Snord116 deletion model of the imprinted disorder Prader–Willi syndrome (PWS). More than 23,000 diurnally rhythmic CpGs are identified in wild-type cortex, with nearly all lost or phase-shifted in PWS. Circadian dysregulation of a second imprinted Snord cluster at the Temple/Kagami-Ogata syndrome locus is observed at the level of methylation, transcription, and chromatin, providing mechanistic evidence of cross-talk. Genes identified by diurnal epigenetic changes in PWS mice overlapped rhythmic and PWS-specific genes in human brain and are enriched for PWS-relevant phenotypes and pathways. These results support the proposed evolutionary relationship between imprinting and sleep, and suggest possible chronotherapy in the treatment of PWS and related disorders.Rhythmic oscillations of physiological processes depend on integrating the circadian clock and diurnal environment. DNA methylation is epigenetically responsive to daily rhythms, as a subset of CpG dinucleotides in brain exhibit diurnal rhythmic methylation. Here, we show a major genetic effect on rhythmic methylation in a mouse  Many genes have oscillating gene expression pattern in circadian centers of the brain. This study shows cortical diurnal DNA methylation oscillation in a mouse model of Prader-Willi syndrome, and describes corresponding changes in gene expression and chromatin compaction. The genetically encoded circadian cycle and the environmentally regulated diurnal cycle are integrated by a complex regulatory feedback network which acts at the chromatin, transcriptional, and translational levels to coordinate biological and environmental rhythms6. In mammals, the core circadian clock resides in the suprachiasmatic nucleus of the hypothalamus; however, almost half of all transcripts, both protein-coding and non-coding, exhibit diurnal rhythms in one or more peripheral tissues8. The epigenetic mechanisms involved in the diurnal rhythms of the cerebral cortex are less well characterized; however, increasing evidence indicates a role for DNA methylation in these rhythms. In contrast to the historically accepted view of DNA methylation as a stably maintained epigenetic mark, ~6%  of CpG sites assayed by 450k array are dynamically regulated throughout diurnal and seasonal cycles9. This epigenetic plasticity plays an important role in circadian entrainment and the resiliency of the circadian clock to changes in the diurnal environment12. Studies of shift work have demonstrated that sleep deprivation (SD) results in the epigenetic and transcriptional dysregulation of core circadian genes and increased risk for adverse metabolic phenotypes and cancer14. The epigenetic and metabolic dysregulation associated with SD in healthy individuals provides a basis for understanding these characteristics in the context of neurodevelopmental disorders, which often share a core sleep phenotype among their unique distinguishing features15.Daily and seasonal cycles of light, temperature, and feeding govern energy and activity of organisms from all branches of life. These environmental and metabolic inputs play an important role in the synchronization of the core circadian clock with the rhythmic patterns of many physiological and behavioral processes in peripheral tissues15. This includes Prader–Willi syndrome (PWS) and Angelman syndrome (AS), which are characterized by excessive daytime sleepiness and shorter sleep duration, respectively19. Genomic imprinting is an epigenetic inheritance pattern that evolved during mammalian radiation concordant with the development of rapid eye movement (REM) sleep, a state of high energy usage in the brain characterized by increased neuronal and metabolic activity and changes in thermoregulation20. PWS is characterized by a failure to thrive and hypotonia in early infancy, followed by hyperphagia as well as metabolic, behavioral, cognitive, and sleep abnormalities22. Small deletions have defined the minimal PWS critical region to encompass SNORD11626. Because this locus is maternally imprinted, with expression exclusively from the paternal allele, deletions specifically of paternal SNORD116 result in PWS. The SNORD116 locus includes repeats of small nucleolar RNAs (snoRNAs) processed from a long polycistronic transcript originating at the protein-coding gene SNRPN and extending through non-coding RNAs SNORD116, SNORD115, and the antisense to the maternally expressed UBE3A (UBE3A-AS). The spliced host gene of SNORD116 (116HG) forms a nuclear long non-coding RNA “cloud” that localizes to its site of transcription and is diurnally regulated in size28. Studies of a PWS mouse model (Snord116+/−) carrying a ~150 kb paternal deletion of Snord116 revealed a specific role for 116HG in the diurnal regulation of genes with circadian, metabolic, and epigenetic functions29. Interestingly, these changes were phase specific, with transcriptional and metabolic dysregulation observed predominately during the light phase28.Sleep disturbances are common in human neurodevelopmental disorders associated with the 15q11–q13 imprinted locus32. Imprinted inheritance patterns of a second imprinted human chromosome locus, 14q32.2, are also observed in Temple and Kagami-Ogata syndromes (TS and KOS). TS and KOS are reciprocally imprinted disorders, with TS caused by maternal uniparental disomy 14 (UPD(14)mat), and KOS caused by paternal uniparental disomy 14 (UPD(14)pat). The 14q32.2 imprinted locus bears striking similarity to the PWS locus, as it encodes the only other repetitive cluster of snoRNAs in the mammalian genome , which are maternally expressed and exhibit allele-specific chromatin decondensation in neurons, similar to SNORD116 and SNORD11535. TS is caused by the loss of paternal gene expression at this locus, resulting in aberrantly high expression of maternal non-coding RNAs, whereas KOS results from the loss of maternally expressed, non-coding RNAs and the upregulation of paternally expressed DLK1. Interestingly, TS phenocopies PWS, in which paternally expressed non-coding RNAs are lost, suggesting that these two imprinted loci may perform similar functions and share common pathways38. The loss of Snord116 expression in PWS results in the upregulation of genes in the TS locus in mouse whole cerebral cortex, indicating that the two loci may interact through a cross-regulatory network28. In support of this hypothesis, IPW from the PWS locus has been shown to regulate the TS locus in an induced pluripotent stem cell line of PWS39. Though both the PWS and TS loci show circadian oscillations in gene expression, the mechanism of this regulation and the impact of circadian rhythms on their cross-regulation are not understood41.Imprinted genes function cooperatively in a regulatory network to coordinate many biological processes underlying complex phenotypes and molecular mechanisms involved in growth and development, metabolism, and sleep116HG in the Snord116+/− mouse model of PWS disrupts the oscillatory pattern of DNA methylation, transcription, and chromatin compaction in the cortex , we characterized the effect of Snord116 loss on the rhythmicity of the diurnal light cycle to understand the previously described light-phase-specific changes to lipid metabolism and transcription28. To identify potential epigenetic alterations in the Snord116+/− mice that could explain these diurnal phenotypes, DNA methylation in prefrontal cerebral cortex was assayed in adult (P120-160) males by whole genome bisulfite sequencing (WGBS) and compared to wild-type (WT) littermates . In order to assess the relevance of our mouse study to human PWS, we utilized previously published WGBS data to identify human cortex PWS DMRs . We next utilized data from a recent study which evaluated the diurnal and seasonal rhythmicity of DNA methylation in Alzheimer’s disease (AD) and control human cortex to determine if genes with dysregulated methylation in human PWS exhibited diurnal rhythmicity9. To define a diurnally rhythmic gene set from this study, we empirically required 9% of CpGs assayed for each gene to demonstrate diurnal rhythmicity  revealed that genotype-specific DMRs could also identify time differences between WT, but not PWS samples for light hours (ZT0–ZT6–ZT12) but not dark hours . The largest represented group of rhythmic CpGs, those that reached their nadir at ZT6, was also significantly greater in WT than in Snord116+/−  as an isolated group, with nearly all ZT6 CpG nadirs being lost in Snord116+/− mice. During dark hours, the opposite pattern was observed in which the total number of rhythmic CpGs (either ZT16 nadir or zenith) was significantly greater in Snord116+/− than WT . Similarly, the largest group of rhythmic CpGs, those that reached their nadir at ZT16, was also significantly greater in Snord116+/− than in WT , with most ZT16 CpG nadirs being gained specifically in Snord116+/− cortex. Overall, WT cortex exhibits much greater diurnal rhythmicity in DNA methylation during light hours than PWS cortex; however, this rhythmicity is not completely lost, with a subset of CpGs exhibiting a shift of rhythmicity to the dark phase. Together, these results demonstrate that rhythmic DNA methylation patterns in the cortex are dependent on diurnal time and are disrupted by the loss of Snord116.To further characterize the individual CpG methylation patterns observed during light hours in WT cortex, we determined which methylation differences between ZT0, ZT6, and ZT12 were rhythmic, defined as reaching either a nadir or zenith at ZT6. Individual CpGs were extracted from each DMR identified between ZT0 and ZT6 or between ZT6 and ZT12 and intersected independently for each genotype Figs.  and 5. DSnord116+/− mice, we performed a genomic annotation of differentially cycling CpGs by enrichment analysis of promoters, gene body, enhancers, and other intergenic regions. Almost all classes of PWS differentially cycling CpGs were enriched in both promoters and enhancers except the largest class, CpGs that lose their ZT6 nadir in PWS, which were not enriched in promoters  and transcription factor binding sites in promoters and enhancers. Changes in diurnal expression are therefore associated with shifted diurnal methylation across light and dark periods, potentially through rhythmic transcription factor binding at enhancers.24.7% of genes that lost a ZT6 nadir overlapped those that gained a ZT16 nadir, indicating a nadir shift from light to dark hours in PWS Fig. . We prevT16 Fig. . EnrichmSnord116+/− cortex, we performed an analysis of gene functions and human phenotypes , these genes were enriched for many phenotypes relating to body mass index (BMI), cholesterol, waist circumference, triglycerides, and other PWS-relevant phenotypes. These analyses provide support for the relevance of the disruption of the diurnally rhythmic transcriptome and epigenome in the pathology of PWS, integrating the sleep and metabolic phenotypes observed in these patients.For the genes associated with ZT6 nadir gain (enriched in promoters and enhancers) that were also upregulated at ZT6 in pes Fig. . KEGG pa20. To explore the potential co-regulation of imprinted loci with respect to the diurnal cycle, we examined the enrichment of time- and Snord116-specific differentially methylated CpGs within imprinted gene clusters  sleep patterns, although the direct mechanism is unknownice Fig. 28.Fig. 634. Chromatin decondensation of the PWS paternal allele and TS maternal allele, as well as the distance between the two was measured in WT and Snord116+/− cortical neurons at ZT0, ZT3, ZT6, ZT9, ZT12, and ZT16  and 4.4 × 10−2 (0.14 µm), respectively).To further characterize the diurnal mechanism of rhythmic methylation and expression between