Vertebrate skin appendages are constructed of keratins produced by multigene families. Alpha (α) keratins are found in all vertebrates, while beta (β) keratins are found exclusively in reptiles and birds. We have studied the molecular evolution of these gene families in the genomes of 48 phylogenetically diverse birds and their expression in the scales and feathers of the chicken.We found that the total number of α-keratins is lower in birds than mammals and non-avian reptiles, yet two α-keratin genes (KRT42 and KRT75) have expanded in birds. The β-keratins, however, demonstrate a dynamic evolution associated with avian lifestyle. The avian specific feather β-keratins comprise a large majority of the total number of β-keratins, but independently derived lineages of aquatic and predatory birds have smaller proportions of feather β-keratin genes and larger proportions of keratinocyte β-keratin genes. Additionally, birds of prey have a larger proportion of claw β-keratins. Analysis of α- and β-keratin expression during development of chicken scales and feathers demonstrates that while α-keratins are expressed in these tissues, the number and magnitude of expressed β-keratin genes far exceeds that of α-keratins.These results support the view that the number of α- and β-keratin genes expressed, the proportion of the β-keratin subfamily genes expressed and the diversification of the β-keratin genes have been important for the evolution of the feather and the adaptation of birds into multiple ecological niches.The online version of this article (doi:10.1186/s12862-014-0249-1) contains supplementary material, which is available to authorized users. The integument of amniotes has evolved from a basic cornified epidermis for protection against the environment and the retention of water into an elaborate covering with epidermal structures used additionally for sexual display, camouflage, locomotion, and thermoregulation . The claThe Type I (acidic) and Type II  α-keratins form obligatory heterodimers ,12 that The avian β-keratins were originally grouped into four subfamilies  based on expression profiles and sequence heterogeneity -31. MoreThe Type I and II α-keratins are found on two unlinked genomic loci. In mammals, the Type I α-keratin locus is separated by a small cluster of keratin associated proteins (KAPs). However this Type I mammalian locus still shows a high level of synteny with the green anole lizard, chicken and zebra finch which lacks the KAPs . In birdAll four β-keratin subfamilies  have been localized to a single locus in both the chicken and zebra finch; microchromosome 25. However, several other unlinked loci contain feather β-keratins . FurtherHere we have taken advantage of the sequencing of 48 bird genomes  spanningWe searched the genomes of 48 avian species that span the avian phylogeny, 2 crocodilians and 2 turtles -44 for αmentclass2pt{minimet al. [The differences in copy number of Type I and II α-keratins among vertebrate groups suggest a dynamic gain or loss of this gene family. To test this hypothesis, we annotated all of the bird, crocodilian, turtle, human and green anole α-keratins to determine which α-keratin genes may have been lost in the avian lineage . KRT75, a Type II α-keratin gene associated with a feather rachis anomaly , is highThe KRT75 expansion in birds led us to investigate whether an increase in copy number is related to mutations in KRT75 genes such as the mutation that causes the chicken frizzle feather phenotype . The assFrom all 48 bird genomes combined, we found 1623 complete β-keratin genes with both start and stop codons and without unknown sequence (−NNN-) or frame shift mutations. We also found 1084 incomplete avian β-keratins , while the proportion of keratinocyte β-keratins is significantly higher for aquatic and semi-aquatic birds than land birds . This includes aquatic and semi-aquatic species that are considered to have been independently derived according to the genome scale phylogeny in our companion study  indicating more strongly that the change in β-keratin gene numbers could be associated with an aquatic lifestyle. We next considered birds with a predatory lifestyle  and keratinocyte β-keratins  is significantly higher in predatory birds than other birds while the proportion of feather β-keratins  is significantly lower for predatory birds. Like the aquatic species, this finding occurs for independent lineages of predatory birds. We did not find any significant differences in copy number for the four β-keratin subfamilies between the major stem lineages of birds: Paleognathae vs. Neognathae or Paleognathae and Galloanserae vs. Neoaves; see also Jarvis et al. [In order to ascertain if β-keratin copy number differences in birds correlate to species phylogenetic relatedness or lifestyle  indicats et al. ). TogethTo further our understanding of the evolution of the α- and β-keratin multigene families in birds we performed phylogenetic analyses and examined their genomic orientation , howeve1,46 = 5.76; standard ANOVA p = 0.021; phylogenetic ANOVA p = 0.057) when the eagles are grouped with land birds.The phylogenetic analyses of Type I and II α-keratins Figures A and 2A Previous studies ,38,39 haWe performed transcriptome analyses of scale, dorsal feather and wing feather tissues during chicken development using a customized version of the chicken 44 K Agilent microarray . We custTissue samples from the chicken scutate scale, dorsal feather and wing feather were from embryonic day 17 and 19 and scutate scale and dorsal feather at day 8. Although feather morphogenesis begins as early as day 6.5 to 7, the cellular differentiation of barbs and barbules and the accumulation of β-keratin does not begin until ~ day 12 of embryogenesis ,51,52. SComparison of day 8 scutate scale and day 8 dorsal feather tissues showed that 6 α-keratins and 18 β-keratins were differentially expressed, but the fold change values were 5 or below Table . Day 8 aAlthough the scale β-keratins were annotated based upon their expression in scale tissue , it appeFour sample comparisons had genes from the feather β-keratin subfamily that were up and down regulated Table . The dayWe found that the basal BKJ genes of the feather β-keratin clade  and their number describes their orientation in a 5′ to 3′ direction. Alternatively, only one keratinocyte β-keratin (GALGA25_Ktn6) is consistently differentially expressed from the other keratinocyte β-keratins on GALGA 25. These results indicate that while β-keratins from all subfamilies are being expressed in these tissues, intra-locus differential expression of GALGA 25 β-keratins and inter-locus differential expression of feather β-keratins may contribute to the structural complexity of these and other avian epidermal appendages.This study made use of the newly published genomes of 45 birds in addition to the 3 previously published bird genomes  to investigate the multigene families of α- and β-keratins . IncomplThe α-keratin nomenclature used in this study is based upon mammals and more specifically humans . While met al. [Our discovery of the KRT42 and KRT75 expansion in the avian lineage and Ng et al.  discoveret al. .The fact that the extreme statistical outliers from the average number of ~34 β-keratins across bird species are all species that have undergone various degrees of domestication , indicate that there could be an association between these observations. In support of this relationship, both the Peking duck (46) and turkey (46), the remaining domesticated species among the birds, have an above average number of β-keratins. Given that domestication may increase recombination rate ,59, the Numerous studies have examined the biochemical and molecular make up of embryonic and adult feathers, as well as their component parts ,57,60-64et al. [et al. [Recently, Kowata et al.  found th [et al. . However [et al. -10, sugg [et al.  examinedet al. [Bird diversification is marked by evolution into novel habitats and ways of life such as predatory and aquatic lifestyles. Birds of prey are identified by their powerful beaks and claws. In the case of the claw, studies indicate that the morphology of the claw of birds of prey  differs from non-raptorial birds and between different Orders of birds of prey ,68 . In addet al. , claw β-et al. . In thisThe feathers of aquatic birds have been shown to have a higher hydrophobicity than the feathers of terrestrial birds . This maThe number of α-keratin genes is reduced in the avian lineage, and while still important for feather development, for example during rachis morphogenesis , their let al. [et al. [The genome build information and statistics for the birds used in this study are detailed in Zhang et al. . Additio [et al.  and listAlpha keratin sequences for the chicken, green anole and human were downloaded via NCBI and their accession numbers and names are listed in Additional file et al. [Beta keratin searches of the bird genomes were performed using 18 peer reviewed avian β-keratins sequences from the Swiss-prot database  libraries of the chicken , turkey , duck , pigeon  and zebra finch . We identified 484 chicken, 48 turkey, 2 duck, 6 pigeon and 82 zebra finch ESTs as potential α-keratins and annotated them using the above method. Of the potential α-keratin ESTs, 219 chicken, 11 turkey and 6 zebra finch ESTs were annotated as α-keratins while none of the duck or pigeon ESTs were annotated as α-keratins.Species have a shared history that can be represented by a phylogeny and as such, species cannot be considered independent data points in statistical analyses, we therefore employed computer simulations to produce phylogenetic ANOVA p-values in our copy number assessments. As we do not have a species tree that includes the birds, mammals and reptiles used in this study, the statistical analyses of copy number differences for α-keratins were performed by calculating the mean for each group and fold change between the groups.For the β-keratins, we tested the null hypothesis that the variance of each β-keratin subfamily  is equal to the variance of the total number of β-keratins using Levene’s test and the SPSS software 21 .In order to test the null hypothesis that the proportions of β-keratin subfamilies  are the same for all birds, we utilized standard and phylogenetic ANOVA implemented in the R package GEIGER v 2.0.1 ,77 and tFor the phylogenetic ANOVA analyses, we grouped birds according to their phylogenetic relatedness or lifestyle (aquatic or predatory). For species phylogenetic relatedness, we performed two analyses. The first grouped Paleognathae (ostrich and tinamou) compared to all other birds (Neognathae), the second grouped Paleognathae and Galloanserae  compared to all other birds  for birds with at least one genomic locus with at least 2 β-keratin subfamilies so we could ascertain the orientation of each genomic locus relative to the other birds. A total of 20 phylogenetically diverse species are included in Figure We investigated the expression profiles of α- and β-keratins during feather and scale development in the chicken using a customized version of the chicken 44 K Agilent microarray . This 60Macro tissue dissections from the chicken dorsal feather and scutate scale were taken at day 8, 17 and 19 of embryonic development, while the wing feather dissections were taken at day 17 and 19. All tissues, immediately following dissection, were fixed in RNAlater . Chicken tissue samples were kindly provided by Richard Goodwin at the University Of South Carolina School Of Medicine in Columbia, SC. RNA extractions were performed using the Qiagen miRNeasy Mini Kit . RNA quality and quantity were checked by both the Agilent Technologies 2100 Bioanalyzer  and Thermo Scientific NanoDrop 2000c spectrometer .RNA was converted to cDNA, labeled and hybridized to the aforementioned single color custom Agilent microarray gene chip at the South Carolina College of Pharmacy Microarray Core Facility . All samples were analyzed in replicates of four, except for day 8 scutate scale which was analyzed in triplicate. The 8 samples were used to perform 14 sample comparisons (See Table http://phybirds.genomics.org.cn) and the GigaScience Genome Downloads (http://gigadb.org/dataset/101000) [The data sets supporting the results of this study can be found at Beijing Genomics Institute (/101000) ,42.


