However, many disease-associated variants throughout the region remain of unknown significance, residing predominantly within non-coding regions of the MHC. The characterization of non-coding RNA transcripts throughout the MHC is thus central to understanding the genetic contribution of these variants. Therefore, we characterize novel miRNA transcripts throughout the MHC by performing deep RNA sequencing of two B lymphoblastoid cell lines with completely characterized MHC haplotypes. Our analysis identifies 89 novel miRNA transcripts, 48 of which undergo Dicer-dependent biogenesis and are loaded onto the Argonaute silencing complex. Several of the identified mature miRNA and pre-miRNA transcripts are unique to specific MHC haplotypes and overlap common SNPs. Furthermore, 43 of the 89 identified novel miRNA transcripts lie within linkage disequilibrium blocks that contain a disease-associated SNP. These disease associated SNPs are associated with 65 unique disease phenotypes, suggesting that these transcripts may play a role in the etiology of numerous diseases associated with the MHC. Additional  However, despite the numerous disease associations throughout the MHC, the functional contribution of these variants remains unclear, due in part to the complex linkage disequilibrium (LD) pattern within the MHC and the numerous SNPs that lie within non-coding regions of the MHC. Although elucidating the functional significance of these SNPs remains a significant challenge, recent research suggests that ~90% of causal autoimmune disease variants reside within non-coding regions of the human genome, with ~60% mapping to immune cell enhancer-like elements which gain histone acetylation following immune stimulation, hence contributing to the transcription of non-coding RNAs5. Consequently, the identification and characterization of functional non-coding transcripts within the MHC is an essential step towards elucidating the contribution of non-coding variants within the MHC to human health and disease.The major histocompatibility complex (MHC) is a ~4 Mbp stretch of the human genome located on the short arm of chromosome 6, which encompasses numerous genes involved in a variety of immunological processes. It is amongst the most gene dense and variable regions of the human genome6. MicroRNAs (miRNAs) are a class of single stranded, non-coding RNA (ncRNA) transcripts, approximately 22 nucleotides in length that attenuate the translation of targeted mRNA transcripts7 following loading of the miRNA transcript onto the Argonaute (Ago) RNA induced silencing complex (RISC)8. Recent research to characterize the miRNA transcriptome of various tissues has led to the discovery of numerous novel miRNA transcripts15, greatly expanding upon the currently annotated set of 2,813 mature miRNA transcripts (miRBase database release 21)6. One miRNA in particular that was born out of this effort, miR-6891-5p11, has been shown to originate from within a highly conserved intronic segment of the HLA-B gene. This miRNA we recently reported to regulate the expression of numerous immunologically related transcripts, including those encoding the heavy chain of IgA16. These findings raise the possibility that additional miRNA transcripts that originate from within polymorphic regions of the MHC including other HLA genes, may play a significant role in regulating a number of biological processes.The MHC has been reported to encode 12 annotated precursor miRNA hairpin (pre-miRNA) loci (miRBase release 21)Despite the discovery of thousands of novel miRNA transcripts located throughout the genome, the identification and quantification of miRNA transcripts originating from within polymorphic loci such as the MHC remains a significant challenge due to inherent sequence differences between the reference genome and the transcripts originating from any given individual. These differences are particularly problematic when mapping miRNA transcripts, since most miRNA mapping pipelines allow for no more than one mismatch between a sequenced read and the reference genome. Although stringent read mapping parameters are necessary to reduce the number of spuriously mapped reads, such an approach is inherently unable to align short reads that differ from the locus of origin within the reference genome by more than one base. Therefore, in order to identify and better characterize the miRNA transcripts originating from within the polymorphic MHC, we performed deep RNA sequencing of miRNA transcripts obtained from two B lymphoblastoid cell lines (BLCLs), with completely characterized, homozygous MHC haplotypes.2. Analysis of the mapped reads obtained from these two cell lines reveals the existence of numerous novel miRNA transcripts originating from within the MHC including several that are only present within specific MHC haplotypes. Nearly half of the identified novel miRNA transcripts (n = 89) are located within a LD block that also contains a disease associated SNP, suggesting that these novel miRNA transcripts may play a role in the etiology of the numerous diseases associated with the MHC. Additional, in silico analysis of the two MHC haplotype sequences (PGF and COX) reveals the potential for thousands of additional putative pre-miRNA encoding loci throughout the MHC, which may be expressed in various tissues, phenotypes and developmental states.Our approach facilitates the accurate mapping of short RNA-seq reads derived from the miRNA transcripts of two MHC homozygous BLCLs (PGF and COX) to their respective reference MHC haplotype sequence12, these efforts do not adequately describe the profile of haplotype and allele specific miRNA transcripts originating from within the polymorphic MHC. For this reason, our experimental design and analysis pipeline were developed, facilitating the identification and characterization of novel miRNA transcripts within the MHC. A modified version of a previously established analytical pipeline9 was utilized for the discovery of novel miRNAs encoded within the MHC using mapped RNA-seq reads from the two biological replicates of each cell line  or exonic, protein coding sequence were retained for further analysis. In total, 89 novel mature miRNAs were identified from the analysis of RNA-Seq data obtained from the two cell lines. The majority (82%) of identified novel miRNAs lie within intergenic regions of the MHC, with 16 identified novel mature miRNAs residing within the introns of 14 unique genes; ATF6B, C2, CSNK2B, DDX39B, GABBR1, HGC20, HLA-DRB5, LY6G6C, MSH5, NFKBIL1, NOTCH4, SLC39A7, TNXB, and TRIM31.Two biological replicates of each cell line were sequenced, generating a total of four RNA-seq datasets. The reads from each sample were mapped to the reference genome (HG38) that had been modified to include either the PGF or COX MHC haplotype reference sequence, depending on the cell line of origin. Approximately 90% of the raw reads generated by each sequencing run were aligned to their respective haplotype specific reference genome. The set of aligned reads was subsequently used for the identification of novel miRNAs transcripts using the miRDeep* algorithm20, Dicer knockdown or silencing experiments have been used to identify mature miRNAs that are formed in a Dicer-dependent manner21. Our Dicer silencing experiment demonstrates that the expression of 54/89 (60%) of the identified novel miRNAs are significantly attenuated following Dicer silencing  as evaluated by qPCR, indicating that the biogenesis of at least these 54 mature miRNAs is Dicer dependent.Since the majority of mature miRNA transcripts are formed through the canonical Dicer-dependent biogenesis pathway8. For this reason, Ago CLIP-seq datasets, in which the Ago protein is immunoprecipitated and the bound RNA fraction sequenced, have been previously used to identify functional miRNA targets and validate functional novel miRNAs10. We performed a meta-analysis on 41 Ago CLIP-seq datasets from 4 independent studies25 in order to provide evidence that our 89 novel miRNA transcripts are functional miRNAs that are loaded onto the Ago silencing complex. Our results indicate that 81 of the 89 (91%) identified novel miRNAs described by this study are found to be loaded onto the Ago silencing complex. These results along with the Dicer silencing results demonstrate that 48/89 of the novel miRNAs were found to be formed in a Dicer dependent manner and also loaded onto the Ago silencing complex. The genomic locus of origin and supporting evidence (Ago support and Dicer dependency) for each identified novel mature miRNA is provided in Supplemental Table Mature miRNAs facilitate the suppression of targeted mRNA transcripts following miRNA loading onto the Ago RISCIn order to determine the presence of each identified novel mature and pre-miRNA encoding sequence within the set of annotated complete (PGF and COX) as well as partially complete MHC haplotypes , each miRNA sequence was compared with each annotated MHC haplotype sequence using BLAST. Only perfectly matched sequences (100% sequence identity between the query sequence and the reference MHC haplotype) were considered to be conserved within a particular MHC haplotype Fig. . Our resIt should also be noted that the presence of the 89 miRNA sequences in the PGF and COX MHC sequences determined computationally Fig.  is disti26, miR-19628, miR-59029, miR-50830 and miR-14331.The set of identified 89 novel mature miRNAs were compared against all known, previously annotated miRNAs (miRBase release 21) in order to identify closely related miRNAs that share partial sequence homology, which may be indicative of a shared target repertoire and physiological function. For this purpose, each identified novel miRNA was aligned pairwise with every annotated mature miRNA sequences (miRBase release 21). Several identified novel miRNAs closely matched the sequences of annotated miRNAs of known physiological function that have been demonstrated to play a role in oncogenesis Fig. , includiab initio computational pre-miRNA annotation pipeline.Many recently discovered miRNAs, including those described within our current work, rely on the identification of novel miRNA from RNA-seq data derived from a variety of tissue types. However, such an approach is inherently limited to discovering only the set of expressed miRNA transcribed within the interrogated tissue types. Given the demonstrated tissue specific RNA expression patterns and limited number of cell lines with completely characterized MHC haplotypes, we have developed a computational pipeline to identify all putative pre-miRNA encoding loci within the reference MHC haplotype sequences of both PGF and COX that may be expressed by various cell types and developmental stages. The developed pipeline is a multi-step process designed to exhaustively interrogate the propensity of genomic loci to form stable, pre-miRNA hairpin structures Fig. . Our ana33, we identified the subset of the miRNAs that lie within LD blocks of annotated disease associated variants within the MHC. Our results indicate that 43 of the 89, (~48%) identified novel miRNA transcripts from the analysis of RNA-seq datasets are within LD blocks containing 87 unique disease associated SNPs. These 87 SNPs have been associated with 65 unique phenotypes  accumulation of mature miRNA transcripts prior to Dicer silencing, 2) differential transcription of pre-miRNA hairpin transcripts amongst the two biological replicates or 3) differential Dicer processing of