these two imprinted loci, we performed DNA fluorescence in situ hybridization (FISH) to examine chromatin dynamics over time, since both snoRNA-containing loci undergo chromatin decondensation of only the active allele specifically in neurons116HG and chromatin to which it is bound was isolated, indicates that the TS and PWS loci colocalize within the 116HG RNA cloud formed by the PWS locus; however, DNA FISH analysis revealed heterogeneity in the distance between the two loci for each neuron, possibly reflecting an unsynchronized interaction, with only a fraction of neurons exhibiting colocalization at any given time  analysis, in which the ime Fig. 28. The iThis study surveyed PWS across seven diurnal time points, integrating genome-wide sequencing with targeted molecular approaches, resulting in multiple novel insights into the relationship between circadian rhythms, epigenetics, and two human imprinting disorders. First, we demonstrated the rhythmic pattern of the cortical methylome and its disruption in a genetic mouse model of PWS, showing conservation of rhythmic methylation across two mammalian species. Second, we identified genes significantly enriched for PWS-relevant phenotypes of body mass index (BMI) and circadian-entrained metabolic pathways from the genes that lost rhythmic DNA methylation and gained expression in PWS mice. Finally, we identified the only other imprinted snoRNA cluster in the mammalian genome as reciprocally altered in diurnal chromatin dynamics, providing a potential mechanism for the time-shift of DNA methylation patterns.46. Despite differences in size and synaptic complexity between the mouse and human cerebral cortex, its role in cognitive and executive function is common between species. Due to the nocturnal rhythm of mice and differences in PWS phenotype between mice and humans, we evaluated the significance of our mouse study using comparative analyses across species for phenotype and rhythmicity. Significant overlap between mouse and human PWS differentially methylated genes demonstrates the relevance of Snord116+/− mice in modeling human PWS. In addition, PWS differentially methylated genes in both species significantly overlapped with previously described rhythmically methylated genes11, despite genomic coverage differences in assays. The light-specific rhythmic CpGs we observed in mouse comprised 0.39% of all CpGs meeting the criteria for DMR consideration during light hours, which is significantly greater than expected by chance (0.034%) and significantly greater than PWS rhythmicity (0.11%) 11. The difference in the proportion of rhythmically methylated regions between our studies is most likely due to the 450k array representing a limited subset of CpGs that are preferentially selected to be associated with known promoters and enhancers, regions that we found to be preferentially enriched for rhythmic methylation. Due to the light phase specific transcriptional and metabolic dysregulation demonstrated previously in this PWS mouse model28, we sampled the dark phase at a much lower density than the light phase, thereby limiting the potential identification of DNA methylation nadirs and zeniths during the dark phase. Future studies with increased dark phase sampling could improve the resolution to distinguish between loss and phase shift of rhythmicity in PWS cortex. Despite these limitations, the concordance between human and mouse in these comparative analyses indicate strong biological significance of rhythmic DNA methylation in the human brain and specifically in the pathogenesis of PWS.Unique evolutionary pressures between mouse and human have introduced important differences between the species which must be considered in the translation of findings from mouse studies to humans47. Pathways enriched for PWS diurnally altered genes were also enriched for functions involving circadian entrainment, AMPK signaling pathways, insulin resistance, as well as axon guidance and dopaminergic synapses relevant to the neurodevelopmental abnormalities in PWS. These new analyses of DNA methylation in the context of both Snord116 genotype and diurnal time have refined our previous description of transcriptional alterations to Mtor and diurnal metabolism to pinpoint the genes most critical for the PWS phenotype.Remarkably, the intersection of methylation and transcriptional data revealed a set of diurnally dysregulated genes that were enriched for human traits of BMI, cholesterol, triglycerides, and waist circumference, which are all important in the obesity phenotypes in PWS as well as for the emerging link to meal timing and circadian biology in common human obesitySnord116 results in the ZT6 upregulation of Dnmt3a and Dnmt1, potentially mimicking the defect in diurnal synchronization observed in sleep deprivation studies28. Our results are consistent with aberrantly high levels of DNA methyltransferases associated with sleep deprivation, in which epigenetic, transcriptional, and metabolic dysregulation has been observed48. Genes upregulated in ZT6 Snord116+/− cortex showed loss of rhythmic methylation at ZT16, suggesting a time-delayed relationship between rhythms of transcription and DNA methylation in diurnal time. Aberrantly high expression of epigenetic and circadian regulators at ZT6 may lead to the prolonged accumulation of these proteins into the dark phase, resulting in the shifted methylation changes observed in PWS. The rhythmicity of epigenetic enzymes plays a role in many biological processes in a diverse array of organisms. Circadian expression of epigenetic factors including DNMTs, TETs, MBDs, and HDACs have been observed in the central circadian oscillator of the SCN as well as other metabolically important peripheral tissues such as liver and muscle, regulating processes including lipid metabolism, seasonal reproductive rhythms, and torpor-arousal bouts during winter hibernation53. Through its own rhythmic chromatin decondensation and dynamic RNA cloud, Snord116 may act to orchestrate the rhythmic patterns of some of these epigenetic factors within the brain.The apparent mechanism of diurnal methylation changes to 0.39% of CpG sites involves a combination of diurnally dynamic chromatin accessibility of two imprinted snoRNA clusters and the epigenetic factors mediating the changes that are also regulated by circadian rhythms. Interestingly, loss of 55. Interestingly, despite the earlier evolutionary origin of the TS locus, acquisition of imprinting control regions and subsequent establishment of imprinting status at these two loci evolved concurrently within the eutherian lineage56. The gain of imprinting at the TS locus concomitant with the assembly of the PWS locus supports the hypothesis of a cross-regulatory network between the two loci. In the absence of the 116HG, the TS locus, carrying the only other snoRNA cluster in the genome, loses rhythmic methylation marks and may decondense to compensate for PWS locus compaction in a diurnally regulated manner.Unlike the TS locus, in which chromosomal arrangement is conserved between marsupials and eutherians, the genomic organization of the PWS locus is unique to eutherian mammals7. The short half-life of many of these drugs and the rhythmic expression of their targets suggests that the time of administration plays a potentially important role in the efficacy of treatment. By characterizing the epigenetic mechanisms of PWS with respect to the diurnal cycle, this study demonstrates the potential application of chronotherapy for the treatment of PWS as well as other epigenetically regulated neurodevelopmental, metabolic, and sleep disorders.Understanding the diurnal timing of dysregulation provides important insights into the molecular pathogenesis of PWS and the best timing for therapies. The majority of commonly used drugs in the United States target diurnally oscillating genes (56 of the top 100)42. A mapping stringency of three mismatches for reads without adapters  and two mismatches for adapter trimmed reads  was used for alignment by BSseeker2.Mice were sacrificed at ZT0, 3, 6, 9, 12, and 16. Genomic DNA was isolated from 5 to 10 mg prefrontal cortex using the Gentra Puregene kit (Qiagen) and 500ng DNA was bisulfite converted using the EZ DNA Methylation-Lightening kit (Zymo). Libraries were prepared with 100ng bisulfite converted DNA using the TruSeq DNA Methylation kit (Illumina) and barcoded using TruSeq DNA Methylation Kit Index PCR Primers (Illumina). Libraries were sequenced on the HiSeq4000 at the Vincent J. Coates Genomics Sequencing Laboratory at UC Berkeley. Reads were aligned as described previously42. To call a DMR, all samples must have at least one read in each CpG, each DMR must contain a minimum of three CpGs, each CpG must be within 300 bp of the previous CpG to be included in the same DMR, the smoothed methylation difference between the two groups must be at least 10%, and the unadjusted p-value for each CpG must be less than 0.05 to be included in a DMR.DMRs were called as described previously42. Cortical tissue was sampled from BA9  and BA19 , which are regions implicated in the cognitive deficits of PWS and the phenotypically related TS.DMRs were called as in mouse using previously published WGBS data to identify human cortex PWS DMRs (GEO GSE81541)Snord116+/− mice. If a site was hypomethylated between ZT0 and ZT6 and hypermethylated between ZT6 and ZT12, it was called diurnally cycling, specifically with a nadir at ZT6. DMRs and CpGs with the opposite pattern were identified as having a zenith at ZT6. For increased stringency, any sites called as a DMR between ZT0 and ZT12 were removed, as methylation at these sites did not return to baseline during the cycle. All DMRs and CpGs were intersected with shared total assayed sites to ensure that coverage requirements were met for all samples. A background set of all covered genomic locations assayed for DMRs was used for normalization. This analysis was repeated for cycling during dark hours, analyzing differential methylation between ZT12–ZT16 and ZT16–ZT0 comparisons for each genotype. WT and Snord116+/− sites were intersected to identify diurnal rhythmicity that was lost, gained, and maintained across genotypes during light and dark hours. A null distribution for rhythmicity was created by permutation (3× per genotype) of light hour ZTs used for original rhythmicity analysis. Biological replicates of ZT0, ZT6, and ZT12 were shuffled and reassigned to each time point. DMRs were then called for each permutation as described previously and rhythmicity analysis was performed for each permutation as described for experimental groups. Permutations were averaged to obtain a null expected rhythmicity for each genotype and rhythmicity was tested against the null distribution for each genotype as well as between WT and PWS by Fisher’s exact test. Results of permutation tests are shown in Supplementary Table Total DMRs and CpGs assayed during DMR analysis were intersected for all samples during light and dark hours to ensure that all sites analyzed for diurnal cycling were represented in each sample. To identify diurnal rhythmicity during light hours, DMRs and CpGs that were called as differentially methylated for both the ZT0–ZT6 and ZT6–ZT12 comparisons were examined for both WT and https://github.com/kwdunaway/WGBS_Tools—roi). Percent methylation of each sample was normalized to the mean methylation of all diurnal time points in WT cortex. DMRs were sorted using Ward’s method of hierarchical clustering.Methylation was called for every sample across each DMR identified as diurnally rhythmic during ZT6 in WT cortex using a custom roi script . DMRs were binned by average coverage