Two artificial freezing protocols were tested: one lowered the temperature continuously by 1°C h–1 to the treatment temperature and another lowered the temperature in stages of 24h each to the treatment temperature. Electrolyte leakage and rhizome sprouting assays after the cold treatment assessed plant and tissue viability. Results from the continuous-cooling trial showed that Miscanthus rhizomes from all genotypes tolerated temperatures as low as –6.5 °C; however, the slower, staged-cooling procedure enabled rhizomes from two diploid lines to survive temperatures as low as –14 °C. Allopolyploid genotypes showed no change in the lethal temperature threshold between the continuous and staged-cooling procedure, indicating that they have little ability to acclimate to subzero temperatures. The results demonstrated that rhizomes from diploid Miscanthus lines have superior cold tolerance that could be exploited to improve performance in more productive polyploid lines. With expected levels of soil insulation, low winter air temperatures should not harm rhizomes of tolerant diploid genotypes of Miscanthus in temperate to sub-boreal climates (up to 60°N); however, the observed winter cold in sub-boreal climates could harm rhizomes of existing polyploid varieties of Miscanthus and thus reduce stand performance.The cold tolerance of winter-dormant rhizomes was evaluated in diploid, allotriploid, and allotetraploid hybrids of  Miscanthus×giganteus (Miscanthus) is a C4 perennial grass that is a leading second-generation crop for bioenergy production in temperate latitudes , the other leading C4 bioenergy crop being developed for temperate climates, and is two to three times that of promising C3 biofuel crops  was –3.4 °C, while the LT50 of the diploid Miscanthus sinensis plants was –6.5 °C  of –22 °C; however, there was no incubation time at the target temperature, so the finding is tentative. Spartina pectinata has an LT50 of −23 to −24 °C in southern Ontario, Canada  temperature at the rate of 1 °C h–1 . In the ardiness .Miscanthus sacchariflorus and M. sinensis were harvested from field plantations maintained by New Energy Farms (http://newenergyfarms.com) in Leamington, Ontario . The varieties are currently being considered for introduction into Canada because of their high yield potential in cool climates . For the continuous-cooling experiment, rhizomes sampled from Miscanthus Amuri genotypes M115 and M147 , genotypes M116 (Nagara) and M161 (Miscanthus×giganteus Illinois) , and M118  were assessed . In the winter of 2009–2010, six dataloggers were spaced at least 30 m apart with one thermistor placed 1cm deep and the other 7cm deep in the soil at the site of a rhizome mass for the genotypes in the study. These depths corresponded to where most of the rhizomes occurred. A weather station (Hobo Micro Station data logger H21-002) in the centre of the field site recorded the air temperature, solar radiation, and wind speed. During the winter of 2010–2011, eight dataloggers were spaced 20 m apart and an OWL2pe weather station  was used to measure the air temperature. In addition, meteorological data was collected from Environment Canada, Kingsville station , 11 km distant from the field site.Soil temperatures were monitored at the field site in order to characterize the thermal environment of the rhizomes prior to sampling. To monitor the soil temperature, thermistors were attached to Hobo U23-003 two-channel dataloggers  from 84 weather stations at 73 locations across Canada. The stations reported between 3 and 22 years of recorded data. The lowest soil temperature reported between 1984 and 2006 was identified and is reported with the associated air temperature and snow pack in Supplementary Table S1 at JXB online for all 73 locations.To evaluate the potential for Canadian landscapes to support http://www.thermotron.com) and following procedures evaluated by All artificial freezing experiments were conducted using Thermotron programmable test chambers . Rhizome temperature profiles during a freezing experiment are shown by Rhizomes were sampled from 3- to 4-year-old plants growing in a sandy-loam soil near Leamington, Ontario. At each collection time, six plants of each sampled genotype were randomly chosen and harvested by digging below the rhizome cluster and shaking the dirt from the cluster. In the winter, pick axes were used to trench around the plants in order to lift them out of the frozen soil. Sampled root masses were bagged and transported to the University of Toronto for cold treatment, with each plant providing a pair of rhizomes for each treatment. One rhizome was used for the electrolyte leakage assay and the other for the regrowth assay. Before being placed in the programmable freezer, rhizome pairs were covered with moist soil in trays that were subsequently enclosed in plastic wrap. In the winter, rhizome temperature during transport to Toronto was near –1 °C. In August 2010, rhizomes were near 27 °C during transport. For the continuous-cooling rate experiment, treatments were randomly assigned to each of the three programmable freezers. Rhizomes were stored at –1 °C (or 21 °C in August 2010) until their treatment started. No rhizome was stored for longer than 5 days. For the staged-cooling rate experiment, rhizomes were immediately moved to the programmable freezers after placement in the soil trays and the experiment started. Plants were sampled during November 2009, January 2010, and August 2010 for the continuous-cooling trial, and in January 2011 and February 2011 for the staged-cooling experiment. During the artificial freezing experiments, rhizome temperature was monitored using 36-gauge copper/constantan thermocouples that were inserted 4mm deep into seven rhizomes (at least one rhizome of each genotype). Thermocouples were measured with either the Thermotron 8200 datalogger or a Veriteq Spectrum 1700 thermocouple datalogger  to the treatment (nadir) temperature at 1 °C h–1 and then incubated at this temperature for 24h. The temperature was once again lowered at the same cooling rate to –5 °C. After 24h at this temperature, rhizome pairs were sampled and the samples were transferred to the Thermotron 2800 set at the treatment temperature and then thawed at 1 °C h–1 to 21 °C. The temperature of the Thermotron 8200 was sequentially lowered to –7, –10, –12, –14, –18, and –22 °C at 1 °C h–1 and the cycle (incubation at the nadir temperature followed by sampling) repeated at each of these treatment temperature (except that the –22 °C treatment was only tested in February 2011). After thawing, one rhizome of the pair was used for the electrolyte leakage measurement and the other for regrowth assays. The complete trial lasted 14 days.For the staged-cooling trials, all rhizomes were placed inside a Thermotron 8200 freezer at –1 °C. The temperature of the chambers was then lowered to –2.5 °C at the rate of 1 °C hsample). Tissues damaged by cold released greater levels of electrolytes, thus raising the solution conductivity . The relative conductivity is given by the formula 100×ELsample/ELtotal.The electrolyte assay consisted of incubating a rhizome sample in a vial containing 7ml of deionized water for 24h. After this period, an Ultrameter 4P conductivity meter  was used to determine the electrolyte conductivity of the incubated solution  and observing whether the rhizome grew new roots and/or shoots in the following 6 weeks. A rhizome that produced new tissue was scored as alive; one that failed to produce new tissues after 6 weeks was scored as dead.50) or relative conductivity (LEL50) where rhizomes had a 50% chance of mortality.In the continuous-cooling rate experiment, the experimental design consisted of replicated trials on three sampling dates . For the staged-cooling experiment, the experimental design was the same, but with just two sampling dates (January 2011 and February 2011). For each trial, rhizome clusters from six different plants per genotype were harvested randomly in the respective field plots. Each plant provided rhizomes for all temperature treatments from that trial, such that the plant was considered the unit of replication in the statistical analysis. Results of both the regrowth and electrolyte leakage assays were analysed using a logistic regression given by a generalized linear mixed-effects model on the binary distribution because survivability and relative conductivity are constrained between 0 and 1. These models were fitted using the package ‘lme4’ . Post-hohttp://weather.gc.ca/; Supplementary Table S1) showed that in southern Ontario, southern British Columbia, southern Quebec, and southeast Labrador, most weather stations did not record a soil temperature below –6.5 °C at a 5cm depth between 1984 and 2006  were as cold tolerant as winter-sampled rhizomes in