pre-miRNA hairpins19, resulting in the expression of miRNA isoforms (isomiRs) that may not be an optimal qPCR substrate for amplification using our designed set of primers.Deep RNA sequencing of the miRNA transcriptome from two BLCLs with completely characterized MHC haplotypes (PGF and COX) has enabled the accurate alignment of short RNA-seq reads to each respective MHC haplotype sequence, facilitating the identification of 89 novel miRNA transcripts originating from within the polymorphic MHC region  originate from within the intergenic regions of the MHC, while 16/89 are located within an intron of an annotated host gene. We also find that 12/73 identified intergenic miRNAs are antisense to an annotated protein-coding gene. The mature miRNAs that lie within an intron, “mirtrons”, are formed following splicing of the mRNA transcript and have been shown to exist throughout the human genome40. Furthermore, an assessment of the haplotype conservation amongst the set of identified computationally predicted putative pre-miRNA encoding loci, reveals that ~50%  of these sequences are 100% conserved between the PGF and COX MHC haplotypes. Together these data suggest a diverse miRNA transcriptome, with the potential for a variety of isomiR transcripts that are defined by inherent differences amongst MHC haplotypes. It is anticipated that each of these isomiRs has a distinct target repertoire governed by the sequence specific interaction of the mature miRNA with targeted mRNA transcripts41.The existence of MHC encoded miRNAs raises many questions related to the existence and prevalence of haplotype specific miRNAs. Although the majority of identified mature miRNA sequences are conserved across all eight known MHC haplotypes, several mature miRNA and pre-miRNA hairpin sequences are found to be unique to specific MHC haplotypes Fig. . It is h43, we sought to identify every potential miRNA encoding locus throughout the MHC using only the DNA reference haplotype sequences of PGF and COX as a guide. Our computational pipeline has identified thousands of potential pre-miRNA encoding loci throughout the MHC and serves as an atlas for the future identification and quantification of MHC encoded miRNA transcripts, which may be expressed by various tissue types, cellular phenotypes and developmental stages. Previous research suggests that there may be as many as 55,000 pre-miRNA encoding loci throughout the human genome44, which would mean, if equally distributed, that the MHC would be expected to harbor ~74 pre-miRNA hairpins. However, our computational estimates suggests the existence of many more pre-miRNA hairpins than anticipated within the MHC, suggesting that the gene dense MHC may harbor more miRNAs than previously calculated. It is only after we are able to characterize the totality of miRNA transcripts across a variety of tissue types, diseases and developmental states from diverse populations that we can begin to understand the full spectrum of miRNA diversity within the MHC. In order to properly study allele and haplotype specific miRNA expression patterns of MHC encoded miRNAs, it is first necessary to resolve the sequence of an individual’s MHC haplotype so that expressed transcripts can be accurately mapped to an individual’s MHC haplotype sequence. Working toward this goal, we have developed a long fragment DNA enrichment method45, capable of generating long DNA fragments that may be utilized by single molecule sequencing platforms to generate long reads, which has been previously utilized by our group for de novo assembly of the MHC46. These efforts lay the foundation for studying allele and haplotype specific transcript expression patterns across diverse sample populations and may be extended to any portion of the genome of interest.The availability of an accurate, reference genome sequence is critical to ensure accurate mapping of short RNA-seq reads for the identification and quantification of novel miRNAs and isomiRs that are transcribed from within highly polymorphic loci such as the MHC. Because there are only two complete MHC haplotype sequences currently available (PGF and COX), our study is limited to the identification and quantification of novel miRNAs expressed by these two cell lines. However, because miRNA and isomiRs are expressed in a tissue and phenotype specific manner5, it is possible that these miRNAs may play a role in the pathophysiology of the associated diseases and warrants further experimental investigation. Furthermore, five of the identified novel miRNAs share considerable sequence homology with oncogenic miRNAs (oncomiRs) including miR-59029, miR-19628, miR-48926, miR-50830 and miR-14331. These novel miRNAs are shown to have a high degree of sequence homology with already known and previously described annotated miRNAs  http://www.ihwg.org/hla/index.html]. PGF cells were obtained from the Coriell Biorepository (Cat #GM03107). They are both homozygous for chromosome 6. COX HLA typing is: HLA-A 01:01:01:01, HLA-B 08:01:01, HLA-C 07:01:01, HLA-DRB1 03:01:01:01, HLA-DQB1 02:01, HLA-DPB1 03:01. PGF HLA typing is: HLA-A 03:01:01:01, HLA-B 07:02:01, HLA-C 07:02:01:03, HLA-DRB1 15:01:01:01, HLA-DQB1 06:02:01, HLA-DPB1 04:01. Cells were cultured in RPMI-1640 medium with 15% FBS (Sigma Cat # F2442-500ML).). 50-base-pair single-end reads were generated on the Illumina NextSeq. 500 sequencing platform and stored in FASTQ format.Total RNA was extracted from two biological replicates  of PGF (n = 2) and COX (n = 2) cells using the Qiagen miRNeasy kit (Cat #217084) per manufacturer’s protocol. RNA was quantified on a Nanodrop ND-100 spectrophotometer, followed by RNA quality assessment on an Agilent 2200 TapeStation . Library construction, workflow analysis and sequencing runs were performed following standard Illumina TruSeq Small RNA protocol (15004197 Revision GRaw sequencing reads from each cell line were mapped to a haplotype specific version of the reference genome (HG38) using Novoalign, allowing up to one mismatch per read as compared to the reference genome. Reads generated from PGF were mapped to the reference genome (GRCh38), which excluded all other MHC haplotype assemblies. Reads generated from COX were mapped to the reference genome (GRCh38), which included only the COX MHC haplotype assembly.17 generated from each biological replicate of PGF (n = 2) and COX (n = 2) cell lines independently. The following miRDeep* parameters were used: minimum phred score of 15, miRNA length of 16–28 25 were interrogated for the presence of the 89 identified novel mature miRNA sequences. The raw data (fastq) files from each dataset were interrogated in order to find the number of Ago supporting reads for each of the 89 novel miRNAs identified by our analysis. Only reads containing the exact, ungapped sequence of each mature miRNA were considered as supporting reads. The number of Ago CLIP-seq reads supporting each novel miRNA was tabulated. Only those miRNAs that were supported by 500 reads or more within all datasets were considered to be Ago Supported.41 Argonaut (Ago) CLIP-seq datasets from 4 independent studies5 COX and PGF cells were plated in 6 well plates, and 2 ml of fresh scrambled or Dicer silencing lentivirus was added along with 4 mg/ml polybrene. The plate was centrifuged at 2500 rpm for 90 minutes. After 10 hours, 2 ml of additional virus with polybrene was added and the plate was centrifuged at 2500 rpm for 90 minutes. After 16 hours, 2 ml of media was discarded and 2 ml of fresh virus and polybrene were added, and the plate was centrifuged at 2500 rpm for 90 minutes. Transduction was allowed to continue for an additional 24 hours before cells were collected for RNA extraction. RNA was extracted using the miRNeasy kit (Qiagen). Total RNA was reverse transcribed using the Qiagen miScript II RT kit (Cat #218160), with either (1) the miScript HiFlex Buffer (for quantification of Dicer mRNA) or (2) MiScript HiSpec Buffer (for specific quantification of mature miRNA only). Q-PCR was performed on cDNA generated by reverse transcription using a miSCRIPT SYBR Green PCR kit (Cat #21803). For the purposes of validating Dicer silencing, cDNA generated using the MiScript HiFlex buffer was quantified with Dicer specific qPCR primers . For the purpose of assessing the quantities of mature miRNA in both Dicer silencing and scrambled control conditions, cDNA generated using the MiScript HiSpec Buffer was quantified using miRNA specific primers. The primers were DNA oligos corresponding to the full length mature miRNA sequences. Sequences of mature miRNAs are included in supplemental Table 47. Even though we tested various small nuclear RNAs as reference, they were not expressed in equivalent and reproducible amounts in COX and PGF cells, therefore beta-actin was chosen as providing reproducible and comparable expression in COX and PGF cells. A p-value less than 0.05 was deemed significant.Dicer silencing was performed by designing a small hairpin RNA (shRNA) vector, which was subsequently transfected into a lentiviral plasmid for transduction into COX and PGF cells, effectively silencing Dicer expression by RNA interference (RNAi) within COX and PGF cells. The lentiviral plasmid containing the Dicer shRNA insert  was generated in cultured HEK293T cells by transfecting with a psi-LVRH1GP vector (GeneCopoeia). A lentiviral plasmid containing a “scrambled” sequence insert  was similarly generated in HEK293T cells. Media was discarded after 24 hours post-transfection and packaging media was added to the plate. Scrambled and shRNA Dicer viruses were collected every 24 hours for 2 days. For transduction, 1.5 × 1048. Only perfectly matched sequences (100% sequence identity between the query sequence and the reference MHC haplotype) were considered to be conserved.Novel miRNA sequence conservation across annotated MHC haplotype sequences was interrogated using BLAST. All eight available MHC haplotype sequences  were scanned for the existence of every identified mature and pre-miRNA sequence  and all annotated miRNAs within miRBase (release 21), each novel miRNA sequence was aligned pairwise with every annotated miRNA sequence using the semi-global Needleman-Wunsch algorithm implemented within MATLAB (2014a). For each identified novel miRNA, the closest matched annotated miRNA  was reported. In the case in which a novel miRNA aligned to multiple annotated miRNA with the same maximal alignment score, every match was reported  using BEDtools52 and ranged from HG38 coordinates chr6:28510019-33383765 and chr6_cox_hap2:1-4795371 for PGF and COX haplotypes respectively. The pipeline begins by first partitioning the FASTA file provided as input into overlapping segments of variable length using a 1 bp sliding window (58 ≤ Length ≥ 110), generating sequences from both the forward and reverse strands (output in the 5′− > 3′ direction) for each iteration so as to enumerate every possible pre-miRNA encoding locus throughout the MHC. In silico RNA folding was subsequently performed for each putative pre-miRNA transcript to determine the minimum free energy (MFE) and secondary structure of each theoretical transcript using RNAfold53. The acceptable parameter range for both window length (pre-miRNA transcript length) and MFE were selected as the 95th and 5th percentile value for both distributions of annotated human pre-miRNA within miRbase release 21 6, corresponding to a length of 58–110 bp and a maximum MFE of −20Kcal/mol respectively. In order to further