Rapid and, in many cases, unprecedented Arctic climate changes are having far-reaching impacts on natural and human systems. Despite state-of-the-art climate models capturing the rapid nature of Arctic climate change, termed Arctic amplification, they significantly disagree on its magnitude. Using a regional, process-oriented surface energy budget analysis, we argue that differences in seasonal energy exchanges in sea ice retreat regions via increased absorption and storage of sunlight in summer and increased upward surface turbulent fluxes in fall/winter contribute to the inter-model spread. Models able to more widely disperse energy drawn from the surface in sea ice retreat regions warm more, suggesting that differences in the local Arctic atmospheric circulation response contribute to the inter-model spread. We find that the principle mechanisms driving the inter-model spread in Arctic amplification operate locally on regional scales, requiring an improved understanding of atmosphere-ocean-sea ice interactions in sea ice retreat regions to reduce the spread. State-of-the-art climate models disagree on the magnitude of Arctic climate change. Here the authors show that the principle processes driving the disagreement operate locally in sea ice retreat regions, necessitating comprehensive observations to improve our understanding of atmosphere-ocean-sea ice energy exchanges. AA is evident in surface temperature observations over the last century4, in model projections made starting in the 1970’s6 and suggested by Arrhenius7 more than 100 years ago. Arctic climate change has global consequences by influencing glacial melt and sea level rise, permafrost thaw and the carbon cycle, atmospheric and oceanic circulations, and potentially extreme mid-latitude weather9. Because of the significant implications for the physical climate and human systems, accurate projections of AA are needed. Unfortunately, climate models project a wide range of possible futures for the Arctic—a larger inter-model spread than any other region. The 2 °C globally-averaged warming specified in the Paris Climate Agreement equates to an Arctic warming between 3 and 7 °C, according to Coupled Model Intercomparison 5 (CMIP510) models. Narrowing the inter-model spread requires an understanding of how Arctic feedback processes and their interactions shape the temperature response.The Arctic has warmed 2–3 times faster than globally-averaged warming, a phenomenon known as Arctic amplification (AA)16, changes in longwave and/or temperature feedbacks18, cloud changes21, intraseasonal cycling of heat23, and poleward energy transport27. While the surface albedo feedback (SAF) has often been cited as the leading contributor to AA28, idealized climate simulations show that AA can occur in its absence30. Recent studies argue for a remote forcing of observed AA, whereby atmospheric heat transport into the Arctic from lower latitudes drives warming and thinner sea ice32, whereas others attribute observed AA to local mechanisms such as the surface albedo and evaporation feedbacks11. Our understanding of AA mechanisms has evolved significantly over the last decade, yet the relative importance of each feedback and its contributions to the inter-model spread in Arctic warming projections remains under debate.A collection of interacting processes support AA: sea ice loss and surface albedo feedback17. CMIP5 models show an increased inter-model spread in surface temperature and sea ice compared to CMIP333. Pithan and Mauritsen18 argue that temperature feedbacks explain these inter-model differences, however our analysis and interpretation differ. Previous studies have analyzed AA in CMIP5 models, yet generally lack the regional perspective required to isolate the energy exchanges that appear to regulate AA in observations34. Moreover, CMIP5 models exhibit large differences in the seasonal cycles of radiative fluxes, clouds, and turbulent fluxes36 and a seasonality in the inter-model spread in projected warming.This study offers a seasonal, process-oriented surface energy budget decomposition using the multi-model CMIP5 archive and methods introduced in Lu and Cai Our results outline the primary drivers of inter-model spread in AA found in CMIP5 models using a surface energy budget perspective highlighting the important contribution of seasonal energy exchange in sea ice retreat regions facilitated by ocean heat storage to the inter-model spread in Arctic warming. We argue that the atmospheric and ocean processes that modulate the seasonal energy exchange in sea ice retreat regions drive model differences in projected Arctic warming. The models that more effectively disperse energy drawn from the surface in sea ice retreat regions warm more. Therefore, reconciling the differences in AA projections requires constraining the representation of atmosphere-ocean-sea ice interactions in sea ice retreat regions.2  shows increasing model differences at more northerly latitudes, approaching an amplification factor spread of 1.6–3.6 Fig. . The das37.Arctic warming projections display stark regional contrasts Fig.  where seThe spatial warming pattern varies significantly between models; some show larger temperature increases over the central Arctic Ocean and others show the greatest warming in regions of the largest sea ice loss. In general, the inter-model spread is greatest in the sea ice retreat regions Fig. , particu38 illustrated for clouds. Our SEB decomposition approach analyzes the energy flux changes and how they contribute to AA in each model (see Methods) by linearly decomposing the total surface temperature change into partial temperature contributions (PTCs). These terms include SAF, cloud radiative effect (CRE), changes in shortwave clear-sky radiation unrelated to SAF (SWCS), longwave clear-sky radiation (LWCS), ocean heat storage and transport (HSTOR), and surface turbulent fluxes . In this decomposition, the LWCS term includes the effects of CO2, air temperature, and water vapor changes from both local and remote sources. We do not separate these effects as in previous work18 because biases due to the decomposition approach interfere with the assessment of inter-model differences. The HSTOR term represents the surface energy imbalance, including surface heat storage and ocean heat transport, computed as a residual (see Methods). Since the heat storage capacity of land is small compared to ocean, this term represents ocean heat content. Ocean heat transport, while potentially important to the inter-model spread, could not be assessed separately because few CMIP5 models archived the necessary output. This limits our ability to consider ocean heat storage and transport separately; however, previous work demonstrated that ocean heat storage in the mixed layer dominates changes in ocean heat transport in CMIP5 models over the 21st century15; thus, we consider HSTOR changes to be from ocean heat storage.The presence of surface ice and the massive amounts of energy sequestered and released during water phase change indicates that the surface energy budget (SEB) is more relevant to the Arctic surface temperature than the top-of-atmosphere (TOA) energy budget. Previous work demonstrates that the surface and TOA perspectives can show opposite signs for the individual feedback contributions to warming, as Taylor et al.Annual mean PTCs and formulae are listed in Table The strong seasonality of the Arctic SEB renders the annual mean picture incomplete. Important factors influencing AA—HSTOR, CRE, and HFLUX—exhibit strong seasonal variations. Applying the decomposition methodology to monthly SEB changes enables the assessment of seasonal energy exchanges Fig. .Fig. 3Se39. Figure 41. The negative HSTOR PTC (April–September) represents the accumulation of solar energy during spring and summer (more energy into the surface than out). Evidenced by the approximate equivalence of the combined SAF + CRE to HSTOR, changes in SAF and CRE primarily determine the summer HSTOR PTC. In fall/winter, HSTOR and HFLUX are approximately equivalent indicating that model HFLUX determines the fall/winter surface cooling rate change. From a process perspective, HFLUX is controlled by the air–sea temperature contrast, strongly influenced by both the presence of sea ice and atmospheric advection42. The negative fall/winter HFLUX PTCs in Fig. The seasonality of the PTCs from CMIP5 remains unchanged from CMIP3 model analysis and is consistent with reanalysisThe model spread between the contributions of individual SEB terms is represented by the inter-model standard deviation in Table R = −0.89) is found between annual mean albedo changes (peaking in summer) and AA (peaking in winter) even though the SAF seasonal cycle is out-of-phase with the maximum warming. The inter-model range in HSTOR (from −2 to −11 K) also maximizes in summer, aligning with the SAF inter-model spread. The inter-model spread in the CRE PTCs is the largest in fall (from +1 to +7 K) and smallest during summer. Models with the largest summer SAF do not exhibit the largest negative CRE PTC and therefore do not compensate for sea ice loss by simulating more reflective clouds. HFLUX exhibits its largest inter-model spread in winter (from −1 to −7 K) and smallest spread in summer (from −1 to +0.5 K). Seasonal cycles of the PTCs exhibit greater amplitude over ocean than land as in Laîné et al.15 (not shown), with HSTOR and HFLUX over the ocean having the largest inter-model range in winter of ~11 K between models. The SAF and HSTOR over the ocean show the largest inter-model range in summer of 18 K and 15 K, respectively.The strong seasonality of the PTCs renders the annual mean picture potentially