the M115 and M147 lines but were less cold tolerant by approximately 2–3 °C in the triploid (Nagara and Illinois) and tetraploid (M118) lines  lines . For exa50 was equivalent for all genotypes at all collection times and varied between 15 and 22% , with no rhizome surviving below –15 °C  to –6.5 °C (diploids) in winter-hardened conditions (Miscanthus rhizomes were continuously cooled in winter at 1 °C h–1 (–4.4 to –6.7 °C). However, when the temperature was lowered in a more gradual, staged manner, the rhizomes of diploid hybrids of Miscanthus acclimated such that the lethal temperature was not observed until nearly –14 °C. Allopolyploid hybrids did not show such a shift, as they were still killed by exposure near –6.5 °C. Staged-cooled rhizomes of diploid Miscanthus resprouted even though they had higher electrolyte leakage than allopolyploids and continuous-cooled rhizomes, indicating greater tolerance of moderate tissue injury. These results showed that rhizomes from diploid Miscanthus lines have a substantial ability to acclimate to slowly decreasing winter temperatures, in contrast to rhizomes of the more-productive polyploid hybrids. Ploidy thus appears to affect potential cold hardiness, with the polyploids losing some of the genetic potential for cold tolerance that is present in the diploid lines. If widespread in the polyploid lines, this lack of tolerance to conditions colder than –10 °C could represent a significant limitation on attempts to cultivate Miscanthus at latitudes above the warm temperate zone. The greater cold hardiness evident in the diploid rhizomes, however, demonstrated that there is genetic potential for survival below –10 °C in the Miscanthus gene pool. This potential could be exploited through targeted breeding efforts or molecular transformation that introduces cold-tolerance genes into highly productive genotypes.Prior research has shown that nditions . These nMiscanthus, nitrogen mobilization to the rhizomes begins in August and might be an indication of the beginning of cold acclimation  in Ontario sites at Leamington, Elora, and Kemptville. At Leamington and Elora, diploids M115 and M147, triploids Nagara and Illinois, and tetraploid M118 had winter survival above 99%. In Elora, Miscanthus×giganteus genotype Illinois in an colder-than-average winter where soil temperature at a 2cm depth reached to –6 °C. This rhizome loss was concentrated in the upper region of the rhizome mass, leading M.×giganteus can be safely grown. Consistently, in Kemptville, which is in a slightly colder climate zone than Elora, second-year stands of the diploids M115 and M147 had a survivability of 49 and 88%, respectively, while the allotriploid Nagara had 40% survivability, Illinois had 2.8%, and the allotetraploid M118 had no survivability in 2009 , might be considered (S. pectinata and P. virgatum tolerate soil temperatures down to at least –20 °C (M.×giganteus lines (M.×giganteus potential, the best strategy for realizing the C4 productive potential in bioenergy plantations of higher latitudes may be to identify and transfer the cold-tolerance mechanisms present in the diploid lines into the more-productive allopolyploid lines.JXB online.Supplementary data are available at Supplementary Table S1. List of the weather stations surveyed and indicated on


Objective reliable markers to assess traumatic brain injury (TBI) and predict outcome soon after injury are a highly needed tool for optimizing management of pediatric TBI. We assessed serum concentrations of Glial Fibrillary Acidic Protein (GFAP) and Ubiquitin C-Terminal Hydrolase-L1 (UCH-L1) in a cohort of 45 children with clinical diagnosis of TBI  and 40 healthy subjects, evaluated their associations with clinical characteristics and outcomes, and compared their performance to previously published data on two well-studied blood biomarkers, S100B and MBP. We observed higher serum levels of GFAP and UCH-L1 in brain-injured children compared with controls and also demonstrated a step-wise increase of biomarker concentrations over the continuum of severity from mild to severe TBI. Furthermore, while we found that only the neuronal biomarker UCH-L1 holds potential to detect acute intracranial lesions as assessed by computed tomography (CT), both markers were substantially increased in TBI patients even with a normal CT suggesting the presence of undetected microstructural injuries. Serum UCH-L1 and GFAP concentrations also strongly predicted poor outcome and performed better than S100B and MBP. Our results point to a role of GFAP and UCH-L1 as candidate biomarkers for pediatric TBI. Further studies are warranted. Traumatic brain injury (TBI) is a leading cause of death and acquired disability among children and adolescents world-wide23Over the past few years, there have been a large number of studies focusing on pathobiologically and structurally diverse biomarkers such as S100B and myelin-basic protein (MBP) which can be measured in blood. Overall, these studies have demonstrated statistically higher concentrations of these biomarkers in children with TBI compared with controls, but these differences have not translated into clinical utility67910Ubiquitin C-terminal hydrolase (UCH-L1) is a proteolytically stable and abundant protein found almost exclusively in the cytoplasm of neurons. Previous adult studies have demonstrated that it is increased in serum after TBI1213131719In this study, we examined whether or not serum concentrations of UCH-L1 and GFAP were significantly elevated in children who suffered mild to severe TBI compared with uninjured controls, and determined their relationship with clinical characteristics and outcomes. We also compared their performance to two-well studied biomarkers - S100B and MBP - using previously published data from our group21The study included 45 cases and 40 controls. The mean (SD) age of cases was 3.8 (3.7) years; 62% were male. Among controls, the mean (SD) age was 3.9 (3.8) years, 58% were male. Cases and controls were well-matched with regard to demographic characteristics. The median (range) Glasgow Coma Scale (GCS) score was 10 (3–15). Of the cases, 19 (42%) had severe TBI, 6 (13%) had moderate TBI and 20 (45%) had mild TBI. Median (range) time between injury and biomarker measurement was 4.7 (0.5–20.6) hours after injury. Serum GFAP and UCH-L1 were significantly higher in cases vs. controls. S100B was also increased in TBI patients compared with controls; while there was no difference in serum MBP. R = −0.45, p = 0.003 and R = −0.55, p = 0.0003, respectively). In particular, UCH-L1 concentrations during the first year of age were significantly higher than those measured at later ages , with the highest individual concentrations observed in infants <3 months of age. No other correlations were found. Among cases, there was a weak negative correlation between UCH-L1 and age , though the correlation was no longer significant after adjusting for injury severity. Biomarker concentrations were not related to gender and there was no correlation with the time after injury when blood was collected. Among cases, GFAP and UCH-L1 concentrations were strongly correlated  as was GFAP and S100B . S100B concentrations also correlated with UCH-L1 .Among controls, both UCH-L1 and S100B concentrations correlated with age (p = 0.035 or p < 0.0001) and for UCH-L1 compared to MBP (p < 0.0001). Addition of S100 and MBP did not improve AUC values of GFAP and UCH-L1.The diagnostic accuracy of both GFAP and UCH-L1 for differentiating cases and controls was good . The sensitivity of GFAP and UCH-L1 was high , although the specificity was moderate to low . Diagnostic accuracy was significantly higher for GFAP compared to S100B (AUC 0.79 [95% CI 0.69 to 0.90] or MBP (AUC 0.44 [95% CI 0.31 to 0.57]) . A highly statistically significant trend for increasing concentration of GFAP and UCH-L1 across severity groups/categories was found    . In part= 0.009) . Furtherp = 0.004) or skull fracture (p = 0.02). Conversely, GFAP concentrations did not differ between these groups, though there was a trend towards higher GFAP concentration in children with positive vs. negative CT. Children with a negative CT or skull fracture had increased levels of both UCH-L1 and GFAP compared with controls (p = 0.0008) (p = 0.31). Inclusion of GFAP, S100 and MBP did not improve upon UCH-L1 alone in discriminating between patients who have intracranial lesions detectable on CTs and those with normal CTs.Among cases, UCH-L1 concentrations were significantly higher in patients with ICI compared with those with both a negative CT (controls . Using R 0.0008) . GFAP wap = 0.003; UCH-L1, R = −0.61, p < 0.0001; S100B, R = −0.44, p = 0.009), but not with MBP. Serum GFAP, UCH-L1 and S100B levels were significantly higher in children with unfavorable outcome