reduce the search space, pre-miRNA hairpin structures containing bulge loops or multi-stem structures and those with a MFE ≥ −20 Kcal/mol were removed, leaving only characteristic linear pre-miRNA hairpin with a permissible MFE. The machine-learning algorithm, miRBoost54 was then utilized to identify high confidence pre-miRNA hairpin structures. The miRBoost positive and negative control training sets comprised of the human pre-miRNA present within miRBase (release 21) and the provided internal miRBoost negative Human control dataset respectively. Lastly, the BED formatted output file was subsequently filtered to remove any entries that overlap an annotated exon (GENCODE v24)55 and the remaining entries then merged so as to remove overlapping entries and create an atlas of loci throughout the MHC that contain at least one putative pre-miRNA hairpin locus.A computational pipeline was developed Fig.  in orderin silico analysis of both the PGF (n = 9019) and COX (n = 9207) MHC haplotypes. The sequences of each putative pre-miRNA encoding loci identified from both haplotypes were aligned pairwise using BLAST and filtered to include only those loci whose sequences matched 100% (minimum overlap defined by the shortest of the two compared sequences).BLAST was used to determine sequence homology amongst computationally predicted pre-miRNA encoding loci identified from the www.ebi.ac.uk/gwas, accessed on March 1, 2017)33. The linkage disequilibrium (LD) block of each disease associated SNP was calculated using SNAP (HapMap release 22) with a minimum r2 of 0.956. The LD blocks defined by each disease associated SNP were then intersected with the set of empirically derived novel miRNA as well as the set of identified computationally predicted pre-miRNA encoding loci using BEDtools52 in order to determine which novel miRNA encoding loci lie within the LD block of each disease associated SNP.Annotated disease associated SNPs within the MHC were collected from the GWAS catalog (Supplemental Material


Deep whole genome sequencing (WGS) allows for the comprehensive study of genetic landscapes at finer resolution than array based methods. We conducted deep WGS on children with the polyarticular form of juvenile idiopathic arthritis (JIA), using 2 independent cohorts to ascertain the sequencing fidelity. Genome wide SNP density analysis identified 18 SNP hotspots with comparison to the 1000 Genome Projects (1KGP) data. A subset of the genes adjacent to SNP hotspots showed statistically significant enrichment in immunological processes. Genes adjacent to indel hotspots were functionally related to G-protein coupled signaling pathways. Further analyses elucidated significantly more JIA SNPs with regulatory potential compared to 1KGP data. Furthermore, SNPs located within linkage disequibilium (LD) blocks containing previously identified JIA-associated SNPs demonstrated higher regulation potential compared to SNPs outside LD blocks. We also demonstrated enrichment of novel JIA variants in histone modification peaks and DNase hypersensitivity sites in B cells. This study greatly expands the number of genetic variants that may contribute to JIA and give us some clues into what may trigger this disease. To date, this study is the first deep WGS effort on children with JIA and provides useful genetic resources for research communities particularly in understanding JIA etiology. In JIA, the search for causal variants that contribute to disease risk has been hampered by the small effect sizes2. Furthermore, although multiple risk loci for JIA have been identified via candidate gene approaches and genetic fine mapping studies4, these approaches carry an inherent bias in their assumption that JIA is an “autoimmune disease”. Genome-wide association studies are free from inherent bias about disease pathogenesis5, but these studies provide only a rough estimate of genetic risk loci and are limited in their capacity to detect regions conferring subtle but biologically significant risk. Finally, the task of finding causal variants is further complicated by the fact that known genetic risk for JIA, resides primarily within the non-coding genome6.JIA is an archetypal complex trait, in which small but measurable genetic susceptibility can be identified in multiple genetic loci, and where the environment contributes significant riskWhole genome sequencing (WGS), with sufficient depth, may provide, in an unbiased manner, a new opportunity to obtain a finer resolution for regions of genetic risk in complex traits, identify new regions not identified through chip-based methods that query targeted regions on assumptions regarding underlying pathogenic mechanisms. To explore this possibility, we performed WGS on children with polyarticular JIA. We report here multiple new genetic variations and identify epigenetic landscapes surrounding these genetic mutations that may cast light on previously unrecognized disease mechanisms.As our sequencing data were obtained through two independent sequencing experiments, we conducted a variants concordance check for the SNPs and indels identified in these two sets of data . We define concordance rate as a ratio of the number of shared variants between two sets to the total number of variants in the observed set. We observed averages of 77.14% and 66.63% concordance rates for SNPs and indels respectively , of which 6,028,802 (55.88%) are common SNPs, defined as alternative allele frequency AAF >5%. However, 1,205,197 are novel or absent in dbSNP141 , resulting in an overall novelty rate of 26.9%  for SVs. When we stratified SV by type, we found novelty rates of 12.2%, 68.3% and 85% for DEL, INV and DUP respectively. High novelty rates for INV and DUP were expected, as the DGVa database contained relatively small proportions of INV  and DUP  from the total 120,045 SVs in its archive.Next, we considered Loss of Function (LoF) variants, i.e., variants with potential to disrupt the function of protein coding genes. In total, we found 66,074 LoF variants  and 10,504 low frequency (AAF <5%) JIA SNPs annotated with class 1 regulation evidence. To determine whether those SNPs were genuine mutations or sequencing artifacts, we compared the sequencing depth of 51,258 JIA SNPs with RegulomeDB class 1 regulation evidence (hereafter we denote these SNPs as class 1 JIA SNPs) and all other SNPs . This comparison showed that class 1 JIA SNPs exhibited a statistically significant higher sequencing depth than other SNPs . In addition, we examined variant quality and found higher quality scores in class 1 JIA SNPs compared with the other SNPs; higher quality scores indicate higher confidence calls . When we investigated the mapping quality of reads supporting class 1 JIA SNPs and other SNPs, we also identified statistical evidence for better mapping quality for class 1 JIA SNPs compared with the other SNPs . Taken together, the three metrics that quantified the fidelity of the detected SNPs , make it reasonable to infer that class 1 JIA SNPs are genuine mutation in JIA genomes.7.When we performed gene ontology (GO) enrichment analysis using genes of JIA SNPs with class 1 regulation evidence, significant GO terms included carboxylic acid metabolism and ATP hydrolysis coupled proton transport  that demonstrated a minimum of 1.5 fold greater density for JIA variants than for the 1KGP variants with Fisher exact test p-values less than 0.05. We identified 18 SNPs hotspots and 19 indel hotspots when comparing JIA WGS with 1KGP data . Of these, 81 novel SNPs were annotated to have regulation potential (RP) scores ≥0.5, significantly higher than known SNPs . When comparing SNPs within hotspots and SNPs outside hotspots, SNPs within hotspots have more regulation potential relative to SNPs outside hotspots . A representative genome browser view that depicts regulation properties of JIA variant hotspots is given in Supplementary Figure We next examined the new variants discovered on WGS in the context of previously identified risk loci for JIA. We found that JIA genetic variants, particularly those SNPs that co-localized within LD blocks containing previously identified JIA-associated SNPs, demonstrated higher regulation potential compared to SNPs outside the JIA risk-associated LD blocks.et al.3 and Hersh et al.4 identified 9,423 SNPs  and 1,112 indels , indicating that SNPs co-segregating with JIA-associated LD blocks exhibited higher regulation ability compared to SNPs outside those LD blocks.The intersection between JIA variants with LD blocks containing regions of known genetic risk for JIA (hereafter denoted as LD blocks), as reported by Hinks SNPs 1,01 novel anIn order to determine whether the LD blocks were more immunologically active due to JIA genetic mutation or effects of variation from the general healthy cohort, we performed the same analysis on 1KGP data . We overlapped 1KGP SNPs with LD blocks and obtained SNPs RP scores with reference to UCSC RP score databases. This was followed by generating a contingency table  that contained the number of 1KGP SNPs within (and outside) LD blocks, segregated by RP scores ≥0.5 and less than 0.5. We did not obtain statistical significant evidence of enrichment for the 1KGP SNPs within the LD blocks, i.e., having RP score ≥0.5 relative to those 1KGP SNPs outside LD blocks . This finding supports the hypothesis that those LD blocks are more likely to be immunologically active due to the regulation potential of JIA SNPs, and are not generally impacted by SNPs from healthy subjects (1KGP).3, which co-localized with novel SNPs and novel indels from WGS. We note that this region also overlaps with neutrophil H3K27ac and H3K4me1 signals6 and spans multiple transcription factor binding sites, suggesting that this is a particularly active genomic region.In Fig. https://ghr.nlm.nih.gov/condition/rheumatoid-arthritis#genes) that are associated with rheumatoid arthritis (RA) and or JIA . We did not observe enrichment of novel JIA variants in H3K27ac or H3K4me1 histone marks in any of the three cell types. In Supplementary Figure We started with histone modification integration analysis. We observed significant enrichment of JIA novel SNPs within H3K4me3 and H3K27me3 marks in CD20+ B cells . Both of these histone marks identify promoter activities18. We found that novel JIA SNPs/indels were enriched in the DNase hypersensitivity sites of CD20+ B cells . In addition, we found that novel JIA SNPs were enriched in CD14+ monocytes DNase hypersensitivity sites . We did not see enrichment evidence of novel JIA variants within DNase hypersensitivity sites of CD4+ T cells. We provide an example of a genomic region where novel JIA SNPs/indels overlap DNase hypersensitivity sites of all three immune system cell types in Supplementary Figure 19. This gene is expressed in JIA neutrophils6 , so subtype-specific data are still lacking.The role of genetic risk for JIA has been well-established by family studieset al.3 were able to explain no more than 18% of the risk for JIA. We therefore conducted deep WGS on 48 children with the polyarticular form of JIA over 2 independent cohorts, comparing results with publically available WGS data from healthy individuals (1KGP). The JIA specific genetic variation achieved an average of 72% concordance between the 2 cohorts despite the ethnic heterogeneity between them.Deep WGS provides an unprecedented opportunity to comprehensively study genetic landscapes at finer resolution than can be achieved with chip-based