misleading. Despite a modest annual mean inter-model spread (~40%), the SAF shows the widest inter-model range in any month, between +2 to +13 K in July Fig. . A stron45. For instance, the pattern of sea ice loss modulates the position of regional baroclinic zones that are favored regions of cyclogenesis47. Moreover, Overland et al.8 suggest that the spatial pattern of warming and sea ice loss alters the mid-latitude circulation. Several recent studies indicate that local feedbacks in sea ice-retreat areas accelerate warming48. Moreover, future Arctic climate change is likely to occur in response to episodic energy fluxes, via surface turbulent fluxes and poleward heat transport50. Since these factors act on a regional level, it is important to assess model feedbacks and differences spatially.Regional differences are important because spatial patterns of sea ice loss affect Arctic climate variability and the warming responsea–f) and the corresponding spatial inter-model standard deviation for each PTC (g–l). The largest model differences occur in seasonal sea ice regions where small differences in sea ice extent correspond to large differences in HSTOR  dominates Arctic warming50 and teleconnections with the tropical climate54. Atmospheric energy convergence from midlatitude moisture fluxes into the Arctic has increased LWDN and contributed to observed Arctic temperature trends between 1989 and 200951.Figure 55. Increased HFLUX subsequently warms, moistens, and increases clouds, contributing to increased LWDN39. Increased LWDN and warmer Arctic temperatures in fall/winter promote thinner sea ice and further sea ice loss48, reinforcing the feedback loop by increasing the potential for ocean heat storage56.The local mechanism represents the combined surface albedo (radiative) and ice-insulation (non-radiative) feedbacks whereby a warmer Arctic with less sea ice stores more energy in the ocean in summer via the SAF supporting increased HFLUX in fall/winter. The increased absorbed radiation is not immediately radiated away but stored and transferred from summer to fall/winter delaying fall sea ice freeze-up and providing an energy source to the atmosphere58 —a layer of warm water below the shallow summer ocean mixed layer (OML). In fall, the OML cools, deepens, and transfers heat to the atmosphere via HFLUX and longwave radiation59. More ice-free ocean supports greater upper ocean mixing by atmospheric winds, which can entrain warm water from the near-surface temperature maximum layer and delay fall sea ice freeze-up58. However, this process is likely poorly represented across CMIP5 models due to insufficient upper ocean vertical mixing60.Ocean mixed layer processes and heat transport modulate the local and remote mechanisms. The summer SAF warms the upper ocean making it less dense and more stably stratified. Heat entering the ocean is trapped near the surface, where it melts and thins sea ice [Graham et al. 2013]. Sea ice melt freshens the mixed layer and increases stratification encouraging the development of a near-surface temperature maximum27 and model output point to ocean heat transport as a potential source of AA and inter-model spread61. Oceanic heat transport into the Barents Sea influences Arctic climate variability and the North Atlantic Oscillation63 by reducing sea ice, enhancing HFLUX, and lowering the local atmospheric pressure. However, ocean heat transport in CMIP5 models contributes little to projected warming over 21st century15. These interactions suggest that changes in the position and intensity of the jet stream and frequency of synoptic cyclones can modulate the local and remote mechanisms by influencing sea ice, HFLUX, and the OML, representing a process link between the two mechanisms.Ocean heat transport into the Arctic influences temperature and sea ice, passes energy to the atmosphere via HFLUX, and modifies the Arctic circulation. Reconstructions of long-term records64. It is clear that the remote forcing mechanism can accelerate the local mechanism by increasing LWDN and influencing sea ice52. It is an open question if changes in the Arctic drive changes in the mid-latitude circulation . OHFSEASONAL is defined as the difference between the month of minimum and maximum HSTOR and ΔOHFSEASONAL represents its change by 2100. Figure SEASONAL and AA (r = 0.89), supporting our hypothesis. Using ΔOHFSEASONAL as an observational constraint on projected AA may lack practical relevance if the signal emergence does not occur before warming. Conversely, the remote forcing mechanism, represented by ΔAPHT (see Methods), anticorrelates with AA suggesting that it dampens the inter-model spread . Figure Figure SEASONAL and ΔAPHT  climatology was determined from the average of the first (last) 20 years of the simulation. Using available data, surface energy budget can be computed asQ represents the storage of heat for all surface types as well as oceanic transport, α is the surface albedo defined by the ratio of upward to downward shortwave clear-sky fluxes, Sdn is incident solar radiation, Fdn is the downwelling longwave radiation, εσTs4 is the longwave emission from the surface at temperature Ts , and (S + L) are the sensible and latent heat fluxes (defined as positive upward). All variables are available from the CMIP5 data portal except for Q, which is obtained as a residual. After solving for T, a perturbation form of (1) yieldsData analyzed during this study comes from 21 CMIP5 models, listed in Supplementary Table 17, the change in the cloud radiative effect (CRE) is calculated asσTs3 yieldsFollowing Lu and CaiRTOA is the net incoming radiation at TOA, and Fao is the horizontal flux divergence for atmosphere and ocean (poleward heat transport term). Averaging over timescales greater than one year makes the energy storage term RTOA and Fao. RTOA is calculated using CMIP5 model outputs at TOA for downwelling shortwave radiation , upwelling shortwave radiation , and upwelling longwave radiation . The equation for the combined atmospheric and oceanic poleward heat transport then becomesAPHT) is determined by taking the difference between RTOA from the surface energy imbalance. After averaging over timescales greater than one year and neglecting The annual mean net radiation balance for latitudes poleward of ~40° is negative, meaning it emits more energy than it receives, requiring poleward heat transport. The energy balance of the Arctic domain can be written as in 5)5\documenComputer code used for the analysis was written in IDL and is available from the authors upon request.Peer Review FileSupplementary Information


Musa spp.) are an important crop worldwide. Most modern cultivars resulted from a complex polyploidization history that comprised three whole genome duplications (WGDs) shaping the haploid Musa genome, followed by inter- and intra-specific crosses between Musa acuminata and M. balbisiana . Unresolved hybridizations finally led to banana diversification into several autotriploid (AAA) and allotriploid cultivars (AAB and ABB). Using transcriptomic data, we investigated the impact of the genome structure on gene expression patterns in roots of 12 different triploid genotypes covering AAA, AAB and ABB subgenome constitutions.Bananas (We demonstrate that (i) there are different genome structures, (ii) expression patterns go beyond the predicted genomic groups, and (iii) the proportion of the B genome influences the gene expression.The presence of the B genome is associated with a higher expression of genes involved in flavonoid biosynthesis, fatty acid metabolism, amino sugar and nucleotide sugar metabolism and oxidative phosphorylation. There are cultivar-specific chromosome regions with biased B:A gene expression ratios that demonstrate homoeologous exchanges (HE) between A and B sub-genomes. In two cultivars, aneuploidy was detected. We identified 3674 genes with a different expression level between allotriploid and autotriploid with ~ 57% having recently duplicated copies . We propose a Paralog Inclusive Expression (PIE) analysis that appears to be suitable for genomes still in a downsizing and fractionation process following whole genome duplications. Our approach allows highlighting the genes with a maximum likelihood to affect the plant phenotype.This study on banana is a good case to investigate the effects of alloploidy in crops. We conclude that allopolyploidy triggered changes in the genome structure of a crop and it clearly influences the gene.The online version of this article (10.1186/s12864-019-5618-0) contains supplementary material, which is available to authorized users. Musa spp.), including dessert and cooking types, are monocotyledonous plants of the Zingiberales, one of the major orders of the Commelinidae clade, also comprising Poales (grasses) and Arecales . Bananas are one of the main fruit crops grown worldwide and are critical for food security in many tropical and subtropical countries.Bananas , are mainly triploid (2n = 3x = 33), as their genome is composed of three subgenomes, i.e. three sets of 11 chromosomes. Triploidy-induced sterility combined with parthenocarpy resulted in edible fruits without seeds. A first level of cultivar classification is based on the subgenome origin: autotriploid cultivars contain three subgenomes derived from M. acuminata (AAA), whereas allotriploid cultivars contain one or two M. balbisiana-derived subgenomes . Once triploidy is established in combination with sterility, plant propagation becomes exclusively vegetative. Therefore, differentiation caused by mutation and clonal selection by farmers resulted in new cultivars, which all derived from a common foundation event.Banana cultivars, which