than in patients with favorable outcome , but MBP concentrations did not differ between these subpopulations. The diagnostic accuracy of serum GFAP and UCH-L1 for the prediction of unfavorable outcome were 0.76 (95% CI 0.60 to 0.92) and 0.86 (95% CI 0.72 to 1.00), respectively. A cut-off of 16.97 ng/ml for GFAP and 2.22 ng/ml for UCH-L1 yielded a diagnostic specificity of 100%, while sensitivities were 9% and 27%, respectively. The combination of the 2 markers did not provide a higher level of predictive power compared to UCH-L1 alone.The concentration on admission of GFAP, UCH-L1 and S100B correlated with the GOS score  and demonstrated significantly increased serum GFAP and UCH-L1 concentrations after injury. Importantly, we demonstrated a step-wise increase of biomarker concentrations over the continuum of severity from mild to severe TBI providing evidence that concentrations of GFAP and UCH-L1 reflect the magnitude of brain injury. Furthermore, our results indicate that UCH-L1 holds potential to detect acute intracranial lesions as assessed by CT and both markers might be used to predict outcome in pediatric TBI.An intriguing observation arising from our study is that the concentrations of GFAP and UCH-L1 were highest in TBI subjects with ICI, but also increased in patients with a skull fracture and in those with a negative CT. This suggests the possibility of undetected brain damage in these subjects. Consistently with this hypothesis, a growing number of studies have demonstrated that CT provides poor sensitivity compared to advanced MRI methods for detecting, quantifying and characterizing small structural lesions and pathophysiologic alterations that can occur following TBI23242627283031In this study, we found that serum GFAP and UCH-L1 within 24hrs after injury correlate with GCS, with levels substantially raised in patients more severely injured (GCS 3–8) compared with those with mild TBI (GCS 13–15). Of note, some variability in biomarker levels, especially UCH-L1, was observed in children wih GCS between 9 and 12, which might be due to the small number of subjects in this group (n = 6). An alternative explanation is that this reflects some inconsistencies between biomarker and GCS assessment, as a consequence of the limited ability of the GCS score to stratify children with TBI, particularly in the grey zone of neurotrauma33Furthermore, our results are in line with previous studies showing that UCH-L1 and GFAP within the first 24hrs of admission predicted long-term clinical outcome15Taken together, our findings provide compelling evidence that these biomarkers may be useful in risk stratification and support the idea that the use of a biomarker-based classification of injury severity, possibly in combination with clinical and imaging data, may improve categorization and identification of subsets of children at risk for poor outcome across the spectrum of TBI severity. These observations have high clinical relevance as a classification system for pediatric TBI using acute serum markers may not only be useful in diagnosis and prognosis but could also provide important insights into the injury-specific and patient-specific vulnerability facilitating early clinical interventions or stratification into clinical trialsEmerging technologies providing sensitivity and precision that are increased an order of magnitude over the present assays are now becoming available37It is also noteworthy that, although GFAP and S100B are both glial markers, our results support the idea that GFAP can be a better biomarker of TBI than S100B. This may be attributable to two of the known limitations of S100B – its age dependence, particularly in very young children4244There are several limitations of this study. First, our study has a relatively modest sample size that precluded meaningful multivariate analysis for independent predictors of outcomes. Second, because subjects were not enrolled consecutively, but rather when study personnel were available to obtain consent, the proportion of cases with severe TBI and those with a positive CT is artificially increased. Third, the heterogeneity of the CT abnormalities within this small group did not allow us to evaluate biomarker concentrations in relation to different types of ICI. Finally, another limitation of the study was the lack of treatment information, which would have helped to determine the ability of UCH-L1 and GFAP to predict the need for intervention, such as neurosurgical procedures. This will be an important avenue for future investigation.In conclusion, the results of the current exploratory study point to a role of GFAP and UCH-L1 as candidate biomarkers for pediatric TBI. As a traumatic insult on a developing brain may affect future brain maturation, neural connectivity, and children’s ultimate functional capabilitiesThis prospective cohort study included a convenience sample of children suffering from TBI admitted to the Emergency Department at Children’s Hospital of Pittsburgh of UPMC (CHP). The protocol was approved by the University of Pittsburgh Institutional Review Board. Informed consent was obtained from parents. The study was conducted in accordance with the principles of the Declaration of Helsinki and the institutional guidelines.Children were eligible for enrollment as cases if they were less than 15 years of age, with a history of an isolated closed head injury, had a head CT as part of clinical care and had blood collected for biomarker measurement within 24 hours of injury.Children were eligible as controls if they were less than 15 years of age and had blood collected as part of an evaluation in which there was no concern for closed head injury .All children received standard care for their injuries. A GCS score was assigned on arrival by the trauma team for each patient, as is standard practice in our institution. Demographic and clinical characteristics including age, gender, GCS and the result of the initial head CT were collected. Injury severity was classified as mild (GCS score 13–15), moderate (GCS score 9–12) or severe (GCS score ≤ 8). CT scans were evaluated as part of routine clinical care by an experienced pediatric neuroradiologist who was blinded to biomarker information. In subjects who had more than one CT scan performed, data were collected for the first CT. For the purposes of analysis, CT scans were classified as ‘positive’ if there was evidence of intracranial injury, either hemorrhage, contusion or edema, ‘skull fracture only’ if there was an isolated skull fracture without ICI and ‘negative’ if there was no skull fracture or ICI.o C until analysis. Blinded sample analysis was conducted in a central laboratory . Serum UCH-L1 was measured using a standard UCHL1 sandwich ELISA protocol as previously described1321Blood was collected as soon as possible after arrival to the hospital. Blood samples were centrifuged at 5,000 RPM for 10 minutes. Serum was removed, aliquoted in single vials to ensure a single freeze/thaw cycle per aliquot and then stored at −80Outcome was assessed according to the Glasgow Outcome Scale (GOS)p value < 0.05 was considered significant.Statistical analyses were carried out using Stata software  and SAS . Data were assessed for equality of variance and distribution. Descriptive statistics with means and median, as appropriate, and proportions were used to describe continuous and categorical variables. For statistical comparison of biomarkers values, non-normally distributed continuous variables, we used the Mann–Whitney U test in case of two groups and the Kruskal- Wallis test in case of three or more groups. To test significant trend for increasing biomarker concentration in a given injury severity group (e.g. mild vs. moderate vs. severe) we used the Jonckheere-Terpstra test for non-parametric trend analysis. Correlation analyses were performed by means of the nonparametric Spearman rank correlation test. Spearman partial correlation analysis was used to adjust for the effect of other potential confounding variables. ROC curve analysis was used to calculate diagnostic accuracy of brain damage biomarkers for distinguishing between children with TBI and controls, for detecting ICI and for predicting a poor outcome. The area under the ROC curve (AUC) indicates the discriminative ability, ranging from 0.5 (random guessing) and 1 (perfect discrimination). As, in controls, serum UCH-L1 and S100B were associated with age, this covariate was incorporated into the analysis and age-adjusted ROC curves were estimated where appropriate. All statistical tests were two-tailed and a How to cite this article: Mondello, S. et al. Serum Concentrations of Ubiquitin C-Terminal Hydrolase-L1 and Glial Fibrillary Acidic Protein after Pediatric Traumatic Brain Injury. Sci. Rep.6, 28203; doi: 10.1038/srep28203 (2016).