methods. While previous candidate gene approaches and genome-wide association studies have revealed useful information about genetic risk in JIA, a finer mapping is needed to decipher genetic landscapes of children with this disease to gain insights into pathogenesis. For example, even with dense genotyping using the Illumina Immunochip, Hinks 24. Thus, the presence of risk-associated variants within this genetically-rich region should not be taken as prima facie evidence that the causal variants include or are limited to MHC molecules. In the final analysis, functional studies in a broad spectrum of relevant cells will be required to identify causal variants. While this work has significantly expanded the variants that need to be queried, technologies now are available to accomplish this task25.Using WGS, we were able to identify multiple new loci within JIA genomes that can now be considered candidate JIA risk regions suitable for further investigation. These loci consist of “hotspots” that were enriched for genetic variants discovered by WGS. Chromosome 6, where the major histocompatibility (MHC) genes are located, was a particularly prominent hotspot, supporting the existing view that JIA pathogenesis involves aberrant adaptive immune responses. However, the MHC locus is extraordinarily complex, and, in addition to genes that regulate the T cell responses, is rich in non-coding functional elements that include H3K4me1/H3K27ac-marked enhancers and non-coding RNA species in several relevant cell typeset al.3 are enriched for H3K4me1/H3K27ac-marked enhancers in both CD4+ T cells and neutrophils6. In the current study, the newly-identified genetic variants were located within regions of functional elements in CD4+ T cells and CD20+ B cells as annotated by the ENCODE and Roadmap Epigenomics projects. Thus, JIA resembles almost every other complex trait, in which genetic risk lies largely in the functional, non-coding genome. This means that the field will be faced with the task of performing functional studies in a broad spectrum of cells, as there is no way to determine, a priori, the specific cell type in which genetic risk is most likely to operate. The appearance of JIA-associated genetic variants within functional elements in B cells, for example, was unexpected. Our findings invite additional inquiry into the role of B cells in this disease.This work corroborates the importance of the non-coding, functional genome in conferring risk for JIA. We have recently reported that the loci reported by Hinks One finding of this JIA genomics study is that it corroborates the importance of previously identified JIA genetic risk loci. When we examined the newly identified genetic variants, those located within the LD blocks containing the known risk-associated SNPs had higher regulatory potential than variants situated outside those LD blocks. On the other hand, when using 1KGP SNPs, we did not observed higher regulation potential within those LD blocks than 1KGP SNPs outside LD blocks, indicating that JIA SNPs within LD blocks very have functional significance. As noted above, however, our current work suggests that functional studies will need to be performed on a broad spectrum of cell types in order to clarify the causal link between genetic variance and immune dysfunction in JIA.To date, this is the first deep WGS on JIA samples, serve as important data resource that has been instrumental in driving the progress biomedical and clinical research. We envision such data resource can often be benchmarked to deliver scientific impact in such a way that it facilitates the design of future hypothesis or experiment. Furthermore, genomic information from this data resource could lead to translational impact typically guides the changing of clinical practices, imposing clinical validity and industry relevance.disease-specific epigenetic data, and concomitant genetic studies to determine whether and how underlying genetic variation alters epigenetic marks and genome function in pathologically-relevant cells. We are now learning that disease states are characterized by specific epigenetic signatures27. Recently, Peeters et al. identified28 novel enhancer marks in memory T cells derived from synovial fluid of children with JIA. The degree to which these regulatory elements are essential to the disease process or even JIA-specific remains uncertain, however. Enhancer localization in inflammatory cells is highly dependent on the local microenvironment30, and it seems likely that similar findings would emerge from the study of inflammatory cells located at any site of chronic inflammation.We are aware that these studies are just scratching the surface of where we want to go in building multi-dimensional genomic models for JIA. Such models will invariably include In summary, these studies demonstrate the utility of WGS, even on relatively small sample numbers, for elucidating underlying genetics of JIA. Segregation of variants from WGS of JIA individuals within known disease-associated LD blocks overcomes the limitation of depth and coverage of arrays in searching for JIA risk variants. Distribution of JIA SNPs/indels in regulatory elements serves as valuable resource from which to develop insights into epigenetic alterations underlying the effects of genetic variants. The long-term goal for this genomic study will be to integrate existing (and newly-developed) epigenetics resources and prospective JIA clinical records. Based on such data, “Precision Medicine” clinical decisions can be efficiently made to address clinical needs as well as answering important questions about JIA disease pathogenesis, clinical course, and the underlying biology of treatment response.31. Fourteen of these children had detectable antinuclear antibodies at presentation. There were 29 samples in the first batch (B1), which included one pair of technical replicates (one subject was sequenced twice), while the second batch (B2) consisted of 19 subjects. All samples came from children of European or mixed European-American Indian ancestry and were obtained via a University of Oklahoma Institutional Review Board approved protocol. Informed consent was obtained from the parents of all patients. All research was carried out in accordance with the IRB-approved protocol and in compliance with relevant state and Federal regulations.We performed WGS using Illumina X Ten on 50 samples from 48 individuals with JIA over two independent cohorts , aligned using Burrows-Wheeler Aligner (BWA)34 practices in calling SNPs and small insertions or deletions . In order to identify high quality variants, we combined GATK variant quality score recalibration filtering and retained SNPs/indels that passed filtration criteria consisting of read depth ≥20X, genotype quality ≥20, variant quality ≥30 and minor-read ratio (MRR) ≥0.2. To access variant novelty, we denoted novel variants (SNPs/indels) as those that were absent in dbSNP141 build 3735 and known variants as those present in the dbSNP141.We adopted the Genome Analysis Toolkit (GATK)36 to detect deletions (DEL), duplications (DUP), and inversions (INV), with reads of mapping quality ≥20. We denoted novel structural variants (SVs) as those absent in Database of Genomic Variants archive (DGVa)37.For structural variation , we used DELLYWe first performed sample ethnicity screening by applying principal component analysis (PCA) over genotype information of JIA SNPs and 1KGP SNPs to compare our sample ethnicities against the reference to 1KGP data. This resulted in the JIA samples clustering together with the American samples of 1KGP .We next examined per sample variant heterozygous to homozygous ratios (Het/Hom) to ensure that the ratio fell within the expected range of genomic scale at 1.5.Finally, for variant discovery QC, we examined the distribution of genetic variants across all autosomal chromosomes for each sample. We found that all samples demonstrate similar numbers of discovered variants  at the same chromosomes , novelty, alternative allele frequency and genomic features  scoreset al.43. LoF SNPs were categorized to one of the following genomic features: non-synonymous, start lost, stop gained, stop lost and splice site. Indels that are classified as frameshift or splice sites are considered LoF indels. We predicted the functional effects of LoF SNPs using Polyphen44 and categorized the prediction into two classes (damaging and benign). For LoF indels, we used the SIFT INDEL45 bioinformatics tool to predict indels’ functional effects as either damaging or benign. In addition, we conducted regulation potential predictions for LoF variants by mapping them to the RegulomeDB46 database to obtain the associated regulation evidence  variants following the definition used by MacArthur th bin with the fold ratio given by:i is the number of JIA variant in ith bin and total_JIA_var represents genome wide total number of JIA variants. Similarly, num_1KGP_Var_bini is the number of 1KGP variant in ith bin and total_1KGP_var represents genome wide total number of 1KGP variants, noting that 1KGP samples as healthy controls for this variant distribution analysis. We used a ratio of at least 1.5 and a Fisher exact test p-value ≤ 0.05 to define SNP/indel hotspots. For the detected variant hotspots, we searched for associated genes, arbitrarily designated as those genes located within the hotspot regions including genes located 5kbp upstream and downstream of hotspot regions. We then conducted gene ontology (GO) enrichment analysis using Gene Ontology enRIchment anaLysis and visuaLizAtion tool (GOrilla)8 to assess the functions of the protein encoding genes within the variant hotspots. REduce Visualize gene ontology (REVigo) was used to classify GO terms base on semantic similarity measurement9.To investigate the distribution of SNPs and indels within JIA genomes, we computed variant density by counting number of variants in non-overlapping bins of 1 M bp size over the entire genome. To identify variant hotspots, we compared JIA SNPs (indels) density and 1KGP SNPs (indels) density, iet al. and Hinks et al.4. Bedtools47 was used to intersect the discovered JIA SNPs/indels within those LD blocks . We focused on four histone marks: H3K27ac, H3K4me1, H3K27me3 and H3K4me3, for integration with JIA genetic variants. For this analysis, we downloaded histone modification ChIP sequencing (ChIPSeq) data from GEO databases (Supplementary Table 53, with default parameter settings for each histone mark independently. Next, we searched for co-localization between JIA SNPs/indels and chromatin accessible genomic regions. We obtained DNase hypersensitivity site data from the ENCODE project. We also investigated the co-segregation of JIA genetic variants and CTCF binding sites, which regulate gene expression through the organization of three dimensional chromatin structure54. For CD4+ T cells we used CTCF binding site data identified by Cuddapah et al.55 whereas for CD20+ B cells and CD14+ monocytes, we obtained the data from ENCODE (Supplementary Table Interplays between epigenetic mechanisms and genetic variants may reveal important phenotype genotype relationshipsWe used bedtools to intersect the aforementioned epigenetic elements and JIA SNPs/indels. Enrichment analysis of novel SNPs/indels was conducted by Fisher exact test relative to known variants.Supplementary Information