originated from inter- and intra-specific crosses between M. acuminata reference sequence (A genome) revealed that the ancestral monocot genome underwent three whole genome duplications (WGDs) independent from those that took place in the evolution of other monocot lineages (e.g. Poaceae) . Subsequently, a second RNAseq dataset was produced with ten cultivars to broaden our observations with a larger representation of allopolyploids including additional five ABB, one AAB and three AAA genomes in addition to Musa ABB ‘Cachaco’  and in three other large terminal regions, the frequency of B variants was 100% for almost all the SNPs   genes of ‘Cachaco’ do not have any A subgenome homeoallele while 861 (2.56%) have a double dose of the A homeoallele.The genome constitution of ‘Cachaco’ being ABB, the B variant frequency distribution along the 11 11) Fig. . In thesThe same approach was applied to all allotriploid cultivars  analysis clearly distinguished ABB from AAA cultivars, whereas the unique AAB cultivar ‘Pisang Ceylan’ had an intermediate position Fig. .Fig. 3PaGenes with a higher expression in the cultivars containing the B genome were enriched in the following pathways: flavonoid biosynthesis, fatty acid metabolism, amino sugar and nucleotide sugar metabolisms and oxidative phosphorylation Fig. .Fig. 4PaA set of 3674 genes was found significantly differentially expressed between ‘Cachaco’ and both AAA cultivars, ‘Grande Naine’ and ‘Mbwazirume’ , or, in the case of ‘Pisang Ceylan’ (AAB) from the expected 1B:2A ratio (2B:1A or 0B:3A) implies that recombinant events left their tracks in the allotriploid genomes. It was already hypothesized that most, if not all, banana cultivars have genomes consisting of different proportions of A- and B-genome chromosomes and/or recombinant chromosomes based on chromosomal, nuclear and cytoplasmic DNA and protein data [Polyploidy is known to induce changes in genome structure and gene expression  and banaein data ; this waein data .Musa annotated genes lie in genomic regions where the ‘Cachaco’ genome ratio is 3B:A0, but, when only the DEGs between ‘Cachaco’ and both AAA cultivars were considered (3674 genes), the 3B:A0 gene fraction is significantly higher . The presence of regions with deviating homeoallele contributions could impact the phenotypes, in particular for the regions 3B:0A (or 0B:3A), where the missing A (or B) genome regions induce a localized lack of inter-genomic heterozygosity and highlights the possible expression differences between the A and B subgenomes. For instance, around 9% of the We aimed to understand the impact of the B subgenome presence on the total gene expression in allopolyploids. The general expression level is altered between the 3 genomic groups  genome. The two more recent and “almost simultaneous” WGDs (α and β) have left more than one-third of genes in multiple copies , 2. WGDsa genome , the hig species , 25 and  species  Fig. 4)Musa genoIn our analyses, we found paralogs for the large majority of DEGs selected in ABB/AAA comparisons. The presence of paralogous gene copies , makes it trickier to interpret differential gene expression results. Considering an enzymatic function ensured by more than one gene, the impact of regulation changes needs to be considered for all the gene copies Fig. .A panel of genes showing highly significant different expression between ‘Cachaco’ (ABB) and AAA cultivars was analyzed to identify paralogs and to determine the expression changes and the possible impact on the phenotype by a Paralog Inclusive Expression (PIE) analysis  its expression without consequences for the phenotype due to the presence of redundant paralogs, and be maintained in the other genome, inducing a significant different expression between A and B homeoalleles but with no obvious effect on the phenotype. However, the impact of several enriched genes in the same pathway or function gives confidence of their potential effect on the phenotype.For some genes, these copies may be still diverging in their expression after the We observed marked differences between auto and allotriploid transcriptomes, the most significant DEGs often being correlated with the presence or absence of the B subgenome. There is a large variability among the ABB cultivars due to the different genomic structures detected in the sampled cultivars (homoeologous exchanges), which testify independent foundation events for the ABB subgroup and, likely, with different A and B genome contributions.Since the occurrence of WGDs is one of most frequent evolutionary events in plants, the occurrence of paralogs could be generalized for all plant species. However, the impact of duplicated and functionally redundant genes is inversely correlated with the time elapsed from the most recent WGD and with the specific evolution rate. In fact, the progress of the fractionation and the neo- and sub-functionalization of duplicated genes reduce the occurrence of functionally redundant genes and, consequently, increase the probability that an observed expression difference impacts the phenotype.The presence of multi-copy genes adds to the background noise in whole genome expression analysis, thus increasing the need for expert analyses to draw correct conclusions. For species having genomes with a high number of paralogs, an automatic pipeline for paralogous detection combined with the evaluation of regulation changes in gene multi-copy context would be relevant.Musa Germplasm Transit Centre (ITC), hosted at KU Leuven, Belgium  v2 [RNA-Seq reads were quality-filtered using the Illumina purity filter. Quality of reads was checked using FastQC v0.11.2 . Reads whang) v2 , 29 usinhang) v2  in a 2-phang) v2 . SNPs wehang) v2 . An overMusa genome ).Our dataset of genotypes ‘Mbwazirume’, ‘Cachaco’ and ‘Grande Naine’ was used as a starting point for further analysis. All the reads were realigned to the newest version of the The allotriploid genomic structure was verified by checking the 2/3 expected contribution of B homeoallele variants  of SNPs in the expressed genes along the 11 chromosomes. In order to reduce the bias introduced by SNPs due to the A sequence polymorphism, only SNP positions monomorphic in available A genomes (DH Pahang and RNA-Seq data of AAA cultivars ‘Grande Naine’ and ‘Mbwazirume’) were taken into account. At each retained SNP position, the alternative nucleotide to the reference variant was considered the B genome variant  of the larger sample dataset. 35,307 annotated genes were considered as X variables and the number of B chromosomes as Y variables . Normalized reads were subjected to ANOVA analysis and Benjamini correction .http://banana-genome-hub.southgreen.fr/convert). Entrez gene IDs were submitted to DAVID [To analyse possible gene enrichment, the Benjamini corrected variables were selected according to their ABB or AAA profile and their accession numbers were converted to entrez gene IDs using the conversion tool  at ≤5%. Data were normalized using RLE [The number of reads in genes were counted with HTSeq-count  using thsing RLE .n = 3)  was performed using Orthofinder v2.2.1 with protein sequences from Musa acuminata v2, Oryza sativa v7, Arabidopsis thaliana v10 and from Vitis vinifera v1. The number of paralogs was defined by counting the number of gene occurrence of Musa within each orthogroup.Identification of Musa genome for a limited number of selected DEGs was verified by BLASTp analysis on Non-redundant protein database at NCBI [Musa genes, having higher scores than genes from any other species, were considered in-paralogs. Even if orthology/paralogy is defined based on phylogenetic analysis, we considered our simplified approach based on sequence similarity appropriate to find paralogs and the possible bias due to lack of identification of paralogs with high sequence divergence as negligible. The same approach was used in [The presence of lineage specific paralogs  in the  at NCBI . Musa ge used in ). When oAdditional file 1:Sequencing and genome mapping statistics for the 12 genotypes considered in the study. Since ‘Cachaco’ was present in both the experiments, it was represented twice. (PNG 1026 kb)Additional file 2:Chromosome intervals (defined on genes coordinates) showing B:A ratio deviating from the expected genomic constitution. CDM indicates the three cultivars sharing the same pattern of deviating regions . (DOCX 16 kb)Additional file 3:p-value and FDR are reported for both comparisons. (XLSX 513 kb)List of 3674 genes showing different expression levels between ‘Cachaco’ (ABB) and both AAA cultivars (‘Grande Naine’ and ‘Mbwazirume’). LogFC, logCPM, Additional file 4:Distribution of the 3674 DEGs by number of paralogs. (PNG 128 kb)Additional file 5:Sample of genes differentially expressed in ‘Cachaco’ (ABB) compared to AAA cultivars (‘Mbwazirume’ and ‘Grande Naine’). (DOCX 18 kb)Additional file 6:Histograms representing the paralog included expression  for 58 genes having significant higher expression in ‘Cachaco’ (Ca) than in ‘Grande Naine’ (GN) and ‘Mbwazirume’ (Mb). Blue color represents expression level of DEG, other colors expression level of respective paralogs. (PPTX 146 kb)Additional file 7:Schematic view of the bioinformatics workflow for differential gene expression and genome structure identification. (PNG 310 kb)