Although many efforts have recently contributed to improve our knowledge of molecular pathogenesis of multiple myeloma (MM), the role and significance of long non-coding RNAs (lncRNAs) in plasma cells (PC) malignancies remains virtually absent. To this aim, we developed a custom annotation pipeline of microarray data investigating lncRNA expression in PCs from 20 monoclonal gammopathies of undetermined significance, 33 smoldering MM, 170 MM, and 36 extra-medullary MMs/plasma cell leukemia patients, and 9 healthy donors. Our study identified 31 lncRNAs deregulated in tumor samples compared to normal controls; among these, the upregulation of MALAT1 appeared associated in MM patients with molecular pathways involving cell cycle regulation, p53-mediated DNA damage response, and mRNA maturation processes. Furthermore, we found 21 lncRNAs whose expression were progressively deregulated trough the more aggressive stages of PC dyscrasia, suggesting a possible role in the progression of the disease. Finally, in the context of molecular heterogeneity of MM, we identified a transcriptional fingerprint in hyperdiploid patients, characterized by the upregulation of lncRNAs/pseudogenes related to ribosomal protein genes, known to be upregulated in this molecular group. Overall, the data provides an important resource for future studies on the functions of lncRNAs in the pathology. Multiple myeloma (MM) is a malignant proliferation of antibody-secreting bone marrow plasma cells (PCs) characterized by a wide clinical spectrum ranging from the presumed pre-malignant condition called monoclonal gammopathy of undetermined significance (MGUS), to smoldering MM (SMM), truly overt and symptomatic MM, and extra-medullary myeloma/plasma cell leukemia (PCL) –3. DespiDuring the years that have followed human genome sequencing, it has become evident that over 90% of the genome is actively transcribed , 6, the Long non-coding RNAs (lncRNAs) are a very heterogeneous group that lack mRNA properties, although they exhibit a structure and biogenesis that does not differ greatly from mRNAs: indeed, they can be polyadenylated and may operate in either nuclear and/or cytoplasmic fractions. LncRNAs represent more than half of the mammalian noncoding transcriptome and are involved in many biological processes, such as transcriptional gene regulation, maintenance of genomic integrity, X-chromosome inactivation, genomic imprinting, cell differentiation, and development . Among lThe knowledge of the role of lncRNAs in MM is virtually absent. A recent paper investigated lncRNAs as biomarkers for predicting survival in MM patients ; in addiThe present study was aimed at investigating the lncRNA expression profiles in a large and representative cohort of PC dyscrasia, including MGUS, SMM, MM, and PCL patients, and in normal bone marrow PC controls. We have identified deregulated lncRNAs putatively associated with MM pathogenesis and possibly linked to the accepted multi-step process underlining the different stages of the disease.The expression profiles of lncRNAs have been investigated by Gene 1.0 ST array in a large cohort of 268 patients included in two different datasets and representative of the major forms of plasma cell dyscrasia. The panel totally included 20 MGUS, 33 SMM, 170 MM, 36 PCL patients and 9 normal bone marrow PCs samples. To be confident in detecting specifically lncRNAs, we applied a custom annotation pipeline able to remap the probes included in the original array to distinct lncRNAs according to the LNCipedia-v3.1 database , as did 19 of 20 MGUS cases  and SMM patients (P = 0.0049). Notably, the lncRNAs strongly upregulated in normal samples  molecules. Among the 19 upregulated lncRNAs, we found seven transcripts classified in Ensembl as pseudogenes; for each lncRNA/pseudogene, we looked for the corresponding putative parental gene when identifiable by the blat search (UCSC Blat tool at http://genome.ucsc.edu/index.html). Specifically, as shown in Table SNRPE gene, which encodes a protein belonging to the large family of small nuclear ribonucleoproteins (snRNPs), the building blocks of the spliceosome complex. In addition, we found a significant upregulation of lnc-ANGPTL1-3 and lnc-RLIM-6 that map antisense to the Ras-specific guanine nucleotide-releasing factor RALGPS2 and to the thyroid hormone transporter SLC16A2, respectively; and lnc-ARFIP1-5 localizing antisense to the E3 ubiquitin ligase FBXW7 gene but also overlapping the MIR4453 locus. Finally, lnc-SENP5-4 and lnc–AP1M2-1 localize head to head to the nuclear cap binding protein NCBP2 gene, and interleukin enhancer binding factor 3 (ILF3), respectively. Among the 12 lncRNAs downregulated in the pathological samples compared to normal plasma cells, we found lnc-SNURF-1 and lnc-SNURF-3 that are host genes for SNORD115 and SNORD116 families. Two pseudogenes of the RN7SL gene at 14q21 were found also downregulated. This gene encodes the small cytoplasmic RNA component of the signal recognition particle (SRP) that associates with the ribosome and targets nascent proteins to the endoplasmic reticulum for secretion or membrane insertion. Other four pseudogenes are included in the downregulated group: lnc-STOM-7 that is a remnant of the Alpha-1,3-galactosyltransferase (GGTA1) gene encoding an enzyme present in most mammals except man; lnc-HSFY2-10 that is positioned in sense orientation to its parental CD24 gene; lnc-CISH-3, and lnc-SEL1L3-6. Furthermore, we found the down-regulation of lnc-LRRC47-1, described below.Next, we compared normal samples with MGUS, SMM, MM or PCL patients, and identified a set of 160 lncRNAs showing highly significant differential expression between normal PCs and those from the four different clinical entities . In partIn order to identify deregulated lncRNAs possibly associated with the different clinico-biological forms of the disease, we searched for lncRNAs whose expression was significantly and progressively increased/decreased from normal PCs to PCLs (Jonckheere–Terpstra test). As reported in Table TP73 gene. In agreement with the previous analyses, we found a significant increasing expression trend of lnc-ANGPTL1-3, lnc-RLIM-6, and lnc-SENP5-4 from normal to pathological samples. Among the first group, we have also identified lnc-WHAMM-2 and lnc-SERPINC1-1, also known as SNHG21 and GAS5, respectively, which are host genes for SNORD molecules, and both showing a significant positive expression trend. Finally, this group also included three pseudogenes, the above described lnc-HSFY2-10, lnc-SNX29P2-3 that maps antisense to the NPIPL1 gene encoding a nuclear pore complex-interacting protein member, and lnc-CPSF2-2, which is a pseudogene of Prothymosin alpha (PTMA) and is localized antisense to the thyroid hormone receptor interactor 11 (TRIP11) gene.Representative of the first group was lnc-LRRC47-1, whose expression showed a significant decreasing trend being significantly downregulated in MGUS, MM and PCL samples as compared to normal control (see above). In details, lnc-LRRC47-1 could be transcribed in 23 different transcripts, seven of which correspond to the reported TP73-AS1 transcripts that map antisense to the 3′UTR of the tumor suppressor RN7SL gene. Lnc-SAFB2-3 and lnc-KIF20B-7 are pseudogenes of SNRPE and SNRPD2, respectively, which encode proteins included in the snRNPs; notably, the expression levels of these lncRNAs resulted significantly correlated with those of corresponding parental genes  data from a limited proprietary paired cohort of 19 MM patients at diagnosis and relapse/PCL progression corroborated the deregulated expression of lnc-SENP5-4, lnc-CPSF2-2, and lnc-LRRC47-1 during disease progression in this subset of patients .DLEU2. Patients carrying t translocation upregulated lnc-WHSC2-2, a pseudogene located in the intron 19 of MMSET gene, which encodes the histone methyltransferase deregulated as the result of t translocation. Finally, MM samples with either the t or t translocations upregulated lnc-LIPG-3, the host gene of SCARNA17, and lnc-JAM2-2, which overlaps the MIR155HG locus.Based on the commonly accepted notion of the great heterogeneity in MM patients, we aimed at identifying lncRNAs deregulation distinctive of the major MM molecular subgroups. We focused on the proprietary panel of 129 MM patients, which is completely characterized for the major molecular alterations, i.e. chromosomal translocations involving the immunoglobulin heavy chain (IGH) locus, hyperdiploid (HD) status, deletions of 13q, 17p13, and gain of 1q. As shown in Figure GAS5) and lnc-CD46-4 that maps antisense to miR-102 gene. Patients with del13 significantly deregulated 14 lncRNAs, five of which reside on 13q14 region. It is worth mentioning the downregulation of lnc-SPRYD7-1, also known as DLEU2, whose expression significantly correlated with that of miR-15a and miR-16-1 located in the same region  the relationship between lncRNAs/pseudogenes and matching parental genes. The results, reported in Tables KDM5D gene that map ∼112 kb apart on chromosome Y, the other four lncRNAs correlated with genes located on different chromosomes, suggesting in trans interplay for such couples.Based on expression levels analysis, we evaluated for each of the differentially expressed lncRNA reported in Tables The expression levels of six relevant lncRNAs found differentially expressed in our analyses were investigated by qRT-PCR performed in 60 MM samples for whom RNA material was available. Specifically, we validated the expression levels of the well-known MALAT1, GAS5, and DLEU2 lncRNAs. Furthermore PCR results confirmed the significant upregulation of GAS5 in samples with 1q gain lesion, and the downregulation of DLEU2 in patients carrying del13 Figure –4B. LikeAlthough we found several lncRNAs deregulated in the different form of PC dyscrasia, only a very few number have been functionally validated in biological