across all samples and the methylation for each CpG at each coverage level was graphed for the original time points used to call rhythmicity .Methylation and coverage for each sample across all DMRs identified as diurnally rhythmic during ZT6 in WT cortex was called using a custom roi script . The processed data contained 420,132 probes belonging to autosomal CpG sites that passed quality control. This data was filtered for significant  diurnal rhythmicity, which left 25,476 CpG sites. The CpG IDs were mapped to hg19 using the FDb.InfiniumMethylation.hg19 Bioconductor package (http://bioconductor.org/packages/FDb.InfiniumMethylation.hg19/). The genomic coordinates were then annotated to genes using the Genomic Regions Enrichment of Annotations Tool (GREAT)57.The human data was obtained from Lim et al.2O, then placed in the PCR product bead mix to capture target regions. Probes were then placed sequentially in 70% ethanol, PyroMark Denaturation Buffer (Qiagen), and 1× PyroMark Wash Buffer (Qiagen). Vacuum was removed and beads were washed from probes in the sequencing primer mix. The plate was heated at 80 °C for 2 min, then cooled to room temperature. Enzyme, Substrate, and dNTPs  were added to the PyroMark Q96 Cartridge 0007 (Qiagen) according to PyroMark CpG Software 1.0.11 specifications for each assay design. Three biological replicates were sequenced in duplicate for each genotype and time condition. Methylation was averaged across CpGs for each DMR and DMR methylation for each condition was graphed with S.E.M.DNA was isolated and bisulfite converted as for WGBS. PCR and sequencing primers were designed using the PyroMark Assay Design Software 2.0 (Qiagen). DMRs were amplified using the PyroMark PCR kit (Qiagen) according to the manufacturer protocol. Pyrosequencing was performed using the PyroMark CpG Software 1.0.11 (Qiagen) on the PSQ 96MA (Biotage). 4 µl of Streptavidin Sepharose High Performance beads  and 36 µl of PyroMark Binding Buffer (Qiagen) were added to each PyroMark PCR product and shaken for 10 min. 2 µl of 10 µM sequencing primer was added to 38 µl PyroMark Annealing Buffer (Qiagen). Vacuum probes were washed twice with ddHTGAGGAAAGGGTTTGGAAGGTTAT (Forward)TCCAAAAACATCATACAACCTATATCTAA TGGAAGGTTATTTGTAGT (Sequencing)AAGTTGAGTTGGAGTAGTGG (Forward)CCCAACTTTCCTTTCACC ATTGAAGTGTATTTGTTAGG (Sequencing)https://github.com/kwdunaway/WGBS_Tools#roi). To identify time-specific differences in methylation, Principal Component Analysis was performed on DMRs across diurnal time points using the prcomp function and ggbiplot package in R. The 95% confidence interval was plotted as ellipses for each group. Non-overlapping ellipses indicate a statistically significantly difference in methylation pattern between groups (p < 0.05).All diurnal time points were pooled for each genotype and DMRs were called on the basis of genotype (WT vs. PWS). Percent methylation was calculated over each DMR for every genotype-time condition using a custom roi script (http://chromosome.sdsc.edu/mouse/download.html). For all enhancers, chromatin signature data for mouse cortex was used. Because enhancers are often difficult to assign to the nearest gene due to their long-range effects, enhancers believed to regulate the expression of the ZT6 upregulated genes were selected using cortex-specific enhancer–promoter units (EPUs), which were filtered for EPUs involving genes upregulated at ZT6.Enhancers were assigned using data from the Ren lab (UCSD) Mouse Encode Project (https://github.com/AndreasHeger/gat). The workspace (effective genome) included all CpGs assayed by the DMR finder, which requires coverage of at least one read in all samples and three CpGs with ≤300 bp between each site. Annotation files contained all regions to be tested for enrichment, which were analyzed together and corrected for multiple hypothesis testing (Benjamini–Hochberg). Isochores were included to account for variable GC composition and 100,000 iterations were used for permutation testing.Enrichment of diurnally rhythmic CpGs within genomic regions  and imprinted loci was assayed using GAT—Genomic Association Tester (http://homer.ucsd.edu/homer/index.html). All possible DMRs assayed, chopped to the size distribution of significant DMRs, were used as custom background regions and percent CpG content was used for normalization.DMRs were examined for enrichment of known transcription factor binding motifs using Hypergeometric Optimization of Motif EnRichment (HOMER) 34. Slides were imaged on an Axioplan 2 fluorescence microscope (Carl Zeiss) equipped with a Qimaging Retiga EXi high-speed uncooled digital camera and analyzed using iVision software (BioVision Technologies). Images were captured using a ×100 oil objective and ×1 camera zoom with blue, red, and green filters at 0.1 µm sections and z-sections were stacked. PWS and TS locus decondensation was measured as the distance along the appropriate fluorescent signal. The compact allele at each locus is much smaller and dimmer than the decondensed allele, and all measurements were taken for the active, decondensed allele. Exposure time was optimized for the intensity of the bright decondensed alleles. The distance between the two loci was measured as the shortest distance between the two loci divided by the nuclear diameter in the same plane. All measurements were converted from pixel counts to microns according to objective and zoom (1px = 0.069 µm). All measurements were blinded to minimize bias.BACs were ordered from BACPAC Resources . PWS locus BACs: RP24-275J20, RP23-358G20, RP23-410L2, RP24-147O20, RP23-141P24. TS locus BAC: RP23-60E10. Nick translation of DIG and biotin labeled probes and DNA FISH were performed as described previouslyAll experiments were conducted in accordance with the National Institutes of Health Guidelines for the Care and Use of Laboratory Animals. All procedures were approved by the Institutional Animal Care and Use Committee of the University of California, Davis.https://github.com/kwdunaway/WGBS_Tools. Custom scripts for DMR calling are available at https://github.com/cemordaunt/DMRfinder.Custom scripts for WGBS analysis are available at GSE103249. Figures Whole genome bisulfite sequencing data that support the findings of this study have been deposited in the Gene Expression Omnibus (GEO) with the accession number Supplementary Information(PDF 2891 kb)Peer Review File(PDF 278 kb)Description of Additional Supplementary Files(PDF 131 kb)Supplementary Data 1(XLSX 60 kb)Supplementary Data 2(XLSX 400 kb)Supplementary Data 3(XLSX 304 kb)Supplementary Data 4(XLSX 2057 kb)Supplementary Data 5(XLSX 256 kb)Supplementary Data 6(XLSX 31 kb)Supplementary Data 7(XLSX 152 kb)Supplementary Data 8(XLSX 70 kb)Supplementary Data 9(XLSX 49 kb)Supplementary Data 10(XLSX 31 kb)Supplementary Data 11(XLSX 45 kb)


Stream ecosystems are the primary receivers of nutrient and organic carbon exported from terrestrial ecosystems and are profoundly influenced by the land use of the surrounding landscape. The aquatic impacts of anthropogenic land use are often first observed in stream benthic biofilms. We studied the benthic biofilms in streams flowing through forest (upstream) and anthropogenic land use (downstream) areas in southwestern China. The results showed that anthropogenic land use increased nutrient and organic carbon in both stream water and benthic biofilms, which are closely related to the differences in the microbial communities. The taxonomic dissimilarity of the communities was significantly correlated with the functional gene dissimilarity, and the upstream sites had more distinct functional genes. Network analysis showed that upstream sites had more highly connected microbial networks. Furthermore, downstream sites had higher relative abundances of anammox and denitrification suggesting stronger nitrogen removal than upstream sites. Increased nutrients in both the stream water and biofilms caused by anthropogenic land use had severe impacts on the nitrogen cycle in stream ecosystems. Downstream sites also had stronger carbon metabolism than upstream sites. This study provides insights into the influences of anthropogenic land use on microbial community structure and functions of stream benthic biofilms. Aquatic ecosystems are sentinels and integrators of terrestrial processes because they are closely related to changes in terrestrial landscapes through the transport and storage of water, nutrients, and energy2. As the primary receivers of nutrient and organic matter exported from terrestrial ecosystems, stream ecosystems are profoundly influenced by the land use of their surrounding landscapes5. In streams heavily influenced by agricultural and urban land uses, elevated nutrient and organic matter concentrations are expected8.Natural landscapes are transformed by anthropogenic activities to satisfy human needs through processes such as agriculture and urbanization10, which are likely linked to watershed conditions because land use significantly controls the export of carbon (C), nitrogen (N), and phosphorus (P) from catchments to aquatic ecosystems13, potentially shifting the in situ microbial community structure and bacteria mediated processes. Previous studies have demonstrated the variations in stream benthic algae composition in relation to landcover change from forest to agriculture to urban areas16, close relationships between longitudinal patterns of stream biofilm biomass and pasture degradation17, and an influence of landcover conditions on biofilm stoichiometry18.The aquatic impacts of land use can often first be observed in their effects on stream benthic biofilms19, contributing substantially to the metabolism and biogeochemical cycles through nutrient uptake, transfer of nutrients to higher trophic levels, and remineralization23. Various heterotrophic and autotrophic taxa are tightly linked to each other via the transfer of various types of organic matters and nutrient associated with different biological processes28. Catchment land use significantly controls the export of organic matter and nutrient from catchments to lake ecosystems through streams and rivers and ultimately impacts the suitability of water resources13. In particular, the nitrogen metabolism in streams is one of the most important ecosystem functions to remove nitrogen24 and consequently protect downstream lakes from eutrophication. Moreover, bacteria can account for a substantial portion of the transformation and use of organic matter in riverine systems30, with implications for ecosystem processes, food webs, and greenhouse gases31. Thus, the major metabolisms in riverine systems are of importance because of the potential for riverine ecosystems to store, mineralize, and transport organic matter and nutrient before reaching the downstream aquatic ecosystems34. Considerable work focused on export, source, transport, retention, and metabolism of nitrogen and carbon in streams that are impacted by anthropogenic activities35. However, the influences of anthropogenic land use on microbial community structures and functional potentials of stream biofilms are remain unclear.In streams, biofilms are hot spots of microbial activity37. Understanding the microbial community and the major metabolic pathways is crucial to obtain insights into ecosystem structures and processes39. In this work, we studied the benthic biofilms in streams flowing through forests and anthropogenic land use (agriculture and urban) areas in the Erhai Lake watershed in southwestern China. The microbial communities of benthic biofilms in upstream and downstream of anthropogenic land use area were compared. We determined the bacterial communities using high-throughput 