Shigella, and an outbreak of an uncommon serotype (O117 : H7) of Shiga-toxin-producing Escherichia coli (STEC). Here, we used microbial genomics on 6 outbreak and 30 sporadic STEC O117 : H7 isolates to explore the origins and pathogenic drivers of the STEC O117 : H7 emergence in MSM. Using genomic epidemiology, we found that the STEC O117 : H7 outbreak lineage was potentially imported from Latin America and likely continues to circulate both in the UK MSM population and in Latin America. We found genomic relationships consistent with existing symptomatic evidence for chronic infection with this STEC serotype. Comparative genomic analysis indicated the existence of a novel Shiga toxin 1-encoding prophage in the outbreak isolates, and evidence of horizontal gene exchange among the STEC O117 : H7 outbreak lineage and other enteric pathogens. There was no evidence of increased virulence in the outbreak strains relative to contextual isolates, but the outbreak lineage was associated with azithromycin resistance. Comparing these findings with similar genomic investigations of emerging MSM-associated Shigella in the UK highlighted many parallels, the most striking of which was the importance of the azithromycin phenotype for STEI emergence in this patient group.Sexually transmissible enteric infections (STEIs) are commonly associated with transmission among men who have sex with men (MSM). In the past decade, the UK has experienced multiple parallel STEI emergences in MSM caused by a range of bacterial species of the genus  Three supplementary figures and two supplementary tables are available with the online version of this article. All supporting data and protocols have been provided within these supplementary files or the main article.This study investigates, using genomics with contextual data, the emerging problem of sexually transmissible enteric infections (STEIs) among men who have sex with men (MSM). It demonstrates that relatively uncommon enteric pathogens can emerge as domestically transmitting epidemics in this unique patient group associated with antimicrobial resistance, in the absence of important virulence factors. Furthermore, the parallels drawn between this study and similar studies on other emerging STEIs in the same MSM community indicates how microbial genomic studies build on each other to reveal common drivers of disease processes.Shigella, Escherichia, Salmonella and Campylobacter, and the parasites Giardia lamblia and Entamoeba histolytica [Shigella since 2009 [Escherichia coli (STEC) O117 : H7 was observed in 2014 in the UK MSM community [Certain enteric pathogens traditionally associated with food-borne or person–person spread can also be transmitted sexually. Outbreaks of such sexually transmissible enteric infections (STEIs) are frequently associated with transmission in men who have sex with men (MSM) in whom oro-anal and genital-anal contact is common. The wide variety of STEIs includes bacteria of the genera tolytica , and a ntolytica . Specifince 2009 , which gnce 2009 . In addiommunity .Shigella sonnei at local laboratories owing to their biochemical similarities and is typically isolated from patients reporting recent travel abroad [Shigella epidemics  [In the UK, STEC O117 : H7 is a rare serotype, often misidentified as l abroad . Hence, artners) .Shigella epidemics, we performed whole-genome sequence analysis (WGSA) on six isolates from the 2014 STEC O117 : H7 outbreak, and a further 30 contextual STEC O117 : H7 isolates collected between 2006 and 2017.To investigate the emergence of this rare STEC serotype in MSM, and explore parallels and contrasts with the concurrent n=5) and other investigations (n=5); isolates sequenced as part of the outbreak investigation (n=6); and all O117 : H7 isolated since the implementation of routine WGSA surveillance at PHE (n=20). Basic patient epidemiological data from sample submission forms  were also retrieved. Whole-genome sequencing was performed at PHE using Nextera XT DNA sample preparation kits and sequencing on an Illumina HiSeq 2500 instrument [fastqc (v 0.11.4), and adapter sequences and 5′ and 3′ ends  were trimmed to remove sequence per base content biases using trim galore v. 0.4.5 (cutadapt v 1.13). Existing sequencing data was retrieved from the Short Read Archive, where novel data was also deposited, under bioproject PRJNA315192 . Draft genome assemblies were generated from quality-trimmed data using spades v. 3.11.0 [sspace [prokka v 1.11 [In total, Illumina sequence data from 36 isolates of STEC O117 : H7 submitted to, and sequenced by, the Gastrointestinal Bacteria Reference Unit (GBRU) at Public Health England (PHE) were examined in this study . All avastrument . Sequenc [sspace  and gap a v 1.11 .hgap v3 [Complete (but unfinished) genomes were generated for two outbreak isolates  through SM-RT sequencing . For all isolates after mapping, >90 % of the genome was covered, with repeat mappings discarded (i.e. reads with multiple best mappings). Overall, between 88 and 95 % of reads for each isolate mapped with a mean depth of 20–90 times coverage (Table S1). Variants were called with Samtools [gubbins [SNP-sites [To infer a phylogenetic tree describing the evolutionary relationships among isolates, trimmed sequencing data were mapped against the complete genome of isolate 23169 using Samtools  to gener[gubbins . In totaNP-sites . To faciNP-sites .phast analysis [blast [emboss stretcher modification of the Needleman–Wunsch algorithm that allows larger sequences to be globally aligned [gyrA codons 83 and 87; parC codons 80, 84 and 129. Plasmid visualizations were made using the blast ring image generator [To determine the basic architecture of the genome, the identities of contiguous sequences from the complete genome assemblies of isolates 23168 and 23169 were compared with existing sequences and each other. Features common to both isolates were considered part of the basic genome architecture. For detection of PAIs, sequences for the LEE-PAI, locus of adhesion and autoaggregation (LAA) PAI and the subtilase-encoding PAI (SE-PAI) were downloaded from the National Center for Biotechnology Information (NCBI) database, using accession numbers AF022236, AFDQ01000026.1 (positions 385984–472336) and JQ9944271, respectively. Genome annotations and  aligned . For the aligned , and themphA gene occurrence in outbreak clade and non-outbreak clade isolates was calculated using a two-tailed Fisher’s exact test.Phenotypic testing of a subset of isolates for reduced susceptibility against azithromycin was performed using the Etest macromethod (bioMérieux), and statistical comparison of the frequency of the To gain epidemiological insight into the outbreak of this rare STEC serotype, the evolutionary relationships among the isolates were combined with the patient epidemiological metadata. This revealed that the six epidemiologically confirmed outbreak isolates shared close genetic relationships with four non-epidemiologically investigated isolates contained within the monophyletic outbreak clade . Of the E. coli O157 : H7 strain Sakai and each other . Comparison of the two complete genomes with the NCBI non-redundant database (Table S2) and each other revealed a common architecture consisting of a single bacterial chromosome and three plasmids . These assembled into 12 and 15 contiguous sequences, respectively (Table S2). The two genomes were broadly syntenic with plasmids . The smaei Ss046 . The secei Ss046 . Finallyei Ss046 , and thastx prophage. This revealed the existence of seven ubiquitous  virulence determinants and seven non-ubiquitous virulence determinants among the 36 STEC O117 : H7 isolates . Outbreak isolate 23 169 did not carry the LEE-PAI or SE-PAI, but did possess some elements of the recently described modular LAA-PAI. Specifically, positions 3928350–3935630 of the 23169 genome (contiguous sequence 1) shared 97 % identity with part of module 1, inclusive of genes encoding the AtoSC two-component system [sisA or hes. Similarly, positions 1502495–1517274 shared 93 % identity with part of module 4 of LAA-PAI, inclusive of ORFs 66–80 (including the known virulence factor antigen 43 [66) in isolate 23169 . The Stx-encoding prophage was found in positions 3711427–3799540, with its closest described relative being a novel stx1a phage Ss-VASD, recently isolated from S. sonnei taken from human patients with diarrhoea who had recently travelled to Mexico [Escherichia phage GER2’ and was deposited separately in GenBank under accession number MG710528, with annotation information from the phast pipeline.To investigate why an STEC serotype that is normally rare in the UK caused an outbreak, the presence of virulence determinants was characterized for all isolates . The presence of azithromycin-resistance genes was enriched in the outbreak clade relative to the remainder of the tree  though this association fell short of traditional significance measures . Although both isolates for which complete genomes were sequenced contained the mphA gene in their draft genomes (and had phenotypic azithromycin resistance confirmed) only one of the two (23168) had the mphA gene in the complete genome, suggesting it may have been lost during re-culture. All ten isolates in the outbreak clade had a mutation in the quinolone-resistance-determining region of gyrA (S83L), which is associated with reduced susceptibility against fluoroquinolones [The draft genomes of all 36 isolates and complete genomes of the two SM-RT-sequenced isolates were examined for genes and mutations known to confer reduced susceptibility to antimicrobials. The isolates were found to have numerous genetic determinants of resistance against a variety of antimicrobial classes , with alinolones , though Shigella flexneri 3a that emerged in the UK, which was also potentially imported from Latin America [Combining the evolutionary analysis of outbreak and contextual STEC O117 : H7 isolates with traditional epidemiological markers yielded insights into the possible origins, and potentially ongoing nature of the outbreak. In addition