and disease processes. Among these, we found MALAT1, the abundant nuclear-retained lncRNA found overexpressed in several cancers associated with high proliferation and metastasis, although the underlying mechanism(s) behind this deregulation and its significance in tumorigenesis is still poorly understood. Our data demonstrates a significant overexpression of MALAT1 in tumor samples compared to healthy controls Figure . To gainLncRNAs are an emerging field of investigation as they are suggested to regulate key biological processes, including cellular proliferation and differentiation, and their aberrant expression being strongly associated with cancer .MALAT1 that has been widely reported to be upregulated in a large variety of solid tumors in association with progression and cancer metastasis [To the best of our knowledge, this is the first report describing the global expression profiles of lncRNAs in a large cohort of samples representing all the major different forms of plasma cell dyscrasias. Our study identified 31 lncRNAs that were specifically deregulated in this pathology compared to normal bone marrow PC controls . To datthelioma , where trocesses . AccordiMIR155HG gene, i.e. the host gene of the miR-155 found also upregulated in translocated MAF samples [MMSET, is relevant. Notably, the consistent deregulation of lnc-WHSC2-2 in translocated MM resembles that of the snoRNA ACA11 that maps in the adjacent 3′ intron of MMSET on the sense strand [In the context of MM, we demonstrated that deregulated patterns of lncRNAs expression are associated with distinct molecular subtypes, as it has long been commonly accepted for mRNAs, miRNAs, and snoRNAs –33. Desp samples . Considee strand , 37. It TP73 gene [Although every resultant lncRNA from our study warrants further investigation to elucidate its possible biological role, some of them are suggestive of a pathological function merely by relying on their genomic position. In particular, the overexpression of lnc-PTPDC1-7, lnc-USP25-2, and lnc-MON2-2 that overlap host genes for members of the let-7 family may somehow be related to this miRNA downregulation often detected in cancer . Other lP73 gene .At present, there is strong evidence of a major role for lncRNAs in tumorigenesis based on several hundred lncRNAs already identified as being aberrantly expressed in cancer . HoweverIn conclusion, our study reported many lncRNAs deregulated in the different forms of plasma cell dyscrasia. The challenge now and in the future is to identify the truly oncogenic and functionally relevant lncRNAs.The study was performed in a cohort of 268 patients included in two different datasets, and representative of all the major forms of plasma cell dyscrasia. Specifically, the proprietary dataset publicly available at the NCBI Gene Expression Omnibus repository (accession #GSE66293) included 4 normal controls , 129 MM, 24 pPCL, and 12 sPCL patients; the other  includedThe proprietary 129 MM tumors employed for the study were representative of the major molecular characteristics of the disease. Samples were characterized for the presence of the most frequent chromosomal translocations and the ploidy status based on fluorescence in situ hybridization (FISH) evaluation criteria, as previously described . Specifiaroma.affymetrix package for all samples , based on the chromosome localization of the target sequence identified by each probe; afterwards, we considered only probes univocally referable to lncRNA transcripts (i.e. that do not overlap with Ensembl transcripts). To summarize probes related to each lncRNA, we considered their median expression value. Finally, we added a further step of batch adjustment using the sva function in the homonymous package of the R software, to avoid any possible cohort-specific bias due to the merging of different datasets. Through this annotation pipeline, we selectively detected 1852 lncRNAs from LNCipedia database.GEP of the 268 samples was generated using GeneChip® Gene 1.0 ST Array  as previously described  Supplem. Then, wprcomp function in the stats package, and the results were visualized using the plot3d function in the rgl package of the R software (shiny package of the R software (https://github.com/MikeJSeo/SAM). The cutoff point for statistical significance  was determined by tuning the Δ parameter on the false discovery rate and controlling the q-value of the selected probes. Hierarchical agglomerative clustering of patients based on the most significant probesets found was performed adopting Pearson's correlation and average as distance and linkage methods, respectively. DNA-Chip Analyzer software (dChip) [http://toppgene.cchmc.org) for functional enrichment analysis using the ToppFun application [The principal component analysis (PCA) of the samples was performed by singular value decomposition of the considered data expression matrix using the software . Supervi (dChip)  was usedlication . Microarlication . MiRNA elication .house-keeping 18S rRNA. Real-time PCR to validate lnc-LRRC47-1, lnc-ANGPTL1-3, and lnc-SENP5-4 was performed using 10 ng of total RNA and SYBR™ Green master mixes with custom primers (GAPDH gene. The threshold cycle (CT) was defined as the fractional cycle number at which the fluorescence passes the fixed threshold. LncRNA expression was relatively quantified using the 2−ΔCt method (Applied Biosystems User Bulletin No. 2), and expressed as the relative quantity of target lncRNA normalized to the house-keeping 18S rRNA.For qRT-PCR, 100 ng of total RNA underwent reverse transcription using random primer mix and reagents from INVITROGEN . Real-time PCR was performed in triplicate using TaqMan® Non-coding RNA Assays  together with the TaqMan® Fast Universal PCR Master Mix on an Applied Biosystems 7900 Sequence Detection System. RNA samples were normalized based on the  primers . RNA samclinfun package to investigate the significance of the trend from normal donors through PCL cases in the expression levels of the selected lncRNA list generated on Gene 1.0 ST array. For robustness, to reduce biases that might be due to numerical imbalances within the groups (with the MM group being largely overrepresented) and to gain the most significant trends, an additional criterion was imposed that only steadily ascending or descending median values were allowed. In addition, a differential expression should be observed in at least 1 condition . The Benjamini and Hochberg correction was used to adjust significance of multiple tests (adjusted p < 0.05).Conventional statistical procedures were applied using standard packages of the R software . The Jonckheere–Terpstra test function was used in the


P = 0.014). Preliminary results of this research show already a promising role of differential AR expression in predicting clinical relapse after PCa treatment with primary EBRT. Further research is needed to validate these findings. Hopefully this can lead to a better understanding of PCa evolution and eventually lead to better therapy strategies.The androgen receptor (AR) and its signaling pathway play an important role in the development and progression of prostate cancer (PCa). In the setting of primary treatment of PCa with radiotherapy (RT), where the AR can be expected to be of more importance, studies evaluating the AR expression are lacking. The goal of this research is to evaluate AR protein expression in hormone-naive PCa patients treated by RT and investigate its possible prognostic role. Primary biopsy samples of 18 patients treated with primary RT were analyzed including the corresponding clinical information. AR protein expression of the tumor epithelium (with highest Gleason pattern) and the surrounding stroma was quantified using the Quick score for steroid receptors. The differential expression between epithelium and stroma, respectively, between tumor and normal tissue (ΔTumor − ΔBenign >2 versus ≤2), was predictive for clinical progression-free survival in the biopsy samples ( The epithelial compartment of the prostate consists of basal epithelial cells, intermediate cells, neuroendocrine cells, and luminal secretory epithelial cells. Stroma, surrounding the epithelial compartment, predominantly consists of connective tissue, smooth muscle cells, and fibroblasts. In the development of the prostate, the epithelial budding is androgen-dependent and requires intricate stromal-epithelial interactions. Androgens act on the stroma to (indirectly) induce prostate epithelial outgrowth during development and further homeostasis in adulthood .Prostate cells depend on androgens to stimulate growth, function, and proliferation. In the 1940s, it was shown that castration of men with prostate cancer (PCa) halted the disease progression . Since tThe AR is one of the most important gatekeepers of prostate development and physiology. Due to the importance of the AR pathway, alteration in its signaling can potentially contribute to PCa development and/or progression –7.changes in intratumoral ligand concentration ,AR overexpression  with increased sensitivity to low ligand levels,AR mutations with, for example, broadened ligand specificity, hypersensitivity,ligand-independent AR activation, for example, splice variants, activating mutations in the LBD, and cross talk with other signaling pathways ,increased transcriptional activity due to changes in coregulatory molecules.However, AR independent mechanisms such as the deregulation of oncogenes , tumor suppressor genes , and the formation of fusion genes, have also been described [The main mechanisms for both PCa development and resistance to ADT are therefore AR dependent:escribed , 8, 9.External beam radiotherapy (EBRT) is a standard treatment for localized and locally advanced PCa . The golIn the literature, several authors have tried to evaluate the prognostic role of AR expression in PCa after initial treatment with radical prostatectomy (RP) , 15. HowThe goal of this research is to evaluate AR protein expression in hormone-naive PCa patients treated by EBRT with curative intent (with or without ADT). Also, to see if AR protein expression can act as a prognostic and/or predictive biomarker, before and after correction for known prognostic factors such as PSA, Gleason score, and TNM-classification.A retrospective database was created with clinical PCa information and follow-up of patients receiving primary treatment for intermediate-, high-, and very-high-risk PCa. For this pilot study a retrospective search was done in patient records for PCa patients with primary diagnosis, lymph-node dissection, and EBRT treatment at our centre, starting from patients who had their treatment more than 4 years ago. When 18 patients were found, their clinical information was coded into this database.Approval by the local ethics committee was obtained for this study (EC UZG 2011/718).Biochemical progression after primary EBRT was defined as a rising PSA level by 2 ng/mL or more above the posttreatment nadir (cf. Phoenix definition) . Clinicaμm slides were taken from the fragment with the most aggressive tumor site (highest Gleason grade) and deparaffinized. A first slide was stained with haematoxylin-eosin to confirm presence of tumor and Gleason pattern. A second slide was stained according to the Masson's trichrome method, to easily differentiate collagen from smooth muscle fibers. A third slide was stained with the monoclonal mouse anti-human AR antibody  to analyze AR expression.From archived formalin-fixed, paraffin embedded tissue samples 5 Gleason score was determined according to the 2005 International Society of Urological Pathology (ISUP) recommendations .AR immunohistochemistry (IHC) samples were analyzed by minimal 2 readers, blinded to each other's results and blinded to the corresponding clinical information. AR expression of the tumor epithelium (with highest Gleason pattern) and the surrounding stroma was quantified using the Quick score for steroid receptors, which evaluates the proportion of cells staining and the staining intensity of these cells . When a Five prostatectomy specimens were analyzed before beginning analysis of the biopsy specimen. These 5 patients treated with primary RP progressed despite secondary EBRT + ADT and died due to PCa.Quick scores ranged 6 to 8 in tumor epithelium, 0 to 1 in stroma surrounding tumor, and 2 to 6 in stroma surrounding benign epithelium. To correct for the expression in NE and surrounding NS, a new variable was considered in further analysis (ΔTumor − ΔBenign). This is the absolute difference in expression between epithelium and surrounding stroma, this from, respectively, tumor with the highest Gleason pattern and normal tissue.χ2-test and the Fisher's exact test were used where appropriate. For continuous variables with comparison between two unpaired nonparametric samples the Mann-Whitney U test was used. Correlation tests and other hypothesis tests were also performed. Relapse analysis was performed using the Kaplan-Meier method and log-rank test.Analysis of the formatted database was performed using IBM SPSS v22.0. Next to descriptive statistics, comparative statistics were performed by means of different hypothesis tests. For categorical variables, the P value < 0.05 was considered as statistically significant. Testing was performed two-sided. Graphical representations and figures of the analysis were created using SPSS.A Eighteen diagnostic tissue samples of patients treated by primary EBRT were analyzed for AR protein expression. Of these, 7 were treated with prostate only EBRT and 11 with whole pelvis EBRT.Sixteen patients received ADT and 2 patients did not. One patient, with intermediate-risk PCa, received 6 months of concomitant/adjuvant ADT and had no relapse documented in a follow-up period of 55 months. The remainder received long-term ADT treatment. No biopsy showed perineural invasion.Eleven patients (61%) in this group of mostly high- and very-high-risk PCa patients had documented clinical relapse. Three patients (16.67%) died during follow-up, 2 directly due to PCa and one from colorectal cancer. P = 0.001 and 0.046). AR expression in TE was higher compared to NE. On the other hand, surrounding TS had lower AR expression compared to NS. The absolute difference between epithelium and stroma (ΔTumor − ΔBenign) was highly significantly different between tumor and benign tissue (P < 0.001). Quick scores differed between TE and NE and TS and NS .No significant differences could be found when comparing Quick scores between the 2 groups. Only with ΔTumor − ΔBenign seen as a continuous variable, the difference was highly significant (n = 4) in the relapse group. The differential expression (ΔTumor − ΔBenign >2 versus ≤2) was associated with clinical relapse within the biopsy samples (P = 0.035). All patients without clinical relapse during follow-up had a ΔTumor − ΔBenign ≤2, compared to only 36.36%  versus ≥2 (expression) and the differential expression (ΔTumor − ΔBenign) >2 versus ≤2.P = 0.257). However, TS had a lower expression compared to NS (P = 0.043). The difference in expression between epithelium and surrounding stroma was higher when comparing tumor with benign tissue (P = 0.039). However, no correlation could be made with prognostic factors or survival.In the 5 prostatectomy specimens analyzed before beginning analysis of the biopsy specimen the AR protein expression was not different between TE (the highest Gleason pattern) and NE (P = 0.014). Kaplan-Meier curves subdivided by TS AR expression are shown in When looking at all analyzed samples (18 biopsy + 5 prostatectomy specimens), the (loss in) expression of AR in the TS (Quick score <2 versus ≥2) was predictive for the clinical progression-free survival rates (log-rank: P = 0.017) in all samples (P = 0.886).The differential expression (ΔTumor − ΔBenign >2 versus ≤2) was also predictive for the clinical progression-free survival  in different PCa compartments may help further research . In the During prostate carcinogenesis the surrounding stroma undergoes elaborate changes by different mesenchymal-epithelial interactions and paracrine influences. The amount of smooth muscle cells reduces and cancer-associated fibroblasts appear. The hormonal PCa development seems to be independent of epithelial AR. The cross talk between tumor and surrounding stroma is still not fully understood. Stromal AR seems essential for epithelial proliferation control and is able to modulate the surrounding tumor promoting microenvironment –33.Henshall et al. showed already in 2001 that the concurrent overexpression of AR in the TE and loss of AR in the surrounding stroma were associated with a poor clinical outcome in PCa after RP . This inQuantification and evaluation of the expression profile of certain markers of reactive stroma have been proposed to supply additional information to other prognostic factors , 35. It In this report, the AR protein expression also was evaluated before primary treatment with EBRT on PCa biopsy specimen. Surrounding TS had lower AR expression compared to NS see . Low strThe reciprocal interactions between neoplastic cells and supporting stromal cells are very complex. The tumor has its own microenvironment with cancer-associated fibroblasts. Epithelial-mesenchymal transition of epithelial cell enables them to invade and to resist apoptosis and to disseminate . StromalThe different therapeutic possibilities in PCa make it even more complex to understand all interactions between cancer cells and their surroundings during and after treatment. ADT has been shown to cause epithelial-mesenchymal transition in both PCa and normal prostate tissue, using a feedback loop involving the AR . Arora eNext to the AR, other potential markers have been suggested. Decreased PTEN expression has been associated with an increased risk of recurrence in patients with clinically localized PCa treated with RP, independent of known clinicopathological factors . Also esα and -β, seems to have increased [Necrotic cell death releases proinflammatory signals into the surrounding tissue microenvironment, in contrast to apoptosis and autophagy . Researcncreased . Researcncreased . This cancreased .An important shortcoming is the small sample size of analyzed AR IHC specimens. Further analysis of other available specimens and clinical data was not done for this pilot study. However, the displayed results are already very promising.Further sample analysis is needed to validate if the differential expression of AR protein expression can be of importance in the primary EBRT setting. This research report emphasizes the role of the AR in this setting due to the link between AR, DNA repair, and ADT. The goal is to see if AR protein expression can act as a prognostic marker (of patient outcome independent of given therapy) and/or a predictive marker (of the possible response to a specific therapy) in the setting of EBRT with or without ADT .The role of different hormones and the alterations in PCa progression have to be further investigated. This will help to explore benefits of different treatments and/or even play its role in personalized medicine. The interaction with stroma should be taken into account in cell culture research . TargetiThe setting of primary EBRT can be very interesting to evaluate the prognostic and predictive role of AR protein expression, especially due to interaction of ADT (often given concomitant/adjuvant with EBRT) and the AR. The differences in AR expression observed in this research suggest that a changed AR expression of epithelium and its surrounding stroma could be of importance.Preliminary results show already a promising role of differential AR expression in predicting clinical relapse after treatment of PCa with primary EBRT. Further validation of these findings is necessary.