16S rRNA gene sequencing and investigated carbon and nitrogen metabolic pathways from the PICRUSt (Phylogenetic Investigation of Communities by Reconstruction of Unobserved STates) predicted functional genes encoding the enzymes required in carbon and nitrogen metabolic pathways. Although metagenomics only reflects a potential rather than a realized functional capacity, our data offered an approach to understand and discuss the anthropogenic influences on the microbial community structures and functions in stream benthic biofilms. We hypothesized that (1) anthropogenic land use leads to nutrient and organic carbon increase and alters microbial community structure, and (2) anthropogenic land use alters the nitrogen and carbon metabolic pathways, which are associated with the community structure and nutrient concentrations.Aquatic ecosystems face increasing pressures and threats from disturbances of watersheds associated with land useth largest lake and the 7th largest freshwater lake in China. The forest coverage of Cangshan Mountain is above 90%, and the stream reaches flowing through the mountain are difficult for humans to access and are still pristine. However, when the streams flow out of the mountain, they flow through a narrow plain (around 5 km wide) between Cangshan Mountain and Erhai Lake  was determined by ion chromatography (EPA 300.0). Total phosphorus (TP) was analyzed using the ammonium molybdate method after oxidation (EPA 365.3). Soluble reactive phosphorus (SRP) was analyzed using the ammonium molybdate method (EPA 365.3). Ammonium (NH4+) was analyzed using the indophenol colorimetric method (EPA 350.1). Dissolved organic carbon (DOC) was analyzed on a TOC analyzer .Samples were collected in August 2016. At each sample site, we randomly sampled 6–9 submerged rocks from the river cross-section. The benthic biofilm was removed by rigorously brushing an area of 4.5 cm in diameter from the upper surface of each stone using a sterilized nylon brush (changed between samples) and rinsing the slurry with sterile water. For microbial analysis, 10 mL of mixed slurry was filtered through 0.2 μm membrane filters and immediately frozen in liquid nitrogen in the field and transported to the lab for DNA extraction and subsequent analyses. For the biofilm chemical analysis, 20 mL of mixed slurry was filtered through 0.2 μm GF/F filters that were dried at 60 °C. Biofilm carbon (Bio-C) and biofilm nitrogen (Bio-N) was analyzed using an element analyzer (LECO 628). Biofilm phosphorus (Bio-P) was analyzed spectrophotometrically by ascorbic acid colorimetry following digestion with potassium persulfate (EPA 365.3). At each sample site, water samples were collected for nitrogen and phosphorus analysis. Total nitrogen (TN) was analyzed by ion chromatography after persulfate oxidation (EPA 300.0). Nitrate  following the manufacturer’s protocols. The V3–V4 regions of the 16 S rRNA gene were amplified using 338F-ACTCCTACGGGAGGCAGCA and 806R-GGACTACHVGGGTWTCTAAT . PCR was performed with a model 2720 thermal cycler  using the following program: 1-min hot start at 80 °C, 94 °C for 5 min followed by 30 cycles of denaturation at 94 °C for 30 s, followed by annealing at 52 °C for 30 s and 72 °C for 1 min 30 s, with a final extension step at 72 °C for 10 min. Amplified DNA was verified by electrophoresis of the PCR mixtures in 1.0% agarose in 1X TAE buffer and purified using the Gel Extraction Kit . Samples were sent for sequencing on a MiSeq platform.40 and then clustered into operational taxonomic units (OTUs) with a complete linkage algorithm at a 97% sequence identity level. The metagenomes were predicted from 16 S data using PICRUSt41. The functional genes associated with nitrogen and carbon metabolism were identified from KEGG (Kyoto Encyclopedia of Genes and Genomes) database42. Redundancy analysis (RDA) was conducted to reveal the association of the microbial communities in relation to nutrient factors based on OTU and functional gene abundances (Vegan package 2.4 in R 3.3.2). Correlation analysis was conducted to assess the relationships between the nutrient factors and nitrogen cycle pathways, and the P-value was adjusted with a Bonferroni correction (psych package 1.7.5 in R 3.3.2). PERMANOVA was used to test whether upstream and downstream sites harbor significantly different microbial communities or metagenomes (using PAST 3.0). Mantel tests were run to assess correlations between functional and taxonomic community dissimilarity matrices based on Bray-Curtis distance. Non-metric multidimensional scaling (NMDS) was applied to reveal differences in community composition between microbial assemblages in upstream and downstream sites (using R 3.3.2 and Vegan package 2.4–1). For network analysis, the relative abundances of the OTUs in each sample were used to construct matrices for visualizing interactions between OTUs in the networks . A Spearman correlation coefficient R score and a P-value were calculated pairwise between the OTUs (the1000 most abundant OTUs) using the Hmisc package (version 4.0–1) in R (version 3.3.2). Only strong (Spearman’s correlation coefficient R > 0.95 or R < −0.95) and strongly significant (P < 0.01) correlations were considered. These correlations were visualized using Cytoscape (version 3.5.0). Each node represents an OTU, and each edge represents a strong and significant correlation. To describe the network topology, a set of node/edge metrics was analyzed using the Network Analyzer plugin within Cytoscape43. The modular structures of the networks were analyzed using the ClusterMaker in Cytoscape. Modularity values > 0.4 suggests that the network is modular44.Bacterial 16 S rRNA sequence data were cleaned using the software package QIIME3− than upstream sites  than upstream sites  followed by Cyanobacteria (39.3%) and Bacteroidetes (7.5%) Fig. . The Bra3−, SRP, Bio-C, Bio-N, and Bio-P showed significant relationships with the functional gene composition of the biofilm communities  and downstream biofilms . Moreover, the slope of the linear relationships was smaller in the downstream sites than in the upstream sites.To evaluate whether taxonomic differences between microbial communities affect their functional potential, we conducted Mantel tests to analyses the relationships between Bray-Curtis dissimilarities based on the taxon abundances and the functional gene abundances of microbial communities in stream biofilms. Mantel correlation tests revealed significantly positive correlations between the functional and taxonomic dissimilarities in upstream biofilms  and two oxidation pathways (nitrification and anammox) Fig. . CompareFor the carbon metabolism, we detected 234 functional genes encoding the core carbon metabolism pathways Fig. . There w13. Agriculture and urban use increase NO3−, NH4+, and SRP concentrations as a result of excess fertilizer application45, as well as sewage and septic inputs47. Increased nutrient loading can lead to reduced water quality48 and altered stream communities50. Biofilm nitrogen and phosphorus contents increase with increasing nutrient availability in streams52. In our study, downstream sites had higher nutrient concentrations in both stream water and benthic biofilms than upstream sites. The nutrient contents in benthic biofilms were closely related to stream-water nutrient concentrations. N and P availability may limit the growth of both biofilm autotrophs and heterotrophs54. It has been demonstrated that the community composition, diversity, biomass, and stoichiometry of biofilms change significantly with available nutrients in streams56. The composition of microbial communities can change in streams influenced by catchment land-use features60. In our study, downstream sites had a high abundance of proteobacteria while upstream sites had a high abundance of cyanobacteria. The differences in microbial communities were closely correlated with nutrients in both the stream water and biofilms.Land use significantly controls the export of nutrient and organic matter from catchments to aquatic ecosystems63. It has been demonstrated that functional beta diversity is strongly correlated with taxonomic beta diversity across soil microbial communities61. In our study, both upstream and downstream sites had significant linear relationships between OTU dissimilarities and functional gene dissimilarities. The high linear regression slope suggested that the microorganisms in upstream sites had more distinct functional genes than in downstream sites. The microorganisms in downstream sites share more functions than upstream sites, indicating high functional gene redundancy. This also indicated that intense anthropogenic impacts can cause the reduction of functional diversity66.Different biomes harbor distinct microbial assemblages67. Network topology (the node distributions and interactions) can affect the stability of the system68. In previous macroecological studies, communities with tightly connected species were shown to be more susceptible to disturbance70. The highly connected microbial network of microorganisms in upstream sites suggested that microorganisms have stronger co-occurrence relationships in upstream sites than in downstream sites. A minor disturbance might cause large impacts on upstream network because of the complex relationships among OTUs. Furthermore, the microbial assemblages in both upstream and downstream sites exhibited a modular structure  to microbes and is another catabolic pathway that can retain the nitrogen in the system in a bioavailable form (NH4+) for further biological processes79. Nitrogen fixation is an energetically demanding process requiring 16 molecules of ATP to break the triple-N bond. Only a few organisms, including cyanobacteria and certain other genera, can carry out this process81. Low NH4+ can stimulate some N2-fixing bacteria84. Nitrification is an essential process in the nitrogen cycle performed by nitrifiers converting ammonium to nitrate. As a competing process to nitrate reduction pathways, denitrification is the main biological process for the removal of N from freshwater systems85. Anammox plays an important role in the nitrogen cycle, turning nitrite and ammonia to dinitrogen86, and is another microbial process that releases fixed nitrogen from the environment as dinitrogen90. In our study, a higher relative abundance of denitrification, nitrification, and anammox suggested that nitrogen removal was stronger in downstream sites than in upstream sites.Nitrogen has various chemical forms and is cycled by a suite of biogeochemical processes92, nutrient availability, and other abiotic factors. High concentrations of NO3− can stimulate microorganisms to increase populations containing the functional genes associated with nitrate reduction. Anammox is influenced by NO3− concentration, NH4+ concentration, C/N ratio, and pH93. Low NH4+ can stimulate functional genes associated with nitrogen fixation84. Nitrification can be affected by temperature, salinity, light, organic matter concentration, substrate concentrations, pH, and oxygen concentration94. Our study showed that anammox was positively correlated with TN, NO3−, Bio-C, Bio-N and Bio-P. Assimilatory nitrate reduction was negatively correlated with TN, NO3−, and Bio-C. Dissimilatory nitrate reduction was negatively correlated with NH4+. Denitrification was positively correlated with Bio-C, Bio-N and Bio-P. Nitrification was positively correlated with Bio-C and Bio-P (Table Furthermore, nitrogen metabolic pathways are regulated by environmental redox states29. Carbon transported by streams provides a major source of energy for aquatic food webs and is a significant component of the