to the six isolates known to belong to the outbreak, the encompassing outbreak clade included a further four isolates, three of which were recovered from patients recently returned from travel to Latin America in 2010, 2013 and 2016. This suggests that the outbreak strain may have originated in, and continues to circulate in, Latin America. Though more work is needed to investigate this link, this mirrors the findings for MSM-associated  America . The fouS. flexneri 3a among UK MSM where isolates serially isolated from patients ≤154 days apart were genetically similar (0–4 SNPs) [The evolutionary analysis also provided genomic evidence for chronic infection with STEC O117 : H7. Specifically, two isolates (outside of the outbreak clade) from the same patient taken 4 months apart were indistinguishable in this analysis . This supports the epidemiological investigation of the outbreak, where symptoms of ≥1 month were reported [E. coli and shigellae. This was supported by the presence of plasmid p09EL50 in the outbreak isolate; a plasmid originally described in a 2009 STEC O104 : H4 strain from the Republic of Georgia [S. sonnei (MSM-associated sublineage 3) that was circulating in UK MSM over a similar timeframe to the 2014 STEC outbreak, providing a plausible opportunity for direct or indirect genetic exchange between these two pathogen subtypes. The finding of shared AMR determinants among distinct STEIs in the MSM community complements results from a recent WGSA study of overlapping Shigella epidemics in UK MSM where a resistance plasmid, pKSR100, was shared among five co-circulating polyphyletic Shigella sublineages  [Analysis of the accessory genome of the outbreak isolate indicated likely horizontal gene flow between pathogenic  Georgia . Similarneri 3a) .stx1a and stx1b on a novel prophage most closely related with an stx1a phage isolated from S. sonnei from human patients in Mexico [sigA. However, these key virulence determinants typically associated with Shigella  were also ubiquitous among the contextual isolates. To summarize then, virulence determinants were not markedly different between outbreak and contextual isolates, so were not likely to have contributed to the aberrant emergence of this rare serotype in MSM.To explore a possible pathogenic basis for the emergence of this rare STEC serotype in MSM, outbreak isolates were investigated for the presence of virulence determinants. Virulence determinants associated with more severe disease presentations, including Stx2a and the LEE-PAI, were not present in the outbreak clade. Increasing evidence suggests that LEE-negative STEC may have common virulence determinants that contribute to their clinical presentation such as the SE- and LAA-PAIs . Althougn Mexico , and alsShigella causing outbreaks among UK MSM were also associated with azithromycin resistance relative to the contextual isolates in those studies [Outbreak and contextual isolates were distinguishable, however, through the enrichment of genes encoding azithromycin resistance among isolates in the outbreak clade, highlighting the increasingly apparent role of AMR in epidemics of STEIs in MSM. The three subtypes of  studies . This co studies  and that studies . These tThese results have implications for public health practice. Outbreaks of enteric illnesses are not typically investigated for association with sexual activity in MSM (though this is changing in the UK consequent to the recent epidemics). The continued finding of STEI emergence associated with azithromycin resistance indicates that it may be useful to investigate and manage azithromycin-resistant enteric illness outbreaks as being potentially associated with sexual transmission. Furthermore, the tendency of STEIs in MSM to be resistant to azithromycin, which is not necessarily the primary treatment for enteric illness, suggests that treatment for other illnesses, including non-enteric bacterial STIs, may be creating a sustained selection pressure for resistance in other pathogens. This selection pressure can then drive pathogen evolution through horizontal gene transfer and potentially cause unexpected changes in epidemiology, such as the emergence of a rare serotype. To work toward a public-health framework where such rapid changes in epidemiology are de-mystified, we must continue to scrutinize pathogens at the genomic level, characterize gene flow among different bacterial populations, and integrate this information with detailed epidemiological data.Public Health England, Sequence Read Archive, Bioproject number: PRJNA315192 (2017).Baker KS. Figshare. doi: 10.6084/m9.figshare.6272216 (2018).


SMARCB1-deficient sinonasal carcinoma (SDSC) is an aggressive subtype of head and neck cancers that has a poor prognosis despite multimodal therapy. We present a unique case with next generation sequencing data of a patient who had SDSC with perineural invasion to the trigeminal nerve that progressed to a brain metastasis and eventually leptomeningeal spread.A 42 year old female presented with facial pain and had resection of a tumor along the V2 division of the trigeminal nerve on the right. She underwent adjuvant stereotactic radiation. She developed further neurological symptoms and imaging demonstrated the tumor had infiltrated into the cavernous sinus as well as intradurally. She had surgical resection for removal of her brain metastasis and decompression of the cavernous sinus. Following her second surgery, she had adjuvant radiation and chemotherapy. Several months later she had quadriparesis and imaging was consistent with leptomeningeal spread. She underwent palliative radiation and ultimately transitioned quickly to comfort care and expired. Overall survival from time of diagnosis was 13 months. Next generation sequencing was carried out on her primary tumor and brain metastasis. The brain metastatic tissue had an increased tumor mutational burden in comparison to the primary.This is the first report of SDSC with perineural invasion progressing to leptomeningeal carcinomatosis. Continued next generation sequencing of the primary and metastatic tissue by clinicians is encouraged toprovide further insights into metastatic progression of rare solid tumors.The online version of this article (10.1186/s12885-019-6043-0) contains supplementary material, which is available to authorized users. Sinonasal carcinomas have recently gained increased interest for their histogenetically diverse characteristics brought to light by advances in molecular genetics . These aSDSC is a rare, often fatal tumor characterized by distinct inactivating alternations of the tumor suppressor gene SMARCB1 located on chromosome 22q11.2, basaloid/rhabdoid differentiation, and histologic loss of INI1 expression . With feA 42 year old female presented with a right sided headache with associated right sided facial pain. A Computed Tomography (CT) scan showed a mass on the V2 division of the trigeminal nerve Fig. a. A maxiA few months after the GKSRS, the patient developed a right cranial nerve 6 palsy and severe facial pain refractory to medications. Interval imaging revealed an increase in the mass with extension along the trigeminal nerve into the cavernous sinus Fig. b and an DNA and RNA sequencing were performed on two patient’s tumor specimens, one from right temporal lobe of the brain, and the other from the primary sinonasal tumor, using the xT Laboratory Developed Test at Tempus’ Clinical Laboratory Improvement Amendments/College of American Pathologists-accredited laboratory in Chicago, IL. Tumor DNA was extracted from tumor tissue sections with tumor cellularity higher than 20% and proteinase K digested. Total nucleic acid extraction is performed with a Chemagic360 instrument using a source-specific magnetic bead protocol. Total nucleic acid is utilized for DNA library construction, while RNA is further purified by DNaseI digestion and magnetic bead purification. The nucleic acid is quantified by a Quant-iT picogreen dsDNA reagent Kit or Quant-iT Ribogreen RNA Kit (Life Technologies), and quality is confirmed using a LabChip GX Touch HT Genomic DNA Reagent Kit or LabChip RNA High HT Pico Sensitivity Reagent Kit (PerkinElmer).For the DNA library construction, one hundred nanograms of DNA for each tumor and normal sample was mechanically sheared to an average size of 200 base pairs using a Covaris ultrasonicator. The libraries were prepared using the KAPA Hyper Prep Kit. Briefly, DNA underwent enzymatic end-repair and A-tailing, followed by adapter ligation, bead-based size selection, and PCR. After library preparation, each sample was hybridized to a custom designed probe set. Recovery and washing of captured targets was performed using the SeqCap hybridization and wash kit. The captured DNA targets were amplified using the KAPA HiFi HotStart ReadyMix. The amplified target-captured libraries were sequenced on an Illumina HiSeq 4000 System utilizing patterned flow cell technology.Generated reads were aligned to the human reference genome (hg19) using BWA aligner , and subRNA Library construction. One hundred nanograms of RNA per tumor sample was fragmented with heat in the presence of magnesium to an average size of 200 base pairs. The RNA then underwent first strand cDNA synthesis using random primers, followed by combined second strand synthesis and A-tailing, adapter ligation, bead-based cleanup, and library amplification. After library preparation, samples were hybridized with the IDT xGEN Exome Research Panel. Target recovery was performed using Streptavidin-coated beads, followed by amplification using the KAPA HiFi Library Amplification Kit. The RNA libraries were sequenced to obtain approximately 65 million