Epigenetic modifications are increasingly recognized as playing an important role in the pathogenesis of infectious diseases. They represent a critical mechanism regulating transcriptional profiles in the immune system that contributes to the cell-type and stimulus specificity of the transcriptional response. Recent data highlight how epigenetic changes impact macrophage functional responses and polarization, influencing the innate immune system through macrophage tolerance and training. In this review we will explore how post-translational modifications of histone tails influence immune function to specific infectious diseases. We will describe how these may influence outcome, highlighting examples derived from responses to acute bacterial pathogens, models of sepsis, maintenance of viral latency and HIV infection. We will discuss how emerging classes of pharmacological agents, developed for use in oncology and other settings, have been applied to models of infectious diseases and their potential to modulate key aspects of the immune response to bacterial infection and HIV therapy. Traditionally innate immunity has been viewed as generic and rapid, as lacking immunological memory and occurring with similar magnitude on rechallenge while adaptive immunity is more specific but requires time to mature. Innate immunity is mediated by pattern recognition receptors , nucleotide-binding oligomerization domain protein like receptors (NLR) and retinoic acid inducible gene 1 like receptors (RLR), that recognize pathogen associated microbial patterns (PAMPs) and mediate an inducible response to micro-organisms . PRR andRegulation of the inducible transcriptional program to pathogens includes post-transcriptional control . TranscrT-cell receptors and immunoglobulin are much more diverse than PRR . The devA key characteristic of both innate and adaptive effector functions is that functional subsets demonstrate significant plasticity, allowing a more flexible response to pathogens. Regulatory T-cells (Treg) can become Th17, while other conditions allow them to develop into follicular T helper cells (Tfh) . Th17 ca2Epigenetics is defined as a “ stably heritable phenotype resulting from changes in a chromosome without changes in the DNA sequence” . The terHistone PTMs have been the subject of particularly intensive investigation and we will focus on these in this review since these dynamic changes allow modulation of the immune response to infection, even though these are not necessarily inheritable.Although these modifications may be transient they are more sustained than the transient protein PTMs observed with signaling molecules and thus allow a mechanism for extending the response period to external stimuli . Histone3. The interactions between the innate immune system and the pathogen are a key factor in determining susceptibility to disease and likelihood of clinical infection  (a class I and II HDACi) leads to increased global levels of H3 and H4 acetylations. This results in an increase in the release of the pro-inflammatory cytokine TNF-α. However, this effect is not seen in mature macrophages suggesting that this reversible chromatin modification and its capacity to influence TNF-α expression are only present during a fixed window of the maturation process . Thus thThe relationship between histone PTMs and gene expression does not conclusively demonstrate directionality. This is illustrated by the case of differentiation and whether histone PTMs are a consequence of gene activation during differentiation or a key driver of the differentiation process has been debated.Nevertheless the example of TSA influencing acetylation levels in monocytes prior to differentiation suggests that alterations of histone PTMs precede differentiation and influence the process . The epi3.3The different macrophage activation states have different transcriptional profiles  and diffTnfa, Il6 and Ifnb  genes. These responses are associated with different kinetics of nuclear factor-κΒ (NF-κΒ) binding and more sustained and higher level transcription. As an example of this mechanism it has been shown that chromatin remodeling allows access of c-Rel-containing NF-κΒ to the IL-12b promoter , illustrating another potential mechanism linking shifts in metabolism to epigenetic changes that enhance the innate immune response to infection -1α, leading to enhanced release of the pro-inflammatory cytokine IL-1β . This prnfection . It has nfection . Finallynfection .3.4.The concept of trained immunity and tolerance has been debated but has emerged as a framework to explain the potential existence of innate immune memory. The process by which prior stimulation with β-glucan and BCG enhance responsiveness and protection to subsequent infectious challenge, has been termed trained immunity . Recent Babesia microti in mice vaccinated with Bacille Calmette–Guerin (BCG)  . This obin (BCG) . Recentlalbicans . The traalbicans . Chromatalbicans . This mealbicans , suggestalbicans . Moreovealbicans .In contrast to a heightened response associated with training, it has long been established that macrophages can become tolerant following LPS challenge, leading to a decreased responsiveness to LPS and other stimuli on rechallenge. Clinically this phenotype is demonstrated by the immune paralysis associated with critical illness and sepsis. Mice pre-treated with LPS demonstrated increased survival in a polymicrobial model of sepsis caused by cecal ligation and puncture . ToleranA separate phenomenon that appears distinct from tolerance is seen when immune cells are rechallenged with certain toxins. This phenomenon, which has been described for anthrax lethal toxin, is termed toxin-induced resistance. When macrophages are exposed to a sub-lethal dose of the toxin they are then resistant to the cytolytic effects of higher potentially lethal doses on rechallenge. A small proportion of macrophages (2–4%) can retain this resistance for 5–6 weeks and the mechanism of this phenomenon appears to involve HDAC8 . HDAC8 rOverall a wealth of information on the role of epigenetic responses in regulating innate responses, particularly in macrophages is emerging. However, if these are to be exploited therapeutically clarification is still required on how long the changes documented in vitro last in vivo, clarification of which enzymes regulating histone PTMs have the degree of specificity to allow their selective modulation and finally identification of which processes have significant enough functional consequences to justify their targeting.4The regulation of the adaptive immune system also involves epigenetic modifications . LymphocIL-4 and IFN-γ genes  as a repressive mark to enable transcriptional gene silencing , since deletion of this but not cagA or other factors induced the dephosphorylation of H3S10 , a Suvar3–9, enhancer-of-zeste, Trithorax (SET) domain-containing methyltransferase, which causes H3K14me3 of the host cell and reduces H3K14ac , resulting in translocation of a host cell class 3 HDAC, sirtuin 2 (SIRT2), to the nucleus. In this location SIRT2 deacetylates H3K18ac. This is associated with a decrease in expression of genes involved in DNA-binding and immune responses , which causes dephosphorylation of H3S10 and decreases the level of H4ac  is associated with clear survival benefits following cecal ligation and puncture  and TSA, macrophage's microbial killing ability was impaired . The macpuncture . Similarpuncture . This wapuncture . Furtherpuncture .E. coli and Salmonella typhimurium , a TLR generated microbicidal response in human macrophages and increased intracellular killing of himurium . Broad s6Epigenetic changes are also seen in viral infections involving viruses such as human adenovirus , influenEpigenetic modifications regulate the balance between viral latency and replication as seen with herpes viruses . A prote6.1+ cells, mainly CD4+ T-lymphocytes  aim to block replication principally targeting key enzymes involved in reverse transcription, integration and maturation of the HIV-1 proteins by the HIV-derived protease. Therapeutic approaches also target steps involved in the entry process, involving binding to the chemokine receptor CCR5 and fusion to the plasma membrane . All exi6.2+ T-cells despite HAART . However, a diminishing response to successive vorinostat treatment was observed when administered on three successive days over eight weekly cycles (SAHA (vorinostat), like valproic acid has also enhanced HIV-1 replication . Vorinos mg dose . Total cy cycles . ImportaThe proof of concept that HDACi can increase viral replication from latent reservoirs has spurred experimentation with alternative and more selective HDACi. Antagonism of class I HDACs induced the desired acetylation of LTR-associated histones, whereas antagonism of class II HDACs had no significant effect . A numbeThe epigenetic mechanism by which post-integration pro-viral latency develops could be targeted in combination with approaches to increase TF availability. Activation of cytoplasmic NF-κΒ by the phorbol ester prostratin stimulates its translocation to the nucleus, where it activates HIV transcription . ValproiHistone lysine methyltransferase (HKMT) inhibitors have also been investigated as a potential therapeutic option. Methylated H3 has been demonstrated to be dimethylated  and trim+ T-cell, which is latently infected with the complete HIV-1 genome that is transcriptionally inhibited by hypermethylation at the promoter region. When used in combination, aza-CdR plus the NF-κΒ activator prostratin, stimulated greater HIV-1 expression in the immortalized CD4+ T-cell, compared to prostratin alone  act as adaptors that link histone acetylation status to transcription by incompletely defined mechanisms . HoweverRecent advances in the field of clustered regularly interspaced short palindromic repeats (CRISPR) technology have enabled targeted modification of the epigenome in an effort to manipulate gene regulation . The CRIAn inactive Cas9 (CRISPR-associated protein 9) was used as a programmable CRISPR/CAS9 construct fused with an acetyltransferase (p300) allowing the acetylation of the promoter region of specific genes . The fusThe CRISPR–Cas9 system has also been used to with a deactivated Cas9 enzyme fused to LSD1 (a histone demethylase), and subsequently programmed to target regions of DNA believed to enhance the expression of a range of genes . The resThis ground-breaking development paves the way for a reversible editing of the epigenome and thereby a specific approach targeting cell function by influencing gene expression without altering the underlying DNA. This could lead to the targeted resolution of inflammatory processes or to the manipulation of individual epigenomes to reduce the susceptibility to disease or prevent the relapse of malignancies. Future work, focusing on a range of alternative chromatin modifiers and orthogonal dCas9 systems will allow researchers to perform even more complex epigenome engineering.8P. falciparum HDACs to limit parasite growth and development  clinical research training fellowship.J. Cole is supported by a MRC (MR/M017931/1) and Wellcome Trust funding and has received research funding from GSK (COL100037467) and help with conference attendance, speaker or advisory board fees from ViiV, Gilead, BMS, JCG or MSD.D. H. Dockrell is supported by Biotechnology and Biological Sciences Research Council UK (BB/M012166/1) and the Engineering and Physical Sciences Research Council UK.M. J. Dickman is supported by the The funding sources had no involvement in this work.