Cre/+; ARfl/y) which lacks androgen receptor in the pituitary gland. Both circulating testosterone and gonadotrophins are unchanged in adulthood, demonstrating that AR signalling is dispensable in the male mouse pituitary for testosterone-dependent regulation of LH secretion. In contrast, Foxg1Cre/+; ARfl/y males have a significant increase in circulating prolactin, suggesting that, rather than controlling gonadotrophins, AR-signalling in the pituitary acts to suppress aberrant prolactin production in males.Production of the androgen testosterone is controlled by a negative feedback loop within the hypothalamic-pituitary-gonadal (HPG) axis. Stimulation of testicular Leydig cells by pituitary luteinising hormone (LH) is under the control of hypothalamic gonadotrophin releasing hormone (GnRH), while suppression of LH secretion by the pituitary is controlled by circulating testosterone. Exactly how androgens exert their feedback control of gonadotrophin secretion (and whether this is at the level of the pituitary), as well as the role of AR in other pituitary cell types remains unclear. To investigate these questions, we exploited a transgenic mouse line (Foxg1 Production of testosterone by testicular Leydig cells is tightly regulated by the hypothalamic-pituitary-gonadal (HPG) axis, forming a homeostatic negative feedback loop. Gonadotrophin-releasing hormone (GnRH), secreted by the hypothalamus, stimulates secretion of luteinising hormone (LH) from the pituitary, which stimulates testosterone production by Leydig cells. Testosterone then feeds back to the hypothalamic-pituitary element to negatively regulate further LH secretion in a dose-dependent manner , 2. DeveTfm) mice  . AR is foduction ), levelsoduction . LH leveoduction . Unfortuoduction . In addioduction –10, suggHowever, there is some evidence for a role for androgen signalling in the pituitary. Testosterone has been shown to suppress LH secretion in rat gonadotroph cultures , 12 and Taken together, these data paint a confusing picture of the role of AR signalling in the pituitary that is inconsistent with the widely accepted paradigm. Intriguingly, AR is widely expressed in several other cell populations within the pituitary in addition to the gonadotrophs , 15, sugIn a previous study, we utilised Foxg1-Cre to ablate AR from the caput epididymal epithelium, demonstrating that local ablation of AR perturbed initial segment development, an observation confirmed by a second group using an epididymis-specific Cre Recombinase line . FurtherloxP technology. Male congenic 129svev mice carrying a carrying a random insertion of Foxg1-Cre  [16 [1629] [t levels  or ESR1 t levels  in Foxg1Gr transcript levels, but no difference is noted between Foxg1Cre/+; ARfl/y and control animals  expression , we quan animals , suggest animals , the expPrl, Cre/+; ARfl/y 1062 ±144.7 pg/ml, Cre/+; ARfl/y mice compared to controls. Furthermore, seminal vesicle weight is significantly increased in Foxg1Cre/+; ARfl/y mice  to empirically determine the role of pituitary androgen receptor in the control of male pituitary hormone production. Foxg1-Cre was active from fetal life and ablated androgen receptor in the pituitary. Surprisingly this resulted in no change in circulating testosterone levels, questioning the currently accepted paradigm of testosterone-mediated feedback at the level of the pituitary. Further to this observation, in this study we demonstrate that circulating gonadotrophin levels are unaffected by loss of pituitary AR, suggesting androgens do not mediate negative feedback on the HPG axis at the level of the pituitary in males. Instead, we show that pituitary AR is required for repression of prolactin production and release by the male pituitary.Production of testosterone by testicular Leydig cells is tightly regulated by the hypothalamic-pituitary-gonadal (HPG) axis, forming a homeostatic negative feedback loop, but exactly how this is affected is not entirely understood. In this study we examined a mouse line with ablation of androgen receptor in the pituitary  and pituitary (LH production). Our data show no change in circulating LH or testosterone  levels wale rats  which doCre/+; ARfl/y compared to controls. This could potentially be a site of androgen feedback by decreasing the number of pituitary receptors for GnRHR to bind and stimulate a response. However, this is contrary to published literature which shows that castration has no effect on pituitary GnRHR levels in the mouse [Gnrhr, we show that circulating gonadotrophin levels do not change. Interestingly, a recently published model of specific androgen receptor ablation in gonadotrophs in female mice also showed that basal LH level is not changed, although pre-ovulatory LH surge is decreased [Interestingly, levels of gonadotrophin-releasing hormone receptor (Gnrhr) transcript are significantly reduced in the Foxg1he mouse , and desecreased . Basal aet al [Cre/+; ARfl/y mouse. There are two possibilities to explain this discrepancy. Firstly, the mouse model used has a total ablation of AR, which extends the possibility that the phenotype is a result of AR ablation in another cell/tissue type. Second, the use of a cell culture system does not take into account the behaviour of pituitary cells as a network in vivo.The HPG axis paradigm focuses on androgen receptor feedback to pituitary gonadotrophs. Despite this, our results show that a sub-population of all other pituitary endocrine cell types express androgen receptor, implying that androgen signalling is involved in their function. A role for pituitary AR in the regulation of pituitary glucocorticoid receptor expression has been postulated by Miyamoto et al . The autvia ERα to cause the neurons to undergo apoptosis. Females produce no gonadal steroids at this age so the neurons persist [Cre/+; ARfl/y mice there is no change in somatotroph volume, and no significant difference in circulating GH levels, implying that pituitary androgen receptor is not involved in the development of this dimorphism.The hypothalamic-pituitary unit is sexually dimorphic and structural and functional differences between males and females are potentiated by steroid exposure both neonatally and at puberty. An example of neonatal steroids affecting development is the oxytocin pulse generator that is neonatally inactivated in males as testicular testosterone is aromatised into estrogen and acts  persist . The cel persist . This diCre/+; ARfl/y mouse was the increase in both pituitary transcript and serum hormone prolactin levels, which validates a previously published postulation of androgen repression of prolactin release [via a balance between stimulation by estrogen and inhibition by dopamine produced by the tuberoinfundibular dopaminergic neurones of the hypothalamus [Prl mRNA levels in rats [The most striking result identified in the Foxg1 release . Controlthalamus , 49. Est in rats , which svia estrogen is instead responsible for the number of lactotrophs and orchidectomy of rats removes both androgens and androgens as the source of estrogen through aromatisation.Male rats have been observed to have fewer lactotrophs than females . NeonataCre/+; ARfl/y.Despite its well-characterised role in lactogenesis in female mammals, the role of prolactin in males is ill defined. Prolactin treatment has been shown to increase testosterone secretion and endogenous prolactin is required for the complete expression of the stimulatory action of LH on T secretion in adult male rats . Both prCre/+; ARfl/y mice. Ablation of AR from the embryonic pituitary gland (as occurs in this model) means that the Foxg1Cre/+; ARfl/y pituitary has developed without exposure to androgens . Because of this it is difficult to elucidate whether any changes are due to a change in development of the pituitary or an acute effect of the lack of AR signalling. Addressing this would require a model of adult-onset genetic ablation of pituitary androgen receptor.Despite AR expression being present in all pituitary cell types, there was limited effect of AR ablation in most cell types other than the striking effect on prolactin secretion. Volume of pituitary cell types as a percentage of the anterior lobe does not change; there are also no gross differences between morphology and size of pituitaries of control and Foxg1In conclusion, this study provides new insight into the regulation of pituitary endocrine hormones by androgens. It is a central paradigm of male reproductive biology that androgens act at both the hypothalamus and pituitary to repress the release of LH. However growing evidence (including that generated in this study) demonstrates that the feedback mechanism is independent of AR in the pituitary. The results we present here also strongly suggest a role of AR is to repress prolactin secretion in males, and that ablating AR from the pituitary removes this repression resulting in a novel model of hyperprolactinaemia.