global carbon cycle95. Anthropogenic land use contributed significantly to accelerated transport of carbon from terrestrial environments to aquatic ecosystems98. On the other hand, anthropogenic land use can accelerate the transition of streams from transporters to transformers of carbon31. In stream ecosystems, a substantial portion of the organic matter are derived from terrestrial environments and serve as a substantial resource fueling riverine bacterial communities29. Small streams can mineralize large quantities of the organic carbon load because of the bacterial carbon metabolism, which results in increased losses of CO2 to the atmosphere and with implications for ecosystem services99. Moreover, the increased nutrient availability can further stimulate whole-stream metabolism of terrestrial carbon100. In our study, the overall carbon metabolism of the benthic biofilms was enhanced by the intense anthropogenic activities.In stream ecosystems, the processing of organic matter is another major function that bacterial communities mediate101. Furthermore, anthropogenic land use accelerates the transition of streams from transporters to transformers of nutrient and organic carbon31. In our study, anthropogenic influenced downstream sites had higher relative abundances of functional gene encoding nitrogen and carbon metabolisms compared to upstream sites with relatively pristine catchments. The results allow us to understand how microbial community structures and metabolism response to influences of anthropogenic land use.Anthropogenic activities have caused the increase of nitrogen and carbon loads in streams and rivers. A portion of increased nitrogen and carbon loads are exported to downstream lake and coastal waters, while a substantial proportion are also transformed along streams and rivers


In this study, we developed an immunofluorescent cell-based assay to detect and characterize CTCs in MM.Multiple myeloma (MM) remains an incurable disease despite recent therapeutic improvements. The ability to detect and characterize MM circulating tumour cells (CTCs) in peripheral blood provides an alternative to replace or augment invasive bone marrow (BM) biopsies with a simple blood draw, providing real-time, clinically relevant information leading to improved disease management and therapy selection. Here we have developed and qualified an enrichment-free, cell-based immunofluorescence MM CTC assay that utilizes an automated digital pathology algorithm to distinguish MM CTCs from white blood cells (WBCs) on the basis of CD138 and CD45 expression levels, as well as a number of morphological parameters. These MM CTCs were further characterized for expression of phospho-ribosomal protein S6 (pS6) as a readout for PI3K/AKT pathway activation. Clinical feasibility of the assay was established by testing blood samples from a small cohort of patients, where we detected populations of both CD138 Multiple myeloma (MM) is a neoplasm of plasma cells and is the second most common blood malignancy worldwide , account4clinicaltrials.org).A number of signalling pathways are known to be dysregulated in MM, which contributes to disease progression. In particular, aberrant activation of growth factor pathways results in downstream activation of the PI3K/AKT signalling cascade, promoting cell proliferation, survival and tumour growth , 11. Tar12Further improvements in MM treatment will require development of novel targeted therapies in combination with diagnostic tests, as well as sensitive technologies to monitor residual disease in patients during remission. Since cancer is a heterogeneous and dynamic disease as demonstrated by inter-patient variability , 17 and 17neg, which have been shown to possess stem cell-like qualities and a higher clonogenic potential than their CD138pos counterparts [38While originating in the BM, MM tumour cells are able to migrate into the peripheral blood stream, from where they can be isolated and characterized , a common downstream readout in PI3K/AKT signalling pathway studies [The MM CTCs assay used a combination of CD138 and CD45 antibodies as well as a DAPI nuclear stain and morphology. Although CD138 has been shown to be highly expressed on most MM CTCs and is required for myeloma cell adhesion , 49, a c38 studies . This apBlood samples were collected in 10 mL cell-free DNA preservative blood tubes  and shipped to Epic Sciences for processing. Sample processing and slide preparation procedures were described previously , 44 and Traditional MM CTCs: CD138pos/CD45neg cells with intact, DAPI stained nuclei and morphologically distinct from surrounding white blood cells (WBCs)MM CTC clusters: two or more adjacent CTCs with shared cytoplasmic boundaries, containing at least one traditional MM CTCneg MM CTCsCD138: CD138neg/CD45neg cells with intact DAPI nuclei and morphologically distinct from surrounding WBCsApoptotic CTCs: CD138pos/CD45neg cells with DAPI pattern of chromosomal condensation/fragmentation and/or membrane blebbing.The number of CTCs detected was reported as either CTC/slide or CTC/ml of blood. Data analysis and graphing were performed using Excel (Microsoft) and Prism software (GraphPad).2.For assay development and qualification, cell line cell (CLC) control slides were prepared following standard sample preparation procedures described above using healthy donor (HD) samples spiked with appropriate numbers of the immunoglobulin A lambda myeloma-derived, MM.1S cells. MM.1S cells were purchased from ATCC  and cultured in RPMI-1640 media supplemented with 10% foetal bovine serum at 37 °C with 5% COSix concentrations of a mouse monoclonal antibody against human CD138  were tested to determine the optimal antibody concentration ranging from 0 μg/mL to 10 μg/mL. Fixation and permeabilization conditions were also optimized to obtain best signal-to-noise ratios. To determine the specificity of the anti-CD138 antibodies, control slides were stained with antibody cocktails with either the anti-CD138 antibody omitted (no primary control) or substituted with an appropriate isotype control antibody (isotype control).For assay qualification, serial dilutions of MM.1S cells were spiked-in to HD WBCs at 1, 10, 100 and 1000 CLCs per three million WBCs per slide. Three sets of three slides at each CLC to WBC ratio listed above were prepared. The slides were stained in triplicate to determine repeatability and on three separate days to determine reproducibility. Assay sensitivity, accuracy and linearity were determined by plotting number of expected CLCs against number of recovered CLCs. In addition, assay specificity was assessed by staining one slide each of HD WBC-only samples from five individual healthy donors.Blood samples from three Stage III MM patients (on active treatment) were sourced from Conversant Bio , collected in EDTA (BD Vacutainer®) tubes and shipped overnight to Epic Sciences for processing. Standard sample processing procedures were followed except blood was lysed using BD Phosflow™ Lyse/Fix Buffer 5X , and two slides from each patient were immunofluorescently labelled with an antibody cocktail against CD138, CD45, pS6 and stained with DAPI for MM CTC analysis. Following staining, CTCs were identified using Epic's proprietary algorithm as described above, and expression levels of CD138 and pS6 were measured.pos/CD45neg cells  and an accuracy of 97.5% between number of expected cells and number of recovered cells ranging from 1 to 1000 cells  of the MM CTC assay was determined by staining triplicate slides per titration point, while reproducibility (inter-assay variability) of the assay was determined by staining three sets of slides on three separate days. The results showed that the assay is highly repeatable and reproducible. At each titration , the intra-assay variability expressed as % CV between the triplicate slides , and theTaken together, these results demonstrated that the MM CTC assay was specific, sensitive, linear, accurate, unbiased, repeatable and reproducible for detecting MM-derived CLCs.neg/CD45neg MM CTCs, we multiplexed the MM CTC assay to include pS6 as a marker for PI3K/AKT pathway activation.To determine the clinical feasibility of the MM CTC assay, we tested blood samples from three Stage III MM patients undergoing active treatment. Since previous work demonstrated the utility of pS6 in cytometry-based multiple myeloma CTC assays  and becapos MM CTCs in all three patient samples tested  and CTC clusters (defined as two or more touching CTCs) were also detected in these samples . These CD138neg/pS6pos cells were classified as ‘putative’ MM CTCs based on CD45 negativity and distinct morphology from surrounding WBCs .pos/CD45neg MM CTCs, a population of CD138neg/pS6pos/CD45neg cells was also identified based on their pS6 expression and distinct MM-like cellular morphology. CD138neg ‘putative’ MM CTCs are of particular interest as they have previously been reported to possess increased proliferative potential and correlate with poor prognosis [38Patient feasibility of the MM CTC assay was established by testing three patient samples, where MM CTCs were identified in all three patients ranging from 63 CTCs/mL to 498 CTCs/mL and pS6 expression levels were quantitated. Patient MM CTCs exhibited a wide range of CD138 expression levels similar to that observed in MM.1S cells. The intra- and inter-patient heterogeneity of MM CTCs was also exemplified by varying cellular morphologies as well as pS6 signal levels. Importantly, in addition to CD138gnosis 38. A furthex vivo culturing of the MM CTCs. The multiplexing and downstream cell-picking capabilities of the Epic platform allow for biomarker expression and genomic profiling using the MM CTC assay described here with single-cell resolution in their native environment, providing a unique opportunity to understand disease heterogeneity.Basic understanding of the significance of MM CTCs and how they differ from clonal MM cells in the BM has been hampered by the lack of a sensitive and unbiased method to molecularly characterize these cells. Others have endeavoured to profile MM CTCs by capturing, culturing and comparing these cells to their resident BM counterparts . HoweverThe ability to assess and characterize MM CTCs also opens up new avenues for improving patient disease management. The invasiveness of BM biopsies limits their clinical utility typically to time of diagnosis or at disease progression. While they have been invaluable for diagnosis and for stratifying patients into proper treatment groups and clinical studies, they provide just a snapshot of a patient's disease state and are not feasible for continuous routine observations. Blood tests, on the other hand, are minimally invasive and are already performed at follow-up visits for routine disease monitoring. Adding a blood-based MM CTC assay to the current standard of care could present physicians with a movie rather than a snapshot of patients' disease evolution in real time, allowing for monitoring of treatment response, emergence of treatment resistance, appearance of potential new molecular targets and informing treatment selection.pos/CD45neg MM CTCs, in two of the three patients a clinically important population of CD138neg/pS6pos/CD45neg cells was also detected, based on their pS6 expression and distinct MM-like cellular morphology. This approach may prove useful clinically for pharmacodynamic testing in new therapeutics development and for monitoring and characterizing an individual MM patient's disease via liquid biopsies of the blood.Due to the heterogeneous nature of patient CTCs, a method capable of unbiased detection and analysis of all CTCs is required to ensure accurate assessment of a patient's disease state. The MM CTC assay described here provides an advantage over enrichment-based technologies, which are inherently incapable of detecting the full array of CTCs within each patient. Importantly, in addition to CD138