Chronic low back pain is one of the most common conditions encountered in the middle-age population. Identifying the primary pain generator is notoriously difficult. The computed tomography–single-photon emission computed tomography (CT–SPECT) is emerging as a new diagnostic modality for this purpose.This 68-year-old Caucasian male presented with intractable low back pain refractory to maximal conservative treatment, including medication and extensive physical therapy. The lumbar computed tomography, magnetic resonance imaging, and flexion–extension X-rays showed advanced degenerative changes throughout the lumbar spine, but no single level significantly worse than the others. The CT–SPECT showed markedly increased uptake at the L1–2 disc level and only minimal uptake at the other levels. The patient underwent a minimally invasive lateral L1–2 fusion with near-complete resolution of his low back pain.The CT–SPECT may provide a unique tool in establishing the primary pain generator in patients with degenerative spine disease. Low back pain (LBP) is the most common cause of disability between the ages of 20 and 45 in the United States . While tSingle-photon emission computed tomography (SPECT) uses detection of 99mTc-Technetium bound to osteoblasts  to gain We report an illustrative case of a patient whose pain source could not be identified by the traditional imaging techniques. The primary pain generator was identified by CT–SPECT and treated by a minimally invasive spinal fusion, which relieved the pain and thus confirmed the diagnosis.A 68-year-old Caucasian man presented with several years history of low back pain, occasionally radiating to the left lower extremity. The pain was described as 10/10 in intensity , incapacitating, improved in supine position and increased by walking or standing. The patient has already tried extensive conservative treatment, including medication  and physical therapy, with minimal relief. His past medical and surgical history was significant for hypertension and depression, and a thoracic 11–12 laminectomy and fusion about 8 years prior to presentation, respectively. The patient had a 15-pack-year history of smoking cigarettes, but had quit smoking 20 years prior to presentation. The neurological examination was normal. Preoperative routine laboratory tests: complete blood count (CBC), basic metabolic panel (BMP), prothrombin time (PT), partial thromboplastin time (PTT), and urinalysis (UA) were normal.The flexion–extension and lateral bending lumbar X-rays revealed extensive degenerative changes in the lumbar spine, but no grossly abnormal motion. A lumbar CT confirmed the loss of disc height at all lumbar levels and sclerotic changes, particularly at L1–2 and L2–3  decreased from 3 to 4 per day before the surgery to occasionally one per week at 3-months postoperatively. The rest of medication  usage remained the same.The patient was advised regarding the results of imaging and was told that, based on the available information, we could not reliably identify a single pain generator. A CT–SPECT was obtained and demonstrated significantly increased uptake at the L1–2 disc level, with only minimal uptake at the other degenerated levels Fig. . The patSPECT has been used for several decades to identify bone abnormalities . RecentlIdentifying the primary pain generator in patients with degenerative spine pathology makes the difference between therapeutic success and failure. Surgical fusions may provide superior results compared to medical treatment in patients with an obvious primary pain generator  . The sitAs previously demonstrated, the SPECT is based on 99mTc-Technetium uptake by the osteoblasts . The stuThe CT–SPECT may provide a unique diagnostic advantage over the current modalities in identifying the primary pain generator in patients with pain of degenerative spinal origin.


Liver regeneration is a spontaneous process that occurs after liver injury, but acute liver failure is a complex and fatal disease which is difficult to treat. Cell-based therapies are promising alternative therapeutic approach for liver failure and different cell sources have been tested in this regard. We investigated the comparative hepatogenic potential of human bone marrow stem cells (BMSC) with stem cells derived from human dental pulp (DPSC), apical papilla (SCAP) and follicle (DFSC) during this study. Hepatogenic potential of stem cells was assessed by functional assays at both genetic and protein level. We observed higher expression of most of the hepatic markers post differentiation in DPSCs compared to other cell types. LC-MS/MS analysis of stem cell secretome revealed the presence of different proteins related to hepatogenic lineage like growth arrest specific protein 6, oncostatin M, hepatocyte growth factor receptor etc. Interactome and Reactome pathway analysis revealed the interaction of DPSC/SCAP secretome proteins and these proteins were found to be associated with various pathways involved in lipid transport and metabolism. To the best of our knowledge, this is the first study regarding detailed investigation of hepatogenic potential of BMSCs v/s DMSCs  along-with secretome characterization. Defining a suitable source of stem cells for obtaining functional hepatocytes is also crucial for development of effective liver regeneration therapy. Functional hepatocytes have been successfully derived from various types of stem cells like embryonic stem cells (ESCs)3, induced pluripotent stem cells (iPSCs)4, bone marrow stem cells (BMSCs)5, adipose derived stem cells (ADSCs)6, umbilical cord derived stem cells (UC-MSCs)7 etc. Earlier, Khanjani etal. reported that menstrual blood derived stem cells (MnSCs) had higher hepatic differentiation potential compared to BMSCs. This conclusion was based on higher expression of hepatocyte markers in differentiated MnSCs8. A comparative analysis of hepatogenic potential between placenta derived stem cells (PDSC), ADSC, & BMSC showed PDSC having higher tendency to form functional hepatocytes based on hepatic gene expression and other parameters like urea production, elevated expression of albumin (ALB) and various cytokines9. However, in another study, Visconti etal. found no significant difference in the potential of ADSC & BMSC to differentiate into hepatocytes10. Apart from stem cells derived from different sources, difference in hepatic differentiation potential of stem cells from different donors has also been reported. A study by Kajiwara and colleagues shed light on donor-dependent variations in for hepatic differentiation in various clones of human induced pluripotent stem cells (hiPSCs). This study established that origin of stem cells but not the derivation method is an important contributor for hepatic differentiation of stem cells.Liver transplantation is the only therapeutic option for many congenital and acquired liver diseases. The widespread application of liver transplantation is limited by a paucity of liver donors, risk of surgical complications, graft-versus-host disease, and high medical costs. There is a need for development of alternative methods of treatment and regenerative medicine offers a novel approach for treatment of liver disease. Currently, cell therapy and tissue/organ engineering are the main regenerative medicine techniques. Cell therapy is less expensive and invasive compared to organ transplantation or tissue engineering. Liver regeneration can be stimulated by cell therapy with hepatocytes, hematopoietic stem cells, or mesenchymal stem cells (MSCs). Mesenchymal stem cells work either by providing trophic support factors at the site of injury or by differentiation of some of the stem cells into hepatocytes. Although there has been a significant improvement in differentiation protocols to improve the efficacy and functionality of hepatocyte differentiation11. Although hepatic differentiation of dental pulp stem cells has been reported before12 but a clear comparison between the efficacy of hepatic differentiation between bone marrow and dental stem cells  has not been reported so far. Micro-environmental cues like biological scaffolds or paracrine factors can determine the differentiation ability of stem cells. Wang etal. have demonstrated the role of biological matrix in directing iPSC differentiation to osteoblasts which can be highly beneficial for tissue engineering purposes13. A numbers of evidences suggest that paracrine factors released by stem cells might act as major facilitators of the therapeutic action of stem cell and the concept of secretory molecules, collectively called as secretome is rapidly gaining importance in regenerative medicine16. The paracrine factors produced by adipose stem cells have been shown to increase the liver regeneration in mice through JAK/STAT signaling17. So we analyzed the proteomic profile of the secretome from these four stem cells populations for any hepatic lineage related proteins which can explain the hepato-protective role of secretome obtained from these cells. To the best of our knowledge this is the first report of comparative secretome analysis pertaining to hepatic lineage proteins from stem cells of different origins. Furthermore, we have also defined the signatures peaks of BMSCs and DMSCs on hepatic differentiation by Fourier transform Infrared spectroscopy (FTIR).Derivation of terminally differentiated cells for different diseases is important in the field of regenerative medicine, so there is a continuous search for specific cells suitable for efficient differentiation into a committed lineage. Dental tissue derived stem cells offer an exciting area of research to explore new avenues in stem cell therapy. These cells are sturdy and versatile in natureTissue was processed from five young donors (6–25 years age) visiting PGIMER OPD for tooth extraction or impacted third molar removal. All experimental protocols were approved by Ethics Review Committee of PGI (IEC No. 9195/PG-12 ITRG/2571-72) and carried out as per Helsinki declaration. Written informed consent was obtained from all patients or guardian if the age of patient was less than 15 years. Extracted teeth were disinfected and carried to the cell culture lab for further processing.18. BMSCs were separated by ficoll density gradient. DMSCs and BMMSCs were cultured in 5% CO2 and a temperature of 37 °C in α-MEM, 10% FBS, 10ng/ml epidermal growth factor (EGF), 10 ng/ml platelet derived growth factor (PDGF), L-ascorbic acid, glutamine, penicillin/streptomycin, L-glutamine and supplemented with 10 μl/ml ITS .Five tissue samples for each stem cell type were obtained in sterilized conditions. Extracted dental pulp from molar, apical papilla and follicle were processed and cultured in α-MEM media by explant culture method as reported beforeCells were dissociated from culture surface