might become more common and widespread in the future Arctic Ocean with frequent lead formation due to thinner and more dynamic sea ice despite projected increases in high-Arctic snowfall. This could alter productivity, marine food webs and carbon sequestration in the Arctic Ocean.

Minimal access mitral valve repair in a patient with a right pneumonectomy for Scimitar syndrome.

A 61-year old man known with chronic atrial fibrillation was referred to our unit via the multidisciplinary team meeting, with severe mitral regurgitation secondary to prolapse of anterior mitral leaflet. In 1968, he had undergone right pneumonectomy due to Scimitar syndrome. Dense adhesions due to previous interventions, such as thoracotomy, make it difficult to insert ports, and this is therefore considered a relative contraindication to port access approach to the mitral valve. The anatomical position of the heart in the mediastinum was completely distorted due to the shift of the mediastinum to the right following previous surgery. Our report illustrates the operative success that can be achieved in such complex situations. Computed tomography scanning and on table transoesophageal and transthoracic echocardiography were the tools used in conjunction to achieve the best possible approach. This case promotes the use of minimal access approach in the experienced hands so that such complex procedures can be carried out without any complication and yield good results.

Radiogenomics Monitoring in Breast Cancer Identifies Metabolism and Immune Checkpoints as Early Actionable Mechanisms of Resistance to Anti-angiogenic Treatment.

Anti-VEGF antibody bevacizumab has prolonged progression-free survival in several cancer types, however acquired resistance is common. Adaption has been observed pre-clinically, but no human study has shown timing and genes involved, enabling formulation of new clinical paradigms. In a window-of-opportunity study in 35 ductal breast cancer patients for 2weeks prior to neoadjuvant chemotherapy, we monitored bevacizumab response by Dynamic Contrast-Enhanced Magnetic Resonance [DCE-MRI], transcriptomic and pathology. Initial treatment response showed significant overall decrease in DCE-MRI median K(trans), angiogenic factors such ESM1 and FLT1, and proliferation. However, it also revealed great heterogeneity, spanning from downregulation of blood vessel density and central necrosis to continued growth with new vasculature. Crucially, significantly upregulated pathways leading to resistance included glycolysis and pH adaptation, PI3K-Akt and immune checkpoint signaling, for which inhibitors exist, making a strong case to investigate such combinations. These findings support that anti-angiogenesis trials should incorporate initial enrichment of patients with high K(trans), and a range of targeted therapeutic options to meet potential early resistance pathways. Multi-arm adaptive trials are ongoing using molecular markers for targeted agents, but our results suggest this needs to be further modified by much earlier adaptation when using drugs affecting the tumor microenvironment.

A novel multivariate performance optimization method based on sparse coding and hyper-predictor learning.

In this paper, we investigate the problem of optimization of multivariate performance measures, and propose a novel algorithm for it. Different from traditional machine learning methods which optimize simple loss functions to learn prediction function, the problem studied in this paper is how to learn effective hyper-predictor for a tuple of data points, so that a complex loss function corresponding to a multivariate performance measure can be minimized. We propose to present the tuple of data points to a tuple of sparse codes via a dictionary, and then apply a linear function to compare a sparse code against a given candidate class label. To learn the dictionary, sparse codes, and parameter of the linear function, we propose a joint optimization problem. In this problem, the both the reconstruction error and sparsity of sparse code, and the upper bound of the complex loss function are minimized. Moreover, the upper bound of the loss function is approximated by the sparse codes and the linear function parameter. To optimize this problem, we develop an iterative algorithm based on descent gradient methods to learn the sparse codes and hyper-predictor parameter alternately. Experiment results on some benchmark data sets show the advantage of the proposed methods over other state-of-the-art algorithms.

Genome-wide identification of hypoxia-induced enhancer regions.

Here we present a genome-wide method for de novo identification of enhancer regions. This approach enables massively parallel empirical investigation of DNA sequences that mediate transcriptional activation and provides a platform for discovery of regulatory modules capable of driving context-specific gene expression. The method links fragmented genomic DNA to the transcription of randomer molecule identifiers and measures the functional enhancer activity of the library by massively parallel sequencing. We transfected a Drosophila melanogaster library into S2 cells in normoxia and hypoxia, and assayed 4,599,881 genomic DNA fragments in parallel. The locations of the enhancer regions strongly correlate with genes up-regulated after hypoxia and previously described enhancers. Novel enhancer regions were identified and integrated with RNAseq data and transcription factor motifs to describe the hypoxic response on a genome-wide basis as a complex regulatory network involving multiple stress-response pathways. This work provides a novel method for high-throughput assay of enhancer activity and the genome-scale identification of 31 hypoxia-activated enhancers in Drosophila.

Rapid mapping and identification of mutations in Caenorhabditis elegans by restriction site-associated DNA mapping and genomic interval pull-down sequencing.

Forward genetic screens provide a powerful approach for inferring gene function on the basis of the phenotypes associated with mutated genes. However, determining the causal mutation by traditional mapping and candidate gene sequencing is often the rate-limiting step, especially when analyzing many mutants. We report two genomic approaches for more rapidly determining the identity of the affected genes in Caenorhabditis elegans mutants. First, we report our use of restriction site-associated DNA (RAD) polymorphism markers for rapidly mapping mutations after chemical mutagenesis and mutant isolation. Second, we describe our use of genomic interval pull-down sequencing (GIPS) to selectively capture and sequence megabase-sized portions of a mutant genome. Together, these two methods provide a rapid and cost-effective approach for positional cloning of C. elegans mutant loci, and are also applicable to other genetic model systems.