Livers were analyzed for fibrosis, inflammation and remodeling 48 and 96 hours after the last injection. Old mice developed more severe fibrosis compared to young ones as evaluated by sirius red morphometry. Expression of pro-fibrogenic genes was equally induced in the two age-groups but enhanced fibrolysis in young mice was demonstrated by a significantly higher Mmp13 induction and collagenase activity. While fibrosis resolution occurred in young mice within 96 hours, no significant fibrosis attenuation was observed in old mice. Although recruitment of monocytes-derived macrophages was similar in young and old livers, young macrophages had globally a remodeling phenotype while old ones, a pro-fibrogenic phenotype. Moreover, we observed a higher proportion of thick fibers and enhanced expression of enzymes involved in collagen maturation in old mice.Clinical data identify age as a factor for severe liver fibrosis. We evaluate whether and how aging modulates the fibrotic response in a mouse model. Liver fibrosis was induced by CClImpaired fibrolysis of a matrix less prone to remodeling associated with a pro-inflammatory phenotype of infiltrated macrophages contribute to a more severe fibrosis in old mice. Liver fibrosis results from a sustained wound healing response due to chronic liver injury and occurs when extracellular matrix (ECM) production exceeds ECM degradation. Activated hepatic stellate cells (aHSCs) are the main cells involved in fibrogenesis as the key source of ECM compounds and a major modulator of hepatic inflammation. Next to aHSCs, the hepatic macrophages also promote fibrosis progression by driving HSCs activation, by releasing pro-inflammatory and pro-fibrogenic factors and by supporting the infiltration of pro-fibrogenic immune cells .Liver fibrosis reversibility has been documented for several years. In animal models, liver damages reverse and fibrotic scar degradation occurs when the hepatotoxic agent is removed or when a normal biliary outflow is restored after common bile duct ligation ,3. EvideThe human liver is affected by aging. It manifests by a reduced volume and blood flow as well as by cellular changes such as increased oxidative stress, decreased number and dysfunction of mitochondria, accelerated cellular senescence and decreased regenerative ability . Aging i4). We provide explanations for mechanisms contributing to age-related fibrosis.In this work, as previously described in other studies ,18, we r4 injections (3x/week for 2 weeks) in young and old mice  and old (aged 15 months) mice (data not shown). Fibrosis was induced by CCle Figure . Liver dfibrosis .4, fibrosis was significant in both age-groups compared to matched control animals but old mice developed more severe fibrosis compared to young ones as evaluated by sirius red quantification  mRNA was more expressed in the old group compared to the young one, the expression of the main genes related to pro-fibrogenic processes such as Collagen I and alpha Smooth muscle actin  was equally induced by CCl4 injections  and Timp2 in the two age-groups but we observed a significantly higher induction of Mmp13 in young mice than in old ones. In addition, Chemokine (C-X-C motif) ligand 9 (Cxcl9), a potent MMP-13 inducer, is upregulated in young fibrotic livers but not in old ones  and degradation (fibrolysis). As we showed that fibrogenic processes were unlikely to account for the difference in intensity of fibrosis observed between groups, we studied the gene expression of matrix remodeling enzymes. CCl4 injection), remodeling was significant in young mice with only some residual collagen deposition. By contrast, there was no significant attenuation of liver fibrosis between the 48- and the 96-hour time points in old mice  ligand 2 (Ccl2), Vascular endothelial growth factor (Vegf) and Macrophages migration inhibitor factor (Mif), all soluble factors involved in tissue recruitment of monocytes [Mif was significantly more expressed in old mice compared to young ones, no difference of Ccl2, Vegf or CD11b gene expression was observed between age-groups suggesting similar inflammatory cells recruitment in young and old livers , LOX like 2 (LOXL2), a disintegrin and metalloproteinase with thrombospondin type I motif (ADAMTS2) and, while more controversial, tissue transglutaminase 2 (TG2) mediate ECM stabilization –29. We ed Figure . By conts Figure . More prThe worldwide increasing proportion of elderly people leads to an increasing incidence of some hepatic disorders and complications for which age is a risk factor, among them fibrosis. Moreover, liver disease related treatments are more and more frequently proposed to elderly people, such as HCV-infected patients. Therefore, the impact of aging on liver fibrosis, the main adverse effect of chronic liver disorders, is of major interest.4 [In this animal study, we applied the same profibrotic regimen to young and old mice and we observed a more severe fibrosis in old mice compared to young ones at the peak of fibrosis. These results are in concordance first with clinical observations suggesting that susceptibility to fibrosis increases with age in many liver diseases –15 and, 4 ,18.4 disrupts centrilobular hepatocytes integrity that wound healing processes tend to restore. In case of repeated exposures, recurrent profibrotic stimulation occurs prior to the resolution of the previous healing round [Collagen I and alphaSma mRNA were significantly upregulated in treated groups compared to controls but no difference was observed between age-groups, mitigating the role of variable fibrogenic processes in the severity of ECM deposition. Rather, this is in favor of an equal propensity to initiate profibrotic events in response to a toxic injury. Moreover, here, as in previous works [4-induced hepatotoxicity was similar in young and old animals suggesting that CCl4 was transformed in toxic species in a same way independently of the age.A single dose of CClng round . In our us works , acute CMmp13 gene expression in young mice at peak of fibrosis while old mice expressed significantly less Mmp13 mRNA. No difference was noticed concerning TIMPs expression suggesting that the balance MMP/TIMP was overtly tilted in favor of matrix degradation in young mice but less so in old ones. This was confirmed by the nearly complete clearance of scar matrix in young animals 4 days after the last toxic injection while virtually no remodeling occurred in old mice, and by the reduced collagenolytic activity in this last group. In addition, CXCL9, a canonical inducer of MMP-13 [Long time described as a static and irreversible phenomenon, fibrotic scar deposition is now recognized as a dynamic and bidirectional process emphasizing the role of ECM remodeling enzymes . MMPs arf MMP-13 , was alsFibrosis and inflammation are closely linked and immune cells are strongly involved in all steps of the fibrotic process from the inflammatory response following initial injury to fibrosis regression . At steaTg2 deletion in mice does not promote fibrosis reversal in a model of advanced liver fibrosis [Next to the variable inflammatory response, we demonstrated a higher proportion of thick and dense fibers in old mice as well as an enhanced expression of the enzymes involved in collagen maturation changes. Septal features limit fibrosis remodeling: old, pauci-cellular, thick and heavily cross-linked septae resist to proteases degradation . ADAMTS2fibrosis . More thfibrosis . As theyfibrosis ;50 but nTo date, no antifibrotic therapy exists besides the suppression of the causative agent. Our work, demonstrating that liver fibrosis is less prone to reverse in old animals, has several clinical implications. First, as already pointed by Karsdal et al , impact Male 7-weeks-old (the young mice) and 15-months-old (the old mice) C57BL/6J mice were purchased from Elevage Janvier, Le-Genest-St-Ile, France. They were exposed to a 12h light/12h dark cycle, were maintained at a constant temperature of 20°C–22°C and received food and drink ad libitum. Young and old animals were maintained in parallel in the same facility for a total duration of one month until sacrifice. Animal care was provided in accordance to the guidelines for humane care for laboratory animals established by the Université catholique de Louvain in accordance with European regulations and in conformity with ARRIVE guidelines. The study protocol was approved by the university ethics committee (2012UCLMD026).4 intra-peritoneal injection 750μl/kg body weight of CCl4 diluted in corn oil, 3 times a week for 2 weeks) was applied to young and old mice and started one week after their arrival in the facility. Control animals received injections of the same volume of corn oil (n=6 in the young group and n=6 in the old group). Mice were sacrificed 48 hours (n=6 in the young group and n=6 in the old group) or 96 hours (n=6 in the young group and n=6 in the old group) after the last injection. At the time of sacrifice, mice where anesthetized and the blood was drawn by cardiac puncture. The liver was rapidly dissected out. A part of the liver was immediately immersed in 4% formalin for histological analyses and the rest snap-frozen in liquid nitrogen and kept at −80°C until analyses.A fibrotic regimen  was performed using Tissue IA software  after digitalization with a SCN400 slide scanner . The same stained sections were then observed under polarized light microscopy to evaluate fiber thickness according to their color as previously described: thin fibers range from green to yellow while thick fibers range from orange to red –54. The Kupffer cells were identified by F4/80 immunostaining using a primary rat anti-mouse F4/80 monoclonal Ab , a rabbit anti-rat immunoglobulin , and then a goat anti-rabbit streptavidin horseradish peroxidase-conjugated Ab . Peroxidase activity was revealed with diaminobenzidine (DAB) and slides counterstained with hematoxylin. Activated stellate cells were identified by alphaSMA immuno-staining using a primary mouse anti-mouse alphaSMA monoclonal Ab  and a goat anti-mouse streptavidin horseradish peroxidase-conjugated Ab . Peroxidase activity was revealed with diaminobenzidine (DAB) and slides counterstained with hematoxylin. Immunostaining quantification (percentage of stained area in the section) was performed using Tissue IA software  after digitalization with a SCN400 slide scanner .Total RNA was extracted from frozen liver samples using TRIzol Isolation Reagent . cDNA was synthesized from 1μg RNA using High-Capacity cDNA Reverse Transcription Kit . Real time PCR analysis was performed in duplicate with the StepOnePlus real-time PCR System  using SYBRgreen. Primer pairs for transcripts of interest were designed using primer express design software  and are listed in g for 5 min at 4°C and the supernatant was stored at −80°C.100 mg of frozen liver tissue samples were homogenized in ice-cold lysis buffer . The homogenates were centrifuged at 10,000 × Collagenase activity of 100μl of liver homogenate was evaluated using FITC-labeled telopeptide-free soluble bovine type I collagen  and following the manufacturer instructions.All the data are presented as means ± standard error of the mean (SEM). Statistical analysis was performed by two-way ANOVA for repeated measures followed by Bonferroni's post-hoc correction. Statistical significance was assumed for p values <0.05 . GraphPad Prism software  was used for graphs and statistics.