by using trypsin (Sigma) and incubated with corresponding FITC/PE conjugated antibodies for stem cell markers CD-90, CD-105, CD-73, endothelial marker CD-34, and hematopoietic marker CD-45. Cells were analyzed atleast in triplicates by using BD FACS Canto flow cytometer. Unstained cells were used to normalize background fluorescence and were gated as population P1 while positive population was indicated in gate P2.3 cells of each stem cell type were cultured in presence of complete culture media supplemented with 10% FBS for a duration of 48hrs. After 48 hrs; EZBlue dye was added (10% volume of media) and procedure was performed as per manufacturer’s instructions. Optical density was recorded at a measurement wavelength of 570 nm by taking 600 nm as reference wavelength (Tecan ELISA reader cum spectrophotometer).5 × 1018. Briefly, Osteogenic differentiation was done with supplementation of dexamethasone, β glycerophosphate and mono-potassium phosphate while insulin and indomethacin in combination with dexamethasone were used for adipogenic differentiation. Neural differentiation was achieved by adding B-27, N2, G5, EGF and bFGF supplements in neurobasal media for 40 days. Osteogenic and adipogenic differentiation were assessed by alizarin red S and Oil Red O stain while neural differentiation was confirmed by immunofluorescence staining for Neurofilament antibody. Hepatic differentiation was established by treating the cells with a differentiation medium, consisting of low glucose DMEM supplemented with 20 ng/ml hepatocyte growth factor (HGF), 2 ng/ml epidermal growth factor (EGF), 0.1 μM dexamethasone, and 50 mg/ml ITS + premix for 14 days, followed by maturation medium  for another 14 days. Parallel control studies were performed and medium was changed when required. Cell number was counted to confirm cell density.Multilineage differentiation potential of BMSCs and DMSCs was assessed by differentiating them into cellular derivatives of ectoderm , mesoderm (osteocytes and adipocytes) and endoderm (hepatocytes). The differentiation protocols were followed as reported previouslyRNA was isolated at the end of differentiation protocol using TRIZOL™ reagent (Invitrogen). Primer probes were used to determine transcript levels in triplicate for housekeeping gene and different genes of interest viz. α-Fetoprotein, Tyrosine aminotransferase, Albumin, and expression was normalized by housekeeping gene β-Actin/GAPDH.Hepatic differentiation was evaluated by using LDL functional hepatocyte assay kit (Abcam) and procedure was performed as per manufacturer’s instructions. A combination of LDL DyLight fluorochrome and LDL primary and secondary antibodies were used for detection of LDL uptake and expression of LDL receptor respectively. After differentiation, live cells were stained with LDL DyLight fluorochrome (1:100) and observed under fluorescence microscope (Nikon Eclipse TS100). Background fluorescence was normalized equally for all cell types by employing NIS- Elements D4.13.00 software package supplied with microscope and exposure settings were kept same for all cells. Cells were then fixed, permeabilized, put in blocking solution and incubated with antibodies (primary and secondary) as per manufacturer’s instructions. DAPI was used as nuclear stain and cells were then photographed in FITC/DAPI and PI excitation (488/350/536 nm respectively) and emission wavelengths (532/470/617 nm respectively) (EVOS LED microscope). Minor brightness corrections were done equally in whole of the fluorescent image after combining individual images from all four differentiated stem cells. Fluorescence attained by each cell as a result of antibody expression and LDL uptake was measured individually for each cell with the help of Image cytometer (Invitrogen-TALI Image based Cytometer). Cells were excited using FITC/PI wavelength under red/green fluorescence function provided with instrument software and various parameters like intensity values, percentage positivity of cells for red (LDL uptake-LDL Dylight fluorochrome) and green (LDL receptor-LDL antibody) were recorded for every cell. These values were then used to quantify the LDL uptake and distribution of LDL receptor in differentiated hepatocytes from BMSCs and DMSCs.Differentiated hepatocytes from all four stem cell sources were trypsinized and washed in phosphate buffer saline. Cells were sonicated and then centrifuged at 4,000 rpm for duration of five minutes to remove cell debris. These cell lysates were then run into Roche Modular P autoanalyzer and various parameters of liver function like AST (Aspartate aminotransferase), ALT  and ALP  were recorded according to manufacturer’s instructions to correlate with the hepatic differentiation potential of different stem cell types.Cells from all four stem cell types (control as well as differentiated hepatocytes) were trypsinized to obtain the cell pellet which was dried for night at a temperature of 156 °C. Dried pellets in powder form were added with equal amount of KBR (potassium bromide). The spectra were acquired using FTIR  by running atleast 52 scans per sample. The peaks thus obtained were searched against corresponding FTIR database.2 culture flasks in the presence of αMEM alone (48 hrs) without any growth factors or FBS after repeated washings in PBS. Conditioned media obtained after culture was harvested in cold conditions, centrifuged for five minutes at 3,000 rpm, and filtered. Secretome was obtained between passages numbered 3rd–7th and total protein was isolated by phenol chloroform method. The final protein profile was checked in SDS-PAGE. LC-MS/MS profiling of the secretome was done on WATERS NanoLC-SYNAPT G2 HDMS instrument. The criterion used for identifying true Protein Hits was the false discovery rate (FDR) parameter set as 4%. (PLGS inbuilt cut off for Protein Identification). Per protein at least 2 peptides were matched with sample. Additionally, at least 5 MSMS fragment ions were found in the Protein and at least 3 Fragment MSMS ions per peptides. Protein analysis was done using WATERS Protein Lynx Global Server (PLGS) v4.1 against the respective UNIPROT database. Bioinformatics analysis was performed using STRING software19. Proteins were classified using functional annotation and interaction of hepatic lineage proteins were observed in evidence view.For secretome harvesting, stem cells at 60–70% confluence were cultured in 75 cmAll experiments were performed atleast in triplicates and were repeated three times. Data was reported as means ± SD. All statistical analyses were performed using one-way ANOVA and paired t test.Detailed methodology and associated supporting data is available with the corresponding author on special request.rd–4th day though migration rate was not uniform and in some tissues, cells migrated on 7th–8th day. Different transitional stages were observed before formation of a confluent primary culture which involved initial bubble like stage with small rounded cells coming out of tissue. These cells initially formed a mesh- like network and subsequently achieved spindle shaped morphology in confluent culture by 18th day  were scaled up further & characterized at 3X. Differentiation studies were carried out in cells from passages 3–8.DMSCs were obtained from tooth pulp (DPSC), papilla (SCAP) and follicle (DFSC) of extracted dental tissues (age 6–25 yrs). Impacted third molar was successfully removed by following proper procedure which involved screening of tooth by OPG, local anesthesia to the patient, incision in the area where tooth was to be removed. Incision flap was made to expose the tooth which was then cut and removed from the socket. Tissue was further transported to tissue culture laboratory for establishment of primary culture. It was observed that on the average, hDMSC migrated from the seeded tissues after 1–2 days of initial seeding. Thereafter, they formed small globule like cells which appeared in culture on 3day Fig. . ConflueStem cell characterization showed that all type of stem cells  exhibited >90% positivity for stemness markers (CD90 and CD73) while and negligible expression (<5%) for CD34 and CD45 Fig. . HoweverHepatic differentiation potential of stem cells was assessed by functional uptake of low density lipoproteins by differentiated hepatocytes via functional LDL-hepatocyte assay kit, real time PCR analysis and evaluation of levels of certain liver parameters. Hepatic differentiation was carried out for a total period of 28 days. We observed that all cells acquired an oval morphology upon differentiation as compared to spindle shape morphology in undifferentiated state . Functionally differentiated hepatocytes are characterized by expression of LDL receptor and uptake of low density lipoproteins (LDL). A labeled LDL approach can make the real time analysis of LDL uptake simpler. So we characterized the differentiated hepatocytes from all four stem cell sources for expression of LDL receptor and by the degree of LDL uptake. Characterization of cells was done by LDL functional hepatocyte staining kit in which LDL antibody (green) bound to the LDL receptor present on the differentiated hepatocytes while functional LDL uptake was represented by LDL DyLight fluorochrome (red). All differentiated cells showed positive staining with LDL antibody as well as LDL DyLight fluorochrome as shown in Fig. Albumin (ALB) and tyrosine amino transferase (TAT) are important markers for mature hepatocytes while α-fetoprotein (AFP) is an early hepatocyte marker. The sequence of gene primers used for real time is shown in Table S1. Expression of these markers in differentiated cells by real time PCR analysis revealed a significantly higher expression of ALB in differentiated DPSC as compared to BMSC while SCAP and DFSC showed minimal increase as shown in Fig. AST (Aspartate aminotransferase), ALT  and ALP  are important enzymes associated with liver function and of potential importance in depicting the differentiation of stem cells into functional hepatocytes. Measurements of these parameters were performed biochemically Fig.  to get a−1 (except differentiated DFSC) and a secondary peak from 1542–1641 cm−1 as shown in Table The molecular characterization of hepatic differentiation from four stem cell populations was carried out using Fourier Transform Infrared Spectroscopy (FTIR) and the peak profile for the same are shown in the Fig. Based on all the above parameters, a score was assigned to each stem cell (BMSCs & DMSCs) for assessment of degree of differentiation in hepatic lineage and the score was found to be highest for DPSCs (please see Table S2).in vitro culture). Twelve proteins were obtained in SCAP secretome while BMSC secretome showed six different proteins related to hepatic cell growth and export of drugs from hepatocytes. DPSC secretome