reads on an Illumina HiSeq 4000 System utilizing patterned flow cell technology.From RNA sequencing variant calling, the best practices published by the Broad Institute were followed. Namely, after a double pass alignment step with Star aligner  to the hThe patient’s primary tumor and brain metastasis underwent next generation sequencing. We found over 500 variant calls on both samples that were screened and selected based on their mapping quality and coverage (>20x). Due to the rarity of this malignancy, we overlapped those selected variants to the mutations reported on The Cancer Genome Atlas (TCGA) using ActiveDriverDB database. We reported calls that were up to three amino acids away from the variant location and were implicated in changes in protein signaling or posttranslational modification in any malignancy Fig. .Fig. 6SThree mutations matched TCGA cases of head and neck squamous cell carcinoma (HNSC). Namely, glutamate metabotropic receptor 3 (GRM3 D279E), ret. proto-oncogene (RET T946A), and BCR (BCR K724 N), but those mutations only appeared on the sinonasal sample. We also identified a mutation on the marker of proliferation Ki 67 MIK67 A1218T) found on brain lower grade glioma only on the sinonasal sample. Additional file 218T founCommon variants overlapping TCGA mutations on the sinonasal and brain samples were found primarily on the filaggrin (FLG) gene, notably a missense mutation on FLG R1469H for lung squamous cell carcinoma, and another on FLG R1469L for colon adenocarcinoma. Moreover, other cancer types shown mutations close by the SNP rs768563961: skin cutaneous melanoma, lung adenocarcinoma, rectum adenocarcinoma and glioblastoma multiform. Table Brain variants overlapping TCGA mutations were considerably less than the sinonasal counterpart. There was one variant on major histocompatibility complex I gene HLA-C with one case of lung adenocarcinoma (HLA-C G276 V) and another mutation found on colon adenocarcinoma (HLA-C G276R). Another variant was found on proto-oncogene MAF  close to a reported mutation on stomach adenocarcinoma (MAF V286A). Finally, on the Aurora kinase gene AURKB (rs101079278) a close mutation was reported on bladder urothelial carcinoma (AURKB S37Y).A variant found on the apoptotic activator BCL2L11 (rs762818079) in a case of uterine corpus endometrial carcinoma shows post transcriptional modifications of the binding sequence motif for the kinases AKT1, MAPK10, MAPK8, and MAPK9.Gene expression data demonstrated that ten genes  had at least a one log fold change in decreased expression when comparing the brain sample to the primary sample. All other genes were all less than one log fold change in expression  on the SNP rs5751602 at the RNA level. Another place that shows LOH is at rs140855897 (deletion in the brain specimen) in the major histocompatibility complex I gene HLA-A. Also, the immune system genes HLA-A and HLA-C were both highly upregulated on the sinonasal sample. Interestingly, in both samples immunohistochemistry demonstrated less than 1% PD-L1 expression in both tumor cells and tumor associated immune cells.We report a rare case of a patient with SMARC-B1 deficient tumor with perineural spread that progressed to develop into an intradural metastasis. Following treatment failure, the patient eventually developed leptomeningeal spread. Previous studies have reported the presence of brain metastasis from this tumor in 3 patients but only one patient has been previously reported as having concordant perineural spread and intradural disease, however no sequencing data was reported , 17. TheTo our knowledge, this is the first report that sequencing data has been reported for the primary SMARCB1 tumor with perineural spread and a matched brain metastasis. Previous whole exome sequencing of primary tumors and their accompanying brain metastasis demonstrated the potential to identify actionable alterations . With thIn other rhabdoid tumors, such as atypical teratoid/rhabdoid tumors (AT/RT) of the CNS, loss of INI1 is associated with overexpression of cell-cycle regulatory protein cyclin D1, leading to cell cycle progression and untethered cellular proliferation , 20. IntHistopathological diagnosis of this tumor can be complex as a number of malignant neoplasms can show similar histologic features and rhabdoid morphology in this location and should be considered in the differential diagnosis. ATRT has been reported in the sellar region of adults but these tumors were confined to the intracranial compartment and did not appear to predominantly involve the skull base and sinonasal regions . Given tThe progression of this patient’s tumor to leptomeningeal spread is an extremely rare occurrence. For cutaneous carcinomas, perineural invasion progressing to leptomeningeal spread has been previously reported . HoweverAdditional file 1:List of DNA mutations identified in each sample. (XLSX 22 kb)Additional file 2:Quantitative Gene Expression differences between samples. (XLSX 10 kb)Additional file 3:List of RNA mutational variants identified in each sample. (XLSX 13 kb)


Acute pain serves as a protective mechanism, guiding the organism away from actual or potential tissue injury. In contrast, chronic pain is a debilitating condition without any obvious physiological function. The transition to, and the maintenance of chronic pain require new gene expression to support biochemical and structural changes within the pain pathway. The regulation of gene expression at the level of mRNA translation has emerged as an important step in the control of protein expression in the cell. Recent studies show that signaling pathways upstream of mRNA translation, such as mTORC1 and ERK, are upregulated in chronic pain conditions, and their inhibition effectively alleviates pain in several animal models. Despite this progress, mRNAs whose translation is altered in chronic pain conditions remain largely unknown. Here, we performed genome-wide translational profiling of dorsal root ganglion (DRG) and spinal cord dorsal horn tissues in a mouse model of neuropathic pain, spared nerve injury (SNI), using the ribosome profiling technique. We identified distinct subsets of mRNAs that are differentially translated in response to nerve injury in both tissues. We discovered key converging upstream regulators and pathways linked to mRNA translational control and neuropathic pain. Our data are crucial for the understanding of mechanisms by which mRNA translation promotes persistent hypersensitivity after nerve injury. Chronic pain debilitates over twenty percent of the population worldwide, and is the leading cause of long-term disability in humans . The mosThe inadequate management of chronic pain is a consequence of our incomplete understanding of the mechanisms underlying the induction and maintenance of pain states, leading to treatments that only target symptomatology without addressing the etiology of the disease. Sensitization of nociceptive circuits, both in the central and peripheral nervous systems, leads to mechanical hypersensitivity , which is a hallmark of many chronic pain conditions. This sensitization is supported by the expression of new genes, which are required for the biochemical and structural reorganization of the pain pathway. With advancements in microarray and sequencing technologies, transcriptional changes associated with chronic pain have been extensively studied, providing important insights into the transcriptional landscape and identification of a subset of genes with differential expression in various chronic pain conditions .Cellular abundance of proteins is highly controlled at the level of mRNA translation . Transla(ERK) pathway and mechanistic target of rapamycin complex 1 (mTORC1) kinase and its downstream effectors have been extensively studied to understand the contribution of translation in the development of hypersensitivity  dorsal horn tissues from mice subjected to SNI and mapped the translational and transcriptional landscapes. In addition, we carried out meta-gene analysis by Ingenuity Pathway Analysis (IPA) and identified commonly affected pathways.To understand the global pattern of translational control, and identify which mRNAs are differentially regulated following nerve injury, we performed genome-wide translational profiling of DRG and dorsal horn of the spinal cord in the SNI assay of neuropathic pain. For the analysis, we collected L3 to L5 DRG and the corresponding lumbar segment of the spinal cord  30 days in vivo genome-wide translational efficiency of mRNAs in DRG and spinal cord, we adopted the ribosome profiling methodology  in the DRG of SNI mice as compared to sham animals, while translation was downregulated  for 31 mRNAs (Footprints and mRNA densities were computed in units of reads per kb per million (RPKM) to normalize for gene length and total reads per sequencing run. All conditions demonstrated a strong correlation between biological replicates  of differentially regulated genes  in SNI revealed top cellular functions and subcellular localizations in the DRG C and spiMyh 7, Mobp, 1500009C09Rik, Sall1, Grin2b, Olig2 and 3110035E14Rik) are transcriptionally down regulated but translationally upregulated. In the spinal cord, four genes  are transcriptionally upregulated but translationally down regulated, whereas Tmem54 is transcriptionally downregulated but translationally upregulated. Several genes that have been previously studied in relation to pain show opposite changes in mRNA levels and their translation efficiency . For example, Scn4a gene codes for the alpha subunit of the voltage-dependent sodium channel, and mutations in this gene have been associated with sodium channel myotonia  was