Jatropha curcas L. (Family – Euphorbiaceae) is a perennial tree of special interest due to its potential as a biofuel plant with high carbon sequestration. In this study, physiological investigations coupled with transcriptomics in relation to photosynthesis were evaluated in Jatropha grown under ambient (395 ppm) and elevated (550 ppm) CO2 atmosphere. Morphophysiological analysis revealed that Jatropha sustained enhanced photosynthesis during its growth under elevated CO2 for one year which might be linked to improved CO2 assimilation physiology and enhanced sink activity. We sequenced and analyzed the leaf transcriptome of Jatropha after one year of growth in both conditions using Illumina HiSeq platform. After optimized assembly, a total of 69,581 unigenes were generated. The differential gene expression (DGE) analysis revealed 3013 transcripts differentially regulated in elevated CO2 conditions. The photosynthesis regulatory genes were analysed for temporal expression patterns at four different growth phases which highlighted probable events contributing to enhanced growth and photosynthetic capacity including increased reducing power, starch synthesis and sucrose mobilization under elevated CO2. Overall, our data on physiological and transcriptomic analyses suggest an optimal resource allocation to the available and developing sink organs thereby sustaining improved photosynthetic rates during long-term growth of Jatropha under CO2 enriched environment. The adaptive capacity of plants to increased [CO2] in atmosphere depends on a range of physiological processes which varies among different plant species. Earlier studies have reported that the initial stimulation in photosynthetic potential under prolonged exposure to elevated [CO2] was followed by an acclimatory down-regulation in photosynthesis leading to less than predicted yields in many plant species3. However, some fast growing coppice plantations with high yield potential have been able to sustain increased growth and productivity under elevated [CO2] over a long period of time suggesting that maintenance of short rotation plantations is an effective strategy for mitigation of increasing atmospheric [CO2] proportionally5. The rapid growth of these trees also allows the analysis of their responses to elevated [CO2] over a complete production cycle6. The ability of such plants to sense and respond to the elevated CO2 environment requires transcriptional cascades operating at cellular level to adjust their morphology, physiology and phenotype accordingly7. Hence, studying the molecular mechanisms associated with growth of these trees to predicted elevated [CO2] can provide an insight into certain key candidate genes and the pathways controlled by them in order to understand the relationship between gene expression and adaptation to varying external environments.Atmospheric carbon dioxide concentration , a total of 3013 unigenes were differentially expressed in response to CO2 treatment in leaves which comprised 833 (~27%) up-regulated unigenes and 2180 (~73%) down-regulated unigenes (Supplementary Table 2-grown Jatropha were annotated based on EuKaryotic Orthologous Group (KOG) classification , PS (II) CP47 chlorophyll apoprotein (PsbB), PS (II) CP43 chlorophyll apoprotein (PsbC), oxygen evolving complex protein (PsbP) and oxygen evolving enhancer protein 3-1], and PSI protein complex [PSI P700 chlorophyll a apoprotein A1 (PsaA)] (Table + reductase (FNR) and F-type H+-transporting ATPase subunit beta (FTA), which play an important role in photosynthetic electron transport and ATP synthesis (Table 2 grown plants (Table Elevated CO)] Table . Similaris Table . The DGEFurther, the expression of DEGs putatively encoding granule bound starch synthase (GBSS), hexokinase 3 (HXK3), phosphofructokinase (PFK), cytosolic aldolase, cytosolic TPI, sucrose phosphate synthase 1 (SPS1), fructose 1,6-bisphosphatase (FB), and phosphoglucomutase (PGM) was found to be up-regulated Table . Interes2 enriched conditions in Jatropha. Uroporphyrinogen decarboxylase, which catalyzes the first committed step of chlorophyll biosynthesis, and magnesium-protoporphyrin O-methyltransferase encoding transcripts were the only two members of chlorophyll biosynthesis which were significantly up-regulated , PS I (PsaA), NADH dehydrogenase complex (B and K), FNR and ATP synthase complex (FTA) were found to be either up-regulated or no change at different time points of growth in elevated CO2 ] during the course of growth of plants in elevated COCO2 Fig. . HoweverCO2 Fig. .Figure 32 grown Jatropha  and glutamine synthetase (GS) was also significantly induced under elevated COP < 0.05) in the leaves of elevated CO2 grown Jatropha at all four time points in comparison to ambient grown plants  activities at all four time points in elevated conditions  higher by ~40% in elevated conditions  of Jatropha under elevated CO2 suggesting an enhanced electron transport rates in coordination with other subunits to facilitate increase in reducing equivalent production to be utilised in carbon reduction cycle. However, no significant variation in expression during vegetative growth stage suggest possible involvement of other subunits associated with photosynthetic electron transport which has several rate-limiting steps, with FNR catalysing just one of them30. Similarly, differential regulation of both subunits of NDH complex, B and K suggests an optimum balance of electron flow around PSI and PSII in maintaining increased photosynthesis capacity of Jatropha under elevated CO232. There was no correlation in the DGE expression and qRT-PCR expression of FTA indicating probable involvement of other subunits in optimising sustained ATP synthesis to maintain enhanced photosynthesis in elevated CO2 grown Jatropha33. Our data on photochemical and non-photochemical quenching clearly indicate that most of the absorbed light energy was efficiently used in qP rather than NPQ in elevated CO2-grown Jatropha plants. Our results also demonstrated no significant changes in the chlorophyll concentrations and chlorophyll a/b ratio in elevated CO2 conditions at all growth stages regardless of high photosynthetic rates which suggests efficient management of light absorption capacity and radiative energy balance of the photosystems to sustain high photosynthetic rates34. The up-regulation of transcripts associated with both chlorophyll biosynthesis and breakdown in the DGE analysis indicate a possible co-regulation to maintain the chlorophyll concentrations in Jatropha under elevated CO2 conditions. Altogether, elevated CO2 increased photosynthesis, photochemistry  and linear electron flow in Jatropha suggesting PSII-adaptive improved growth and productivity performance in elevated CO2 environment.The physiological results demonstrate that increased atmospheric CO2 into organic forms to be utilized for growth and development. Most evidently, the DGE and temporal expression analysis showed up-regulation in transcript levels of enzymes of PCR cycle including rubisco (both RL and RS), SB, and PRK in elevated CO2 grown Jatropha plants. The temporal expression pattern of rubisco (both subunits), SB and PRK, especially at 180 and 360 DAT which is the seed setting stage of Jatropha under elevated CO2, indicates enhanced production of carbon intermediates for carbon fixation which correlates with our physiological and biochemical findings of sustained enhanced photosynthetic rates, biomass accumulation, reproductive yields and rubisco activity35. The up-regulation of these transcripts possibly ensures enhanced supply of carbon skeletons to manage the demand from newly formed reproductive sink tissues. Further, GAPDH showed up-regulation only at 270 DAT suggesting enhanced photosynthesis and photochemistry of Jatropha is independent of GAPDH expression under elevated CO2. The upregulation of RA at 180 DAT suggests activation and maintenance of rubisco catalytic activity by promoting the removal of any tightly bound, inhibitory, sugar phosphates from the catalytic site of both the carbamylated and decarbamylated forms of rubisco under elevated CO236. However, there was no variations in expression levels of RA at 90, 270 and 360 DAT indicating balanced ATP/ADP ratios under elevated CO2 to sustain enhanced photosynthesis in Jatropha under elevated CO237. Further, the measured RA enzyme activity did not correlate with the transcript levels which may be attributed to the complexity of the relationship between RNA levels, protein levels, and physiological changes38. We also observed significant enrichment in transcripts of enzymes participating in pentose phosphate pathway (PPP), G6PD, TKL and chloroplastic TPI which generates NADPH and carbon intermediates to meet the demand for carbon skeletons for both vegetative and reproductive sinks in elevated CO2 grown Jatropha during both growth seasons39. Furthermore, the expression analysis of RPI, which maintains pentose and hexose carbon pools in equilibrium with one another as the fluxes through the major metabolic pathways change in response to external environmental conditions did not correlate with DGE analysis with only up-regulation recorded at 270 DAT suggest involvement of other players in the regulatory network in maintaining the pentose and hexose carbon ratio40. An increase in foliar carbohydrate levels, particularly starch, is a common observation in elevated CO2 environments which has been predicted to be responsible for photosynthetic acclimation in the absence of sink tissues6. Our biochemical results demonstrated increased starch levels in leaf which correlated with significant upregulation in transcripts encoding granule bound starch synthase (GBSS) in elevated CO2 conditions41. We have earlier proposed that Jatropha was able to escape photosynthetic downregulation in the later stages of growth due to availability of sufficient sinks like increased tertiary branches, flowers and fruits to sustain enhanced growth22. This implies efficient source-sink interaction and sustained photosynthetic potential of Jatropha under elevated [CO2]. Moreover, differential regulation of nitrogen metabolism associated gene, NR indicate improved nitrogen allocation and nitrogen use efficiency in the leaves of elevated CO2 grown Jatropha. However, GS and GD did not demonstrate any differential expression in Jatropha under elevated CO2 environment indicating nitrogen use is not regulated by these two genes at these four growth stages.The most important pathway for any plant is the photosynthetic carbon reduction (PCR) cycle which fixes the atmospheric CO2-grown Jatropha plants. Starch and sucrose metabolisms are finely co-regulated involving key intermediate metabolites at the cross-road of catabolic and anabolic pathways for the control of carbon flux in the plant cell. There was significant up-regulation of transcripts pertaining to sucrose metabolism and glycolytic pathway. The significant up-regulation of transcripts pertaining to PGM, cytosolic FB, cytosolic TPI and cytosolic ALD