showed five proteins one of which included Growth arrest specific protein 6 (GAS6) which is mainly associated with hepatic regeneration. Interactome analysis of these proteins by STRING bioinformatics software , umbilical cord stem cells  and BMSCs (low cell number and highly invasive procedure). According to the minimal criteria defined by the International Society for Cellular Therapy, a stem cell should show adherence to the plastic adherence and characteristic expression of surface markers such as CD73, CD90, and CD105, they also display a negative expression of CD14, CD34, and CD45, and they should be capable of osteogenic, chondrogenic, and adipogenic differentiation22. We employed a two-step strategy of HGF in first stage and hepatic maturation by OSM in the second stage. HGF is a potent mitogen for mature hepatocytes and is involved in proliferation and differentiation of oval cells in combination with other factors by a paracrine mechanism23. OSM induces the expression of hepatic genes like tyrosine amino transferase, glucose-6-phosphatase and accumulation of glycogen granules in developing hepatocytes and induce hepatic maturation by activating STAT324. OSM also upregulate and initiate the secretion of albumin. Insulin has been reported to increase hepatic differentiation by activation of PI3/AKT pathway25. Hepatic differentiation is assessed based on some important parameters like enhanced expression of hepatic genes, uptake of low density lipoproteins (LDL), and by other biochemical parameters important for liver functions.Hepatic differentiation of stem cells includes induction followed by cell maturation. Stem cells can be made to differentiate into hepatocytes by various chemicals like hepatocyte growth factor (HGF), Oncostatin M (OSM), insulin, transferrin26. α-fetoprotein is another early hepatocyte marker which is expressed at initial stages endodermal differentiation27. Contrary to ALB, its expression decreases in mature hepatocytes. Tyrosine amino transferase (TAT) is a marker for pre or postnatal hepatocyte differentiation as it is absent prior to birth but rapidly increases its levels during neonatal development28. A significantly higher expression of mature hepatocyte markers ALB and TAT as compared to AFP (early hepatocyte marker) in differentiated DPSCs reflected a more potent and mature hepatocyte formation in case of DPSCs in comparison to other stem cells.Increased expression of various genes like albumin, α-fetoprotein (AFP) and Tyrosine amino transferase (TAT) in differentiated cells showed presence of hepatocyte-like cells. Albumin (ALB) is the most abundant protein synthesized by hepatocytes. Fetal hepatocytes begin synthesizing ALB which increases in adult/mature hepatocytes making it an important marker of hepatocyte maturation29. Presence of a higher percentage of functional hepatocytes  after differentiation of DPSCs reflected it’s superiority for hepatic differentiation in comparison to rest of the stem cells. A low percentage of only green and only red cells in this population again confirmed this point. Further validation of the same by higher MFI in hepatocytes from differentiated DPSC strongly supported the notion that DPSCs have an edge over others for deriving functional hepatocytes.Uptake of low density lipoproteins (LDL) is an important assay which is often used to demonstrate the activity of hepatocyte like cells on differentiation. Deficiency of LDL receptor has been reported to be associated with diseases like familial hypercholesterolemia (FH)30. Increased levels of AST, ALT and ALP during serum tests serve as important markers for release of these enzymes by ruptured membrane of hepatocytes. We obtained these enzymes in lysate of differentiated hepatocytes from all four types of stem cells. Increase in the levels of AST, ALT and ALP in differentiated hepatocytes derived from DPSC again reflected the better hepatogenic potential of these cells as compared to three other stem cell types.Alanine amino transferase (ALT), Aspartate amino transferase (AST) and alkaline phosphatase (ALP) are three important enzymes associated with liver function. ALT is expressed at a higher concentration in liver as compared to other tissues of body. AST is an important enzyme associated with liver activity and ALP is associated with an increased proliferation of hepatocyteset al. have characterized the spectroscopic signatures of differentiated hepatocytes at early and late stage by using mouse embryonic stem cells (mESCs)31. Amide I and II (ranged from 1500–1700 cm−1) bands have been shown to be associated with hepatic differentiation with Amide I found to be more potent during later stages of hepatic differentiation while Amide II to be more dominant for initial endodermal induction (induction of hepatic differentiation). This group reported the predominance of Amide II band in initial stages of endodermal induction while Amide I band was found to be associated with presence of α-helix protein structure of albumin (1656 cm−1)32. Interestingly the group also showed a peak shift of 1627–1641 cm−1 from induction to maturation during hepatic differentiation which also reflects a shift from β sheet structure to α helix. In differentiated BMSCs the band envelope of Amide I was observed at peak with wave number 1641.1 cm−1 which arises mainly due to the stretching and bending type of vibrations in backbone of the protein. This peak is also a characteristic peak for urea (related to detoxifying function of hepatocytes). An Amide II band was observed during differentiation of DFSCs (1542.8 cm−1) which also arises due to similar kind of vibrations. α- helix is generally centered at ~1650 cm−1 and the bands corresponding to this position were present in differentiated BMSCs, DPSCs (1632 cm−1) and SCAP (1638.1 cm−1). This can be explained by increase in the proteins which are α-helical in nature like albumin (the major protein produced in differentiated hepatocytes) during hepatic differentiation.Very few reports are available about molecular signature profiling of hepatic differentiation by FTIR analysis. Thumanu −1 and 1641 cm−1 in differentiated BMSCs and DPSCs respectively.As albumin is a mature hepatocyte marker, the real time PCR results showed maximum increase in ALB in DPSCs and BMSCs in our differentiation system which was in agreement with maturation peak of 1632 cm33.The secretory factors present in niche can effect a cell’s decision for a specific cell fate and these micro environmental cues may be extrinsic or intrinsic. However, the cascade of molecules which orchestrate these signals are still not very clear. An elevated expression of the secretory factors during differentiation might play a role in regulating the differentiation potential of stem cells by a paracrine or autocrine manner. Higher expression of the secretory protein, NPTX1 during neural differentiation of induced pluripotent stem cells (iPSCs), might explain the neural promotory effect of NPTX1 on iPSCs35. It acts as a regulator of Wnt signaling pathway by controlling the hepatoma formation and has been found to be associated with accelerated liver regeneration after partial hepatectomy by modulating Wnt signaling. It also acts in a synergistic way with hepatocyte growth factor (HGF) to increase cell migration36. Retrotransposon-derived protein paternally expressed 10 (PEG10) (expressed in BMSC & DFSC secretome) has been shown to increase cell survival and growth in hepatic cancer37. ABCB4 (in BMSC and SCAP secretome) and hepatocyte nuclear factor 4 (in SCAP secretome only) have been reported to control the transport of organic anions and drugs and hence aiding in liver regeneration38. Apolipoprotein C-III (APOC3) is a VLDL protein and abrogate the hepatic and lipoprotein lipase39. Presence of this protein only in SCAP secretome might be a suggestive of limited hepatic potential of SCAP in comparison to DPSCs. Growth arrest-specific protein 6 (GAS6) is another important protein which boost hepatic regeneration in combination with its receptor Axl by increasing proliferation in hepatocytes. In an interesting study addition of GAS6 in culture was found to enhance the number of WB-F344 cells (hepatocyte precursor cell line)40. Presence of GAS6 in DMSC  secretome and its absence in BMSC secretome might account for increased hepatic differentiation of these cells. Oncostatin M (OSM) (found in DFSC secretome) has been found to induce hepatic maturation by STAT3 signaling pathway24 and was also involved in termination of embryonic hematopoiesis in fetal liver41. Hepatocyte growth factor receptor (HGFR) (observed in DFSC secretome) acts as a receptor to HGF and has been associated with hepatic differentiation  and also in regulation of hepatic metabolism by stimulating glucose uptake42. In addition, presence of different LDL receptor proteins (LRP) in secretome of different stem cells reflected the role of stem cell secretome proteins in controlling lipid metabolism and transport which ultimately determines the activity of differentiated hepatocytes. Presence of different signalling molecules pertaining to lipid transport and metabolism in the secretome proteins of DPSCs and SCAP is an important indicator of enhanced tendency of these stem cells towards hepatic lineage. Thus, LC-MS/MS analysis revealed that in addition to the intracellular genes and proteins, complex interplay of secretome proteins may also contribute to the increased hepatic differentiation potential of DPSCs as compared to other stem cells. Differences based on the tissue of origin in stem cells play an important role in regulating the micro-environment which ultimately determine a stem cell’s inherent tendency to differentiate towards a particular lineage18. The secretome from DMSCs provides microenvironmental cues for hepatocyte differentiation.In similar lines, we sought for the presence of potential hepatic lineage associated proteins in stem cell secretome of all four populations. The divergent behavior of these cells towards hepatic differentiation was revealed by LC-MS/MS analysis. We obtained twenty different proteins pertaining to hepatic lineage maintenance and difference in secretome of these four stem cells as shown in Table Our study provides strong evidence for expanding potential of dental tissue as a viable source of mesenchymal stem cells as compared to bone marrow in context of hepatic lineage and regeneration. This study also emphasizes the fact that proteins secreted by BMSCs and DMSCs in their milieu also contribute towards inherent hepatic differentiation of these stem cells. These secretory proteins can offer a viable alternative to replace stem cell therapy with “cell free” therapy and may be future hotspots for developing potential therapies for various debilitating diseases.Supplementary Information