identified as a signature gene associated with pain experienced in sickle cell disease  is a member of the transient receptor potential superfamily which is known to be involved in a number of sensory functions, ranging from detection of light, force, osmolality, temperature, odour, taste, and pain . Lumbar DRG (level L3–L5) were excised for all animals. Next, the lumbar region of the spinal cord at which the L3–L5 DRG branch into was excised and placed on a bed of dry ice/metal plate and allowed to freeze after which it was cut along the frontal plane to separate the dorsal horn section. The DRG and dorsal horn were collected in non-stick, RNase free microcentrifuge tubes , immediately snap-frozen in liquid nitrogen, and stored at −80 °C until further processing.To collect DRG and dorsal horn of the spinal cord, animals were sacrificed by brief isoflurane anesthesia followed by decapitation. The animal was secured on a bed of dry ice and the spinal cord was exposed and doused with RNA2, 1 mM DTT and 100 μg/ml cyclohexamide, 8% glycerol), 1% Triton X-100 and 25 U/ml Turbo DNase I) in a glass homogenizer system. The total lysate was divided into two fractions. A fraction containing at least 150 μg of total RNA was reserved for ribosome footprinting (RFP fraction) and the remaining (at least 100 μg) was processed for mRNA-Seq.Flash frozen DRG and dorsal horn tissue was lysed in ice-cold cell lysis buffer (1% Polysome buffer (20 mM TrisCl (pH 7.4), 150 mM NaCl, 5 mM MgClg at 4 °C for 30 min. Purified RNA was resolved on a 15% polyacrylamide urea gel  and bands corresponding to 28–32 nucleotides, containing the desired ribosome footprints (RFPs), was excised and extracted for RNA using Costar Spin-X column .Ribosome footprinting was carried out as previously described  with min2CO3, 90 mM NaHCO3, pH 9.2) for 20 min at 95 °C. The reaction was stopped by addition of the precipitation solution (300 mM NaOAc pH 5.5 and 2 μl GlycoBlue), followed by Isopropanol. Fragmented mRNAs were size-selected on a denaturing 10% polyacrylamide-urea gel and the bands corresponding to 30–50 nucleotides were excised, eluted, and precipitated with Isopropanol.Poly (A)+ mRNAs were purified from 100 µg of cytoplasmic RNA, using magnetic oligo-dT DynaBeads. The purified RNA was then subjected to alkaline fragmentation by treating it with an equal volume of 2× alkaline fragmentation solution  and SuperScript III reverse transcriptase (Invitrogen) according to the manufacturer’s instruction manual. The resulting cDNA was purified by size selection on a 10% polyacrylamide Tris/Borate/EDTA-urea (TBE-urea) gel. The cDNA was then circularized using CircLigase . Products arising from ribosomal sequences were depleted using biotinylated rDNA complementary oligos  and MyOnRaw sequencing data were de-multiplexed by the sequencing facility (Genome Quebec). Sequences were analyzed using a custom developed bioinformatics pipeline adapted from Reads Per Kilobase of transcript per Million mapped reads (RPKM) was calculated using an in-house R-script described in Pathway Analysis was performed using the Ingenuity Pathway Analysis Software . Datasets previously filtered to include only differentially expressed and differentially translated genes were submitted to IPA. Location and Type information were obtained from the IPA annotated datasets to determine the % of genes from each dataset belonging to individual subcellular localization and molecular type/function categories. Data was plotted as% of genes in each category, with category “other” not shown. IPA annotated datasets were submitted to Core Analysis with analysis parameters set to include “Direct and indirect interactions” and “Experimentally observed data only”. Network data was obtained for all datasets and a Molecular Activity Predictor (MAP) analysis was applied based on the differentially regulated genes belonging to each individual network.


However, radiotracers beyond [18F]FDG have been proposed to investigate BAT activity, targeting various aspects of BAT metabolism. It remains questionable which tracer is best suited to detect metabolic BAT activity and to what extent those results correlate with ex vivo metabolic BAT activity.Brown adipose tissue (BAT) is considered as a potential target for combating obesity in humans where active BAT metabolizes glucose and fatty acids as fuel resulting in heat production. Prospective studies in humans have been set up to further study the presence and metabolic activity of BAT mostly using Positron Emission Tomography (PET) imaging in cold-stimulated conditions with the radiolabeled glucose derivative FDG visualized BAT. Glucose metabolism, fatty acid metabolism, noradrenergic stimulation and blood perfusion showed a clear tracer-dependent twofold increase in BAT uptake upon cold exposure. Only the tracer for the amino acid transport system did not show BAT specific uptake under any of the experimental conditions. MRS demonstrated that cold-acclimated animals had BAT with a stronger heat-production compared to control animals.At room temperature, only [18F]FDG was also able to show BAT activity under non-stimulated conditions (room temperature). The variances in uptake of the different tracers should be taken into account when developing future clinical applications in humans.BAT activity following cold exposure in rats was visualized by several tracers, while only [ Brown adipose tissue (BAT) has gained considerable attention over the last decade, as its appearance and function in humans is becoming more elucidated. The main function of BAT is to dissipate energy in the form of heat, a process mediated by the mitochondrial uncoupling protein 1 (UCP1), in response to cold exposure . BAT is 2 < 0.3) [18F]fluorodeoxyglucose ([18F]FDG), 14-[18F]fluoro-6-thia-heptadecanoic acid ([18F]FTHA), [99mTc]Tc-2-methoxy-isobutyl-isonitrile ([99mTc]TcMIBI) and [123I]-metaiodobenzylguanidine ([123I]MIBG). These radiopharmaceuticals are able to visualize and quantify BAT activity in humans [18F]FDG mainly visualizes glucose transport and is the most frequently used tracer to image BAT due to its high availability. However fatty acids are the main metabolized substance class in BAT [18F]FDG might largely underestimate BAT activity. In addition [18F]FDG BAT uptake is verifiably reduced in diabetic patients [18F]FTHA visualizes uptake of free fatty acids  (targeting LAT1-4 amino acid transport system density) was used. [123I]IPA has never been reported to visualize BAT clinically or preclinically, but as it correlates to a key amino acid transport system we feel it is important to include it in our study.The presence and/or activity of BAT can be non-invasively visualized in humans using magnetic resonance imaging (MRI), computed tomography (CT), thermography and molecular imaging –9. Therm2 < 0.3) , this te2 < 0.3) . These tIBI) and 3I-metaion humans . 18F]FDFFD2 < 0.s in BAT  and therpatients  due to tvia CD36 ). Fatty terminal . NorepinBI) and 1I-metaiodterminal  and therIBI) and 3I-metaionditions . In acut18F]FDG uptake on PET images correlates positively to cold outside temperatures and negatively to a subjects BMI, in correspondence with predictions from in vitro work FDG PET uptake in humans FDG, [18F]FTHA, [123I]MIBG, [99mTc]TcMIBI and ([123I]IPA). Furthermore, using MRS, we visualized and quantified BAT in rat following acute cold or cold acclimation by means of gradient-echo sequence (Fast Low Angle Shot (FLASH)).In our study, we primarily investigated to what extent different radiopharmaceuticals are suitable to quantify response of BAT to acute cold and cold acclimation in rats. For this reason, we used a rat model which was acutely exposed to cold or acclimated to cold. We compared tracers that were previously described to visualize BAT in clinical setting, namely FDG was purchased from GE Healthcare. Sestamibi (MIBI) was labeled with technetium-99m according to manufacturer guidelines . Non-carrier added [123I]MIBG was prepared as suggested by the manufacturer of the precursor [123I]MIBG over a cation exchange filter, rinsing with saline solution and finally collecting the non-carrier-added tracer by a phosphoric acid/ascorbic acid solution. After neutralization with NaOH a final volume of about 2ml is reached and the solution is ready for injection. The synthesis of ([123I]IPA) was adapted from earlier publications [18F]FTHA was prepared according to an adapted procedure from FDG, [18F]FTHA, [123I]MIBG, [99mTc]TcMIBI and [123I]IPA and group F was used for MRI (n = 8). Imaging started at 1 pm. Prior to any scan or animal sacrifice, animals were fasted for 4 hours at either room temperature (baseline control conditions) or at 4 ⁰C (exposure to cold). PET/SPECT imaging was performed under pentobarbital anesthesia , as this sedative was reported to show the least side effects on BAT activityFDG (group A), 20–50 MBq [18F]FTHA (group B), 100MBq [123I]MIBG (group C), 100 MBq [99mTc]TcMIBI (group D) or 100 MBq [123I]IPA (group E). The imaging room was conditioned to room temperature (21 ⁰C).Animals were sedated and subsequently injected in a tail vein under the camera  with 20-50MBq FDG images showed pronounced uptake in heart and brain, and to some extent in iBAT   volume change.In comparison, the myocardium and the liver showed no significant changes in SUVIn addition, there was no difference in data obtained from images that were acquired by injecting the tracer in cold-acclimated animals under the camera, in a room where the animal was no longer exposed to cold during tracer distribution, and images that were acquired after the tracer had been given the chance to distribute in the cold-acclimated animal for 30 minutes in a cold