imply improved sucrose metabolism in the cytosol35. The enzyme activity of cytosolic FB complied with the temporal expression patterns as indicated in our results. The up-regulation of these transcripts also imply increased hexose sugar pools for various carbon skeletons to be provided to the developing sinks under elevated CO2. However, PFK showed up-regulation at only 360 DAT suggesting its non-regulatory role under elevated CO2 as it has been implicated in adaptation of plants in non-optimal conditions42. Interestingly, we found differential regulation in transcripts encoding isoforms of hexokinase and sucrose phosphate synthase. The differential regulation of both SPS and HXK isoforms may have played a role in exerting regulatory influence on sucrose biosynthesis and hexose sugar pools to accommodate enhanced photosynthesis in Jatropha grown in CO2 enriched atmosphere43. Also, this suggests the optimum balance in the catalytic and signaling function of HXK and also SPS, which may be one of the adaptive strategy during morning hours for sustained photosynthesis in elevated CO2-grown Jatropha44. The enzyme activity of SPS1 was in agreement with the transcript expression at all stages of growth while the activity of HXK did not alter at the reproductive development stage suggesting regulatory effect of sucrose on hexose sugar pools45. The cytosolic glycolytic network may provide an essential metabolic flexibility that facilitates plant growth and development under elevated CO2. We recorded significant up-regulation of transcripts pertaining to chloroplastic TPT at all stages of growth under elevated CO2 indicating a coordinated metabolic interaction between cytosol and chloroplast for high levels of carbon skeletons to be utilized for sustained growth and development46. The downregulation of GPT suggests preferential exchange of triose phosphate intermediates between subcellular compartments during morning and may be one of the adaptive strategy for sustained photosynthesis in Jatropha.Our foliar biochemical results in this study showed increase in soluble sugar content in elevated COJatropha under elevated CO2.In conclusion, the present study highlights the importance of differential expression of key regulatory genes of photosynthetic electron transport in chloroplasts and central carbohydrate metabolism in maintaining the improved photosynthetic capacity during long-term growth in Jatropha curcas seeds (Variety: CG-20) were surface sterilized with 0.5% sodium hypochlorite and grown in polythene bags. After 10 days, three Jatropha seedlings were transplanted in three pits with a spacing of 2 × 2 m in octagonal-shaped open top chambers (OTCs). The establishment of plant growth, experimental setup, microclimatic growth conditions and CO2 treatment inside the OTCs were same as described in our previous study22. Two OTCs were used for elevated CO2 treatment (mean CO2 concentration −550 ppm) and two for ambient CO2 (mean CO2 concentration −395 ppm) in this study. Three Jatropha plants were grown and maintained in each OTC for one year. The plants were maintained as coppice plantations after completion of harvest of seeds which was done once every six months. The experiment started in the month of January and the first growth season ended in June and the second growth season was from July to December. The two growth seasons were chosen with respect to the growth behaviour of Jatropha as described earlier26. Morphological and physiological parameters like plant height, light response curves (A/Q curves) and chlorophyll a fluorescence were performed after 90, 180, 270 and 360 days of growth on both ambient and elevated CO2 grown plants as described previously48. Similarly, chlorophyll, soluble carbohydrates and starch content were measured on the same leaves used for physiological measurements22. Above ground biomass and reproductive yields were assessed at 180 and 360 days after coppicing the plants. These morphological, physiological and biochemical measurements were performed to analyse the consistency in response of Jatropha under elevated CO2 at different seasons. Further, upper canopy young green leaves (preferably 3rd from top) used for physiological measurements were collected at the above mentioned four time points  from both ambient and elevated CO2 grown Jatropha plants during its one year of growth and stored at −80 °C until use. Three independent biological replicates of stored young green leaves of 360 days from each ambient and elevated grown plants were used for sequencing . The transcriptome generated at this time point was used for deciphering differential regulation of key regulatory genes associated with photosynthesis and carbohydrate metabolism at the four different time points and correlate their expression patterns with morphophysiological analysis. The four time points selected for morphophysiological, biochemical and molecular analyses reflect two distinct stages of Jatropha across two seasons. The 90 and 270 days are the stages when the vegetative growth rate is at maximum for both seasons. At 180 and 360 days for both seasons, both vegetative and reproductive growth simultaneously occur.2-grown Jatropha plants respectively. The concentration, intactness and purity of RNA were checked with Agilent 2100 Bioanalyzer . Samples having RNA integrity number (RIN) value greater than 8 were used for library preparation. Three biological replicates were sequenced from both ambient and elevated CO2-grown Jatropha plants. Paired end cDNA library preparation for ambient and elevated samples was performed by the genomics facility  following Illumina TruSeq RNA library protocol outlined in “TruSeq RNA Sample Preparation Guide” . The prepared library was quantified using Nanodrop and validated for quality by running an aliquot on High Sensitivity Bioanalyzer Chip . Sequencing of constructed cDNA library was performed on Illumina HiSeq. 2000 sequencer on high output mode and RNA-Seq data were generated in Fastq format. Sequencing resulted in generation of 101 bp raw reads having attached adapter sequences in tissues from both sample A and E. These raw reads were subjected to filtering through the standard Illumina pipeline. The filtered Fastq files were further subjected for quality control using SeqQC 2.149 and BBDuk (https://sourceforge.net/projects/bbmap/) to remove adapters, B-block, low quality bases towards 3′ ends and contaminant reads.Agilent plant RNA isolation kit  was used to isolate total RNA from leaf tissue of ambient (sample A) and elevated (sample E) CO51. The high quality filtered reads were de novo assembled for transcript generation. Various k-mer assemblies were performed and the best hash length assembly was selected  considering various parameters like total number of transcripts generated, maximum transcript length, total transcript length and less number of N’s. Further, as the Jatropha genome is available, the reference based assembly was also performed by mapping the trimmed reads onto the J. curcas reference genome and exclusively to gene models using Rsubread package on the downloaded files from NCBI (NCBI GCA_000696525.1) [http://www.ncbi.nlm.nih.gov/genome/annotation_euk/Jatropha_curcas/100/]52. After mapping, the mapped file in *.bam format were used to count mapped reads and to provide the genomic coordinates, GTF file was downloaded from NCBI site. The mapped reads were counted using featureCounts program within the Rsubread package53. The unaligned reads to the genome were also assembled. The clustering of assembled transcripts from both libraries to generate unigenes was performed using CD-HIT54. The length of the assembled unigenes for further study were selected as ≥200. Gene IDs were assigned to unigenes according to Jatropha genome version JatCur_1.0 (http://www.ncbi.nlm.nih.gov/genome/jatrophacurcas).Assembly was performed using the high quality reads after removing duplicate reads from libraries for both the samples using Velvet 1.2.10 and Oases 0.2.08 at different k-mer lengths55. The alignment of reads of both sample A and E was performed using Bowtie tool56. The read count profile for reads from both A and E was generated and DGE analysis carried out using shrinkage estimation for dispersions and fold changes to improve stability and interpretability of estimates. The relative expression levels of each annotated unigene was estimated separately for both samples and also as a joint estimate from both samples and presented as its mean expression level , the fold change from sample A to sample E and the logarithm (to basis 2) of the fold change. An expression matrix at the gene and transcript level was also generated in the form of FPKM values for trimmed reads aligned to reference genome. The differentially expressed genes (DEGs) were identified with log-fold (log2) expression change ≥1 or ≤−1 using a statistically significant P-value (P < 0.05) and FDR (FDR < 0.01).The differential gene expression (DGE) analysis in the sequenced cDNA library generated from leaves of both sample A and E was carried out using DESeq tool, considering sample A as control and sample E as treated from the unigenes generatedr) and Swiss-Prot58. The hits with an E-value ≤ 1E-05 and blast score ≥80 were considered to be significant. GO (Gene Ontology) terms were assigned to impart a broad overview of their functions and categorized into biological process, molecular function and cellular component using in house Perl scripts. Also, KOG (Eukaryotic Orthologous Groups) was used to identify the transcript homologues from other organisms and thus assigning a probable function to transcripts. KAAS [KEGG (Kyoto Encyclopedia of Genes and Genomes) Automatic Annotation Server] was used for metabolic pathway analysis using Arabidopsis thaliana and Oryza sativa L. ssp. japonica as reference organisms to identify the enriched metabolic pathways in various gene sets59. The unigenes were classified into various transcription factors (TFs) using Transcription factor Family Data Base (TFDB)60.Transcript annotation was done by performing BLASTX analysis against the non-redundant protein database  with 18SrRNA as housekeeping gene for normalization of data62. The fold change values were log transformed with base 2 so that ~1.00 fold was used to identify differentially expressed genes.Validation of differential gene expression data was carried out using the qRT-PCR analysis at the four different time points. Gene specific primers were designed for certain key regulatory enzymes involved in photosynthesis, carbohydrate and nitrogen metabolism  values were calculated for each sample. The significance of the difference for all physiological (n = 6–15), biochemical (n = 6–15) and molecular were tested by using Analysis of Variance (ANOVA) and the comparisons were tested by Dunnett’s multiple comparison analysis. All statistical analysis was performed using SIGMA PLOT 11.0.Supplementary informationSupplementary Table S1Supplementary Table S4Supplementary Table S5Supplementary Table S6Supplementary Table S7