environment also see .Analysis of biodistribution data obtained from dissected tissue  confirmsThe use of the gradient-echo sequence with a fat suppression pulse made it possible to visualize the position of BAT in rat . The FLAmRNA expression of genes related to BAT metabolism  were higher in iBAT compared to iWAT. Similar results were observed for GLUT4 mRNA expression when comparing iBAT to iWAT although the results did not reach statistical significance (data not shown). When examining protein expression, control animals showed higher levels of UCP1 and GLUT4 protein expression in iBAT compared to WAT, however CD36 protein expression was similar between iBAT and WAT (p = 0.43).Cold acclimation did not alter mRNA expression of the selected genes in BAT. PRDM16 mRNA expression decreased following cold acclimation in BAT (p = 0.013) . No chan18F]FDG, [18F]FTHA, [123I]MIBG, [99mTc]TcMIBI and [123I]IPA in baseline and different cold-stimulated conditions. Additionally we investigated temperature of BAT and muscle as a negative control in cold stimulated conditions by MRS and analyzed mRNA and protein expression of a number of relevant genes.In this study, we used a rat model to assess BAT molecular imaging with FDG allowed iBAT visualization in warm conditions as well as in animals exposed to acute cold and cold-acclimated animals. If animals were exposed to cold, cBAT was visible as well. [18F]FTHA, [123I]MIBG and [99mTc]TcMIBI poorly visualized iBAT in warm conditions, but clearly showed iBAT and to some extent also cBAT in animals exposed to acute cold and when animals were acclimated to cold. [123I]IPA did not visualize iBAT in any setting and is therefore not suited as a BAT imaging tracer.FDG, [18F]FTHA and [123I]MIBG in animals exposed to acute cold was about double, while for [99mTc]TcMIBI the increase was by a factor of 1.3. Considering the time window of 4 hours cold exposure, the factor of 1.3 is probably due to an increase in perfusion / blood flow and not to an increase in mitochondrial density. This confirms earlier findings of increased blood flow due to cold exposure with other tracers such as [201Tl]thallium chloride FDG and [99mTc]MIBI. For [18F]FTHA and [123I]MIBG the increase in volume already starts after acute cold exposure, and then increases further upon cold-acclimation. These findings are to some extent not congruent with those found in literature from clinical studies, in which [18F]FTHA showed a lack of uptake under acute cold exposure and less uptake compared to FDG upon cold stimulation [18F]FTHA may be due to a metabolic difference in animal species (rat vs. human), but may also be due to the higher difficulty of delineating brown adipose tissue in humans compared to rats. The volume increase of active BAT is in accordance with the results obtained from MRS and the biodistribution where increased BAT temperature or volume could only be found after cold acclimation.The volume of tissue with a high tracer uptake shows a different pattern, as the volume after cold exposure is similar to that in warm conditions and is only increased upon cold-acclimation for IPA were able to visualize BAT under acute and acclimated cold conditions. With the support of the different tracer we were able to investigate different aspects of BAT metabolism and features during cold exposure and compare these results to other measurement techniques. We were able to show BATs dependence on glucose and fatty acid metabolism by [18F]FDG and [18F]FTHA by PET and uptake was increased due to acute cold exposure. With [99mTc]MIBI we were able to visualize BATs perfusion and with [123I]MIBG expression of sympathetic nerve endings which are responsible for norepinephrine release and therefore for BAT activation. Both tracers could show increased uptake after acute cold exposure. Only [123I]IPA was not able to visualize BAT under all temperature conditions, indicating an independence of BAT on LAT1-4 amino acid transport system. Invasive measurements of BAT volume were coherent with results obtained by MRS and dissection of iBAT.All tracer except for FDG, [18F]FTHA, [123I]MIBG and [99mTc]TcMIBI, but not [123I]IPA, are suitable for imaging aspects of BAT activity using non-invasive molecular imaging, while MRS is able to quantify heat production in BAT, even in an animal model where classical ex vivo techniques fail to show significant trends. The uptake in BAT of each tracer responds differently to acute cold exposure, and cold-acclimation does not increase BAT tracer uptake as strongly in our animal model as it does in humans. Those differences in tracer uptake characteristics should be considered during study planning as well as it should be translated to clinical applications. in iBAT and visceral WAT after dissection and workup. Cold-acclimation = animals exposed to cold for 6h per day for 4 weeks. (†) indicates a statistically significant difference between brown adipose tissue and white adipose tissue.(DOC)Click here for additional data file.S1 Dataset(XLS)Click here for additional data file.S2 Dataset(XLSX)Click here for additional data file.S1 Text(DOC)Click here for additional data file.S2 Text(PDF)Click here for additional data file.


Based on international guidelines, the elaboration of national carbon (C) budgets in many countries has tended to set aside the capacity of grazing lands to sequester C as soil organic carbon (SOC). A widely applied simple method assumes a steady state for SOC stocks in grasslands and a long-term equilibrium between annual C gains and losses. This article presents a theoretical method based on the annual conversion of belowground biomass into SOC to include the capacity of grazing-land soils to sequester C in greenhouse gases (GHG) calculations. Average figures from both methods can be combined with land-use/land-cover data to reassess the net C sequestration of the rural sector from a country. The results of said method were validated with empirical values based on peer-reviewed literature that provided annual data on SOC sequestration.This methodology offers important differences over pre-existing GHG landscape approach calculation methods:•improves the estimation about the capacity of grazing-land soils to sequester C assuming these lands are not in a steady state and•counts C gains when considering that grazing lands are managed at low livestock densities. Specification TableExtensively used international guidelines such as those of Intergovernmental Panel on Climate Change (IPCC) Tier 1 1996, 2006) provide thorough procedures to estimate national carbon (C) emissions. Nevertheless, methods to assess the capacity of plants and soils to capture and store C raise uncertainty because these set aside the capacity of grazing lands to sequester C as soil organic carbon (SOC). National communication reports on greenhouse gases (GHG) that applied IPCC Tier 1 procedure 006 provi. This meIn Data on land-use/land-cover by biome types was provided by the HYDE 3.1 global database [2O emissions from agriculture, agricultural waste burning and large scale biomass burning) for each MERCOSUR country was allocated on a spatial 0.1° x 0.1° grid. Because all calculations were expressed in terms of C, a factor = 0.273 (IPCC 2006/ [2 eq year−1) into C emissions (ton C year−1).Annual data on GHG emissions (E) due to land-use change and livestock/crop production was provided by the global database EDGAR  4.2 [SOC), which reflected an annual gain, loss or equilibrium in SOC figures.The theoretical estimation of the annual C balance (CB) was the result of the difference between carbon emissions (E) and soil organic carbon (SOC) change ; gAGB is the annual growth of aboveground biomass expressed in ton dry matter (DM) ha−1 year−1; BGB/AGB is the relationship of each specific biome type; 0.47 is the carbon factor used to convert DM biomass into C biomass suggested by IPCC [L represents the loss of carbon from BGB due to respiration under different thermal conditions before being converted into SOC [On the other hand, Sinto SOC .−1. DM estimations for natural forests, forest plantations, shrublands, savannas, grasslands, cultivated pastures, desert steppes and croplands were provided by a global database that comprised 685 geographical sites [The annual SOC sequestration was estimated following a sequence of three steps. The first one consisted of determining, for each biome and climatic region, the stock of AGB expressed in tons DM haal sites . The thial sites . Thus, r2 enriched atmosphere). The final database cases from 366 peer-reviewed publications for different climatic regions (See Table S2).To validate results from theoretical method, annual data on SOC from various published peer-reviewed independent studies of dominant biomes and regions  was usedThese collected data were counteracted with the results obtained with the theoretical method. The objective was to submit it to a validation process to verify its strength. For this, a simple linear regression model was used. The statistical significance of the model and the values of the coefficients of determination and correlation, together with the behaviour of the residuals, were verified. The high and significant relationship between the results coming from the proposed theoretical method with the empirical data that emerged from meta-analysis, gave it additional strength when estimating soil carbon sequestration. The relevance of this method is that it allows obtaining different carbon balance results when incorporating carbon sequestration in grazing lands into calculations. Examples of estimation and results obtained with this methodology compared with IPCC Tier 1 results can be seen in Viglizzo et al. .